feat: add more baseline expirements

README.md

@@ -65,7 +65,7 @@ stages:
 python main.py --env development
 ```
 
-## NER Processing
+## NER Processing (Optional)
 
 This project implements a custom named entity recognition (NER) pipeline tailored for Congolese names. 
 Its main objective is to accurately identify and tag the different components of a Congolese name, 
@@ -75,7 +75,7 @@ specifically distinguishing between the native part and the surname.
 python ner.py --env development
 ```
 
-Once you've built and train the NER model you can use it to annotate **CoMPOSE** name in the original dataset 
+Once you've built and train the NER model you can use it to annotate **COMPOSE** name in the original dataset 
 
 **Running the Pipeline with NER Annotation**
 ```yaml
@@ -104,20 +104,55 @@ you can define model features, training parameters, and evaluation metrics in th
 **Running Experiments**
 
 ```bash
+# bigru
 python train.py --name="bigru" --type="baseline" --env="development"
-python train.py --name="cnn" --type="baseline" --env="development"
+python train.py --name="bigru_native" --type="baseline" --env="development"
-python train.py --name="lightgbm" --type="baseline" --env="development"
+python train.py --name="bigru_surname" --type="baseline" --env="development"
 
-python train.py --name="logistic_regression_fullname" --type="baseline" --env="development"
+# cnn
+python train.py --name="cnn" --type="baseline" --env="development"
+python train.py --name="cnn_native" --type="baseline" --env="development"
+python train.py --name="cnn_surname" --type="baseline" --env="development"
+
+# lightgbm
+python train.py --name="lightgbm" --type="baseline" --env="development"
+python train.py --name="lightgbm_native" --type="baseline" --env="development"
+python train.py --name="lightgbm_surname" --type="baseline" --env="development"
+
+# logistic regression
+python train.py --name="logistic_regression" --type="baseline" --env="development"
 python train.py --name="logistic_regression_native" --type="baseline" --env="development"
 python train.py --name="logistic_regression_surname" --type="baseline" --env="development"
 
