Move to a blacklist model for checks

csrank/tests/test_estimators.py

@@ -3,9 +3,10 @@
 https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator
 """
 
+from functools import partial
+
 import pytest
-from sklearn.utils.estimator_checks import check_parameters_default_constructible
-from sklearn.utils.estimator_checks import check_no_attributes_set_in_init
+from sklearn.utils.estimator_checks import check_estimator
 
 from csrank.choicefunction import CmpNetChoiceFunction
 from csrank.choicefunction import FATEChoiceFunction
@@ -42,36 +43,66 @@
     "Estimator",
     [
         CmpNet,
-        # CmpNetChoiceFunction,
-        # CmpNetDiscreteChoiceFunction,
-        # ExpectedRankRegression,
-        # FATEChoiceFunction,
-        # FATEDiscreteChoiceFunction,
-        # FATELinearChoiceFunction,
-        # FATELinearDiscreteChoiceFunction,
-        # FATELinearObjectRanker,
-        # FATEObjectRanker,
-        # FETAChoiceFunction,
-        # FETADiscreteChoiceFunction,
-        # FETALinearChoiceFunction,
-        # FETALinearDiscreteChoiceFunction,
-        # FETALinearObjectRanker,
-        # FETAObjectRanker,
-        # GeneralizedLinearModel,
-        # GeneralizedNestedLogitModel,
-        # ListNet,
-        # MixedLogitModel,
-        # MultinomialLogitModel,
-        # NestedLogitModel,
-        # PairedCombinatorialLogit,
-        # PairwiseSVMChoiceFunction,
-        # PairwiseSVMDiscreteChoiceFunction,
-        # RankNet,
-        # RankNetChoiceFunction,
-        # RankNetDiscreteChoiceFunction,
-        # RankSVM,
+        CmpNetChoiceFunction,
+        CmpNetDiscreteChoiceFunction,
+        ExpectedRankRegression,
+        FATEChoiceFunction,
+        FATEDiscreteChoiceFunction,
+        FATELinearChoiceFunction,
+        FATELinearDiscreteChoiceFunction,
+        FATELinearObjectRanker,
+        FATEObjectRanker,
+        FETAChoiceFunction,
+        FETADiscreteChoiceFunction,
+        FETALinearChoiceFunction,
+        FETALinearDiscreteChoiceFunction,
+        FETALinearObjectRanker,
+        FETAObjectRanker,
+        GeneralizedLinearModel,
+        GeneralizedNestedLogitModel,
+        ListNet,
+        MixedLogitModel,
+        MultinomialLogitModel,
+        NestedLogitModel,
+        PairedCombinatorialLogit,
+        PairwiseSVMChoiceFunction,
+        PairwiseSVMDiscreteChoiceFunction,
+        RankNet,
+        RankNetChoiceFunction,
+        RankNetDiscreteChoiceFunction,
+        RankSVM,
     ],
 )
 def test_all_estimators(Estimator):
-    check_parameters_default_constructible("default_constructible", Estimator)
-    check_no_attributes_set_in_init("no_attributes_set_in_init", Estimator())
+    def get_check_name(check):
+        if isinstance(check, partial):
+            return check.func.__name__
+        else:
+            return check.__name__
+
+    for (estimator, check) in check_estimator(Estimator, generate_only=True):
+        # checks that attempt to call "fit" do not work since our estimators
+        # expect a 3-dimensional data shape while scikit-learn assumes two
+        # dimensions (an array of 1d data).
+        if not get_check_name(check) in {
+            "check_estimators_dtypes",
+            "check_fit_score_takes_y",
+            "check_estimators_fit_returns_self",
+            "check_complex_data",
+            "check_dtype_object",
+            "check_estimators_empty_data_messages",
+            "check_pipeline_consistency",
+            "check_estimators_nan_inf",
+            "check_estimators_overwrite_params",
+            "check_estimator_sparse_data",
+            "check_estimators_pickle",
+            "check_fit2d_predict1d",
+            "check_methods_subset_invariance",
+            "check_fit2d_1sample",
+            "check_fit2d_1feature",
+            "check_dict_unchanged",
+            "check_dont_overwrite_parameters",
+            "check_fit_idempotent",
+            "check_fit1d",
+        }:
+            check(estimator)