{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cancer Type Classification using Deep-Learning\n",
    "## S.Ravichandran"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document will explain how to use genomic expression data for classifying different cancer/tumor sites/types. This workshop is a follow-up to the NCI-DOE Pilot1 benchmark also called TC1. You can read about the project here, https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/TC1\n",
    "\n",
    "For classification, we use a Deep-Learning procedure called 1D-Convolutional Neural Network (CONV1D; https://en.wikipedia.org/wiki/Convolutional_neural_network. \n",
    "NCI Genomic Data Commons (GDC; https://gdc.cancer.gov/) is the source of RNASeq expression data. \n",
    "\n",
    "First we will start with genomic data preparation and then we will show how to use the data to build CONV1D model that can classify different cancer types. Please note that there are more than ways to extract data from GDC. What I am describing is one possible way. \n",
    "\n",
    "This is a continuation of data preparation which can be accessed from here, \n",
    "https://github.com/ravichas/ML-TC1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part-2: Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load some libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import os, sys, gzip, glob, json, time, argparse\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "from keras import backend as K\n",
    "from keras.layers import Input, Dense, Dropout, Activation, Conv1D, MaxPooling1D, Flatten\n",
    "from keras import optimizers\n",
    "from keras.optimizers import SGD, Adam, RMSprop\n",
    "from keras.models import Sequential, Model, model_from_json, model_from_yaml\n",
    "from keras.utils import np_utils\n",
    "from keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us read the input data and outcome class data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read features and output files\n",
    "TC1data3 = pd.read_csv(\"Data/TC1-data3stypes.tsv\", sep=\"\\t\", low_memory = False)\n",
    "outcome = pd.read_csv(\"Data/TC1-outcome-data3stypes.tsv\", sep=\"\\t\", low_memory=False, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>60400</th>\n",
       "      <th>60401</th>\n",
       "      <th>60482</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.716923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.951998</td>\n",
       "      <td>1.167483</td>\n",
       "      <td>0.667981</td>\n",
       "      <td>1.274099</td>\n",
       "      <td>1.258272</td>\n",
       "      <td>1.837351</td>\n",
       "      <td>1.000251</td>\n",
       "      <td>1.991821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.979573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.939303</td>\n",
       "      <td>0.946014</td>\n",
       "      <td>0.828050</td>\n",
       "      <td>1.338521</td>\n",
       "      <td>1.215231</td>\n",
       "      <td>2.298950</td>\n",
       "      <td>1.974058</td>\n",
       "      <td>1.744890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.681222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.016686</td>\n",
       "      <td>0.789298</td>\n",
       "      <td>0.930981</td>\n",
       "      <td>1.167504</td>\n",
       "      <td>1.026718</td>\n",
       "      <td>2.058239</td>\n",
       "      <td>1.776646</td>\n",
       "      <td>1.510484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.640044</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.669994</td>\n",
       "      <td>0.821958</td>\n",
       "      <td>0.426876</td>\n",
       "      <td>1.214174</td>\n",
       "      <td>1.673027</td>\n",
       "      <td>1.904529</td>\n",
       "      <td>0.867674</td>\n",
       "      <td>1.526440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.800725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.013062</td>\n",
       "      <td>0.743211</td>\n",
       "      <td>0.652487</td>\n",
       "      <td>0.935054</td>\n",
       "      <td>1.102839</td>\n",
       "      <td>2.068075</td>\n",
       "      <td>1.405575</td>\n",
       "      <td>1.674716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0    1         2         3         4         5         6         7  \\\n",
       "0  1.716923  0.0  1.951998  1.167483  0.667981  1.274099  1.258272  1.837351   \n",
       "1  1.979573  0.0  1.939303  0.946014  0.828050  1.338521  1.215231  2.298950   \n",
       "2  1.681222  0.0  2.016686  0.789298  0.930981  1.167504  1.026718  2.058239   \n",
       "3  1.640044  0.0  1.669994  0.821958  0.426876  1.214174  1.673027  1.904529   \n",
       "4  1.800725  0.0  2.013062  0.743211  0.652487  0.935054  1.102839  2.068075   \n",
       "\n",
       "          8         9  60400  60401  60482  \n",
       "0  1.000251  1.991821    0.0    0.0    0.0  \n",
       "1  1.974058  1.744890    0.0    0.0    0.0  \n",
       "2  1.776646  1.510484    0.0    0.0    0.0  \n",
       "3  0.867674  1.526440    0.0    0.0    0.0  \n",
       "4  1.405575  1.674716    0.0    0.0    0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TC1data3.iloc[[0,1,2,3,4],[0,1,2,3,4,5,6,7,8,9,60400,60401,60482]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outcome[0].value_counts()\n",
    "outcome = outcome[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(data):\n",
    "    print('Shape of data (BEFORE encode): %s' % str(data.shape))\n",
    "    encoded = to_categorical(data)\n",
    "    print('Shape of data (AFTER  encode): %s\\n' % str(encoded.shape))\n",
    "    return encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data (BEFORE encode): (150,)\n",
