nmt_utils.py

import numpy as np
from faker import Faker
import random
from tqdm import tqdm
from babel.dates import format_date
from keras.utils import to_categorical
import keras.backend as K
import matplotlib.pyplot as plt

fake = Faker()
fake.seed(12345)
random.seed(12345)

# Define format of the data we would like to generate
FORMATS = ['short',
           'medium',
           'long',
           'full',
           'full',
           'full',
           'full',
           'full',
           'full',
           'full',
           'full',
           'full',
           'full',
           'd MMM YYY', 
           'd MMMM YYY',
           'dd MMM YYY',
           'd MMM, YYY',
           'd MMMM, YYY',
           'dd, MMM YYY',
           'd MM YY',
           'd MMMM YYY',
           'MMMM d YYY',
           'MMMM d, YYY',
           'dd.MM.YY']

# change this if you want it to work with another language
LOCALES = ['en_US']

def load_date():
    """
        Loads some fake dates 
        :returns: tuple containing human readable string, machine readable string, and date object
    """
    dt = fake.date_object()

    try:
        human_readable = format_date(dt, format=random.choice(FORMATS),  locale='en_US') # locale=random.choice(LOCALES))
        human_readable = human_readable.lower()
        human_readable = human_readable.replace(',','')
        machine_readable = dt.isoformat()

    except AttributeError as e:
        return None, None, None

    return human_readable, machine_readable, dt

def load_dataset(m):
    """
        Loads a dataset with m examples and vocabularies
        :m: the number of examples to generate
    """

    human_vocab = set()
    machine_vocab = set()
    dataset = []
    Tx = 30


    for i in tqdm(range(m)):
        h, m, _ = load_date()
        if h is not None:
            dataset.append((h, m))
            human_vocab.update(tuple(h))
            machine_vocab.update(tuple(m))

    human = dict(zip(sorted(human_vocab) + ['<unk>', '<pad>'], 
                     list(range(len(human_vocab) + 2))))
    inv_machine = dict(enumerate(sorted(machine_vocab)))
    machine = {v:k for k,v in inv_machine.items()}

    return dataset, human, machine, inv_machine

def preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty):

    X, Y = zip(*dataset)

    X = np.array([string_to_int(i, Tx, human_vocab) for i in X])
    Y = [string_to_int(t, Ty, machine_vocab) for t in Y]

    Xoh = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), X)))
    Yoh = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y)))

    return X, np.array(Y), Xoh, Yoh

def string_to_int(string, length, vocab):
    """
    Converts all strings in the vocabulary into a list of integers representing the positions of the
    input string's characters in the "vocab"

    Arguments:
    string -- input string, e.g. 'Wed 10 Jul 2007'
    length -- the number of time steps you'd like, determines if the output will be padded or cut
    vocab -- vocabulary, dictionary used to index every character of your "string"

    Returns:
    rep -- list of integers (or '<unk>') (size = length) representing the position of the string's character in the vocabulary
    """

    #make lower to standardize
    string = string.lower()
    string = string.replace(',','')

    if len(string) > length:
        string = string[:length]

    rep = list(map(lambda x: vocab.get(x, '<unk>'), string))

    if len(string) < length:
        rep += [vocab['<pad>']] * (length - len(string))

    #print (rep)
    return rep


def int_to_string(ints, inv_vocab):
    """
    Output a machine readable list of characters based on a list of indexes in the machine's vocabulary

    Arguments:
    ints -- list of integers representing indexes in the machine's vocabulary
    inv_vocab -- dictionary mapping machine readable indexes to machine readable characters 

    Returns:
    l -- list of characters corresponding to the indexes of ints thanks to the inv_vocab mapping
    """

    l = [inv_vocab[i] for i in ints]
    return l


EXAMPLES = ['3 May 1979', '5 Apr 09', '20th February 2016', 'Wed 10 Jul 2007']

def run_example(model, input_vocabulary, inv_output_vocabulary, text):
    encoded = string_to_int(text, TIME_STEPS, input_vocabulary)
    prediction = model.predict(np.array([encoded]))
    prediction = np.argmax(prediction[0], axis=-1)
    return int_to_string(prediction, inv_output_vocabulary)

def run_examples(model, input_vocabulary, inv_output_vocabulary, examples=EXAMPLES):
    predicted = []
    for example in examples:
        predicted.append(''.join(run_example(model, input_vocabulary, inv_output_vocabulary, example)))
        print('input:', example)
        print('output:', predicted[-1])
    return predicted


def softmax(x, axis=1):
    """Softmax activation function.
    # Arguments
        x : Tensor.
        axis: Integer, axis along which the softmax normalization is applied.
    # Returns
        Tensor, output of softmax transformation.
    # Raises
        ValueError: In case `dim(x) == 1`.
    """
    ndim = K.ndim(x)
    if ndim == 2:
        return K.softmax(x)
    elif ndim > 2:
        e = K.exp(x - K.max(x, axis=axis, keepdims=True))
        s = K.sum(e, axis=axis, keepdims=True)
        return e / s
    else:
        raise ValueError('Cannot apply softmax to a tensor that is 1D')


def plot_attention_map(model, input_vocabulary, inv_output_vocabulary, text, n_s = 128, num = 6, Tx = 30, Ty = 10):
    """
    Plot the attention map.

    """
    attention_map = np.zeros((10, 30))
    Ty, Tx = attention_map.shape

    s0 = np.zeros((1, n_s))
    c0 = np.zeros((1, n_s))
    layer = model.layers[num]

    encoded = np.array(string_to_int(text, Tx, input_vocabulary)).reshape((1, 30))
    encoded = np.array(list(map(lambda x: to_categorical(x, num_classes=len(input_vocabulary)), encoded)))

    f = K.function(model.inputs, [layer.get_output_at(t) for t in range(Ty)])
    r = f([encoded, s0, c0])

    for t in range(Ty):
        for t_prime in range(Tx):
            attention_map[t][t_prime] = r[t][0,t_prime,0]

    # Normalize attention map
#     row_max = attention_map.max(axis=1)
#     attention_map = attention_map / row_max[:, None]

    prediction = model.predict([encoded, s0, c0])

    predicted_text = []
    for i in range(len(prediction)):
        predicted_text.append(int(np.argmax(prediction[i], axis=1)))

    predicted_text = list(predicted_text)
    predicted_text = int_to_string(predicted_text, inv_output_vocabulary)
    text_ = list(text)

    # get the lengths of the string
    input_length = len(text)
    output_length = Ty

    # Plot the attention_map
    plt.clf()
    f = plt.figure(figsize=(8, 8.5))
    ax = f.add_subplot(1, 1, 1)

    # add image
    i = ax.imshow(attention_map, interpolation='nearest', cmap='Blues')

    # add colorbar
    cbaxes = f.add_axes([0.2, 0, 0.6, 0.03])
    cbar = f.colorbar(i, cax=cbaxes, orientation='horizontal')
    cbar.ax.set_xlabel('Alpha value (Probability output of the "softmax")', labelpad=2)

    # add labels
    ax.set_yticks(range(output_length))
    ax.set_yticklabels(predicted_text[:output_length])

    ax.set_xticks(range(input_length))
    ax.set_xticklabels(text_[:input_length], rotation=45)

    ax.set_xlabel('Input Sequence')
    ax.set_ylabel('Output Sequence')

    # add grid and legend
    ax.grid()

    #f.show()

    return attention_map