pyod/models/base.py
# -*- coding: utf-8 -*-
"""Base class for all outlier detector models
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause
from __future__ import division
from __future__ import print_function
import abc
import warnings
from collections import defaultdict
from inspect import signature
import numpy as np
from numpy import percentile
from scipy.special import erf
from scipy.stats import binom
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils import deprecated
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import check_is_fitted
from .sklearn_base import _pprint
from ..utils.utility import precision_n_scores
class BaseDetector(metaclass=abc.ABCMeta):
"""Abstract class for all outlier detection algorithms.
Parameters
----------
contamination : float in (0., 0.5), optional (default=0.1)
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 decision function.
Attributes
----------
decision_scores_ : numpy array of shape (n_samples,)
The outlier scores of the training data.
The higher, the more abnormal. Outliers tend to have higher
scores. This value is available once the detector is fitted.
threshold_ : float
The threshold is based on ``contamination``. It is the
``n_samples * contamination`` most abnormal samples in
``decision_scores_``. The threshold is calculated for generating
binary outlier labels.
labels_ : int, either 0 or 1
The binary labels of the training data. 0 stands for inliers
and 1 for outliers/anomalies. It is generated by applying
``threshold_`` on ``decision_scores_``.
"""
@abc.abstractmethod
def __init__(self, contamination=0.1):
if (isinstance(contamination, (float, int))):
if not (0. < contamination <= 0.5):
raise ValueError("contamination must be in (0, 0.5], "
"got: %f" % contamination)
# allow arbitrary input such as PyThreshld object
self.contamination = contamination
# noinspection PyIncorrectDocstring
@abc.abstractmethod
def fit(self, X, y=None):
"""Fit detector. y is ignored in unsupervised methods.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : object
Fitted estimator.
"""
pass
@abc.abstractmethod
def decision_function(self, X):
"""Predict raw anomaly scores of X using the fitted detector.
The anomaly score of an input sample is computed based on the fitted
detector. For consistency, outliers are assigned with
higher anomaly scores.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples. Sparse matrices are accepted only
if they are supported by the base estimator.
Returns
-------
anomaly_scores : numpy array of shape (n_samples,)
The anomaly score of the input samples.
"""
pass
@deprecated()
def fit_predict(self, X, y=None):
"""Fit detector first and then predict whether a particular sample
is an outlier or not. y is ignored in unsupervised models.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
outlier_labels : numpy array of shape (n_samples,)
For each observation, tells whether
it should be considered as an outlier according to the
fitted model. 0 stands for inliers and 1 for outliers.
.. deprecated:: 0.6.9
`fit_predict` will be removed in pyod 0.8.0.; it will be
replaced by calling `fit` function first and then accessing
`labels_` attribute for consistency.
"""
self.fit(X, y)
return self.labels_
def predict(self, X, return_confidence=False):
"""Predict if a particular sample is an outlier or not.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
return_confidence : boolean, optional(default=False)
If True, also return the confidence of prediction.
Returns
-------
outlier_labels : numpy array of shape (n_samples,)
For each observation, tells whether
it should be considered as an outlier according to the
fitted model. 0 stands for inliers and 1 for outliers.
confidence : numpy array of shape (n_samples,).
Only if return_confidence is set to True.
"""
check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
pred_score = self.decision_function(X)
if isinstance(self.contamination, (float, int)):
prediction = (pred_score > self.threshold_).astype('int').ravel()
# if this is a PyThresh object
else:
prediction = self.contamination.eval(pred_score)
if return_confidence:
confidence = self.predict_confidence(X)
return prediction, confidence
return prediction
def predict_proba(self, X, method='linear', return_confidence=False):
"""Predict the probability of a sample being outlier. Two approaches
are possible:
1. simply use Min-max conversion to linearly transform the outlier
scores into the range of [0,1]. The model must be
fitted first.
2. use unifying scores, see :cite:`kriegel2011interpreting`.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
method : str, optional (default='linear')
probability conversion method. It must be one of
'linear' or 'unify'.
return_confidence : boolean, optional(default=False)
If True, also return the confidence of prediction.
Returns
-------
outlier_probability : numpy array of shape (n_samples, n_classes)
For each observation, tells whether or not
it should be considered as an outlier according to the
fitted model. Return the outlier probability, ranging
in [0,1]. Note it depends on the number of classes, which is by
default 2 classes ([proba of normal, proba of outliers]).
