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SMS Spam Detection using TensorFlow in Python

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  • Difficulty Level : Medium
  • Last Updated : 02 Aug, 2022
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In today’s society, practically everyone has a mobile phone, and they all get communications (SMS/ email) on their phone regularly. But the essential point is that majority of the messages received will be spam, with only a few being ham or necessary communications. Scammers create fraudulent text messages to deceive you into giving them your personal information, such as your password, account number, or Social Security number. If they have such information, they may be able to gain access to your email, bank, or other accounts.

In this article, we are going to develop various deep learning models using Tensorflow for SMS spam detection and also analyze the performance metrics of different models.

We will be using  SMS Spam Detection Dataset, which contains SMS text and corresponding label (Ham or spam)

Dataset can be downloaded from here https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset

Implementation

We will import all the required libraries

Python3




import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers


Load the Dataset using pandas function .read_csv()

Python3




# Reading the data
df = pd.read_csv("/content/spam.csv",encoding='latin-1')
df.head()


Lets, Understand the data

 

As we can see that the dataset contains three unnamed columns with null values. So we drop those columns and rename the columns v1 and v2 to label and Text, respectively. Since the target variable is in string form, we will encode it numerically using pandas function .map().

Python3




df = df.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'],axis=1)
df = df.rename(columns={'v1':'label','v2':'Text'})
df['label_enc'] = df['label'].map({'ham':0,'spam':1})
df.head()


Output after the above data preprocessing.

 

Let’s visualize the distribution of Ham and Spam data.

Python3




sns.countplot(x=df['label'])
plt.show()


 

The ham data is comparatively higher than spam data, it’s natural. Since we are going to use embeddings in our deep learning model, we need not balance the data. Now, let’s find the average number of words in all the sentences in SMS data.

Python3




# Find average number of tokens in all sentences
avg_words_len=round(sum([len(i.split()) for i in df['Text']])/len(df['Text']))
print(avg_words_len)


Output

Average number of words in each sentence in SMS data

Now, let’s find the total number of unique words in the corpus

Python3




# Finding Total no of unique words in corpus
s = set()
for sent in df['Text']:
  for word in sent.split():
    s.add(word)
total_words_length=len(s)
print(total_words_length)


 

Now,  splitting the data into training and testing parts using train_test_split() function.

Python3




# Splitting data for Training and testing
from sklearn.model_selection import train_test_split
  
X, y = np.asanyarray(df['Text']), np.asanyarray(df['label_enc'])
new_df = pd.DataFrame({'Text': X, 'label': y})
X_train, X_test, y_train, y_test = train_test_split(
    new_df['Text'], new_df['label'], test_size=0.2, random_state=42)
X_train.shape, y_train.shape, X_test.shape, y_test.shape


Shape of train and test data

 

Building the models

First, we will build a baseline model and then we’ll try to beat the performance of the baseline model using deep learning models (embeddings, LSTM, etc)

Here, we will chose MultinomialNB(), which performs well for text classification when the features are discrete like word counts of the words or tf-idf vectors. The tf-idf is a measure that tells how important or relevant a word is the document.

Python3




from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report,accuracy_score
  
tfidf_vec = TfidfVectorizer().fit(X_train)
X_train_vec,X_test_vec = tfidf_vec.transform(X_train),tfidf_vec.transform(X_test)
  
baseline_model = MultinomialNB()
baseline_model.fit(X_train_vec,y_train)


Performance of baseline model

 

Confusion matrix for the baseline model

 

Model 1: Creating custom Text vectorization and embedding layers:

Text vectorization is the process of converting text into a numerical representation. Example: Bag of words frequency, Binary Term frequency, etc.;

A word embedding is a learned representation of text in which words with related meanings have similar representations. Each word is assigned to a single vector, and the vector values are learned like that of a neural network.

Now, we’ll create a custom text vectorization layer using TensorFlow.

Python3




from tensorflow.keras.layers import TextVectorization
  
MAXTOKENS=total_words_length
OUTPUTLEN=avg_words_len
  
text_vec = TextVectorization(
    max_tokens=MAXTOKENS,
    standardize='lower_and_strip_punctuation',
    output_mode='int',
    output_sequence_length=OUTPUTLEN
)
text_vec.adapt(X_train)


  • MAXTOKENS is the maximum size of the vocabulary which was found earlier
  • OUTPUTLEN is the length to which the sentences should be padded irrespective of the sentence length.

Output of a sample sentence using text vectorization is shown below:

 

Now let’s create an embedding layer

Python3




embedding_layer = layers.Embedding(
    input_dim=MAXTOKENS,
    output_dim=128,
    embeddings_initializer='uniform',
    input_length=OUTPUTLEN
)


  • input_dim is the size of vocabulary
  • output_dim is the dimension of the embedding layer i.e, the size of the vector in which the words will be embedded
  • input_length is the length of input sequences

Now, let’s build and compile model 1 using the Tensorflow Functional API

Python3




input_layer = layers.Input(shape=(1,), dtype=tf.string)
vec_layer = text_vec(input_layer)
embedding_layer_model = embedding_layer(vec_layer)
x = layers.GlobalAveragePooling1D()(embedding_layer_model)
x = layers.Flatten()(x)
x = layers.Dense(32, activation='relu')(x)
output_layer = layers.Dense(1, activation='sigmoid')(x)
model_1 = keras.Model(input_layer, output_layer)
  
model_1.compile(optimizer='adam', loss=keras.losses.BinaryCrossentropy(
    label_smoothing=0.5), metrics=['accuracy'])


Summary of the model 1

 

Training the model-1

 

Plotting the history of model-1

 

Let’s create helper functions for compiling, fitting, and evaluating the model performance.

