gemseo_mlearning / quality_measures

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mae_measure module

The mean absolute error to measure the quality of a regression algorithm.

The mean absolute error (MAE) is defined by

\[\operatorname{MAE}(\hat{y})=\frac{1}{n}\sum_{i=1}^n\|\hat{y}_i-y_i\|,\]

where \(\hat{y}\) are the predictions and \(y\) are the data points.

class gemseo_mlearning.quality_measures.mae_measure.MAEMeasure(algo, fit_transformers=False)[source]

Bases: MLErrorMeasure

The mean absolute error measure for machine learning.

Parameters:

  • algo (MLRegressionAlgo) – A machine learning algorithm for supervised learning.

  • 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 False.

class EvaluationMethod(value)

Bases: StrEnum

The evaluation method.

BOOTSTRAP = 'BOOTSTRAP'

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

KFOLDS = 'KFOLDS'

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

LEARN = 'LEARN'

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

LOO = 'LOO'

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

TEST = 'TEST'

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

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

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

    By default it is set to True.

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

  • as_dict (bool) –

    Whether to express the measure as a dictionary whose keys are the output names.

    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, as_dict=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) –

    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.

  • as_dict (bool) –

    Whether to express the measure as a dictionary whose keys are the output names.

    By default it is set to False.

Returns:

The value of the quality measure.

Return type:

MeasureType

evaluate_learn(samples=None, multioutput=True, as_dict=False)

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) –

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

    By default it is set to True.

  • as_dict (bool) –

    Whether to express the measure as a dictionary whose keys are the output names.

    By default it is set to False.

Returns:

The value of the quality measure.

Return type:

MeasureType

evaluate_loo(samples=None, multioutput=True, as_dict=False)

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.

  • as_dict (bool) –

    Whether to express the measure as a dictionary whose keys are the output names.

    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, as_dict=False)

Evaluate the quality measure using a test dataset.

Parameters:

  • test_data (IODataset) – 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.

  • as_dict (bool) –

    Whether to express the measure as a dictionary whose keys are the output names.

    By default it is set to False.

Returns:

The value of the quality measure.

Return type:

MeasureType

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

SMALLER_IS_BETTER: ClassVar[bool] = True

Whether to minimize or maximize the measure.

algo: MLAlgo

The machine learning algorithm usually trained.