Merge branch 'sktime:main' into estimator-overview-detector

.all-contributorsrc

@@ -3191,6 +3191,15 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "mjste",
+      "name": "Michał Stefanik",
+      "avatar_url": "https://avatars.githubusercontent.com/mjste",
+      "profile": "https://github.com/mjste",
+      "contributions": [
+        "doc"
+      ]
     }
   ]
 }

examples/01c_forecasting_hierarchical_global.ipynb

@@ -279,7 +279,7 @@
     "* instance index: the first element of pairs in `obj.index` is interpreted as an instance index. \n",
     "* variables: columns of `obj` correspond to different variables\n",
     "* variable names: column names `obj.columns`\n",
-    "* time points: rows of `obj` with the same `\"timepoints\"` index correspond correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond correspond to the different time points.\n",
+    "* time points: rows of `obj` with the same `\"timepoints\"` index correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond to the different time points.\n",
     "* time index: the second element of pairs in `obj.index` is interpreted as a time index. \n",
     "* capabilities: can represent panels of multivariate series; can represent unequally spaced series; can represent panels of unequally supported series; cannot represent panels of series with different sets of variables."
    ]
@@ -416,7 +416,7 @@
     "* hierarchy: the non-time-like indices in `obj.index` are interpreted as a hierarchy identifying index. \n",
     "* variables: columns of `obj` correspond to different variables\n",
     "* variable names: column names `obj.columns`\n",
-    "* time points: rows of `obj` with the same `\"timepoints\"` index correspond correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond correspond to the different time points.\n",
+    "* time points: rows of `obj` with the same `\"timepoints\"` index correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond to the different time points.\n",
     "* time index: the last element of tuples in `obj.index` is interpreted as a time index. \n",
     "* capabilities: can represent hierarchical series; can represent unequally spaced series; can represent unequally supported hierarchical series; cannot represent hierarchical series with different sets of variables."
    ]

examples/AA_datatypes_and_datasets.ipynb

@@ -479,7 +479,7 @@
     "* instance index: the first element of pairs in `obj.index` (0-th level value) is interpreted as an instance index, we call it \"instance index\" below.\n",
     "* instances: rows with the same \"instance index\" index value correspond to the same instance; rows with different \"instance index\" values correspond to different instances. \n",
     "* time index: the second element of pairs in `obj.index` (1-st level value) is interpreted as a time index, we call it \"time index\" below. \n",
-    "* time points: rows of `obj` with the same \"time index\" value correspond correspond to the same time point; rows of `obj` with different \"time index\" index correspond correspond to the different time points.\n",
+    "* time points: rows of `obj` with the same \"time index\" value correspond to the same time point; rows of `obj` with different \"time index\" index correspond to the different time points.\n",
     "* variables: columns of `obj` correspond to different variables\n",
     "* variable names: column names `obj.columns`\n",
     "* capabilities: can represent panels of multivariate series; can represent unequally spaced series; can represent panels of unequally supported series; cannot represent panels of series with different sets of variables."
@@ -759,7 +759,7 @@
     "* hierarchy level: rows with the same non-time-like index values correspond to the same hierarchy unit; rows with different non-time-like index combination correspond to different hierarchy unit.\n",
     "* hierarchy: the non-time-like indices in `obj.index` are interpreted as a hierarchy identifying index. \n",
     "* time index: the last element of tuples in `obj.index` is interpreted as a time index. \n",
-    "* time points: rows of `obj` with the same `\"timepoints\"` index correspond correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond correspond to the different time points.\n",
+    "* time points: rows of `obj` with the same `\"timepoints\"` index correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond to the different time points.\n",
     "* variables: columns of `obj` correspond to different variables\n",
     "* variable names: column names `obj.columns`\n",
     "* capabilities: can represent hierarchical series; can represent unequally spaced series; can represent unequally supported hierarchical series; cannot represent hierarchical series with different sets of variables."

sktime/annotation/tests/test_all_annotators.py

@@ -31,6 +31,15 @@ class AnnotatorsFixtureGenerator(BaseFixtureGenerator):
 
     estimator_type_filter = "detector"
 
+    fixture_sequence = [
+        "estimator_class",
+        "estimator_instance",
+        "fitted_estimator",
+        "scenario",
+        "method_nsc",
+        "method_nsc_arraylike",
+    ]
+
 
 class TestAllAnnotators(AnnotatorsFixtureGenerator, QuickTester):
     """Module level tests for all sktime annotators."""
@@ -39,16 +48,60 @@ def test_output_type(self, estimator_instance):
         """Test annotator output type."""
         estimator = estimator_instance
 
