One-Hot-Encode分类变量和同时缩放连续的变量

Question

我很困惑,因为如果你第一次这样做会成为一个问题OneHotEncoder然后StandardScaler因为缩放器也将缩放之前转换过的列OneHotEncoder.有没有办法同时执行编码和缩放,然后将结果连接在一起？

Answer 1

当然可以.只需根据需要单独缩放和单独编码单独的列:

# Import libraries and download example data
from sklearn.preprocessing import StandardScaler, OneHotEncoder

dataset = pd.read_csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
print(dataset.head(5))

# Define which columns should be encoded vs scaled
columns_to_encode = ['rank']
columns_to_scale  = ['gre', 'gpa']

# Instantiate encoder/scaler
scaler = StandardScaler()
ohe    = OneHotEncoder(sparse=False)

# Scale and Encode Separate Columns
scaled_columns  = scaler.fit_transform(dataset[columns_to_scale]) 
encoded_columns =    ohe.fit_transform(dataset[columns_to_encode])

# Concatenate (Column-Bind) Processed Columns Back Together
processed_data = np.concatenate([scaled_columns, encoded_columns], axis=1)

Answer 2

0.20 版的 Scikit-learn 提供sklearn.compose.ColumnTransformer了使用 Mixed Types来做Column Transformer。您可以缩放数字特征并将分类特征进行单热编码。以下是官方示例（您可以在此处找到代码）：

# Author: Pedro Morales <part.morales@gmail.com>
#
# License: BSD 3 clause

from __future__ import print_function

import pandas as pd
import numpy as np

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV

np.random.seed(0)

# Read data from Titanic dataset.
titanic_url = ('https://raw.githubusercontent.com/amueller/'
               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')
data = pd.read_csv(titanic_url)

# We will train our classifier with the following features:
# Numeric Features:
# - age: float.
# - fare: float.
# Categorical Features:
# - embarked: categories encoded as strings {'C', 'S', 'Q'}.
# - sex: categories encoded as strings {'female', 'male'}.
# - pclass: ordinal integers {1, 2, 3}.

# We create the preprocessing pipelines for both numeric and categorical data.
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', preprocessor),
                      ('classifier', LogisticRegression(solver='lbfgs'))])

X = data.drop('survived', axis=1)
y = data['survived']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

clf.fit(X_train, y_train)
print("model score: %.3f" % clf.score(X_test, y_test))

注意：此方法是实验性的，在不弃用的情况下，某些行为可能会在不同版本之间发生变化。

Answer 3

目前有多种方法可以实现 OP 所需的结果。有 3 种方法可以做到这一点

1. np.concatenate()- 请参阅已发布的OP问题的答案

2. scikit-learn的ColumnTransformer

   • 最初在这个SO回答OP的问题中建议

3. scikit-learn的FeatureUnion

   • 也显示在这个SO答案中

使用@Max Power在这里发布的示例，下面是一个最小的工作片段，它执行OP正在寻找的操作，并将转换后的列汇集到单个 Pandas 数据框中。显示了所有 3 种方法的输出

所有 3 种方法的通用代码是

import numpy as np
import pandas as pd

# Import libraries and download example data
from sklearn.preprocessing import StandardScaler, OneHotEncoder

dataset = pd.read_csv("https://stats.idre.ucla.edu/stat/data/binary.csv")

# Define which columns should be encoded vs scaled
columns_to_encode = ['rank']
columns_to_scale  = ['gre', 'gpa']

# Instantiate encoder/scaler
scaler = StandardScaler()
ohe    = OneHotEncoder(sparse=False)

方法1.看这里的代码。要显示输出，可以使用

print(pd.DataFrame(processed_data).head())

方法 1 的输出。

          0         1    2    3    4    5
0 -1.800263  0.579072  0.0  0.0  1.0  0.0
1  0.626668  0.736929  0.0  0.0  1.0  0.0
2  1.840134  1.605143  1.0  0.0  0.0  0.0
3  0.453316 -0.525927  0.0  0.0  0.0  1.0
4 -0.586797 -1.209974  0.0  0.0  0.0  1.0

方法2.

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline


p = Pipeline(
    [("coltransformer", ColumnTransformer(
        transformers=[
            ("assessments", Pipeline([("scale", scaler)]), columns_to_scale),
            ("ranks", Pipeline([("encode", ohe)]), columns_to_encode),
        ]),
    )]
)

print(pd.DataFrame(p.fit_transform(dataset)).head())

方法 2 的输出。

          0         1    2    3    4    5
0 -1.800263  0.579072  0.0  0.0  1.0  0.0
1  0.626668  0.736929  0.0  0.0  1.0  0.0
2  1.840134  1.605143  1.0  0.0  0.0  0.0
3  0.453316 -0.525927  0.0  0.0  0.0  1.0
4 -0.586797 -1.209974  0.0  0.0  0.0  1.0

方法3.

from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FeatureUnion


class ItemSelector(BaseEstimator, TransformerMixin):
    def __init__(self, key):
        self.key = key
    def fit(self, x, y=None):
        return self
    def transform(self, df):
        return df[self.key]

p = Pipeline([("union", FeatureUnion(
    transformer_list=[
        ("assessments", Pipeline([
            ("selector", ItemSelector(key=columns_to_scale)),
            ("scale", scaler)
            ]),
        ),
        ("ranks", Pipeline([
            ("selector", ItemSelector(key=columns_to_encode)),
            ("encode", ohe)
            ]),
        ),
    ]))
])

print(pd.DataFrame(p.fit_transform(dataset)).head())

方法 3 的输出。

          0         1    2    3    4    5
0 -1.800263  0.579072  0.0  0.0  1.0  0.0
1  0.626668  0.736929  0.0  0.0  1.0  0.0
2  1.840134  1.605143  1.0  0.0  0.0  0.0
3  0.453316 -0.525927  0.0  0.0  0.0  1.0
4 -0.586797 -1.209974  0.0  0.0  0.0  1.0

解释

1. 方法1.已经解释过了。

2. 方法 2. 和 3. 接受完整数据集，但仅对数据子集执行特定操作。修改/处理的子集被汇集（组合）成最终输出。

细节

pandas==0.23.4
numpy==1.15.2
scikit-learn==0.20.0

补充笔记

这里显示的 3 种方法可能不是唯一的可能性......我确信还有其他方法可以做到这一点。

使用的来源

binary.csv更新了数据集链接