make_plots

mastml.plots.make_plots(plots, y_true, y_pred, groups, dataset_stdev, metrics, model, residuals, model_errors, has_model_errors, savepath, data_type, X_test=None, show_figure=False, recalibrate_errors=False, model_errors_cal=None, splits_summary=False, file_extension='.csv', image_dpi=250)

Helper function to make collections of different types of plots after a single or multiple data splits are evaluated.

Args:

plots: (list of str), list denoting which types of plots to make. Viable entries are “Scatter”, “Histogram”, “Error”

y_true: (pd.Series), series containing the true y data

y_pred: (pd.Series), series containing the predicted y data

groups: (list), list denoting the group label for each data point

dataset_stdev: (float), the dataset standard deviation

metrics: (list of str), list denoting the metric names to evaluate. See mastml.metrics.Metrics.metrics_zoo_ for full list

model: (mastml.models object), a MAST-ML model object, e.g. SklearnModel or EnsembleModel

residuals: (pd.Series), series containing the residuals (true model errors)

model_errors: (pd.Series), series containing the as-obtained uncalibrated model errors

has_model_errors: (bool), whether the model type used can be subject to UQ and thus have model errors calculated

savepath: (str), string denoting the path to save output to

data_type: (str), string denoting the data type analyzed, e.g. train, test, leftout

show_figure: (bool), whether or not the generated figure is output to the notebook screen (default False)

recalibrate_errors: (bool), whether or not the model errors have been recalibrated (default False)

model_errors_cal: (pd.Series), series containing the calibrated predicted model errors

splits_summary: (bool), whether or not the data used in the plots comes from a collection of many splits (default False), False denotes a single split folder

Returns:

None.