[ENH] Some tests for coverage

aeon/transformations/collection/dictionary_based/_sfa.py

@@ -280,9 +280,6 @@ def _transform(self, X, y=None):
         List of dictionaries containing SFA words
         """
         X = X.squeeze(1)
-
-        # with warnings.catch_warnings():
-        # warnings.simplefilter("ignore", category=NumbaTypeSafetyWarning)
         transform = Parallel(n_jobs=self._n_jobs, prefer="threads")(
             delayed(self._transform_case)(
                 X[i, :],
@@ -327,10 +324,6 @@ def transform_mft(self, X):
         -------
         Array of Fourier coefficients
         """
-        # X = X.squeeze(1)
-
-        # with warnings.catch_warnings():
-        #    warnings.simplefilter("ignore", category=NumbaTypeSafetyWarning)
         transform = Parallel(n_jobs=self._n_jobs, prefer="threads")(
             delayed(self._mft)(X[i, :]) for i in range(X.shape[0])
         )

aeon/transformations/collection/dictionary_based/tests/test_sfa.py

@@ -6,6 +6,9 @@
 import pytest
 
 from aeon.datasets import load_unit_test
+from aeon.testing.data_generation import (
+    make_example_3d_numpy,
+)
 from aeon.transformations.collection.dictionary_based import SFA, SFAFast
 
 
@@ -92,9 +95,6 @@ def test_dft_mft(use_fallback_dft, norm):
     assert len(mft[0]) == word_length
 
 
-test_dft_mft(True, True)
-
-
 @pytest.mark.parametrize("binning_method", ["equi-depth", "information-gain"])
 def test_sfa_anova(binning_method):
     """Test the SFA transformer with ANOVA one-sided test."""
@@ -249,3 +249,31 @@ def test_sfa_fast_transform_after_fit():
         and np.all(x.indptr == y.indptr)
         and np.allclose(x.data, y.data)
     )
+
+
+def test_incorrect_paras():
+    """Test incorrect parameters in SFA."""
+    X, y = make_example_3d_numpy(n_cases=20, n_channels=1, n_timepoints=49)
+    sfa = SFA(alphabet_size=1, word_length=0, binning_method="information-gain")
+    with pytest.raises(ValueError, match="must be an integer greater than 2"):
+        sfa._fit(X)
+    sfa.alphabet_size = 4
+    with pytest.raises(ValueError, match="must be an integer greater than 1"):
+        sfa._fit(X)
+    sfa.word_length = 2
+    with pytest.raises(ValueError, match="Class values must be provided"):
+        sfa._fit(X)
+    sfa.binning_method = "Arsenal"
+    with pytest.raises(TypeError, match="binning_method must be one of"):
+        sfa._fit(X)
+    sfa = SFA(word_length=64, alphabet_size=8)
+    sfa.max_bits = 128
+    sfa._typed_dict = True
+    with pytest.raises(ValueError, match="Typed Dictionaries can only handle 64 bit"):
+        sfa._fit(X, y)
+    sfa = SFA(typed_dict=True, levels=16)
+    with pytest.raises(ValueError, match="Dictionaries can only handle 15 levels"):
+        sfa._fit(X, y)
+    sfa = SFA(typed_dict=True, save_words=True, n_jobs=2)
+    sfa.fit_transform(X, y)
+    assert isinstance(sfa.words, np.ndarray)

aeon/transformations/series/tests/test_base.py

@@ -3,6 +3,10 @@
 import numpy as np
 import pytest
 
+from aeon.testing.data_generation import (
+    make_example_1d_numpy,
+    make_example_2d_numpy_series,
+)
 from aeon.testing.mock_estimators import (
     MockMultivariateSeriesTransformer,
     MockSeriesTransformerNoFit,
@@ -53,3 +57,16 @@ def test_fit_transform_univariate():
     x3 = transformer.transform(x1, x2)
     x4 = transformer.fit_transform(x1, x2)
     np.testing.assert_array_almost_equal(x3, x4)
+
+
+def test_check_y():
+    """Test check_y function in BaseTransformer."""
+    y = make_example_2d_numpy_series(100, 3)
+    t = MockSeriesTransformerNoFit()
+    with pytest.raises(TypeError, match="must be a np.array or a pd.Series"):
+        t._check_y("Arsenal")
+    with pytest.raises(TypeError, match="must be 1-dimensional"):
+        t._check_y(y)
+    y = make_example_1d_numpy(100)
+    with pytest.raises(ValueError, match="Mismatch in number of cases"):
+        t._check_y(y, 50)