显然有一个很酷的方法来做到这一点！，对于这个df：
df_with_cat = pd.DataFrame({
'A' : ['ios', 'android', 'web', 'NaN'],
'B' : [4, 4, 'NaN', 2],
'target' : [1, 1, 0, 0]
})
如果您不介意将您的 sklearn 升级到 0.20.2，请运行：
pip3 install scikit-learn==0.20.2
并使用此解决方案（如@AI_learning 建议的那样）：
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
columnTransformer = ColumnTransformer(
transformers=[
('cat', OneHotEncoder(), CATEGORICAL_FEATURES),
('num', Imputer( strategy='most_frequent'), NUMERICAL_FEATURES)
])
然后：
columnTransformer.fit(df_with_cat)
但如果您使用的是较早的 sklearn 版本，请使用此版本：
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import LabelBinarizer, LabelEncoder
CATEGORICAL_FEATURES = ['A']
NUMERICAL_FEATURES = ['B']
TARGET = ['target']
numerical_pipline = Pipeline([
('selector', DataFrameSelector(NUMERICAL_FEATURES)),
('imputer', Imputer(strategy='most_frequent'))
])
categorical_pipeline = Pipeline([
('selector', DataFrameSelector(CATEGORICAL_FEATURES)),
('cat_encoder', LabelBinarizerPipelineFriendly())
])
如果你注意到我们错过了DataFrameSelector，它不是sklearn的一部分，所以让我们写在这里：
from sklearn.base import BaseEstimator, TransformerMixin
class DataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names].values
让我们统一它们：
from sklearn.pipeline import FeatureUnion, make_pipeline
preprocessing_pipeline = FeatureUnion(transformer_list=[
('numerical_pipline', numerical_pipline),
('categorical_pipeline', categorical_pipeline)
])
就是这样，现在让我们运行：
preprocessing_pipeline.fit_transform(df_with_cat[CATEGORICAL_FEATURES+NUMERICAL_FEATURES])
现在让我们更疯狂！
将它们与分类器管道统一起来：
from sklearn import tree
clf = tree.DecisionTreeClassifier()
full_pipeline = make_pipeline(preprocessing_pipeline, clf)
并一起训练他们：
full_pipeline.fit(df_with_cat[CATEGORICAL_FEATURES+NUMERICAL_FEATURES], df_with_cat[TARGET])
只需打开一个 Jupyter 笔记本，获取代码片段并自己尝试一下！
下面是LabelBinarizerPipelineFriendly()的定义：
class LabelBinarizerPipelineFriendly(LabelBinarizer):
'''
Wrapper to LabelBinarizer to allow usage in sklearn.pipeline
'''
def fit(self, X, y=None):
"""this would allow us to fit the model based on the X input."""
super(LabelBinarizerPipelineFriendly, self).fit(X)
def transform(self, X, y=None):
return super(LabelBinarizerPipelineFriendly, self).transform(X)
def fit_transform(self, X, y=None):
return super(LabelBinarizerPipelineFriendly, self).fit(X).transform(X)
这种方法的主要优点是您可以将经过训练的模型连同所有管道转储到 pkl 文件中，然后您可以实时使用相同的模型（生产中的预测）