slickml.regression._xgboostcv#

Module Contents#

Classes#

XGBoostCVRegressor

XGBoost CV Regressor.
class slickml.regression._xgboostcv.XGBoostCVRegressor[source]#

Bases: slickml.regression._xgboost.XGBoostRegressor

XGBoost CV Regressor.

This is wrapper using XGBoostRegressor to train a XGBoost [xgboost-api] model with using the optimum number of boosting rounds from the inputs. It used xgboost.cv() model with n-folds cross-validation and train model based on the best number of boosting round to avoid over-fitting.

Parameters:

  • num_boost_round (int, optional) – Number of boosting rounds to fit a model, by default 200

  • n_splits (int, optional) – Number of folds for cross-validation, by default 4

  • metrics (str, optional) – Metrics to be tracked at cross-validation fitting time with possible values of "rmse", "rmsle", "mae". Note this is different than eval_metric that needs to be passed to params dict, by default “rmse”

  • early_stopping_rounds (int, optional) – The criterion to early abort the xgboost.cv() phase if the test metric is not improved, by default 20

  • random_state (int, optional) – Random seed number, by default 1367

  • shuffle (bool, optional) – Whether to shuffle data to have the ability of building stratified folds in xgboost.cv(), by default True

  • sparse_matrix (bool, optional) – Whether to convert the input features to sparse matrix with csr format or not. This would increase the speed of feature selection for relatively large/sparse datasets. Consequently, this would actually act like an un-optimize solution for dense feature matrix. Additionally, this feature cannot be used along with scale_mean=True standardizing the feature matrix to have a mean value of zeros would turn the feature matrix into a dense matrix. Therefore, by default our API banned this feature, by default False

  • scale_mean (bool, optional) – Whether to standarize the feauture matrix to have a mean value of zero per feature (center the features before scaling). As laid out in sparse_matrix, scale_mean=False when using sparse_matrix=True, since centering the feature matrix would decrease the sparsity and in practice it does not make any sense to use sparse matrix method and it would make it worse. The StandardScaler object can be accessed via cls.scaler_ if scale_mean or scale_strd is used unless it is None, by default False

  • scale_std (bool, optional) – Whether to scale the feauture matrix to have unit variance (or equivalently, unit standard deviation) per feature. The StandardScaler object can be accessed via cls.scaler_ if scale_mean or scale_strd is used unless it is None, by default False

  • importance_type (str, optional) – Importance type of xgboost.train() with possible values "weight", "gain", "total_gain", "cover", "total_cover", by default “total_gain”

  • params (Dict[str, Union[str, float, int]], optional) – Set of parameters required for fitting a Booster, by default {“eval_metric”: “rmse”, “tree_method”: “hist”, “objective”: “reg:squarederror”, “learning_rate”: 0.05, “max_depth”: 2, “min_child_weight”: 1, “gamma”: 0.0, “reg_alpha”: 0.0, “reg_lambda”: 1.0, “subsample”: 0.9, “max_delta_step”: 1, “verbosity”: 0, “nthread”: 4} Other options for objective: "reg:logistic", "reg:squaredlogerror"

  • verbose (bool, optional) – Whether to log the final results of xgboost.cv(), by default True

  • callbacks (bool, optional) – Whether to logging standard deviation of metrics on train data and track the early stopping criterion, by default False

fit(X_train, y_train)[source]#

Fits a XGBoost.Booster to input training data. Proper dtrain_ matrix based on chosen options i.e. sparse_matrix, scale_mean, scale_std is being created based on the passed X_train and y_train

predict(X_test, y_test)#

Returns prediction target values

get_cv_results()[source]#

Returns the mean value of the metrics in n_splits cross-validation for each boosting round

get_params()#

Returns final set of train parameters. The default set of parameters will be updated with the new ones that passed to params

get_default_params()#

Returns the default set of train parameters. The default set of parameters will be used when params=None

get_feature_importance()#

Returns the feature importance of the trained booster based on the given importance_type

get_shap_explainer()#

Returns the shap.TreeExplainer

plot_cv_results()[source]#

Visualizes cross-validation results

plot_shap_summary()#

Visualizes Shapley values summary plot

plot_shap_waterfall()#

Visualizes Shapley values waterfall plot

cv_results_#

The mean value of the metrics in n_splits cross-validation for each boosting round

Type:

pd.DataFrame

feature_importance_#

Features importance based on the given importance_type

Type:

pd.DataFrame

scaler_#

Standardization object when scale_mean=True or scale_std=True unless it is None

Type:

StandardScaler, optional

X_train_#

Fitted and Transformed features when scale_mean=True or scale_std=True. In other case, it will be the same as the passed X_train features

Type:

pd.DataFrame

X_test_#

Transformed features when scale_mean=True or scale_std=True using clf.scaler_ that has be fitted on X_train and y_train data. In other case, it will be the same as the passed X_train features

