[英]ValueError when fitting a model
我正在运行此代码只是为了检查线性回归模型如何在python中工作:
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');
弹出以下错误信息。 这是什么意思,以及如何解决?
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
对于此用例,这看起来像是add_constant
函数的设计问题。
从文档字符串:
“对于ndarrays和pandas.DataFrames,请检查以确保不包含常量。如果存在至少一列的常量,则返回原始对象。”
http://statsmodels.sourceforge.net/devel/_modules/statsmodels/tools/tools.html#add_constant
我认为以这种方式定义此方法是为了避免使用奇异的设计矩阵进行估算,但是predict
也将适用于奇异的矩阵。
我的猜测是,您的validation
数据只有一列具有所有相同的值,例如它们都可能来自同一年。 如果这是故意的,则需要将常量手动添加到数据框。
如果add_constant
有一个选项可以改变这种行为,那会更好。
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