Adding custom checks

skore lets you extend the built-in automated checks with your own. This example shows how to write a custom check function and register it with a report via add().

Writing a custom check for a single estimator

We start by defining a simple check that flags models with a very large number of features. The check inspects the test data attached to the report. We throw an exception when the test data is not available to avoid running the check when it is not applicable. The check function is wrapped in a Check instance and registered with the report via add().

The docs_url argument is optional. When provided as a full URL (starting with "http"), it is used as-is. When it is a plain anchor string it points to the skore automated checks user guide. When omitted entirely, no documentation link is shown.

We set the severity to “tip” to indicate that this is not an issue to fix, but a cautionary note about the dataset. Severity can also be set to “issue” to indicate when there is an issue to fix.

import numpy as np
from skore import Check, CheckNotApplicable


class CustomCheck1(Check):
    code = "CSTM001"
    title = "High feature count"
    report_types = ["estimator"]
    severity = "tip"
    docs_url = "https://scikit-learn.org/stable/modules/feature_selection.html#feature-selection"

    def check_function(self, report):
        """Flag when the number of features exceeds a threshold."""
        if report.X_test is None:
            raise CheckNotApplicable()

        n_features = report.X_test.shape[1]
        if n_features > 50:
            return (
                f"The dataset has {n_features} features which may hurt model performance. "
                "Consider feature selection or dimensionality reduction."
            )
        return None

Registering the check

add() accepts a list of Check instances, and registers them. The next call to summarize() runs any newly added checks on top of the built-in checks.

We can then find the new check in the Tips tab of the checks summary, along another tip informing us that the dataset is not standardized.

import pandas as pd
from sklearn.linear_model import LinearRegression
from skore import evaluate

rng = np.random.default_rng(42)
X = pd.DataFrame(
    rng.normal(size=(200, 80)), columns=[f"feature_{i}" for i in range(80)]
)
y = pd.Series(X.iloc[:, 0] + rng.normal(scale=0.1, size=200))

report = evaluate(LinearRegression(), X, y)
report.checks.add([CustomCheck1()])
report.checks.summarize()

Cross-validation level checks

CrossValidationReport and ComparisonReport can also receive custom checks scoped to their report type.

The report_types attribute of Check is a list that controls which reports the check runs on. Let’s write a check that is specific to cross-validation reports: it flags metrics with high variance across splits. We set the severity to “issue” to indicate that this is an issue to fix.

We will corrupt the first fold of the target to illustrate the check.

We see that our new check appears along another similar issue that detects folds that are outliers in terms of performance metrics.

import pandas as pd

y_noisy = y.copy()
y_noisy[: len(y_noisy) // 5] = rng.normal(size=len(y_noisy) // 5)
cv_report = evaluate(LinearRegression(), X, y_noisy, splitter=5)


class CustomCheck2(Check):
    code = "CSTM002"
    title = "High score variance across CV splits"
    report_types = ["cross-validation"]
    docs_url = None
    severity = "issue"

    def check_function(self, report):
        """Flag high score variance across CV splits."""
        frames = [
            sub_report.metrics.summarize(data_source="test").summary
            for sub_report in report.reports_
        ]
        scores = pd.concat(frames, ignore_index=True)

        high_var_metrics = [
            metric_name
            for metric_name, group in scores.groupby("verbose_name")
            if group["score"].std() > 0.1
        ]

        if high_var_metrics:
            return f"Metrics with high variance: {', '.join(high_var_metrics)}."
        return None


cv_report.checks.add([CustomCheck2()])
cv_report.checks.summarize()

Estimator-scoped checks on cross-validation and comparison reports

Estimator-scoped checks such as CustomCheck1 are only executed on EstimatorReport instances, not on CrossValidationReport instances. To run the same logic on both report types, set report_types = ["estimator", "cross-validation"] and adapt the check function to handle each report type.

Comparison reports aggregate checks across their component reports.

from sklearn.ensemble import RandomForestRegressor

comparison_report = evaluate(
    [LinearRegression(), RandomForestRegressor()], X, y, splitter=5
)
comparison_report.checks.add([CustomCheck1(), CustomCheck2()])
comparison_report.checks.summarize()