[#162] Integrate LightGBM with training step 3

hlink/linking/training/link_step_save_model_metadata.py

@@ -104,6 +104,15 @@ def _run(self):
                 zip(true_column_names, true_categories, weights, gains),
                 "feature_name: string, category: int, weight: double, average_gain_per_split: double",
             ).sort("feature_name", "category")
+        elif model_type == "lightgbm":
+            num_splits = model.getFeatureImportances("split")
+            total_gains = model.getFeatureImportances("gain")
+            label = "Feature importances (number of splits and total gains)"
+
+            features_df = self.task.spark.createDataFrame(
+                zip(true_column_names, true_categories, num_splits, total_gains),
+                "feature_name: string, category: int, num_splits: double, total_gain: double",
+            ).sort("feature_name", "category")
         else:
             try:
                 feature_imp = model.coefficients

hlink/tests/training_test.py

@@ -486,6 +486,14 @@ def test_step_3_with_lightgbm_model(
 
     training.run_all_steps()
 
+    importances_df = spark.table("training_feature_importances")
+    assert importances_df.columns == [
+        "feature_name",
+        "category",
+        "num_splits",
+        "total_gain",
+    ]
+
 
 @requires_lightgbm
 def test_lightgbm_with_interacted_features(
@@ -536,6 +544,14 @@ def test_lightgbm_with_interacted_features(
 
     training.run_all_steps()
 
+    importances_df = spark.table("training_feature_importances")
+    assert importances_df.columns == [
+        "feature_name",
+        "category",
+        "num_splits",
+        "total_gain",
+    ]
+
 
 @requires_xgboost
 def test_step_3_with_xgboost_model(