      "Shape of data (AFTER  encode): (150, 3)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# One hot encoding \n",
    "# Done run more than once \n",
    "outcome = encode(outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Image\n",
    "Image(filename='Img/Train-Test.png',width = 600, height = 800 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the Test data for validatation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data into training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train/test split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(TC1data3, outcome,\n",
    "                                                    train_size=0.75,\n",
    "                                                    test_size=0.25,\n",
    "                                                    random_state=123,\n",
    "                                                    stratify = outcome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us define some parameters\n",
    "\n",
    "* activation to be RELU\n",
    "* batch_size is set to 20 \n",
    "* number of classes is three (chosen a small number for performace) for this exercise. The code that is available from NIH FTP site will model 15 cancer site outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "activation='relu'\n",
    "batch_size=20\n",
    "# Number of sites\n",
    "classes=3\n",
    "\n",
    "drop = 0.1\n",
    "feature_subsample = 0\n",
    "loss='categorical_crossentropy'\n",
    "\n",
    "# metrics='accuracy'\n",
    "out_act='softmax'\n",
    "\n",
    "shuffle = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note epochs should be greather than 10. For hands-on, I have chosen a smaller number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs=5\n",
    "optimizer = optimizers.SGD(lr=0.1)\n",
    "metrics = ['acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_len = X_train.shape[1]\n",
    "\n",
    "X_train = np.expand_dims(X_train, axis=2)\n",
    "X_test = np.expand_dims(X_test, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Please note that the `filters = 128` gave the best results. For the purpose of demonstration via cloud, I might choose a smaller number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filters = 128\n",
    "filters = 128\n",
    "filter_len = 20\n",
    "stride = 1\n",
    "\n",
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and initialize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 300
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Image\n",
    "Image(filename='Img/TC1-arch.png',width = 300, height = 400 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# model.add  1. CONV1D\n",
    "model.add(Conv1D(filters = filters,\n",
    "                 kernel_size = filter_len,\n",
    "                 strides = stride,\n",
    "                 padding='valid',\n",
    "                 input_shape=(x_train_len, 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the topology of the architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# 3. MaxPooling\n",
    "model.add(MaxPooling1D(pool_size = 1))\n",
    "\n",
    "# 4. Conv1D: filters:128, filter_len=20, stride=1\n",
    "model.add(Conv1D(filters=filters,\n",
    "                 kernel_size=filter_len,\n",
    "                 strides=stride,\n",
    "                 padding='valid'))\n",
    "\n",
    "# 5. Activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# 6. MaxPooling\n",
    "model.add(MaxPooling1D(pool_size = 10))\n",
    "\n",
    "# 7. Flatten\n",
    "model.add(Flatten())\n",
    "\n",
    "# 8. Dense\n",
    "model.add(Dense(200))\n",
    "\n",
    "# 9. activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# 10. dropout\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "#11. Dense\n",
    "model.add(Dense(20))\n",
    "\n",
    "#12. Activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "#13. dropout\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "# 14. dense\n",
    "model.add(Dense(3))\n",
    "\n",
    "# 15. Activation\n",
    "model.add(Activation(out_act))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile and show the model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 60464, 128)        2688      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 60464, 128)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 60464, 128)        0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 60445, 128)        327808    \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 60445, 128)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 6044, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 773632)            0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 200)               154726600 \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                4020      \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 63        \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 155,061,179\n",
      "Trainable params: 155,061,179\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile( loss= loss,\n",
    "              optimizer = optimizer,\n",
    "              metrics = metrics )\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save\n",
    "save = '.'\n",
    "output_dir = \"Model\"\n",
    "\n",
    "if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "model_name = 'tc1'\n",
    "path = '{}/{}.autosave.model.h5'.format(output_dir, model_name)\n",
    "checkpointer = ModelCheckpoint(filepath=path,\n",
    "                               verbose=1,\n",
    "                               save_weights_only=True,\n",
    "                               save_best_only=True)\n",
    "\n",
