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FIX ensemble RAM usage #59

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FIX ensemble RAM usage #59

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45 changes: 22 additions & 23 deletions expertise/models/multifacet_recommender/ensemble.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,32 +62,31 @@ def embed_publications(self, specter_publications_path=None, mfr_publications_pa
def all_scores(self, specter_publications_path=None, mfr_publications_path=None,
specter_submissions_path=None, mfr_submissions_path=None,
scores_path=None):
preliminary_scores_map = {}
print("SPECTER:")
specter_scores_path = os.path.join(self.specter_predictor.work_dir, "specter_affinity.csv")
self.specter_predictor.all_scores(specter_publications_path, specter_submissions_path, specter_scores_path)
self.specter_predictor.all_scores(specter_publications_path, specter_submissions_path, specter_scores_path,
ensemble_mode=True, scores_map=preliminary_scores_map,
score_weight=self.merge_alpha)
print("SPECTER: Scores computed")
print("MFR:")
mfr_scores_path = os.path.join(self.mfr_predictor.work_dir, "mfr_affinity.csv")
self.mfr_predictor.all_scores(mfr_publications_path, mfr_submissions_path, mfr_scores_path)

# Convert preliminary scores of SPECTER to a dictionary
csv_scores = []
self.preliminary_scores = []
specter_preliminary_scores_map = {}
for entry in self.specter_predictor.preliminary_scores:
specter_preliminary_scores_map[(entry[0], entry[1])] = entry[2]

for entry in self.mfr_predictor.preliminary_scores:
new_score = specter_preliminary_scores_map[(entry[0], entry[1])] * self.merge_alpha + \
entry[2] * (1 - self.merge_alpha)
csv_line = '{note_id},{reviewer},{score}'.format(note_id=entry[0], reviewer=entry[1],
score=new_score)
csv_scores.append(csv_line)
self.preliminary_scores.append((entry[0], entry[1], new_score))

self.mfr_predictor.all_scores(mfr_publications_path, mfr_submissions_path, mfr_scores_path,
ensemble_mode=True, scores_map=preliminary_scores_map,
score_weight=(1 - self.merge_alpha))
print("MFR: Scores computed")

self.preliminary_scores = [(score_key[0], score_key[1], score_val)
for score_key, score_val in preliminary_scores_map.items()]
del preliminary_scores_map
if scores_path:
print("Writing scores")
with open(scores_path, 'w') as f:
for csv_line in csv_scores:
f.write(csv_line + '\n')
for entry in self.preliminary_scores:
csv_line = '{note_id},{reviewer},{score}\n'.format(note_id=entry[0], reviewer=entry[1],
score=entry[2])
f.write(csv_line)
print("Scores written")

return self.preliminary_scores

Expand Down Expand Up @@ -115,14 +114,15 @@ def sparse_scores(self, scores_path=None):

print('Sorting...')
self.preliminary_scores.sort(key=lambda x: (x[0], x[2]), reverse=True)
print('preliminary', self.preliminary_scores, len(self.preliminary_scores))
print('Sort 1 complete')
all_scores = set()
# They are first sorted by note_id
all_scores = self._sparse_scores_helper(all_scores, 0)

# Sort by profile_id
print('Sorting...')
self.preliminary_scores.sort(key=lambda x: (x[1], x[2]), reverse=True)
print('Sort 2 complete')
all_scores = self._sparse_scores_helper(all_scores, 1)

print('Final Sort...')
Expand All @@ -131,6 +131,5 @@ def sparse_scores(self, scores_path=None):
with open(scores_path, 'w') as f:
for note_id, profile_id, score in all_scores:
f.write('{0},{1},{2}\n'.format(note_id, profile_id, score))

print('ALL SCORES', all_scores)
print('Sparse scores written')
return all_scores
41 changes: 26 additions & 15 deletions expertise/models/multifacet_recommender/multifacet_recommender.py
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Original file line number Diff line number Diff line change
Expand Up @@ -823,7 +823,8 @@ def embed_publications(self, publications_path=None):
print('NOTE: Archive publication embeddings are not explicitly computed. '
'Reviewer embeddings are computed and consumed with submission embeddings during score calculation')

def all_scores(self, publications_path=None, submissions_path=None, scores_path=None):
def all_scores(self, publications_path=None, submissions_path=None, scores_path=None,
ensemble_mode=False, scores_map=None, score_weight=1.0):
print("Loading data...")
seed_all_randomness(self.seed, self.use_cuda)
idx2word_freq, user_idx2word_freq, tag_idx2word_freq, dataloader_train_arr, _,\
Expand Down Expand Up @@ -897,29 +898,39 @@ def all_scores(self, publications_path=None, submissions_path=None, scores_path=

