refactor: refactor code to use new Config class

main.py

@@ -2,15 +2,16 @@
 
 from loguru import logger
 
+from power_consumption.config import Config
 from power_consumption.model import ConsumptionModel
 from power_consumption.preprocessing import DataProcessor
-from power_consumption.utils import load_config, plot_actual_vs_predicted, plot_feature_importance, visualise_results
+from power_consumption.utils import plot_actual_vs_predicted, plot_feature_importance, visualise_results
 
 # Load configuration
-config = load_config("./configs/project_configs.yml")
+config = Config.from_yaml("./configs/project_configs.yml")
 
 # Initialise data processor and preprocess data
-processor = DataProcessor(dataset_id=849, config=config)
+processor = DataProcessor(config=config)
 processor.preprocess_data()
 
 # Split data into train and test sets
@@ -31,17 +32,17 @@
 
 # Log Mean Squared Error for each target
 logger.info("Mean Squared Error for each target:")
-for i, target in enumerate(config["target"]):
+for i, target in enumerate(config.target.target):
     logger.info(f"{target}: {mse[i]:.4f}")
 
 # Log R-squared for each target
 logger.info("\nR-squared for each target:")
-for i, target in enumerate(config["target"]):
+for i, target in enumerate(config.target.target):
     logger.info(f"{target}: {r2[i]:.4f}")
 
 # Visualise results
-visualise_results(y_test, y_pred, config["target"])
-plot_actual_vs_predicted(y_test.values, y_pred, config["target"])
+visualise_results(y_test, y_pred, config.target.target)
+plot_actual_vs_predicted(y_test.values, y_pred, config.target.target)
 
 # Get and plot feature importance
 feature_importance, feature_names = model.get_feature_importance()

notebooks/week_1.ipynb

@@ -8,17 +8,16 @@
    "source": [
     "from power_consumption.preprocessing import DataProcessor\n",
     "from power_consumption.model import ConsumptionModel\n",
-    "\n",
-    "from power_consumption.utils import load_config"
+    "from power_consumption.config import Config"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "config = load_config(\"../configs/project_configs.yml\")"
+    "config = Config.from_yaml(\"../configs/project_configs.yml\")"
    ]
   },
   {
@@ -27,7 +26,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "processor = DataProcessor(dataset_id=849, config=config)\n",
+    "\n",
+    "processor = DataProcessor(config=config)\n",
     "processor.preprocess_data()\n",
     "X_train, X_test, y_train, y_test = processor.split_data()\n",
     "X_train_transformed, X_test_transformed = processor.fit_transform_features(X_train, X_test)"
@@ -76,11 +76,11 @@
    "outputs": [],
    "source": [
     "print(\"Mean Squared Error for each target:\")\n",
-    "for i, target in enumerate(config['target']):\n",
+    "for i, target in enumerate(config.target.target):\n",
     "    print(f\"{target}: {mse[i]:.4f}\")\n",
     "\n",
     "print(\"\\nR-squared for each target:\")\n",
-    "for i, target in enumerate(config['target']):\n",
+    "for i, target in enumerate(config.target.target):\n",
     "    print(f\"{target}: {r2[i]:.4f}\")"
    ]
   },
@@ -99,8 +99,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "visualise_results(y_test, y_pred, config['target'])\n",
-    "plot_actual_vs_predicted(y_test.values, y_pred, config['target'])\n",
+    "visualise_results(y_test, y_pred, config.target.target)\n",
+    "plot_actual_vs_predicted(y_test.values, y_pred, config.target.target)\n",
     "\n",
     "feature_importance, feature_names = model.get_feature_importance()\n",
     "plot_feature_importance(feature_importance, feature_names)"

power_consumption/model/rf_model.py

@@ -2,7 +2,7 @@
 Main module for the power consumption model.
 """
 
-from typing import Dict, Self, Tuple
+from typing import Self, Tuple
 
 import numpy as np
 from sklearn.compose import ColumnTransformer
@@ -11,6 +11,8 @@
 from sklearn.multioutput import MultiOutputRegressor
 from sklearn.pipeline import Pipeline
 
