waterfall_plot

class virtualitics_sdk.elements.waterfall_plot.DataType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

CATEGORICAL = 'categorical'

NUMERICAL = 'numerical'

class virtualitics_sdk.elements.waterfall_plot.WaterfallPlot(title, expected, returned, pred_explanation, x_axis_label, data, description='', positive_color=None, negative_color=None, expected_title=None, predicted_title=None, show_title=True, show_description=True, **kwargs)

Bases: Plot

Waterfall plots are usually generated by the Explainer class and are not recommended creating manually.

EXAMPLE:

# Imports 
from xgboost import XGBRegressor
from virtualitics_sdk.assets.explainer import Explainer
. . . 
# Example usage
def produce_waterfall_xai_plot(data):
     . . .
     # Creating Virtualitics Ensemble Model
     xgb = Model(
         XGBRegressor(learning_rate=0.1, 
                      n_jobs=-1, 
                      n_estimators=100, 
                      max_depth=5, 
                      eval_metric="mae", 
                      random_state=42),
         label="pmx",
         name="remaining useful life predictor",
     )
     # Training Virtualitics Ensemble Model
     xgb.fit(modeling_data[ohe_features], modeling_data[target])
     # Create explainer training data asset
     explain_data = Dataset(
         modeling_data[ohe_features],
         label="pmx",
         name="explainer modeling set",
         categorical_cols=categorical_ft_cols,
         encoding=DataEncoding.ONE_HOT,
     )
     # Create explainer for ensemble model
     explainer = Explainer(
         model=xgb,
         training_data=explain_data,
         output_names=target,
         mode="regression",
         label="pmx project",
         name="ensemble model",
         use_shap=True,
         use_lime=False,
     )
     plot = explainer.explain(
         explain_instances[ohe_features],
         method="manual",
         titles=titles,
         expected_title="Average Gearbox Lifespan",
         predicted_title="Predicted Gearbox Lifespan",
         return_as="plots",
     )

to_json()

Convert the element to a JSON.

Returns:

A JSON dictionary of the element.

class virtualitics_sdk.elements.waterfall_plot.WaterfallPlotData(name, weight, value, explanation, type, _min=None, _max=None, percentile=None, count=None, frequency=None)

Bases: PlotDataPoint

to_json()