bci_4_tl_sub2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "bci_4_tl_sub2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zabir-nabil/eeg-rsenet/blob/master/bci_4_tl_sub2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "8CKgAFkiFaSt",
        "colab_type": "code",
        "outputId": "b8dbe696-c636-4e7d-c3bd-f69daa9568f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "cell_type": "code",
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NKrq6sysFgIR",
        "colab_type": "code",
        "outputId": "070c5ad7-9f02-40d4-c7aa-26893915e003",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "% cd /content/drive/My Drive/subject 2 to 9/subject 2/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/subject 2 to 9/subject 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fvuKVJ0vFrq2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import glob\n",
        "\n",
        "cls1_ch1 = []\n",
        "cls1_ch2 = []\n",
        "cls1_ch3 = []\n",
        "\n",
        "cls2_ch1 = []\n",
        "cls2_ch2 = []\n",
        "cls2_ch3 = []\n",
        "\n",
        "for f in glob.glob('/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 0/*.png'):\n",
        "  cls1_ch1.append(f)\n",
        "  \n",
        "for f in glob.glob('/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/*.png'):\n",
        "  cls1_ch2.append(f)\n",
        "  \n",
        "for f in glob.glob('/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 2/*.png'):\n",
        "  cls1_ch3.append(f)\n",
        "  \n",
        "for f in glob.glob('/content/drive/My Drive/subject 2 to 9/subject 2/lvl 2/ch 0/*.png'):\n",
        "  cls2_ch1.append(f)\n",
        "  \n",
        "for f in glob.glob('/content/drive/My Drive/subject 2 to 9/subject 2/lvl 2/ch 1/*.png'):\n",
        "  cls2_ch2.append(f)\n",
        "  \n",
        "for f in glob.glob('/content/drive/My Drive/subject 2 to 9/subject 2/lvl 2/ch 2/*.png'):\n",
        "  cls2_ch3.append(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_3siVkxUF_PP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cls1_ch1.sort()\n",
        "cls1_ch2.sort()\n",
        "cls1_ch3.sort()\n",
        "\n",
        "cls2_ch1.sort()\n",
        "cls2_ch2.sort()\n",
        "cls2_ch3.sort()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CXSTQPxSGEFW",
        "colab_type": "code",
        "outputId": "13b0137c-c39b-43f5-e190-af50596b285f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "cell_type": "code",
      "source": [
        "print(len(cls1_ch1))\n",
        "print(len(cls1_ch2))\n",
        "print(len(cls1_ch3))\n",
        "\n",
        "print(len(cls2_ch1))\n",
        "print(len(cls2_ch2))\n",
        "print(len(cls2_ch3))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60\n",
            "60\n",
            "60\n",
            "60\n",
            "60\n",
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LTgx3sKqGvxs",
        "colab_type": "code",
        "outputId": "996b5039-f074-421e-9c69-9956119e2972",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1109
        }
      },
      "cell_type": "code",
      "source": [
        "cls1_ch2"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_0_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_10_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_11_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_12_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_13_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_14_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_15_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_16_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_17_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_18_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_19_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_1_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_20_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_21_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_22_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_23_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_24_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_25_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_26_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_27_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_28_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_29_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_2_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_30_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_31_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_32_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_33_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_34_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_35_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_36_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_37_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_38_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_39_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_3_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_40_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_41_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_42_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_43_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_44_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_45_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_46_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_47_