def _get_local_region(self, X_test_norm):
""" Get local region for each test instance
Parameters
----------
X_test_norm : numpy array, shape (n_samples, n_features)
Normalized test data
Returns
-------
final_local_region_list : List of lists, shape [n_samples, [local_region]]
Indices of training samples in the local region of each test sample
"""
# Initialize the local region list
local_region_list = [[]] * X_test_norm.shape[0]
if self.local_max_features > 1.0:
warnings.warn(
"Local max features greater than 1.0, reducing to 1.0")
self.local_max_features = 1.0
# perform multiple iterations
for _ in range(self.local_region_iterations):
# randomly generate feature subspaces
features = generate_bagging_indices(
self.random_state,
bootstrap_features=False,
n_features=self.X_train_norm_.shape[1],
min_features=int(self.X_train_norm_.shape[1] *
self.local_min_features),
max_features=int(self.X_train_norm_.shape[1] *
self.local_max_features))
# build KDTree out of training subspace
tree = KDTree(self.X_train_norm_[:, features])
# Find neighbors of each test instance
_, ind_arr = tree.query(X_test_norm[:, features],
k=self.local_region_size)
# add neighbors to local region list
for j in range(X_test_norm.shape[0]):
local_region_list[j] = local_region_list[j] + ind_arr[
j, :].tolist()
# keep nearby points which occur at least local_region_threshold times
final_local_region_list = [[]] * X_test_norm.shape[0]
for j in range(X_test_norm.shape[0]):
final_local_region_list[j] = [
item for item, count in collections.Counter(
local_region_list[j]).items()
if count > self.local_region_threshold
]
return final_local_region_list
def _get_local_region(self, X_test_norm):
""" Get local region for each test instance
Parameters
----------
X_test_norm : numpy array, shape (n_samples, n_features)
Normalized test data
Returns
-------
final_local_region_list : List of lists, shape [n_samples, [local_region]]
Indices of training samples in the local region of each test sample
"""
# Initialize the local region list
local_region_list = [[]] * X_test_norm.shape[0]
if self.local_max_features > 1.0:
warnings.warn(
"Local max features greater than 1.0, reducing to 1.0")
self.local_max_features = 1.0
# perform multiple iterations
for _ in range(self.local_region_iterations):
# randomly generate feature subspaces
features = generate_bagging_indices(
self.random_state,
bootstrap_features=False,
n_features=self.X_train_norm_.shape[1],
min_features=int(
self.X_train_norm_.shape[1] * self.local_min_features),
max_features=int(
self.X_train_norm_.shape[1] * self.local_max_features))
# build KDTree out of training subspace
tree = KDTree(self.X_train_norm_[:, features])
# Find neighbors of each test instance
_, ind_arr = tree.query(X_test_norm[:, features],
k=self.local_region_size)
# add neighbors to local region list
for j in range(X_test_norm.shape[0]):
local_region_list[j] = local_region_list[j] + \
ind_arr[j, :].tolist()
# keep nearby points which occur at least local_region_threshold times
final_local_region_list = [[]] * X_test_norm.shape[0]
for j in range(X_test_norm.shape[0]):
final_local_region_list[j] = [item for item, count in
collections.Counter(
local_region_list[j]).items() if
count > self.local_region_threshold]
return final_local_region_list
def _get_local_region(self, X_test_norm):
""" Get local region for each test instance
Parameters
----------
X_test_norm : numpy array, shape (n_samples, n_features)
Normalized test data
Returns
-------
final_local_region_list : List of lists, shape of [n_samples, [local_region]]
Indices of training samples in the local region of each test sample
"""
# Initialize the local region list
local_region_list = [[]] * X_test_norm.shape[0]
if self.local_max_features > 1.0:
warnings.warn(
"Local max features greater than 1.0, reducing to 1.0")
self.local_max_features = 1.0
if self.X_train_norm_.shape[1] * self.local_min_features < 1:
warnings.warn(
"Local min features smaller than 1, increasing to 1.0")
self.local_min_features = 1.0
# perform multiple iterations
for _ in range(self.local_region_iterations):
# if min and max are the same, then use all features
if self.local_max_features == self.local_min_features:
features = range(0, self.X_train_norm_.shape[1])
warnings.warn("Local min features equals local max features; "
"use all features instead.")
else:
# randomly generate feature subspaces
features = generate_bagging_indices(
self.random_state,
bootstrap_features=False,
n_features=self.X_train_norm_.shape[1],
min_features=int(
self.X_train_norm_.shape[1] * self.local_min_features),
max_features=int(
self.X_train_norm_.shape[1] * self.local_max_features))
# build KDTree out of training subspace
tree = KDTree(self.X_train_norm_[:, features])
# Find neighbors of each test instance
_, ind_arr = tree.query(X_test_norm[:, features],
k=self.local_region_size)
# add neighbors to local region list
for j in range(X_test_norm.shape[0]):
local_region_list[j] = local_region_list[j] + \
ind_arr[j, :].tolist()
# keep nearby points which occur at least local_region_threshold times
final_local_region_list = [[]] * X_test_norm.shape[0]
for j in range(X_test_norm.shape[0]):
tmp = [item for item, count in collections.Counter(
local_region_list[j]).items() if
count > self.local_region_threshold]
decrease_value = 0
while len(tmp) < 2:
decrease_value = decrease_value + 1
assert decrease_value < self.local_region_threshold
tmp = [item for item, count in
collections.Counter(local_region_list[j]).items() if
count > (self.local_region_threshold - decrease_value)]
final_local_region_list[j] = tmp
return final_local_region_list
y_train_scores = clf.decision_scores_
toeplitz_time.append(time.time() - start)
toeplitz_roc.append(roc_auc_score(y, y_train_scores))
toeplitz_prn.append(precision_n_scores(y, y_train_scores))
X_transformer = PCA_sklearn(n_components=dim_new).fit_transform(X)
start = time.time()
clf.fit(X_transformer)
y_train_scores = clf.decision_scores_
pca_time.append(time.time() - start)
pca_roc.append(roc_auc_score(y, y_train_scores))
pca_prn.append(precision_n_scores(y, y_train_scores))
selected_features = generate_bagging_indices(random_state=j,
bootstrap_features=False,
n_features=int(
X.shape[1]),
min_features=dim_new,
max_features=dim_new + 1)
assert (dim_new == len(selected_features))
X_transformer = X[:, selected_features]
start = time.time()
clf.fit(X_transformer)
y_train_scores = clf.decision_scores_
rp_time.append(time.time() - start)
rp_roc.append(roc_auc_score(y, y_train_scores))
rp_prn.append(precision_n_scores(y, y_train_scores))
print()
print(mat_file_name)
print('original', np.round(np.average(original_time), decimals=4),
np.round(np.average(original_roc), decimals=4),