Create Skimu #2100
Create Skimu #2100
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Actionable comments posted: 2
🔭 Outside diff range comments (1)
Skimu (1)
60-76: 🛠️ Refactor suggestionEncapsulate example code in a main block.
The example code at the bottom of the file will run when the module is imported, which might not be intended.
+if __name__ == "__main__": + # Load a sample dataset (e.g., Iris dataset) + data = load_iris() + X = data.data + y = data.target + + # Split into training and testing sets + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) + + # Initialize the model + model = RandomForestClassifier() + + # Create an instance of StatefulWeightedDomainGraph + graph = StatefulWeightedDomainGraph(model, retrain_f1_threshold=0.75) + + # Train and evaluate the model + graph.train_model(X_train, y_train, X_test, y_test) -# Load a sample dataset (e.g., Iris dataset) -data = load_iris() -X = data.data -y = data.target - -# Split into training and testing sets -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) - -# Initialize the model -model = RandomForestClassifier() - -# Create an instance of StatefulWeightedDomainGraph -graph = StatefulWeightedDomainGraph(model, retrain_f1_threshold=0.75) - -# Train and evaluate the model -graph.train_model(X_train, y_train, X_test, y_test)
🧹 Nitpick comments (3)
Skimu (3)
32-35: Replace print statements with logging.Using print statements for output is not flexible for production environments. Consider using the logging module instead.
- print(f"Cross-Validation F1 Scores: {cv_scores}") - print(f"Mean Cross-Validation F1 Score: {cv_scores.mean():.4f}") + import logging + logging.info(f"Cross-Validation F1 Scores: {cv_scores}") + logging.info(f"Mean Cross-Validation F1 Score: {cv_scores.mean():.4f}")
46-58: Improve error handling in check_retrain method.The method should handle potential exceptions and could be more robust.
def check_retrain(self, X_test, y_test): """ Checks whether retraining is needed based on the F1 score. :param X_test: Test feature set :param y_test: Test labels :return: True if retraining is needed, False otherwise """ if self.model: - y_pred = self.model.predict(X_test) - f1 = f1_score(y_test, y_pred, average='weighted') - return f1 < self.retrain_f1_threshold + try: + y_pred = self.model.predict(X_test) + f1 = f1_score(y_test, y_pred, average='weighted') + return f1 < self.retrain_f1_threshold + except Exception as e: + import logging + logging.error(f"Error during retrain check: {e}") + return True # Conservative approach: retrain if evaluation fails return False
7-7: Class name may not align with its functionality.The name
StatefulWeightedDomainGraphsuggests a graph-based implementation, but the class appears to be a wrapper for ML model training and evaluation with no graph-related functionality.Consider renaming to something more descriptive of its actual purpose, such as
ModelTrainingManagerorAutoRetrainingModel.
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🔇 Additional comments (1)
Skimu (1)
7-17: Class definition looks good.The class definition is clear with appropriate documentation and a sensible default value for the F1 threshold parameter.
-pip install scikit-learnfrom sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_learn] # Archive and Compressed Files
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from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score
class StatefulWeightedDomainGraph:
def init(self, model, retrain_f1_threshold=0.8):
"""
Initializes the graph with the model and the F1 threshold for retraining.
Load a sample dataset (e.g., Iris dataset)
data = load_iris()
X = data.data
y = data.target
Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
Initialize the model
model = RandomForestClassifier()
Create an instance of StatefulWeightedDomainGraph
graph = StatefulWeightedDomainGraph(model, retrain_f1_threshold=0.75)
Train and evaluate the model
graph.train_model(X_train, y_train, X_test, y_test)
List_of_file_formats scronym .pdf