# Objectives#

EvalML standard and custom objectives.

## Package Contents#

### Classes Summary#

 AccuracyBinary Accuracy score for binary classification. AccuracyMulticlass Accuracy score for multiclass classification. AUC AUC score for binary classification. AUCMacro AUC score for multiclass classification using macro averaging. AUCMicro AUC score for multiclass classification using micro averaging. AUCWeighted AUC Score for multiclass classification using weighted averaging. BalancedAccuracyBinary Balanced accuracy score for binary classification. BalancedAccuracyMulticlass Balanced accuracy score for multiclass classification. BinaryClassificationObjective Base class for all binary classification objectives. 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. ExpVariance Explained variance score for regression. F1 F1 score for binary classification. F1Macro F1 score for multiclass classification using macro averaging. F1Micro F1 score for multiclass classification using micro averaging. F1Weighted F1 score for multiclass classification using weighted averaging. FraudCost Score the percentage of money lost of the total transaction amount process due to fraud. Gini Gini coefficient for binary classification. LeadScoring Lead scoring. LogLossBinary Log Loss for binary classification. LogLossMulticlass Log Loss for multiclass classification. MAE Mean absolute error for regression. MAPE Mean absolute percentage error for time series regression. Scaled by 100 to return a percentage. MaxError Maximum residual error for regression. MCCBinary Matthews correlation coefficient for binary classification. MCCMulticlass Matthews correlation coefficient for multiclass classification. MeanSquaredLogError Mean squared log error for regression. MedianAE Median absolute error for regression. MSE Mean squared error for regression. MulticlassClassificationObjective Base class for all multiclass classification objectives. ObjectiveBase Base class for all objectives. Precision Precision score for binary classification. PrecisionMacro Precision score for multiclass classification using macro-averaging. PrecisionMicro Precision score for multiclass classification using micro averaging. PrecisionWeighted Precision score for multiclass classification using weighted averaging. R2 Coefficient of determination for regression. Recall Recall score for binary classification. RecallMacro Recall score for multiclass classification using macro averaging. RecallMicro Recall score for multiclass classification using micro averaging. RecallWeighted Recall score for multiclass classification using weighted averaging. RegressionObjective Base class for all regression objectives. RootMeanSquaredError Root mean squared error for regression. RootMeanSquaredLogError Root mean squared log error for regression. SensitivityLowAlert Create instance of SensitivityLowAlert.

### Functions#

 get_all_objective_names Get a list of the names of all objectives. get_core_objective_names Get a list of all valid core objectives. get_core_objectives Returns all core objective instances associated with the given problem type. get_non_core_objectives Get non-core objective classes. get_objective Returns the Objective class corresponding to a given objective name. get_optimization_objectives Get objectives for optimization. get_ranking_objectives Get objectives for pipeline rankings. ranking_only_objectives Get ranking-only objective classes.

### Contents#

class evalml.objectives.AccuracyBinary[source]#

Accuracy score for binary classification.

Example

>>> y_true = pd.Series([0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(AccuracyBinary().objective_function(y_true, y_pred), 0.6363636)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Accuracy Binary perfect_score 1.0 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 Objective function for accuracy score for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for accuracy score for binary classification.

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, 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]

• 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.

class evalml.objectives.AccuracyMulticlass[source]#

Accuracy score for multiclass classification.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(AccuracyMulticlass().objective_function(y_true, y_pred), 0.5454545)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Accuracy Multiclass perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for accuracy score for multiclass classification. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for accuracy score for multiclass classification.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.AUC[source]#

AUC score for binary classification.

Example

>>> y_true = pd.Series([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(AUC().objective_function(y_true, y_pred), 0.5714285)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name AUC perfect_score 1.0 problem_types [ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY] score_needs_proba True

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 Objective function for AUC score for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for AUC score for binary classification.

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, 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]

• 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.

class evalml.objectives.AUCMacro[source]#

AUC score for multiclass classification using macro averaging.

Example

>>> y_true = [0, 1, 2, 0, 2, 1]
>>> y_pred = [[0.7, 0.2, 0.1],
...           [0.1, 0.0, 0.9],
...           [0.1, 0.3, 0.6],
...           [0.9, 0.1, 0.0],
...           [0.6, 0.1, 0.3],
...           [0.5, 0.5, 0.0]]
>>> np.testing.assert_almost_equal(AUCMacro().objective_function(y_true, y_pred), 0.75)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name AUC Macro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba True

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for AUC score for multiclass classification using macro-averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for AUC score for multiclass classification using macro-averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.AUCMicro[source]#

AUC score for multiclass classification using micro averaging.

