[MNT] testing remaining cleanup (#2349)

* remove old stuff

* testing tidy

* testing tidy

* example results files

MANIFEST.in

@@ -1,6 +1,6 @@
 recursive-include aeon *.py
-recursive-include aeon/benchmarking/example_results *.csv
 recursive-include aeon/datasets *.csv *.arff *.txt *.ts *.tsv *.tsf
+recursive-include aeon/testing/example_results_files *.csv
 include aeon/registry/README.md
 include .coveragerc
 include conftest.py

aeon/benchmarking/tests/test_results_loaders.py

@@ -7,6 +7,7 @@
 import pytest
 from pytest import raises
 
+import aeon
 from aeon.benchmarking.results_loaders import (
     CONNECTION_ERRORS,
     NAME_ALIASES,
@@ -72,8 +73,10 @@ def test_get_available_estimators():
 
 cls = ["HIVECOTEV2", "FreshPRINCE", "InceptionTime"]
 data = ["Chinatown", "ItalyPowerDemand", "Tools"]
-test_path = os.path.dirname(__file__)
-data_path = os.path.join(test_path, "../example_results/")
+data_path = os.path.join(
+    os.path.dirname(aeon.__file__),
+    "testing/example_results_files/",
+)
 
 
 @pytest.mark.skipif(

aeon/classification/compose/tests/test_pipeline.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import pytest
+from numpy.testing import assert_array_almost_equal
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import StandardScaler
 
@@ -16,7 +17,6 @@
     make_example_3d_numpy_list,
 )
 from aeon.testing.mock_estimators import MockCollectionTransformer
-from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,
@@ -61,7 +61,7 @@ def test_classifier_pipeline(transformers):
         X_test = t.transform(X_test)
 
     c.fit(X_train, y_train)
-    _assert_array_almost_equal(y_pred, c.predict(X_test))
+    assert_array_almost_equal(y_pred, c.predict(X_test))
 
 
 @pytest.mark.parametrize(
@@ -99,7 +99,7 @@ def test_sklearn_classifier_pipeline(transformers):
         X_test = t.transform(X_test)
 
     c.fit(X_train, y_train)
-    _assert_array_almost_equal(y_pred, c.predict(X_test))
+    assert_array_almost_equal(y_pred, c.predict(X_test))
 
 
 def test_unequal_tag_inference():

aeon/clustering/compose/tests/test_pipeline.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import pytest
+from numpy.testing import assert_array_almost_equal
 from sklearn.cluster import KMeans
 from sklearn.preprocessing import StandardScaler
 
@@ -14,7 +15,6 @@
     make_example_3d_numpy_list,
 )
 from aeon.testing.mock_estimators import MockCollectionTransformer
-from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,
@@ -60,7 +60,7 @@ def test_clusterer_pipeline(transformers):
         X_test = t.transform(X_test)
 
     c.fit(X_train, y_train)
-    _assert_array_almost_equal(y_pred, c.predict(X_test))
+    assert_array_almost_equal(y_pred, c.predict(X_test))
 
 
 @pytest.mark.parametrize(
@@ -98,7 +98,7 @@ def test_sklearn_clusterer_pipeline(transformers):
         X_test = t.transform(X_test)
 
     c.fit(X_train, y_train)
-    _assert_array_almost_equal(y_pred, c.predict(X_test))
+    assert_array_almost_equal(y_pred, c.predict(X_test))
 
 
 def test_unequal_tag_inference():

aeon/performance_metrics/tests/test_stats.py

@@ -13,7 +13,7 @@
 
 data_path = os.path.join(
     os.path.dirname(aeon.__file__),
-    "benchmarking/example_results/",
+    "testing/example_results_files/",
 )
 
 

aeon/regression/compose/tests/test_pipeline.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import pytest
+from numpy.testing import assert_array_almost_equal
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import StandardScaler
 
@@ -16,7 +17,6 @@
     make_example_3d_numpy_list,
 )
 from aeon.testing.mock_estimators import MockCollectionTransformer
-from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,
@@ -61,7 +61,7 @@ def test_regressor_pipeline(transformers):
         X_test = t.transform(X_test)
 
     r.fit(X_train, y_train)
-    _assert_array_almost_equal(y_pred, r.predict(X_test))
+    assert_array_almost_equal(y_pred, r.predict(X_test))
 
 
 @pytest.mark.parametrize(
@@ -99,7 +99,7 @@ def test_sklearn_regressor_pipeline(transformers):
         X_test = t.transform(X_test)
 
     r.fit(X_train, y_train)
-    _assert_array_almost_equal(y_pred, r.predict(X_test))
+    assert_array_almost_equal(y_pred, r.predict(X_test))
 
 
 def test_unequal_tag_inference():

aeon/segmentation/_eagglo.py

@@ -72,13 +72,11 @@ class EAggloSegmenter(BaseSegmenter):
 
