GenoML_IPDGC_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "  <img width=\"400\" height=\"400\" src=\"logo.png\">\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GenoML Demo for IPDGC London Workshop 2019\n",
    "\n",
    "## Details\n",
    "- **Put together by:** Hampton Leonard, Mary Makarious, and Mike Nalls\n",
    "- **Project:** GenoML Demo for IPDGC London 2019 \n",
    "- **PIs:** Mike Nalls, PhD and Andrew Singleton, PhD from the Laboratory of Neurogenetics NIH/NIA/NINDS\n",
    "- **Collaborators:** Full list of collaborators can be found [here](https://genoml.github.io/docs/about.html)\n",
    "- **Date Last Updated:** 12.17.2019\n",
    "\t- Last Update: Added interpretation of results + quick descriptions \n",
    "\n",
    "### Disclaimer\n",
    "The people at LNG are working hard to finish up the Python package for users to be able to run locally. The scripts will not be identical to what was used in this workshop, but the structure on how to run will be quite similar (and documentation will be provided!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Table of Contents\n",
    "### [0. Getting Started](#0)\n",
    "This section is for importing the necessary packages into the environment so things can run properly and smoothly. This includes automatically choosing the right version of PLINK to path for munging to work properly \n",
    "\n",
    "### [1. Data Munging](#1)\n",
    "This section is to clean and preprocess the data. First, we will look at what the input files look like, these are your typical PLINK binary files. The script has a number of arguments that can be chosen, and the script will start a dialogue with the user on what will be running. To see the arguments you can use, scroll up to the `munging.py` script in the scripts folder\n",
    "\n",
    "### [2. Training the Model](#2)\n",
    "This section is to train the model. Here, the `training.py` script automagically competes the machine learning algorithms on the data, and chooses the best one based on the AUC curve. A few plots are generated in this process, including the ROC of the best algorithm and the probability density plots. The script has a number of arguments that can be chosen, and the script will start a dialogue with the user on what will be running. To see the arguments you can use, scroll up to the `training.py` script in the scripts folder\n",
    " \n",
    "### [3. Tuning the Model](#3)\n",
    "This section takes the best algorithm and takes it a step further. Here, the `tuning.py` script tunes the best model from the step prior. Randomized search cross-validation is used, and the outcomes following each hyperparameter tune is communicated to the user. The script has a number of arguments that can be chosen, and the script will start a dialogue with the user on what will be running. To see the arguments you can use, scroll up to the `tuning.py` script in the scripts folder \n",
    " \n",
    "### [4. Interpreting Results](#4)\n",
    "This section is a step-by-step introduction to the outputs that GenoML has made during your munging, training, and tuning scripts. We can generate things, but we cannot do the thinking for you :) \n",
    "\n",
    "\n",
    " ### [5. Introduction to Kipoi](#5)\n",
    "This section is to advertise and advocate for the use of Kipoi. Kipoi is a repository for predictive models in genomics. Models come with code to process and load input data in major file formats, which allows for easy application to new datasets and creation of new derived models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id=\"0\"></a>\n",
    "## Part 0: Getting Started "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary packages \n",
    "import os\n",
    "import sys\n",
    "import argparse\n",
    "import math\n",
    "import time\n",
    "import h5py\n",
    "import joblib\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Additional packages for VIF calculation\n",
    "import random\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "# Additional packages for training\n",
    "import xgboost\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "\n",
    "# Additional packages for tuning\n",
    "from scipy.stats import randint as sp_randint\n",
    "from scipy.stats import uniform as sp_randfloat\n",
    "\n",
    "# Viewing Images\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLINK has successfully been loaded to your path!\n"
     ]
    }
   ],
   "source": [
    "# Check the platform to load the right PLINK to path\n",
    "    # This will load a PLINK v1.9 which is necessary for data munging \n",
    "    # If running this in Docker/Binder - nothing to worry about :) \n",
    "from sys import platform\n",
    "def get_platform():\n",
    "    platforms = {\n",
    "        \"linux\" : \"linux\",\n",
    "        \"linux1\" : \"linux\",\n",
    "        \"linux2\" : \"linux\",\n",
    "        \"darwin\" : \"mac\"\n",
    "    }\n",
    "    if sys.platform not in platforms:\n",
    "        return \"GenoML is not supported on this platform. Please try Mac or Linux\"\n",
    "    elif platforms[sys.platform] == \"mac\":\n",
    "        filename_mac = \"plink/mac/\"\n",
    "        directory_mac = os.getcwd() + \"/\" + filename_mac\n",
    "        os.environ[\"PATH\"] = os.environ[\"PATH\"] + os.pathsep + directory_mac\n",
    "    else:\n",
    "        filename_linux = \"plink/linux/\"\n",
    "        directory_linux = os.getcwd() + \"/\" + filename_linux\n",
    "        os.environ[\"PATH\"] = os.environ[\"PATH\"] + os.pathsep + directory_linux\n",
    "    return print(\"PLINK has successfully been loaded to your path!\")\n",
    "\n",
    "# Run and get the proper PLINK to path\n",
    "get_platform()\n",
    "\n",
    "# Set seed for reproducibility!\n",
    "random.seed(75629814343)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id=\"1\"></a>\n",
    "## Part 1: Data Munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>PHENO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        ID  PHENO\n",
       "0  sample1      1\n",
       "1  sample2      1\n",
       "2  sample3      1\n",
       "3  sample4      0\n",
       "4  sample5      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look at what the input files look like \n",
    "    # 1 = case\n",
    "    # 0 = control \n",
    "pheno_file = pd.read_csv(\"example_inputs/training_pheno.csv\")\n",
    "pheno_file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is a glimpse of what the bim file (with the SNP info) looks like\n",
      "   1  snp410  0  10638604  T  C\n",
      "0  1  snp403  0  12013215  T  C\n",
      "1  1  snp164  0  15857302  C  T\n",
      "2  1  snp363  0  15903085  T  G\n",
      "3  1  snp439  0  16306012  G  C\n",
      "4  1  snp370  0  16326935  A  G\n",
      "\n",
      "Here is a glimpse of what the fam file (with the sex amd phenotype info) looks like\n",
      "     sample81 sample81 0 0 2 1\n",
      "0  sample158 sample158 0 0 1 2\n",
      "1  sample216 sample216 0 0 2 2\n",
      "2    sample36 sample36 0 0 1 1\n",
      "3  sample346 sample346 0 0 2 2\n",
      "4  sample274 sample274 0 0 2 1\n",
      "\n",
      "The .bed file is a binary file - so no need to look at it now :) \n"
     ]
    }
   ],
   "source": [
    "# What about how the geno files look like?\n",
    "    # These are standard PLINK binaries \n",
    "geno_bim = pd.read_csv(\"example_inputs/training.bim\", delimiter='\\t')\n",
    "geno_fam = pd.read_csv(\"example_inputs/training.fam\")\n",
    "\n",
    "print(\"Here is a glimpse of what the bim file (with the SNP info) looks like\")\n",
    "print(geno_bim.head())\n",
