如何处理多个非序分类变量？

Question

我在网上获取了一个数据集，其中包含今年 NBA 球员的数据。我正在尝试对数据集运行线性回归，以查看给定玩家在给定以下特征的情况下平均可以得分多少分：团队名称、位置、年龄、每场比赛的上场时间。但是，我不知道如何处理前两列，它们是我的分类变量。我刚刚开始了关于 Udemy 的数据科学课程，讲师还没有真正解释在这种情况下该怎么做，因为他的 OneHotEncoding 示例仅适用于具有一个分类变量的数据集。

我的代码：

#Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#Import Dataset

dataset = pd.read_csv('nba_clean.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

#Encode Dataset

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers = [('encoder', OneHotEncoder(), [0, 1])], remainder = 'passthrough')
X = np.array(ct.fit_transform(X))

#Splitting the Dataset into Training set and Test Set

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state = 0)

#Perform Multiple Linear Regression on Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

#Compare predicted values to true values
y_pred = regressor.predict(X_test)
np.set_printoptions(precision = 2)
new_y_pred = y_pred.reshape(len(y_pred), 1)
new_y_test = y_test.reshape(len(y_test), 1)
print(np.concatenate((new_y_pred, new_y_test), 1))

Answer 1

您的列转换器必须处理所有不同的列类型： 你必须更换

 ct = ColumnTransformer(transformers = [('encoder', OneHotEncoder(), [0, 1])], remainder = 'passthrough')

使用以下类型的代码：

首先定义你的列类型列表：

num_f  = ['age', 'points', ...]
ord_f  = ['bbb', 'ccc', ...]
cat_f  = ['aaa', 'ddd', ...]
drop_f = []

然后为每种类型的值创建一个转换器

# create a transformer for the categorical values
cat_tr = Pipeline(steps=[
    ('onehot', OneHotEncoder())])

# create a transformer for the categorical ordinal values
ord_tr = Pipeline(steps=[
    ('ordinal', OrdinalEncoder())])

# create a transformed for the numerical values
num_tr = Pipeline(steps=[
    ('scaler', StandardScaler())])

ct = ColumnTransformer(transformers=[
    ("drop",'drop' ,drop_f)
    ,("cat", cat_tr, cat_f)
    ,("ord", ord_tr, ord_f)
    ,("num", num_tr, num_f)
    ],remainder='passthrough')

Answer 2

您可以使用 pandas 函数将某些列转换为 one-hot：

pandas.get_dummies(data, column=["TeamName", "Position"])

像这样：

df = pd.DataFrame({
        "Player": ['player1', 'player2', 'player3'],
        "TeamName": ['Lakers', 'Spurs', 'Lakers'],
        "Position":['point guard', 'center', 'forward']
        })
    
df
           Player TeamName     Position
       0  player1   Lakers  point guard
       1  player2    Spurs       center
       2  player3   Lakers      forward


pd.get_dummies(df, columns=['TeamName', 'Position'], prefix='', prefix_sep='')

    Player   Lakers   Spurs   center   forward   point guard
0  player1        1       0        0         0             1
1  player2        0       1        1         0             0
2  player3        1       0        0         1             0