     Examples
     --------
-    >>> from aeon.testing.data_generation import piecewise_normal_multivariate
+    >>> from aeon.testing.data_generation import make_example_dataframe_series
     >>> from aeon.segmentation import EAggloSegmenter
-    >>> X = piecewise_normal_multivariate(means=[[1, 3], [4, 5]], lengths=[3, 4],
-    ...     random_state = 10)
+    >>> X = make_example_dataframe_series(n_channels=2, random_state=10)
     >>> model = EAggloSegmenter()
-    >>> model.fit_predict(X, axis=0)
-    array([0, 0, 0, 1, 1, 1, 1])
+    >>> y = model.fit_predict(X, axis=0)
     """
 
     _tags = {

aeon/segmentation/_ggs.py

@@ -435,12 +435,10 @@ class GreedyGaussianSegmenter(BaseSegmenter):
 
     Examples
     --------
-    >>> from aeon.testing.data_generation import piecewise_normal_multivariate
+    >>> from aeon.testing.data_generation import make_example_dataframe_series
     >>> from sklearn.preprocessing import MinMaxScaler
     >>> from aeon.segmentation import GreedyGaussianSegmenter
-    >>> X = piecewise_normal_multivariate(lengths=[10, 10, 10, 10],
-    ...     means=[[0.0, 1.0], [11.0, 10.0], [5.0, 3.0], [2.0, 2.0]],
-    ...     variances=0.5)
+    >>> X = make_example_dataframe_series(n_channels=2, random_state=10)
     >>> X_scaled = MinMaxScaler(feature_range=(0, 1)).fit_transform(X)
     >>> ggs = GreedyGaussianSegmenter(k_max=3, max_shuffles=5)
     >>> y = ggs.fit_predict(X_scaled, axis=0)

aeon/segmentation/_igts.py

@@ -152,19 +152,6 @@ class _IGTS:
        "Information gain-based metric for recognizing transitions in human activities.",
        Pervasive and Mobile Computing, 38, 92-109, (2017).
        https://www.sciencedirect.com/science/article/abs/pii/S1574119217300081
-
-    Examples
-    --------
-    >>> from aeon.testing.data_generation import piecewise_normal_multivariate
-    >>> from sklearn.preprocessing import MinMaxScaler
-    >>> from aeon.segmentation import InformationGainSegmenter
-    >>> X = piecewise_normal_multivariate(lengths=[10, 10, 10, 10],
-    ...     means=[[0.0, 1.0], [11.0, 10.0], [5.0, 3.0], [2.0, 2.0]],
-    ...     variances=0.5)
-    >>> X_scaled = MinMaxScaler(feature_range=(0, 1)).fit_transform(X)
-    >>> igts = InformationGainSegmenter(k_max=3, step=2)
-    >>> y = igts.fit_predict(X_scaled, axis=0)
-
     """
 
     # init attributes
@@ -256,7 +243,8 @@ def find_change_points(self, X: npt.ArrayLike) -> list[int]:
         current_change_points = self.identity(X)
 
         for k in range(self.k_max):
-            ig_max = 0
+            best_candidate = -1
+            ig_max = -1
             # find a point which maximizes score
             for candidate in self.get_candidates(n_samples, current_change_points):
                 try_change_points = {candidate}
@@ -335,12 +323,10 @@ class InformationGainSegmenter(BaseSegmenter):
 
     Examples
     --------
-    >>> from aeon.testing.data_generation import piecewise_normal_multivariate
+    >>> from aeon.testing.data_generation import make_example_dataframe_series
     >>> from sklearn.preprocessing import MinMaxScaler
     >>> from aeon.segmentation import InformationGainSegmenter
-    >>> X = piecewise_normal_multivariate(lengths=[10, 10, 10, 10],
-    ...     means=[[0.0, 1.0], [11.0, 10.0], [5.0, 3.0], [2.0, 2.0]],
-    ...     variances=0.5)
+    >>> X = make_example_dataframe_series(n_channels=2, random_state=10)
     >>> X_scaled = MinMaxScaler(feature_range=(0, 1)).fit_transform(X)
     >>> igts = InformationGainSegmenter(k_max=3, step=2)
     >>> y = igts.fit_predict(X_scaled, axis=0)

aeon/testing/data_generation/__init__.py

@@ -14,15 +14,6 @@
     "make_example_2d_numpy_series",
     "make_example_pandas_series",
     "make_example_dataframe_series",
-    # other
-    "piecewise_normal_multivariate",
-    "piecewise_normal",
-    "piecewise_multinomial",
-    "piecewise_poisson",
-    "labels_with_repeats",
-    "label_piecewise_normal",
-    "_make_hierarchical",
-    "_bottom_hier_datagen",
 ]
 
 
@@ -41,15 +32,3 @@
     make_example_dataframe_series,
     make_example_pandas_series,
 )
-from aeon.testing.data_generation.hierarchical import (
-    _bottom_hier_datagen,
-    _make_hierarchical,
-)
-from aeon.testing.data_generation.segmentation import (
-    label_piecewise_normal,
-    labels_with_repeats,
-    piecewise_multinomial,
-    piecewise_normal,
-    piecewise_normal_multivariate,
-    piecewise_poisson,
-)