TSCGridSearchCV#

class TSCGridSearchCV(estimator, param_grid, scoring=None, n_jobs='deprecated', refit=True, cv=None, verbose=0, pre_dispatch='deprecated', error_score=nan, return_train_score=False, tune_by_variable=False, greater_is_better='auto', backend=None, backend_params=None)[source]#

Exhaustive search over specified parameter values for a classifier.

Optimizes hyper-parameters of estimator by exhaustive grid search, using sktime native backtesting via classification.model_evaluation.evaluate.

In fit, each parameter combination in param_grid is backtested on the data passed, using the cross-validation scheme cv and the metric scoring. All candidates are evaluated on identical folds.

The parameter combination with the best mean test score is set as best_params_, and a clone of estimator with those parameters is set as best_estimator_. If refit is not False, best_estimator_ is fitted to the entire data, and predict and predict-like methods of the tuner call the respective method of best_estimator_.

Parameters:

estimatorsktime classifier, BaseClassifier instance or interface compatible

The classifier to tune, must implement the sktime classifier interface.

param_griddict or list of dictionaries

Dictionary with parameters names (str) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.

scoringNone, str, callable, sklearn scorer, or list or dict of these

Metric or metrics to evaluate the cross-validated model with.

a callable must have signature (y_true, y_pred) -> float, e.g., accuracy_score from sklearn.metrics. Its value is reported as is
a string must name a scikit-learn scorer, e.g., "accuracy". Values are reported with the sign convention of the scorer, so values of "neg_log_loss" are negative
a list or dict selects multiple metrics. The first is used to rank candidates, unless refit names another. Dict keys are used as the metric names in cv_results_
if None, defaults to accuracy_score

n_jobsint, optional, default=”deprecated”

Number of jobs to run in parallel over the parameter candidates.

Deprecated, and will be removed in sktime 1.3.0. If passed, the value is written to backend_params, and backend defaults to "loky", so behaviour is unchanged. To retain the behaviour after removal, pass backend="loky" and backend_params={"n_jobs": ...} instead.

refitbool, str, or callable, default=True

Refit best_estimator_ using the best found parameters on the whole dataset. If False, predict and predict-like methods raise, and the tuner can be used only to tune hyper-parameters, e.g., as a parameter estimator via get_fitted_params.

For multi-metric evaluation, this can be a str naming the metric to select the best parameters by.

Where there are considerations other than the best score in choosing the best parameters, refit can be a callable, which is applied to cv_results_ and returns the selected best_index_. In that case best_score_ is not available.

The refitted estimator is available at the best_estimator_ attribute, and permits calling predict directly on the tuner.

cvint, cross-validation generator, iterable of splits, or None, default=None

Determines the cross-validation splitting strategy. Possible inputs for cv are:

None, to use the default 5-fold cross-validation,
integer, to specify the number of folds in a (Stratified)KFold,
a cross-validation splitter with a split method,
an iterable yielding (train, test) splits as arrays of indices.

For integer and None inputs, StratifiedKFold is used if y is binary or multiclass, and KFold otherwise. Both are instantiated with shuffle=False, so the splits are the same across calls.

Splits are computed once, before the search, so that all parameter candidates are evaluated on the same folds.

verboseint, default=0

Controls the verbosity. If positive, the number of fits is printed.

pre_dispatchint or str, optional, default=”deprecated”

Number of jobs dispatched during parallel execution, a joblib parameter.

Deprecated, and will be removed in sktime 1.3.0. If passed, the value is written to backend_params, and backend defaults to "loky", so behaviour is unchanged. To retain the behaviour after removal, pass backend="loky" and backend_params={"pre_dispatch": ...} instead.

error_score‘raise’ or numeric, default=np.nan

Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.

return_train_scorebool, default=False

Whether to include training scores in cv_results_.

Deprecated, and will be removed in sktime 1.3.0. Train scores are not computed by the native grid search, so the value passed is ignored and cv_results_ contains test scores only. Passing True raises a warning.

tune_by_variablebool, optional (default=False)

Whether to tune parameter by each time series variable separately, in case of multivariate data passed to the tuning estimator. Only applies if time series passed are strictly multivariate. If True, clones of the estimator will be fit to each variable separately, and are available in fields of the classifiers_ attribute. Has the same effect as applying ColumnEnsembleClassifier wrapper to self. If False, the same best parameter is selected for all variables.

greater_is_better“auto”, bool, optional, default=”auto”

Whether higher values of the reported metric are better, used to rank the parameter candidates.

“auto” determines the direction from the metric. Scikit-learn scorers are higher-is-better by their sign convention. For metric callables, the direction is inferred from the metric, e.g., accuracy_score is higher-is-better, and log_loss is lower-is-better
True or False set the direction explicitly, for all metrics

backendstr, optional, default=None

Parallelization backend for the search over parameter candidates.

