slickml.visualization._xgboost#

Module Contents#

Functions#

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.
slickml.visualization._xgboost.plot_xgb_cv_results(cv_results: pandas.DataFrame, *, figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = (8, 5), linestyle: Optional[str] = '--', train_label: Optional[str] = 'Train', test_label: Optional[str] = 'Test', train_color: Optional[str] = 'navy', train_std_color: Optional[str] = '#B3C3F3', test_color: Optional[str] = 'purple', test_std_color: Optional[str] = '#D0AAF3', save_path: Optional[str] = None, display_plot: Optional[bool] = False, return_fig: Optional[bool] = False) → Optional[matplotlib.figure.Figure][source]#

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

See also

slickml.classification.XGBoostCVClassifier

References

Returns:

Figure, optional

slickml.visualization._xgboost.plot_xgb_feature_importance(feature_importance: pandas.DataFrame, *, figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = (8, 5), color: Optional[str] = '#87CEEB', marker: Optional[str] = 'o', markersize: Optional[Union[int, float]] = 10, markeredgecolor: Optional[str] = '#1F77B4', markerfacecolor: Optional[str] = '#1F77B4', markeredgewidth: Optional[Union[int, float]] = 1, 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 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

See also

slickml.classification.XGBoostClassifier, slickml.classification.XGBoostCVClassifier, slickml.regression.XGBoostRegressor, slickml.regression.XGBoostCVRegressor

References

Returns:

Figure, optional