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test_word2vec.py
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from os import listdir, scandir
from os.path import isfile, join
import numpy as np
from naive_bayes import NaiveBayes
from naive_bayes_basic import NaiveBayesBasic
from gensim.models import Word2Vec
# from gensim.models.keyedvectors import WordEmbeddingsKeyedVectors
# import gensim
from transformers import AutoTokenizer, AutoModel
import torch
import numpy as np
class EmbeddingBayes(NaiveBayes):
def __init__(self):
self.ham_total = 0
self.ham_freq = {}
self.spam_total = 0
self.spam_freq = {}
# self.tokenizer = None
# self.model = None
# self.spam_centroid = None
# self.ham_centroid = None
self.spam_embeddings = None
self.ham_embeddings = None
self.nbb = NaiveBayesBasic()
self.spam_similarities = {}
self.ham_similarities = {}
def get_embedding(self, sentences):
#get w2v model
# vector_length = 100
# return WordEmbeddingsKeyedVectors(vector_length)
return Word2Vec(sentences, window=5, workers=4, min_count=1)
def calculate_centroid(self, embeddings, document):
document_embeddings = [embeddings.wv[word] for word in [line for line in document]]
# for line in document:
# wv = embedding.wv
# enumerable = wv.index_to_key
# # [wv[word] for word in enumerate(enumerable)]
print(document_embeddings)
return np.mean(document_embeddings, axis=0)
# return np.mean(embeddings, axis=0)
def train(self):
# https://stackoverflow.com/questions/3207219/how-do-i-list-all-files-of-a-directory
SPAM_FILES = []
HAM_FILES = []
TESTING_PATH = "./Training"
TESTING_FOLDERS_PATHS = [f.path for f in scandir(TESTING_PATH) if f.is_dir()]
#for testing_folder_path in TESTING_FOLDERS_PATHS:
testing_folder_path = TESTING_FOLDERS_PATHS[0]
TEST_SPAM_PATH = join(testing_folder_path, "spam")
TEST_HAM_PATH = join(testing_folder_path, "ham")
SPAM_FILES += [join(TEST_SPAM_PATH, file) for file in listdir(TEST_SPAM_PATH) if
isfile(join(TEST_SPAM_PATH, file))]
HAM_FILES += [join(TEST_HAM_PATH, file) for file in listdir(TEST_HAM_PATH) if
isfile(join(TEST_HAM_PATH, file))]
ham_words = []
spam_words = []
# self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# self.model = AutoModel.from_pretrained("bert-base-uncased")
print("training hams...")
for ham_file in HAM_FILES:
file = open(ham_file, "r", encoding="ISO-8859-1")
ham_words.extend([line.strip().split() for line in file])
file.close()
# embeddings = self.get_embedding(words)
# ham_embeddings.append(embeddings)
ham_embeddings = self.get_embedding(ham_words)
print("training spams...")
for spam_file in SPAM_FILES:
file = open(spam_file, "r", encoding="ISO-8859-1")
spam_words.extend([line.strip().split() for line in file])
file.close()
# embeddings = self.get_embedding(words)
# spam_embeddings.append(embeddings)
spam_embeddings = self.get_embedding(spam_words)
self.spam_embeddings = spam_embeddings
self.ham_embeddings = ham_embeddings
self.nbb.train()
print("done training!")
# self.ham_total = float(ham_total)
# self.ham_freq = ham_freq
# self.spam_total = float(spam_total)
# self.spam_freq = spam_freq
# spam_words = list(spam_freq.keys())
# ham_words = list(ham_freq.keys())
batch_size = 1024
# spam_embeddings = []
# for i in range(0, len(spam_words), batch_size):
# batch = spam_words[i:i + batch_size]
# embeddings = self.get_embedding(batch)
# spam_embeddings.extend(embeddings)
# ham_embeddings = []
# for i in range(0, len(ham_words), batch_size):
# batch = ham_words[i:i + batch_size]
# embeddings = self.get_embedding(batch)
# ham_embeddings.extend(embeddings)
#centroid calculation stuff
# ham_centroids = []
# spam_centroids = []
# for ham_file in HAM_FILES:
# file = open(ham_file, "r", encoding="ISO-8859-1")
# ham_centroids.append(self.calculate_centroid(ham_embeddings, [line.strip().split() for line in file]))
# file.close()
# # embeddings = self.get_embedding(words)
# # ham_embeddings.append(embeddings)
# for spam_file in SPAM_FILES:
# file = open(spam_file, "r", encoding="ISO-8859-1")
# spam_centroids.append(self.calculate_centroid(spam_embeddings, [line.strip().split() for line in file]))
# file.close()
# self.spam_centroids = spam_centroids
# self.ham_centroids = ham_centroids
# self.spam_centroid = self.calculate_centroid(spam_embeddings)
# self.ham_centroid = self.calculate_centroid(ham_embeddings)
def get_max_similarity_words(self, word, embeddings, k):
maxima = {}
wv = embeddings.wv
# take first column - i'm not sure why these embeddings are 2x100
norm_new = np.linalg.norm(word[0, :])
for embedding in enumerate(wv.index_to_key):
cos = np.dot(word[0, :], wv[embedding][0, :])/(norm_new*np.linalg.norm(wv[embedding][0, :]))
if len(maxima) < k or float(cos) > min(maxima, key=maxima.get)[0]:
maxima[embedding] = cos
return maxima.keys()
def predict(self, filename):
file = open(filename, "r", encoding="ISO-8859-1")
new_embedding = self.get_embedding(file.read())
file_words = []
for line in file:
file_words.extend(line.strip().split())
file.close()
# distance_to_spam = np.linalg.norm(new_embedding - self.spam_centroid)
# distance_to_ham = np.linalg.norm(new_embedding - self.ham_centroid)
for word in enumerate(new_embedding.wv.index_to_key):
ham_max_words = None
spam_max_words = None
if word in self.spam_similarities:
spam_max_words = self.spam_similarities[word]
else:
spam_max_words = self.get_max_similarity_words(new_embedding.wv[word], self.spam_embeddings, 5)
self.spam_similarities[word] = spam_max_words
if word in self.ham_similarities:
ham_max_words = self.ham_similarities[word]
else:
ham_max_words = self.get_max_similarity_words(new_embedding.wv[word], self.ham_embeddings, 5)
self.ham_similarities[word] = spam_max_words
file_words.extend(spam_max_words)
file_words.extend(ham_max_words)
print("done padding!")
return self.nbb.predict_wordlist(file_words)
if __name__ == "__main__":
nb = EmbeddingBayes()
nb.train()
accuracy = nb.test()
print(accuracy)