postprocessing

This consists of modules which handles the output of Model after the model has been trained i.e. after .fit method has been called on it.

Please note that the SeqMetrics sub-module has been deprecated. Please use SeqMetrics library instead.

• explain
  • ShapExplainer
  • LimeMLExplainer
  • PermutationImportance
  • PartialDependencePlot
  • explain_model
  • explain_model_with_lime
  • explain_model_with_shap
• Model Interpretation
  • Interpret
• Performance Metrics
  • SeqMetrics
  • RegressionMetrics
  • ClassificationMetrics
  • Utils
• Model Visualization
  • Visualize

ProcessPredictions

class ai4water.postprocessing.ProcessPredictions(mode: str, forecast_len: Optional[int] = None, output_features: Optional[Union[str, list]] = None, wandb_config: Optional[dict] = None, path: Optional[str] = None, dpi: int = 300, show=1, save: bool = True, plots: Optional[Union[str, list]] = None, quantiles: Optional[int] = None)

Bases: Plot

post processing of results after training.

__init__(mode: str, forecast_len: Optional[int] = None, output_features: Optional[Union[str, list]] = None, wandb_config: Optional[dict] = None, path: Optional[str] = None, dpi: int = 300, show=1, save: bool = True, plots: Optional[Union[str, list]] = None, quantiles: Optional[int] = None)

Parameters:

• mode (str) – either “regression” or “classification”

• forecast_len (int, optional (default=None)) – forecast length, only valid when mode is regression

• output_features (str, optional) – names of output features

• plots (int/list, optional (default=None)) –

  the names of plots to draw. Following plots are avialble.

  residual regression prediction errors fdc murphy edf

• path (str) – folder in which to save the results/plots

• show (bool) – whether to show the plots or not

• save (bool) – whether to save the plots or not

• wandb_config – weights and bias configuration dictionary

• dpi (int) – determines resolution of saved figure

Examples

>>> import numpy as np
>>> from ai4water.postprocessing import ProcessPredictions
>>> true = np.random.random(100)
>>> predicted = np.random.random(100)
>>> processor = ProcessPredictions("regression", forecast_len=1,
...    plots=['prediction', 'regression', 'residual'])
>>> processor(true, predicted)

# for postprocessing of classification, we need to set the mode

>>> true = np.random.randint(0, 2, (100, 1))
>>> predicted = np.random.randint(0, 2, (100, 1))
>>> processor = ProcessPredictions("classification")
>>> processor(true, predicted)

__call__(true_outputs, predicted, metrics='minimal', prefix='test', index=None, inputs=None, model=None)

Call self as a function.

available_plots = ['regression', 'prediction', 'residual', 'murphy', 'fdc', 'errors', 'edf']

average_target_across_feature(true, predicted, feature)

confusion_matrix(true, predicted, prefix=None, cmap='Blues', **kwargs)

plots confusion matrix.

cmap : **kwargs :

any keyword arguments for imshow

edf_plot(true, predicted, prefix, where, **kwargs)

cumulative distribution function of absolute error between true and predicted.

Parameters:

• true – array like

• predicted – array like

• prefix –

• where –

errors_plot(true, predicted, prefix, where, **kwargs)

fdc_plot(true, predicted, prefix, where, **kwargs)

horizon_plots(errors: dict, fname='')

maybe_not_3d_data(true, predicted)

murphy_plot(true, predicted, prefix, where, inputs, **kwargs)

n_classes(array)

plot_all_qs(true_outputs, predicted, save=False)

plot_quantile(true_outputs, predicted, min_q: int, max_q, st=0, en=None, save=False)

plot_quantiles1(true_outputs, predicted, st=0, en=None, save=True)

plot_quantiles2(true_outputs, predicted, st=0, en=None, save=True)

plot_results(true, predicted: DataFrame, prefix, where, inputs=None)

# kwargs can be any/all of followings

# fillstyle: # marker: # linestyle: # markersize: # color:

precision_recall_curve(estimator, x, y, prefix=None)

prediction_distribution_across_feature(true, predicted, feature)

prediction_plot(true, predicted, prefix, where)

process_binary(true, predicted, metrics, prefix, index, model=None)

process_cls_results(true: ndarray, predicted: ndarray, metrics='minimal', prefix=None, index=None, inputs=None, model=None)

post-processes classification results.

process_multiclass(true, predicted, metrics, prefix, index)

process_multilabel(true, predicted, metrics, prefix, index)

process_quantiles(true, predicted)

process_rgr_results(true: ndarray, predicted: ndarray, metrics='minimal', prefix=None, index=None, remove_nans=True, inputs=None)

predicted, true are arrays of shape (examples, outs, forecast_len).

property quantiles

regression_plot(true, predicted, target_name, where, annotate_with='r2')

residual_plot(true, predicted, prefix, where, hist_kws: Optional[dict] = None, **kwargs)

Makes residual plot

Parameters:

• true – array like

• predicted – array like

• prefix –

• where –

• hist_kws –

roc_curve(estimator, x, y, prefix=None)

save_or_show(show=None, **kwargs)