pandas 错误：找到带有暗淡 3 的数组。估计器预期 <= 2
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Error: Found array with dim 3. Estimator expected <= 2
提问by ZJAY
I have a 14x5 data matrix titled data. The first column (Y) is the dependent variable followed by 4 independent variables (X,S1,S2,S3). When trying to fit a regression model to a subset of the independent variables ['S2'][:T] I get the following error:
我有一个 14x5 的数据矩阵，名为 data。第一列 (Y) 是因变量，后跟 4 个自变量 (X,S1,S2,S3)。当尝试将回归模型拟合到自变量 ['S2'][:T] 的子集时，我收到以下错误：
ValueError: Found array with dim 3. Estimator expected <= 2.
I'd appreciate any insight on a fix. Code below.
我很感激任何有关修复的见解。代码如下。
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
data = pd.read_csv('C:/path/Macro.csv')
T=len(data['X'])-1
#Fit variables
X = data['X'][:T]
S1 = data['S1'][:T]
S2 = data['S2'][:T]
S3 = data['S3'][:T]
Y = data['Y'][:T]
regressor = LinearRegression()
regressor.fit([[X,S1,S2,S3]], Y)
回答by Igor Raush
You are passing a 3-dimensional array as the first argument to fit(). X, S1, S2, S3 are all Seriesobjects (1-dimensional), so the following
您将一个 3 维数组作为第一个参数传递给fit()。X、S1、S2、S3都是Series对象（一维），所以下面
[[X, S1, S2, S3]]
is 3-dimensional. sklearnestimators expect an array of feature vectors (2-dimensional).
是 3 维的。sklearn估计器需要一组特征向量（二维）。
Try something like this:
尝试这样的事情：
# pandas indexing syntax
# data.ix[ row index/slice, column index/slice ]
X = data.ix[:T, 'X':] # rows up to T, columns from X onward
y = data.ix[:T, 'Y'] # rows up to T, Y column
regressor = LinearRegression()
regressor.fit(X, y)