Example

>>> y_true = [0, 1, 2, 0, 2, 1]
>>> y_pred = [[0.7, 0.2, 0.1],
...           [0.3, 0.5, 0.2],
...           [0.1, 0.3, 0.6],
...           [0.9, 0.1, 0.0],
...           [0.3, 0.1, 0.6],
...           [0.5, 0.5, 0.0]]
>>> np.testing.assert_almost_equal(AUCMicro().objective_function(y_true, y_pred), 0.9861111)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name AUC Micro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba True

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for AUC score for multiclass classification using micro-averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for AUC score for multiclass classification using micro-averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.AUCWeighted[source]#

AUC Score for multiclass classification using weighted averaging.

Example

>>> y_true = [0, 1, 2, 0, 2, 1]
>>> y_pred = [[0.7, 0.2, 0.1],
...           [0.1, 0.0, 0.9],
...           [0.1, 0.3, 0.6],
...           [0.1, 0.2, 0.7],
...           [0.6, 0.1, 0.3],
...           [0.5, 0.2, 0.3]]
>>> np.testing.assert_almost_equal(AUCWeighted().objective_function(y_true, y_pred), 0.4375)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name AUC Weighted perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba True

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for AUC Score for multiclass classification using weighted averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for AUC Score for multiclass classification using weighted averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.BalancedAccuracyBinary[source]#

Balanced accuracy score for binary classification.

Example

>>> y_true = pd.Series([0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(BalancedAccuracyBinary().objective_function(y_true, y_pred), 0.60)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Balanced Accuracy Binary perfect_score 1.0 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 Objective function for accuracy score for balanced accuracy for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for accuracy score for balanced accuracy for binary classification.

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, 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]

• 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.

class evalml.objectives.BalancedAccuracyMulticlass[source]#

Balanced accuracy score for multiclass classification.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(BalancedAccuracyMulticlass().objective_function(y_true, y_pred), 0.5555555)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Balanced Accuracy Multiclass perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for accuracy score for balanced accuracy for multiclass classification. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for accuracy score for balanced accuracy for multiclass classification.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.BinaryClassificationObjective[source]#

Base class for all binary classification objectives.

Attributes

 problem_types [ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY]

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. expected_range Returns the expected range of the objective, which is not necessarily the possible ranges. greater_is_better Returns a boolean determining if a greater score indicates better model performance. is_bounded_like_percentage Returns whether this objective is bounded between 0 and 1, inclusive. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. name Returns a name describing the objective. objective_function Computes the relative value of the provided predictions compared to the actual labels, according a specified metric. optimize_threshold Learn a binary classification threshold which optimizes the current objective. perfect_score Returns the score obtained by evaluating this objective on a perfect model. 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. score_needs_proba Returns a boolean determining if the score() method needs probability estimates. 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)[source]#

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

property expected_range(cls)#

Returns the expected range of the objective, which is not necessarily the possible ranges.

For example, our expected R2 range is from [-1, 1], although the actual range is (-inf, 1].

property greater_is_better(cls)#

Returns a boolean determining if a greater score indicates better model performance.

property is_bounded_like_percentage(cls)#

Returns whether this objective is bounded between 0 and 1, inclusive.

classmethod is_defined_for_problem_type(cls, problem_type)#

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

property name(cls)#

Returns a name describing the objective.

abstract classmethod objective_function(cls, y_true, y_predicted, X=None, sample_weight=None)#

Computes the relative value of the provided predictions compared to the actual labels, according a specified metric.

Args:

y_predicted (pd.Series): Predicted values of length [n_samples] y_true (pd.Series): Actual class labels 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

Numerical value used to calculate score

optimize_threshold(self, ypred_proba, y_true, X=None)[source]#

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.

property perfect_score(cls)#

Returns the score obtained by evaluating this objective on a perfect model.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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

property score_needs_proba(cls)#

Returns a boolean determining if the score() method needs probability estimates.

This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.

validate_inputs(self, y_true, y_predicted)[source]#

Validate inputs for scoring.

class evalml.objectives.CostBenefitMatrix(true_positive, true_negative, false_positive, false_negative)[source]#

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, X=None, sample_weight=None)[source]#

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

Parameters
• y_predicted (pd.Series) – Predicted labels.

