model_rnn.py

"""
@author: lilianweng
"""
import numpy as np
import os
import random
import re
import shutil
import time
import tensorflow as tf

import matplotlib.pyplot as plt

from tensorflow.contrib.tensorboard.plugins import projector


class LstmRNN(object):
    def __init__(self, sess, stock_count,
                 lstm_size=128,
                 num_layers=1,
                 num_steps=30,
                 input_size=1,
                 keep_prob=0.8,
                 logs_dir="logs",
                 plots_dir="images"):
        """
        Construct a RNN model using LSTM cell.

        Args:
            sess:
            stock_count:
            lstm_size:
            num_layers
            num_steps:
            input_size:
            keep_prob:
            checkpoint_dir
        """
        self.sess = sess
        self.stock_count = stock_count

        self.lstm_size = lstm_size
        self.num_layers = num_layers
        self.num_steps = num_steps
        self.input_size = input_size
        self.keep_prob = keep_prob
        self.logs_dir = logs_dir
        self.plots_dir = plots_dir

        self.build_graph()

    def build_graph(self):
        """
        The model asks for three things to be trained:
        - input: training data X
        - targets: training label y
        - learning_rate:
        """
        # inputs.shape = (number of examples, number of input, dimension of each input).
        self.learning_rate = tf.placeholder(tf.float32, None, name="learning_rate")

        # Stock symbols are mapped to integers.
        self.symbols = tf.placeholder(tf.int32, [None, 1], name='stock_labels')

        self.inputs = tf.placeholder(tf.float32, [None, self.num_steps, self.input_size], name="inputs")
        self.targets = tf.placeholder(tf.float32, [None, self.input_size], name="targets")

        def _create_one_cell():
            lstm_cell = tf.contrib.rnn.LSTMCell(self.lstm_size, state_is_tuple=True)
            if self.keep_prob < 1.0:
                lstm_cell = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=self.keep_prob)
            return lstm_cell

        cell = tf.contrib.rnn.MultiRNNCell(
            [_create_one_cell() for _ in range(self.num_layers)],
            state_is_tuple=True
        ) if self.num_layers > 1 else _create_one_cell()


        # Run dynamic RNN
        val, state_ = tf.nn.dynamic_rnn(cell, self.inputs, dtype=tf.float32, scope="dynamic_rnn")

        # Before transpose, val.get_shape() = (batch_size, num_steps, lstm_size)
        # After transpose, val.get_shape() = (num_steps, batch_size, lstm_size)
        val = tf.transpose(val, [1, 0, 2])

        last = tf.gather(val, int(val.get_shape()[0]) - 1, name="lstm_state")
        ws = tf.Variable(tf.truncated_normal([self.lstm_size, self.input_size]), name="w")
        bias = tf.Variable(tf.constant(0.1, shape=[self.input_size]), name="b")
        self.pred = tf.matmul(last, ws) + bias

        self.last_sum = tf.summary.histogram("lstm_state", last)
        self.w_sum = tf.summary.histogram("w", ws)
        self.b_sum = tf.summary.histogram("b", bias)
        self.pred_summ = tf.summary.histogram("pred", self.pred)

        # self.loss = -tf.reduce_sum(targets * tf.log(tf.clip_by_value(prediction, 1e-10, 1.0)))
        self.loss = tf.reduce_mean(tf.square(self.pred - self.targets), name="loss_mse")
        self.optim = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss, name="rmsprop_optim")

        self.loss_sum = tf.summary.scalar("loss_mse", self.loss)
        self.learning_rate_sum = tf.summary.scalar("learning_rate", self.learning_rate)

        self.t_vars = tf.trainable_variables()
        self.saver = tf.train.Saver()

    def train(self, dataset_list, config):
        """
        Args:
            dataset_list (<StockDataSet>)
            config (tf.app.flags.FLAGS)
        """
        assert len(dataset_list) > 0
        self.merged_sum = tf.summary.merge_all()

        # Set up the logs folder
        self.writer = tf.summary.FileWriter(os.path.join("./logs", self.model_name))
        self.writer.add_graph(self.sess.graph)
        tf.global_variables_initializer().run()

        # Merged test data of different stocks.
        merged_test_X = []
        merged_test_y = []
        merged_test_labels = []

        for label_, d_ in enumerate(dataset_list):
            merged_test_X += list(d_.test_X)
            merged_test_y += list(d_.test_y)
            merged_test_labels += [[label_]] * len(d_.test_X)

        merged_test_X = np.array(merged_test_X)
        merged_test_y = np.array(merged_test_y)
        merged_test_labels = np.array(merged_test_labels)

        print "len(merged_test_X) =", len(merged_test_X)
        print "len(merged_test_y) =", len(merged_test_y)
        print "len(merged_test_labels) =", len(merged_test_labels)

        test_data_feed = {
            self.learning_rate: 0.0,
            self.inputs: merged_test_X,
            self.targets: merged_test_y,
            self.symbols: merged_test_labels,
        }

        global_step = 0

        num_batches = sum(len(d_.train_X) for d_ in dataset_list) // config.batch_size
        random.seed(time.time())

