Fixed imports from examples and tests, also reformatted tests

examples/advanced/ex04_acsd.py

@@ -1,7 +1,6 @@
 import os
 
 import mala
-import numpy as np
 from mala.datahandling.data_repo import data_repo_path
 
 data_path = os.path.join(data_repo_path, "Be2")

examples/advanced/ex05_checkpoint_hyperparameter_optimization.py

@@ -1,7 +1,6 @@
 import os
 
 import mala
-from mala import printout
 
 from mala.datahandling.data_repo import data_repo_path
 

examples/advanced/ex06_distributed_hyperparameter_optimization.py

@@ -1,7 +1,6 @@
 import os
 
 import mala
-from mala import printout
 
 from mala.datahandling.data_repo import data_repo_path
 

examples/advanced/ex08_visualize_observables.py

@@ -1,8 +1,6 @@
 import os
 
-from ase.io import read
 import mala
-import numpy as np
 
 from mala.datahandling.data_repo import data_repo_path
 

examples/basic/ex04_hyperparameter_optimization.py

@@ -1,7 +1,6 @@
 import os
 
 import mala
-from mala import printout
 
 from mala.datahandling.data_repo import data_repo_path
 

examples/basic/ex06_ase_calculator.py

@@ -1,7 +1,6 @@
 import os
 
 import mala
-from mala import printout
 from ase.io import read
 
 from mala.datahandling.data_repo import data_repo_path

test/basic_gpu_test.py

@@ -20,6 +20,7 @@
 import torch
 
 from mala.datahandling.data_repo import data_repo_path
+
 data_path = os.path.join(data_repo_path, "Be2")
 
 test_checkpoint_name = "test"
@@ -36,8 +37,10 @@ class TestGPUExecution:
 
     Tests whether a GPU is available and then the execution on it.
     """
-    @pytest.mark.skipif(torch.cuda.is_available() is False,
-                        reason="No GPU detected.")
+
+    @pytest.mark.skipif(
+        torch.cuda.is_available() is False, reason="No GPU detected."
+    )
     def test_gpu_performance(self):
         """
         Test whether GPU training brings performance improvements.
@@ -104,12 +107,27 @@ def __run(use_gpu):
 
         # Add a snapshot we want to use in to the list.
         for i in range(0, 6):
-            data_handler.add_snapshot("Be_snapshot0.in.npy", data_path,
-                                      "Be_snapshot0.out.npy", data_path, "tr")
-        data_handler.add_snapshot("Be_snapshot1.in.npy", data_path,
-                                  "Be_snapshot1.out.npy", data_path, "va")
-        data_handler.add_snapshot("Be_snapshot2.in.npy", data_path,
-                                  "Be_snapshot2.out.npy", data_path, "te")
+            data_handler.add_snapshot(
+                "Be_snapshot0.in.npy",
+                data_path,
+                "Be_snapshot0.out.npy",
+                data_path,
+                "tr",
+            )
+        data_handler.add_snapshot(
+            "Be_snapshot1.in.npy",
+            data_path,
+            "Be_snapshot1.out.npy",
+            data_path,
+            "va",
+        )
+        data_handler.add_snapshot(
+            "Be_snapshot2.in.npy",
+            data_path,
+            "Be_snapshot2.out.npy",
+            data_path,
+            "te",
+        )
         data_handler.prepare_data()
         printout("Read data: DONE.", min_verbosity=0)
 
@@ -120,16 +138,17 @@ def __run(use_gpu):
         # but it is safer this way.
         ####################
 
-        test_parameters.network.layer_sizes = [data_handler.
-                                               input_dimension,
-                                               100,
-                                               data_handler.
-                                               output_dimension]
+        test_parameters.network.layer_sizes = [
+            data_handler.input_dimension,
+            100,
+            data_handler.output_dimension,
+        ]
 
