refactoring: add initial pipeline configuration and model classes

research/models/ensemble_model.py

@@ -0,0 +1,97 @@
+import numpy as np
+import pandas as pd
+from sklearn.base import BaseEstimator
+from sklearn.ensemble import VotingClassifier, RandomForestClassifier
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import Pipeline
+
+from research.experiment import ExperimentConfig
+from research.traditional_model import TraditionalModel
+
+
+class EnsembleModel(TraditionalModel):
+    """Ensemble model combining multiple base models"""
+
+    @property
+    def architecture(self) -> str:
+        """Return the architecture type"""
+        return "ensemble"
+
+    def __init__(self, config: ExperimentConfig):
+        super().__init__(config)
+        self.base_models = []
+        self.model_weights = None
+
+    def build_model(self) -> BaseEstimator:
+        params = self.config.model_params
+        base_model_types = params.get(
+            "base_models", ["logistic_regression", "random_forest", "naive_bayes"]
+        )
+
+        # Create base models with simplified configs
+        estimators = []
+        for model_type in base_model_types:
+            if model_type == "logistic_regression":
+                model = Pipeline(
+                    [
+                        (
+                            "vectorizer",
+                            CountVectorizer(analyzer="char", ngram_range=(2, 4), max_features=5000),
+                        ),
+                        (
+                            "classifier",
+                            LogisticRegression(max_iter=1000, random_state=self.config.random_seed),
+                        ),
+                    ]
+                )
+                estimators.append((f"logistic_regression", model))
+
+            elif model_type == "random_forest":
+                model = Pipeline(
+                    [
+                        (
+                            "vectorizer",
+                            TfidfVectorizer(analyzer="char", ngram_range=(2, 3), max_features=3000),
+                        ),
+                        (
+                            "classifier",
+                            RandomForestClassifier(
+                                n_estimators=50, random_state=self.config.random_seed
+                            ),
+                        ),
+                    ]
+                )
+                estimators.append((f"rf", model))
+
+            elif model_type == "naive_bayes":
+                model = Pipeline(
+                    [
+                        (
+                            "vectorizer",
+                            CountVectorizer(analyzer="char", ngram_range=(1, 3), max_features=4000),
+                        ),
+                        ("classifier", MultinomialNB()),
+                    ]
+                )
+                estimators.append((f"nb", model))
+
+        voting_type = params.get("voting", "soft")  # 'hard' or 'soft'
+        return VotingClassifier(estimators=estimators, voting=voting_type)
+
+    def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
+        text_features = []
+
+        for feature_type in self.config.features:
+            if feature_type.value in X.columns:
+                text_features.append(X[feature_type.value].astype(str))
+
+        if len(text_features) == 1:
+            return text_features[0].values
+        else:
+            combined = text_features[0].astype(str)
+            for feature in text_features[1:]:
+                combined = combined + " " + feature.astype(str)
+            return combined.values