Changed grader to use the new grading system

Author: Andy Wu

Exercise1/utils.py

@@ -19,7 +19,9 @@ def __init__(self):
                       'Computing Cost (for multiple variables)',
                       'Gradient Descent (for multiple variables)',
                       'Normal Equations']
-        super().__init__('linear-regression', part_names)
+        part_names_key = ['DCRbJ', 'BGa4S', 'b65eO', 'BbS8u', 'FBlE2', 'RZAZC', '7m5Eu']
+        assignment_key = 'UkTlA-FyRRKV5ooohuwU6A'
+        super().__init__('linear-regression', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 8):

Exercise2/utils.py

@@ -119,7 +119,9 @@ def __init__(self):
                       'Predict',
                       'Regularized Logistic Regression Cost',
                       'Regularized Logistic Regression Gradient']
-        super().__init__('logistic-regression', part_names)
+        part_names_key = ['sFxIn', 'yvXBE', 'HerlY', '9fxV6', 'OddeL', 'aUo3H']
+        assignment_key = 'JvOPouj-S-ys8KjYcPYqrg'
+        super().__init__('logistic-regression', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 7):

Exercise3/utils.py

@@ -79,8 +79,9 @@ def __init__(self):
                       'One-vs-All Classifier Training',
                       'One-vs-All Classifier Prediction',
                       'Neural Network Prediction Function']
-
-        super().__init__('multi-class-classification-and-neural-networks', part_names)
+        part_names_key = ['jzAIf', 'LjDnh', '3yxcY', 'yNspP']
+        assignment_key = '2KZRbGlpQnyzVI8Ki4uXjw'
+        super().__init__('multi-class-classification-and-neural-networks', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 5):

Exercise4/utils.py

@@ -193,7 +193,9 @@ def __init__(self):
                       'Sigmoid Gradient',
                       'Neural Network Gradient (Backpropagation)',
                       'Regularized Gradient']
-        super().__init__('neural-network-learning', part_names)
+        part_names_key = ['aAiP2', '8ajiz', 'rXsEO', 'TvZch', 'pfIYT']
+        assignment_key = 'xolSVXukR72JH37bfzo0pg'
+        super().__init__('neural-network-learning', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 6):

Exercise5/utils.py

@@ -138,7 +138,9 @@ def __init__(self):
                       'Learning Curve',
                       'Polynomial Feature Mapping',
                       'Validation Curve']
-        super().__init__('regularized-linear-regression-and-bias-variance', part_names)
+        part_names_key = ['a6bvf', 'x4FhA', 'n3zWY', 'lLaa4', 'gyJbG']
+        assignment_key = '-wEfetVmQgG3j-mtasztYg'
+        super().__init__('regularized-linear-regression-and-bias-variance', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 6):

Exercise6/utils.py

@@ -695,7 +695,9 @@ def __init__(self):
                       'Parameters (C, sigma) for Dataset 3',
                       'Email Processing',
                       'Email Feature Extraction']
-        super().__init__('support-vector-machines', part_names)
+        part_names_key = ['drOLk', 'JYt9Q', 'UHwLk', 'RIiFh']
+        assignment_key = 'xHfBJWXxTdKXrUG7dHTQ3g'
+        super().__init__('support-vector-machines', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 5):

Exercise7/utils.py

@@ -211,7 +211,9 @@ def __init__(self):
                       'PCA',
                       'Project Data (PCA)',
                       'Recover Data (PCA)']
-        super().__init__('k-means-clustering-and-pca', part_names)
+        part_names_key = ['7yN0U', 'G1WGM', 'ixOMV', 'AFoJK', 'vf9EL']
+        assignment_key = 'rGGTuM9gQoaikOnlhLII1A'
+        super().__init__('k-means-clustering-and-pca', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 6):

