From c27183050a21f7a6ce5d704e2755a5403bd266f0 Mon Sep 17 00:00:00 2001 From: Yue Zhao Date: Mon, 29 Jul 2019 20:06:27 +0800 Subject: [PATCH] update documentation --- README.rst | 22 +++++++++++----------- docs/example.rst | 22 +++++++++++----------- 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/README.rst b/README.rst index c58f91b..06c80d5 100644 --- a/README.rst +++ b/README.rst @@ -419,23 +419,23 @@ demonstrates the basic API of combining multiple base outlier detectors. #. Evaluate the prediction using ROC and Precision @ Rank n. - .. code-block:: python + .. code-block:: python - # evaluate and print the results - print("\nOn Training Data:") - evaluate_print(clf_name, y_train, y_train_scores) - print("\nOn Test Data:") - evaluate_print(clf_name, y_test, y_test_scores) + # evaluate and print the results + print("\nOn Training Data:") + evaluate_print(clf_name, y_train, y_train_scores) + print("\nOn Test Data:") + evaluate_print(clf_name, y_test, y_test_scores) #. See sample outputs on both training and test data. - .. code-block:: bash + .. code-block:: bash - On Training Data: - Aggregation by Averaging ROC:0.9994, precision @ rank n:0.95 + On Training Data: + Aggregation by Averaging ROC:0.9994, precision @ rank n:0.95 - On Test Data: - Aggregation by Averaging ROC:1.0, precision @ rank n:1.0 + On Test Data: + Aggregation by Averaging ROC:1.0, precision @ rank n:1.0 ---- diff --git a/docs/example.rst b/docs/example.rst index 43a6672..d040f62 100644 --- a/docs/example.rst +++ b/docs/example.rst @@ -195,22 +195,22 @@ demonstrates the basic API of combining multiple base outlier detectors. #. Evaluate the prediction using ROC and Precision @ Rank n. - .. code-block:: python + .. code-block:: python - # evaluate and print the results - print("\nOn Training Data:") - evaluate_print(clf_name, y_train, y_train_scores) - print("\nOn Test Data:") - evaluate_print(clf_name, y_test, y_test_scores) + # evaluate and print the results + print("\nOn Training Data:") + evaluate_print(clf_name, y_train, y_train_scores) + print("\nOn Test Data:") + evaluate_print(clf_name, y_test, y_test_scores) #. See sample outputs on both training and test data. - .. code-block:: bash + .. code-block:: bash - On Training Data: - Aggregation by Averaging ROC:0.9994, precision @ rank n:0.95 + On Training Data: + Aggregation by Averaging ROC:0.9994, precision @ rank n:0.95 - On Test Data: - Aggregation by Averaging ROC:1.0, precision @ rank n:1.0 + On Test Data: + Aggregation by Averaging ROC:1.0, precision @ rank n:1.0