utils.py

import csv
import torch
from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report, confusion_matrix
import shutil
import numpy as np



class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


class Logger(object):

    def __init__(self, path, header,opening_mode='w'):
        self.log_file = open(path, opening_mode)
        self.logger = csv.writer(self.log_file, delimiter='\t')
        if opening_mode =='w':
          self.logger.writerow(header)
        self.header = header

    def __del(self):
        self.log_file.close()

    def log(self, values):
        write_values = []
        for col in self.header:
            assert col in values
            write_values.append(values[col])

        self.logger.writerow(write_values)
        self.log_file.flush()



class Queue:
    # Constructor creates a list
    def __init__(self, max_size, n_classes):
        self.queue = list(np.zeros((max_size, n_classes), dtype=float).tolist())
        self.max_size = max_size
        self.median = None
        self.ma = None
        self.ewma = None

    # Adding elements to queue
    def enqueue(self, data):
        self.queue.insert(0, data)
        self.median = self._median()
        self.ma = self._ma()
        self.ewma = self._ewma()
        return True

    # Removing the last element from the queue
    def dequeue(self):
        if len(self.queue) > 0:
            return self.queue.pop()
        return ("Queue Empty!")

    # Getting the size of the queue
    def size(self):
        return len(self.queue)

    # printing the elements of the queue
    def printQueue(self):
        return self.queue

    # Average
    def _ma(self):
        return np.array(self.queue[:self.max_size]).mean(axis=0)

    # Median
    def _median(self):
        return np.median(np.array(self.queue[:self.max_size]), axis=0)

    # Exponential average
    def _ewma(self):
        weights = np.exp(np.linspace(-1., 0., self.max_size))
        weights /= weights.sum()
        average = weights.reshape(1, self.max_size).dot(np.array(self.queue[:self.max_size]))
        return average.reshape(average.shape[1], )


def LevenshteinDistance(a, b):
    # This is a straightforward implementation of a well-known algorithm, and thus
    # probably shouldn't be covered by copyright to begin with. But in case it is,
    # the author (Magnus Lie Hetland) has, to the extent possible under law,
    # dedicated all copyright and related and neighboring rights to this software
    # to the public domain worldwide, by distributing it under the CC0 license,
    # version 1.0. This software is distributed without any warranty. For more
    # information, see <http://creativecommons.org/publicdomain/zero/1.0>
    "Calculates the Levenshtein distance between a and b."
    n, m = len(a), len(b)
    if n > m:
        # Make sure n <= m, to use O(min(n,m)) space
        a, b = b, a
        n, m = m, n

    current = range(n + 1)
    for i in range(1, m + 1):
        previous, current = current, [i] + [0] * n
        for j in range(1, n + 1):
            add, delete = previous[j] + 1, current[j - 1] + 1
            change = previous[j - 1]
            if a[j - 1] != b[i - 1]:
                change = change + 1
            current[j] = min(add, delete, change)
    if current[n]<0:
        return 0
    else:
        return current[n]


def load_value_file(file_path):
    with open(file_path, 'r') as input_file:
        value = float(input_file.read().rstrip('\n\r'))

    return value


def calculate_accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


def calculate_precision(outputs, targets):

    _, pred = outputs.topk(1, 1, True)
    pred = pred.t()
    return  precision_score(targets.view(-1), pred.view(-1), average = 'macro')


def calculate_recall(outputs, targets):

    _, pred = outputs.topk(1, 1, True)
    pred = pred.t()
    return  recall_score(targets.view(-1), pred.view(-1), average = 'macro')


def save_checkpoint(state, is_best, opt):
    torch.save(state, '%s/%s_checkpoint.pth' % (opt.result_path, opt.store_name))
    if is_best:
        shutil.copyfile('%s/%s_checkpoint.pth' % (opt.result_path, opt.store_name),'%s/%s_best.pth' % (opt.result_path, opt.store_name))


def adjust_learning_rate(optimizer, epoch, opt):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr_new = opt.learning_rate * (0.1 ** (sum(epoch >= np.array(opt.lr_steps))))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr_new
        #param_group['lr'] = opt.learning_rate