Exercise8/utils.py

@@ -235,7 +235,9 @@ def __init__(self):
                       'Collaborative Filtering Gradient',
                       'Regularized Cost',
                       'Regularized Gradient']
-        super().__init__('anomaly-detection-and-recommender-systems', part_names)
+        part_names_key = ['WGzrg', '80Tcg', 'KDzSh', 'wZud3', 'BP3th', 'YF0u1']
+        assignment_key = 'JvOPouj-S-ys8KjYcPYqrg'
+        super().__init__('anomaly-detection-and-recommender-systems', assignment_key, part_names, part_names_key)
 
     def __iter__(self):
         for part_id in range(1, 7):

submission.py

@@ -1,21 +1,21 @@
-from urllib.parse import urlencode
-from urllib.request import urlopen
-import pickle
 import json
+import os
+import pickle
 from collections import OrderedDict
+
 import numpy as np
-import os
+import requests
 
 
 class SubmissionBase:
-
-    submit_url = 'https://www-origin.coursera.org/api/' \
-                 'onDemandProgrammingImmediateFormSubmissions.v1'
+    submit_url = 'https://www.coursera.org/api/onDemandProgrammingScriptSubmissions.v1?includes=evaluation'
     save_file = 'token.pkl'
 
-    def __init__(self, assignment_slug, part_names):
+    def __init__(self, assignment_slug, assignment_key, part_names, part_names_key):
         self.assignment_slug = assignment_slug
+        self.assignment_key = assignment_key
         self.part_names = part_names
+        self.part_names_key = part_names_key
         self.login = None
         self.token = None
         self.functions = OrderedDict()
@@ -28,24 +28,25 @@ def grade(self):
         # Evaluate the different parts of exercise
         parts = OrderedDict()
         for part_id, result in self:
-            parts[str(part_id)] = {'output': sprintf('%0.5f ', result)}
-        result, response = self.request(parts)
+            parts[self.part_names_key[part_id - 1]] = {'output': sprintf('%0.5f ', result)}
+        response = self.request(parts)
         response = json.loads(response.decode("utf-8"))
 
         # if an error was returned, print it and stop
-        if 'errorMessage' in response:
-            print(response['errorMessage'])
+        if 'errorCode' in response:
+            print(response['message'], response['details']['learnerMessage'])
             return
 
         # Print the grading table
         print('%43s | %9s | %-s' % ('Part Name', 'Score', 'Feedback'))
         print('%43s | %9s | %-s' % ('---------', '-----', '--------'))
-        for part in parts:
-            part_feedback = response['partFeedbacks'][part]
-            part_evaluation = response['partEvaluations'][part]
+        for index, part in enumerate(parts):
+            part_feedback = response['linked']['onDemandProgrammingScriptEvaluations.v1'][0]['parts'][str(part)][
+                'feedback']
+            part_evaluation = response['linked']['onDemandProgrammingScriptEvaluations.v1'][0]['parts'][str(part)]
             score = '%d / %3d' % (part_evaluation['score'], part_evaluation['maxScore'])
-            print('%43s | %9s | %-s' % (self.part_names[int(part) - 1], score, part_feedback))
-        evaluation = response['evaluation']
+            print('%43s | %9s | %-s' % (self.part_names[int(index) - 1], score, part_feedback))
+        evaluation = response['linked']['onDemandProgrammingScriptEvaluations.v1'][0]
         total_score = '%d / %d' % (evaluation['score'], evaluation['maxScore'])
         print('                                  --------------------------------')
         print('%43s | %9s | %-s\n' % (' ', total_score, ' '))
@@ -71,18 +72,15 @@ def login_prompt(self):
                 pickle.dump((self.login, self.token), f)
 
     def request(self, parts):
-        params = {
-            'assignmentSlug': self.assignment_slug,
+        payload = {
+            'assignmentKey': self.assignment_key,
+            'submitterEmail': self.login,
             'secret': self.token,
-            'parts': parts,
-            'submitterEmail': self.login}
-
-        params = urlencode({'jsonBody': json.dumps(params)}).encode("utf-8")
-        f = urlopen(self.submit_url, params)
-        try:
-            return 0, f.read()
-        finally:
-            f.close()
+            'parts': dict(eval(str(parts)))}
+        headers = {}
+
+        r = requests.post(self.submit_url, data=json.dumps(payload), headers=headers)
+        return r.content
 
     def __iter__(self):
         for part_id in self.functions: