TimeSeriesForestRegressor¶

class TimeSeriesForestRegressor(base_estimator=None, n_estimators=200, n_intervals='sqrt', min_interval_length=3, max_interval_length=inf, time_limit_in_minutes=None, contract_max_n_estimators=500, random_state=None, n_jobs=1, parallel_backend=None)[source]¶

Time series forest (TSF) regressor.

A time series forest is an ensemble of decision trees built on random intervals. Overview: Input n series length m. For each tree

sample sqrt(m) intervals,

find mean, std and slope for each interval, concatenate to form new

data set, - build a decision tree on new data set.

Ensemble the trees with averaged predictions.

This implementation deviates from the original in minor ways. It samples intervals with replacement and does not use the tree splitting criteria refinement described in [1].

Parameters:

base_estimatorBaseEstimator or None, default=None

scikit-learn BaseEstimator used to build the interval ensemble. If None, use a simple decision tree.

n_estimatorsint, default=200

Number of estimators to build for the ensemble.

n_intervalsint, str, list or tuple, default=”sqrt”

Number of intervals to extract per tree for each series_transformers series.

An int input will extract that number of intervals from the series, while a str input will return a function of the series length (may differ per series_transformers output) to extract that number of intervals. Valid str inputs are:

“sqrt”: square root of the series length.

“sqrt-div”: sqrt of series length divided by the number
of series_transformers.

A list or tuple of ints and/or strs will extract the number of intervals using the above rules and sum the results for the final n_intervals. i.e. [4, “sqrt”] will extract sqrt(n_timepoints) + 4 intervals.

Different number of intervals for each series_transformers series can be specified using a nested list or tuple. Any list or tuple input containing another list or tuple must be the same length as the number of series_transformers.

min_interval_lengthint, float, list, or tuple, default=3

Minimum length of intervals to extract from series. float inputs take a proportion of the series length to use as the minimum interval length.

Different minimum interval lengths for each series_transformers series can be specified using a list or tuple. Any list or tuple input must be the same length as the number of series_transformers.

max_interval_lengthint, float, list, or tuple, default=np.inf

Maximum length of intervals to extract from series. float inputs take a proportion of the series length to use as the maximum interval length.

Different maximum interval lengths for each series_transformers series can be specified using a list or tuple. Any list or tuple input must be the same length as the number of series_transformers.

time_limit_in_minutesint, default=0

Time contract to limit build time in minutes, overriding n_estimators. Default of 0 means n_estimators are used.

contract_max_n_estimatorsint, default=500

Max number of estimators when time_limit_in_minutes is set.

random_stateint, RandomState instance or None, default=None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

parallel_backendstr, ParallelBackendBase instance or None, default=None

Specify the parallelisation backend implementation in joblib, if None a ‘prefer’ value of “threads” is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.

Attributes:

n_cases_int: The number of train cases in the training set.
n_channels_int: The number of dimensions per case in the training set.
n_timepoints_int: The length of each series in the training set.
total_intervals_int: Total number of intervals per tree from all representations.
estimators_list of shape (n_estimators) of BaseEstimator: The collections of estimators trained in fit.
intervals_list of shape (n_estimators) of BaseTransformer: Stores the interval extraction transformer for all estimators.

References

[1]

H.Deng, G.Runger, E.Tuv and M.Vladimir, “A time series forest for classification and feature extraction”, Information Sciences, 239, 2013

Examples

>>> from aeon.regression.interval_based import TimeSeriesForestRegressor
>>> from aeon.testing.data_generation import make_example_3d_numpy
>>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12,
...                              return_y=True, regression_target=True,
...                              random_state=0)
>>> reg = TimeSeriesForestRegressor(n_estimators=10, random_state=0)
>>> reg.fit(X, y)
TimeSeriesForestRegressor(n_estimators=10, random_state=0)
>>> reg.predict(X)
array([0.7252543 , 1.50132442, 0.95608366, 1.64399016, 0.42385504,
       0.60639322, 1.01919317, 1.30157483, 1.66017354, 0.2900776 ])

