Python/Sklearn 中的循环回归模型 [英] Looped regression model in Python/Sklearn
问题描述
我正在尝试在同一组输入/自变量上系统地回归几个不同的因变量(国家/地区)，并希望使用 Sklearn 在 Python 中以循环方式执行此操作.因变量如下所示:
I'm trying to systematically regress a couple of different dependent variables (countries) on the same set of inputs/independent variables, and want to do this in a looped fashion in Python using Sklearn. The dependant variables look like this:
Europe UK Japan USA Canada
Jan-10 10 13 39 42 16
Feb-10 13 16 48 51 19
Mar-10 15 18 54 57 21
Apr-10 12 15 45 48 18
May-10 11 14 42 45 17
而自变量看起来像这样:
while the independent variables look like this:
Input 1 Input 2 Input 3 Input 4
Jan-10 90 50 3 41
Feb-10 95 54 5 43
Mar-10 92 52 1 45
Apr-10 91 60 1 49
May-10 90 67 11 49
我发现手动回归它们 + 一次存储一个预测很容易(即所有四个输入上的欧洲，然后是日本等)，但还没有想出如何编写一个循环函数来完成所有这些操作去.我怀疑我可能需要使用列表/字典来存储因变量并一一调用它们，但不太知道如何以 Pythonic 的方式编写它.
I find it easy to manually regress them + store predictions one at a time (ie Europe on all four inputs, then Japan etc) but haven't figured out how to program a single looped function that could do them all in one go. I suspect I may need to use a list/dictionary to store the dependent variables and call them one-by-one but don't quite know how to write this in a Pythonic way.
单个循环的现有代码如下所示:
The existing code for a single loop looks like this:
x = pd.DataFrame('countryinputs.csv')
countries = pd.DataFrame('countryoutputs.csv')
y = countries['Europe']
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X, y)
y_pred = regressor.predict(X)
推荐答案
只需遍历列名.然后将 name 传递给定义的函数.实际上，您可以将过程包装在字典推导中并传递给 DataFrame 构造函数以返回预测值的数据帧(与原始数据帧的形状相同):
Simply iterate through the column names. Then pass name into a defined function. In fact, you can wrap the process in a dictionary comprehension and pass into DataFrame constructor to return a dataframe of predicted values (same shape as original dataframe):
X = pd.DataFrame(...)
countries = pd.DataFrame(...)
def reg_proc(label):
y = countries[label]
regressor = LinearRegression()
regressor.fit(X, y)
y_pred = regressor.predict(X)
return(y_pred)
pred_df = pd.DataFrame({lab: reg_proc(lab) for lab in countries.columns},
columns = countries.columns)
用随机的种子数据来演示以下工具是您的国家:
To demonstrate with random, seeded data where tools below would be your countries:
数据
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
np.random.seed(7172018)
tools = pd.DataFrame({'pandas': np.random.uniform(0,1000,50),
'r': np.random.uniform(0,1000,50),
'julia': np.random.uniform(0,1000,50),
'sas': np.random.uniform(0,1000,50),
'spss': np.random.uniform(0,1000,50),
'stata': np.random.uniform(0,1000,50)
},
columns=['pandas', 'r', 'julia', 'sas', 'spss', 'stata'])
X = pd.DataFrame({'Input1': np.random.randn(50)*10,
'Input2': np.random.randn(50)*10,
'Input3': np.random.randn(50)*10,
'Input4': np.random.randn(50)*10})
模型
def reg_proc(label):
y = tools[label]
regressor = LinearRegression()
regressor.fit(X, y)
y_pred = regressor.predict(X)
return(y_pred)
pred_df = pd.DataFrame({lab: reg_proc(lab) for lab in tools.columns},
columns = tools.columns)
print(pred_df.head(10))
# pandas r julia sas spss stata
# 0 547.631679 576.025733 682.390046 507.767567 246.020799 557.648181
# 1 577.334819 575.992992 280.579234 506.014191 443.044139 396.044620
# 2 430.494827 576.211105 541.096721 441.997575 386.309627 558.472179
# 3 440.662962 524.582054 406.849303 420.017656 508.701222 393.678200
# 4 588.993442 472.414081 453.815978 479.208183 389.744062 424.507541
# 5 520.215513 489.447248 670.708618 459.375294 314.008988 516.235188
# 6 515.266625 459.292370 477.485995 436.398180 446.777292 398.826234
# 7 423.930650 414.069751 629.444118 378.059735 448.760240 449.062734
# 8 549.769034 406.531405 653.557937 441.425445 348.725447 456.089921
# 9 396.826924 399.327683 717.285415 361.235709 444.830491 429.967976
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