Updated three more tests

Author: seanlaw

tests/test_aamp_mmotifs.py

@@ -51,17 +51,15 @@
 
 
 def test_aamp_mmotifs_default_parameters():
-    motif_distances_ref = np.array(
-        [[0.0, 0.06315749, 0.25275899, 0.34087884, 0.3452315]]
-    )
-    motif_indices_ref = np.array([[19, 77, 63, 52, 71]])
-    motif_subspaces_ref = [np.array([2])]
+    motif_distances_ref = np.array([[0.0, 0.10660435, 0.27927693]])
+    motif_indices_ref = np.array([[19, 41, 34]])
+    motif_subspaces_ref = [np.array([1])]
     motif_mdls_ref = [
-        np.array([411.60964047, 423.69925001, 449.11032383, 476.95855027, 506.62406252])
+        np.array([411.60964047, 433.21928095, 429.77443751, 457.74925683, 477.80793557])
     ]
 
-    np.random.seed(0)
-    T = np.random.rand(500).reshape(5, 100)
+    pytest.fix_rng_state()
+    T = pytest.RNG.random(size=(5, 100))
 
     m = 5
     excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))
@@ -78,6 +76,8 @@ def test_aamp_mmotifs_default_parameters():
     npt.assert_array_almost_equal(motif_subspaces_ref, motif_subspaces_cmp)
     npt.assert_array_almost_equal(motif_mdls_ref, motif_mdls_cmp)
 
+    pytest.unfix_rng_state()
+
 
 @pytest.mark.parametrize("T", test_data)
 def test_aamp_mmotifs_max_distance(T):

tests/test_aamp_motifs.py

@@ -15,7 +15,7 @@
         np.array([0.0, 1.0, 2.0]),
         np.array([0.1, 1.0, 2.0, 3.0, -1.0, 0.1, 1.0, 2.0, -0.5]),
     ),
-    (np.random.uniform(-1000, 1000, [8]), np.random.uniform(-1000, 1000, [64])),
+    (pytest.RNG.uniform(-1000, 1000, [8]), pytest.RNG.uniform(-1000, 1000, [64])),
 ]
 
 
@@ -71,12 +71,12 @@ def test_aamp_motifs_one_motif():
 def test_aamp_motifs_two_motifs():
     # Fix seed, because in some case motifs can be off by an index resulting in test
     # fails, which is caused since one of the motifs is not repeated perfectly in T.
-    np.random.seed(1234)
+    pytest.fix_rng_state()
 
     # The time series is random noise with two motifs for m=10:
     # * (almost) identical step functions at indices 10, 110 and 210
     # * identical linear slopes at indices 70 and 170
-    T = np.random.normal(size=300)
+    T = pytest.RNG.normal(size=300)
     m = 20
 
     T[10:30] = 1
@@ -125,7 +125,7 @@ def test_aamp_motifs_two_motifs():
     npt.assert_almost_equal(left_profile_values, right_distance_values, decimal=6)
 
     # Reset seed
-    np.random.seed(None)
+    pytest.unfix_rng_state()
 
 
 def test_aamp_naive_match_exact():

tests/test_aamp_ostinato.py

@@ -20,78 +20,81 @@ def dask_cluster():
     cluster.close()
 
 
-@pytest.mark.parametrize(
-    "seed", np.random.choice(np.arange(10000), size=25, replace=False)
-)
-def test_random_ostinato(seed):
-    m = 50
-    np.random.seed(seed)
-    Ts = [np.random.rand(n) for n in [64, 128, 256]]
-
-    ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m)
-    comp_radius, comp_Ts_idx, comp_subseq_idx = aamp_ostinato(Ts, m)
-
-    npt.assert_almost_equal(ref_radius, comp_radius)
-    npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
-    npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
-
-
-@pytest.mark.parametrize("seed", [41, 88, 290, 292, 310, 328, 538, 556, 563, 570])
-def test_deterministic_ostinato(seed):
-    m = 50
-    np.random.seed(seed)
-    Ts = [np.random.rand(n) for n in [64, 128, 256]]
+def test_random_ostinato():
+    for _ in range(25):
+        m = 50
+        Ts = [pytest.RNG.random(n) for n in [64, 128, 256]]
 
-    for p in [1.0, 2.0, 3.0]:
-        ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m, p=p)
-        comp_radius, comp_Ts_idx, comp_subseq_idx = aamp_ostinato(Ts, m, p=p)
+        ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m)
+        comp_radius, comp_Ts_idx, comp_subseq_idx = aamp_ostinato(Ts, m)
 
         npt.assert_almost_equal(ref_radius, comp_radius)
         npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
         npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
 