    "\n",
    "print()\n",
    "print(\"Here is a glimpse of what the fam file (with the sex amd phenotype info) looks like\")\n",
    "print(geno_fam.head())\n",
    "\n",
    "print()\n",
    "print(\"The .bed file is a binary file - so no need to look at it now :) \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Here is some basic info on the command you are about to run.\n",
      "Python version info...\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "CLI argument info...\n",
      "The output prefix for this run is ./example_outputs/test_discrete_geno and will be appended to later runs of GenoML.\n",
      "Working with genotype data? ./example_inputs/training\n",
      "Working with additional predictors? nope\n",
      "Where is your phenotype file? ./example_inputs/training_pheno.csv\n",
      "Any use for an external set of GWAS summary stats? nope\n",
      "If you plan on using external GWAs summary stats for SNP filtering, we'll only keep SNPs at what P value? 0.001\n",
      "How strong is your VIF filter? 5\n",
      "How many iterations of VIF filtering are you doing? 1\n",
      "The imputation method you picked is using the column median to fill in any remaining NAs.\n",
      "Give credit where credit is due, for this stage of analysis we use code from the great contributors to python packages: os, sys, argparse, numpy, pandas, joblib, math and time. We also use PLINKv1.9 from https://www.cog-genomics.org/plink/1.9/.\n",
      "\n",
      "No additional features as predictors? No problem, we'll stick to genotypes.\n",
      "So you don't want to filter on P values from external GWAS? No worries, we don't usually either (if the dataset is large enough).\n",
      "Pruning your data and exporting a reduced set of genotypes.\n",
      "A list of pruned variants and the allele being counted in the dosages (usually the minor allele) can be found here  ./example_outputs/test_discrete_geno.variants_and_alleles.tab.\n",
      "\n",
      "You have just imputed your genotype features, covering up NAs with the column median so that analyses don't crash due to missing data.\n",
      "Now your genotype features might look a little better (showing the first few lines of the left-most and right-most columns)...\n",
      "##############################\n",
      "         snp410_T    snp403_T    snp164_C    snp439_G    snp370_A    snp389_T  \\\n",
      "count  500.000000  500.000000  500.000000  500.000000  500.000000  500.000000   \n",
      "mean     0.866000    0.104000    0.936000    0.188000    0.698000    0.622000   \n",
      "std      0.716289    0.305566    0.696329    0.415935    0.683909    0.660294   \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "50%      1.000000    0.000000    1.000000    0.000000    1.000000    1.000000   \n",
      "75%      1.000000    0.000000    1.000000    0.000000    1.000000    1.000000   \n",
      "max      2.000000    1.000000    2.000000    2.000000    2.000000    2.000000   \n",
      "\n",
      "         snp475_T    snp399_A     snp87_C    snp308_G  ...     snp90_T  \\\n",
      "count  500.000000  500.000000  500.000000  500.000000  ...  500.000000   \n",
      "mean     0.250000    0.416000    0.754000    0.808000  ...    0.080000   \n",
      "std      0.460352    0.565317    0.662088    0.672339  ...    0.285943   \n",
      "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "50%      0.000000    0.000000    1.000000    1.000000  ...    0.000000   \n",
      "75%      0.000000    1.000000    1.000000    1.000000  ...    0.000000   \n",
      "max      2.000000    2.000000    2.000000    2.000000  ...    2.000000   \n",
      "\n",
      "          snp95_A    snp377_G    snp382_T    snp450_A    snp107_A    snp224_C  \\\n",
      "count  500.000000  500.000000  500.000000  500.000000  500.000000  500.000000   \n",
      "mean     0.266000    0.572000    0.056000    0.534000    0.048000    0.642000   \n",
      "std      0.493692    0.640028    0.230152    0.652453    0.223149    0.656275   \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
      "75%      0.000000    1.000000    0.000000    1.000000    0.000000    1.000000   \n",
      "max      2.000000    2.000000    1.000000    2.000000    2.000000    2.000000   \n",
      "\n",
      "         snp368_G    snp304_C   snp306_G  \n",
      "count  500.000000  500.000000  500.00000  \n",
      "mean     0.270000    0.250000    0.02200  \n",
      "std      0.491511    0.473231    0.14683  \n",
      "min      0.000000    0.000000    0.00000  \n",
      "25%      0.000000    0.000000    0.00000  \n",
      "50%      0.000000    0.000000    0.00000  \n",
      "75%      0.000000    0.000000    0.00000  \n",
      "max      2.000000    2.000000    1.00000  \n",
      "\n",
      "[8 rows x 449 columns]\n",
      "##############################\n",
      "\n",
      "Stripping erroneous space, dropping non-numeric columns...\n",
      "Drop any rows where at least one element is missing...\n",
      "Keeping only numerical columns...\n",
      "Checking datatypes...\n",
      "Sampling 100 rows at random to reduce memory overhead...\n",
      "Dropping columns that are not SNPs...\n",
      "Dropped!\n",
      "Cleaned!\n",
      "\n",
      "                \n",
      "\n",
      "\n",
      "                Iteration 1\n",
      "                \n",
      "\n",
      "\n",
      "                \n",
      "Shuffling columns...\n",
      "Shuffled!\n",
      "Generating chunked, randomized dataframes...\n",
      "The number of dataframes you have moving forward is 5\n",
      "Complete!\n",
      "Dropping columns with a VIF threshold greater than 5\n",
      "Dropping \"snp89_G\" with VIF = 345.06\n",
      "Dropping \"snp120_G\" with VIF = 232.93\n",
      "Dropping \"snp354_C\" with VIF = 97.77\n",
      "Dropping \"snp300_T\" with VIF = 92.99\n",
      "Dropping \"snp232_G\" with VIF = 54.53\n",
      "Dropping \"snp396_G\" with VIF = 41.80\n",
      "Dropping \"snp383_A\" with VIF = 38.40\n",
      "Dropping \"snp200_T\" with VIF = 26.76\n",
      "Dropping \"snp234_A\" with VIF = 22.67\n",
      "Dropping \"snp499_C\" with VIF = 20.89\n",
      "Dropping \"snp22_G\" with VIF = 17.33\n",
      "Dropping \"snp108_T\" with VIF = 16.80\n",
      "Dropping \"snp150_A\" with VIF = 14.42\n",
      "Dropping \"snp434_G\" with VIF = 13.62\n",
      "Dropping \"snp191_G\" with VIF = 11.54\n",
      "Dropping \"snp133_G\" with VIF = 11.41\n",
      "Dropping \"snp478_G\" with VIF = 10.36\n",
      "Dropping \"snp241_C\" with VIF = 10.26\n",
      "Dropping \"snp370_A\" with VIF = 10.13\n",
      "Dropping \"snp142_A\" with VIF = 9.69\n",
      "Dropping \"snp378_C\" with VIF = 9.39\n",
      "Dropping \"snp262_C\" with VIF = 8.13\n",
      "Dropping \"snp63_T\" with VIF = 7.59\n",
      "Dropping \"snp432_G\" with VIF = 7.39\n",
      "Dropping \"snp19_G\" with VIF = 7.24\n",
      "Dropping \"snp293_G\" with VIF = 6.90\n",
      "Dropping \"snp57_G\" with VIF = 6.65\n",
      "Dropping \"snp422_C\" with VIF = 6.19\n",
      "Dropping \"snp8_A\" with VIF = 6.18\n",
      "Dropping \"snp141_A\" with VIF = 5.99\n",
      "Dropping \"snp41_G\" with VIF = 5.47\n",
      "Dropping \"snp207_T\" with VIF = 5.26\n",
      "Dropping \"snp48_A\" with VIF = 5.09\n",
      "\n",
      "VIF calculation on all chunks complete! \n",
      "\n",
      "Gluing the dataframe back together...\n",
      "Full VIF-filtered dataframe generated!\n",
      "\n",
      "        \n",
      "\n",
      "\n",
      "            Iterations Complete!\n",
      "        \n",
      "\n",
      "\n",
      "        \n",
      "\n",
