gemseo / mlearning / quality_measures

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rmse_measure module

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

The mse_measure module implements the concept of root mean squared error measures for machine learning algorithms.

This concept is implemented through the RMSEMeasure class and overloads the MSEMeasure.evaluate_*() methods.

The root mean squared error (RMSE) is defined by

\[\begin{split}\\operatorname{RMSE}(\\hat{y})=\\sqrt{\\frac{1}{n}\\sum_{i=1}^n(\\hat{y}_i-y_i)^2},\end{split}\]

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

class gemseo.mlearning.quality_measures.rmse_measure.RMSEMeasure(algo, fit_transformers=True)[source]

Bases: MSEMeasure

The root mean Squared Error measure for machine learning.

Parameters:

  • algo (BaseMLRegressionAlgo) – 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.

compute_bootstrap_measure(n_replicates=100, samples=None, multioutput=True, seed=None, as_dict=False, store_resampling_result=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 (int | 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.

  • store_resampling_result (bool) –

    Whether to store the \(n\) machine learning algorithms and associated predictions generated by the resampling stage where \(n\) is the number of bootstrap replicates.

    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, store_resampling_result=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) –

    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) –

    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.

  • store_resampling_result (bool) –

    Whether to store the \(n\) machine learning algorithms and associated predictions generated by the resampling stage where \(n\) is the number of folds.

    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_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

algo: BaseMLSupervisedAlgo

The machine learning algorithm whose quality we want to measure.

Examples using RMSEMeasure

Cross-validation

Cross-validation

Leave-one-out

Leave-one-out

RMSE for regression models

RMSE for regression models