Spam Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Step-1:Download the dataset using requests library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datasetfile = pathlib.Path(\"smsspamcollection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset already exists\n"
     ]
    }
   ],
   "source": [
    "if datasetfile.is_file():\n",
    "    print('dataset already exists')\n",
    "else:\n",
    "    import requests\n",
    "    import zipfile\n",
    "    import io  \n",
    "    r = requests.get(\"http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\",stream=True)\n",
    "    z = zipfile.ZipFile(io.BytesIO(r.content))\n",
    "    z.extractall() #download's and extracts to current directory\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  Useful URL's <br/>\n",
    "https://github.com/dbsheta/spam-detection-using-deep-learning/blob/master/spam_detection.ipynb <br/>\n",
    "http://mindmech.net/?p=61 <br/>\n",
    "http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection <br/>\n",
    "https://www.kaggle.com/jacklinggu/tfidf-to-keras-dense-neural-network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Step-2:  Create dataframe with tab seperated data of the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pandas.read_csv('smsspamcollection', sep='\\t',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = ['label', 'msg'] # set column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### print the top 5 lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>msg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                                msg\n",
       "0   ham  Go until jurong point, crazy.. Available only ...\n",
       "1   ham                      Ok lar... Joking wif u oni...\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3   ham  U dun say so early hor... U c already then say...\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "msgs = df['msg'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = [1 if x == \"spam\" else 0 for x in df['label'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ok lar... Joking wif u oni...',\n",
       "       \"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\",\n",
       "       'U dun say so early hor... U c already then say...',\n",
       "       \"Nah I don't think he goes to usf, he lives around here though\"], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msgs[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 0, 0]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Step-3:  Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras.preprocessing.text as kpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found  unique tokens 9009\n"
     ]
    }
   ],
   "source": [
    "tokenizer = kpt.Tokenizer()\n",
    "tokenizer.fit_on_texts(msgs)\n",
    "sequences = tokenizer.texts_to_sequences(msgs)\n",
    "word2index = tokenizer.word_index\n",
    "num_words = len(word2index)\n",
    "print(\"Found  unique tokens \" + num_words.__str__());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MAX_WORDS_IN_SEQ=9009#1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras.preprocessing.sequence as kps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras.utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import sklearn.model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 1, 0, 0, 1, 0, 0, 1, 1]\n",
      "[[ 1.  0.]\n",
      " [ 1.  0.]\n",
      " [ 0.  1.]\n",
      " [ 1.  0.]\n",
      " [ 1.  0.]\n",
      " [ 0.  1.]\n",
      " [ 1.  0.]\n",
      " [ 1.  0.]\n",
      " [ 0.  1.]\n",
      " [ 0.  1.]]\n",
      "Shape of data tensor: (5572, 9009)\n",
      "Shape of label tensor: (5572, 2)\n"
     ]
    }
   ],
   "source": [
    "data = kps.pad_sequences(sequences, maxlen=MAX_WORDS_IN_SEQ, padding='post', truncating='post')\n",
    "print(labels[:10])\n",
    "labels = keras.utils.to_categorical(labels)\n",
    "print(labels[:10])\n",
    "\n",
    "print('Shape of data tensor:', data.shape)\n",
    "print('Shape of label tensor:', labels.shape)\n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(data, labels, test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###### Step-4:  Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Conv1D, GlobalMaxPooling1D, Dropout, Dense, Input, Embedding, MaxPooling1D, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Model, load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "EMBED_DIM = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_seq = Input(shape=[MAX_WORDS_IN_SEQ, ], dtype='int32')\n",
    "embed_seq = Embedding(num_words, EMBED_DIM, embeddings_initializer='glorot_uniform', input_length=MAX_WORDS_IN_SEQ)(\n",
    "    input_seq)\n",
    "conv_1 = Conv1D(128, 5, activation='relu')(embed_seq)\n",
    "conv_1 = MaxPooling1D(pool_size=5)(conv_1)\n",
    "conv_2 = Conv1D(128, 5, activation='relu')(conv_1)\n",
    "conv_2 = MaxPooling1D(pool_size=5)(conv_2)\n",
    "conv_3 = Conv1D(128, 5, activation='relu')(conv_2)\n",
    "conv_3 = MaxPooling1D(pool_size=35)(conv_3)\n",
    "flat = Flatten()(conv_3)\n",
    "# flat = Dropout(0.25)(flat)\n",
    "fc1 = Dense(128, activation='relu')(flat)\n",
    "# dense_1 = Dropout(0.25)(flat)\n",
    "fc2 = Dense(2, activation='softmax')(fc1)\n",
    "\n",
    "model = Model(input_seq, fc2)\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 9009)              0         \n",
      "_________________________________________________________________\n",
      "embedding_2 (Embedding)      (None, 9009, 100)         900900    \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 9005, 128)         64128     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1 (None, 1801, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 1797, 128)         82048     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1 (None, 359, 128)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 355, 128)          82048     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1 (None, 10, 128)           0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1280)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               163968    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 1,293,350\n",
      "Trainable params: 1,293,350\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Testing ---------------------------------------\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " 864/4457 [====>.........................] - ETA: 389s - loss: 0.3500 - acc: 0.8877"
     ]
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=100, batch_size=1, verbose=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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