我有一个数据集，其中包含有关美国auto_sales的各种值。
我正在尝试使用简单的OLS回归预测2010年10月的auto_sales。
df2 = pd.read_csv('Paul_data/question12_prediction_data.csv')
window_size = 7 #-1 due to zero-indexing of array
window = df2.ix[0:window_size,:]
print window
result = sm.ols(formula="log_sales ~ log_sales_l2 + vehicleshopping_l2 + vehiclebrand_l2 + actual_sales_edmunds_l1 + isSummer + isWinter", data=df2).fit()
print result.predict()[df2[(df2.month == 10) & (df2.year == 2015)].index[0]]
窗口是以下数据：
year month auto_sales log_sales log_sales_l1 log_sales_l2 \
0 2015 3 83352 11.330828 11.294807 11.317823
1 2015 4 83871 11.337035 11.330828 11.294807
2 2015 5 85489 11.356143 11.337035 11.330828
3 2015 6 84123 11.340035 11.356143 11.337035
4 2015 7 85320 11.354164 11.340035 11.356143
5 2015 8 NaN NaN 11.354164 11.340035
6 2015 9 NaN NaN NaN 11.354164
7 2015 10 NaN NaN NaN NaN
log_sales_l3 GT_vehicleshopping GT_vehiclemaintenance GT_suvs \
0 11.313523 0.1320 0.694 0.0680
1 11.317823 0.1150 0.745 0.0525
2 11.294807 0.1060 0.754 0.0560
3 11.330828 0.0950 0.785 0.0550
4 11.337035 0.1025 0.870 0.1075
5 11.356143 0.1140 0.794 0.1240
6 11.340035 NaN NaN NaN
7 NaN NaN NaN NaN
... vansminivans_l2 isWinter isSummer vehiclebrands \
0 ... 0.0900 1 0 0.08
1 ... 0.1250 0 0 0.09
2 ... 0.1580 0 0 0.09
3 ... 0.1750 0 1 0.12
4 ... 0.1920 0 1 0.17
5 ... 0.2100 0 1 NaN
6 ... 0.2175 0 0 NaN
7 ... NaN NaN NaN NaN
vehiclebrand_l1 vehiclebrand_l2 actual_sales_edmunds edmund_forecast \
0 0.05 0.03 1542841 1522881
1 0.08 0.05 1451790 1464176
2 0.09 0.08 1631234 1591221
3 0.09 0.09 1473142 1484487
4 0.12 0.09 1507643 1478025
5 0.17 0.12 1573573 1538958
6 NaN 0.17 NaN NaN
7 NaN NaN NaN NaN
actual_sales_edmunds_l1 edmund_forecast_l1
0 1255458 1285019
1 1542841 1522881
2 1451790 1464176
3 1631234 1591221
4 1473142 1484487
5 1507643 1478025
6 1573573 1538958
7 NaN NaN
[8 rows x 32 columns]
但是我收到以下错误：
IndexError Traceback (most recent call last)
<ipython-input-83-16bf72335e7f> in <module>()
5
6 result = sm.ols(formula="log_sales ~ log_sales_l2 + vehicleshopping_l2 + vehiclebrand_l2 + actual_sales_edmunds_l1 + isSummer + isWinter", data=df2).fit()
----> 7 print result.predict()[df2[(df2.month == 10) & (df2.year == 2015)].index[0]]
8 #np.exp(result.predict(df2.ix[x+(window_size)]))
IndexError: index 7 is out of bounds for axis 0 with size 5
我不知道如何继续这一点，我知道我正在尝试做出样本预测，但到目前为止我尝试的所有内容都未能解决问题。
答案 0 :(得分：0)
我相信，你的问题是你回归的数据只有5个条目，其中并非所有输入都是NaN。因此：
result.predict()
返回一个包含5个元素的数组，但是：
df2[(df2.month == 10) & (df2.year == 2015)].index[0]
返回'7'，因为您执行的切片返回一行，该行对应于原始数据帧中的第8行。所以你要问“给我这个长度为5的数组的第8个元素”然后它会中断。
答案 1 :(得分：0)
user333700是正确的，这解决了我的问题：
df2 = pd.read_csv('Paul_data/question12_prediction_data.csv')
window_size = 4 #-1 due to zero-indexing of array
window = df2.ix[0:window_size,:]
result = sm.ols(formula="log_sales ~ log_sales_l2 + vehicleshopping_l2 + vehiclebrand_l2 + actual_sales_edmunds_l1 + isSummer + isWinter", data=window).fit()
index = df2[(df2.month == 10) & (df2.year == 2015)].index[0] -1
print result.predict(df2)[index]