chemo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "chemo.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "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/iwatobipen/chemo_info/blob/master/chemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "c20nBaAf1yYV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9e24d2a-3b48-400b-ab82-fa4f709fa922"
      },
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PInm4lFu2Uyq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1394
        },
        "outputId": "145e842d-937d-413f-eba9-b50000d49d5d"
      },
      "cell_type": "code",
      "source": [
        "!wget -c https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
        "!time bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
        "!time conda install -q -y -c conda-forge rdkit\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-10-31 13:08:02--  https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
            "Resolving repo.continuum.io (repo.continuum.io)... 104.16.19.10, 104.16.18.10, 2400:cb00:2048:1::6810:120a, ...\n",
            "Connecting to repo.continuum.io (repo.continuum.io)|104.16.19.10|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "\n",
            "    The file is already fully retrieved; nothing to do.\n",
            "\n",
            "PREFIX=/usr/local\n",
            "reinstalling: python-3.7.0-hc3d631a_0 ...\n",
            "Python 3.7.0\n",
            "reinstalling: ca-certificates-2018.03.07-0 ...\n",
            "reinstalling: conda-env-2.6.0-1 ...\n",
            "reinstalling: libgcc-ng-8.2.0-hdf63c60_1 ...\n",
            "reinstalling: libstdcxx-ng-8.2.0-hdf63c60_1 ...\n",
            "reinstalling: libffi-3.2.1-hd88cf55_4 ...\n",
            "reinstalling: ncurses-6.1-hf484d3e_0 ...\n",
            "reinstalling: openssl-1.0.2p-h14c3975_0 ...\n",
            "reinstalling: xz-5.2.4-h14c3975_4 ...\n",
            "reinstalling: yaml-0.1.7-had09818_2 ...\n",
            "reinstalling: zlib-1.2.11-ha838bed_2 ...\n",
            "reinstalling: libedit-3.1.20170329-h6b74fdf_2 ...\n",
            "reinstalling: readline-7.0-h7b6447c_5 ...\n",
            "reinstalling: tk-8.6.8-hbc83047_0 ...\n",
            "reinstalling: sqlite-3.24.0-h84994c4_0 ...\n",
            "reinstalling: asn1crypto-0.24.0-py37_0 ...\n",
            "reinstalling: certifi-2018.8.24-py37_1 ...\n",
            "reinstalling: chardet-3.0.4-py37_1 ...\n",
            "reinstalling: idna-2.7-py37_0 ...\n",
            "reinstalling: pycosat-0.6.3-py37h14c3975_0 ...\n",
            "reinstalling: pycparser-2.18-py37_1 ...\n",
            "reinstalling: pysocks-1.6.8-py37_0 ...\n",
            "reinstalling: ruamel_yaml-0.15.46-py37h14c3975_0 ...\n",
            "reinstalling: six-1.11.0-py37_1 ...\n",
            "reinstalling: cffi-1.11.5-py37he75722e_1 ...\n",
            "reinstalling: setuptools-40.2.0-py37_0 ...\n",
            "reinstalling: cryptography-2.3.1-py37hc365091_0 ...\n",
            "reinstalling: wheel-0.31.1-py37_0 ...\n",
            "reinstalling: pip-10.0.1-py37_0 ...\n",
            "reinstalling: pyopenssl-18.0.0-py37_0 ...\n",
            "reinstalling: urllib3-1.23-py37_0 ...\n",
            "reinstalling: requests-2.19.1-py37_0 ...\n",