• y_true (pd.Series) – True labels.

• 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, 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]

• 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.

class evalml.objectives.ExpVariance[source]#

Explained variance score for regression.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(ExpVariance().objective_function(y_true, y_pred), 0.7760736)


Attributes

 expected_range None greater_is_better True is_bounded_like_percentage False name ExpVariance perfect_score 1.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for explained variance score for regression. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for explained variance score for regression.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.F1[source]#

F1 score for binary classification.

Example

>>> y_true = pd.Series([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(F1().objective_function(y_true, y_pred), 0.25)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name F1 perfect_score 1.0 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 Objective function for F1 score for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for F1 score for binary classification.

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, 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]

• 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.

class evalml.objectives.F1Macro[source]#

F1 score for multiclass classification using macro averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(F1Macro().objective_function(y_true, y_pred), 0.5476190)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name F1 Macro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for F1 score for multiclass classification using macro averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for F1 score for multiclass classification using macro averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.F1Micro[source]#

F1 score for multiclass classification using micro averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(F1Micro().objective_function(y_true, y_pred), 0.5454545)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name F1 Micro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for F1 score for multiclass classification. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for F1 score for multiclass classification.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.F1Weighted[source]#

F1 score for multiclass classification using weighted averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(F1Weighted().objective_function(y_true, y_pred), 0.5454545)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name F1 Weighted perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for F1 score for multiclass classification using weighted averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for F1 score for multiclass classification using weighted averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.FraudCost(retry_percentage=0.5, interchange_fee=0.02, fraud_payout_percentage=1.0, amount_col='amount')[source]#

Score the percentage of money lost of the total transaction amount process due to fraud.

Parameters
• retry_percentage (float) – What percentage of customers that will retry a transaction if it is declined. Between 0 and 1. Defaults to 0.5.

• interchange_fee (float) – How much of each successful transaction you pay. Between 0 and 1. Defaults to 0.02.

• fraud_payout_percentage (float) – Percentage of fraud you will not be able to collect. Between 0 and 1. Defaults to 1.0.

• amount_col (str) – Name of column in data that contains the amount. Defaults to “amount”.

Attributes

 expected_range None greater_is_better False is_bounded_like_percentage True name Fraud Cost perfect_score 0.0 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 Calculate amount lost to fraud per transaction given predictions, true values, and dataframe with transaction amount. 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, X, sample_weight=None)[source]#

Calculate amount lost to fraud per transaction given predictions, true values, and dataframe with transaction amount.

Parameters
• y_predicted (pd.Series) – Predicted fraud labels.

• y_true (pd.Series) – True fraud labels.

• X (pd.DataFrame) – Data with transaction amounts.

• sample_weight (pd.DataFrame) – Ignored.

Returns

Amount lost to fraud per transaction.

Return type

float

Raises

ValueError – If amount_col is not a valid column in the input data.

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, 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]

• 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.

evalml.objectives.get_all_objective_names()[source]#

Get a list of the names of all objectives.

Returns

Objective names

Return type

list (str)

evalml.objectives.get_core_objective_names()[source]#

Get a list of all valid core objectives.

Returns

Objective names.

Return type

list[str]

evalml.objectives.get_core_objectives(problem_type)[source]#

Returns all core objective instances associated with the given problem type.

Core objectives are designed to work out-of-the-box for any dataset.

Parameters

problem_type (str/ProblemTypes) – Type of problem

Returns

List of ObjectiveBase instances

Examples

>>> for objective in get_core_objectives("regression"):
...     print(objective.name)
ExpVariance
MaxError
MedianAE
MSE
MAE
R2
Root Mean Squared Error
>>> for objective in get_core_objectives("binary"):
...     print(objective.name)
MCC Binary
Log Loss Binary
Gini
AUC
Precision
F1
Balanced Accuracy Binary
Accuracy Binary

evalml.objectives.get_non_core_objectives()[source]#

Get non-core objective classes.

Non-core objectives are objectives that are domain-specific. Users typically need to configure these objectives before using them in AutoMLSearch.

Returns

List of ObjectiveBase classes

evalml.objectives.get_objective(objective, return_instance=False, **kwargs)[source]#

Returns the Objective class corresponding to a given objective name.

Parameters
• objective (str or ObjectiveBase) – Name or instance of the objective class.

• return_instance (bool) – Whether to return an instance of the objective. This only applies if objective is of type str. Note that the instance will be initialized with default arguments.

