utils.py

import torch
import numpy as np
from torch.autograd import Variable
from collections import defaultdict, Counter, OrderedDict

class OrderedCounter(Counter, OrderedDict):
    'Counter that remembers the order elements are first encountered'

    def __repr__(self):
        return '%s(%r)' % (self.__class__.__name__, OrderedDict(self))

    def __reduce__(self):
        return self.__class__, (OrderedDict(self),)

def to_var(x, volatile=False):
    if torch.cuda.is_available():
        x = x.cuda()
    return Variable(x, volatile=volatile)


def idx2word(idx, i2w, pad_idx):

    sent_str = [str()]*len(idx)

    for i, sent in enumerate(idx):

        for word_id in sent:

            if word_id == pad_idx:
                break
            sent_str[i] += i2w[str(word_id)] + " "

        sent_str[i] = sent_str[i].strip()


    return sent_str


def interpolate(start, end, steps):

    interpolation = np.zeros((start.shape[0], steps + 2))

    for dim, (s,e) in enumerate(zip(start,end)):
        interpolation[dim] = np.linspace(s,e,steps+2)

    return interpolation.T

def expierment_name(args, ts):

    exp_name = str()
    exp_name += "BS=%i_"%args.batch_size
    exp_name += "LR={}_".format(args.learning_rate)
    exp_name += "EB=%i_"%args.embedding_size
    exp_name += "%s_"%args.rnn_type.upper()
    exp_name += "HS=%i_"%args.hidden_size
    exp_name += "L=%i_"%args.num_layers
    exp_name += "BI=%i_"%args.bidirectional
    exp_name += "LS=%i_"%args.latent_size
    exp_name += "WD={}_".format(args.word_dropout)
    exp_name += "ANN=%s_"%args.anneal_function.upper()
    exp_name += "K={}_".format(args.k)
    exp_name += "X0=%i_"%args.x0
    exp_name += "TS=%s"%ts

    return exp_name