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#

All of the checks that evaluate model performance, such as Train Test Performance come with default metrics.

The default metrics by task type are:

Classification#

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'

Token Classification (NLP only)#

Classification metrics averaged over the tokens:

  • Accuracy 'accuracy'

  • Precision 'precision_macro'

  • Recall 'recall_macro'

Classification metrics per token class:

  • F1 'f1_per_class'

  • Precision 'precision_per_class'

  • Recall 'recall_per_class'

Object Detection (Vision only)#

  • Mean average precision 'average_precision_per_class'

  • Mean average recall 'average_recall_per_class'

Regression#

  • Negative RMSE 'neg_rmse'

  • Negative MAE 'neg_mae'

  • R2 'r2'

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.

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 Class Performance check:

from deepchecks.vision.checks import ClassPerformance
from deepchecks.vision.datasets.classification import mnist_torch as mnist
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)

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 for both vision and tabular.

  • 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. This is the method for developing custom tabular metrics, and is also the advised method for developing custom vision metrics for classification tasks.

  • Deepchecks Metrics for vision Metrics implemented by Deepchecks. These are dedicated metrics for object detection and semantic segmentation, such as the MeanIoU metric. Using them is advised when, for example, defining custom confidence or custom IoU thresholds is needed. You can import them from deepchecks.vision.metrics.

  • For cases in which a new vision custom metrics is needed, such as for implementing additional object detection or segmentation metrics, deepchecks also supports custom metric classes.

Jump to the Custom Metrics section for further information about implementing your own metrics using the Scikit-learn Scorer api or a custom metric class.

Example for passing strings:

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

Example for passing Deepchecks metrics:

from deepchecks.vision.metrics import MeanDice
from deepchecks.vision.datasets.segmentation.segmentation_coco import load_dataset, load_model

coco_dataset = load_dataset()
metric = {'mean_dice': MeanDice(threshold=0.9)}

check = SingleDatasetPerformance(scorers=metric)
result = check.run(coco_dataset)

List of Supported Strings#

In addition to the strings listed below, all Sklearn scorer strings apply for all tabular task types, and for computer vision classification tasks.

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 Working with Models and Predictions for additional information.

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

Token Classification#

String

Metric

Comments

‘accuracy’

classification accuracy across all tokens

‘precision_per_class’

precision per token class - no averaging

‘precision_macro’

precision per token class with macro averaging

‘precision_micro’

precision per token class with micro averaging

‘recall’

recall

suffixes apply as with ‘precision’

‘f1’

f1

suffixes apply as with ‘precision’

Object Detection#

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’

Regression#

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 note.

‘mae’

mean absolute error

not recommended, see note.

‘mse’

mean squared error

not recommended, see note.

‘r2’

R2 score

Custom Metrics#

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

For tabular metrics and vision classification tasks the custom metrics function should follow the sklearn scorer API, which is a function that accepts the parameters: (model, x, y_true), and returns a score with the convention that higher is better.

For other computer vision tasks, you should implement a Deepchecks CustomMetric. A Deepchecks CustomMetric is an object that calculates a metric by accumulating information about the labels and predictions batch by batch, and then finalizes the metric computation once all batches have been processed. The metric must inherit from deepchecks.vision.metrics_utils.CustomMetric and implement the following methods: reset, update and compute:

  • reset - Resets the metric to its initial state, resets any internal variables. Called by deepchecks before first call to the update method.

  • update - Called once for each batch in the data, this method updates the metric’s internal state based on the labels and predictions of one batch. The method’s signature should be update(self, output), where output is a tuple containing first y_pred which is the model’s output and second y_true is the ground truth, both given as lists of numpy objects, adhering to the deepchecks format. For example, an object detection label would be a list where each element is a numpy array of bounding boxes annotations, and the prediction would be a list where each element is a numpy array of bounding boxes predictions, both in the deepchecks format.

  • compute - Returns the metric’s value based on the internal state. Can be either a single number, or a numpy array of containing a number for each class. This method is called only once, after all batches have been processed.

The update method is called on each batch of data, and the compute method is called to compute the final metric.

Note that in all cases, 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#

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#

import numpy as np
import typing as t
from deepchecks.vision.checks import SingleDatasetPerformance
from deepchecks.vision.metrics_utils import CustomMetric

# For simplicity, we will implement the accuracy metric, although it is already implemented in deepchecks and
# can be passed as a string, and even otherwise we'd recommend using the sklearn API for custom classification metrics.

class CustomAccuracy(CustomMetric):

    def __init__(self):
        super().__init__()

    def reset(self):
        self._correct = 0
        self._total = 0
        super().reset()

    def update(self, output: t.Tuple[t.List[np.ndarray], t.List[int]]):
        y_pred, y = output
        y_pred = np.array(y_pred).argmax(axis=1)
        y = np.array(y)
        self._correct += (y_pred == y).sum()
        self._total += y_pred.shape[0]
        super().update(output)

    def compute(self):
        return self._correct / self._total

check = SingleDatasetPerformance(scorers={'accuracy': CustomAccuracy()})
result = check.run(train_ds)

NLP Example#

Currently unavailable, will be added in future releases.

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