move definitions from test/facet/__init__ to test/conftest (#121)

test/test/conftest.py

@@ -1,7 +1,7 @@
 import functools
 import logging
 import operator
-from typing import List, Set
+from typing import Any, List, Mapping, Optional, Sequence, Set
 
 import numpy as np
 import pandas as pd
@@ -28,7 +28,6 @@
 )
 
 import facet
-from .facet import STEP_IMPUTE, STEP_ONE_HOT_ENCODE
 from facet.crossfit import LearnerCrossfit
 from facet.data import Sample
 from facet.inspection import LearnerInspector, TreeExplainerFactory
@@ -51,6 +50,9 @@
 K_FOLDS = 5
 N_BOOTSTRAPS = 30
 
+STEP_IMPUTE = "impute"
+STEP_ONE_HOT_ENCODE = "one-hot-encode"
+
 
 @pytest.fixture
 def boston_target() -> str:
@@ -299,3 +301,46 @@ def iris_sample_binary_dual_target(
         iris_sample_binary.features.join(target).join(target.rename(iris_target_2)),
         target_name=[iris_sample_binary.target_name, iris_target_2],
     )
+
+
+def check_ranking(
+    ranking: List[LearnerEvaluation],
+    expected_scores: Sequence[float],
+    expected_learners: Optional[Sequence[type]],
+    expected_parameters: Optional[Mapping[int, Mapping[str, Any]]],
+) -> None:
+    """
+    Test helper to check rankings produced by learner rankers
+
+    :param ranking: a list of LearnerEvaluations
+    :param expected_scores: expected ranking scores, rounded to 3 decimal places
+    :param expected_learners: expected learner classes
+    :param expected_parameters: expected learner parameters
+    :return: None
+    """
+
+    if expected_learners is None:
+        expected_learners = [None] * len(ranking)
+
+    for rank, (learner_eval, score_expected, learner_expected) in enumerate(
+        zip(ranking, expected_scores, expected_learners)
+    ):
+        score_actual = round(learner_eval.ranking_score, 3)
+        assert score_actual == pytest.approx(score_expected, abs=0.1), (
+            f"unexpected score for learner at rank #{rank + 1}: "
+            f"got {score_actual} but expected {score_expected}"
+        )
+        if learner_expected is not None:
+            learner_actual = learner_eval.pipeline.final_estimator
+            assert type(learner_actual) == learner_expected, (
+                f"unexpected class for learner at rank #{rank}: "
+                f"got {type(learner_actual)} but expected {learner_expected}"
+            )
+
+    if expected_parameters is not None:
+        for rank, parameters_expected in expected_parameters.items():
+            parameters_actual = ranking[rank].parameters
+            assert parameters_actual == parameters_expected, (
+                f"unexpected parameters for learner at rank #{rank}: "
+                f"got {parameters_actual} but expected {parameters_expected}"
+            )

test/test/facet/__init__.py

@@ -1,51 +0,0 @@
-from typing import Any, List, Mapping, Optional, Sequence
-
-import pytest
-
-from facet.selection import LearnerEvaluation
-
-STEP_IMPUTE = "impute"
-STEP_ONE_HOT_ENCODE = "one-hot-encode"
-
-
-def check_ranking(
-    ranking: List[LearnerEvaluation],
-    expected_scores: Sequence[float],
-    expected_learners: Optional[Sequence[type]],
-    expected_parameters: Optional[Mapping[int, Mapping[str, Any]]],
-) -> None:
-    """
-    Test helper to check rankings produced by learner rankers
-
-    :param ranking: a list of LearnerEvaluations
-    :param expected_scores: expected ranking scores, rounded to 3 decimal places
-    :param expected_learners: expected learner classes
-    :param expected_parameters: expected learner parameters
-    :return: None
-    """
-
-    if expected_learners is None:
-        expected_learners = [None] * len(ranking)
-
-    for rank, (learner_eval, score_expected, learner_expected) in enumerate(
-        zip(ranking, expected_scores, expected_learners)
-    ):
-        score_actual = round(learner_eval.ranking_score, 3)
-        assert score_actual == pytest.approx(score_expected, abs=0.1), (
-            f"unexpected score for learner at rank #{rank + 1}: "
-            f"got {score_actual} but expected {score_expected}"
-        )
-        if learner_expected is not None:
-            learner_actual = learner_eval.pipeline.final_estimator
-            assert type(learner_actual) == learner_expected, (
-                f"unexpected class for learner at rank #{rank}: "
-                f"got {type(learner_actual)} but expected {learner_expected}"
-            )
-
-    if expected_parameters is not None:
-        for rank, parameters_expected in expected_parameters.items():
-            parameters_actual = ranking[rank].parameters
-            assert parameters_actual == parameters_expected, (
-                f"unexpected parameters for learner at rank #{rank}: "
-                f"got {parameters_actual} but expected {parameters_expected}"
-            )

test/test/facet/test_crossfit.py

@@ -6,7 +6,7 @@
 from sklearndf.pipeline import ClassifierPipelineDF, RegressorPipelineDF
 from sklearndf.regression import RandomForestRegressorDF
 
-from . import check_ranking
+from ..conftest import check_ranking
 from facet.data import Sample
 from facet.selection import LearnerGrid, LearnerRanker
 from facet.validation import StratifiedBootstrapCV

test/test/facet/test_inspection.py

@@ -20,7 +20,7 @@
 )
 from sklearndf.pipeline import ClassifierPipelineDF, RegressorPipelineDF
 
-from . import check_ranking
+from ..conftest import check_ranking
 from facet.crossfit import LearnerCrossfit
 from facet.data import Sample
 from facet.inspection import (

test/test/facet/test_selection.py

@@ -19,7 +19,7 @@
 )
 from sklearndf.regression.extra import LGBMRegressorDF
 
-from . import check_ranking
+from ..conftest import check_ranking
 from facet.crossfit import LearnerCrossfit
 from facet.data import Sample
 from facet.selection import LearnerEvaluation, LearnerGrid, LearnerRanker