            "reinstalling: conda-4.5.11-py37_0 ...\n",
            "unlinking: ca-certificates-2018.10.15-ha4d7672_0\n",
            "unlinking: certifi-2018.10.15-py37_1000\n",
            "unlinking: conda-4.5.11-py37_1000\n",
            "unlinking: openssl-1.0.2p-h470a237_1\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /usr/local\n",
            "\n",
            "real\t0m15.630s\n",
            "user\t0m13.287s\n",
            "sys\t0m4.019s\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs: \n",
            "    - rdkit\n",
            "\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "    ca-certificates: 2018.03.07-0      --> 2018.10.15-ha4d7672_0 conda-forge\n",
            "    certifi:         2018.8.24-py37_1  --> 2018.10.15-py37_1000  conda-forge\n",
            "    conda:           4.5.11-py37_0     --> 4.5.11-py37_1000      conda-forge\n",
            "    openssl:         1.0.2p-h14c3975_0 --> 1.0.2p-h470a237_1     conda-forge\n",
            "\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "\n",
            "real\t0m40.560s\n",
            "user\t0m38.017s\n",
            "sys\t0m1.007s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AFT7w0WD2bj_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import sys\n",
        "import os\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L_QLM4zNOp5A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import DataStructs\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit.Chem import RDConfig\n",
        "from rdkit import rdBase\n",
        "from rdkit.Chem.Draw import IPythonConsole"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cP4ELmt67TwY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "07cdd998-d145-40ec-d780-f07482291c9a"
      },
      "cell_type": "code",
      "source": [
        "rdBase.rdkitVersion"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2018.09.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "uJ66JY_Y6TJg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8507632c-790e-4881-f6c4-69235c3dc87b"
      },
      "cell_type": "code",
      "source": [
        "trainsdf = Chem.SDMolSupplier(os.path.join( RDConfig.RDDocsDir, 'Book/data/solubility.train.sdf'))\n",
        "testsdf = Chem.SDMolSupplier(os.path.join( RDConfig.RDDocsDir, 'Book/data/solubility.test.sdf'))\n",
        "train_mols = [mol for mol in trainsdf if mol != None]\n",
        "test_mols = [mol for mol in testsdf if mol != None]\n",
        "print(len(train_mols), len(test_mols))\n",
        "\n",
        "sol_class = {\"(A) low\":0, \"(B) medium\":1, \"(C) high\": 2}"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1025 257\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "c7LO9wFe6_l9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# 2048 bit vector\n",
        "def mol2arr(mol):\n",
        "  arr = np.zeros((1,))\n",
        "  fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2)\n",
        "  DataStructs.ConvertToNumpyArray(fp, arr)\n",
        "  return arr"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1mRyMr18Sxny",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d5eefcae-c608-41e4-edbf-dd2616bddd1c"
      },
      "cell_type": "code",
      "source": [
        "np.sum(mol2arr(train_mols[0]))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "ZtFfizuNS5MJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ae1a7daf-3cc5-4a6a-9274-e3bab5443275"
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2048,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4eHTvTNDUJcY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "from keras import Model\n",
        "from keras.layers import Activation, Dense, Dropout, Input\n",
        "from keras.utils import np_utils\n",
        "\n",
        "\n",
        "trainX = np.array([mol2arr(mol) for mol in train_mols])\n",
        "trainY = [sol_class[mol.GetProp(\"SOL_classification\")] for mol in train_mols]\n",
        "trainY = np_utils.to_categorical(trainY)\n",
        "\n",
        "testX = np.array([mol2arr(mol) for mol in test_mols])\n",
        "testY = [sol_class[mol.GetProp(\"SOL_classification\")] for mol in test_mols]\n",
        "testY = np_utils.to_categorical(testY)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0O692Z2mUqC7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "25672f7f-5763-4043-e4c7-6c10731d4538"
      },
      "cell_type": "code",
      "source": [
        "inputs = Input(shape=(2048,), name='input')\n",
        "x = Dense(100, activation='relu', name='Layer1')(inputs)\n",
        "x = Dense(20, activation='relu', name='Layer2')(x)\n",
        "x = Dense(2, activation='relu', name='Layer3')(x)\n",
        "predictions = Dense(3, activation='softmax', name='output')(x)\n",
        "\n",
        "model = Model(inputs=inputs, outputs = predictions)\n",
        "model.compile(optimizer=tf.train.RMSPropOptimizer(0.001),\n",
        "                           loss='categorical_crossentropy',\n",
        "                           metrics=['accuracy'])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model_int = Model(inputs=inputs, outputs=model.get_layer(name='Layer3').output)\n",
        "model_int.summary()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input (InputLayer)           (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "Layer1 (Dense)               (None, 100)               204900    \n",
            "_________________________________________________________________\n",
            "Layer2 (Dense)               (None, 20)                2020      \n",
            "_________________________________________________________________\n",
            "Layer3 (Dense)               (None, 2)                 42        \n",
            "_________________________________________________________________\n",
            "output (Dense)               (None, 3)                 9         \n",
            "=================================================================\n",
            "Total params: 206,971\n",
            "Trainable params: 206,971\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input (InputLayer)           (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "Layer1 (Dense)               (None, 100)               204900    \n",
            "_________________________________________________________________\n",
            "Layer2 (Dense)               (None, 20)                2020      \n",
            "_________________________________________________________________\n",
            "Layer3 (Dense)               (None, 2)                 42        \n",
