Repository: sktime/sktime
Pull request #8028 (2025-03-23 10:55:58)

[BUG] Fix failing tests for SklearnClustererPipeline, TimeSeriesDBSCAN, ClustererPipeline
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#### Reference Issues/PRs
Fix failing tests for SklearnClustererPipeline, TimeSeriesDBSCAN, and ClustererPipeline tracked in #8026. Bugs discovered via #7971.


#### What does this implement/fix? Explain your changes.
Updated the default implementation of predict_proba for clusterers to return an array of shape (n_instances, n_clusters). This is the intended shape, but the implementation was instead returning an array of shape (n_samples, n_clusters).

Also updated the tests to allow for instances to not be assigned to any cluster (indicated by cluster -1). This means that the probability assigned for each instance may be less than 1.

#### Does your contribution introduce a new dependency? If yes, which one?
No
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No. This is covered by existing tests.
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Patch:
diff --git a/sktime/clustering/base.py b/sktime/clustering/base.py
index 9075c49b6..6eb793eeb 100644
--- a/sktime/clustering/base.py
+++ b/sktime/clustering/base.py
@@ -312,7 +312,7 @@ class BaseClusterer(BaseEstimator):
             n_clusters = self.n_clusters
         else:
             n_clusters = max(preds) + 1
-        dists = np.zeros((X.shape[0], n_clusters))
+        dists = np.zeros((n_instances, n_clusters))
         for i in range(n_instances):
             # preds[i] can be -1, in this case there is no cluster for this instance
             if preds[i] > -1:
diff --git a/sktime/forecasting/prophetverse.py b/sktime/forecasting/prophetverse.py
index 3f4ce270f..eed16324d 100644
--- a/sktime/forecasting/prophetverse.py
+++ b/sktime/forecasting/prophetverse.py
@@ -7,11 +7,12 @@ from typing import Any, Optional, Union
 
 import pandas as pd
 
-from sktime.forecasting.base._delegate import BaseForecaster, _DelegatedForecaster
+from sktime.forecasting.base._delegate import _DelegatedForecaster
 from sktime.utils.dependencies import _placeholder_record
 
 
-@_placeholder_record("prophetverse.sktime", dependencies="prophetverse>=0.3.0")
+# TODO 0.38.0: update upper and lower bounds when Prophetverse 0.6.0 is released
+@_placeholder_record("prophetverse.sktime", dependencies="prophetverse>=0.3.0,<0.6.0")
 class Prophetverse(_DelegatedForecaster):
     """Univariate prophetverse forecaster - prophet model implemented in numpyro.
 
@@ -207,8 +208,9 @@ class Prophetverse(_DelegatedForecaster):
         self._delegate = Prophet(**self.get_params())
 
 
-@_placeholder_record("prophetverse.sktime")
-class HierarchicalProphet(BaseForecaster):
+# TODO 0.38.0: update upper and lower bounds when Prophetverse 0.6.0 is released
+@_placeholder_record("prophetverse.sktime", dependencies="prophetverse>=0.3.0,<0.6.0")
+class HierarchicalProphet(_DelegatedForecaster):
     """A Bayesian hierarchical time series forecasting model based on Meta's Prophet.
 
     This method forecasts all bottom series in a hierarchy at once, using a
@@ -314,6 +316,8 @@ class HierarchicalProphet(BaseForecaster):
     >>> forecaster.predict(fh=[1])
     """
 
+    _delegate_name = "_delegate"
+
     _tags = {
         # packaging info
         # --------------
@@ -388,3 +392,8 @@ class HierarchicalProphet(BaseForecaster):
         self.rng_key = rng_key
 
         super().__init__()
+
+        # delegation, only for prophetverse 0.2.X
+        from prophetverse.sktime import HierarchicalProphet
+
+        self._delegate = HierarchicalProphet(**self.get_params())
diff --git a/sktime/transformations/base.py b/sktime/transformations/base.py
index 8121cbf26..e9b430c07 100644
--- a/sktime/transformations/base.py
+++ b/sktime/transformations/base.py
@@ -1394,15 +1394,26 @@ class BaseTransformer(BaseEstimator):
                 store_behaviour="freeze",
             )
         elif output_scitype == "Primitives":
+            # vectorization causes a superfluous zero level
+            # if we have a Series input that is vectorized,
+            # as in that case the index should be "0-level" (no levels)
+            # but this is not possible in pandas, so it will have a level
+            # which always has the entry 0.
+            # in this case, we need to strip this level
+            Xt_has_superfluous_zero_level = (
+                X_input_scitype != "Series"
+                and case == "case 3: requires vectorization"
+                and isinstance(Xt, (pd.DataFrame, pd.Series))
+            )
             # we ensure the output is pd_DataFrame_Table
             # & ensure the returned index is sensible
             # for return index, we need to deal with last level, constant 0
-            if isinstance(Xt, (pd.DataFrame, pd.Series)):
+            if Xt_has_superfluous_zero_level:
                 # if index is multiindex, last level is constant 0
                 # and other levels are hierarchy
                 if isinstance(Xt.index, pd.MultiIndex):
                     Xt.index = Xt.index.droplevel(-1)
-                # else this is only zeros and should be reset to RangeIndex
+                # we have an index with only zeroes, and should be reset to RangeIndex
                 else:
                     Xt = Xt.reset_index(drop=True)
             return convert_to(