"""
check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
train_scores = self.decision_scores_
test_scores = self.decision_function(X)
probs = np.zeros([X.shape[0], int(self._classes)])
if method == 'linear':
scaler = MinMaxScaler().fit(train_scores.reshape(-1, 1))
probs[:, 1] = scaler.transform(
test_scores.reshape(-1, 1)).ravel().clip(0, 1)
probs[:, 0] = 1 - probs[:, 1]
if return_confidence:
confidence = self.predict_confidence(X)
return probs, confidence
return probs
elif method == 'unify':
# turn output into probability
pre_erf_score = (test_scores - self._mu) / (
self._sigma * np.sqrt(2))
erf_score = erf(pre_erf_score)
probs[:, 1] = erf_score.clip(0, 1).ravel()
probs[:, 0] = 1 - probs[:, 1]
if return_confidence:
confidence = self.predict_confidence(X)
return probs, confidence
return probs
else:
raise ValueError(method,
'is not a valid probability conversion method')
def predict_confidence(self, X):
"""Predict the model's confidence in making the same prediction
under slightly different training sets.
See :cite:`perini2020quantifying`.
Parameters
-------
X : numpy array of shape (n_samples, n_features)
The input samples.
Returns
-------
confidence : numpy array of shape (n_samples,)
For each observation, tells how consistently the model would
make the same prediction if the training set was perturbed.
Return a probability, ranging in [0,1].
"""
check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
n = len(self.decision_scores_)
# todo: this has an optimization opportunity since the scores may
# already be available
test_scores = self.decision_function(X)
count_instances = np.vectorize(
lambda x: np.count_nonzero(self.decision_scores_ <= x))
n_instances = count_instances(test_scores)
# Derive the outlier probability using Bayesian approach
posterior_prob = np.vectorize(lambda x: (1 + x) / (2 + n))(n_instances)
if not isinstance(self.contamination, (float, int)):
contam = np.sum(self.labels_) / n
# if this is a PyThresh object
else:
contam = self.contamination
# Transform the outlier probability into a confidence value
confidence = np.vectorize(
lambda p: 1 - binom.cdf(n - int(n * contam), n, p))(
posterior_prob)
if isinstance(self.contamination, (float, int)):
prediction = (test_scores > self.threshold_).astype('int').ravel()
# if this is a PyThresh object
else:
prediction = self.contamination.eval(test_scores)
np.place(confidence, prediction == 0, 1 - confidence[prediction == 0])
return confidence
def _predict_rank(self, X, normalized=False):
"""Predict the outlyingness rank of a sample by a fitted model. The
method is for outlier detector score combination.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
normalized : bool, optional (default=False)
If set to True, all ranks are normalized to [0,1].
Returns
-------
ranks : array, shape (n_samples,)
Outlying rank of a sample according to the training data.
"""
check_is_fitted(self, ['decision_scores_'])
test_scores = self.decision_function(X)
train_scores = self.decision_scores_
sorted_train_scores = np.sort(train_scores)
ranks = np.searchsorted(sorted_train_scores, test_scores)
if normalized:
# return normalized ranks
ranks = ranks / ranks.max()
return ranks
@deprecated()
def fit_predict_score(self, X, y, scoring='roc_auc_score'):
"""Fit the detector, predict on samples, and evaluate the model by
predefined metrics, e.g., ROC.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
y : Ignored
Not used, present for API consistency by convention.
scoring : str, optional (default='roc_auc_score')
Evaluation metric:
- 'roc_auc_score': ROC score
- 'prc_n_score': Precision @ rank n score
Returns
-------
score : float
.. deprecated:: 0.6.9
`fit_predict_score` will be removed in pyod 0.8.0.; it will be
replaced by calling `fit` function first and then accessing
`labels_` attribute for consistency. Scoring could be done by
calling an evaluation method, e.g., AUC ROC.