            "=================================================================\n",
            "Total params: 206,962\n",
            "Trainable params: 206,962\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M-OtvArzWXGC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1717
        },
        "outputId": "9d27253f-f1cd-46e5-a3e4-df16ddddd189"
      },
      "cell_type": "code",
      "source": [
        "epochs = 50\n",
        "hist = model.fit(np.array(trainX), trainY, batch_size=32, epochs=epochs)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "1025/1025 [==============================] - 0s 478us/step - loss: 1.0971 - acc: 0.4322\n",
            "Epoch 2/50\n",
            "1025/1025 [==============================] - 0s 175us/step - loss: 1.0896 - acc: 0.4244\n",
            "Epoch 3/50\n",
            "1025/1025 [==============================] - 0s 176us/step - loss: 1.0620 - acc: 0.4117\n",
            "Epoch 4/50\n",
            "1025/1025 [==============================] - 0s 178us/step - loss: 1.0195 - acc: 0.4351\n",
            "Epoch 5/50\n",
            "1025/1025 [==============================] - 0s 181us/step - loss: 0.9382 - acc: 0.5307\n",
            "Epoch 6/50\n",
            "1025/1025 [==============================] - 0s 173us/step - loss: 0.8520 - acc: 0.6010\n",
            "Epoch 7/50\n",
            "1025/1025 [==============================] - 0s 177us/step - loss: 0.7807 - acc: 0.6654\n",
            "Epoch 8/50\n",
            "1025/1025 [==============================] - 0s 189us/step - loss: 0.7262 - acc: 0.6995\n",
            "Epoch 9/50\n",
            "1025/1025 [==============================] - 0s 190us/step - loss: 0.6837 - acc: 0.7239\n",
            "Epoch 10/50\n",
            "1025/1025 [==============================] - 0s 186us/step - loss: 0.6427 - acc: 0.7434\n",
            "Epoch 11/50\n",
            "1025/1025 [==============================] - 0s 195us/step - loss: 0.6119 - acc: 0.7532\n",
            "Epoch 12/50\n",
            "1025/1025 [==============================] - 0s 188us/step - loss: 0.5826 - acc: 0.7610\n",
            "Epoch 13/50\n",
            "1025/1025 [==============================] - 0s 244us/step - loss: 0.5492 - acc: 0.7698\n",
            "Epoch 14/50\n",
            "1025/1025 [==============================] - 0s 182us/step - loss: 0.5258 - acc: 0.7776\n",
            "Epoch 15/50\n",
            "1025/1025 [==============================] - 0s 181us/step - loss: 0.4976 - acc: 0.8449\n",
            "Epoch 16/50\n",
            "1025/1025 [==============================] - 0s 193us/step - loss: 0.4642 - acc: 0.9580\n",
            "Epoch 17/50\n",
            "1025/1025 [==============================] - 0s 184us/step - loss: 0.4318 - acc: 0.9600\n",
            "Epoch 18/50\n",
            "1025/1025 [==============================] - 0s 188us/step - loss: 0.3990 - acc: 0.9649\n",
            "Epoch 19/50\n",
            "1025/1025 [==============================] - 0s 192us/step - loss: 0.3606 - acc: 0.9756\n",
            "Epoch 20/50\n",
            "1025/1025 [==============================] - 0s 187us/step - loss: 0.3201 - acc: 0.9824\n",
            "Epoch 21/50\n",
            "1025/1025 [==============================] - 0s 200us/step - loss: 0.2810 - acc: 0.9854\n",
            "Epoch 22/50\n",
            "1025/1025 [==============================] - 0s 187us/step - loss: 0.2484 - acc: 0.9824\n",
            "Epoch 23/50\n",
            "1025/1025 [==============================] - 0s 183us/step - loss: 0.2151 - acc: 0.9844\n",
            "Epoch 24/50\n",
            "1025/1025 [==============================] - 0s 184us/step - loss: 0.1897 - acc: 0.9824\n",
            "Epoch 25/50\n",
            "1025/1025 [==============================] - 0s 184us/step - loss: 0.1650 - acc: 0.9863\n",
            "Epoch 26/50\n",
            "1025/1025 [==============================] - 0s 198us/step - loss: 0.1451 - acc: 0.9854\n",
            "Epoch 27/50\n",
            "1025/1025 [==============================] - 0s 186us/step - loss: 0.1268 - acc: 0.9863\n",
            "Epoch 28/50\n",
            "1025/1025 [==============================] - 0s 190us/step - loss: 0.1071 - acc: 0.9824\n",
            "Epoch 29/50\n",
            "1025/1025 [==============================] - 0s 188us/step - loss: 0.0798 - acc: 0.9844\n",
            "Epoch 30/50\n",
            "1025/1025 [==============================] - 0s 183us/step - loss: 0.0574 - acc: 0.9873\n",
            "Epoch 31/50\n",
            "1025/1025 [==============================] - 0s 188us/step - loss: 0.0482 - acc: 0.9893\n",
            "Epoch 32/50\n",
            "1025/1025 [==============================] - 0s 199us/step - loss: 0.0440 - acc: 0.9863\n",
            "Epoch 33/50\n",
            "1025/1025 [==============================] - 0s 186us/step - loss: 0.0395 - acc: 0.9893\n",
            "Epoch 34/50\n",
            "1025/1025 [==============================] - 0s 184us/step - loss: 0.0351 - acc: 0.9873\n",
            "Epoch 35/50\n",
            "1025/1025 [==============================] - 0s 182us/step - loss: 0.0393 - acc: 0.9854\n",
            "Epoch 36/50\n",
            "1025/1025 [==============================] - 0s 183us/step - loss: 0.0372 - acc: 0.9883\n",
            "Epoch 37/50\n",
            "1025/1025 [==============================] - 0s 193us/step - loss: 0.0362 - acc: 0.9834\n",
            "Epoch 38/50\n",
            "1025/1025 [==============================] - 0s 187us/step - loss: 0.0348 - acc: 0.9873\n",
            "Epoch 39/50\n",
            "1025/1025 [==============================] - 0s 190us/step - loss: 0.0295 - acc: 0.9893\n",
            "Epoch 40/50\n",
            "1025/1025 [==============================] - 0s 193us/step - loss: 0.0320 - acc: 0.9873\n",
            "Epoch 41/50\n",
            "1025/1025 [==============================] - 0s 208us/step - loss: 0.0272 - acc: 0.9883\n",
            "Epoch 42/50\n",
            "1025/1025 [==============================] - 0s 243us/step - loss: 0.0372 - acc: 0.9873\n",
            "Epoch 43/50\n",
            "1025/1025 [==============================] - 0s 184us/step - loss: 0.0325 - acc: 0.9873\n",
            "Epoch 44/50\n",
            "1025/1025 [==============================] - 0s 187us/step - loss: 0.0304 - acc: 0.9873\n",
            "Epoch 45/50\n",
            "1025/1025 [==============================] - 0s 187us/step - loss: 0.0251 - acc: 0.9902\n",
            "Epoch 46/50\n",
