def evaluation(u=None, d=None):
if u is None or d is None:
return render_template("evaluate_landing.html")
import numpy
department = app.db.query(Department).join(University) \
.filter(University.abbreviation==u) \
.filter(Department.abbreviation==d) \
.first()
if department is None:
abort(404) # department not found
# Retrieve the set of predicted and ground truth knowledge area labels
# for each course.
try:
knowledge_areas = {
'predicted': {
course.id:
predicted_knowledge_areas(course, result_set=g.result_set_raw)
for course in department.courses
},
'truth': {
course.id: ground_truth_knowledge_areas(course)
for course in department.courses
},
}
except RuntimeError:
# Return empty knowledge area lists if an error is encountered.
knowledge_areas = {
'predicted': {course.id: []
for course in department.courses},
'truth': {course.id: []
for course in department.courses},
}
# Calculate the jaccard coefficient and percentage correct of the
# prediction/truth sets, use these as 'correctness' metrics.
knowledge_areas['jaccard'] = {
course.id: float(
jaccard(knowledge_areas['predicted'][course.id],
knowledge_areas['truth'][course.id]))
for course in department.courses if knowledge_areas['truth'][course.id]
}
knowledge_areas['percent'] = {
course.id:
float(len(set(knowledge_areas['predicted'][course.id])\
.intersection(set(knowledge_areas['truth'][course.id])))\
/ len(knowledge_areas['truth'][course.id]))
for course in department.courses
if knowledge_areas['truth'][course.id]
}
return render_template(
"evaluate_department.html",
department=department,
knowledge_areas=knowledge_areas,
)
def evaluation(u=None, d=None):
if u is None or d is None:
return render_template("evaluate_landing.html")
import numpy
department = app.db.query(Department).join(University) \
.filter(University.abbreviation==u) \
.filter(Department.abbreviation==d) \
.first()
if department is None:
abort(404) # department not found
# Retrieve the set of predicted and ground truth knowledge area labels
# for each course.
try:
knowledge_areas = {
'predicted': {
course.id: predicted_knowledge_areas(
course,
result_set=g.result_set_raw)
for course in department.courses
},
'truth': {
course.id: ground_truth_knowledge_areas(course)
for course in department.courses
},
}
except RuntimeError:
# Return empty knowledge area lists if an error is encountered.
knowledge_areas = {
'predicted': {course.id: [] for course in department.courses},
'truth': {course.id: [] for course in department.courses},
}
# Calculate the jaccard coefficient and percentage correct of the
# prediction/truth sets, use these as 'correctness' metrics.
knowledge_areas['jaccard'] = {
course.id: float(jaccard(
knowledge_areas['predicted'][course.id],
knowledge_areas['truth'][course.id]
)) for course in department.courses
if knowledge_areas['truth'][course.id]
}
knowledge_areas['percent'] = {
course.id:
float(len(set(knowledge_areas['predicted'][course.id])\
.intersection(set(knowledge_areas['truth'][course.id])))\
/ len(knowledge_areas['truth'][course.id]))
for course in department.courses
if knowledge_areas['truth'][course.id]
}
return render_template("evaluate_department.html",
department=department,
knowledge_areas=knowledge_areas,)
print("Begin.")
print("Read from database...")
gmu_cs = session.query(Department).filter(Department.abbreviation=="CS")\
.join(University).filter(University.abbreviation=="GMU")\
.first()
gmu = gmu_cs.university
result_sets = session.query(ResultSet).all()
print("Done.")
print("Compute predicted and truth knowledge areas...")
knowledge_areas = [
{
'predicted': {
course.id: predicted_knowledge_areas(course, rs)
for course in gmu_cs.courses
},
'truth': {
course.id: ground_truth_knowledge_areas(course)
for course in gmu_cs.courses
},
} for rs in result_sets
]
print("Done.")
print("Calculate jaccard and percent metrics...")
for ka_dict in knowledge_areas:
ka_dict['jaccard'] = {
course.id: jaccard(
ka_dict['predicted'][course.id],
