Fixed #1061, Fixed #1080 Failed Snippets Tests

tests/naive.py

@@ -1591,16 +1591,18 @@ def mpdist_snippets(
     mpdist_percentage=0.05,
     mpdist_k=None,
     mpdist_T_subseq_isconstant=None,
+    D=None,
 ):
-    D = get_all_mpdist_profiles(
-        T,
-        m,
-        percentage,
-        s,
-        mpdist_percentage,
-        mpdist_k,
-        mpdist_T_subseq_isconstant=mpdist_T_subseq_isconstant,
-    )
+    if D is None:  # pragma: no cover
+        D = get_all_mpdist_profiles(
+            T,
+            m,
+            percentage,
+            s,
+            mpdist_percentage,
+            mpdist_k,
+            mpdist_T_subseq_isconstant=mpdist_T_subseq_isconstant,
+        )
 
     snippets = np.empty((k, m))
     snippets_indices = np.empty(k, dtype=np.int64)
@@ -1718,8 +1720,12 @@ def aampdist_snippets(
     mpdist_percentage=0.05,
     mpdist_k=None,
     p=2.0,
+    D=None,
 ):
-    D = get_all_aampdist_profiles(T, m, percentage, s, mpdist_percentage, mpdist_k, p=p)
+    if D is None:  # pragma: no cover
+        D = get_all_aampdist_profiles(
+            T, m, percentage, s, mpdist_percentage, mpdist_k, p=p
+        )
 
     pad_width = (0, int(m * np.ceil(T.shape[0] / m) - T.shape[0]))
     T_padded = np.pad(T, pad_width, mode="constant", constant_values=np.nan)

tests/test_aampdist_snippets.py

@@ -4,7 +4,7 @@
 import pytest
 
 from stumpy import config, rng
-from stumpy.aampdist_snippets import aampdist_snippets
+from stumpy.aampdist_snippets import _get_all_aampdist_profiles, aampdist_snippets
 
 test_data = [rng.RNG.uniform(-1000, 1000, size=64).astype(np.float64)]
 s = [6, 7, 8]
@@ -13,21 +13,55 @@
 k = [1, 2, 3]
 
 
+@pytest.mark.parametrize("T", test_data)
+@pytest.mark.parametrize("m", m)
+def test_get_all_aampdist_profiles(T, m):
+    ref_profiles = naive.get_all_aampdist_profiles(T, m)
+    cmp_profiles = _get_all_aampdist_profiles(T, m)
+
+    npt.assert_almost_equal(
+        ref_profiles, cmp_profiles, decimal=config.STUMPY_TEST_PRECISION
+    )
+
+
+@pytest.mark.parametrize("T", test_data)
+@pytest.mark.parametrize("m", m)
+@pytest.mark.parametrize("s", s)
+def test_get_all_aampdist_profiles_s(T, m, s):
+    ref_profiles = naive.get_all_aampdist_profiles(T, m, s=s)
+    cmp_profiles = _get_all_aampdist_profiles(T, m, s=s)
+
+    npt.assert_almost_equal(
+        ref_profiles, cmp_profiles, decimal=config.STUMPY_TEST_PRECISION
+    )
+
+
 @pytest.mark.parametrize("T", test_data)
 @pytest.mark.parametrize("m", m)
 @pytest.mark.parametrize("k", k)
 def test_aampdist_snippets(T, m, k):
     for p in [1.0, 2.0, 3.0]:
+        D = _get_all_aampdist_profiles(
+            T,
+            m,
+            p=p,
+        )
         (
-            ref_snippets,
+            ref_aampdist_snippets,
             ref_indices,
             ref_profiles,
             ref_fractions,
             ref_areas,
             ref_regimes,
-        ) = naive.aampdist_snippets(T, m, k, p=p)
+        ) = naive.aampdist_snippets(
+            T,
+            m,
+            k,
+            p=p,
+            D=D,
+        )
         (
-            cmp_snippets,
+            cmp_aampdist_snippets,
             cmp_indices,
             cmp_profiles,
             cmp_fractions,
@@ -36,7 +70,9 @@ def test_aampdist_snippets(T, m, k):
         ) = aampdist_snippets(T, m, k, p=p)
 