            "1025/1025 [==============================] - 0s 183us/step - loss: 0.0319 - acc: 0.9883\n",
            "Epoch 47/50\n",
            "1025/1025 [==============================] - 0s 192us/step - loss: 0.0265 - acc: 0.9893\n",
            "Epoch 48/50\n",
            "1025/1025 [==============================] - 0s 185us/step - loss: 0.0306 - acc: 0.9893\n",
            "Epoch 49/50\n",
            "1025/1025 [==============================] - 0s 190us/step - loss: 0.0284 - acc: 0.9883\n",
            "Epoch 50/50\n",
            "1025/1025 [==============================] - 0s 184us/step - loss: 0.0300 - acc: 0.9873\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "alpYfIqaX1s2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "ee1bc3e2-d2d9-4fd8-d9fc-46190cb3487c"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(range(epochs), hist.history['acc'])\n",
        "plt.plot(range(epochs), hist.history['loss'])"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9e210f7550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YHz+veGc4wgt/reBvG6tw2A2+tGQKl51RmnQhHgj4qatrs7qMo0KRED/b+Dh7\nWyuZXziXO2b9/bCE+Whr90iRdqeXgbQ7EPD3GVpJ0yM/wumwM21cDtPG5XAlPWPeB2rbWbm1hlXb\nanhn3UHeWXeQ0sJMTp9RyMdbqqltCjEukMFd15ZRWphpdRNSgtfh5VunLeXnm59gQ+1mYrEod8y+\nBact6f6lhEh6A+qRK6UeBs4CTOABrfXnjttWSn0fWKy1vuBk6xpqj/xkn1qRaIwtuxv4aEs1m3c3\nEI2ZGMDli8Zz/XmTcTqSdyx3tPZUuqJhfrH5ScqbKpidP4M7Z9+K0+5M2PpHa7uHm7Q7vQx7j1wp\ndT4wTWu9WCk1E3gcWHzcPLOAJUD3QIoeLg67jfnTA8yfHqA1GGbTrnpKAhlMKcm2sqyU5ra7uHfu\nHTy25bdsbdjJL7f8lm/M+Rouu8vq0oRIGwPpol4MrADQWu8AcpVSWcfN8xDwLwmubUiyfC7Om1ci\nIT4CXHYnd8+5jdn5M9nRWM4vNj9JdyxidVlCpI2BDGgWA71PTl0Xf6wVQCl1O/A+sG8gT5ib68Mx\nhINwAoHUvjjyyYz2tv9z4Jv8aOWvWFe1mT/sXcF9i25PyDVQR3u7h4u0O70Mpd2D2TJ19J2plMoD\n7gAuAcYOZOGmpuAgnrJHuo6fQfK0/ZZpN9HQ3sxHlWvIJJNrp1wxpPUlS7sTTdqdXgY4Rt7ntIEM\nrVTR0wM/ogSojt++CAgAHwKvAAviG0ZFmnLZXdw993YC3nzeqvwrHx1abXVJQqS8gQT528CNAEqp\nBUCV1roNQGv9B631LK31WcD1wKda628PW7UiKfhdmXxz3lIynRk8X76CrfU7rC5JiJTWb5BrrVcC\n65VSK4HlwDKl1O1KqeuHvTqRtAp9Bdwz93bsho3fbHua/a0HrS5JiJSVdEd2puP4GSRv2zfWbeXX\nW57C78rkuwuXke/NO6Xlk7XdQyXtTi9D3Y88eY+QEUnhtMBsbph2La3hNh7d9DjB7pDVJQmRciTI\nxbC7sPRcLiw9l5pgLY9ve5poLGp1SUKkFAlyMSK+NPUayvJnsKOxnJcrXre6HCFSigS5GBE2w8Yd\nZTczJqOIvx38WHZLFCKBJMjFiPE6PNwz93YynD6eL19BeVOF1SUJkRIkyMWIKvDmc9fsr2Fg8Ost\nv6c2WG91SUIkPQlyMeKm5U7mK+p6OiJBfrn5SUIR2ZNFiKGQIBeWOLvkTC4qPa9nT5atz8ieLEIM\ngQS5sMz1U69mVr5ie6PmpYrXGOmD04RIFRLkwjI2w8bXy26hJKOY9w+u5G8HP7a6JCGSkgS5sJTX\n4eHeeXeQ5fLz0q7X2FS3zep2CxXOAAAPZklEQVSShEg6EuTCcnmeXO6dewdOm4Mntz1DZesBq0sS\nIqlIkItRYXzWOO4ou5nuWIRfbH6ShlCT1SUJkTQkyMWoMTdQdvQEW7/Y/ITslijEAEmQi1HlwtJz\nOX/cOVR11PDrLb8nIrslCtEvCXIx6tw47VrmFMxkZ9MuHln9hOxjLkQ/JMjFqGMzbNw+62YmZ09k\n5YH1/Gbb00RiEavLEmLUkiAXo5LH4WbZvKWUFU5nU91WfrXld3RHu60uS4hRSYJcjFoeh5t/Om8Z\nM/Oms7VhJ7/Y/CThaNjqsoQYdSTIxajmcri4e85tR8fMH930OJ2RLqvLEmJUkSAXo57T7uTO2bdy\nWmAOu5r38LNNv5ZdE4XoRYJcJAWHzcHXy27m9KLT2NNSyU83/Jpgd9DqsoQYFSTIRdKw2+zcNusr\nnFV8OpVtB1i+8Vd0SJgLIUEukovNsHHLzBs5e8wZHGg7xPINj9He3WF1WUJYSoJcJB2bYePvZ9zA\nOSWLONhexfINj9EWbre6LCEsI0EukpLNsPEVdT1Lxi7mUHu1hLlIaxLkImnZDBs3Tb/u6LlZfrzh\nl7SG26wuS4gRJ0EukpphGHx52he4sPRcajoO86P1j3KovdrqsoQYUY6BzKSUehg4CzCBB7TWa3tN\nuxD4PhAFNHCn1jo2DLUKcUKGYXDD1Gtx21y8VflXfrjup9w0/XoWjzkdwzCsLk+IYddvj1wpdT4w\nTWu9GFgKLD9ulseAG7XW5wB+4IqEVylEPwzD4NopV3DP3Ntx2pw8vfNFntrxAl1ySL9IAwMZWrkY\nWAGgtd4B5CqlsnpNX6i1Phi/XQfkJ7ZEIQZuTsEsvnfGA0zwl/JJzXp+sO6n1HQctrosIYaVYZrm\nSWdQSj0GvKG1/mP8/ofAUq11+XHzjQE+BBZprRv6Wl8kEjUdDvuQCxfiZCLRCE9tepk3d72H2+7i\nrtNvZsnERVaXJcRQ9DlOOKAx8v5WppQqBF4DvnmyEAdoahr8kXiBgJ+6uvTcKyFd2z6Udl9TeiVj\n3eP4/Y4XeeSTJ1m3fys3Tb8Ot92V4CoTT17v9DKQdgcC/j6nDSTIq4DiXvdLgKO7BcSHWd4E/kVr\n/fYA1ifEiJlfOIdxmSU8vu1pVlevY29LJV8vu4Vx/hKrSxMiYQYyRv42cCOAUmoBUKW17v3R8RDw\nsNb6rWGoT4ghC/jy+c7Cb3Jx6RIOB+v44fpHeP/gSvobVhQiWfQ7Rg6glPpPYAkQA5YB84EW4M9A\nE7Cq1+zPaK0f62tddXVtg373pOvXLkjftie63Vvrd/DUjhdo7+5gXkEZt8z8MhlOX8LWnyjyeqeX\nAQ6tDG2MXGv9veMe2tTrtnsg6xBiNJhdMJN/OvNBntz2LJvqt7F/zSH+Tl3HnIJZVpcmxKDJkZ0i\n7eS4s7l//je4ZtJltIRb+cXmJ3l00+PUBuusLk2IQRnMXitCJD2bYePKSZcwLzCbF3e9yraGnejG\nXVw0fgmXT7gIj0O+aIrkIT1ykdZKMou5/7S7WDr7q/hdft6ufI9/X/1D1tZskI2hImlIkIu0ZxgG\nCwrn8t/P+i5XTryYjkiQJ7c/y0PrH6Wiea/V5QnRLwlyIeJcdhfXTL6cf1v0XU4LzGFvayUPf/pz\nfrn5t3KYvxjVZIxciOMUePO4a86t7GmpZEXFG2yu38aW+u2cXXIGV026lBx3ttUlCnEMCXIh+jA5\newLfXnAvW+q388fdb/Jx1RrW1mzg4vHnc9mEC3AlwaH+Ij1IkAtxEoZhMDdQRln+DFbXrOONPW/z\n5r53WF29juunXsWCwnlyznNhORkjF2IA7DY755Qs4r+f9Y9cNuFC2sJtPL7tGX684RccbKuyujyR\n5iTIhTgFHoebL065kn9d9F3mFMyionkv/7n2JzynX6G9u8Pq8kSakiAXYhACvnzumXs7y+YtpdBX\nwIeHVvEfq/+LXU17rC5NpCEJciGGYFa+4p/P/DbXTbmKYCTE8o2P8eGhVf0vKEQCSZALMUQOm4NL\nJ1zAfafdhc/h5Tn9Cs/ufIlILGJ1aSJNSJALkSDTc6fwj6ffz9jMMXxU9QnLNzxGazj9TskqRp4E\nuRAJlO/N5TsLl7GgcC67W/bx/9YuZ3/rwf4XFGIIJMiFSDC33cXXy27hC5OvoKWrlR99+ih/3f8B\nMTNmdWkiRUmQCzEMDMPg8okXcc/c23Hb3bxU8ToPf/pzDnfUWl2aSEES5EIMo9kFM/nXRd9hQeFc\n9rRU8v21P+ad/e9L71wklAS5EMPM78pk6eyvsnT2V3Hb3bxS8QY/Wv8oNdI7Fwki51oRYoQsKJzL\ntJzJvFC+gk9rN/P9tT+mLE8xI286M/KmEfDmy3lbxKBIkAsxgo70zhfUbuHV3W+yqX4bm+q3AZDv\nyWNG3jRm5k1nnncaZsyJ3Wa3uGKRDCTIhbDA/MI5zC+cQ12wgZ1N5exs3IVuquDjqk/4uOoT2AoG\nBjnubPI8ueR7c8n35JLvyaPAm0eBN59sdxY2Q0ZHhQS5EJYK+PIJ+BZz3tjFRGNRKtsOUt5UQUus\nmarmWhpCTexp2cfuls9fcs5h2Mnz5lLgyafAm0e+N48CTx753nwKvLl4HV4ATNMkFAnR0NlEQ2cT\njaFGmrtacdoceJ1evA4PXocXn6PntsvuwmE4cNjsOG1OHDY7DpsDu2G3ZOgn2B2koztEnidHvqH0\nQYJciFHCbrMzOXsCk7MnEAj4qavrOSo0GovS1NVCQ6iRhs4m6kMNPT+djTSEGtke1CdcX4bDR6Yr\nk5auFjqjXUOuz8DAZXfisrlw2V1Hb/ucXop8AYp8hRRnFFLkKyTLlXlM6MfMGMHuEB3dHQQjnWS5\nMsn15JzwG4VpmhwO1rKlfgdbG3awp6WSmBnDZtjIc+cQ8BUQ8OYT8OaT582LfwB58cU/kDwO9yl9\nUzFNk4bORsAgr4+aRjsJciFGObvNHh9OyTvh9FCkk/pQIw3xYK8PNVLf2UBDqIn2cDt5ntyjwzNH\nbue6s4maMUKREMHuEKFIZ8/tSIhwrJtILBL/iR693R3rJhzrJhwNE4520xpuIxwN0x2LsKOx/Jia\nvA4vBZ5cuqJhOrqDBCMhTMxj5nEYdgq8+QR8BRR6Cyjw5tN2oJk1BzZR39kI9Hx4TMwaT8CXT32o\nkbpQPTsay9lxkr+XgYHX4SHgLaAoI/4B4wtQlFFIgTef5s4W9rcd5EDbISrjv0OREABOm5MiX4Di\njEKKfYUUZRTic3hp7GyK/zTT0NlIY2cz7eF2PA4PPocXn/PINxofXoebSCxCVzRMV7Tr6O9wLMIV\nEy7ijOL5g/9n6IMEuRBJzuvwUOovodRfYsnzd0Y6ORyso6ajlppgLYfjv2uCdXgcbvxuP8UZRWQ6\nfWQ4fXgcHlrDbdQFG6gN1VMTPHY3TI/dzfzAHOYUzGJWvsLvyvzc8/WEegNNnU0E4x9CoUgnwUiI\nUCREe7iDQ+1VVLYd6Lf+Ql8BZfkKIF57HQfb+75YiIFBtjuLIl+AzmgXbd3t1IbqT3psgMvmxO1w\nEzGj/dYzGBLkQogh8Tg8TMgqZUJW6Skva5omHd1BakP11IcaGF9YRAFFOGx9R5PH4WGcv4Rx/Xxw\nxcwYDaEmaoKHORys43BHLbWherJdWYzPGsd4/zhK/SVHtyX0Xq6ps7nnw6ijls5oF3nunKPfaHLc\n2Z+rzzRNOqNdBLtDdEY7cdocuO1u3PaeYajhHq4ZUJArpR4GzgJM4AGt9dpe0y4B/i8QBf6ktf6P\n4ShUCJF6DMMg05VBpivjc9sGhspm2OIbk/OZc4rL5cc3HpflzxjQMoZhxDcaewZX7BD1+zGhlDof\nmKa1XgwsBZYfN8ty4AbgHOAypdSshFcphBCiTwPp718MrADQWu8AcpVSWQBKqclAo9b6gNY6Bvwp\nPr8QQogRMpAgLwbqet2viz92omm1wJjElCaEEGIgBrOx82RHBPR7tEBurg+HY/A79QcC/kEvm+zS\nte3S7vQi7T51