[#174] Pass just decision into predict_with_thresholds() instead of t…

…he whole training config

This makes it clear which part of the config predict_with_thresholds() is using
and makes it easier to call. It also means that predict_with_thresholds() does
not need to know about the structure of the config.

hlink/linking/core/threshold.py

@@ -40,8 +40,8 @@ def predict_using_thresholds(
     pred_df: DataFrame,
     alpha_threshold: float,
     threshold_ratio: float,
-    training_conf: dict[str, Any],
     id_col: str,
+    decision: str | None,
 ) -> DataFrame:
     """Adds a prediction column to the given pred_df by applying thresholds.
 
@@ -57,17 +57,17 @@ def predict_using_thresholds(
         to the "a" record's next best probability value.
         Only used with the "drop_duplicate_with_threshold_ratio"
         configuration value.
-    training_conf: dictionary
-        the training config section
     id_col: string
         the id column
+    decision: str | None
+        how to apply the thresholds
 
     Returns
     -------
     A Spark DataFrame containing the "prediction" column as well as other intermediate columns generated to create the prediction.
     """
     use_threshold_ratio = (
-        training_conf.get("decision", "") == "drop_duplicate_with_threshold_ratio"
+        decision is not None and decision == "drop_duplicate_with_threshold_ratio"
     )
 
     if use_threshold_ratio:

hlink/linking/matching/link_step_score.py

@@ -96,12 +96,13 @@ def _run(self):
         threshold_ratio = threshold_core.get_threshold_ratio(
             config[training_conf], chosen_model_params, default=1.3
         )
+        decision = config[training_conf].get("decision")
         predictions = threshold_core.predict_using_thresholds(
             score_tmp,
             alpha_threshold,
             threshold_ratio,
-            config[training_conf],
             config["id_column"],
+            decision,
         )
         predictions.write.mode("overwrite").saveAsTable(f"{table_prefix}predictions")
         pmp = self.task.spark.table(f"{table_prefix}potential_matches_pipeline")

hlink/linking/model_exploration/link_step_train_test_models.py

@@ -411,20 +411,21 @@ def _evaluate_threshold_combinations(
                 f"{this_alpha_threshold=} and {this_threshold_ratio=}"
             )
             logger.debug(diag)
+            decision = training_settings.get("decision")
             start_predict_time = perf_counter()
             predictions = threshold_core.predict_using_thresholds(
                 thresholding_predictions,
                 this_alpha_threshold,
                 this_threshold_ratio,
-                training_settings,
                 id_column,
+                decision,
             )
             predict_train = threshold_core.predict_using_thresholds(
                 thresholding_predict_train,
                 this_alpha_threshold,
                 this_threshold_ratio,
-                training_settings,
                 id_column,
+                decision,
             )
 
             end_predict_time = perf_counter()

hlink/tests/core/threshold_test.py

@@ -26,7 +26,7 @@ def test_predict_using_thresholds_default_decision(spark: SparkSession) -> None:
 
     # We are using the default decision, so threshold_ratio will be ignored
     predictions = predict_using_thresholds(
-        df, alpha_threshold=0.6, threshold_ratio=0.0, training_conf={}, id_col="id"
+        df, alpha_threshold=0.6, threshold_ratio=0.0, id_col="id", decision=None
     )
 
     output_rows = (
@@ -64,13 +64,12 @@ def test_predict_using_thresholds_drop_duplicates_decision(spark: SparkSession)
         (3, "E", 0.8),
     ]
     df = spark.createDataFrame(input_rows, schema=["id_a", "id_b", "probability"])
-    training_conf = {"decision": "drop_duplicate_with_threshold_ratio"}
     predictions = predict_using_thresholds(
         df,
         alpha_threshold=0.5,
         threshold_ratio=2.0,
-        training_conf=training_conf,
         id_col="id",
+        decision="drop_duplicate_with_threshold_ratio",
     )
 
     output_rows = (

hlink/tests/matching_scoring_test.py

@@ -51,8 +51,8 @@ def test_step_2_alpha_beta_thresholds(
         score_tmp,
         alpha_threshold,
         threshold_ratio,
-        matching_conf["training"],
         matching_conf["id_column"],
+        matching_conf["training"].get("decision"),
     )
     predictions.write.mode("overwrite").saveAsTable("predictions")