-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathpre_llm_tester.py
More file actions
169 lines (144 loc) · 5.96 KB
/
pre_llm_tester.py
File metadata and controls
169 lines (144 loc) · 5.96 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
import os,csv,json
import routes as G
import numpy as np
import getter as UG
import metrics as UM
import pre_llm as PL
cur_seed = 5
# condition on 10, generate 100, should be at least 20 songs length
# 500 samples
csv_dir = os.path.join(__file__.split(os.sep)[0], 'data')
res_dir = os.path.join(__file__.split(os.sep)[0], 'res')
#playlist_csvs = list(os.listdir(csv_dir))
playlist_csvs = ['num_splits/num_tracks-50.csv']
num_csvs = len(playlist_csvs)
rec_cols = ['artist_name', 'track_name', 'id']
rec_cols2 = rec_cols + ['dist']
rng = np.random.default_rng(cur_seed)
def sample_playlists(_rng, min_length = 20, sample_num=500, max_tries=10):
cur_num = 0
pl_pid = set()
playlists = []
csv_path = os.path.join(csv_dir, playlist_csvs[0])
with open(csv_path, 'r') as f:
csvr = csv.DictReader(f)
cur_playlists = [x for x in csvr]
num_playlists = len(cur_playlists)
while cur_num < sample_num:
pl_len = 0
cur_tries = 0
while pl_len < min_length and cur_tries < max_tries:
pl_idx = rng.integers(num_playlists)
cur_pl = cur_playlists[pl_idx]
cur_len = int(cur_pl['num_splits/num_tracks'])
cur_id = int(cur_pl['pid'])
if cur_len < min_length or cur_id in pl_pid:
max_tries += 1
else:
pl_pid.add(cur_id)
cur_dict = {'file': playlist_csvs[0], 'idx': pl_idx, 'num_splits/num_tracks': cur_len, 'pid': cur_id}
playlists.append(cur_dict)
cur_num += 1
pl_len = cur_len
return playlists
def get_playlists(sample_num=500):
cur_playlists = None
csv_path = os.path.join(csv_dir, playlist_csvs[0])
with open(csv_path, 'r') as f:
csvr = csv.DictReader(f)
cur_playlists = [x for x in csvr]
return cur_playlists[:sample_num]
if __name__ == "__main__":
"""
cur_header = ['file', 'idx', 'num_splits/num_tracks', 'pid']
res = sample_playlists(rng, sample_num=500)
with open(os.path.join(res_dir, 'pre_llm_test_playlists.csv'), 'w') as f:
csvw = csv.DictWriter(f, fieldnames=cur_header)
csvw.writeheader()
for playlist in res:
csvw.writerow(playlist)
print(res)
"""
weights = {'danceability':1.0,
'energy':1.0,
'key':1.0,
'loudness':1.0,
'mode':1.0,
'speechiness':1.0,
'acousticness':1.0,
'instrumentalness':1.0,
'liveness':1.0,
'valence':1.0,
'tempo': 1.0}
weights = {'danceability': 0.75,
'energy':1.0,
'key':0.0,
'loudness':0.0,
'mode':0.5,
'speechiness':1.0,
'acousticness':1.5,
'instrumentalness':1.5,
'liveness':0.25,
'valence':1.0,
'tempo': 2.5}
#weights = None
cond_num = 10
gen_num = 100
sample_num = 1000
res_dir2 = os.path.join(__file__.split(os.sep)[0], 'res', f'prellm_test0-{cond_num}_{gen_num}_{sample_num}')
pl = get_playlists(sample_num = sample_num)
num_runs = 1000
mheader = ['expr_idx', 'pl_idx', 'r_prec', 'dcg', 'idcg', 'ndcg', 'clicks']
r2_path = os.path.join(res_dir2, f'metrics-{cond_num}_{gen_num}_{sample_num}.csv')
w_path = os.path.join(res_dir2, f'weights-{cond_num}_{gen_num}_{sample_num}.csv')
#weights = [1.0, 0.8,0.6,0.4,0.2]
if os.path.exists(res_dir2) == False:
os.mkdir(res_dir2)
if weights != None:
if 'dict' in type(weights).__name__:
with open(w_path, 'w') as f:
csvw = csv.DictWriter(f, fieldnames = UG.comp_feat)
csvw.writeheader()
csvw.writerow({x:y for (x,y) in weights.items() if x in UG.comp_feat})
else:
wlen = len(weights)
fieldnames = [f'w{i}' for i in range(1, wlen+1)]
wzip = {y:x for (x,y) in zip(weights,fieldnames)}
with open(w_path, 'w') as f:
csvw = csv.DictWriter(f, fieldnames = fieldnames)
csvw.writeheader()
csvw.writerow(wzip)
runs = []
cnx, cursor = UG.connect_to_nct()
all_song_df, all_song_feat, txs = PL.load_all_songs(cnx, normalize=True, pca=0,seed=cur_seed)
for pl_i, pl_dict in enumerate(pl):
if pl_i < num_runs:
#if True:
print(pl_i)
print('-----')
pl_c = UG.get_playlist(pl_dict['file'], int(pl_dict['idx']))
pl_songs, res_songs, res_cos_sim = PL.get_closest_songs_to_playlist(cnx, pl_c, all_song_feat, all_song_df, metric='l1', mask=cond_num, k=gen_num, weights = weights, tx=txs)
truth_ids = pl_songs['id'].to_numpy()[cond_num:]
retr_ids = res_songs['id'].to_numpy()
r_prec = UM.r_precision(truth_ids, retr_ids)
dcg = UM.dcg(truth_ids, retr_ids)
idcg = UM.idcg(truth_ids, retr_ids)
ndcg = UM.ndcg(truth_ids, retr_ids)
clicks = UM.rec_songs_clicks(truth_ids, retr_ids, max_clicks=99998)
g_path = os.path.join(res_dir2, f'ground_truth-{cond_num}_{gen_num}_{sample_num}-{pl_i}.csv')
r_path = os.path.join(res_dir2, f'retrieved-{cond_num}_{gen_num}_{sample_num}-{pl_i}.csv')
pl_songs[cond_num:][rec_cols].to_csv(g_path)
res_songs = res_songs.assign(dist = res_cos_sim)
res_songs[rec_cols2].to_csv(r_path)
mdict = {'expr_idx': pl_i, 'pl_idx': int(pl_dict['idx']), 'r_prec': r_prec, 'dcg': dcg, 'idcg': idcg, 'ndcg': ndcg, 'clicks': clicks}
runs.append(mdict)
print('r_prec', r_prec)
print('dcg', dcg)
print('idcg', idcg)
print('ndcg', ndcg)
print('clicks', clicks)
with open(r2_path, 'w') as f:
csvw = csv.DictWriter(f, fieldnames = mheader)
csvw.writeheader()
for run in runs:
csvw.writerow(run)