chemo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/iwatobipen/playground/blob/master/chemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "c20nBaAf1yYV",
    "outputId": "cf6b8342-50d5-40ac-d9fc-a3b231e735bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.7.0\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iyKF-r93mUku"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jCnwE9tRjoFf"
   },
   "source": [
    "Install rdkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1394
    },
    "colab_type": "code",
    "id": "PInm4lFu2Uyq",
    "outputId": "dd0ad815-e634-4f80-9cc6-5553ec54004b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-10-31 14:40:18--  https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
      "Resolving repo.continuum.io (repo.continuum.io)... 104.16.18.10, 104.16.19.10, 2400:cb00:2048:1::6810:120a, ...\n",
      "Connecting to repo.continuum.io (repo.continuum.io)|104.16.18.10|: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\t0m15.274s\n",
      "user\t0m12.988s\n",
      "sys\t0m4.010s\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\t0m40.676s\n",
      "user\t0m38.224s\n",
      "sys\t0m1.171s\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mK9s1rCdjuMM"
   },
   "source": [
    "Append RDKit path and import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AFT7w0WD2bj_"
   },
   "outputs": [],
   "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/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L_QLM4zNOp5A"
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cP4ELmt67TwY",
    "outputId": "2f23c8a6-294e-4254-91c9-c30618753cf0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2018.09.1'"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdBase.rdkitVersion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MWzwrp1nj6c1"
   },
   "source": [
    "LOAD data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "uJ66JY_Y6TJg",
    "outputId": "6c9f716f-3a33-42c1-a809-739ce1acc080"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1025 257\n"
     ]
    }
   ],
   "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}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c7LO9wFe6_l9"
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EbJSxd4tkAtN"
   },
   "source": [
    "import tensorflow and use keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4eHTvTNDUJcY",
    "outputId": "7fd4f12c-cfff-4d45-e88a-ec8c262fdc9c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 561
    },
    "colab_type": "code",
    "id": "0O692Z2mUqC7",
    "outputId": "f12e8353-77a8-4349-b0c6-63124e7def39"
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1717
    },
    "colab_type": "code",
    "id": "M-OtvArzWXGC",
    "outputId": "71813d6c-99e5-4b90-876e-5a315a8f861a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1025/1025 [==============================] - 1s 1ms/step - loss: 1.0888 - acc: 0.3424\n",
      "Epoch 2/50\n",
      "1025/1025 [==============================] - 0s 202us/step - loss: 1.0732 - acc: 0.3883\n",
      "Epoch 3/50\n",
      "1025/1025 [==============================] - 0s 185us/step - loss: 1.0208 - acc: 0.4273\n",
      "Epoch 4/50\n",
      "1025/1025 [==============================] - 0s 189us/step - loss: 0.9094 - acc: 0.5190\n",
      "Epoch 5/50\n",
      "1025/1025 [==============================] - 0s 189us/step - loss: 0.7589 - acc: 0.6459\n",
      "Epoch 6/50\n",
      "1025/1025 [==============================] - 0s 187us/step - loss: 0.6307 - acc: 0.7102\n",
      "Epoch 7/50\n",
      "1025/1025 [==============================] - 0s 208us/step - loss: 0.5441 - acc: 0.7415\n",
      "Epoch 8/50\n",
      "1025/1025 [==============================] - 0s 182us/step - loss: 0.4598 - acc: 0.7727\n",
      "Epoch 9/50\n",
      "1025/1025 [==============================] - 0s 184us/step - loss: 0.3952 - acc: 0.8195\n",
      "Epoch 10/50\n",
      "1025/1025 [==============================] - 0s 182us/step - loss: 0.3463 - acc: 0.8712\n",
      "Epoch 11/50\n",
      "1025/1025 [==============================] - 0s 185us/step - loss: 0.3072 - acc: 0.9112\n",
      "Epoch 12/50\n",
      "1025/1025 [==============================] - 0s 195us/step - loss: 0.2761 - acc: 0.9327\n",
      "Epoch 13/50\n",
      "1025/1025 [==============================] - 0s 189us/step - loss: 0.2553 - acc: 0.9454\n",
      "Epoch 14/50\n",
      "1025/1025 [==============================] - 0s 186us/step - loss: 0.2331 - acc: 0.9600\n",
      "Epoch 15/50\n",
      "1025/1025 [==============================] - 0s 186us/step - loss: 0.2211 - acc: 0.9659\n",
      "Epoch 16/50\n",
      "1025/1025 [==============================] - 0s 188us/step - loss: 0.2096 - acc: 0.9776\n",
      "Epoch 17/50\n",
      "1025/1025 [==============================] - 0s 186us/step - loss: 0.2005 - acc: 0.9707\n",
      "Epoch 18/50\n",
      "1025/1025 [==============================] - 0s 198us/step - loss: 0.1889 - acc: 0.9824\n",
      "Epoch 19/50\n",
      "1025/1025 [==============================] - 0s 192us/step - loss: 0.1790 - acc: 0.9844\n",
      "Epoch 20/50\n",
      "1025/1025 [==============================] - 0s 185us/step - loss: 0.1711 - acc: 0.9844\n",
      "Epoch 21/50\n",
      "1025/1025 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.9844\n",
      "Epoch 22/50\n",
      "1025/1025 [==============================] - 0s 180us/step - loss: 0.1578 - acc: 0.9834\n",
      "Epoch 23/50\n",
      "1025/1025 [==============================] - 0s 191us/step - loss: 0.1493 - acc: 0.9873\n",
      "Epoch 24/50\n",
      "1025/1025 [==============================] - 0s 189us/step - loss: 0.1433 - acc: 0.9883\n",
