FER_perfo_mypart_kNN_minibatchkmeans.ipynb

{
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "G5pCzCXSYTpm",
        "outputId": "d51a0f05-cf72-4fdb-e9e9-ac84c05ce745"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive/', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kyo6niXPbYbK"
      },
      "outputs": [],
      "source": [
        "from IPython.display import clear_output\n",
        "!pip install tf_explain\n",
        "! pip install segmentation-models\n",
        "clear_output()\n",
        "from tf_explain.core.grad_cam import GradCAM\n",
        "\n",
        "#!pip install git+git://github.com/stared/livelossplot.git\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "#import utils\n",
        "import os\n",
        "%matplotlib inline\n",
        "\n",
        "import tensorflow as tf\n",
        "from skimage.color import rgb2gray\n",
        "from tensorflow.keras import Input\n",
        "from tensorflow.keras.models import Model, load_model, save_model\n",
        "from tensorflow.keras.layers import Input, Activation, BatchNormalization, Dropout, Lambda, Conv2D, Conv2DTranspose, MaxPooling2D, concatenate, Flatten, Dense, Reshape, Multiply, Add,UpSampling2D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "from keras import regularizers\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from tensorflow.keras.layers import Dense, Input, Dropout,Flatten, Conv2D\n",
        "from tensorflow.keras.layers import BatchNormalization, Activation, MaxPooling2D\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "from IPython.display import SVG, Image\n",
        "# from livelossplot import PlotLossesTensorFlo\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import os,cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 20, 10\n",
        "\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWZg9CV0bE7a"
      },
      "source": [
        "# kNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iYoiFH2Zba6J"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"gdrive/MyDrive/fer2013.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KnzJ5rif-_bh"
      },
      "outputs": [],
      "source": [
        "traindf = df[df['Usage']=='Training']"
      ]
    },
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      "execution_count": null,
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        "colab": {
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        "outputId": "6878d0b5-a1bd-4dfe-cb8e-b8c75bf5034d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   emotion                                             pixels     Usage\n",
              "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
              "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
              "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
              "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
              "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
            ],
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      "execution_count": null,
      "metadata": {
        "id": "jIT667Ou6eiv"
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      "outputs": [],
      "source": [
        "x_data = traindf['pixels']\n",
        "y_data = traindf['emotion']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(x=\"emotion\", data=df)\n"
      ],
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            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
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oUOrq6rLXXnvlJz/5SZL/uXOlpqYms2fPzj777JO6urqMHDkyy5Ytyy9/+csMHjw49fX1Oe6447Jq1arKMVevXp0zzjgjffr0Sbdu3XLAAQfkgQceSJL85S9/yUEHHZQk6dWrV2pqavLpT386yRsfTfbiiy/mhBNOSK9evbL11lvnsMMOyxNPPFHZP2PGjPTs2TOzZ8/O4MGDs+222+bQQw/Ns88+W/hvDQAAKE2IAQAAOoQpU6bk+9//fqZPn55HH300Z555Zo4//vg0NTVV1lxwwQW56qqrcs8992TJkiX5+Mc/nqlTp+amm27KzJkzc8cdd+TKK6+srD/nnHPy05/+NDfccEMeeuih7LLLLhk9enReeOGFDBgwID/96U+TJIsWLcqzzz6byy+//B/O9ulPfzoPPvhgbrvttsyfPz9tbW05/PDDs3bt2sqaVatW5Zvf/Gb+4z/+I3fffXcWL16cs846q9DfFgAAsKl4RwwAALDZW716dXr37p1f/epXaWxsrGz/zGc+k1WrVuXUU0/NQQcdlF/96lc5+OCDkyQXX3xxJk2alKeeeirvfOc7kySnnXZa/vKXv2TWrFlZuXJlevXqlRkzZuS4445LkqxduzbveMc7MmHChJx99tn/6ztiDjzwwOy9996ZOnVqnnjiibz73e/OvHnz8oEPfCBJ8vzzz2fAgAG54YYbcswxx2TGjBk56aST8uSTT+Zd73pXkuTb3/52LrroojQ3N2+Kv0IAAKAQ74gBAAA2e08++WRWrVqVD3/4w+22r1mzJvvss0/l+5577ln5c0NDQ7beeutKhHlt2/33358keeqpp7J27drsv//+lf1bbbVV3ve+9+Xxxx//p2d7/PHH06VLlwwfPryybbvttstuu+3W7jhbb711JcIkSb9+/bJs2bJ/+vcAAABvT0IMAACw2Xv55ZeTJDNnzsyOO+7Ybl9tbW2eeuqpJP8TUl5TU1PT7vtr21pbWwtP+4/9o1k8wAAAADZ/3hEDAABs9oYMGZLa2tosXrw4u+yyS7vPgAED1uuY73rXu9K1a9fMmzevsm3t2rV54IEHMmTIkCRJ165dkyTr1q37X48zePDgvPrqq7nvvvsq255//vksWrSochwAAKDjckcMAACw2evevXvOOuusnHnmmWltbc0BBxyQFStWZN68eamvr8/AgQPf8jG32WabnH766Tn77LPTu3fv7LzzzrnkkkuyatWqjB07NkkycODA1NTU5Pbbb8/hhx+eurq6bLvttu2Os+uuu+bII4/MKaecku985zvp3r17zj333Oy444458sgjN8r5AwAAb1/uiAEAADqEL3/5yznvvPMyZcqUDB48OIceemhmzpyZQYMGrfcxL7744hx99NH51Kc+lX333TdPPvlkZs+enV69eiVJdtxxx1x44YU599xz09DQkPHjx//D41x//fUZOnRoPvKRj6SxsTFtbW35xS9+8YbHkQEAAB1PTZuHDgMAAAAAABThjhgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBChBgAAAAAAIBC/j/weS2u88AUaQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inference: An imbalance of data is observed as some classes like 1 have very low instances while class 3 has more no. of instances"
      ],
      "metadata": {
        "id": "nE-T9qtN2klR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To overcome, we go for resampling the data by generating new data of minority classes and store it as new train dataset."
