practice_examples/02_Spacy_Sklearn_ML.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-9QBCl9lDov"
      },
      "source": [
        "# Classical Pipeline-based ML\n",
        "\n",
        "Amazon rating classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2KMBYHjLjeQI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe461057-ac8a-457d-fe9b-a7cce0732362"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Sep 25 20:02:01 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   47C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "5Sd5ZVKhq98s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0bc7b4d7-192b-409d-f18c-ea5b72f67c53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.1.2)\n",
            "Collecting pip\n",
            "  Downloading pip-23.2.1-py3-none-any.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (67.7.2)\n",
            "Collecting setuptools\n",
            "  Downloading setuptools-68.2.2-py3-none-any.whl (807 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m807.9/807.9 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (0.41.2)\n",
            "Installing collected packages: setuptools, pip\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 67.7.2\n",
            "    Uninstalling setuptools-67.7.2:\n",
            "      Successfully uninstalled setuptools-67.7.2\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 23.1.2\n",
            "    Uninstalling pip-23.1.2:\n",
            "      Successfully uninstalled pip-23.1.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed pip-23.2.1 setuptools-68.2.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "_distutils_hack",
                  "pkg_resources",
                  "setuptools"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: spacy[cuda120] in /usr/local/lib/python3.10/dist-packages (3.6.1)\n",
            "\u001b[33mWARNING: spacy 3.6.1 does not provide the extra 'cuda120'\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (1.0.4)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (1.0.9)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (2.0.7)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (3.0.8)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (2.4.7)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (2.0.9)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (0.9.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (0.10.2)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (4.66.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (1.23.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (1.10.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (68.2.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (23.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy[cuda120]) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy[cuda120]) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy[cuda120]) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy[cuda120]) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy[cuda120]) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy[cuda120]) (2023.7.22)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy[cuda120]) (0.7.10)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy[cuda120]) (0.1.2)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy[cuda120]) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy[cuda120]) (2.1.3)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "#Update Spacy for 3.0 functionality (new features), install GPU support for better performance\n",
        "!pip install -U pip setuptools wheel\n",
        "!pip install --upgrade 'spacy[cuda120]'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YYG2E8KMoC8C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19ebe703-3a99-427b-cd2f-e10cf4c4d93a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-09-25 20:02:35.902344: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-09-25 20:02:37.071442: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2023-09-25 20:02:40.860697: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2023-09-25 20:02:40.861346: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2023-09-25 20:02:40.861589: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "Collecting en-core-web-sm==3.6.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.6.0/en_core_web_sm-3.6.0-py3-none-any.whl (12.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.7.0,>=3.6.0 in /usr/local/lib/python3.10/dist-packages (from en-core-web-sm==3.6.0) (3.6.1)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.4)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.9)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.7)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.8)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.4.7)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.9)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.9.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.10.2)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (4.66.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.23.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.10.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (68.2.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (23.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2023.7.22)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.7.10)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.1.2)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.1.3)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy download en_core_web_sm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "NNxtBhYWjUEB"
      },
