r2_measure module¶

The R2 to measure the quality of a regression algorithm.

The r2_measure module implements the concept of R2 measures for machine learning algorithms.

This concept is implemented through the R2Measure class and overloads the MLErrorMeasure._compute_measure() method.

The R2 is defined by

\[R_2(\hat{y}) = 1 - \frac{\sum_i (\hat{y}_i - y_i)^2} {\sum_i (y_i-\bar{y})^2},\]

where \(\hat{y}\) are the predictions, \(y\) are the data points and \(\bar{y}\) is the mean of \(y\).

class gemseo.mlearning.quality_measures.r2_measure.R2Measure(algo, fit_transformers=True)[source]

The R2 measure for machine learning.

Parameters:

algo (MLRegressionAlgo) – A machine learning algorithm for regression.
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 (list[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.
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:

NoReturn

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 (list[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

SMALLER_IS_BETTER: ClassVar[bool] = False: Whether to minimize or maximize the measure.