图形化流水线#
本 Notebook 的内容：#
理解什么是图形化流水线
理解图形化流水线的 API
简单流水线的示例以及如何使用图形化流水线实现它们。
更复杂的图形化流水线（预测 + 网格搜索）
使用图形化流水线进行网格搜索
什么是图形化流水线？#
回顾顺序流水线
许多任务是非顺序的。解决此问题有两种可能性
嵌套顺序流水线。
使用图形化流水线。
因此，存在广义图形化流水线。
图形化意味着不同的步骤可以共享同一个前驱步骤，或者将它们的输出提供给同一个后继步骤（数据流可以分支和合并）。
广义意味着流水线可用于多种任务（例如预测、分类等）。
注意
图形化流水线是一个新功能，因此，如果您遇到任何问题，我们非常乐意获得关于图形化流水线的反馈。
潜在用例#
图形化流水线存在多种潜在用例。接下来，我们将重点介绍预测和分类流水线。 #### 图形化流水线的预测用例
预测器的输入依赖于其他预测器的输出，而这些预测器使用相同的输入。
预测器可以使用相同的预处理（数据流分支）
预测器可以使用多个前驱步骤的输出（数据流合并）
注意：图形化流水线目前的实验状态尚未完全支持此用例。但是，我们正在为此努力。如果您对此用例感兴趣并希望做出贡献，请联系我们。
致谢#
图形化流水线最初由 pyWATTS [1] 开发，随后被适配用于 sktime。原始实现可以在 pyWATTS 找到。pyWATTS 是一个由 KIT 应用信息学与自动化研究所开发并由 HelmholtzAI 资助的开源库。
[1] Heidrich, Benedikt, et al. “pyWATTS: Python workflow automation tool for time series.” arXiv preprint arXiv:2106.10157 (2021)。
注意：图形化流水线目前的实验状态尚未完全支持此用例。但是，我们正在为此努力。如果您对此用例感兴趣并希望做出贡献，请联系我们。
如何构建图形化流水线#
首先让我们可视化一个我们要构建的简单预测流水线
然后我们有两种方法使用图形化流水线构建此流水线
在初始化时将所有步骤传递给流水线，就像顺序流水线一样。
[1]:
from sktime.forecasting.sarimax import SARIMAX
from sktime.pipeline.pipeline import Pipeline
from sktime.transformations.series.difference import Differencer
differencer = Differencer()
general_pipeline = Pipeline(
[
{"skobject": differencer, "name": "differencer", "edges": {"X": "y"}},
{
"skobject": SARIMAX(),
"name": "sarimax",
"edges": {"X": "X", "y": "differencer"},
},
{
"skobject": differencer,
"name": "differencer_inv",
"edges": {"X": "sarimax"},
"method": "inverse_transform",
},
]
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
创建一个流水线对象并逐个添加步骤。
[2]:
general_pipeline = Pipeline()
differencer = Differencer()
general_pipeline = general_pipeline.add_step(
differencer, "differencer", edges={"X": "y"}
)
general_pipeline = general_pipeline.add_step(
SARIMAX(), "sarimax", edges={"X": "X", "y": "differencer"}
)
general_pipeline = general_pipeline.add_step(
differencer, "differencer_inv", edges={"X": "sarimax"}, method="inverse_transform"
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
参数解释#
add_step 的参数或初始化时步骤列表中的字典键为
skobject: 添加到流水线的 sktime 对象
name: 步骤的名称
edges: 字典的键指示 skobject 的输入（X 或 y），值是要连接到输入参数的步骤名称。注意支持使用
__进行子集选择以及通过列表进行特征联合。method: 应该调用的 skobject 方法。如果未提供，将根据添加的 skobject 推断默认方法。此参数用于 inverse_transform 方法。可选。
kwargs: 传递给 sktime 对象的附加关键字参数。可选。
现在让我们拟合流水线并进行预测
[3]:
from sktime.datasets import load_longley
from sktime.forecasting.model_selection import temporal_train_test_split
y, X = load_longley()
y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)
general_pipeline.fit(y=y_train, X=X_train, fh=[1, 2, 3, 4])
general_pipeline.predict(X=X_test)
[3]:
1959 67213.735362
1960 68328.076310
1961 68737.861398
1962 71322.894026
Freq: A-DEC, Name: TOTEMP, dtype: float64
更多示例#
分类流水线#
使用图形化流水线实现的简单分类流水线。
[4]:
from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
from sktime.transformations.series.exponent import ExponentTransformer
general_pipeline = Pipeline()
general_pipeline = general_pipeline.add_step(
ExponentTransformer(), "exponent", edges={"X": "X"}
)
general_pipeline = general_pipeline.add_step(
KNeighborsTimeSeriesClassifier(), "classifier", edges={"X": "exponent", "y": "y"}
