| |
| |
|
|
| import numpy as np |
|
|
| from ._metrics_engine import metric_by_group |
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|
| def selection_rate(y_true, y_pred, *, pos_label=1, sample_weight=None): |
| """Calculate the fraction of predicted labels matching the 'good' outcome. |
| |
| The argument `pos_label` specifies the 'good' outcome. |
| """ |
| selected = (np.squeeze(np.asarray(y_pred)) == pos_label) |
| s_w = np.ones(len(selected)) |
| if sample_weight is not None: |
| s_w = np.squeeze(np.asarray(sample_weight)) |
|
|
| return np.dot(selected, s_w) / s_w.sum() |
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|
|
| def group_selection_rate(y_true, y_pred, group_membership, |
| *, pos_label=1, sample_weight=None): |
| """Wrap :func:`selection_rate` as a group metric. |
| |
| The arguments are the same, with the addition of the |
| `group_membership` array. |
| """ |
| def internal_sel_wrapper(y_true, y_pred, sample_weight=None): |
| return selection_rate(y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight) |
|
|
| return metric_by_group(internal_sel_wrapper, |
| y_true, y_pred, group_membership, |
| sample_weight=sample_weight) |
|
|
