def course(u=None, d=None, cid=None):
course = app.db.query(Course).join(Department).join(University) \
.filter(University.abbreviation==u) \
.filter(Department.abbreviation==d) \
.filter(Course.id==cid).first()
if course is None:
abort(404)
return render_template("course.html",
course=course,
topics=topic_list(course, result_set=g.result_set_raw))
def course(u=None, d=None, cid=None):
course = app.db.query(Course).join(Department).join(University) \
.filter(University.abbreviation==u) \
.filter(Department.abbreviation==d) \
.filter(Course.id==cid).first()
if course is None:
abort(404)
return render_template("course.html",
course=course,
topics=topic_list(course,
result_set=g.result_set_raw))
def predicted_knowledge_areas(course, result_set=None):
"""
Compute the set of ACM knowledge areas assigned to this course by
determining conceptual overlap between the target course and the
knowledge areas' inferred topics.
"""
from trajectory.utils.vector import topic_list
from trajectory.models import University, Department, Course
from trajectory.models.meta import session
# Handle empty case.
if course is None:
return []
# Query database for knowledge areas.
knowledge_areas = session.query(Department).join(University)\
.filter(University.abbreviation=="ACM")\
.filter(Department.abbreviation=="KA")\
.first()
# Handle case where ACM/KA is not present in the database.
if knowledge_areas is None:
raise RuntimeError("Knowledge areas not defined.")
# This is the list of course objects representing knowledge areas.
knowledge_areas = knowledge_areas.courses
knowledge_areas_by_topic = {
ka: set(topic_list(ka, result_set=result_set))
for ka in knowledge_areas
}
course_topics = set(topic_list(course, result_set=result_set))
# Generate the list of knowledge areas with conceptual overlap.
inferred_knowledge_areas = set([
ka for ka in knowledge_areas_by_topic
if (course_topics & knowledge_areas_by_topic[ka])
])
return inferred_knowledge_areas
def predicted_knowledge_areas(course, result_set=None):
"""
Compute the set of ACM knowledge areas assigned to this course by
determining conceptual overlap between the target course and the
knowledge areas' inferred topics.
"""
from trajectory.utils.vector import topic_list
from trajectory.models import University, Department, Course
from trajectory.models.meta import session
# Handle empty case.
if course is None:
return []
# Query database for knowledge areas.
knowledge_areas = session.query(Department).join(University)\
.filter(University.abbreviation=="ACM")\
.filter(Department.abbreviation=="KA")\
.first()
# Handle case where ACM/KA is not present in the database.
if knowledge_areas is None:
raise RuntimeError("Knowledge areas not defined.")
# This is the list of course objects representing knowledge areas.
knowledge_areas = knowledge_areas.courses
knowledge_areas_by_topic = {
ka:set(topic_list(ka, result_set=result_set))
for ka in knowledge_areas
}
course_topics = set(topic_list(course, result_set=result_set))
# Generate the list of knowledge areas with conceptual overlap.
inferred_knowledge_areas = set([
ka for ka in knowledge_areas_by_topic
if (course_topics & knowledge_areas_by_topic[ka])
])
return inferred_knowledge_areas
def compare_departments(daid=None, dbid=None):
if None in [daid, dbid]:
departments = app.db.query(Department).all()
return render_template("compare_departments_landing.html",
departments=departments)
# Look up references to requested departments.
department_a = app.db.query(Department).get(daid)
department_b = app.db.query(Department).get(dbid)
# If either department isn't found, or if there is no result set
# (meaning no topics to infer) then simply 404.
if department_a is None or department_b is None or g.result_set_raw is None:
abort(404)
# Identify a set of topics for each department.
department_a_topics = set(topic_list(department_a, g.result_set_raw))
department_b_topics = set(topic_list(department_b, g.result_set_raw))
# Generate topic vectors for the two departments.
a_vector = topic_vector(department_a, g.result_set_raw)
b_vector = topic_vector(department_b, g.result_set_raw)
a_vector_string = a_vector.unpack(one=b'1', zero=b'0').decode('utf-8')
b_vector_string = b_vector.unpack(one=b'1', zero=b'0').decode('utf-8')
