Gitlab CI/CD错误机器学习Python
我已经在Modelrf类中编写了一个随机森林回归模型。它是一个单独的python文件,名为RandomForest.py RandomForest.py的代码:Gitlab CI/CD错误机器学习Python,python,git,machine-learning,continuous-integration,gitlab,Python,Git,Machine Learning,Continuous Integration,Gitlab,我已经在Modelrf类中编写了一个随机森林回归模型。它是一个单独的python文件,名为RandomForest.py RandomForest.py的代码: from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split import numpy as
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
class Modelrf():
def __init__(self, train = "train.csv", test = "test.csv"):
self.X_train = pd.read_csv(train)
self.X_test = pd.read_csv(test)
self.linear_reg = LinearRegression()
self.random_forest = RandomForestRegressor()
def split(self):
self.X_train.dropna(axis=0, subset=['salary'], inplace=True)
self.X_test.dropna(axis=0, subset=['salary'], inplace=True)
self.y_train = self.X_train.final_hourly_fee
self.y_test = self.X_test.final_hourly_fee
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 = Modelrf()
model_instance.split()
model_instance.fit()
model_instance.predict()
print(model_instance.result)
print("Accuracy: ", model_instance.model.score(model_instance.X_test, model_instance.y_test))
output = pd.DataFrame({'Id': model_instance.X_test.index,'Y Original': model_instance.y_test, 'Y predicted':model_instance.result})
output.to_csv('outputTest.txt', index=False)
现在,我已经将类Modelrf导入main.py
main.py的代码:
import pandas as pd
import numpy as np
from RandomForest import Modelrf
from RandomForest import X_test,y_test,result
if __name__ == '__main__':
model_instance = Modelrf()
model_instance.split()
model_instance.fit()
model_instance.predict()
print(model_instance.result)
print("Accuracy: ", model_instance.model.score(model_instance.X_test, model_instance.y_test))
output = pd.DataFrame({'Id': model_instance.X_test.index,'Y Original': model_instance.y_test, 'Y predicted':model_instance.result})
output.to_csv('outputTest.txt', index=False)
我想在gitlab上启用CI/CD如何编写“.gitlab CI.yml”文件代码?如果我正确理解了情况,您需要转到存储库: 设置>CI/CD>跑步者 然后,您需要决定是使用私人跑步者还是共享跑步者:
您包含了python代码,但您的CI没有使用python代码,您的问题是关于通用CI?@TinNguyen是的。我想知道我应该以何种方式编写.yml文件,以便CI能够推送它。共享运行程序是否可以使用我创建的.yml文件?您使用了标记“CI”,我不知道是否有带有此标记的共享运行程序。跑步者拾取与其标记集匹配的任务:这些标记通常指示跑步者是否支持运行CI阶段所需的技术。在您的情况下,由于您的脚本,您需要一个带有标记“shell”或“linux”等的运行程序。那么我应该如何选择一个兼容的运行程序?另外,当我创建运行程序时,我已经将shell添加为标记,请确保在“设置”>“CI/CD”>“运行程序”下激活共享运行程序,然后标记“gitlab org”就可以了