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Changed grader to use the new grading system

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Andy Wu
2021-07-14 18:39:55 +08:00
parent a6b0678c06
commit 3c04c2c2cf
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)',
'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):

4
Exercise2/utils.py
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@@ -119,7 +119,9 @@ class Grader(SubmissionBase):
'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):

5
Exercise3/utils.py
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@@ -79,8 +79,9 @@ class Grader(SubmissionBase):
'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):

4
Exercise4/utils.py
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@@ -193,7 +193,9 @@ class Grader(SubmissionBase):
'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):

4
Exercise5/utils.py
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@@ -138,7 +138,9 @@ class Grader(SubmissionBase):
'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):

4
Exercise6/utils.py
View File

@@ -695,7 +695,9 @@ class Grader(SubmissionBase):
'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):

4
Exercise7/utils.py
View File

@@ -211,7 +211,9 @@ class Grader(SubmissionBase):
'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):

4
Exercise8/utils.py
View File

@@ -235,7 +235,9 @@ class Grader(SubmissionBase):
'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):

54
submission.py
View File

@@ -1,21 +1,21 @@
from urllib.parse import urlencode
from urllib.request import urlopen
import pickle
import json
from collections import OrderedDict
import numpy as np
import os
import pickle
from collections import OrderedDict
import numpy as np
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 @@ class SubmissionBase:
# 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 @@ class SubmissionBase:
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}
'parts': dict(eval(str(parts)))}
headers = {}
params = urlencode({'jsonBody': json.dumps(params)}).encode("utf-8")
f = urlopen(self.submit_url, params)
try:
return 0, f.read()
finally:
f.close()
r = requests.post(self.submit_url, data=json.dumps(payload), headers=headers)
return r.content
def __iter__(self):
for part_id in self.functions: