Repository: scikit-learn/scikit-learn
File: sklearn/metrics/_ranking.py (Python)

Code before:
        Sample weights.

    Returns
    -------
    fps : ndarray of shape (n_thresholds,)
        A count of false positives, at index i being the number of negative
        samples assigned a score >= thresholds[i]. The total number of
        negative samples is equal to fps[-1] (thus true negatives are given by
        fps[-1] - fps).

    tps : ndarray of shape (n_thresholds,)
        An increasing count of true positives, at index i being the number
        of positive samples assigned a score >= thresholds[i]. The total
        number of positive samples is equal to tps[-1] (thus false negatives
        are given by tps[-1] - tps).

    thresholds : ndarray of shape (n_thresholds,)
        Decreasing score values.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import binary_classification_curve
    >>> y_true = np.array([0., 0., 1., 1.])
    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
    >>> fps, tps, thresholds = binary_classification_curve(y_true, y_scores)
    >>> fps
    array([0., 1., 1., 2.])
    >>> tps
    array([1., 1., 2., 2.])
    >>> thresholds
    array([0.8 , 0.4 , 0.35, 0.1 ])
    """
    # Check to make sure y_true is valid
    y_type = type_of_target(y_true, input_name="y_true")
    if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):
        raise ValueError("{0} format is not supported".format(y_type))

    check_consistent_length(y_true, y_score, sample_weight)
    y_true = column_or_1d(y_true)
    y_score = column_or_1d(y_score)
    assert_all_finite(y_true)
    assert_all_finite(y_score)

    # Filter out zero-weighted samples, as they should not impact the result
    if sample_weight is not None:
        sample_weight = column_or_1d(sample_weight)
        sample_weight = _check_sample_weight(sample_weight, y_true)
        nonzero_weight_mask = sample_weight != 0
        y_true = y_true[nonzero_weight_mask]
        y_score = y_score[nonzero_weight_mask]

Review comment (suggestion):
```suggestion
    >>> fps, tps, thresholds = binary_classification_curve(y_true, y_score)
```

Code after:
        if not np.array_equal(classes, labels):
            raise ValueError("Parameter 'labels' must be ordered")
        if len(classes) != y_score.shape[1]:
            raise ValueError(
                "Number of given labels, {0}, not equal to the number "
                "of columns in 'y_score', {1}".format(len(classes), y_score.shape[1])
            )
        if len(np.setdiff1d(y_true, classes)):
            raise ValueError("'y_true' contains labels not in parameter 'labels'")
    else:
        classes = _unique(y_true)
        if len(classes) != y_score.shape[1]:
            raise ValueError(
                "Number of classes in y_true not equal to the number of "
                "columns in 'y_score'"
            )

    if multi_class == "ovo":
        if sample_weight is not None:
            raise ValueError(
                "sample_weight is not supported "
                "for multiclass one-vs-one ROC AUC, "
                "'sample_weight' must be None in this case."
            )
        y_true_encoded = _encode(y_true, uniques=classes)
        # Hand & Till (2001) implementation (ovo)
        return _average_multiclass_ovo_score(
            _binary_roc_auc_score, y_true_encoded, y_score, average=average
        )
    else:
        # ovr is same as multi-label
        y_true_multilabel = label_binarize(y_true, classes=classes)
        return _average_binary_score(
            _binary_roc_auc_score,
            y_true_multilabel,
            y_score,
            average,
            sample_weight=sample_weight,
        )


@validate_params(
    {
        "y_true": ["array-like"],
        "y_score": ["array-like"],
        "pos_label": [Real, str, "boolean", None],
        "sample_weight": ["array-like", None],
    },
    prefer_skip_nested_validation=True,
)
def confusion_matrix_at_thresholds(y_true, y_score, pos_label=None, sample_weight=None):