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_tsf.py
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_tsf.py
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# -*- coding: utf-8 -*-
# copyright: aeon developers, BSD-3-Clause License (see LICENSE file)
"""Time Series Forest (TSF) Regressor.
Interval-based TSF regressor, extracts basic summary features from random intervals.
"""
__author__ = ["Matthew Middlehurst"]
__all__ = ["TimeSeriesForestRegressor"]
import numpy as np
from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
from aeon.regression import BaseRegressor
class TimeSeriesForestRegressor(BaseIntervalForest, BaseRegressor):
"""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_estimator : BaseEstimator or None, default=None
scikit-learn BaseEstimator used to build the interval ensemble. If None, use a
simple decision tree.
n_estimators : int, default=200
Number of estimators to build for the ensemble.
n_intervals : int, 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_length : int, 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_length : int, 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_minutes : int, default=0
Time contract to limit build time in minutes, overriding n_estimators.
Default of 0 means n_estimators are used.
contract_max_n_estimators : int, default=500
Max number of estimators when time_limit_in_minutes is set.
save_transformed_data : bool, default=False
Save the data transformed in fit for use in _get_train_preds.
random_state : int, 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_jobs : int, default=1
The number of jobs to run in parallel for both `fit` and `predict`.
``-1`` means using all processors.
parallel_backend : str, 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_instances_ : 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.
transformed_data_ : list of shape (n_estimators) of ndarray with shape
(n_instances_ ,total_intervals * att_subsample_size)
The transformed dataset for all regressors. Only saved when
save_transformed_data is true.
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.datasets 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 ])
"""
_tags = {
"capability:multivariate": True,
"capability:train_estimate": True,
"capability:contractable": True,
"capability:multithreading": True,
"algorithm_type": "interval",
}
def __init__(
self,
base_estimator=None,
n_estimators=200,
n_intervals="sqrt",
min_interval_length=3,
max_interval_length=np.inf,
time_limit_in_minutes=None,
contract_max_n_estimators=500,
save_transformed_data=False,
random_state=None,
n_jobs=1,
parallel_backend=None,
):
super(TimeSeriesForestRegressor, self).__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
interval_selection_method="random",
n_intervals=n_intervals,
min_interval_length=min_interval_length,
max_interval_length=max_interval_length,
interval_features=None,
series_transformers=None,
att_subsample_size=None,
replace_nan=0,
time_limit_in_minutes=time_limit_in_minutes,
contract_max_n_estimators=contract_max_n_estimators,
save_transformed_data=save_transformed_data,
random_state=random_state,
n_jobs=n_jobs,
parallel_backend=parallel_backend,
)
@classmethod
def get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, 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
-------
params : dict 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`.
"""
return {
"n_estimators": 2,
"n_intervals": 2,
}