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class watex.exlib.gbm.XGBClassifier(*, objective='binary:logistic', **kwargs)[source]#

Bases: XGBModel, ClassifierMixin

Implementation of the scikit-learn API for XGBoost classification. See /python/sklearn_estimator for more information.

Parameters:

  • n_estimators (Optional[int]) – Number of boosting rounds.

  • max_depth (Optional[int]) – Maximum tree depth for base learners.

  • max_leaves – Maximum number of leaves; 0 indicates no limit.

  • max_bin – If using histogram-based algorithm, maximum number of bins per feature

  • grow_policy – Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change.

  • learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)

  • verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType]) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).

  • booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.

  • tree_method (Optional[str]) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from the parameters document tree method

  • n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.

  • gamma (Optional[float]) – (min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree.

  • min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.

  • max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.

  • subsample (Optional[float]) – Subsample ratio of the training instance.

  • sampling_method –

    Sampling method. Used only by the GPU version of hist tree method.

    • uniform: select random training instances uniformly.

    • gradient_based select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)

  • colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.

  • colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.

  • colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.

  • reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).

  • reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).

  • scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.

  • base_score (Optional[float]) – The initial prediction score of all instances, global bias.

  • random_state (Optional[Union[numpy.random.RandomState, int]]) –

    Random number seed.

    Note

    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, default np.nan) – Value in the data which needs to be present as a missing value.

  • num_parallel_tree (Optional[int]) – Used for boosting random forest.

  • monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.

  • interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g. [[0, 1], [2, 3, 4]], where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more information

  • importance_type (Optional[str]) –

    The feature importance type for the feature_importances_ property:

    • For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

    • For linear model, only “weight” is defined and it’s the normalized coefficients without bias.

  • device (Optional[str]) –

    New in version 2.0.0.

    Device ordinal, available options are cpu, cuda, and gpu.

  • validate_parameters (Optional[bool]) – Give warnings for unknown parameter.

  • enable_categorical (bool) –

    New in version 1.5.0.

    Note

    This parameter is experimental

    Experimental support for categorical data. When enabled, cudf/pandas.DataFrame should be used to specify categorical data type. Also, JSON/UBJSON serialization format is required.

  • feature_types (Optional[FeatureTypes]) –

    New in version 1.7.0.

    Used for specifying feature types without constructing a dataframe. See DMatrix for details.

  • max_cat_to_onehot (Optional[int]) –

    New in version 1.6.0.

    Note

    This parameter is experimental

    A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Also, enable_categorical needs to be set to have categorical feature support. See Categorical Data and cat-param for details.

  • max_cat_threshold (Optional[int]) –

    New in version 1.7.0.

    Note

    This parameter is experimental

    Maximum number of categories considered for each split. Used only by partition-based splits for preventing over-fitting. Also, enable_categorical needs to be set to have categorical feature support. See Categorical Data and cat-param for details.

  • multi_strategy (Optional[str]) –

    New in version 2.0.0.

    Note

    This parameter is working-in-progress.

    The strategy used for training multi-target models, including multi-target regression and multi-class classification. See /tutorials/multioutput for more information.

    • one_output_per_tree: One model for each target.

    • multi_output_tree: Use multi-target trees.

  • eval_metric (Optional[Union[str, List[str], Callable]]) –

    New in version 1.6.0.

    Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in sklearn.metrics, or any other user defined metric that looks like sklearn.metrics.

    If custom objective is also provided, then custom metric should implement the corresponding reverse link function.

    Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.

    For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see xgboost.callback.EarlyStopping.

    See Custom Objective and Evaluation Metric for more.

    Note

    This parameter replaces eval_metric in fit() method. The old one receives un-transformed prediction regardless of whether custom objective is being used.

    from sklearn.datasets import load_diabetes
from sklearn.metrics import mean_absolute_error
X, y = load_diabetes(return_X_y=True)
reg = xgb.XGBRegressor(
    tree_method="hist",
    eval_metric=mean_absolute_error,
)
reg.fit(X, y, eval_set=[(X, y)])

  • early_stopping_rounds (Optional[int]) –

    New in version 1.6.0.

    • Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set in fit().

    • If early stopping occurs, the model will have two additional attributes: best_score and best_iteration. These are used by the predict() and apply() methods to determine the optimal number of trees during inference. If users want to access the full model (including trees built after early stopping), they can specify the iteration_range in these inference methods. In addition, other utilities like model plotting can also use the entire model.

    • If you prefer to discard the trees after best_iteration, consider using the callback function xgboost.callback.EarlyStopping.

    • If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping.

    Note

    This parameter replaces early_stopping_rounds in fit() method.

  • callbacks (Optional[List[TrainingCallback]]) –

    List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API.

    Note

    States in callback are not preserved during training, which means callback objects can not be reused for multiple training sessions without reinitialization or deepcopy.

    for params in parameters_grid:
    # be sure to (re)initialize the callbacks before each run
    callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]
    reg = xgboost.XGBRegressor(**params, callbacks=callbacks)
    reg.fit(X, y)

  • kwargs (dict, optional) –

    Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

    Note

    **kwargs unsupported by scikit-learn

    **kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.

    Note

    Custom objective function

    A custom objective function can be provided for the objective parameter. In this case, it should have the signature objective(y_true, y_pred) -> grad, hess:

    y_true: array_like of shape [n_samples]

    The target values

    y_pred: array_like of shape [n_samples]

    The predicted values

    grad: array_like of shape [n_samples]

    The value of the gradient for each sample point.

    hess: array_like of shape [n_samples]

    The value of the second derivative for each sample point

property classes_#
fit(X, y, *, sample_weight=None, base_margin=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, base_margin_eval_set=None, feature_weights=None, callbacks=None)[source]#

Fit gradient boosting classifier.

