From 8db73a5db3d53a207b44b9dfc43d7a55dc5583e4 Mon Sep 17 00:00:00 2001
From: Yu-Ting Hsiung <bear890707@gmail.com>
Date: Sun, 11 Oct 2026 11:18:28 +0800
Subject: [PATCH] [MINOR][ML][PYTHON] Clean up FM example typing and formatting

---
 examples/src/main/python/ml/fm_classifier_example.py | 11 ++++++-----
 examples/src/main/python/ml/fm_regressor_example.py  | 11 ++++++-----
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/examples/src/main/python/ml/fm_classifier_example.py b/examples/src/main/python/ml/fm_classifier_example.py
index d3fba01b85f54..b081a46d19d01 100644
--- a/examples/src/main/python/ml/fm_classifier_example.py
+++ b/examples/src/main/python/ml/fm_classifier_example.py
@@ -20,7 +20,7 @@
 """
 # $example on$
 from pyspark.ml import Pipeline
-from pyspark.ml.classification import FMClassifier
+from pyspark.ml.classification import FMClassificationModel, FMClassifier
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
 from pyspark.ml.feature import MinMaxScaler, StringIndexer
 
@@ -65,12 +65,13 @@
     evaluator = MulticlassClassificationEvaluator(
         labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
     accuracy = evaluator.evaluate(predictions)
-    print("Test set accuracy = %g" % accuracy)
+    print(f"Test set accuracy = {accuracy:g}")
 
     fmModel = model.stages[2]
-    print("Factors: " + str(fmModel.factors))  # type: ignore
-    print("Linear: " + str(fmModel.linear))  # type: ignore
-    print("Intercept: " + str(fmModel.intercept))  # type: ignore
+    assert isinstance(fmModel, FMClassificationModel)
+    print(f"Factors: {fmModel.factors}")
+    print(f"Linear: {fmModel.linear}")
+    print(f"Intercept: {fmModel.intercept}")
     # $example off$
 
     spark.stop()
diff --git a/examples/src/main/python/ml/fm_regressor_example.py b/examples/src/main/python/ml/fm_regressor_example.py
index 02527a9a34f69..325af151beaf4 100644
--- a/examples/src/main/python/ml/fm_regressor_example.py
+++ b/examples/src/main/python/ml/fm_regressor_example.py
@@ -22,7 +22,7 @@
 from pyspark.ml import Pipeline
 from pyspark.ml.evaluation import RegressionEvaluator
 from pyspark.ml.feature import MinMaxScaler
-from pyspark.ml.regression import FMRegressor
+from pyspark.ml.regression import FMRegressionModel, FMRegressor
 
 # $example off$
 from pyspark.sql import SparkSession
@@ -62,12 +62,13 @@
     evaluator = RegressionEvaluator(
         labelCol="label", predictionCol="prediction", metricName="rmse")
     rmse = evaluator.evaluate(predictions)
-    print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)
+    print(f"Root Mean Squared Error (RMSE) on test data = {rmse:g}")
 
     fmModel = model.stages[1]
-    print("Factors: " + str(fmModel.factors))  # type: ignore
-    print("Linear: " + str(fmModel.linear))  # type: ignore
-    print("Intercept: " + str(fmModel.intercept))  # type: ignore
+    assert isinstance(fmModel, FMRegressionModel)
+    print(f"Factors: {fmModel.factors}")
+    print(f"Linear: {fmModel.linear}")
+    print(f"Intercept: {fmModel.intercept}")
     # $example off$
 
     spark.stop()