bigframes.ml.metrics.roc_curve

bigframes.ml.metrics.roc_curve(y_true: DataFrame | Series, y_score: DataFrame | Series, *, drop_intermediate: bool = True) → Tuple[Series, Series, Series]

Compute Receiver operating characteristic (ROC).

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.ml.metrics

>>> y_true = bpd.DataFrame([1, 1, 2, 2])
>>> y_score = bpd.DataFrame([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve(y_true, y_score, drop_intermediate=False)
>>> fpr
0    0.0
1    0.0
2    0.0
3    0.0
4    0.0
Name: fpr, dtype: Float64

>>> tpr
0         0.0
1    0.333333
2         0.5
3    0.833333
4         1.0
Name: tpr, dtype: Float64

>>> thresholds
0     inf
1     0.8
2     0.4
3    0.35
4     0.1
Name: thresholds, dtype: Float64

Parameters:

• y_true – Series or DataFrame of shape (n_samples,) True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given.

• y_score – Series or DataFrame of shape (n_samples,) Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).

• drop_intermediate – bool, default=True Default to True. Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.

Returns:

Increasing false positive rates such that element i is the false

positive rate of predictions with score >= thresholds[i].

tpr:

Increasing true positive rates such that element i is the true positive rate of predictions with score >= thresholds[i].

thresholds:

Decreasing thresholds on the decision function used to compute fpr and tpr. thresholds[0] represents no instances being predicted and is arbitrarily set to max(y_score) + 1.

Return type:

fpr