gemseo / mlearning / quality_measures

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.

evaluate_bootstrap(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) –
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)[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.
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)[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) –
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)[source]

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)[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) –
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

algo: MLAlgo: The machine learning algorithm usually trained.

Examples using MLErrorMeasure¶

Calibration of a polynomial regression

Machine learning algorithm selection example

MSE example - test-train split

Quality measure for surrogate model comparison

Advanced mixture of experts