      "outputs": [],
      "source": [
        "import spacy\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVmzVQcvjfwu"
      },
      "source": [
        "# Using Pandas and Sklearn for NLP-based classification\n",
        "\n",
        "Download Dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "\n",
        "#Download a larger text corpus\n",
        "def download_from_drive(link, filename):\n",
        "  id = link.split(\"/\")[-2]\n",
        "  !wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id={id}' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id={id}\" -O {\"/content/\"+filename} && rm -rf /tmp/cookies.txt\n",
        "\n",
        "download_from_drive(\"https://drive.google.com/file/d/1abJU_E85xrSSFfchUvrODOuWyJ_1AHeR/view?usp=sharing\", \"amazon_alexa.tsv\")"
      ],
      "metadata": {
        "id": "uwNo5xQ0rRbz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba0ae06c-1545-4ae2-c857-ddff3e07b78a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-25 20:03:05--  https://docs.google.com/uc?export=download&confirm=&id=1abJU_E85xrSSFfchUvrODOuWyJ_1AHeR\n",
            "Resolving docs.google.com (docs.google.com)... 142.251.2.138, 142.251.2.100, 142.251.2.139, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.251.2.138|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-00-3s-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/4oee134ng9hhulun942moli9gjgsde2i/1695672150000/08371275558726478285/*/1abJU_E85xrSSFfchUvrODOuWyJ_1AHeR?e=download&uuid=e9ecc7a8-0e15-4b91-9924-a8cbdbc658b6 [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-09-25 20:03:06--  https://doc-00-3s-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/4oee134ng9hhulun942moli9gjgsde2i/1695672150000/08371275558726478285/*/1abJU_E85xrSSFfchUvrODOuWyJ_1AHeR?e=download&uuid=e9ecc7a8-0e15-4b91-9924-a8cbdbc658b6\n",
            "Resolving doc-00-3s-docs.googleusercontent.com (doc-00-3s-docs.googleusercontent.com)... 142.250.141.132, 2607:f8b0:4023:c0b::84\n",
            "Connecting to doc-00-3s-docs.googleusercontent.com (doc-00-3s-docs.googleusercontent.com)|142.250.141.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 514752 (503K) [text/tab-separated-values]\n",
            "Saving to: ‘/content/amazon_alexa.tsv’\n",
            "\n",
            "/content/amazon_ale 100%[===================>] 502.69K  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-09-25 20:03:06 (4.01 MB/s) - ‘/content/amazon_alexa.tsv’ saved [514752/514752]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Parse to pandas"
      ],
      "metadata": {
        "id": "MOlaA2Tdrfjp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"amazon_alexa.tsv\", sep='\\t')"
      ],
      "metadata": {
        "id": "GEZ7n9lLrRTr"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "MF9o13ctl8f-",
        "outputId": "e2bf64b6-e814-4882-f7a7-f2de1c95db1f"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   rating       date         variation  \\\n",
              "0       5  31-Jul-18  Charcoal Fabric    \n",
              "1       5  31-Jul-18  Charcoal Fabric    \n",
              "2       4  31-Jul-18    Walnut Finish    \n",
              "3       5  31-Jul-18  Charcoal Fabric    \n",
              "4       5  31-Jul-18  Charcoal Fabric    \n",
              "\n",
              "                                    verified_reviews  feedback  \n",
              "0                                      Love my Echo!         1  \n",
              "1                                          Loved it!         1  \n",
              "2  Sometimes while playing a game, you can answer...         1  \n",
              "3  I have had a lot of fun with this thing. My 4 ...         1  \n",
              "4                                              Music         1  "
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXoOvSbbheUO",
        "outputId": "9e293287-01dc-4800-ae2a-5caeddb979e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3150 entries, 0 to 3149\n",
            "Data columns (total 5 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   rating            3150 non-null   int64 \n",
            " 1   date              3150 non-null   object\n",
            " 2   variation         3150 non-null   object\n",
            " 3   verified_reviews  3150 non-null   object\n",
            " 4   feedback          3150 non-null   int64 \n",
            "dtypes: int64(2), object(3)\n",
            "memory usage: 123.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "oRDUJ7rirpr1",
        "outputId": "f232fb0e-c673-4b2f-b365-e02faf8c1613"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            rating     feedback\n",
              "count  3150.000000  3150.000000\n",
              "mean      4.463175     0.918413\n",
              "std       1.068506     0.273778\n",
              "min       1.000000     0.000000\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da0e3406-681e-44a2-8f8c-a1d0827bf698 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-da0e3406-681e-44a2-8f8c-a1d0827bf698');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c2cb2e1f-6a37-4889-9045-78653893aff9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2cb2e1f-6a37-4889-9045-78653893aff9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2cb2e1f-6a37-4889-9045-78653893aff9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"rating\").describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "YgY2UQqHr5ZU",
        "outputId": "6c84b32b-6714-4234-af17-d98f5ca1cfd1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       feedback                                   \n",
