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_stsf.py
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"""Supervised Time Series Forest Classifier (STSF).
Interval-based STSF classifier extracting summary features from intervals selected
through a supervised process.
"""
__author__ = ["MatthewMiddlehurst"]
__all__ = ["SupervisedTimeSeriesForest"]
import numpy as np
from sklearn.preprocessing import FunctionTransformer
from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
from aeon.classification.base import BaseClassifier
from aeon.transformations.collection import PeriodogramTransformer
from aeon.utils.numba.general import first_order_differences_3d
from aeon.utils.numba.stats import (
row_iqr,
row_mean,
row_median,
row_numba_max,
row_numba_min,
row_slope,
row_std,
)
class SupervisedTimeSeriesForest(BaseIntervalForest, BaseClassifier):
"""Supervised Time Series Forest (STSF).
An ensemble of decision trees built on intervals selected through a supervised
process as described in [1]_.
Overview: Input n series length m
For each tree
- sample X using class-balanced bagging
- sample intervals for all 3 representations and 7 features using supervised
- method
- find mean, median, std, slope, iqr, min and max using their corresponding
- interval for each representation, concatenate to form new data set
- build a decision tree on new data set
Ensemble the trees with averaged probability estimates.
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.
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.
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.
use_pyfftw : bool, default=True
Whether to use the pyfftw library for FFT calculations. Requires the pyfftw
package to be installed.
save_transformed_data : bool, default="deprecated"
Save the data transformed in ``fit``.
Deprecated and will be removed in v0.8.0. Use ``fit_predict`` and
``fit_predict_proba`` to generate train estimates instead.
``transformed_data_`` will also be removed.
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.
n_classes_ : int
Number of classes. Extracted from the data.
classes_ : ndarray of shape (n_classes_)
Holds the label for each class.
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 TransformerMixin
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 estimators. Only saved when
save_transformed_data is true.
Notes
-----
For the Java version, see
`TSML <https://github.com/uea-machine-learning/tsml/blob/master/src/main/
java/tsml/classifiers/interval_based/STSF.java>`_.
References
----------
.. [1] Cabello, Nestor, et al. "Fast and Accurate Time Series Classification
Through Supervised Interval Search." IEEE ICDM 2020
Examples
--------
>>> from aeon.classification.interval_based import SupervisedTimeSeriesForest
>>> from aeon.testing.utils.data_gen import make_example_3d_numpy
>>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12,
... return_y=True, random_state=0)
>>> clf = SupervisedTimeSeriesForest(n_estimators=10, random_state=0)
>>> clf.fit(X, y)
SupervisedTimeSeriesForest(n_estimators=10, random_state=0)
>>> clf.predict(X)
array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])
"""
_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,
min_interval_length=3,
time_limit_in_minutes=None,
contract_max_n_estimators=500,
use_pyfftw=False,
save_transformed_data="deprecated",
random_state=None,
n_jobs=1,
parallel_backend=None,
):
self.use_pyfftw = use_pyfftw
if use_pyfftw:
self.set_tags(**{"python_dependencies": "pyfftw"})
series_transformers = [
None,
FunctionTransformer(func=first_order_differences_3d, validate=False),
PeriodogramTransformer(use_pyfftw=use_pyfftw),
]
interval_features = [
row_mean,
row_std,
row_slope,
row_median,
row_iqr,
row_numba_min,
row_numba_max,
]
super().__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
interval_selection_method="supervised",
n_intervals=1,
min_interval_length=min_interval_length,
max_interval_length=np.inf,
interval_features=interval_features,
series_transformers=series_transformers,
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,
)
def _fit(self, X, y):
return super()._fit(X, y)
def _predict(self, X) -> np.ndarray:
return super()._predict(X)
def _predict_proba(self, X) -> np.ndarray:
return super()._predict_proba(X)
def _fit_predict(self, X, y) -> np.ndarray:
return super()._fit_predict(X, y)
def _fit_predict_proba(self, X, y) -> np.ndarray:
return super()._fit_predict_proba(X, y)
@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.
SupervisedTimeSeriesForest provides the following special sets:
"results_comparison" - used in some classifiers to compare against
previously generated results where the default set of parameters
cannot produce suitable probability estimates
"contracting" - used in classifiers that set the
"capability:contractable" tag to True to test contacting
functionality
"train_estimate" - used in some classifiers that set the
"capability:train_estimate" tag to True to allow for more efficient
testing when relevant parameters are available
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`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10}
elif parameter_set == "contracting":
return {
"time_limit_in_minutes": 5,
"contract_max_n_estimators": 2,
}
else:
return {"n_estimators": 2}