RegressionAdaBoost.py 文件源码

def parameterChoosing(self):
        dts = []
        dts.append(DecisionTreeRegressor(max_depth=5, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=7, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=9, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=11, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=12, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=14, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=15, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=17, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=19, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=21, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=22, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=24, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=26, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=27, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=31, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=33, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=35, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=37, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=39, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=41, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=43, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=45, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=47, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=49, max_features='auto'))
        dts.append(DecisionTreeRegressor(max_depth=50, max_features='auto'))


        tuned_parameters = [{'base_estimator': dts,
                             'n_estimators': range(5,700),
                             'learning_rate': [1, 2, 3]
                             }
                            ]

        reg = GridSearchCV(AdaBoostRegressor(), tuned_parameters, cv=5, scoring='mean_squared_error')
        reg.fit(self.X_train, self.y_train.ravel())

        print "Best parameters set found on development set:\n"
        print reg.best_params_

        print "Grid scores on development set:\n"
        for params, mean_score, scores in reg.grid_scores_:
            print "%0.3f (+/-%0.03f) for %r\n" % (mean_score, scores.std() * 2, params)

        print "MSE for test data set:\n"
        y_true, y_pred = self.y_test, reg.predict(self.X_test)
        print mean_squared_error(y_true, y_pred)