dask_ml.xgboost
.XGBClassifier¶

class
dask_ml.xgboost.
XGBClassifier
(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)¶ Attributes: coef_
Coefficients property
feature_importances_
Feature importances property
intercept_
Intercept (bias) property
Methods
apply
(self, X[, ntree_limit])Return the predicted leaf every tree for each sample. evals_result
(self)Return the evaluation results. fit
(self, X[, y, classes, eval_set, …])Fit a gradient boosting classifier get_booster
(self)Get the underlying xgboost Booster of this model. get_num_boosting_rounds
(self)Gets the number of xgboost boosting rounds. get_params
(self[, deep])Get parameters. get_xgb_params
(self)Get xgboost type parameters. load_model
(self, fname)Load the model from a file. predict
(self, X)Predict with data. predict_proba
(self, data[, ntree_limit])Predict the probability of each data example being of a given class. save_model
(self, fname)Save the model to a file. score
(self, X, y[, sample_weight])Returns the mean accuracy on the given test data and labels. set_params
(self, \*\*params)Set the parameters of this estimator. 
__init__
(self, max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.

apply
(self, 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]
 coef_ : array of shape

evals_result
(self)¶ 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]
 feature_importances_ : array of shape

fit
(self, X, y=None, classes=None, eval_set=None, sample_weight_eval_set=None, eval_metric=None, early_stopping_rounds=None)¶ Fit a gradient boosting classifier
Parameters:  X : arraylike [n_samples, n_features]
Feature Matrix. May be a dask.array or dask.dataframe
 y : arraylike
Labels
 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 ith validation set.
 eval_metric : str, list of str, or callable, optional
If a str, should be a builtin evaluation metric to use. See doc/parameter.rst. # noqa: E501 If a list of str, should be the list of multiple builtin evaluation metrics to use. If callable, a custom evaluation metric. The call signature is
func(y_predicted, y_true)
wherey_true
will be a DMatrix object such that you may need to call theget_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
andclf.best_ntree_limit
. classes : sequence, optional
The unique values in y. If no specified, this will be eagerly computed from y before training.
Returns:  self : XGBClassifier
Notes
This differs from the XGBoost version in three ways
 The
sample_weight
andverbose
fit kwargs are not
supported. The labels are not automatically labelencoded
 The
classes_
andn_classes_
attributes are not learned

get_booster
(self)¶ 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
(self)¶ Gets the number of xgboost boosting rounds.

get_params
(self, deep=False)¶ Get parameters.

get_xgb_params
(self)¶ Get xgboost type 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]
 intercept_ : array of shape

load_model
(self, fname)¶ Load the model from a file.
The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.
Parameters:  fname : string or a memory buffer
Input file name or memory buffer(see also save_raw)

predict
(self, 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 callpredict()
.Note
Using
predict()
with DART boosterIf the booster object is DART type,
predict()
will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results ifdata
is not the training data. To obtain correct results on test sets, setntree_limit
to a nonzero value, e.g.preds = bst.predict(dtest, ntree_limit=num_round)
Parameters:  data : DMatrix
The dmatrix storing the input.
 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

predict_proba
(self, data, ntree_limit=None)¶ Predict the probability of each data example being of a given class.
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 predictParameters:  data : DMatrix
The dmatrix storing the input.
 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
a numpy array with the probability of each data example being of a given class.

save_model
(self, fname)¶ Save the model to a file.
The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.
Parameters:  fname : string
Output file name

score
(self, X, y, sample_weight=None)¶ Returns the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters:  X : arraylike, shape = (n_samples, n_features)
Test samples.
 y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
 sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns:  score : float
Mean accuracy of self.predict(X) wrt. y.

set_params
(self, **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