      ],
      "metadata": {
        "id": "Gk2WvTAK2x1a"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtrs4kUJ9hbM",
        "outputId": "875d14e3-86e8-4604-9099-5dcbff2ed178"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50505, 1)   (50505,)\n"
          ]
        }
      ],
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "oversampler = RandomOverSampler(sampling_strategy='auto')\n",
        "\n",
        "x_data, y_data = oversampler.fit_resample(x_data.values.reshape(-1,1), y_data)\n",
        "print(x_data.shape,\" \",y_data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZtfW9XN9osn",
        "outputId": "f3ebd973-3056-46ff-db8a-9e8420aaceaa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.27450982, 0.3137255 , 0.32156864, ..., 0.41568628, 0.42745098,\n",
              "        0.32156864],\n",
              "       [0.5921569 , 0.5882353 , 0.5764706 , ..., 0.75686276, 0.7176471 ,\n",
              "        0.72156864],\n",
              "       [0.90588236, 0.83137256, 0.6117647 , ..., 0.34509805, 0.43137255,\n",
              "        0.59607846],\n",
              "       ...,\n",
              "       [0.3019608 , 0.30588236, 0.30980393, ..., 0.49019608, 0.2627451 ,\n",
              "        0.26666668],\n",
              "       [0.33333334, 0.32941177, 0.3529412 , ..., 0.22745098, 0.28627452,\n",
              "        0.32941177],\n",
              "       [1.        , 0.99607843, 1.        , ..., 0.99607843, 1.        ,\n",
              "        1.        ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "x_data = pd.Series(x_data.flatten())\n",
        "x_data = np.array(list(map(str.split, x_data)), np.float32)\n",
        "x_data /= 255\n",
        "x_data[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iKT_Rvmw-NRQ"
      },
      "outputs": [],
      "source": [
        "y_data = np.array(y_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZ5m6ZPc-EQE",
        "outputId": "d2815b8f-a901-454f-af2c-73de42ad22bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50505, 48, 48, 1), (50505, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "x_data = x_data.reshape(-1, 48, 48, 1)\n",
        "y_data = y_data.reshape(y_data.shape[0], 1)\n",
        "x_data.shape,y_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yq0RX-vY-SLx",
        "outputId": "4dda7d02-eb27-4269-c824-54f62b923dbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train: (45454, 48, 48, 1)\n",
            "X_test: (2526, 48, 48, 1)\n",
            "X_val: (2525, 48, 48, 1)\n",
            "y_train: (45454, 1)\n",
            "y_test: (2526, 1)\n",
            "y_val: (2525, 1)\n"
          ]
        }
      ],
      "source": [
        "x_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size = 0.1, random_state = 42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42)\n",
        "print(\"x_train: \"+str(x_train.shape))\n",
        "print(\"X_test: \"+str(X_test.shape))\n",
        "print(\"X_val: \"+str(X_val.shape))\n",
        "print(\"y_train: \"+str(y_train.shape))\n",
        "print(\"y_test: \"+str(y_test.shape))\n",
        "print(\"y_val: \"+str(y_val.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rCiI19uI-YRP",
        "outputId": "562419bc-58e9-44e0-b0b5-b0992a62008f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train: (45454, 48, 48, 1)\n",
            "X_test: (2526, 2304)\n",
            "X_val: (2525, 2304)\n",
            "y_train: (45454, 1)\n",
            "y_test: (2526, 1)\n",
            "y_val: (2525, 1)\n"
          ]
        }
      ],
      "source": [
        "from builtins import range\n",
        "from builtins import object\n",
        "\n",
        "num_training = x_train.shape[0]\n",
        "mask = list(range(num_training))\n",
        "X_train = x_train[mask]\n",
        "y_train = y_train[mask]\n",
        "\n",
        "num_test = X_test.shape[0]\n",
        "mask = list(range(num_test))\n",
        "X_test = X_test[mask]\n",
        "y_test = y_test[mask]\n",
        "\n",
        "num_val = X_val.shape[0]\n",
        "mask = list(range(num_val))\n",
        "X_val = X_val[mask]\n",
        "y_val = y_val[mask]\n",
        "\n",
        "# Reshape the image data into rows\n",
        "X_train = np.reshape(x_train, (x_train.shape[0], -1))\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
        "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
        "\n",
        "print(\"x_train: \"+str(x_train.shape))\n",
        "print(\"X_test: \"+str(X_test.shape))\n",
        "print(\"X_val: \"+str(X_val.shape))\n",
        "print(\"y_train: \"+str(y_train.shape))\n",
        "print(\"y_test: \"+str(y_test.shape))\n",
        "print(\"y_val: \"+str(y_val.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vhm_5t3GHyCc"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS9X6mn3NnHY"
      },
      "source": [
        "Reshape 4D x_train np array (45454,48,48,1) into 2D array by taking row size as 45454 and columns as 48x48x1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WoVTZSCBIhxN"
      },
      "outputs": [],
      "source": [
        "nsamples, nx, ny, nz = x_train.shape\n",
        "d2_train_dataset = x_train.reshape((nsamples,nx*ny*nz))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "AgXNcUC1FGBM",
        "outputId": "45ebe6b2-c905-44c1-a61c-cdbcc1106360"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Accuracy for k=1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "print(\"Val Accuracy for k=1\")\n",
        "classifier = KNeighborsClassifier(n_neighbors=1)\n",
        "classifier.fit(d2_train_dataset, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miwnHexhJQ2R",
        "outputId": "7576c5a7-4678-4a1b-9fd5-5db431be0db8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the model for validation dataset:\n",
            "0.7572277227722772\n"
          ]
        }
      ],
      "source": [
        "# Calculate the accuracy of the model \n",
        "print(\"Accuracy of the model for validation dataset:\")\n",
        "print(classifier.score(X_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxMWYeB9I4WP",
        "outputId": "fee51eda-e51e-4210-a3c6-44ee1c6018ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the model for test dataset:\n",
            "0.7743467933491687\n"