Type:

pd.DataFrame

dtrain_#

Training data matrix via xgboost.DMatrix(clf.X_train_, clf.y_train)

Type:

xgb.DMatrix

dtest_#

Testing data matrix via xgboost.DMatrix(clf.X_test_, clf.y_test) or xgboost.DMatrix(clf.X_test_, None) when y_test is not available in inference

Type:

xgb.DMatrix

shap_values_train_#

Shapley values from TreeExplainer using X_train_

Type:

np.ndarray

shap_values_test_#

Shapley values from TreeExplainer using X_test_

Type:

np.ndarray

shap_explainer_#

Shap TreeExplainer object

Type:

shap.TreeExplainer

model_#

XGBoost Booster object

Type:

xgboost.Booster

See also

slickml.regression.XGBoostRegressor

References

__slots__ = []#
callbacks :Optional[bool] = False#
early_stopping_rounds :Optional[int] = 20#
importance_type :Optional[str] = total_gain#
metrics :Optional[str] = rmse#
n_splits :Optional[int] = 4#
num_boost_round :Optional[int] = 200#
params :Optional[Dict[str, Union[str, float, int]]]#
random_state :Optional[int] = 1367#
scale_mean :Optional[bool] = False#
scale_std :Optional[bool] = False#
shuffle :Optional[bool] = True#
sparse_matrix :Optional[bool] = False#
verbose :Optional[bool] = True#
__getstate__()#
__post_init__() → None[source]#

Post instantiation validations and assignments.

__repr__(N_CHAR_MAX=700)#

Return repr(self).

__setstate__(state)#
fit(X_train: Union[pandas.DataFrame, numpy.ndarray], y_train: Union[List[float], numpy.ndarray, pandas.Series]) → None[source]#

Fits a XGBoost.Booster to input training data based on the best number of boostring round.

Parameters:

  • X_train (Union[pd.DataFrame, np.ndarray]) – Input data for training (features)

  • y_train (Union[List[float], np.ndarray, pd.Series]) – Input ground truth for training (targets)

See also

xgboost.cv() xgboost.train()

Returns:

None

get_cv_results() → pandas.DataFrame[source]#

Returns cross-validiation results.

Returns:

pd.DataFrame

get_default_params() → Dict[str, Union[str, float, int]]#

Returns the default set of train parameters.

The default set of parameters will be used when params=None.

See also

get_params()

Returns:

Dict[str, Union[str, float, int]]

get_feature_importance() → pandas.DataFrame#

Returns the feature importance of the trained booster based on the given importance_type.

Returns:

pd.DataFrame

get_params() → Optional[Dict[str, Union[str, float, int]]]#

Returns the final set of train parameters.

The default set of parameters will be updated with the new ones that passed to params.

See also

get_default_params()

Returns:

Dict[str, Union[str, float, int]]

get_shap_explainer() → shap.TreeExplainer#

Returns the shap.TreeExplainer object.

Returns:

shap.TreeExplainer

plot_cv_results(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 cross-validation results and evolution of metrics through number of boosting rounds.

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

Returns:

Figure, optional

plot_feature_importance(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]#

Visualizes the XGBoost feature importance as bar chart.

Parameters:

  • feature importance (pd.DataFrame) – Feature importance (feature_importance_ attribute)

  • figsize (Tuple[Union[int, float], Union[int, float]], 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

Returns:

Figure, optional

plot_shap_summary(validation: Optional[bool] = True, 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#

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]. Note that this function should be ran after the predict() to make sure the X_test is being instansiated or set validation=False.

Parameters:

  • validation (bool, optional) – Whether to calculate Shap values of using the validation data X_test or not. When validation=False, Shap values are calculated using X_train, be default True

  • 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

Returns:

None

plot_shap_waterfall(validation: Optional[bool] = True, 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]#

Visualizes the Shapley values as a waterfall plot.

Notes

Waterfall is defined as the cumulitative/composite ratios of shap values per feature. Therefore, it can be easily seen with each feature how much explainability we can achieve. Note that this function should be ran after the predict() to make sure the X_test is being instansiated or set validation=False.

Parameters:

  • validation (bool, optional) – Whether to calculate Shap values of using the validation data X_test or not. When validation=False, Shap values are calculated using X_train, be default True

  • 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 [2]_, 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

Returns:

Figure, optional

predict(X_test: Union[pandas.DataFrame, numpy.ndarray], y_test: Optional[Union[List[float], numpy.ndarray, pandas.Series]] = None) → numpy.ndarray#

Returns the prediction target (response) values.

Parameters:

  • X_test (Union[pd.DataFrame, np.ndarray]) – Input data for testing (features)

  • y_test (Union[List[float], np.ndarray, pd.Series], optional) – Input ground truth for testing (targets)

Returns:

np.ndarray

score(X, y, sample_weight=None)#

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score (float) – \(R^2\) of self.predict(X) wrt. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self (estimator instance) – Estimator instance.