if args.docs_small_random:
print(f"Downsizing to {args.docs_small_random} docs")
data = {
"docs": random.sample(data["docs"], args.docs_small_random),
"queries": []
}
if args.center:
print("Centering")
data = center_data(data)
if args.std:
print("Dividing by std")
data = zscore_data(data, center=False)
if args.norm:
print("Normalizing")
data = norm_data(data)
if len(data["queries"]) != 0:
print("First query element[:4]", data["queries"][0][:4], "Norm:",
np.linalg.norm(data["queries"][0]))
print("Output shape", data["docs"][0].shape)
# pickler does not support serialization for objects over 4GB
data["docs"] = [x for x in data["docs"]]
data["queries"] = [x for x in data["queries"]]
print("Saving")
save_pickle(args.data_out, data)
sum([len(x) for x in data_relevancy]),
"relevancies total",
np.average([len(x) for x in data_relevancy]),
"relevancies average",
sum([len(x) for x in data_relevancy_articles]),
"articles total",
np.average([len(x) for x in data_relevancy_articles]),
"articles average",
)
save_pickle(
args.data_out, {
"queries": data_query,
"docs": data_docs,
"relevancy": data_relevancy,
"relevancy_articles": data_relevancy_articles,
"docs_articles": data_docs_articles,
"boundaries": {
"train": query_train_max,
"dev": query_dev_max,
"test": query_test_max
}
})
print("data_query[0]:")
print(data_query[0])
print("\ndata_docs[0]:")
print(data_docs[0])
print("\ndata_relevancy[0]:")
print(data_relevancy[0])
print("\ndata_relevancy_articles[0]:")
print(data_relevancy_articles[0])
print("\ndata_docs_articles[0]:")
"relevancy": data["relevancy"],
}
model = SimDistilModel(
args.model,
args.dimension,
batchSize=args.batch_size,
learningRate=args.learning_rate,
dataOrganization=args.data_organization,
merge=not args.not_merge,
similarityModel=args.similarity_model,
similarityGold=args.similarity_gold,
)
# model = SimDistillationFromAutoencoderModel(
# args.model, args.dimension,
# batchSize=args.batch_size,
# learningRate=args.learning_rate,
# )
print(model)
model.trainModel(data, args.epochs, args.post_cn)
model.train(False)
# encode data
with torch.no_grad():
encoded = {
"queries": model.encode1(data["queries"]).cpu().numpy(),
"docs": model.encode2(data["docs"]).cpu().numpy(),
"relevancy": data["relevancy"],
}
report(f"Final:", encoded, data.cpu())
save_pickle(encoded, args.data_out)
def comp_acc(data, data_dev):
val_acc = acc_ip(
data_dev["queries"], data["docs"], data_dev["relevancy"], n=10
)
return val_acc
cur_time = time.time()
val_acc = comp_acc(data, data_dev)
logdata_all = []
logdata_all.append({"acc": val_acc})
traindata_all = []
for i in range(10):
print(f"#### Pass {i+1}", flush=True)
traindata, logitem = filter_step(data, data_dev, cur_time=cur_time)
cur_time = time.time()
val_acc = comp_acc(data, data_dev)
logitem["acc"] = val_acc
logdata_all.append(logitem)
traindata_all.append(traindata)
# continuously overwrite logfile
save_json(args.logfile, logdata_all)
save_pickle(args.traindata, traindata_all)
