def _get_decision_scores(self, X):
# ensure local region size is within acceptable limits
self.local_region_size = max(self.local_region_size, self.local_region_min)
self.local_region_size = min(self.local_region_size, self.local_region_max)
# standardize test data and get local region for each test instance
X_test_norm = X
ind_arr = self._get_local_region(X_test_norm)
# calculate test scores
test_scores = np.zeros([X_test_norm.shape[0], self.n_clf])
for k, estimator in enumerate(self.estimator_list):
test_scores[:, k] = estimator.decision_function(X_test_norm)
# generate standardized scores
train_scores_norm, test_scores_norm = standardizer(self.train_scores_, test_scores)
# generate pseudo target for training --> for calculating weights
self.training_pseudo_label_ = np.max(train_scores_norm, axis=1).reshape(-1, 1)
# placeholder for predictions
pred_scores_ens = np.zeros([X_test_norm.shape[0], ])
# iterate through test instances (ind_arr indices correspond to x_test)
for i, ind_k in enumerate(ind_arr):
# get pseudo target and training scores in local region of test instance
local_pseudo_ground_truth = self.training_pseudo_label_[ind_k,].ravel()
local_train_scores = train_scores_norm[ind_k, :]
# calculate pearson correlation between local pseudo ground truth and local train scores
pearson_corr_scores = np.zeros([self.n_clf, ])
for d in range(self.n_clf):
pearson_corr_scores[d,] = pearsonr(local_pseudo_ground_truth, local_train_scores[:, d])[0]
# return best score
pred_scores_ens[i,] = np.mean(
test_scores_norm[i, self._get_competent_detectors(pearson_corr_scores)])
return pred_scores_ens
def _get_decision_scores(self, X):
""" Helper function for getting outlier scores on test data X (note:
model must already be fit)
Parameters
----------
X : numpy array, shape (n_samples, n_features)
Test data
Returns
-------
pred_scores_ens : numpy array, shape (n_samples,)
Outlier scores for test samples
"""
# raise warning if local region size is outside acceptable limits
if (self.local_region_size < self.local_region_min) or (
self.local_region_size > self.local_region_max):
warnings.warn("Local region size of {} is outside "
"recommended range [{}, {}]".format(
self.local_region_size, self.local_region_min,
self.local_region_max))
# standardize test data and get local region for each test instance
X_test_norm = X
test_local_regions = self._get_local_region(X_test_norm)
# calculate test scores
test_scores = np.zeros([X_test_norm.shape[0], self.n_clf])
for k, detector in enumerate(self.detector_list):
test_scores[:, k] = detector.decision_function(X_test_norm)
# generate standardized scores
train_scores_norm, test_scores_norm = standardizer(self.train_scores_,
test_scores)
# generate pseudo target for training --> for calculating weights
self.training_pseudo_label_ = np.max(train_scores_norm,
axis=1).reshape(-1, 1)
# placeholder for ensemble predictions
pred_scores_ens = np.zeros([X_test_norm.shape[0], ])
# iterate through test instances (test_local_regions
# indices correspond to x_test)
for i, test_local_region in enumerate(test_local_regions):
# get pseudo target and training scores in local region of
# test instance
local_pseudo_ground_truth = self.training_pseudo_label_[
test_local_region,].ravel()
local_train_scores = train_scores_norm[test_local_region, :]
# calculate pearson correlation between local pseudo ground truth
# and local train scores
pearson_corr_scores = np.zeros([self.n_clf, ])
for d in range(self.n_clf):
pearson_corr_scores[d,] = pearsonr(
local_pseudo_ground_truth, local_train_scores[:, d])[0]
# return best score
pred_scores_ens[i,] = np.mean(
test_scores_norm[
i, self._get_competent_detectors(pearson_corr_scores)])
return pred_scores_ens
def _get_decision_scores(self, X):
""" Helper function for getting outlier scores on test data X (note:
model must already be fit)
Parameters
----------
X : numpy array, shape (n_samples, n_features)
Test data
Returns
-------
pred_scores_ens : numpy array, shape (n_samples,)
Outlier scores for test samples
"""
# raise warning if local region size is outside acceptable limits
if (self.local_region_size < self.local_region_min) or (
self.local_region_size > self.local_region_max):
warnings.warn("Local region size of {} is outside "
"recommended range [{}, {}]".format(
self.local_region_size, self.local_region_min,
self.local_region_max))
# standardize test data and get local region for each test instance
X_test_norm = X
test_local_regions = self._get_local_region(X_test_norm)
# calculate test scores
test_scores = np.zeros([X_test_norm.shape[0], self.n_clf])
for k, detector in enumerate(self.detector_list):
test_scores[:, k] = detector.decision_function(X_test_norm)
# generate standardized scores
train_scores_norm, test_scores_norm = standardizer(self.train_scores_,
test_scores)
# generate pseudo target for training --> for calculating weights
self.training_pseudo_label_ = np.max(train_scores_norm,
axis=1).reshape(-1, 1)
# placeholder for ensemble predictions
pred_scores_ens = np.zeros([X_test_norm.shape[0], ])
# iterate through test instances (test_local_regions
# indices correspond to x_test)
for i, test_local_region in enumerate(test_local_regions):
# get pseudo target and training scores in local region of
# test instance
local_pseudo_ground_truth = self.training_pseudo_label_[
test_local_region,].ravel()
local_train_scores = train_scores_norm[test_local_region, :]
# calculate pearson correlation between local pseudo ground truth
# and local train scores
pearson_corr_scores = np.zeros([self.n_clf, ])
for d in range(self.n_clf):
pearson_corr_scores[d,] = pearsonr(
local_pseudo_ground_truth, local_train_scores[:, d])[0]
# return best score
pred_scores_ens[i,] = np.mean(
test_scores_norm[
i, self._get_competent_detectors(pearson_corr_scores)])
return pred_scores_ens