def __init__(
self,
predicted: np.ndarray,
expected: np.ndarray = None,
featuresize: int = None,
split_origin: np.array = None,
**kwargs,
):
"""
Instantiates a QQ plot
Parameters
----------
predicted: nd.array
The predicted values
expected: np.ndarray
Optional, the true values. If this attribute is None, the predicted array is assumed to contain the already
standardized residuals.
featuresize: int
number of features
split_origin: np.ndarray
Optional, if the data used for the predictions includes unseen test data.
These residuals can be marked explicitly in the plot. This attribute must have the same dimensionality
as the predictions and expected array. Each entry in this array must be one of the strings ['train', 'test']
to denote from which split this observation originates.
"""
if expected is not None:
std_res = helper.calculate_standardized_residual(
predicted, expected=expected, featuresize=featuresize
)
else:
std_res = predicted
plot = self.__qq_plot(std_res, split_origin)
super(QQPlotWidget, self).__init__(plot, **kwargs)
def update_values(
self,
predicted: np.ndarray,
expected: np.ndarray = None,
featuresize: int = None,
split_origin: np.ndarray = None,
):
"""
Update the QQ plot values
Parameters
----------
predicted: nd.array
The predicted values
expected: np.ndarray
Optional, the true values. If this attribute is None, the predicted array is assumed to contain the already
standardized residuals.
featuresize: int
number of features
split_origin: np.ndarray
Optional, if the data used for the predictions includes unseen test data.
These residuals can be marked explicitly in the plot. This attribute must have the same dimensionality
as the predictions and expected array. Each entry in this array must be one of the strings ['train', 'test']
to denote from which split this observation originates.
"""
plot = self.__qq_plot(
standardized_residuals=helper.calculate_standardized_residual(
predicted, expected, featuresize
),
split_origin=split_origin,
)
self.update({"data": plot.data}, overwrite=True)
self.update_layout()
def __create_resplots(
self,
model,
x: np.ndarray,
y: np.ndarray,
x_test: np.ndarray = None,
y_test: np.ndarray = None,
) -> widgets.VBox:
logger = get_logger()
with fit_if_not_fitted(model, x, y) as fitted_model:
fitted = fitted_model.predict(x)
fitted_residuals = fitted - y
if x_test is not None and y_test is not None:
pred = fitted_model.predict(x_test)
prediction_residuals = pred - y_test
predictions = np.concatenate((fitted, pred))
residuals = np.concatenate((fitted_residuals, prediction_residuals))
split_origin = np.concatenate(
(np.repeat("train", fitted.shape[0]), np.repeat("test", pred.shape[0]))
)
x = np.concatenate((x, x_test))
y = np.concatenate((y, y_test))
else:
predictions = fitted
residuals = fitted_residuals
split_origin = None
logger.info("Calculated model residuals")
self.display.move_progress()
tukey_anscombe_widget = TukeyAnscombeWidget(
predictions, residuals, split_origin=split_origin
)
logger.info("Calculated Tunkey-Anscombe Plot")
self.figures.append(tukey_anscombe_widget)
self.display.move_progress()
qq_plot_widget = QQPlotWidget(
predictions, y, split_origin=split_origin, featuresize=x.shape[1]
)
logger.info("Calculated Normal QQ Plot")
self.figures.append(qq_plot_widget)
self.display.move_progress()
standardized_residuals = helper.calculate_standardized_residual(
predictions, y, None
)
model_norm_residuals_abs_sqrt = np.sqrt(np.abs(standardized_residuals))
scale_location_widget = ScaleLocationWidget(
predictions, model_norm_residuals_abs_sqrt, split_origin=split_origin
)
logger.info("Calculated Scale-Location Plot")
self.figures.append(scale_location_widget)
self.display.move_progress()
leverage = helper.leverage_statistic(np.array(x))
n_model_params = len(model.get_params())
distance = helper.cooks_distance(
standardized_residuals, leverage, n_model_params=n_model_params
)
cooks_distance_widget = CooksDistanceWidget(
leverage,
distance,
standardized_residuals,
n_model_params,
split_origin=split_origin,
)
logger.info("Calculated Residual vs Leverage Plot inc. Cook's distance")
self.figures.append(cooks_distance_widget)
self.display.move_progress()
items_layout = Layout(width="1000px")
h0 = widgets.HBox(self.figures[:2], layout=items_layout)
h1 = widgets.HBox(self.figures[2:], layout=items_layout)
return widgets.VBox([h0, h1])