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
Alternative Metrics - How to pass to the checks your desired metrics and override the default ones.
List of Supported Strings - List of the names of the pre-implemented metrics that can be passed to the checks.
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 SingleDatasetPerformance </checks_gallery/vision/model_evaluation/plot_single_dataset_performance>, 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 (negative RMSE so that the convention that greater is better is kept) ‘neg_rmse’
Negative MAE ‘neg_mae’
R2 ‘r2’

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’

To run a check with the default metrics, run it without passing any value to the “scorer” parameter. For example:

ClassPerformance:

from deepchecks.vision.checks import ClassPerformance
from deepchecks.vision.datasets.classification import mnist
mnist_model = mnist.load_model()
train_ds = mnist.load_dataset(train=True, object_type='VisionData')
test_ds = mnist.load_dataset(train=False, object_type='VisionData')
check = ClassPerformance()
result = check.run(train_ds, test_ds, mnist_model)

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} as a parameter to the check.

The metrics in the dict can be some of the existing:

Strings from Deepchecks’ 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.

or your own implementation.

ClassPerformance:

from deepchecks.tabular.checks import TrainTestPerformance
from deepchecks.tabular.datasets.classification import adult
train_ds, test_ds = adult.load_data(data_format='Dataset', as_train_test=True)
model = adult.load_fitted_model()

scorer = ['precision_per_class', 'recall_per_class', 'fnr_macro']
check = TrainTestPerformance(scorers=scorer)
result = check.run(train_ds, test_ds, model)

List of Supported Strings#

In addition to the strings listed below, all Sklearn scorer strings apply.

Supported Strings#
String	Metric	Comments
‘neg_rmse’	negative root mean squared error	scikit-learn
‘neg_mae’	negative mean absolute error	scikit-learn
‘accuracy’	classification accuracy	scikit-learn
‘roc_auc’	Area Under the Receiver Operating Characteristic Curve (ROC AUC) - binary	scikit-learn, for multiclass options check 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
‘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’
‘precision’	precision	suffixes apply as with ‘f1’
‘recall’ , ‘sensitivity’	recall (sensitivity)	suffixes apply as with ‘f1’
‘average_precision_per_class’	average precision for object detection	only for object detection
‘average_recall_per_class’	average recall for object detection	only for object detection

Custom Metrics#

You can also pass your own custom metric to relevant checks and suites.

Custom metrics should follow the Ignite Metric API for computer vision or sklearn scorer API for tabular.

Tabular Example#

from deepchecks.tabular.datasets.classification import adult
from deepchecks.tabular.suites import model_evaluation
from sklearn.metrics import cohen_kappa_score, fbeta_score, make_scorer

f1_scorer = make_scorer(fbeta_score, labels=[0, 1], average=None, beta=0.2)
ck_scorer = make_scorer(cohen_kappa_score)
custom_scorers = {'f1': f1_scorer, 'cohen': ck_scorer}

train_ds, test_ds = adult.load_data(data_format='Dataset', as_train_test=True)
model = adult.load_fitted_model()
suite = model_evaluation(scorers=custom_scorers)
result = suite.run(train_ds, test_ds, model)

Vision Example#

from ignite.metrics import Precision
from deepchecks.vision.checks import SingleDatasetPerformance

precision = Precision(average=True)
double_precision = 2 * precision

check = SingleDatasetPerformance({'precision2': double_precision})
result = check.run(train_ds, mnist_model)

Configure Check Conditions

Drift User Guide