tuning.py 文件源码

python
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项目:kaggle-Kobe-Bryant-Shot-Selection 作者: shiba24 项目源码 文件源码
def score(self, params):
        print "Training with params : "
        print params
        N_boost_round=[]
        Score=[]
        skf = cross_validation.StratifiedKFold(self.train_y, n_folds=6, shuffle=True, random_state=25)
        for train, test in skf:
            X_Train, X_Test, y_Train, y_Test = self.train_X[train], self.train_X[test], self.train_y[train], self.train_y[test]
            dtrain = xgb.DMatrix(X_Train, label=y_Train)
            dvalid = xgb.DMatrix(X_Test, label=y_Test)
            watchlist = [(dtrain, 'train'),(dvalid, 'eval')]
            model = xgb.train(params, dtrain, num_boost_round=150, evals=watchlist, early_stopping_rounds=10)
            predictions = model.predict(dvalid)
            N = model.best_iteration
            N_boost_round.append(N)
            score = model.best_score
            Score.append(score)
        Average_best_num_boost_round = np.average(N_boost_round)
        Average_best_score = np.average(Score)
        print "\tAverage of best iteration {0}\n".format(Average_best_num_boost_round)
        print "\tScore {0}\n\n".format(Average_best_score)
        return {'loss': Average_best_score, 'status': STATUS_OK, 'Average_best_num_boost_round': Average_best_num_boost_round}
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