[ENH] clusterer test scenario with unequal length time series; fix cl…

…usterer tags (#6277)

Fixes #6276:

* corrects tag `capability:unequal_length` for `TimeSeriesKMeansTslearn`
(to `False`)
* adds a clusterer test scenario with unequal length time series panels

sktime/clustering/k_means/_k_means_tslearn.py

@@ -107,7 +107,7 @@ class TimeSeriesKMeansTslearn(_TslearnAdapter, BaseClusterer):
         # estimator type
         # --------------
         "capability:multivariate": True,
-        "capability:unequal_length": True,
+        "capability:unequal_length": False,
     }
 
     # defines the name of the attribute containing the tslearn estimator

sktime/clustering/k_shapes.py

@@ -57,7 +57,7 @@ class TimeSeriesKShapes(_TslearnAdapter, BaseClusterer):
         # estimator type
         # --------------
         "capability:multivariate": True,
-        "capability:unequal_length": True,
+        "capability:unequal_length": False,
     }
 
     # defines the name of the attribute containing the tslearn estimator

sktime/utils/_testing/scenarios_clustering.py

@@ -7,7 +7,11 @@
 
 __all__ = ["scenarios_clustering"]
 
+from inspect import isclass
+
 from sktime.base import BaseObject
+from sktime.registry import scitype
+from sktime.utils._testing.hierarchical import _make_hierarchical
 from sktime.utils._testing.panel import _make_panel_X, make_clustering_problem
 from sktime.utils._testing.scenarios import TestScenario
 
@@ -43,11 +47,52 @@ def get_args(self, key, obj=None, deepcopy_args=True):
 
         return super().get_args(key=key, obj=obj, deepcopy_args=deepcopy_args)
 
+    def is_applicable(self, obj):
+        """Check whether scenario is applicable to obj.
+
+        Parameters
+        ----------
+        obj : class or object to check against scenario
+
+        Returns
+        -------
+        applicable: bool
+            True if self is applicable to obj, False if not
+        """
+
+        def get_tag(obj, tag_name):
+            if isclass(obj):
+                return obj.get_class_tag(tag_name)
+            else:
+                return obj.get_tag(tag_name)
+
+        # applicable only if obj inherits from BaseClassifier, BaseEarlyClassifier or
+        #   BaseRegressor. currently we test both classifiers and regressors using these
+        #   scenarios
+        if scitype(obj) != "clusterer":
+            return False
+
+        # if X is multivariate, applicable only if can handle multivariate
+        is_multivariate = not self.get_tag("X_univariate")
+        if is_multivariate and not get_tag(obj, "capability:multivariate"):
+            return False
+
+        # if X is unequal length, applicable only if can handle unequal length
+        is_unequal_length = self.get_tag("X_unequal_length")
+        if is_unequal_length and not get_tag(obj, "capability:unequal_length"):
+            return False
+
+        return True
+
 
 class ClustererFitPredict(ClustererTestScenario):
     """Fit/predict with panel Xmake_clustering_problem."""
 
-    _tags = {"X_univariate": True, "is_enabled": True}
+    _tags = {
+        "X_univariate": True,
+        "X_unequal_length": False,
+        "is_enabled": True,
+    }
 
     @property
     def args(self):
@@ -59,6 +104,39 @@ def args(self):
     default_method_sequence = ["fit", "predict"]
 
 
+class ClustererFitPredictUnequalLength(ClustererTestScenario):
+    """Fit/predict with univariate panel X, unequal length series."""
+
+    _tags = {
+        "X_univariate": True,
+        "X_unequal_length": True,
+        "is_enabled": True,
+    }
+
+    @property
+    def args(self):
+        X_unequal_length = _make_hierarchical(
+            hierarchy_levels=(10,),
+            min_timepoints=10,
+            max_timepoints=15,
+            random_state=RAND_SEED,
+        )
+        X_unequal_length_test = _make_hierarchical(
+            hierarchy_levels=(5,),
+            min_timepoints=10,
+            max_timepoints=15,
+            random_state=RAND_SEED,
+        )
+        return {
+            "fit": {"X": X_unequal_length},
+            "predict": {"X": X_unequal_length_test},
+        }
+
+    default_method_sequence = ["fit", "predict", "predict_proba", "decision_function"]
+    default_arg_sequence = ["fit", "predict", "predict", "predict"]
+
+
 scenarios_clustering = [
     ClustererFitPredict,
+    ClustererFitPredictUnequalLength,
 ]