Intelegentny_Pszczelarz/.venv/Lib/site-packages/sklearn/ensemble/_iforest.py
2023-06-19 00:49:18 +02:00

546 lines
19 KiB
Python

# Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numbers
import numpy as np
from scipy.sparse import issparse
from warnings import warn
from numbers import Integral, Real
from ..tree import ExtraTreeRegressor
from ..tree._tree import DTYPE as tree_dtype
from ..utils import (
check_random_state,
check_array,
gen_batches,
get_chunk_n_rows,
)
from ..utils._param_validation import Interval, StrOptions
from ..utils.validation import check_is_fitted, _num_samples
from ..base import OutlierMixin
from ._bagging import BaseBagging
__all__ = ["IsolationForest"]
class IsolationForest(OutlierMixin, BaseBagging):
"""
Isolation Forest Algorithm.
Return the anomaly score of each sample using the IsolationForest algorithm
The IsolationForest 'isolates' observations by randomly selecting a feature
and then randomly selecting a split value between the maximum and minimum
values of the selected feature.
Since recursive partitioning can be represented by a tree structure, the
number of splittings required to isolate a sample is equivalent to the path
length from the root node to the terminating node.
This path length, averaged over a forest of such random trees, is a
measure of normality and our decision function.
Random partitioning produces noticeably shorter paths for anomalies.
Hence, when a forest of random trees collectively produce shorter path
lengths for particular samples, they are highly likely to be anomalies.
Read more in the :ref:`User Guide <isolation_forest>`.
.. versionadded:: 0.18
Parameters
----------
n_estimators : int, default=100
The number of base estimators in the ensemble.
max_samples : "auto", int or float, default="auto"
The number of samples to draw from X to train each base estimator.
- If int, then draw `max_samples` samples.
- If float, then draw `max_samples * X.shape[0]` samples.
- If "auto", then `max_samples=min(256, n_samples)`.
If max_samples is larger than the number of samples provided,
all samples will be used for all trees (no sampling).
contamination : 'auto' or float, default='auto'
The amount of contamination of the data set, i.e. the proportion
of outliers in the data set. Used when fitting to define the threshold
on the scores of the samples.
- If 'auto', the threshold is determined as in the
original paper.
- If float, the contamination should be in the range (0, 0.5].
.. versionchanged:: 0.22
The default value of ``contamination`` changed from 0.1
to ``'auto'``.
max_features : int or float, default=1.0
The number of features to draw from X to train each base estimator.
- If int, then draw `max_features` features.
- If float, then draw `max(1, int(max_features * n_features_in_))` features.
Note: using a float number less than 1.0 or integer less than number of
features will enable feature subsampling and leads to a longerr runtime.
bootstrap : bool, default=False
If True, individual trees are fit on random subsets of the training
data sampled with replacement. If False, sampling without replacement
is performed.
n_jobs : int, default=None
The number of jobs to run in parallel for both :meth:`fit` and
:meth:`predict`. ``None`` means 1 unless in a
:obj:`joblib.parallel_backend` context. ``-1`` means using all
processors. See :term:`Glossary <n_jobs>` for more details.
random_state : int, RandomState instance or None, default=None
Controls the pseudo-randomness of the selection of the feature
and split values for each branching step and each tree in the forest.
Pass an int for reproducible results across multiple function calls.
See :term:`Glossary <random_state>`.
verbose : int, default=0
Controls the verbosity of the tree building process.
warm_start : bool, 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. See :term:`the Glossary <warm_start>`.
.. versionadded:: 0.21
Attributes
----------
estimator_ : :class:`~sklearn.tree.ExtraTreeRegressor` instance
The child estimator template used to create the collection of
fitted sub-estimators.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
base_estimator_ : ExtraTreeRegressor instance
The child estimator template used to create the collection of
fitted sub-estimators.
.. deprecated:: 1.2
`base_estimator_` is deprecated and will be removed in 1.4.
Use `estimator_` instead.
estimators_ : list of ExtraTreeRegressor instances
The collection of fitted sub-estimators.
estimators_features_ : list of ndarray
The subset of drawn features for each base estimator.
estimators_samples_ : list of ndarray
The subset of drawn samples (i.e., the in-bag samples) for each base
estimator.
max_samples_ : int
The actual number of samples.
offset_ : float
Offset used to define the decision function from the raw scores. We
have the relation: ``decision_function = score_samples - offset_``.
``offset_`` is defined as follows. When the contamination parameter is
set to "auto", the offset is equal to -0.5 as the scores of inliers are
close to 0 and the scores of outliers are close to -1. When a
contamination parameter different than "auto" is provided, the offset
is defined in such a way we obtain the expected number of outliers
(samples with decision function < 0) in training.