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_48_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_49_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_4_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_50_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_51_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_52_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_53_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_54_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_55_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_56_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_57_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_58_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_59_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_5_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_6_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_7_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_8_1_1.png',\n",
              " '/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_9_1_1.png']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "Ji7VSsvqGPFV",
        "colab_type": "code",
        "outputId": "1725ac72-e244-4b5d-eb5e-7ac113ef3224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1109
        }
      },
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "\n",
        "x1 = []\n",
        "x2 = []\n",
        "x3 = []\n",
        "\n",
        "y = []\n",
        "\n",
        "for i in range(len(cls1_ch1)):\n",
        "  x1.append(cv2.resize(cv2.imread(cls1_ch1[i]), (224, 224)))\n",
        "  print(cls1_ch2[i])\n",
        "  x2.append(cv2.resize(cv2.imread(cls1_ch2[i]), (224, 224)))\n",
        "  x3.append(cv2.resize(cv2.imread(cls1_ch3[i]), (224, 224)))\n",
        "  \n",
        "  y.append(0)\n",
        "  \n",
        "for i in range(len(cls2_ch1)):\n",
        "  x1.append(cv2.resize(cv2.imread(cls2_ch1[i]), (224, 224)))\n",
        "  x2.append(cv2.resize(cv2.imread(cls2_ch2[i]), (224, 224)))\n",
        "  x3.append(cv2.resize(cv2.imread(cls2_ch3[i]), (224, 224)))\n",
        "  \n",
        "  y.append(1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_0_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_10_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_11_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_12_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_13_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_14_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_15_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_16_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_17_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_18_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_19_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_1_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_20_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_21_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_22_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_23_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_24_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_25_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_26_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_27_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_28_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_29_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_2_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_30_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_31_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_32_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_33_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_34_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_35_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_36_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_37_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_38_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_39_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_3_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_40_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_41_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_42_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_43_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_44_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_45_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_46_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_47_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_48_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_49_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_4_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_50_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_51_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_52_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_53_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_54_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_55_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_56_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_57_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_58_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_59_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_5_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_6_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_7_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_8_1_1.png\n",
