spam_detector_tensorflow.py

#!/usr/bin/env python3
import numpy as np
from tf_keras.layers import Dense, Flatten
from tf_keras.layers import Embedding
from tf_keras.models import Sequential
from tf_keras.optimizers.legacy import SGD
from tf_keras.preprocessing.sequence import pad_sequences
from tf_keras.preprocessing.text import Tokenizer

np.set_printoptions(suppress=True)

# Depends on the selection of the pre-trained embedding dictionary
EMBEDDING_VECTOR_LENGTH = 100

# The percentage of dataset to use for validation (the rest is for training)
VALIDATION_SPLIT = 0.1

# How sure (in percentage) the network has to be to consider a classification of an individual sample successful
SUCCESS_THRESHOLD = 0.9

# The overall classification success threshold
TRAINING_SUCCESS_THRESHOLD = 0.97

# Load dataset
messages = []  # list of text samples
labels = []  # 1 spam; 0 ham
with open('dataset.csv', newline='') as dataset_file:
    i = 0
    for row in dataset_file:
        row_split = row.split(",", 1)
        if "spam" == row_split[0]:
            labels.append(1)
        elif "ham" == row_split[0]:
            labels.append(0)
        else:
            # Skip if it's an unknown category
            continue

        messages.append(row_split[1])
print('Found %s texts.' % len(messages))

# Make them numpy arrays and shuffle
perm = np.random.permutation(len(messages))
messages = np.array(messages)[perm]
labels = np.array(labels)[perm]

# Tokenize words and convert messages into sequences
tokenizer = Tokenizer()
tokenizer.fit_on_texts(messages)
sequences = tokenizer.texts_to_sequences(messages)
word_index = tokenizer.word_index
print('Found and tokenized %s unique words.' % len(word_index))

# Use pre-trained GloVe embeddings (vector length is 100): https://github.com/stanfordnlp/GloVe
embeddings_dict = {}
print('Loading pre-trained embeddings...')
with open("glove.6B.100d.txt") as f:
    for line in f:
        word, coeffs = line.split(maxsplit=1)
        coeffs = np.fromstring(coeffs, "f", sep=" ")
        embeddings_dict[word] = coeffs
print("Found %s word vectors." % len(embeddings_dict))

# Prepare embedding matrix
word_number = len(word_index) + 1  # +1 because 0 is reserved for misses
hits = 0
misses = 0
embedding_matrix = np.zeros((word_number, EMBEDDING_VECTOR_LENGTH))
for word, index in word_index.items():
    embedding_vector = embeddings_dict.get(word)
    if embedding_vector is not None:
        # Words not found in embedding index (and index 0 of course) will be a vector of all zeros.
        embedding_matrix[index] = embedding_vector
        hits += 1
    else:
        misses += 1
print("Converted %d words (%d misses)." % (hits, misses))

# Split dataset on validation and training
validation_texts_number = int(VALIDATION_SPLIT * len(sequences))
train_sequences = sequences[:-validation_texts_number]
validation_sequences = sequences[-validation_texts_number:]
train_labels = labels[:-validation_texts_number]
validation_labels = labels[-validation_texts_number:]

# Padding sequences by the longest one (adds 0s for smaller sequences)
longest_sequence = 0
for sequence in sequences:
    longest_sequence = longest_sequence if len(sequence) <= longest_sequence else len(sequence)
print("Longest sequence is %s." % longest_sequence)
padded_train_sequences = pad_sequences(train_sequences, maxlen=longest_sequence, padding='post')
padded_validation_sequences = pad_sequences(validation_sequences, maxlen=longest_sequence, padding='post')

# Build the prediction model
model = Sequential()
model.add(Embedding(input_dim=word_number,
                    output_dim=EMBEDDING_VECTOR_LENGTH,
                    weights=[embedding_matrix],
                    input_length=longest_sequence,
                    trainable=False))
model.add(Flatten())
model.add(Dense(128, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer=SGD(learning_rate=0.01), loss='mean_squared_error')
model.summary()

# Train the model
epoch_count = 1
print("Training started...")
while True:
    model.fit(padded_train_sequences, train_labels)
    classifications = model.predict(padded_validation_sequences)

    # I can do this better with evaluations, but ok for now
    success_count = 0
    for i in range(len(classifications)):
        if classifications[i] > SUCCESS_THRESHOLD and validation_labels[i] == 1:
            success_count += 1
        elif classifications[i] < 1 - SUCCESS_THRESHOLD and validation_labels[i] == 0:
            success_count += 1
    success_rate = success_count / len(padded_validation_sequences)
    print("----------------------------------------")
    print("[End of epoch %d]" % epoch_count)
    print("Success rate: %f" % success_rate)
    print("----------------------------------------")
    if success_rate >= TRAINING_SUCCESS_THRESHOLD:
        break
    epoch_count += 1

# Print false classifications
classifications = model.predict(padded_validation_sequences)
print("---------------------------------------")
print("[False classifications in the validation sample (sequences)]")
for i in range(len(classifications)):
    if ((classifications[i] > SUCCESS_THRESHOLD and validation_labels[i] == 0)
            or (classifications[i] < 1 - SUCCESS_THRESHOLD and validation_labels[i] == 1)
            or (SUCCESS_THRESHOLD > classifications[i] > 1 - SUCCESS_THRESHOLD)):
        print("- " + str(tokenizer.sequences_to_texts(validation_sequences)[i]) + ": " + str(classifications[i]))
print("---------------------------------------")

# Classify a couple of samples that are not in the dataset
print("---------------------------------------")
print("[Manual examples]")
samples = [
    'Do you mind booking this hotel for our trip? Last cheap room here: https://somewebsite.com/foo',
    'Cheap booking, last chance: https://somewebsite.com/foo. Act now before it\'s too late'
]
sample_sequences = tokenizer.texts_to_sequences(samples)
padded_sample_sequences = pad_sequences(sample_sequences, maxlen=longest_sequence, padding='post')
sample_classifications = model.predict(padded_sample_sequences)
for i in range(len(sample_classifications)):
    print("- " + str(samples[i]) + ": " + str(sample_classifications[i]))