Question

我正在尝试通过GridSearchCV找到最好的xgboost模型，并且作为cross_validation我想使用April目标数据。这是代码：

    x_train.head()

x_train

    y_train.head()

y_train

    from sklearn.model_selection import GridSearchCV
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import mean_squared_error
    from sklearn.metrics import make_scorer
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.model_selection import TimeSeriesSplit
    import xgboost as xg

    xgb_parameters={'max_depth':[3,5,7,9],'min_child_weight':[1,3,5]}
    xgb=xg.XGBRegressor(learning_rate=0.1, n_estimators=100,max_depth=5, min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8)
    model=GridSearchCV(n_jobs=2,estimator=xgb,param_grid=xgb_parameters,cv=train_test_split(x_train,y_train,test_size=len(y_train['2016-04':'2016-04']), random_state=42, shuffle=False),scoring=my_func)
    model.fit(x_train,y_train)
    model.grid_scores_
    model.best_params_

但是我在训练模型时遇到了这个错误。

Error

有人可以帮帮我吗？或者有人可以建议我如何在上个月将非清洗数据拆分为训练/测试以验证模型？

感谢您的帮助

Answer 1

此错误的根本原因是您在cv电话中使用GridSearchCV()参数的方式：

cv=train_test_split(x_train,y_train,test_size=len(y_train['2016-04':'2016-04'])

以下是cv参数的文档字符串的摘录：

cv : int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:
      - None, to use the default 3-fold cross validation,
      - integer, to specify the number of folds in a `(Stratified)KFold`,
      - An object to be used as a cross-validation generator.
      - An iterable yielding train, test splits.

    For integer/None inputs, if the estimator is a classifier and ``y`` is
    either binary or multiclass, :class:`StratifiedKFold` is used. In all
    other cases, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.

然而train_test_split(x_train,y_train)返回4个数组：

X_train, X_test, y_train, y_test

这导致：ValueError too many values to unpack (expected 2)错误。

作为一种解决方法，您可以指定上面指定的选项之一（cv参数的docstring）...

Python：ValueError要解压的值太多（预期为2）

1 个答案: