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Fix limiting CBPE confidence bands to 0 #360

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Fix limiting CBPE confidence bands to 0 #360

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60 changes: 33 additions & 27 deletions nannyml/performance_estimation/confidence_based/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -270,13 +270,14 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:

chunk_record[f'realized_{column_name}'] = self._realized_performance(chunk_data)

confidence_lower_bound, confidence_upper_bound = self._get_confidence_bounds()
chunk_record[f'upper_confidence_boundary_{column_name}'] = np.minimum(
self.confidence_upper_bound or np.inf,
confidence_upper_bound,
estimated_metric_value + SAMPLING_ERROR_RANGE * metric_estimate_sampling_error,
)

chunk_record[f'lower_confidence_boundary_{column_name}'] = np.maximum(
self.confidence_lower_bound or -np.inf,
confidence_lower_bound,
estimated_metric_value - SAMPLING_ERROR_RANGE * metric_estimate_sampling_error,
)

Expand All @@ -287,6 +288,13 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:

return chunk_record

def _get_confidence_bounds(self) -> Tuple[float, float]:
"""Returns constraints for the confidence bounds of the metric."""
return (
-np.inf if self.confidence_lower_bound is None else self.confidence_lower_bound,
np.inf if self.confidence_upper_bound is None else self.confidence_upper_bound,
)


class MetricFactory:
"""A factory class that produces Metric instances based on a given magic string or a metric specification."""
Expand Down Expand Up @@ -1281,18 +1289,19 @@ def get_true_pos_info(self, chunk_data: pd.DataFrame) -> Dict:
true_pos_info['sampling_error_true_positive'] = sampling_error_true_positives
true_pos_info['realized_true_positive'] = self._true_positive_realized_performance(chunk_data)

confidence_lower_bound, confidence_upper_bound = self._get_confidence_bounds()
if self.normalize_confusion_matrix is None:
true_pos_info['upper_confidence_boundary_true_positive'] = (
estimated_true_positives + SAMPLING_ERROR_RANGE * sampling_error_true_positives
)
else:
true_pos_info['upper_confidence_boundary_true_positive'] = np.minimum(
self.confidence_upper_bound,
confidence_upper_bound,
estimated_true_positives + SAMPLING_ERROR_RANGE * sampling_error_true_positives,
)

true_pos_info['lower_confidence_boundary_true_positive'] = np.maximum(
self.confidence_lower_bound, estimated_true_positives - SAMPLING_ERROR_RANGE * sampling_error_true_positives
confidence_lower_bound, estimated_true_positives - SAMPLING_ERROR_RANGE * sampling_error_true_positives
)

true_pos_info['upper_threshold_true_positive'] = self.true_positive_upper_threshold
Expand Down Expand Up @@ -1333,18 +1342,19 @@ def get_true_neg_info(self, chunk_data: pd.DataFrame) -> Dict:
true_neg_info['sampling_error_true_negative'] = sampling_error_true_negatives
true_neg_info['realized_true_negative'] = self._true_negative_realized_performance(chunk_data)

confidence_lower_bound, confidence_upper_bound = self._get_confidence_bounds()
if self.normalize_confusion_matrix is None:
true_neg_info['upper_confidence_boundary_true_negative'] = (
estimated_true_negatives + SAMPLING_ERROR_RANGE * sampling_error_true_negatives
)
else:
true_neg_info['upper_confidence_boundary_true_negative'] = np.minimum(
self.confidence_upper_bound,
confidence_upper_bound,
estimated_true_negatives + SAMPLING_ERROR_RANGE * sampling_error_true_negatives,
)

true_neg_info['lower_confidence_boundary_true_negative'] = np.maximum(
self.confidence_lower_bound, estimated_true_negatives - SAMPLING_ERROR_RANGE * sampling_error_true_negatives
confidence_lower_bound, estimated_true_negatives - SAMPLING_ERROR_RANGE * sampling_error_true_negatives
)

