run black

examples/other_files/ML_AI_Plugin/mea_column_model_training_smtgenn.py

@@ -40,25 +40,29 @@ def create_model(x_train, z_train, grad_train):
     # we have x_train = (n_m, n_x), z_train = (n_m, n_y) and grad_train = (n_y, n_m, n_x)
     n_m, n_x = np.shape(x_train)
     _, n_y = np.shape(z_train)
-    
+
     # check dimensions using grad_train
     assert np.shape(grad_train) == (n_y, n_m, n_x)
-    
+
     # reshape arrays
     X = np.reshape(x_train, (n_x, n_m))
     Y = np.reshape(z_train, (n_y, n_m))
     J = np.reshape(grad_train, (n_y, n_x, n_m))
-    
+
     # set up and train model
 
     # Train neural net
-    model = Model.initialize(X.shape[0], Y.shape[0], deep=2, wide=6)  # 2 hidden layers with 6 neurons each
+    model = Model.initialize(
+        X.shape[0], Y.shape[0], deep=2, wide=6
+    )  # 2 hidden layers with 6 neurons each
     model.train(
         X=X,  # input data
         Y=Y,  # output data
         J=J,  # gradient data
         num_iterations=25,  # number of optimizer iterations per mini-batch
-        mini_batch_size=int(np.floor(n_m/5)), # used to divide data into training batches (use for large data sets)
+        mini_batch_size=int(
+            np.floor(n_m / 5)
+        ),  # used to divide data into training batches (use for large data sets)
         num_epochs=20,  # number of passes through data
         alpha=0.15,  # learning rate that controls optimizer step size
         beta1=0.99,  # tuning parameter to control ADAM optimization
@@ -79,6 +83,7 @@ def create_model(x_train, z_train, grad_train):
 
     return model
 
+
 # Main code
 
 # import data
@@ -95,7 +100,7 @@ def create_model(x_train, z_train, grad_train):
 
 xmax, xmin = xdata.max(axis=0), xdata.min(axis=0)
 zmax, zmin = zdata.max(axis=0), zdata.min(axis=0)
-xdata, zdata = np.array(xdata), np.array(zdata) # (n_m, n_x) and (n_m, n_y)
+xdata, zdata = np.array(xdata), np.array(zdata)  # (n_m, n_x) and (n_m, n_y)
 gdata = np.stack([np.array(grad0_data), np.array(grad1_data)])  # (2, n_m, n_x)
 
 model_data = np.concatenate(
@@ -110,7 +115,7 @@ def create_model(x_train, z_train, grad_train):
 model = create_model(x_train=xdata, z_train=zdata, grad_train=gdata)
 
 with open("mea_column_model_smt.pkl", "wb") as file:
-     pickle.dump(model, file)
+    pickle.dump(model, file)
 
 # load model as pickle format
 with open("mea_column_model_smt.pkl", "rb") as file:

foqus_lib/framework/graph/node.py

@@ -651,10 +651,7 @@ def sub_symbols(i):
             )[0]
         elif self.trainer == "smt":
             self.scaled_outputs = self.model.evaluate(
-                np.reshape(
-                    np.array(self.scaled_inputs, ndmin=2),
-                    (self.model._n_x, 1)
-                    )
+                np.reshape(np.array(self.scaled_inputs, ndmin=2), (self.model._n_x, 1))
             )
         else:  # this shouldn't occur, adding failsafe just in case
             raise AttributeError(
@@ -1226,22 +1223,28 @@ def setSim(self, newType=None, newModel=None, force=False, ids=None):
             elif (
                 extension == ".pkl"
             ):  # use importlib/pickle loading syntax for SciKitLearn models
-                pickle_loaded = False  # use a flag so we don't overload the model unnecessarily
-                try: # try Scikitlearn first
+                pickle_loaded = (
+                    False  # use a flag so we don't overload the model unnecessarily
+                )
+                try:  # try Scikitlearn first
                     if not pickle_loaded:
                         with open(str(self.modelName) + extension, "rb") as file:
                             self.model = skl_pickle_load(file)
                         pickle_loaded = True
                 except ModuleNotFoundError as e:
-                    _logger.info(e)  # will print that sklearn is not installed but won't just fail
+                    _logger.info(
+                        e
+                    )  # will print that sklearn is not installed but won't just fail
 
