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Fix stupid web editor merge
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@nnansters
nnansters committed Feb 12, 2024
1 parent 5f71340 commit cce5029
Showing 1 changed file with 27 additions and 12 deletions.
39 changes: 27 additions & 12 deletions nannyml/performance_estimation/confidence_based/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -277,14 +277,13 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:
chunk_record[f'sampling_error_{column_name}'] = metric_estimate_sampling_error
chunk_record[f'realized_{column_name}'] = self._realized_performance(chunk_data)
chunk_record[f'upper_confidence_boundary_{column_name}'] = np.minimum(
np.inf if self.upper_threshold_value_limit is None else self.upper_threshold_value_limit,
estimated_metric_value + SAMPLING_ERROR_RANGE * metric_estimate_sampling_error,
)

chunk_record[f'lower_confidence_boundary_{column_name}'] = np.maximum(
-np.inf if self.lower_threshold_value_limit is None else self.lower_threshold_value_limit,
estimated_metric_value - SAMPLING_ERROR_RANGE * metric_estimate_sampling_error,
)
np.inf if self.upper_threshold_value_limit is None else self.upper_threshold_value_limit,
estimated_metric_value + SAMPLING_ERROR_RANGE * metric_estimate_sampling_error,
)
chunk_record[f'lower_confidence_boundary_{column_name}'] = np.maximum(
-np.inf if self.lower_threshold_value_limit is None else self.lower_threshold_value_limit,
estimated_metric_value - SAMPLING_ERROR_RANGE * metric_estimate_sampling_error,
)
chunk_record[f'upper_threshold_{column_name}'] = self.upper_threshold_value
chunk_record[f'lower_threshold_{column_name}'] = self.lower_threshold_value
chunk_record[f'alert_{column_name}'] = self.alert(estimated_metric_value)
Expand Down Expand Up @@ -340,6 +339,7 @@ def create(cls, key: str, use_case: ProblemType, **kwargs) -> Metric:
@classmethod
def register(cls, metric: str, use_case: ProblemType) -> Callable:
"""Register a Metric in the MetricFactory registry."""

def inner_wrapper(wrapped_class: Type[Metric]) -> Type[Metric]:
if metric in cls.registry:
if use_case in cls.registry[metric]:
Expand All @@ -355,6 +355,7 @@ def inner_wrapper(wrapped_class: Type[Metric]) -> Type[Metric]:
@MetricFactory.register('roc_auc', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationAUROC(Metric):
"""CBPE binary classification AUROC Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -450,6 +451,7 @@ def estimate_roc_auc(y_pred_proba: pd.Series) -> float:
@MetricFactory.register('f1', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationF1(Metric):
"""CBPE binary classification f1 Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -536,6 +538,7 @@ def estimate_f1(y_pred: pd.DataFrame, y_pred_proba: pd.DataFrame) -> float:
@MetricFactory.register('precision', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationPrecision(Metric):
"""CBPE binary classification precision Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -622,6 +625,7 @@ def estimate_precision(y_pred: pd.DataFrame, y_pred_proba: pd.DataFrame) -> floa
@MetricFactory.register('recall', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationRecall(Metric):
"""CBPE binary classification recall Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -707,6 +711,7 @@ def estimate_recall(y_pred: pd.DataFrame, y_pred_proba: pd.DataFrame) -> float:
@MetricFactory.register('specificity', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationSpecificity(Metric):
"""CBPE binary classification specificity Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -793,6 +798,7 @@ def estimate_specificity(y_pred: pd.DataFrame, y_pred_proba: pd.DataFrame) -> fl
@MetricFactory.register('accuracy', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationAccuracy(Metric):
"""CBPE binary classification accuracy Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -860,6 +866,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('confusion_matrix', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationConfusionMatrix(Metric):
"""CBPE binary classification confusion matrix Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -1324,7 +1331,7 @@ def get_true_pos_info(self, chunk_data: pd.DataFrame) -> Dict:

true_pos_info['lower_confidence_boundary_true_positive'] = np.maximum(
-np.inf if self.lower_threshold_value_limit is None else self.lower_threshold_value_limit,
estimated_true_positives - SAMPLING_ERROR_RANGE * sampling_error_true_positives
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 @@ -1372,7 +1379,7 @@ def get_true_neg_info(self, chunk_data: pd.DataFrame) -> Dict:

true_neg_info['lower_confidence_boundary_true_negative'] = np.maximum(
-np.inf if self.lower_threshold_value_limit is None else self.lower_threshold_value_limit,
estimated_true_negatives - SAMPLING_ERROR_RANGE * sampling_error_true_negatives
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 @@ -1526,6 +1533,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('business_value', ProblemType.CLASSIFICATION_BINARY)
class BinaryClassificationBusinessValue(Metric):
"""CBPE binary classification business value Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -1717,6 +1725,7 @@ def _ensure_targets(self, data: pd.DataFrame) -> Optional[pd.DataFrame]:
@MetricFactory.register('roc_auc', ProblemType.CLASSIFICATION_MULTICLASS)
class MulticlassClassificationAUROC(_MulticlassClassificationMetric):
"""CBPE multiclass classification AUROC Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -1783,6 +1792,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('f1', ProblemType.CLASSIFICATION_MULTICLASS)
class MulticlassClassificationF1(_MulticlassClassificationMetric):
"""CBPE multiclass classification f1 Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -1852,6 +1862,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('precision', ProblemType.CLASSIFICATION_MULTICLASS)
class MulticlassClassificationPrecision(_MulticlassClassificationMetric):
"""CBPE multiclass classification precision Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -1921,6 +1932,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('recall', ProblemType.CLASSIFICATION_MULTICLASS)
class MulticlassClassificationRecall(_MulticlassClassificationMetric):
"""CBPE multiclass classification recall Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -1990,6 +2002,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('specificity', ProblemType.CLASSIFICATION_MULTICLASS)
class MulticlassClassificationSpecificity(_MulticlassClassificationMetric):
"""CBPE multiclass classification specificity Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -2063,6 +2076,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('accuracy', ProblemType.CLASSIFICATION_MULTICLASS)
class MulticlassClassificationAccuracy(_MulticlassClassificationMetric):
"""CBPE multiclass classification accuracy Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -2129,6 +2143,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
@MetricFactory.register('confusion_matrix', ProblemType.CLASSIFICATION_MULTICLASS)
class MulticlassClassificationConfusionMatrix(Metric):
"""CBPE multiclass classification confusion matrix Metric Class."""

def __init__(
self,
y_pred_proba: ModelOutputsType,
Expand Down Expand Up @@ -2343,7 +2358,7 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:
)
chunk_record[f'upper_confidence_boundary_true_{true_class}_pred_{pred_class}'] = min(
np.inf if self.upper_threshold_value_limit is None else self.upper_threshold_value_limit,
upper_confidence_boundary
upper_confidence_boundary,
)

lower_confidence_boundary = (
Expand All @@ -2353,7 +2368,7 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:
)
chunk_record[f'lower_confidence_boundary_true_{true_class}_pred_{pred_class}'] = max(
-np.inf if self.lower_threshold_value_limit is None else self.lower_threshold_value_limit,
lower_confidence_boundary
lower_confidence_boundary,
)

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

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