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Merge pull request #82 from andywot/new-grader

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Changed grader to use the new grading system
This commit is contained in:
Gerges Dib
2021-07-15 00:26:17 -07:00
committed by GitHub
parent a6b0678c06 3c04c2c2cf
commit 10207d04db
9 changed files with 50 additions and 37 deletions
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4
Exercise1/utils.py
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@@ -19,7 +19,9 @@ class Grader(SubmissionBase):
'Computing Cost (for multiple variables)', 'Computing Cost (for multiple variables)',
'Gradient Descent (for multiple variables)', 'Gradient Descent (for multiple variables)',
'Normal Equations'] '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): def __iter__(self):
for part_id in range(1, 8): for part_id in range(1, 8):

4
Exercise2/utils.py
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@@ -119,7 +119,9 @@ class Grader(SubmissionBase):
'Predict', 'Predict',
'Regularized Logistic Regression Cost', 'Regularized Logistic Regression Cost',
'Regularized Logistic Regression Gradient'] '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): def __iter__(self):
for part_id in range(1, 7): for part_id in range(1, 7):

5
Exercise3/utils.py
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@@ -79,8 +79,9 @@ class Grader(SubmissionBase):
'One-vs-All Classifier Training', 'One-vs-All Classifier Training',
'One-vs-All Classifier Prediction', 'One-vs-All Classifier Prediction',
'Neural Network Prediction Function'] 'Neural Network Prediction Function']
part_names_key = ['jzAIf', 'LjDnh', '3yxcY', 'yNspP']
super().__init__('multi-class-classification-and-neural-networks', part_names) assignment_key = '2KZRbGlpQnyzVI8Ki4uXjw'
super().__init__('multi-class-classification-and-neural-networks', assignment_key, part_names, part_names_key)
def __iter__(self): def __iter__(self):
for part_id in range(1, 5): for part_id in range(1, 5):

4
Exercise4/utils.py
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@@ -193,7 +193,9 @@ class Grader(SubmissionBase):
'Sigmoid Gradient', 'Sigmoid Gradient',
'Neural Network Gradient (Backpropagation)', 'Neural Network Gradient (Backpropagation)',
'Regularized Gradient'] '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): def __iter__(self):
for part_id in range(1, 6): for part_id in range(1, 6):

4
Exercise5/utils.py
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@@ -138,7 +138,9 @@ class Grader(SubmissionBase):
'Learning Curve', 'Learning Curve',
'Polynomial Feature Mapping', 'Polynomial Feature Mapping',
'Validation Curve'] '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): def __iter__(self):
for part_id in range(1, 6): for part_id in range(1, 6):

4
Exercise6/utils.py
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@@ -695,7 +695,9 @@ class Grader(SubmissionBase):
'Parameters (C, sigma) for Dataset 3', 'Parameters (C, sigma) for Dataset 3',
'Email Processing', 'Email Processing',
'Email Feature Extraction'] '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): def __iter__(self):
for part_id in range(1, 5): for part_id in range(1, 5):

4
Exercise7/utils.py
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@@ -211,7 +211,9 @@ class Grader(SubmissionBase):
'PCA', 'PCA',
'Project Data (PCA)', 'Project Data (PCA)',
'Recover 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): def __iter__(self):
for part_id in range(1, 6): for part_id in range(1, 6):

4
Exercise8/utils.py
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@@ -235,7 +235,9 @@ class Grader(SubmissionBase):
'Collaborative Filtering Gradient', 'Collaborative Filtering Gradient',
'Regularized Cost', 'Regularized Cost',
'Regularized Gradient'] '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): def __iter__(self):
for part_id in range(1, 7): for part_id in range(1, 7):

