def get_exercise_parents_lookup_table():
"""Return a dictionary with exercise ids as keys and topic_ids as values."""
global exercise_parents_lookup_table
if exercise_parents_lookup_table:
return exercise_parents_lookup_table
### topic tree for traversal###
tree = get_topic_nodes_with_children(parent="root")
#3 possible layers
for topic in tree:
for subtopic_id in topic['children']:
exercises = get_topic_contents(topic_id=subtopic_id,
kinds=["Exercise"])
for ex in exercises:
if ex['id'] not in exercise_parents_lookup_table:
exercise_parents_lookup_table[ex['id']] = {
"subtopic_id": subtopic_id,
"topic_id": topic['id'],
}
return exercise_parents_lookup_table
def get_exercise_parents_lookup_table():
"""Return a dictionary with exercise ids as keys and topic_ids as values."""
global exercise_parents_lookup_table
if exercise_parents_lookup_table:
return exercise_parents_lookup_table
### topic tree for traversal###
tree = get_topic_nodes_with_children(parent="root")
#3 possible layers
for topic in tree:
for subtopic_id in topic['children']:
exercises = get_topic_contents(topic_id=subtopic_id, kinds=["Exercise"])
if exercises is None:
raise RuntimeError("Caught exception, tried to find topic contents for {}".format(subtopic_id))
for ex in exercises:
if ex['id'] not in exercise_parents_lookup_table:
exercise_parents_lookup_table[ ex['id'] ] = {
"subtopic_id": subtopic_id,
"topic_id": topic['id'],
}
return exercise_parents_lookup_table
def get_neighbors_at_dist_1(topic_index, subtopic_index, topic):
"""Return a list of the neighbors at distance 1 from the specified subtopic."""
neighbors = [] #neighbor list to be returned
tree = get_topic_nodes_with_children(parent="root")
#pointers to the previous and next subtopic (list indices)
prev = subtopic_index - 1
next = subtopic_index + 1
#if there is a previous topic (neighbor to left)
if (prev > -1) and prev < len(topic['children']):
neighbors.append(topic['children'][prev] +
' 1') # neighbor on the left side
#else check if there is a neighboring topic (left)
else:
if (topic_index - 1) > -1 and topic_index - 1 < len(tree) and len(
tree[(topic_index - 1)]['children']) > 1:
neighbor_length = len(tree[(topic_index - 1)]['children'])
neighbors.append(tree[(topic_index -
1)]['children'][(neighbor_length - 1)] +
' 4')
else:
neighbors.append(' ') # no neighbor to the left
#if there is a neighbor to the right
if (next < len(topic['children'])):
neighbors.append(topic['children'][next] +
' 1') # neighbor on the right side
#else check if there is a neighboring topic (right)
else:
if (topic_index + 1) < len(tree) and tree[(topic_index +
1)]['children']:
#the 4 denotes the # of nodes in path to this other node, will always be 4
neighbors.append(tree[(topic_index + 1)]['children'][0] + ' 4')
else:
neighbors.append(' ') # no neighbor on right side
return neighbors
def get_neighbors_at_dist_1(topic_index, subtopic_index, topic):
"""Return a list of the neighbors at distance 1 from the specified subtopic."""
neighbors = [] #neighbor list to be returned
tree = get_topic_nodes_with_children(parent="root")
#pointers to the previous and next subtopic (list indices)
prev = subtopic_index - 1
next = subtopic_index + 1
#if there is a previous topic (neighbor to left)
if (prev > -1 ) and prev < len(topic['children']):
neighbors.append(topic['children'][prev] + ' 1') # neighbor on the left side
#else check if there is a neighboring topic (left)
else:
if (topic_index-1) > -1 and topic_index - 1 < len(tree) and len(tree[(topic_index-1)]['children']) > 1:
neighbor_length = len(tree[(topic_index-1)]['children'])
neighbors.append(tree[(topic_index-1)]['children'][(neighbor_length-1)] + ' 4')
else:
neighbors.append(' ') # no neighbor to the left
#if there is a neighbor to the right
if(next < len(topic['children'])):
neighbors.append(topic['children'][next] + ' 1') # neighbor on the right side
#else check if there is a neighboring topic (right)
else:
if (topic_index + 1) < len(tree) and tree[(topic_index+1)]['children']:
#the 4 denotes the # of nodes in path to this other node, will always be 4
neighbors.append(tree[(topic_index+1)]['children'][0] + ' 4')
else:
neighbors.append(' ') # no neighbor on right side
return neighbors
def generate_recommendation_data():
"""Traverses topic tree to generate a dictionary with related subtopics per subtopic."""
