classify.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using machine learning to predict IBD and Ulcerative Colitis from the microbes present ... and using that data to identify the most important bacteria\n",
    "\n",
    "The IBDMD consortium have made a whole bunch of sequencing data related to IBD and UC available on [their website](https://ibdmdb.org/). In this example, I use two data sets to show how you can use pandas data frames and scikit-learn's random forest classifier to build a classifier that can detect IBD and UC, and use that classifier to identify the most important bacteria for discriminating IBD/UC from healthy samples.\n",
    "\n",
    "*First a note about the data and analysis:* [The consortium](https://ibdmdb.org/project/) have provided this data as-is and have not yet published it. This analysis was performed because I wanted to understand what the discriminatory bacteria were in this data set. It is not a publication-quality analysis as you will see, and _I will not publish the analysis. You should not publish the analysis either._ I provide this here as a cool machine learning example using free data that you can repeat and probably find something different.\n",
    "\n",
    "## Downloading the data\n",
    "\n",
    "There is a lot of data in their repository, but we are going to use two files:\n",
    "1. [The metadata file](https://ibdmdb.org/tunnel/cb/document/Public/HMP2/Metadata/hmp2_metadata.csv). This file has the list of sample IDs and whether they have IBD, UC, or are a control. There is a lot more data in this file than that, but for this simple example we are going to just use those two columns.\n",
    "2. [The taxonomic profiles from the metagenomes](https://ibdmdb.org/tunnel/cb/document/Public/HMP2/WGS/1812/taxonomic_profiles.tsv.gz). Their processing pipeline is not exactly clear, but it was done by the Huttenhower lab, so its probably done using [biobakery](https://www.ncbi.nlm.nih.gov/pubmed/29194469).\n",
    "\n",
    "I have downloaded and uncompressed those two files.\n",
    "\n",
    "_Note 1:_ I just uncompressed them to avoid using gzip.open, but you might want to consider that.\n",
    "\n",
    "_Note 2:_ I am just parsing the tsv file using a regular expression. You might want to consider downloading and importing the biom file which will allow a more robust interface to the data and allow you to explore other options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Before we start, load our environment\n",
    "%matplotlib inline\n",
    "import os\n",
    "import re\n",
    "import sys\n",
    "import argparse\n",
    "import csv\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the metadata for the HMP project\n",
    "\n",
    "This is the data downloaded from the [IBDMD site](https://ibdmdb.org/tunnel/cb/document/Public/HMP2/Metadata/hmp2_metadata.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diagnosis: CD : 2671 occurrences\n",
      "Diagnosis: UC : 1501 occurrences\n",
      "Diagnosis: nonIBD : 1338 occurrences\n"
     ]
    }
   ],
   "source": [
    "def read_metadata(fname='hmp2_metadata.csv'):\n",
    "    metadata={}\n",
    "    counts = {}\n",
    "    with open(fname, 'r') as fin:\n",
    "        header = fin.readline()\n",
    "        csvreader = csv.reader(fin, delimiter=',')\n",
    "        for row in csvreader:\n",
    "            metadata[row[1]] = row[69]\n",
    "            counts[row[69]] = counts.get(row[69], 0)+1\n",
    "\n",
    "    for row in counts:\n",
    "        print(\"Diagnosis: {} : {} occurrences\".format(row, counts[row]))\n",
    "\n",
    "    return metadata\n",
    "md=read_metadata()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the taxonomy file\n",
    "\n",
    "This is a taxonomy TSV file downloaded from the [IBDMD site too](https://ibdmdb.org/tunnel/cb/document/Public/HMP2/WGS/1812/taxonomic_profiles.tsv.gz)\n",
    "\n",
    "We just read this file in and identifier the species using a regular expression. This line\n",
    "```\n",
    "    m=re.search('\\|s__(\\w+)$', row[0])\n",
    "```\n",
    "\n",
    "is looking for something that starts '|s__' at the end of the first element in the table. That is the species counts, and so we keep those rows. If you change the s here to a g it will pull out genera. \n",
    "\n",
    "We are making a dict of dicts where our species are the first keys and our samples are the second keys. We'll see in a second how easy it is to convert that to a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 572 species in our file\n"