• kwargs (Any) – Any keyword arguments to pass into the objective. Only used when return_instance=True.

Returns

ObjectiveBase if the parameter objective is of type ObjectiveBase. If objective is instead a valid objective name, function will return the class corresponding to that name. If return_instance is True, an instance of that objective will be returned.

Raises
• TypeError – If objective is None.

• TypeError – If objective is not a string and not an instance of ObjectiveBase.

• ObjectiveNotFoundError – If input objective is not a valid objective.

• ObjectiveCreationError – If objective cannot be created properly.

evalml.objectives.get_optimization_objectives(problem_type)[source]#

Get objectives for optimization.

Parameters

problem_type (str/ProblemTypes) – Type of problem

Returns

List of ObjectiveBase instances

evalml.objectives.get_ranking_objectives(problem_type)[source]#

Get objectives for pipeline rankings.

Parameters

problem_type (str/ProblemTypes) – Type of problem

Returns

List of ObjectiveBase instances

class evalml.objectives.Gini[source]#

Gini coefficient for binary classification.

Example

>>> y_true = pd.Series([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(Gini().objective_function(y_true, y_pred), 0.1428571)


Attributes

 expected_range None greater_is_better True is_bounded_like_percentage False name Gini perfect_score 1.0 problem_types [ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY] score_needs_proba True

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 Objective function for Gini coefficient for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for Gini coefficient for binary classification.

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, 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]

• 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.

Parameters
• true_positives (int) – Reward for a true positive. Defaults to 1.

• false_positives (int) – Cost for a false positive. Should be negative. Defaults to -1.

Attributes

 expected_range None greater_is_better True is_bounded_like_percentage False name Lead Scoring 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 Calculate the profit per lead. 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, X=None, sample_weight=None)[source]#

Parameters
• y_predicted (pd.Series) – Predicted labels

• y_true (pd.Series) – True labels

• X (pd.DataFrame) – Ignored.

• sample_weight (pd.DataFrame) – Ignored.

Returns

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, 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]

• 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.

class evalml.objectives.LogLossBinary[source]#

Log Loss for binary classification.

Example

>>> y_true = pd.Series([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(LogLossBinary().objective_function(y_true, y_pred), 18.8393325)


Attributes

 expected_range [0, 1] greater_is_better False is_bounded_like_percentage False name Log Loss Binary perfect_score 0.0 problem_types [ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY] score_needs_proba True

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 Objective function for log loss for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for log loss for binary classification.

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, 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]

• 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.

class evalml.objectives.LogLossMulticlass[source]#

Log Loss for multiclass classification.

Example

>>> y_true = [0, 1, 2, 0, 2, 1]
>>> y_pred = [[0.7, 0.2, 0.1],
...           [0.3, 0.5, 0.2],
...           [0.1, 0.3, 0.6],
...           [0.9, 0.1, 0.0],
...           [0.3, 0.1, 0.6],
...           [0.5, 0.5, 0.0]]
>>> np.testing.assert_almost_equal(LogLossMulticlass().objective_function(y_true, y_pred), 0.4783301)


Attributes

 expected_range [0, 1] greater_is_better False is_bounded_like_percentage False name Log Loss Multiclass perfect_score 0.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba True

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for log loss for multiclass classification. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for log loss for multiclass classification.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MAE[source]#

Mean absolute error for regression.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(MAE().objective_function(y_true, y_pred), 0.2727272)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage True name MAE perfect_score 0.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for mean absolute error for regression. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for mean absolute error for regression.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MAPE[source]#

Mean absolute percentage error for time series regression. Scaled by 100 to return a percentage.

Only valid for nonzero inputs. Otherwise, will throw a ValueError.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(MAPE().objective_function(y_true, y_pred), 15.9848484)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage False name Mean Absolute Percentage Error perfect_score 0.0 problem_types [ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for mean absolute percentage error for time series regression. positive_only If True, this objective is only valid for positive data. score Returns a numerical score indicating performance based on the differences between the predicted and actual values. validate_inputs Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for mean absolute percentage error for time series regression.

positive_only(self)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MaxError[source]#

Maximum residual error for regression.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(MaxError().objective_function(y_true, y_pred), 1.0)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage False name MaxError perfect_score 0.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for maximum residual error for regression. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for maximum residual error for regression.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MCCBinary[source]#

Matthews correlation coefficient for binary classification.

