slickml.visualization._shap#

Module Contents#

Functions#

plot_shap_summary(→ None)

Visualizes shap beeswarm plot as summary of shapley values.

plot_shap_waterfall(, bar_color, bar_thickness, ...)

Visualizes the Shapley values as a waterfall plot. pl
slickml.visualization._shap.plot_shap_summary(shap_values: numpy.ndarray, features: Union[pandas.DataFrame, numpy.ndarray], *, plot_type: Optional[str] = 'dot', figsize: Optional[Union[str, Tuple[float, float]]] = 'auto', color: Optional[str] = None, cmap: Optional[matplotlib.colors.LinearSegmentedColormap] = None, max_display: Optional[int] = 20, feature_names: Optional[List[str]] = None, layered_violin_max_num_bins: Optional[int] = 10, title: Optional[str] = None, sort: Optional[bool] = True, color_bar: Optional[bool] = True, class_names: Optional[List[str]] = None, class_inds: Optional[List[int]] = None, color_bar_label: Optional[str] = 'Feature Value', save_path: Optional[str] = None, display_plot: Optional[bool] = True) → None[source]#

Visualizes shap beeswarm plot as summary of shapley values.

Notes

This is a helper function to plot the shap summary plot based on all types of shap.Explainer including shap.LinearExplainer for linear models, shap.TreeExplainer for tree-based models, and shap.DeepExplainer deep neural network models. More on details are available at [shap-api].

Parameters:

  • shap_values (np.ndarray) – Calculated SHAP values array. For single output explanations such as binary classification problems, this will be a matrix of SHAP values with a shape of (n_samples, n_features). Additionally, for multi-output explanations this would be a list of such matrices of SHAP values (List[np.ndarray])

  • features (Union[pd.DataFrame, np.ndarray]) – The feature matrix that was used to calculate the SHAP values. For the case of Numpy array it is recommened to pass the feature_names list as well for better visualization results

  • plot_type (str, optional) – The type of summary plot where possible options are “bar”, “dot”, “violin”, “layered_violin”, and “compact_dot”. Recommendations are “dot” for single-output such as binary classifications, “bar” for multi-output problems, “compact_dot” for Shap interactions, by default “dot”

  • figsize (tuple, optional) – Figure size where “auto” is auto-scaled figure size based on the number of features that are being displayed. Passing a single float will cause each row to be that many inches high. Passing a pair of floats will scale the plot by that number of inches. If None is passed then the size of the current figure will be left unchanged, by default “auto”

  • color (str, optional) – Color of plots when plot_type="violin" and plot_type=layered_violin" are “RdBl” color-map while color of the horizontal lines when plot_type="bar" is “#D0AAF3”, by default None

  • cmap (LinearSegmentedColormap, optional) – Color map when plot_type="violin" and plot_type=layered_violin", by default “RdBl”

  • max_display (int, optional) – Limit to show the number of features in the plot, by default 20

  • feature_names (List[str], optional) – List of feature names to pass. It should follow the order of features, by default None

  • layered_violin_max_num_bins (int, optional) – The number of bins for calculating the violin plots ranges and outliers, by default 10

  • title (str, optional) – Title of the plot, by default None

  • sort (bool, optional) – Flag to plot sorted shap vlues in descending order, by default True

  • color_bar (bool, optional) – Flag to show a color bar when plot_type="dot" or plot_type="violin"

  • class_names (List[str], optional) – List of class names for multi-output problems, by default None

  • class_inds (List[int], optional) – List of class indices for multi-output problems, by default None

  • color_bar_label (str, optional) – Label for color bar, by default “Feature Value”

  • save_path (str, optional) – The full or relative path to save the plot including the image format such as “myplot.png” or “../../myplot.pdf”, by default None

  • display_plot (bool, optional) – Whether to show the plot, by default True

References

Returns:

None

slickml.visualization._shap.plot_shap_waterfall(shap_values: numpy.ndarray, features: Union[pandas.DataFrame, numpy.ndarray], *, figsize: Optional[Tuple[float, float]] = (8, 5), bar_color: Optional[str] = '#B3C3F3', bar_thickness: Optional[Union[float, int]] = 0.5, line_color: Optional[str] = 'purple', marker: Optional[str] = 'o', markersize: Optional[Union[int, float]] = 7, markeredgecolor: Optional[str] = 'purple', markerfacecolor: Optional[str] = 'purple', markeredgewidth: Optional[Union[int, float]] = 1, max_display: Optional[int] = 20, title: Optional[str] = None, fontsize: Optional[Union[int, float]] = 12, save_path: Optional[str] = None, display_plot: Optional[bool] = True, return_fig: Optional[bool] = False) → Optional[matplotlib.figure.Figure][source]#

Visualizes the Shapley values as a waterfall plot. pl This function is a helper function to plot the shap summary plot based on all types of shap explainers including tree, linear, and dnn.

Parameters:

  • shap_values (np.ndarray) – Calculated SHAP values array. For single output explanations such as binary classification problems, this will be a matrix of SHAP values with a shape of (n_samples, n_features). Additionally, for multi-output explanations this would be a list of such matrices of SHAP values (List[np.ndarray])

  • features (Union[pd.DataFrame, np.ndarray]) – The feature matrix that was used to calculate the SHAP values. For the case of Numpy array it is recommened to pass the feature_names list as well for better visualization results

  • figsize (Tuple[float, float], optional) – Figure size, by default (8, 5)

  • bar_color (str, optional) – Color of the horizontal bar lines, “#B3C3F3”

  • bar_thickness (Union[float, int], optional) – Thickness (hight) of the horizontal bar lines, by default 0.5

  • line_color (str, optional) – Color of the line plot, by default “purple”

  • marker (str, optional) – Marker style of the lollipops. More valid marker styles can be found at [markers-api], by default “o”

  • markersize (Union[int, float], optional) – Markersize, by default 7

  • markeredgecolor (str, optional) – Marker edge color, by default “purple”

  • markerfacecolor (str, optional) – Marker face color, by default “purple”

  • markeredgewidth (Union[int, float], optional) – Marker edge width, by default 1

  • max_display (int, optional) – Limit to show the number of features in the plot, by default 20

  • title (str, optional) – Title of the plot, by default None

  • fontsize (Union[int, float], optional) – Fontsize for xlabel and ylabel, and ticks parameters, by default 12

  • save_path (str, optional) – The full or relative path to save the plot including the image format such as “myplot.png” or “../../myplot.pdf”, by default None

  • display_plot (bool, optional) – Whether to show the plot, by default True

  • return_fig (bool, optional) – Whether to return figure object, by default False

References

Returns:

Figure, optional