     ]
    }
   ],
   "source": [
    "def read_taxonomy(fname='taxonomic_profiles.tsv'):\n",
    "\n",
    "    header = None\n",
    "    counts = {}\n",
    "    with open(fname, 'r') as fin:\n",
    "        csvreader = csv.reader(fin, delimiter='\\t')\n",
    "        for row in csvreader:\n",
    "            if not header:\n",
    "                for i in range(1, len(row)):\n",
    "                    row[i]=row[i].replace('_taxonomic_profile', '')\n",
    "                header = row                    \n",
    "                continue\n",
    "            m=re.search('\\|s__(\\w+)$', row[0])\n",
    "            if not m:\n",
    "                continue\n",
    "            for hix in range(1, len(header)):\n",
    "                if m.groups()[0] not in counts:\n",
    "                    counts[m.groups()[0]] = {}\n",
    "                counts[m.groups()[0]][header[hix]] = float(row[hix])\n",
    "    return counts\n",
    "counts=read_taxonomy()\n",
    "print(\"There are {} species in our file\".format(len(counts)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a pandas data frame from the data.\n",
    "\n",
    "Now we have both the metadata parsed and the species counts, we can make a metadata table using pandas. Note that we want to have the species in columns and the samples in rows which is why we set the dictionary up that way!\n",
    "\n",
    "This is trivial to convert to a table, and then we use head to have a look at the first few rows and see that we have numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abiotrophia_defectiva</th>\n",
       "      <th>Acidaminococcus_fermentans</th>\n",
       "      <th>Acidaminococcus_intestini</th>\n",
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       "      <th>Acidaminococcus_sp_HPA0509</th>\n",
       "      <th>Acidaminococcus_unclassified</th>\n",
       "      <th>Acinetobacter_lwoffii</th>\n",
       "      <th>Acinetobacter_unclassified</th>\n",
       "      <th>Acinetobacter_ursingii</th>\n",
       "      <th>Actinobacillus_unclassified</th>\n",
       "      <th>...</th>\n",
       "      <th>Veillonella_ratti</th>\n",
       "      <th>Veillonella_unclassified</th>\n",
       "      <th>Vicia_cryptic_virus</th>\n",
       "      <th>Weissella_cibaria</th>\n",
       "      <th>Weissella_confusa</th>\n",
       "      <th>Weissella_phage_phiYS61</th>\n",
       "      <th>Weissella_unclassified</th>\n",
       "      <th>Wohlfahrtiimonas_chitiniclastica</th>\n",
       "      <th>Yersinia_enterocolitica</th>\n",
       "      <th>Yersinia_unclassified</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 572 columns</p>\n",
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      "text/plain": [
       "            Abiotrophia_defectiva  Acidaminococcus_fermentans  \\\n",
       "CSM5FZ3N_P                    0.0                         0.0   \n",
       "CSM5FZ3R_P                    0.0                         0.0   \n",
       "CSM5FZ3T_P                    0.0                         0.0   \n",
       "CSM5FZ3V_P                    0.0                         0.0   \n",
       "CSM5FZ3X_P                    0.0                         0.0   \n",
       "\n",
       "            Acidaminococcus_intestini  Acidaminococcus_sp_BV3L6  \\\n",
       "CSM5FZ3N_P                        0.0                       0.0   \n",
       "CSM5FZ3R_P                        0.0                       0.0   \n",
       "CSM5FZ3T_P                        0.0                       0.0   \n",
       "CSM5FZ3V_P                        0.0                       0.0   \n",
       "CSM5FZ3X_P                        0.0                       0.0   \n",
       "\n",
       "            Acidaminococcus_sp_HPA0509  Acidaminococcus_unclassified  \\\n",
       "CSM5FZ3N_P                         0.0                      0.000000   \n",
       "CSM5FZ3R_P                         0.0                      0.000000   \n",
       "CSM5FZ3T_P                         0.0                      0.000000   \n",
       "CSM5FZ3V_P                         0.0                      0.001363   \n",
       "CSM5FZ3X_P                         0.0                      0.000000   \n",
       "\n",
       "            Acinetobacter_lwoffii  Acinetobacter_unclassified  \\\n",
       "CSM5FZ3N_P                    0.0                         0.0   \n",
