Python xgboost.XGBModel() Examples

def optimize_hyperparam(self, X, y, test_size=.2, n_eval=100):
        X_trn, X_val, y_trn, y_val = train_test_split(X, y, test_size=test_size, shuffle=self.shuffle)

        def objective(hyperparams):
            model = XGBModel(n_estimators=self.n_est, **self.params, **hyperparams)
            model.fit(X=X_trn, y=y_trn,
                      eval_set=[(X_val, y_val)],
                      eval_metric=self.metric,
                      early_stopping_rounds=self.n_stop,
                      verbose=False)
            score = model.evals_result()['validation_0'][self.metric][model.best_iteration] * self.loss_sign

            return {'loss': score, 'status': STATUS_OK, 'model': model}

        trials = Trials()
        best = hyperopt.fmin(fn=objective, space=self.space, trials=trials,
                             algo=tpe.suggest, max_evals=n_eval, verbose=1,
                             rstate=self.random_state)

        hyperparams = space_eval(self.space, best)
        return hyperparams, trials 

def fit(self, X, y):
        self.model = XGBModel(n_estimators=self.n_best, **self.params)
        self.model.fit(X=X[self.features], y=y, eval_metric='mae', verbose=False)
        return self 

def plot_importance(self, ax=None, height=0.2,
                        xlim=None, title='Feature importance',
                        xlabel='F score', ylabel='Features',
                        grid=True, **kwargs):

        """Plot importance based on fitted trees.

        Parameters
        ----------
        ax : matplotlib Axes, default None
            Target axes instance. If None, new figure and axes will be created.
        height : float, default 0.2
            Bar height, passed to ax.barh()
        xlim : tuple, default None
            Tuple passed to axes.xlim()
        title : str, default "Feature importance"
            Axes title. To disable, pass None.
        xlabel : str, default "F score"
            X axis title label. To disable, pass None.
        ylabel : str, default "Features"
            Y axis title label. To disable, pass None.
        kwargs :
            Other keywords passed to ax.barh()

        Returns
        -------
        ax : matplotlib Axes
        """

        import xgboost as xgb

        if not isinstance(self._df.estimator, xgb.XGBModel):
            raise ValueError('estimator must be XGBRegressor or XGBClassifier')
        # print(type(self._df.estimator.booster), self._df.estimator.booster)
        return xgb.plot_importance(self._df.estimator,
                                   ax=ax, height=height, xlim=xlim, title=title,
                                   xlabel=xlabel, ylabel=ylabel, grid=True, **kwargs) 

def to_graphviz(self, num_trees=0, rankdir='UT',
                    yes_color='#0000FF', no_color='#FF0000', **kwargs):

        """Convert specified tree to graphviz instance. IPython can automatically plot the
        returned graphiz instance. Otherwise, you shoud call .render() method
        of the returned graphiz instance.

        Parameters
        ----------
        num_trees : int, default 0
            Specify the ordinal number of target tree
        rankdir : str, default "UT"
            Passed to graphiz via graph_attr
        yes_color : str, default '#0000FF'
            Edge color when meets the node condigion.
        no_color : str, default '#FF0000'
            Edge color when doesn't meet the node condigion.
        kwargs :
            Other keywords passed to graphviz graph_attr

        Returns
        -------
        ax : matplotlib Axes
        """

        import xgboost as xgb

        if not isinstance(self._df.estimator, xgb.XGBModel):
            raise ValueError('estimator must be XGBRegressor or XGBClassifier')
        return xgb.to_graphviz(self._df.estimator,
                               num_trees=num_trees, rankdir=rankdir,
                               yes_color=yes_color, no_color=no_color, **kwargs) 

def plot_tree(self, num_trees=0, rankdir='UT', ax=None, **kwargs):

        """Plot specified tree.

        Parameters
        ----------
        booster : Booster, XGBModel
            Booster or XGBModel instance
        num_trees : int, default 0
            Specify the ordinal number of target tree
        rankdir : str, default "UT"
            Passed to graphiz via graph_attr
        ax : matplotlib Axes, default None
            Target axes instance. If None, new figure and axes will be created.
        kwargs :
            Other keywords passed to to_graphviz

        Returns
        -------
        ax : matplotlib Axes

        """

        import xgboost as xgb

        if not isinstance(self._df.estimator, xgb.XGBModel):
            raise ValueError('estimator must be XGBRegressor or XGBClassifier')
        return xgb.plot_tree(self._df.estimator,
                             num_trees=num_trees, rankdir=rankdir, **kwargs) 

def load_pkl(name):
    """Load xgboost model from pickle and perform conversion from version
    0.90 if necessary.

    :return:
        XGBoost model
    """
    import pickle
    import xgboost
    with open(name, 'rb') as f:
        try:
            model = pickle.load(f)
            return model
        except xgboost.core.XGBoostError as e:
            if "Check failed: header == serialisation_header_" in str(e):
                # pylint: disable=unused-import
                import xgboost_prev
                import tempfile

                class Unpickler(pickle.Unpickler):
                    def find_class(self, module, name):
                        if module.startswith("xgboost"):
                            return pickle.Unpickler.find_class(
                                self, module.replace(
                                    "xgboost", "xgboost_prev"),
                                name)
                        return pickle.Unpickler.find_class(self, module, name)
                f.seek(0)
                model = Unpickler(f).load()
                temp_file = tempfile.NamedTemporaryFile(
                    prefix='xgboost_migration', suffix='.model')
                model.save_model(temp_file.name)
                migrated_model = xgboost.XGBModel()
                migrated_model.load_model(temp_file.name)
                return migrated_model
            raise