              "          count mean  std  min  25%  50%  75%  max\n",
              "rating                                            \n",
              "1         161.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
              "2          96.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
              "3         152.0  1.0  0.0  1.0  1.0  1.0  1.0  1.0\n",
              "4         455.0  1.0  0.0  1.0  1.0  1.0  1.0  1.0\n",
              "5        2286.0  1.0  0.0  1.0  1.0  1.0  1.0  1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a2eca59c-fb6f-4943-9884-a6fc996e3de7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">feedback</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rating</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>161.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>152.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>455.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2286.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2eca59c-fb6f-4943-9884-a6fc996e3de7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a2eca59c-fb6f-4943-9884-a6fc996e3de7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a2eca59c-fb6f-4943-9884-a6fc996e3de7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b7990c6b-abf0-4c76-93bd-9a408548c56e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b7990c6b-abf0-4c76-93bd-9a408548c56e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b7990c6b-abf0-4c76-93bd-9a408548c56e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load pipeline! We don't need NER and DEP parsing"
      ],
      "metadata": {
        "id": "loDf6U4rrrkQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "7JM0TgDscxWO"
      },
      "outputs": [],
      "source": [
        "# *EXTRA CODE*: require_gpu() is called to force spacy to use GPU\n",
        "spacy.require_gpu()\n",
        "nlp = spacy.load(\"en_core_web_sm\", disable=[\"ner\",\"parser\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define additional columns"
      ],
      "metadata": {
        "id": "320bL0mAsIJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"length\"] = df[\"verified_reviews\"].apply(len)"
      ],
      "metadata": {
        "id": "1Xy7QXY4sCXK"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "t6WOoNrWsCUt",
        "outputId": "fed7389d-8e16-46f8-c802-44a2374a23bf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   rating       date         variation  \\\n",
              "0       5  31-Jul-18  Charcoal Fabric    \n",
              "1       5  31-Jul-18  Charcoal Fabric    \n",
              "2       4  31-Jul-18    Walnut Finish    \n",
              "3       5  31-Jul-18  Charcoal Fabric    \n",
              "4       5  31-Jul-18  Charcoal Fabric    \n",
              "\n",
              "                                    verified_reviews  feedback  length  \n",
              "0                                      Love my Echo!         1      13  \n",
              "1                                          Loved it!         1       9  \n",
              "2  Sometimes while playing a game, you can answer...         1     195  \n",
              "3  I have had a lot of fun with this thing. My 4 ...         1     172  \n",
              "4                                              Music         1       5  "
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "        document.querySelector('#df-2f3fd8eb-f9c9-4d4f-81c1-1346854ab960 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "oHewNaLcsCSN"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.length.hist(bins=300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "E3aHwsAksCMd",
        "outputId": "7ec1c411-5b50-4b3f-d92f-ad1682f760c7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.length.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8C3FR1BusPKd",
        "outputId": "3a748625-7513-4946-b6cd-0486367ececf"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3150.000000\n",
              "mean      132.049524\n",
              "std       182.099952\n",
              "min         1.000000\n",
              "25%        30.000000\n",
              "50%        74.000000\n",
              "75%       165.000000\n",
              "max      2851.000000\n",
              "Name: length, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(column=\"length\", by=\"rating\", bins=50, figsize=(10,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "-S1X-dpbsRtj",
        "outputId": "9e0fcb8b-66b3-4ac8-c0c5-0cdb2a6d4bcf"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': '1'}>, <Axes: title={'center': '2'}>],\n",
              "       [<Axes: title={'center': '3'}>, <Axes: title={'center': '4'}>],\n",
              "       [<Axes: title={'center': '5'}>, <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run our pipeline by with batched processing (not line-by-line with df.apply)"
      ],
      "metadata": {
        "id": "W7QoAJbIsZ-1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Input: A list of strings\n",
        "doc_list = nlp.pipe(df.verified_reviews.to_list())\n",
        "\n",
        "# We want to obtain a list of tokens for each string --> list[list[spacy.Doc]]\n",
        "tokens = []\n",
        "\n",
        "for doc in doc_list:\n",
        "  token_list = []\n",
        "  for token in doc:\n",
        "    token_list.append(token)\n",
        "  tokens.append(token_list)\n",
        "\n",
        "df['tokens'] = tokens"
      ],
      "metadata": {
        "id": "2hJ5ky3ksRrI"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "Qg0___CpsPAe",
        "outputId": "e961bbdb-972d-44e5-d7ad-bfcd68f13080"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   rating       date         variation  \\\n",
              "0       5  31-Jul-18  Charcoal Fabric    \n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's clean the tokens by removing punctuations, stopwords, lemmatizing and transforming to lower case.\n",
        "\n",
        "Warning for older Spacy versions: *One of spaCy's quirks, is that it lemmatizes pronouns to a placeholder token `\"-PRON-\"`. We used some if-statement logic to avoid this behaviour.*"
      ],
      "metadata": {
        "id": "peX6cpRltaWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(tokens):\n",
        "  cleaned_tokens = []\n",
        "\n",
        "  for token in tokens:\n",
        "    if not token.is_stop and token.pos_ != \"PUNCT\":\n",
        "      if token.lemma_ != \"-PRON-\":\n",
        "        cleaned_tokens.append(token.lemma_.lower().strip())\n",
        "      else:\n",
        "        cleaned_tokens.append(token.lower_.strip())\n",
        "    else:\n",
        "      pass\n",
        "\n",
        "  return cleaned_tokens"
      ],
      "metadata": {
        "id": "xonYJq5nsuZ8"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"cleaned tokens\"] = df[\"tokens\"].apply(clean_text)"
      ],
      "metadata": {
        "id": "JX9cyTYftdVn"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "gYkavkmetdTU",
        "outputId": "84befbea-fbc7-4837-b421-40df71bca2db"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   rating       date         variation  \\\n",
              "0       5  31-Jul-18  Charcoal Fabric    \n",
              "1       5  31-Jul-18  Charcoal Fabric    \n",
              "2       4  31-Jul-18    Walnut Finish    \n",
              "3       5  31-Jul-18  Charcoal Fabric    \n",
              "4       5  31-Jul-18  Charcoal Fabric    \n",
              "\n",
              "                                    verified_reviews  feedback  length  \\\n",
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              "3  I have had a lot of fun with this thing. My 4 ...         1     172   \n",
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              "                                              tokens  \\\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9a6ed1fb-14f0-4292-af69-4ad7c108553a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9a6ed1fb-14f0-4292-af69-4ad7c108553a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6916e7bc-7d66-4157-9396-e301913d6303\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6916e7bc-7d66-4157-9396-e301913d6303')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6916e7bc-7d66-4157-9396-e301913d6303 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vectorize our tokens!\n",
        "1. By Count-based BOW"
      ],
      "metadata": {
        "id": "aw7Edjzytlb4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "df_train, df_test = train_test_split(df, test_size=0.2, shuffle=True, random_state=42)\n",
        "\n",
        "def dummy(doc):\n",
        "  return doc\n",
        "\n",
        "vectorizer = CountVectorizer(analyzer=\"word\", tokenizer=dummy, preprocessor=dummy, token_pattern=None)"
      ],
      "metadata": {
        "id": "zi_N8_votdQv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dummy is needed because we have already done our tokenizing and preprocessing steps, so we just want an identity transformation there."
      ],
      "metadata": {
        "id": "hCOMzDNmuZEz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = vectorizer.fit_transform(df_train[\"cleaned tokens\"])\n",
        "y_train = df_train[\"rating\"].to_numpy()\n",
        "x_test = vectorizer.transform(df_test[\"cleaned tokens\"])\n",
        "y_test = df_test[\"rating\"].to_numpy()"
      ],
      "metadata": {
        "id": "nFfUCQA3tdJQ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tPLEFwczOZT",
        "outputId": "774b269a-5807-4afc-99eb-8ce6393c2bc3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<2520x2986 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 26223 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A simple discriminative logistic regression model:"
      ],
      "metadata": {
        "id": "lLgI1W-Dums1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg_model = LogisticRegression()\n",
        "\n",
        "logreg_model.fit(x_train, y_train);"
      ],
      "metadata": {
        "id": "ZYzFUg9iulEb"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's pretty straightforward to get back the prediction of our model based on what it has learned: just call the *predict* function on a given input x.\n",
        "Note: call it on your *x_test*!\n",
        "\n",
        "Let's see our model's accuracy. It is the most commonly used metric.\n",
        "As our dataset is highly inbalanced we should get [balanced accuracy score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html) too."
      ],
      "metadata": {
        "id": "v3ENozdpuqyD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import balanced_accuracy_score\n",
        "\n",
        "y_pred = logreg_model.predict(x_test)\n",
        "print(round(logreg_model.score(x_test, y_test), 3))\n",
        "\n",
        "balanced_accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTPyC4w2ulAl",
        "outputId": "0aada156-162d-4ec3-ff51-cca1f1f49506"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.795\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4936175213675213"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot the confusion matrix. It should be diagonal for a perfect classifier."
