slickml.visualization¶

Functions¶

`plot_binary_classification_metrics`(, save_path, ...)	Visualizes binary classification metrics using `plotting_dict_` attribute of `BinaryClassificationMetrics`.
`plot_glmnet_coeff_path`(, linestyle, fontsize, ...)	Visualizes the GLMNet coefficients' paths.
`plot_glmnet_cv_results`(, marker, markersize, ...)	Visualizes the GLMNet cross-validation results.
`plot_regression_metrics`(, save_path, display_plot, ...)	Visualizes regression metrics using `plotting_dict_` attribute of `RegressionMetrics`.
`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
`plot_xfs_cv_results`(, internalcvcolor, ...)	Visualizies the cross-validation results of `XGBoostFeatureSelector`.
`plot_xfs_feature_frequency`(, show_freq_pct, color, ...)	Visualizes the selected features frequency as a bar chart.
`plot_xgb_cv_results`(, linestyle, train_label, ...)	Visualizes the cv_results of `XGBoostCVClassifier`.
`plot_xgb_feature_importance`(, color, marker, ...)	Visualizes the XGBoost feature importance as a bar chart.

Package Contents¶

slickml.visualization.plot_binary_classification_metrics(figsize: Tuple[float, float] | None = (12, 12), save_path: str | None = None, display_plot: bool | None = False, return_fig: bool | None = False, **kwargs: Dict[str, Any]) → matplotlib.figure.Figure | None[source]¶

Visualizes binary classification metrics using plotting_dict_ attribute of BinaryClassificationMetrics.

Parameters:

figsize (Tuple[float, float], optional) – Figure size, by default (12, 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 False
return_fig (bool, optional) – Whether to return figure object, by default False
**kwargs (Dict[str, Any]) – Key-value pairs of regression metrics plot

Returns:

Figure, optional

Visualizes the GLMNet coefficients’ paths.

Parameters:

figsize (tuple, optional) – Figure size, by default (8, 5)
linestyle (str, optional) – Linestyle of paths, by default “-”
fontsize (Union[int, float], optional) – Fontsize of the title. The fontsizes of xlabel, ylabel, tick_params, and legend are resized with 0.85, 0.85, 0.75, and 0.85 fraction of title fontsize, respectively, by default 12
grid (bool, optional) – Whether to show (x,y) grid on the plot or not, by default True
legend (bool, optional) – Whether to show legend on the plot or not, by default True
legendloc (Union[int, str], optional) – Location of legend, by default “center”
xlabel (str, optional) – Xlabel of the plot, by default “-Log(Lambda)”
ylabel (str, optional) – Ylabel of the plot, by default “Coefficients”
title (str, optional) – Title of the plot, by default “Best {lambda_best} with {n} Features”
yscale (str, optional) – Scale for y-axis (coefficients). Possible options are "linear", "log", "symlog", "logit" [yscale], by default “linear”
bbox_to_anchor (Tuple[float, float], optional) – Relative coordinates for legend location outside of the plot, by default (1.1, 0.5)
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
**kwargs (Dict[str, Any]) – Key-value pairs of results. results_ attribute can be used

References

[yscale]

https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html

Returns:: Figure, optional

Visualizes the GLMNet cross-validation results.

Notes

This plotting function can be used along with results_ attribute of any of GLMNetCVClassifier, or GLMNetCVRegressor classes as kwargs.

Parameters:

figsize (tuple, optional) – Figure size, by default (8, 5)
marker (str, optional) – Marker style of the metric to distinguish the error bars. More valid marker styles can be found at [markers-api], by default “o”
markersize (Union[int, float], optional) – Markersize, by default 5
color (str, optional) – Line and marker color, by default “red”
errorbarcolor (str, optional) – Error bar color, by default “black”
maxlambdacolor (str, optional) – Color of vertical line for lambda_max_, by default “purple”
bestlambdacolor (str, optional) – Color of vertical line for lambda_best_, by default “navy”
linestyle (str, optional) – Linestyle of vertical lambda lines, by default “–”
fontsize (Union[int, float], optional) – Fontsize of the title. The fontsizes of xlabel, ylabel, tick_params, and legend are resized with 0.85, 0.85, 0.75, and 0.85 fraction of title fontsize, respectively, by default 12
grid (bool, optional) – Whether to show (x,y) grid on the plot or not, by default True
legend (bool, optional) – Whether to show legend on the plot or not, by default True
legendloc (Union[int, str], optional) – Location of legend, by default “best”
xlabel (str, optional) – Xlabel of the plot, by default “-Log(Lambda)”
ylabel (str, optional) – Ylabel of the plot, by default “{n_splits}-Folds CV Mean {metric}”
title (str, optional) – Title of the plot, by default “Best {lambda_best} with {n} Features”
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
**kwargs (Dict[str, Any]) – Key-value pairs of results. results_ attribute can be used

References

[markers-api]

https://matplotlib.org/stable/api/markers_api.html

Returns:: Figure, optional

slickml.visualization.plot_regression_metrics(figsize: Tuple[float, float] | None = (12, 16), save_path: str | None = None, display_plot: bool | None = False, return_fig: bool | None = False, **kwargs: Dict[str, Any]) → matplotlib.figure.Figure[source]¶

Visualizes regression metrics using plotting_dict_ attribute of RegressionMetrics.

