Add Initial Example for Comet.

Author: ParagEkbote

.github/workflows/comet.yml

@@ -0,0 +1,39 @@
+name: comet
+
+on:
+  schedule:
+    - cron: '0 15 * * *'
+  pull_request:
+    paths:
+      - 'comet/**'
+      - '.github/workflows/comet.yml'
+
+jobs:
+  examples:
+    if: (github.event_name == 'schedule' && github.repository == 'optuna/optuna-examples') || (github.event_name != 'schedule')
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: setup-python${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install (Python)
+      run: |
+        python -m pip install --upgrade pip
+        pip install --progress-bar off -U setuptools
+        pip install git+https://github.com/optuna/optuna.git
+        python -c 'import optuna'
+        pip install git+https://github.com/optuna/optuna-integration.git
+        python -c 'import optuna_integration'
+
+        pip install -r comet/requirements.txt
+    - name: Run examples
+      run: |
+        python comet/comet_callback.py
+      env:
+        OMP_NUM_THREADS: 1

README.md

@@ -111,6 +111,7 @@ In addition, integration modules are available for the following libraries, prov
 * [Pruning with Tensorflow Integration Module](./tensorflow/tensorflow_estimator_integration.py)
 * [Pruning with XGBoost Integration Module](./xgboost/xgboost_integration.py)
 * [Pruning with XGBoost Integration Module (Cross Validation Version)](./xgboost/xgboost_cv_integration.py)
+* [Pruning with Comet Integration Module](./comet/comet_callback.py)
 </details>
 
 <details open>

comet/comet_callback.py

@@ -0,0 +1,40 @@
+
+import comet_ml
+import optuna
+
+comet_ml.login(project_name="comet-sklearn-example")
+
+import numpy as np
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
+
+random_state = 42
+def evaluate(y_test, y_pred):
+    return {
+        "f1": f1_score(y_test, y_pred),
+        "precision": precision_score(y_test, y_pred),
+        "recall": recall_score(y_test, y_pred),
+    }
+experiment = comet_ml.start()
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(
+    cancer.data, cancer.target, stratify=cancer.target, random_state=random_state
+)
+
+clf = RandomForestClassifier()
+clf.fit(X_train, y_train)
+y_train_pred = clf.predict(X_train)
+with experiment.train():
+    metrics = evaluate(y_train, y_train_pred)
+    experiment.log_metrics(metrics)
+
+y_test_pred = clf.predict(X_test)
+
+with experiment.test():
+    metrics = evaluate(y_test, y_test_pred)
+    experiment.log_metrics(metrics)
+
+experiment.end()