当需要一维数组时传递列向量 y [英] A column-vector y was passed when a 1d array was expected

问题描述

我需要从 sklearn.ensemble 中拟合 RandomForestRegressor.

I need to fit RandomForestRegressor from sklearn.ensemble.

forest = ensemble.RandomForestRegressor(**RF_tuned_parameters)
model = forest.fit(train_fold, train_y)
yhat = model.predict(test_fold)

这段代码一直有效，直到我对数据进行了一些预处理 (train_y).错误消息说:

This code always worked until I made some preprocessing of data (train_y). The error message says:

DataConversionWarning:当需要一维数组时，传递了列向量 y.请将 y 的形状更改为 (n_samples,)，例如使用 ravel().

DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().

model = Forest.fit(train_fold, train_y)

model = forest.fit(train_fold, train_y)

以前 train_y 是一个系列，现在它是 numpy 数组(它是一个列向量).如果我应用 train_y.ravel()，那么它会变成一个行向量并且不会出现错误消息，整个预测步骤需要很长时间(实际上它永远不会完成......).

Previously train_y was a Series, now it's numpy array (it is a column-vector). If I apply train_y.ravel(), then it becomes a row vector and no error message appears, through the prediction step takes very long time (actually it never finishes...).

在 RandomForestRegressor 的文档中，我发现 train_y 应该定义为 y : array-like, shape = [n_samples] or [n_samples, n_outputs]知道如何解决这个问题吗?

In the docs of RandomForestRegressor I found that train_y should be defined as y : array-like, shape = [n_samples] or [n_samples, n_outputs] Any idea how to solve this issue?

推荐答案

更改这一行:

model = forest.fit(train_fold, train_y)

到:

model = forest.fit(train_fold, train_y.values.ravel())

.values 将给出数组中的值.(形状:(n,1)

.values will give the values in an array. (shape: (n,1)

.ravel 将该数组形状转换为 (n, )

.ravel will convert that array shape to (n, )

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