[#162] Add two training tests for lightgbm

One of these is failing because there's a bug where LightGBM throws an error on
interacted features.

hlink/tests/training_test.py

@@ -6,6 +6,7 @@
 import pytest
 from pyspark.ml import Pipeline
 import hlink.linking.core.pipeline as pipeline_core
+from hlink.tests.markers import requires_lightgbm
 
 
 @pytest.mark.quickcheck
@@ -432,6 +433,111 @@ def test_step_3_with_probit_model(
     )
 
 
+@requires_lightgbm
+def test_step_3_with_lightgbm_model(
+    spark, training, training_conf, datasource_training_input, state_dist_path
+):
+    training_data_path, prepped_df_a_path, prepped_df_b_path = datasource_training_input
+    training_conf["comparison_features"] = [
+        {
+            "alias": "regionf",
+            "column_name": "region",
+            "comparison_type": "fetch_a",
+            "categorical": True,
+        },
+        {
+            "alias": "namelast_jw",
+            "column_name": "namelast",
+            "comparison_type": "jaro_winkler",
+        },
+        {
+            "alias": "state_distance",
+            "key_count": 1,
+            "column_name": "bpl",
+            "comparison_type": "geo_distance",
+            "loc_a": "statecode1",
+            "loc_b": "statecode2",
+            "distance_col": "dist",
+            "table_name": "state_distances_lookup",
+            "distances_file": state_dist_path,
+        },
+    ]
+    training_conf["training"]["dataset"] = training_data_path
+    training_conf["training"]["dependent_var"] = "match"
+    training_conf["training"]["independent_vars"] = [
+        "namelast_jw",
+        "regionf",
+        "state_distance",
+    ]
+    training_conf["training"]["chosen_model"] = {
+        "type": "lightgbm",
+        "maxDepth": 7,
+        "numIterations": 5,
+        "minDataInLeaf": 1,
+        "threshold": 0.5,
+    }
+    training_conf["training"]["score_with_model"] = True
+    training_conf["training"]["feature_importances"] = True
+
+    prepped_df_a = spark.read.csv(prepped_df_a_path, header=True, inferSchema=True)
+    prepped_df_b = spark.read.csv(prepped_df_b_path, header=True, inferSchema=True)
+
+    prepped_df_a.write.mode("overwrite").saveAsTable("prepped_df_a")
+    prepped_df_b.write.mode("overwrite").saveAsTable("prepped_df_b")
+
+    training.run_all_steps()
+
+
+@requires_lightgbm
+def test_lightgbm_with_interacted_features(
+    spark, training, training_conf, datasource_training_input
+):
+    training_data_path, prepped_df_a_path, prepped_df_b_path = datasource_training_input
+    training_conf["comparison_features"] = [
+        {
+            "alias": "regionf",
+            "column_name": "region",
+            "comparison_type": "fetch_a",
+            "categorical": True,
+        },
+        {
+            "alias": "namelast_jw",
+            "column_name": "namelast",
+            "comparison_type": "jaro_winkler",
+        },
+    ]
+    training_conf["pipeline_features"] = [
+        {
+            "input_columns": ["regionf", "namelast_jw"],
+            "output_column": "regionf_interacted_namelast_jw",
+            "transformer_type": "interaction",
+        }
+    ]
+    training_conf["training"]["dataset"] = training_data_path
+    training_conf["training"]["dependent_var"] = "match"
+    training_conf["training"]["independent_vars"] = [
+        "namelast_jw",
+        "regionf",
+        "regionf_interacted_namelast_jw",
+    ]
+    training_conf["training"]["chosen_model"] = {
+        "type": "lightgbm",
+        "maxDepth": 7,
+        "numIterations": 5,
+        "minDataInLeaf": 1,
+        "threshold": 0.5,
+    }
+    training_conf["training"]["score_with_model"] = True
+    training_conf["training"]["feature_importances"] = True
+    prepped_df_a = spark.read.csv(prepped_df_a_path, header=True, inferSchema=True)
+    prepped_df_b = spark.read.csv(prepped_df_b_path, header=True, inferSchema=True)
+
+    prepped_df_a.write.mode("overwrite").saveAsTable("prepped_df_a")
+    prepped_df_b.write.mode("overwrite").saveAsTable("prepped_df_b")
+
+    training.run_all_steps()
+
+
 def test_step_3_requires_table(training_conf, training):
     training_conf["training"]["feature_importances"] = True
     with pytest.raises(RuntimeError, match="Missing input tables"):