          ]
        }
      ],
      "source": [
        "# Calculate the accuracy of the model \n",
        "print(\"Accuracy of the model for test dataset:\")\n",
        "print(classifier.score(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3gb1irvLPre",
        "outputId": "f0dcd412-1ae5-40a8-ff38-84abd705751e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6265, 2304), (6265, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Calculate the accuracy of the test FER dataset\n",
        "testdf = df[df['Usage']=='PublicTest']\n",
        "# testdf.head()\n",
        "X_test = testdf['pixels']\n",
        "y_test = testdf['emotion']\n",
        "oversamp = RandomOverSampler(sampling_strategy='auto')\n",
        "Xt, yt = oversamp.fit_resample(X_test.values.reshape(-1,1), y_test)\n",
        "Xt = pd.Series(Xt.flatten())\n",
        "x_test = np.array(list(map(str.split, Xt)), np.float32)\n",
        "x_test /= 255\n",
        "Y_test = np.array(yt)\n",
        "x_test = x_test.reshape(-1, 48, 48, 1)\n",
        "Y_test = Y_test.reshape(Y_test.shape[0], 1)\n",
        "\n",
        "nsamp, nxt, nyt, nzt = x_test.shape\n",
        "x_test = x_test.reshape((nsamp,nxt*nyt*nzt))\n",
        "\n",
        "x_test.shape,Y_test.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZuyZDsKP14m",
        "outputId": "fcc12ab9-458c-4c13-b78e-c2ef701882b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the model for test dataset:\n",
            "0.4244213886671987\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy of the model for test dataset:\")\n",
        "print(classifier.score(x_test, Y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "PA7qQwu4R5RS",
        "outputId": "66534867-5724-4ad9-a441-da5401223f50"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using SKLEARN\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "max acc at k=1 acc of 0.4244213886671987\n"
          ]
        }
      ],
      "source": [
        "print(\"Using SKLEARN\")\n",
        "lix = []\n",
        "liy = []\n",
        "index=0\n",
        "acc=0\n",
        "for k in range(1, 25):\n",
        "    neigh = KNeighborsClassifier(n_neighbors=k)\n",
        "    neigh.fit(d2_train_dataset, y_train)\n",
        "    liy.append(neigh.score(x_test, Y_test))\n",
        "    if liy[k-1]>acc:\n",
        "        acc=liy[k-1]\n",
        "        index=k-1\n",
        "    lix.append(k)\n",
        "\n",
        "plt.plot(lix, liy)\n",
        "plt.show()\n",
        "print(\"max acc at k=\"+str(index+1)+\" acc of \"+str(acc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ub6YmSfyhdOJ",
        "outputId": "5502b206-07b3-4091-fbb6-a8d057934253"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using SKLEARN\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "max acc at k=1 acc of 0.4244213886671987\n"
          ]
        }
      ],
      "source": [
        "print(\"Using SKLEARN\")\n",
        "lix = []\n",
        "liy = []\n",
        "index=0\n",
        "acc=0\n",
        "for k in range(1, 100):\n",
        "    neigh = KNeighborsClassifier(n_neighbors=k)\n",
        "    neigh.fit(d2_train_dataset, y_train)\n",
        "    liy.append(neigh.score(x_test, Y_test))\n",
        "    if liy[k-1]>acc:\n",
        "        acc=liy[k-1]\n",
        "        index=k-1\n",
        "    lix.append(k)\n",
        "\n",
        "plt.plot(lix, liy)\n",
        "plt.show()\n",
        "print(\"max acc at k=\"+str(index+1)+\" acc of \"+str(acc))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### trying custom knn function"
      ],
      "metadata": {
        "id": "ng-Ir0zoKSIk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H6SmZSY3VBUA"
      },
      "outputs": [],
      "source": [
        "def most_common(lst):\n",
        "    '''Returns the most common element in a list'''\n",
        "    return max(set(lst), key=lst.count)\n",
        "def euclidean(point, data):\n",
        "    '''Euclidean distance between a point  & data'''\n",
        "    return np.sqrt(np.sum((point - data)**2, axis=1))\n",
        "class KNeighborsClassifier():\n",
        "    def __init__(self, k=5, dist_metric=euclidean):\n",
        "        self.k = k\n",
        "        self.dist_metric = dist_metric\n",
        "    def fit(self, X_train, y_train):\n",
        "        self.X_train = X_train\n",
        "        self.y_train = y_train\n",
        "    def predict(self, X_test):\n",
        "        neighbors = []\n",
        "        for x in X_test:\n",
        "            distances = self.dist_metric(x, self.X_train)\n",
        "            y_sorted = [y for _, y in sorted(zip(distances, self.y_train))]\n",
        "            neighbors.append(y_sorted[:self.k])\n",
        "        return list(map(most_common, neighbors))\n",
        "    def evaluate(self, X_test, y_test):\n",
        "        y_pred = self.predict(X_test)\n",
        "        accuracy = sum(y_pred == y_test) / len(y_test)\n",
        "        return accuracy\n",
        "accuracies = []\n",
        "ks = range(1, 30)\n",
        "for k in ks:\n",
        "    knn = KNeighborsClassifier(k=k)\n",
        "    knn.fit(d2_train_dataset, y_train)\n",
        "    accuracy = knn.evaluate(X_test, y_test)\n",
        "    accuracies.append(accuracy)\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(ks, accuracies)\n",
        "ax.set(xlabel=\"k\",\n",
        "       ylabel=\"Accuracy\",\n",
        "       title=\"Performance of knn\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z4Q2rBGxE6TT"
      },
      "outputs": [],
      "source": [
        "# class KNearestNeighbor(object):\n",
        "#     \"\"\" a kNN classifier with L2 distance \"\"\"\n",
        "\n",
        "#     def __init__(self):\n",
        "#         pass\n",
        "\n",
        "#     def predict_label(self, dists, k=1):\n",
        "#         num_test = dists.shape[0]\n",
        "#         y_pred = np.zeros(num_test)\n",
        "#         for i in range(num_test):\n",
        "#             closest_y = []\n",
        "#             closest_y = self.y_train[np.argsort(dists[i])][0:k]\n",
        "#             y_pred[i] = np.bincount(closest_y).argmax()\n",
        "#         return y_pred\n",
        "\n",
        "#     def train(self, X, y):\n",
