gemseo / mlearning / quality_measures

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cluster_measure module

Here is the baseclass to measure the quality of machine learning algorithms.

The concept of clustering quality measure is implemented with the BaseMLClusteringMeasure class and proposes different evaluation methods.

class gemseo.mlearning.quality_measures.cluster_measure.BaseMLClusteringMeasure(algo, fit_transformers=True)[source]

Bases: BaseMLQualityMeasure

An abstract clustering measure for clustering algorithms.

Parameters:

  • algo (BaseMLClusteringAlgo) – A machine learning algorithm for 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_learning_measure(samples=None, multioutput=True)[source]

Evaluate the quality measure from the learning dataset.

Parameters:

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

evaluate_learn(samples=None, multioutput=True)

Evaluate the quality measure from the learning dataset.

Parameters:

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

algo: BaseMLClusteringAlgo

The machine learning algorithm whose quality we want to measure.

class gemseo.mlearning.quality_measures.cluster_measure.MLPredictiveClusteringMeasure(algo, fit_transformers=True)[source]

Bases: BaseMLClusteringMeasure

An abstract clustering measure for predictive clustering algorithms.

Parameters:

  • algo (BaseMLPredictiveClusteringAlgo) – 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=None, multioutput=True, seed=None, store_resampling_result=False)[source]

Evaluate the quality measure using the bootstrap technique.

Parameters:

  • n_replicates (int) –

    The number of bootstrap replicates.

    By default it is set to 100.

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

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

Evaluate the quality measure using the k-folds technique.

Parameters:

  • n_folds (int) –

    The number of folds.

    By default it is set to 5.

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

compute_test_measure(test_data, samples=None, multioutput=True)[source]

Evaluate the quality measure using a test dataset.

Parameters:

  • test_data (Dataset) – The test dataset.

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

evaluate_bootstrap(n_replicates=100, samples=None, multioutput=True, seed=None, store_resampling_result=False)

Evaluate the quality measure using the bootstrap technique.

Parameters:

  • n_replicates (int) –

    The number of bootstrap replicates.

    By default it is set to 100.

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=True, seed=None, store_resampling_result=False)

Evaluate the quality measure using the k-folds technique.

Parameters:

  • n_folds (int) –

    The number of folds.

    By default it is set to 5.

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

evaluate_test(test_data, samples=None, multioutput=True)

Evaluate the quality measure using a test dataset.

Parameters:

  • test_data (Dataset) – The test dataset.

  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • 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 value of the quality measure.

Return type:

MeasureType

algo: BaseMLPredictiveClusteringAlgo

The machine learning algorithm whose quality we want to measure.

Examples using BaseMLClusteringMeasure

Advanced mixture of experts

Advanced mixture of experts

Examples using MLPredictiveClusteringMeasure

Advanced mixture of experts

Advanced mixture of experts