Python ValueError：形状（124,1）的不可广播输出操作数与广播形状（124,13）不匹配

Question

我想在sklearn.preprocessing 中使用MinMaxScaler 规范化训练和测试数据集。但是，该包似乎不接受我的测试数据集。

import pandas as pd
import numpy as np

# Read in data.
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', 
                      header=None)
df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
                   'Alcalinity of ash', 'Magnesium', 'Total phenols',
                   'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
                   'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',
                   'Proline']

# Split into train/test data.
from sklearn.model_selection import train_test_split
X = df_wine.iloc[:, 1:].values
y = df_wine.iloc[:, 0].values
X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.3, 
                                                    random_state = 0)

# Normalize features using min-max scaling.
from sklearn.preprocessing import MinMaxScaler
mms = MinMaxScaler()
X_train_norm = mms.fit_transform(X_train)
X_test_norm = mms.transform(X_test)

执行此操作时，我得到一个 DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample. 和一个 ValueError: operands could not be broadcast together with shapes (124,) (13,) (124,)。

重塑数据仍然会产生错误。

X_test_norm = mms.transform(X_test.reshape(-1, 1))

这种重塑会产生错误ValueError: non-broadcastable output operand with shape (124,1) doesn't match the broadcast shape (124,13)。

任何有关如何修复此错误的信息都会有所帮助。

Answer 1

必须按照与train_test_split() 函数的输入数组相同的顺序指定训练/测试数据的分区，以便它按照该顺序解包它们。

显然，当订单指定为 X_train, y_train, X_test, y_test 时，y_train (len(y_train)=54) 和 X_test (len(X_test)=124) 的结果形状被交换，从而导致 ValueError。

相反，您必须：

# Split into train/test data.
#                   _________________________________
#                   |       |                        \
#                   |       |                         \
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)                                        
# |          |                                      /
# |__________|_____________________________________/
# (or)
# y_train, y_test, X_train, X_test = train_test_split(y, X, test_size=0.3, random_state=0)

# Normalize features using min-max scaling.
from sklearn.preprocessing import MinMaxScaler
mms = MinMaxScaler()
X_train_norm = mms.fit_transform(X_train)
X_test_norm = mms.transform(X_test)

产生：

X_train_norm[0]
array([ 0.72043011,  0.20378151,  0.53763441,  0.30927835,  0.33695652,
        0.54316547,  0.73700306,  0.25      ,  0.40189873,  0.24068768,
        0.48717949,  1.        ,  0.5854251 ])

X_test_norm[0]
array([ 0.72849462,  0.16386555,  0.47849462,  0.29896907,  0.52173913,
        0.53956835,  0.74311927,  0.13461538,  0.37974684,  0.4364852 ,
        0.32478632,  0.70695971,  0.60566802])