       "CSM5FZ3R_P                    0.0                         0.0   \n",
       "CSM5FZ3T_P                    0.0                         0.0   \n",
       "CSM5FZ3V_P                    0.0                         0.0   \n",
       "CSM5FZ3X_P                    0.0                         0.0   \n",
       "\n",
       "            Acinetobacter_ursingii  Actinobacillus_unclassified  \\\n",
       "CSM5FZ3N_P                     0.0                          0.0   \n",
       "CSM5FZ3R_P                     0.0                          0.0   \n",
       "CSM5FZ3T_P                     0.0                          0.0   \n",
       "CSM5FZ3V_P                     0.0                          0.0   \n",
       "CSM5FZ3X_P                     0.0                          0.0   \n",
       "\n",
       "                    ...            Veillonella_ratti  \\\n",
       "CSM5FZ3N_P          ...                          0.0   \n",
       "CSM5FZ3R_P          ...                          0.0   \n",
       "CSM5FZ3T_P          ...                          0.0   \n",
       "CSM5FZ3V_P          ...                          0.0   \n",
       "CSM5FZ3X_P          ...                          0.0   \n",
       "\n",
       "            Veillonella_unclassified  Vicia_cryptic_virus  Weissella_cibaria  \\\n",
       "CSM5FZ3N_P                  0.000000                  0.0                0.0   \n",
       "CSM5FZ3R_P                  0.000000                  0.0                0.0   \n",
       "CSM5FZ3T_P                  0.000000                  0.0                0.0   \n",
       "CSM5FZ3V_P                  0.000123                  0.0                0.0   \n",
       "CSM5FZ3X_P                  0.000000                  0.0                0.0   \n",
       "\n",
       "            Weissella_confusa  Weissella_phage_phiYS61  \\\n",
       "CSM5FZ3N_P                0.0                      0.0   \n",
       "CSM5FZ3R_P                0.0                      0.0   \n",
       "CSM5FZ3T_P                0.0                      0.0   \n",
       "CSM5FZ3V_P                0.0                      0.0   \n",
       "CSM5FZ3X_P                0.0                      0.0   \n",
       "\n",
       "            Weissella_unclassified  Wohlfahrtiimonas_chitiniclastica  \\\n",
       "CSM5FZ3N_P                     0.0                               0.0   \n",
       "CSM5FZ3R_P                     0.0                               0.0   \n",
       "CSM5FZ3T_P                     0.0                               0.0   \n",
       "CSM5FZ3V_P                     0.0                               0.0   \n",
       "CSM5FZ3X_P                     0.0                               0.0   \n",
       "\n",
       "            Yersinia_enterocolitica  Yersinia_unclassified  \n",
       "CSM5FZ3N_P                      0.0                    0.0  \n",
       "CSM5FZ3R_P                      0.0                    0.0  \n",
       "CSM5FZ3T_P                      0.0                    0.0  \n",
       "CSM5FZ3V_P                      0.0                    0.0  \n",
       "CSM5FZ3X_P                      0.0                    0.0  \n",
       "\n",
       "[5 rows x 572 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(counts)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a column for the classification\n",
    "\n",
    "Now we can add the metadata to our table. This is whether the patients have UC, IBD, or are a control. We add this to a new column called `diagnoses`, and that is what we are going to train our classifier on!\n",
    "\n",
    "However, our random forest requires that this is numeric, so we use `factorize` to convert this to a list of elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['diagnoses'] = pd.Series(md)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick peek at the table see that it looks correct. If you scroll all the way to the right you will see the new column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abiotrophia_defectiva</th>\n",
       "      <th>Acidaminococcus_fermentans</th>\n",
       "      <th>Acidaminococcus_intestini</th>\n",
       "      <th>Acidaminococcus_sp_BV3L6</th>\n",
       "      <th>Acidaminococcus_sp_HPA0509</th>\n",
