[Train]_SIBI_Lang_Estimation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eP5SHGKzKGHn"
      },
      "source": [
        "## SIBI (Sistem Isyarat Bahasa Indonesia) Language Estimation\n",
        "\n",
        "Pada colab ini berisi sistem yang dapat mengestimasi bahasa SIBI yang diucapkan oleh pengguna. Pada proyek ini menggunakan data koordinat (x dan y) dari hasil pembacaaan *Hand Landmark*. Kemudian pada proyek ini menggunakan metode spatiotemporal untuk melakukan ekstraksi fitur dari obyek yang akan diamati. Untuk proses *Learning* dan *Prediction* menggunakan metode *Support Vector Machine* (SVM).\n",
        "\n",
        "Pada proyek ini membutuhkan beberapa library tambahan guna menunjang proyek ini. Library tambahan tersebut antara lain:\n",
        "1.   Mediapipe\n",
        "2.   OpenCV\n",
        "3.   Scikit-learn\n",
        "4.   Pandas\n",
        "5.   Pickle\n",
        "\n",
        "Masing-masing dari library diatas akan dijelaskan ditiap shell.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFDYUsaFL4-M"
      },
      "source": [
        "# Install Mediapipe Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tof3rgir78ky",
        "outputId": "74160d28-308e-4be5-a2aa-f7c42148b2ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mediapipe\n",
            "  Downloading mediapipe-0.8.10.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (32.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 32.9 MB 1.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mediapipe) (1.21.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mediapipe) (3.2.2)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from mediapipe) (1.2.0)\n",
            "Requirement already satisfied: protobuf<4,>=3.11 in /usr/local/lib/python3.7/dist-packages (from mediapipe) (3.17.3)\n",
            "Requirement already satisfied: attrs>=19.1.0 in /usr/local/lib/python3.7/dist-packages (from mediapipe) (22.1.0)\n",
            "Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.7/dist-packages (from mediapipe) (4.6.0.66)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf<4,>=3.11->mediapipe) (1.15.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mediapipe) (4.1.1)\n",
            "Installing collected packages: mediapipe\n",
            "Successfully installed mediapipe-0.8.10.1\n"
          ]
        }
      ],
      "source": [
        "# Install Mediapipe Library\n",
        "!pip install mediapipe "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XC9j9qvL91g"
      },
      "source": [
        "# Import Required Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IU4G2vdU8URW"
      },
      "outputs": [],
      "source": [
        "# import library and framework\n",
        "import cv2\n",
        "import csv\n",
        "import numpy as np\n",
        "import mediapipe as mp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7OhPbG2MG84"
      },
      "source": [
        "# Call mediapipe library and assign to object"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EmQZrK-r8q-2"
      },
      "outputs": [],
      "source": [
        "# create object for draw and get hand detection\n",
        "mp_drawing = mp.solutions.drawing_utils\n",
        "mp_drawing_styles = mp.solutions.drawing_styles\n",
        "mp_hands = mp.solutions.hands"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JYp0T49O94n"
      },
      "source": [
        "# Data Acquisition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9vWJJmkE89A"
      },
      "source": [
        "Pada bagian ini akan melakukan pengambilan data koordinat dari *hand landmark* dengan menggunakan metode spatiotemporal. Spatiotemporal adalah proses analisa yang melibatkan ruang dan waktu. Dengan spatiotemporal sistem dapat mengetahui perubahan pose dari obyek yang diamati pada tiap frame. Pada proyek ini akan mengambil data sebanyak 210 data. Data tersebut didapat dari perhitungan sebagai berikut:\n",
        "\n",
        "  spatio_data = jumlah keypoint yang diambil x 2 x jumlah frame yang akan diambil\n",
        "\n",
        "Pada proyek ini jumlah keypoint yang diambil sebanyak 21 titik. Nilai '2' diatas diambil dari jumlah koordinat yang akan dihitung yaitu X dan Y dan jumlah frame yang akan diambil sebanyak 5 frame. Untuk penjelasan mengena spatiotemporal biasa dilihat referensi dibawah ini. \n",
        "\n",
        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) \n",
        "\n",
