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python - 如何在 Flask 的 sklearn 管道中使用自定义转换？

问题描述

我无法在 Flask 中使用带有自定义转换的 sklearn 管道，使用picke.dump().

假设我想在泡菜中使用以下管道来服务 Flask。它选择变量，创建虚拟变量并分离进入模型的变量。

# Custom Transformer that extracts columns passed as argument to its constructor
class FeatureSelector(BaseEstimator, TransformerMixin):
    # Class Constructor
    def __init__(self, feature_names):
        self._feature_names = feature_names

    # Return self nothing else to do here
    def fit(self, X, y=None):
        return self

    # Method that describes what we neeed this transformer to do
    def transform(self, X, y=None):
        return X[self._feature_names]

# Defining the steps in the categorical pipeline
categorical_features = ['marital', 'contact', 'job']

# Converts certain features to binary
class CategoricalBinary(TransformerMixin):
    # Class Constructor
    #def __init__(self):

    # Return self nothing else to do here
    def fit(self, X, y=None):
        return self

    # Faz as transformações com a função get_dummies
    def transform(self, X, y=None):
        X = pd.get_dummies(X, columns=X.columns.tolist())
        return X

# Custom Transformer that extracts columns passed as argument to its constructor
class ModelFeatureSelector(BaseEstimator, TransformerMixin):
    # Class Constructor
    def __init__(self, feature_names):
        self._feature_names = feature_names

    # Return self nothing else to do here
    def fit(self, X, y=None):
        return self

    # Method that describes what we neeed this transformer to do
    def transform(self, X, y=None):
        return X[self._feature_names]

model_features = ['marital_divorced', 'marital_married', 'marital_single',
                        'contact_cellular', 'job_admin.', 'job_blue-collar',
                        'job_entrepreneur', 'job_housemaid', 'job_management',
                        'job_retired', 'job_self-employed', 'job_services',
                        'job_student', 'job_technician', 'job_unemployed']

categorical_transform = Pipeline(steps=[('feature_selector', FeatureSelector(categorical_features)),
                                        ('categorical_dummy', CategoricalBinary()),
                                        ('model_features', ModelFeatureSelector(model_features)),
                                        ('logreg', LogisticRegression(class_weight='balanced', solver='liblinear'))])

# Fits
categorical_transform.fit(X_train, y_train)

# Save on pickle
with open('categorical_transform.pkl', 'wb') as f:
    pickle.dump(categorical_transform, f)

我编写了 Flask

# Importa as classes necessárias do pacote `flask`
from flask import Flask, request, jsonify

# Importa o pacote de interação com o sistema `os`, o pacote `pandas` para maniulação da informação
# e o pacote `pickle` para carregar nosso modelo.
import os
import pandas as pd
import pickle
from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.impute import SimpleImputer
import outros

# Cria a aplicação
app = Flask(__name__)

# Model
model = pickle.load(open('categorical_transform.pkl', 'rb'))

# TAREFA: PREENCHA O MÉTODO DE REQUISIÇÃO
@app.route('/predict', methods=['POST'])
def predictor():
    # Recebe o conteúdo da postagem no formato json.
    content = request.json
    features = pd.DataFrame([content])

    # Aplica a predição do modelo
    predito = model.predict(features)

    # Cria e envia uma resposta para o 'chamador' da API
    return jsonify(status='completed', predict=float(predito[1][1]))

# Essa linha garante que a aplicação execute no localhost, ou seja, no IP "0.0.0.0"
# e que esteja na porta padrão do sistema ou, caso ela não exista, na porta 8080.
if __name__ == '__main__':
    app.run(debug=True,host='0.0.0.0',port=os.environ.get('PORT', 8080))

引发以下异常：

AttributeError: Can't get attribute 'FeatureSelector'

标签： pythonpandasscikit-learn

解决方案

您已经FeatureSelector在一些 python 脚本中创建了一个自定义转换器。在 Flask 脚本中，您导入该自定义类的父类，但没有导入FeatureSelector自身。因此，Flask 脚本中没有自定义类的定义。因此，pickle 无法重建对象。

假设FeatureSelector是在 custom_classes.py 中定义的。然后你的 Flask 脚本应该包含一行：

from custom_classes import FeatureSelector

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