clean up / pepify

.gitignore

@@ -12,3 +12,4 @@ talos.egg-info
 *.h5
 *.json
 *.npy
+test.sh

talos/commands/deploy.py

@@ -12,11 +12,10 @@ class Deploy:
 
     def __init__(self, scan_object, model_name, metric='val_acc', asc=False):
 
-
         '''Deploy a model to be used later or in a different system.
 
         NOTE: for a metric that is to be minimized, set asc=True or otherwise
-        you will end up with the model that has the highest loss. 
+        you will end up with the model that has the highest loss.
 
         Deploy() takes in the object from Scan() and creates a package locally
         that can be later activated with Restore().
@@ -26,10 +25,10 @@ def __init__(self, scan_object, model_name, metric='val_acc', asc=False):
         model_name : str
             Name for the .zip file to be created.
         metric : str
-            The metric to be used for picking the best model. 
+            The metric to be used for picking the best model.
         asc: bool
-            Make this True for metrics that are to be minimized (e.g. loss) , and
-            False when the metric is to be maximized (e.g. acc)
+            Make this True for metrics that are to be minimized (e.g. loss) ,
+            and False when the metric is to be maximized (e.g. acc)
 
         '''
 

talos/commands/kerasmodel.py

@@ -73,7 +73,8 @@ def _create_input_model(self, x_train, y_train, x_val, y_val, params):
                         activation=params['last_activation']))
 
         # bundle the optimizer with learning rate changes
-        optimizer = params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer']))
+        optimizer = params['optimizer'](lr=lr_normalizer(params['lr'],
+                                                         params['optimizer']))
 
         # compile the model
         model.compile(optimizer=optimizer,

talos/commands/predict.py

@@ -1,8 +1,3 @@
-from numpy import mean, std
-
-from sklearn.metrics import f1_score
-
-from ..utils.validation_split import kfold
 from ..utils.best_model import best_model, activate_model
 
 

talos/commands/reporting.py

@@ -1,11 +1,10 @@
 from pandas import read_csv
 from ..utils.connection_check import is_connected
+from ..metrics.names import metric_names
 
 if is_connected() is True:
     from astetik import line, hist, corr, regs, bargrid, kde, box
 
-from ..metrics.names import metric_names
-
 
 class Reporting:
 

talos/commands/restore.py

@@ -8,8 +8,16 @@
 
 class Restore:
 
-    '''Utility class for restoring the assets from Deploy()
-    package.'''
+    '''Restores the scan_object that had been stored locally as a result
+    of talos.Deploy(scan_object, 'example')
+
+    USE:
+
+    diabetes = ta.Scan(x, y, p, input_model)
+    ta.Deploy(diabetes, 'diabetes')
+    ta.Restore('diabetes.zip')
+
+    '''
 
     def __init__(self, path_to_zip):