None: executes loop sequentially, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses joblib.Parallel loops
“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

Recommendation: use “dask” or “loky” for parallel grid search. “threading” is unlikely to see speed ups due to the GIL.

backend_paramsdict, optional

Additional parameters passed to the backend as config, directly passed to utils.parallel.parallelize. Valid keys depend on backend, see there for details.

Attributes:

cv_results_dict of str to numpy (masked) ndarray

A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame. Contains params, one param_<name> column per searched parameter, fit and score timings, and per metric the per-fold scores split<i>_test_<name>, their mean mean_test_<name>, standard deviation std_test_<name>, and rank rank_test_<name>, 1 being the best.

For a single metric, <name> is score, e.g., mean_test_score. For multiple metrics, <name> is the name of the respective metric.

best_estimator_estimator

Clone of estimator with the best found parameters set, i.e., the parameters which gave the best mean test score on the held out data. Fitted to the entire data if refit is not False, otherwise unfitted. See the refit parameter for more information on allowed values.

best_score_float

Mean cross-validated score of best_estimator_. Not available if refit is a callable.

best_params_dict

Parameter setting that gave the best results on the hold out data.

best_index_int

The index in the cv_results_ arrays which corresponds to the best candidate parameter setting.

The dict at cv_results_["params"][best_index_] gives the parameter setting for the best model, i.e., is identical with best_params_.

scorer_callable or dict of callable

Metric used on the held out data to choose the best parameters. For multi-metric evaluation, a dict of metric name to metric.

n_splits_int

The number of cross-validation splits (folds/iterations).

refit_time_float

Seconds used for refitting the best model on the whole dataset. This is present only if refit is not False.

multimetric_bool

Whether multiple metrics were passed in scoring.

classes_ndarray of shape (n_classes,)

The class labels seen in fit.

See also

ParameterGrid: Generates all the combinations of a hyperparameter grid.
sktime.classification.model_evaluation.evaluate: Backtesting used internally.
sklearn.metrics.make_scorer: Make a scorer from a performance metric or loss function.

Examples

>>> from sklearn.metrics import accuracy_score
>>> from sktime.classification.dummy import DummyClassifier
>>> from sktime.classification.model_selection import TSCGridSearchCV
>>> from sktime.datasets import load_unit_test
>>>
>>> X, y = load_unit_test(split="train")
>>> tuned = TSCGridSearchCV(
...     DummyClassifier(),
...     param_grid={"strategy": ["most_frequent", "prior"]},
...     scoring=accuracy_score,
...     cv=2,
... )
>>> tuned = tuned.fit(X, y)
>>> y_pred = tuned.predict(X)
>>> best_params = tuned.best_params_

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.

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

Return testing parameter settings for the estimator.

Parameters:

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. For classifiers, a “default” set of parameters should be provided for general testing, and a “results_comparison” set for comparing against previously recorded results if the general set does not produce suitable probabilities to compare against.

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.

check_is_fitted(method_name=None)[source]#

Check if the estimator has been fitted.

Check if _is_fitted attribute is present and True. The is_fitted attribute should be set to True in calls to an object’s fit method.

If not, raises a NotFittedError.

Parameters:

method_namestr, optional: Name of the method that called this function. If provided, the error message will include this information.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters and config.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Equivalent to constructing a new instance of type(self), with parameters of self, that is, type(self)(**self.get_params(deep=False)).

If configs were set on self, the clone will also have the same configs as the original, equivalent to calling cloned_self.set_config(**self.get_config()).

Also equivalent in value to a call of self.reset, with the exception that clone returns a new object, instead of mutating self like reset.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

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

Clone tags from another object as dynamic override.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of 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.

clone_tags sets dynamic tag overrides from another object, estimator.

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

The dynamic tags are set to the values of the tags in estimator, with the names specified in tag_names.

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

Current tag values can be inspected by get_tags or get_tag.

Parameters:

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]#

Construct an instance of the class, using first test parameter set.

Parameters:

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]#

Create list of all test instances and a list of names for them.

Parameters:

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)[source]#

Fit time series classifier to training data.

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

Parameters:

Xsktime compatible time series panel data container of Panel scitype

time series to fit the 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.

ysktime compatible tabular data container, Table scitype

1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame

Returns:

selfReference to self.

fit_predict(X, y, cv=None, change_state=True)[source]#

Fit and predict labels for sequences in X.

Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.

Writes to self, if change_state=True:: Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.

Does not update state if change_state=False.

Parameters:

Xsktime compatible time series panel data container of Panel scitype

time series to fit to and predict labels for.

