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refactor: reorganize project structure and enhance model verbosity

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This commit is contained in:
bernard-ng
2025-08-06 21:57:10 +02:00
parent ad8db43748
commit d7aa24a935
23 changed files with 1209 additions and 1416 deletions
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research/model_trainer.py
+33 -32
View File
@@ -11,6 +11,7 @@ from core.utils.data_loader import DataLoader
from research.experiment import FeatureType, ExperimentConfig
from research.experiment.experiment_runner import ExperimentRunner
from research.experiment.experiment_tracker import ExperimentTracker
from research.model_registry import MODEL_REGISTRY
class ModelTrainer:
@@ -21,25 +22,24 @@ class ModelTrainer:
self.data_loader = DataLoader(self.config)
self.experiment_runner = ExperimentRunner(self.config)
self.experiment_tracker = ExperimentTracker(self.config)
self.logger = logging.getLogger(__name__)
# Setup model artifacts directory
self.models_dir = self.config.paths.models_dir
self.models_dir.mkdir(parents=True, exist_ok=True)
def train_single_model(
self,
model_name: str,
model_type: str = "logistic_regression",
features: List[str] = None,
model_params: Dict[str, Any] = None,
save_artifacts: bool = True,
self,
model_name: str,
model_type: str = "logistic_regression",
features: List[str] = None,
model_params: Dict[str, Any] = None,
save_artifacts: bool = True,
) -> str:
"""
Train a single model and save its artifacts.
Returns the experiment ID.
"""
self.logger.info(f"Training {model_type} model: {model_name}")
logging.info(f"Training {model_type} model: {model_name}")
if features is None:
features = ["full_name"]
@@ -60,10 +60,10 @@ class ModelTrainer:
experiment = self.experiment_tracker.get_experiment(experiment_id)
if experiment and experiment.test_metrics:
self.logger.info("Training completed successfully!")
self.logger.info(f" Experiment ID: {experiment_id}")
self.logger.info(f" Test Accuracy: {experiment.test_metrics.get('accuracy', 0):.4f}")
self.logger.info(f" Test F1-Score: {experiment.test_metrics.get('f1', 0):.4f}")
logging.info("Training completed successfully!")
logging.info(f"Experiment ID: {experiment_id}")
logging.info(f"Test Accuracy: {experiment.test_metrics.get('accuracy', 0):.4f}")
logging.info(f"Test F1-Score: {experiment.test_metrics.get('f1', 0):.4f}")
if save_artifacts:
self.save_model_artifacts(experiment_id)
@@ -71,12 +71,15 @@ class ModelTrainer:
return experiment_id
def train_multiple_models(
self, base_name: str, model_configs: List[Dict[str, Any]], save_all: bool = True
self,
base_name: str,
model_configs: List[Dict[str, Any]],
save_all: bool = True
) -> List[str]:
"""
Train multiple models with different configurations.
"""
self.logger.info(f"Training {len(model_configs)} models...")
logging.info(f"Training {len(model_configs)} models...")
experiment_ids = []
@@ -94,10 +97,10 @@ class ModelTrainer:
experiment_ids.append(exp_id)
except Exception as e:
self.logger.error(f"Failed to train {model_name}: {e}")
logging.error(f"Failed to train {model_name}: {e}")
continue
self.logger.info(f"Completed training {len(experiment_ids)} models successfully")
logging.info(f"Completed training {len(experiment_ids)} models successfully")
return experiment_ids
def save_model_artifacts(self, experiment_id: str) -> Dict[str, str]:
@@ -145,7 +148,7 @@ class ModelTrainer:
df = self.data_loader.load_csv_complete(data_path)
# Generate learning curve
self.logger.info("Generating learning curve...")
logging.info("Generating learning curve...")
