'Refactored by Sourcery'

Author: Sourcery AI

Plotting/interactive_decision_tree.py

@@ -31,17 +31,16 @@ def _get_tree_info(X, tree_model, target_names, target_colors, color_map):
     :return:
         dictionary of useful information
     '''
-    # classify features into 3 types: binary, float and int
-    binary_features = []
-    for col in X.columns.values:
-        if list(sorted(np.unique(X[col].values))) == [0, 1]:
-            binary_features.append(col)
-
-    int_features = []
-    for col in list(set(X.columns.values) - set(binary_features)):
-        if list(X[col].map(int).values) == list(X[col].values):
-            int_features.append(col)
-
+    binary_features = [
+        col
+        for col in X.columns.values
+        if list(sorted(np.unique(X[col].values))) == [0, 1]
+    ]
+    int_features = [
+        col
+        for col in list(set(X.columns.values) - set(binary_features))
+        if list(X[col].map(int).values) == list(X[col].values)
+    ]
     # get feature names
     feature_names = X.columns.values
 
@@ -51,23 +50,19 @@ def _get_tree_info(X, tree_model, target_names, target_colors, color_map):
 
     # color mapping for targets
     if target_colors is None:
-        if color_map is not None:
-            cm = plt.get_cmap(color_map)
-        else:
-            cm = plt.get_cmap('tab20')
-        target_colors = []
-        for n in range(tree_model.tree_.n_classes[0]):
-            target_colors.append(str(matplotlib.colors.rgb2hex(cm(n + 1))))
-
-    tree_info = {
+        cm = plt.get_cmap('tab20') if color_map is None else plt.get_cmap(color_map)
+        target_colors = [
+            str(matplotlib.colors.rgb2hex(cm(n + 1)))
+            for n in range(tree_model.tree_.n_classes[0])
+        ]
+    return {
         'tree_model': tree_model,
         'features': [feature_names[i] for i in tree_model.tree_.feature],
         'binary_features': binary_features,
         'int_features': int_features,
         'target_names': target_names,
-        'target_colors': target_colors
+        'target_colors': target_colors,
     }
-    return tree_info
 
 
 def _parse_tree(node_id, parent, pos, tree_info):
@@ -86,30 +81,33 @@ def _parse_tree(node_id, parent, pos, tree_info):
         complete tree structure
     '''
     tree_model = tree_info['tree_model']
-    features = tree_info['features']
-    binary_features = tree_info['binary_features']
-    int_features = tree_info['int_features']
     target_names = tree_info['target_names']
 
     node = {}
     if parent == 'null':
         node['name'] = "HEAD"
     else:
+        features = tree_info['features']
         feature = features[parent]
+        binary_features = tree_info['binary_features']
+        int_features = tree_info['int_features']
         if pos == 'left':
             if feature in binary_features:
-                node['name'] = feature + ': 0'
+                node['name'] = f'{feature}: 0'
             elif feature in int_features:
-                node['name'] = feature + " <= " + str(int(tree_model.tree_.threshold[parent]))
+                node['name'] = f"{feature} <= {int(tree_model.tree_.threshold[parent])}"
             else:
-                node['name'] = feature + " <= " + str(round(tree_model.tree_.threshold[parent], 3))
+                node[
+                    'name'
+                ] = f"{feature} <= {str(round(tree_model.tree_.threshold[parent], 3))}"
+        elif feature in binary_features:
+            node['name'] = f'{feature}: 1'
+        elif feature in int_features:
+            node['name'] = f"{feature} > {int(tree_model.tree_.threshold[parent])}"
         else:
-            if feature in binary_features:
-                node['name'] = feature + ': 1'
-            elif feature in int_features:
-                node['name'] = feature + " > " + str(int(tree_model.tree_.threshold[parent]))
-            else:
-                node['name'] = feature + " > " + str(round(tree_model.tree_.threshold[parent], 3))
+            node[
+                'name'
+            ] = f"{feature} > {str(round(tree_model.tree_.threshold[parent], 3))}"
     try:
         node['parent'] = int(parent)
     except:
@@ -125,12 +123,12 @@ def _parse_tree(node_id, parent, pos, tree_info):
 
