def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model',
type=str,
defaule='aae',
# All possible method should appear here
choices=['cm', 'svd', 'ae', 'aae', 'mlp'],
help="Specify the model to use [aae]")
parser.add_argument('--epochs',
type=int,
default=20,
help="Specify the number of training epochs [50]")
parser.add_argument('--hidden',
type=int,
default=200,
help="Number of hidden units [100]")
parser.add_argument('--no-title',
action='store_false',
default=True,
dest='use_title',
help="Do not use the playlist titles")
parser.add_argument('--max-items',
type=int,
default=75000,
help="Limit the max number of considered items")
parser.add_argument(
'--vocab-size',
type=int,
default=50000,
help="Limit the max number of distinct condition words")
parser.add_argument('-j',
'--jobs',
type=int,
default=4,
help="Number of jobs for data loading [4].")
parser.add_argument('-o',
'--outfile',
default="submission.csv",
type=str,
help="Write submissions to this path")
parser.add_argument('--use-embedding',
default=False,
action='store_true',
help="Use embedding (SGNS GoogleNews) [false]")
parser.add_argument('--dont-aggregate',
action='store_false',
dest='aggregate',
default=True,
help="Aggregate track metadata as side info input")
parser.add_argument('--debug',
action='store_true',
default=False,
help="Activate debug mode, run only on small sample")
parser.add_argument(
'-x',
'--exclude',
type=argparse.FileType('r'),
default=None,
help="Path to file with slice filenames to exclude for training")
parser.add_argument(
'--dev',
type=str,
default=None,
help='Path to dev set, use in combination with (-x, --exclude)')
parser.add_argument('--no-idf',
action='store_false',
default=True,
dest='use_idf',
help="Do **not** use idf re-weighting")
parser.add_argument('--lr',
type=float,
default=0.001,
help="Initial learning rate [0.001]")
parser.add_argument('--code',
type=int,
default=100,
help="Code dimension [50]")
args = parser.parse_args()
# Either exclude and dev set, or no exclude and test set
assert (args.dev is None) == (args.exclude is None)
if args.dev is not None:
print("Making submission for dev set:", args.dev)
assert os.path.isfile(args.dev)
# Dump args into submission file
if os.path.exists(args.outfile) and \
input("Path '{}' exists. Overwrite? [y/N]"
.format(args.outfile)) != 'y':
exit(-1)
with open(args.outfile, 'w') as out:
print('#', args, file=out)
if args.use_embedding:
print("Loading embedding:", W2V_PATH)
vectors = KeyedVectors.load_word2vec_format(W2V_PATH,
binary=W2V_IS_BINARY)
else:
vectors = None
# Create the model as specified by command line args
# Count-based never uses title
# Decoding recommender always uses title
tfidf_params = {'max_features': args.vocab_size, 'use_idf': args.use_idf}
model = {
'cm':
Countbased(),
'svd':
SVDRecommender(use_title=args.use_title),
'ae':
AAERecommender(use_title=args.use_title,
adversarial=False,
n_hidden=args.hidden,
n_code=args.code,
n_epochs=args.epochs,
embedding=vectors,
lr=args.lr,
tfidf_params=tfidf_params),
'aae':
AAERecommender(
use_title=args.use_title,
adversarial=True,
n_hidden=args.hidden,
n_code=args.code,
n_epochs=args.epochs,
gen_lr=args.lr,
reg_lr=args.lr, # same gen and reg lrs
embedding=vectors,
tfidf_params=tfidf_params),
'mlp':
DecodingRecommender(n_epochs=args.epochs,
n_hidden=args.hidden,
embedding=vectors,
tfidf_params=tfidf_params)
}[args.model]
track_attrs = TRACK_INFO if args.aggregate else None
if args.exclude is not None:
# Dev set case, exclude dev set data
exclude = [line.strip() for line in args.exclude]
else:
# Real submission case, do not exclude any training data
exclude = None
# = Training =
print("Loading data from {} using {} jobs".format(DATA_PATH, args.jobs))
playlists = playlists_from_slices(DATA_PATH,
n_jobs=args.jobs,
debug=args.debug,
without=exclude)
print("Unpacking playlists")
train_set = Bags(*unpack_playlists(playlists, aggregate=track_attrs))
print("Building vocabulary of {} most frequent items".format(
args.max_items))
vocab, __counts = train_set.build_vocab(max_features=args.max_items,
apply=False)
train_set = train_set.apply_vocab(vocab)
print("Training set:", train_set, sep='\n')
print("Training for {} epochs".format(args.epochs))
try:
model.train(train_set)
except KeyboardInterrupt:
print("Training interrupted by keyboard, pass.")
# Not required anymore
del train_set
# = Predictions =
if args.dev is not None:
print("Loading and unpacking DEV set")
data, index2playlist, side_info = unpack_playlists(
load(args.dev), aggregate=track_attrs)
else:
print("Loading and unpacking test set")
data, index2playlist, side_info = unpack_playlists(
load(TEST_PATH), aggregate=track_attrs)
test_set = Bags(data, index2playlist, side_info)
# Apply same vocabulary as in training
test_set = test_set.apply_vocab(vocab)
print("Test set:", test_set, sep='\n')
pred = model.predict(test_set)
if sp.issparse(pred):
pred = pred.toarray()
else:
pred = np.asarray(pred)
print("Scaling and removing non-missing items")
pred = remove_non_missing(pred, test_set.tocsr(), copy=False)
index2trackid = {v: k for k, v in vocab.items()}
print("Making submission:", args.outfile)
make_submission(pred, index2playlist, index2trackid, outfile=args.outfile)
print("Success.")
print("Make sure to verify the submission format via", VERIFY_SCRIPT)
if dataset == "pubmed" or dataset == "acm" or dataset == "dblp":
x_dim = "References"
elif dataset == "mpd":
x_dim = "Tracks"
else:
x_dim = "Labels"
print("Plotting papers' distribution by number of their {} on file".format(
x_dim.lower()))
y_dim = "Papers" if dataset != "mpd" else "Playlists"
# show the y-value for the bar at x=mark_x_set in the plot
plot(set_cnts, dataset, x_dim, y_dim, mark_x_set)
bags = bags.build_vocab(apply=True)
csr = bags.tocsr()
print("N ratings:", csr.sum())
column_sums = csr.sum(0).flatten()
row_sums = csr.sum(1).flatten()
print(column_sums.shape)
print(row_sums.shape)
FMT = "N={}, Min={}, Max={} Median={}, Mean={}, Std={}"
print("Items per document")
print(FMT.format(*compute_stats(row_sums)))
print("Documents per item")
print(FMT.format(*compute_stats(column_sums)))