refactor: include province and annotation pipeline

pipeline/gender/models/__init__.py

@@ -0,0 +1,80 @@
+import argparse
+from dataclasses import dataclass
+from typing import Optional
+
+from sklearn.metrics import (
+    accuracy_score, precision_recall_fscore_support,
+    classification_report, confusion_matrix
+)
+
+from misc import logging
+
+def evaluate_proba(y_true, y_proba, threshold, class_names):
+    y_pred = (y_proba[:, 1] >= threshold).astype(int)
+    acc = accuracy_score(y_true, y_pred)
+    pr, rc, f1, _ = precision_recall_fscore_support(y_true, y_pred, average="binary")
+    cm = confusion_matrix(y_true, y_pred)
+
+    logging.info(f"Accuracy: {acc:.4f} | Precision: {pr:.4f} | Recall: {rc:.4f} | F1: {f1:.4f}")
+    print("Confusion Matrix:\n", cm)
+    print("\nClassification Report:\n", classification_report(y_true, y_pred, target_names=class_names))
+
+
+@dataclass
+class BaseConfig:
+    """
+    Represents the base configuration for a dataset and its associated parameters.
+
+    This class serves as a foundational configuration handler to encapsulate
+    dataset-related parameters and options. It allows customization of dataset
+    behavior, including threshold values, size, cross-validation settings, and
+    whether to save derived configurations. It can also manage configurations
+    for balanced datasets if necessary.
+    """
+    dataset_path: str = "names_featured.csv"
+    size: Optional[int] = None
+    threshold: float = 0.5
+    cv: Optional[int] = None
+    save: bool = False
+    balanced: bool = False
+
+    epochs: int = 10
+    test_size: float = 0.2
+    random_state: int = 42
+
+
+def load_config(description: str) -> BaseConfig:
+    """
+    Parses command-line arguments and loads the configuration for the logistic regression model.
+
+    This function sets up an argument parser for various command-line options including
+    the dataset path, dataset size, dataset balancing, classification threshold,
+    cross-validation folds, and saving the model and its associated artifacts. Once parsed,
+    it transfers the configurations to a ``BaseConfig`` instance and returns it.
+    """
+    parser = argparse.ArgumentParser(description)
+
+    parser.add_argument("--dataset", type=str, default="names_featured.csv", help="Path to the dataset file")
+    parser.add_argument("--size", type=int, help="Number of rows to load from the dataset")
+    parser.add_argument("--balanced", action="store_true", help="Load balanced dataset")
+    parser.add_argument("--threshold", type=float, default=0.5, help="Probability threshold for classification")
+    parser.add_argument("--cv", type=int, help="Number of folds for cross-validation")
+    parser.add_argument("--save", action="store_true", help="Save the model and artifacts after training")
+
+    parser.add_argument("--epochs", type=int, default=10, help="Number of epochs for training")
+    parser.add_argument("--test_size", type=float, default=0.2, help="Proportion of the dataset to include in the test split")
+    parser.add_argument("--random_state", type=int, default=42, help="Random seed for reproducibility")
+
+    args = parser.parse_args()
+
+    return BaseConfig(
+        dataset_path=args.dataset,
+        size=args.size,
+        threshold=args.threshold,
+        cv=args.cv,
+        save=args.save,
+        balanced=args.balanced,
+        epochs=args.epochs,
+        test_size=args.test_size,
+        random_state=args.random_state
+    )