kwcoco.metrics.functional module¶
- kwcoco.metrics.functional.fast_confusion_matrix(y_true, y_pred, n_labels, sample_weight=None)[source]¶
faster version of sklearn confusion matrix that avoids the expensive checks and label rectification
- Parameters:
y_true (ndarray) – ground truth class label for each sample
y_pred (ndarray) – predicted class label for each sample
n_labels (int) – number of labels
sample_weight (ndarray | None) – weight of each sample Extended typing
ndarray[Any, int | Float]
- Returns:
matrix where rows represent real and cols represent pred and the value at each cell is the total amount of weight Extended typing
ndarray[Shape['*, *'], Int64 | Float64]- Return type:
ndarray
Example
>>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 0, 0, 1]) >>> y_pred = np.array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1]) >>> fast_confusion_matrix(y_true, y_pred, 2) array([[4, 2], [3, 1]]...) >>> fast_confusion_matrix(y_true, y_pred, 2).ravel() array([4, 2, 3, 1]...)
- kwcoco.metrics.functional._truncated_roc(y_df, bg_idx=-1, fp_cutoff=None)[source]¶
Computes truncated ROC info
- kwcoco.metrics.functional._pr_curves(y)[source]¶
Compute a PR curve from a method
- Parameters:
y (pd.DataFrame | DataFrameArray) – output of detection_confusions
- Returns:
Tuple[float, ndarray, ndarray]
Example
>>> # xdoctest: +REQUIRES(module:sklearn) >>> import pandas as pd >>> y1 = pd.DataFrame.from_records([ >>> {'pred': 0, 'score': 10.00, 'true': -1, 'weight': 1.00}, >>> {'pred': 0, 'score': 1.65, 'true': 0, 'weight': 1.00}, >>> {'pred': 0, 'score': 8.64, 'true': -1, 'weight': 1.00}, >>> {'pred': 0, 'score': 3.97, 'true': 0, 'weight': 1.00}, >>> {'pred': 0, 'score': 1.68, 'true': 0, 'weight': 1.00}, >>> {'pred': 0, 'score': 5.06, 'true': 0, 'weight': 1.00}, >>> {'pred': 0, 'score': 0.25, 'true': 0, 'weight': 1.00}, >>> {'pred': 0, 'score': 1.75, 'true': 0, 'weight': 1.00}, >>> {'pred': 0, 'score': 8.52, 'true': 0, 'weight': 1.00}, >>> {'pred': 0, 'score': 5.20, 'true': 0, 'weight': 1.00}, >>> ]) >>> import kwcoco as nh >>> import kwarray >>> y2 = kwarray.DataFrameArray(y1) >>> _pr_curves(y2) >>> _pr_curves(y1)