CK+.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CK+.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ShivamSinghal1/FacialEmotionRecognition/blob/master/CK%2B.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MN95rZ6r2O8B"
      },
      "source": [
        "import keras\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential , load_model\n",
        "from keras.layers import Convolution2D, Activation, BatchNormalization, MaxPooling2D, Dropout, Dense, Flatten, AveragePooling2D\n",
        "from keras.initializers import  RandomNormal\n",
        "from keras.layers import Conv2D,MaxPooling2D\n",
        "from keras.optimizers import RMSprop,SGD,Adam\n",
        "from keras.callbacks import ModelCheckpoint, TensorBoard ,EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlYJ7azviO-W",
        "outputId": "be00f424-0862-406f-9676-bd2289dcde23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNfkS10H28kE",
        "outputId": "c4c21a94-9b72-4bff-98df-0fa8f68b1868",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "data_dir = \"/content/drive/My Drive/Dataset/CK+48\"\n",
        "datagen = ImageDataGenerator(rescale=1. / 255,\n",
        "                             shear_range=0.3,\n",
        "                             zoom_range=0.2,\n",
        "                             validation_split = 0.2,\n",
        "                             horizontal_flip=True)\n",
        "train_generator = datagen.flow_from_directory(data_dir,\n",
        "                                              target_size=(48,48),\n",
        "                                              batch_size=4,\n",
        "                                              color_mode=\"grayscale\",\n",
        "                                              subset=\"training\",\n",
        "                                              class_mode='categorical')\n",
        "validation_generator = datagen.flow_from_directory(data_dir,\n",
        "                                              target_size=(48,48),\n",
        "                                              batch_size=4,\n",
        "                                              color_mode=\"grayscale\",\n",
        "                                              subset=\"validation\",\n",
        "                                              class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 788 images belonging to 7 classes.\n",
            "Found 193 images belonging to 7 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiAcyBw2_UBw"
      },
      "source": [
        "def get_cnn_model():\n",
        "    model = Sequential()\n",
        "\n",
        "    model.add(Convolution2D(64, (3, 1), padding='same', input_shape=(48,48,1)))\n",
        "    model.add(Convolution2D(64, (1, 3), padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Convolution2D(128, (3, 1), padding='same'))\n",
        "    model.add(Convolution2D(128, (1, 3), padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Convolution2D(256, (3, 1), padding='same'))\n",
        "    model.add(Convolution2D(256, (1, 3), padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Convolution2D(512, (3, 1), padding='same'))\n",
        "    model.add(Convolution2D(512, (1, 3), padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Flatten())\n",
        "\n",
        "    model.add(Dense(512))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Dense(256))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Dense(7))\n",
        "    model.add(Activation('softmax'))\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51Y-miqhF2A6"
      },
      "source": [
        "model = get_cnn_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-G4V0ha6O2-d",
        "outputId": "7b7dc2d7-38eb-4856-be76-3878b192e8c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 48, 48, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 48, 48, 64)        12352     \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 48, 48, 64)        256       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 48, 48, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 24, 24, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 24, 24, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 24, 24, 128)       24704     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 24, 24, 128)       49280     \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 24, 24, 128)       512       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 24, 24, 128)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 12, 12, 256)       98560     \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 12, 12, 256)       196864    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 12, 12, 256)       1024      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 12, 12, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 6, 6, 512)         393728    \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 6, 6, 512)         786944    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 6, 6, 512)         2048      \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 6, 6, 512)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 512)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 3, 3, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 7)                 1799      \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 7)                 0         \n",
            "=================================================================\n",
            "Total params: 4,062,535\n",
            "Trainable params: 4,059,079\n",
            "Non-trainable params: 3,456\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3byxk8VGWUc"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D7g1OnpHd9I"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "# Saving model each time it achieves lower loss on the validation set\n",
