How to use the pyod.utils.data.check_consistent_shape function in pyod
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yzhao062 / pyod / examples / ocsvm_example.py View on Github 
"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
return
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
return
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
return
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
return
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
return
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))The color of outliers.
"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
return
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))"""
plt.axis("equal")
plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
color=inlier_color, s=40)
plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
label='outliers', color=outlier_color, s=50, marker='^')
plt.title(sub_plot_title, fontsize=15)
plt.xticks([])
plt.yticks([])
plt.legend(loc=3, prop={'size': 10})
return
# check input data shapes are consistent
X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred)
if X_train.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_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
X_train, y_train_pred)
X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
X_test, y_test_pred)
# plot ground truth vs. predicted results
fig = plt.figure(figsize=(12, 10))pyod
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
Package Health Score
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