        "#         \"\"\"\n",
        "#         Train the classifier. For k-nearest neighbors this is just\n",
        "#         memorizing the training data.\n",
        "\n",
        "#         Inputs:\n",
        "#         - X: A numpy array of shape (num_train, D) containing the training data\n",
        "#           consisting of num_train samples each of dimension D.\n",
        "#         - y: A numpy array of shape (N,) containing the training labels, where\n",
        "#              y[i] is the label for X[i].\n",
        "#         \"\"\"\n",
        "#         self.x_tr = X\n",
        "#         self.y_tr = y\n",
        "\n",
        "#     def predict(self, X, k=1):\n",
        "#         \"\"\"\n",
        "#         Predict labels for test data using this classifier.\n",
        "\n",
        "#         Inputs:\n",
        "#         - X: A numpy array of shape (num_test, D) containing test data consisting\n",
        "#              of num_test samples each of dimension D.\n",
        "#         - k: The number of nearest neighbors that vote for the predicted labels.\n",
        "#         - num_loops: Determines which implementation to use to compute distances\n",
        "#           between training points and testing points.\n",
        "\n",
        "#         Returns:\n",
        "#         - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
        "#           test data, where y[i] is the predicted label for the test point X[i].\n",
        "#         \"\"\"\n",
        "#         dists = self.compute_distances_no_loops(X)\n",
        "\n",
        "#         return self.predict_labels(dists, k=k)\n",
        "\n",
        "#     def compute_distances_no_loops(self, X):\n",
        "#         \"\"\"\n",
        "#         Compute the distance between each test point in X and each training point\n",
        "#         in self.x_tr using no explicit loops.\n",
        "\n",
        "#         Input / Output: Same as compute_distances_two_loops\n",
        "#         \"\"\"\n",
        "#         num_test = X.shape[0]\n",
        "#         num_train = self.x_tr.shape[0]\n",
        "#         dists = np.zeros((num_test, num_train))\n",
        "#         #########################################################################\n",
        "#         dists = np.sqrt((X ** 2).sum(axis=1, keepdims=1) + (self.x_tr ** 2).sum(axis=1) - 2 * X.dot(self.x_tr.T))\n",
        "\n",
        "#         return dists\n",
        "\n",
        "#     def predict_labels(self, dists, k=1):\n",
        "#         \"\"\"\n",
        "#         Given a matrix of distances between test points and training points,\n",
        "#         predict a label for each test point.\n",
        "\n",
        "#         Inputs:\n",
        "#         - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n",
        "#           gives the distance betwen the ith test point and the jth training point.\n",
        "\n",
        "#         Returns:\n",
        "#         - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
        "#           test data, where y[i] is the predicted label for the test point X[i].\n",
        "#         \"\"\"\n",
        "#         num_test = dists.shape[0]\n",
        "#         y_pred = np.zeros(num_test)\n",
        "#         for i in range(num_test):\n",
        "#             # A list of length k storing the labels of the k nearest neighbors to\n",
        "#             # the ith test point.\n",
        "#             closest_y = []\n",
        "#             closest_y = self.y_tr[np.argsort(dists[i])][0:k]\n",
        "#             closest_y = closest_y.astype(int)\n",
        "#             y_pred[i] = np.bincount(closest_y).argmax()\n",
        "#         return y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XvR4BF_KGDEm"
      },
      "outputs": [],
      "source": [
        "# dists = classifier.compute_distances_no_loops(X_val)\n",
        "# y_val_pred = classifier.predict_labels(dists, k=1)\n",
        "# # num_correct = np.sum(y_val_pred == y_val)\n",
        "# accuracy = float(num_correct) / num_val\n",
        "# print('Got %d / %d correct => accuracy: %f' % (num_correct, num_val, accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multiclassification with knn"
      ],
      "metadata": {
        "id": "2hOjOA7nKZRz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.multiclass import OneVsRestClassifier"
      ],
      "metadata": {
        "id": "AGXWIuKRKWD_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting training dataset and training with 75% of dataset"
      ],
      "metadata": {
        "id": "kVG_XzLyUHVc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train2, X_test2, y_train2, y_test2 = train_test_split(x_data, y_data, test_size=0.25, random_state=0)\n",
        "ns, nx, ny, nz = X_train2.shape\n",
        "X_train2 = X_train2.reshape(ns,nx*ny*nz)\n",
        "nst2, nxt2, nyt2, nzt2 = X_test2.shape\n",
        "X_test2 = X_test2.reshape(nst2,nxt2*nyt2*nzt2)"
      ],
      "metadata": {
        "id": "tm3lgxe2NuZb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = OneVsRestClassifier(KNeighborsClassifier())"
      ],
      "metadata": {
        "id": "84VblpCROupU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(X_train2,y_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "NG2F4KboO4nR",
        "outputId": "c546c541-48cb-4629-d38f-3f963b7bd17f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=KNeighborsClassifier())"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=KNeighborsClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneVsRestClassifier</label><div class=\"sk-toggleable__content\"><pre>OneVsRestClassifier(estimator=KNeighborsClassifier())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn.predict(X_test2[0].reshape(1,-1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioI4XSBKTrdk",
        "outputId": "4e31410e-08bc-41fa-989d-e1846b20c6a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test2[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exssokgfTw7F",
        "outputId": "593987cd-5c17-452b-f77f-06475515232f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn.predict(X_test2[:10].reshape(10,-1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxaPnfDJT04h",