 
-@pytest.mark.parametrize(
-    "seed", np.random.choice(np.arange(10000), size=25, replace=False)
-)
-def test_random_ostinatoed(seed, dask_cluster):
-    with Client(dask_cluster) as dask_client:
+def test_deterministic_ostinato():
+    pytest.fix_rng_state()
+
+    for _ in range(10):
         m = 50
-        np.random.seed(seed)
-        Ts = [np.random.rand(n) for n in [64, 128, 256]]
+        Ts = [pytest.RNG.random(n) for n in [64, 128, 256]]
 
-        ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m)
-        comp_radius, comp_Ts_idx, comp_subseq_idx = aamp_ostinatoed(dask_client, Ts, m)
+        for p in [1.0, 2.0, 3.0]:
+            ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m, p=p)
+            comp_radius, comp_Ts_idx, comp_subseq_idx = aamp_ostinato(Ts, m, p=p)
 
-        npt.assert_almost_equal(ref_radius, comp_radius)
-        npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
-        npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
+            npt.assert_almost_equal(ref_radius, comp_radius)
+            npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
+            npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
 
+    pytest.unfix_rng_state()
 
-@pytest.mark.parametrize("seed", [41, 88, 290, 292, 310, 328, 538, 556, 563, 570])
-def test_deterministic_ostinatoed(seed, dask_cluster):
+
+def test_random_ostinatoed(dask_cluster):
     with Client(dask_cluster) as dask_client:
-        m = 50
-        np.random.seed(seed)
-        Ts = [np.random.rand(n) for n in [64, 128, 256]]
+        for _ in range(25):
+            m = 50
+            Ts = [pytest.RNG.random(n) for n in [64, 128, 256]]
 
-        for p in [1.0, 2.0, 3.0]:
-            ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m, p=p)
+            ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m)
             comp_radius, comp_Ts_idx, comp_subseq_idx = aamp_ostinatoed(
-                dask_client, Ts, m, p=p
+                dask_client, Ts, m
             )
 
             npt.assert_almost_equal(ref_radius, comp_radius)
             npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
             npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
 
 
+@pytest.mark.parametrize("seed", [41, 88, 290, 292, 310, 328, 538, 556, 563, 570])
+def test_deterministic_ostinatoed(seed, dask_cluster):
+    pytest.fix_rng_state()
+
+    with Client(dask_cluster) as dask_client:
+        for _ in range(10):
+            m = 50
+            Ts = [pytest.RNG.random(n) for n in [64, 128, 256]]
+
+            for p in [1.0, 2.0, 3.0]:
+                ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m, p=p)
+                comp_radius, comp_Ts_idx, comp_subseq_idx = aamp_ostinatoed(
+                    dask_client, Ts, m, p=p
+                )
+
+                npt.assert_almost_equal(ref_radius, comp_radius)
+                npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
+                npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
+
+    pytest.unfix_rng_state()
+
+
 def test_input_not_overwritten_ostinato():
     # aamp_ostinato preprocesses its input, a list of time series,
     # by replacing nan value with 0 in each time series.
     # This test ensures that the original input is not overwritten
     m = 50
-    Ts = [np.random.rand(n) for n in [64, 128, 256]]
+    Ts = [pytest.RNG.random(n) for n in [64, 128, 256]]
     for T in Ts:
         T[0] = np.nan
 
@@ -107,7 +110,7 @@ def test_input_not_overwritten_ostinato():
 def test_extract_several_consensus_ostinato():
     # This test is to further ensure that the function `aamp_ostinato`
     # does not tamper with the original data.
-    Ts = [np.random.rand(n) for n in [256, 512, 1024]]
+    Ts = [pytest.RNG.random(n) for n in [256, 512, 1024]]
     Ts_ref = [T.copy() for T in Ts]
     Ts_comp = [T.copy() for T in Ts]
 
@@ -145,7 +148,7 @@ def test_input_not_overwritten_ostinatoed(dask_cluster):
     # This test ensures that the original input is not overwritten
     with Client(dask_cluster) as dask_client:
         m = 50
-        Ts = [np.random.rand(n) for n in [64, 128, 256]]
+        Ts = [pytest.RNG.random(n) for n in [64, 128, 256]]
         for T in Ts:
             T[0] = np.nan
 
@@ -163,7 +166,7 @@ def test_input_not_overwritten_ostinatoed(dask_cluster):
 def test_extract_several_consensus_ostinatoed(dask_cluster):
     # This test is to further ensure that the function `ostinatoed`
     # does not tamper with the original data.
-    Ts = [np.random.rand(n) for n in [256, 512, 1024]]
+    Ts = [pytest.RNG.random(n) for n in [256, 512, 1024]]
     Ts_ref = [T.copy() for T in Ts]
     Ts_comp = [T.copy() for T in Ts]