        # Select samples for plotting.
        sample_labels = range(min(config.sample_size, len(dataset_list)))
        sample_indices = {}
        for l in sample_labels:
            sym = dataset_list[l].stock_sym
            target_indices = np.array([
                i for i, sym_label in enumerate(merged_test_labels)
                if sym_label[0] == l])
            sample_indices[sym] = target_indices
        print sample_indices

        print "Start training for stocks:", [d.stock_sym for d in dataset_list]
        for epoch in xrange(config.max_epoch):
            epoch_step = 0
            learning_rate = config.init_learning_rate * (
                config.learning_rate_decay ** max(float(epoch + 1 - config.init_epoch), 0.0)
            )

            for label_, d_ in enumerate(dataset_list):
                for batch_X, batch_y in d_.generate_one_epoch(config.batch_size):
                    global_step += 1
                    epoch_step += 1
                    batch_labels = np.array([[label_]] * len(batch_X))
                    train_data_feed = {
                        self.learning_rate: learning_rate,
                        self.inputs: batch_X,
                        self.targets: batch_y,
                        self.symbols: batch_labels,
                    }
                    train_loss, _, train_merged_sum = self.sess.run(
                        [self.loss, self.optim, self.merged_sum], train_data_feed)
                    self.writer.add_summary(train_merged_sum, global_step=global_step)

                    if np.mod(global_step, len(dataset_list) * 100 / config.input_size) == 1:
                        test_loss, test_pred = self.sess.run([self.loss, self.pred], test_data_feed)

                        print "Step:%d [Epoch:%d] [Learning rate: %.6f] train_loss:%.6f test_loss:%.6f" % (
                            global_step, epoch, learning_rate, train_loss, test_loss)

                        # Plot samples
                        for sample_sym, indices in sample_indices.iteritems():
                            image_path = os.path.join(self.model_plots_dir, "{}_epoch{:02d}_step{:04d}.png".format(
                                sample_sym, epoch, epoch_step))
                            sample_preds = test_pred[indices]
                            sample_truth = merged_test_y[indices]
                            self.plot_samples(sample_preds, sample_truth, image_path, stock_sym=sample_sym)

                        self.save(global_step)

        final_pred, final_loss = self.sess.run([self.pred, self.loss], test_data_feed)

        # Save the final model
        self.save(global_step)
        return final_pred

    @property
    def model_name(self):
        name = "stock_rnn_lstm%d_step%d_input%d" % (
            self.lstm_size, self.num_steps, self.input_size)

        return name

    @property
    def model_logs_dir(self):
        model_logs_dir = os.path.join(self.logs_dir, self.model_name)
        if not os.path.exists(model_logs_dir):
            os.makedirs(model_logs_dir)
        return model_logs_dir

    @property
    def model_plots_dir(self):
        model_plots_dir = os.path.join(self.plots_dir, self.model_name)
        if not os.path.exists(model_plots_dir):
            os.makedirs(model_plots_dir)
        return model_plots_dir

    def save(self, step):
        model_name = self.model_name + ".model"
        self.saver.save(
            self.sess,
            os.path.join(self.model_logs_dir, model_name),
            global_step=step
        )

    def load(self):
        print(" [*] Reading checkpoints...")
        ckpt = tf.train.get_checkpoint_state(self.model_logs_dir)
        if ckpt and ckpt.model_checkpoint_path:
            ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
            self.saver.restore(self.sess, os.path.join(self.model_logs_dir, ckpt_name))
            counter = int(next(re.finditer("(\d+)(?!.*\d)", ckpt_name)).group(0))
            print(" [*] Success to read {}".format(ckpt_name))
            return True, counter

        else:
            print(" [*] Failed to find a checkpoint")
            return False, 0

    def plot_samples(self, preds, targets, figname, stock_sym=None):
        def _flatten(seq):
            return [x for y in seq for x in y]

        truths = _flatten(targets)[-200:]
        preds = _flatten(preds)[-200:]
        days = range(len(truths))[-200:]

        plt.figure(figsize=(12, 6))
        plt.plot(days, truths, label='truth')
        plt.plot(days, preds, label='pred')
        plt.legend(loc='upper left', frameon=False)
        plt.xlabel("day")
        plt.ylabel("normalized price")
        plt.ylim((min(truths), max(truths)))
        plt.grid(ls='--')

        if stock_sym:
            plt.title(stock_sym + " | Last %d days in test" % len(truths))

        plt.savefig(figname, format='png', bbox_inches='tight', transparent=True)
        plt.close()