Log_regression_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.8.5\r\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g781SNSs0V8N"
   },
   "source": [
    "### Data Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w-x-6bEK0aLT",
    "outputId": "dcfc6d97-3ae6-4958-e81e-42f601338ece"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/gdrive', force_remount=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "irtbx68K0V8S"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "class Sentiment:\n",
    "    NEGATIVE = \"NEGATIVE\"\n",
    "    NEUTRAL = \"NEUTRAL\"\n",
    "    POSITIVE = \"POSITIVE\"\n",
    "\n",
    "class Review:\n",
    "    def __init__(self, text, score):\n",
    "        self.text = text\n",
    "        self.score = score\n",
    "        self.sentiment = self.get_sentiment()\n",
    "        \n",
    "    def get_sentiment(self):\n",
    "        if self.score <= 2:\n",
    "            return Sentiment.NEGATIVE\n",
    "        elif self.score == 3:\n",
    "            return Sentiment.NEUTRAL\n",
    "        else: #Score of 4 or 5\n",
    "            return Sentiment.POSITIVE\n",
    "\n",
    "class ReviewContainer:\n",
    "    def __init__(self, reviews):\n",
    "        self.reviews = reviews\n",
    "        \n",
    "    def get_text(self):\n",
    "        return [x.text for x in self.reviews]\n",
    "    \n",
    "    def get_sentiment(self):\n",
    "        return [x.sentiment for x in self.reviews]\n",
    "        \n",
    "    def evenly_distribute(self):\n",
    "        negative = list(filter(lambda x: x.sentiment == Sentiment.NEGATIVE, self.reviews))\n",
    "        positive = list(filter(lambda x: x.sentiment == Sentiment.POSITIVE, self.reviews))\n",
    "        positive_shrunk = positive[:len(negative)]\n",
    "        self.reviews = negative + positive_shrunk\n",
    "        random.shuffle(self.reviews)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1fg6K8Ml0V8U"
   },
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cFwDGvVA0V8U",
    "outputId": "5ee80225-5c8c-48b7-bcd5-c6af3266d27b"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "file_name = './Books_small_10000.json'\n",
    "\n",
    "reviews = []\n",
    "with open(file_name) as f:\n",
    "    for line in f:\n",
    "        review = json.loads(line)\n",
    "        reviews.append(Review(review['reviewText'], review['overall']))\n",
    "        \n",
    "print(reviews[0].sentiment)\n",
    "print(reviews[0].text)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U_iulOtm0V8V"
   },
   "source": [
    "### Prep Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yq-tnP3h0V8W"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "training, test = train_test_split(reviews, test_size=0.20)\n",
    "\n",
    "train_container = ReviewContainer(training)\n",
    "\n",
    "test_container = ReviewContainer(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VTZfpB5K0V8W",
    "outputId": "a1d4ab1b-b6e0-44dc-92cc-20c5abb7e139"
   },
   "outputs": [],
   "source": [
    "train_container.evenly_distribute()\n",
    "train_x = train_container.get_text()\n",
    "train_y = train_container.get_sentiment()\n",
    "\n",
    "test_container.evenly_distribute()\n",
    "test_x = test_container.get_text()\n",
    "test_y = test_container.get_sentiment()\n",
    "\n",
    "print(train_y.count(Sentiment.POSITIVE))\n",
    "print(train_y.count(Sentiment.NEGATIVE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-qH795BN0V8X"
   },
   "source": [
    "#### Bag of words vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TYnxEOxL0V8X",
    "outputId": "fcc098fb-aa8b-4bc9-8fff-5561c06fce86",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer(max_features=10000)\n",
    "train_x_vectors = vectorizer.fit_transform(train_x)\n",
    "test_x_vectors = vectorizer.transform(test_x)\n",
    "\n",
    "# print(train_x[0])\n",
    "# print(train_x_vectors[0].toarray())\n",
    "print(train_x_vectors[11].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(vectorizer, open(\"./models/tfidf.pickle\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'./models/tfidf.pickle', 'rb') as f:\n",
    "    vectorizer = pickle.load(f)\n",
    "    train_x_vectors = vectorizer.transform(train_x)\n",
    "    test_x_vectors = vectorizer.transform(test_x)\n",
    "    print(train_x_vectors[11].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TzywuszZ0V8Y"
   },
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qTiP5WsR0V8a"
   },
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sxH1-_KK0V8a",
    "outputId": "590af8b9-8701-4a6f-a6a7-eec138716417"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf_log = LogisticRegression()\n",
    "clf_log.fit(train_x_vectors, train_y)\n",
    "\n",
    "clf_log.predict(test_x_vectors[5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3wuSjp-Y0V8b"
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BbV2MJoz0V8b",
    "outputId": "3dad607d-6080-4062-fc64-2b67efb688ca"
   },
   "outputs": [],
   "source": [
    "# Mean Accuracy\n",
    "print(clf_log.score(test_x_vectors, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "caDRT7tS0V8b",
    "outputId": "85332c0f-9da8-4d55-8ce6-bea7708d7f70"
   },
   "outputs": [],
   "source": [
    "# F1 Scores\n",
    "from sklearn.metrics import f1_score\n",
    "f1_score(test_y, clf_log.predict(test_x_vectors), average=None, labels=[Sentiment.POSITIVE, Sentiment.NEUTRAL, Sentiment.NEGATIVE])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ttUNuo2W0V8c",
    "outputId": "0e8f92fa-c04c-404a-d5c3-ede1e2db87ed"
   },
   "outputs": [],
   "source": [
    "test_set =[\"good book\"]\n",
    "test_set_vector = vectorizer.transform(test_set)\n",
    "clf_log.predict(test_set_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kOoE0JPP0V8d",
    "outputId": "2332b70f-039a-4c32-ebbb-50c0579d42c5"
   },
   "outputs": [],
   "source": [
    "print(clf.score(test_x_vectors, test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fEuiqYNB0V8d"
   },
   "source": [
    "## Saving Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zPACSv2i0V8d"
   },
   "source": [
    "#### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZWNs4VL30V8e"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('./models/sentiment_classifier.pkl', 'wb') as f:\n",
    "    pickle.dump(clf_log, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sr0vricD0V8e"
   },
   "source": [
    "#### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ozG9J8HR0V8e"
   },
   "outputs": [],
   "source": [
    "with open('./models/sentiment_classifier.pkl', 'rb') as f:\n",
    "    loaded_clf = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q37WXLBI0V8e",
    "outputId": "79415c9e-a413-43cd-b023-1d6aef6a401d"
   },
   "outputs": [],
   "source": [
    "print(test_x[0])\n",
    "loaded_clf.predict(test_x_vectors[0])"
   ]
  }
 ],
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