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)
Implementation
We will import all the required libraries
Pythonimportnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltimportseabornassnsimporttensorflowastffromtensorflowimportkerasfromtensorflow.kerasimportlayers
Load the Dataset using pandas function .read_csv()
Python# Reading the datadf=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 tolabel andText, respectively. Since the target variable is in string form, we will encode it numerically using pandas function.map().
Pythondf=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 ofHamandSpamdata.
Pythonsns.countplot(x=df['label'])plt.show()
countplotThe 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.
Python# Find average number of tokens in all sentencesavg_words_len=round(sum([len(i.split())foriindf['Text']])/len(df['Text']))print(avg_words_len)
Output
16
Now, let's find the total number of unique words in the corpus
Python# Finding Total no of unique words in corpuss=set()forsentindf['Text']:forwordinsent.split():s.add(word)total_words_length=len(s)print(total_words_length)
Output
15686
Now, splitting the data into training and testing parts usingtrain_test_split()function.
Python# Splitting data for Training and testingfromsklearn.model_selectionimporttrain_test_splitX,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.shapeShape 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 chooseMultinomialNB(),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.
Pythonfromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.naive_bayesimportMultinomialNBfromsklearn.metricsimportclassification_report,accuracy_scoretfidf_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 vectorizationis the process of converting text into a numerical representation. Example: Bag of words frequency, Binary Term frequency, etc.;
Aword 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.
Pythonfromtensorflow.keras.layersimportTextVectorizationMAXTOKENS=total_words_lengthOUTPUTLEN=avg_words_lentext_vec=TextVectorization(max_tokens=MAXTOKENS,standardize='lower_and_strip_punctuation',output_mode='int',output_sequence_length=OUTPUTLEN)text_vec.adapt(X_train)
- MAXTOKENSis the maximum size of the vocabulary which was found earlier
- OUTPUTLENis the length to which the sentences should be padded irrespective of the sentence length.
Outputof a sample sentence using text vectorization is shown below:
Now let's create an embedding layer
Pythonembedding_layer=layers.Embedding(input_dim=MAXTOKENS,output_dim=128,embeddings_initializer='uniform',input_length=OUTPUTLEN)
- input_dimis the size of vocabulary
- output_dimis the dimension of the embedding layer i.e, the size of the vector in which the words will be embedded
- input_lengthis the length of input sequences
Now, let's build and compile model 1 using the Tensorflow Functional API
Pythoninput_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.
Pythonfromsklearn.metricsimportprecision_score,recall_score,f1_scoredefcompile_model(model):'''simply compile the model with adam optimzer'''model.compile(optimizer=keras.optimizers.Adam(),loss=keras.losses.BinaryCrossentropy(),metrics=['accuracy'])defevaluate_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}returnmodel_results_dictdeffit_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 '''# Check if validation data is providedifX_testisnotNoneandy_testisnotNone:history=model.fit(X_train,y_train,epochs=epochs,validation_data=(X_test,y_test))#Removed validation steps argumentelse:# Handle case where validation data is not providedhistory=model.fit(X_train,y_train,epochs=epochs)returnhistory# This code is modified by Susobhan AkhuliModel -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
Pythoninput_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 modelhistory_2=fit_model(model_2,epochs=5,X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test)# fit the model# This code is modified by Susobhan Akhuli
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.
Pythonimporttensorflow_hubashubimporttensorflowastffromtensorflowimportkerasfromtensorflow.kerasimportlayers# Disable XLA compilationtf.config.optimizer.set_jit(False)# model with functional apiinput_layer=keras.Input(shape=[],dtype=tf.string)# universal-sentence-encoder layer# directly from tfhubuse_layer=hub.KerasLayer("https://tfhub.dev/google/universal-sentence-encoder/4",trainable=False,input_shape=[],dtype=tf.string,name='USE')# Create a Lambda layer to wrap the use_layer call# Specify the output shape of the USE layerx=layers.Lambda(lambdax:use_layer(x),output_shape=(512,))(input_layer)x=layers.Dropout(0.2)(x)x=layers.Dense(64,activation=keras.activations.relu)(x)output_layer=layers.Dense(1,activation=keras.activations.sigmoid)(x)model_3=keras.Model(input_layer,output_layer)compile_model(model_3)history_3=fit_model(model_3,epochs=5)# This code is modified by Susobhan AkhuliCompiling and training the model
Training model with USEAnalyzing our Model Performance
We will use the helper function which we created earlier to evaluate model performance.
Pythonbaseline_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_resultsOutput
baseline model resultsPlotting 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 learningmodel gives the best accuracy and f1-score.
Get the Complete notebook:
Notebook: click here.
Dataset: click here.
SMS Spam Detection using TensorFlow in Python