无法对 OLS 模型进行预测

Question

我正在构建 OLS 模型，但无法做出任何预测。

你能解释一下我做错了什么吗？

构建模型：

import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm 
import matplotlib.pyplot as plt

d = {'City': ['Tokyo','Tokyo','Lisbon','Tokyo','Madrid','New York','Madrid','London','Tokyo','London','Tokyo'], 
     'Card': ['Visa','Visa','Visa','Master Card','Bitcoin','Master Card','Bitcoin','Visa','Master Card','Visa','Bitcoin'],
     'Colateral':['Yes','Yes','No','No','Yes','No','No','Yes','Yes','No','Yes'],
     'Client Number':[1,2,3,4,5,6,7,8,9,10,11],
     'Total':[100,100,200,300,10,20,40,50,60,100,500]}

d = pd.DataFrame(data=d).set_index('Client Number')

df = pd.get_dummies(d,prefix='', prefix_sep='')

X = df[['Lisbon','London','Madrid','New York','Tokyo','Bitcoin','Master Card','Visa','No','Yes']]
Y = df['Total']

X1 = sm.add_constant(X)
reg = sm.OLS(Y, X1).fit()

reg.summary()

预测：

d1 = {'City': ['Tokyo','Tokyo','Lisbon'], 
     'Card': ['Visa','Visa','Visa'],
     'Colateral':['Yes','Yes','No'],
     'Client Number':[11,12,13],
     'Total':[0,0,0]}

df1 = pd.DataFrame(data=d1).set_index('Client Number')

df1 = pd.get_dummies(df1,prefix='', prefix_sep='')
y_new = df1[['Lisbon','Tokyo','Visa','No','Yes']]
x_new = df1['Total']
mod = sm.OLS(y_new, x_new)

mod.predict(reg.params)

然后显示：ValueError：形状 (3,1) 和 (11,) 未对齐：1 (dim 1) != 11 (dim 0)

我做错了什么？

Answer 1

最大的问题是您没有使用相同的虚拟转换。也就是说，df1 中的某些值不存在。您可以使用以下代码（来自 here）添加缺少的 values/columns：

d1 = {'City': ['Tokyo','Tokyo','Lisbon'], 
 'Card': ['Visa','Visa','Visa'],
 'Colateral':['Yes','Yes','No'],
 'Client Number':[11,12,13],
 'Total':[0,0,0]}

df1 = pd.DataFrame(data=d1).set_index('Client Number')
df1 = pd.get_dummies(df1,prefix='', prefix_sep='')
print(df1.shape)  # Shape is 3x6 but it has to be 3x11
# Get missing columns in the training test
missing_cols = set( df.columns ) - set( df1.columns )
# Add a missing column in test set with default value equal to 0
for c in missing_cols:
    df1[c] = 0
# Ensure the order of column in the test set is in the same order than in train set
df1 = df1[df.columns]
print(df1.shape)  # Shape is 3x11

此外，您混淆了 x_new 和 y_new。所以应该是：

x_new = df1.drop(['Total'], axis=1).values
y_new = df1['Total'].values
mod = sm.OLS(y_new, x_new)

mod.predict(reg.params)

请注意，我使用 x_new = df1.drop(['Total'], axis=1).values 而不是 df1[['Lisbon','Tokyo','Visa','No','Yes']]，因为它更方便（就 1）不太容易（打字）错误和 2）代码较少

Answer 2

首先，您需要 string-index 所有单词，或者 one-hot 对值进行编码。 ML 模型不接受文字，只接受数字。接下来，您希望 X 和 y 为：

X = d.iloc[:,:-1]
y = d.iloc[:,-1]

这样 X 的形状为 [11,3]，y 的形状为 [11,]，这是所需的正确形状。

Answer 3

这是代码的固定预测部分和我的评论：

d1 = {'City': ['Tokyo','Tokyo','Lisbon'], 
     'Card': ['Visa','Visa','Visa'],
     'Colateral':['Yes','Yes','No'],
     'Client Number':[11,12,13],
     'Total':[0,0,0]}

df1 = pd.DataFrame(data=d1).set_index('Client Number')
df1 = pd.get_dummies(df1,prefix='', prefix_sep='')
x_new = df1.drop(columns='Total')

主要问题是训练 X1 和 x_new 数据集的假人数量不同。下面我添加了缺失的虚拟列并用零填充：

x_new = x_new.reindex(columns = X1.columns, fill_value=0)

现在 x_new 的列数等于训练数据集 X1:

               const  Lisbon  London  Madrid  ...  Master Card  Visa  No  Yes
Client Number                                 ...                            
11                 0       0       0       0  ...            0     1   0    1
12                 0       0       0       0  ...            0     1   0    1
13                 0       1       0       0  ...            0     1   1    0

[3 rows x 11 columns]

最终使用先前训练的模型 reg 对新数据集 x_new 进行预测：

reg.predict(x_new)

结果：

Client Number
11     35.956284
12     35.956284
13    135.956284
dtype: float64

附录

根据要求，我在下面附上完全可重现的代码来测试训练和预测任务：

import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm 
import matplotlib.pyplot as plt

d = {'City': ['Tokyo','Tokyo','Lisbon','Tokyo','Madrid','New York','Madrid','London','Tokyo','London','Tokyo'], 
     'Card': ['Visa','Visa','Visa','Master Card','Bitcoin','Master Card','Bitcoin','Visa','Master Card','Visa','Bitcoin'],
     'Colateral':['Yes','Yes','No','No','Yes','No','No','Yes','Yes','No','Yes'],
     'Client Number':[1,2,3,4,5,6,7,8,9,10,11],
     'Total':[100,100,200,300,10,20,40,50,60,100,500]}

d = pd.DataFrame(data=d).set_index('Client Number')

df = pd.get_dummies(d,prefix='', prefix_sep='')

X = df[['Lisbon','London','Madrid','New York','Tokyo','Bitcoin','Master Card','Visa','No','Yes']]
Y = df['Total']

X1 = sm.add_constant(X)
reg = sm.OLS(Y, X1).fit()

reg.summary()

###
d1 = {'City': ['Tokyo','Tokyo','Lisbon'], 
     'Card': ['Visa','Visa','Visa'],
     'Colateral':['Yes','Yes','No'],
     'Client Number':[11,12,13],
     'Total':[0,0,0]}

df1 = pd.DataFrame(data=d1).set_index('Client Number')
df1 = pd.get_dummies(df1,prefix='', prefix_sep='')
x_new = df1.drop(columns='Total')

x_new = x_new.reindex(columns = X1.columns, fill_value=0)

reg.predict(x_new)

无法对 OLS 模型进行预测

Cant make Prediction on OLS Model

python

linear-regression

dataframe

pandas

statsmodels