      "Thanks for munging some data with GenoML!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%run -i ./scripts/munging.py\\\n",
    "--prefix ./example_outputs/test_discrete_geno\\\n",
    "--pheno ./example_inputs/training_pheno.csv\\\n",
    "--geno ./example_inputs/training\\\n",
    "--vif 5\\\n",
    "--iter 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id=\"2\"></a>\n",
    "## Part 2: Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Here is some basic info on the command you are about to run.\n",
      "Python version info...\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "CLI argument info...\n",
      "Are you ranking features, even though it is pretty slow? Right now, GenoML runs general recursive feature ranking. You chose to run this part.\n",
      "Working with dataset ./example_outputs/test_discrete_geno from previous data munging efforts.\n",
      "Give credit where credit is due, for this stage of analysis we use code from the great contributors to Python packages: argparse, xgboost, sklearn, pandas, numpy, time, matplotlib and seaborn.\n",
      "As a note, in all exported probabilities and other graphics, case status is treated as a 0 or 1, with 1 representing a positive case.\n",
      "\n",
      "\n",
      "Your data looks like this (showing the first few lines of the left-most and right-most columns)...\n",
      "##############################\n",
      "         snp229_A    snp423_C    snp148_G    snp263_G    snp332_A    snp381_T  \\\n",
      "count  100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
      "mean     0.030000    0.110000    0.110000    0.090000    0.580000    0.140000   \n",
      "std      0.171447    0.314466    0.314466    0.287623    0.638496    0.376588   \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    0.000000    0.500000    0.000000   \n",
      "75%      0.000000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
      "max      1.000000    1.000000    1.000000    1.000000    2.000000    2.000000   \n",
      "\n",
      "         snp264_G    snp225_G     snp77_C    snp380_G  ...    snp231_T  \\\n",
      "count  100.000000  100.000000  100.000000  100.000000  ...  100.000000   \n",
      "mean     0.420000    0.530000    0.080000    0.180000  ...    0.320000   \n",
      "std      0.589127    0.593823    0.307482    0.386123  ...    0.529532   \n",
      "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "75%      1.000000    1.000000    0.000000    0.000000  ...    1.000000   \n",
      "max      2.000000    2.000000    2.000000    1.000000  ...    2.000000   \n",
      "\n",
      "         snp242_T    snp323_A    snp211_G    snp91_T    snp433_G    snp151_T  \\\n",
      "count  100.000000  100.000000  100.000000  100.00000  100.000000  100.000000   \n",
      "mean     0.030000    0.280000    0.050000    0.95000    0.860000    0.420000   \n",
      "std      0.171447    0.494004    0.219043    0.74366    0.725022    0.589127   \n",
      "min      0.000000    0.000000    0.000000    0.00000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.00000    0.000000    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    1.00000    1.000000    0.000000   \n",
      "75%      0.000000    1.000000    0.000000    1.25000    1.000000    1.000000   \n",
      "max      1.000000    2.000000    1.000000    2.00000    2.000000    2.000000   \n",
      "\n",
      "        snp451_C    snp335_A       PHENO  \n",
      "count  100.00000  100.000000  100.000000  \n",
      "mean     0.85000    0.110000    0.700000  \n",
      "std      0.74366    0.314466    0.460566  \n",
      "min      0.00000    0.000000    0.000000  \n",
      "25%      0.00000    0.000000    0.000000  \n",
      "50%      1.00000    0.000000    1.000000  \n",
      "75%      1.00000    0.000000    1.000000  \n",
      "max      2.00000    1.000000    1.000000  \n",
      "\n",
      "[8 rows x 417 columns]\n",
      "##############################\n",
      "\n",
      "\n",
      "Now let's compete these algorithms!\n",
      "We'll update you as each algorithm runs, then summarize at the end.\n",
      "Here we test each algorithm under default settings using the same training and test datasets derived from a 70% training and 30% testing split of your data.\n",
      "For each algorithm, we will output the following metrics...\n",
      "Algorithm name, hoping that's pretty self-explanatory. Plenty of resources on these common ML algorithms at https://scikit-learn.org and https://xgboost.readthedocs.io/.\n",
      "AUC_percent, this is the area under the curve from reciever operating characteristic analyses. This is the most common metric of classifier performance in biomedical literature, we express this as a percent. We calculate AUC based on the predicted probability of being a case.\n",
      "Accuracy_percent, this is the simple accuracy of the classifer, how many predictions were correct from best classification cutoff (python default).\n",
      "Balanced_Accuracy_Percent, consider this as the accuracy resampled to a 1:1 mix of cases and controls. Imbalanced datasets can give funny results for simple accuracy.\n",
      "Log_Loss, this is essentially the inverse of the likelihood function for a correct prediction, you want to minimize this.\n",
      "Sensitivity, proportion of cases correcctly identified.\n",
      "Specificity, proportion of controls correctly identified.\n",
      "PPV, this is the positive predictive value, the probability that subjects with a positive result actually have the disease.\n",
      "NPV, this is the negative predictive value, the probability that subjects with a negative result don't have the disease.\n",
      "We also log the runtimes per algorithm.\n",
      "\n",
      "Algorithm summaries incoming...\n",
      "\n",
      "##############################\n",
      "LogisticRegression\n",
      "AUC: 43.1818%\n",
      "Accuracy: 66.6667%\n",
      "Balanced Accuracy: 49.4318%\n",
      "Log Loss: 0.9333\n",
      "Runtime in seconds: 0.02462\n",
      "\n",
      "##############################\n",
      "RandomForestClassifier\n",
      "AUC: 42.3295%\n",
      "Accuracy: 50.0000%\n",
      "Balanced Accuracy: 38.0682%\n",
      "Log Loss: 0.6923\n",
      "Runtime in seconds: 0.01817\n",
      "\n",
      "##############################\n",
      "AdaBoostClassifier\n",
      "AUC: 35.2273%\n",
      "Accuracy: 40.0000%\n",
      "Balanced Accuracy: 35.2273%\n",
      "Log Loss: 0.721\n",
      "Runtime in seconds: 0.0672\n",
      "\n",
      "##############################\n",
      "GradientBoostingClassifier\n",
      "AUC: 31.2500%\n",
      "Accuracy: 66.6667%\n",
      "Balanced Accuracy: 45.4545%\n",
      "Log Loss: 1.24\n",
      "Runtime in seconds: 0.1135\n",
      "\n",
      "##############################\n",
      "SGDClassifier\n",
      "AUC: 46.0227%\n",
      "Accuracy: 50.0000%\n",
      "Balanced Accuracy: 46.0227%\n",
      "Log Loss: 17.27\n",
      "Runtime in seconds: 0.009317\n",
      "\n",
      "##############################\n",
      "SVC\n",
      "AUC: 54.5455%\n",
      "Accuracy: 73.3333%\n",
      "Balanced Accuracy: 50.0000%\n",
      "Log Loss: 0.5799\n",
      "Runtime in seconds: 0.02336\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leonardhl/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n",
      "./scripts/training.py:125: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  NPV = TN/(TN+FN)\n",
      "/Users/leonardhl/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################\n",
      "MLPClassifier\n",
      "AUC: 51.1364%\n",
      "Accuracy: 66.6667%\n",
      "Balanced Accuracy: 49.4318%\n",
      "Log Loss: 0.9528\n",
