Merge pull request #4 from jungtaekkim/cleanse-tests

Cleanse tests

bayeso/bo.py

@@ -137,7 +137,7 @@ def _optimize(self, fun_negative_acquisition, str_initial_method, int_samples):
         return next_point.flatten(), next_points
 
     def optimize(self, X_train, Y_train,
-        str_initial_method=constants.STR_OPTIMIZER_INITIALIZATION,
+        str_initial_method=constants.STR_AO_INITIALIZATION,
         int_samples=constants.NUM_ACQ_SAMPLES,
         is_normalized=True,
     ):

bayeso/constants.py

@@ -12,8 +12,8 @@
 STR_OPTIMIZER_METHOD_GP = 'BFGS'
 STR_GP_COV = 'se'
 STR_BO_ACQ = 'ei'
-STR_BO_INITIALIZATION = 'sobol'
-STR_OPTIMIZER_INITIALIZATION = 'sobol'
+STR_BO_INITIALIZATION = 'uniform'
+STR_AO_INITIALIZATION = 'uniform'
 
 NUM_BO_GRID = 50
 NUM_BO_RANDOM = 1000

bayeso/utils/utils_bo.py

@@ -12,8 +12,9 @@
 def get_grid(arr_ranges, int_grid):
     assert isinstance(arr_ranges, np.ndarray)
     assert isinstance(int_grid, int)
-    assert len(arr_ranges.shape)
+    assert len(arr_ranges.shape) == 2
     assert arr_ranges.shape[1] == 2
+    assert (arr_ranges[:, 0] <= arr_ranges[:, 1]).all()
 
     list_grid = []
     for range_ in arr_ranges:
@@ -67,7 +68,7 @@ def get_next_best_acquisition(arr_points, arr_acquisitions, cur_points):
     return next_point
 
 def optimize_many_(model_bo, fun_target, X_train, Y_train, int_iter,
-    str_initial_method_ao=constants.STR_OPTIMIZER_INITIALIZATION,
+    str_initial_method_ao=constants.STR_AO_INITIALIZATION,
     int_samples_ao=constants.NUM_ACQ_SAMPLES,
 ):
     assert isinstance(model_bo, bo.BO)
@@ -113,7 +114,7 @@ def optimize_many_(model_bo, fun_target, X_train, Y_train, int_iter,
     return X_final, Y_final
 
 def optimize_many(model_bo, fun_target, X_train, int_iter,
-    str_initial_method_ao=constants.STR_OPTIMIZER_INITIALIZATION,
+    str_initial_method_ao=constants.STR_AO_INITIALIZATION,
     int_samples_ao=constants.NUM_ACQ_SAMPLES,
 ):
     assert isinstance(model_bo, bo.BO)
@@ -142,7 +143,7 @@ def optimize_many(model_bo, fun_target, X_train, int_iter,
 
 def optimize_many_with_random_init(model_bo, fun_target, int_init, int_iter,
     str_initial_method_bo=constants.STR_BO_INITIALIZATION,
-    str_initial_method_ao=constants.STR_OPTIMIZER_INITIALIZATION,
+    str_initial_method_ao=constants.STR_AO_INITIALIZATION,
     int_samples_ao=constants.NUM_ACQ_SAMPLES,
     int_seed=None,
 ):

docs/index.rst

@@ -10,6 +10,8 @@ Welcome to bayeso's documentation!
 
 The code can be found in `our GitHub repository <https://github.com/jungtaekkim/bayeso>`_.
 
+It is still under construction. We will update soon.
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents:

tests/test_benchmarks.py

@@ -37,4 +37,3 @@ def test_branin():
     val_fun = benchmarks.branin(X)
     assert len(val_fun.shape) == 1
     assert val_fun.shape[0] == X.shape[0]
-

tests/test_bo.py

@@ -124,11 +124,19 @@ def test_optimize():
         model_bo.optimize(X, Y, str_initial_method='abc')
     with pytest.raises(AssertionError) as error:
         model_bo.optimize(X, Y, int_samples='abc')
+    with pytest.raises(AssertionError) as error:
+        model_bo.optimize(X, Y, is_normalized='abc')
 
     next_point, next_points, acquisitions, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(X, Y)
     assert isinstance(next_point, np.ndarray)
+    assert isinstance(next_points, np.ndarray)
+    assert isinstance(acquisitions, np.ndarray)
     assert isinstance(cov_X_X, np.ndarray)
     assert isinstance(inv_cov_X_X, np.ndarray)
     assert isinstance(hyps, dict)
     assert len(next_point.shape) == 1
+    assert len(next_points.shape) == 2
+    assert len(acquisitions.shape) == 1
     assert next_point.shape[0] == dim_X
+    assert next_points.shape[1] == dim_X
+    assert next_points.shape[0] == acquisitions.shape[0]

tests/test_gp.py

@@ -45,6 +45,8 @@ def test_get_kernels():
         gp.get_kernels(X, hyps, 1)
     with pytest.raises(ValueError) as error:
         gp.get_kernels(X, hyps, 'abc')
+    with pytest.raises(AssertionError) as error:
+        gp.get_kernels(X, hyps, 'se', debug=1)
 
     cov_X_X, inv_cov_X_X = gp.get_kernels(X, hyps, 'se')
     print(cov_X_X)
@@ -89,6 +91,10 @@ def test_log_ml():
         gp.log_ml(X, np.expand_dims(np.arange(0, 4), axis=1), arr_hyps, str_cov, prior_mu_X)
     with pytest.raises(AssertionError) as error:
         gp.log_ml(X, Y, arr_hyps, str_cov, np.expand_dims(np.arange(0, 4), axis=1))
+    with pytest.raises(AssertionError) as error:
+        gp.log_ml(X, Y, arr_hyps, str_cov, prior_mu_X, is_fixed_noise=1)
+    with pytest.raises(AssertionError) as error:
+        gp.log_ml(X, Y, arr_hyps, str_cov, prior_mu_X, debug=1)
 