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
或者通过构造器 API 定义。
[5]:
general_pipeline = Pipeline(
[
{"skobject": ExponentTransformer(), "name": "exponent", "edges": {"X": "X"}},
{
"skobject": KNeighborsTimeSeriesClassifier(),
"name": "classifier",
"edges": {"X": "exponent", "y": "y"},
},
]
)
此流水线可以如下可视化
[6]:
from sktime.datasets import load_arrow_head
X, y = load_arrow_head(split="train", return_X_y=True)
general_pipeline.fit(X=X, y=y)
general_pipeline.predict(X=X)
[6]:
array(['0', '1', '2', '0', '1', '2', '0', '1', '2', '0', '1', '2', '0',
'1', '2', '0', '1', '2', '0', '1', '2', '0', '1', '2', '0', '1',
'2', '0', '1', '2', '0', '1', '2', '0', '1', '2'], dtype='<U1')
更复杂的示例#
考虑的用例是使用实际国内生产总值、实际可支配个人收入和失业率的预测值来预测通货膨胀率。此外，失业率的预测使用相同的特征，但排除失业率本身。
数据取自 statsmodels 包的 macrodata 数据集。
注意 下面我们将坚持使用 add_step。
创建图形化流水线实例
[7]:
pipe = Pipeline()
pipe.set_config(warnings="off")
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
[7]:
Pipeline()请重新运行此单元格以显示 HTML 表示或信任此 Notebook。
Pipeline()
添加预处理
[8]:
from sklearn.preprocessing import StandardScaler
from sktime.transformations.series.adapt import TabularToSeriesAdaptor
from sktime.transformations.series.detrend import Deseasonalizer
pipe = pipe.add_step(
TabularToSeriesAdaptor(StandardScaler()),
name="scaler",
edges={"X": "X__realgdp_realdpi_unemp"},
)
pipe = pipe.add_step(
Deseasonalizer(sp=4), name="deseasonalizer", edges={"X": "X__realgdp_realdpi"}
)
添加 GDP 和 DPI 的预测器
[9]:
from sklearn.linear_model import Lasso, Ridge
from sktime.forecasting.compose import make_reduction
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_gdp",
edges={"y": "deseasonalizer__realgdp"},
)
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_dpi",
edges={"y": "deseasonalizer__realdpi"},
)
添加依赖于 GDP 和 DPI 预测值的失业率预测器
[10]:
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_unemp",
edges={
"y": "scaler__unemp",
"X": [
"forecaster_gdp",
"forecaster_dpi",
],
},
)
添加依赖于预测 DPI 和失业率的通货膨胀预测器
[11]:
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_inflation",
edges={"X": ["forecaster_dpi", "forecaster_unemp"], "y": "y"},
)
加载数据并将其分为训练集和测试集
[12]:
from sktime.datasets import load_macroeconomic
from sktime.forecasting.base import ForecastingHorizon
data = load_macroeconomic()
X = data[["realgdp", "realdpi", "unemp"]]
y = data[["infl"]]
fh = ForecastingHorizon([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
y_train, y_test, X_train, X_test = temporal_train_test_split(y, X=X, fh=fh)
X_train
[12]:
| realgdp | realdpi | unemp | |
|---|---|---|---|
| 时期 | |||
| 1959Q1 | 2710.349 | 1886.9 | 5.8 |
| 1959Q2 | 2778.801 | 1919.7 | 5.1 |
| 1959Q3 | 2775.488 | 1916.4 | 5.3 |
| 1959Q4 | 2785.204 | 1931.3 | 5.6 |
| 1960Q1 | 2847.699 | 1955.5 | 5.2 |
| ... | ... | ... | ... |
| 2005Q3 | 12683.153 | 9308.0 | 5.0 |
| 2005Q4 | 12748.699 | 9358.7 | 4.9 |
| 2006Q1 | 12915.938 | 9533.8 | 4.7 |
| 2006Q2 | 12962.462 | 9617.3 | 4.7 |
| 2006Q3 | 12965.916 | 9662.5 | 4.7 |
191 行 × 3 列
[13]:
pipe.fit(y=y_train, X=X_train, fh=fh)
result = pipe.predict(X=None, fh=y_test.index)
result
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
[13]:
| infl | |
|---|---|
| 时期 | |
| 2006Q4 | 3.090428 |
| 2007Q1 | 1.676421 |
| 2007Q2 | 0.219586 |
| 2007Q3 | 1.570087 |
| 2007Q4 | 0.350137 |
| 2008Q1 | 0.438966 |
| 2008Q2 | 0.615457 |
| 2008Q3 | 0.119022 |
| 2008Q4 | 0.257887 |
| 2009Q1 | 0.129785 |
| 2009Q2 | -0.056094 |
| 2009Q3 | -0.066123 |
[14]:
((result - y_test) ** 2).mean()
[14]:
infl 20.103326
dtype: float64
使用图形化流水线进行网格搜索#
此流水线有多个可以测试以找到最佳配置的参数。这些参数包括
应该为哪个变量使用哪个预测器 ->
MultiplexForecaster预测器的超参数应该是什么
不同的预测器应该使用哪些特征 -> 调整图形化流水线的 edges!
由于我们进行预测，因此使用 ForecastingGridSearchCV。
创建流水线的蓝图
[15]:
from sktime.forecasting.compose import MultiplexForecaster
pipe = Pipeline()
sklearn_scaler = StandardScaler()
sktime_scaler = TabularToSeriesAdaptor(sklearn_scaler)
deseasonalizer = Deseasonalizer(sp=4)
pipe = pipe.add_step(
sktime_scaler, name="scaler", edges={"X": "X__realgdp_realdpi_unemp"}
)
pipe = pipe.add_step(
deseasonalizer, name="deseasonalizer", edges={"X": "X__realgdp_realdpi"}
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_gdp",
edges={"y": "deseasonalizer__realgdp"},
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_dpi",
edges={"y": "deseasonalizer__realdpi"},
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_unemp",
edges={
"y": "scaler__unemp",
"X": [
"forecaster_gdp",
"forecaster_dpi",
],
},
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_inflation",
edges={"X": ["forecaster_dpi", "forecaster_unemp"], "y": "y"},
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
指定参数网格
字典的键是流水线中的参数，值指定应该测试哪些选项。键具有以下结构：步骤参数 <步骤名称>__skobject__<参数名称> 和步骤的输入 edges <步骤名称>__edges_<Xory>。
[16]:
param_grid = {
"forecaster_inflation__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_unemp__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_dpi__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_gdp__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_inflation__edges__X": [
["forecaster_unemp"],
["forecaster_unemp", "forecaster_dpi"],
],
"forecaster_unemp__edges__X": [
[],
["forecaster_dpi"],
["forecaster_gdp", "forecaster_dpi"],
],
"deseasonalizer__edges__X": ["X__realgdp_realdpi", "scaler__realgdp_realdpi"],
}
使用流水线、交叉验证策略、评分和 param_grid 初始化网格搜索。
[17]:
from sktime.forecasting.model_selection import (
ForecastingGridSearchCV,
SlidingWindowSplitter,
)
from sktime.performance_metrics.forecasting import mean_absolute_error
gridcv = ForecastingGridSearchCV(
pipe,
cv=SlidingWindowSplitter(
window_length=len(X_train) - 20,
step_length=4,
fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
),
scoring=mean_absolute_error,
param_grid=param_grid,
)
在 gridsearch 对象上调用 fit 方法。
[18]:
gridcv.fit(y=y_train, X=X_train)
/Users/benediktheidrich/code/sktime/sktime/forecasting/model_selection/_tune.py:201: UserWarning: in ForecastingGridSearchCV, n_jobs and pre_dispatch parameters are deprecated and will be removed in 0.27.0. Please use n_jobs and pre_dispatch directly in the backend_params argument instead.
warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.997e+02, tolerance: 1.843e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.997e+02, tolerance: 1.843e-01
model = cd_fast.enet_coordinate_descent(
[18]:
ForecastingGridSearchCV(cv=SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12],
step_length=4,
window_length=171),
forecaster=Pipeline(steps=[{'edges': {'X': 'X__realgdp_realdpi_unemp'},
'kwargs': {},
'method': None,
'name': 'scaler',
'skobject': TabularToSeriesAdaptor(transformer=StandardScaler())},
{'edges': {'X': 'X__realgdp_realdpi'},
'kwargs': {},
'method': Non...
'forecaster_inflation__edges__X': [['forecaster_unemp'],
['forecaster_unemp',
'forecaster_dpi']],
'forecaster_inflation__skobject__selected_forecaster': ['ridge',
'lasso'],
'forecaster_unemp__edges__X': [[],
['forecaster_dpi'],
['forecaster_gdp',
'forecaster_dpi']],
'forecaster_unemp__skobject__selected_forecaster': ['ridge',
'lasso']},
scoring=<function mean_absolute_error at 0x172c7e980>)请重新运行此单元格以显示 HTML 表示或信任此 Notebook。ForecastingGridSearchCV(cv=SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12],
step_length=4,
window_length=171),
forecaster=Pipeline(steps=[{'edges': {'X': 'X__realgdp_realdpi_unemp'},
'kwargs': {},
'method': None,
'name': 'scaler',
'skobject': TabularToSeriesAdaptor(transformer=StandardScaler())},
{'edges': {'X': 'X__realgdp_realdpi'},
'kwargs': {},
'method': Non...
'forecaster_inflation__edges__X': [['forecaster_unemp'],
['forecaster_unemp',
'forecaster_dpi']],
'forecaster_inflation__skobject__selected_forecaster': ['ridge',
'lasso'],
'forecaster_unemp__edges__X': [[],
['forecaster_dpi'],
['forecaster_gdp',
'forecaster_dpi']],
'forecaster_unemp__skobject__selected_forecaster': ['ridge',
'lasso']},
scoring=<function mean_absolute_error at 0x172c7e980>)SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], step_length=4,
window_length=171)Pipeline(steps=[{'edges': {'X': 'X__realgdp_realdpi_unemp'}, 'kwargs': {},
'method': None, 'name': 'scaler',
'skobject': TabularToSeriesAdaptor(transformer=StandardScaler())},
{'edges': {'X': 'X__realgdp_realdpi'}, 'kwargs': {},
'method': None, 'name': 'deseasonalizer',
'skobject': Deseasonalizer(sp=4)},
{'edges': {'y': 'deseasonalizer__realgdp'}, 'kwargs': {},
'method': None...