            "/content/drive/My Drive/subject 2 to 9/subject 2/lvl 1/ch 1/mi_9_1_1.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rrFmsSrKGTNo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "50f6558a-f5e1-4df0-9430-7642a5274e92"
      },
      "cell_type": "code",
      "source": [
        "! pip install Augmentor "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting Augmentor\n",
            "  Downloading https://files.pythonhosted.org/packages/4d/f4/b0eaa9d3b4120a5450ac92d4417907ca60fad5749c1f50ed95f720792350/Augmentor-0.2.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: future>=0.16.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (0.16.0)\n",
            "Requirement already satisfied: Pillow>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (4.0.0)\n",
            "Requirement already satisfied: tqdm>=4.9.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (4.28.1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (1.14.6)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from Pillow>=4.0.0->Augmentor) (0.46)\n",
            "Installing collected packages: Augmentor\n",
            "Successfully installed Augmentor-0.2.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MUqRVe_CKQcV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z = list(zip(x1,x2,x3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MTYOOuuRKUOV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d0eaa81c-0900-4ef5-d17a-4d89c306d50b"
      },
      "cell_type": "code",
      "source": [
        "print(len((z)))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "120\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ui7qI-A7KW1n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def unzip(z):\n",
        "  x1_p = [a[0] for a in z]\n",
        "  x2_p = [a[1] for a in z]\n",
        "  x3_p = [a[2] for a in z]\n",
        "  return x1_p, x2_p, x3_p"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kcWrWlFeKY6w",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(1997)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n82MCpLCKatu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import Augmentor\n",
        "\n",
        "p = Augmentor.DataPipeline(z, y)\n",
        "\n",
        "p.rotate(probability=0.2, max_left_rotation=5, max_right_rotation=5)\n",
        "p.flip_left_right(probability=0.2)\n",
        "p.zoom_random(probability=0.1, percentage_area=0.8)\n",
        "p.flip_top_bottom(probability=0.3)\n",
        "p.gaussian_distortion(probability=0.05, grid_width=4, grid_height=4, magnitude=3, corner='bell', method='in', mex=0.5, mey=0.5, sdx=0.05, sdy=0.05)\n",
        "p.random_brightness(probability=0.05, min_factor=0.7, max_factor=1.3)\n",
        "p.random_color(probability=0.05, min_factor=0.6, max_factor=0.9)\n",
        "p.random_contrast(probability=0.05, min_factor=0.6, max_factor=0.9)\n",
        "p.random_distortion(probability=0.2, grid_width=4, grid_height=4, magnitude=2)\n",
        "\n",
        "augmented_images, labels = p.sample(1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MSEZ9wq-Kc1X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x1_p, x2_p, x3_p = unzip(augmented_images)\n",
        "y_p = labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MBsy5wyEKpKm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6327532b-d6cf-4d72-9bf0-1f54166c96de"
      },
      "cell_type": "code",
      "source": [
        "print(augmented_images[9][2].shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "O9lVZS8eKrQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "0eb9d959-01f9-4a46-9e65-607c85fcb0ee"
      },
      "cell_type": "code",
      "source": [
        "print(len(x1_p))\n",
        "print(len(x2_p))\n",
        "print(len(x3_p))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1000\n",
            "1000\n",
            "1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_lVXToQ9Ktbg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x1.extend(x1_p)\n",
        "x2.extend(x2_p)\n",
        "x3.extend(x3_p)\n",
        "y.extend(y_p)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_zUqiZAjKv5P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "bac487cb-4d62-4a23-9ff7-54a30481a489"
      },
      "cell_type": "code",
      "source": [
        "print(len(x1))\n",
        "print(len(x2))\n",
        "print(len(x3))\n",
        "print(len(y))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1120\n",
            "1120\n",
            "1120\n",
            "1120\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iE5Nqpb2KyJg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(100)\n",
        "zz = list(zip(x1, x2, x3, y))\n",
        "random.shuffle(zz)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kT8WGi6BK0-X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z1 = [a[0] for a in zz]\n",
        "z2 = [a[1] for a in zz]\n",
        "z3 = [a[2] for a in zz]\n",
        "zy = [a[3] for a in zz]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5kFZCdLfK3Dd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a89f9bd2-0144-48c5-ceb2-adbed50d7f0d"
      },
      "cell_type": "code",
      "source": [
        "split = int(0.8*(len(z1)))\n",
        "print(split)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "896\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gecYUdEhK5D5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x1_tr = z1[:split]\n",
        "x2_tr = z2[:split]\n",
        "x3_tr = z3[:split]\n",
        "y_tr = zy[:split]\n",
        "\n",
        "x1_ts = z1[split:]\n",
        "x2_ts = z2[split:]\n",
        "x3_ts = z3[split:]\n",
        "y_ts = zy[split:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BmHHFVj3K7cm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "x1_tr = np.array(x1_tr, dtype = 'float32')\n",
        "x2_tr = np.array(x2_tr, dtype = 'float32')\n",
        "x3_tr = np.array(x3_tr, dtype = 'float32')\n",
        "y_tr = np.array(y_tr, dtype = 'float32')\n",
        "y_ts = np.array(y_ts, dtype = 'float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qi9yHRP6K9yv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7139842e-c25f-4bfb-fdd1-b529934cf164"
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(x1_tr))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "151.0883\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "l1NEzIPHK_uV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x1_tr = x1_tr/255.\n",
        "x2_tr = x2_tr/255.\n",
        "x3_tr = x3_tr/255."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OwcM6pjQLB2v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "400d04a7-52c2-4a03-ff79-bf6881c244b1"
      },
      "cell_type": "code",
      "source": [
        "np.mean(x1_tr)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.59250474"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "dnvZrKhfLD4X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x1_ts = np.array(x1_ts, dtype = 'float32')\n",
        "x2_ts = np.array(x2_ts, dtype = 'float32')\n",
        "x3_ts = np.array(x3_ts, dtype = 'float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OkqUKFu-LFx9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "013bdd08-5803-44c7-9f38-5f555bf68ca8"
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(x1_ts))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "153.14502\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AjJwOUvRLH22",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x1_ts = x1_ts/255.\n",
        "x2_ts = x2_ts/255.\n",
        "x3_ts = x3_ts/255."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lpBQfIorLKBn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf1103ab-c450-4620-cc37-ad3c273d71c9"
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(x1_ts))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.60056776\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-V3Nk5WBLL7e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "94a2961f-6285-4ba3-b6f6-fb1a794bd0d2"
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.resnet50 import ResNet50\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.resnet50 import preprocess_input\n",
        "\n",
        "model = ResNet50(weights='imagenet', include_top=False, pooling=\"avg\")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94658560/94653016 [==============================] - 69s 1us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VzmcvTnXLOm-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2312cd82-08fb-4155-b788-d1f622617a15"
      },
      "cell_type": "code",
      "source": [
        "x1_tr.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(896, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "-rB6ROFxLdgK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "1cf3b039-e106-44a6-90a9-1369ef536e31"
      },
      "cell_type": "code",
      "source": [
        "fx1_tr = model.predict(x1_tr)\n",
        "print(fx1_tr.shape)\n",
        "\n",
        "\n",
        "fx2_tr = model.predict(x2_tr)\n",
        "print(fx2_tr.shape)\n",
        "\n",
        "fx3_tr = model.predict(x3_tr)\n",
        "print(fx3_tr.shape)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(896, 2048)\n",
            "(896, 2048)\n",
            "(896, 2048)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VoaVId9dLfeJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bac18aa6-f6ba-4b77-90d1-3b3b43c0be34"
      },
      "cell_type": "code",
      "source": [
        "fx_tr = np.concatenate((fx1_tr, fx2_tr, fx3_tr), axis=-1)\n",
        "print(fx_tr.shape)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(896, 6144)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VB7vUiPGOIGv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "d1d21284-6628-4fa8-8336-dbb272ee7468"
      },
      "cell_type": "code",
      "source": [
        "fx1_ts = model.predict(x1_ts)\n",
        "print(fx1_ts.shape)\n",
        "\n",
        "\n",
        "fx2_ts = model.predict(x2_ts)\n",
        "print(fx2_ts.shape)\n",
        "\n",
        "fx3_ts = model.predict(x3_ts)\n",
        "print(fx3_ts.shape)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 2048)\n",
            "(224, 2048)\n",
            "(224, 2048)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uMeCTD-BOLKu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fd89163e-3546-4c2e-852e-27fe3d2501ba"
      },
      "cell_type": "code",
      "source": [
        "fx_ts = np.concatenate((fx1_ts, fx2_ts, fx3_ts), axis=-1)\n",
        "print(fx_ts.shape)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 6144)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0RkRW6PNORq4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "tsne = TSNE(n_components=2, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9Xc0m5b-OUfg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_2d = tsne.fit_transform(fx_tr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VCGlquiEOW73",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f97a042e-8eed-44bd-d66e-b1d5287c2953"
      },
      "cell_type": "code",
      "source": [
        "X_2d.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(896, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "cHLqmYXsOcdG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931
        },
        "outputId": "464a52a4-ed34-4845-b5ae-7c3bced84e33"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(20,14))\n",
        "\n",
        "B_SIZE = 10\n",
        "axis_font = {'fontname':'Arial', 'size':'30'}\n",
        "plt.rc('font', size=B_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=B_SIZE+5)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=B_SIZE+5)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=B_SIZE-5)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=B_SIZE-5)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=B_SIZE+5)    # legend fontsize\n",
        "plt.rc('figure', titlesize=B_SIZE)  # fontsize of the figure title\n",
        "#plt.rc('xlabel', titlesize=B_SIZE+10)\n",
        "#plt.rc('ylabel', titlesize=B_SIZE+10)\n",
        "labels = [0, 1]\n",
        "class_label = ['Class 0', 'Class 1']\n",
        "\n",
        "plt.figure(figsize=(20,15))\n",
        "plt.title('t-SNE for eeg features (subject 2)', fontweight='bold')\n",
        "plt.xlabel('dim 1')\n",
        "plt.ylabel('dim 2')\n",
        "colors = 'b', 'g'\n",
        "for i, c, label in zip(labels, colors, class_label):\n",
        "    plt.scatter(X_2d[y_tr == i, 0], X_2d[y_tr == i, 1], c=c,\n",
        "                label=label, alpha=0.8, linewidths=2, edgecolors ='k',\n",
        "               s=100)\n",
        "plt.legend()\n",
        "# plt.savefig('tsne_feature_plot_2.png', bbox_inches='tight')\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1008 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MvXsTsFpVAgJi0OvDAbBYtqGqADEoSuP/7WkCivB4bGi1oWg0zVCUvjid2axZ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\ncn70o59SV7d4Wud3psaaDSbEH4rXyt9dIf7QyO+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6erKkUgnAJRgM8Na3votPfeqfpbJI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CTbtt3I0PCT5Bt5YutiRLoilCfL5J7J8eyXnoEIWEmT2Pr43GsTByaYeiDPwYP7ufPOz9PZmVqM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2DSOFjSZDGFY9HrMFx5ekr28vv/3tr7j22g9O+Leb7SjBSKFQzApmalRx7IR26aWXj5TMqWih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Qbz/vCHP2Db+YAPyKOz8zV6ejQWLfoqc+b89bDPnw7BYJBf/OJn/OEPbwIa73jHO/jTP71u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\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+ngwW3QQ0UlCQP6L9izp03INWrbqX3/72N0Sj27HtFQhxHrpejMt1FMf5K7m5drfgddeJ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\nf/7nfx7tl1aUTuNlhPdY6Ku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77DM88cQT2PbbQBu9/SeGlEeAbQC43flDzhJUFEVRFEVR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\n9CsYDCoYnCTDuFDl5Q/0e14sVq/c3MT5AAAAAIDxjyVpAPp13XXzlJ9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            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CFGTjXWTOfOI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "eceed309-89db-42ae-cdc7-69780bdc3b1d"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(fx_tr)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "\n",
        "Xtr = scaler.transform(fx_tr)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.00000000e+00 0.00000000e+00 3.95875873e-05 ... 1.99013342e+00\n",
            " 0.00000000e+00 0.00000000e+00]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JCTkPZkZOys4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "8cffc23b-1a37-4f8a-9c30-7d1936b666a4"
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(Xtr)) # MEAN IS ZERO \n",
        "print(np.var(Xtr)) # VARIANCE IS 1"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-1.2639377e-09\n",
            "0.36718744\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r94pXT8qO3l_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class_weight = {0: 0.5, 1: 0.5} "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gx_54Ho1O6sf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "20ecbbf0-12e9-4c47-ea52-817a9a3bf8d1"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf1 = svm.SVC(kernel='linear', C=10, class_weight = class_weight)\n",
        "scores = cross_val_score(clf1, Xtr, y_tr, cv=10)\n",
        "print('SVM 10 fold CV Accuracy: ', scores.mean(), scores.std())\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf2 = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf2, Xtr, y_tr, cv=10)\n",
        "print('Random Forest 10 fold Accuracy: ', scores.mean(), scores.std())\n",
        "\n",
        "\n",
        "\n",
        "from sklearn import tree\n",
        "clf3 = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf3, Xtr, y_tr, cv=10)\n",
        "print('Decision Tree 10 fold Accuracy: ', scores.mean(), scores.std())\n",
        "\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "neigh = KNeighborsClassifier(n_neighbors=3)\n",
        "scores = cross_val_score(neigh, Xtr, y_tr, cv=10)\n",
        "print('KNN 10 fold Accuracy: ', scores.mean(), scores.std())"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVM 10 fold CV Accuracy:  0.9185354844905408 0.021837622562075307\n",
            "Random Forest 10 fold Accuracy:  0.7935115446351402 0.05131134216496535\n",
            "Decision Tree 10 fold Accuracy:  0.7778677751711459 0.0362439969217487\n",
            "KNN 10 fold Accuracy:  0.8193045780686228 0.047088242586991264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YYDvojTiO9Uo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "bfafddcc-492e-49c4-bd39-9b18ec2c0221"
      },
      "cell_type": "code",
      "source": [
        "# test\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(fx_ts)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "\n",
