使用Flask构建机器学习模型API

1. Python环境设置和Flask基础

Anaconda

使用“Anaconda”创建一个虚拟环境。如果你需要在Python中创建你的工作流程，并将依赖项分离出来，或者共享环境设置，“Anaconda”发行版是一个不错的选择。
- 安装here
- wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
- bash Miniconda3-latest-Linux-x86_64.sh
- source .bashrc
- conda create --name <environment-name> python=3.6
- source activate <environment-name>
- 安装必要的Python包: flask & gunicorn.
尝试一个简单的“Flask”Hello-World应用程序，并使用gunicorn提供服务:
- hello-world.py
- 编写代码:
```
from flask import Flask

app = Flask(__name__)

@app.route('/users/<string:username>')
def hello_world(username=None):

    return("Hello {}!".format(username))
```
- 保存
- gunicorn --bind 0.0.0.0:8000 hello-world:app
- 如果你得到了下面的响应，你就走上了正确的道路:
- 在浏览器上访问https://localhost:8000/users/any-name

您编写了第一个Flask应用程序。正如您现在通过几个简单的步骤所体验到的，我们能够创建可以在本地访问的web端点。未来的路也很简单。

使用“Flask”，我们可以很容易地封装我们的机器学习模型，并将它们作为Web api来使用。此外，如果我们想创建更复杂的web应用程序(包括JavaScript ' gasps ')，我们只需要进行一些修改。

2. 构建机器学习模型

以机器学习比赛为例: Loan Prediction Competition. 主要目标是设置预处理管道和创建ML模型，目标是在部署时简化ML预测。
数据：链接：https://pan.baidu.com/s/1VjqNZxvdKm0G5iBtTs-4TA 提取码：n5ag

import os 
import json
import numpy as np
import pandas as pd
from sklearn.externals import joblib
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.ensemble import RandomForestClassifier

from sklearn.pipeline import make_pipeline

import warnings
warnings.filterwarnings("ignore")

data = pd.read_csv('../data/training.csv')

list(data.columns)

['Loan_ID',
 'Gender',
 'Married',
 'Dependents',
 'Education',
 'Self_Employed',
 'ApplicantIncome',
 'CoapplicantIncome',
 'LoanAmount',
 'Loan_Amount_Term',
 'Credit_History',
 'Property_Area',
 'Loan_Status']

data.shape

(614, 13)

Finding out the null/Nan values in the columns:

for _ in data.columns:
    print("The number of null values in:{} == {}".format(_, data[_].isnull().sum()))

The number of null values in:Loan_ID == 0
The number of null values in:Gender == 13
The number of null values in:Married == 3
The number of null values in:Dependents == 15
The number of null values in:Education == 0
The number of null values in:Self_Employed == 32
The number of null values in:ApplicantIncome == 0
The number of null values in:CoapplicantIncome == 0
The number of null values in:LoanAmount == 22
The number of null values in:Loan_Amount_Term == 14
The number of null values in:Credit_History == 50
The number of null values in:Property_Area == 0
The number of null values in:Loan_Status == 0

Next step is creating training and testing datasets:

pred_var = ['Gender','Married','Dependents','Education','Self_Employed','ApplicantIncome','CoapplicantIncome',
            'LoanAmount','Loan_Amount_Term','Credit_History','Property_Area']

X_train, X_test, y_train, y_test = train_test_split(data[pred_var], data['Loan_Status'], 
                                                    test_size=0.25, random_state=42)

To make sure that the pre-processing steps are followed religiously even after we are done with experimenting and we do not miss them while predictions, we'll create a custom pre-processing Scikit-learn estimator.

To follow the process on how we ended up with this estimator, read up on this notebook

from sklearn.base import BaseEstimator, TransformerMixin

class PreProcessing(BaseEstimator, TransformerMixin):
    """Custom Pre-Processing estimator for our use-case
    """

    def __init__(self):
        pass

    def transform(self, df):
        """Regular transform() that is a help for training, validation & testing datasets
           (NOTE: The operations performed here are the ones that we did prior to this cell)
        """
        pred_var = ['Gender','Married','Dependents','Education','Self_Employed','ApplicantIncome',
                    'CoapplicantIncome','LoanAmount','Loan_Amount_Term','Credit_History','Property_Area']
        
        df = df[pred_var]
        
        df['Dependents'] = df['Dependents'].fillna(0)
        df['Self_Employed'] = df['Self_Employed'].fillna('No')
        df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna(self.term_mean_)
        df['Credit_History'] = df['Credit_History'].fillna(1)
        df['Married'] = df['Married'].fillna('No')
        df['Gender'] = df['Gender'].fillna('Male')
        df['LoanAmount'] = df['LoanAmount'].fillna(self.amt_mean_)
        
