def submit():
s = Submission(homework, part_names, srcs, output)
try:
s.submit()
except Exception as ex:
template = "An exception of type {0} occured. Messsage:\n{1!r}"
message = template.format(type(ex).__name__, ex.args)
print(message)
def _getSubmissions(self):
filenames = os.listdir('./')
submissions = {}
for filename in filenames:
# we only focus on directory
if not os.path.isdir(filename):
continue
sub = Submission(filename)
netid = sub.netid
submissions[netid] = sub
return submissions
def submissions():
submissionsDatabase = app.mongo.db.submissions
problemsDatabase = app.mongo.db.problems
print(submissionsDatabase)
submissionsCursor = submissionsDatabase.find({}).limit(100).sort([('Submission Time', -1)])
submissions = list()
for submission in submissionsCursor:
submissions.append(Submission(submission, problemsDatabase))
for submission in submissions:
print(submission.submissionTime)
return app.render_template('submissions.html', submissions=submissions)
def viewOneSubmissions(submissionId, form):
file = open("static/css/styles/styles.txt", "r")
themes = list()
for line in file:
themes.append(line[:-1])
file.close()
if form.get("themes") != None:
preferedTheme = form.get("themes")
else:
preferedTheme = "atom-one-dark"
submissionsDatabase = app.mongo.db.submissions
submission = Submission(submissionsDatabase.find({"Submission Id": submissionId})[0], app.mongo.db.problems)
language = str(submission.language).lower()
return app.render_template('submitted_Code_viewer.html', submission=submission, language=language, themes=themes,
preferedTheme=preferedTheme)
def __init__(self, welcome=False, **kwargs):
super(MainScreen, self).__init__(**kwargs)
self.orientation = 'vertical'
self.current_ex = 'ex1'
self.current_file = 'warmUpExercise.py'
self.submit_ob = Submission()
self.element = resourceHandler()
if welcome:
welcome_popup = Popup(title='Coursera ML in Python',
content=Label(text='Hello World'),
size_hint=(1, 1))
self.add_widget(welcome_popup)
welcome_popup.open()
Clock.schedule_once(self.start_app, 3)
else:
self.bind(size=self.draw_screen)
def filter_submissions(path, rerun_flag):
with open("FailedNBs.txt", "w") as f:
f.write('')
submissions = list(sorted(path.glob('*.ipynb')))
#print(submissions)
final_submissions = []
ids = []
for s in range(len(submissions)-1, -1, -1):
fname, _ = submissions[s].name.split(".") # ID_TRIAL.ext
student_id, trial_no = fname.split("_") #Split ID and TRIAL nr
#print('Student ID:', student_id, ',Trial NO:', trial_no, ' -->', submissions[s])
if student_id in ids:
continue
else:
try:
processed_submission = Submission(student_id, str(submissions[s]), trial_no, rerun_flag)
final_submissions.append(processed_submission)
ids.append(student_id)
except Exception as e:
with open("FailedNBs.txt", "a") as f:
f.write(str(student_id) + '\n')
print('Error 1.1: Error Reading Submission of Student ID ' + student_id + '-->', e)
return final_submissions
fname = srcs[part_id - 1].rsplit('.', 1)[0]
mod = __import__(fname, fromlist=[fname], level=1)
func = getattr(mod, fname)
if part_id == 1:
idx = func(X, C)
return sprintf('%0.5f ', idx[1] + 1)
elif part_id == 2:
centroids = func(X, idx, 3)
return sprintf('%0.5f ', centroids)
elif part_id == 3:
U, S, V = func(X)
return sprintf('%0.5f ', abs(np.hstack(
(U.T.flatten(), S.T.flatten()))))
elif part_id == 4:
X_proj = func(X, Z, 5)
return sprintf('%0.5f ', X_proj.T.flatten())
elif part_id == 5:
X_rec = func(X[:, :5], Z, 5)
return sprintf('%0.5f ', X_rec.T.flatten())