Test patch:
diff --git a/sktime/clustering/tests/test_all_clusterers.py b/sktime/clustering/tests/test_all_clusterers.py
index 9bd49b3a8..5770da930 100644
--- a/sktime/clustering/tests/test_all_clusterers.py
+++ b/sktime/clustering/tests/test_all_clusterers.py
@@ -76,16 +76,25 @@ class TestAllClusterers(ClustererFixtureGenerator, QuickTester):
         # run predict
         y_pred = scenario.run(estimator_instance, method_sequence=["predict"])
 
+        shape_msg = "Incorrect shape returned by predict"
+
         # check predict
-        assert isinstance(y_pred, np.ndarray)
-        assert y_pred.shape == (X_new_instances,)
+        msg = f"Incorrect type returned by predict ({type(y_pred)})"
+        assert isinstance(y_pred, np.ndarray), msg
+        assert y_pred.shape == (X_new_instances,), shape_msg
 
         # check predict proba (all clusterers have predict_proba by default)
         y_proba = scenario.run(estimator_instance, method_sequence=["predict_proba"])
-        assert isinstance(y_proba, np.ndarray)
-        assert y_proba.shape[0] == X_new_instances
+        msg = f"Incorrect type returned by predict_proba ({type(y_proba)})"
+        assert isinstance(y_proba, np.ndarray), msg
+        assert y_proba.shape[0] == X_new_instances, shape_msg
 
         if hasattr(estimator_instance, "n_clusters") and estimator_instance.n_clusters:
             assert y_proba.shape[1] == estimator_instance.n_clusters
 
-        np.testing.assert_almost_equal(y_proba.sum(axis=1), 1, decimal=4)
+        # If a sample doesn't belong to a cluster, it may not contribute to the total
+        # probability. Check that total probability in range [0,1] + rounding error
+        msg = "Total assigned probability exceeds 1"
+        assert np.all(y_proba.sum(axis=1) < 1.0001), msg
+        msg = "Total assigned probability negative"
+        assert np.all(y_proba.sum(axis=1) >= 0), msg
diff --git a/sktime/transformations/tests/test_base.py b/sktime/transformations/tests/test_base.py
index cc64b1d39..f89b23184 100644
--- a/sktime/transformations/tests/test_base.py
+++ b/sktime/transformations/tests/test_base.py
@@ -27,6 +27,7 @@ from sktime.transformations.panel.tsfresh import (
 from sktime.transformations.series.boxcox import BoxCoxTransformer
 from sktime.transformations.series.exponent import ExponentTransformer
 from sktime.transformations.series.summarize import SummaryTransformer
+from sktime.utils._testing.hierarchical import _make_hierarchical
 from sktime.utils._testing.scenarios_transformers import (
     TransformerFitTransformHierarchicalMultivariate,
     TransformerFitTransformHierarchicalUnivariate,
@@ -802,3 +803,65 @@ def test_wrong_y_is_not_passed_to_transformer():
 
     model = make_pipeline(noise_filter, interpolator, regressor)
     model.fit(X, y)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("sktime.transformations"),
+    reason="run test only if anything in sktime.transformations module has changed",
+)
+def test_series_to_primitives_hierarchical():
+    """Test that the correct index is returned for hierarchical Series-to-Primitives.
+
+    Tests the vectorization case and the plain case.
+
+    Setting: transformer has tags
+        "scitype:transform-input" = "Series"
+        "scitype:transform-output" = "Panel"
+
+        Case 1: "X_inner_mtype" does not support Hierarchical, vectorizes
+        Case 2: "X_inner_mtype" does support Hierarchical, does not vectorize
+
+    X input to fit/transform has Hierarchical mtype
+    X output from fit/transform should be Table with hierarchical index, one level less
+    """
+    # case 1
+    # example of Series-to-Primitives not supporting Hierarchical
+    cls_vectorizes = SummaryTransformer
+    est = cls_vectorizes.create_test_instance()
+    # ensure cls is a good example, if this fails, choose another example
+    #   (if this changes, it may be due to implementing more scitypes)
+    #   (then this is not a failure of cls, but we need to choose another example)
+    assert "Series" in inner_X_scitypes(est)
+    assert "Panel" not in inner_X_scitypes(est)
+    assert "Hierarchical" not in inner_X_scitypes(est)
+    assert est.get_tag("scitype:transform-input") == "Series"
+    assert est.get_tag("scitype:transform-output") == "Primitives"
+
+    X = _make_hierarchical()
+    Xt = est.fit_transform(X)
+    ix = Xt.index
+
+    # check that Xt.index is the same as X.index with time level dropped and made unique
+    assert (X.index.droplevel(-1).unique() == ix).all()
+
+    # case 2
+    from sktime.clustering.dbscan import TimeSeriesDBSCAN
+    from sktime.registry import coerce_scitype
+
+    # example of Series-to-Primitives supporting Hierarchical
+    clust = TimeSeriesDBSCAN.create_test_instance()
+    est = coerce_scitype(clust, "transformer")
+
+    # ensure est is a good example, if this fails, choose another example
+    #   (if this changes, it may be due to implementing more scitypes)
+    #   (then this is not a failure of est, but we need to choose another example)
+    assert "Hierarchical" in inner_X_scitypes(est)
+    assert est.get_tag("scitype:transform-input") == "Series"
+    assert est.get_tag("scitype:transform-output") == "Primitives"
+
+    X = _make_hierarchical()
+    Xt = est.fit_transform(X)
+    ix = Xt.index
+
+    # check that Xt.index is the same as X.index with time level dropped and made unique
+    assert (X.index.droplevel(-1).unique() == ix).all()