         npt.assert_almost_equal(
-            ref_snippets, cmp_snippets, decimal=config.STUMPY_TEST_PRECISION
+            ref_aampdist_snippets,
+            cmp_aampdist_snippets,
+            decimal=config.STUMPY_TEST_PRECISION,
         )
         npt.assert_almost_equal(
             ref_indices, cmp_indices, decimal=config.STUMPY_TEST_PRECISION
@@ -57,17 +93,22 @@ def test_aampdist_snippets(T, m, k):
 @pytest.mark.parametrize("m", m)
 @pytest.mark.parametrize("k", k)
 @pytest.mark.parametrize("percentage", percentage)
-def test_mpdist_snippets_percentage(T, m, k, percentage):
+def test_aampdist_snippets_percentage(T, m, k, percentage):
+    D = _get_all_aampdist_profiles(
+        T,
+        m,
+        percentage=percentage,
+    )
     (
-        ref_snippets,
+        ref_aampdist_snippets,
         ref_indices,
         ref_profiles,
         ref_fractions,
         ref_areas,
         ref_regimes,
-    ) = naive.aampdist_snippets(T, m, k, percentage=percentage)
+    ) = naive.aampdist_snippets(T, m, k, percentage=percentage, D=D)
     (
-        cmp_snippets,
+        cmp_aampdist_snippets,
         cmp_indices,
         cmp_profiles,
         cmp_fractions,
@@ -76,7 +117,9 @@ def test_mpdist_snippets_percentage(T, m, k, percentage):
     ) = aampdist_snippets(T, m, k, percentage=percentage)
 
     npt.assert_almost_equal(
-        ref_snippets, cmp_snippets, decimal=config.STUMPY_TEST_PRECISION
+        ref_aampdist_snippets,
+        cmp_aampdist_snippets,
+        decimal=config.STUMPY_TEST_PRECISION,
     )
     npt.assert_almost_equal(
         ref_indices, cmp_indices, decimal=config.STUMPY_TEST_PRECISION
@@ -98,16 +141,27 @@ def test_mpdist_snippets_percentage(T, m, k, percentage):
 @pytest.mark.parametrize("k", k)
 @pytest.mark.parametrize("s", s)
 def test_aampdist_snippets_s(T, m, k, s):
+    D = _get_all_aampdist_profiles(
+        T,
+        m,
+        s=s,
+    )
     (
-        ref_snippets,
+        ref_aampdist_snippets,
         ref_indices,
         ref_profiles,
         ref_fractions,
         ref_areas,
         ref_regimes,
-    ) = naive.aampdist_snippets(T, m, k, s=s)
+    ) = naive.aampdist_snippets(
+        T,
+        m,
+        k,
+        s=s,
+        D=D,
+    )
     (
-        cmp_snippets,
+        cmp_aampdist_snippets,
         cmp_indices,
         cmp_profiles,
         cmp_fractions,
@@ -116,7 +170,9 @@ def test_aampdist_snippets_s(T, m, k, s):
     ) = aampdist_snippets(T, m, k, s=s)
 
     npt.assert_almost_equal(
-        ref_snippets, cmp_snippets, decimal=config.STUMPY_TEST_PRECISION
+        ref_aampdist_snippets,
+        cmp_aampdist_snippets,
+        decimal=config.STUMPY_TEST_PRECISION,
     )
     npt.assert_almost_equal(
         ref_indices, cmp_indices, decimal=config.STUMPY_TEST_PRECISION

tests/test_precision.py

@@ -2,19 +2,19 @@
 from unittest.mock import patch
 
 import naive
-import numba
 import numpy as np
 import numpy.testing as npt
 import pytest
 from numba import cuda
 
