Fix errors

jungtaekkim · Oct 8, 2021 · 4b3027e · 4b3027e
1 parent 9d89d1a
commit 4b3027e
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Showing 2 changed files with 136 additions and 23 deletions.
diff --git a/bayeso/bo/base_bo.py b/bayeso/bo/base_bo.py
@@ -247,7 +247,7 @@ def _get_samples_halton(self, num_samples: int,
         assert isinstance(num_samples, int)
         assert isinstance(seed, (int, constants.TYPE_NONE))
 
-        sampler = qmcpy.Halton(self.num_dim, randomize='OWEN', seed=seed)
+        sampler = qmcpy.Halton(self.num_dim, randomize='OWEN', generalize=False, seed=seed)
         samples = sampler.gen_samples(num_samples)
 
         samples = samples * (self.range_X[:, 1].flatten() - self.range_X[:, 0].flatten()) \

diff --git a/tests/common/test_bo_bo_w_gp.py b/tests/common/test_bo_bo_w_gp.py
@@ -120,21 +120,58 @@ def test_get_samples():
 
     arr_initials_ = model_bo.get_samples('sobol', num_samples=3)
     arr_initials = model_bo.get_samples('sobol', num_samples=3, seed=42)
+
+    print('sobol')
+    for elem_1 in arr_initials:
+        for elem_2 in elem_1:
+            print(elem_2)
+
     truth_arr_initials = np.array([
-        [5.051551531068981, 0.8090446023270488, -1.0847891168668866],
-        [1.4649059670045972, -1.925125477835536, 1.4882571692578495],
-        [3.202530408743769, 1.6943757990375161, -3.383688726462424],
+        [
+            8.78516613971442,
+            -0.6113853892311454,
+            -4.274874331895262,
+        ],
+        [
+            2.7565963030792773,
+            1.627942705526948,
+            1.6141902864910662,
+        ],
+        [
+            1.0978685109876096,
+            -1.511254413984716,
+            -1.5049133892171085,
+        ],
     ])
 
     assert (np.abs(arr_initials - truth_arr_initials) < TEST_EPSILON).all()
 
     arr_initials_ = model_bo.get_samples('halton', num_samples=3)
     arr_initials = model_bo.get_samples('halton', num_samples=3, seed=42)
+
+    print('halton')
+    for elem_1 in arr_initials:
+        for elem_2 in elem_1:
+            print(elem_2)
+
     truth_arr_initials = np.array([
-        [4.325625705206888, 1.9045673771707823, 1.0981007622257621],
-        [9.325625705206889, 0.5712340438374492, -2.9018992377742396],
-        [1.825625705206888, -0.7620992894958845, 3.098100762225762],
+        [
+            3.3124358790165855,
+            -1.4099903230606423,
+            -0.4462920191434243,
+        ],
+        [
+            8.312435879016585,
+            1.2566763436060246,
+            -2.446292019143424,
+        ],
+        [
+            0.8124358790165853,
+            -0.07665698972730861,
+            -4.446292019143424,
+        ],
     ])
+
     assert (np.abs(arr_initials - truth_arr_initials) < TEST_EPSILON).all()
 
     arr_initials_ = model_bo.get_samples('uniform', num_samples=3)
@@ -182,11 +219,29 @@ def test_get_initials():
 
     arr_initials = model_bo.get_initials('sobol', 3)
     arr_initials = model_bo.get_initials('sobol', 3, seed=42)
+
+    for elem_1 in arr_initials:
+        for elem_2 in elem_1:
+            print(elem_2)
+
     truth_arr_initials = np.array([
-        [5.051551531068981, 0.8090446023270488, -1.0847891168668866],
-        [1.4649059670045972, -1.925125477835536, 1.4882571692578495],
-        [3.202530408743769, 1.6943757990375161, -3.383688726462424],
+        [
+            8.78516613971442,
+            -0.6113853892311454,
+            -4.274874331895262,
+        ],
+        [
+            2.7565963030792773,
+            1.627942705526948,
+            1.6141902864910662,
+        ],
+        [
+            1.0978685109876096,
+            -1.511254413984716,
+            -1.5049133892171085,
+        ],
     ])
+
     assert (np.abs(arr_initials - truth_arr_initials) < TEST_EPSILON).all()
 
     arr_initials = model_bo.get_initials('uniform', 3, seed=42)
@@ -757,19 +812,63 @@ def test_compute_acquisitions():
     cov_X_X, inv_cov_X_X, _ = covariance.get_kernel_inverse(X, hyps, model_bo.str_cov)
 
