如何使用 JMP18 Python 腳本執行機器學習建模？你好，
我正在使用 JMP Python 腳本 (JMP18) 建立 LightGBM 模型。
我目前的設定如下：
- X 變數：「train1.csv」文件，D1 至 D1776 列
- Y 變數：「y1.csv」文件，「活動」列
- 預測目標「Activity」：「test1.csv」文件，D1 至 D1776 列
- 注意：原始文件「train_original.csv」文件，列「活動」至 D1776。來自 Kaggle.com（ 預測生物反應）
如果有人能為以下問題提供指導，我將不勝感激：
- 無法在 JMP18 python 環境中執行 LightGBM 建模
我在下麵包含了我的 JMP Python 腳本供您參考。詳情請參閱以下內容。
先感謝您的寶貴時間！ 
import jmp
import jmputils
# update to latest version of pip (Package Installer of python) and setuptools then install numpy & pandas
jmputils.jpip('install --upgrade', 'pip setuptools')
jmputils.jpip('install', 'pandas numpy scikit-learn keras lightgbm')
# Import package
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Load data
train1 = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/train1.csv')
y1 = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/y1.csv')
test1 = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/test1.csv')
train1.head() # No Result in JMP, but it's okay. It's fine to open the datatable using 'jmp.open'
y1.head() # No Result in JMP, but it's okay. It's fine to open the datatable using 'jmp.open'
# train1 = jmp.open('D:/steve.kim/Kaggle/BI Biological Response/train1.csv')
# y1 = jmp.open('D:/steve.kim/Kaggle/BI Biological Response/y1.csv')
# 01 Modeling training - Library import
from lightgbm import LGBMClassifier
# 02 Modeling training - LGBM Baseline model without hyperparameter tuning
lgb = LGBMClassifier()
# 03 Modeling training - define X (factors) and Y (responses) variables
lgb.fit(train1, y1) # lightgbm.basic.LightGBMError: Length of labels differs from the length of #data
# 04 Predict
predslgb = lgb.predict_proba(test1)
預測建模和機器學習回覆：如何使用 JMP18 Python 腳本執行機器學習建模 (LBM)？哦...在我將程式碼“head()”視為終端環境而不是 jupyter 筆記本之後，它運作得很好！
y1 = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/y1.csv')
print(y1.head()) 然後我可以從嵌入的日誌中獲取程式碼結果！
所以我想我也可以解決其餘的事情。
Activity
0 1
1 1
2 1
3 1
4 1
回覆：如何使用 JMP18 Python 腳本執行機器學習建模？
抱歉問這個問題！
python pandas 和 lightGBM 在 JMP18 python 腳本中運作良好！
import jmp
import jmputils
# update to latest version of pip (Package Installer of python) and setuptools then install numpy & pandas
jmputils.jpip('install --upgrade', 'pip setuptools')
jmputils.jpip('install', 'pandas numpy scikit-learn keras lightgbm')
# Checking package version
jmputils.jpip('list')
# Import package
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Any results you write to the current directory are saved as output.
import os
print (os.listdir("D:/steve.kim/Kaggle/BI Biological Response"))
# Load data
train_x = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/train_x.csv')
print(train_x.head())
train_y = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/train_y.csv')
print(train_y.head())
test_x = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/test_x.csv')
print(test_x)
# Modeling
from lightgbm import LGBMClassifier
lgb = LGBMClassifier(colsample_bytree=0.6, subsample=0.8)
lgb.fit(train_x, train_y)
preds_lgb = lgb.predict_proba(test_x)
sub_lgb = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/test_y.csv')
sub_lgb["Activity"] = preds_lgb[:,1]
print(sub_lgb.head())
test_answer = pd.read_csv('D:/steve.kim/Kaggle/BI Biological Response/test_answer.csv')
# Evaluation
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
print('LightGBM MAE')
print(mean_absolute_error(test_answer, sub_lgb))
print('LightGBM MSE')
print(mean_squared_error(test_answer, sub_lgb))
