.. _metrics_guide: ==================== Metrics Guide ==================== In this guide we'll explain how to customize the metrics that deepchecks uses to validate and monitor your model performance. Controlling the metrics helps you shape the checks and suites according to the specifics of your use case. **Structure:** * `Default Metrics <#default-metrics>`__ * `Alternative Metrics <#alternative-metrics>`__ - How to pass to the checks your desired metrics and override the default ones. * `List of Supported Strings <#list-of-supported-strings>`__ - List of the names of the pre-implemented metrics that can be passed to the checks. * `Custom Metrics <#custom-metrics>`__ - How to pass to the checks a custom metric that is not in the pre-implemented ones. Default Metrics =============== All of the checks that evaluate model performance, such as :doc:`SingleDatasetPerformance ` come with default metrics. The default metrics by task type are: Tabular _______ Binary classification: * Accuracy ``'accuracy'`` * Precision ``'precision'`` * Recall ``'recall'`` Multiclass classification averaged over the classes: * Accuracy ``'accuracy'`` * Precision ``'precision_macro'`` * Recall ``'recall_macro'`` Multiclass classification per class: * F1 ``'f1_per_class'`` * Precision ``'precision_per_class'`` * Recall ``'recall_per_class'`` Regression: * Negative RMSE ``'neg_rmse'`` * Negative MAE ``'neg_mae'`` * R2 ``'r2'`` .. _metrics_guide_note_regression: .. Note:: Deepchecks follow the convention that greater metric value represent better performance. Therefore, it is recommended to only use metrics that follow this convention, for example, Negative MAE instead of MAE. Vision ______ Classification: * Precision ``'precision_per_class'`` * Recall ``'recall_per_class'`` Object detection: * Mean average precision ``'average_precision_per_class'`` * Mean average recall ``'average_recall_per_class'`` Running a Check with Default Metrics ____________________________________ To run a check with the default metrics, run it without passing any value to the "scorers" parameter. We will demonstrate it using the :doc:`ClassPerformance ` check: .. literalinclude:: ../../../../examples/examples_metrics_guide.py :language: python :lines: 2-8 :tab-width: 0 Alternative Metrics =================== Sometimes the defaults don't fit the specifics of the use case. If this is the case, you can pass a list of supported metric strings or a dict in the format {``metric_name_string``: ``metric``} to the scorers parameter of the check or suite. The metrics in the dict can be some of the existing: * Strings from Deepchecks' `supported strings <#list-of-supported-strings>`__ for both vision and tabular. * `Ignite Metrics `__ for vision. An Ignite Metric is a class with the methods: reset, compute, and update, that iterates over batches of data and aggregates the result. * `Scikit-learn Scorers `__ for both vision and tabular. A Scikit-learn Scorer is a function that accepts the parameters: (model, x, y_true), and returns a score with the convention that higher is better. * `Your own implementation <#custom-metrics>`__. .. literalinclude:: ../../../../examples/examples_metrics_guide.py :language: python :lines: 11-18 :tab-width: 0 List of Supported Strings ========================= In addition to the strings listed below, all Sklearn `scorer strings `__ apply. Regression __________ .. list-table:: :widths: 25 75 75 :header-rows: 1 * - String - Metric - Comments * - 'neg_rmse' - negative root mean squared error - higher value represents better performance * - 'neg_mae' - negative mean absolute error - higher value represents better performance * - 'rmse' - root mean squared error - not recommended, see :ref:`note `. * - 'mae' - mean absolute error - not recommended, see :ref:`note `. * - 'mse' - mean squared error - not recommended, see :ref:`note `. * - 'r2' - R2 score - Classification ______________ .. Note:: For classification tasks, Deepchecks requires an ordered list of all possible classes (Can also be inferred from provided data and model). It is also recommended to supply the model's output probabilities per class, as they are required for some metrics and checks which will not work without them. See :doc:`link ` for additional information. .. list-table:: :widths: 25 75 75 :header-rows: 1 * - String - Metric - Comments * - 'accuracy' - classification accuracy - scikit-learn * - ‘roc_auc’ - Area Under the Receiver Operating Characteristic Curve (ROC AUC) - binary - for multiclass averaging options see `scikit-learn's documentation `__ * - ‘roc_auc_per_class’ - Area Under the Receiver Operating Characteristic Curve (ROC AUC) - score per class - for multiclass averaging options see `scikit-learn's documentation `__ * - 'f1' - F-1 - binary - * - 'f1_per_class' - F-1 per class - no averaging - * - 'f1_macro' - F-1 - macro averaging - * - 'f1_micro' - F-1 - micro averaging - * - 'f1_weighted' - F-1 - macro, weighted by support - * - 'precision' - precision - suffixes apply as with 'f1' * - 'recall' , 'sensitivity' - recall (sensitivity) - suffixes apply as with 'f1' * - 'fpr' - False Positive Rate - binary - suffixes apply as with 'f1' * - 'fnr' - False Negative Rate - binary - suffixes apply as with 'f1' * - 'tnr', 'specificity' - True Negative Rate - binary - suffixes apply as with 'f1' * - 'roc_auc' - AUC - binary - * - 'roc_auc_per_class' - AUC per class - no averaging - * - 'roc_auc_ovr' - AUC - One-vs-rest - * - 'roc_auc_ovo' - AUC - One-vs-One - * - 'roc_auc_ovr_weighted' - AUC - One-vs-rest, weighted by support - * - 'roc_auc_ovo_weighted' - AUC - One-vs-One, weighted by support - Object Detection ________________ .. list-table:: :widths: 25 75 75 :header-rows: 1 * - String - Metric - Comments * - 'average_precision_per_class' - average precision for object detection - * - 'average_precision_macro' - average precision macro averaging - * - 'average_precision_weighted' - average precision macro, weighted by support - * - 'average_recall_per_class' - average recall for object detection - suffixes apply as with 'average_precision' Custom Metrics ============== You can also pass your own custom metric to relevant checks and suites. For computer vision the custom metrics should support the `Ignite Metric `__ API. For tabular metrics the custom metrics should support the `sklearn scorer `__ API. Multiclass classification scorers should assume that the labels are given in a `multi-label format `__ (a binary matrix). Binary classification scorers should assume that the labels are given as 0 and 1. Tabular Example _______________ .. literalinclude:: ../../../../examples/examples_metrics_guide.py :language: python :lines: 21-32 :tab-width: 0 Vision Example ______________ .. literalinclude:: ../../../../examples/examples_metrics_guide.py :language: python :lines: 35-42 :tab-width: 0