        "outputId": "f0679348-7360-4865-e464-9112f2df656f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 4, 2, 2, 6, 1, 1, 6, 3, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test2[:10].reshape(1,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zX_8-7YtT7wV",
        "outputId": "9f6991a1-b990-4e76-e65d-3624d9a3d5c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5, 6, 2, 5, 0, 1, 1, 0, 4, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn.predict(x_test[0].reshape(1,-1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8HkGDnpPWGE",
        "outputId": "509e1c40-b5c9-4905-bb64-71a7c8bf008f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7OY3qafTdJJ",
        "outputId": "5d06bb93-cce0-4622-b938-a634808ab10f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn.predict(x_test[:10].reshape(10,-1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GdxR5I4P0Du",
        "outputId": "fc0256f6-0374-4c75-f186-12c13ae3f357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 1, 0, 4, 4, 6, 6, 6, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test[:10].reshape(1,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oXM7m-PTLTR",
        "outputId": "31a999c8-4a07-47ad-a86c-8878064d7a8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 4, 6, 3, 3, 2, 0, 2, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prediction for training dataset test set"
      ],
      "metadata": {
        "id": "TdVuI15bfbpx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_test = knn.predict(x_test)"
      ],
      "metadata": {
        "id": "e73NXPT_Uj11"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time\n",
        "print('KNN Accuracy: %.3f' % accuracy_score(Y_test,predictions_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0GrxP86Ut_U",
        "outputId": "f0e0808b-d526-4b10-dce7-74c27e1416c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2 µs, sys: 0 ns, total: 2 µs\n",
            "Wall time: 6.2 µs\n",
            "KNN Accuracy: 0.360\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf = confusion_matrix(Y_test,predictions_test)\n",
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(conf,annot=True, fmt='.3f', linewidths=.5)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "all_sample_title = 'Accuracy Score: {0}'.format(accuracy_score(Y_test,predictions_test))\n",
        "plt.title(all_sample_title,size=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "za6pObHqUt4a",
        "outputId": "02be4ba3-7d81-4e74-b381-2646584d7b51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score: 0.3600957701516361')"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Same procedure for heatmap with training dataset split"
      ],
      "metadata": {
        "id": "uawFuGwAWAhH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = knn.predict(X_test2)"
      ],
      "metadata": {
        "id": "VjYNHCuOWEwe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time\n",
        "print('KNN Accuracy: %.3f' % accuracy_score(y_test2,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jbd2RcjLWG_7",
        "outputId": "75a433fd-2cda-4cbb-e3e2-e69b658b0ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
            "Wall time: 8.11 µs\n",
            "KNN Accuracy: 0.511\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test,predictions)\n",
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(cm,annot=True, fmt='.3f')\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "all_sample_title = 'Accuracy Score: {0}'.format(accuracy_score(y_test,predictions))\n",
        "plt.title(all_sample_title,size=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "1jQ1kP0VXMjv",
        "outputId": "61389c9e-6cce-411e-bdc5-320e927fbe65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score: 0.5106517779361686')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtIVagggbJuw"
      },
      "source": [
        "# ALGO 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qb8FXRlFbbmf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waQDL8j9bMVC"
      },
      "source": [
        "# ALGO 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IycqO7XobcUs"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import MiniBatchKMeans"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_clusters = len(np.unique(Y_test))\n",
        "# Initialize the K-Means model\n",
        "kmeans = MiniBatchKMeans(n_clusters = total_clusters)\n",
        "# Fitting the model to training set\n",
        "kmeans.fit(X_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "esm7YXAPBrsM",
        "outputId": "1cb446c4-9336-4c2d-930d-666282f08f16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 3 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MiniBatchKMeans(n_clusters=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MiniBatchKMeans(n_clusters=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MiniBatchKMeans</label><div class=\"sk-toggleable__content\"><pre>MiniBatchKMeans(n_clusters=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.labels_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53GjKLNiCZri",
        "outputId": "bc6321ce-eedd-4c7f-84eb-7c595be9bb08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 1, 1, ..., 4, 1, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(kmeans.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WP4nCQ1LDSQk",
        "outputId": "fa10c9af-eec3-4bde-a360-668a1ac92810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37878"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8ur8mGKDVWJ",
        "outputId": "af9ea372-01ec-455d-f5b7-743664ee84bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37878"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdO-pmwuDbmL",
        "outputId": "33faa731-a607-49e7-cc8f-f10b55ae04aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37878"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_info(cluster_labels,y_train):\n",
        "    '''\n",
        "    Associates most probable label with each cluster in KMeans model\n",
        "    returns: dictionary of clusters assigned to each label\n",
        "    '''\n",
        "    # Initializing\n",
        "    reference_labels = {}\n",
        "    # For loop to run through each label of cluster label\n",
        "    for i in range(len(np.unique(kmeans.labels_))):\n",