      "Runtime in seconds: 0.1386\n",
      "\n",
      "##############################\n",
      "KNeighborsClassifier\n",
      "AUC: 61.3636%\n",
      "Accuracy: 60.0000%\n",
      "Balanced Accuracy: 52.8409%\n",
      "Log Loss: 0.6185\n",
      "Runtime in seconds: 0.0139\n",
      "\n",
      "##############################\n",
      "QuadraticDiscriminantAnalysis\n",
      "AUC: 52.2727%\n",
      "Accuracy: 53.3333%\n",
      "Balanced Accuracy: 52.2727%\n",
      "Log Loss: 16.12\n",
      "Runtime in seconds: 0.02029\n",
      "\n",
      "##############################\n",
      "BaggingClassifier\n",
      "AUC: 35.5114%\n",
      "Accuracy: 56.6667%\n",
      "Balanced Accuracy: 46.5909%\n",
      "Log Loss: 1.822\n",
      "Runtime in seconds: 0.02328\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leonardhl/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################\n",
      "XGBClassifier\n",
      "AUC: 44.8864%\n",
      "Accuracy: 63.3333%\n",
      "Balanced Accuracy: 43.1818%\n",
      "Log Loss: 0.7915\n",
      "Runtime in seconds: 0.2268\n",
      "\n",
      "##############################\n",
      "\n",
      "This table below is also logged as ./example_outputs/test_discrete_geno.training_withheldSamples_performanceMetrics.csv and is in your current working directory...\n",
      "##############################\n",
      "                       Algorithm  AUC_Percent  Accuracy_Percent  \\\n",
      "0             LogisticRegression    43.181818         66.666667   \n",
      "0         RandomForestClassifier    42.329545         50.000000   \n",
      "0             AdaBoostClassifier    35.227273         40.000000   \n",
      "0     GradientBoostingClassifier    31.250000         66.666667   \n",
      "0                  SGDClassifier    46.022727         50.000000   \n",
      "0                            SVC    54.545455         73.333333   \n",
      "0                  MLPClassifier    51.136364         66.666667   \n",
      "0           KNeighborsClassifier    61.363636         60.000000   \n",
      "0  QuadraticDiscriminantAnalysis    52.272727         53.333333   \n",
      "0              BaggingClassifier    35.511364         56.666667   \n",
      "0                  XGBClassifier    44.886364         63.333333   \n",
      "\n",
      "   Balanced_Accuracy_Percent   Log_Loss  Sensitivity  Specificity       PPV  \\\n",
      "0                  49.431818   0.933301     0.863636        0.125  0.730769   \n",
      "0                  38.068182   0.692286     0.636364        0.125  0.666667   \n",
      "0                  35.227273   0.721005     0.454545        0.250  0.625000   \n",
      "0                  45.454545   1.239939     0.909091        0.000  0.714286   \n",
      "0                  46.022727  17.269388     0.545455        0.375  0.705882   \n",
      "0                  50.000000   0.579904     1.000000        0.000  0.733333   \n",
      "0                  49.431818   0.952840     0.863636        0.125  0.730769   \n",
      "0                  52.840909   0.618455     0.681818        0.375  0.750000   \n",
      "0                  52.272727  16.118096     0.545455        0.500  0.750000   \n",
      "0                  46.590909   1.822086     0.681818        0.250  0.714286   \n",
      "0                  43.181818   0.791452     0.863636        0.000  0.703704   \n",
      "\n",
      "        NPV  Runtime_Seconds  \n",
      "0  0.250000         0.024622  \n",
      "0  0.111111         0.018167  \n",
      "0  0.142857         0.067200  \n",
      "0  0.000000         0.113493  \n",
      "0  0.230769         0.009317  \n",
      "0       NaN         0.023360  \n",
      "0  0.250000         0.138614  \n",
      "0  0.300000         0.013900  \n",
      "0  0.285714         0.020294  \n",
      "0  0.222222         0.023281  \n",
      "0  0.000000         0.226781  \n",
      "##############################\n",
      "\n",
      "Based on your withheld samples, the algorithm with the best AUC is the KNeighborsClassifier ... lets save that model for you.\n",
      "...remember, there are occasionally slight fluxuations in model performance on the same withheld samples...\n",
      "##############################\n",
      "KNeighborsClassifier\n",
      "AUC: 61.3636%\n",
      "Accuracy: 60.0000%\n",
      "Balanced Accuracy: 52.8409%\n",
      "Log Loss: 0.6185\n",
      "##############################\n",
      "... this model has been saved as ./example_outputs/test_discrete_geno.trainedModel.joblib for later use and can be found in your working directory.\n",
      "\n",
      "We are also exporting a ROC curve for you here ./example_outputs/test_discrete_geno.trainedModel_withheldSample_ROC.png this is a graphical representation of AUC in the withheld test data for the best performing algorithm.\n",
      "\n",
      "Preview of the exported predictions for the withheld test data that has been exported as ./example_outputs/test_discrete_geno.trainedModel_withheldSample_Predictions.csv these are pretty straight forward.\n",
      "They generally include the sample ID, the previously reported case status (1 = case), the case probability from the best performing algorithm and the predicted label from that algorithm,\n",
      "##############################\n",
      "         ID  CASE_REPORTED  CASE_PROBABILITY  CASE_PREDICTED\n",
      "0  sample84              0               0.8               1\n",
      "1  sample54              1               0.2               0\n",
      "2  sample71              1               1.0               1\n",
      "3  sample46              1               0.8               1\n",
      "4  sample45              1               0.8               1\n",
      "##############################\n",
      "\n",
      "Preview of the exported predictions for the training samples which is naturally overfit and exported as ./example_outputs/test_discrete_geno.trainedModel_trainingSample_Predictions.csv in the similar format as in the withheld test dataset that was just exported.\n",
      "##############################\n",
      "         ID  CASE_REPORTED  CASE_PROBABILITY  CASE_PREDICTED\n",
      "0  sample12              1               0.6               1\n",
      "1  sample48              1               0.4               0\n",
      "2  sample86              1               0.8               1\n",
      "3  sample29              0               0.8               1\n",
      "4  sample94              0               0.4               0\n",
      "##############################\n",
      "\n",
      "We are also exporting probability density plots to the file ./example_outputs/test_discrete_geno.trainedModel_withheldSample_probabilities.png this is a plot of the probability distributions of being a case, stratified by case and control status in the withheld test samples.\n",
      "Even if you selected to run feature ranking, you can't generate feature ranks using SVC, ComplementNB, KNeighborsClassifier, QuadraticDiscriminantAnalysis or BaggingClassifier... it just isn't possible.\n",
      "\n",
      "Thanks for training a model with GenoML!\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 309x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run -i ./scripts/training.py\\\n",
    "--prefix ./example_outputs/test_discrete_geno\\\n",
    "--rank-features run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id=\"3\"></a>\n",
    "## Part 3: Tuning the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is some basic info on the command you are about to run.\n",
      "Python version info...\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "CLI argument info...\n",