+# lstm
 python train.py --name="lstm" --type="baseline" --env="development"
+python train.py --name="lstm_native" --type="baseline" --env="development"
+python train.py --name="lstm_surname" --type="baseline" --env="development"
+
+# random forest
 python train.py --name="random_forest" --type="baseline" --env="development"
+python train.py --name="random_forest_native" --type="baseline" --env="development"
+python train.py --name="random_forest_surname" --type="baseline" --env="development"
+
+# svm
 python train.py --name="svm" --type="baseline" --env="development"
+python train.py --name="svm_native" --type="baseline" --env="development"
+python train.py --name="svm_surname" --type="baseline" --env="development"
+
+# naive bayes
 python train.py --name="naive_bayes" --type="baseline" --env="development"
+python train.py --name="naive_bayes_native" --type="baseline" --env="development"
+python train.py --name="naive_bayes_surname" --type="baseline" --env="development"
+
+# transformer
 python train.py --name="transformer" --type="baseline" --env="development"
+python train.py --name="transformer_native" --type="baseline" --env="development"
+python train.py --name="transformer_surname" --type="baseline" --env="development"
+
+# xgboost
 python train.py --name="xgboost" --type="baseline" --env="development"
+python train.py --name="xgboost_native" --type="baseline" --env="development"
+python train.py --name="xgboost_surname" --type="baseline" --env="development"
 ```
 
 ## Web Interface

config/research_templates.yaml

@@ -1,4 +1,5 @@
 baseline_experiments:
+  # BiGRU Models
   - name: "bigru"
     description: "Baseline BiGRU with full name features"
     model_type: "bigru"
@@ -10,6 +11,29 @@ baseline_experiments:
       batch_size: 32
     tags: [ "baseline", "neural", "bigru" ]
 
+  - name: "bigru_native"
+    description: "Baseline BiGRU with native name features"
+    model_type: "bigru"
+    features: [ "native_name" ]
+    model_params:
+      embedding_dim: 64
+      gru_units: 32
+      epochs: 2
+      batch_size: 32
+    tags: [ "baseline", "neural", "bigru", "native" ]
+
+  - name: "bigru_surname"
+    description: "Baseline BiGRU with surname features"
+    model_type: "bigru"
+    features: [ "surname" ]
+    model_params:
+      embedding_dim: 64
+      gru_units: 32
+      epochs: 2
+      batch_size: 32
+    tags: [ "baseline", "neural", "bigru", "surname" ]
+
+  ## CNN Models
   - name: "cnn"
     description: "Baseline CNN with character patterns"
     model_type: "cnn"
@@ -23,6 +47,33 @@ baseline_experiments:
       batch_size: 32
     tags: [ "baseline", "neural", "cnn" ]
 
+  - name: "cnn_native"
+    description: "Baseline CNN with native name character patterns"
+    model_type: "cnn"
+    features: [ "native_name" ]
+    model_params:
+      embedding_dim: 64
+      filters: 64
+      kernel_size: 3
+      dropout: 0.5
+      epochs: 2
+      batch_size: 32
+    tags: [ "baseline", "neural", "cnn", "native" ]
+
+  - name: "cnn_surname"
+    description: "Baseline CNN with surname character patterns"
+    model_type: "cnn"
+    features: [ "surname" ]
+    model_params:
+      embedding_dim: 64
+      filters: 64
+      kernel_size: 3
+      dropout: 0.5
+      epochs: 2
+      batch_size: 32
+    tags: [ "baseline", "neural", "cnn", "surname" ]
+
+  ## Ensemble Models
   - name: "ensemble"
     description: "Baseline Ensemble with multiple models"
     model_type: "ensemble"
@@ -33,6 +84,27 @@ baseline_experiments:
       cv_folds: 5
     tags: [ "baseline", "ensemble" ]
 
+  - name: "ensemble_native"
+    description: "Baseline Ensemble with native name"
+    model_type: "ensemble"
+    features: [ "native_name" ]
+    model_params:
+      base_models: [ "logistic_regression", "random_forest", "xgboost" ]
+      voting: "soft"
+      cv_folds: 5
+    tags: [ "baseline", "ensemble", "native" ]
+
+  - name: "ensemble_surname"
+    description: "Baseline Ensemble with surname"
+    model_type: "ensemble"
+    features: [ "surname" ]
+    model_params:
+      base_models: [ "logistic_regression", "random_forest", "xgboost" ]
+      voting: "soft"
+      cv_folds: 5
+    tags: [ "baseline", "ensemble", "surname" ]
+
+  # LightGBM Models
   - name: "lightgbm"
     description: "Baseline LightGBM with engineered features"
     model_type: "lightgbm"
@@ -46,7 +118,34 @@ baseline_experiments:
       colsample_bytree: 0.8
     tags: [ "baseline", "lightgbm" ]
 
-  - name: "logistic_regression_fullname"
+  - name: "lightgbm_native"
+    description: "Baseline LightGBM with native name features"
+    model_type: "lightgbm"
+    features: [ "native_name" ]
+    model_params:
+      n_estimators: 100
+      max_depth: -1
+      learning_rate: 0.1
+      num_leaves: 31
+      subsample: 0.8
+      colsample_bytree: 0.8
+    tags: [ "baseline", "lightgbm", "native" ]
+
+  - name: "lightgbm_surname"
+    description: "Baseline LightGBM with surname features"
+    model_type: "lightgbm"
+    features: [ "surname" ]
+    model_params:
+      n_estimators: 100
+      max_depth: -1
+      learning_rate: 0.1
+      num_leaves: 31
+      subsample: 0.8
+      colsample_bytree: 0.8
+    tags: [ "baseline", "lightgbm", "surname" ]
+
+  # Logistic Regression Models
+  - name: "logistic_regression"
     description: "Baseline logistic regression with full name"
     model_type: "logistic_regression"
     features: [ "full_name" ]
@@ -70,6 +169,7 @@ baseline_experiments:
       max_features: 5000
     tags: [ "baseline", "logistic_regression", "surname" ]
 
+  # LSTM Models
   - name: "lstm"
     description: "Baseline LSTM with full name features"
     model_type: "lstm"
@@ -81,6 +181,29 @@ baseline_experiments:
       batch_size: 64
     tags: [ "baseline", "neural", "lstm" ]
 
+  - name: "lstm_native"
+    description: "Baseline LSTM with native name features"
