Update tests and assertions for compute_posteriors

bayeso/bo/bo_w_gp.py

@@ -210,6 +210,26 @@ def compute_posteriors(self,
 
         """
 
+        assert isinstance(X_train, np.ndarray)
+        assert isinstance(Y_train, np.ndarray)
+        assert isinstance(X_test, np.ndarray)
+        assert isinstance(cov_X_X, np.ndarray)
+        assert isinstance(inv_cov_X_X, np.ndarray)
+        assert isinstance(hyps, dict)
+        assert len(X_train.shape) == 2 or len(X_train.shape) == 3
+        assert len(Y_train.shape) == 2
+        assert len(X_test.shape) == 2 or len(X_test.shape) == 3
+        assert len(cov_X_X.shape) == 2
+        assert len(inv_cov_X_X.shape) == 2
+        assert Y_train.shape[1] == 1
+        assert X_train.shape[0] == Y_train.shape[0]
+        if len(X_train.shape) == 2:
+            assert X_test.shape[1] == X_train.shape[1] == self.num_dim
+        else:
+            assert X_test.shape[2] == X_train.shape[2] == self.num_dim
+        assert cov_X_X.shape[0] == cov_X_X.shape[1] == X_train.shape[0]
+        assert inv_cov_X_X.shape[0] == inv_cov_X_X.shape[1] == X_train.shape[0]
+
         pred_mean, pred_std, _ = gp.predict_with_cov(
             X_train, Y_train, X_test,
             cov_X_X, inv_cov_X_X, hyps, str_cov=self.str_cov,

bayeso/bo/bo_w_tp.py

@@ -201,6 +201,26 @@ def compute_posteriors(self,
 
         """
 
+        assert isinstance(X_train, np.ndarray)
+        assert isinstance(Y_train, np.ndarray)
+        assert isinstance(X_test, np.ndarray)
+        assert isinstance(cov_X_X, np.ndarray)
+        assert isinstance(inv_cov_X_X, np.ndarray)
+        assert isinstance(hyps, dict)
+        assert len(X_train.shape) == 2 or len(X_train.shape) == 3
+        assert len(Y_train.shape) == 2
+        assert len(X_test.shape) == 2 or len(X_test.shape) == 3
+        assert len(cov_X_X.shape) == 2
+        assert len(inv_cov_X_X.shape) == 2
+        assert Y_train.shape[1] == 1
+        assert X_train.shape[0] == Y_train.shape[0]
+        if len(X_train.shape) == 2:
+            assert X_test.shape[1] == X_train.shape[1] == self.num_dim
+        else:
+            assert X_test.shape[2] == X_train.shape[2] == self.num_dim
+        assert cov_X_X.shape[0] == cov_X_X.shape[1] == X_train.shape[0]
+        assert inv_cov_X_X.shape[0] == inv_cov_X_X.shape[1] == X_train.shape[0]
+
         _, pred_mean, pred_std, _ = tp.predict_with_cov(
             X_train, Y_train, X_test,
             cov_X_X, inv_cov_X_X, hyps, str_cov=self.str_cov,

bayeso/bo/bo_w_trees.py

@@ -85,7 +85,7 @@ def compute_posteriors(self,
         """
         It returns posterior mean and standard deviation functions over `X`.
 
-        :param X: inputs. Shape: (l, d) or (l, m, d).
+        :param X: inputs. Shape: (l, d).
         :type X: numpy.ndarray
         :param cov_X_X: kernel matrix over `X_train`. Shape: (n, n).
         :type cov_X_X: numpy.ndarray
@@ -100,6 +100,11 @@ def compute_posteriors(self,
 
         """
 
+        assert isinstance(X, np.ndarray)
+        assert isinstance(trees, list)
+        assert len(X.shape) == 2
+        assert X.shape[1] == self.num_dim
+
         pred_mean, pred_std = trees_common.predict_by_trees(X, trees)
 
         pred_mean = np.squeeze(pred_mean, axis=1)
@@ -115,9 +120,9 @@ def compute_acquisitions(self, X: np.ndarray,
         It computes acquisition function values over 'X',
         where `X_train` and `Y_train` are given.
 
-        :param X: inputs. Shape: (l, d) or (l, m, d).
+        :param X: inputs. Shape: (l, d).
         :type X: numpy.ndarray
-        :param X_train: inputs. Shape: (n, d) or (n, m, d).
+        :param X_train: inputs. Shape: (n, d).
         :type X_train: numpy.ndarray
         :param Y_train: outputs. Shape: (n, 1).
         :type Y_train: numpy.ndarray
@@ -139,10 +144,7 @@ def compute_acquisitions(self, X: np.ndarray,
         if len(X.shape) == 1:
             X = np.atleast_2d(X)
 
-        if len(X_train.shape) == 2:
-            assert X.shape[1] == X_train.shape[1] == self.num_dim
-        else:
-            assert X.shape[2] == X_train.shape[2] == self.num_dim
+        assert X.shape[1] == X_train.shape[1] == self.num_dim
 
         fun_acquisition = utils_bo.choose_fun_acquisition(self.str_acq, constants.GP_NOISE)
 
@@ -165,7 +167,7 @@ def optimize(self, X_train: np.ndarray, Y_train: np.ndarray,
         inverse matrix of the covariance matrix, hyperparameters optimized,
         and execution times.
 
-        :param X_train: inputs. Shape: (n, d) or (n, m, d).
+        :param X_train: inputs. Shape: (n, d).
         :type X_train: numpy.ndarray
         :param Y_train: outputs. Shape: (n, 1).
         :type Y_train: numpy.ndarray

tests/common/test_bo_bo_w_trees.py

@@ -682,3 +682,9 @@ def test_compute_acquisitions():
     assert len(acqs.shape) == 1
     assert X_test.shape[0] == acqs.shape[0]
     assert np.all(np.abs(acqs - truth_acqs) < TEST_EPSILON)
+
+    acqs = model_bo.compute_acquisitions(X_test[0], X, Y, trees)
+
+    assert isinstance(acqs, np.ndarray)
+    assert len(acqs.shape) == 1
+    assert acqs.shape[0] == 1