[DEP] Remove Split mixin (#1613)

* deprecate Split mixin

* remove legacy

* remove legacy

aeon/transformations/collection/hog1d.py

@@ -5,11 +5,11 @@
 
 import numpy as np
 
-from aeon.transformations._split import SplitsTimeSeries
 from aeon.transformations.collection import BaseCollectionTransformer
+from aeon.utils import split_series
 
 
-class HOG1DTransformer(BaseCollectionTransformer, SplitsTimeSeries):
+class HOG1DTransformer(BaseCollectionTransformer):
     """HOG1D transform.
 
     This transformer calculates the HOG1D transform [1] of a collection of time series.
@@ -89,7 +89,7 @@ def _calculate_hog1ds(self, X):
         """
         # Firstly, split the time series into approx equal
         # length intervals
-        splitTimeSeries = self._split(X)
+        splitTimeSeries = split_series(X, self.n_intervals)
         HOG1Ds = []
 
         for x in range(len(splitTimeSeries)):

aeon/transformations/collection/slope.py

@@ -7,11 +7,11 @@
 
 import numpy as np
 
-from aeon.transformations._split import SplitsTimeSeries
 from aeon.transformations.collection import BaseCollectionTransformer
+from aeon.utils import split_series
 
 
-class SlopeTransformer(BaseCollectionTransformer, SplitsTimeSeries):
+class SlopeTransformer(BaseCollectionTransformer):
     """Piecewise slope transformation.
 
     Class to perform a slope transformation on a collection of time series.
@@ -67,8 +67,9 @@ def _transform(self, X, y=None):
         for i in range(n_cases):
             case_data = []
             for j in range(n_channels):
+                splits = split_series(X[i][j], self.n_intervals)
                 # Calculate gradients
-                res = [self._get_gradient(x) for x in self._split(X[i][j])]
+                res = [self._get_gradient(x) for x in splits]
                 case_data.append(res)
             full_data.append(np.asarray(case_data))
 

aeon/utils/__init__.py

@@ -4,6 +4,7 @@
     "get_cutoff",
     "update_data",
     "get_window",
+    "split_series",
     "ALL_TIME_SERIES_TYPES",
     "COLLECTIONS_DATA_TYPES",
     "SERIES_DATA_TYPES",
@@ -16,4 +17,5 @@
     HIERARCHICAL_DATA_TYPES,
     SERIES_DATA_TYPES,
 )
+from aeon.utils._split import split_series
 from aeon.utils.index_functions import get_cutoff, get_window, update_data

aeon/utils/_split.py

@@ -0,0 +1,27 @@
+"""Split function."""
+
+
+def split_series(X, n_intervals):
+    """Split a time series into approximately equal intervals.
+
+    Adopted from = https://stackoverflow.com/questions/2130016/
+                   splitting-a-list-into-n-parts-of-approximately
+                   -equal-length
+
+    Parameters
+    ----------
+    X : a numpy array of shape = [n_timepoints]
+
+    Returns
+    -------
+    output : a numpy array of shape = [self.n_intervals,interval_size]
+    """
+    avg = len(X) / float(n_intervals)
+    output = []
+    beginning = 0.0
+
+    while beginning < len(X):
+        output.append(X[int(beginning) : int(beginning + avg)])
+        beginning += avg
+
+    return output

aeon/transformations/tests/test_split.py → aeon/utils/tests/test_split.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from aeon.transformations._split import SplitsTimeSeries
+from aeon.utils import split_series
 
 X = np.arange(10)
 testdata = [
@@ -13,11 +13,9 @@
 
 
 @pytest.mark.parametrize("X,n_intervals,expected", testdata)
-def test_split_(X, n_intervals, expected):
+def test_split_series(X, n_intervals, expected):
     """Test the splitting of a time series into multiple intervals."""
-    splitter = SplitsTimeSeries()
-    splitter.n_intervals = n_intervals
-    res = splitter._split(X)
+    res = split_series(X, n_intervals)
 
     assert len(res) == n_intervals
     for x, y in zip(res, expected):