     log_ml = gp.log_ml(X, Y, arr_hyps, str_cov, prior_mu_X)
     print(log_ml)
@@ -123,6 +129,10 @@ def test_get_optimized_kernel():
         gp.get_optimized_kernel(X, Y, prior_mu, 'abc')
     with pytest.raises(AssertionError) as error:
         gp.get_optimized_kernel(X, Y, prior_mu, 'se', str_optimizer_method=1)
+    with pytest.raises(AssertionError) as error:
+        gp.get_optimized_kernel(X, Y, prior_mu, 'se', is_fixed_noise=1)
+    with pytest.raises(AssertionError) as error:
+        gp.get_optimized_kernel(X, Y, prior_mu, 'se', debug=1)
 
 def test_predict_test_():
     np.random.seed(42)
@@ -222,3 +232,7 @@ def test_predict_optimized():
         gp.predict_optimized(np.random.randn(10, dim_X), Y, X_test, str_cov='se', prior_mu=prior_mu)
     with pytest.raises(AssertionError) as error:
         gp.predict_optimized(X, np.random.randn(10, 1), X_test, str_cov='se', prior_mu=prior_mu)
+    with pytest.raises(AssertionError) as error:
+        gp.predict_optimized(X, Y, X_test, is_fixed_noise=1)
+    with pytest.raises(AssertionError) as error:
+        gp.predict_optimized(X, Y, X_test, debug=1)

tests/test_utils_bo.py

@@ -83,6 +83,17 @@ def test_get_grid():
         [10., 2., 5.],
     ])
 
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_grid('abc', 3)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_grid(arr_range_1, 'abc')
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_grid(np.arange(0, 10), 3)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_grid(np.ones((3, 3)), 3)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_grid(np.array([[0.0, -2.0], [10.0, 20.0]]), 3)
+
     arr_grid_1 = utils_bo.get_grid(arr_range_1, 3)
     arr_grid_2 = utils_bo.get_grid(arr_range_2, 3)
 
@@ -106,6 +117,40 @@ def test_get_best_acquisition():
     assert best_initial.shape[1] == arr_initials.shape[1]
     assert best_initial == np.array([[1]])
 
+def test_get_next_best_acquisition():
+    arr_points = np.array([
+        [0.0, 1.0],
+        [1.0, -3.0],
+        [-2.0, -4.0],
+        [1.0, 3.0],
+    ])
+    arr_acquisitions = np.array([1.1, 0.2, 0.5, 0.6])
+    cur_points = np.array([
+        [-10.0, 1.0],
+        [1.0, -3.0],
+        [11.0, 2.0],
+    ])
+
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(1, arr_acquisitions, cur_points)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(np.arange(0, 4), arr_acquisitions, cur_points)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(arr_points, 1, cur_points)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(arr_points, np.ones((4, 2)), cur_points)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(arr_points, arr_acquisitions, 1)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(arr_points, arr_acquisitions, np.arange(0, 3))
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(arr_points, np.arange(0, 10), cur_points)
+    with pytest.raises(AssertionError) as error:
+        utils_bo.get_next_best_acquisition(arr_points, arr_acquisitions, np.ones((3, 5)))
+
+    next_point = utils_bo.get_next_best_acquisition(arr_points, arr_acquisitions, cur_points)
+    assert (next_point == np.array([-2.0, -4.0])).all()
+
 def test_optimize_many_():
     np.random.seed(42)
     arr_range = np.array([

tests/test_utils_covariance.py

@@ -34,6 +34,8 @@ def test_convert_hyps():
         utils_covariance.convert_hyps('se', 2.1)
     with pytest.raises(AssertionError) as error:
         utils_covariance.convert_hyps('abc', 2.1)
+    with pytest.raises(AssertionError) as error:
+        utils_covariance.convert_hyps('se', dict(), is_fixed_noise=1)
 
     cur_hyps = {'noise': 0.1, 'signal': 1.0, 'lengthscales': np.array([1.0, 1.0])}
     converted_hyps = utils_covariance.convert_hyps('se', cur_hyps)
@@ -52,9 +54,18 @@ def test_restore_hyps():
         utils_covariance.restore_hyps('abc', 2.1)
     with pytest.raises(AssertionError) as error:
         utils_covariance.restore_hyps('se', np.array([[1.0, 1.0], [1.0, 1.0]]))
+    with pytest.raises(AssertionError) as error:
+        utils_covariance.restore_hyps('se', np.array([1.0, 1.0, 1.0]), is_fixed_noise=1)
+    with pytest.raises(AssertionError) as error:
+        utils_covariance.restore_hyps('se', np.array([1.0, 1.0, 1.0]), fixed_noise='abc')
 
     cur_hyps = np.array([0.1, 1.0, 1.0, 1.0, 1.0])
     restored_hyps = utils_covariance.restore_hyps('se', cur_hyps)
     assert restored_hyps['noise'] == cur_hyps[0]
     assert restored_hyps['signal'] == cur_hyps[1]
     assert (restored_hyps['lengthscales'] == cur_hyps[2:]).all()
+
+    restored_hyps = utils_covariance.restore_hyps('se', cur_hyps, is_fixed_noise=True)
+    assert restored_hyps['noise'] == constants.GP_NOISE
+    assert restored_hyps['signal'] == cur_hyps[0]
+    assert (restored_hyps['lengthscales'] == cur_hyps[1:]).all()