true_neg_info['upper_threshold_true_negative'] = self.true_negative_upper_threshold
Expand Down Expand Up @@ -1385,18 +1395,19 @@ def get_false_pos_info(self, chunk_data: pd.DataFrame) -> Dict:
false_pos_info['sampling_error_false_positive'] = sampling_error_false_positives
false_pos_info['realized_false_positive'] = self._false_positive_realized_performance(chunk_data)

confidence_lower_bound, confidence_upper_bound = self._get_confidence_bounds()
if self.normalize_confusion_matrix is None:
false_pos_info['upper_confidence_boundary_false_positive'] = (
estimated_false_positives + SAMPLING_ERROR_RANGE * sampling_error_false_positives
)
else:
false_pos_info['upper_confidence_boundary_false_positive'] = np.minimum(
self.confidence_upper_bound,
confidence_upper_bound,
estimated_false_positives + SAMPLING_ERROR_RANGE * sampling_error_false_positives,
)

false_pos_info['lower_confidence_boundary_false_positive'] = np.maximum(
self.confidence_lower_bound,
confidence_lower_bound,
estimated_false_positives - SAMPLING_ERROR_RANGE * sampling_error_false_positives,
)

Expand Down Expand Up @@ -1438,18 +1449,19 @@ def get_false_neg_info(self, chunk_data: pd.DataFrame) -> Dict:
false_neg_info['sampling_error_false_negative'] = sampling_error_false_negatives
false_neg_info['realized_false_negative'] = self._false_negative_realized_performance(chunk_data)

confidence_lower_bound, confidence_upper_bound = self._get_confidence_bounds()
if self.normalize_confusion_matrix is None:
false_neg_info['upper_confidence_boundary_false_negative'] = (
estimated_false_negatives + SAMPLING_ERROR_RANGE * sampling_error_false_negatives
)
else:
false_neg_info['upper_confidence_boundary_false_negative'] = np.minimum(
self.confidence_upper_bound,
confidence_upper_bound,
estimated_false_negatives + SAMPLING_ERROR_RANGE * sampling_error_false_negatives,
)

false_neg_info['lower_confidence_boundary_false_negative'] = np.maximum(
self.confidence_lower_bound,
confidence_lower_bound,
estimated_false_negatives - SAMPLING_ERROR_RANGE * sampling_error_false_negatives,
)

Expand Down Expand Up @@ -2277,29 +2289,23 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:
* sampling_error[self.classes.index(true_class), self.classes.index(pred_class)]
)

if self.normalize_confusion_matrix is None:
chunk_record[
f'upper_confidence_boundary_true_{true_class}_pred_{pred_class}'
] = upper_confidence_boundary
else:
chunk_record[f'upper_confidence_boundary_true_{true_class}_pred_{pred_class}'] = min(
self.confidence_upper_bound, upper_confidence_boundary
)

lower_confidence_boundary = (
estimated_cm[self.classes.index(true_class), self.classes.index(pred_class)]
- SAMPLING_ERROR_RANGE
* sampling_error[self.classes.index(true_class), self.classes.index(pred_class)]
)

if self.normalize_confusion_matrix is None:
chunk_record[
f'lower_confidence_boundary_true_{true_class}_pred_{pred_class}'
] = lower_confidence_boundary
else:
chunk_record[f'lower_confidence_boundary_true_{true_class}_pred_{pred_class}'] = max(
self.confidence_lower_bound, lower_confidence_boundary
)
if self.normalize_confusion_matrix is not None:
confidence_lower_bound, confidence_upper_bound = self._get_confidence_bounds()
upper_confidence_boundary = min(confidence_upper_bound, upper_confidence_boundary)
lower_confidence_boundary = max(confidence_lower_bound, lower_confidence_boundary)

chunk_record[
f'upper_confidence_boundary_true_{true_class}_pred_{pred_class}'
] = upper_confidence_boundary
chunk_record[
f'lower_confidence_boundary_true_{true_class}_pred_{pred_class}'
] = lower_confidence_boundary

chunk_record[f'upper_threshold_true_{true_class}_pred_{pred_class}'] = self.alert_thresholds[
f'true_{true_class}_pred_{pred_class}'
Expand Down
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