refactoring: add initial pipeline configuration and model classes

research/models/lightgbm_model.py

@@ -0,0 +1,51 @@
+import lightgbm as lgb
+import numpy as np
+import pandas as pd
+from sklearn.base import BaseEstimator
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import LabelEncoder
+
+from research.traditional_model import TraditionalModel
+
+
+class LightGBMModel(TraditionalModel):
+    """LightGBM with engineered features"""
+
+    def build_model(self) -> BaseEstimator:
+        params = self.config.model_params
+
+        return lgb.LGBMClassifier(
+            n_estimators=params.get("n_estimators", 100),
+            max_depth=params.get("max_depth", -1),
+            learning_rate=params.get("learning_rate", 0.1),
+            num_leaves=params.get("num_leaves", 31),
+            subsample=params.get("subsample", 0.8),
+            colsample_bytree=params.get("colsample_bytree", 0.8),
+            random_state=self.config.random_seed,
+            verbose=-1,
+        )
+
+    def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
+        features = []
+
+        for feature_type in self.config.features:
+            if feature_type.value in X.columns:
+                column = X[feature_type.value]
+
+                if feature_type.value in ["name_length", "word_count"]:
+                    features.append(column.fillna(0).values.reshape(-1, 1))
+                elif feature_type.value in ["full_name", "native_name", "surname"]:
+                    # Character n-grams for text features
+                    vectorizer = CountVectorizer(
+                        analyzer="char", ngram_range=(2, 3), max_features=50
+                    )
+                    char_features = vectorizer.fit_transform(
+                        column.fillna("").astype(str)
+                    ).toarray()
+                    features.append(char_features)
+                else:
+                    le = LabelEncoder()
+                    encoded = le.fit_transform(column.fillna("unknown").astype(str))
+                    features.append(encoded.reshape(-1, 1))
+
+        return np.hstack(features) if features else np.array([]).reshape(len(X), 0)