-        arg = make_annotation_problem(
+        X_train = make_annotation_problem(
             n_timepoints=50, estimator_type=estimator.get_tag("distribution_type")
         )
-        estimator.fit(arg)
-        arg = make_annotation_problem(
+        estimator.fit(X_train)
+        X_test = make_annotation_problem(
             n_timepoints=10, estimator_type=estimator.get_tag("distribution_type")
         )
-        y_pred = estimator.predict(arg)
+        y_test = estimator.predict(X_test)
+        assert isinstance(y_test, (pd.Series, np.ndarray))
+
+    def test_transform_output_type(self, estimator_instance):
+        """Test output type for the transform method."""
+        X_train = make_annotation_problem(
+            n_timepoints=50,
+            estimator_type=estimator_instance.get_tag("distribution_type"),
+        )
+        estimator_instance.fit(X_train)
+        X_test = make_annotation_problem(
+            n_timepoints=10,
+            estimator_type=estimator_instance.get_tag("distribution_type"),
+        )
+        y_test = estimator_instance.transform(X_test)
+        assert isinstance(y_test, (pd.Series, np.ndarray))
+        assert len(y_test) == len(X_test)
+
+    def test_predict_points(self, estimator_instance):
+        X_train = make_annotation_problem(
+            n_timepoints=50,
+            estimator_type=estimator_instance.get_tag("distribution_type"),
+        )
+        estimator_instance.fit(X_train)
+        X_test = make_annotation_problem(
+            n_timepoints=10,
+            estimator_type=estimator_instance.get_tag("distribution_type"),
+        )
+        y_pred = estimator_instance.predict_points(X_test)
         assert isinstance(y_pred, (pd.Series, np.ndarray))
 
+    def test_predict_segments(self, estimator_instance):
+        X_train = make_annotation_problem(
+            n_timepoints=50,
+            estimator_type=estimator_instance.get_tag("distribution_type"),
+        )
+        estimator_instance.fit(X_train)
+
+        X_test = make_annotation_problem(
+            n_timepoints=10,
+            estimator_type=estimator_instance.get_tag("distribution_type"),
+        )
+        y_test = estimator_instance.predict_segments(X_test)
+        assert isinstance(y_test, pd.Series)
+        assert isinstance(y_test.index.dtype, pd.IntervalDtype)
+        assert pd.api.types.is_integer_dtype(y_test)
+
     def test_annotator_tags(self, estimator_class):
         """Check the learning_type and task tags are valid."""
         check_task(estimator_class.get_class_tag("task"))

sktime/datatypes/_panel/_check.py

@@ -427,7 +427,7 @@ class PanelPdMultiIndex(ScitypePanel):
       is interpreted as a time index, we call it "time index" below.
     * time points: rows of ``obj`` with the same "time index" value correspond
       correspond to the same time point; rows of `obj` with different "time index"
-      index correspond correspond to the different time points.
+      index correspond to the different time points.
     * variables: columns of ``obj`` correspond to different variables
     * variable names: column names ``obj.columns``
 

sktime/forecasting/croston.py

@@ -77,6 +77,7 @@ class Croston(BaseForecaster):
         # estimator type
         # --------------
         "requires-fh-in-fit": False,  # is forecasting horizon already required in fit?
+        "ignores-exogeneous-X": True,
     }
 
     def __init__(self, smoothing=0.1):

sktime/tests/_config.py

@@ -203,8 +203,6 @@
         "test_update_predict_single",  # see 2997, sporadic failure, unknown cause
         "test__y_when_refitting",  # see 3176
     ],
-    # GGS inherits from BaseEstimator which breaks this test
-    "GreedyGaussianSegmentation": ["test_inheritance", "test_create_test_instance"],
     "InformationGainSegmentation": [
         "test_inheritance",
         "test_create_test_instance",
@@ -248,6 +246,62 @@
         "test_persistence_via_pickle",
         "test_save_estimators_to_file",
     ],
+    # The following detectors are not interface compliant. See PR 6958
+    "PoissonHMM": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "HMM": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "ClaSPSegmentation": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "ClusterSegmenter": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "AnnotatorPipeline": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "BinarySegmentation": [
+        "test_predict_segments",
+    ],
+    "GreedyGaussianSegmentation": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+        "test_inheritance",
+        "test_create_test_instance",
+    ],
+    "PyODAnnotator": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "GaussianHMM": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "GMMHMM": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
+    "SubLOF": [
+        "test_predict_points",
+        "test_predict_segments",
+        "test_transform_output_type",
+    ],
 }
 
 # exclude tests but keyed by test name

sktime/transformations/series/holiday/_holidayfeats.py

@@ -60,6 +60,7 @@ class HolidayFeatures(BaseTransformer):
 
     Returns country holiday features with custom holiday windows
 
+    >>> from sktime.transformations.series.holiday import HolidayFeatures
     >>> transformer = HolidayFeatures(
     ...    calendar=country_holidays(country="FR"),
     ...    return_categorical=True,