threshold_optimizer package¶
Submodules¶
threshold_optimizer.threshold_optimizer module¶
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class
threshold_optimizer.threshold_optimizer.ThresholdOptimizer(y_score: Union[numpy.ndarray, pandas.core.series.Series, list], y_true: Union[numpy.ndarray, pandas.core.series.Series, list], search_space_size: int = 100)¶ Bases:
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convert_classes(threshold: float) → numpy.ndarray¶ Convert predicted probabilities into binary classes based on a threshold/cutoff value
- Parameters
threshold – The probability threshold value to determine predicted classes. This follows a greater than or equal to format for determining class 1
Returns: 1 dimensional numpy array of classes
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get_best_accuracy_metrics(verbose: bool = True) → Tuple[int, int]¶ Optimizes threshold for accuracy
Returns: best accuracy score and threshold at which best accuracy score occurs
- Parameters
verbose – Optional. Option of whether to output results of optimization
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get_best_f1_metrics(verbose: bool = True) → Tuple[int, int]¶ Optimizes threshold for F1 score
Returns: best F1 score and threshold at which best F1 score occurs
- Parameters
verbose – Optional. Option of whether to output results of optimization
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get_best_precision_metrics(verbose: bool = True) → Tuple[int, int]¶ Optimizes threshold for precision
Returns: best precision score and threshold at which best precision score occurs
- Parameters
verbose – Optional. Option of whether to output results of optimization
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get_best_recall_metrics(verbose: bool = True) → Tuple[int, int]¶ Optimizes threshold for recall
Returns: best recall score and threshold at which best recall score occurs
- Parameters
verbose – Optional. Option of whether to output results of optimization
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get_best_sensitivity_metrics(verbose: bool = True) → Tuple[int, int]¶ Optimizes threshold for sensitivity score
Returns: best sensitivity score and threshold at which best sensitivity score occurs
- Parameters
verbose – Optional. Option of whether to output results of optimization
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get_best_specificity_metrics(verbose: bool = True) → Tuple[int, int]¶ Optimizes threshold for specificity
Returns: best specificity score and threshold at which best specificity score occurs
- Parameters
verbose – Optional. Option of whether to output results of optimization
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optimize_metrics(metrics: list = None, verbose: bool = True)¶ Function to optimize for supported metrics in a batch format
- Parameters
verbose – Optional. Option of whether to output results of optimization
metrics – Optional. Should be specified if only specific supported metrics are to be optimized. input must be a subset one of the supported metrics. If no metrics are applied, all metrics will be optimized for.
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set_search_space(search_space_size: int)¶ set the number of possible probability threshold values to optimze for
This function is useful to reset the size of the search space after initializing the ThresholdOptimizer object.
- Parameters
search_space_size – The number of possible probability threshold values to optimze for
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