      "Working with the dataset and best model corresponding to prefix ./example_outputs/test_discrete_geno the timestamp from the merge is the prefix in most cases.\n",
      "Your maximum number of tuning iterations is 10 and if you are concerned about runtime, make this number smaller.\n",
      "You are running 3 rounds of cross-valdiation, and again... if you are concerned about runtime, make this number smaller.\n",
      "Give credit where credit is due, for this stage of analysis we use code from the great contributors to python packages: argparse, xgboost, sklearn, pandas, numpy, time, matplotlib and seaborn.\n",
      "As a note, in all exported probabilities and other graphics, case status is treated as a 0 or 1, with 1 representing a positive case.\n",
      "\n",
      "\n",
      "Your data looks like this (showing the first few lines of the left-most and right-most columns)...\n",
      "##############################\n",
      "         snp229_A    snp423_C    snp148_G    snp263_G    snp332_A    snp381_T  \\\n",
      "count  100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
      "mean     0.030000    0.110000    0.110000    0.090000    0.580000    0.140000   \n",
      "std      0.171447    0.314466    0.314466    0.287623    0.638496    0.376588   \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    0.000000    0.500000    0.000000   \n",
      "75%      0.000000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
      "max      1.000000    1.000000    1.000000    1.000000    2.000000    2.000000   \n",
      "\n",
      "         snp264_G    snp225_G     snp77_C    snp380_G  ...    snp231_T  \\\n",
      "count  100.000000  100.000000  100.000000  100.000000  ...  100.000000   \n",
      "mean     0.420000    0.530000    0.080000    0.180000  ...    0.320000   \n",
      "std      0.589127    0.593823    0.307482    0.386123  ...    0.529532   \n",
      "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
      "75%      1.000000    1.000000    0.000000    0.000000  ...    1.000000   \n",
      "max      2.000000    2.000000    2.000000    1.000000  ...    2.000000   \n",
      "\n",
      "         snp242_T    snp323_A    snp211_G    snp91_T    snp433_G    snp151_T  \\\n",
      "count  100.000000  100.000000  100.000000  100.00000  100.000000  100.000000   \n",
      "mean     0.030000    0.280000    0.050000    0.95000    0.860000    0.420000   \n",
      "std      0.171447    0.494004    0.219043    0.74366    0.725022    0.589127   \n",
      "min      0.000000    0.000000    0.000000    0.00000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.00000    0.000000    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    1.00000    1.000000    0.000000   \n",
      "75%      0.000000    1.000000    0.000000    1.25000    1.000000    1.000000   \n",
      "max      1.000000    2.000000    1.000000    2.00000    2.000000    2.000000   \n",
      "\n",
      "        snp451_C    snp335_A       PHENO  \n",
      "count  100.00000  100.000000  100.000000  \n",
      "mean     0.85000    0.110000    0.700000  \n",
      "std      0.74366    0.314466    0.460566  \n",
      "min      0.00000    0.000000    0.000000  \n",
      "25%      0.00000    0.000000    0.000000  \n",
      "50%      1.00000    0.000000    1.000000  \n",
      "75%      1.00000    0.000000    1.000000  \n",
      "max      2.00000    1.000000    1.000000  \n",
      "\n",
      "[8 rows x 417 columns]\n",
      "##############################\n",
      "\n",
      "From previous analyses in the training phase, we've determined that the best algorithm for this application is KNeighborsClassifier  ... so lets tune it up and see what gains we can make!\n",
      "Here is a summary of the top 10 iterations of the hyperparameter tune...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leonardhl/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV took 3.09 seconds for 10 candidates parameter iterations.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.550 (std: 0.032)\n",
      "Parameters: {'leaf_size': 96, 'n_neighbors': 1}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.528 (std: 0.113)\n",
      "Parameters: {'leaf_size': 41, 'n_neighbors': 3}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.511 (std: 0.157)\n",
      "Parameters: {'leaf_size': 99, 'n_neighbors': 5}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.511 (std: 0.157)\n",
      "Parameters: {'leaf_size': 58, 'n_neighbors': 5}\n",
      "\n",
      "Model with rank: 5\n",
      "Mean validation score: 0.473 (std: 0.100)\n",
      "Parameters: {'leaf_size': 75, 'n_neighbors': 7}\n",
      "\n",
      "Model with rank: 5\n",
      "Mean validation score: 0.473 (std: 0.100)\n",
      "Parameters: {'leaf_size': 36, 'n_neighbors': 7}\n",
      "\n",
      "Model with rank: 5\n",
      "Mean validation score: 0.473 (std: 0.100)\n",
      "Parameters: {'leaf_size': 62, 'n_neighbors': 7}\n",
      "\n",
      "Model with rank: 5\n",
      "Mean validation score: 0.473 (std: 0.100)\n",
      "Parameters: {'leaf_size': 83, 'n_neighbors': 7}\n",
      "\n",
      "Model with rank: 9\n",
      "Mean validation score: 0.429 (std: 0.107)\n",
      "Parameters: {'leaf_size': 73, 'n_neighbors': 8}\n",
      "\n",
      "Model with rank: 9\n",
      "Mean validation score: 0.429 (std: 0.107)\n",
      "Parameters: {'leaf_size': 94, 'n_neighbors': 8}\n",
      "\n",
      "Here is the cross-validation summary of your best tuned model hyperparameters...\n",
      "Scores per cross-validation of the metric to be maximized, this scoring metric is AUC for discrete phenotypes and explained variance for continuous phenotypes:\n",
      "[0.54583333 0.51304348 0.59130435]\n",
      "Mean cross-validation score:\n",
      "0.55006038647343\n",
      "Standard deviation of the cross-validation score:\n",
      "0.032089374253407646\n",
      "\n",
      "Here is the cross-validation summary of your baseline/default hyperparamters for the same algorithm on the same data...\n",
      "Scores per cross-validation of the metric to be maximized, this scoring metric is AUC for discrete phenotypes and explained variance for continuous phenotypes:\n",
      "[0.50416667 0.27826087 0.67826087]\n",
      "Mean cross-validation score:\n",
      "0.48689613526570047\n",
      "Stnadard deviation of the cross-validation score:\n",
      "0.16375531226223636\n",
      "\n",
      "Just a note, if you have a relatively small variance among the cross-validation iterations, there is a higher chance of your model being more generalizable to similar datasets.\n",
      "\n",
      "Based on comparisons of the default parameters to your hyperparameter tuned model, the tuned model actually performed better.\n",
      "Looks like the tune was worth it, we suggest using this model for maximum performance, lets summarize and export this now.\n",
      "In most cases, if opting to use the tuned model, a separate test dataset is a good idea. GenoML has a module to fit models to external data.\n",
      "\n",
      "We are also exporting a ROC curve for you here ./example_outputs/test_discrete_geno.tunedModel_allSample_ROC.png this is a graphical representation of AUC in all samples for the best performing algorithm.\n",
      "\n",
      "Preview of the exported predictions for the tuning samples which is naturally overfit and exported as ./example_outputs/test_discrete_geno.tunedModel_allSample_Predictions.csv in the similar format as in the initial training phase of GenoML.\n",
      "##############################\n",
      "        ID  CASE_REPORTED  CASE_PROBABILITY  CASE_PREDICTED\n",
      "0  sample1              1               1.0               1\n",
      "1  sample2              1               1.0               1\n",
      "2  sample3              1               1.0               1\n",
      "3  sample4              0               0.0               0\n",
      "4  sample5              0               0.0               0\n",
      "##############################\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leonardhl/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "/Users/leonardhl/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "We are also exporting probability density plots to the file ./example_outputs/test_discrete_geno.tunedModel_allSample_probabilities.png this is a plot of the probability distributions of being a case, stratified by case and control status for all samples.\n",
      "\n",
      "Lets shut everything down, thanks for trying to tune your model with GenoML.\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 309x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run -i ./scripts/tuning.py\\\n",
    "--prefix ./example_outputs/test_discrete_geno\\\n",
    "--max-tune 10\\\n",
    "--n-cv 3\n",
    "\n",
    "# FIXME: Module not callable error?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id=\"4\"></a>\n",
    "## Part 4: Interpreting the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General output from running GenoML:\n",
    "\n",
    "1. test_discrete_geno.best_algorithm.txt\n",
    "2. test_discrete_geno.trainedModel.joblib\n",
    "3. test_discrete_geno.trainedModel_trainingSample_featureImportance.csv\n",
    "4. test_discrete_geno.trainedModel_trainingSample_Predictions.csv\n",
    "5. test_discrete_geno.trainedModel_withheldSample_Predictions.csv\n",
    "6. test_discrete_geno.trainedModel_withheldSample_probabilities.png\n",
    "7. test_discrete_geno.trainedModel_withheldSample_ROC.png\n",
    "8. test_discrete_geno.training_withheldSamples_performanceMetrics.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### best_algorithm.txt stores the name of the top performing algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>QuadraticDiscriminantAnalysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [QuadraticDiscriminantAnalysis]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_alg = pd.read_csv(\"example_outputs/test_discrete_geno.best_algorithm.txt\")\n",
    "best_alg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### trainedModel.joblib is your trained model, load it using joblib.load to use it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
      "  kernel='rbf', max_iter=-1, probability=True, random_state=None,\n",
      "  shrinking=True, tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "model = joblib.load(\"example_outputs/test_discrete_geno.trainedModel.joblib\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model_trainingSample_featureImportance.csv contains your feature rankings, if you've chosen to rank features when training your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>RANK</th>\n",
       "      <th>FEATURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>1</td>\n",
       "      <td>snp446_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1</td>\n",
       "      <td>snp354_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1</td>\n",
       "      <td>snp448_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1</td>\n",
       "      <td>snp83_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1</td>\n",
       "      <td>snp351_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1</td>\n",
       "      <td>snp353_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1</td>\n",
       "      <td>snp497_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1</td>\n",
       "      <td>snp4_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1</td>\n",
       "      <td>snp384_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1</td>\n",
       "      <td>snp88_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>1</td>\n",
       "      <td>snp282_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>1</td>\n",
       "      <td>snp71_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1</td>\n",
       "      <td>snp284_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1</td>\n",
       "      <td>snp461_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1</td>\n",
       "      <td>snp259_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>1</td>\n",
       "      <td>snp103_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1</td>\n",
       "      <td>snp422_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1</td>\n",
       "      <td>snp346_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>1</td>\n",
       "      <td>snp295_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1</td>\n",
       "      <td>snp73_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1</td>\n",
       "      <td>snp436_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1</td>\n",
       "      <td>snp156_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1</td>\n",
       "      <td>snp312_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1</td>\n",
       "      <td>snp36_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1</td>\n",
       "      <td>snp112_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1</td>\n",
       "      <td>snp329_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1</td>\n",
       "      <td>snp271_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1</td>\n",
       "      <td>snp315_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1</td>\n",
       "      <td>snp241_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>1</td>\n",
       "      <td>snp297_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>197</td>\n",
       "      <td>snp469_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>198</td>\n",
       "      <td>snp471_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>199</td>\n",
       "      <td>snp389_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200</td>\n",
       "      <td>snp370_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>201</td>\n",
       "      <td>snp18_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>202</td>\n",
       "      <td>snp212_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>203</td>\n",
       "      <td>snp287_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>204</td>\n",
       "      <td>snp439_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>205</td>\n",
       "      <td>snp185_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>206</td>\n",
       "      <td>snp109_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>207</td>\n",
       "      <td>snp64_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>208</td>\n",
       "      <td>snp164_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>209</td>\n",
       "      <td>snp403_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>210</td>\n",
       "      <td>snp127_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>211</td>\n",
       "      <td>snp162_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>212</td>\n",
       "      <td>snp368_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>213</td>\n",
       "      <td>snp410_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>214</td>\n",
       "      <td>snp398_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>215</td>\n",
       "      <td>snp371_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>216</td>\n",
       "      <td>snp359_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>217</td>\n",
