# -*- coding: utf-8 -*-
"""A collection of model combination functionalities.
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause
from __future__ import division
from __future__ import print_function
try:
import combo
except ImportError:
print('please install combo first for combination by `pip install combo`')
from combo.models.score_comb import aom as combo_aom
from combo.models.score_comb import moa as combo_moa
from combo.models.score_comb import average as combo_average
from combo.models.score_comb import maximization as combo_maximization
from combo.models.score_comb import majority_vote as combo_majority_vote
from combo.models.score_comb import median as combo_median
[docs]
def aom(scores, n_buckets=5, method='static', bootstrap_estimators=False,
random_state=None):
"""Average of Maximum - An ensemble method for combining multiple
estimators. See :cite:`aggarwal2015theoretical` for details.
First dividing estimators into subgroups, take the maximum score as the
subgroup score. Finally, take the average of all subgroup outlier scores.
Parameters
----------
scores : numpy array of shape (n_samples, n_estimators)
The score matrix outputted from various estimators
n_buckets : int, optional (default=5)
The number of subgroups to build
method : str, optional (default='static')
{'static', 'dynamic'}, if 'dynamic', build subgroups
randomly with dynamic bucket size.
bootstrap_estimators : bool, optional (default=False)
Whether estimators are drawn with replacement.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the
random number generator; If RandomState instance, random_state is
the random number generator; If None, the random number generator
is the RandomState instance used by `np.random`.
Returns
-------
combined_scores : Numpy array of shape (n_samples,)
The combined outlier scores.
"""
return combo_aom(scores, n_buckets, method, bootstrap_estimators,
random_state)
[docs]
def moa(scores, n_buckets=5, method='static', bootstrap_estimators=False,
random_state=None):
"""Maximization of Average - An ensemble method for combining multiple
estimators. See :cite:`aggarwal2015theoretical` for details.
First dividing estimators into subgroups, take the average score as the
subgroup score. Finally, take the maximization of all subgroup outlier
scores.
Parameters
----------
scores : numpy array of shape (n_samples, n_estimators)
The score matrix outputted from various estimators
n_buckets : int, optional (default=5)
The number of subgroups to build
method : str, optional (default='static')
{'static', 'dynamic'}, if 'dynamic', build subgroups
randomly with dynamic bucket size.
bootstrap_estimators : bool, optional (default=False)
Whether estimators are drawn with replacement.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the
random number generator; If RandomState instance, random_state is
the random number generator; If None, the random number generator
is the RandomState instance used by `np.random`.
Returns
-------
combined_scores : Numpy array of shape (n_samples,)
The combined outlier scores.
"""
return combo_moa(scores, n_buckets, method, bootstrap_estimators,
random_state)
[docs]
def average(scores, estimator_weights=None):
"""Combination method to merge the outlier scores from multiple estimators
by taking the average.
Parameters
----------
scores : numpy array of shape (n_samples, n_estimators)
Score matrix from multiple estimators on the same samples.
estimator_weights : list of shape (1, n_estimators)
If specified, using weighted average
Returns
-------
combined_scores : numpy array of shape (n_samples, )
The combined outlier scores.
"""
return combo_average(scores, estimator_weights)
[docs]
def maximization(scores):
"""Combination method to merge the outlier scores from multiple estimators
by taking the maximum.
Parameters
----------
scores : numpy array of shape (n_samples, n_estimators)
Score matrix from multiple estimators on the same samples.
Returns
-------
combined_scores : numpy array of shape (n_samples, )
The combined outlier scores.
"""
return combo_maximization(scores)
[docs]
def majority_vote(scores, weights=None):
"""Combination method to merge the scores from multiple estimators
by majority vote.
Parameters
----------
scores : numpy array of shape (n_samples, n_estimators)
Score matrix from multiple estimators on the same samples.
weights : numpy array of shape (1, n_estimators)
If specified, using weighted majority weight.
Returns
-------
combined_scores : numpy array of shape (n_samples, )
The combined scores.
"""
return combo_majority_vote(scores, n_classes=2, weights=weights)