54
submission.py
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@@ -1,21 +1,21 @@
from urllib.parse import urlencode
from urllib.request import urlopen
import pickle
import json import json
from collections import OrderedDict
import numpy as np
import os import os
import pickle
from collections import OrderedDict
import numpy as np
import requests
class SubmissionBase: class SubmissionBase:
submit_url = 'https://www.coursera.org/api/onDemandProgrammingScriptSubmissions.v1?includes=evaluation'
submit_url = 'https://www-origin.coursera.org/api/' \
'onDemandProgrammingImmediateFormSubmissions.v1'
save_file = 'token.pkl' 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_slug = assignment_slug
self.assignment_key = assignment_key
self.part_names = part_names self.part_names = part_names
self.part_names_key = part_names_key
self.login = None self.login = None
self.token = None self.token = None
self.functions = OrderedDict() self.functions = OrderedDict()
@@ -28,24 +28,25 @@ class SubmissionBase:
# Evaluate the different parts of exercise # Evaluate the different parts of exercise
parts = OrderedDict() parts = OrderedDict()
for part_id, result in self: for part_id, result in self:
parts[str(part_id)] = {'output': sprintf('%0.5f ', result)} parts[self.part_names_key[part_id - 1]] = {'output': sprintf('%0.5f ', result)}
result, response = self.request(parts) response = self.request(parts)
response = json.loads(response.decode("utf-8")) response = json.loads(response.decode("utf-8"))
# if an error was returned, print it and stop # if an error was returned, print it and stop
if 'errorMessage' in response: if 'errorCode' in response:
print(response['errorMessage']) print(response['message'], response['details']['learnerMessage'])
return return
# Print the grading table # Print the grading table
print('%43s | %9s | %-s' % ('Part Name', 'Score', 'Feedback')) print('%43s | %9s | %-s' % ('Part Name', 'Score', 'Feedback'))
print('%43s | %9s | %-s' % ('---------', '-----', '--------')) print('%43s | %9s | %-s' % ('---------', '-----', '--------'))
for part in parts: for index, part in enumerate(parts):
part_feedback = response['partFeedbacks'][part] part_feedback = response['linked']['onDemandProgrammingScriptEvaluations.v1'][0]['parts'][str(part)][
part_evaluation = response['partEvaluations'][part] 'feedback']
part_evaluation = response['linked']['onDemandProgrammingScriptEvaluations.v1'][0]['parts'][str(part)]
score = '%d / %3d' % (part_evaluation['score'], part_evaluation['maxScore']) score = '%d / %3d' % (part_evaluation['score'], part_evaluation['maxScore'])
print('%43s | %9s | %-s' % (self.part_names[int(part) - 1], score, part_feedback)) print('%43s | %9s | %-s' % (self.part_names[int(index) - 1], score, part_feedback))
evaluation = response['evaluation'] evaluation = response['linked']['onDemandProgrammingScriptEvaluations.v1'][0]
total_score = '%d / %d' % (evaluation['score'], evaluation['maxScore']) total_score = '%d / %d' % (evaluation['score'], evaluation['maxScore'])
print(' --------------------------------') print(' --------------------------------')
print('%43s | %9s | %-s\n' % (' ', total_score, ' ')) print('%43s | %9s | %-s\n' % (' ', total_score, ' '))
@@ -71,18 +72,15 @@ class SubmissionBase:
pickle.dump((self.login, self.token), f) pickle.dump((self.login, self.token), f)
def request(self, parts): def request(self, parts):
params = { payload = {
'assignmentSlug': self.assignment_slug, 'assignmentKey': self.assignment_key,
'submitterEmail': self.login,
'secret': self.token, 'secret': self.token,
'parts': parts, 'parts': dict(eval(str(parts)))}
'submitterEmail': self.login} headers = {}
params = urlencode({'jsonBody': json.dumps(params)}).encode("utf-8") r = requests.post(self.submit_url, data=json.dumps(payload), headers=headers)
f = urlopen(self.submit_url, params) return r.content
try:
return 0, f.read()
finally:
f.close()
def __iter__(self): def __iter__(self):
for part_id in self.functions: for part_id in self.functions: