Remove estimators in aeon and tsml-eval

README.md

@@ -16,7 +16,7 @@ Please see [`tsml_eval`](https://github.com/time-series-machine-learning/tsml-ev
 is more of a sandbox for testing out new ideas and algorithms. It may contain some
 algorithms and implementations that are not available in the other toolkits.
 
-The current release of `tsml` is v0.5.0.
+The current release of `tsml` is v0.6.0.
 
 ## Installation
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tsml"
-version = "0.5.0"
+version = "0.6.0"
 description = "A development sandbox for time series machine learning algorithms."
 authors = [
     {name = "Matthew Middlehurst", email = "[email protected]"},
@@ -38,25 +38,21 @@ classifiers = [
     "Programming Language :: Python :: 3.12",
 ]
 dependencies = [
-    "numba>=0.55.0,<0.61.0",
-    "numpy>=1.21.0,<2.2.0",
-    "scipy>=1.9.0,<1.14.0",
-    "pandas>=1.5.3,<2.3.0",
-    "scikit-learn>=1.0.0,<1.4.0",
+    "numba>=0.55.0",
+    "numpy>=1.21.0",
+    "scipy>=1.9.0",
+    "pandas>=1.5.3",
+    "scikit-learn>=1.0.0",
     "packaging>=20.0",
 ]
 
 [project.optional-dependencies]
 all_extras = [
     "grailts",
-    "scikit-fda>=0.7.0",
-    "statsmodels>=0.12.1",
-    "stumpy>=1.6.0",
+    # "scikit-fda>=0.7.0",
     "wildboar",
 ]
 unstable_extras = [
-    "pycatch22",
-    "pyfftw>=0.12.0; python_version < '3.12'",  # requires fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html)
     "mrsqm>=0.0.7; platform_system == 'Linux' and python_version < '3.12'",  # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html)
     "mrseql>=0.0.4,<0.1.0; platform_system == 'Linux' and python_version < '3.12'",  # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html)
 ]

tsml/__init__.py

@@ -1,3 +1,3 @@
 """tsml."""
 
-__version__ = "0.5.0"
+__version__ = "0.6.0"

tsml/compose/_channel_ensemble.py

@@ -204,17 +204,17 @@ class ChannelEnsembleClassifier(ClassifierMixin, _BaseChannelEnsemble):
     Examples
     --------
     >>> from tsml.compose import ChannelEnsembleClassifier
-    >>> from tsml.interval_based import IntervalForestClassifier
+    >>> from tsml.dummy import DummyClassifier
     >>> from tsml.utils.testing import generate_3d_test_data
     >>> X, y = generate_3d_test_data(n_samples=8, series_length=10, random_state=0)
     >>> reg = ChannelEnsembleClassifier(
-    ...     estimators=("tsf", IntervalForestClassifier(n_estimators=2), "all-split"),
+    ...     estimators=("d", DummyClassifier(), "all-split"),
     ...     random_state=0,
     ... )
     >>> reg.fit(X, y)
     ChannelEnsembleClassifier(...)
     >>> reg.predict(X)
-    array([0, 1, 1, 0, 0, 1, 0, 1])
+    array([0, 0, 0, 0, 0, 0, 0, 0])
     """
 
     def __init__(self, estimators, remainder="drop", random_state=None):
@@ -349,12 +349,12 @@ def get_test_params(
         params : dict or list of dict
             Parameters to create testing instances of the class.
         """
-        from tsml.interval_based import IntervalForestClassifier
+        from tsml.dummy import DummyClassifier
 
         return {
             "estimators": [
-                ("tsf1", IntervalForestClassifier(n_estimators=2), 0),
-                ("tsf2", IntervalForestClassifier(n_estimators=2), 0),
+                ("d1", DummyClassifier(), 0),
+                ("d2", DummyClassifier(), 0),
             ]
         }
 