     if tree_model.tree_.children_left[node_id] != -1 or tree_model.tree_.children_right[node_id] != -1:
         node['children'] = []
-        if tree_model.tree_.children_left[node_id] != -1:
-            child = tree_model.tree_.children_left[node_id]
-            node['children'].append(_parse_tree(child, node_id, 'left', tree_info))
-        if tree_model.tree_.children_right[node_id] != -1:
-            child = tree_model.tree_.children_right[node_id]
-            node['children'].append(_parse_tree(child, node_id, 'right', tree_info))
+    if tree_model.tree_.children_left[node_id] != -1:
+        child = tree_model.tree_.children_left[node_id]
+        node['children'].append(_parse_tree(child, node_id, 'left', tree_info))
+    if tree_model.tree_.children_right[node_id] != -1:
+        child = tree_model.tree_.children_right[node_id]
+        node['children'].append(_parse_tree(child, node_id, 'right', tree_info))
     return node
 
 
@@ -154,9 +152,7 @@ def _extract_rules(node_id, parent, pos, tree_rules, tree_info):
     features = tree_info['features']
     tree_model = tree_info['tree_model']
 
-    tree_rules[node_id] = {}
-    tree_rules[node_id]['features'] = {}
-
+    tree_rules[node_id] = {'features': {}}
     if parent != "null":
         previous = copy.deepcopy(tree_rules[parent]['features'])
         tree_rules[node_id]['features'] = previous
@@ -202,24 +198,20 @@ def _clean_rules(tree_rules, tree_info):
             for k in node['features'].keys():
                 feat = node['features'][k]
                 if k in tree_info['binary_features']:
-                    if feat[0] == -sys.maxsize:
-                        rule = k + ': 0'
-                    else:
-                        rule = k + ': 1'
+                    rule = f'{k}: 0' if feat[0] == -sys.maxsize else f'{k}: 1'
                 elif k in tree_info['int_features']:
                     if feat[0] == -sys.maxsize:
-                        rule = k + ' <= ' + str(int(feat[1]))
+                        rule = f'{k} <= {int(feat[1])}'
                     elif feat[1] == sys.maxsize:
-                        rule = k + ' > ' + str(int(feat[0]))
+                        rule = f'{k} > {int(feat[0])}'
                     else:
-                        rule = str(int(feat[0])) + ' < ' + k + ' <= ' + str(int(feat[1]))
+                        rule = f'{int(feat[0])} < {k} <= {int(feat[1])}'
+                elif feat[0] == -sys.maxsize:
+                    rule = f'{k} <= {str(round(feat[1], 3))}'
+                elif feat[1] == sys.maxsize:
+                    rule = f'{k} > {str(round(feat[0], 3))}'
                 else:
-                    if feat[0] == -sys.maxsize:
-                        rule = k + ' <= ' + str(round(feat[1], 3))
-                    elif feat[1] == sys.maxsize:
-                        rule = k + ' > ' + str(round(feat[0], 3))
-                    else:
-                        rule = str(round(feat[0], 3)) + ' < ' + k + ' <= ' + str(round(feat[1], 3))
+                    rule = f'{str(round(feat[0], 3))} < {k} <= {str(round(feat[1], 3))}'
                 rules.append(rule)
             rules = sorted(rules, key= lambda x : len(x))
         tree_rules_clean[key] = rules

Python/codon_expt.py

@@ -1,9 +1,7 @@
 import time
 
 def fib(n):
-    if n<=1:
-        return 1
-    return fib(n-1) + fib(n-2)
+    return 1 if n<=1 else fib(n-1) + fib(n-2)
 
 def approximate_pi(num_terms):
     """

Run-time Optimization/codon_expt.py

@@ -1,9 +1,7 @@
 import time
 
 def fib(n):
-    if n<=1:
-        return 1
-    return fib(n-1) + fib(n-2)
+    return 1 if n<=1 else fib(n-1) + fib(n-2)
 
 def approximate_pi(num_terms):
     """