Update tests and improve pylint

bayeso/bo/bo_w_trees.py

@@ -109,8 +109,8 @@ def get_trees(
         assert isinstance(num_trees, int)
         assert isinstance(depth_max, int)
         assert isinstance(size_min_leaf, int)
-        assert len(X_train.shape) == 2
-        assert len(Y_train.shape) == 2
+        assert X_train.ndim == 2
+        assert Y_train.ndim == 2
         assert X_train.shape[0] == Y_train.shape[0]
         assert Y_train.shape[1] == 1
 
@@ -282,9 +282,10 @@ def optimize(
             next_points, np.array(self._get_bounds())
         )
 
-        fun_negative_acquisition = lambda X_test: -1.0 * self.compute_acquisitions(
-            X_test, X_train, Y_train, trees
-        )
+        def fun_negative_acquisition(X_test):
+            return -1.0 * self.compute_acquisitions(
+                X_test, X_train, Y_train, trees
+            )
         acquisitions = fun_negative_acquisition(next_points)
         ind_next_point = np.argmin(acquisitions)
         next_point = next_points[ind_next_point]

bayeso/gp/gp_kernel.py

@@ -89,6 +89,8 @@ def get_optimized_kernel(
     elif str_cov in constants.ALLOWED_COV_SET:
         num_dim = X_train.shape[2]
         use_gradient = False
+    else:
+        raise ValueError
 
     if str_modelselection_method == "ml":
         def neg_log_ml_(hyps):

bayeso/tp/tp_kernel.py

@@ -82,6 +82,8 @@ def get_optimized_kernel(
     elif str_cov in constants.ALLOWED_COV_SET:
         num_dim = X_train.shape[2]
         use_gradient = False
+    else:
+        raise ValueError
 
     def neg_log_ml_(hyps):
         return tp_likelihood.neg_log_ml(

bayeso/utils/utils_bo.py

@@ -246,17 +246,22 @@ def choose_fun_acquisition(
     elif str_acq == "aei":
         assert noise is not None
 
-        fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.aei(
-            pred_mean, pred_std, Y_train, noise
-        )
+        def fun_acquisition(pred_mean, pred_std, Y_train):
+            return acquisition.aei(
+                pred_mean, pred_std, Y_train, noise
+            )
     elif str_acq == "pure_exploit":
-        fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.pure_exploit(
-            pred_mean
-        )
+        def fun_acquisition(pred_mean, pred_std, Y_train):
+            _, _ = pred_std, Y_train
+            return acquisition.pure_exploit(
+                pred_mean
+            )
     elif str_acq == "pure_explore":
-        fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.pure_explore(
-            pred_std
-        )
+        def fun_acquisition(pred_mean, pred_std, Y_train):
+            _, _ = pred_mean, Y_train
+            return acquisition.pure_explore(
+                pred_std
+            )
     else:
         raise NotImplementedError(
             "_choose_fun_acquisition: allowed str_acq,\

tests/common/test_bo_bo_w_tp.py

@@ -161,8 +161,8 @@ def test_get_samples():
     )
     assert (np.abs(arr_initials - truth_arr_initials) < TEST_EPSILON).all()
 
-    arr_initials_ = model_bo.get_samples("sobol", num_samples=3)
-    arr_initials = model_bo.get_samples("sobol", num_samples=3, seed=42)
+    arr_initials_ = model_bo.get_samples("sobol", num_samples=4)
+    arr_initials = model_bo.get_samples("sobol", num_samples=4, seed=42)
 
     print("sobol")
     for elem_1 in arr_initials:
@@ -177,14 +177,19 @@ def test_get_samples():
                 3.06412766687572,
             ],
             [
-                8.698135614395142,
-                -0.250022292137146,
-                -0.012653172016143799,
+                8.69813535362482,
+                -0.2500223182141781,
+                -0.012653125450015068,
             ],
             [
-                5.779154300689697,
-                0.04064440727233887,
-                2.2647011280059814,
+                5.779154505580664,
+                0.04064444452524185,
+                2.2647008765488863,
+            ],
+            [
+                1.3686652854084969,
+                -1.0451578684151173,
+                -4.681709306314588,
             ],
         ]
     )
@@ -291,8 +296,8 @@ def test_get_initials():
     with pytest.raises(AssertionError) as error:
         model_bo.get_initials("abc", 10)
 
-    arr_initials = model_bo.get_initials("sobol", 3)
-    arr_initials = model_bo.get_initials("sobol", 3, seed=42)
+    arr_initials = model_bo.get_initials("sobol", 4)
+    arr_initials = model_bo.get_initials("sobol", 4, seed=42)
 
     print("sobol")
     for elem_1 in arr_initials:
@@ -307,14 +312,19 @@ def test_get_initials():
                 3.06412766687572,
             ],
             [
-                8.698135614395142,
-                -0.250022292137146,
-                -0.012653172016143799,
+                8.69813535362482,
+                -0.2500223182141781,
+                -0.012653125450015068,
+            ],
+            [
+                5.779154505580664,
+                0.04064444452524185,
+                2.2647008765488863,
             ],
             [
-                5.779154300689697,
-                0.04064440727233887,
-                2.2647011280059814,
+                1.3686652854084969,
+                -1.0451578684151173,
+                -4.681709306314588,
             ],
         ]
     )
@@ -761,7 +771,7 @@ def test_compute_posteriors():
 
     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)
+    X_test = model_bo.get_samples("sobol", num_samples=16, seed=111)
 
     with pytest.raises(AssertionError) as error:
         model_bo.compute_posteriors(1, Y, X_test, cov_X_X, inv_cov_X_X, hyps)
@@ -875,7 +885,7 @@ 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)
+    X_test = model_bo.get_samples("sobol", num_samples=8, seed=111)
 
     truth_X_test = np.array(
         [
@@ -919,16 +929,6 @@ def test_compute_acquisitions():
                 0.5484802722930908,
                 -0.9542649984359741,
             ],
-            [
-                4.554623663425446,
-                -1.7126559913158417,
-                1.575535535812378,
-            ],
-            [
-                6.998107433319092,
-                1.1039907932281494,
-                -0.24655908346176147,
-            ],
         ]
     )
 
@@ -969,8 +969,6 @@ def test_compute_acquisitions():
             0.634886228604709,
             0.6350134713825293,
             0.6263764526122838,
-            0.6444373090088434,
-            0.6348254001303183,
         ]
     )