    "csv_logger = CSVLogger('{}/training.log'.format(output_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SR: change epsilon to min_delta\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
    "                              factor=0.1,\n",
    "                              patience=10,\n",
    "                              verbose=1, mode='auto',\n",
    "                              min_delta=0.0001,\n",
    "                              cooldown=0,\n",
    "                              min_lr=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is a time-consuming step and smaller sample sizes will not result in good model. \n",
    "Here are the commands for training and evaluating test accuracy score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 112 samples, validate on 38 samples\n",
      "Epoch 1/5\n",
      "112/112 [==============================] - 216s 2s/step - loss: 2.2790 - acc: 0.2946 - val_loss: 1.0991 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.09910, saving model to Model/tc1.autosave.model.h5\n",
      "Epoch 2/5\n",
      "112/112 [==============================] - 214s 2s/step - loss: 1.1005 - acc: 0.3304 - val_loss: 1.0992 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 1.09910\n",
      "Epoch 3/5\n",
      "112/112 [==============================] - 206s 2s/step - loss: 1.1000 - acc: 0.3214 - val_loss: 1.0985 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.09910 to 1.09846, saving model to Model/tc1.autosave.model.h5\n",
      "Epoch 4/5\n",
      "112/112 [==============================] - 210s 2s/step - loss: 1.0981 - acc: 0.3393 - val_loss: 1.0934 - val_acc: 0.6053\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.09846 to 1.09340, saving model to Model/tc1.autosave.model.h5\n",
      "Epoch 5/5\n",
      "112/112 [==============================] - 208s 2s/step - loss: 1.4125 - acc: 0.4375 - val_loss: 1.1013 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 1.09340\n"
     ]
    }
   ],
   "source": [
    "# batch_size = 20; epochs=5\n",
    "history = model.fit(X_train, Y_train, batch_size=batch_size,\n",
    "                    epochs=epochs, verbose=1, validation_data=(X_test, Y_test),\n",
    "                    callbacks = [checkpointer, csv_logger, reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 1.101348876953125\n",
      "Test accuracy: 0.31578946113586426\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
    "\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word of caution about the accuracy\n",
    "\n",
    "The output loss and accuracy from smalller sample sizes (for example, n = 50) will not reflect the real learning. For good accuracy, we need to use the whole dataset. Here are few epochs from the original dataset modeling (Train: 3375; Validate: 1125)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Image\n",
    "Image(filename='Img/TC1-Acc.PNG',width = 1000, height = 1000 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "tc1results = pd.read_csv(\"Output/tc1results.txt\", index_col='epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b430fa4448>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc1results.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to save the model/weights?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "# JSON JSON\n",
    "# serialize model to json\n",
    "json_model = model.to_json()\n",
    "\n",
    "# save the model architecture to JSON file\n",
    "with open('Model/tc1.model.json', 'w') as json_file:\n",
    "    json_file.write(json_model)\n",
    "\n",
    "\n",
    "# YAML YAML\n",
    "# serialize model to YAML\n",
    "model_yaml = model.to_yaml()\n",
    "\n",
    "# save the model architecture to YAML file\n",
    "with open(\"{}/{}.model.yaml\".format(output_dir, model_name), \"w\") as yaml_file:\n",
    "    yaml_file.write(model_yaml)\n",
    "\n",
    "\n",
    "# WEIGHTS HDF5\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"{}/{}.model.h5\".format(output_dir, model_name))\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "\n",
    "The calculation was carried out on a NIH Biowulf GPU node. Model weights were saved in Python HDF5 grid format. HDF5 is ideal for storing multi-dimensional arrays of numbers. You can read about HDF5 here.\n",
    "http://www.h5py.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "from keras.models import model_from_json\n",
    "\n",
    "# Open the handle\n",
    "json_file = open('Model/tc1.model.json', 'r')\n",
    "\n",
    "# load json and create model\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "\n",
    "# load weights into new model\n",
    "loaded_model.load_weights('Model/tc1.model.h5')\n",
    "print(\"Loaded model from disk\")\n",
    "# loaded_model_json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mimicking the process of external set\n",
    "\n",
    "Note this is a demonstration of how to use external data for inference.  \n",
    "\n",
    "When you bring in an external dataset. Make sure you follow the following steps:\n",
    "\n",
    "a) Make sure you do the same operations that you had done to the data set \n",
    "b) scale the inference dataset in the same way as the training data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "chosen_idx = np.random.choice(38, replace=False, size=5)\n",
    "# X_test[chosen_idx].shape\n",
    "# Y_test[chosen_idx].shape\n",
    "# Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_mini.shape (5, 60483, 1)\n",
      "len(y_minip) 5\n"
     ]
    }
   ],
   "source": [
    "X_mini = X_test[chosen_idx]\n",
    "y_mini = Y_test[chosen_idx]\n",
    "# df_trimmed = X_mini.drop(X_mini.columns[[0]], axis=1, inplace=False)\n",
    "# X_mini = df_trimmed\n",
    "print('X_mini.shape', X_mini.shape)\n",
