train.py

#! /usr/bin/env python
import csv
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Parameters
# ==================================================

# Data loading params
tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
tf.flags.DEFINE_string("positive_data_file", "/home/axp1147/Humor/Anotherbert/positiveid.txt", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "/home/axp1147/Humor/Anotherbert/negativeid.txt", "Data source for the negative data.")

# Model Hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 1024, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")

# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 10000, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
accc = 0.0
losss= 0.0
los= 0.0
acc = 0.0
# Eval Parameters
tf.flags.DEFINE_string("checkpoint_dir", "./runs/1575513572/checkpoints/", "Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", False, "Evaluate on all training data")

FLAGS = tf.flags.FLAGS
# FLAGS._parse_flags()
# print("\nParameters:")
# for attr, value in sorted(FLAGS.__flags.items()):
#     print("{}={}".format(attr.upper(), value))
# print("")

#def logreg(X,y):
 #   score = make_scorer(RMSE, greater_is_better=False)
  #  a = [[0]*1024]*1024
   # for i in range (0, 1024):
    #    for j in range(i+1,1024):
     #       X1  = X[:,i:j+1:j-i]
      #      clf = LogisticRegression(random_state=0, verbose = 1)
	#    clf.compile(loss='mean_squared_error', optimizer='sgd')
	 #   clf.fit(X1[:,:,:100], y, validation_data=)
	  #  a[i][j] = score(clf, X1[:,:,101:125], y)
    #print (a)
     
    

def preprocess():
    # Data Preparation
    # ==================================================

    # Load data
    print("Loading data...")
    x_text, y = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)

    # Build vocabulary
    max_document_length = max([len(x.split(" ")) for x in x_text])
    vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
    x = np.array(list(vocab_processor.fit_transform(x_text)))
    print(x)

    # Randomly shuffle data
    np.random.seed(10)
    shuffle_indices = np.random.permutation(np.arange(len(y)))
    x_shuffled = x[shuffle_indices]
    y_shuffled = y[shuffle_indices]
    #np.random.shuffle(np.transpose(x_shuffled)) #Coloumn shuffle just to try permutation
    x_shuffled, x_test, y_shuffled, y_test = train_test_split( x_shuffled , y_shuffled , test_size=0.25, random_state=42)
    

    # Split train/test set
    # TODO: This is very crude, should use cross-validation
    dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
    x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
    y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
    print(x_train.shape[1])

    del x, y, x_shuffled, y_shuffled
    
    print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
    print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))
    return x_train, y_train, vocab_processor, x_dev, y_dev, x_test, y_test

def train(x_train, y_train, vocab_processor, x_dev, y_dev):
    # Training
    # ==================================================
    with tf.Graph().as_default():
        session_conf = tf.ConfigProto(
          allow_soft_placement=FLAGS.allow_soft_placement,
          log_device_placement=FLAGS.log_device_placement)
        sess = tf.Session(config=session_conf)
        with sess.as_default():
            cnn = TextCNN(
                sequence_length=x_train.shape[1],
                num_classes=y_train.shape[1],
                vocab_size=len(vocab_processor.vocabulary_),
                embedding_size=FLAGS.embedding_dim,
                filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
                num_filters=FLAGS.num_filters,
                l2_reg_lambda=FLAGS.l2_reg_lambda)

            # Define Training procedure
            global_step = tf.Variable(0, name="global_step", trainable=False)
            optimizer = tf.train.AdamOptimizer(learning_rate= 0.05, epsilon= 1e-3)
            grads_and_vars = optimizer.compute_gradients(cnn.loss)
            train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)

            # Keep track of gradient values and sparsity (optional)
            grad_summaries = []
            for g, v in grads_and_vars:
                if g is not None:
                    grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
                    sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
                    grad_summaries.append(grad_hist_summary)
                    grad_summaries.append(sparsity_summary)
            grad_summaries_merged = tf.summary.merge(grad_summaries)

            # Output directory for models and summaries
            timestamp = str(int(time.time()))
            out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
            print("Writing to {}\n".format(out_dir))

            # Summaries for loss and accuracy
            loss_summary = tf.summary.scalar("loss", cnn.loss)
            acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)

            # Train Summaries
            train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])
            train_summary_dir = os.path.join(out_dir, "summaries", "train")
            train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)

            # Dev summaries
            dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
            dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
            dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
            

            # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
            checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
            FLAGS.checkpoint_dir = checkpoint_dir
            dev_path = os.path.join(train_summary_dir, "..", "dev.csv")
            train_path = os.path.join(dev_summary_dir, "..", "train.csv")
            checkpoint_prefix = os.path.join(checkpoint_dir, "model")
            if not os.path.exists(checkpoint_dir):
                os.makedirs(checkpoint_dir)
            saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)

