def fit(self, X, y, eval_set=None, eval_metric=None,
            early_stopping_rounds=None, verbose=True):
        # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
        """
        Fit the gradient boosting model

        Parameters
        ----------
        X : array_like
            Feature matrix
        y : array_like
            Labels
        eval_set : list, optional
            A list of (X, y) tuple pairs to use as a validation set for
            early-stopping
        eval_metric : str, callable, optional
            If a str, should be a built-in evaluation metric to use. See
            doc/parameter.md. 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. This objective is always minimized.
        early_stopping_rounds : int
            Activates early stopping. Validation error needs to decrease at
            least every <early_stopping_rounds> round(s) to continue training.
            Requires at least one item in evals.  If there's more than one,
            will use the last. Returns the model from the last iteration
            (not the best one). If early stopping occurs, the model will
            have two additional fields: bst.best_score and bst.best_iteration.
        verbose : bool
            If `verbose` and an evaluation set is used, writes the evaluation
            metric measured on the validation set to stderr.
        """
        trainDmatrix = DMatrix(X, label=y, missing=self.missing)

        evals_result = {}
        if eval_set is not None:
            evals = list(DMatrix(x[0], label=x[1]) for x in eval_set)
            evals = list(zip(evals, ["validation_{}".format(i) for i in
                                     range(len(evals))]))
        else:
            evals = ()

        params = self.get_xgb_params()

        feval = eval_metric if callable(eval_metric) else None
        if eval_metric is not None:
            if callable(eval_metric):
                eval_metric = None
            else:
                params.update({'eval_metric': eval_metric})

        self._Booster = train(params, trainDmatrix,
                              self.n_estimators, evals=evals,
                              early_stopping_rounds=early_stopping_rounds,
                              evals_result=evals_result, feval=feval,
                              verbose_eval=verbose)

        if evals_result:
            for val in evals_result.items():
                evals_result_key = list(val[1].keys())[0]
                evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
            self.evals_result_ = evals_result

        if early_stopping_rounds is not None:
            self.best_score = self._Booster.best_score
            self.best_iteration = self._Booster.best_iteration
        return self
def fit ( self , X , y , eval_set = None , eval_metric = None , early_stopping_rounds = None , verbose = True ) : trainDmatrix = DMatrix ( X , label = y , missing = self . missing ) evals_result = { } if eval_set is not None : evals = list ( DMatrix ( x [ 0 ] , label = x [ 1 ] ) for x in eval_set ) evals = list ( zip ( evals , [ "validation_{}" . format ( i ) for i in range ( len ( evals ) ) ] ) ) else : evals = ( ) params = self . get_xgb_params ( ) feval = eval_metric if callable ( eval_metric ) else None if eval_metric is not None : if callable ( eval_metric ) : eval_metric = None else : params . update ( { 'eval_metric' : eval_metric } ) self . _Booster = train ( params , trainDmatrix , self . n_estimators , evals = evals , early_stopping_rounds = early_stopping_rounds , evals_result = evals_result , feval = feval , verbose_eval = verbose ) if evals_result : for val in evals_result . items ( ) : evals_result_key = list ( val [ 1 ] . keys ( ) ) [ 0 ] evals_result [ val [ 0 ] ] [ evals_result_key ] = val [ 1 ] [ evals_result_key ] self . evals_result_ = evals_result if early_stopping_rounds is not None : self . best_score = self . _Booster . best_score self . best_iteration = self . _Booster . best_iteration return self
Fit the gradient boosting model

        Parameters
        ----------
        X : array_like
            Feature matrix
        y : array_like
            Labels
        eval_set : list, optional
            A list of (X, y) tuple pairs to use as a validation set for
            early-stopping
        eval_metric : str, callable, optional
            If a str, should be a built-in evaluation metric to use. See
            doc/parameter.md. 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. This objective is always minimized.
        early_stopping_rounds : int
            Activates early stopping. Validation error needs to decrease at
            least every <early_stopping_rounds> round(s) to continue training.
            Requires at least one item in evals.  If there's more than one,
            will use the last. Returns the model from the last iteration
            (not the best one). If early stopping occurs, the model will
            have two additional fields: bst.best_score and bst.best_iteration.
        verbose : bool
            If `verbose` and an evaluation set is used, writes the evaluation
            metric measured on the validation set to stderr.