refactoring: add initial pipeline configuration and model classes

research/experiment/__init__.py

@@ -0,0 +1,91 @@
+from dataclasses import dataclass, field, asdict
+from enum import Enum
+from typing import List, Dict, Any, Optional
+
+import numpy as np
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+
+from .feature_extractor import FeatureType
+
+
+@dataclass
+class ExperimentConfig:
+    """Configuration for a single experiment"""
+
+    # Experiment metadata
+    name: str
+    description: str = ""
+    tags: List[str] = field(default_factory=list)
+
+    # Model configuration
+    model_type: str = "logistic_regression"  # logistic_regression, lstm, transformer, etc.
+    model_params: Dict[str, Any] = field(default_factory=dict)
+
+    # Feature configuration
+    features: List[FeatureType] = field(default_factory=lambda: [FeatureType.FULL_NAME])
+    feature_params: Dict[str, Any] = field(default_factory=dict)
+
+    # Data configuration
+    train_data_filter: Optional[Dict[str, Any]] = None  # Filter criteria for training data
+    test_data_filter: Optional[Dict[str, Any]] = None
+    target_column: str = "sex"
+
+    # Training configuration
+    test_size: float = 0.2
+    random_seed: int = 42
+    cross_validation_folds: int = 5
+
+    # Evaluation configuration
+    metrics: List[str] = field(default_factory=lambda: ["accuracy", "precision", "recall", "f1"])
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization"""
+        result = asdict(self)
+        # Convert enums to strings
+        result["features"] = [f.value for f in self.features]
+        return result
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ExperimentConfig":
+        """Create from dictionary"""
+        if "features" in data:
+            data["features"] = [FeatureType(f) for f in data["features"]]
+        return cls(**data)
+
+
+class ExperimentStatus(Enum):
+    """Experiment execution status"""
+
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+
+
+def calculate_metrics(
+    y_true: np.ndarray, y_pred: np.ndarray, metrics: List[str] = None
+) -> Dict[str, float]:
+    """Calculate specified metrics"""
+
+    if metrics is None:
+        metrics = ["accuracy", "precision", "recall", "f1"]
+
+    results = {}
+
+    if "accuracy" in metrics:
+        results["accuracy"] = accuracy_score(y_true, y_pred)
+
+    if any(m in metrics for m in ["precision", "recall", "f1"]):
+        precision, recall, f1, _ = precision_recall_fscore_support(
+            y_true, y_pred, average="weighted"
+        )
+
+        if "precision" in metrics:
+            results["precision"] = precision
+        if "recall" in metrics:
+            results["recall"] = recall
+        if "f1" in metrics:
+            results["f1"] = f1
+
+    return results