skipgrams ignores seed when given sampling_table

[francolq]

Random seed should be fixed before this call to random.random():

https://github.com/keras-team/keras-preprocessing/blob/6f679b06d10d39edcb066142eec9e3bcd6d9de4b/keras_preprocessing/sequence.py#L199

Sorry for ignoring the guidelines.

[Torbins]

I have fixed it like this, seems to work correctly:

    couples = []
    labels = []
    if seed is None:
        seed = random.randint(0, 10e6)
    random.seed(seed)

    for i, wi in enumerate(sequence):
        if not wi:
            continue
        if sampling_table is not None:
            if sampling_table[wi] < random.random():
                continue

        window_start = max(0, i - window_size)
        window_end = min(len(sequence), i + window_size + 1)
        for j in range(window_start, window_end):
            if j != i:
                wj = sequence[j]
                if not wj:
                    continue
                couples.append([wi, wj])
                if categorical:
                    labels.append([0, 1])
                else:
                    labels.append(1)

    if negative_samples > 0:
        num_negative_samples = int(len(labels) * negative_samples)
        words = [c[0] for c in couples]
        random.seed(seed)
        random.shuffle(words)

        couples += [[words[i % len(words)],
                     random.randint(1, vocabulary_size - 1)]
                    for i in range(num_negative_samples)]
        if categorical:
            labels += [[1, 0]] * num_negative_samples
        else:
            labels += [0] * num_negative_samples

    if shuffle:
        random.seed(seed)
        random.shuffle(couples)
        random.seed(seed)
        random.shuffle(labels)

    return couples, labels