-                try: # try SMT next
+                try:  # try SMT next
                     if not pickle_loaded:
                         with open(str(self.modelName) + extension, "rb") as file:
                             self.model = smt_pickle_load(file)
                         pickle_loaded = True
                 except ModuleNotFoundError as e:
-                    _logger.info(e)  # will print that sklearn is not installed but won't just fail
+                    _logger.info(
+                        e
+                    )  # will print that sklearn is not installed but won't just fail
 
                 # now check which model type was unpickled
                 if "sklearn" in str(type(self.model)):
@@ -1793,22 +1796,28 @@ def runPymodelMLAI(self):
             elif (
                 extension == ".pkl"
             ):  # use importlib/pickle loading syntax for SciKitLearn models
-                pickle_loaded = False  # use a flag so we don't overload the model unnecessarily
-                try: # try Scikitlearn first
+                pickle_loaded = (
+                    False  # use a flag so we don't overload the model unnecessarily
+                )
+                try:  # try Scikitlearn first
                     if not pickle_loaded:
                         with open(str(self.modelName) + extension, "rb") as file:
                             self.model = skl_pickle_load(file)
                         pickle_loaded = True
                 except ModuleNotFoundError as e:
-                    _logger.info(e)  # will print that sklearn is not installed but won't just fail
+                    _logger.info(
+                        e
+                    )  # will print that sklearn is not installed but won't just fail
 
-                try: # try SMT next
+                try:  # try SMT next
                     if not pickle_loaded:
                         with open(str(self.modelName) + extension, "rb") as file:
                             self.model = smt_pickle_load(file)
                         pickle_loaded = True
                 except ModuleNotFoundError as e:
-                    _logger.info(e)  # will print that sklearn is not installed but won't just fail
+                    _logger.info(
+                        e
+                    )  # will print that sklearn is not installed but won't just fail
 
                 # now check which model type was unpickled
                 if "sklearn" in str(type(self.model)):

foqus_lib/gui/tests/test_ml_ai.py

@@ -272,10 +272,7 @@ def test_load_and_run_measmt(self, active_session, simnode):
         pytest.importorskip("sympy", reason="sympy not installed")
         # set sim name and confirm it's the correct model
         simnode.simNameBox.setCurrentIndex(8)
-        assert (
-            simnode.simNameBox.currentText()
-            == "mea_column_model_smt"
-        )
+        assert simnode.simNameBox.currentText() == "mea_column_model_smt"
 
         def test_flowsheet_run_successful(
             self,

foqus_lib/unit_test/node_test.py

@@ -175,7 +175,9 @@ def test_import_sklearn_success(self):
         # check that the returned functions expect the correct input as a way
         # of confirming that the class (function) types are correct
         with pytest.raises(TypeError):
-            skl_pickle_load(None)  # should fail to find 'read' and 'readline' attributes
+            skl_pickle_load(
+                None
+            )  # should fail to find 'read' and 'readline' attributes
 
     def test_import_smt_success(self):
         # skip this test if smt is not available
@@ -187,7 +189,9 @@ def test_import_smt_success(self):
         # check that the returned functions expect the correct input as a way
         # of confirming that the class (function) types are correct
         with pytest.raises(TypeError):
-            smt_pickle_load(None)  # should fail to find 'read' and 'readline' attributes
+            smt_pickle_load(
+                None
+            )  # should fail to find 'read' and 'readline' attributes
 
 
 # ----------------------------------------------------------------------------