对于多次调试的程序来说，重复执行如：读取数据、加载模型、得到导入的外部数据或三方库等重复操作的过程，可以使用网络服务搭建一个类似API一样的操作，一次读取，终身使用

整体的思路如下：

1. 本地搭建一个web服务（如本博客使用FastAPI，也可换别的）
2. 本地的web服务将需要读取、加载的东西先加载，然后赋值给一个变量，放到内存中
3. 反复调试的程序在需要读取固定的数据时，使用本地的web服务获取已保存在内存中的变量值
4. 对于web服务来讲，其实就是请求时返回对应的变量（需要json格式或打包返回）

我们使用tsfresh为例，这是一个可以提取时间序列的特征的库，使用方法可以参考：python 使用tsfresh进行时间序列特征提取

原始程序

import pandas as pd
import numpy as np


def convert_to_series(series: pd.Series):
    covert_df = pd.DataFrame(data=[['id'] * series.shape[0], series.values], index=['id', "value"]).T
    covert_df['value'] = covert_df['value'].astype("float")
    return covert_df


def get_line_features(dataframe: pd.DataFrame):
    """本地：得到曲线的特征"""
    from tsfresh import extract_features  # 这个操作是费时间的
    ext_feature = extract_features(dataframe, column_id="id")
    ext_feature = ext_feature.dropna(axis=1)
    return ext_feature


def main():
    series = pd.Series(data=np.random.rand(100), index=pd.date_range(start="20190101", periods=100))
    # 本地
    conv_df = convert_to_series(series)
    ext_feature = get_line_features(conv_df)

    print(ext_feature.shape)


if __name__ == '__main__':
    main()
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WEB改写

首先先写一个FastAPI的服务：

from fastapi import FastAPI
import uvicorn
from tsfresh import extract_features
import pandas as pd

app = FastAPI()


def get_line_features(data_list: list):
    """得到曲线的特征"""
    # 将 list 转为 dataframe
    covert_df = pd.DataFrame(data=[['id_1'] * len(data_list), data_list], index=['id', "value"]).T
    covert_df['value'] = covert_df['value'].astype("float")
    # 使用 tsfresh 提取特征
    ext_feature = extract_features(covert_df, column_id="id")
    ext_feature = ext_feature.dropna(axis=1)
    return ext_feature


@app.get("/data={data}")
async def data_service(data: str):
    data_list: list = eval(data)
    feature = get_line_features(data_list)
    return feature.to_dict()


if __name__ == "__main__":
    uvicorn.run(app, host='127.0.0.1', port=8999, debug=True)
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在这个文件中我们已经把耗时的from tsfresh import extract_features已经加载了，所以启动web服务可能比较费劲，但是之后就轻松多了

调试的程序如下：

import pandas as pd
import numpy as np


def convert_to_series(series: pd.Series):
    covert_df = pd.DataFrame(data=[['id'] * series.shape[0], series.values], index=['id', "value"]).T
    covert_df['value'] = covert_df['value'].astype("float")
    return covert_df

def get_line_features_service(value_list):
    """使用服务：得到曲线的特征"""
    import requests
    url = "http://127.0.0.1:8999/data={}".format(value_list)  # 这里是访问的url
    request = requests.get(url)
    if request.status_code == 200:
        return pd.read_json(request.text)


def main():
    series = pd.Series(data=np.random.rand(100), index=pd.date_range(start="20190101", periods=100))
    # 远程
    ext_feature = get_line_features_service(series.values.tolist())

    print(ext_feature.shape)


if __name__ == '__main__':
    main()
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由于对数据的变化、加载库等耗时或固定的操作都写在了web服务中，因此主程序也看着简单很多

完整代码

代码文件1：提供web服务

from fastapi import FastAPI
import uvicorn
from tsfresh import extract_features
import pandas as pd

app = FastAPI()


def get_line_features(data_list: list):
    """得到曲线的特征"""
    # 将 list 转为 dataframe
    covert_df = pd.DataFrame(data=[['id_1'] * len(data_list), data_list], index=['id', "value"]).T
    covert_df['value'] = covert_df['value'].astype("float")
    # 使用 tsfresh 提取特征
    ext_feature = extract_features(covert_df, column_id="id")
    ext_feature = ext_feature.dropna(axis=1)
    return ext_feature


@app.get("/data={data}")
async def data_service(data: str):
    data_list: list = eval(data)
    feature = get_line_features(data_list)
    return feature.to_dict()


if __name__ == "__main__":
    uvicorn.run(app, host='127.0.0.1', port=8999, debug=True)
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代码文件2：常规读取、WEB读取

import pandas as pd
import numpy as np


def convert_to_series(series: pd.Series):
    covert_df = pd.DataFrame(data=[['id'] * series.shape[0], series.values], index=['id', "value"]).T
    covert_df['value'] = covert_df['value'].astype("float")
    return covert_df


def get_line_features(dataframe: pd.DataFrame):
    """本地：得到曲线的特征"""
    from tsfresh import extract_features
    ext_feature = extract_features(dataframe, column_id="id")
    ext_feature = ext_feature.dropna(axis=1)
    return ext_feature

def get_line_features_service(value_list):
    """使用服务：得到曲线的特征"""
    import requests
    url = "http://127.0.0.1:8999/data={}".format(value_list)  # 这里是访问的url
    request = requests.get(url)
    if request.status_code == 200:
        return pd.read_json(request.text)


def main():
    series = pd.Series(data=np.random.rand(100), index=pd.date_range(start="20190101", periods=100))
    # 本地
    conv_df = convert_to_series(series)
    ext_feature = get_line_features(conv_df)
    # 远程
    ext_feature = get_line_features_service(series.values.tolist())

    print(ext_feature.shape)


if __name__ == '__main__':
    main()
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