.. versionadded:: 0.20
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
See Also
--------
sklearn.covariance.EllipticEnvelope : An object for detecting outliers in a
Gaussian distributed dataset.
sklearn.svm.OneClassSVM : Unsupervised Outlier Detection.
Estimate the support of a high-dimensional distribution.
The implementation is based on libsvm.
sklearn.neighbors.LocalOutlierFactor : Unsupervised Outlier Detection
using Local Outlier Factor (LOF).
Notes
-----
The implementation is based on an ensemble of ExtraTreeRegressor. The
maximum depth of each tree is set to ``ceil(log_2(n))`` where
:math:`n` is the number of samples used to build the tree
(see (Liu et al., 2008) for more details).
References
----------
.. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
.. [2] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation-based
anomaly detection." ACM Transactions on Knowledge Discovery from
Data (TKDD) 6.1 (2012): 3.
Examples
--------
>>> from sklearn.ensemble import IsolationForest
>>> X = [[-1.1], [0.3], [0.5], [100]]
>>> clf = IsolationForest(random_state=0).fit(X)
>>> clf.predict([[0.1], [0], [90]])
array([ 1, 1, -1])
"""
_parameter_constraints: dict = {
"n_estimators": [Interval(Integral, 1, None, closed="left")],
"max_samples": [
StrOptions({"auto"}),
Interval(Integral, 1, None, closed="left"),
Interval(Real, 0, 1, closed="right"),
],
"contamination": [
StrOptions({"auto"}),
Interval(Real, 0, 0.5, closed="right"),
],
"max_features": [
Integral,
Interval(Real, 0, 1, closed="right"),
],
"bootstrap": ["boolean"],
"n_jobs": [Integral, None],
"random_state": ["random_state"],
"verbose": ["verbose"],
"warm_start": ["boolean"],
}
def __init__(
self,
*,
n_estimators=100,
max_samples="auto",
contamination="auto",
max_features=1.0,
bootstrap=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
):
super().__init__(
estimator=ExtraTreeRegressor(
max_features=1, splitter="random", random_state=random_state
),
# here above max_features has no links with self.max_features
bootstrap=bootstrap,
bootstrap_features=False,
n_estimators=n_estimators,
max_samples=max_samples,
max_features=max_features,
warm_start=warm_start,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
)
self.contamination = contamination
def _set_oob_score(self, X, y):
raise NotImplementedError("OOB score not supported by iforest")
def _parallel_args(self):
# ExtraTreeRegressor releases the GIL, so it's more efficient to use
# a thread-based backend rather than a process-based backend so as
# to avoid suffering from communication overhead and extra memory
# copies.
return {"prefer": "threads"}
def fit(self, X, y=None, sample_weight=None):
"""
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csc_matrix`` for maximum efficiency.
y : Ignored
Not used, present for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
Returns
-------
self : object
Fitted estimator.
"""
self._validate_params()
X = self._validate_data(X, accept_sparse=["csc"], dtype=tree_dtype)
if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
X.sort_indices()
rnd = check_random_state(self.random_state)
y = rnd.uniform(size=X.shape[0])
# ensure that max_sample is in [1, n_samples]:
n_samples = X.shape[0]
if isinstance(self.max_samples, str) and self.max_samples == "auto":
max_samples = min(256, n_samples)
elif isinstance(self.max_samples, numbers.Integral):
if self.max_samples > n_samples:
warn(
"max_samples (%s) is greater than the "
"total number of samples (%s). max_samples "
"will be set to n_samples for estimation."
% (self.max_samples, n_samples)
)
max_samples = n_samples
else:
max_samples = self.max_samples
else: # max_samples is float
max_samples = int(self.max_samples * X.shape[0])
self.max_samples_ = max_samples
max_depth = int(np.ceil(np.log2(max(max_samples, 2))))
super()._fit(
X,
y,
max_samples,
max_depth=max_depth,
sample_weight=sample_weight,
check_input=False,
)
if self.contamination == "auto":
# 0.5 plays a special role as described in the original paper.
# we take the opposite as we consider the opposite of their score.
self.offset_ = -0.5
return self
# else, define offset_ wrt contamination parameter
self.offset_ = np.percentile(self.score_samples(X), 100.0 * self.contamination)
return self
def predict(self, X):
"""
Predict if a particular sample is an outlier or not.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
is_inlier : ndarray of shape (n_samples,)
For each observation, tells whether or not (+1 or -1) it should
be considered as an inlier according to the fitted model.