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.

ysktime compatible tabular data container, Table scitype

1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame

cvNone, int, or sklearn cross-validation object, optional, default=None

None : predictions are in-sample, equivalent to fit(X, y).predict(X)
cv : predictions are equivalent to fit(X_train, y_train).predict(X_test), where multiple X_train, y_train, X_test are obtained from cv folds. returned y is union over all test fold predictions, cv test folds must be non-intersecting
int : equivalent to cv=KFold(cv, shuffle=True, random_state=x), i.e., k-fold cross-validation predictions out-of-sample, and where random_state x is taken from self if exists, otherwise x=None

change_statebool, optional (default=True)

if False, will not change the state of the classifier, i.e., fit/predict sequence is run with a copy, self does not change
if True, will fit self to the full X and y, end state will be equivalent to running fit(X, y)

Returns:

y_predsktime compatible tabular data container, of Table scitype

predicted class labels

1D iterable, of shape [n_instances], or 2D iterable, of shape [n_instances, n_dimensions].

0-th indices correspond to instance indices in X, 1-st indices (if applicable) correspond to multioutput vector indices in X.

1D np.npdarray, if y univariate (one dimension); otherwise, same type as y passed in fit

fit_predict_proba(X, y, cv=None, change_state=True)[source]#

Fit and predict labels probabilities for sequences in X.

Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.

Writes to self, if change_state=True:: Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.

Does not update state if change_state=False.

Parameters:

Xsktime compatible time series panel data container of Panel scitype

time series to fit to and predict labels for.

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.

ysktime compatible tabular data container, Table scitype

1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame

cvNone, int, or sklearn cross-validation object, optional, default=None

None : predictions are in-sample, equivalent to fit(X, y).predict(X)
cv : predictions are equivalent to fit(X_train, y_train).predict(X_test), where multiple X_train, y_train, X_test are obtained from cv folds. returned y is union over all test fold predictions, cv test folds must be non-intersecting
int : equivalent to cv=KFold(cv, shuffle=True, random_state=x), i.e., k-fold cross-validation predictions out-of-sample, and where random_state x is taken from self if exists, otherwise x=None

change_statebool, optional (default=True)

if False, will not change the state of the classifier, i.e., fit/predict sequence is run with a copy, self does not change
if True, will fit self to the full X and y, end state will be equivalent to running fit(X, y)

Returns:

y_pred2D np.array of int, of shape [n_instances, n_classes]: predicted class label probabilities 0-th indices correspond to instance indices in X 1-st indices correspond to class index, in same order as in self.classes_ entries are predictive class probabilities, summing to 1

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

Get class tag value from class, with tag level inheritance from parents.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of 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.

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.

Parameters:

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]#

Get class tags from class, with tag level inheritance from parent classes.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of 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.

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.

Returns:

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]#

Get config flags for self.

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]#

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

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.

Parameters:

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]#

Get a dict of parameters values for this object.

Parameters:

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]#

Get tag value from instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of 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.

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.

Parameters:

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.

Raises:

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

get_tags()[source]#

Get tags from instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of 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.

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]#

Check if the object is composed of other BaseObjects.

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]#

Load object from file location.

Parameters:

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

Returns:

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

classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:

serial1st element of output of cls.save(None)

Returns:

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

predict(X)[source]#

Predicts labels for sequences in X.

Parameters:

Xsktime compatible time series panel data container of Panel scitype

time series to predict labels for.

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:

y_predsktime compatible tabular data container, of Table scitype

predicted class labels

1D iterable, of shape [n_instances], or 2D iterable, of shape [n_instances, n_dimensions].

0-th indices correspond to instance indices in X, 1-st indices (if applicable) correspond to multioutput vector indices in X.

1D np.npdarray, if y univariate (one dimension); otherwise, same type as y passed in fit

predict_proba(X)[source]#

Predicts labels probabilities for sequences in X.

Parameters:

Xsktime compatible time series panel data container of Panel scitype

time series to predict labels for.

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:

y_pred2D np.array of int, of shape [n_instances, n_classes]: predicted class label probabilities 0-th indices correspond to instance indices in X 1-st indices correspond to class index, in same order as in self.classes_ entries are predictive class probabilities, summing to 1

reset()[source]#

Reset the object to a clean post-init state.

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]#

Save serialized self to bytes-like object or to (.zip) file.

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).

Parameters:

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) → float[source]#

Scores predicted labels against ground truth labels on X.

Parameters:

Xsktime compatible time series panel data container of Panel scitype

time series to score predicted labels for.

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.

ysktime compatible tabular data container, Table scitype

1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame

Returns:

float, accuracy score of predict(X) vs y

set_config(**config_dict)[source]#

Set config flags to given values.

Parameters:

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 sequentially, 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]#

Set the parameters of this object.

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>.

Parameters:

**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]#

Set random_state pseudo-random seed parameters for self.

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.

Parameters:

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]#

Set instance level tag overrides to given values.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of 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.

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.

Parameters:

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

Returns:

Self: Reference to self.