Methods

`check_is_fitted`()	Check if the estimator has been fitted.
`clone`([random_state])	Obtain a clone of the object with the same hyperparameters.
`create_test_instance`([parameter_set, ...])	Construct Estimator instance if possible.
`fit`(X, y)	Fit time series regressor to training data.
`fit_predict`(X, y)	Fits the regressor and predicts class labels for X.
`get_class_tag`(tag_name[, tag_value_default, ...])	Get tag value from estimator class (only class tags).
`get_class_tags`()	Get class tags from estimator class and all its parent classes.
`get_fitted_params`([deep])	Get fitted parameters.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from estimator class.
`get_tags`()	Get tags from estimator.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts target variable for time series in X.
`reset`([keep])	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y[, metric, metric_params])	Scores predicted labels against ground truth labels on X.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, metric, metric_params])	Request metadata passed to the `score` method.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`temporal_importance_curves`([return_dict, ...])	Calculate the temporal importance curves for each feature.

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()[source]¶

Check if the estimator has been fitted.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone(random_state=None)[source]¶

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None: Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject: Instance of type(self), clone of self (see above)

classmethod create_test_instance(parameter_set='default', return_first=True)[source]¶

Construct Estimator instance if possible.

Calls the get_test_params method and returns an instance or list of instances using the returned dict or list of dict.

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.
return_firstbool, default=True: If True, return the first instance of the list of instances. If False, return the list of instances.

Returns:

instanceBaseAeonEstimator or list of BaseAeonEstimator: Instance of the class with default parameters. If return_first is False, returns list of instances.

fit(X, y) → BaseCollectionEstimator[source]¶

Fit time series regressor to training data.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float, of shape (n_cases) - regression targets (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

selfBaseRegressor: Reference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

fit_predict(X, y) → ndarray[source]¶

Fits the regressor and predicts class labels for X.

fit_predict produces prediction estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Regressors which override _fit_predict will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float, of shape (n_cases) - regression targets (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

predictionsnp.ndarray: 1D np.array of float, of shape (n_cases) - predicted regression labels indices correspond to instance indices in X

classmethod get_class_tag(tag_name, tag_value_default=None, raise_error=False)[source]¶

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.
raise_errorbool: Whether a ValueError is raised when the tag is not found.

Returns:

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

Raises:

ValueError: if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()[source]¶

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

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 (= BaseAeonEstimator-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
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_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

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

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr: Name of tag to be retrieved.
tag_value_defaultany type, 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_value: Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError: if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()[source]¶

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

classmethod load_from_path(serial)[source]¶

Load object from file location.

Parameters:

serialobject: Result 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:

serialobject: First element of output of cls.save(None).

Returns:

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

predict(X) → ndarray[source]¶

Predicts target variable for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i other types are allowed and converted into one of the above.

Returns:

predictionsnp.ndarray: 1D np.array of float, of shape (n_cases) - predicted regression labels indices correspond to instance indices in X

reset(keep=None)[source]¶

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:: hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None: If None, all attributes are removed except hyper-parameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

self: Reference to self.

save(path=None)[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/.

Returns:

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

score(X, y, metric='r2', metric_params=None) → float[source]¶

Scores predicted labels against ground truth labels on X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float, of shape (n_cases) - regression targets (ground truth) for fitting indices corresponding to instance indices in X.

metricUnion[str, callable], default=”r2”,

Defines the scoring metric to test the fit of the model. For supported strings arguments, check sklearn.metrics.get_scorer_names.

metric_paramsdict, default=None,

Contains parameters to be passed to the scoring function. If None, no parameters are passed.

Returns:

scorefloat: MSE score of predict(X) vs y

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_score_request(*, metric: bool | None | str = '$UNCHANGED$', metric_params: bool | None | str = '$UNCHANGED$') → TimeSeriesForestRegressor[source]¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

metricstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for metric parameter in score.
metric_paramsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for metric_params parameter in score.

Returns:

selfobject: The updated object.

set_tags(**tag_dict)[source]¶

Set dynamic tags to given values.

Parameters:

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

Returns:

self: Reference to self.

temporal_importance_curves(return_dict=False, normalise_time_points=False)[source]¶

Calculate the temporal importance curves for each feature.

Can be finicky with transformers currently.

Parameters:

return_dictbool, default=False: If True, return a dictionary of curves. If False, return a list of names and a list of curves.
normalise_time_pointsbool, default=False: If True, normalise the time points for each feature to the number of splits that used that feature. If False, return the sum of the information gain for each split.

Returns:

nameslist of str: The names of the features.
curveslist of np.ndarray: The temporal importance curves for each feature.