gemseo / mlearning / quality_measures

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error_measure module

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

The concept of error measure is implemented with the MLErrorMeasure class and proposes different evaluation methods.

class gemseo.mlearning.quality_measures.error_measure.MLErrorMeasure(algo, fit_transformers=True)[source]

Bases: MLQualityMeasure

An abstract error measure for machine learning.

Parameters:
  • algo (MLSupervisedAlgo) – 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 True.

compute_bootstrap_measure(n_replicates=100, samples=None, multioutput=True, seed=None, as_dict=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 (None | None) – The seed of the pseudo-random number generator. If None, an unpredictable generator will be used.

  • as_dict (bool) –

    Whether the full quality measure is returned as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

    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, as_dict=False)[source]
as_dict: Whether the full quality measure is returned

as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

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.

  • as_dict (bool) –

    The description is missing.

    By default it is set to False.

Returns:

The value of the quality measure.

Return type:

MeasureType

compute_learning_measure(samples=None, multioutput=True, as_dict=False)[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.

  • as_dict (bool) –

    Whether the full quality measure is returned as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

    By default it is set to False.

Returns:

The value of the quality measure.

Return type:

MeasureType

compute_leave_one_out_measure(samples=None, multioutput=True, as_dict=False)[source]
as_dict: Whether the full quality measure is returned

as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

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.

  • as_dict (bool) –

    The description is missing.

    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, as_dict=False)[source]

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) –

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

    By default it is set to True.

  • as_dict (bool) –

    Whether the full quality measure is returned as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

    By default it is set to False.

Returns:

The value of the quality measure.

Return type:

MeasureType

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) –

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

  • as_dict (bool) –

    Whether the full quality measure is returned as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.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)
as_dict: Whether the full quality measure is returned

as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

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.

  • as_dict (bool) –

    The description is missing.

    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) –

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

    By default it is set to True.

  • as_dict (bool) –

    Whether the full quality measure is returned as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.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)
as_dict: Whether the full quality measure is returned

as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

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.

  • as_dict (bool) –

    The description is missing.

    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) –

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

    By default it is set to True.

  • as_dict (bool) –

    Whether the full quality measure is returned as a mapping from algo.output names to quality measures. Otherwise, the full quality measure as an array stacking these quality measures according to the order of algo.output_names.

    By default it is set to False.

Returns:

The value of the quality measure.

Return type:

MeasureType

algo: MLAlgo

The machine learning algorithm usually trained.

Examples using MLErrorMeasure

Calibration of a polynomial regression

Calibration of a polynomial regression

Machine learning algorithm selection example

Machine learning algorithm selection example

MSE example - test-train split

MSE example - test-train split

Quality measure for surrogate model comparison

Quality measure for surrogate model comparison

Advanced mixture of experts

Advanced mixture of experts