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

clone([random_state])

Obtain a clone of the object with the same hyperparameters.

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[, raise_error, ...])

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()

Sklearn metadata routing.

get_params([deep])

Get parameters for this estimator.

get_tag(tag_name[, raise_error, ...])

Get tag value from estimator class.

get_tags()

Get tags from estimator.

predict(X)

Predicts target variable for time series in X.

reset([keep])

Reset the object to a clean post-init state.

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_tags(**tag_dict)

Set dynamic tags to given values.

temporal_importance_curves([return_dict, ...])

Calculate the temporal importance curves for each feature.

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)

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.

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:
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, raise_error=True, tag_value_default=None)[source]

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

Parameters:
tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:
tag_value

Value of the tag_name tag in cls. 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

If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:
fitted_paramsdict

Fitted parameter names mapped to their values.

get_metadata_routing()[source]

Sklearn metadata routing.

Not supported by aeon estimators.

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, raise_error=True, tag_value_default=None)[source]

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:
tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

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.

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.

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.

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 hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:
selfobject

Reference to self.

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.

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.

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_tags(**tag_dict)[source]

Set dynamic tags to given values.

Parameters:
**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:
selfobject

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.