Source code for pyod.models.base

# -*- 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 warnings
from collections import defaultdict

from inspect import signature

import abc
import six

import numpy as np
from numpy import percentile
from scipy.special import erf
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import roc_auc_score
from sklearn.utils import deprecated
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.multiclass import check_classification_targets

from .sklearn_base import _pprint
from ..utils.utility import precision_n_scores


[docs]@six.add_metaclass(abc.ABCMeta) class BaseDetector(object): """Abstract class for all outlier detection algorithms. .. warning:: pyod would stop supporting Python 2 in the future. Consider move to Python 3.5+. 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 not (0. < contamination <= 0.5): raise ValueError("contamination must be in (0, 0.5], " "got: %f" % contamination) self.contamination = contamination # noinspection PyIncorrectDocstring
[docs] @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
[docs] @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
[docs] @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 or not 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_
[docs] def predict(self, X): """Predict if a particular sample is an outlier or not. Parameters ---------- X : numpy array of shape (n_samples, n_features) The input samples. Returns ------- outlier_labels : numpy array of shape (n_samples,) For each observation, tells whether or not it should be considered as an outlier according to the fitted model. 0 stands for inliers and 1 for outliers. """ check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_']) pred_score = self.decision_function(X) return (pred_score > self.threshold_).astype('int').ravel()
[docs] def predict_proba(self, X, method='linear'): """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'. Returns ------- outlier_probability : numpy array of shape (n_samples,) 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]. """ 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] 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] return probs else: raise ValueError(method, 'is not a valid probability conversion method')
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
[docs] @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 """ self.threshold_ = percentile(self.decision_scores_, 100 * (1 - self.contamination)) self.labels_ = (self.decision_scores_ > self.threshold_).astype( 'int').ravel() # 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
[docs] 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
[docs] 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), ),)