+    model_type: "lstm"
+    features: [ "native_name" ]
+    model_params:
+      embedding_dim: 128
+      lstm_units: 64
+      epochs: 2
+      batch_size: 64
+    tags: [ "baseline", "neural", "lstm", "native" ]
+
+  - name: "lstm_surname"
+    description: "Baseline LSTM with surname features"
+    model_type: "lstm"
+    features: [ "surname" ]
+    model_params:
+      embedding_dim: 128
+      lstm_units: 64
+      epochs: 2
+      batch_size: 64
+    tags: [ "baseline", "neural", "lstm", "surname" ]
+
+  # Naive Bayes Models
   - name: "naive_bayes"
     description: "Baseline Naive Bayes with full name features"
     model_type: "naive_bayes"
@@ -89,6 +212,23 @@ baseline_experiments:
       max_features: 5000
     tags: [ "baseline", "naive_bayes" ]
 
+  - name: "naive_bayes_native"
+    description: "Baseline Naive Bayes with native name features"
+    model_type: "naive_bayes"
+    features: [ "native_name" ]
+    model_params:
+      max_features: 5000
+    tags: [ "baseline", "naive_bayes", "native" ]
+
+  - name: "naive_bayes_surname"
+    description: "Baseline Naive Bayes with surname features"
+    model_type: "naive_bayes"
+    features: [ "surname" ]
+    model_params:
+      max_features: 5000
+    tags: [ "baseline", "naive_bayes", "surname" ]
+
+  # Random Forest Models
   - name: "random_forest"
     description: "Baseline Random Forest with engineered features"
     model_type: "random_forest"
@@ -100,6 +240,29 @@ baseline_experiments:
       min_samples_leaf: 1
     tags: [ "baseline", "random_forest", "engineered" ]
 
+  - name: "random_forest_native"
+    description: "Baseline Random Forest with native name engineered features"
+    model_type: "random_forest"
+    features: [ "native_name" ]
+    model_params:
+      n_estimators: 100
+      max_depth: 10
+      min_samples_split: 2
+      min_samples_leaf: 1
+    tags: [ "baseline", "random_forest", "engineered", "native" ]
+
+  - name: "random_forest_surname"
+    description: "Baseline Random Forest with surname engineered features"
+    model_type: "random_forest"
+    features: [ "surname" ]
+    model_params:
+      n_estimators: 100
+      max_depth: 10
+      min_samples_split: 2
+      min_samples_leaf: 1
+    tags: [ "baseline", "random_forest", "engineered", "surname" ]
+
+  # SVM Models
   - name: "svm"
     description: "Baseline SVM with full name features"
     model_type: "svm"
@@ -111,6 +274,29 @@ baseline_experiments:
       max_features: 5000
     tags: [ "baseline", "svm" ]
 
+  - name: "svm_native"
+    description: "Baseline SVM with native name features"
+    model_type: "svm"
+    features: [ "native_name" ]
+    model_params:
+      C: 1.0
+      kernel: "rbf"
+      ngram_range: [ 2, 4 ]
+      max_features: 5000
+    tags: [ "baseline", "svm", "native" ]
+
+  - name: "svm_surname"
+    description: "Baseline SVM with surname features"
+    model_type: "svm"
+    features: [ "surname" ]
+    model_params:
+      C: 1.0
+      kernel: "rbf"
+      ngram_range: [ 2, 4 ]
+      max_features: 5000
+    tags: [ "baseline", "svm", "surname" ]
+
+  # Transformer Models
   - name: "transformer"
     description: "Baseline Transformer with attention mechanism"
     model_type: "transformer"
@@ -123,6 +309,31 @@ baseline_experiments:
       batch_size: 64
     tags: [ "baseline", "neural", "transformer" ]
 
+  - name: "transformer_native"
+    description: "Baseline Transformer with native name attention mechanism"
+    model_type: "transformer"
+    features: [ "native_name" ]
+    model_params:
+      embedding_dim: 128
+      num_heads: 4
+      num_layers: 2
+      epochs: 2
+      batch_size: 64
+    tags: [ "baseline", "neural", "transformer", "native" ]
+
+  - name: "transformer_surname"
+    description: "Baseline Transformer with surname attention mechanism"
+    model_type: "transformer"
+    features: [ "surname" ]
+    model_params:
+      embedding_dim: 128
+      num_heads: 4
+      num_layers: 2
+      epochs: 2
+      batch_size: 64
+    tags: [ "baseline", "neural", "transformer", "surname" ]
+
+  # XGBoost Models
   - name: "xgboost"
     description: "Baseline XGBoost with engineered features"
     model_type: "xgboost"
@@ -135,6 +346,30 @@ baseline_experiments:
       colsample_bytree: 0.8
     tags: [ "baseline", "xgboost" ]
 
+  - name: "xgboost_native"
+    description: "Baseline XGBoost with native name engineered features"
+    model_type: "xgboost"
+    features: [ "native_name" ]
+    model_params:
+      n_estimators: 100
+      max_depth: 6
+      learning_rate: 0.1
+      subsample: 0.8
+      colsample_bytree: 0.8
+    tags: [ "baseline", "xgboost", "native" ]
+
+  - name: "xgboost_surname"
+    description: "Baseline XGBoost with surname engineered features"
+    model_type: "xgboost"
+    features: [ "surname" ]
+    model_params:
+      n_estimators: 100
+      max_depth: 6
+      learning_rate: 0.1
+      subsample: 0.8
+      colsample_bytree: 0.8
+    tags: [ "baseline", "xgboost", "surname" ]
+
 
 # Advanced Experiments Configuration
 advanced_experiments:

research/models/cnn_model.py

@@ -12,6 +12,8 @@ from tensorflow.keras.layers import (
     SpatialDropout1D,
 )
 from tensorflow.keras.models import Sequential
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 from research.neural_network_model import NeuralNetworkModel
 
@@ -64,9 +66,6 @@ class CNNModel(NeuralNetworkModel):
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
         """Prepare sequences for CNN using extracted features"""
         # X here contains the features already extracted by FeatureExtractor
-        from tensorflow.keras.preprocessing.text import Tokenizer
-        from tensorflow.keras.preprocessing.sequence import pad_sequences
-
         # Get text data from extracted features - use character level for CNN
         text_data = self._collect_text_corpus(X)