        "Xts = scaler.transform(fx_ts)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.00000000e+00 0.00000000e+00 8.74522292e-06 ... 1.97464153e+00\n",
            " 0.00000000e+00 0.00000000e+00]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ghzYhvHVPN2n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "ba3a048a-7fad-449e-960d-5d79bacdc155"
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(Xts)) # MEAN IS ZERO \n",
        "print(np.var(Xts)) # VARIANCE IS 1"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.0357313e-09\n",
            "0.35139978\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "py6-qUEEPQLl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "clf1.fit(Xtr,y_tr)\n",
        "y_p1 = clf1.predict(Xts)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UMoAztTuPSM-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "224ac2a2-19df-4f9e-804b-2bf79da927cc"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import recall_score, average_precision_score, f1_score, accuracy_score, confusion_matrix\n",
        "print('SVM test result:\\nrecall\\nprecision\\nf1\\naccuracy\\nconfusion_matrix')\n",
        "print(recall_score(y_ts, y_p1))\n",
        "print(average_precision_score(y_ts, y_p1))\n",
        "print(f1_score(y_ts, y_p1))\n",
        "print(accuracy_score(y_ts, y_p1))\n",
        "print(confusion_matrix(y_ts, y_p1))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVM test result:\n",
            "recall\n",
            "precision\n",
            "f1\n",
            "accuracy\n",
            "confusion_matrix\n",
            "0.963302752293578\n",
            "0.9290894761078248\n",
            "0.9545454545454546\n",
            "0.9553571428571429\n",
            "[[109   6]\n",
            " [  4 105]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "isM8a0RIPVyf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "clf2.fit(Xtr,y_tr)\n",
        "y_p1 = clf2.predict(Xts)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kacwZLwjPYS0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "5b324711-6750-49f8-8c70-8d15068cf29c"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import recall_score, average_precision_score, f1_score, accuracy_score, confusion_matrix\n",
        "print('RF test result:\\nrecall\\nprecision\\nf1\\naccuracy\\nconfusion_matrix')\n",
        "print(recall_score(y_ts, y_p1))\n",
        "print(average_precision_score(y_ts, y_p1))\n",
        "print(f1_score(y_ts, y_p1))\n",
        "print(accuracy_score(y_ts, y_p1))\n",
        "print(confusion_matrix(y_ts, y_p1))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RF test result:\n",
            "recall\n",
            "precision\n",
            "f1\n",
            "accuracy\n",
            "confusion_matrix\n",
            "0.8165137614678899\n",
            "0.7813783311489734\n",
            "0.8317757009345795\n",
            "0.8392857142857143\n",
            "[[99 16]\n",
            " [20 89]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WJmHrjw2Paaw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "clf3.fit(Xtr,y_tr)\n",
        "y_p1 = clf3.predict(Xts)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mGne2IXlPczQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "c08ccae5-e306-45be-86cb-502c4a4ea4d5"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import recall_score, average_precision_score, f1_score, accuracy_score, confusion_matrix\n",
        "print('DTree test result:\\nrecall\\nprecision\\nf1\\naccuracy\\nconfusion_matrix')\n",
        "print(recall_score(y_ts, y_p1))\n",
        "print(average_precision_score(y_ts, y_p1))\n",
        "print(f1_score(y_ts, y_p1))\n",
        "print(accuracy_score(y_ts, y_p1))\n",
        "print(confusion_matrix(y_ts, y_p1))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DTree test result:\n",
            "recall\n",
            "precision\n",
            "f1\n",
            "accuracy\n",
            "confusion_matrix\n",
            "0.7706422018348624\n",
            "0.6213232448581542\n",
            "0.711864406779661\n",
            "0.6964285714285714\n",
            "[[72 43]\n",
            " [25 84]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HLyXF3SYPfQm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "neigh.fit(Xtr,y_tr)\n",
        "y_p1 = neigh.predict(Xts)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hOEQFkEaPhtn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "3ae82945-3bd9-4bbe-a9a2-8fdd7562ea6d"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import recall_score, average_precision_score, f1_score, accuracy_score, confusion_matrix\n",
        "print('KNN test result:\\nrecall\\nprecision\\nf1\\naccuracy\\nconfusion_matrix')\n",
        "print(recall_score(y_ts, y_p1))\n",
        "print(average_precision_score(y_ts, y_p1))\n",
        "print(f1_score(y_ts, y_p1))\n",
        "print(accuracy_score(y_ts, y_p1))\n",
        "print(confusion_matrix(y_ts, y_p1))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN test result:\n",
            "recall\n",
            "precision\n",
            "f1\n",
            "accuracy\n",
            "confusion_matrix\n",
            "0.8899082568807339\n",
            "0.8041896974186564\n",
            "0.8660714285714286\n",
            "0.8660714285714286\n",
            "[[97 18]\n",
            " [12 97]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lcRR_IwPPkQg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}