adjustments for upcoming revision

Author: rasbt

code/ch09/1st_flask_app_2/templates/_formhelpers.html

@@ -9,4 +9,5 @@
     </ul>
   {% endif %}
   </dd>
-{% endmacro %}
+  </dt>
+{% endmacro %}

code/ch09/ch09.ipynb

@@ -500,7 +500,7 @@
     "example = ['I love this movie']\n",
     "X = vect.transform(example)\n",
     "print('Prediction: %s\\nProbability: %.2f%%' %\\\n",
-    "      (label[clf.predict(X)[0]], np.max(clf.predict_proba(X))*100))"
+    "      (label[clf.predict(X)[0]], clf.predict_proba(X).max()*100))"
    ]
   },
   {

code/ch09/movieclassifier/static/style.css

@@ -2,6 +2,6 @@ body{
 	width:600px;
 }
 
-#button{
+.button{
 	padding-top: 20px;
-}
+}

code/ch09/movieclassifier_with_update/static/style.css

@@ -2,6 +2,6 @@ body{
 	width:600px;
 }
 
-#button{
+.button{
 	padding-top: 20px;
-}
+}

code/ch09/movieclassifier_with_update/update.py

@@ -20,20 +20,20 @@ def update_model(db_path, model, batch_size=10000):
 
         classes = np.array([0, 1])
         X_train = vect.transform(X)
-        clf.partial_fit(X_train, y, classes=classes)
+        model.partial_fit(X_train, y, classes=classes)
         results = c.fetchmany(batch_size)
 
     conn.close()
-    return None
+    return model
 
 cur_dir = os.path.dirname(__file__)
 
 clf = pickle.load(open(os.path.join(cur_dir,
-                 'pkl_objects',
-                 'classifier.pkl'), 'rb'))
+                  'pkl_objects',
+                  'classifier.pkl'), 'rb'))
 db = os.path.join(cur_dir, 'reviews.sqlite')
 
-update_model(db_path=db, model=clf, batch_size=10000)
+clf = update_model(db_path=db, model=clf, batch_size=10000)
 
 # Uncomment the following lines if you are sure that
 # you want to update your classifier.pkl file