`TimeSeriesKMeans`#

类 TimeSeriesKMeans(n_clusters: int = 8, init_algorithm: str | Callable = 'random', metric: str | Callable = 'dtw', n_init: int = 10, max_iter: int = 300, tol: float = 1e-06, verbose: bool = False, random_state: int | RandomState = None, averaging_method: str | Callable[[ndarray], ndarray] = 'mean', distance_params: dict = None, average_params: dict = None)[source]#

时间序列 K-均值实现。

参数:

n_clusters: int, 默认值 = 8

要形成的聚类数量以及要生成的质心数量。

init_algorithm: str, np.ndarray (3d array of shape (n_clusters, n_dimensions,

series_length)), defaults = ‘random’ Method for initializing cluster centers or an array of initial cluster centers. If string, any of the following strings are valid

[‘kmeans++’, ‘random’, ‘forgy’].

If 3D np.ndarray, initializes cluster centers with the provided array. The array: 必须具有 (n_clusters, n_dimensions, series_length) 的形状，并且数组中的聚类数量必须与提供给 n_clusters 参数的值相同。

metric: str 或 Callable, 默认值 = ‘dtw’

用于计算时间序列之间相似度的距离度量。以下任何一个都有效：[‘dtw’, ‘euclidean’, ‘erp’, ‘edr’, ‘lcss’, ‘squared’, ‘ddtw’, ‘wdtw’, ‘wddtw’]

n_init: int, 默认值 = 10

使用不同质心种子运行 k-均值算法的次数。最终结果将是 n_init 次连续运行中根据惯性（inertia）表现最好的输出。

max_iter: int, 默认值 = 300

k-均值算法单次运行的最大迭代次数。

tol: float, 默认值 = 1e-6

两次连续迭代中聚类中心差异的 Frobenius 范数方面的相对容差，用于判断收敛。

verbose: bool, 默认值 = False

详细模式。

random_state: int 或 np.random.RandomState 实例或 None, 默认值 = None

确定质心初始化的随机数生成。

averaging_method: str 或 Callable, 默认值 = ‘mean’

计算聚类平均值的方法。以下任何字符串都有效：[‘mean’, ‘dba’]。如果提供了 Callable，则必须采用 Callable[[np.ndarray], np.ndarray] 的形式。

average_params: dict, 默认值 = None = 无参数

包含 averaging_method 关键字参数的字典。

distance_params: dict, 默认值 = None = 无参数

包含所用距离度量关键字参数的字典。

属性:

cluster_centers_: np.ndarray (3d array of shape (n_clusters, n_dimensions,: series_length) 的 3d 数组) 代表每个聚类中心的时间序列。如果算法在完全收敛之前停止，这些将与 labels_ 不一致。
labels_: np.ndarray (形状为 (n_instance,) 的 1d 数组): 每个时间序列所属聚类的索引标签。
inertia_: float: 样本到其最近聚类中心的平方距离之和，如果提供了样本权重，则按样本权重加权。
n_iter_: int: 运行的迭代次数。

示例

>>> from sktime.datasets import load_arrow_head
>>> from sktime.clustering.k_means import TimeSeriesKMeans
>>> X_train, y_train = load_arrow_head(split="train")
>>> X_test, y_test = load_arrow_head(split="test")
>>> clusterer = TimeSeriesKMeans(n_clusters=3)  
>>> clusterer.fit(X_train)  
TimeSeriesKMeans(n_clusters=3)
>>> y_pred = clusterer.predict(X_test)  

方法

`check_is_fitted`([method_name])	检查估计器是否已拟合。
`clone`()	获取具有相同超参数和配置的对象的克隆。
`clone_tags`(estimator[, tag_names])	将另一个对象的标签作为动态覆盖克隆。
`create_test_instance`([parameter_set])	使用第一个测试参数集构造类的实例。
`create_test_instances_and_names`([parameter_set])	创建所有测试实例的列表及其名称列表。
`fit`(X[, y])	将时间序列聚类器拟合到训练数据。
`fit_predict`(X[, y])	计算聚类中心并预测每个时间序列的聚类索引。
`get_class_tag`(tag_name[, tag_value_default])	从类中获取类标签值，并考虑父类的标签继承。
`get_class_tags`()	从类中获取类标签，并考虑父类的标签继承。
`get_config`()	获取自身的配置标志。
`get_fitted_params`([deep])	获取已拟合参数。
`get_param_defaults`()	获取对象的参数默认值。
`get_param_names`([sort])	获取对象的参数名称。
`get_params`([deep])	获取此对象的参数值字典。
`get_tag`(tag_name[, tag_value_default, ...])	从实例获取标签值，并考虑标签级别的继承和覆盖。
`get_tags`()	从实例获取标签，并考虑标签级别的继承和覆盖。
`get_test_params`([parameter_set])	返回估计器的测试参数设置。
`is_composite`()	检查对象是否由其他 BaseObject 组成。
`load_from_path`(serial)	从文件位置加载对象。
`load_from_serial`(serial)	从序列化内存容器加载对象。
`predict`(X[, y])	预测 X 中每个样本所属的最接近的聚类。
`predict_proba`(X)	预测 X 中序列的标签概率。
`reset`()	将对象重置为初始化后的干净状态。
`save`([path, serialization_format])	将序列化的自身保存到字节类对象或到 (.zip) 文件。
`score`(X[, y])	评估聚类器的质量得分。
`set_config`(**config_dict)	将配置标志设置为给定值。
`set_params`(**params)	设置此对象的参数。
`set_random_state`([random_state, deep, ...])	为自身设置 random_state 伪随机种子参数。
`set_tags`(**tag_dict)	将实例级别的标签覆盖设置为给定值。

check_is_fitted(method_name=None)[source]#

检查估计器是否已拟合。

检查 _is_fitted 属性是否存在且为 True。在调用对象的 fit 方法时，is_fitted 属性应设置为 True。

如果不是，则引发 NotFittedError。

参数:

method_namestr, 可选: 调用此方法的名称。如果提供，错误消息将包含此信息。

引发:

NotFittedError: 如果估计器尚未拟合。

clone()[source]#

获取具有相同超参数和配置的对象的克隆。

克隆是处于初始化后状态的不同对象，不共享引用。此函数等同于返回 sklearn.clone 的 self。

等同于构造一个 type(self) 的新实例，使用 self 的参数，即 type(self)(**self.get_params(deep=False))。

如果 self 上设置了配置，克隆也将具有与原始对象相同的配置，等同于调用 cloned_self.set_config(**self.get_config())。

其值也等同于调用 self.reset，不同之处在于 clone 返回一个新对象，而不是像 reset 那样修改 self。

引发:

如果克隆由于错误的 __init__ 而不符合规范，则引发 RuntimeError。

classmethod clone_tags(estimator, tag_names=None)[source]#

将另一个对象的标签作为动态覆盖克隆。

每个兼容 scikit-base 的对象都有一个标签字典。标签可用于存储对象的元数据，或控制对象的行为。

标签是实例 self 特定的键值对，它们是对象构造后不会更改的静态标志。

clone_tags 从另一个对象 estimator 设置动态标签覆盖。

clone_tags 方法只能在对象的 __init__ 方法中、构造期间或通过 __init__ 构造后直接调用。

动态标签被设置为 estimator 中标签的值，名称由 tag_names 指定。

The default of tag_names writes all tags from estimator to self.

Current tag values can be inspected by get_tags or get_tag.

参数:

estimatorAn instance of :class:BaseObject or derived class
tag_namesstr or list of str, default = None: Names of tags to clone. The default (None) clones all tags from estimator.

Returns:

self: Reference to self.

classmethod create_test_instance(parameter_set='default')[source]#

使用第一个测试参数集构造类的实例。

参数:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

classmethod create_test_instances_and_names(parameter_set='default')[source]#

创建所有测试实例的列表及其名称列表。

参数:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i])
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests. The naming convention is {cls.__name__}-{i} if more than one instance, otherwise {cls.__name__}

fit(X, y=None)[source]#

将时间序列聚类器拟合到训练数据。

State change: Changes state to “fitted”.
Writes to self: Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.

参数:

Xsktime compatible time series panel data container of Panel scitype

time series to fit estimator to.

Can be in any mtype of Panel scitype, for instance

pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER

for specifications, see examples/AA_datatypes_and_datasets.ipynb

Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.

yignored, exists for API consistency reasons.

Returns:

selfReference to self.

fit_predict(X, y=None) → ndarray[source]#

计算聚类中心并预测每个时间序列的聚类索引。

Convenience method; equivalent of calling fit(X) followed by predict(X)

参数:

Xsktime compatible time series panel data container of Panel scitype

time series to cluster.

Can be in any mtype of Panel scitype, for instance

pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER

for specifications, see examples/AA_datatypes_and_datasets.ipynb

Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.

y: ignored, exists for API consistency reasons.

Returns:

np.ndarray (1d array of shape (n_instances,)): Index of the cluster each time series in X belongs to.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

从类中获取类标签值，并考虑父类的标签继承。

Every scikit-base compatible object has a dictionary of tags, which are used to store metadata about the object.

The get_class_tag method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns the value of the tag with name tag_name from the object, taking into account tag overrides, in the following order of descending priority

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

To retrieve tag values with potential instance overrides, use the get_tag method instead.

