plotting

plot_comet(y_train, y_test, y_pred, scoring=None, **kwargs)

Given a train-test-split of panel data (y_train, y_test) and forecast y_pred, returns a Comet plot i.e. scatterplot of volatility per entity in y_train against the forecast scores.

Parameters:

Name	Type	Description	Default
y_train	DataFrame	Panel DataFrame of train dataset.	required
y_test	DataFrame	Panel DataFrame of test dataset.	required
y_pred	DataFrame	Panel DataFrame of forecasted values to score against y_test.	required
scoring	Optional[metric]	If None, defaults to SMAPE.	None

Returns:

Name	Type	Description
figure	Figure	Plotly scatterplot.

plot_entities(y, **kwargs)

Given panel DataFrame of observed values y, returns bar chart of entity counts, which is the number of observed values per entity.

Parameters:

Name	Type	Description	Default
y	DataFrame | LazyFrame	Panel DataFrame of observed values.	required
**kwargs		Additional keyword arguments to pass to a plotly.graph_objects.Layout object.	{}

Returns:

Name	Type	Description
figure	Figure	Plotly bar chart.

plot_forecasts(*, y_true, y_pred, num_series=None, num_cols=None, num_points=None, seed=None, layout_kwargs=None, line_kwargs=None)

Given panel DataFrames of observed values y and forecasts y_pred, returns subplots for each individual entity / time-series.

Parameters:

Name	Type	Description	Default
y	Union[DataFrame, LazyFrame]	Panel DataFrame of observed values.	required
num_series	Optional[int]	Number of entities / time-series to plot. If None, plot all entities. Defaults to None.	None
num_points	Optional[int]	Plot last_n most recent values in y. If None, plot all points. Defaults to None.	None
num_cols	Optional[int]	Number of columns to arrange subplots. Defaults to 2.	None
seed	Optional[int]	Random seed for sampling entities / time-series. Defaults to None.	None
layout_kwargs	Optional[Dict[str, Any]]	Additional keyword arguments to pass to plotly.graph_objects.Figure.update_layout or, equivalently, a plotly.graph_objects.Layout object.	None
line_kwargs	Optional[Dict[str, Any]]	Additional keyword arguments to pass to a plotly.graph_objects.Line object.	None

Returns:

Name	Type	Description
figure	Figure	Plotly instance of Figure with all the subplots.

plot_fva(y_true, y_pred, y_pred_bench, scoring=None, **kwargs)

Given two panel data forecasts y_pred and y_pred_bench, returns scatterplot of benchmark scores against forecast scores. Each dot represents a single entity / time-series.

Parameters:

Name	Type	Description	Default
y_true	DataFrame	Panel DataFrame of test dataset.	required
y_pred	DataFrame	Panel DataFrame of forecasted values.	required
y_pred_bench	DataFrame	Panel DataFrame of benchmark forecast values.	required
scoring	Optional[metric]	If None, defaults to SMAPE.	None

Returns:

Name	Type	Description
figure	Figure	Plotly scatterplot.

plot_panel(y, *, num_series=None, num_cols=None, num_points=None, seed=None, layout_kwargs=None, line_kwargs=None)

Given panel DataFrames of observed values y, returns subplots for each individual entity / time-series.

Parameters:

Name	Type	Description	Default
y	Union[DataFrame, LazyFrame]	Panel DataFrame of observed values.	required
num_series	Optional[int]	Number of entities / time-series to plot. If None, plot all entities. Defaults to None.	None
num_points	Optional[int]	Plot last_n most recent values in y. If None, plot all points. Defaults to None.	None
num_cols	Optional[int]	Number of columns to arrange subplots. Defaults to 2.	None
seed	Optional[int]	Random seed for sampling entities / time-series. Defaults to None.	None
layout_kwargs	Optional[Dict[str, Any]]	Additional keyword arguments to pass to a plotly.graph_objects.Layout object.	None
line_kwargs	Optional[Dict[str, Any]]	Additional keyword arguments to pass to a plotly.graph_objects.Line object.	None

Returns:

Name	Type	Description
figure	Figure	Plotly instance of Figure with all the subplots.

plot_residuals(y_resids, n_bins=None, **kwargs)

Given panel DataFrame of residuals across splits y_resids, returns binned counts plot of forecast residuals colored by entity / time-series.

Useful for residuals analysis (bias and normality) at scale.

Parameters:

Name	Type	Description	Default
y_resids	Union[DataFrame, LazyFrame]	Panel DataFrame of forecast residuals (i.e. observed less forecast).	required
n_bins	int	Number of bins.	None

Returns:

Name	Type	Description
figure	Figure	Plotly histogram.