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机器学习管道 pipeline

一般通用pipeline的实现流程：

1.构建多个转换器（transformer），来实现对特征的预处理

2.构建 ColumnTransformer，将不同的预处理应用于不同的列子集，构造一个完备的转化器

3.构建完整的 Pipeline，将预处理器和模型串联起来

通用pipeline

如果要实现一个简单的适用于所有机器学习模型的pipeline，我觉得可以通过类的封装来实现：

具体代码实例如下：

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
from sklearn.impute import SimpleImputer
import pandas as pd
from sklearn.model_selection import train_test_splitclass GenericPipelineBuilder:#构造函数传入需要的参数，包括模型，数值特征，适合进行独热编码的特征，适合进行序数编码的特征，指定每个序数编码特征的类别顺序列表def __init__(self, model, numeric_features, onehot_features, ordinal_features, ordinal_categories=None):self.model = modelself.numeric_features = numeric_featuresself.onehot_features = onehot_featuresself.ordinal_features = ordinal_featuresself.ordinal_categories = ordinal_categories if ordinal_categories else [[] for _ in range(len(ordinal_features))]# 构建数值特征的处理管道，包括缺失值填充和标准化def build_numeric_transformer(self):return Pipeline(steps=[('imputer', SimpleImputer(strategy='mean')),('scaler', StandardScaler())])# 构建适合进行独热编码的特征的处理管道，包括缺失值填充和独热编码def build_onehot_transformer(self):return Pipeline(steps=[('imputer', SimpleImputer(strategy='most_frequent')),('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))])# 构建适合进行序数编码的特征的处理管道，包括缺失值填充和序数编码def build_ordinal_transformer(self):return Pipeline(steps=[('imputer', SimpleImputer(strategy='most_frequent')),('ordinal', OrdinalEncoder(categories=self.ordinal_categories, handle_unknown='use_encoded_value',unknown_value=-1))])# 构建整个预处理的ColumnTransformer，将不同的特征处理管道应用到不同的特征def build_preprocessor(self):return ColumnTransformer(transformers=[('num', self.build_numeric_transformer(), self.numeric_features),('onehot', self.build_onehot_transformer(), self.onehot_features),('ordinal', self.build_ordinal_transformer(), self.ordinal_features)],remainder='passthrough')# 构建完整的机器学习流水线，包括预处理和模型def build_pipeline(self):return Pipeline(steps=[('preprocessor', self.build_preprocessor()),('classifier', self.model)])

使用的话直接实例化类进行操作即可

例子如下：

    data = pd.read_csv('your_data.csv')y = data['target_column']X = data.drop('target_column', axis=1)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)numeric_features = X.select_dtypes(include=['number']).columns.tolist()onehot_features = ['Color', 'City']ordinal_features = ['Education_Level']ordinal_categories = [['High School', 'Bachelor', 'Master', 'PhD']]from sklearn.ensemble import RandomForestClassifiermodel = RandomForestClassifier(random_state=42)builder = GenericPipelineBuilder(model, numeric_features, onehot_features, ordinal_features, ordinal_categories)pipeline = builder.build_pipeline()pipeline.fit(X_train, y_train)print("模型训练完成")

@浙大疏锦行