Example

>>> y_true = pd.Series([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(MCCBinary().objective_function(y_true, y_pred), 0.2390457)


Attributes

 expected_range None greater_is_better True is_bounded_like_percentage False name MCC Binary perfect_score 1.0 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 Objective function for Matthews correlation coefficient for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for Matthews correlation coefficient for binary classification.

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, 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]

• 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.

class evalml.objectives.MCCMulticlass[source]#

Matthews correlation coefficient for multiclass classification.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(MCCMulticlass().objective_function(y_true, y_pred), 0.325)


Attributes

 expected_range None greater_is_better True is_bounded_like_percentage False name MCC Multiclass perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for Matthews correlation coefficient for multiclass classification. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for Matthews correlation coefficient for multiclass classification.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MeanSquaredLogError[source]#

Mean squared log error for regression.

Only valid for nonnegative inputs. Otherwise, will throw a ValueError.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(MeanSquaredLogError().objective_function(y_true, y_pred), 0.0171353)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage False name Mean Squared Log Error perfect_score 0.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for mean squared log error for regression. positive_only If True, this objective is only valid for positive data. score Returns a numerical score indicating performance based on the differences between the predicted and actual values. validate_inputs Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for mean squared log error for regression.

positive_only(self)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MedianAE[source]#

Median absolute error for regression.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(MedianAE().objective_function(y_true, y_pred), 0.25)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage False name MedianAE perfect_score 0.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for median absolute error for regression. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for median absolute error for regression.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MSE[source]#

Mean squared error for regression.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(MSE().objective_function(y_true, y_pred), 0.1590909)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage False name MSE perfect_score 0.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for mean squared error for regression. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for mean squared error for regression.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.MulticlassClassificationObjective[source]#

Base class for all multiclass classification objectives.

Attributes

 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS]

Methods

 calculate_percent_difference Calculate the percent difference between scores. expected_range Returns the expected range of the objective, which is not necessarily the possible ranges. greater_is_better Returns a boolean determining if a greater score indicates better model performance. is_bounded_like_percentage Returns whether this objective is bounded between 0 and 1, inclusive. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. name Returns a name describing the objective. objective_function Computes the relative value of the provided predictions compared to the actual labels, according a specified metric. perfect_score Returns the score obtained by evaluating this objective on a perfect model. 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. score_needs_proba Returns a boolean determining if the score() method needs probability estimates. validate_inputs Validates the input based on a few simple checks.
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 expected_range(cls)#

Returns the expected range of the objective, which is not necessarily the possible ranges.

For example, our expected R2 range is from [-1, 1], although the actual range is (-inf, 1].

property greater_is_better(cls)#

Returns a boolean determining if a greater score indicates better model performance.

property is_bounded_like_percentage(cls)#

Returns whether this objective is bounded between 0 and 1, inclusive.

classmethod is_defined_for_problem_type(cls, problem_type)#

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

property name(cls)#

Returns a name describing the objective.

abstract classmethod objective_function(cls, y_true, y_predicted, X=None, sample_weight=None)#

Computes the relative value of the provided predictions compared to the actual labels, according a specified metric.

Args:

y_predicted (pd.Series): Predicted values of length [n_samples] y_true (pd.Series): Actual class labels 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

Numerical value used to calculate score

property perfect_score(cls)#

Returns the score obtained by evaluating this objective on a perfect model.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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

property score_needs_proba(cls)#

Returns a boolean determining if the score() method needs probability estimates.

This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.

validate_inputs(self, y_true, y_predicted)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.ObjectiveBase[source]#

Base class for all objectives.

Attributes

 problem_types None

Methods

 calculate_percent_difference Calculate the percent difference between scores. expected_range Returns the expected range of the objective, which is not necessarily the possible ranges. greater_is_better Returns a boolean determining if a greater score indicates better model performance. is_bounded_like_percentage Returns whether this objective is bounded between 0 and 1, inclusive. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. name Returns a name describing the objective. objective_function Computes the relative value of the provided predictions compared to the actual labels, according a specified metric. perfect_score Returns the score obtained by evaluating this objective on a perfect model. 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. score_needs_proba Returns a boolean determining if the score() method needs probability estimates. validate_inputs Validates the input based on a few simple checks.
classmethod calculate_percent_difference(cls, score, baseline_score)[source]#

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 expected_range(cls)#

Returns the expected range of the objective, which is not necessarily the possible ranges.

