How to use the pyod.utils.data.get_outliers_inliers function in pyod

To help you get started, we’ve selected a few pyod examples, based on popular ways it is used in public projects.

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yzhao062 / pyod / examples / ocsvm_example.py View on Github

# 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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)
    _add_sub_plot(X_train_inliers_pred, X_train_outliers_pred,
                  'Train Set Prediction', inlier_color='blue',
                  outlier_color='orange')

yzhao062 / pyod / examples / mcd_example.py View on Github

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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

yzhao062 / pyod / pyod / utils / example.py View on Github

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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)

yzhao062 / pyod / examples / feature_bagging_example.py View on Github

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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)

yzhao062 / pyod / examples / cblof_example.py View on Github

# 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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)
    _add_sub_plot(X_train_inliers_pred, X_train_outliers_pred,
                  'Train Set Prediction', inlier_color='blue',
                  outlier_color='orange')

yzhao062 / pyod / examples / loci_example.py View on Github

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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)
    _add_sub_plot(X_train_inliers_pred, X_train_outliers_pred,
                  'Train Set Prediction', inlier_color='blue',
                  outlier_color='orange')

yzhao062 / pyod / examples / loci_example.py View on Github

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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)

yzhao062 / pyod / examples / hbos_example.py View on Github

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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)
    _add_sub_plot(X_train_inliers_pred, X_train_outliers_pred,
                  'Train Set Prediction', inlier_color='blue',
                  outlier_color='orange')

yzhao062 / pyod / examples / iforest_example.py View on Github

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.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

How to use the pyod.utils.data.get_outliers_inliers function in pyod

To help you get started, we’ve selected a few pyod examples, based on popular ways it is used in public projects.

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