trained_model.generate_learning_curve(df, df[experiment.config.target_column])
# Plot and save learning curve
@@ -169,7 +172,7 @@ class ModelTrainer:
json.dump(trained_model.training_history, f, indent=2)
except Exception as e:
self.logger.warning(f"Could not generate learning curves: {e}")
logging.warning(f"Could not generate learning curves: {e}")
# Save artifacts metadata
metadata = {
@@ -193,17 +196,17 @@ class ModelTrainer:
with open(metadata_path, "w") as f:
json.dump(metadata, f, indent=2)
self.logger.info(f"Model artifacts saved to: {model_dir}")
self.logger.info(f" - Complete model: {model_path.name}")
self.logger.info(f" - Configuration: {config_path.name}")
self.logger.info(f" - Results: {results_path.name}")
self.logger.info(f" - Metadata: {metadata_path.name}")
logging.info(f"Model artifacts saved to: {model_dir}")
logging.info(f" - Complete model: {model_path.name}")
logging.info(f" - Configuration: {config_path.name}")
logging.info(f" - Results: {results_path.name}")
logging.info(f" - Metadata: {metadata_path.name}")
if learning_curve_path and learning_curve_path.exists():
self.logger.info(f" - Learning curve: {learning_curve_path.name}")
logging.info(f" - Learning curve: {learning_curve_path.name}")
if training_history_path and training_history_path.exists():
self.logger.info(f" - Training history: {training_history_path.name}")
logging.info(f" - Training history: {training_history_path.name}")
return {
"model_dir": str(model_dir),
@@ -231,16 +234,14 @@ class ModelTrainer:
metadata = json.load(f)
model_type = metadata["model_type"]
from research.model_registry import MODEL_REGISTRY
model_class = MODEL_REGISTRY[model_type]
# Load the complete model
loaded_model = model_class.load(str(model_path))
self.logger.info(f"Loaded model: {metadata['model_name']}")
self.logger.info(f" Type: {model_type}")
self.logger.info(f" Accuracy: {metadata['test_accuracy']:.4f}")
logging.info(f"Loaded model: {metadata['model_name']}")
logging.info(f" Type: {model_type}")
logging.info(f" Accuracy: {metadata['test_accuracy']:.4f}")
return loaded_model
@@ -259,10 +260,10 @@ class ModelTrainer:
metadata = json.load(f)
models_data.append(metadata)
except Exception as e:
self.logger.warning(f"Could not read metadata for {model_dir.name}: {e}")
logging.warning(f"Could not read metadata for {model_dir.name}: {e}")
if not models_data:
self.logger.info("No saved models found.")
logging.info("No saved models found.")
return pd.DataFrame()
df = pd.DataFrame(models_data)
research/models/lightgbm_model.py
+1 -1
View File
@@ -22,7 +22,7 @@ class LightGBMModel(TraditionalModel):
subsample=params.get("subsample", 0.8),
colsample_bytree=params.get("colsample_bytree", 0.8),
random_state=self.config.random_seed,
verbose=-1,
verbose=2,
)
def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
research/models/logistic_regression_model.py
+3 -1
View File
@@ -20,7 +20,9 @@ class LogisticRegressionModel(TraditionalModel):
)
classifier = LogisticRegression(
max_iter=params.get("max_iter", 1000), random_state=self.config.random_seed
max_iter=params.get("max_iter", 1000),
random_state=self.config.random_seed,
verbose=2
)
return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])
research/models/random_forest_model.py
+1
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@@ -18,6 +18,7 @@ class RandomForestModel(TraditionalModel):
n_estimators=params.get("n_estimators", 100),
max_depth=params.get("max_depth", None),
random_state=self.config.random_seed,
verbose=2
)
def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
research/models/svm_model.py
+1
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@@ -25,6 +25,7 @@ class SVMModel(TraditionalModel):
gamma=params.get("gamma", "scale"),
probability=True, # Enable probability prediction
random_state=self.config.random_seed,
verbose=2
)
return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])
research/models/xgboost_model.py
+1
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@@ -22,6 +22,7 @@ class XGBoostModel(TraditionalModel):
colsample_bytree=params.get("colsample_bytree", 0.8),
random_state=self.config.random_seed,
eval_metric="logloss",
verbosity=2
)
def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
research/neural_network_model.py
+4 -1
View File
@@ -49,6 +49,7 @@ class NeuralNetworkModel(BaseModel):
# Now we can build the model with known vocab size
vocab_size = len(self.tokenizer.word_index) + 1 if self.tokenizer else 1000
logging.info(f"Vocabulary size: {vocab_size}")
# Get additional model parameters
max_len = self.config.model_params.get("max_len", 6)
@@ -58,16 +59,18 @@ class NeuralNetworkModel(BaseModel):
)
# Train the neural network
logging.info(f"Fitting model with {X_prepared.shape[0]} samples and {X_prepared.shape[1]} features")
history = self.model.fit(
X_prepared,
y_encoded,
epochs=self.config.model_params.get("epochs", 10),
batch_size=self.config.model_params.get("batch_size", 64),
validation_split=0.1,
verbose=1,
verbose=2,
)
# Store training history
self.training_history = {
"accuracy": history.history["accuracy"],
"loss": history.history["loss"],
research/traditional_model.py
+2 -1
View File
@@ -50,7 +50,8 @@ class TraditionalModel(BaseModel):
y_encoded = self.label_encoder.transform(y)
# Train model
self.model.fit(X_prepared, y_encoded)
logging.info(f"Fitting model with {X_prepared.shape[0]} samples and {X_prepared.shape[1]} features")
self.model.fit(X_prepared, y_encoded, verbose=2)
self.is_fitted = True
return self