     X_test = model_bo.get_samples('sobol', num_samples=10, seed=111)
+
     truth_X_test = np.array([
-        [1.5372224315069616, 0.04384007956832647, -2.2484372765757143],
-        [7.530325392726809, -1.5013302871957421, 3.6598239350132644],
-        [5.009974606800824, 1.4473280012607574, -2.9132778802886605],
-        [4.057266886811703, -0.9762288630008698, 1.8228407809510827],
-        [2.9087040200829506, 1.576258834451437, 3.9799577672965825],
-        [6.793604656122625, -0.0973438061773777, -0.07283419137820601],
-        [9.274512701667845, 0.914928319863975, 0.8972382079809904],
-        [0.4274896439164877, -1.380226788111031, -4.483412243425846],
-        [1.1341158207505941, 1.000061221420765, 0.24217900820076466],
-        [9.78826540056616, -0.5445895120501518, -3.8301817141473293],
+        [
+            3.328958908095956,
+            -1.8729291455820203,
+            0.2839687094092369,
+        ],
+        [
+            8.11741182114929,
+            0.3799784183502197,
+            -0.05574141861870885,
+        ],
+        [
+            6.735238193068653,
+            -0.9264274807646871,
+            3.631770429201424,
+        ],
+        [
+            2.13300823001191,
+            1.3245289996266365,
+            -3.547573888208717,
+        ],
+        [
+            0.6936023756861687,
+            -0.018464308232069016,
+            -2.1043178741820157,
+        ],
+        [
+            5.438151848502457,
+            1.7285785367712379,
+            2.0298107899725437,
+        ],
+        [
+            9.085266247857362,
+            -1.2144776917994022,
+            -4.31197423255071,
+        ],
+        [
+            4.468362366314977,
+            0.5345162367448211,
+            4.0739051485434175,
+        ],
+        [
+            3.9395463559776545,
+            -0.5726078534498811,
+            -4.846788686700165,
+        ],
+        [
+            9.92871844675392,
+            1.1744442842900753,
+            4.774723623413593,
+        ],
     ])
-    print(X_test)
+
+    for elem_1 in X_test:
+        for elem_2 in elem_1:
+            print(elem_2)
 
     assert np.all(np.abs(X_test - truth_X_test) < TEST_EPSILON)
 
@@ -789,9 +888,23 @@ def test_compute_acquisitions():
         model_bo.compute_acquisitions(X_test, X, Y, cov_X_X, inv_cov_X_X, 'abc')
 
     acqs = model_bo.compute_acquisitions(X_test, X, Y, cov_X_X, inv_cov_X_X, hyps)
-    print(acqs)
 
-    truth_acqs = np.array([0.9602042008680384, 0.7893457649458702, 0.7874870212950252, 0.8113392160307042, 0.7900800170056282, 0.789580990650518, 0.7893341902282358, 0.8667465389980766, 0.23928549511387842, 0.7893341160443801])
+    print('acqs')
+    for elem_1 in acqs:
+        print(elem_1)
+
+    truth_acqs = np.array([
+        0.9140836833364618,
+        0.7893422923284443,
+        0.7893819649518585,
+        0.780516205172671,
+        1.170379060386938,
+        0.7889956503605072,
+        0.7893345684226016,
+        0.789773864915061,
+        0.7908883762985802,
+        0.7893339801719917,
+    ])
 
     assert isinstance(acqs, np.ndarray)
     assert len(acqs.shape) == 1