       "      <td>snp493_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>218</td>\n",
       "      <td>snp151_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>219</td>\n",
       "      <td>snp141_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>220</td>\n",
       "      <td>snp293_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>221</td>\n",
       "      <td>snp99_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>222</td>\n",
       "      <td>snp361_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>223</td>\n",
       "      <td>snp183_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>224</td>\n",
       "      <td>snp256_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>225</td>\n",
       "      <td>snp304_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>226</td>\n",
       "      <td>snp306_G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>449 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RANK   FEATURE\n",
       "406     1  snp446_C\n",
       "147     1  snp354_C\n",
       "298     1  snp448_T\n",
       "150     1   snp83_G\n",
       "151     1  snp351_T\n",
       "152     1  snp353_C\n",
       "153     1  snp497_A\n",
       "291     1    snp4_G\n",
       "290     1  snp384_A\n",
       "156     1   snp88_C\n",
       "157     1  snp282_C\n",
       "288     1   snp71_A\n",
       "146     1  snp284_A\n",
       "159     1  snp461_C\n",
       "161     1  snp259_A\n",
       "286     1  snp103_G\n",
       "163     1  snp422_C\n",
       "164     1  snp346_T\n",
       "284     1  snp295_G\n",
       "167     1   snp73_G\n",
       "168     1  snp436_A\n",
       "283     1  snp156_A\n",
       "170     1  snp312_C\n",
       "422     1   snp36_T\n",
       "172     1  snp112_G\n",
       "160     1  snp329_C\n",
       "299     1  snp271_C\n",
       "144     1  snp315_C\n",
       "143     1  snp241_C\n",
       "425     1  snp297_G\n",
       "..    ...       ...\n",
       "265   197  snp469_T\n",
       "229   198  snp471_T\n",
       "5     199  snp389_T\n",
       "4     200  snp370_A\n",
       "368   201   snp18_C\n",
       "228   202  snp212_C\n",
       "328   203  snp287_G\n",
       "3     204  snp439_G\n",
       "187   205  snp185_G\n",
       "110   206  snp109_G\n",
       "222   207   snp64_C\n",
       "2     208  snp164_C\n",
       "1     209  snp403_T\n",
       "306   210  snp127_T\n",
       "191   211  snp162_G\n",
       "446   212  snp368_G\n",
       "0     213  snp410_T\n",
       "266   214  snp398_T\n",
       "337   215  snp371_T\n",
       "390   216  snp359_C\n",
       "224   217  snp493_A\n",
       "226   218  snp151_T\n",
       "225   219  snp141_A\n",
       "124   220  snp293_G\n",
       "171   221   snp99_A\n",
       "219   222  snp361_G\n",
       "140   223  snp183_T\n",
       "223   224  snp256_T\n",
       "447   225  snp304_C\n",
       "448   226  snp306_G\n",
       "\n",
       "[449 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp = pd.read_csv(\"example_outputs/test_discrete_geno.trainedModel_trainingSample_featureImportance.csv\")\n",
    "feat_imp.sort_values(by=\"RANK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SNPs above with rank 1 make the largest contributions to the model, the SNPs near the bottom of the above dataframe make the smallest contributions.\n",
    "\n",
    "This can be used for manual feature selection, remove the features contributing the least to the model and train again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### trainedModel_trainingSample_Predictions.csv contains predictions for the data the model is trained on - this is by nature overfit\n",
    "\n",
    "#### test_discrete_geno.trainedModel_withheldSample_Predictions.csv is more inportant, this contains predictions for the withheld data - effectively estimating how the model would perform on unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CASE_REPORTED</th>\n",
       "      <th>CASE_PROBABILITY</th>\n",
       "      <th>CASE_PREDICTED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample48</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample86</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample29</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample94</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sample6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sample67</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sample66</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sample36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sample17</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  CASE_REPORTED  CASE_PROBABILITY  CASE_PREDICTED\n",
       "1  sample48              1               1.0               1\n",
       "2  sample86              1               1.0               1\n",
       "3  sample29              0               0.0               0\n",
       "4  sample94              0               0.0               0\n",
       "5   sample6              1               1.0               1\n",
       "6  sample67              1               1.0               1\n",
       "7  sample66              1               1.0               1\n",
       "8  sample36              0               0.0               0\n",
       "9  sample17              1               1.0               1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred = pd.read_csv(\"example_outputs/test_discrete_geno.trainedModel_trainingSample_Predictions.csv\")\n",
    "train_pred[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>ID</th>\n",
       "      <th>CASE_REPORTED</th>\n",
       "      <th>CASE_PROBABILITY</th>\n",
       "      <th>CASE_PREDICTED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample54</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample71</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample46</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample45</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sample40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sample23</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sample81</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sample11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sample1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  CASE_REPORTED  CASE_PROBABILITY  CASE_PREDICTED\n",
       "1  sample54              1               0.0               0\n",
       "2  sample71              1               0.0               0\n",
       "3  sample46              1               1.0               1\n",
       "4  sample45              1               1.0               1\n",
       "5  sample40              1               0.0               0\n",
       "6  sample23              1               1.0               1\n",
       "7  sample81              0               0.0               0\n",
       "8  sample11              1               1.0               1\n",
       "9   sample1              1               1.0               1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred = pd.read_csv(\"example_outputs/test_discrete_geno.trainedModel_withheldSample_Predictions.csv\")\n",