      ],
      "metadata": {
        "id": "2l6DGMqzu6Pl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "cnf = ConfusionMatrixDisplay.from_estimator(logreg_model, x_test, y_test, normalize=\"true\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "t7aYxpUbuk5a",
        "outputId": "8104d7d0-a858-4afb-868a-bff53fa20cbd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Try your ideas for dealing with:\n",
        "- Vectorization\n",
        "- Non-linear classification\n",
        "- Imbalanced data"
      ],
      "metadata": {
        "id": "rK01EuhavAFL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example:\n",
        "- Using TF-IDF with class_weighted Random Forest classifier"
      ],
      "metadata": {
        "id": "lkRzGKfBzBro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "\n",
        "\"A\", \"fox\", \"ate\", \"plum\", \"jumped\", \"high\"\n",
        "\n",
        "\"A fox ate plum\"\n",
        "\"A fox jumped high\"\n",
        "\n",
        "# Count vectors (BOW):\n",
        "[1 1 1 1 0 0]\n",
        "[1 1 0 0 1 1]\n",
        "\n",
        "# (N+1)/(df+1) -> idf:\n",
        "[1 1 1.5 1.5 1.5 1.5]\n",
        "\n",
        "# IDF\n",
        "[0 0 0.4 0.4 0.4 0.4]\n",
        "\n",
        "#TF-IDF\n",
        "[0  0  0.4 0.4  0   0]\n",
        "[0  0   0   0  0.4 0.4]\n",
        "\n",
        "# TF-IDF Term-frequency-inverse document frequency\n",
        "# tf * log((N+1)/(df+1))\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "hQ0QzC9p1H68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "d845582d-7cbf-4904-8c14-4590e3fda5b6"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n\\n\"A\", \"fox\", \"ate\", \"plum\", \"jumped\", \"high\"\\n\\n\"A fox ate plum\"\\n\"A fox jumped high\"\\n\\n# Count vectors (BOW):\\n[1 1 1 1 0 0]\\n[1 1 0 0 1 1]\\n\\n# (N+1)/(df+1) -> idf:\\n[1 1 1.5 1.5 1.5 1.5]\\n\\n# IDF\\n[0 0 0.4 0.4 0.4 0.4]\\n\\n#TF-IDF\\n[0  0  0.4 0.4  0   0]\\n[0  0   0   0  0.4 0.4]\\n\\n# TF-IDF Term-frequency-inverse document frequency\\n# tf * log((N+1)/(df+1))\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "metadata": {
        "id": "SgxMEAcwy_ub"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer_tfidf = TfidfVectorizer(analyzer=\"word\", tokenizer=dummy, preprocessor=dummy, token_pattern=None, norm=\"l2\")\n",
        "\n",
        "x_train_tfidf = vectorizer_tfidf.fit_transform(df_train[\"cleaned tokens\"])\n",
        "y_train_tfidf = df_train[\"rating\"].to_numpy()\n",
        "x_test_tfidf = vectorizer_tfidf.transform(df_test[\"cleaned tokens\"])\n",
        "y_test_tfidf = df_test[\"rating\"].to_numpy()"
      ],
      "metadata": {
        "id": "MiHBFv4xyEyJ"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "cls, cnt = np.unique(y_train_tfidf, return_counts = True)\n",
        "\n",
        "# f = N_class / N_all\n",
        "# cw = 1/f = N_all / N_class\n",
        "\n",
        "cw = ((cnt.sum()/(cnt+1))/len(cnt))\n",
        "\n",
        "weight_dict = dict(zip(cls, cw))"
      ],
      "metadata": {
        "id": "gA26JQdz41Gc"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weight_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOsGHQ6el2Ya",
        "outputId": "c1d90688-12e1-4b53-f2ae-7ce3d9b8c690"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1: 3.8769230769230765,\n",
              " 2: 7.098591549295774,\n",
              " 3: 4.1652892561983474,\n",
              " 4: 1.3770491803278688,\n",
              " 5: 0.2743603701687534}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rndf_model = RandomForestClassifier(class_weight=weight_dict)\n",
        "\n",
        "rndf_model.fit(x_train_tfidf.toarray(), y_train_tfidf);\n",
        "\n",
        "y_pred_rndf = rndf_model.predict(x_test_tfidf.toarray())\n",
        "print(round(rndf_model.score(x_test_tfidf.toarray(), y_test_tfidf), 3))\n",
        "\n",
        "balanced_accuracy_score(y_test_tfidf, y_pred_rndf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjicqWOyzxhc",
        "outputId": "83671107-29af-4a21-f009-bbd890817d9c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.797\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5053995726495726"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnf = ConfusionMatrixDisplay.from_estimator(rndf_model, x_test_tfidf, y_test_tfidf, normalize=\"true\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "pVHT16nOzxdX",
        "outputId": "6f83fe3f-6908-49f3-f195-e61c4af37a97"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UY7c03MTqXw5"
      },
      "execution_count": 35,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}