do not load model if save is false

polyssifier/polyssifier.py

@@ -221,7 +221,7 @@ def fit_clf(args, clf_name, val, n_fold, project_name, save, scoring):
     file_name = 'poly_{}/models/{}_{}.p'.format(
         project_name, clf_name, n_fold + 1)
     start = time.time()
-    if os.path.isfile(file_name):
+    if save and os.path.isfile(file_name):
         logger.info('Loading {} {}'.format(file_name, n_fold))
         clf = joblib.load(file_name)
     else:
@@ -296,22 +296,25 @@ def create_polynomial(data, degree):
     :return: a new data matrix of the specified degree (for polynomial fitting purposes)
     '''
 
-    #First we make an empty matrix which is the size of what we wish to pass through to linear regress
+    # First we make an empty matrix which is the size of what we wish to pass through to linear regress
     height_of_pass_through = data.shape[0]
     width_of_pass_through = degree * data.shape[1]
-    to_pass_through = np.zeros(shape=(height_of_pass_through, width_of_pass_through))
+    to_pass_through = np.zeros(
+        shape=(height_of_pass_through, width_of_pass_through))
 
-    #These are the width and height of each "exponeneted" matrix
+    # These are the width and height of each "exponeneted" matrix
     height_exponential_matrix = data.shape[0]
     width_exponential_matrix = data.shape[1]
 
     for i in range(degree):
         to_add_in = data ** (i + 1)
         for j in range(height_exponential_matrix):
             for k in range(width_exponential_matrix):
-                to_pass_through.itemset((j, k + i * width_exponential_matrix), (to_add_in.item(j, k)))
+                to_pass_through.itemset(
+                    (j, k + i * width_exponential_matrix), (to_add_in.item(j, k)))
     return to_pass_through
 
+
 def polyr(data, label, n_folds=10, scale=True, exclude=[],
           feature_selection=False, num_degrees=1, save=False, scoring='r2',
           project_name='', concurrency=1, verbose=True):
@@ -337,9 +340,9 @@ def polyr(data, label, n_folds=10, scale=True, exclude=[],
     '''
     if num_degrees != 1:
         polynomial_data = create_polynomial(data, num_degrees)
-        return polyr(data = polynomial_data, label = label, n_folds=n_folds, scale=scale, exclude=exclude,
-              feature_selection=feature_selection, num_degrees=1, save=save, scoring=scoring,
-              project_name=project_name, concurrency=concurrency, verbose=verbose)
+        return polyr(data=polynomial_data, label=label, n_folds=n_folds, scale=scale, exclude=exclude,
+                     feature_selection=feature_selection, num_degrees=1, save=save, scoring=scoring,
+                     project_name=project_name, concurrency=concurrency, verbose=verbose)
 
     assert label.shape[0] == data.shape[0],\
         "Label dimesions do not match data number of rows"