parametrize tests for proba and score_one

river/compose/test_.py

@@ -1,6 +1,7 @@
 import pandas as pd
+import pytest
 
-from river import compose, linear_model, preprocessing
+from river import compose, linear_model, preprocessing, anomaly
 
 
 def test_pipeline_funcs():
@@ -53,29 +54,31 @@ def b(x):
         assert str(pipeline) == "a + b"
 
 
-def test_no_learn_unsupervised_predict_one():
+@pytest.mark.parametrize("func", [compose.Pipeline.predict_one, compose.Pipeline.predict_proba_one])
+def test_no_learn_unsupervised_one(func):
     pipeline = compose.Pipeline(
         ("scale", preprocessing.StandardScaler()),
-        ("lin_reg", linear_model.LinearRegression()),
+        ("log_reg", linear_model.LogisticRegression()),
     )
 
     dataset = [(dict(a=x, b=x), x) for x in range(100)]
 
     for x, y in dataset:
         counts_pre = dict(pipeline.steps["scale"].counts)
-        pipeline.predict_one(x, learn_unsupervised=True)
+        func(pipeline, x, learn_unsupervised=True)
         counts_post = dict(pipeline.steps["scale"].counts)
-        pipeline.predict_one(x, learn_unsupervised=False)
+        func(pipeline, x, learn_unsupervised=False)
         counts_no_learn = dict(pipeline.steps["scale"].counts)
 
         assert counts_pre != counts_post
         assert counts_post == counts_no_learn
 
 
-def test_no_learn_unsupervised_predict_many():
+@pytest.mark.parametrize("func", [compose.Pipeline.predict_many, compose.Pipeline.predict_proba_many])
+def test_no_learn_unsupervised_many(func):
     pipeline = compose.Pipeline(
         ("scale", preprocessing.StandardScaler()),
-        ("lin_reg", linear_model.LinearRegression()),
+        ("log_reg", linear_model.LogisticRegression()),
     )
 
     dataset = [(dict(a=x, b=x), x) for x in range(100)]
@@ -84,9 +87,27 @@ def test_no_learn_unsupervised_predict_many():
         X = pd.DataFrame([x for x, y in dataset][i : i + 5])
 
         counts_pre = dict(pipeline.steps["scale"].counts)
-        pipeline.predict_many(X, learn_unsupervised=True)
+        func(pipeline, X, learn_unsupervised=True)
         counts_post = dict(pipeline.steps["scale"].counts)
-        pipeline.predict_many(X, learn_unsupervised=False)
+        func(pipeline, X, learn_unsupervised=False)
+        counts_no_learn = dict(pipeline.steps["scale"].counts)
+
+        assert counts_pre != counts_post
+        assert counts_post == counts_no_learn
+
+def test_no_learn_unsupervised_score_one():
+    pipeline = compose.Pipeline(
+        ("scale", preprocessing.StandardScaler()),
+        ("anomaly", anomaly.HalfSpaceTrees()),
+    )
+
+    dataset = [(dict(a=x, b=x), x) for x in range(100)]
+
+    for x, y in dataset:
+        counts_pre = dict(pipeline.steps["scale"].counts)
+        pipeline.score_one(x, learn_unsupervised=True)
+        counts_post = dict(pipeline.steps["scale"].counts)
+        pipeline.score_one(x, learn_unsupervised=False)
         counts_no_learn = dict(pipeline.steps["scale"].counts)
 
         assert counts_pre != counts_post