z_lstm_test.py

__author__ = 'luoshalin'

import theano, theano.tensor as T
import numpy as np
import random
from theano_lstm import Embedding, LSTM, RNN, StackedCells, Layer, create_optimization_updates, masked_loss


# ------- / part1: Collect Language Data / ------- #
class Sampler:
    def __init__(self, prob_table):
        total_prob = 0.0
        if type(prob_table) is dict:
            for key, value in prob_table.items():
                total_prob += value
        elif type(prob_table) is list:
            prob_table_gen = {}
            for key in prob_table:
                prob_table_gen[key] = 1.0 / (float(len(prob_table)))
            total_prob = 1.0
            prob_table = prob_table_gen
        else:
            print("__init__ takes either a dict or a list as its first argument")
        if total_prob <= 0.0:
            raise ValueError("Probability is not strictly positive.")
        self._keys = []
        self._probs = []
        for key in prob_table:
            self._keys.append(key)
            self._probs.append(prob_table[key] / total_prob)

    def __call__(self):
        sample = random.random()
        seen_prob = 0.0
        for key, prob in zip(self._keys, self._probs):
            if (seen_prob + prob) >= sample:
                return key
            else:
                seen_prob += prob
        return key

samplers = {
    "punctuation": Sampler({".": 0.49, ",": 0.5, ";": 0.03, "?": 0.05, "!": 0.05}),
    "stop": Sampler({"the": 10, "from": 5, "a": 9, "they": 3, "he": 3, "it" : 2.5, "she": 2.7, "in": 4.5}),
    "noun": Sampler(["cat", "broom", "boat", "dog", "car", "wrangler", "mexico", "lantern", "book", "paper", "joke","calendar", "ship", "event"]),
    "verb": Sampler(["ran", "stole", "carried", "could", "would", "do", "can", "carry", "catapult", "jump", "duck"]),
    "adverb": Sampler(["rapidly", "calmly", "cooly", "in jest", "fantastically", "angrily", "dazily"])
    }


def generate_nonsense(word = ""):
    if word.endswith("."):
        return word
    else:
        if len(word) > 0:
            word += " "
        word += samplers["stop"]()
        word += " " + samplers["noun"]()
        if random.random() > 0.7:
            word += " " + samplers["adverb"]()
            if random.random() > 0.7:
                word += " " + samplers["adverb"]()
        word += " " + samplers["verb"]()
        if random.random() > 0.8:
            word += " " + samplers["noun"]()
            if random.random() > 0.9:
                word += "-" + samplers["noun"]()
        if len(word) > 500:
            word += "."
        else:
            word += " " + samplers["punctuation"]()
        return generate_nonsense(word)

def generate_dataset(total_size, ):
    sentences = []
    for i in range(total_size):
        sentences.append(generate_nonsense())
    return sentences

# generate dataset
lines = generate_dataset(100)

### Utilities:
class Vocab:
    __slots__ = ["word2index", "index2word", "unknown"]

    def __init__(self, index2word = None):
        self.word2index = {}
        self.index2word = []

        # add unknown word:
        self.add_words(["**UNKNOWN**"])
        self.unknown = 0

        if index2word is not None:
            self.add_words(index2word)

    def add_words(self, words):
        for word in words:
            if word not in self.word2index:
                self.word2index[word] = len(self.word2index)
                self.index2word.append(word)

    def __call__(self, line):
        """
        Convert from numerical representation to words
        and vice-versa.
        """
        if type(line) is np.ndarray:
            return " ".join([self.index2word[word] for word in line])
        if type(line) is list:
            if len(line) > 0:
                if line[0] is int:
                    return " ".join([self.index2word[word] for word in line])
            indices = np.zeros(len(line), dtype=np.int32)
        else:
            line = line.split(" ")
            indices = np.zeros(len(line), dtype=np.int32)

        for i, word in enumerate(line):
            indices[i] = self.word2index.get(word, self.unknown)

        return indices

    @property
    def size(self):
        return len(self.index2word)

    def __len__(self):
        return len(self.index2word)

vocab = Vocab()
for line in lines:
    vocab.add_words(line.split(" "))

def pad_into_matrix(rows, padding = 0):
    if len(rows) == 0:
        return np.array([0, 0], dtype=np.int32)
    lengths = map(len, rows)
    width = max(lengths)
    height = len(rows)
    mat = np.empty([height, width], dtype=rows[0].dtype)
    mat.fill(padding)
    for i, row in enumerate(rows):
        mat[i, 0:len(row)] = row
    return mat, list(lengths)

# transform into big numerical matrix of sentences:
numerical_lines = []
for line in lines:
    numerical_lines.append(vocab(line))
numerical_lines, numerical_lengths = pad_into_matrix(numerical_lines)

# ------- / part2: BUILD MODEL / ------- #
def softmax(x):
    """
    Wrapper for softmax, helps with
    pickling, and removing one extra
    dimension that Theano adds during
    its exponential normalization.
    """
    return T.nnet.softmax(x.T)

def has_hidden(layer):
    """
    Whether a layer has a trainable
    initial hidden state.
    """
    return hasattr(layer, 'initial_hidden_state')

def matrixify(vector, n):
    return T.repeat(T.shape_padleft(vector), n, axis=0)

def initial_state(layer, dimensions = None):
    """
    Initalizes the recurrence relation with an initial hidden state
    if needed, else replaces with a "None" to tell Theano that
    the network **will** return something, but it does not need
    to send it to the next step of the recurrence
    """
    if dimensions is None:
        return layer.initial_hidden_state if has_hidden(layer) else None
    else:
        return matrixify(layer.initial_hidden_state, dimensions) if has_hidden(layer) else None

