add

graphstorm-processing/tests/test_dist_numerical_transformation.py

@@ -20,7 +20,7 @@
 import numpy as np
 from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_almost_equal
 from pyspark.sql import SparkSession, DataFrame, functions as F
-from pyspark.sql.types import ArrayType, FloatType, DoubleType, StructField, StructType, StringType
+from pyspark.sql.types import ArrayType, FloatType, DoubleType, StructField, StructType, StringType, LongType
 from scipy.special import erfinv
 
 from graphstorm_processing.data_transformations.dist_transformations import (
@@ -113,6 +113,27 @@ def test_numerical_transformation_without_transformation(input_df: DataFrame, ch
         assert row["salary"] == expected_salary
 
 
+@pytest.mark.parametrize("out_dtype", ["float32", "float64"])
+def test_numerical_min_max_transformation_precision(spark: SparkSession, out_dtype):
+    """Test numerical transformation without any transformation applied"""
+    # Adjust the number to be an integer
+    high_precision_integer = 1.2345678901234562
+    data = [(high_precision_integer,)]
+    schema = StructType([StructField("age", FloatType(), True)])
+    input_df = spark.createDataFrame(data, schema=schema)
+
+    dist_numerical_transformation = DistNumericalTransformation(
+        ["age"], imputer="none", normalizer="min-max", out_dtype=out_dtype
+    )
+
+    transformed_df = dist_numerical_transformation.apply(input_df)
+    column_data_type = [field.dataType for field in transformed_df.schema.fields if field.name == "age"][0]
+    if out_dtype == "float32":
+        assert isinstance(column_data_type, FloatType), f"The column 'age' is not of type FloatType."
+    elif out_dtype == "float64":
+        assert isinstance(column_data_type, DoubleType), f"The column 'age' is not of type DoubleType."
+
+
 def test_numerical_transformation_with_median_imputer_and_std_norm(
     input_df: DataFrame, check_df_schema
 ):