window_length=5))])},
{'edges': {'X': ['forecaster_dpi', 'forecaster_unemp'],
'y': 'y'},
'kwargs': {}, 'method': None, 'name': 'forecaster_inflation',
'skobject': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])}])检查网格搜索的结果
[19]:
gridcv.cv_results_
[19]:
| mean_test__DynamicForecastingErrorMetric | mean_fit_time | mean_pred_time | params | rank_test__DynamicForecastingErrorMetric | |
|---|---|---|---|---|---|
| 0 | 1.539329 | 0.075673 | 0.023929 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 107.5 |
| 1 | 1.720565 | 0.076208 | 0.025338 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 119.5 |
| 2 | 1.394329 | 0.141800 | 0.046452 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 97.5 |
| 3 | 1.942051 | 0.151181 | 0.045115 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 129.5 |
| 4 | 2.033714 | 0.160442 | 0.059711 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 136.0 |
| ... | ... | ... | ... | ... | ... |
| 187 | 1.329079 | 0.096761 | 0.037935 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 188 | 1.329079 | 0.109997 | 0.040909 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 189 | 1.329079 | 0.100659 | 0.047549 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 190 | 1.329079 | 0.105045 | 0.055426 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 191 | 1.329079 | 0.106906 | 0.039190 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
192 行 × 5 列
使用已拟合的网格搜索，利用最佳超参数进行预测
[20]:
result = gridcv.predict(X=None, fh=y_test.index)
result
[20]:
| infl | |
|---|---|
| 时期 | |
| 2006Q4 | 2.188182 |
| 2007Q1 | 2.124281 |
| 2007Q2 | 1.045280 |
| 2007Q3 | 1.857716 |
| 2007Q4 | 1.790664 |
| 2008Q1 | 1.649457 |
| 2008Q2 | 1.874361 |
| 2008Q3 | 1.855627 |
| 2008Q4 | 1.858207 |
| 2009Q1 | 1.909693 |
| 2009Q2 | 1.905106 |
| 2009Q3 | 1.910452 |
如何通过嵌套顺序流水线实现上述流水线的简化版本#
简化：失业率的预测不依赖于 GDP 和 DPI。
创建用于预测 GDP、DPI 和失业率的顺序流水线。
[21]:
from sktime.forecasting.compose import ColumnEnsembleForecaster, ForecastX
from sktime.transformations.series.subset import ColumnSelect
forecasting_pipeline_gdp = (
ColumnSelect(["realgdp"]) # To train the forecaster only on the realgdp column
* Deseasonalizer()
* MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
)
)
forecasting_pipeline_dpi = (
ColumnSelect(["realdpi"])
* Deseasonalizer()
* MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
)
)
forecasting_pipeline_unemp = (
ColumnSelect(["unemp"])
* Deseasonalizer()
* MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
)
)
使用 ColumnEnsembleForecaster 组合 DPI、GDP、UNEMP 的预测值。（预测联合）
[22]:
input_inflation_forecast = ColumnEnsembleForecaster(
[
("realdpi", forecasting_pipeline_dpi, "realdpi"),
("realgdp", forecasting_pipeline_gdp, "realgdp"),
("unemp", forecasting_pipeline_unemp, "unemp"),
]
)
创建通货膨胀预测器。
[23]:
inflation_forecast = ForecastX(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
input_inflation_forecast,
)
[24]:
inflation_forecast.fit(y=y_train, X=X_train, fh=fh)
[24]:
ForecastX(forecaster_X=ColumnEnsembleForecaster(forecasters=[('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length...
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')]),
forecaster_y=MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]))请重新运行此单元格以显示 HTML 表示或信任此 Notebook。ForecastX(forecaster_X=ColumnEnsembleForecaster(forecasters=[('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length...
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')]),
forecaster_y=MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]))ColumnSelect(columns=['realdpi'])
Deseasonalizer()
Ridge()
Lasso()
ColumnSelect(columns=['realgdp'])
Deseasonalizer()
Ridge()
Lasso()
ColumnSelect(columns=['unemp'])
Deseasonalizer()
Ridge()
Lasso()
Ridge()
Lasso()
[25]:
inflation_forecast.predict()
[25]:
| infl | |
|---|---|
| 2006Q4 | 3.979318 |
| 2007Q1 | 2.347512 |
| 2007Q2 | 1.443598 |
| 2007Q3 | 3.914533 |
| 2007Q4 | 2.533117 |
| 2008Q1 | 3.278010 |
| 2008Q2 | 3.861517 |
| 2008Q3 | 3.487510 |
| 2008Q4 | 4.195074 |
| 2009Q1 | 4.294984 |
| 2009Q2 | 4.433578 |
| 2009Q3 | 4.858610 |
[26]:
inflation_forecast.get_params(True)
[26]:
{'behaviour': 'update',
'columns': None,
'fh_X': None,
'fit_behaviour': 'use_actual',
'forecaster_X': ColumnEnsembleForecaster(forecasters=[('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realdpi'),
('r...