    "print('len(y_minip)', len(y_mini))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_mini.shape (5, 60483, 1)\n",
      "y_mini.shape (5, 3)\n"
     ]
    }
   ],
   "source": [
    "print('X_mini.shape', X_mini.shape)\n",
    "print('y_mini.shape', y_mini.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.00%\n"
     ]
    }
   ],
   "source": [
    "# evaluate loaded model on test data\n",
    "loaded_model.compile(loss='categorical_crossentropy', optimizer='sgd', \n",
    "                     metrics=['accuracy'])\n",
    "score = loaded_model.evaluate(X_mini, y_mini, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised learning plots (PCA and tSNE)\n",
    "\n",
    "### *This section was based on Dr. Andrew Weissman's code template. Check out Andrew's Gihub here, https://github.com/andrew-weisman*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the custom `tc1_library.py` file, which contains the unsupervised learning plotting function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tc1_library\n",
    "import importlib\n",
    "\n",
    "importlib.reload(tc1_library);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decode the outcome matrix back into a single vector (remember earlier that we one-hot-encoded it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let us explore some outcome values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome.shape # 150,3\n",
    "outcome[0:3]  \n",
    "outcome[50:53]  \n",
    "outcome[75:80]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let us explore some outcome_decoded values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_decoded = [x.argmax() for x in outcome]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_decoded[0:3] #[1,1,1]\n",
    "outcome_decoded[50:53] #[2,2,0]\n",
    "outcome_decoded[75:80] # [0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform the PCA and t-SNE using scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 PCA explained variance ratios: [0.38811713 0.12041275 0.0499922  0.03027331 0.02528322 0.02271728\n",
      " 0.01852503 0.01623522 0.01457019 0.01271466]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sklearn.decomposition as sk_decomp\n",
    "tc1_library.run_and_plot_pca_and_tsne(TC1data3, outcome_decoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a binary dataset instead of a multi-class one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Current outcome distribution of the 150 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    50\n",
       "1    50\n",
       "0    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(outcome_decoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the indexes of the data that correspond to classes 0 or 1 only (excluding class 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_indexes = np.where(np.array(outcome_decoded)!=2)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recreate the data structures of the same types that was used in the original analysis above, except this time with just two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "TC1data2 = TC1data3.iloc[binary_indexes,:]\n",
    "outcome2 = outcome[binary_indexes,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decode the new outcome matrix just like we did above, and print out the outcome distribution of the new set of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    50\n",
       "0    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_decoded2 = [x.argmax() for x in outcome2]\n",
    "pd.value_counts(outcome_decoded2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check our new dataset by performing the same unsupervised learning that we did above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 PCA explained variance ratios: [0.49686232 0.04021868 0.03354306 0.03221509 0.02649848 0.0183547\n",
      " 0.01706743 0.01658734 0.01308181 0.01172459]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc1_library.run_and_plot_pca_and_tsne(TC1data2, outcome_decoded2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pick up with the Jupyter notebook from the cell that splits the data into the training and test sets, but now replacing TC1data3 with TC1data2 and outcome with outcome2\n",
    "* Work through the notebook until at least the model has been trained (the cell with history = model.fit(...)), resulting in a binary classifier\n",
    "* Apply gene/feature importance tools to the resulting model in order to determine which genes best contribute to discriminating between cancer classes 0 and 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving two-class datafiles to disk\n",
    "### Save the data and labels to CSV format in the repository's data directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "TC1data2.reset_index(drop=True).to_csv('Data/X_two_classes.csv')\n",
    "\n",
    "pd.Series(outcome_decoded2).to_csv('Data/y_two_classes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test that we've exported the data correctly by reading them back in and running the unsupervised learning analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 PCA explained variance ratios: [0.49686232 0.04021867 0.03354307 0.03221509 0.02649846 0.01835562\n",
      " 0.01706924 0.01658787 0.01310717 0.01186719]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.read_csv('Data/X_two_classes.csv', index_col=0)\n",
    "\n",
    "y = pd.read_csv('Data/y_two_classes.csv', index_col=0, squeeze=True)\n",
    "\n",
    "tc1_library.run_and_plot_pca_and_tsne(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "You are viewing the Jupyter Notebook from ML-TC1 GitHub repository, https://github.com/ravichas/ML-TC1 "
   ]
  }
 ],
 "metadata": {
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}