用Python安排培训和测试脚本

我有一个简单的随机森林回归模型，它进行训练和测试，然后打印预测和模型精度。我想写一个python自动脚本来安排这段代码，它将每月进行一次培训，每周自动进行一次测试

型号代码：

from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np

import pandas as pd
from main import data as df


class Model():
    def __init__(self):
        self.df = df
        self.linear_reg = LinearRegression()
        self.random_forest = RandomForestRegressor()
    def split(self, test_size):
        X = np.array(self.df[['age','experience','education','certificates']])
        y = np.array(self.df['salary'])
        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size = test_size, random_state = 42)

    def fit(self):
        self.model = self.random_forest.fit(self.X_train, self.y_train)

    def predict(self):

        self.result = self.random_forest.predict(self.X_test)
        return self.result


if __name__ == '__main__':
    model_instance = Model()
    model_instance.split(0.2)
    model_instance.fit()
    model_instance.predict()
    print(model_instance.result)
    print("Accuracy: ", model_instance.model.score(model_instance.X_test, model_instance.y_test))

这是我想每月安排一次的培训部分

    def fit(self):
        self.model = self.random_forest.fit(self.X_train, self.y_train)

这是我想每周安排一次的测试部分

     def predict(self):

        self.result = self.random_forest.predict(self.X_test)
        return self.result

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如何编写在所述时间范围内安排培训和测试的代码？

可以使用以下方法实现以下解决方案：

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你的问题是什么？我想写一个python脚本，用于每月和每周自动安排培训和测试。Brian问的是：你想在计划或培训+测试方面得到帮助吗？您希望解决的实际问题是什么？然而，你这篇文章的目的是什么？如果你每月训练一次，每3周的测试结果将保持不变。@LeonidGlanz是的，我需要帮助安排训练+测试。测试数据将每周不断变化。请您帮忙。我已经添加了一个答案，使用模块时间表来执行所需的任务。非常感谢。另外，如何在云上实现自动化？云上的自动化取决于云提供商，但这将是另一个问题。

import schedule
import time
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np

import pandas as pd
from main import data as df

class Model():
    def __init__(self):
        self.df = df
        self.linear_reg = LinearRegression()
        self.random_forest = RandomForestRegressor()
    def split(self, test_size):
        X = np.array(self.df[['age','experience','education','certificates']])
        y = np.array(self.df['salary'])
        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size = test_size, random_state = 42)

    def fit(self):
        self.model = self.random_forest.fit(self.X_train, self.y_train)

    def predict(self):

        self.result = self.random_forest.predict(self.X_test)
        print(self.result)
        print("Accuracy: ", self.model.score(self.X_test, self.y_test))


if __name__ == '__main__':
    model_instance = Model()
    model_instance.split(0.2)
    schedule.every(28).day.at("07:00").do(model_instance.fit())
    schedule.every(7).day.at("07:00").do(model_instance.predict())
    while 1:
        schedule.run_pending()
        time.sleep(1)