Source code for pyod.utils.utility

# -*- coding: utf-8 -*-
"""A set of utility functions to support outlier detection.
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause

from __future__ import division
from __future__ import print_function

import numpy as np
from numpy import percentile
import numbers

import sklearn
from sklearn.metrics import precision_score
from sklearn.preprocessing import StandardScaler

from sklearn.utils import column_or_1d
from sklearn.utils import check_array
from sklearn.utils import check_consistent_length

from sklearn.utils import check_random_state
from sklearn.utils.random import sample_without_replacement

MAX_INT = np.iinfo(np.int32).max
MIN_INT = -1 * MAX_INT


[docs]def check_parameter(param, low=MIN_INT, high=MAX_INT, param_name='', include_left=False, include_right=False): """Check if an input is within the defined range. Parameters ---------- param : int, float The input parameter to check. low : int, float The lower bound of the range. high : int, float The higher bound of the range. param_name : str, optional (default='') The name of the parameter. include_left : bool, optional (default=False) Whether includes the lower bound (lower bound <=). include_right : bool, optional (default=False) Whether includes the higher bound (<= higher bound). Returns ------- within_range : bool or raise errors Whether the parameter is within the range of (low, high) """ # param, low and high should all be numerical if not isinstance(param, (numbers.Integral, np.integer, float)): raise TypeError('{param_name} is set to {param} Not numerical'.format( param=param, param_name=param_name)) if not isinstance(low, (numbers.Integral, np.integer, float)): raise TypeError('low is set to {low}. Not numerical'.format(low=low)) if not isinstance(high, (numbers.Integral, np.integer, float)): raise TypeError('high is set to {high}. Not numerical'.format( high=high)) # at least one of the bounds should be specified if low is MIN_INT and high is MAX_INT: raise ValueError('Neither low nor high bounds is undefined') # if wrong bound values are used if low > high: raise ValueError( 'Lower bound > Higher bound') # value check under different bound conditions if (include_left and include_right) and (param < low or param > high): raise ValueError( '{param_name} is set to {param}. ' 'Not in the range of [{low}, {high}].'.format( param=param, low=low, high=high, param_name=param_name)) elif (include_left and not include_right) and ( param < low or param >= high): raise ValueError( '{param_name} is set to {param}. ' 'Not in the range of [{low}, {high}).'.format( param=param, low=low, high=high, param_name=param_name)) elif (not include_left and include_right) and ( param <= low or param > high): raise ValueError( '{param_name} is set to {param}. ' 'Not in the range of ({low}, {high}].'.format( param=param, low=low, high=high, param_name=param_name)) elif (not include_left and not include_right) and ( param <= low or param >= high): raise ValueError( '{param_name} is set to {param}. ' 'Not in the range of ({low}, {high}).'.format( param=param, low=low, high=high, param_name=param_name)) else: return True
[docs]def check_detector(detector): """Checks if fit and decision_function methods exist for given detector Parameters ---------- detector : pyod.models Detector instance for which the check is performed. """ if not hasattr(detector, 'fit') or not hasattr(detector, 'decision_function'): raise AttributeError("%s is not a detector instance." % (detector))
[docs]def standardizer(X, X_t=None, keep_scalar=False): """Conduct Z-normalization on data to turn input samples become zero-mean and unit variance. Parameters ---------- X : numpy array of shape (n_samples, n_features) The training samples X_t : numpy array of shape (n_samples_new, n_features), optional (default=None) The data to be converted keep_scalar : bool, optional (default=False) The flag to indicate whether to return the scalar Returns ------- X_norm : numpy array of shape (n_samples, n_features) X after the Z-score normalization X_t_norm : numpy array of shape (n_samples, n_features) X_t after the Z-score normalization scalar : sklearn scalar object The scalar used in conversion """ X = check_array(X) scaler = StandardScaler().fit(X) if X_t is None: if keep_scalar: return scaler.transform(X), scaler else: return scaler.transform(X) else: X_t = check_array(X_t) if X.shape[1] != X_t.shape[1]: raise ValueError( "The number of input data feature should be consistent" "X has {0} features and X_t has {1} features.".format( X.shape[1], X_t.shape[1])) if keep_scalar: return scaler.transform(X), scaler.transform(X_t), scaler else: return scaler.transform(X), scaler.transform(X_t)