    "test_pred[1:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### trainedModel_withheldSample_probabilities.png is a visualization of how prediction probabilities for witheld samples are distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"example_outputs/test_discrete_geno.trainedModel_withheldSample_probabilities.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### trainedModel_withheldSample_ROC.png displays the ROC curve for your model (the true positive rate vs the false positive rate for predictions made by your model on withheld data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAAAAAAAAAMhBAoQAAAAAAAAAAAAAAAAAkIMECAEAAAAAAAAAAAAAAAAgBwkQAgAAAAAAAAAAAAAAAEAO+v+0Td03ySxZSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"example_outputs/test_discrete_geno.trainedModel_withheldSample_ROC.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### training_withheldSamples_performanceMetrics.csv summarizes performance for all models tested in terms of metrics such as AUC, sensitivity, specificity, and more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Algorithm</th>\n",
       "      <th>AUC_Percent</th>\n",
       "      <th>Accuracy_Percent</th>\n",
       "      <th>Balanced_Accuracy_Percent</th>\n",
       "      <th>Log_Loss</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>Runtime_Seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>50.568182</td>\n",
       "      <td>63.333333</td>\n",
       "      <td>43.181818</td>\n",
       "      <td>0.805640</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.703704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>49.147727</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>42.613636</td>\n",
       "      <td>0.635800</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.030165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>55.681818</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>56.818182</td>\n",
       "      <td>0.660945</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.112529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>34.659091</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>49.431818</td>\n",
       "      <td>1.046900</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.132865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>43.181818</td>\n",
       "      <td>63.333333</td>\n",
       "      <td>43.181818</td>\n",
       "      <td>12.664218</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.703704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVC</td>\n",
       "      <td>51.136364</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.583587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MLPClassifier</td>\n",
       "      <td>46.022727</td>\n",
       "      <td>63.333333</td>\n",
       "      <td>43.181818</td>\n",
       "      <td>0.950493</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.703704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>38.068182</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>44.886364</td>\n",
       "      <td>2.877772</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.014110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>QuadraticDiscriminantAnalysis</td>\n",
       "      <td>50.568182</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>50.568182</td>\n",
       "      <td>14.966803</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.018940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BaggingClassifier</td>\n",
       "      <td>58.238636</td>\n",
       "      <td>63.333333</td>\n",
       "      <td>55.113636</td>\n",
       "      <td>0.630396</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.025850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>51.136364</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>53.409091</td>\n",
       "      <td>0.780503</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.229350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Algorithm  AUC_Percent  Accuracy_Percent  \\\n",
       "0              LogisticRegression    50.568182         63.333333   \n",
       "1          RandomForestClassifier    49.147727         56.666667   \n",
       "2              AdaBoostClassifier    55.681818         60.000000   \n",
       "3      GradientBoostingClassifier    34.659091         66.666667   \n",
       "4                   SGDClassifier    43.181818         63.333333   \n",
       "5                             SVC    51.136364         73.333333   \n",
       "6                   MLPClassifier    46.022727         63.333333   \n",
       "7            KNeighborsClassifier    38.068182         60.000000   \n",
       "8   QuadraticDiscriminantAnalysis    50.568182         56.666667   \n",
       "9               BaggingClassifier    58.238636         63.333333   \n",
       "10                  XGBClassifier    51.136364         66.666667   \n",
       "\n",
       "    Balanced_Accuracy_Percent   Log_Loss  Sensitivity  Specificity       PPV  \\\n",
       "0                   43.181818   0.805640     0.863636        0.000  0.703704   \n",
       "1                   42.613636   0.635800     0.727273        0.125  0.695652   \n",
       "2                   56.818182   0.660945     0.636364        0.500  0.777778   \n",
       "3                   49.431818   1.046900     0.863636        0.125  0.730769   \n",
       "4                   43.181818  12.664218     0.863636        0.000  0.703704   \n",
       "5                   50.000000   0.583587     1.000000        0.000  0.733333   \n",
       "6                   43.181818   0.950493     0.863636        0.000  0.703704   \n",
       "7                   44.886364   2.877772     0.772727        0.125  0.708333   \n",
       "8                   50.568182  14.966803     0.636364        0.375  0.736842   \n",
       "9                   55.113636   0.630396     0.727273        0.375  0.761905   \n",
       "10                  53.409091   0.780503     0.818182        0.250  0.750000   \n",
       "\n",
       "         NPV  Runtime_Seconds  \n",
       "0   0.000000         0.029198  \n",
       "1   0.142857         0.030165  \n",
       "2   0.333333         0.112529  \n",
       "3   0.250000         0.132865  \n",
       "4   0.000000         0.010719  \n",
       "5        NaN         0.022147  \n",
       "6   0.000000         0.088084  \n",
       "7   0.166667         0.014110  \n",
       "8   0.272727         0.018940  \n",
       "9   0.333333         0.025850  \n",
       "10  0.333333         0.229350  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.read_csv(\"example_outputs/test_discrete_geno.training_withheldSamples_performanceMetrics.csv\")\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id=\"5\"></a>\n",
    "## Part 5: Introduction to Kipoi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kipoi: A Model Zoo for Genomics\n",
    "\n",
    "A repository for predictive models in genomics, facilitates easy application of models to new data and creation of new derived models. The primary goal is lowering the entry barrier to modeling genomics\n",
    "\n",
    "The website to Kipoi can be found here: https://kipoi.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "If you made it this far, congratulations! Thank you for working through the workshop. \n",
    "\n",
    "This effort would not have been possible without these people:\n",
    "- Andy Singleton (NIH-NIA)\n",
    "- Mike Nalls (DTI / NIH-NIA)\n",
    "- Faraz Faghri (NIA-NIH / UIUC)\n",
    "- Cornelis Blauwendraat (NIH-NIA)\n",
    "- Hirotaka Iwaki (NIA-NIH / MJFF / DTI)\n",
    "- Sayed Hadi Hashemi (UIUC)\n",
    "- David Saffo (NortheasternU)\n",
    "- Roy H. Campbell (UIUC)\n",
    "- Juan A. Botia (UM)\n",
    "- Lana Sargeant (VCU)\n",
    "- Susan Chacko (Biowulf-NIH)\n",
    "- Rafael Jordá Muñoz (UM)\n",
    "- Jeff Kim (NIH-NIA)\n",
    "\n",
    "...or these open-source initiatives:\n",
    "- JupyterLab/Notebook\n",
    "- Binder \n",
    "- GitHub\n",
    "- Kipoi \n",
    "- Python and all its packages\n",
    "- PLINK \n",
    "- ...and many more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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