我正在运行此代码只是为了检查线性回归模型在python中的工作原理：
import pandas as pd
import numpy as np
import statsmodels.api as sm
train = pd.read_csv('data/train.csv', parse_dates=[0])
test = pd.read_csv('data/test.csv', parse_dates=[0])
print train.head()
#Feature engineering
temp_train = pd.DatetimeIndex(train['datetime'])
train['year'] = temp_train.year
train['month'] = temp_train.month
train['hour'] = temp_train.hour
train['weekday'] = temp_train.weekday
temp_test = pd.DatetimeIndex(test['datetime'])
test['year'] = temp_test.year
test['month'] = temp_test.month
test['hour'] = temp_test.hour
test['weekday'] = temp_test.weekday
#Define features vector
features = ['season', 'holiday', 'workingday', 'weather',
'temp', 'atemp', 'humidity', 'windspeed', 'year',
'month', 'weekday', 'hour']
#The evaluation metric is the RMSE in the log domain,
#so we should transform the target columns into log domain as well.
for col in ['casual', 'registered', 'count']:
train['log-' + col] = train[col].apply(lambda x: np.log1p(x))
#Split train data set into training and validation sets
training, validation = train[:int(0.8*len(train))], train[int(0.8*len(train)):]
# Create a linear model
X = sm.add_constant(training[features])
model = sm.OLS(training['log-count'],X) # OLS stands for Ordinary Least Squares
f = model.fit()
ypred = f.predict(sm.add_constant(validation[features]))
print(ypred)
plt.figure();
plt.plot(validation[features], ypred, 'o', validation[features], validation['log-count'], 'b-');
plt.title('blue: true, red: OLS');
弹出以下错误消息。它是什么意思以及如何解决它？
Traceback (most recent call last):
File "C:/TestModel/linear_regression.py", line 99, in <module>
ypred = f.predict(sm.add_constant(validation[features]))
File "C:\Python27\lib\site-packages\statsmodels\base\model.py", line 749, in predict
return self.model.predict(self.params, exog, *args, **kwargs)
File "C:\Python27\lib\site-packages\statsmodels\regression\linear_model.py", line 359, in predict
return np.dot(exog, params)
ValueError: shapes (2178,12) and (13,) not aligned: 12 (dim 1) != 13 (dim 0)
这是数据样本：
print training.head()
datetime season holiday workingday weather temp atemp \
0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395
1 2011-01-01 01:00:00 1 0 0 1 9.02 13.635
2 2011-01-01 02:00:00 1 0 0 1 9.02 13.635
3 2011-01-01 03:00:00 1 0 0 1 9.84 14.395
4 2011-01-01 04:00:00 1 0 0 1 9.84 14.395
humidity windspeed casual registered count year month hour weekday \
0 81 0 3 13 16 2011 1 0 5
1 80 0 8 32 40 2011 1 1 5
2 80 0 5 27 32 2011 1 2 5
3 75 0 3 10 13 2011 1 3 5
4 75 0 0 1 1 2011 1 4 5
log-casual log-registered log-count
0 1.386294 2.639057 2.833213
1 2.197225 3.496508 3.713572
2 1.791759 3.332205 3.496508
3 1.386294 2.397895 2.639057
4 0.000000 0.693147 0.693147
print validation.head()
datetime season holiday workingday weather temp atemp \
8708 2012-08-05 05:00:00 3 0 0 1 29.52 34.850
8709 2012-08-05 06:00:00 3 0 0 1 29.52 34.850
8710 2012-08-05 07:00:00 3 0 0 1 30.34 35.605
8711 2012-08-05 08:00:00 3 0 0 1 31.16 36.365
8712 2012-08-05 09:00:00 3 0 0 1 32.80 38.635
humidity windspeed casual registered count year month hour \
8708 74 16.9979 1 18 19 2012 8 5
8709 79 16.9979 7 12 19 2012 8 6
8710 74 19.9995 18 50 68 2012 8 7
8711 66 22.0028 27 81 108 2012 8 8
8712 59 23.9994 61 168 229 2012 8 9
weekday log-casual log-registered log-count
8708 6 0.693147 2.944439 2.995732
8709 6 2.079442 2.564949 2.995732
8710 6 2.944439 3.931826 4.234107
8711 6 3.332205 4.406719 4.691348
8712 6 4.127134 5.129899 5.438079
答案 0 :(得分：2)
这看起来像这个用例的predict函数的设计问题。
：
” 对于ndarrays和pandas.DataFrames，检查以确保常量不是 已包括在内。如果至少有一列，那么 返回原始对象。 “
http://statsmodels.sourceforge.net/devel/_modules/statsmodels/tools/tools.html#add_constant
我认为这是以这种方式定义的，以避免用于估计的奇异设计矩阵，但validation也适用于奇异矩阵。
我的猜测是，您的add_constant数据有一列具有相同的值，例如它们可能都来自同一年。
如果这是故意的，那么您需要手动将常量添加到数据帧。
如果[DisplayFormat(ApplyFormatInEditMode = true, DataFormatString = "{0:yyyy-MMMMM-dd}")]
可以选择转换此行为，那会更好。