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import pandas as pd |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from sklearn.model_selection import train_test_split |
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import tensorflow as tf |
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from sklearn.metrics import classification_report, confusion_matrix, accuracy_score |
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print("---------------------- Downloading Dataset -------------------------\n") |
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dataset = pd.read_csv('https://raw.githubusercontent.com/adityaiiitmk/Datasets/master/SMSSpamCollection',sep='\t',names=['label','message']) |
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print("---------------------- -------------------------\n") |
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print(dataset.head()) |
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print("---------------------- -------------------------") |
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print(dataset.groupby('label').describe()) |
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print("---------------------- -------------------------") |
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dataset['label'] = dataset['label'].map( {'spam': 1, 'ham': 0} ) |
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X = dataset['message'].values |
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y = dataset['label'].values |
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print("---------------------- Train Test Split -------------------------\n") |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) |
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tokeniser = tf.keras.preprocessing.text.Tokenizer() |
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tokeniser.fit_on_texts(X_train) |
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encoded_train = tokeniser.texts_to_sequences(X_train) |
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encoded_test = tokeniser.texts_to_sequences(X_test) |
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print(encoded_train[0:2]) |
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print("---------------------- Padding -------------------------\n") |
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max_length = 10 |
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padded_train = tf.keras.preprocessing.sequence.pad_sequences(encoded_train, maxlen=max_length, padding='post') |
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padded_test = tf.keras.preprocessing.sequence.pad_sequences(encoded_test, maxlen=max_length, padding='post') |
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print(padded_train[0:2]) |
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print("---------------------- -------------------------\n") |
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vocab_size = len(tokeniser.word_index)+1 |
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print("---------------------- Modelling -------------------------\n") |
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model=tf.keras.models.Sequential([ |
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tf.keras.layers.Embedding(input_dim=vocab_size,output_dim= 24, input_length=max_length), |
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tf.keras.layers.SimpleRNN(24, return_sequences=False), |
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tf.keras.layers.Dense(64, activation='relu'), |
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tf.keras.layers.Dense(32, activation='relu'), |
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tf.keras.layers.Dense(1, activation='sigmoid') |
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]) |
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) |
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print("---------------------- -------------------------\n") |
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print(model.summary()) |
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print("---------------------- -------------------------\n") |
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early_stop = tf.keras.callbacks.EarlyStopping(monitor='accuracy', mode='min', patience=10) |
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print("---------------------- Training -------------------------\n") |
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model.fit(x=padded_train, |
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y=y_train, |
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epochs=50, |
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validation_data=(padded_test, y_test), |
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callbacks=[early_stop] |
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) |
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print("---------------------- -------------------------\n") |
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def c_report(y_true, y_pred): |
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print("Classification Report") |
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print(classification_report(y_true, y_pred)) |
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acc_sc = accuracy_score(y_true, y_pred) |
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print(f"Accuracy : {str(round(acc_sc,2)*100)}") |
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return acc_sc |
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def plot_confusion_matrix(y_true, y_pred): |
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mtx = confusion_matrix(y_true, y_pred) |
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sns.heatmap(mtx, annot=True, fmt='d', linewidths=.5, cmap="Blues", cbar=False) |
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plt.ylabel('True label') |
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plt.xlabel('Predicted label') |
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plt.savefig(r"C:\Users\Dell\Documents\Sem !!!\Deep Learning\App\Results\test.jpg") |
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preds = (model.predict(padded_test) > 0.5).astype("int32") |
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c_report(y_test, preds) |
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plot_confusion_matrix(y_test, preds) |
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model.save("RNN/results/model/spam_model") |
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