calour.training.regress¶

calour.training.regress(exp: calour.experiment.Experiment, field, estimator, cv=<calour.training.RepeatedSortedStratifiedKFold object>, params=None)[source]¶

Evaluate regression during cross validation.

Note

This function is also available as a class method Experiment.regress()

Parameters:

exp (Experiment) – Input experiment object. field (str) – column name in the sample metadata, which contains the variable we want to predict. estimator (estimator object implementing fit and predict) – scikit-learn estimator. e.g. sklearn.ensemble.RandomForestRegressor cv (int, cross-validation generator or an iterable) – similar to the cv parameter in sklearn.model_selection.GridSearchCV params (dict of string to sequence, or sequence of such) – For example, the output of sklearn.model_selection.ParameterGrid or sklearn.model_selection.ParameterSampler. By default, it uses whatever default parameters of the estimator set in scikit-learn

Yields:

pandas.DataFrame – The result of prediction per sample for a given parameter set. It contains the following columns: Y_TRUE: the true value for the samples SAMPLE: sample IDs CV: which split of the cross validation Y_PRED: the predicted value for the samples