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       "      <td>CD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSM5FZ3T_P</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSM5FZ3V_P</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSM5FZ3X_P</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 573 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Abiotrophia_defectiva  Acidaminococcus_fermentans  \\\n",
       "CSM5FZ3N_P                    0.0                         0.0   \n",
       "CSM5FZ3R_P                    0.0                         0.0   \n",
       "CSM5FZ3T_P                    0.0                         0.0   \n",
       "CSM5FZ3V_P                    0.0                         0.0   \n",
       "CSM5FZ3X_P                    0.0                         0.0   \n",
       "\n",
       "            Acidaminococcus_intestini  Acidaminococcus_sp_BV3L6  \\\n",
       "CSM5FZ3N_P                        0.0                       0.0   \n",
       "CSM5FZ3R_P                        0.0                       0.0   \n",
       "CSM5FZ3T_P                        0.0                       0.0   \n",
       "CSM5FZ3V_P                        0.0                       0.0   \n",
       "CSM5FZ3X_P                        0.0                       0.0   \n",
       "\n",
       "            Acidaminococcus_sp_HPA0509  Acidaminococcus_unclassified  \\\n",
       "CSM5FZ3N_P                         0.0                      0.000000   \n",
       "CSM5FZ3R_P                         0.0                      0.000000   \n",
       "CSM5FZ3T_P                         0.0                      0.000000   \n",
       "CSM5FZ3V_P                         0.0                      0.001363   \n",
       "CSM5FZ3X_P                         0.0                      0.000000   \n",
       "\n",
       "            Acinetobacter_lwoffii  Acinetobacter_unclassified  \\\n",
       "CSM5FZ3N_P                    0.0                         0.0   \n",
       "CSM5FZ3R_P                    0.0                         0.0   \n",
       "CSM5FZ3T_P                    0.0                         0.0   \n",
       "CSM5FZ3V_P                    0.0                         0.0   \n",
       "CSM5FZ3X_P                    0.0                         0.0   \n",
       "\n",
       "            Acinetobacter_ursingii  Actinobacillus_unclassified    ...      \\\n",
       "CSM5FZ3N_P                     0.0                          0.0    ...       \n",
       "CSM5FZ3R_P                     0.0                          0.0    ...       \n",
       "CSM5FZ3T_P                     0.0                          0.0    ...       \n",
       "CSM5FZ3V_P                     0.0                          0.0    ...       \n",
       "CSM5FZ3X_P                     0.0                          0.0    ...       \n",
       "\n",
       "            Veillonella_unclassified  Vicia_cryptic_virus  Weissella_cibaria  \\\n",
       "CSM5FZ3N_P                  0.000000                  0.0                0.0   \n",
       "CSM5FZ3R_P                  0.000000                  0.0                0.0   \n",
       "CSM5FZ3T_P                  0.000000                  0.0                0.0   \n",
       "CSM5FZ3V_P                  0.000123                  0.0                0.0   \n",
       "CSM5FZ3X_P                  0.000000                  0.0                0.0   \n",
       "\n",
       "            Weissella_confusa  Weissella_phage_phiYS61  \\\n",
       "CSM5FZ3N_P                0.0                      0.0   \n",
       "CSM5FZ3R_P                0.0                      0.0   \n",
       "CSM5FZ3T_P                0.0                      0.0   \n",
       "CSM5FZ3V_P                0.0                      0.0   \n",
       "CSM5FZ3X_P                0.0                      0.0   \n",
       "\n",
       "            Weissella_unclassified  Wohlfahrtiimonas_chitiniclastica  \\\n",
       "CSM5FZ3N_P                     0.0                               0.0   \n",
       "CSM5FZ3R_P                     0.0                               0.0   \n",
       "CSM5FZ3T_P                     0.0                               0.0   \n",
       "CSM5FZ3V_P                     0.0                               0.0   \n",
       "CSM5FZ3X_P                     0.0                               0.0   \n",
       "\n",
       "            Yersinia_enterocolitica  Yersinia_unclassified  diagnoses  \n",
       "CSM5FZ3N_P                      0.0                    0.0         CD  \n",
       "CSM5FZ3R_P                      0.0                    0.0         CD  \n",