s = Submission(homework, part_names, srcs, output)
try:
s.submit()
except Exception as ex:
template = "An exception of type {0} occured. Messsage:\n{1!r}"
message = template.format(type(ex).__name__, ex.args)
print(message)
fname = srcs[part_id - 1].rsplit(".", 1)[0]
mod = __import__(fname, fromlist=[fname], level=1)
func = getattr(mod, fname)
if part_id == 1:
sim = func(x1, x2, 2)
return sprintf("%0.5f ", sim)
elif part_id == 2:
data = scipy.io.loadmat("ex6data3.mat")
X = data["X"]
y = data["y"].flatten()
Xval = data["Xval"]
yval = data["yval"].flatten()
C, sigma = func(X, y, Xval, yval)
return sprintf("%0.5f ", np.hstack((C, sigma)))
elif part_id == 3:
word_indices = np.array(func(ec))
return sprintf("%d ", (word_indices + 1).tolist())
elif part_id == 4:
x = func(wi)
return sprintf("%d", x)
s = Submission(homework, part_names, srcs, output)
try:
s.submit()
except Exception as ex:
template = "An exception of type {0} occured. Messsage:\n{1!r}"
message = template.format(type(ex).__name__, ex.args)
print message
from Submission import Submission
from sklearn.metrics import accuracy_score, auc, roc_auc_score, classification_report
from sklearn.model_selection import train_test_split, cross_val_score
from config import *
print('test began')
#objects
dataProcessor = Preprocessor()
transactionData = dataProcessor.get_data() #original given data
featureFilter = FeatureFilter()
algorithm = Models()
result = ResultLog()
miniProcessor = MiniProcessor(transactionData)
submission = Submission()
#data for training
X_train = dataProcessor.get_train_attributes()
y_train = dataProcessor.get_train_labels()
#data for test
X_test = dataProcessor.get_test_attributes()
y_test = dataProcessor.get_test_labels()
'''
First Learning to classify the rows into early, ontime, late
'''
trainedModel = algorithm.decision_tree(decision_tree_dict, X_train, y_train,
X_test, y_test)
#save trained model
def main(notify):
db = sqlite3.connect('data/submissions.sqlite')
g = nx.Graph()
for row in db.execute('SELECT * FROM submissions'):
submission = Submission(row)
from_sub = submission.subreddit.lower()
initialize_node(g, from_sub)
g.node[from_sub]['post_count'] += 1
# weighten edges
if submission.is_x_post():
to_sub = submission.get_sub_from_url().lower()
initialize_node(g, to_sub)
g.node[from_sub]['x_post_count'] += 1
g.node[to_sub]['x_post_to_count'] += 1
if g.has_edge(from_sub, to_sub):
g[from_sub][to_sub]['weight'] += 1
else:
g.add_edge(from_sub, to_sub, weight=1)
# keep track of self posts
if submission.is_self_post():
g.node[from_sub]['self_count'] += 1
"""
Now I want to remove reddits who primarily link to
other content on reddit (z.B. x-post reddits like bestof and worstof)
And also remove nodes that are relatively weakly linked, I.E. degree of 1 or 0
"""
# remove nodes (subs) that are x-post too heavily (>40%)
# or those whose weighted degree is less than a certain number
to_remove = []
for n in g:
data = g.node[n]
removed = False
# avoid division by zero in the next if-block
if data['post_count'] == 0: data['post_count'] = 1
# the too heavy x-posters
if data['x_post_count'] / data['post_count'] > .2:
print "%s removed" % n
to_remove.append(n)
removed = True
# the low weights
weight = 0
for e in g.edges(n, data=True):
weight += e[2]['weight']
if weight < 15 and not removed:
print "%s for low weight" % n
to_remove.append(n)
# now remove them
# TODO do better than this
for n in to_remove:
g.remove_node(n)
db.close()
nx.write_gexf(g, 'data/output.gexf')