        "        index = np.where(cluster_labels == i,1,0)\n",
        "        if y_train.size:\n",
        "          num = np.bincount(y_train[index==1]).argmax()\n",
        "          reference_labels[i] = num\n",
        "    return reference_labels"
      ],
      "metadata": {
        "id": "2foJv23dChQ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ref_labels = retrieve_info(kmeans.labels_,y_train2.flatten('C'))\n",
        "number_labels = np.random.rand(len(kmeans.labels_))\n",
        "for i in range(len(kmeans.labels_)):\n",
        "    number_labels[i] = ref_labels[kmeans.labels_[i]]"
      ],
      "metadata": {
        "id": "pGOL10K9C0-w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "number_labels[:20].astype(int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcG7TsyHEVpv",
        "outputId": "daea9d55-e970-465d-a873-ae815bd321d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 5, 5, 2, 2, 4, 2, 2, 3, 5, 4, 2, 6, 4, 5, 3, 6, 4, 4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train2[1:20].reshape(1,19)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoH9bbIXEggN",
        "outputId": "14f6ca6a-583e-4460-cc2e-b42f0cecb14e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 3, 5, 6, 3, 5, 4, 0, 2, 5, 5, 5, 0, 6, 4, 6, 4, 0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(number_labels,y_train2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWDEXacaFHGC",
        "outputId": "33fb56f2-c269-4afe-fb2e-2a9f5d11ec18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.19578647235862506\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import homogeneity_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "zDX40WC8Fo6t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We optimize k-means using 3 performance metrics: Accuracy, Inertia and Homogeneity score<br>**Accuracy** score is the percentage of correctly predicted values.<br>**Inertia** is a measure of how internally coherent clusters are. It is inversely proportional to sum of squares of distances between data points and their respective cluster’s centroid. Higher the number of clusters, lower is the inertia score.<br>**Homogeneity** score is a measure of data points of a particular cluster belonging to a single class."
      ],
      "metadata": {
        "id": "SJryvf66JuSp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_metrics(model,output):\n",
        "  print('Number of clusters is {}'.format(model.n_clusters))\n",
        "  print('Inertia : {}'.format(model.inertia_))\n",
        "  print('Homogeneity : {}'.format(homogeneity_score(output,model.labels_)))"
      ],
      "metadata": {
        "id": "X-a3PAblFTqg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_number = [4,8,10,13,20,36,48,64,144,256]\n",
        "for i in cluster_number:\n",
        "    total_clusters = len(np.unique(y_test2))\n",
        "\n",
        "    kmeans = MiniBatchKMeans(n_clusters = i)\n",
        "\n",
        "    kmeans.fit(X_train2)\n",
        "    \n",
        "    calculate_metrics(kmeans,y_train2.flatten('C'))\n",
        "\n",
        "    reference_labels = retrieve_info(kmeans.labels_,y_train2.flatten('C'))\n",
        "\n",
        "    number_labels = np.random.rand(len(kmeans.labels_))\n",
        "    for i in range(len(kmeans.labels_)):\n",
        "        number_labels[i] = reference_labels[kmeans.labels_[i]]\n",
        " \n",
        "    print('Accuracy score : {}'.format(accuracy_score(number_labels,y_train2)))\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GsYgUT5oFuy5",
        "outputId": "9ea4ef4b-4e79-49bb-930d-54c0cf1930dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters is 4\n",
            "Inertia : 3877825.5\n",
            "Homogeneity : 0.013550036901865129\n",
            "Accuracy score : 0.1926976081102487\n",
            "\n",
            "Number of clusters is 8\n",
            "Inertia : 3497851.25\n",
            "Homogeneity : 0.017261314230683125\n",
            "Accuracy score : 0.19998415967052113\n",
            "\n",
            "Number of clusters is 10\n",
            "Inertia : 3383480.5\n",
            "Homogeneity : 0.018805556423197114\n",
            "Accuracy score : 0.20038016790749247\n",
            "\n",
            "Number of clusters is 13\n",
            "Inertia : 3282949.0\n",
            "Homogeneity : 0.020441029810177355\n",
            "Accuracy score : 0.20420824753154865\n",
            "\n",
            "Number of clusters is 20\n",
            "Inertia : 3111492.5\n",
            "Homogeneity : 0.023589479873704355\n",
            "Accuracy score : 0.20901314747346744\n",
            "\n",
            "Number of clusters is 36\n",
            "Inertia : 2940026.0\n",
            "Homogeneity : 0.028587897853857003\n",
            "Accuracy score : 0.21796293362901947\n",
            "\n",
            "Number of clusters is 48\n",
            "Inertia : 2864481.5\n",
            "Homogeneity : 0.034793271552809174\n",
            "Accuracy score : 0.22514388299276625\n",
            "\n",
            "Number of clusters is 64\n",
            "Inertia : 2770362.5\n",
            "Homogeneity : 0.03929392416349252\n",
            "Accuracy score : 0.23187602302127885\n",
            "\n",
            "Number of clusters is 144\n",
            "Inertia : 2578718.75\n",
            "Homogeneity : 0.07230330319343464\n",
            "Accuracy score : 0.25993980674798034\n",
            "\n",
            "Number of clusters is 256\n",
            "Inertia : 2432472.0\n",
            "Homogeneity : 0.10995390034127407\n",
            "Accuracy score : 0.29064364538782406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inference: The highest accuracy is returned by no. of clusters taken as 256 at 29%<br>We also notice inertia parameter decreases as no. of clusters reaches best possible. This is because distance (SSD) decreases between pixel data and centroids <br>"
      ],
      "metadata": {
        "id": "WxHnPKvmIDft"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Other inferences: Accuracy scores increases as no. of clusters increase and homogeneity score as clusters become more and more differentiable"
      ],
      "metadata": {