Python3




from sklearn.metrics import precision_score, recall_score, f1_score
  
def compile_model(model):
    '''
    simply compile the model with adam optimzer
    '''
    model.compile(optimizer=keras.optimizers.Adam(),
                  loss=keras.losses.BinaryCrossentropy(),
                  metrics=['accuracy'])
  
def fit_model(model, epochs, X_train=X_train, y_train=y_train,
              X_test=X_test, y_test=y_test):
    '''
    fit the model with given epochs, train 
    and test data
    '''
    history = model.fit(X_train,
                        y_train,
                        epochs=epochs,
                        validation_data=(X_test, y_test),
                        validation_steps=int(0.2*len(X_test)))
    return history
  
def evaluate_model(model, X, y):
    '''
    evaluate the model and returns accuracy, 
    precision, recall and f1-score 
    '''
    y_preds = np.round(model.predict(X))
    accuracy = accuracy_score(y, y_preds)
    precision = precision_score(y, y_preds)
    recall = recall_score(y, y_preds)
    f1 = f1_score(y, y_preds)
  
    model_results_dict = {'accuracy': accuracy,
                          'precision': precision,
                          'recall': recall,
                          'f1-score': f1}
  
    return model_results_dict


Model -2 Bidirectional LSTM

A bidirectional LSTM (Long short-term memory) is made up of two LSTMs, one accepting input in one direction and the other in the other. BiLSTMs effectively improve the network’s accessible information, boosting the context for the algorithm (e.g. knowing what words immediately follow and precede a word in a sentence).

Building and compiling the model-2

Python3




input_layer = layers.Input(shape=(1,), dtype=tf.string)
vec_layer = text_vec(input_layer)
embedding_layer_model = embedding_layer(vec_layer)
bi_lstm = layers.Bidirectional(layers.LSTM(
    64, activation='tanh', return_sequences=True))(embedding_layer_model)
lstm = layers.Bidirectional(layers.LSTM(64))(bi_lstm)
flatten = layers.Flatten()(lstm)
dropout = layers.Dropout(.1)(flatten)
x = layers.Dense(32, activation='relu')(dropout)
output_layer = layers.Dense(1, activation='sigmoid')(x)
model_2 = keras.Model(input_layer, output_layer)
  
compile_model(model_2)  # compile the model
history_2 = fit_model(model_2, epochs=5# fit the model


Training the model

 

Model -3 Transfer Learning with USE Encoder

Transfer Learning

Transfer learning is a machine learning approach in which a model generated for one job is utilized as the foundation for a model on a different task.

USE Layer (Universal Sentence Encoder)

The Universal Sentence Encoder converts text into high-dimensional vectors that may be used for text categorization, semantic similarity, and other natural language applications.

The USE can be downloaded from tensorflow_hub and can be used as a layer using .kerasLayer() function.

Python3




import tensorflow_hub as hub
  
# model with Sequential api
model_3 = keras.Sequential()
  
# universal-sentence-encoder layer 
# directly from tfhub
                           trainable=False,
                           input_shape=[],
                           dtype=tf.string,
                           name='USE')
model_3.add(use_layer)
model_3.add(layers.Dropout(0.2))
model_3.add(layers.Dense(64, activation=keras.activations.relu))
model_3.add(layers.Dense(1, activation=keras.activations.sigmoid))
  
compile_model(model_3)
  
history_3 = fit_model(model_3, epochs=5)


Compiling and training the model

Training model with USE

Analyzing our Model Performance

We will use the helper function which we created earlier to evaluate model performance.

Python3




baseline_model_results = evaluate_model(baseline_model, X_test_vec, y_test)
model_1_results = evaluate_model(model_1, X_test, y_test)
model_2_results = evaluate_model(model_2, X_test, y_test)
model_3_results = evaluate_model(model_3, X_test, y_test)
  
total_results = pd.DataFrame({'MultinomialNB Model':baseline_model_results,
                             'Custom-Vec-Embedding Model':model_1_results,
                             'Bidirectional-LSTM Model':model_2_results,
                             'USE-Transfer learning Model':model_3_results}).transpose()
  
total_results


Output

 

Plotting the results

 

Metrics

All four models deliver excellent results. (All of them have greater than 96 percent accuracy), thus comparing them might be difficult.

Problem

We have an unbalanced dataset; most of our data points contain the label “ham,” which is natural because most SMS are ham. Accuracy cannot be an appropriate metric in certain situations. Other measurements are required.

Which metric is better?

  • False negative and false positive are significant in this problem. Precision and recall are the metrics that allow us the ability to calculate them, but there is one more, ‘f1-score.’
  • The f1-score is the harmonic mean of accuracy and recall. Thus, we can get both with a single shot.
  • USE-Transfer learning model gives the best accuracy and f1-score.

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