参数:

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

从类中获取类标签，并考虑父类的标签继承。

每个兼容 scikit-base 的对象都有一个标签字典。标签可用于存储对象的元数据，或控制对象的行为。

标签是实例 self 特定的键值对，它们是对象构造后不会更改的静态标志。

The get_class_tags method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns a dictionary with keys being keys of any attribute of _tags set in the class or any of its parent classes.

Values are the corresponding tag values, with overrides in the following order of descending priority

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Instances can override these tags depending on hyper-parameters.

To retrieve tags with potential instance overrides, use the get_tags method instead.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

For including overrides from dynamic tags, use get_tags.

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or clone_tags.

get_config()[source]#

获取自身的配置标志。

Configs are key-value pairs of self, typically used as transient flags for controlling behaviour.

get_config returns dynamic configs, which override the default configs.

Default configs are set in the class attribute _config of the class or its parent classes, and are overridden by dynamic configs set via set_config.

Configs are retained under clone or reset calls.

Returns:

config_dictdict: Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_fitted_params(deep=True)[source]#

获取已拟合参数。

State required: Requires state to be “fitted”.

参数:

deepbool, default=True

Whether to return fitted parameters of components.

If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include

always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()[source]#

Get object’s parameter defaults.

Returns:

default_dict: dict[str, Any]: Keys are all parameters of cls that have a default defined in __init__. Values are the defaults, as defined in __init__.

classmethod get_param_names(sort=True)[source]#

Get object’s parameter names.

参数:

sortbool, default=True: Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]: List of parameter names of cls. If sort=False, in same order as they appear in the class __init__. If sort=True, alphabetically ordered.

get_params(deep=True)[source]#

获取此对象的参数值字典。

参数:

deepbool, default=True

Whether to return parameters of components.

If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

从实例获取标签值，并考虑标签级别的继承和覆盖。

每个兼容 scikit-base 的对象都有一个标签字典。标签可用于存储对象的元数据，或控制对象的行为。

标签是实例 self 特定的键值对，它们是对象构造后不会更改的静态标志。

The get_tag method retrieves the value of a single tag with name tag_name from the instance, taking into account tag overrides, in the following order of descending priority

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

参数:

tag_namestr: Name of tag to be retrieved
tag_value_defaultany type, optional; default=None: Default/fallback value if tag is not found
raise_errorbool: whether a ValueError is raised when the tag is not found

Returns:

tag_valueAny: Value of the tag_name tag in self. If not found, raises an error if raise_error is True, otherwise it returns tag_value_default.

引发:

ValueError, if raise_error is True.: The ValueError is then raised if tag_name is not in self.get_tags().keys().

get_tags()[source]#

从实例获取标签，并考虑标签级别的继承和覆盖。

每个兼容 scikit-base 的对象都有一个标签字典。标签可用于存储对象的元数据，或控制对象的行为。

标签是实例 self 特定的键值对，它们是对象构造后不会更改的静态标志。

The get_tags method returns a dictionary of tags, with keys being keys of any attribute of _tags set in the class or any of its parent classes, or tags set via set_tags or clone_tags.

Values are the corresponding tag values, with overrides in the following order of descending priority

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()[source]#

检查对象是否由其他 BaseObject 组成。

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool: Whether an object has any parameters whose values are BaseObject descendant instances.

property is_fitted[source]#

Whether fit has been called.

Inspects object’s _is_fitted` attribute that should initialize to ``False during object construction, and be set to True in calls to an object’s fit method.

Returns:

bool: Whether the estimator has been fit.

classmethod load_from_path(serial)[source]#

从文件位置加载对象。

参数:

serialresult of ZipFile(path).open(“object)

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)[source]#

从序列化内存容器加载对象。

参数:

serial1st element of output of cls.save(None)

Returns:

deserialized self resulting in output serial, of cls.save(None)

predict(X, y=None) → ndarray[source]#

预测 X 中每个样本所属的最接近的聚类。

参数:

Xsktime compatible time series panel data container of Panel scitype

time series to cluster.

Can be in any mtype of Panel scitype, for instance

pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER

for specifications, see examples/AA_datatypes_and_datasets.ipynb

Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.

y: ignored, exists for API consistency reasons.

Returns:

np.ndarray (1d array of shape (n_instances,)): Index of the cluster each time series in X belongs to.

predict_proba(X)[source]#

预测 X 中序列的标签概率。

Default behaviour is to call _predict and set the predicted class probability to 1, other class probabilities to 0. Override if better estimates are obtainable.

参数:

Xsktime compatible time series panel data container of Panel scitype

time series to cluster.

Can be in any mtype of Panel scitype, for instance

pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER

for specifications, see examples/AA_datatypes_and_datasets.ipynb

Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.

Returns:

y2D array of shape [n_instances, n_classes] - predicted class probabilities: 1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

reset()[source]#

将对象重置为初始化后的干净状态。

Results in setting self to the state it had directly after the constructor call, with the same hyper-parameters. Config values set by set_config are also retained.