        "id": "5CSoaDaYImBg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing model on testing sub-dataset (X_test2 and y_test2) for k=256\n",
        "\n",
        "kmeans = MiniBatchKMeans(n_clusters = 256)\n",
        "\n",
        "kmeans.fit(X_test2)\n",
        "calculate_metrics(kmeans,y_test2.flatten('C'))\n",
        "ref_labels = retrieve_info(kmeans.labels_,y_test2.flatten('C'))\n",
        "# ‘number_labels’ is a list which denotes the number displayed in image\n",
        "number_labels = np.random.rand(len(kmeans.labels_))\n",
        "for i in range(len(kmeans.labels_)):\n",
        "    number_labels[i] = ref_labels[kmeans.labels_[i]]\n",
        " \n",
        "print('Accuracy score : {}'.format(accuracy_score(number_labels,y_test2)))\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3FWCjaUICll",
        "outputId": "ce800a54-748d-46b0-96c0-b24d492f8e3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters is 256\n",
            "Inertia : 799935.25\n",
            "Homogeneity : 0.125900051754282\n",
            "Accuracy score : 0.3009424249623822\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Minibatch K-means for performance analysis"
      ],
      "metadata": {
        "id": "1OZo-MMSTYlY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mini-batch k-means uses the idea of taking random smaller samples from our original high-dimensional dataset and applying k-means clustering<br>The clusters are updated with a different portion of the dataset in each iteration"
      ],
      "metadata": {
        "id": "OWbgSrn8Ui0f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import MiniBatchKMeans"
      ],
      "metadata": {
        "id": "pc_XHdbuAIUg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_clusters = len(np.unique(Y_test))\n",
        "# Initialize the K-Means model\n",
        "kmeans = MiniBatchKMeans(n_clusters = total_clusters)\n",
        "# Fitting the model to training set\n",
        "kmeans.fit(X_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "1cb446c4-9336-4c2d-930d-666282f08f16",
        "id": "judyqbmSV8zC"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 3 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MiniBatchKMeans(n_clusters=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MiniBatchKMeans(n_clusters=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MiniBatchKMeans</label><div class=\"sk-toggleable__content\"><pre>MiniBatchKMeans(n_clusters=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.labels_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc6321ce-eedd-4c7f-84eb-7c595be9bb08",
        "id": "3xKdGQ-wV8zD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 1, 1, ..., 4, 1, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(kmeans.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa10c9af-eec3-4bde-a360-668a1ac92810",
        "id": "P8orVYpqV8zD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37878"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af9ea372-01ec-455d-f5b7-743664ee84bd",
        "id": "7zuvLJBsV8zD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37878"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We infer that each data point in X_train2 has been labeled by k-means and the labels are stored in kmeans.labels_"
      ],
      "metadata": {
        "id": "ejQhFLRTVAw7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_info(cluster_labels,y_train):\n",
        "    '''\n",
        "    Associates most probable label with each cluster in KMeans model\n",
        "    returns: dictionary of clusters assigned to each label\n",
        "    '''\n",
        "    reference_labels = {}\n",
        "    # For loop to run through each label of cluster label\n",
        "    for i in range(len(np.unique(kmeans.labels_))):\n",
        "        index = np.where(cluster_labels == i,1,0)\n",
        "        if y_train.size: # Check for emptiness and None\n",
        "          num = np.bincount(y_train[index==1]).argmax() # Find the highest probability label\n",
        "          reference_labels[i] = num\n",
        "    return reference_labels"
      ],
      "metadata": {
        "id": "GhvL_5pYV8zE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ref_labels = retrieve_info(kmeans.labels_,y_train2.flatten('C'))\n",
        "number_labels = np.random.rand(len(kmeans.labels_))\n",
        "for i in range(len(kmeans.labels_)):\n",
        "    number_labels[i] = ref_labels[kmeans.labels_[i]]"
      ],
      "metadata": {
        "id": "A7vEV_yZV8zE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "number_labels[:20].astype(int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "daea9d55-e970-465d-a873-ae815bd321d8",
        "id": "S-9XMgegV8zE"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 5, 5, 2, 2, 4, 2, 2, 3, 5, 4, 2, 6, 4, 5, 3, 6, 4, 4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train2[1:20].reshape(1,19)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14f6ca6a-583e-4460-cc2e-b42f0cecb14e",
        "id": "uE_mil4zV8zF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 3, 5, 6, 3, 5, 4, 0, 2, 5, 5, 5, 0, 6, 4, 6, 4, 0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(number_labels,y_train2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33fb56f2-c269-4afe-fb2e-2a9f5d11ec18",
        "id": "3ibE2rVwV8zF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.19578647235862506\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We get a low accuracy of 19.5% on training sub-dataset. To improve it, we go for modifying k values i.e. no. of clusters assumed"
      ],
      "metadata": {
        "id": "jIzJ4a4DTFwO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import homogeneity_score"
      ],
      "metadata": {
        "id": "8utitICaV8zF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We optimize k-means using 3 performance metrics: Accuracy, Inertia and Homogeneity score<br>**Accuracy** score is the percentage of correctly predicted values.<br>**Inertia** is a measure of how internally coherent clusters are. It is inversely proportional to sum of squares of distances between data points and their respective cluster’s centroid. Higher the number of clusters, lower is the inertia score.<br>**Homogeneity** score is a measure of data points of a particular cluster belonging to a single class."
