def classify(query):
num_classifiers = 5
answers = []
word_index = get_word_index("bagging_vocab.txt")
print(word_index)
test_query = [format_query(query)]
max_query_len = 55
X_test, y_test = vectorize_stories(test_query, word_index, max_query_len)
for i in range(num_classifiers):
model = load_model('trained_models/bagging_RNN_10_epochs_' + str(i) +
'.h5')
pred_results = model.predict(([X_test]))
answers.append(pred_results[0][0])
print(pred_results[0][0])
sum_answers = 0
for j in range(0, num_classifiers):
sum_answers = sum_answers + answers[j]
mean = sum_answers / num_classifiers
if mean > 0.5:
status = 'malicious'
else:
status = 'normal'
print('Query: ' + query + ' is ' + status)
from keras.layers.embeddings import Embedding
from keras.layers import Input, Activation, Dense, Permute, Dropout, add, dot, concatenate, LSTM, Bidirectional
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from preprocessing import format_query, vectorize_stories, get_word_index
all_data = []
train_data = []
test_data = []
dataset = 'norm'
with open('datasets/' + dataset + 'Test.txt', 'r') as f:
for line in f:
all_data.append(format_query(line))
word_index = get_word_index(dataset + '_vocab.txt')
vocab_len = len(word_index) + 1
train_data, test_data = train_test_split(all_data,
test_size=0.25,
random_state=1)
# Now we are going to calculate the longest query
# We need this for the Keras pad sequences.
all_query_lens = [len(data[0]) for data in all_data]
max_query_len = (max(all_query_lens))
print(max_query_len)
X_train, y_train = vectorize_stories(train_data, word_index, max_query_len)
def test_ensemble_n(num_tests, num_classifiers, test_file, vocab, model_name,
max_query_len, debug_flag):
timestamp = time.time()
log_file_name = "logs/" + str(timestamp) + "_ensemble_HARD" + ".txt"
f = open(log_file_name, "a")
f.write("Ensemble - Hard voting with " + str(num_classifiers) +
" classifiers \n")
f.write("Tested with data-set: " + test_file + " at " +
time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp)) +
" \n")
f.write("Model tested: " + model_name + " \n")
f.write(str(num_tests) + " queries tested" + "\n")
f.write("---------------" + "\n")
f.write("Mis-classifications: " + "\n")
word_index = get_word_index(vocab)
output = []
answers = []
queries = []
print('Testing ensemble (HARD VOTING)...')
# Load models
for i in range(num_classifiers):
model = load_model('trained_models/' + model_name + str(i) + '.h5')
# For each model, predict 10 answers
with open(test_file) as fp:
for n in range(num_tests):
line = fp.readline()
queries.append(line)
test_query = [format_query(line)]
X_test, y_test = vectorize_stories(test_query, word_index,
max_query_len)
pred_results = model.predict(([X_test]))
output.append(pred_results[0][0])
# Save answers first time
if i == 0:
answers.append(y_test[0])
# Start comparing model outputs
true_positives = 0
true_negatives = 0
false_positives = 0
false_negatives = 0
for j in range(0, num_tests):
normal = 0
malicious = 0
for k in range(0, num_classifiers):
if output[(k * num_tests) + j] > 0.5:
malicious = malicious + 1
else:
normal = normal + 1
if malicious > normal:
if answers[j] == 1:
true_positives = true_positives + 1
if debug_flag:
print('M_Correct')
else:
f = open(log_file_name, "a")
f.write("False positive - at line " + str(j + 1) + " - " +
queries[j] + "\n")
false_positives = false_positives + 1
if debug_flag:
print('M_Wrong')
else:
if answers[j] == 0:
true_negatives = true_negatives + 1
if debug_flag:
print('N_Correct')
else:
f = open(log_file_name, "a")
f.write("False negative - at line " + str(j + 1) + " - " +
queries[j] + "\n")
false_negatives = false_negatives + 1
if debug_flag:
print('N_Wrong')
if debug_flag:
print('-------')
line_1 = 'True positives: ' + str(true_positives) + " / " + str(num_tests)
line_2 = 'True negatives: ' + str(true_negatives) + " / " + str(num_tests)
line_3 = 'False positives: ' + str(false_positives) + " / " + str(
num_tests)
line_4 = 'False negatives: ' + str(false_negatives) + " / " + str(
num_tests)
line_5 = 'Accuracy: ' + str(true_negatives +
true_positives) + "/" + str(num_tests)
f = open(log_file_name, "a")
f.write("---------------------------- \n")
f.write(line_1 + "\n")
f.write(line_2 + "\n")
f.write(line_3 + "\n")
f.write(line_4 + "\n")
f.write(line_5 + "\n")
print(line_1)
print(line_2)
print(line_3)
print(line_4)
print(line_5)
def test_individual(num_tests, test_file, vocab, model_name, max_query_len,
debug_flag):
temp_name = model_name.replace("/", "_")
timestamp = time.time()
log_file_name = "logs/" + str(
timestamp) + "_individual_" + temp_name + ".txt"
f = open(log_file_name, "a")
f.write("Tested with data-set: " + test_file + " at " +
time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp)) +
" \n")
f.write("Model tested: " + model_name + " \n")
f.write(str(num_tests) + " queries tested" + "\n")
f.write("---------------" + "\n")
f.write("Mis-classifications: " + "\n")
word_index = get_word_index(vocab)
output = []
answers = []
queries = []
# Load model
model = load_model('trained_models/' + model_name + '.h5')
# For each model, predict 10 answers
with open(test_file) as fp:
for n in range(num_tests):
line = fp.readline()
queries.append(line)
test_query = [format_query(line)]
X_test, y_test = vectorize_stories(test_query, word_index,
max_query_len)
pred_results = model.predict(([X_test]))
if pred_results[0][0] > 0.5:
output.append(1)
else:
output.append(0)
answers.append(y_test[0])
# Start comparing model outputs
true_positives = 0
true_negatives = 0
false_positives = 0
false_negatives = 0
for j in range(0, num_tests):
if output[j] > 0.5:
if answers[j] == 1:
true_positives = true_positives + 1
if debug_flag:
print('M_Correct')
else:
f = open(log_file_name, "a")
f.write("False positive - at line " + str(j + 1) + " - " +
queries[j] + "\n")
false_positives = false_positives + 1
if debug_flag:
print('M_Wrong')
else:
if answers[j] == 0:
true_negatives = true_negatives + 1
if debug_flag:
print('N_Correct')
else:
f = open(log_file_name, "a")
f.write("False negative - at line " + str(j + 1) + " - " +
queries[j] + "\n")
false_negatives = false_negatives + 1
if debug_flag:
print('N_Wrong')
if debug_flag:
print('-------')
line_1 = 'True positives: ' + str(true_positives) + " / " + str(num_tests)
line_2 = 'True negatives: ' + str(true_negatives) + " / " + str(num_tests)
line_3 = 'False positives: ' + str(false_positives) + " / " + str(
num_tests)
line_4 = 'False negatives: ' + str(false_negatives) + " / " + str(
num_tests)
line_5 = 'Accuracy: ' + str(true_negatives +
true_positives) + "/" + str(num_tests)
f = open(log_file_name, "a")
f.write("---------------------------- \n")
f.write(line_1 + "\n")
f.write(line_2 + "\n")
f.write(line_3 + "\n")
f.write(line_4 + "\n")
f.write(line_5 + "\n")
print(line_1)
print(line_2)
print(line_3)
print(line_4)
print(line_5)