A reset call deletes any object attributes, except

hyper-parameters = arguments of __init__ written to self, e.g., self.paramname where paramname is an argument of __init__
object attributes containing double-underscores, i.e., the string “__”. For instance, an attribute named “__myattr” is retained.
config attributes, configs are retained without change. That is, results of get_config before and after reset are equal.

Class and object methods, and class attributes are also unaffected.

Equivalent to clone, with the exception that reset mutates self instead of returning a new object.

After a self.reset() call, self is equal in value and state, to the object obtained after a constructor call``type(self)(**self.get_params(deep=False))``.

Returns:

self: Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

save(path=None, serialization_format='pickle')[source]#

将序列化的自身保存到字节类对象或到 (.zip) 文件。

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

参数:

pathNone or file location (str or Path)

if None, self is saved to an in-memory object if file location, self is saved to that file location. If

path=”estimator” then a zip file estimator.zip will be made at cwd.
path=”/home/stored/estimator” then a zip file estimator.zip will be

stored in /home/stored/.

serialization_format: str, default = “pickle”

Module to use for serialization. The available options are “pickle” and “cloudpickle”. Note that non-default formats might require installation of other soft dependencies.

Returns:

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file

score(X, y=None) → float[source]#

评估聚类器的质量得分。

参数:

Xnp.ndarray (2d or 3d array of shape (n_instances, series_length) or shape: (n_instances, n_dimensions, series_length)) or pd.DataFrame (where each column is a dimension, each cell is a pd.Series (any number of dimensions, equal or unequal length series)). Time series instances to train clusterer and then have indexes each belong to return.
y: ignored, exists for API consistency reasons.

Returns:

scorefloat: Score of the clusterer.

set_config(**config_dict)[source]#

将配置标志设置为给定值。

参数:

config_dictdict

Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below

displaystr, “diagram” (default), or “text”

how jupyter kernels display instances of self

“diagram” = html box diagram representation
“text” = string printout

print_changed_onlybool, default=True

whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False). Does not nest, i.e., only affects self and not component estimators.

warningsstr, “on” (default), or “off”

whether to raise warnings, affects warnings from sktime only

“on” = will raise warnings from sktime
“off” = will not raise warnings from sktime

backend:parallelstr, optional, default=”None”

backend to use for parallelization when broadcasting/vectorizing, one of

“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses joblib.Parallel
“joblib”: custom and 3rd party joblib backends, e.g., spark
“dask”: uses dask, requires dask package in environment
“ray”: uses ray, requires ray package in environment

backend:parallel:paramsdict, optional, default={} (no parameters passed)

additional parameters passed to the parallelization backend as config. Valid keys depend on the value of backend:parallel

“None”: no additional parameters, backend_params is ignored
“loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“joblib”: custom and 3rd party joblib backends, e.g., spark. Any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“dask”: any valid keys for dask.compute can be passed, e.g., scheduler
“ray”: The following keys can be passed
- “ray_remote_args”: dictionary of valid keys for ray.init
- “shutdown_ray”: bool, default=True; False prevents ray from
  shutting down after parallelization.
- “logger_name”: str, default=”ray”; name of the logger to use.
- “mute_warnings”: bool, default=False; if True, suppresses warnings

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**params)[source]#

设置此对象的参数。

The method works on simple skbase objects as well as on composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

参数:

**paramsdict: BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

set_random_state(random_state=None, deep=True, self_policy='copy')[source]#

为自身设置 random_state 伪随机种子参数。

Finds random_state named parameters via self.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in self, depending on self_policy, and remaining component objects if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base object, even those without a random_state parameter.

参数:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in skbase object valued parameters, i.e., component estimators.

If False, will set only self’s random_state parameter, if exists.
If True, will set random_state parameters in component objects as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

“copy” : self.random_state is set to input random_state
“keep” : self.random_state is kept as is
“new” : self.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to self

set_tags(**tag_dict)[source]#

将实例级别的标签覆盖设置为给定值。

Every scikit-base compatible object has a dictionary of tags, which are used to store metadata about the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object. They may be used for metadata inspection, or for controlling behaviour of the object.

set_tags sets dynamic tag overrides to the values as specified in tag_dict, with keys being the tag name, and dict values being the value to set the tag to.

The set_tags method should be called only in the __init__ method of an object, during construction, or directly after construction via __init__.

Current tag values can be inspected by get_tags or get_tag.

参数:

**tag_dictdict: Dictionary of tag name: tag value pairs.

Returns:

Self: Reference to self.

classmethod get_test_params(parameter_set='default')[source]#

返回估计器的测试参数设置。

参数:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return "default" set.

Returns:

paramsdict or list of dict, default = {}: Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

TimeSeriesKMeans#

`TimeSeriesKMeans`#