"""
check_is_fitted(self)
decision_func = self.decision_function(X)
is_inlier = np.ones_like(decision_func, dtype=int)
is_inlier[decision_func < 0] = -1
return is_inlier
def decision_function(self, X):
"""
Average anomaly score of X of the base classifiers.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, which is equivalent to
the number of splittings required to isolate this point. In case of
several observations n_left in the leaf, the average path length of
a n_left samples isolation tree is added.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
scores : ndarray of shape (n_samples,)
The anomaly score of the input samples.
The lower, the more abnormal. Negative scores represent outliers,
positive scores represent inliers.
"""
# We subtract self.offset_ to make 0 be the threshold value for being
# an outlier:
return self.score_samples(X) - self.offset_
def score_samples(self, X):
"""
Opposite of the anomaly score defined in the original paper.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, which is equivalent to
the number of splittings required to isolate this point. In case of
several observations n_left in the leaf, the average path length of
a n_left samples isolation tree is added.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples.
Returns
-------
scores : ndarray of shape (n_samples,)
The anomaly score of the input samples.
The lower, the more abnormal.
"""
# code structure from ForestClassifier/predict_proba
check_is_fitted(self)
# Check data
X = self._validate_data(X, accept_sparse="csr", reset=False)
# Take the opposite of the scores as bigger is better (here less
# abnormal)
return -self._compute_chunked_score_samples(X)
def _compute_chunked_score_samples(self, X):
n_samples = _num_samples(X)
if self._max_features == X.shape[1]:
subsample_features = False
else:
subsample_features = True
# We get as many rows as possible within our working_memory budget
# (defined by sklearn.get_config()['working_memory']) to store
# self._max_features in each row during computation.
#
# Note:
# - this will get at least 1 row, even if 1 row of score will
# exceed working_memory.
# - this does only account for temporary memory usage while loading
# the data needed to compute the scores -- the returned scores
# themselves are 1D.
chunk_n_rows = get_chunk_n_rows(
row_bytes=16 * self._max_features, max_n_rows=n_samples
)
slices = gen_batches(n_samples, chunk_n_rows)
scores = np.zeros(n_samples, order="f")
for sl in slices:
# compute score on the slices of test samples:
scores[sl] = self._compute_score_samples(X[sl], subsample_features)
return scores
def _compute_score_samples(self, X, subsample_features):
"""
Compute the score of each samples in X going through the extra trees.
Parameters
----------
X : array-like or sparse matrix
Data matrix.
subsample_features : bool
Whether features should be subsampled.
"""
n_samples = X.shape[0]
depths = np.zeros(n_samples, order="f")
for tree, features in zip(self.estimators_, self.estimators_features_):
X_subset = X[:, features] if subsample_features else X
leaves_index = tree.apply(X_subset)
node_indicator = tree.decision_path(X_subset)
n_samples_leaf = tree.tree_.n_node_samples[leaves_index]
depths += (
np.ravel(node_indicator.sum(axis=1))
+ _average_path_length(n_samples_leaf)
- 1.0
)
denominator = len(self.estimators_) * _average_path_length([self.max_samples_])
scores = 2 ** (
# For a single training sample, denominator and depth are 0.
# Therefore, we set the score manually to 1.
-np.divide(
depths, denominator, out=np.ones_like(depths), where=denominator != 0
)
)
return scores
def _more_tags(self):
return {
"_xfail_checks": {
"check_sample_weights_invariance": (
"zero sample_weight is not equivalent to removing samples"
),
}
}
def _average_path_length(n_samples_leaf):
"""
The average path length in a n_samples iTree, which is equal to
the average path length of an unsuccessful BST search since the
latter has the same structure as an isolation tree.
Parameters
----------
n_samples_leaf : array-like of shape (n_samples,)
The number of training samples in each test sample leaf, for
each estimators.
Returns
-------
average_path_length : ndarray of shape (n_samples,)
"""
n_samples_leaf = check_array(n_samples_leaf, ensure_2d=False)
n_samples_leaf_shape = n_samples_leaf.shape
n_samples_leaf = n_samples_leaf.reshape((1, -1))
average_path_length = np.zeros(n_samples_leaf.shape)
mask_1 = n_samples_leaf <= 1
mask_2 = n_samples_leaf == 2
not_mask = ~np.logical_or(mask_1, mask_2)
average_path_length[mask_1] = 0.0
average_path_length[mask_2] = 1.0
average_path_length[not_mask] = (
2.0 * (np.log(n_samples_leaf[not_mask] - 1.0) + np.euler_gamma)
- 2.0 * (n_samples_leaf[not_mask] - 1.0) / n_samples_leaf[not_mask]
)
return average_path_length.reshape(n_samples_leaf_shape)