# Convert distances to scores
num_special_token = 3
csv_scores = []
self.preliminary_scores = []
if not ensemble_mode:
self.preliminary_scores = []
else:
assert scores_map is not None
if scores_path:
fout = open(scores_path, 'w')
else:
fout = None
dist_arr = np.loadtxt(os.path.join(self.work_dir, "reviewer_submission_dist_arr.txt"))
assert dist_arr.shape[0] == len(self.submission_paper_ids_list)
assert dist_arr.shape[1] == len(user_idx2word_freq)
sim_arr = 1. - dist_arr
for j in range(num_special_token, len(user_idx2word_freq)):
user_raw = user_idx2word_freq[j][0]
user_name = user_raw
if '|' in user_raw:
suffix_start = user_raw.index('|')
user_name = user_raw[:suffix_start]
for i, paper_id in enumerate(self.submission_paper_ids_list):
csv_line = '{note_id},{reviewer},{score}'.format(note_id=paper_id, reviewer=user_name,
score=sim_arr[i, j])
csv_scores.append(csv_line)
self.preliminary_scores.append((paper_id, user_name, sim_arr[i, j]))

if scores_path:
with open(scores_path, 'w') as f:
for csv_line in csv_scores:
f.write(csv_line + '\n')
if fout is not None:
csv_line = '{note_id},{reviewer},{score}\n'.format(note_id=paper_id, reviewer=user_name,
score=1. - dist_arr[i, j])
fout.write(csv_line)
if ensemble_mode:
scores_map[(paper_id, user_name)] = scores_map.get((paper_id, user_name), 0.0) + \
score_weight * (1. - dist_arr[i, j])
else:
self.preliminary_scores.append((paper_id, user_name, (1. - dist_arr[i, j])))
if fout is not None:
fout.flush()
fout.close()

if ensemble_mode:
return
return self.preliminary_scores

def _sparse_scores_helper(self, all_scores, id_index):
Expand All @@ -946,14 +957,15 @@ def sparse_scores(self, scores_path=None):

print('Sorting...')
self.preliminary_scores.sort(key=lambda x: (x[0], x[2]), reverse=True)
print('preliminary', self.preliminary_scores, len(self.preliminary_scores))
print('Sort 1 complete')
all_scores = set()
# They are first sorted by note_id
all_scores = self._sparse_scores_helper(all_scores, 0)

# Sort by profile_id
print('Sorting...')
self.preliminary_scores.sort(key=lambda x: (x[1], x[2]), reverse=True)
print('Sort 2 complete')
all_scores = self._sparse_scores_helper(all_scores, 1)

print('Final Sort...')
Expand All @@ -963,5 +975,4 @@ def sparse_scores(self, scores_path=None):
for note_id, profile_id, score in all_scores:
f.write('{0},{1},{2}\n'.format(note_id, profile_id, score))

print('ALL SCORES', all_scores)
return all_scores
40 changes: 26 additions & 14 deletions expertise/models/multifacet_recommender/specter.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -281,7 +281,8 @@ def embed_publications(self, publications_path=None):
False)
manager.run()

def all_scores(self, publications_path=None, submissions_path=None, scores_path=None):
def all_scores(self, publications_path=None, submissions_path=None, scores_path=None,
ensemble_mode=False, scores_map=None, score_weight=1.0):
def load_emb_file(emb_file):
paper_emb_size_default = 768
id_list = []
Expand Down Expand Up @@ -323,8 +324,14 @@ def load_emb_file(emb_file):
for i in range(paper_num_test):
p2p_aff[i, :] = torch.sum(paper_emb_test[i, :].unsqueeze(dim=0) * paper_emb_train, dim=1)

csv_scores = []
self.preliminary_scores = []
if not ensemble_mode:
self.preliminary_scores = []
else:
assert scores_map is not None
if scores_path:
fout = open(scores_path, 'w')
else:
fout = None
for reviewer_id, train_note_id_list in self.pub_author_ids_to_note_id.items():
if len(train_note_id_list) == 0:
continue
Expand All @@ -339,16 +346,21 @@ def load_emb_file(emb_file):
elif self.max_score:
all_paper_aff = train_paper_aff_j.max(dim=1)[0]
for j in range(paper_num_test):
csv_line = '{note_id},{reviewer},{score}'.format(note_id=test_id_list[j], reviewer=reviewer_id,
score=all_paper_aff[j].item())
csv_scores.append(csv_line)
self.preliminary_scores.append((test_id_list[j], reviewer_id, all_paper_aff[j].item()))

if scores_path:
with open(scores_path, 'w') as f:
for csv_line in csv_scores:
f.write(csv_line + '\n')
if fout is not None:
csv_line = '{note_id},{reviewer},{score}\n'.format(note_id=test_id_list[j], reviewer=reviewer_id,
score=all_paper_aff[j].item())
fout.write(csv_line)
if ensemble_mode:
scores_map[(test_id_list[j], reviewer_id)] = scores_map.get((test_id_list[j], reviewer_id), 0.0) + \
score_weight * all_paper_aff[j].item()
else:
self.preliminary_scores.append((test_id_list[j], reviewer_id, all_paper_aff[j].item()))
if fout is not None:
fout.flush()
fout.close()

if ensemble_mode:
return
return self.preliminary_scores

def _sparse_scores_helper(self, all_scores, id_index):
Expand All @@ -375,14 +387,15 @@ def sparse_scores(self, scores_path=None):

print('Sorting...')
self.preliminary_scores.sort(key=lambda x: (x[0], x[2]), reverse=True)
print('preliminary', self.preliminary_scores, len(self.preliminary_scores))
print('Sort 1 complete')
all_scores = set()
# They are first sorted by note_id
all_scores = self._sparse_scores_helper(all_scores, 0)

# Sort by profile_id
print('Sorting...')
self.preliminary_scores.sort(key=lambda x: (x[1], x[2]), reverse=True)
print('Sort 2 complete')
all_scores = self._sparse_scores_helper(all_scores, 1)

print('Final Sort...')
Expand All @@ -392,5 +405,4 @@ def sparse_scores(self, scores_path=None):
for note_id, profile_id, score in all_scores:
f.write('{0},{1},{2}\n'.format(note_id, profile_id, score))

print('ALL SCORES', all_scores)
return all_scores