NPP#
- class mlquantify.evaluation.protocol.NPP(models: List[str | Quantifier] | str | Quantifier, learner: BaseEstimator | None = None, n_jobs: int = 1, random_state: int = 32, verbose: bool = False, return_type: str = 'predictions', measures: List[str] | None = None)[source]#
Natural Prevalence Protocol.
This approach splits a test into several samples varying sample size, with n iterations. For a list of Quantifiers, it computes training and testing for each one and returns either a table of results with error measures or just the predictions.
- Parameters:
- modelsUnion[List[Union[str, Quantifier]], str, Quantifier]
List of quantification models, a single model name, or ‘all’ for all models.
- batch_sizeUnion[List[int], int]
Size of the batches to be processed, or a list of sizes.
- learnerBaseEstimator, optional
Machine learning model to be used with the quantifiers. Required for model methods.
- n_iterationsint, optional
Number of iterations for the protocol. Default is 1.
- n_jobsint, optional
Number of jobs to run in parallel. Default is 1.
- random_stateint, optional
Seed for random number generation. Default is 32.
- verbosebool, optional
Whether to print progress messages. Default is False.
- return_typestr, optional
Type of return value (‘predictions’ or ‘table’). Default is ‘predictions’.
- measuresList[str], optional
List of error measures to calculate. Must be in MEASURES or None. Default is None.
- Attributes:
- modelsList[Quantifier]
List of quantification models.
- batch_sizeUnion[List[int], int]
Size of the batches to be processed.
- learnerBaseEstimator
Machine learning model to be used with the quantifiers.
- n_iterationsint
Number of iterations for the protocol.
- n_jobsint
Number of jobs to run in parallel.
- random_stateint
Seed for random number generation.
- verbosebool
Whether to print progress messages.
- return_typestr
Type of return value (‘predictions’ or ‘table’).
- measuresList[str]
List of error measures to calculate.
- fit(X_train, y_train)[source]#
Fits the models with the training data.
- Parameters:
- X_trainnp.ndarray
Features of the training set.
- y_trainnp.ndarray
Labels of the training set.
- Returns:
- Protocol
Fitted protocol.
- predict(X_test: ndarray, y_test: ndarray) Any[source]#
Predicts the prevalence for the test set.
- Parameters:
- X_testnp.ndarray
Features of the test set.
- y_testnp.ndarray
Labels of the test set.
- Returns:
- Any
Predictions for the test set. Can be a table or a tuple with the quantifier names, real prevalence, and predicted prevalence.