move ml track to simpler system

soa/tracks/__init__.py

@@ -1,4 +1,4 @@
 from .core import load_tracks, trackmap, taskmap
 
 # Import tracks here please
-from . import python
+from . import python, ml

soa/tracks/ml/1.md

@@ -18,8 +18,4 @@ Please write the full command. ( answer is case sensitive )
 <form method='POST'>  
   <input name='answer'>  
   <input type='submit' value='Submit'>  
-  <code class='code_checker'>  
-    def answer(s):  
-        return s == 'df.mean()'  
-  </code>  
 </form>  

soa/tracks/ml/2.md

@@ -26,8 +26,4 @@ Write the command to One Hot Encode Column named 'company' using pandas function
 <form method='POST'>
   <input name='answer'>
   <input type='submit' value='Submit'>
-  <code class='code_checker'>
-    def answer(s):
-        return s == "df['company'].get_dummies()"
-  </code>
 </form>

soa/tracks/ml/3.md

@@ -23,8 +23,4 @@ We hope you've gone through the code and other resources provided along. Let's w
 <form method='POST'>  
   <input name='answer'>  
   <input type='submit' value='Submit'>  
-  <code class='code_checker'>  
-    def answer(s):  
-        return s.lower() == 'metrics'  
-  </code>  
 </form>  

soa/tracks/ml/4.md

@@ -24,9 +24,4 @@ What is the number of estimators used for Random Forest Classifier?
 <form method='POST'>
   <input name='answer'>
   <input type='submit' value='Submit'>
-  <code class='code_checker'>
-
-  def answer(s):
-  	return s=='200'
-  </code>
 </form>

soa/tracks/ml/5.md

@@ -26,8 +26,4 @@ In terms of the bias-variance trade-off, which of the following is/are substanti
 <form method='POST'>
   <input name='answer'>
   <input type='submit' value='Submit'>
-  <code class='code_checker'>
-  def answer(s):
-  	return s == '3'
-  </code>
 </form>

soa/tracks/ml/6.md

@@ -24,8 +24,4 @@ What is the name of the linkage that we have used in Agglomerative Clustering?
 <form method='POST'>
   <input name='answer'>
   <input type='submit' value='Submit'>
-  <code class='code_checker'>
-  def answer(s):
-  	return s.lower() == 'ward'
-  </code>
 </form>

soa/tracks/ml/7.md

@@ -23,8 +23,4 @@ Kernel PCA cannot be used for non linear data. (True / False)
 <form method='POST'>
     <input name='answer'>
     <input type='submit' value='Submit'>
-    <code class='code_checker'>
-    def answer(s):
-        return s.lower() == 'False'
- </code>
  </form>

soa/tracks/ml/8.md

@@ -27,8 +27,4 @@ Which of the following algorithm are not an example of ensemble learning algorit
 <form method='POST'>
   <input name='answer'>
   <input type='submit' value='Submit'>
-  <code class='code_checker'>
-  def answer(s):
-      return s =='5'
-  </code>
 </form>

soa/tracks/ml/__init__.py

@@ -3,6 +3,38 @@
 from bottle import request
 
 
+def a1(s):
+    return s == "df.mean()"
+
+
+def a2(s):
+    return s == "df['company'].get_dummies()"
+
+
+def a3(s):
+    return s.lower() == "metrics"
+
+
+def a4(s):
+    return s == "200"
+
+
+def a5(s):
+    return s == "3"
+
+
+def a6(s):
+    return s.lower() == "ward"
+
+
+def a7(s):
+    return s.lower() == "False"
+
+
+def a8(s):
+    return s == "5"
+
+
 class ML(Track):
     slug = "ml"
     title = "Machine Learning"
@@ -13,3 +45,14 @@ def is_locked(self):
         if request.user.is_.track_owner:
             return False
         return False if settings.is_dev else True
+
+    tasks_meta = {
+        "ml.1": {"order": 1, "md": "1.md", "check": {"answer": a1}},
+        "ml.2": {"order": 2, "md": "2.md", "check": {"answer": a2}},
+        "ml.3": {"order": 3, "md": "3.md", "check": {"answer": a3}},
+        "ml.4": {"order": 4, "md": "4.md", "check": {"answer": a4}},
+        "ml.5": {"order": 5, "md": "5.md", "check": {"answer": a5}},
+        "ml.6": {"order": 6, "md": "6.md", "check": {"answer": a6}},
+        "ml.7": {"order": 7, "md": "7.md", "check": {"answer": a7}},
+        "ml.8": {"order": 8, "md": "8.md", "check": {"answer": a8}},
+    }