"""
self.fit(X)
if scoring == 'roc_auc_score':
score = roc_auc_score(y, self.decision_scores_)
elif scoring == 'prc_n_score':
score = precision_n_scores(y, self.decision_scores_)
else:
raise NotImplementedError('PyOD built-in scoring only supports '
'ROC and Precision @ rank n')
print("{metric}: {score}".format(metric=scoring, score=score))
return score
# def score(self, X, y, scoring='roc_auc_score'):
# """Returns the evaluation resulted on the given test data and labels.
# ROC is chosen as the default evaluation metric
#
# :param X: The input samples
# :type X: numpy array of shape (n_samples, n_features)
#
# :param y: Outlier labels of the input samples
# :type y: array, shape (n_samples,)
#
# :param scoring: Evaluation metric
#
# -' roc_auc_score': ROC score
# - 'prc_n_score': Precision @ rank n score
# :type scoring: str, optional (default='roc_auc_score')
#
# :return: Evaluation score
# :rtype: float
# """
# check_is_fitted(self, ['decision_scores_'])
# if scoring == 'roc_auc_score':
# score = roc_auc_score(y, self.decision_function(X))
# elif scoring == 'prc_n_score':
# score = precision_n_scores(y, self.decision_function(X))
# else:
# raise NotImplementedError('PyOD built-in scoring only supports '
# 'ROC and Precision @ rank n')
#
# print("{metric}: {score}".format(metric=scoring, score=score))
#
# return score
def _set_n_classes(self, y):
"""Set the number of classes if `y` is presented, which is not
expected. It could be useful for multi-class outlier detection.
Parameters
----------
y : numpy array of shape (n_samples,)
Ground truth.
Returns
-------
self
"""
self._classes = 2 # default as binary classification
if y is not None:
check_classification_targets(y)
self._classes = len(np.unique(y))
warnings.warn(
"y should not be presented in unsupervised learning.")
return self
def _process_decision_scores(self):
"""Internal function to calculate key attributes:
- threshold_: used to decide the binary label
- labels_: binary labels of training data
Returns
-------
self
"""
if isinstance(self.contamination, (float, int)):
self.threshold_ = percentile(self.decision_scores_,
100 * (1 - self.contamination))
self.labels_ = (self.decision_scores_ > self.threshold_).astype(
'int').ravel()
# if this is a PyThresh object
else:
self.labels_ = self.contamination.eval(self.decision_scores_)
self.threshold_ = self.contamination.thresh_
if not self.threshold_:
self.threshold_ = np.sum(self.labels_) / len(self.labels_)
# calculate for predict_proba()
self._mu = np.mean(self.decision_scores_)
self._sigma = np.std(self.decision_scores_)
return self
# noinspection PyMethodParameters
def _get_param_names(cls):
# noinspection PyPep8
"""Get parameter names for the estimator
See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
and sklearn/base.py for more information.
"""
# fetch the constructor or the original constructor before
# deprecation wrapping if any
init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
if init is object.__init__:
# No explicit constructor to introspect
return []
# introspect the constructor arguments to find the model parameters
# to represent
init_signature = signature(init)
# Consider the constructor parameters excluding 'self'
parameters = [p for p in init_signature.parameters.values()
if p.name != 'self' and p.kind != p.VAR_KEYWORD]
for p in parameters:
if p.kind == p.VAR_POSITIONAL:
raise RuntimeError("scikit-learn estimators should always "
"specify their parameters in the signature"
" of their __init__ (no varargs)."
" %s with constructor %s doesn't "
" follow this convention."
% (cls, init_signature))
# Extract and sort argument names excluding 'self'
return sorted([p.name for p in parameters])
# noinspection PyPep8
def get_params(self, deep=True):
"""Get parameters for this estimator.
See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
and sklearn/base.py for more information.
Parameters
----------
deep : bool, optional (default=True)
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : mapping of string to any
Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
# We need deprecation warnings to always be on in order to
# catch deprecated param values.
# This is set in utils/__init__.py but it gets overwritten
# when running under python3 somehow.
warnings.simplefilter("always", DeprecationWarning)
try:
with warnings.catch_warnings(record=True) as w:
value = getattr(self, key, None)
if len(w) and w[0].category == DeprecationWarning:
# if the parameter is deprecated, don't show it
continue
finally:
warnings.filters.pop(0)
# XXX: should we rather test if instance of estimator?
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
def set_params(self, **params):
# noinspection PyPep8
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.
See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
and sklearn/base.py for more information.
Returns
-------
self : object
"""
if not params:
# Simple optimization to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
nested_params = defaultdict(dict) # grouped by prefix
for key, value in params.items():
key, delim, sub_key = key.partition('__')
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self))
if delim:
nested_params[key][sub_key] = value
else:
setattr(self, key, value)
for key, sub_params in nested_params.items():
valid_params[key].set_params(**sub_params)
return self
def __repr__(self):
# noinspection PyPep8
"""
See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
and sklearn/base.py for more information.
"""
class_name = self.__class__.__name__
return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
offset=len(class_name), ),)