/
_bagging.py
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/
_bagging.py
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"""Bagging time series classifiers."""
__author__ = ["fkiraly"]
__all__ = ["BaggingClassifier"]
from math import ceil
import numpy as np
import pandas as pd
from sktime.classification.base import BaseClassifier
class BaggingClassifier(BaseClassifier):
"""Bagging ensemble of time series classifiers.
Fits ``n_estimators`` clones of a classifier on
datasets which are instance sub-samples and/or variable sub-samples.
On ``predict_proba``, the mean average of probabilistic predictions is returned.
For a deterministic classifier, this results in majority vote for ``predict``.
The estimator allows to choose sample sizes for instances, variables,
and whether sampling is with or without replacement.
Direct generalization of ``sklearn``'s ``BaggingClassifier``
to the time series classification task.
Note: if ``n_features=1``, ``BaggingClassifier`` turns a univariate classifier into
a multivariate classifier, because slices seen by ``estimator`` are all univariate.
This can be used to give a univariate classifier multivariate capabilities.
Parameters
----------
estimator : sktime classifier, descendant of BaseClassifier
classifier to use in the bagging estimator
n_estimators : int, default=10
number of estimators in the sample for bagging
n_samples : int or float, default=1.0
The number of instances drawn from ``X`` in ``fit`` to train each clone
If int, then indicates number of instances precisely
If float, interpreted as a fraction, and rounded by ``ceil``
n_features : int or float, default=1.0
The number of features/variables drawn from ``X`` in ``fit`` to train each clone
If int, then indicates number of instances precisely
If float, interpreted as a fraction, and rounded by ``ceil``
Note: if n_features=1, BaggingClassifier turns a univariate classifier into
a multivariate classifier (as slices seen by ``estimator`` are all univariate).
bootstrap : boolean, default=True
whether samples/instances are drawn with replacement (True) or not (False)
bootstrap_features : boolean, default=False
whether features/variables are drawn with replacement (True) or not (False)
random_state : int, RandomState instance or None, optional (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``.
Attributes
----------
estimators_ : list of of sktime classifiers
clones of classifier in ``estimator`` fitted in the ensemble
Examples
--------
>>> from sktime.classification.ensemble import BaggingClassifier
>>> from sktime.classification.kernel_based import RocketClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train") # doctest: +SKIP
>>> X_test, y_test = load_unit_test(split="test") # doctest: +SKIP
>>> clf = BaggingClassifier(
... RocketClassifier(num_kernels=100),
... n_estimators=10,
... ) # doctest: +SKIP
>>> clf.fit(X_train, y_train) # doctest: +SKIP
BaggingClassifier(...)
>>> y_pred = clf.predict(X_test) # doctest: +SKIP
"""
_tags = {
# packaging info
# --------------
"authors": ["fkiraly"],
# estimator type
# --------------
"capability:multivariate": True,
"capability:missing_values": True,
"capability:predict_proba": True,
"X_inner_mtype": ["pd-multiindex", "nested_univ"],
}
def __init__(
self,
estimator,
n_estimators=10,
n_samples=1.0,
n_features=1.0,
bootstrap=True,
bootstrap_features=False,
random_state=None,
):
self.estimator = estimator
self.n_estimators = n_estimators
self.n_samples = n_samples
self.n_features = n_features
self.bootstrap = bootstrap
self.bootstrap_features = bootstrap_features
self.random_state = random_state
super().__init__()
if n_features == 1:
# if n_features == 1, this turns a univariate classifier into multivariate
tags_to_clone = ["capability:missing_values"]
else:
tags_to_clone = ["capability:multivariate", "capability:missing_values"]
self.clone_tags(estimator, tags_to_clone)
def _fit(self, X, y):
"""Fit time series classifier to training data.
Parameters
----------
X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
if self.get_tag("X_inner_mtype") = "numpy3D":
3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
if self.get_tag("X_inner_mtype") = "nested_univ":
pd.DataFrame with each column a dimension, each cell a pd.Series
for list of other mtypes, see datatypes.SCITYPE_REGISTER
for specifications, see examples/AA_datatypes_and_datasets.ipynb
y : 1D np.array of int, of shape [n_instances] - class labels for fitting
indices correspond to instance indices in X
Returns
-------
self : Reference to self.
"""
estimator = self.estimator
n_estimators = self.n_estimators
n_samples = self.n_samples
n_features = self.n_features
bootstrap = self.bootstrap
bootstrap_ft = self.bootstrap_features
random_state = self.random_state
np.random.seed(random_state)
if isinstance(X.index, pd.MultiIndex):
inst_ix = X.index.droplevel(-1).unique()
else:
inst_ix = X.index
col_ix = X.columns
n = len(inst_ix)
m = len(col_ix)
if isinstance(n_samples, float):
n_samples_ = ceil(n_samples * n)
else:
n_samples_ = n_samples
if isinstance(n_features, float):
n_features_ = ceil(n_features * m)
else:
n_features_ = n_features
self.estimators_ = []
for _i in range(n_estimators):
esti = estimator.clone()
row_iloc = pd.RangeIndex(n)
row_ss = _random_ss_ix(row_iloc, size=n_samples_, replace=bootstrap)
inst_ix_i = inst_ix[row_ss]
col_ix_i = _random_ss_ix(col_ix, size=n_features_, replace=bootstrap_ft)
# if we bootstrap, we need to take care to ensure the
# indices end up unique
if not isinstance(X.index, pd.MultiIndex):
Xi = X.loc[inst_ix_i, col_ix_i]
Xi = Xi.reset_index(drop=True)
else:
Xis = [X.loc[[ix], col_ix_i].droplevel(0) for ix in inst_ix_i]
Xi = pd.concat(Xis, keys=pd.RangeIndex(len(inst_ix_i)))
if bootstrap_ft:
Xi.columns = pd.RangeIndex(len(col_ix_i))
yi = y[row_ss]
self.estimators_ += [esti.fit(Xi, yi)]
return self
def _predict_proba(self, X) -> np.ndarray:
"""Predicts labels probabilities for sequences in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predict probabilities for.
Returns
-------
y : array-like, shape = [n_instances, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
classes = pd.Index(self.classes_)
y_probas = [est.predict_proba(X) for est in self.estimators_]
est_shape = (len(y_probas[0]), len(classes))
y_proba_np = np.zeros((len(y_probas), est_shape[0], est_shape[1]))
y_proba_np = np.zeros((est_shape[0], est_shape[1], len(y_probas)))
for i, y_proba in enumerate(y_probas):
cls_ix = self.estimators_[i].classes_
ixer = classes.get_indexer_for(cls_ix)
y_proba_np[:, ixer, i] = y_proba
y_proba = np.mean(y_proba_np, axis=2)
return y_proba
@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``.
"""
from sktime.classification.dummy import DummyClassifier
params1 = {"estimator": DummyClassifier()}
params2 = {
"estimator": DummyClassifier(),
"n_samples": 0.5,
"n_features": 0.5,
}
params3 = {
"estimator": DummyClassifier(),
"n_samples": 7,
"n_features": 2,
"bootstrap": False,
"bootstrap_features": True,
}
# force-create a classifier that cannot handle multivariate
univariate_dummy = DummyClassifier()
univariate_dummy.set_tags(**{"capability:multivariate": False})
# this should still result in a multivariate classifier
params4 = {
"estimator": univariate_dummy,
"n_features": 1,
}
return [params1, params2, params3, params4]
def _random_ss_ix(ix, size, replace=True):
a = range(len(ix))
ixs = ix[np.random.choice(a, size=size, replace=replace)]
return ixs