Мобильная версия- У меня есть обученная модель Модель XGB, использующая некоторые функции (с плавающей запятой) для прогнозирования одного
значения (интенсивности выбросов углерода). - Я хочу сохранить модель в виде файла и загрузить модель для использования в
Яваскрипт. Он попросит пользователя ввести одну запись (22 функции) в JavaScript и перейти к модели ML, которая, наконец, вернет и распечатает прогнозируемое значение (интенсивность выбросов углекислого газа). - Я знаю эту модель можно сохранить как файл json/pickle/joblib.
# 1. Load the Data
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# Load the dataset from an Excel file
df = pd.read_excel(r"C:\Users\RY\Desktop\Jupiter\test.xlsx", sheet_name="Sheet1")
# Keep only the relevant columns (excluding the first two columns)
df = df.iloc[:, 2:]
# Categorize features based on their module (boiler, turbine, power, coal, carbon emissions)
# Features: 1(date) + 7(boiler) + 6(turbine) + 3(power) + 5(coal quality) + 2(carbon emissions) + 1(runtime) + 1(predicted carbon intensity)
# Define feature names for each module (adjust based on your actual column names)
boiler_features = ['Boiler Feedwater Temperature', 'Air Supply Temperature', 'Oxygen Level',
'Air Preheater Leakage Rate', 'Calculated Flue Gas Temperature',
'Oxygen Content (%)', 'Flue Gas Flow Rate']
turbine_features = ['Main Steam Temperature', 'Main Steam Pressure', 'Reheat Steam Temperature',
'Exhaust Steam Temperature', 'Vacuum', 'Average Load']
power_features = ['Load', 'Plant Power Consumption Rate', 'Operating Hours']
coal_features = ['Carbon Content (Air Dry, %)', 'Volatile Matter (Received, %)',
'Ash Content (Received, %)', 'Net Calorific Value (kJ/kg, Received)',
'Moisture Content (%)']
carbon_emission_features = ['CO2 Concentration', 'Carbon Emission', 'Carbon Intensity']
# Combine features in the specified order
sorted_columns = boiler_features + turbine_features + power_features + coal_features + carbon_emission_features
df = df[sorted_columns]
# Split data into features (X1) and target (y1)
X1 = df.drop('Carbon Intensity', axis=1).drop('Carbon Emission', axis=1)
y1 = df['Carbon Intensity']
# 2. Split Data into Training and Testing Sets
from sklearn.model_selection import train_test_split
X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.2, random_state=42)
# Display basic statistics of the feature dataset
X1.describe()
# 3. Train the Model
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error
# Initialize the XGBoost regressor
xgb_model = XGBRegressor(
n_estimators=300, # Number of trees
max_depth=3, # Maximum tree depth
learning_rate=0.1, # Learning rate
subsample=0.8, # Fraction of samples per tree
colsample_bytree=0.8, # Fraction of features per tree
random_state=42
)
# Train the model on the training data
xgb_model.fit(X1_train, y1_train)
# Make predictions on the test set
xgb_y1_pred = xgb_model.predict(X1_test)
# 4. Make Predictions on one record
# Example: Predict carbon intensity for a single data point
xgb_model.predict([X1_train.iloc[0].values])
# 5. Save the Model
# Save the trained model as a JSON file
xgb_model.save_model("xgb_model.json")
как сохранить, а затем загрузить модель в javascript?
Пример ввода и вывода одной записи:
[array([ 2.42860000e+02, 1.97800000e+01, 4.59000000e+00, 2.43000000e+00,
1.27770000e+02, 9.60153257e-02, 6.51605003e+05, 5.42320000e+02,
1.26000000e+01, 5.12910000e+02, 3.18000000e+01, -9.78800000e+01,
1.97080000e+02, 1.93096475e+02, 5.45000000e+00, 2.40000000e+01,
5.36500000e+01, 2.41800000e+01, 2.18900000e+01, 1.86225000e+04,
1.53000000e+01, 3.33225000e+03])]
array([865.6147], dtype=float32)
Подробнее здесь: https://stackoverflow.com/questions/792 ... to-call-th
