Merge pull request #363 from rasbt/docformatting

Fix documentation parsing errors

docs/ipynb2markdown.py

@@ -54,10 +54,7 @@ def ipynb_to_md(ipynb_path):
             if line.lstrip().startswith('#'):
                 break
         for line in f:
-            if line.lstrip().startswith('```'):
-                continue
-            else:
-                new_s.append(line[4:])
+            new_s.append(line[4:])
 
     with open(md_name, 'w') as f:
         f.write(''.join(new_s))

docs/make_api.py

@@ -41,6 +41,7 @@ def docstring_to_markdown(docstring):
         line = line.strip()
         if set(line) in ({'-'}, {'='}):
             new_docstring_lst[idx - 1] = '**%s**' % new_docstring_lst[idx - 1]
+
         elif line.startswith('>>>'):
             line = '    %s' % line
         new_docstring_lst.append(line)
@@ -62,8 +63,16 @@ def docstring_to_markdown(docstring):
 
     clean_lst = []
     for line in new_docstring_lst:
-        if set(line.strip()) not in ({'-'}, {'='}):
+        if line.startswith('\n>>>'):
+            clean_lst.append('\n')
+            clean_lst.append('    ' + line[1:])
+        elif line.startswith('        ```'):
+            clean_lst.append(line[8:])
+        elif line.startswith('    ```'):
+            clean_lst.append(line[4:])
+        elif set(line.strip()) not in ({'-'}, {'='}):
             clean_lst.append(line)
+
     return clean_lst
 
 
@@ -338,7 +347,7 @@ def summarize_methdods_and_functions(api_modules, out_dir,
         new_output = []
         if str_above_header:
             new_output.append(str_above_header)
-        for p in module_paths:
+        for p in sorted(module_paths):
             with open(p, 'r') as r:
                 new_output.extend(r.readlines())
 
@@ -406,4 +415,4 @@ def summarize_methdods_and_functions(api_modules, out_dir,
                                      clean=args.clean,
                                      str_above_header=('mlxtend'
                                                        ' version: %s \n' % (
-                                                       package.__version__)))
+                                                        package.__version__)))

docs/sources/user_guide/evaluate/bootstrap_point632_score.ipynb

@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -201,17 +201,17 @@
       "\n",
       "**Examples**\n",
       "\n",
+      "\n",
       "    >>> from sklearn import datasets, linear_model\n",
       "    >>> from mlxtend.evaluate import bootstrap_point632_score\n",
-      "    >>> import numpy as np\n",
       "    >>> iris = datasets.load_iris()\n",
       "    >>> X = iris.data\n",
       "    >>> y = iris.target\n",
       "    >>> lr = linear_model.LogisticRegression()\n",
       "    >>> scores = bootstrap_point632_score(lr, X, y)\n",
       "    >>> acc = np.mean(scores)\n",
       "    >>> print('Accuracy:', acc)\n",
-      "    Accuracy: 0.953023146884\n",
+      "    0.953023146884\n",
       "    >>> lower = np.percentile(scores, 2.5)\n",
       "    >>> upper = np.percentile(scores, 97.5)\n",
       "    >>> print('95%% Confidence interval: [%.2f, %.2f]' % (lower, upper))\n",
@@ -226,6 +226,13 @@
     "    s = f.read() \n",
     "print(s)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -245,7 +252,19 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.4"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,

docs/sources/user_guide/frequent_patterns/apriori.ipynb

@@ -955,7 +955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -971,8 +971,9 @@
       "\n",
       "- `df` : pandas DataFrame\n",
       "\n",
-      "    pandas DataFrame in one-hot encoded format. For example\n",
-      "    ```\n",
+      "    pandas DataFrame the encoded format. For example,\n",
+      "\n",
+      "```\n",
       "    Apple  Bananas  Beer  Chicken  Milk  Rice\n",
       "    0      1        0     1        1     0     1\n",
       "    1      1        0     1        0     0     1\n",
@@ -982,7 +983,8 @@
       "    5      0        0     1        0     1     1\n",
       "    6      0        0     1        0     1     0\n",
       "    7      1        1     0        0     0     0\n",
-      "    ```\n",
+      "```\n",
+      "\n",
       "\n",
       "- `min_support` : float (default: 0.5)\n",
       "\n",
@@ -1005,7 +1007,8 @@
       "**Returns**\n",
       "\n",
       "pandas DataFrame with columns ['support', 'itemsets'] of all itemsets\n",
-      "    that are >= `min_support` and < than `max_len` (if `max_len` is not None).\n",
+      "    that are >= `min_support` and < than `max_len`\n",
+      "    (if `max_len` is not None).\n",
       "\n",
       "\n"
      ]
@@ -1015,6 +1018,13 @@
     "with open('../../api_modules/mlxtend.frequent_patterns/apriori.md', 'r') as f:\n",
     "    print(f.read())"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1035,6 +1045,18 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.4"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,