        gender_values = {'Female' : 0, 'Male' : 1} 
        married_values = {'No' : 0, 'Yes' : 1}
        education_values = {'Graduate' : 0, 'Not Graduate' : 1}
        employed_values = {'No' : 0, 'Yes' : 1}
        property_values = {'Rural' : 0, 'Urban' : 1, 'Semiurban' : 2}
        dependent_values = {'3+': 3, '0': 0, '2': 2, '1': 1}
        df.replace({'Gender': gender_values, 'Married': married_values, 'Education': education_values, 
                    'Self_Employed': employed_values, 'Property_Area': property_values, 
                    'Dependents': dependent_values}, inplace=True)
        
        return df.as_matrix()

    def fit(self, df, y=None, **fit_params):
        """Fitting the Training dataset & calculating the required values from train
           e.g: We will need the mean of X_train['Loan_Amount_Term'] that will be used in
                transformation of X_test
        """
        
        self.term_mean_ = df['Loan_Amount_Term'].mean()
        self.amt_mean_ = df['LoanAmount'].mean()
        return self

Convert y_train & y_test to np.array:

y_train = y_train.replace({'Y':1, 'N':0}).as_matrix()
y_test = y_test.replace({'Y':1, 'N':0}).as_matrix()

We'll create a pipeline to make sure that all the preprocessing steps that we do are just a single scikit-learn estimator.

pipe = make_pipeline(PreProcessing(),
                    RandomForestClassifier())

pipe

Pipeline(memory=None,
     steps=[('preprocessing', PreProcessing()), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False))])

To search for the best hyper-parameters (degree for PolynomialFeatures & alpha for Ridge), we'll do a Grid Search:

Defining param_grid:

param_grid = {"randomforestclassifier__n_estimators" : [10, 20, 30],
             "randomforestclassifier__max_depth" : [None, 6, 8, 10],
             "randomforestclassifier__max_leaf_nodes": [None, 5, 10, 20], 
             "randomforestclassifier__min_impurity_split": [0.1, 0.2, 0.3]}

Running the Grid Search:

grid = GridSearchCV(pipe, param_grid=param_grid, cv=3)

Fitting the training data on the pipeline estimator:

grid.fit(X_train, y_train)

GridSearchCV(cv=3, error_score='raise-deprecating',
       estimator=Pipeline(memory=None,
     steps=[('preprocessing', PreProcessing()), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impu...bs=None,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False))]),
       fit_params=None, iid='warn', n_jobs=None,
       param_grid={'randomforestclassifier__n_estimators': [10, 20, 30], 'randomforestclassifier__max_depth': [None, 6, 8, 10], 'randomforestclassifier__max_leaf_nodes': [None, 5, 10, 20], 'randomforestclassifier__min_impurity_split': [0.1, 0.2, 0.3]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring=None, verbose=0)

Let's see what parameter did the Grid Search select:

print("Best parameters: {}".format(grid.best_params_))

Best parameters: {'randomforestclassifier__max_depth': None, 'randomforestclassifier__max_leaf_nodes': None, 'randomforestclassifier__min_impurity_split': 0.3, 'randomforestclassifier__n_estimators': 30}

Let's score:

print("Validation set score: {:.2f}".format(grid.score(X_test, y_test)))

Validation set score: 0.79

3. 保存机器学习模型:序列化和反序列化

# 保存模型
from sklearn.externals import joblib
joblib.dump(grid, 'loan_model.pkl')

['loan_model.pkl']

# 加载模型
grid = joblib.load('loan_model.pkl')

# 读取测试数据
test_df = pd.read_csv('../data/test.csv', encoding="utf-8-sig")
test_df = test_df.head()

test_df

	Loan_ID	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area
0	LP001015	Male	Yes	0	Graduate	No	5720	0	110.0	360.0	1.0	Urban
1	LP001022	Male	Yes	1	Graduate	No	3076	1500	126.0	360.0	1.0	Urban
2	LP001031	Male	Yes	2	Graduate	No	5000	1800	208.0	360.0	1.0	Urban
3	LP001035	Male	Yes	2	Graduate	No	2340	2546	100.0	360.0	NaN	Urban
4	LP001051	Male	No	0	Not Graduate	No	3276	0	78.0	360.0	1.0	Urban

# 使用模型进行预测
grid.predict(test_df)

array([1, 1, 1, 1, 1], dtype=int64)

4. 使用Flask创建API

我们将保持文件夹结构尽可能简单:

Folder Struct

构建包装函数有三个重要部分， apicall():

获取请求数据
加载模型
预测并响应

HTTP消息由头和正文组成。作为标准，发送的主体内容大部分是“json”格式。我们将发送(' POST url-endpoint/ ')传入的数据作为批处理，以获得预测。