        "filepath='/content/drive/My Drive/Dataset/ck_vgg16_v2.hdf5'\n",
        "checkpointer = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')\n",
        "\n",
        "earlystop = EarlyStopping(monitor='val_loss',\n",
        "                          min_delta=0,\n",
        "                          patience=8,\n",
        "                          verbose=1,\n",
        "                          restore_best_weights=True, mode = 'auto'\n",
        "                          )\n",
        "\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
        "                              factor=0.9,\n",
        "                              patience=3,\n",
        "                              verbose=1,\n",
        "                              min_delta=0.0001)\n",
        "\n",
        "\n",
        "history = model.fit(train_generator,\n",
        "                    epochs=300,\n",
        "                    validation_data=validation_generator,\n",
        "                    callbacks=[checkpointer,earlystop,reduce_lr]\n",
        "                             )\n",
        "\n",
        "pd.DataFrame(history.history).to_csv(\"/content/drive/My Drive/Dataset/ck_history.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THYKAJEtLMIm"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.subplot(2,1,1)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='lower right')\n",
        "\n",
        "plt.subplot(2,1,2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right')\n",
        "\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTEfzc8NScyf"
      },
      "source": [
        "def eval_model(history, model):\n",
        "    batch_size = 32\n",
        "    score = model.evaluate(X_test, Y_test, steps=(int)(len(X_test) / batch_size))\n",
        "    print('Evaluation loss: ', score[0])\n",
        "    print('Evaluation accuracy: ', score[1])\n",
        "    # summarize history for accuracy\n",
        "    plt.plot(history.history['acc'], color='b', label='Training')\n",
        "    plt.plot(history.history['val_acc'], color='g', label='Validation')\n",
        "    plt.title('Accuracy')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "    # summarize history for loss\n",
        "    plt.plot(history.history['loss'], color='b', label='Training')\n",
        "    plt.plot(history.history['val_loss'], color='g', label='Validation')\n",
        "    plt.title('model loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(loc='lower left')\n",
        "    plt.show()\n",
        "    y_pred = model.predict_classes(X_test)\n",
        "    y_true = np.asarray([np.argmax(i) for i in Y_test])\n",
        "\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    import seaborn as sns\n",
        "\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    cm_normalised = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    sns.set(font_scale=1.5) \n",
        "    fig, ax = plt.subplots(figsize=(10,10))\n",
        "    ax = sns.heatmap(cm_normalised, annot=True, linewidths=0, square=False, \n",
        "                        cmap=\"Greens\", yticklabels=labels, xticklabels=labels, vmin=0, vmax=np.max(cm_normalised), \n",
        "                        fmt=\".2f\", annot_kws={\"size\": 20})\n",
        "    ax.set(xlabel='Predicted label', ylabel='True label')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1kedkNbUNod"
      },
      "source": [
        "model.evaluate(validation_generator)\n",
        "model.save('/content/drive/My Drive/Dataset/final_ck_vgg16_v2.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrfgCCxMUPEd"
      },
      "source": [
        "model = load_model('/content/drive/My Drive/Dataset/ck_vgg16_v2.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWjYWdrJUhvr"
      },
      "source": [
        "model.evaluate(validation_generator)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnVQjKkMUyLn"
      },
      "source": [
        "y_test = []\n",
        "X_test = []\n",
        "for i in range(0,49):\n",
        "    X = validation_generator.__getitem__(i)[0]\n",
        "    Y = validation_generator.__getitem__(i)[1]\n",
        "    for y in Y:\n",
        "      y_test.append(np.argmax(y))\n",
        "    for x in X:\n",
        "      X_test.append(x)\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-sFglUQegzY",
        "outputId": "9066c568-0d94-4149-efac-67bbf74f5624",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "y_predict = model.predict_classes(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-11-e4f2425f2518>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpiHvumhVUE4"
      },
      "source": [
        "# Plot confusion matrix \n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def create_confmat(true_labels, predicted_labels, columns, colour = 'Greens', size = (20,14)):\n",
        "    \n",
        "    cm = confusion_matrix(true_labels, predicted_labels) \n",
        "    cm_df = pd.DataFrame(cm,\n",
        "    index = [col for col in columns], \n",
        "    columns = [col for col in columns])\n",
        "    plt.figure(figsize=(18,16))\n",
        "    sns.heatmap(cm_df, annot = True, cmap = colour, fmt='g', linewidths=.2)\n",
        "    plt.title('Confusion Matrix', fontsize = 20)\n",
        "    plt.ylabel('True label', fontsize = 18)\n",
        "    plt.xlabel('Predicted label', fontsize = 18)\n",
        "    plt.tick_params(axis='both', labelsize=14)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKbnNBE3VZLm",
        "outputId": "42ff309a-9f51-45fa-a411-ea4227e345ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 965
        }
      },
      "source": [
        "create_confmat(y_test , y_predict , ['anger' , 'contempt' , 'disgust' , 'fear' , 'happy' , 'sadness' , 'surprise'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1S4SVoLiV1rB",
        "outputId": "088df358-5e8c-4ca7-8657-aa7d5823520c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test , y_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.85      0.81        27\n",
            "           1       1.00      0.90      0.95        10\n",
            "           2       0.95      1.00      0.97        35\n",
            "           3       0.87      0.87      0.87        15\n",
            "           4       0.98      1.00      0.99        41\n",
            "           5       0.87      0.81      0.84        16\n",
            "           6       1.00      0.92      0.96        49\n",
            "\n",
            "    accuracy                           0.93       193\n",
            "   macro avg       0.92      0.91      0.91       193\n",
            "weighted avg       0.93      0.93      0.93       193\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQWH7MCNJWpf"
      },
      "source": [
        "# SVM Trial Version "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5BUg9TqJidY"
      },
      "source": [
        "y_test = []\n",
        "X_test = []\n",
        "for i in range(0,49):\n",
        "    X = validation_generator.__getitem__(i)[0]\n",
        "    Y = validation_generator.__getitem__(i)[1]\n",
        "    for y in Y:\n",
        "      y_test.append(np.argmax(y))\n",
        "    for x in X:\n",
        "      X_test.append(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "985bYiqKJkmR",
        "outputId": "47e75f92-979f-41bc-f351-dabbcb997a10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_test = np.array(X_test)\n",
        "X_test = X_test.reshape(X_test.shape[0],224*224*3)\n",
        "y_test = np.array(y_test)\n",
        "X_test.shape , y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((193, 150528), (193,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYmh2ClNMJiu"
      },
      "source": [
        "y_train = []\n",
        "X_train = []\n",
        "for i in range(0,197):\n",
        "    X = train_generator.__getitem__(i)[0]\n",
        "    Y = train_generator.__getitem__(i)[1]\n",
        "    for y in Y:\n",
        "      y_train.append(np.argmax(y))\n",
        "    for x in X:\n",
        "      X_train.append(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-u_gl2HMTSl",
        "outputId": "f0135ab3-b2ff-4d2e-9ae2-0ecf2a3881d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_train = np.array(X_train)\n",
        "X_train = X_train.reshape(X_train.shape[0],224*224*3)\n",
        "y_train = np.array(y_train)\n",
        "X_train.shape , y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((788, 150528), (788,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfmD__N-OzLm",
        "outputId": "eb4b9eb3-3dbd-410b-c3b8-d1e2a795f219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC()\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2G3wqiBO6Tu",
        "outputId": "a04f4e15-e679-476a-aa44-38a75dabc6eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "model.score(X_test , y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6269430051813472"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwxaURglmSB8",
        "outputId": "5303afba-a8c1-4ca3-f870-4148f8092d44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC(kernel='linear', probability=True)\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctIZlKeXQIqU",
        "outputId": "2dcb1024-5f20-4ad4-d611-3b94832f115c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "model.score(X_test , y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.694300518134715"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0M-CX9Ja4PdN",
        "outputId": "e2e6d953-1fa5-4e12-dd0c-66f5c0252222",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC(kernel='rbf', probability=True)\n",
        "model.fit(X_train,y_train)\n",
        "model.score(X_test , y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6373056994818653"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vm-rIjgPvPY6",
        "outputId": "200157ea-a2a2-4f04-cb4b-9ad9c65195a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000],  \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001], \n",
        "              'kernel': ['rbf']} \n",
        "\n",
        "          # , 0.001, 0.005, 0.01, 0.1\n",
        "# for sklearn version 0.16 or prior, the class_weight parameter value is 'auto'\n",
        "clf = GridSearchCV(SVC(kernel='linear', class_weight='balanced'), param_grid,refit = True, verbose = 3)\n",
        "clf = clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  3.6min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  7.2min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.185, total= 3.6min\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.210, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.139, total= 3.7min\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.139, total= 3.7min\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.185, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.210, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.185, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.210, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.139, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.185, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.223, total= 3.7min\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.297, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.323, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.357, total= 3.6min\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.484, total= 3.6min\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.177, total= 3.6min\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.177, total= 3.6min\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.177, total= 3.6min\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.210, total= 3.6min\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.177, total= 