       "CSM5FZ3T_P                      0.0                    0.0         CD  \n",
       "CSM5FZ3V_P                      0.0                    0.0         CD  \n",
       "CSM5FZ3X_P                      0.0                    0.0         CD  \n",
       "\n",
       "[5 rows x 573 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract the features that we want to use in our random forest\n",
    "\n",
    "Now that we have merged everything, we extract the column names of the features that we want to use in our random forest. This just creates an index of the species that we have. Note that if you use genus you will need to change the 492 to something else!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = df.columns[:492]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create test and training sets.\n",
    "\n",
    "For our classifier, we are going to use some part of the data to train the random forest classifier, and some part of the data to test it. We are going to make a new column that says whether it is testing or training, and populate it so that 75% of the data is training and 25% of the data is for testing. You can change those variables here. Note also that in this study they have different data sets available, so you could use one data set for testing and one for training. (Remember the caveat that I am not going to publish this analysis, so there are a lot of things I _should_ do, but don't. This is one of those places a reviewer would likely ask _why?_)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data: (1638, 574)\n",
      "Training: (1253, 574)\n",
      "Testing: (385, 574)\n"
     ]
    }
   ],
   "source": [
    "df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75\n",
    "train, test = df[df['is_train']==True], df[df['is_train']==False]\n",
    "print(\"Data: {}\\nTraining: {}\\nTesting: {}\".format(df.shape, train.shape, test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a factorized list of diagnoses\n",
    "\n",
    "The random forest requires the diagnoses to just be a list of integers rather than labels, and so we use factorize to split out the labels and the indexes. This will be the input to our random forest classifier. Note that we are just using the training set here, not the whole data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CD', 'UC', 'nonIBD'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diagnosisfactors, labels = pd.factorize(train['diagnoses'])\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start a random classifier\n",
    "\n",
    "I leave most of this at the default (which is to use Gini as the measure of quality), and to bootstrap the trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the random forest\n",
    "\n",
    "Now we train our random forest on the training data using just the features that we are looking for and with our factorized list of diagnoses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train[features], diagnosisfactors)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test our classifier using the test data set\n",
    "\n",
    "This makes predictions for each of the test data sets. For each sample we get a 0, 1, or 2 depending on whether we think it is CD, UC, or neither.\n",
    "\n",
    "Then we convert those predictions to the appropriate labels. We now have an array of predictions the same length as our test data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CD', 'CD', 'CD', 'UC', 'UC', 'CD', 'CD', 'CD', 'CD', 'nonIBD'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(test[features])\n",
    "predictions=labels[clf.predict(test[features])]\n",
    "predictions[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can compare those predictions to the test data set. Seems like we get the first ten correct!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CSM5FZ3R_P    CD\n",
       "CSM5FZ44_P    CD\n",
       "CSM5FZ4G_P    UC\n",
       "CSM5FZ4K_P    UC\n",
       "CSM5MCUY_P    UC\n",
       "CSM5MCVN      CD\n",
       "CSM5MCVV_P    CD\n",
       "CSM5MCW4_P    CD\n",
       "CSM5MCWE      CD\n",
       "CSM5MCWK_P    CD\n",
       "Name: diagnoses, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.diagnoses[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How accurate is our classifier?\n",
    "\n",