Parameters:

figsize (Tuple[float, float], optional) – Figure size, by default (12, 16)
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 False
return_fig (bool, optional) – Whether to return figure object, by default False
**kwargs (Dict[str, Any]) – Key-value pairs of regression metrics plot

Returns:

Figure, optional

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

[shap-api]

https://shap-lrjball.readthedocs.io/en/latest/generated/shap.summary_plot.html

Returns:: None

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

[markers-api]

https://matplotlib.org/stable/api/markers_api.html

Returns:: Figure, optional

Visualizies the cross-validation results of XGBoostFeatureSelector.

Notes

It visualizes the internal and external cross-validiation performance during the selection process. The internal refers to the performance of the train/test folds during the xgboost.cv() using metrics rounds to help the best number of boosting round while the external refers to the performance of xgboost.train() based on watchlist using eval_metric. Additionally, sns.distplot previously was used which is now deprecated. More details in [seaborn-distplot-deprecation].

Parameters:

figsize (tuple, optional) – Figure size, by default (10, 8)
internalcvcolor (str, optional) – Color of the histograms for internal cv results, by default “#4169E1”
externalcvcolor (str, optional) – Color of the histograms for external cv results, by default “#8A2BE2”
sharex (bool, optional) – Whether to share “X” axis for each column of subplots, by default False
sharey (bool, optional) – Whether to share “Y” axis for each row of subplots, by default False
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
kwargs (Dict[str, Any]) – Required plooting elements (plotting_cv_ attribute of XGBoostFeatureSelector)

See also

slickml.selection.XGBoostFeatureSelector, Refereces, ---------,

Returns:: Figure, optional

Visualizes the selected features frequency as a bar chart.

This plotting function can be used along with feature_frequency_ attribute of any frequency-based feature selection algorithm such as XGBoostFeatureSelector.

feature importancepd.DataFrame
Feature importance (feature_frequency_ attribute)

figsizetuple, optional
Figure size, by default (8, 4)

show_freq_pctbool, optional
Whether to show the features frequency in percent, by default True

colorstr, optional: Color of the horizontal lines of lollipops, by default “#87CEEB”
markerstr, optional: Marker style of the lollipops. More valid marker styles can be found at [markers-api], by default “o”
markersizeUnion[int, float], optional: Markersize, by default 10
markeredgecolorstr, optional: Marker edge color, by default “#1F77B4”
markerfacecolorstr, optional: Marker face color, by defualt “#1F77B4”
markeredgewidthUnion[int, float], optional: Marker edge width, by default 1
fontsizeUnion[int, float], optional: Fontsize for xlabel and ylabel, and ticks parameters, by default 12
save_pathstr, 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_plotbool, optional: Whether to show the plot, by default True
return_figbool, optional: Whether to return figure object, by default False

References

[markers-api]

https://matplotlib.org/stable/api/markers_api.html

Returns:: Figure, optional

Visualizes the cv_results of XGBoostCVClassifier.

Parameters:

cv_results (pd.DataFrame) – Cross-validation results
figsize (Tuple[Union[int, float], Union[int, float]], optional) – Figure size, by default (8, 5)
linestyle (str, optional) – Style of lines [linestyles-api], by default “–”
train_label (str, optional) – Label in the figure legend for the train line, by default “Train”
test_label (str, optional) – Label in the figure legend for the test line, by default “Test”
train_color (str, optional) – Color of the training line, by default “navy”
train_std_color (str, optional) – Color of the edge color of the training std bars, by default “#B3C3F3”
test_color (str, optional) – Color of the testing line, by default “purple”
test_std_color (str, optional) – Color of the edge color of the testing std bars, by default “#D0AAF3”
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 False
return_fig (bool, optional) – Whether to return figure object, by default False

References

[linestyles-api]

https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html

Returns:: Figure, optional

Visualizes the XGBoost feature importance as a bar chart.

Notes

This plotting function can be used along with feature_importance_ attribute of any of XGBoostClassifier, XGBoostCVClassifier, XGBoostRegressor, or XGBoostCVRegressor classes.

Parameters:

feature importance (pd.DataFrame) – Feature importance (feature_importance_ attribute)
figsize (tuple, optional) – Figure size, by default (8, 5)
color (str, optional) – Color of the horizontal lines of lollipops, by default “#87CEEB”
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 10
markeredgecolor (str, optional) – Marker edge color, by default “#1F77B4”
markerfacecolor (str, optional) – Marker face color, by defualt “#1F77B4”
markeredgewidth (Union[int, float], optional) – Marker edge width, by default 1
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

[markers-api]

https://matplotlib.org/stable/api/markers_api.html

Returns:: Figure, optional