For example, our expected R2 range is from [-1, 1], although the actual range is (-inf, 1].

property greater_is_better(cls)#

Returns a boolean determining if a greater score indicates better model performance.

property is_bounded_like_percentage(cls)#

Returns whether this objective is bounded between 0 and 1, inclusive.

classmethod is_defined_for_problem_type(cls, problem_type)[source]#

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

property name(cls)#

Returns a name describing the objective.

abstract classmethod objective_function(cls, y_true, y_predicted, X=None, sample_weight=None)[source]#

Computes the relative value of the provided predictions compared to the actual labels, according a specified metric.

Args:

y_predicted (pd.Series): Predicted values of length [n_samples] y_true (pd.Series): Actual class labels 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

Numerical value used to calculate score

property perfect_score(cls)#

Returns the score obtained by evaluating this objective on a perfect model.

positive_only(cls)#

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

score(self, y_true, y_predicted, X=None, sample_weight=None)[source]#

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]

• 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

property score_needs_proba(cls)#

Returns a boolean determining if the score() method needs probability estimates.

This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.

validate_inputs(self, y_true, y_predicted)[source]#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.Precision[source]#

Precision score for binary classification.

Example

>>> y_true = pd.Series([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(Precision().objective_function(y_true, y_pred), 1.0)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Precision perfect_score 1.0 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 Objective function for precision score for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for precision score for binary classification.

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, 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]

• 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.

class evalml.objectives.PrecisionMacro[source]#

Precision score for multiclass classification using macro-averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(PrecisionMacro().objective_function(y_true, y_pred), 0.5555555)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Precision Macro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for precision score for multiclass classification using macro-averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for precision score for multiclass classification using macro-averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.PrecisionMicro[source]#

Precision score for multiclass classification using micro averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(PrecisionMicro().objective_function(y_true, y_pred), 0.5454545)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Precision Micro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for precision score for binary classification using micro-averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for precision score for binary classification using micro-averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.PrecisionWeighted[source]#

Precision score for multiclass classification using weighted averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(PrecisionWeighted().objective_function(y_true, y_pred), 0.5606060)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Precision Weighted perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for precision score for multiclass classification using weighted averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for precision score for multiclass classification using weighted averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.R2[source]#

Coefficient of determination for regression.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(R2().objective_function(y_true, y_pred), 0.7638036)


Attributes

 expected_range None greater_is_better True is_bounded_like_percentage False name R2 perfect_score 1 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for coefficient of determination for regression. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for coefficient of determination for regression.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

evalml.objectives.ranking_only_objectives()[source]#

Get ranking-only objective classes.

Ranking-only objectives are objectives that are useful for evaluating the performance of a model, but should not be used as an optimization objective during AutoMLSearch for various reasons.

Returns

List of ObjectiveBase classes

class evalml.objectives.Recall[source]#

Recall score for binary classification.

Example

>>> y_true = pd.Series([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1])
>>> y_pred = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
>>> np.testing.assert_almost_equal(Recall().objective_function(y_true, y_pred), 0.1428571)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Recall perfect_score 1.0 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 Objective function for recall score for binary classification. 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, X=None, sample_weight=None)[source]#

Objective function for recall score for binary classification.

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, 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]

• 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.

class evalml.objectives.RecallMacro[source]#

Recall score for multiclass classification using macro averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(RecallMacro().objective_function(y_true, y_pred), 0.5555555)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Recall Macro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for recall score for multiclass classification using macro-averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for recall score for multiclass classification using macro-averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.RecallMicro[source]#

Recall score for multiclass classification using micro averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(RecallMicro().objective_function(y_true, y_pred), 0.5454545)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Recall Micro perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for recall score for multiclass classification using micro-averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for recall score for multiclass classification using micro-averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.RecallWeighted[source]#

Recall score for multiclass classification using weighted averaging.

Example

>>> y_true = pd.Series([0, 1, 0, 2, 0, 1, 2, 1, 2, 0, 2])
>>> y_pred = pd.Series([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
>>> np.testing.assert_almost_equal(RecallWeighted().objective_function(y_true, y_pred), 0.5454545)


Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Recall Weighted perfect_score 1.0 problem_types [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for recall score for multiclass classification using weighted averaging. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for recall score for multiclass classification using weighted averaging.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.RegressionObjective[source]#

Base class for all regression objectives.