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realgdp'),
('unemp',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')]),
'forecaster_y': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__forecasters': [('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realdpi'),
('realgdp',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realgdp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realgdp'),
('unemp',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')],
'forecaster_X__realdpi': TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'forecaster_X__realgdp': TransformedTargetForecaster(steps=[ColumnSelect(columns=['realgdp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'forecaster_X__unemp': TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'forecaster_X__realdpi__steps': [ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])],
'forecaster_X__realdpi__ColumnSelect': ColumnSelect(columns=['realdpi']),
'forecaster_X__realdpi__Deseasonalizer': Deseasonalizer(),
'forecaster_X__realdpi__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__realdpi__ColumnSelect__columns': ['realdpi'],
'forecaster_X__realdpi__ColumnSelect__index_treatment': 'remove',
'forecaster_X__realdpi__ColumnSelect__integer_treatment': 'col',
'forecaster_X__realdpi__Deseasonalizer__model': 'additive',
'forecaster_X__realdpi__Deseasonalizer__sp': 1,
'forecaster_X__realdpi__MultiplexForecaster__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],
'forecaster_X__realdpi__MultiplexForecaster__selected_forecaster': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_X__realdpi__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator': Ridge(),
'forecaster_X__realdpi__MultiplexForecaster__ridge__pooling': 'local',
'forecaster_X__realdpi__MultiplexForecaster__ridge__transformers': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge__window_length': 5,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__alpha': 1.0,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__copy_X': True,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__fit_intercept': True,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__max_iter': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__positive': False,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__random_state': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__solver': 'auto',
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__tol': 0.0001,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator': Lasso(),
'forecaster_X__realdpi__MultiplexForecaster__lasso__pooling': 'local',
'forecaster_X__realdpi__MultiplexForecaster__lasso__transformers': None,
'forecaster_X__realdpi__MultiplexForecaster__lasso__window_length': 5,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__alpha': 1.0,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__copy_X': True,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__fit_intercept': True,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__max_iter': 1000,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__positive': False,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__precompute': False,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__random_state': None,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__selection': 'cyclic',
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__tol': 0.0001,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__warm_start': False,
'forecaster_X__realgdp__steps': [ColumnSelect(columns=['realgdp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])],
'forecaster_X__realgdp__ColumnSelect': ColumnSelect(columns=['realgdp']),
'forecaster_X__realgdp__Deseasonalizer': Deseasonalizer(),
'forecaster_X__realgdp__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__realgdp__ColumnSelect__columns': ['realgdp'],
'forecaster_X__realgdp__ColumnSelect__index_treatment': 'remove',
'forecaster_X__realgdp__ColumnSelect__integer_treatment': 'col',
'forecaster_X__realgdp__Deseasonalizer__model': 'additive',
'forecaster_X__realgdp__Deseasonalizer__sp': 1,
'forecaster_X__realgdp__MultiplexForecaster__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],
'forecaster_X__realgdp__MultiplexForecaster__selected_forecaster': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_X__realgdp__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator': Ridge(),
'forecaster_X__realgdp__MultiplexForecaster__ridge__pooling': 'local',
'forecaster_X__realgdp__MultiplexForecaster__ridge__transformers': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge__window_length': 5,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__alpha': 1.0,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__copy_X': True,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__fit_intercept': True,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__max_iter': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__positive': False,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__random_state': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__solver': 'auto',
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__tol': 0.0001,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator': Lasso(),
'forecaster_X__realgdp__MultiplexForecaster__lasso__pooling': 'local',
'forecaster_X__realgdp__MultiplexForecaster__lasso__transformers': None,