      ],
      "metadata": {
        "id": "VadelJliV8zF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_metrics(model,output):\n",
        "  print('Number of clusters is {}'.format(model.n_clusters))\n",
        "  print('Inertia : {}'.format(model.inertia_))\n",
        "  print('Homogeneity : {}'.format(homogeneity_score(output,model.labels_)))"
      ],
      "metadata": {
        "id": "27UcGJHbV8zG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_number = [4,8,10,13,20,36,48,64,144,256]\n",
        "for i in cluster_number:\n",
        "    total_clusters = len(np.unique(y_test2))\n",
        "\n",
        "    kmeans = MiniBatchKMeans(n_clusters = i)\n",
        "\n",
        "    kmeans.fit(X_train2)\n",
        "    \n",
        "    calculate_metrics(kmeans,y_train2.flatten('C'))\n",
        "\n",
        "    reference_labels = retrieve_info(kmeans.labels_,y_train2.flatten('C'))\n",
        "\n",
        "    number_labels = np.random.rand(len(kmeans.labels_))\n",
        "    for i in range(len(kmeans.labels_)):\n",
        "        number_labels[i] = reference_labels[kmeans.labels_[i]]\n",
        " \n",
        "    print('Accuracy score : {}'.format(accuracy_score(number_labels,y_train2)))\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ea4ef4b-4e79-49bb-930d-54c0cf1930dc",
        "id": "lWbL41QJV8zG"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters is 4\n",
            "Inertia : 3877825.5\n",
            "Homogeneity : 0.013550036901865129\n",
            "Accuracy score : 0.1926976081102487\n",
            "\n",
            "Number of clusters is 8\n",
            "Inertia : 3497851.25\n",
            "Homogeneity : 0.017261314230683125\n",
            "Accuracy score : 0.19998415967052113\n",
            "\n",
            "Number of clusters is 10\n",
            "Inertia : 3383480.5\n",
            "Homogeneity : 0.018805556423197114\n",
            "Accuracy score : 0.20038016790749247\n",
            "\n",
            "Number of clusters is 13\n",
            "Inertia : 3282949.0\n",
            "Homogeneity : 0.020441029810177355\n",
            "Accuracy score : 0.20420824753154865\n",
            "\n",
            "Number of clusters is 20\n",
            "Inertia : 3111492.5\n",
            "Homogeneity : 0.023589479873704355\n",
            "Accuracy score : 0.20901314747346744\n",
            "\n",
            "Number of clusters is 36\n",
            "Inertia : 2940026.0\n",
            "Homogeneity : 0.028587897853857003\n",
            "Accuracy score : 0.21796293362901947\n",
            "\n",
            "Number of clusters is 48\n",
            "Inertia : 2864481.5\n",
            "Homogeneity : 0.034793271552809174\n",
            "Accuracy score : 0.22514388299276625\n",
            "\n",
            "Number of clusters is 64\n",
            "Inertia : 2770362.5\n",
            "Homogeneity : 0.03929392416349252\n",
            "Accuracy score : 0.23187602302127885\n",
            "\n",
            "Number of clusters is 144\n",
            "Inertia : 2578718.75\n",
            "Homogeneity : 0.07230330319343464\n",
            "Accuracy score : 0.25993980674798034\n",
            "\n",
            "Number of clusters is 256\n",
            "Inertia : 2432472.0\n",
            "Homogeneity : 0.10995390034127407\n",
            "Accuracy score : 0.29064364538782406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inference: The highest accuracy is returned by no. of clusters taken as 256 at 29%<br>We also notice inertia parameter decreases as no. of clusters reaches best possible. This is because distance (SSD) decreases between pixel data and centroids <br>"
      ],
      "metadata": {
        "id": "VVxfJvzgV8zG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Other inferences: Accuracy scores increases as no. of clusters increase and homogeneity score as clusters become more and more differentiable"
      ],
      "metadata": {
        "id": "OwmfhGTtV8zG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing model on testing sub-dataset (X_test2 and y_test2) for k=256\n",
        "\n",
        "kmeans = MiniBatchKMeans(n_clusters = 256)\n",
        "\n",
        "kmeans.fit(X_test2)\n",
        "calculate_metrics(kmeans,y_test2.flatten('C'))\n",
        "ref_labels = retrieve_info(kmeans.labels_,y_test2.flatten('C'))\n",
        "# ‘number_labels’ is a list which denotes the number displayed in image\n",
        "number_labels = np.random.rand(len(kmeans.labels_))\n",
        "for i in range(len(kmeans.labels_)):\n",
        "    number_labels[i] = ref_labels[kmeans.labels_[i]]\n",
        " \n",
        "print('Accuracy score : {}'.format(accuracy_score(number_labels,y_test2)))\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce800a54-748d-46b0-96c0-b24d492f8e3f",
        "id": "Kyy5NuH5V8zH"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters is 256\n",
            "Inertia : 799935.25\n",
            "Homogeneity : 0.125900051754282\n",
            "Accuracy score : 0.3009424249623822\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thus, we have improved the original accuracy from 19.5% to 30%"
      ],
      "metadata": {
        "id": "YCsWC-GsTQpS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inference:\n",
        "As concluded by PCA, we understand that k-means is not a suitable clustering method to find the classes of FER dataset by seeing the low accuracy score when compared with original labels.\n",
        "<br>We also notice that the no. of classes according to our benchmark labeled dataset was 7 but k-means provided a highest accuracy for no. of clusters as 256. This shows that k-means groups the images based on different features such as orientation of the face or similar structure."
      ],
      "metadata": {
        "id": "kXNZAaVsTWOu"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}