+from power_consumption.config import Config
+
 
 class ConsumptionModel:
     """
@@ -31,7 +33,7 @@ class ConsumptionModel:
         The complete model pipeline including preprocessing and regression steps.
     """
 
-    def __init__(self, preprocessor: ColumnTransformer, config: Dict) -> None:
+    def __init__(self, preprocessor: ColumnTransformer, config: Config) -> None:
         """
         Initialise the ConsumptionModel.
         """
@@ -43,8 +45,8 @@ def __init__(self, preprocessor: ColumnTransformer, config: Dict) -> None:
                     "regressor",
                     MultiOutputRegressor(
                         RandomForestRegressor(
-                            n_estimators=config["parameters"]["n_estimators"],
-                            max_depth=config["parameters"]["max_depth"],
+                            n_estimators=config.hyperparameters.n_estimators,
+                            max_depth=config.hyperparameters.max_depth,
                             random_state=42,
                         )
                     ),

power_consumption/preprocessing/data_preprocessor.py

@@ -1,7 +1,9 @@
 """Data preprocessing module for the power consumption dataset."""
 
+from __future__ import annotations
+
 import os
-from typing import Any, Dict, Optional, Tuple
+from typing import Optional, Tuple
 
 import numpy as np
 import pandas as pd
@@ -12,18 +14,19 @@
 from sklearn.preprocessing import OneHotEncoder, StandardScaler
 from ucimlrepo import fetch_ucirepo
 
+from power_consumption.config import Config
 from power_consumption.schemas.processed_data import PowerConsumptionSchema
 
 
 class DataProcessor:
-    def __init__(self, config: Dict[str, Any]):
+    def __init__(self, config: Config):
         """
         Initialise the DataProcessor.
         """
         self.data: Optional[pd.DataFrame] = None
         self.X: Optional[pd.DataFrame] = None
         self.y: Optional[pd.DataFrame] = None
-        self.config: Dict[str, Any] = config
+        self.config: Config = config
         self.preprocessor: Optional[ColumnTransformer] = None
         self.load_data()
 
@@ -41,7 +44,7 @@ def load_data(self) -> None:
         If loading from UCI ML Repository fails, the method will attempt to load
         the data from '../data/Tetuan City power consumption.csv'.
         """
-        dataset_id = self.config["dataset"]["id"]
+        dataset_id = self.config.dataset.id
         try:
             dataset = fetch_ucirepo(id=dataset_id)
             logger.info(
@@ -76,8 +79,8 @@ def create_preprocessor(self) -> ColumnTransformer:
         ColumnTransformer
             The preprocessing pipeline.
         """
-        numeric_features = self.config["num_features"]
-        categorical_features = self.config["cat_features"]
+        numeric_features = self.config.features.num_features
+        categorical_features = self.config.features.cat_features
 
         logger.info(f"Numeric features: {numeric_features}")
         logger.info(f"Categorical features: {categorical_features}")
@@ -141,8 +144,8 @@ def split_data(self, test_size: float = 0.2) -> Tuple[pd.DataFrame, pd.DataFrame
         tuple of pd.DataFrame
             X_train, X_test, y_train, y_test
         """
-        target_columns = self.config["target"]
-        feature_columns = self.config["num_features"] + self.config["cat_features"]
+        target_columns = self.config.target.target
+        feature_columns = self.config.features.num_features + self.config.features.cat_features
 
         X = self.data[feature_columns]
         y = self.data[target_columns]

power_consumption/utils.py

@@ -1,30 +1,7 @@
 """Utility functions for the power consumption project."""
 
-from typing import Any, Dict
-
 import matplotlib.pyplot as plt
 import numpy as np
-import yaml
-from loguru import logger
-
-
-def load_config(config_path: str) -> Dict[str, Any]:
-    """
-    Load configuration from a YAML file.
-
-    Parameters
-    ----------
-    config_path : str
-        Path to the configuration YAML file.
-
-    Returns
-    -------
-    Dict[str, Any]
-        Configuration dictionary.
-    """
-    logger.info(f"Loading configuration from {config_path}")
-    with open(config_path, "r") as file:
-        return yaml.safe_load(file)
 
 
 def visualise_results(y_test, y_pred, target_names):