def main(args):
progress = WorkSplitter()
table_path = 'tables/'
test = load_numpy(path=args.path, name=args.dataset + args.test)
df = pd.DataFrame({
'model': [
'AutoRec', 'AutoRec', 'AutoRec', 'InitFeatureEmbedAE',
'InitFeatureEmbedAE', 'InitFeatureEmbedAE', 'AlterFeatureEmbedAE',
'ConcatFeatureEmbedAE', 'UnionSampleAE', 'WRSampleAE',
'BatchSampleAE', 'BridgeLabelAE', 'RefineLabelAE', 'DeepAutoRec',
'DeepAutoRec', 'SoftLabelAE', 'HintAE'
],
'way': [
None, 'unif', 'combine', 'user', 'item', 'both', None, None, None,
None, None, None, None, None, 'unif', None, None
]
})
progress.subsection("Reproduce")
frame = []
for idx, row in df.iterrows():
row = row.to_dict()
row['metric'] = ['NLL', 'AUC']
row['rank'] = 200
result = execute(test, row, folder=args.model_folder + args.dataset)
frame.append(result)
results = pd.concat(frame)
save_dataframe_csv(results, table_path, args.name)
def main(args):
progress = WorkSplitter()
table_path = 'tables/'
test = load_numpy(path=args.path, name=args.dataset + args.test)
df = pd.DataFrame({
'model': [
"BiasedMF", "BiasedMF", "BiasedMF", "PropensityMF",
"InitFeatureEmbedMF", "InitFeatureEmbedMF", "InitFeatureEmbedMF",
"AlterFeatureEmbedMF", "ConcatFeatureEmbedMF", "CausalSampleMF",
"UnionSampleMF", "WRSampleMF", "BatchSampleMF", "BridgeLabelMF",
"RefineLabelMF"
],
'way': [
None, "unif", "combine", None, "user", "item", "both", None, None,
None, None, None, None, None, None
]
})
progress.subsection("Reproduce")
frame = []
for idx, row in df.iterrows():
row = row.to_dict()
row['metric'] = ['NLL', 'AUC']
row['rank'] = 10
result = execute(test, row, folder=args.model_folder + args.dataset)
frame.append(result)
results = pd.concat(frame)
save_dataframe_csv(results, table_path, args.name)
def main(args):
table_path = load_yaml('config/global.yml', key='path')['tables']
df = find_best_hyperparameters(table_path + args.problem, 'NDCG')
R_train = load_numpy(path=args.path, name=args.train)
R_valid = load_numpy(path=args.path, name=args.valid)
R_test = load_numpy(path=args.path, name=args.test)
R_train = R_train + R_valid
topK = [5, 10, 15, 20, 50]
frame = []
for idx, row in df.iterrows():
start = timeit.default_timer()
row = row.to_dict()
row['metric'] = ['R-Precision', 'NDCG', 'Precision', 'Recall', "MAP"]
row['topK'] = topK
result = execute(R_train,
R_test,
row,
models[row['model']],
gpu_on=args.gpu)
stop = timeit.default_timer()
print('Time: ', stop - start)
frame.append(result)
results = pd.concat(frame)
save_dataframe_csv(results, table_path, args.name)
def main(args):
progress = WorkSplitter()
table_path = 'tables/'
test = load_numpy(path=args.path, name=args.dataset + args.test)
df = pd.DataFrame({
'model': [
'RestrictedBatchSampleMF', 'RestrictedBatchSampleMF',
'RestrictedBatchSampleMF', 'RestrictedBatchSampleMF',
'RestrictedBatchSampleMF'
],
'way': [None, 'head_users', 'tail_users', 'head_items', 'tail_items']
})
progress.subsection("Gain Analysis")
frame = []
for idx, row in df.iterrows():
row = row.to_dict()
row['metric'] = ['NLL', 'AUC']
row['rank'] = 10
result = execute(test, row, folder=args.model_folder + args.dataset)
frame.append(result)
results = pd.concat(frame)
save_dataframe_csv(results, table_path, args.name)
def main(args):
table_path = load_yaml('config/global.yml', key='path')['tables']
df = find_best_hyperparameters(table_path+args.tuning_result_path, 'MAP@10')
R_train = load_numpy(path=args.path, name=args.train)
R_valid = load_numpy(path=args.path, name=args.valid)
R_test = load_numpy(path=args.path, name=args.test)
R_train = R_train + R_valid
# R_train[(R_train <= 3).nonzero()] = 0
# R_test[(R_test <= 3).nonzero()] = 0
# R_train[(R_train > 3).nonzero()] = 1
# R_test[(R_test > 3).nonzero()] = 1
# import ipdb; ipdb.set_trace()
topK = [5, 10, 15, 20, 50]
frame = []
for idx, row in df.iterrows():
start = timeit.default_timer()
row = row.to_dict()
row['metric'] = ['R-Precision', 'NDCG', 'Precision', 'Recall', "MAP"]
row['topK'] = topK
result = execute(R_train, R_test, row, models[row['model']])
stop = timeit.default_timer()
print('Time: ', stop - start)
frame.append(result)
results = pd.concat(frame)
save_dataframe_csv(results, table_path, args.name)