dask_ml.xgboost.XGBRegressor

class dask_ml.xgboost.XGBRegressor(objective='reg:squarederror', **kwargs)
Attributes:
coef_

Coefficients property

feature_importances_

Feature importances property

intercept_

Intercept (bias) property

Methods

apply(X[, ntree_limit]) Return the predicted leaf every tree for each sample.
evals_result() Return the evaluation results.
fit(X[, y, eval_set, …]) Fit the gradient boosting model
get_booster() Get the underlying xgboost Booster of this model.
get_num_boosting_rounds() Gets the number of xgboost boosting rounds.
get_params([deep]) Get parameters.
get_xgb_params() Get xgboost specific parameters.
load_model(fname) Load the model from a file.
predict(X) Predict with data.
save_model(fname) Save the model to a file.
score(X, y[, sample_weight]) Return the coefficient of determination R^2 of the prediction.
set_params(**params) Set the parameters of this estimator.
__init__(objective='reg:squarederror', **kwargs)

Initialize self. See help(type(self)) for accurate signature.

apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters:
X : array_like, shape=[n_samples, n_features]

Input features matrix.

ntree_limit : int

Limit number of trees in the prediction; defaults to 0 (use all trees).
Returns:
X_leaves : array_like, shape=[n_samples, n_trees]

For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering.
coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns:
coef_ : array of shape [n_features] or [n_classes, n_features]
evals_result()

Return the evaluation results.

If eval_set is passed to the fit function, you can call evals_result() to get evaluation results for all passed eval_sets. When eval_metric is also passed to the fit function, the evals_result will contain the eval_metrics passed to the fit function.

Returns:
evals_result : dictionary
feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns:
feature_importances_ : array of shape [n_features]
fit(X, y=None, eval_set=None, sample_weight_eval_set=None, eval_metric=None, early_stopping_rounds=None)

Fit the gradient boosting model

Parameters:
X : array-like [n_samples, n_features]
y : array-like
Returns:
self : the fitted Regressor

Notes

This differs from the XGBoost version not supporting the eval_set, eval_metric, early_stopping_rounds and verbose fit kwargs. eval_set : list, optional

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.
sample_weight_eval_set : list, optional
A list of the form [L_1, L_2, …, L_n], where each L_i is a list of instance weights on the i-th validation set.
eval_metric : str, list of str, or callable, optional
If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. # noqa: E501 If a list of str, should be the list of multiple built-in evaluation metrics to use. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. The callable custom objective is always minimized.
early_stopping_rounds : int
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. The method returns the model from the last iteration (not the best one). 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. If early stopping occurs, the model will have three additional fields: clf.best_score, clf.best_iteration and clf.best_ntree_limit.
get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns:
booster : a xgboost booster of underlying model
get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=True)

Get parameters.

get_xgb_params()

Get xgboost specific parameters.

intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns:
intercept_ : array of shape (1,) or [n_classes]
load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded.

Parameters:
fname : string

Input file name.
predict(X)

Predict with data.

Note

This function is not thread safe.

For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call xgb.copy() to make copies of model object and then call predict().

preds = bst.predict(dtest, ntree_limit=num_round)
Parameters:
data : numpy.array/scipy.sparse

Data to predict with

output_margin : bool

Whether to output the raw untransformed margin value.

ntree_limit : int

Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees).

validate_features : bool

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.

Returns
——-
prediction : numpy array
save_model(fname: str)

Save the model to a file.

The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be saved.

Parameters:
fname : string

Output file name
score(X, y, sample_weight=None)

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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, 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 R2 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. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search.

Returns:
self