/
_ensemble.py
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/
_ensemble.py
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#!/usr/bin/env python3 -u
# -*- coding: utf-8 -*-
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Implements a composite Time series Forest Regressor that accepts a pipeline."""
__author__ = ["Markus Löning", "AyushmaanSeth"]
__all__ = ["ComposableTimeSeriesForestRegressor"]
import numbers
from warnings import warn
import numpy as np
from joblib import Parallel, delayed
from sklearn.ensemble._base import _partition_estimators
from sklearn.ensemble._forest import (
_generate_unsampled_indices,
_get_n_samples_bootstrap,
)
from sklearn.metrics import r2_score
from sklearn.pipeline import Pipeline
from sklearn.tree import DecisionTreeRegressor
from sktime.regression.base import BaseRegressor
from sktime.series_as_features.base.estimators._ensemble import BaseTimeSeriesForest
from sktime.transformations.panel.summarize import RandomIntervalFeatureExtractor
from sktime.utils.slope_and_trend import _slope
from sktime.utils.validation.panel import check_X, check_X_y
class ComposableTimeSeriesForestRegressor(BaseTimeSeriesForest, BaseRegressor):
"""Time-Series Forest Regressor.
A time series forest is a meta estimator and an adaptation of the random
forest for time-series/panel data that fits a number of decision tree
regressors on various sub-samples of a transformed dataset and uses
averaging to improve the predictive accuracy and control over-fitting.
The sub-sample size is always the same as the original input sample size
but the samples are drawn with replacement if `bootstrap=True` (default).
Parameters
----------
estimator : Pipeline
A pipeline consisting of series-to-tabular transformations
and a decision tree regressor as final estimator.
n_estimators : integer, optional (default=100)
The number of trees in the forest.
criterion : string, optional (default="mse")
The function to measure the quality of a split. Supported criteria
are "mse" for the mean squared error, which is equal to variance
reduction as feature selection criterion and minimizes the L2 loss
using the mean of each terminal node, "friedman_mse", which uses mean
squared error with Friedman's improvement score for potential splits,
and "mae" for the mean absolute error, which minimizes the L1 loss
using the median of each terminal node.
max_depth : integer or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`int(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_leaf_nodes : int or None, optional (default=None)
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, optional (default=0.)
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
min_impurity_split : float, (default=1e-7)
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
bootstrap : boolean, optional (default=True)
Whether bootstrap samples are used when building trees.
oob_score : bool (default=False)
Whether to use out-of-bag samples to estimate
the generalization accuracy.
n_jobs : int or None, optional (default=None)
The number of jobs to run in parallel for both `fit` and `predict`.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors.
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`.
verbose : int, optional (default=0)
Controls the verbosity when fitting and predicting.
warm_start : bool, optional (default=False)
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest.
None, optional (default=None)
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}].
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown.
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
Attributes
----------
estimators_ : list of DecisionTreeRegressor
The collection of fitted sub-estimators.
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
feature_importances_ : array of shape = [n_features]
The feature importances (the higher, the more important the feature).
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
oob_decision_function_ : array of shape = [n_samples, n_classes]
Decision function computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
`oob_decision_function_` might contain NaN.
class_weight: dict, list of dicts, "balanced", "balanced_subsample" or \
None, optional (default=None)
Not needed here, added in the constructor to align with base class \
sharing both Classifier and Regressor parameters.
"""
def __init__(
self,
estimator=None,
n_estimators=100,
criterion="mse",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
max_samples=None,
):