        "Spatiotemporal reference: [documentation](https://drive.google.com/file/d/18DpgE5vpjp3kihcZ48wzgN0cfSk5AUNo/view?usp=sharing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFnU9GsufcrD"
      },
      "source": [
        "### Getting data from video or live webcam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u5F1E89x81GI"
      },
      "outputs": [],
      "source": [
        "# Define global variable for hand keypoints data\n",
        "hand_keypoint_data = np.array([])\n",
        "\n",
        "# For webcam input:\n",
        "cap = cv2.VideoCapture(0)\n",
        "with mp_hands.Hands(\n",
        "    model_complexity=0,\n",
        "    min_detection_confidence=0.5,\n",
        "    min_tracking_confidence=0.5) as hands:\n",
        "    # Read until video is completed\n",
        "  while cap.isOpened():\n",
        "    # Capture frame-by-frame\n",
        "    success, image = cap.read()\n",
        "    if not success:\n",
        "      print(\"Ignoring empty camera frame.\")\n",
        "      # If loading a video, use 'break' instead of 'continue'.\n",
        "      continue\n",
        "    \n",
        "    try:\n",
        "      # To improve performance, optionally mark the image as not writeable to\n",
        "      # pass by reference.\n",
        "      image.flags.writeable = False\n",
        "      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "      results = hands.process(image)\n",
        "\n",
        "      # Draw the hand annotations on the image.\n",
        "      image.flags.writeable = True\n",
        "      image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
        "      if results.multi_hand_landmarks:\n",
        "        for hand_landmarks in results.multi_hand_landmarks:\n",
        "          mp_drawing.draw_landmarks(\n",
        "              image,\n",
        "              hand_landmarks,\n",
        "              mp_hands.HAND_CONNECTIONS,\n",
        "              mp_drawing_styles.get_default_hand_landmarks_style(),\n",
        "              mp_drawing_styles.get_default_hand_connections_style())\n",
        "      \n",
        "      # Checking keypoints if complete will do this block of code\n",
        "      if len(results.multi_hand_landmarks[0].landmark) >= 21:\n",
        "        # define variable for centering and scaling process\n",
        "        centering = np.array([])\n",
        "        scaling = np.array([])\n",
        "\n",
        "        # Centering X coordinate Process\n",
        "        for indexPoint in range(21):\n",
        "          centering = np.append(centering, (\n",
        "            results.multi_hand_landmarks[0].landmark[0].x - results.multi_hand_landmarks[0].landmark[indexPoint].x))\n",
        "\n",
        "        # Centering Y coordinate Process\n",
        "        for indexPoint in range(21):\n",
        "          centering = np.append(centering, (\n",
        "            results.multi_hand_landmarks[0].landmark[0].y - results.multi_hand_landmarks[0].landmark[indexPoint].y))\n",
        "\n",
        "        centering = centering.reshape(2, 21)\n",
        "        \n",
        "        # Scaling Process\n",
        "        for indexIter in range(2):\n",
        "          for jointIter in range(21):\n",
        "            scaling = np.append(scaling, centering[indexIter][jointIter] / np.max(\n",
        "              np.absolute(centering[indexIter])) * 320)\n",
        "        \n",
        "        # Normalization Process\n",
        "        for jointIter in range(42):\n",
        "          hand_keypoint_data = np.append(hand_keypoint_data, (scaling[jointIter] + 320))\n",
        "\n",
        "        # Write spatiodata from hand keypoints coordinate\n",
        "        if len(hand_keypoint_data) >= 210:\n",
        "          # Write spatiodata to csv\n",
        "          with open('/content/<write hand keypoint data>.csv', 'a', newline='') as f:\n",
        "            writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
        "            writer.writerow(hand_keypoint_data)\n",
        "\n",
        "          # deleted 42 old data \n",
        "          deletedIndex = np.arange(42)\n",
        "          hand_keypoint_data = np.delete(hand_keypoint_data, deletedIndex)\n",
        "\n",
        "    except Exception as e:\n",
        "      continue\n",
        "\n",
        "    finally:\n",
        "      # Show the result\n",
        "      cv2.imshow('Result', image)\n",
        "\n",
        "      if cv2.waitKey(5) & 0xFF == 27:\n",
        "        break\n",
        "\n",
        "cap.release()\n",
        "cv2.destroyAllWindows()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2nmoaPr2Abpr"
      },
      "source": [
        "# Training and Creating SVM Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cj1eILaMBWlP"
      },
      "outputs": [],
      "source": [
        "# Import library\n",
        "import pandas as pd\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "theqQ5zuAbGF",
        "outputId": "73f6b241-c1bf-4f25-f62b-7585bbf40b3f"
      },
      "outputs": [
        {
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              "      <th>y_18_5</th>\n",
              "      <th>y_19_5</th>\n",
              "      <th>y_20_5</th>\n",
              "      <th>classes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>320.0</td>\n",
              "      <td>485.152943</td>\n",
              "      <td>572.228051</td>\n",
              "      <td>611.015991</td>\n",
              "      <td>640.0</td>\n",
              "      <td>503.889783</td>\n",
              "      <td>538.078134</td>\n",
              "      <td>555.553512</td>\n",
              "      <td>540.435086</td>\n",
              "      <td>422.656361</td>\n",
              "      <td>...</td>\n",
              "      <td>121.286176</td>\n",
              "      <td>82.360586</td>\n",
              "      <td>13.063771</td>\n",
              "      <td>97.376312</td>\n",
              "      <td>131.739908</td>\n",
              "      <td>93.934650</td>\n",
              "      <td>46.712579</td>\n",
              "      <td>109.061526</td>\n",
              "      <td>138.803534</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>320.0</td>\n",
              "      <td>465.964144</td>\n",
              "      <td>559.274410</td>\n",
              "      <td>591.684841</td>\n",
              "      <td>640.0</td>\n",
              "      <td>496.860392</td>\n",
              "      <td>525.258322</td>\n",
              "      <td>523.136214</td>\n",
              "      <td>505.315559</td>\n",
              "      <td>411.365575</td>\n",
              "      <td>...</td>\n",
              "      <td>121.803245</td>\n",
              "      <td>80.713003</td>\n",
              "      <td>16.623943</td>\n",
              "      <td>102.249387</td>\n",
              "      <td>128.441406</td>\n",
              "      <td>90.945055</td>\n",
              "      <td>46.858668</td>\n",
              "      <td>110.469978</td>\n",
              "      <td>134.988855</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>320.0</td>\n",
              "      <td>462.766483</td>\n",
              "      <td>556.857238</td>\n",
              "      <td>589.850807</td>\n",
              "      <td>640.0</td>\n",
              "      <td>505.518389</td>\n",
              "      <td>537.883128</td>\n",
              "      <td>533.152127</td>\n",
              "      <td>512.368892</td>\n",
              "      <td>424.009098</td>\n",
              "      <td>...</td>\n",
              "      <td>118.260449</td>\n",
              "      <td>77.857223</td>\n",
              "      <td>15.038736</td>\n",
              "      <td>101.801804</td>\n",
              "      <td>125.593594</td>\n",
              "      <td>86.919297</td>\n",
              "      <td>49.423377</td>\n",
              "      <td>114.858841</td>\n",
              "      <td>136.562579</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>320.0</td>\n",
              "      <td>461.931064</td>\n",
              "      <td>554.799106</td>\n",
              "      <td>587.140571</td>\n",
              "      <td>640.0</td>\n",
              "      <td>508.268696</td>\n",
              "      <td>544.488910</td>\n",
              "      <td>535.330896</td>\n",
              "      <td>511.087601</td>\n",
              "      <td>426.029084</td>\n",
              "      <td>...</td>\n",
              "      <td>123.819147</td>\n",
              "      <td>80.897288</td>\n",
              "      <td>17.182610</td>\n",
              "      <td>104.476018</td>\n",
              "      <td>134.503585</td>\n",
              "      <td>90.175464</td>\n",
              "      <td>47.319703</td>\n",
              "      <td>112.033595</td>\n",