Note that calling fit() multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly pass xgb_model argument.

Parameters:

  • X –

    Feature matrix. See py-data for a list of supported types.

    When the tree_method is set to hist, internally, the QuantileDMatrix will be used instead of the DMatrix for conserving memory. However, this has performance implications when the device of input data is not matched with algorithm. For instance, if the input is a numpy array on CPU but cuda is used for training, then the data is first processed on CPU then transferred to GPU.

  • y – Labels

  • sample_weight – instance weights

  • base_margin – global bias for each instance.

  • eval_set – A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.

  • eval_metric (str, list of str, or callable, optional) –

    Deprecated since version 1.6.0.

    Use eval_metric in __init__() or set_params() instead.

  • early_stopping_rounds (int) –

    Deprecated since version 1.6.0.

    Use early_stopping_rounds in __init__() or set_params() instead.

  • verbose – If verbose is True and an evaluation set is used, the evaluation metric measured on the validation set is printed to stdout at each boosting stage. If verbose is an integer, the evaluation metric is printed at each verbose boosting stage. The last boosting stage / the boosting stage found by using early_stopping_rounds is also printed.

  • xgb_model – file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).

  • sample_weight_eval_set – A list of the form [L_1, L_2, …, L_n], where each L_i is an array like object storing instance weights for the i-th validation set.

  • base_margin_eval_set – A list of the form [M_1, M_2, …, M_n], where each M_i is an array like object storing base margin for the i-th validation set.

  • feature_weights – Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a ValueError is thrown.

  • callbacks –

    Deprecated since version 1.6.0: Use callbacks in __init__() or set_params() instead.

predict(X, output_margin=False, validate_features=True, base_margin=None, iteration_range=None)[source]#

Predict with X. If the model is trained with early stopping, then best_iteration is used automatically. The estimator uses inplace_predict by default and falls back to using DMatrix if devices between the data and the estimator don’t match.

Note

This function is only thread safe for gbtree and dart.

Parameters:

  • X – Data to predict with.

  • output_margin – Whether to output the raw untransformed margin value.

  • validate_features – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

  • base_margin – Margin added to prediction.

  • iteration_range –

    Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.

    New in version 1.4.0.

Return type:

prediction

predict_proba(X, validate_features=True, base_margin=None, iteration_range=None)[source]#

Predict the probability of each X example being of a given class. If the model is trained with early stopping, then best_iteration is used automatically. The estimator uses inplace_predict by default and falls back to using DMatrix if devices between the data and the estimator don’t match.

Note

This function is only thread safe for gbtree and dart.

Parameters:

  • X – Feature matrix. See py-data for a list of supported types.

  • validate_features – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same.

  • base_margin – Margin added to prediction.

  • iteration_range – Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying iteration_range=(10, 20), then only the forests built during [10, 20) (half open set) rounds are used in this prediction.

Returns:

a numpy array of shape array-like of shape (n_samples, n_classes) with the probability of each data example being of a given class.

Return type:

prediction

set_fit_request(*, base_margin='$UNCHANGED$', base_margin_eval_set='$UNCHANGED$', callbacks='$UNCHANGED$', early_stopping_rounds='$UNCHANGED$', eval_metric='$UNCHANGED$', eval_set='$UNCHANGED$', feature_weights='$UNCHANGED$', sample_weight='$UNCHANGED$', sample_weight_eval_set='$UNCHANGED$', verbose='$UNCHANGED$', xgb_model='$UNCHANGED$')#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • base_margin (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for base_margin parameter in fit.

  • base_margin_eval_set (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for base_margin_eval_set parameter in fit.

  • callbacks (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for callbacks parameter in fit.

  • early_stopping_rounds (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for early_stopping_rounds parameter in fit.

  • eval_metric (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for eval_metric parameter in fit.

  • eval_set (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for eval_set parameter in fit.

  • feature_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for feature_weights parameter in fit.

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

  • sample_weight_eval_set (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight_eval_set parameter in fit.

  • verbose (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for verbose parameter in fit.

  • xgb_model (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for xgb_model parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_predict_proba_request(*, base_margin='$UNCHANGED$', iteration_range='$UNCHANGED$', validate_features='$UNCHANGED$')#

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict_proba.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • base_margin (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for base_margin parameter in predict_proba.

  • iteration_range (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for iteration_range parameter in predict_proba.

  • validate_features (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for validate_features parameter in predict_proba.

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, base_margin='$UNCHANGED$', iteration_range='$UNCHANGED$', output_margin='$UNCHANGED$', validate_features='$UNCHANGED$')#

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • base_margin (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for base_margin parameter in predict.

  • iteration_range (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for iteration_range parameter in predict.

  • output_margin (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for output_margin parameter in predict.

  • validate_features (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for validate_features parameter in predict.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight='$UNCHANGED$')#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class watex.exlib.gbm.xgboostdoc#

Bases: object

Extreme Gradient Boosting

XGBoost stands for Extreme Gradient Boosting, is an open-source software library that implements optimized distributed gradient boosting machine learning algorithms under the Gradient Boosting framework.

XgBoost, which was proposed by the researchers at the University of Washington. It is a library written in C++ which optimizes the training for Gradient Boosting [1]_. Before understanding the XGBoost, we first need to understand the trees especially the decision tree.

Indeed , a Decision tree(DT) is a flowchart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label. A tree can be ‘learned’ by splitting the source set into subsets based on an attribute value test. This process is repeated on each derived subset in a recursive manner called recursive partitioning. The recursion is completed when the subset at a node all has the same value of the target variable, or when splitting no longer adds value to the predictions [2]_.

References

..[1] https://www.geeksforgeeks.org/xgboost/ ..[2] https://www.nvidia.com/en-us/glossary/data-science/xgboost/

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