Adjust how choose_classifier handles seed parameters

Dockerfile

@@ -1,8 +1,8 @@
 ARG PYTHON_VERSION=3.10
-FROM python:${PYTHON_VERSION}
+FROM python:${PYTHON_VERSION}-bookworm
 ARG HLINK_EXTRAS=dev
 
-RUN apt-get update && apt-get install default-jre-headless -y
+RUN apt-get update && apt-get install openjdk-17-jre-headless -y
 
 RUN mkdir /hlink
 WORKDIR /hlink

hlink/linking/core/classifier.py

@@ -30,6 +30,17 @@
     _xgboost_available = True
 
 
+def _ensure_seeded(params: dict[str, Any]) -> dict[str, Any]:
+    """
+    Ensure that the given dictionary of parameters has a "seed" parameter.
+
+    This is useful for making results reproducible across different linking
+    runs. If the user doesn't set the "seed" parameter, then this function sets
+    it to 2133 (which is just as good as any other number, I suppose).
+    """
+    return {"seed": 2133, **params}
+
+
 def choose_classifier(model_type: str, params: dict[str, Any], dep_var: str):
     """Given a model type and hyper-parameters for the model, return a
     classifier of that type with those hyper-parameters, along with a
@@ -60,11 +71,11 @@ def choose_classifier(model_type: str, params: dict[str, Any], dep_var: str):
     post_transformer = SQLTransformer(statement="SELECT * FROM __THIS__")
     features_vector = "features_vector"
     if model_type == "random_forest":
+        params = _ensure_seeded(params)
         classifier = RandomForestClassifier(
             **params,
             labelCol=dep_var,
             featuresCol=features_vector,
-            seed=2133,
             probabilityCol="probability_array",
         )
         post_transformer = SQLTransformer(
@@ -93,23 +104,23 @@ def choose_classifier(model_type: str, params: dict[str, Any], dep_var: str):
         )
 
     elif model_type == "decision_tree":
+        params = _ensure_seeded(params)
         classifier = DecisionTreeClassifier(
             **params,
             featuresCol=features_vector,
             labelCol=dep_var,
             probabilityCol="probability_array",
-            seed=2133,
         )
         post_transformer = SQLTransformer(
             statement="SELECT *, parseProbVector(probability_array, 1) as probability FROM __THIS__"
         )
 
     elif model_type == "gradient_boosted_trees":
+        params = _ensure_seeded(params)
         classifier = GBTClassifier(
             **params,
             featuresCol=features_vector,
             labelCol=dep_var,
-            seed=2133,
         )
         post_transformer = (
             hlink.linking.transformers.rename_prob_column.RenameProbColumn()
@@ -122,6 +133,8 @@ def choose_classifier(model_type: str, params: dict[str, Any], dep_var: str):
                 "its dependencies. Try installing hlink with the lightgbm extra: "
                 "\n\n    pip install hlink[lightgbm]"
             )
+
+        params = _ensure_seeded(params)
         classifier = synapse.ml.lightgbm.LightGBMClassifier(
             **params,
             featuresCol=features_vector,
@@ -138,6 +151,8 @@ def choose_classifier(model_type: str, params: dict[str, Any], dep_var: str):
                 "the xgboost library and its dependencies. Try installing hlink with "
                 "the xgboost extra:\n\n    pip install hlink[xgboost]"
             )
+
+        params = _ensure_seeded(params)
         classifier = xgboost.spark.SparkXGBClassifier(
             **params,
             features_col=features_vector,

hlink/tests/core/classifier_test.py

@@ -3,6 +3,8 @@
 # in this project's top-level directory, and also on-line at:
 #   https://github.com/ipums/hlink
 
+import pytest
+
 from hlink.linking.core.classifier import choose_classifier
 from hlink.tests.markers import requires_lightgbm, requires_xgboost
 
@@ -30,3 +32,17 @@ def test_choose_classifier_supports_xgboost():
     }
     classifier, _post_transformer = choose_classifier("xgboost", params, "match")
     assert classifier.getLabelCol() == "match"
+
+
+@pytest.mark.parametrize(
+    "classifier", ["random_forest", "decision_tree", "gradient_boosted_trees"]
+)
+def test_choose_classifier_can_set_seed_in_params(spark, classifier) -> None:
+    """
+    Ensure that you can pass a "seed" parameter to the classifier. This used to
+    cause an error because of manual handling of the seed parameter. See GitHub
+    Issue #221.
+    """
+    params = {"seed": 151015}
+    classifier, _post_transformer = choose_classifier(classifier, params, "match")
+    assert classifier.getSeed() == 151015