              "      <td>138.702828</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>320.0</td>\n",
              "      <td>464.205083</td>\n",
              "      <td>556.428947</td>\n",
              "      <td>589.834202</td>\n",
              "      <td>640.0</td>\n",
              "      <td>507.870735</td>\n",
              "      <td>544.482767</td>\n",
              "      <td>538.405151</td>\n",
              "      <td>516.912143</td>\n",
              "      <td>426.546836</td>\n",
              "      <td>...</td>\n",
              "      <td>123.995003</td>\n",
              "      <td>79.947167</td>\n",
              "      <td>14.567389</td>\n",
              "      <td>102.702474</td>\n",
              "      <td>129.519895</td>\n",
              "      <td>87.379546</td>\n",
              "      <td>43.844527</td>\n",
              "      <td>109.514312</td>\n",
              "      <td>134.206767</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 211 columns</p>\n",
              "</div>\n",
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              "          const element = document.querySelector('#df-e91291a4-3970-4403-aabf-2c9f911f78a8');\n",
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              "  "
            ],
            "text/plain": [
              "   x_0_1       x_1_1       x_2_1       x_3_1  x_4_1       x_5_1       x_6_1  \\\n",
              "0  320.0  485.152943  572.228051  611.015991  640.0  503.889783  538.078134   \n",
              "1  320.0  465.964144  559.274410  591.684841  640.0  496.860392  525.258322   \n",
              "2  320.0  462.766483  556.857238  589.850807  640.0  505.518389  537.883128   \n",
              "3  320.0  461.931064  554.799106  587.140571  640.0  508.268696  544.488910   \n",
              "4  320.0  464.205083  556.428947  589.834202  640.0  507.870735  544.482767   \n",
              "\n",
              "        x_7_1       x_8_1       x_9_1  ...      y_12_5     y_13_5     y_14_5  \\\n",
              "0  555.553512  540.435086  422.656361  ...  121.286176  82.360586  13.063771   \n",
              "1  523.136214  505.315559  411.365575  ...  121.803245  80.713003  16.623943   \n",
              "2  533.152127  512.368892  424.009098  ...  118.260449  77.857223  15.038736   \n",
              "3  535.330896  511.087601  426.029084  ...  123.819147  80.897288  17.182610   \n",
              "4  538.405151  516.912143  426.546836  ...  123.995003  79.947167  14.567389   \n",
              "\n",
              "       y_15_5      y_16_5     y_17_5     y_18_5      y_19_5      y_20_5  \\\n",
              "0   97.376312  131.739908  93.934650  46.712579  109.061526  138.803534   \n",
              "1  102.249387  128.441406  90.945055  46.858668  110.469978  134.988855   \n",
              "2  101.801804  125.593594  86.919297  49.423377  114.858841  136.562579   \n",
              "3  104.476018  134.503585  90.175464  47.319703  112.033595  138.702828   \n",
              "4  102.702474  129.519895  87.379546  43.844527  109.514312  134.206767   \n",
              "\n",
              "   classes  \n",
              "0        A  \n",
              "1        A  \n",
              "2        A  \n",
              "3        A  \n",
              "4        A  \n",
              "\n",
              "[5 rows x 211 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# insert your dataset\n",
        "SIBI_Lang = pd.read_csv('/content/SIBI_Lang_Spatio.csv')\n",
        "SIBI_Lang.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hINDrmHtA0Nx",
        "outputId": "f740d87b-5537-4d72-c9ac-66985cdc1d2a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[320.      , 485.15295 , 572.228   , ...,  46.712578, 109.06152 ,\n",
              "        138.80353 ],\n",
              "       [320.      , 465.96414 , 559.2744  , ...,  46.85867 , 110.46998 ,\n",
              "        134.98886 ],\n",
              "       [320.      , 462.76648 , 556.85724 , ...,  49.423378, 114.85884 ,\n",
              "        136.56258 ],\n",
              "       ...,\n",
              "       [320.      , 469.24905 , 594.7948  , ...,  44.107727,  46.26379 ,\n",
              "         81.53687 ],\n",
              "       [320.      , 467.83725 , 595.1465  , ...,  44.048576,  46.13349 ,\n",
              "         81.403465],\n",
              "       [320.      , 468.94113 , 596.2404  , ...,  46.110588,  45.692066,\n",