    "The _confusion matrix_ is a really good way of figuring out if the classification works. In this matrix we have the actual dignoses in the rows, and the predicted diagnoses in the columns. The numbers in the diagonals tell us how many we got right, while the numbers off the diagonals tell us what we got wrong ... where we were confused!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Diagnoses</th>\n",
       "      <th>CD</th>\n",
       "      <th>UC</th>\n",
       "      <th>nonIBD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Diagnoses</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CD</th>\n",
       "      <td>136</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UC</th>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nonIBD</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Diagnoses   CD  UC  nonIBD\n",
       "Actual Diagnoses                    \n",
       "CD                   136   4       5\n",
       "UC                    29  90       1\n",
       "nonIBD                23   7      90"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(test['diagnoses'], predictions, rownames=['Actual Diagnoses'], colnames=['Predicted Diagnoses'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the most important genus/species for discriminating between disease states\n",
    "\n",
    "This takes the random forest importance feature plot and sorts by the importance. Note that we sort in reverse order to highlight the most important things.\n",
    "\n",
    "In this list the values are the importance of each variable, in this case, the importance of the species in predicting the diagnoses of the disease.\n",
    "\n",
    "_Bifidobacterium adolescentis_ is the most important predictor, but note that this does not tell you why! It may be that this is abundant in a disease state, or it may be that this is _not_ abundant in the disease state.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Clostridium_bolteae', 0.019955005634273815),\n",
       " ('Dorea_formicigenerans', 0.019117687403742544),\n",
       " ('Alistipes_putredinis', 0.018784510477231182),\n",
       " ('Bacteroides_eggerthii', 0.017740048402737379),\n",
       " ('Faecalibacterium_prausnitzii', 0.01632631562173251),\n",
       " ('Lachnospiraceae_bacterium_7_1_58FAA', 0.016100671464421241),\n",
       " ('Burkholderiales_bacterium_1_1_47', 0.015775906343368677),\n",
       " ('Bacteroides_ovatus', 0.01576729126412128),\n",
       " ('Bacteroides_vulgatus', 0.015005463409777722),\n",
       " ('Eubacterium_ventriosum', 0.014795295341909945),\n",
       " ('Parabacteroides_goldsteinii', 0.014761052247161881),\n",
       " ('Bacteroides_thetaiotaomicron', 0.014756343610461553),\n",
       " ('Bacteroides_finegoldii', 0.014092533164758097),\n",
       " ('Bacteroides_stercoris', 0.013941982940559983),\n",
       " ('Lachnospiraceae_bacterium_3_1_46FAA', 0.01381138919321959),\n",
       " ('Bifidobacterium_adolescentis', 0.01358989874839966),\n",
       " ('Eubacterium_siraeum', 0.013267032630585979),\n",
       " ('Bacteroides_fragilis', 0.013028884476596923),\n",
       " ('Eubacterium_hallii', 0.0126931473760728),\n",
       " ('Bacteroides_uniformis', 0.012642876122358721),\n",
       " ('Bacteroides_dorei', 0.012567787375555393),\n",
       " ('Bifidobacterium_longum', 0.012380586262986101),\n",
       " ('Odoribacter_splanchnicus', 0.012219489695726385),\n",
       " ('Alistipes_shahii', 0.011675593960315263),\n",
       " ('Eubacterium_rectale', 0.011506452671337413),\n",
       " ('Dorea_longicatena', 0.011429918332199794),\n",
       " ('Alistipes_onderdonkii', 0.01082073564432442),\n",
       " ('Akkermansia_muciniphila', 0.010663369753780931),\n",
       " ('Haemophilus_parainfluenzae', 0.010636098925907483),\n",
       " ('Parabacteroides_merdae', 0.010225401683731056),\n",
       " ('Roseburia_intestinalis', 0.010083626542399526),\n",
       " ('Bacteroides_xylanisolvens', 0.0096547841982348014),\n",
       " ('Clostridium_leptum', 0.0095723979952375884),\n",
       " ('Alistipes_finegoldii', 0.0094358023092352451),\n",
       " ('Oscillibacter_unclassified', 0.0092828658325404788),\n",
       " ('Parasutterella_excrementihominis', 0.0091198081957850688),\n",
       " ('Prevotella_copri', 0.0087558286810463064),\n",
       " ('Parabacteroides_unclassified', 0.0087337174074648676),\n",
       " ('Ruminococcaceae_bacterium_D16', 0.0087231959485360704),\n",
       " ('Collinsella_aerofaciens', 0.0087156023350945036)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered=list(sorted(zip(train[features], clf.feature_importances_), key=lambda species:species[1], reverse=True))\n",
