Merge pull request #7 from jungtaekkim/cleanse-tests

Cleanse tests

bayeso/bo.py

@@ -1,6 +1,6 @@
 # bo
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: July 04, 2018
+# last updated: July 09, 2018
 
 import numpy as np
 import time
@@ -34,6 +34,8 @@ def __init__(self, arr_range,
         assert len(arr_range.shape) == 2
         assert arr_range.shape[1] == 2
         assert (arr_range[:, 0] <= arr_range[:, 1]).all()
+        assert str_cov in constants.ALLOWED_GP_COV
+        assert str_acq in constants.ALLOWED_BO_ACQ
 
         self.arr_range = arr_range
         self.num_dim = arr_range.shape[0]
@@ -115,7 +117,6 @@ def _optimize_objective(self, fun_acquisition, X_train, Y_train, X_test, cov_X_X
         return acquisitions
 
     def _optimize(self, fun_negative_acquisition, str_initial_method, int_samples):
-        assert str_initial_method in constants.ALLOWED_INITIALIZATIONS_OPTIMIZER
         list_bounds = []
         for elem in self.arr_range:
             list_bounds.append(tuple(elem))
@@ -153,6 +154,7 @@ def optimize(self, X_train, Y_train,
         assert X_train.shape[0] == Y_train.shape[0]
         assert X_train.shape[1] == self.num_dim
         assert int_samples > 0
+        assert str_initial_method in constants.ALLOWED_INITIALIZATIONS_AO
 
         time_start = time.time()
 

bayeso/constants.py

@@ -1,6 +1,6 @@
 # constants
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: June 23, 2018
+# last updated: July 09, 2018
 
 import numpy as np
 
@@ -31,8 +31,10 @@
 TIME_PAUSE = 2.0
 RANGE_SHADE = 1.96
 
+ALLOWED_GP_COV = ['se', 'matern32', 'matern52']
+ALLOWED_BO_ACQ = ['pi', 'ei', 'ucb']
 ALLOWED_INITIALIZATIONS_BO = ['sobol', 'uniform', 'latin']
-ALLOWED_INITIALIZATIONS_OPTIMIZER = ALLOWED_INITIALIZATIONS_BO + ['grid']
+ALLOWED_INITIALIZATIONS_AO = ALLOWED_INITIALIZATIONS_BO + ['grid']
 
 COLORS = [
     'red',

bayeso/covariance.py

@@ -1,6 +1,6 @@
 # covariance
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: June 23, 2018
+# last updated: July 09, 2018
 
 import numpy as np
 
@@ -26,6 +26,7 @@ def cov_main(str_cov, X, Xs, hyps, jitter=constants.JITTER_COV):
     assert isinstance(jitter, float)
     assert len(X.shape) == 2
     assert len(Xs.shape) == 2
+    assert str_cov in constants.ALLOWED_GP_COV
 
     num_X = X.shape[0]
     num_d_X = X.shape[1]

tests/test_bo.py

@@ -44,8 +44,12 @@ def test_load_bo():
         model_bo = bo.BO(arr_range_4)
     with pytest.raises(AssertionError) as error:
         model_bo = bo.BO(arr_range_1, str_cov=1)
+    with pytest.raises(AssertionError) as error:
+        model_bo = bo.BO(arr_range_1, str_cov='abc')
     with pytest.raises(AssertionError) as error:
         model_bo = bo.BO(arr_range_1, str_acq=1)
+    with pytest.raises(AssertionError) as error:
+        model_bo = bo.BO(arr_range_1, str_acq='abc')
     with pytest.raises(AssertionError) as error:
         model_bo = bo.BO(arr_range_1, is_ard=1)
     with pytest.raises(AssertionError) as error:

tests/test_covariance.py

@@ -49,7 +49,7 @@ def test_cov_main():
     with pytest.raises(AssertionError) as error:
         covariance.cov_main('se', np.zeros((10, 3)), np.zeros((20, 3)), cur_hyps, 1)
 
-    with pytest.raises(ValueError) as error:
+    with pytest.raises(AssertionError) as error:
         covariance.cov_main('abc', np.zeros((10, 3)), np.zeros((20, 3)), cur_hyps, 0.001)
 
     cur_hyps.pop('signal', None)

tests/test_gp.py

@@ -1,6 +1,6 @@
 # test_gp
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: June 20, 2018
+# last updated: July 09, 2018
 
 import numpy as np
 import pytest
@@ -43,7 +43,7 @@ def test_get_kernels():
         gp.get_kernels(X, 1, 'se')
     with pytest.raises(AssertionError) as error:
         gp.get_kernels(X, hyps, 1)
-    with pytest.raises(ValueError) as error:
+    with pytest.raises(AssertionError) as error:
         gp.get_kernels(X, hyps, 'abc')
     with pytest.raises(AssertionError) as error:
         gp.get_kernels(X, hyps, 'se', debug=1)
@@ -63,6 +63,42 @@ def test_get_kernels():
     ]
     assert (cov_X_X - truth_cov_X_X < TEST_EPSILON).all()
     assert (inv_cov_X_X - truth_inv_cov_X_X < TEST_EPSILON).all()
+    assert cov_X_X.shape == inv_cov_X_X.shape
+
+def test_get_kernel_cholesky():
+    dim_X = 3
+    X = np.reshape(np.arange(0, 9), (3, dim_X))
+    hyps = utils_covariance.get_hyps('se', dim_X)
+
+    with pytest.raises(AssertionError) as error:
+        gp.get_kernel_cholesky(1, hyps, 'se')
+    with pytest.raises(AssertionError) as error:
+        gp.get_kernel_cholesky(np.arange(0, 10), hyps, 'se')
+    with pytest.raises(AssertionError) as error:
+        gp.get_kernel_cholesky(X, 1, 'se')
+    with pytest.raises(AssertionError) as error:
+        gp.get_kernel_cholesky(X, hyps, 1)
+    with pytest.raises(AssertionError) as error:
+        gp.get_kernel_cholesky(X, hyps, 'abc')
+    with pytest.raises(AssertionError) as error:
+        gp.get_kernel_cholesky(X, hyps, 'se', debug=1)
+
+    cov_X_X, lower = gp.get_kernel_cholesky(X, hyps, 'se')
+    print(cov_X_X)
+    print(lower)
+    truth_cov_X_X = [
+        [1.00011000e+00, 1.37095909e-06, 3.53262857e-24],
+        [1.37095909e-06, 1.00011000e+00, 1.37095909e-06],
+        [3.53262857e-24, 1.37095909e-06, 1.00011000e+00],
+    ]
+    truth_lower = [
+        [1.00005500e+00, 0.00000000e+00, 0.00000000e+00],
+        [1.37088369e-06, 1.00005500e+00, 0.00000000e+00],
+        [3.53243429e-24, 1.37088369e-06, 1.00005500e+00],
+    ]
+    assert (cov_X_X - truth_cov_X_X < TEST_EPSILON).all()
+    assert (lower - truth_lower < TEST_EPSILON).all()
+    assert cov_X_X.shape == lower.shape
 
 def test_log_ml():
     dim_X = 3