cost_benefit_matrix

Cost-benefit matrix objective.

Module Contents

Classes Summary

CostBenefitMatrix

Score using a cost-benefit matrix. Scores quantify the benefits of a given value, so greater numeric scores represents a better score. Costs and scores can be negative, indicating that a value is not beneficial. For example, in the case of monetary profit, a negative cost and/or score represents loss of cash flow.

Contents

class evalml.objectives.cost_benefit_matrix.CostBenefitMatrix(true_positive, true_negative, false_positive, false_negative)

Score using a cost-benefit matrix. Scores quantify the benefits of a given value, so greater numeric scores represents a better score. Costs and scores can be negative, indicating that a value is not beneficial. For example, in the case of monetary profit, a negative cost and/or score represents loss of cash flow.

Parameters

• true_positive (float) – Cost associated with true positive predictions.

• true_negative (float) – Cost associated with true negative predictions.

• false_positive (float) – Cost associated with false positive predictions.

• false_negative (float) – Cost associated with false negative predictions.

Attributes

expected_range	None
greater_is_better	True
is_bounded_like_percentage	False
name	Cost Benefit Matrix
perfect_score	None
problem_types	[ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY]
score_needs_proba	False

Methods

calculate_percent_difference	Calculate the percent difference between scores.
can_optimize_threshold	Returns a boolean determining if we can optimize the binary classification objective threshold.
decision_function	Apply a learned threshold to predicted probabilities to get predicted classes.
is_defined_for_problem_type	Returns whether or not an objective is defined for a problem type.
objective_function	Calculates cost-benefit of the using the predicted and true values.
optimize_threshold	Learn a binary classification threshold which optimizes the current objective.
positive_only	If True, this objective is only valid for positive data. Defaults to False.
score	Returns a numerical score indicating performance based on the differences between the predicted and actual values.
validate_inputs	Validate inputs for scoring.

classmethod calculate_percent_difference(cls, score, baseline_score)

Calculate the percent difference between scores.

Parameters

• score (float) – A score. Output of the score method of this objective.

• baseline_score (float) – A score. Output of the score method of this objective. In practice, this is the score achieved on this objective with a baseline estimator.

Returns

The percent difference between the scores. Note that for objectives that can be interpreted

as percentages, this will be the difference between the reference score and score. For all other objectives, the difference will be normalized by the reference score.

Return type

float

property can_optimize_threshold(cls)

Returns a boolean determining if we can optimize the binary classification objective threshold.

This will be false for any objective that works directly with predicted probabilities, like log loss and AUC. Otherwise, it will be true.

Returns

Whether or not an objective can be optimized.

Return type

bool

decision_function(self, ypred_proba, threshold=0.5, X=None)

Apply a learned threshold to predicted probabilities to get predicted classes.

Parameters

• ypred_proba (pd.Series, np.ndarray) – The classifier’s predicted probabilities

• threshold (float, optional) – Threshold used to make a prediction. Defaults to 0.5.

• X (pd.DataFrame, optional) – Any extra columns that are needed from training data.

Returns

predictions

classmethod is_defined_for_problem_type(cls, problem_type)

Returns whether or not an objective is defined for a problem type.

objective_function(self, y_true, y_predicted, y_train=None, X=None, sample_weight=None)

Calculates cost-benefit of the using the predicted and true values.

Parameters

• y_predicted (pd.Series) – Predicted labels.

• y_true (pd.Series) – True labels.

• y_train (pd.Series) – Ignored.

• X (pd.DataFrame) – Ignored.

• sample_weight (pd.DataFrame) – Ignored.

Returns

Cost-benefit matrix score

Return type

float

optimize_threshold(self, ypred_proba, y_true, X=None)

Learn a binary classification threshold which optimizes the current objective.

Parameters

• ypred_proba (pd.Series) – The classifier’s predicted probabilities

• y_true (pd.Series) – The ground truth for the predictions.

• X (pd.DataFrame, optional) – Any extra columns that are needed from training data.

Returns

Optimal threshold for this objective.

Raises

RuntimeError – If objective cannot be optimized.

positive_only(cls)

If True, this objective is only valid for positive data. Defaults to False.

score(self, y_true, y_predicted, y_train=None, X=None, sample_weight=None)

Returns a numerical score indicating performance based on the differences between the predicted and actual values.

Parameters

• y_predicted (pd.Series) – Predicted values of length [n_samples]

• y_true (pd.Series) – Actual class labels of length [n_samples]

• y_train (pd.Series) – Observed training values of length [n_samples]

• X (pd.DataFrame or np.ndarray) – Extra data of shape [n_samples, n_features] necessary to calculate score

• sample_weight (pd.DataFrame or np.ndarray) – Sample weights used in computing objective value result

Returns

score

validate_inputs(self, y_true, y_predicted)

Validate inputs for scoring.