AwnyKj7rgQOUANV9TBsbf6xPTU3BU6nvGIncEJJs0rXt0u70Iu0++Tx9GcjQytvA\njQBKqQVAlda6DUBrvQ/IUkpNVEo5gGvi8wshhBgh/fbItdYrlVLrlVIrgRiwTCl1O9CitX4FuBd4\nNj7781rr8j5WJYQQYhgMaIxca/294x7a1GvaB8DiRBYlhBBi4AzTNPufSwghxKiVfKf5EkIIcQwJ\nciGESHIS5EIIkeQkyIUQIslJkAshRJKTIBdCiCQnQS6EEEkuaa4QdLKLW6QipdRs4I/Aw1rrR5RS\npcBTgJ2ec93cqrUe+hV1Rxml1A+A8+j53/w+sJYUbrdSygc8CRQBHuA/6DngLmXbfDyllBfYSk/b\n3yXF266UugB4EdgWf2gL8AOG0O6k6JEP4OIWKUUplQH8lJ5/6iP+HfiZ1vo8oAL4uhW1DSel1IXA\n7PjrfAXwY1K/3dcC67TW5wM3AT8i9dt8vH8FGuO306Xt72utL4j/3McQ250UQc5JLm6RorqAqzj2\nTJIXAK/Gb78GXDLCNY2ED4Avx283AxmkeLu11s9rrX8Qv1sKHCTF29ybUmoGMAt4I/7QBaRJ249z\nAUNod7IMrRQD63vdP3Jxi1ZryhleWusIEFFK9X44o9dXrZS8gIfWOgp0xO8upeeKU5enersB4iel\nG0fPGUTfSYc2xz0EfAu4LX4/5f/P42YppV4F8oD/xRDbnSw98uP1ewGLFJfS7VdKfZGeIP/WcZNS\ntt1a67OBLwC/59h2pmyblVJfA1Zprff2MUuqtn0XPeH9RXo+wH7DsZ3qU253sgT5yS5ukS7a4xuF\nYAAX8EhWSqnLgX8BrtRat5Di7VZKLYxvyEZrvZGeN3RbKre5l6uBLyqlVgN3Av9Gir/eAFrrQ/Eh\nNVNrvRuooWe4eNDtTpYg7/PiFmnkHeCG+O0bgLcsrGVYKKWygR8C12itj2z8SvV2LwG+A6CUKgIy\nSf02A6C1/jut9Rla67OAX9Oz10rKt10pdYtS6rvx28X07LH0BENod9KcxlYp9Z/0/NPHgGVa6039\nLJK0lFIL6Rk7nAh0A4eAW+jZTc0DVAJ3aK27LSpxWCilvgH8T6D3xUluo+dNnpLtjvfCfkPPhk4v\nPV+51wG/I0XbfCJKqf8J7AP+TIq3XSnlB54BcgAXPa/5BobQ7qQJciGEECeWLEMrQggh+iBBLoQQ\nSU6CXAghkpwEuRBCJDkJciGESHIS5EIIkeQkyIUQIsn9f/1oLyPOYlLHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9e210f7518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8gu88IAMX-KM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "62e8eab4-0af0-496f-fe56-e636f23bc18e"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "predY = model.predict(testX)\n",
        "predY = [np.argmax(y) for y in predY]\n",
        "Y = [np.argmax(y) for y in testY]\n",
        "print(classification_report(Y, predY))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.78      0.73      0.75       102\n",
            "          1       0.69      0.78      0.73       115\n",
            "          2       0.77      0.60      0.68        40\n",
            "\n",
            "avg / total       0.74      0.73      0.73       257\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hZqQQxbmhEjD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "intdata = model_int.predict(testX)\n",
        "colormap = {0:\"red\", 1:\"blue\", 2:\"green\"}\n",
        "c = [colormap[i] for i in Y]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "v9_QrO9sgiPU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f53a13a8-71b5-44d0-ebd5-ddd4f2730499"
      },
      "cell_type": "code",
      "source": [
        "intdata[:,0].shape"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(257,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "metadata": {
        "id": "gGjLQ3vwgwjv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "4a0ed8e8-1c84-4f5f-da40-b1dce9742168"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(intdata[:,0], intdata[:,1], c=c)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f9e20090e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U2bN1jBhh4MQJr4vm228NPPSQky++sIsQZoHgJiEsv+rylcA+b92mjfloSe6Q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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9e213dd3c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "OVZGl0EairPr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}