gemseo.mlearning.clustering.quality.base_predictive_clusterer_quality module#

The base class to assess the quality of a predictive clusterer.

class BasePredictiveClustererQuality(algo, fit_transformers=True)[source]#

Bases: BaseClustererQuality

The base class to assess the quality of a predictive clusterer.

Parameters:

  • algo (BasePredictiveClusterer) -- A machine learning algorithm for predictive clustering.

  • fit_transformers (bool) --

    Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

    By default it is set to True.

compute_bootstrap_measure(n_replicates=100, samples=(), multioutput=True, seed=None, store_resampling_result=False)[source]#

Evaluate the quality of the ML model using the bootstrap technique.

Parameters:

  • n_replicates (int) --

    The number of bootstrap replicates.

    By default it is set to 100.

  • samples (Sequence[int]) --

    The indices of the learning samples. If empty, use the whole learning dataset.

    By default it is set to ().

  • multioutput (bool) --

    Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.

    By default it is set to True.

  • seed (int | None) -- The seed of the pseudo-random number generator. If None, an unpredictable generator will be used.

  • store_resampling_result (bool) --

    Whether to store the \(n\) machine learning algorithms and associated predictions generated by the resampling stage where \(n\) is the number of bootstrap replicates.

    By default it is set to False.

Returns:

The quality of the ML model.

Return type:

MeasureType

compute_cross_validation_measure(n_folds=5, samples=(), multioutput=True, randomize=True, seed=None, store_resampling_result=False)[source]#

Evaluate the quality of the ML model using the k-folds technique.

Parameters:

  • n_folds (int) --

    The number of folds.

    By default it is set to 5.

  • samples (Sequence[int]) --

    The indices of the learning samples. If empty, use the whole learning dataset.

    By default it is set to ().

  • multioutput (bool) --

    Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.

    By default it is set to True.

  • randomize (bool) --

    Whether to shuffle the samples before dividing them in folds.

    By default it is set to True.

  • seed (int | None) -- The seed of the pseudo-random number generator. If None, an unpredictable generator is used.

  • store_resampling_result (bool) --

    Whether to store the \(n\) machine learning algorithms and associated predictions generated by the resampling stage where \(n\) is the number of folds.

    By default it is set to False.

Returns:

The quality of the ML model.

Return type:

MeasureType

compute_test_measure(test_data, samples=(), multioutput=True)[source]#

Evaluate the quality of the ML model from a test dataset.

Parameters:

  • test_data (Dataset) -- The test dataset.

  • samples (Sequence[int]) --

    The indices of the learning samples. If empty, use the whole learning dataset.

    By default it is set to ().

  • multioutput (bool) --

    Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.

    By default it is set to True.

Returns:

The quality of the ML model.

Return type:

MeasureType

algo: BasePredictiveClusterer#

The machine learning algorithm whose quality we want to measure.