3.6min\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.177, total= 3.6min\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.177, total= 3.6min\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.210, total= 3.6min\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.184, total= 3.6min\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.177, total= 3.6min\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.190, total= 3.6min\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.210, total= 3.6min\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.373, total= 3.6min\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.418, total= 3.6min\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.449, total= 3.6min\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.446, total= 3.6min\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.363, total= 3.6min\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.658, total= 2.8min\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.709, total= 2.7min\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.747, total= 2.7min\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.764, total= 2.8min\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.815, total= 2.8min\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.259, total= 3.6min\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.259, total= 3.6min\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.380, total= 3.6min\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.456, total= 3.6min\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.424, total= 3.6min\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.471, total= 3.6min\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.433, total= 3.6min\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.810, total= 2.9min\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.766, total= 2.9min\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.842, total= 2.8min\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.841, total= 2.9min\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.879, total= 2.9min\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.259, total= 3.6min\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.259, total= 3.6min\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.255, total= 3.7min\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.380, total= 3.6min\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.456, total= 3.6min\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.424, total= 3.6min\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.471, total= 3.6min\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.433, total= 3.6min\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.804, total= 2.9min\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.772, total= 2.9min\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.848, total= 2.9min\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.841, total= 2.9min\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.860, total= 2.9min\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.259, total= 3.6min\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.253, total= 3.6min\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.259, total= 3.6min\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.255, total= 3.6min\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.380, total= 3.6min\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.456, total= 3.6min\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.424, total= 3.6min\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.471, total= 3.6min\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.433, total= 3.6min\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.804, total= 2.9min\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.772, total= 2.9min\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.848, total= 2.9min\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.841, total= 2.9min\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.860, total= 2.9min\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed: 436.1min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdeMFupprF7j",
        "outputId": "6c8d4f8a-201c-475b-dd13-d163cfe26ef4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(clf.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVC(C=10, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7i_Jfb06vWUM",
        "outputId": "4c9e5a1e-22fb-470c-fab4-00f812d5c17d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "#SVC(C=10, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,\n",
        " #   decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
        "  #  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
        "   # tol=0.001, verbose=False)\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.38      0.70      0.49        27\n",
            "           1       0.40      0.40      0.40        10\n",
            "           2       0.54      0.63      0.58        35\n",
            "           3       0.50      0.27      0.35        15\n",
            "           4       0.97      0.90      0.94        41\n",
            "           5       0.50      0.06      0.11        16\n",
            "           6       0.86      0.78      0.82        49\n",
            "\n",
            "    accuracy                           0.65       193\n",
            "   macro avg       0.59      0.53      0.53       193\n",
            "weighted avg       0.68      0.65      0.64       193\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsDoQP2BtTfC"
      },
      "source": [
        "create_confmat(y_test , y_pred , ['anger' , 'contempt' , 'disgust' , 'fear' , 'happy' , 'sadness' , 'surprise'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_-v2QkyRZOB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}