gemseo / mlearning / qual_measure

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 MLClusteringMeasure class and proposes different evaluation methods.

class gemseo.mlearning.qual_measure.cluster_measure.MLClusteringMeasure(algo, fit_transformers=True)[source]

Bases: MLQualityMeasure

An abstract clustering measure for clustering algorithms.

Parameters:

  • algo (MLAlgo) – 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.

evaluate(method='learn', samples=None, **options)

Evaluate the quality measure.

Parameters:

  • method (str) –

    The name of the method to evaluate the quality measure.

    By default it is set to “learn”.

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

  • **options (OptionType | None) – The options of the estimation method (e.g. test_data for the test method, n_replicates for the bootstrap one, …).

Returns:

The value of the quality measure.

Raises:

ValueError – When the name of the method is unknown.

Return type:

float | ndarray

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

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) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    By default it is set to True.

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

Returns:

The value of the quality measure.

Return type:

NoReturn

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

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) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    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, then an unpredictable generator will be used.

Returns:

The value of the quality measure.

Return type:

NoReturn

evaluate_learn(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 to return the quality measure for each output component. If not, average these measures.

    By default it is set to True.

Returns:

The value of the quality measure.

Return type:

float | ndarray

evaluate_loo(samples=None, multioutput=True)

Evaluate the quality measure using the leave-one-out technique.

Parameters:

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

  • multioutput (bool) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    By default it is set to True.

Returns:

The value of the quality measure.

Return type:

float | ndarray

abstract 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) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    By default it is set to True.

Returns:

The value of the quality measure.

Return type:

NoReturn

classmethod is_better(val1, val2)

Compare the quality between two values.

This method returns True if the first one is better than the second one.

For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.

Parameters:

  • val1 (float) – The value of the first quality measure.

  • val2 (float) – The value of the second quality measure.

Returns:

Whether val1 is of better quality than val2.

Return type:

bool

BOOTSTRAP: ClassVar[str] = 'bootstrap'

The name of the method to evaluate the measure by bootstrap.

KFOLDS: ClassVar[str] = 'kfolds'

The name of the method to evaluate the measure by cross-validation.

LEARN: ClassVar[str] = 'learn'

The name of the method to evaluate the measure on the learning dataset.

LOO: ClassVar[str] = 'loo'

The name of the method to evaluate the measure by leave-one-out.

SMALLER_IS_BETTER: ClassVar[bool] = True

Whether to minimize or maximize the measure.

TEST: ClassVar[str] = 'test'

The name of the method to evaluate the measure on a test dataset.

algo: MLAlgo

The machine learning algorithm usually trained.

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

Bases: MLClusteringMeasure

An abstract clustering measure for predictive clustering algorithms.

Parameters:

  • algo (MLPredictiveClusteringAlgo) – 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.

evaluate(method='learn', samples=None, **options)

Evaluate the quality measure.

Parameters:

  • method (str) –

    The name of the method to evaluate the quality measure.

    By default it is set to “learn”.

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

  • **options (OptionType | None) – The options of the estimation method (e.g. test_data for the test method, n_replicates for the bootstrap one, …).

Returns:

The value of the quality measure.

Raises:

ValueError – When the name of the method is unknown.

Return type:

float | ndarray

evaluate_bootstrap(n_replicates=100, samples=None, multioutput=True, seed=None)[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) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    By default it is set to True.

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

Returns:

The value of the quality measure.

Return type:

float | ndarray

evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=True, seed=None)[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) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    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, then an unpredictable generator will be used.

Returns:

The value of the quality measure.

Return type:

float | ndarray

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 to return the quality measure for each output component. If not, average these measures.

    By default it is set to True.

Returns:

The value of the quality measure.

Return type:

float | ndarray

evaluate_loo(samples=None, multioutput=True)

Evaluate the quality measure using the leave-one-out technique.

Parameters:

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

  • multioutput (bool) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    By default it is set to True.

Returns:

The value of the quality measure.

Return type:

float | ndarray

evaluate_test(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) –

    If True, return the quality measure for each output component. Otherwise, average these measures.

    By default it is set to True.

Returns:

The value of the quality measure.

Return type:

float | ndarray

classmethod is_better(val1, val2)

Compare the quality between two values.

This method returns True if the first one is better than the second one.

For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.

Parameters:

  • val1 (float) – The value of the first quality measure.

  • val2 (float) – The value of the second quality measure.

Returns:

Whether val1 is of better quality than val2.

Return type:

bool

BOOTSTRAP: ClassVar[str] = 'bootstrap'

The name of the method to evaluate the measure by bootstrap.

KFOLDS: ClassVar[str] = 'kfolds'

The name of the method to evaluate the measure by cross-validation.

LEARN: ClassVar[str] = 'learn'

The name of the method to evaluate the measure on the learning dataset.

LOO: ClassVar[str] = 'loo'

The name of the method to evaluate the measure by leave-one-out.

SMALLER_IS_BETTER: ClassVar[bool] = True

Whether to minimize or maximize the measure.

TEST: ClassVar[str] = 'test'

The name of the method to evaluate the measure on a test dataset.

algo: MLAlgo

The machine learning algorithm usually trained.

Examples using MLClusteringMeasure

Advanced mixture of experts

Advanced mixture of experts

Advanced mixture of experts

Examples using MLPredictiveClusteringMeasure

Advanced mixture of experts

Advanced mixture of experts

Advanced mixture of experts