self.estimator = estimator
# Assign values, even though passed on to base estimator below,
# necessary here for cloning
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.max_samples = max_samples
# Pass on params.
super(ComposableTimeSeriesForestRegressor, self).__init__(
base_estimator=None,
n_estimators=n_estimators,
estimator_params=None,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=max_samples,
)
# We need to add is-fitted state when inheriting from scikit-learn
self._is_fitted = False
def _validate_estimator(self):
if not isinstance(self.n_estimators, numbers.Integral):
raise ValueError(
"n_estimators must be an integer, "
"got {0}.".format(type(self.n_estimators))
)
if self.n_estimators <= 0:
raise ValueError(
"n_estimators must be greater than zero, "
"got {0}.".format(self.n_estimators)
)
# Set base estimator
if self.estimator is None:
# Set default time series forest
features = [np.mean, np.std, _slope]
steps = [
(
"transform",
RandomIntervalFeatureExtractor(
n_intervals="sqrt",
features=features,
random_state=self.random_state,
),
),
("clf", DecisionTreeRegressor(random_state=self.random_state)),
]
self.estimator_ = Pipeline(steps)
else:
# else check given estimator is a pipeline with prior
# transformations and final decision tree
if not isinstance(self.estimator, Pipeline):
raise ValueError("`estimator` must be pipeline with transforms.")
if not isinstance(self.estimator.steps[-1][1], DecisionTreeRegressor):
raise ValueError(
"Last step in `estimator` must be DecisionTreeRegressor."
)
self.estimator_ = self.estimator
# Set parameters according to naming in pipeline
estimator_params = {
"criterion": self.criterion,
"max_depth": self.max_depth,
"min_samples_split": self.min_samples_split,
"min_samples_leaf": self.min_samples_leaf,
"min_weight_fraction_leaf": self.min_weight_fraction_leaf,
"max_features": self.max_features,
"max_leaf_nodes": self.max_leaf_nodes,
"min_impurity_decrease": self.min_impurity_decrease,
"min_impurity_split": self.min_impurity_split,
}
final_estimator = self.estimator_.steps[-1][0]
self.estimator_params = {
f"{final_estimator}__{pname}": pval
for pname, pval in estimator_params.items()
}
# Set renamed estimator parameters
for pname, pval in self.estimator_params.items():
self.__setattr__(pname, pval)
def fit(self, X, y, **kwargs):
"""Wrap BaseForest._fit.
This is a temporary measure prior to the BaseRegressor refactor.
"""
X, y = check_X_y(X, y, coerce_to_numpy=True, enforce_univariate=True)
return BaseTimeSeriesForest._fit(self, X, y, **kwargs)
def predict(self, X):
"""Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the trees in the forest.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted values.
"""
self.check_is_fitted()
# Check data
X = check_X(X, enforce_univariate=True)
X = self._validate_X_predict(X)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# Parallel loop
y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
delayed(e.predict)(X, check_input=True) for e in self.estimators_
)
return np.sum(y_hat, axis=0) / len(self.estimators_)
def _set_oob_score(self, X, y):
"""Compute out-of-bag scores."""
X, y = check_X_y(X, y, enforce_univariate=True)
n_samples = y.shape[0]
predictions = np.zeros((n_samples, self.n_outputs_))
n_predictions = np.zeros((n_samples, self.n_outputs_))
n_samples_bootstrap = _get_n_samples_bootstrap(n_samples, self.max_samples)
for estimator in self.estimators_:
final_estimator = estimator.steps[-1][1]
unsampled_indices = _generate_unsampled_indices(
final_estimator.random_state, n_samples, n_samples_bootstrap
)
p_estimator = estimator.predict(X[unsampled_indices, :], check_input=False)
if self.n_outputs_ == 1:
p_estimator = p_estimator[:, np.newaxis]
predictions[unsampled_indices, :] += p_estimator
n_predictions[unsampled_indices, :] += 1
if (n_predictions == 0).any():
warn(
"Some inputs do not have OOB scores. "
"This probably means too few trees were used "
"to compute any reliable oob estimates."
)
n_predictions[n_predictions == 0] = 1
predictions /= n_predictions
self.oob_prediction_ = predictions
if self.n_outputs_ == 1:
self.oob_prediction_ = self.oob_prediction_.reshape((n_samples,))
self.oob_score_ = 0.0
for k in range(self.n_outputs_):
self.oob_score_ += r2_score(y[:, k], predictions[:, k])
self.oob_score_ /= self.n_outputs_
def _validate_y_class_weight(self, y):
# in regression, we don't validate class weights
# TODO remove from regression
return y, None
def _fit(self, X, y):
"""Empty method to satisfy abstract parent. Needs refactoring."""
def _predict(self, X):
"""Empty method to satisfy abstract parent. Needs refactoring."""
@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.
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": 3}