-from stumpy import cache, config, core, fastmath, rng, sdp
+from stumpy import config, core, rng, sdp
 
 if cuda.is_available():
     from stumpy.gpu_stump import gpu_stump
 else:  # pragma: no cover
     from stumpy.core import _gpu_stump_driver_not_found as gpu_stump  # noqa: F401
-from stumpy.snippets import snippets
+
+from stumpy.snippets import _get_all_profiles, snippets
 
 try:
     from numba.errors import NumbaPerformanceWarning
@@ -118,18 +118,25 @@ def test_calculate_squared_distance():
                 npt.assert_almost_equal(ref, comp, decimal=14)
 
 
-def test_snippets():
-    # This test function raises an error if there is a considerable loss of precision
-    # that violates the symmetry property of a distance measure.
-    m = 10
-    k = 3
-    s = 3
-    with rng.fix_seed(332):
-        T = rng.RNG.uniform(-1000.0, 1000.0, [64])
+@pytest.mark.parametrize(
+    "seed, m, k, s",
+    [(2135137202, 10, 3, 3), (2636, 9, 3, 3), (332, 10, 3, 3), (1615, 10, 3, 3)],
+)
+def test_snippets(seed, m, k, s):
+    with rng.fix_seed(seed):
+        T = rng.RNG.uniform(-1000, 1000, [64]).astype(np.float64)
 
         isconstant_custom_func = functools.partial(
             naive.isconstant_func_stddev_threshold, quantile_threshold=0.05
         )
+
+        D = _get_all_profiles(
+            T,
+            m,
+            s=s,
+            mpdist_T_subseq_isconstant=isconstant_custom_func,
+        )
+
         (
             ref_snippets,
             ref_indices,
@@ -138,9 +145,13 @@ def test_snippets():
             ref_areas,
             ref_regimes,
         ) = naive.mpdist_snippets(
-            T, m, k, s=s, mpdist_T_subseq_isconstant=isconstant_custom_func
+            T,
+            m,
+            k,
+            s=s,
+            mpdist_T_subseq_isconstant=isconstant_custom_func,
+            D=D,
         )
-
         (
             cmp_snippets,
             cmp_indices,
@@ -150,29 +161,6 @@ def test_snippets():
             cmp_regimes,
         ) = snippets(T, m, k, s=s, mpdist_T_subseq_isconstant=isconstant_custom_func)
 
-        if (
-            not np.allclose(ref_snippets, cmp_snippets) and not numba.config.DISABLE_JIT
-        ):  # pragma: no cover
-            # Revise fastmath flags by removing reassoc (to improve precision),
-            # recompile njit functions, and re-compute snippets.
-            fastmath._set(
-                "core",
-                "_calculate_squared_distance",
-                {"nsz", "arcp", "contract", "afn"},
-            )
-            cache._recompile()
-
-            (
-                cmp_snippets,
-                cmp_indices,
-                cmp_profiles,
-                cmp_fractions,
-                cmp_areas,
-                cmp_regimes,
-            ) = snippets(
-                T, m, k, s=s, mpdist_T_subseq_isconstant=isconstant_custom_func
-            )
-
         npt.assert_almost_equal(
             ref_snippets, cmp_snippets, decimal=config.STUMPY_TEST_PRECISION
         )
@@ -190,11 +178,6 @@ def test_snippets():
         )
         npt.assert_almost_equal(ref_regimes, cmp_regimes)
 
-    if not numba.config.DISABLE_JIT:  # pragma: no cover
-        # Revert fastmath flag back to their default values
-        fastmath._reset("core", "_calculate_squared_distance")
-        cache._recompile()
-
 
 @pytest.mark.filterwarnings("ignore", category=NumbaPerformanceWarning)
 @patch("stumpy.config.STUMPY_THREADS_PER_BLOCK", TEST_THREADS_PER_BLOCK)
@@ -215,7 +198,7 @@ def test_distance_symmetry_property_in_gpu():
 
         # This test raises an error if arithmetic operation in ...
         # ... `gpu_stump._compute_and_update_PI_kernel` does not
-        # generates the same result if values of variable for mean and std
+        # generate the same result if values of variable for mean and std
         # are swapped.
 
         T_A = T[i : i + m]