@@ -411,19 +411,19 @@ class ChannelEnsembleRegressor(RegressorMixin, _BaseChannelEnsemble):
     Examples
     --------
     >>> from tsml.compose import ChannelEnsembleRegressor
-    >>> from tsml.interval_based import IntervalForestRegressor
+    >>> from tsml.dummy import DummyRegressor
     >>> from tsml.utils.testing import generate_3d_test_data
     >>> X, y = generate_3d_test_data(n_samples=8, series_length=10,
     ...                              regression_target=True, random_state=0)
     >>> reg = ChannelEnsembleRegressor(
-    ...     estimators=("tsf", IntervalForestRegressor(n_estimators=2), "all-split"),
+    ...     estimators=("d", DummyRegressor(), "all-split"),
     ...     random_state=0,
     ... )
     >>> reg.fit(X, y)
     ChannelEnsembleRegressor(...)
     >>> reg.predict(X)
-    array([0.31798318, 1.41426301, 1.06414747, 0.6924721 , 0.56660146,
-           1.26538944, 0.52324808, 1.0939405 ])
+    array([0.8672557, 0.8672557, 0.8672557, 0.8672557, 0.8672557, 0.8672557,
+           0.8672557, 0.8672557], dtype=float32)
     """
 
     def __init__(self, estimators, remainder="drop", random_state=None):
@@ -518,12 +518,12 @@ def get_test_params(
         params : dict or list of dict
             Parameters to create testing instances of the class.
         """
-        from tsml.interval_based import IntervalForestRegressor
+        from tsml.dummy import DummyRegressor
 
         return {
             "estimators": [
-                ("tsf1", IntervalForestRegressor(n_estimators=2), 0),
-                ("tsf2", IntervalForestRegressor(n_estimators=2), 0),
+                ("d1", DummyRegressor(), 0),
+                ("d2", DummyRegressor(), 0),
             ]
         }
 

tsml/compose/tests/test_channel_ensemble.py

@@ -9,25 +9,21 @@
     _check_key_type,
     _get_channel,
 )
-from tsml.interval_based import IntervalForestClassifier, IntervalForestRegressor
+from tsml.dummy import DummyClassifier, DummyRegressor
 from tsml.utils.testing import generate_3d_test_data, generate_unequal_test_data
 
 
 def test_single_estimator():
     """Test that a single estimator is correctly applied to all channels."""
     X, y = generate_3d_test_data(n_channels=3)
 
-    ens = ChannelEnsembleClassifier(
-        estimators=[("tsf", IntervalForestClassifier(n_estimators=2), "all")]
-    )
+    ens = ChannelEnsembleClassifier(estimators=[("d", DummyClassifier(), "all")])
     ens.fit(X, y)
 
     assert len(ens.estimators_[0][2]) == 3
     assert ens.predict(X).shape == (X.shape[0],)
 
-    ens = ChannelEnsembleRegressor(
-        estimators=[("tsf", IntervalForestRegressor(n_estimators=2), "all")]
-    )
+    ens = ChannelEnsembleRegressor(estimators=[("d", DummyRegressor(), "all")])
     ens.fit(X, y)
 
     assert len(ens.estimators_[0][2]) == 3
@@ -38,18 +34,14 @@ def test_single_estimator_split():
     """Test that a single split estimator correctly creates an estimator per channel."""
     X, y = generate_3d_test_data(n_channels=3)
 
-    ens = ChannelEnsembleClassifier(
-        estimators=("tsf", IntervalForestClassifier(n_estimators=2), "all-split")
-    )
+    ens = ChannelEnsembleClassifier(estimators=("d", DummyClassifier(), "all-split"))
     ens.fit(X, y)
 
     assert len(ens.estimators_) == 3
     assert isinstance(ens.estimators_[0][2], int)
     assert ens.predict(X).shape == (X.shape[0],)
 
-    ens = ChannelEnsembleRegressor(
-        estimators=("tsf", IntervalForestRegressor(n_estimators=2), "all-split")
-    )
+    ens = ChannelEnsembleRegressor(estimators=("d", DummyRegressor(), "all-split"))
     ens.fit(X, y)
 
     assert len(ens.estimators_) == 3
@@ -62,17 +54,17 @@ def test_remainder():
     X, y = generate_3d_test_data(n_channels=3)
 
     ens = ChannelEnsembleClassifier(
-        estimators=[("tsf", IntervalForestClassifier(n_estimators=2), 0)],
-        remainder=IntervalForestClassifier(n_estimators=2),
+        estimators=[("d", DummyClassifier(), 0)],
+        remainder=DummyClassifier(),
     )
     ens.fit(X, y)
 
     assert len(ens._remainder[2]) == 2
     assert ens.predict(X).shape == (X.shape[0],)
 
     ens = ChannelEnsembleRegressor(
-        estimators=[("tsf", IntervalForestRegressor(n_estimators=2), 0)],
-        remainder=IntervalForestRegressor(n_estimators=2),
+        estimators=[("d", DummyRegressor(), 0)],
+        remainder=DummyRegressor(),
     )
     ens.fit(X, y)