[#134] Add tests for the array feature selection

This includes some failing tests which provide 1 or 3 input columns instead of
just 2. #134 should make these tests pass.

hlink/tests/core/transforms_test.py

@@ -0,0 +1,92 @@
+from pyspark.sql import Row, SparkSession
+import pytest
+
+from hlink.linking.core.transforms import generate_transforms
+from hlink.linking.link_task import LinkTask
+
+
+@pytest.mark.parametrize("is_a", [True, False])
+def test_generate_transforms_array_transform_1_col(
+    spark: SparkSession, preprocessing: LinkTask, is_a: bool
+) -> None:
+    df = spark.createDataFrame(
+        [[1, "Leto II", 3508], [2, "Hwi", 26], [3, "Siona", 25]],
+        schema=["id", "name", "age"],
+    )
+    feature_selections = [
+        {
+            "transform": "array",
+            "input_columns": ["name"],
+            "output_column": "array_column",
+        }
+    ]
+
+    df_result = generate_transforms(
+        spark, df, feature_selections, preprocessing, is_a, "id"
+    )
+    array_column = df_result.select("array_column").collect()
+    assert array_column == [
+        Row(array_column=["Leto II"]),
+        Row(array_column=["Hwi"]),
+        Row(array_column=["Siona"]),
+    ]
+
+
+@pytest.mark.parametrize("is_a", [True, False])
+def test_generate_transforms_array_transform_2_cols(
+    spark: SparkSession, preprocessing: LinkTask, is_a: bool
+) -> None:
+    df = spark.createDataFrame(
+        [[1, "Leto II", 3508], [2, "Hwi", 26], [3, "Siona", 25]],
+        schema=["id", "name", "age"],
+    )
+    feature_selections = [
+        {
+            "transform": "array",
+            "input_columns": ["name", "age"],
+            "output_column": "array_column",
+        }
+    ]
+
+    df_result = generate_transforms(
+        spark, df, feature_selections, preprocessing, is_a, "id"
+    )
+    array_column = df_result.select("array_column").collect()
+    assert array_column == [
+        Row(array_column=["Leto II", "3508"]),
+        Row(array_column=["Hwi", "26"]),
+        Row(array_column=["Siona", "25"]),
+    ]
+
+
+@pytest.mark.parametrize("is_a", [True, False])
+def test_generate_transforms_array_transform_3_cols(
+    spark: SparkSession,
+    preprocessing: LinkTask,
+    is_a: bool,
+) -> None:
+    df = spark.createDataFrame(
+        [
+            [1, "Leto II", 3508, "Arrakis"],
+            [2, "Hwi", 26, "Ix"],
+            [3, "Siona", 25, "Arrakis"],
+        ],
+        schema=["id", "name", "age", "home"],
+    )
+    feature_selections = [
+        {
+            "transform": "array",
+            "input_columns": ["home", "age", "name"],
+            "output_column": "array_column",
+        }
+    ]
+
+    df_result = generate_transforms(
+        spark, df, feature_selections, preprocessing, is_a, "id"
+    )
+    array_column = df_result.select("array_column").collect()
+    assert array_column == [
+        Row(array_column=["Arrakis", "3508", "Leto II"]),
+        Row(array_column=["Ix", "26", "Hwi"]),
+        Row(array_column=["Arrakis", "25", "Siona"]),
+    ]