Merge pull request #305 from ParagEkbote/Add-Comet-Example

Add Example for Comet

Author: c-bata

comet/comet_integration.py

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+"""
+This script integrates Comet ML and Optuna to optimize a Random Forest Classifier
+on the scikit-learn Breast Cancer dataset. It performs the following steps:
+
+1. Initializes a Comet ML experiment for logging.
+2. Loads the Breast Cancer dataset and splits it into training and testing sets.
+3. Defines an evaluation function using accuracy.
+4. Implements an Optuna objective function to optimize hyperparameters
+   (min_samples_leaf, max_depth, and min_samples_split) for the Random Forest model.
+5. Uses Optuna to run multiple trials and identify the best hyperparameters.
+6. Trains the final Random Forest model using the best-found hyperparameters.
+7. Logs training and testing metrics to Comet ML.
+
+You can run this example as follows:
+    $ python comet_integration.py
+"""
+
+import optuna
+from optuna_integration.comet import CometCallback
+
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+
+def objective(trial):
+    """Objective function for optimizing a RandomForestClassifier using Optuna."""
+    data = load_iris()
+    x_train, x_valid, y_train, y_valid = train_test_split(
+        data["data"], data["target"], random_state=42
+    )
+    params = {
+        "min_samples_leaf": trial.suggest_int("min_samples_leaf", 2, 10),
+        "max_depth": trial.suggest_int("max_depth", 5, 20),
+        "min_samples_split": trial.suggest_int("min_samples_split", 2, 10),
+    }
+
+    clf = RandomForestClassifier(**params, random_state=42)
+    clf.fit(x_train, y_train)
+    pred = clf.predict(x_valid)
+    score = accuracy_score(y_valid, pred)
+
+    return score
+
+
+if __name__ == "__main__":
+    study = optuna.create_study(direction="maximize")
+    comet_callback = CometCallback(study, project_name="comet-optuna-sklearn-example")
+
+    study.optimize(objective, n_trials=20, callbacks=[comet_callback])
+
+    print(f"Number of finished trials: {len(study.trials)}\n")
+
+    print("Best trial:")
+    trial = study.best_trial
+
+    print(f"  Value: {trial.value}\n")
+    print("  Params:")
+    for key, value in trial.params.items():
+        print(f"    {key}: {value}")

comet/requirements.txt

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+comet-ml 
+optuna 
+optuna-integration 
+scikit-learn