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embeddings_model.py
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embeddings_model.py
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import os, utils, time
import data_processing as data_proc
import numpy as np
from dl_models import nn_bow_model
from pandas import DataFrame
path = os.getcwd()[:os.getcwd().rfind('/')]
to_write_filename = path + '/stats/embeddings_analysis.txt'
utils.initialize_writer(to_write_filename)
train_filename = "train.txt"
test_filename = "test.txt"
dict_filename = "word_list.txt"
word_filename = "word_list_freq.txt"
# Load the data
x_train = utils.load_file(path + "/res/tokens/tokens_clean_original_" + train_filename)
x_test = utils.load_file(path + "/res/tokens/tokens_clean_original_" + test_filename)
# Make sure all words are lower-case
x_train = [t.lower() for t in x_train]
x_test = [t.lower() for t in x_test]
# Load the labels
y_train = [int(l) for l in utils.load_file(path + "/res/datasets/ghosh/labels_" + train_filename)]
y_test = [int(l) for l in utils.load_file(path + "/res/datasets/ghosh/labels_" + test_filename)]
# Extract just the emojis present in each tweet in each set
x_train_emoji = data_proc.extract_emojis(x_train)
x_test_emoji = data_proc.extract_emojis(x_test)
# Load the deepmoji predictions for each tweet
x_train_deepmoji = utils.get_deepmojis("data_frame_" + train_filename[:-4] + ".csv", threshold=0.05)
x_train_deepmoji = [' '.join(e) for e in x_train_deepmoji]
x_test_deepmoji = utils.get_deepmojis("data_frame_" + test_filename[:-4] + ".csv", threshold=0.05)
x_test_deepmoji = [' '.join(e) for e in x_test_deepmoji]
# Get the concatenation of present and predicted emojis
x_train_all_emojis = [x_train_deepmoji[i] if x_train_emoji[i] == ''
else x_train_emoji[i] + ' ' + x_train_deepmoji[i] for i in range(len(x_train_emoji))]
x_test_all_emojis = [x_test_deepmoji[i] if x_test_emoji[i] == ''
else x_test_emoji[i] + ' ' + x_test_deepmoji[i] for i in range(len(x_test_emoji))]
# Load the embedding maps
embedding_dim = 100
random_word2vec_map = utils.build_random_word2vec(x_train, embedding_dim=embedding_dim, variance=1)
word2vec_map = utils.load_vectors(filename="glove.6B.%dd.txt" % embedding_dim)
emoji2vec_map = utils.load_vectors(filename="emoji_embeddings_%dd.txt" % embedding_dim)
# Settings for the up-coming embedding-extractions
init_unk = True
var = None
weighted = False
# Get embeddings for training
x_train_rand_word_emb = utils.get_tweets_embeddings(x_train, random_word2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_train_word_emb = utils.get_tweets_embeddings(x_train, word2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_train_emoji_emb = utils.get_tweets_embeddings(x_train_emoji, emoji2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_train_deepemoji_emb = utils.get_tweets_embeddings(x_train_deepmoji, emoji2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_train_all_emoji_emb = utils.get_tweets_embeddings(x_train_all_emojis, emoji2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
# Get embeddings for testing
x_test_rand_word_emb = utils.get_tweets_embeddings(x_test, random_word2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_test_word_emb = utils.get_tweets_embeddings(x_test, word2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_test_emoji_emb = utils.get_tweets_embeddings(x_test_emoji, emoji2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_test_deepemoji_emb = utils.get_tweets_embeddings(x_test_deepmoji, emoji2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
x_test_all_emoji_emb = utils.get_tweets_embeddings(x_test_all_emojis, emoji2vec_map, embedding_dim,
init_unk=init_unk, variance=var, weighted_average=weighted)
# Obtain features by concatenating word embeddings with all emoji embeddings
x_train_features_concat = []
for t, e in zip(x_train_word_emb, x_train_all_emoji_emb):
x_train_features_concat.append(np.concatenate((t, e), axis=0))
x_test_features_concat = []
for t, e in zip(x_test_word_emb, x_test_all_emoji_emb):
x_test_features_concat.append(np.concatenate((t, e), axis=0))
# Obtain features by adding together the word embeddings with all emoji embeddings
x_train_features_sum = []
for t, e in zip(x_train_word_emb, x_train_all_emoji_emb):
x_train_features_sum.append(t + e)
x_test_features_sum = []
for t, e in zip(x_test_word_emb, x_test_all_emoji_emb):
x_test_features_sum.append(t + e)
print("Shape of concatenated train features: ", np.array(x_train_features_concat).shape)
print("Shape of concatenated test features: ", np.array(x_test_features_concat).shape)
print("Shape of summed train features: ", np.array(x_train_features_sum).shape)
print("Shape of summed test features: ", np.array(x_test_features_sum).shape)
features = {
'Random emb': [x_train_rand_word_emb, x_test_rand_word_emb],
'Just word emb': [x_train_word_emb, x_test_word_emb],
'Present emojis': [x_train_emoji_emb, x_test_emoji_emb],
'Deepmojis': [x_train_deepemoji_emb, x_test_deepemoji_emb],
'All emojis': [x_train_all_emoji_emb, x_test_all_emoji_emb],
'All concat': [x_train_features_concat, x_test_features_concat],
'All summed': [x_train_features_sum, x_test_features_sum]
}
results = DataFrame()
for k, v in features.items():
utils.print_model_title("SVM analysis for: " + k)
start = time.time()
utils.run_supervised_learning_models(v[0], y_train, v[1], y_test)
end = time.time()
print("Completion time of the %s SVM model: %.3f s = %.3f min" % (k, (end - start), (end - start) / 60.0))
start = time.time()
utils.print_model_title("NN analysis for: " + k)
mode = '_'.join([w.lower() for w in k.split()])
nn_bow_model(x_train_rand_word_emb, y_train, x_test_rand_word_emb, y_test, results, mode)
end = time.time()
print("Completion time of the %s NN model: %.3f s = %.3f min" % (k, (end - start), (end - start) / 60.0))
# Plot the embeddings-NN results obtained for each mode
utils.boxplot_results(results, "embeddings_nn_boxplot.png")