From f439bac315ef0bc2efa5b744c4c4d7f35e9a6945 Mon Sep 17 00:00:00 2001
From: RektPunk <rektpunk@gmail.com>
Date: Sat, 28 Sep 2024 09:14:14 +0900
Subject: [PATCH] update example multiclass

---
 examples/multiclass_engine.py  |  1 +
 examples/multiclass_sklearn.py | 42 ++++++++++++++++++++++++++++++++++
 2 files changed, 43 insertions(+)
 create mode 100644 examples/multiclass_sklearn.py

diff --git a/examples/multiclass_engine.py b/examples/multiclass_engine.py
index 2c90601..02c74b3 100644
--- a/examples/multiclass_engine.py
+++ b/examples/multiclass_engine.py
@@ -77,5 +77,6 @@
 # Evaluate models
 print("\nClassification Report for Standard:")
 print(classification_report(y_test, y_pred_standard_label))
+
 print("\nClassification Report for Imbalanced:")
 print(classification_report(y_test, y_pred_focal_label))
diff --git a/examples/multiclass_sklearn.py b/examples/multiclass_sklearn.py
new file mode 100644
index 0000000..9a37ea9
--- /dev/null
+++ b/examples/multiclass_sklearn.py
@@ -0,0 +1,42 @@
+from sklearn.datasets import make_classification
+from sklearn.metrics import classification_report
+from sklearn.model_selection import train_test_split
+
+import imlightgbm as imlgb
+
+# Generate dataset
+X, y = make_classification(
+    n_samples=5000,
+    n_features=10,
+    n_classes=3,
+    n_informative=5,
+    weights=[0.05, 0.15, 0.8],
+    flip_y=0,
+    random_state=42,
+)
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# Initialize the ImbalancedLGBMClassifier using binary focal loss
+clf = imlgb.ImbalancedLGBMClassifier(
+    objective="multiclass_focal",  # multiclass_weighted
+    gamma=2.0,  # alpha with multiclass_weighted
+    num_class=3,
+    learning_rate=0.05,
+    num_leaves=31,
+)
+
+# Train the classifier on the training data
+clf.fit(X=X_train, y=y_train)
+
+# Make predictions on the test data
+y_pred_focal = clf.predict(X_test)
+
+
+# Evaluate the model performance using accuracy, log loss, and ROC AUC
+# Evaluate models
+print("\nClassification Report:")
+print(classification_report(y_test, y_pred_focal))