将GridSearchCV与TimeSeriesSplit

Question

我有一些代码将使用TimeSeriesSplit来拆分数据。对于每个拆分，我将使用ParametersGrid并遍历每个参数组合，记录最佳参数集，并使用它来预测我的X_test。您可以在文章底部看到该部分的代码

我知道GridSearchCV将为我完成很多工作。我想知道我是否使用以下代码，我的数据在哪里分解成 X_train，X_test，y_train和y_test？将GridSearchCV与TimeSeriesSplit一起使用会在后台处理此问题吗？如果是，那么此代码是否会完成与本帖子底部的原始代码相同的事情？另外，我现在已经尝试了GridSearchCV方法，而且已经快30分钟了，我还没有完成-我的语法正确吗？

X = data.iloc[:, 0:8]
y = data.iloc[:, 8:9]

parameters = [
    {'kernel': ['rbf'],
     'gamma': [.01],
     'C': [1, 10, 100]}]

gsc = GridSearchCV(SVR(), param_grid=parameters, scoring='neg_mean_absolute_error', 
                   cv=TimeSeriesSplit(n_splits=2))
gsc.fit(X,y)
means = gsc.cv_results_['mean_test_score']
for mean in means:
    print(mean)
print('end')

以下原始代码：

# Create the time series split generator
tscv = TimeSeriesSplit(n_splits=3)

for train_index, test_index in tqdm(tscv.split(X)):

X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]

# scale the data set
scaler_X = StandardScaler()
scaler_y = StandardScaler()
scaler_X.fit(X_train)
scaler_y.fit(y_train)
X_train, X_test = scaler_X.transform(X_train), scaler_X.transform(X_test)
y_train, y_test = scaler_y.transform(y_train), scaler_y.transform(y_test)


# optimization area - set params
parameters = [
    {'kernel': ['rbf'],
     'gamma': [.01],
     'C': [ 1,10,100,500,1000]}]


regressor = SVR()
# loop through each of the parameters and find the best set
for e, g in enumerate(ParameterGrid(parameters)):
    regressor.set_params(**g)
    regressor.fit(X_train, y_train.ravel())
    score = metrics.mean_absolute_error(regressor.predict(X_train), y_train.ravel())
    if e == 0:
        best_score = score
        best_params = g
    elif score < best_score:
        best_score = score
        best_params = g


# refit the model with the best set of params

regressor.set_params(**best_params)
regressor.fit(X_train, y_train.ravel())

Answer 1

您需要稍微修改代码。

gsc = GridSearchCV(SVR(), param_grid=parameters, scoring='neg_mean_absolute_error', 
                   cv=TimeSeriesSplit(n_splits=2).split(X))

而且，您可以考虑添加verbose参数来查看运行输出。