Source code for pygod.models.base

# -*- coding: utf-8 -*-
"""Base class for all outlier detector models
# Author: Yue Zhao <>
# License: BSD 2 clause

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.preprocessing import MinMaxScaler
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.multiclass import check_classification_targets

[docs]class BaseDetector(object): """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_``. """ 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 self.decision_scores_ = None
[docs] def fit(self, G): """Fit detector. y is ignored in unsupervised methods. Parameters ---------- G : PyTorch Geometric Data instance ( The input graph. Returns ------- self : object Fitted estimator. """ pass
[docs] def decision_function(self, G): """Predict raw anomaly scores of PyG Graph G 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 ---------- G : PyTorch Geometric Data instance ( The input graph. Returns ------- anomaly_scores : numpy array of shape (n_samples,) The anomaly score of The input graph.. """ pass
[docs] def process_graph(self, G): """Process the raw PyG data object into a tuple of sub data objects needed for the underlying model. For instance, if the training of the model need the node feature and edge index, return (G.x, G.edge_index). Parameters ---------- G : PyTorch Geometric Data instance ( The input graph. Returns ------- processed_data : tuple of data object The necessary information from the raw PyG Data object. """ pass
[docs] def predict(self, G, return_confidence=False): """Predict if a particular sample is an outlier or not. Parameters ---------- G : PyTorch Geometric Data instance ( The input graph. 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. 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(G) prediction = (pred_score > self.threshold_).astype('int').ravel() if return_confidence: confidence = self.predict_confidence(G) return prediction, confidence return prediction
[docs] def predict_proba(self, G, 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 ---------- G : PyTorch Geometric Data instance ( The input graph. 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 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(G) probs = np.zeros([len(test_scores), 2]) 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(G) 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(G) return probs, confidence return probs else: raise ValueError(method, 'is not a valid probability conversion method')
[docs] def predict_confidence(self, G): """Predict the model's confidence in making the same prediction under slightly different training sets. See :cite:`perini2020quantifying`. Parameters ---------- G : PyTorch Geometric Data instance ( The input graph. 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(G) 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) # Transform the outlier probability into a confidence value confidence = np.vectorize( lambda p: 1 - binom.cdf(n - * self.contamination), n, p))( posterior_prob) prediction = (test_scores > self.threshold_).astype('int').ravel(), prediction == 0, 1 - confidence[prediction == 0]) return confidence
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 # noinspection PyMethodParameters def _get_param_names(cls): # noinspection PyPep8 """Get parameter names for the estimator See .BaseEstimator.html and sklearn/ 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 != '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([ for p in parameters]) # noinspection PyPep8
[docs] def get_params(self, deep=True): r"""Get parameters for this estimator. See `sklearn.base.BaseEstimator <>`_ for more information. Parameters ---------- deep : bool, optional If True, will return the parameters for this estimator and contained sub-objects that are estimators. Default: ```True```. 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/, 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 `sklearn.base.BaseEstimator <>`_ 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 class_name = self.__class__.__name__ return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False), offset=len(class_name), ),)
def _pprint(params, offset=0, printer=repr): # noinspection PyPep8 """Pretty print the dictionary 'params' See .BaseEstimator.html and sklearn/ for more information. :param params: The dictionary to pretty print :type params: dict :param offset: The offset in characters to add at the begin of each line. :type offset: int :param printer: The function to convert entries to strings, typically the builtin str or repr :type printer: callable :return: None """ # Do a multi-line justified repr: options = np.get_printoptions() np.set_printoptions(precision=5, threshold=64, edgeitems=2) params_list = list() this_line_length = offset line_sep = ',\n' + (1 + offset // 2) * ' ' for i, (k, v) in enumerate(sorted(params.items())): if type(v) is float: # use str for representing floating point numbers # this way we get consistent representation across # architectures and versions. this_repr = '%s=%s' % (k, str(v)) else: # use repr of the rest this_repr = '%s=%s' % (k, printer(v)) if len(this_repr) > 500: this_repr = this_repr[:300] + '...' + this_repr[-100:] if i > 0: if this_line_length + len(this_repr) >= 75 or '\n' in this_repr: params_list.append(line_sep) this_line_length = len(line_sep) else: params_list.append(', ') this_line_length += 2 params_list.append(this_repr) this_line_length += len(this_repr) np.set_printoptions(**options) lines = ''.join(params_list) # Strip trailing space to avoid nightmare in doctests lines = '\n'.join(l.rstrip(' ') for l in lines.split('\n')) return lines