[docs]def score_to_label(pred_scores, outliers_fraction=0.1): """Turn raw outlier outlier scores to binary labels (0 or 1). Parameters ---------- pred_scores : list or numpy array of shape (n_samples,) Raw outlier scores. Outliers are assumed have larger values. outliers_fraction : float in (0,1) Percentage of outliers. 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. Return the outlier probability, ranging in [0,1]. """ # check input values pred_scores = column_or_1d(pred_scores) check_parameter(outliers_fraction, 0, 1) threshold = percentile(pred_scores, 100 * (1 - outliers_fraction)) pred_labels = (pred_scores > threshold).astype('int') return pred_labels
[docs]def precision_n_scores(y, y_pred, n=None): """Utility function to calculate precision @ rank n. Parameters ---------- y : list or numpy array of shape (n_samples,) The ground truth. Binary (0: inliers, 1: outliers). y_pred : list or numpy array of shape (n_samples,) The raw outlier scores as returned by a fitted model. n : int, optional (default=None) The number of outliers. if not defined, infer using ground truth. Returns ------- precision_at_rank_n : float Precision at rank n score. """ # turn raw prediction decision scores into binary labels y_pred = get_label_n(y, y_pred, n) # enforce formats of y and labels_ y = column_or_1d(y) y_pred = column_or_1d(y_pred) return precision_score(y, y_pred)
[docs]def get_label_n(y, y_pred, n=None): """Function to turn raw outlier scores into binary labels by assign 1 to top n outlier scores. Parameters ---------- y : list or numpy array of shape (n_samples,) The ground truth. Binary (0: inliers, 1: outliers). y_pred : list or numpy array of shape (n_samples,) The raw outlier scores as returned by a fitted model. n : int, optional (default=None) The number of outliers. if not defined, infer using ground truth. Returns ------- labels : numpy array of shape (n_samples,) binary labels 0: normal points and 1: outliers Examples -------- >>> from pyod.utils.utility import get_label_n >>> y = [0, 1, 1, 0, 0] >>> y_pred = [0.1, 0.5, 0.3, 0.2, 0.7] >>> get_label_n(y, y_pred) array([0, 1, 0, 0, 1]) """ # enforce formats of inputs y = column_or_1d(y) y_pred = column_or_1d(y_pred) check_consistent_length(y, y_pred) y_len = len(y) # the length of targets # calculate the percentage of outliers if n is not None: outliers_fraction = n / y_len else: outliers_fraction = np.count_nonzero(y) / y_len threshold = percentile(y_pred, 100 * (1 - outliers_fraction)) y_pred = (y_pred > threshold).astype('int') return y_pred
[docs]def get_intersection(lst1, lst2): """get the overlapping between two lists Parameters ---------- li1 : list or numpy array Input list 1. li2 : list or numpy array Input list 2. Returns ------- difference : list The overlapping between li1 and li2. """ return list(set(lst1) & set(lst2))
[docs]def get_list_diff(li1, li2): """get the elements in li1 but not li2. li1-li2 Parameters ---------- li1 : list or numpy array Input list 1. li2 : list or numpy array Input list 2. Returns ------- difference : list The difference between li1 and li2. """ # if isinstance(li1, (np.ndarray, np.generic)): # li1 = li1.tolist() # if isinstance(li2, (np.ndarray, np.generic)): # li1 = li1.tolist() return (list(set(li1) - set(li2)))
[docs]def get_diff_elements(li1, li2): """get the elements in li1 but not li2, and vice versa Parameters ---------- li1 : list or numpy array Input list 1. li2 : list or numpy array Input list 2. Returns ------- difference : list The difference between li1 and li2. """ # if isinstance(li1, (np.ndarray, np.generic)): # li1 = li1.tolist() # if isinstance(li2, (np.ndarray, np.generic)): # li1 = li1.tolist() return (list(set(li1) - set(li2)) + list(set(li2) - set(li1)))