      "Epoch 25/50\n",
      "1025/1025 [==============================] - 0s 181us/step - loss: 0.1406 - acc: 0.9902\n",
      "Epoch 26/50\n",
      "1025/1025 [==============================] - 0s 186us/step - loss: 0.1319 - acc: 0.9912\n",
      "Epoch 27/50\n",
      "1025/1025 [==============================] - 0s 184us/step - loss: 0.1305 - acc: 0.9902\n",
      "Epoch 28/50\n",
      "1025/1025 [==============================] - 0s 204us/step - loss: 0.1246 - acc: 0.9893\n",
      "Epoch 29/50\n",
      "1025/1025 [==============================] - 0s 185us/step - loss: 0.1167 - acc: 0.9902\n",
      "Epoch 30/50\n",
      "1025/1025 [==============================] - 0s 181us/step - loss: 0.1153 - acc: 0.9844\n",
      "Epoch 31/50\n",
      "1025/1025 [==============================] - 0s 179us/step - loss: 0.1093 - acc: 0.9893\n",
      "Epoch 32/50\n",
      "1025/1025 [==============================] - 0s 183us/step - loss: 0.1065 - acc: 0.9883\n",
      "Epoch 33/50\n",
      "1025/1025 [==============================] - 0s 182us/step - loss: 0.1019 - acc: 0.9893\n",
      "Epoch 34/50\n",
      "1025/1025 [==============================] - 0s 192us/step - loss: 0.0973 - acc: 0.9902\n",
      "Epoch 35/50\n",
      "1025/1025 [==============================] - 0s 188us/step - loss: 0.0948 - acc: 0.9893\n",
      "Epoch 36/50\n",
      "1025/1025 [==============================] - 0s 187us/step - loss: 0.0892 - acc: 0.9912\n",
      "Epoch 37/50\n",
      "1025/1025 [==============================] - 0s 189us/step - loss: 0.0871 - acc: 0.9893\n",
      "Epoch 38/50\n",
      "1025/1025 [==============================] - 0s 185us/step - loss: 0.0839 - acc: 0.9922\n",
      "Epoch 39/50\n",
      "1025/1025 [==============================] - 0s 202us/step - loss: 0.0796 - acc: 0.9893\n",
      "Epoch 40/50\n",
      "1025/1025 [==============================] - 0s 183us/step - loss: 0.0739 - acc: 0.9912\n",
      "Epoch 41/50\n",
      "1025/1025 [==============================] - 0s 181us/step - loss: 0.0742 - acc: 0.9893\n",
      "Epoch 42/50\n",
      "1025/1025 [==============================] - 0s 179us/step - loss: 0.0732 - acc: 0.9883\n",
      "Epoch 43/50\n",
      "1025/1025 [==============================] - 0s 179us/step - loss: 0.0693 - acc: 0.9893\n",
      "Epoch 44/50\n",
      "1025/1025 [==============================] - 0s 186us/step - loss: 0.0676 - acc: 0.9863\n",
      "Epoch 45/50\n",
      "1025/1025 [==============================] - 0s 187us/step - loss: 0.0652 - acc: 0.9893\n",
      "Epoch 46/50\n",
      "1025/1025 [==============================] - 0s 182us/step - loss: 0.0646 - acc: 0.9902\n",
      "Epoch 47/50\n",
      "1025/1025 [==============================] - 0s 181us/step - loss: 0.0597 - acc: 0.9893\n",
      "Epoch 48/50\n",
      "1025/1025 [==============================] - 0s 182us/step - loss: 0.0585 - acc: 0.9922\n",
      "Epoch 49/50\n",
      "1025/1025 [==============================] - 0s 183us/step - loss: 0.0581 - acc: 0.9912\n",
      "Epoch 50/50\n",
      "1025/1025 [==============================] - 0s 194us/step - loss: 0.0538 - acc: 0.9893\n"
     ]
    }
   ],
   "source": [
    "epochs = 50\n",
    "hist = model.fit(np.array(trainX), trainY, batch_size=32, epochs=epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mwkwhWydkHob"
   },
   "source": [
    "maybe 50 epochs is too mutch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "alpYfIqaX1s2",
    "outputId": "4a04b645-d678-4252-d117-ba21f8e8059e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5bec8ebdd8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PXGv9xWueuq5PQGt9Bbhr6SXNbWwixv9+/AyWZfGZ+7ficztWYrXL6kDtfo51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\nns6PACfO9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5be688a668>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epochs), hist.history['acc'])\n",
    "plt.plot(range(epochs), hist.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "8gu88IAMX-KM",
    "outputId": "6a91d805-681f-4de2-aaf2-ec1d14a9c23f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.67      0.80      0.73       102\n",
      "          1       0.75      0.61      0.67       115\n",
      "          2       0.73      0.75      0.74        40\n",
      "\n",
      "avg / total       0.72      0.71      0.71       257\n",
      "\n"
     ]
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hZqQQxbmhEjD"
   },
   "outputs": [],
   "source": [
    "intdata = model_int.predict(testX)\n",
    "colormap = {0:\"red\", 1:\"blue\", 2:\"green\"}\n",
    "c = [colormap[i] for i in Y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "v9_QrO9sgiPU",
    "outputId": "f2e23ae9-17d2-434a-ae0a-436b16ff4d57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(257,)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intdata[:,0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "colab_type": "code",
    "id": "gGjLQ3vwgwjv",
    "outputId": "cb1baa55-cb0c-4291-a52f-bb12c873b5b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f5be2f9f400>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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plJg/30z16jpTpljZvVv2e+3s5GfLum3qZCLmzMS0dw+GzYazRSuSx7yFu07d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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5be439ee80>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(intdata[:,0], intdata[:,1], c=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OVZGl0EairPr"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "chemo.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}