# Run similarity metrics.
similarity = dict()
similarity['jaccard'] = {
'name': 'Jaccard Index',
'range': '[0, 1]',
'description': 'Comparative set cardinality.',
'value': jaccard(department_a_topics, department_b_topics),
}
similarity['cosine'] = {
'name': 'Cosine Similarity',
'range': '[-1, 1]',
'description': 'Geometric cosine distance.',
'value': cosine_similarity(a_vector, b_vector),
}
similarity['euclidean'] = {
'name': 'Euclidean Distance',
'description': 'Geometric vector distance.',
'value': euclidean_distance(a_vector, b_vector),
}
# Remove common topics from the topic sets.
intersection = department_a_topics & department_b_topics
department_a_topics = department_a_topics - intersection
department_b_topics = department_b_topics - intersection
# Number of courses in each department.
num_courses_a = app.db.query(Course).join(Department) \
.filter(Department.id==daid).count()
num_courses_b = app.db.query(Course).join(Department) \
.filter(Department.id==dbid).count()
# Global list of departments for switching over.
departments = app.db.query(Department).all()
return render_template(
"compare_departments.html",
da=department_a,
db=department_b,
da_topics=department_a_topics,
db_topics=department_b_topics,
num_courses_a=num_courses_a,
num_courses_b=num_courses_b,
common_topics=intersection,
departments=departments,
similarity_metrics=similarity,
da_vector=a_vector_string,
db_vector=b_vector_string,
)
def compare(depA, depB, rs):
dep_a_topics = topic_list(depA, rs)
dep_b_topics = topic_list(depB, rs)
return jaccard(dep_a_topics, dep_b_topics)
def compare_departments(daid=None, dbid=None):
if None in [daid, dbid]:
departments = app.db.query(Department).all()
return render_template("compare_departments_landing.html",
departments=departments)
# Look up references to requested departments.
department_a = app.db.query(Department).get(daid)
department_b = app.db.query(Department).get(dbid)
# If either department isn't found, or if there is no result set
# (meaning no topics to infer) then simply 404.
if department_a is None or department_b is None or g.result_set_raw is None:
abort(404)
# Identify a set of topics for each department.
department_a_topics = set(topic_list(department_a, g.result_set_raw))
department_b_topics = set(topic_list(department_b, g.result_set_raw))
# Generate topic vectors for the two departments.
a_vector = topic_vector(department_a, g.result_set_raw)
b_vector = topic_vector(department_b, g.result_set_raw)
a_vector_string = a_vector.unpack(one=b'1', zero=b'0').decode('utf-8')
b_vector_string = b_vector.unpack(one=b'1', zero=b'0').decode('utf-8')
# Run similarity metrics.
similarity = dict()
similarity['jaccard'] = {
'name': 'Jaccard Index',
'range': '[0, 1]',
'description': 'Comparative set cardinality.',
'value': jaccard(department_a_topics, department_b_topics),
}
similarity['cosine'] = {
'name': 'Cosine Similarity',
'range': '[-1, 1]',
'description': 'Geometric cosine distance.',
'value': cosine_similarity(a_vector, b_vector),
}
similarity['euclidean'] = {
'name': 'Euclidean Distance',
'description': 'Geometric vector distance.',
'value': euclidean_distance(a_vector, b_vector),
}
# Remove common topics from the topic sets.
intersection = department_a_topics & department_b_topics
department_a_topics = department_a_topics - intersection
department_b_topics = department_b_topics - intersection
# Number of courses in each department.
num_courses_a = app.db.query(Course).join(Department) \
.filter(Department.id==daid).count()
num_courses_b = app.db.query(Course).join(Department) \
.filter(Department.id==dbid).count()
# Global list of departments for switching over.
departments = app.db.query(Department).all()
return render_template("compare_departments.html",
da=department_a,
db=department_b,
da_topics=department_a_topics,
db_topics=department_b_topics,
num_courses_a=num_courses_a,
num_courses_b=num_courses_b,
common_topics=intersection,
departments=departments,
similarity_metrics=similarity,
da_vector=a_vector_string,
db_vector=b_vector_string,
)