[docs]def argmaxn(value_list, n, order='desc'): """Return the index of top n elements in the list if order is set to 'desc', otherwise return the index of n smallest ones. Parameters ---------- value_list : list, array, numpy array of shape (n_samples,) A list containing all values. n : int The number of elements to select. order : str, optional (default='desc') The order to sort {'desc', 'asc'}: - 'desc': descending - 'asc': ascending Returns ------- index_list : numpy array of shape (n,) The index of the top n elements. """ value_list = column_or_1d(value_list) length = len(value_list) # validate the choice of n check_parameter(n, 1, length, include_left=True, include_right=True, param_name='n') # for the smallest n, flip the value if order != 'desc': n = length - n value_sorted = np.partition(value_list, length - n) threshold = value_sorted[int(length - n)] if order == 'desc': return np.where(np.greater_equal(value_list, threshold))[0] else: # return the index of n smallest elements return np.where(np.less(value_list, threshold))[0]
[docs]def invert_order(scores, method='multiplication'): """ Invert the order of a list of values. The smallest value becomes the largest in the inverted list. This is useful while combining multiple detectors since their score order could be different. Parameters ---------- scores : list, array or numpy array with shape (n_samples,) The list of values to be inverted method : str, optional (default='multiplication') Methods used for order inversion. Valid methods are: - 'multiplication': multiply by -1 - 'subtraction': max(scores) - scores Returns ------- inverted_scores : numpy array of shape (n_samples,) The inverted list Examples -------- >>> scores1 = [0.1, 0.3, 0.5, 0.7, 0.2, 0.1] >>> invert_order(scores1) array([-0.1, -0.3, -0.5, -0.7, -0.2, -0.1]) >>> invert_order(scores1, method='subtraction') array([0.6, 0.4, 0.2, 0. , 0.5, 0.6]) """ scores = column_or_1d(scores) if method == 'multiplication': return scores.ravel() * -1 if method == 'subtraction': return (scores.max() - scores).ravel()
def _get_sklearn_version(): # pragma: no cover """ Utility function to decide the version of sklearn. PyOD will result in different behaviors with different sklearn version Returns ------- sk_learn version : int """ sklearn_version = str(sklearn.__version__) # print(sklearn_version) # if int(sklearn_version.split(".")[1]) < 19 or int( # sklearn_version.split(".")[1]) > 24: # raise ValueError("Sklearn version error") return int(sklearn_version.split(".")[1]) # def _sklearn_version_21(): # pragma: no cover # """ Utility function to decide the version of sklearn # In sklearn 21.0, LOF is changed. Specifically, _decision_function # is replaced by _score_samples # # Returns # ------- # sklearn_21_flag : bool # True if sklearn.__version__ is newer than 0.21.0 # # """ # sklearn_version = str(sklearn.__version__) # if int(sklearn_version.split(".")[1]) > 20: # return True # else: # return False
[docs]def generate_bagging_indices(random_state, bootstrap_features, n_features, min_features, max_features): """ Randomly draw feature indices. Internal use only. Modified from sklearn/ensemble/bagging.py Parameters ---------- random_state : RandomState A random number generator instance to define the state of the random permutations generator. bootstrap_features : bool Specifies whether to bootstrap indice generation n_features : int Specifies the population size when generating indices min_features : int Lower limit for number of features to randomly sample max_features : int Upper limit for number of features to randomly sample Returns ------- feature_indices : numpy array, shape (n_samples,) Indices for features to bag """ # Get valid random state random_state = check_random_state(random_state) # decide number of features to draw random_n_features = random_state.randint(min_features, max_features) # Draw indices feature_indices = generate_indices(random_state, bootstrap_features, n_features, random_n_features) return feature_indices
[docs]def generate_indices(random_state, bootstrap, n_population, n_samples): """ Draw randomly sampled indices. Internal use only. See sklearn/ensemble/bagging.py Parameters ---------- random_state : RandomState A random number generator instance to define the state of the random permutations generator. bootstrap : bool Specifies whether to bootstrap indice generation n_population : int Specifies the population size when generating indices n_samples : int Specifies number of samples to draw Returns ------- indices : numpy array, shape (n_samples,) randomly drawn indices """ # Draw sample indices if bootstrap: indices = random_state.randint(0, n_population, n_samples) else: indices = sample_without_replacement(n_population, n_samples, random_state=random_state) return indices