Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
"""
# -----------------------------------------------------------------------------
def calculate_recommendation(
tags,
time_delta = 3, # how recent papers are we recommending? in days
):
# a bit of preprocessing
x, pids = features['x'], features['pids']
n, d = x.shape
ptoi, itop = {}, {}
for i, p in enumerate(pids):
ptoi[p] = i
itop[i] = p
# loop over all the tags
all_pids, all_scores = {}, {}
for tag, pids in tags.items():
if len(pids) == 0:
continue
# construct the positive set for this tag
y = np.zeros(n, dtype=np.float32)
for pid in pids:
y[ptoi[pid]] = 1.0
# classify
clf = svm.LinearSVC(class_weight='balanced', verbose=False, max_iter=10000, tol=1e-6, C=0.01)
clf.fit(x, y)
s = clf.decision_function(x)
sortix = np.argsort(-s)
pids = [itop[ix] for ix in sortix]
scores = [100*float(s[ix]) for ix in sortix]
# filter by time to only recent papers
deltat = time_delta*60*60*24 # allowed time delta in seconds
keep = [i for i,pid in enumerate(pids) if (tnow - metas[pid]['_time']) < deltat]
pids, scores = [pids[i] for i in keep], [scores[i] for i in keep]
# finally exclude the papers we already have tagged
have = set().union(*tags.values())
keep = [i for i,pid in enumerate(pids) if pid not in have]
pids, scores = [pids[i] for i in keep], [scores[i] for i in keep]
# store results
all_pids[tag] = pids
all_scores[tag] = scores
return all_pids, all_scores
# -----------------------------------------------------------------------------
def render_recommendations(user, tags, tag_pids, tag_scores):
# render the paper recommendations into the html template
# first we are going to merge all of the papers / scores together using a MAX
max_score = {}
max_source_tag = {}
for tag in tag_pids:
for pid, score in zip(tag_pids[tag], tag_scores[tag]):
max_score[pid] = max(max_score.get(pid, -99999), score) # lol
if max_score[pid] == score:
max_source_tag[pid] = tag
# now we have a dict of pid -> max score. sort by score
max_score_list = sorted(max_score.items(), key=lambda x: x[1], reverse=True)
pids, scores = zip(*max_score_list)
# now render the html for each individual recommendation
parts = []
n = min(len(scores), args.num_recommendations)
for score, pid in zip(scores[:n], pids[:n]):
p = pdb[pid]
authors = ', '.join(a['name'] for a in p['authors'])
# crop the abstract
summary = p['summary']
summary = summary[:min(500, len(summary))]
if len(summary) == 500:
summary += '...'
# create the url that will feature this paper on top and also show the most similar papers
url = 'https://arxiv-sanity-lite.com/?rank=pid&pid=' + pid
parts.append(
"""
<tr>
<td valign="top"><div class="s">%.2f</div></td>
<td>
<a href="%s">%s</a> <div class="f">(%s)</div>
<div class="a">%s</div>
<div class="u">%s</div>
</td>
</tr>
""" % (score, url, p['title'], max_source_tag[pid], authors, summary)
)
# render the final html
out = template