refactoring: add initial pipeline configuration and model classes

research/experiment/experiment_builder.py

@@ -0,0 +1,123 @@
+from typing import List
+
+from research.experiment import ExperimentConfig
+from research.experiment.feature_extractor import FeatureType
+
+
+class ExperimentBuilder:
+    """Helper class to build experiment configurations"""
+
+    @staticmethod
+    def create_baseline_experiments() -> List[ExperimentConfig]:
+        """Create a set of baseline experiments for comparison"""
+
+        return [
+            # Full name experiments
+            ExperimentConfig(
+                name="baseline_logistic_regression_fullname",
+                description="Logistic regression with full name",
+                model_type="logistic_regression",
+                features=[FeatureType.FULL_NAME],
+                tags=["baseline", "fullname"],
+            ),
+            # Native name only
+            ExperimentConfig(
+                name="baseline_logistic_regression_native",
+                description="Logistic regression with native name only",
+                model_type="logistic_regression",
+                features=[FeatureType.NATIVE_NAME],
+                tags=["baseline", "native"],
+            ),
+            # Surname only
+            ExperimentConfig(
+                name="baseline_logistic_regression_surname",
+                description="Logistic regression with surname only",
+                model_type="logistic_regression",
+                features=[FeatureType.SURNAME],
+                tags=["baseline", "surname"],
+            ),
+            # Random Forest with engineered features
+            ExperimentConfig(
+                name="baseline_rf_engineered",
+                description="Random Forest with engineered features",
+                model_type="random_forest",
+                features=[FeatureType.NAME_LENGTH, FeatureType.WORD_COUNT, FeatureType.PROVINCE],
+                tags=["baseline", "engineered"],
+            ),
+        ]
+
+    @staticmethod
+    def create_feature_ablation_study() -> List[ExperimentConfig]:
+        """Create experiments for feature ablation study"""
+        base_features = [
+            FeatureType.FULL_NAME,
+            FeatureType.NAME_LENGTH,
+            FeatureType.WORD_COUNT,
+            FeatureType.PROVINCE,
+        ]
+
+        experiments = []
+
+        # Test removing each feature one by one
+        for i, feature_to_remove in enumerate(base_features):
+            remaining_features = [f for f in base_features if f != feature_to_remove]
+
+            experiments.append(
+                ExperimentConfig(
+                    name=f"ablation_remove_{feature_to_remove.value}",
+                    description=f"Ablation study: removed {feature_to_remove.value}",
+                    model_type="logistic_regression",
+                    features=remaining_features,
+                    tags=["ablation", feature_to_remove.value],
+                )
+            )
+
+        return experiments
+
+    @staticmethod
+    def create_name_component_study() -> List[ExperimentConfig]:
+        """Create experiments to study different name components"""
+        experiments = []
+
+        name_components = [
+            (FeatureType.FIRST_WORD, "first_word"),
+            (FeatureType.LAST_WORD, "last_word"),
+            (FeatureType.NATIVE_NAME, "native_name"),
+            (FeatureType.SURNAME, "surname"),
+            (FeatureType.NAME_BEGINNINGS, "name_beginnings"),
+            (FeatureType.NAME_ENDINGS, "name_endings"),
+        ]
+
+        for feature, name in name_components:
+            experiments.append(
+                ExperimentConfig(
+                    name=f"component_study_{name}",
+                    description=f"Study of {name} for gender prediction",
+                    model_type="logistic_regression",
+                    features=[feature],
+                    tags=["component_study", name],
+                )
+            )
+
+        return experiments
+
+    @staticmethod
+    def create_province_specific_study() -> List[ExperimentConfig]:
+        """Create experiments for province-specific analysis"""
+        provinces = ["kinshasa", "bas-congo", "bandundu", "katanga"]  # Add more as needed
+
+        experiments = []
+
+        for province in provinces:
+            experiments.append(
+                ExperimentConfig(
+                    name=f"province_study_{province}",
+                    description=f"Gender prediction for {province} province only",
+                    model_type="logistic_regression",
+                    features=[FeatureType.FULL_NAME],
+                    train_data_filter={"province": province},
+                    tags=["province_study", province],
+                )
+            )
+
+        return experiments