'forecaster_X__realgdp__MultiplexForecaster__lasso__window_length': 5,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__alpha': 1.0,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__copy_X': True,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__fit_intercept': True,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__max_iter': 1000,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__positive': False,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__precompute': False,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__random_state': None,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__selection': 'cyclic',
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__tol': 0.0001,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__warm_start': False,
'forecaster_X__unemp__steps': [ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])],
'forecaster_X__unemp__ColumnSelect': ColumnSelect(columns=['unemp']),
'forecaster_X__unemp__Deseasonalizer': Deseasonalizer(),
'forecaster_X__unemp__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__unemp__ColumnSelect__columns': ['unemp'],
'forecaster_X__unemp__ColumnSelect__index_treatment': 'remove',
'forecaster_X__unemp__ColumnSelect__integer_treatment': 'col',
'forecaster_X__unemp__Deseasonalizer__model': 'additive',
'forecaster_X__unemp__Deseasonalizer__sp': 1,
'forecaster_X__unemp__MultiplexForecaster__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
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'forecaster_X__unemp__MultiplexForecaster__selected_forecaster': None,
'forecaster_X__unemp__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_X__unemp__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator': Ridge(),
'forecaster_X__unemp__MultiplexForecaster__ridge__pooling': 'local',
'forecaster_X__unemp__MultiplexForecaster__ridge__transformers': None,
'forecaster_X__unemp__MultiplexForecaster__ridge__window_length': 5,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__alpha': 1.0,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__copy_X': True,
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'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__max_iter': None,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__positive': False,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__random_state': None,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__solver': 'auto',
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__tol': 0.0001,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator': Lasso(),
'forecaster_X__unemp__MultiplexForecaster__lasso__pooling': 'local',
'forecaster_X__unemp__MultiplexForecaster__lasso__transformers': None,
'forecaster_X__unemp__MultiplexForecaster__lasso__window_length': 5,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__alpha': 1.0,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__copy_X': True,
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'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__max_iter': 1000,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__positive': False,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__precompute': False,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__random_state': None,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__selection': 'cyclic',
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__tol': 0.0001,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__warm_start': False,
'forecaster_y__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
('lasso',
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'forecaster_y__selected_forecaster': None,
'forecaster_y__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_y__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_y__ridge__estimator': Ridge(),
'forecaster_y__ridge__pooling': 'local',
'forecaster_y__ridge__transformers': None,
'forecaster_y__ridge__window_length': 5,
'forecaster_y__ridge__estimator__alpha': 1.0,
'forecaster_y__ridge__estimator__copy_X': True,
'forecaster_y__ridge__estimator__fit_intercept': True,
'forecaster_y__ridge__estimator__max_iter': None,
'forecaster_y__ridge__estimator__positive': False,
'forecaster_y__ridge__estimator__random_state': None,
'forecaster_y__ridge__estimator__solver': 'auto',
'forecaster_y__ridge__estimator__tol': 0.0001,
'forecaster_y__lasso__estimator': Lasso(),
'forecaster_y__lasso__pooling': 'local',
'forecaster_y__lasso__transformers': None,
'forecaster_y__lasso__window_length': 5,
'forecaster_y__lasso__estimator__alpha': 1.0,
'forecaster_y__lasso__estimator__copy_X': True,
'forecaster_y__lasso__estimator__fit_intercept': True,
'forecaster_y__lasso__estimator__max_iter': 1000,
'forecaster_y__lasso__estimator__positive': False,
'forecaster_y__lasso__estimator__precompute': False,
'forecaster_y__lasso__estimator__random_state': None,
'forecaster_y__lasso__estimator__selection': 'cyclic',
'forecaster_y__lasso__estimator__tol': 0.0001,
'forecaster_y__lasso__estimator__warm_start': False}
图形化流水线与嵌套顺序流水线的比较#
图形化流水线的优点#
便于实现复杂流水线
通过嵌套顺序流水线，即使是图形化流水线的简化版本也非常复杂难以实现。
通过嵌套顺序流水线，有些图形化流水线无法实现（例如，带有耦合 ForecastX 的示例）。
预处理步骤无法在不同的预测器之间共享。
顺序流水线的参数结构可能非常复杂。
在复杂场景中，如何微调 edges？
顺序流水线的优点#
构建简单流水线非常容易。
逆操作会自动应用。
与实验性的图形化流水线相比，这是一个成熟的功能。
何时使用哪种？#
如果您的流水线不需要太多嵌套，且主要是顺序的，您可能应该坚持使用标准的流水线实现。
如果您的流水线需要表示具有多个相互影响的预测器的复杂场景，您可能希望使用图形化流水线，因为它使编写代码更容易