            # Write vocabulary
            vocab_processor.save(os.path.join(out_dir, "vocab"))

            # Initialize all variables
            sess.run(tf.global_variables_initializer())

            def train_step(x_batch, y_batch):
                """
                A single training step
                """
                feed_dict = {
                  cnn.input_x: x_batch,
                  cnn.input_y: y_batch,
                  cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
                }
                _, step, summaries, loss, accuracy = sess.run(
                    [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
                    feed_dict)
                time_str = datetime.datetime.now().isoformat()
                print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                train_summary_writer.add_summary(summaries, step)
                return loss, accuracy

            def dev_step(x_batch, y_batch, writer=None):
                """
                Evaluates model on a dev set
                """
                feed_dict = {
                  cnn.input_x: x_batch,
                  cnn.input_y: y_batch,
                  cnn.dropout_keep_prob: 1.0
                }
                step, summaries, loss, accuracy = sess.run(
                    [global_step, dev_summary_op, cnn.loss, cnn.accuracy],
                    feed_dict)
                time_str = datetime.datetime.now().isoformat()
                print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                with open(dev_path, 'a') as devfile:
                    devwriter = csv.writer(devfile, delimiter=' ',quotechar='|', quoting=csv.QUOTE_MINIMAL)
                    devwriter.writerow([step, loss, accuracy])
                if writer:
                    writer.add_summary(summaries, step)
                    
            # Generate batches
            batches = data_helpers.batch_iter(
                list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
            losss=0.0
            los=0.0
            accc=0.0
            acc = 0.0
            # Training loop. For each batch...
            for batch in batches:
                x_batch, y_batch = zip(*batch)
                los,acc = train_step(x_batch, y_batch)
                current_step = tf.train.global_step(sess, global_step)
                losss = los + losss
                accc= acc+ accc
                if current_step % FLAGS.evaluate_every == 0:
                    with open(train_path, 'a') as trainfile:
                    	trainwriter = csv.writer(trainfile, delimiter=' ',quotechar='|', quoting=csv.QUOTE_MINIMAL)
                    	trainwriter.writerow({current_step, float(losss/FLAGS.evaluate_every), float(accc/FLAGS.evaluate_every)} )
                    	losss=0.0
                    	accc=0.0
                    print("\nEvaluation:")
                    dev_step(x_dev, y_dev, writer=dev_summary_writer)
                    print("")
                if current_step % FLAGS.checkpoint_every == 0:
                    path = saver.save(sess, checkpoint_prefix, global_step=current_step)
                    print("Saved model checkpoint to {}\n".format(path))


def testing(x_test, y_test):
    y_test = np.argmax(y_test, axis=1)
    checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
    graph = tf.Graph()
    with graph.as_default():
        session_conf = tf.ConfigProto(
          allow_soft_placement=FLAGS.allow_soft_placement,
          log_device_placement=FLAGS.log_device_placement)
        sess = tf.Session(config=session_conf)
        with sess.as_default():
            # Load the saved meta graph and restore variables
            saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
            saver.restore(sess, checkpoint_file)
    
            # Get the placeholders from the graph by name
            input_x = graph.get_operation_by_name("input_x").outputs[0]
            # input_y = graph.get_operation_by_name("input_y").outputs[0]
            dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
    
            # Tensors we want to evaluate
            predictions = graph.get_operation_by_name("output/predictions").outputs[0]
    
            # Generate batches for one epoch
            batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False)
    
            # Collect the predictions here
            all_predictions = []
    
            for x_test_batch in batches:
                batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
                all_predictions = np.concatenate([all_predictions, batch_predictions])
    
    
    print(all_predictions)
    # Print accuracy if y_test is defined
    if y_test is not None:
        correct_predictions = float(sum(all_predictions == y_test))
        print("Total number of test examples: {}".format(len(y_test)))
        print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))
    
    np.save('file1', x_test)
    np.save('file2', y_test)
    np.save('file3', all_predictions)
    # Save the evaluation to a csv
    out_path = os.path.join(FLAGS.checkpoint_dir, "..", "prediction.csv")
    print("Saving evaluation to {0}".format(out_path))
    with open(out_path, 'w') as f:
        csv.writer(f).writerow({correct_predictions/float(len(y_test))})

def main(argv=None):
    x_train, y_train, vocab_processor, x_dev, y_dev, x_test, y_test = preprocess()
    train(x_train, y_train, vocab_processor, x_dev, y_dev)
    testing(x_test,y_test)
    
    

if __name__ == '__main__':
    tf.app.run()