    "ordered[:40]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the means\n",
    "\n",
    "Since we don't know whether _Bifidobacterium adolescentis_ is important for predicting healthy or diseased states, we can get the mean abundance of that species in the data. We group all the data by the diagnoses, and then get the means of the data.\n",
    "\n",
    "We limit this to the 60 most predictive samples (using the ordering above) so we can plot the data below.\n",
    "\n",
    "Again, our head() command summarizes the data frame, and note that we now have the samples in the same order as the list above! We can see that _Bifidobacterium adolescentis_ is very high in UC (~2x abundance compared to controls and ~6x abundance compared to CD), so perhaps that is a marker for UC versus CD or healthy people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnoses</th>\n",
       "      <th>Clostridium_bolteae</th>\n",
       "      <th>Dorea_formicigenerans</th>\n",
       "      <th>Alistipes_putredinis</th>\n",
       "      <th>Bacteroides_eggerthii</th>\n",
       "      <th>Faecalibacterium_prausnitzii</th>\n",
       "      <th>Lachnospiraceae_bacterium_7_1_58FAA</th>\n",
       "      <th>Burkholderiales_bacterium_1_1_47</th>\n",
       "      <th>Bacteroides_ovatus</th>\n",
       "      <th>Bacteroides_vulgatus</th>\n",
       "      <th>...</th>\n",
       "      <th>Bacteroides_faecis</th>\n",
       "      <th>Clostridium_symbiosum</th>\n",
       "      <th>Ruminococcus_bromii</th>\n",
       "      <th>Bilophila_wadsworthia</th>\n",
       "      <th>Flavonifractor_plautii</th>\n",
       "      <th>Lachnospiraceae_bacterium_5_1_63FAA</th>\n",
       "      <th>Roseburia_hominis</th>\n",
       "      <th>Clostridiaceae_bacterium_JC118</th>\n",
       "      <th>Parabacteroides_distasonis</th>\n",
       "      <th>Ruminococcus_callidus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CD</td>\n",
       "      <td>0.006223</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.028139</td>\n",
       "      <td>0.003993</td>\n",
       "      <td>0.092932</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>0.064718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006102</td>\n",
       "      <td>0.002152</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.001309</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.013520</td>\n",
       "      <td>0.000255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UC</td>\n",
       "      <td>0.002207</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>0.021040</td>\n",
       "      <td>0.018673</td>\n",
       "      <td>0.116517</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.043008</td>\n",
       "      <td>0.087743</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004092</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.002779</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.003057</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.010540</td>\n",
       "      <td>0.000298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nonIBD</td>\n",
       "      <td>0.001763</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.044515</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>0.124156</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.001232</td>\n",
       "      <td>0.018698</td>\n",
       "      <td>0.052504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003299</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.010239</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.007083</td>\n",
       "      <td>0.001312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnoses  Clostridium_bolteae  Dorea_formicigenerans  Alistipes_putredinis  \\\n",
       "0        CD             0.006223               0.000678              0.028139   \n",
       "1        UC             0.002207               0.001029              0.021040   \n",
       "2    nonIBD             0.001763               0.000777              0.044515   \n",
       "\n",
       "   Bacteroides_eggerthii  Faecalibacterium_prausnitzii  \\\n",
       "0               0.003993                      0.092932   \n",
       "1               0.018673                      0.116517   \n",
       "2               0.015688                      0.124156   \n",
       "\n",
       "   Lachnospiraceae_bacterium_7_1_58FAA  Burkholderiales_bacterium_1_1_47  \\\n",
