def visualize(clf_name,
X_train,
y_train,
X_test,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False): # pragma: no cover
"""
Utility function for visualizing the results in examples
Internal use only
:param clf_name: The name of the detector
:type clf_name: str
:param X_train: The training samples
:param X_train: numpy array of shape (n_samples, n_features)
:param y_train: The ground truth of training samples
:type y_train: list or array of shape (n_samples,)
:param X_test: The test samples
:type X_test: numpy array of shape (n_samples, n_features)
:param y_test: The ground truth of test samples
:type y_test: list or array of shape (n_samples,)
:param y_train_pred: The predicted outlier scores on the training samples
:type y_train_pred: numpy array of shape (n_samples, n_features)
:param y_test_pred: The predicted outlier scores on the test samples
:type y_test_pred: numpy array of shape (n_samples, n_features)
:param show_figure: If set to True, show the figure
:type show_figure: bool, optional (default=True)
:param save_figure: If set to True, save the figure to the local
:type save_figure: bool, optional (default=False)
"""
if X_train.shape[1] != 2 or X_test.shape[1] != 2:
raise ValueError("Input data has to be 2-d for visualization. The "
"input data has {shape}.".format(shape=X_train.shape))
X_train, y_train = check_X_y(X_train, y_train)
X_test, y_test = check_X_y(X_test, y_test)
c_train = get_color_codes(y_train)
c_test = get_color_codes(y_test)
fig = plt.figure(figsize=(12, 10))
plt.suptitle("Demo of {clf_name}".format(clf_name=clf_name))
fig.add_subplot(221)
plt.scatter(X_train[:, 0], X_train[:, 1], c=c_train)
plt.title('Train ground truth')
legend_elements = [
Line2D([0], [0],
marker='o',
color='w',
label='normal',
markerfacecolor='b',
markersize=8),
Line2D([0], [0],
marker='o',
color='w',
label='outlier',
markerfacecolor='r',
markersize=8)
]
plt.legend(handles=legend_elements, loc=4)
fig.add_subplot(222)
plt.scatter(X_test[:, 0], X_test[:, 1], c=c_test)
plt.title('Test ground truth')
plt.legend(handles=legend_elements, loc=4)
fig.add_subplot(223)
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train_pred)
plt.title('Train prediction by {clf_name}'.format(clf_name=clf_name))
legend_elements = [
Line2D([0], [0],
marker='o',
color='w',
label='normal',
markerfacecolor='0',
markersize=8),
Line2D([0], [0],
marker='o',
color='w',
label='outlier',
markerfacecolor='yellow',
markersize=8)
]
plt.legend(handles=legend_elements, loc=4)
fig.add_subplot(224)
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test_pred)
plt.title('Test prediction by {clf_name}'.format(clf_name=clf_name))
plt.legend(handles=legend_elements, loc=4)
if save_figure:
plt.savefig('{clf_name}.png'.format(clf_name=clf_name), dpi=300)
if show_figure:
plt.show()
return
def visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred, show_figure=True,
save_figure=False): # pragma: no cover
"""
Utility function for visualizing the results in examples
Internal use only
:param clf_name: The name of the detector
:type clf_name: str
:param X_train: The training samples
:param X_train: numpy array of shape (n_samples, n_features)
:param y_train: The ground truth of training samples
:type y_train: list or array of shape (n_samples,)
:param X_test: The test samples
:type X_test: numpy array of shape (n_samples, n_features)
:param y_test: The ground truth of test samples
:type y_test: list or array of shape (n_samples,)
:param y_train_pred: The predicted outlier scores on the training samples
:type y_train_pred: numpy array of shape (n_samples, n_features)
:param y_test_pred: The predicted outlier scores on the test samples
:type y_test_pred: numpy array of shape (n_samples, n_features)
:param show_figure: If set to True, show the figure
:type show_figure: bool, optional (default=True)
:param save_figure: If set to True, save the figure to the local
:type save_figure: bool, optional (default=False)
"""
if X_train.shape[1] != 2 or X_test.shape[1] != 2:
raise ValueError("Input data has to be 2-d for visualization. The "
"input data has {shape}.".format(shape=X_train.shape))
X_train, y_train = check_X_y(X_train, y_train)
X_test, y_test = check_X_y(X_test, y_test)
c_train = get_color_codes(y_train)
c_test = get_color_codes(y_test)
fig = plt.figure(figsize=(12, 10))
plt.suptitle("Demo of {clf_name}".format(clf_name=clf_name))
fig.add_subplot(221)
plt.scatter(X_train[:, 0], X_train[:, 1], c=c_train)
plt.title('Train ground truth')
legend_elements = [Line2D([0], [0], marker='o', color='w', label='inlier',
markerfacecolor='b', markersize=8),
Line2D([0], [0], marker='^', color='w', label='outlier',
markerfacecolor='r', markersize=8)]
plt.legend(handles=legend_elements, loc=4)
fig.add_subplot(222)
plt.scatter(X_test[:, 0], X_test[:, 1], c=c_test)
plt.title('Test ground truth')
plt.legend(handles=legend_elements, loc=4)
fig.add_subplot(223)
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train_pred)
plt.title('Train prediction by {clf_name}'.format(clf_name=clf_name))
legend_elements = [Line2D([0], [0], marker='o', color='w', label='inlier',
markerfacecolor='0', markersize=8),
Line2D([0], [0], marker='^', color='w', label='outlier',
markerfacecolor='yellow', markersize=8)]
plt.legend(handles=legend_elements, loc=4)
fig.add_subplot(224)
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test_pred)
plt.title('Test prediction by {clf_name}'.format(clf_name=clf_name))
plt.legend(handles=legend_elements, loc=4)
if save_figure:
plt.savefig('{clf_name}.png'.format(clf_name=clf_name), dpi=300)
if show_figure:
plt.show()
return