def initial_state_with_taps(layer, dimensions = None):
    """Optionally wrap tensor variable into a dict with taps=[-1]"""
    state = initial_state(layer, dimensions)
    if state is not None:
        return dict(initial=state, taps=[-1])
    else:
        return None

class Model:
    """
    Simple predictive model for forecasting words from
    sequence using LSTMs. Choose how many LSTMs to stack
    what size their memory should be, and how many
    words can be predicted.
    """
    def __init__(self, hidden_size, input_size, vocab_size, stack_size=1, celltype=LSTM):
        # declare model
        self.model = StackedCells(input_size, celltype=celltype, layers =[hidden_size] * stack_size)
        # add an embedding
        self.model.layers.insert(0, Embedding(vocab_size, input_size))
        # add a classifier:
        self.model.layers.append(Layer(hidden_size, vocab_size, activation = softmax))
        # inputs are matrices of indices,
        # each row is a sentence, each column a timestep
        self._stop_word   = theano.shared(np.int32(999999999), name="stop word")
        self.for_how_long = T.ivector()
        self.input_mat = T.imatrix()
        self.priming_word = T.iscalar()
        self.srng = T.shared_randomstreams.RandomStreams(np.random.randint(0, 1024))
        # create symbolic variables for prediction:
        self.predictions = self.create_prediction()
        # create symbolic variable for greedy search:
        self.greedy_predictions = self.create_prediction(greedy=True)
        # create gradient training functions:
        self.create_cost_fun()
        self.create_training_function()
        self.create_predict_function()

    def stop_on(self, idx):
        self._stop_word.set_value(idx)

    @property
    def params(self):
        return self.model.params

    def create_prediction(self, greedy=False):
        def step(idx, *states):
            # new hiddens are the states we need to pass to LSTMs
            # from past. Because the StackedCells also include
            # the embeddings, and those have no state, we pass
            # a "None" instead:
            new_hiddens = [None] + list(states)

            new_states = self.model.forward(idx, prev_hiddens = new_hiddens)
            if greedy:
                new_idxes = new_states[-1]
                new_idx   = new_idxes.argmax()
                # provide a stopping condition for greedy search:
                return ([new_idx.astype(self.priming_word.dtype)] + new_states[1:-1]), theano.scan_module.until(T.eq(new_idx,self._stop_word))
            else:
                return new_states[1:]
        # in sequence forecasting scenario we take everything
        # up to the before last step, and predict subsequent
        # steps ergo, 0 ... n - 1, hence:
        inputs = self.input_mat[:, 0:-1]
        num_examples = inputs.shape[0]
        # pass this to Theano's recurrence relation function:

        # choose what gets outputted at each timestep:
        if greedy:
            outputs_info = [dict(initial=self.priming_word, taps=[-1])] + [initial_state_with_taps(layer) for layer in self.model.layers[1:-1]]
            result, _ = theano.scan(fn=step,
                                n_steps=200,
                                outputs_info=outputs_info)
        else:
            outputs_info = [initial_state_with_taps(layer, num_examples) for layer in self.model.layers[1:]]
            result, _ = theano.scan(fn=step,
                                sequences=[inputs.T],
                                outputs_info=outputs_info)

        if greedy:
            return result[0]
        # softmaxes are the last layer of our network,
        # and are at the end of our results list:
        return result[-1].transpose((2,0,1))
        # we reorder the predictions to be:
        # 1. what row / example
        # 2. what timestep
        # 3. softmax dimension

    def create_cost_fun (self):
        # create a cost function that
        # takes each prediction at every timestep
        # and guesses next timestep's value:
        what_to_predict = self.input_mat[:, 1:]
        # because some sentences are shorter, we
        # place masks where the sentences end:
        # (for how long is zero indexed, e.g. an example going from `[2,3)`)
        # has this value set 0 (here we substract by 1):
        for_how_long = self.for_how_long - 1
        # all sentences start at T=0:
        starting_when = T.zeros_like(self.for_how_long)

        self.cost = masked_loss(self.predictions,
                                what_to_predict,
                                for_how_long,
                                starting_when).sum()

    def create_predict_function(self):
        self.pred_fun = theano.function(
            inputs=[self.input_mat],
            outputs =self.predictions,
            allow_input_downcast=True
        )

        self.greedy_fun = theano.function(
            inputs=[self.priming_word],
            outputs=T.concatenate([T.shape_padleft(self.priming_word), self.greedy_predictions]),
            allow_input_downcast=True
        )

    def create_training_function(self):
        updates, _, _, _, _ = create_optimization_updates(self.cost, self.params, method="adadelta")
        self.update_fun = theano.function(
            inputs=[self.input_mat, self.for_how_long],
            outputs=self.cost,
            updates=updates,
            allow_input_downcast=True)

    def __call__(self, x):
        return self.pred_fun(x)

# ------- / part3: Construct & train model / ------ #
# construct model & theano functions:
model = Model(
    input_size=10,
    hidden_size=10,
    vocab_size=len(vocab),
    stack_size=1, # make this bigger, but makes compilation slow
    celltype=RNN # use RNN or LSTM
)
model.stop_on(vocab.word2index["."])

# train:
for i in range(10000):
    error = model.update_fun(numerical_lines, numerical_lengths)
    if i % 100 == 0:
        print("epoch %(epoch)d, error=%(error).2f" % ({"epoch": i, "error": error}))
    if i % 500 == 0:
        print(vocab(model.greedy_fun(vocab.word2index["the"])))