Python 将预测结果保存为 CSV
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Saving prediction results to CSV
提问by ZJAY
I am storing the results from a sklearn regression model to the varibla prediction.
我将 sklearn 回归模型的结果存储到 varibla 预测。
prediction = regressor.predict(data[['X']])
print(prediction)
The values of the prediction output looks like this
预测输出的值如下所示
[ 266.77832991 201.06347505 446.00066136 499.76736079 295.15519906
214.50514991 422.1043505 531.13126879 287.68760191 201.06347505
402.68859792 478.85808879 286.19408248 192.10235848]
I am then trying to use the to_csv function to save the results to a local CSV file:
然后我尝试使用 to_csv 函数将结果保存到本地 CSV 文件：
prediction.to_csv('C:/localpath/test.csv')
But the error I get back is:
但我回来的错误是：
AttributeError: 'numpy.ndarray' object has no attribute 'to_csv'
I am using Pandas/Numpy/SKlearn. Any idea on the basic fix?
我正在使用 Pandas/Numpy/SKlearn。关于基本修复的任何想法？
回答by DavidK
You can use pandas. As it's said, numpy arrays don't have a to_csv function.
你可以使用熊猫。如前所述，numpy 数组没有 to_csv 函数。
import numpy as np
import pandas as pd
prediction = pd.DataFrame(predictions, columns=['predictions']).to_csv('prediction.csv')
add ".T" if you want either your values in line or column-like.
如果您希望您的值成行或列状，请添加“.T”。
回答by Ali
You can use the numpy.savetxtfunction:
您可以使用该numpy.savetxt功能：
numpy.savetxt('C:/localpath/test.csv',prediction, ,delimiter=',')
and to load a CSV file you can use numpy.genfromtxtfunction:
并加载一个 CSV 文件，您可以使用numpy.genfromtxt函数：
numpy.genfromtxt('C:/localpath/test.csv', delimiter=',')
回答by Ilker Kurtulus
It is a very detailed solution cases like those but you can use it even in production.
这是一个非常详细的解决方案案例，但您甚至可以在生产中使用它。
First Save the Model
首先保存模型
joblib.dump(regressor, "regressor.sav")
Save columns in order
按顺序保存列
pd.DataFrame(X_train.columns).to_csv("feature_list.csv", index = None)
Save data types of train set
保存训练集的数据类型
pd.DataFrame(X_train.dtypes).reset_index().to_csv("data_types.csv", index = None)
Using it again:
再次使用它：
feature_list = pd.read_csv("feature_list.csv")
feature_list = pd.Index(list(feature_list["0"]))
add_cols = list(feature_list.difference(X_test.columns))
drop_cols = list(X_test.columns.difference(feature_list))
for col in add_cols:
X_test[col] = np.nan
for col in drop_cols:
X_test = X_test.drop(col, axis = 1)
#?reorder columns
X_test = X_test[feature_list]
types = pd.read_csv("data_types.csv")
for i in range(len(types)):
X_test[types.iloc[i,0]] = X_test[types.iloc[i,0]].astype(types.iloc[i,1])
Make Predictions
作出预测
regressor = joblib.load("regressor.sav")
predictions = regressor.predict(X_test)
Save Prediction Results
保存预测结果
res = pd.DataFrame(predictions)
res.index = X_test.index # its important for comparison
res.columns = ["prediction"]
res.to_csv("prediction_results.csv")
Enjoy end to end model/prediction saver code!
享受端到端模型/预测保护程序代码！
