Sentiment Analysis on IMDB Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8855b938",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3566a333",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLP for the IMDB problem\n",
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing import sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7a90fd",
   "metadata": {},
   "source": [
    "## Load and Split dataset in training and testing dataset\n",
    "\n",
    "load the dataset but only keep the top n words, zero the rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd91d196",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words = 5000\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48ba4cfa",
   "metadata": {},
   "source": [
    "## pad_sequences\n",
    "pad_sequences is used to ensure that all sequences in a list have the same length.\n",
    "\n",
    "\n",
    "    tf.keras.preprocessing.sequence.pad_sequences(\n",
    "            sequences, maxlen=None, dtype='int32', padding='pre',\n",
    "            truncating='pre', value=0.0\n",
    "    )\n",
    "\n",
    "\n",
    "\n",
    "This function transforms a list (of length num_samples) of \n",
    "**sequences** (lists of integers) into a 2D Numpy array of shape (num_samples, num_timesteps). \n",
    "num_timesteps is either the **maxlen** argument if provided, or the length of the longest sequence in the list.\n",
    "\n",
    "Sequences that are shorter than num_timesteps are padded with value until they are num_timesteps long.\n",
    "\n",
    "Sequences longer than num_timesteps are truncated so that they fit the desired length.\n",
    "\n",
    "The position where padding or truncation happens is determined by the arguments **padding** and **truncating**, respectively. Pre-padding or removing values from the beginning of the sequence is the default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf065626",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_words = 500\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=max_words)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=max_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb355d4d",
   "metadata": {},
   "source": [
    "## Design MLP Model\n",
    "\n",
    "### Sequential model\n",
    "\n",
    "A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.\n",
    "\n",
    "**add method:** Sequential.add(layer). Adds a layer instance on top of the layer stack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b838856e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 500, 32)           160000    \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 16000)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 250)               4000250   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 251       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,160,501\n",
      "Trainable params: 4,160,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(top_words, 32, input_length=max_words))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(250, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae32c93b",
   "metadata": {},
   "source": [
    "## Fit the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87923000",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "196/196 - 20s - loss: 0.5801 - accuracy: 0.6506 - val_loss: 0.3155 - val_accuracy: 0.8657 - 20s/epoch - 103ms/step\n",
      "Epoch 2/2\n",
      "196/196 - 19s - loss: 0.2233 - accuracy: 0.9126 - val_loss: 0.3114 - val_accuracy: 0.8674 - 19s/epoch - 95ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b862629730>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=128, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a096c6",
   "metadata": {},
   "source": [
    "## Predict The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd78a720",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c0ca35fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.31302392],\n",
       "       [0.9965226 ],\n",
       "       [0.5378991 ],\n",
       "       ...,\n",
       "       [0.2789628 ],\n",
       "       [0.12609693],\n",
       "       [0.56096053]], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "358f4eec",
   "metadata": {},
   "outputs": [
    {
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       " ...]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "rounded = [float(np.round(x)) for x in predict]\n",
    "rounded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761ea264",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2d09122",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1b85c7b57c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "ConfusionMatrixDisplay.from_predictions(y_test, rounded)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18bfd938",
   "metadata": {},
   "source": [
    "## Evaluation of Model\n",
    "\n",
    "Returns the loss value & metrics values for the model in test mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4e0d11d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final evaluation of the model\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a200eb5f",
   "metadata": {},
   "source": [
    "## Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "73220991",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.74%\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0276327",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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