args.classifier_dropout_rate = .0
args.checkpoint_path = "./../outputs/{}/{}_Adam_X/{}_bestModel.pwf".format(
encoder, encoder, encoder)
model = MLP(args)
model.load_state_dict(torch.load(args.checkpoint_path,
map_location=device))
model.to(device)
model.eval()
for min_len, max_len in zip([0, 10, 15, 20], [10, 15, 20, 100]):
dataset = Dataset(args)
dataset.filter_based_on_length(data_set, min_len, max_len)
accu = evaluate(dataset, model, args.batch_size, args.data_set)
P[encoder].append(accu)
print(encoder, min_len, max_len, accu)
sns.set()
plt.figure(1)
for encoder in ["BoW", "LSTM", "biLSTM", "biLSTM_maxp"]:
plt.plot(P[encoder], label=encoder)
plt.legend()
plt.xlabel("mean number of tokens")
plt.ylabel("accuracy")
plt.xticks(np.arange(4), ("0-10", "10-15", "15-20", "20-max"))