global recommendation_data
if recommendation_data:
return recommendation_data
### populate data exploiting structure of topic tree ###
tree = get_topic_nodes_with_children(parent="root")
######## DYNAMIC ALG #########
##
# ITERATION 1 - grabs all immediate neighbors of each subtopic
##
#array indices for the current topic and subtopic
topic_index = 0
subtopic_index = 0
#for each topic
for topic in tree:
subtopic_index = 0
#for each subtopic add the neighbors at distance 0 and 1 (at dist one has 2 for each)
for subtopic_id in topic['children']:
neighbors_dist_1 = get_neighbors_at_dist_1(topic_index,
subtopic_index, topic)
#add to recommendation_data - distance 0 (itself) + distance 1
recommendation_data[subtopic_id] = {
'related_subtopics': ([subtopic_id + ' 0'] + neighbors_dist_1)
}
subtopic_index += 1
topic_index += 1
##
# ITERATION 2 - grabs all subsequent neighbors of each subtopic via
# Breadth-first search (BFS)
##
#loop through all subtopics currently in recommendation_data dict
for subtopic in recommendation_data:
related = recommendation_data[subtopic][
'related_subtopics'] # list of related subtopics (right now only 2)
other_neighbors = get_subsequent_neighbors(related,
recommendation_data,
subtopic)
recommendation_data[subtopic][
'related_subtopics'] += other_neighbors ##append new neighbors
##
# ITERATION 2.5 - Sort all results by increasing distance and to strip the final
# result of all distance values in recommendation_data (note that there are only 3 possible: 0,1,4).
##
#for each item in recommendation_data
for subtopic in recommendation_data:
at_dist_4 = [] #array to hold the subtopic ids of recs at distance 4
at_dist_lt_4 = [
] #array to hold subtopic ids of recs at distance 0 or 1
#for this item, loop through all recommendations
for recc in recommendation_data[subtopic]['related_subtopics']:
if recc.split(" ")[1] == '4': #if at dist 4, add to the array
at_dist_4.append(recc.split(" ")[0])
else:
at_dist_lt_4.append(recc.split(" ")[0])
sorted_related = at_dist_lt_4 + at_dist_4 #append later items at end of earlier
recommendation_data[subtopic]['related_subtopics'] = sorted_related
return recommendation_data
def generate_recommendation_data():
"""Traverses topic tree to generate a dictionary with related subtopics per subtopic."""
global recommendation_data
if recommendation_data:
return recommendation_data
### populate data exploiting structure of topic tree ###
tree = get_topic_nodes_with_children(parent="root")
######## DYNAMIC ALG #########
##
# ITERATION 1 - grabs all immediate neighbors of each subtopic
##
#array indices for the current topic and subtopic
topic_index = 0
subtopic_index = 0
#for each topic
for topic in tree:
subtopic_index = 0
#for each subtopic add the neighbors at distance 0 and 1 (at dist one has 2 for each)
for subtopic_id in topic['children']:
neighbors_dist_1 = get_neighbors_at_dist_1(topic_index, subtopic_index, topic)
#add to recommendation_data - distance 0 (itself) + distance 1
recommendation_data[ subtopic_id ] = { 'related_subtopics' : ([subtopic_id + ' 0'] + neighbors_dist_1) }
subtopic_index+=1
topic_index+=1
##
# ITERATION 2 - grabs all subsequent neighbors of each subtopic via
# Breadth-first search (BFS)
##
#loop through all subtopics currently in recommendation_data dict
for subtopic in recommendation_data:
related = recommendation_data[subtopic]['related_subtopics'] # list of related subtopics (right now only 2)
other_neighbors = get_subsequent_neighbors(related, recommendation_data, subtopic)
recommendation_data[subtopic]['related_subtopics'] += other_neighbors ##append new neighbors
##
# ITERATION 2.5 - Sort all results by increasing distance and to strip the final
# result of all distance values in recommendation_data (note that there are only 3 possible: 0,1,4).
##
#for each item in recommendation_data
for subtopic in recommendation_data:
at_dist_4 = [] #array to hold the subtopic ids of recs at distance 4
at_dist_lt_4 = [] #array to hold subtopic ids of recs at distance 0 or 1
#for this item, loop through all recommendations
for recc in recommendation_data[subtopic]['related_subtopics']:
if recc.split(" ")[1] == '4': #if at dist 4, add to the array
at_dist_4.append(recc.split(" ")[0])
else:
at_dist_lt_4.append(recc.split(" ")[0])
sorted_related = at_dist_lt_4 + at_dist_4 #append later items at end of earlier
recommendation_data[subtopic]['related_subtopics'] = sorted_related
return recommendation_data