              "         78.81219 ]], dtype=float32)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# separate data and convert values to float32\n",
        "X = SIBI_Lang.values[:,:-1]\n",
        "X = X.astype('float32')\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0bV6FYRA2yM",
        "outputId": "3d064e7d-c673-4b34-bbdb-b9655385f9e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['A', 'A', 'A', ..., 'E', 'E', 'E'], dtype=object)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# separate label\n",
        "Y = SIBI_Lang.values[:,-1]\n",
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vW2KY4PUPNyz"
      },
      "outputs": [],
      "source": [
        "# encode labels into numeric values\n",
        "le = LabelEncoder()\n",
        "Y = le.fit_transform(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82VeMZw3MbPe"
      },
      "source": [
        "### Split dataset into train data and test data\n",
        "\n",
        "Pada bagian ini dataset akan dibagi menjadi 2, yaitu data train dan data test. Untuk rincian data train sebanyak 75 % dari dataset dan untuk rincian dari data test sebanyak 25 % dari dataset. Kalian bisa mengubah pembagian dataset dengan memodifikasi parameter 'test_size'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B87Bqn1MA6kz",
        "outputId": "f70f8a54-ac6f-4434-eb45-d2a4e9c7169a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1312, 210) (438, 210) (1312,) (438,)\n"
          ]
        }
      ],
      "source": [
        "# split dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=42)\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOD1YpLmA8tG",
        "outputId": "88389969-355b-4bc9-99a1-e5dfe3f885ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(C=100, probability=True)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# create svm model\n",
        "svm_model = SVC(kernel='rbf', C=100, probability=True) \n",
        "# getting train from your train and label data\n",
        "svm_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0Wpu-A3A-lN",
        "outputId": "45c5d785-c3a2-4019-8dbf-2810484f1e5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ],
      "source": [
        "# check accuracy of svm model\n",
        "y_pred = svm_model.predict(X_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "TWSxW_2UBAOW",
        "outputId": "21cba72d-a86a-495f-8230-85f2a1101f54"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f37edb72990>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# get the confusion matrix for evaluating svm model\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "plot_confusion_matrix(svm_model, X_test, y_test, xticks_rotation='vertical', values_format='d')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pohbeCoEBHt4",
        "outputId": "1dff62d3-ad1d-4aea-e4bc-c4bcea9ddfb8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           A       1.00      1.00      1.00        85\n",
            "           B       1.00      1.00      1.00        93\n",
            "           C       1.00      1.00      1.00        89\n",
            "           D       1.00      1.00      1.00        88\n",
            "           E       1.00      1.00      1.00        83\n",
            "\n",
            "    accuracy                           1.00       438\n",
            "   macro avg       1.00      1.00      1.00       438\n",
            "weighted avg       1.00      1.00      1.00       438\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# get the classification report for evaluating svm model\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxH6rZNsMmP8"
      },
      "source": [
        "# Save SVM model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ogbBjpE9BI5P"
      },
      "outputs": [],
      "source": [
        "# save your svm model\n",
        "import pickle\n",
        "\n",
        "with open('/content/<write the name of the model>.sav', 'wb') as handle:\n",
        "    pickle.dump(svm_model, handle)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "[Train] SIBI-Lang-Estimation.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.7.11 ('my_env')",
      "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": "3.7.11"
    },
    "vscode": {
      "interpreter": {
        "hash": "3fe420644837b9164684aa99b0054c85678c51b2b00629249570993b2a64553c"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}