(NOTE: 您可以直接发送纯文本、XML、csv或图像，但为了格式的可互换性，建议使用“json”)

import pandas as pd
from sklearn.externals import joblib
from flask import Flask, jsonify, request

app = Flask(__name__)


@app.route('/predict', methods=['POST'])
def apicall():
    try:
        # 获取test数据，可通过json，也可通过其他方式
        test_json = request.get_json()
        test = pd.read_json(test_json, orient='records')
        test['Dependents'] = [str(x) for x in list(test['Dependents'])]
        loan_ids = test['Loan_ID']

        # 读取数据库形式
        # sql = "select * from data where unif_cust_id=" + unif_cust_id
        # conn = create_engine('mysql+mysqldb://test:test@localhost:3306/score_card?charset=utf8')
        # data = pd.read_sql(sql, conn)

    except Exception as e:
        raise e

    if test.empty:
        return bad_request()
    else:
        # 加载模型
        print("Loading the model...")
        loaded_model = joblib.load('loan_model.pkl')

        # 预测
        print("The model has been loaded...doing predictions now...")
        predictions = loaded_model.predict(test)

        # 将预测结果存入DataFrame中
        prediction_series = list(pd.Series(predictions))
        final_predictions = pd.DataFrame(list(zip(loan_ids, prediction_series)))

        # 返回接口响应
        responses = jsonify(predictions=final_predictions.to_json(orient="records"))
        responses.status_code = 200

        return responses


@app.errorhandler(400)
def bad_request(error=None):
    message = {
        'status': 400,
        'message': 'Bad Request: ' + request.url + '--> Please check your data payload...',
    }
    resp = jsonify(message)
    resp.status_code = 400

    return resp


if __name__ == '__main__':
    app.run()

 * Serving Flask app "__main__" (lazy loading)
 * Environment: production
   WARNING: Do not use the development server in a production environment.
   Use a production WSGI server instead.
 * Debug mode: off


 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)


Loading the model...
The model has been loaded...doing predictions now...


127.0.0.1 - - [11/Nov/2019 10:05:09] "[37mPOST /predict HTTP/1.1[0m" 200 -

请求API

如果使用jupyter，请另启一个页面进行请求。

import json
import requests
import pandas as pd

"""Setting the headers to send and accept json responses
"""
header = {'Content-Type': 'application/json', 
                  'Accept': 'application/json'}

"""Reading test batch
"""
df = pd.read_csv('../data/test.csv', encoding="utf-8-sig")
df = df.head()

"""Converting Pandas Dataframe to json
"""
data = df.to_json(orient='records')

data

'[{"Loan_ID":"LP001015","Gender":"Male","Married":"Yes","Dependents":"0","Education":"Graduate","Self_Employed":"No","ApplicantIncome":5720,"CoapplicantIncome":0,"LoanAmount":110.0,"Loan_Amount_Term":360.0,"Credit_History":1.0,"Property_Area":"Urban"},{"Loan_ID":"LP001022","Gender":"Male","Married":"Yes","Dependents":"1","Education":"Graduate","Self_Employed":"No","ApplicantIncome":3076,"CoapplicantIncome":1500,"LoanAmount":126.0,"Loan_Amount_Term":360.0,"Credit_History":1.0,"Property_Area":"Urban"},{"Loan_ID":"LP001031","Gender":"Male","Married":"Yes","Dependents":"2","Education":"Graduate","Self_Employed":"No","ApplicantIncome":5000,"CoapplicantIncome":1800,"LoanAmount":208.0,"Loan_Amount_Term":360.0,"Credit_History":1.0,"Property_Area":"Urban"},{"Loan_ID":"LP001035","Gender":"Male","Married":"Yes","Dependents":"2","Education":"Graduate","Self_Employed":"No","ApplicantIncome":2340,"CoapplicantIncome":2546,"LoanAmount":100.0,"Loan_Amount_Term":360.0,"Credit_History":null,"Property_Area":"Urban"},{"Loan_ID":"LP001051","Gender":"Male","Married":"No","Dependents":"0","Education":"Not Graduate","Self_Employed":"No","ApplicantIncome":3276,"CoapplicantIncome":0,"LoanAmount":78.0,"Loan_Amount_Term":360.0,"Credit_History":1.0,"Property_Area":"Urban"}]'

"""POST <url>/predict
"""
resp = requests.post("http://127.0.0.1:5000/predict", 
                    data = json.dumps(data),
                    headers= header)

resp.status_code

resp.json()

{'predictions': '[{"0":"LP001015","1":1},{"0":"LP001022","1":1},{"0":"LP001031","1":1},{"0":"LP001035","1":1},{"0":"LP001051","1":1}]'}

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原文地址：https://www.cnblogs.com/chenxiangzhen/p/11833283.html