rankeval.scoring package

The rankeval.scoring module includes utilities to score a model on a given dataset.

class rankeval.scoring.Scorer(model, dataset)

Bases: object

Class for efficient scoring of an ensemble-based model composed of binary regression trees on a given dataset.

This class can be used for simple or detailed scoring, depending on the mode selected at scoring time. The document scores are cached as to avoid useless re-scoring. Thus, calling multiple times the score method does not involve the scoring activity to be executed again, except for a detailed scoring following a basic scoring. Indeed in this situation the scoring has to be repeated as to analyze in depth the scoring behaviour.

model: RTEnsemble

The model to use for scoring

dataset: Dataset

The dataset to use for scoring

model : RTEnsemble

The model to use for scoring

dataset : Dataset

The dataset to use for scoring

y_pred : numpy array of float

The predicted scores produced by the given model for each sample of the given dataset X

partial_y_pred : numpy 2d-array of float

The predicted score of each tree of the model for each dataset instance

get_partial_predicted_scores()

Provide an accessor to the partial scores produced by the given model for each sample of the given dataset X. Each partial score reflects the score produced by a single tree of the ensemble model to a single dataset instance. Thus, the returned numpy matrix has a shape of (n_instances, n_trees). The partial scores does not take into account the tree weights, thus for producing the final score is needed to multiply each row for the tree weight vector.

scores : numpy 2d-array of float

The predicted score of each tree of the model for each dataset instance

get_predicted_scores()

Provide an accessor to the predicted scores produced by the given model for each sample of the given dataset X

scores : numpy array of float

The predicted scores produced by the given model for each sample of the given dataset X

score(detailed)

detailed : bool

True if the class has to performs a detailed scoring, false otherwise

y : numpy array of float

the predicted scores produced by the given model for each sample of the given dataset X

self.y : array of float

The predicted scores of each dataset instance

Submodules

rankeval.scoring.scorer module

Class for efficient scoring of an ensemble-based model composed of binary regression trees on a given dataset.

class rankeval.scoring.scorer.Scorer(model, dataset)

Bases: object

Class for efficient scoring of an ensemble-based model composed of binary regression trees on a given dataset.

This class can be used for simple or detailed scoring, depending on the mode selected at scoring time. The document scores are cached as to avoid useless re-scoring. Thus, calling multiple times the score method does not involve the scoring activity to be executed again, except for a detailed scoring following a basic scoring. Indeed in this situation the scoring has to be repeated as to analyze in depth the scoring behaviour.

model: RTEnsemble

The model to use for scoring

dataset: Dataset

The dataset to use for scoring

model : RTEnsemble

The model to use for scoring

dataset : Dataset

The dataset to use for scoring

y_pred : numpy array of float

The predicted scores produced by the given model for each sample of the given dataset X

partial_y_pred : numpy 2d-array of float

The predicted score of each tree of the model for each dataset instance

get_partial_predicted_scores()

Provide an accessor to the partial scores produced by the given model for each sample of the given dataset X. Each partial score reflects the score produced by a single tree of the ensemble model to a single dataset instance. Thus, the returned numpy matrix has a shape of (n_instances, n_trees). The partial scores does not take into account the tree weights, thus for producing the final score is needed to multiply each row for the tree weight vector.

scores : numpy 2d-array of float

The predicted score of each tree of the model for each dataset instance

get_predicted_scores()

Provide an accessor to the predicted scores produced by the given model for each sample of the given dataset X

scores : numpy array of float

The predicted scores produced by the given model for each sample of the given dataset X

score(detailed)

detailed : bool

True if the class has to performs a detailed scoring, false otherwise

y : numpy array of float

the predicted scores produced by the given model for each sample of the given dataset X

self.y : array of float

The predicted scores of each dataset instance