将 scikit-learn (sklearn) 预测添加到 Pandas 数据框
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Adding scikit-learn (sklearn) prediction to pandas data frame
提问by bolla
I am trying to add a sklearn prediction to a pandas dataframe, so that I can make a thorough evaluation of the prediction. The relavant piece of code is the following:
我正在尝试向 Pandas 数据帧添加 sklearn 预测，以便我可以对预测进行全面评估。相关代码如下：
clf = linear_model.LinearRegression()
clf.fit(Xtrain,ytrain)
ypred = pd.DataFrame({'pred_lin_regr': pd.Series(clf.predict(Xtest))})
The dataframes look like this:
数据框如下所示：
Xtest
测试
axial_MET cos_theta_r1 deltaE_abs lep1_eta lep1_pT lep2_eta
8000 1.383026 0.332365 1.061852 0.184027 0.621598 -0.316297
8001 -1.054412 0.046317 1.461788 -1.141486 0.488133 1.011445
8002 0.259077 0.429920 0.769219 0.631206 0.353469 1.027781
8003 -0.096647 0.066200 0.411222 -0.867441 0.856115 -1.357888
8004 0.145412 0.371409 1.111035 1.374081 0.485231 0.900024
ytest
测试
8000 1
8001 0
8002 0
8003 0
8004 0
ypred
ypred
pred_lin_regr
0 0.461636
1 0.314448
2 0.363751
3 0.291858
4 0.416056
Concatenating Xtest and ytest works fine:
连接 Xtest 和 ytest 工作正常：
df_total = pd.concat([Xtest, ytest], axis=1)
but the event information is lost on ypred.
但是事件信息在 ypred 上丢失了。
What would be the must python/pandas/numpy-like way to do this?
什么是必须的 python/pandas/numpy-like 方式来做到这一点？
I am using the following versions:
我正在使用以下版本：
argparse==1.2.1
cycler==0.9.0
decorator==4.0.4
ipython==4.0.0
ipython-genutils==0.1.0
matplotlib==1.5.0
nose==1.3.7
numpy==1.10.1
pandas==0.17.0
path.py==8.1.2
pexpect==4.0.1
pickleshare==0.5
ptyprocess==0.5
py==1.4.30
pyparsing==2.0.5
pytest==2.8.2
python-dateutil==2.4.2
pytz==2015.7
scikit-learn==0.16.1
scipy==0.16.1
simplegeneric==0.8.1
six==1.10.0
sklearn==0.0
traitlets==4.0.0
wsgiref==0.1.2
I tried the following:
我尝试了以下方法：
df_total["pred_lin_regr"] = clf.predict(Xtest)
seems to do the job, but I think I can't be sure that the events are matched correctly
似乎可以完成这项工作，但我想我无法确定事件是否正确匹配
采纳答案by Leb
You're correct with your second line, df_total["pred_lin_regr"] = clf.predict(Xtest)and it's more efficient.
你的第二行是正确的，df_total["pred_lin_regr"] = clf.predict(Xtest)而且效率更高。
In that one you're taking the output of clf.predict(), which happens to be an array, and adding it to a dataframe. The output you're receiving from the array itself is in orderto match Xtest, since that's the case, adding it to a numpy array will notchange or alter that order.
在那个中，您要获取 的输出clf.predict()，它恰好是一个数组，并将其添加到数据帧中。您从数组本身接收的输出是为了match Xtest，因为在这种情况下，将它添加到 numpy 数组不会改变或改变该顺序。
Here's a little proof from this example:
下面是这个例子的一个小证明：
Taking the following protion:
采取以下措施：
import numpy as np
import pandas as pd
from sklearn import datasets, linear_model
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
print(regr.predict(diabetes_X_test))
df = pd.DataFrame(regr.predict(diabetes_X_test))
print(df)
The first print()function will give us a numpy array as expected:
第一个print()函数将按预期为我们提供一个 numpy 数组：
[ 225.9732401 115.74763374 163.27610621 114.73638965 120.80385422
158.21988574 236.08568105 121.81509832 99.56772822 123.83758651
204.73711411 96.53399594 154.17490936 130.91629517 83.3878227
171.36605897 137.99500384 137.99500384 189.56845268 84.3990668 ]
That order is identical to the second print()function in which we add the results to a dataframe:
该顺序与print()我们将结果添加到数据帧的第二个函数相同：
0
0 225.973240
1 115.747634
2 163.276106
3 114.736390
4 120.803854
5 158.219886
6 236.085681
7 121.815098
8 99.567728
9 123.837587
10 204.737114
11 96.533996
12 154.174909
13 130.916295
14 83.387823
15 171.366059
16 137.995004
17 137.995004
18 189.568453
19 84.399067
Rerunning the code for a portion of the test, will give us the same ordered results as such:
为测试的一部分重新运行代码，将为我们提供相同的有序结果：
print(regr.predict(diabetes_X_test[0:5]))
df = pd.DataFrame(regr.predict(diabetes_X_test[0:5]))
print(df)
[ 225.9732401 115.74763374 163.27610621 114.73638965 120.80385422]
0
0 225.973240
1 115.747634
2 163.276106
3 114.736390
4 120.803854