         # Setup network and trainer.
         test_network = mala.Network(test_parameters)
-        test_trainer = mala.Trainer(test_parameters, test_network,
-                                    data_handler)
+        test_trainer = mala.Trainer(
+            test_parameters, test_network, data_handler
+        )
         starttime = time.time()
         test_trainer.train_network()
 

test/checkpoint_hyperopt_test.py

@@ -5,6 +5,7 @@
 import numpy as np
 
 from mala.datahandling.data_repo import data_repo_path
+
 data_path = os.path.join(data_repo_path, "Be2")
 
 checkpoint_name = "test_ho"
@@ -30,8 +31,9 @@ def test_hyperopt_checkpoint(self):
         hyperopt.perform_study()
         new_final_test_value = hyperopt.study.best_trial.value
 
-        assert np.isclose(original_final_test_value, new_final_test_value,
-                          atol=accuracy)
+        assert np.isclose(
+            original_final_test_value, new_final_test_value, atol=accuracy
+        )
 
     @staticmethod
     def __original_setup(n_trials):
@@ -84,12 +86,27 @@ def __original_setup(n_trials):
         data_handler = mala.DataHandler(test_parameters)
 
         # Add all the snapshots we want to use in to the list.
-        data_handler.add_snapshot("Be_snapshot0.in.npy", data_path,
-                                  "Be_snapshot0.out.npy", data_path, "tr")
-        data_handler.add_snapshot("Be_snapshot1.in.npy", data_path,
-                                  "Be_snapshot1.out.npy", data_path, "va")
-        data_handler.add_snapshot("Be_snapshot2.in.npy", data_path,
-                                  "Be_snapshot2.out.npy", data_path, "te")
+        data_handler.add_snapshot(
+            "Be_snapshot0.in.npy",
+            data_path,
+            "Be_snapshot0.out.npy",
+            data_path,
+            "tr",
+        )
+        data_handler.add_snapshot(
+            "Be_snapshot1.in.npy",
+            data_path,
+            "Be_snapshot1.out.npy",
+            data_path,
+            "va",
+        )
+        data_handler.add_snapshot(
+            "Be_snapshot2.in.npy",
+            data_path,
+            "Be_snapshot2.out.npy",
+            data_path,
+            "te",
+        )
         data_handler.prepare_data()
         printout("Read data: DONE.", min_verbosity=0)
 
@@ -105,20 +122,28 @@ def __original_setup(n_trials):
         test_hp_optimizer = mala.HyperOpt(test_parameters, data_handler)
 
         # Learning rate will be optimized.
-        test_hp_optimizer.add_hyperparameter("float", "learning_rate",
-                                             0.0000001, 0.01)
+        test_hp_optimizer.add_hyperparameter(
+            "float", "learning_rate", 0.0000001, 0.01
+        )
 
         # Number of neurons per layer will be optimized.
-        test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_00", 10, 100)
-        test_hp_optimizer.add_hyperparameter("int", "ff_neurons_layer_01", 10, 100)
+        test_hp_optimizer.add_hyperparameter(
+            "int", "ff_neurons_layer_00", 10, 100
+        )
+        test_hp_optimizer.add_hyperparameter(
+            "int", "ff_neurons_layer_01", 10, 100
+        )
 
         # Choices for activation function at each layer will be optimized.
-        test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_00",
-                                             choices=["ReLU", "Sigmoid"])
-        test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_01",
-                                             choices=["ReLU", "Sigmoid"])
-        test_hp_optimizer.add_hyperparameter("categorical", "layer_activation_02",
-                                             choices=["ReLU", "Sigmoid"])
+        test_hp_optimizer.add_hyperparameter(
+            "categorical", "layer_activation_00", choices=["ReLU", "Sigmoid"]
+        )
+        test_hp_optimizer.add_hyperparameter(
+            "categorical", "layer_activation_01", choices=["ReLU", "Sigmoid"]
+        )
+        test_hp_optimizer.add_hyperparameter(
+            "categorical", "layer_activation_02", choices=["ReLU", "Sigmoid"]
+        )
 
         # Perform hyperparameter optimization.
         printout("Starting Hyperparameter optimization.", min_verbosity=0)
@@ -136,7 +161,7 @@ def __resume_checkpoint():
             The hyperopt object.
 
         """
-        loaded_params, new_datahandler, new_hyperopt = \
+        loaded_params, new_datahandler, new_hyperopt = (
             mala.HyperOptOptuna.resume_checkpoint(checkpoint_name)
+        )
         return new_hyperopt
-