Attributes

 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION]

Methods

 calculate_percent_difference Calculate the percent difference between scores. expected_range Returns the expected range of the objective, which is not necessarily the possible ranges. greater_is_better Returns a boolean determining if a greater score indicates better model performance. is_bounded_like_percentage Returns whether this objective is bounded between 0 and 1, inclusive. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. name Returns a name describing the objective. objective_function Computes the relative value of the provided predictions compared to the actual labels, according a specified metric. perfect_score Returns the score obtained by evaluating this objective on a perfect model. 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. score_needs_proba Returns a boolean determining if the score() method needs probability estimates. validate_inputs Validates the input based on a few simple checks.
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 expected_range(cls)#

Returns the expected range of the objective, which is not necessarily the possible ranges.

For example, our expected R2 range is from [-1, 1], although the actual range is (-inf, 1].

property greater_is_better(cls)#

Returns a boolean determining if a greater score indicates better model performance.

property is_bounded_like_percentage(cls)#

Returns whether this objective is bounded between 0 and 1, inclusive.

classmethod is_defined_for_problem_type(cls, problem_type)#

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

property name(cls)#

Returns a name describing the objective.

abstract classmethod objective_function(cls, y_true, y_predicted, X=None, sample_weight=None)#

Computes the relative value of the provided predictions compared to the actual labels, according a specified metric.

Args:

y_predicted (pd.Series): Predicted values of length [n_samples] y_true (pd.Series): Actual class labels 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

Numerical value used to calculate score

property perfect_score(cls)#

Returns the score obtained by evaluating this objective on a perfect model.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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

property score_needs_proba(cls)#

Returns a boolean determining if the score() method needs probability estimates.

This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.

validate_inputs(self, y_true, y_predicted)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.RootMeanSquaredError[source]#

Root mean squared error for regression.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(RootMeanSquaredError().objective_function(y_true, y_pred), 0.3988620)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage False name Root Mean Squared Error perfect_score 0.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for root mean squared error for regression. 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 Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for root mean squared error for regression.

positive_only(cls)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

class evalml.objectives.RootMeanSquaredLogError[source]#

Root mean squared log error for regression.

Only valid for nonnegative inputs. Otherwise, will throw a ValueError.

Example

>>> y_true = pd.Series([1.5, 2, 3, 1, 0.5, 1, 2.5, 2.5, 1, 0.5, 2])
>>> y_pred = pd.Series([1.5, 2.5, 2, 1, 0.5, 1, 3, 2.25, 0.75, 0.25, 1.75])
>>> np.testing.assert_almost_equal(RootMeanSquaredLogError().objective_function(y_true, y_pred), 0.13090204)


Attributes

 expected_range None greater_is_better False is_bounded_like_percentage False name Root Mean Squared Log Error perfect_score 0.0 problem_types [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] score_needs_proba False

Methods

 calculate_percent_difference Calculate the percent difference between scores. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Objective function for root mean squared log error for regression. positive_only If True, this objective is only valid for positive data. score Returns a numerical score indicating performance based on the differences between the predicted and actual values. validate_inputs Validates the input based on a few simple checks.
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

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, X=None, sample_weight=None)[source]#

Objective function for root mean squared log error for regression.

positive_only(self)#

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

score(self, y_true, y_predicted, 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]

• 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)#

Validates the input based on a few simple checks.

Parameters
• y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples].

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

Raises

ValueError – If the inputs are malformed.

Parameters

alert_rate (float) – percentage of top scores to classify as high risk.

Attributes

 expected_range [0, 1] greater_is_better True is_bounded_like_percentage True name Sensitivity at Low Alert Rates perfect_score 1.0 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 Determine if an observation is high risk given an alert rate. is_defined_for_problem_type Returns whether or not an objective is defined for a problem type. objective_function Calculate sensitivity across all predictions, using the top alert_rate percent of observations as the predicted positive class. 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, **kwargs)[source]#

Determine if an observation is high risk given an alert rate.

Parameters
• ypred_proba (pd.Series) – Predicted probabilities.

• **kwargs – Additional abritrary parameters.

Returns

Whether or not an observation is high risk given an alert rate.

Return type

pd.Series

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, **kwargs)[source]#

Calculate sensitivity across all predictions, using the top alert_rate percent of observations as the predicted positive class.

Parameters
• y_true (pd.Series) – True labels.

• y_predicted (pd.Series) – Predicted labels based on alert_rate.

• **kwargs – Additional abritrary parameters.

Returns

sensitivity using the observations with the top scores as the predicted positive class.

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, 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]

• 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.