model.py 文件源码

def grid_search(X, y, split, learn=[.01], samples_leaf=[250, 350, 500],
                depth=[10, 15]):
    '''
    Runs a grid search for GBM on split data
    '''
    for l in learn:
        for s in samples_leaf:
            for d in depth:
                model = GradientBoostingRegressor(n_estimators=250,
                                                  learning_rate=l,
                                                  min_samples_leaf=s,
                                                  max_depth=d,
                                                  random_state=42)
                model.fit(X.values[:split], y.values[:split])
                in_score = model.score(X.values[:split], y.values[:split])
                out_score = model.score(X.values[split:], y.values[split:])
                print 'learning_rate: {}, min_samples_leaf: {}, max_depth: {}'.\
                    format(l, s, d)
                print 'in-sample score:', in_score
                print 'out-sample score:', out_score
                print ''