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Model Module for Daily Household Electricity Consumption Predictor
This module handles data preprocessing, model training, evaluation, and prediction
for the electricity consumption prediction model.
"""
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
import joblib
from typing import Tuple, Dict, Any, Optional
import os
class ElectricityConsumptionModel:
"""Linear regression model for predicting daily electricity consumption."""
def __init__(self):
"""Initialize the model with preprocessing pipeline."""
self.model = None
self.preprocessor = None
self.feature_names = None
self.is_trained = False
def _create_preprocessor(self) -> ColumnTransformer:
"""
Create preprocessing pipeline for the features.
Returns:
ColumnTransformer with preprocessing steps
"""
# Numerical features (temperature)
numerical_features = ["temperature"]
numerical_transformer = StandardScaler()
# Categorical features (day_of_week)
categorical_features = ["day_of_week"]
categorical_transformer = OneHotEncoder(drop="first", sparse=False)
# Boolean features (major_event) - no transformation needed
boolean_features = ["major_event"]
boolean_transformer = "passthrough"
# Combine all transformers
preprocessor = ColumnTransformer(
transformers=[
("num", numerical_transformer, numerical_features),
("cat", categorical_transformer, categorical_features),
("bool", boolean_transformer, boolean_features),
],
remainder="drop",
)
return preprocessor
def _create_pipeline(self) -> Pipeline:
"""
Create the complete model pipeline.
Returns:
Pipeline with preprocessing and model
"""
preprocessor = self._create_preprocessor()
model = LinearRegression()
pipeline = Pipeline([("preprocessor", preprocessor), ("regressor", model)])
return pipeline
def prepare_features(self, data: pd.DataFrame) -> pd.DataFrame:
"""
Prepare features for training/prediction.
Args:
data: Input DataFrame with raw features
Returns:
DataFrame with prepared features
"""
required_columns = ["temperature", "day_of_week", "major_event"]
# Validate input data
missing_columns = [col for col in required_columns if col not in data.columns]
if missing_columns:
raise ValueError(f"Missing required columns: {missing_columns}")
# Validate data types and ranges
if not all(data["temperature"].between(15, 35)):
raise ValueError("Temperature must be between 15 and 35 degrees Celsius")
valid_days = [
"Monday",
"Tuesday",
"Wednesday",
"Thursday",
"Friday",
"Saturday",
"Sunday",
]
if not all(day in valid_days for day in data["day_of_week"].unique()):
raise ValueError(f"Day of week must be one of: {valid_days}")
if not all(data["major_event"].isin([0, 1])):
raise ValueError("Major event must be 0 or 1")
return data[required_columns].copy()
def train(self, X_train: pd.DataFrame, y_train: pd.DataFrame) -> Dict[str, float]:
"""
Train the model on the provided data.
Args:
X_train: Training features
y_train: Training targets
Returns:
Dictionary with training metrics
"""
# Prepare features
X_prepared = self.prepare_features(X_train)
# Create and train pipeline
self.model = self._create_pipeline()
self.model.fit(X_prepared, y_train["consumption_kwh"])
# Store feature names for later use
self.feature_names = X_prepared.columns.tolist()
self.is_trained = True
# Calculate training metrics
y_pred = self.model.predict(X_prepared)
metrics = {
"train_mse": mean_squared_error(y_train["consumption_kwh"], y_pred),
"train_rmse": np.sqrt(
mean_squared_error(y_train["consumption_kwh"], y_pred)
),
"train_mae": mean_absolute_error(y_train["consumption_kwh"], y_pred),
"train_r2": r2_score(y_train["consumption_kwh"], y_pred),
}
return metrics
def evaluate(self, X_test: pd.DataFrame, y_test: pd.DataFrame) -> Dict[str, float]:
"""
Evaluate the model on test data.
Args:
X_test: Test features
y_test: Test targets
Returns:
Dictionary with evaluation metrics
"""
if not self.is_trained:
raise ValueError("Model must be trained before evaluation")
# Prepare features
X_prepared = self.prepare_features(X_test)
# Make predictions
y_pred = self.model.predict(X_prepared)
# Calculate metrics
metrics = {
"test_mse": mean_squared_error(y_test["consumption_kwh"], y_pred),
"test_rmse": np.sqrt(mean_squared_error(y_test["consumption_kwh"], y_pred)),
"test_mae": mean_absolute_error(y_test["consumption_kwh"], y_pred),
"test_r2": r2_score(y_test["consumption_kwh"], y_pred),
}
return metrics
def predict(self, temperature: float, day_of_week: str, major_event: int) -> float:
"""
Make a single prediction.
Args:
temperature: Average daily temperature in Celsius
day_of_week: Day of the week
major_event: Whether there's a major event (0 or 1)
Returns:
Predicted electricity consumption in kWh
"""
if not self.is_trained:
raise ValueError("Model must be trained before making predictions")
# Create input DataFrame
input_data = pd.DataFrame(
{
"temperature": [temperature],
"day_of_week": [day_of_week],
"major_event": [major_event],
}
)
# Prepare features
X_prepared = self.prepare_features(input_data)
# Make prediction
prediction = self.model.predict(X_prepared)[0]
return max(0, prediction) # Ensure non-negative prediction
def get_model_coefficients(self) -> Dict[str, Any]:
"""
Get model coefficients and feature names.
Returns:
Dictionary with model coefficients and feature information
"""
if not self.is_trained:
raise ValueError("Model must be trained before accessing coefficients")
# Get feature names from preprocessor
preprocessor = self.model.named_steps["preprocessor"]
feature_names = []
# Numerical features
feature_names.extend(["temperature"])
# Categorical features (one-hot encoded)
cat_transformer = preprocessor.named_transformers_["cat"]
day_names = [
"Tuesday",
"Wednesday",
"Thursday",
"Friday",
"Saturday",
"Sunday",
] # Monday is dropped
feature_names.extend([f"day_{day.lower()}" for day in day_names])
# Boolean features
feature_names.extend(["major_event"])
# Get coefficients
coefficients = self.model.named_steps["regressor"].coef_
intercept = self.model.named_steps["regressor"].intercept_
return {
"feature_names": feature_names,
"coefficients": coefficients.tolist(),
"intercept": float(intercept),
}
def save_model(self, filepath: str) -> None:
"""
Save the trained model to disk.
Args:
filepath: Path to save the model
"""
if not self.is_trained:
raise ValueError("Model must be trained before saving")
# Create directory if it doesn't exist
os.makedirs(os.path.dirname(filepath), exist_ok=True)
# Save model
joblib.dump(self.model, filepath)
def load_model(self, filepath: str) -> None:
"""
Load a trained model from disk.
Args:
filepath: Path to the saved model
"""
if not os.path.exists(filepath):
raise FileNotFoundError(f"Model file not found: {filepath}")
self.model = joblib.load(filepath)
self.is_trained = True
# Extract feature names from the loaded model
preprocessor = self.model.named_steps["preprocessor"]
self.feature_names = ["temperature", "day_of_week", "major_event"]
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