       "0                             0.001031                          0.000817   \n",
       "1                             0.001002                          0.002092   \n",
       "2                             0.000554                          0.001232   \n",
       "\n",
       "   Bacteroides_ovatus  Bacteroides_vulgatus          ...            \\\n",
       "0            0.042000              0.064718          ...             \n",
       "1            0.043008              0.087743          ...             \n",
       "2            0.018698              0.052504          ...             \n",
       "\n",
       "   Bacteroides_faecis  Clostridium_symbiosum  Ruminococcus_bromii  \\\n",
       "0            0.006102               0.002152             0.002086   \n",
       "1            0.004092               0.001320             0.002779   \n",
       "2            0.003299               0.001301             0.010239   \n",
       "\n",
       "   Bilophila_wadsworthia  Flavonifractor_plautii  \\\n",
       "0               0.000393                0.001309   \n",
       "1               0.000173                0.001249   \n",
       "2               0.000340                0.000972   \n",
       "\n",
       "   Lachnospiraceae_bacterium_5_1_63FAA  Roseburia_hominis  \\\n",
       "0                             0.000436           0.002032   \n",
       "1                             0.000453           0.003057   \n",
       "2                             0.000310           0.004495   \n",
       "\n",
       "   Clostridiaceae_bacterium_JC118  Parabacteroides_distasonis  \\\n",
       "0                        0.000082                    0.013520   \n",
       "1                        0.000268                    0.010540   \n",
       "2                        0.000075                    0.007083   \n",
       "\n",
       "   Ruminococcus_callidus  \n",
       "0               0.000255  \n",
       "1               0.000298  \n",
       "2               0.001312  \n",
       "\n",
       "[3 rows x 61 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "differential=df.groupby('diagnoses', as_index=False)[[o[0] for o in ordered[:60]]].mean()\n",
    "differential.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the data\n",
    "\n",
    "We can make a simple plot of this data. Note that I just disable the legend as it would be 60 entries long - one for each bacterial species.\n",
    "\n",
    "However, this is not really an informative plot. We don't want to compare between diagnoses, we want to compare within them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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asIcMa89/6aVX6CfZmGRfkv1Jtsww/+gk13bzb06ydmjeW7v2fUnOWrjSJ1zfN3AXVNNa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\nD7rkuYy4uF6xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0931c805f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = differential.plot(x='diagnoses', kind='bar', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transpose and plot\n",
    "\n",
    "Since we want to compare with a single species, we transpose the data and plot organized by species. We also label the columns appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf = differential.transpose()[1:]\n",
    "tdf = tdf.rename(columns={0:'CD', 1:'UC', 2:'nonIBD'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the data\n",
    "\n",
    "Here we make a larger plot (using figsize - the numbers are in inches not pixels), and manipulate the plot a little to make it look good.\n",
    "\n",
    "This plot shows the abundance of different species in CD, UC, or controls. Almost ready to be published!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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akuxlObkQ8Odn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WGNNoyQDoUa5sGD+1hJLwS2tz0yO7fpIOkRhIBMP0c+yPbfkuJ5ue1TJG0/7HWPyAL1oUaA6sve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\nqVqQf7QVmDsnhsWpMVYFChbkJX2uJRkOYHjRLztZWXWsJxS88Ut6DdHZu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godkr5he4cB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0931621fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tplot=tdf.plot(kind='bar',figsize=(33, 24))\n",
    "tplot.legend(loc='upper center', ncol=3)\n",
    "tfig = tplot.get_figure()\n",
    "tfig.tight_layout()\n",
    "tfig.savefig('ibduc.png')"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}