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[MINOR][ML][PYTHON] Narrow FM model types in examples #59342

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[MINOR][ML][PYTHON] Narrow FM model types in examples #59342

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11 changes: 6 additions & 5 deletions examples/src/main/python/ml/fm_classifier_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand Down Expand Up @@ -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()
11 changes: 6 additions & 5 deletions examples/src/main/python/ml/fm_regressor_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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()