def recommend_course(request):
plan = eval(request.GET['courses'])
code = request.GET['code']
course_list = parse_degree_json(request.GET['courses'])
algo_recommended = get_recommendations(course_list)
d = Degree(code=code, requirements=str(plan))
try:
predictions, prediction_ratings = get_prediction(d, 20)
except:
to_return = []
for course in algo_recommended:
if(course in course_list):
continue
degree = Degree.objects.filter(code=code)[0]
if (int(degree.number_of_enrolments) > 0):
proportion = int(eval(degree.metrics)[course])*100 / int(degree.number_of_enrolments)
else:
proportion = 0
to_return.append({"course": course, "reasoning": '%.2f%% of students in your degree chose this course' % proportion})
return JsonResponse({"response": to_return})
to_return = []
response = course_data_helper.get_all()
algo_courses_rec = 0
for i in range(len(predictions)):
course = predictions[i]
course_rating = prediction_ratings[i]
course-=1
course_code = response[course]["_source"]['code']
student_has_already_completed_course = course_code in course_list
if(student_has_already_completed_course):
continue
if (course_rating<1):
continue
degree = Degree.objects.filter(code = code)[0]
if (int(degree.number_of_enrolments) > 0):
proportion = int(eval(degree.metrics)[course_code])*100 / int(degree.number_of_enrolments)
else:
proportion = 0
course_list.append(course_code)
to_return.append({"course": course_code, "reasoning": '%.2f%% of students in your degree chose this course.' % proportion })
for course in algo_recommended:
if (course in course_list):
continue
degree = Degree.objects.filter(code=code)[0]
to_return.append(
{"course": course, "reasoning": 'You have taken similar courses.'})
return JsonResponse({"response": to_return})
from sklearn.feature_extraction.text import TfidfVectorizer
import operator
from elasticsearch_dsl import Search
from elasticsearch_dsl.query import MultiMatch
from degree.course_data_helper import get_all, es_conn
tfidf = TfidfVectorizer(analyzer='word',
ngram_range=(1, 3),
min_df=0,
stop_words='english',
max_df=0.7)
tfidf.fit(list(map(lambda x: x['_source']['description'], get_all())))
def raw_search(search_object, phrase):
q = MultiMatch(query=phrase,
fields=['title^2', 'description', 'outcome^1.5'])
response = search_object.query(q).execute()
course_list = []
for hit in response['hits']['hits']:
course = {
'code': hit['_source']['code'],
'title': hit['_source']['title']
}
course_list.append(course)
return course_list
import numpy as np
from degree.course_data_helper import get_all
from sklearn.feature_extraction.text import TfidfVectorizer
from degree.course_data_helper import get_data
from degree.models import Degree
import pickle
from sklearn.neural_network import MLPRegressor
from sklearn.externals import joblib
#initial subjects to train into the neural network
SUBJECTS_TO_TRAIN_INIT = [] #["AACRD"]
tfidf = TfidfVectorizer()
course_vectors = tfidf.fit_transform(
list(map(lambda x: x['_source']['description'], get_all())))
course_ids = np.identity(len(get_all())) * 100
def get_course_vector(code):
if (get_data(code) == None):
return None
return tfidf.transform([get_data(code)['description']]).toarray().tolist()
def create_vector(degree_requirements):
reqs = list(eval(degree_requirements).items())
ave_vec = []
for j in range(len(reqs)):
k, v = reqs[j]
for i in range(len(v)):
def recommend_course(request):
plan = eval(request.GET['courses'])
code = request.GET['code']
course_list = parse_degree_json(request.GET['courses'])
if code == 'NDSTE':
if 'ENGN6626' in course_list:
les_recommendations = [
'ENGN6516', 'COMP6780', 'ENGN6223', 'ENGN6627', 'COMP6240',
'ENGN8180', 'ENGN6334', 'ENGN6410', 'ENGN8535', 'COMP6730',
'ENGN6613', 'ENGN6520', 'ENGN6420', 'ENGN8602', 'BUSI7019',
'SCOM8015', 'COMP8400', 'ENGN6521', 'EMET7001', 'MGMT7030',
'EMET6007', 'MGMT7170', 'ENVS6202', 'MGMT8005', 'ENGN6512',
'COMP6340', 'COMP8620', 'COMP7240', 'STAT7055', 'ENGN2228'
]
else:
les_recommendations = [
'ENGN6626', 'ENGN6516', 'ENGN6223', 'COMP6780', 'ENGN6627',
'COMP6240', 'ENGN8180', 'ENGN6334', 'ENGN6410', 'ENGN8535',
'COMP6730', 'ENGN6613', 'ENGN6520', 'ENGN6420', 'ENGN8602',
'SCOM8015', 'BUSI7019', 'COMP8400', 'ENGN6521', 'MGMT7030',
'EMET6007', 'ENGN6601', 'ENGN6512', 'COMP7240', 'ENVS6202',
'EMET7001', 'COMP6340', 'COMP8620', 'ENGN2228', 'SCOM6501'
]
return_stuff = []
for course in les_recommendations[:10]:
if course in course_list:
continue
return_stuff.append({
'course':
course,
'reasoning':
'Other students with your electives also chose this course'
})
return JsonResponse({'response': return_stuff})
algo_recommended = get_recommendations(course_list)
d = Degree(code=code, requirements=str(plan))
try:
predictions, prediction_ratings = get_prediction(d, 20)
except:
to_return = []
for course in algo_recommended:
if course in course_list:
continue
degree = Degree.objects.filter(code=code)[0]
if int(degree.number_of_enrolments) > 0:
proportion = int(eval(degree.metrics)[course]) * 100 / int(
degree.number_of_enrolments)
else:
proportion = 0
to_return.append({
"course":
course,
"reasoning":
'%.2f%% of students in your degree chose this course' %
proportion
})
return JsonResponse({"response": to_return})
to_return = []
response = course_data_helper.get_all()
for i in range(len(predictions)):
course = predictions[i]
course_rating = prediction_ratings[i]
course -= 1
course_code = response[course]["_source"]['code']
student_has_already_completed_course = course_code in course_list
if student_has_already_completed_course:
continue
if course_rating < 1:
continue
degree = Degree.objects.filter(code=code)[0]
if int(degree.number_of_enrolments) > 0:
proportion = int(eval(degree.metrics)[course_code]) * 100 / int(
degree.number_of_enrolments)
else:
proportion = 0
course_list.append(course_code)
to_return.append({
"course":
course_code,
"reasoning":
'%.2f%% of students in your degree chose this course.' % proportion
})
for course in algo_recommended:
if course in course_list:
continue
to_return.append({
"course": course,
"reasoning": 'You have taken similar courses.'
})
return JsonResponse({"response": to_return})