cedar-ISMB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "from collections import defaultdict\n",
    "from scipy.stats import spearmanr\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from math import log\n",
    "import seaborn as sb\n",
    "from sklearn import metrics as metrics\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ete3 import NCBITaxa\n",
    "ncbi = NCBITaxa()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the taxonomy input for karp\n",
    "taxatree = pd.read_csv('/mnt/scratch2/cedar/indices/i100_kraken_Dec6/taxonomy/nodes.dmp', sep='|', header=None)\n",
    "out = open('/mnt/scratch2/cedar/indices/i100_kraken_Dec6/taxonomy/lineage.dmp', 'w')\n",
    "for i, row in taxatree.iterrows():\n",
    "    try:\n",
    "        curtaxa = row[0]\n",
    "        rmap = ncbi.get_rank([curtaxa])\n",
    "        if curtaxa in rmap:\n",
    "            if curtaxa != 1 and \\\n",
    "            ( \\\n",
    "            rmap[curtaxa] == 'species' or \\\n",
    "            rmap[curtaxa] == 'subspecies' or \\\n",
    "            rmap[curtaxa] == 'no rank' \\\n",
    "            ):\n",
    "                lineage = ncbi.get_lineage(curtaxa)\n",
    "            \n",
    "                names = ncbi.get_taxid_translator(lineage)\n",
    "                strr = \"\"\n",
    "                for taxid in lineage:\n",
    "                    strr += names[taxid] + \";\"\n",
    "                out.write(str(curtaxa) + \"\\t\" + strr+\"\\n\")\n",
    "        else:\n",
    "            out.write(str(curtaxa) + \"\\t\" + \"root;no rank\\n\")\n",
    "    except ValueError:\n",
    "        out.write(str(curtaxa) + \"\\t\" + \"root;no rank\\n\")\n",
    "        print(curtaxa)\n",
    "    #break\n",
    "out.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lineage=pd.read_csv('/mnt/scratch2/cedar/indices/i100_kraken_Dec6/taxonomy/lineage.dmp', sep='\\t', header=None)\n",
    "lineage.columns = ['taxid', 'lineage']\n",
    "seq2id=pd.read_csv('/mnt/scratch2/cedar/indices/i100_kraken_Dec6/seqid2taxid.map', sep='\\t', header=None)\n",
    "seq2id.columns = ['seqid', 'taxid']\n",
    "out = pd.merge(seq2id, lineage, on = 'taxid', how='left')\n",
    "out[['seqid', 'lineage']].to_csv('/mnt/scratch2/cedar/indices/i100_kraken_Dec6/seqid2lineage.map', sep='\\t', index=None, header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relDiff(c1, c2, DF, cutoff=0.01, verbose=False):\n",
    "    import pandas as pd\n",
    "    \"\"\"\n",
    "    Computes the relative difference between the values\n",
    "    in columns c1 and c2 of DF.\n",
    "    c1 and c2 are column names and DF is a Pandas data frame.\n",
    "    Values less than cutoff will be treated as 0.\n",
    "    The relative difference is defined as\n",
    "    d(x_i, y_i) =\n",
    "        0.0 if x_i and y_i are 0\n",
    "        (x_i - y_i) / (|x_i + y_i|) otherwise\n",
    "    This function returns two values.\n",
    "    rd is a DataFrame where the \"relDiff\" column contains all\n",
    "    of the computed relative differences.\n",
    "    nz is a set of indexes into rd for data points where\n",
    "    x_i and y_i were not *both* zero.\n",
    "    \"\"\"\n",
    "    import numpy as np\n",
    "    rd = pd.DataFrame(data = {\"Name\" : DF.index, \"relDiff\" : np.zeros(len(DF.index))*np.nan})\n",
    "    rd.set_index(\"Name\", inplace=True)\n",
    "    bothZero = DF.loc[(DF[c1] < cutoff) & (DF[c2] < cutoff)].index\n",
    "    nonZero = DF.index.difference(bothZero)\n",
    "    if (verbose):\n",
    "        print(\"Zero occurs in both columns {} times\".format(len(rd.loc[bothZero])))\n",
    "        print(\"Nonzero occurs in at least 1 column {} times\".format(len(rd.loc[nonZero])))\n",
    "    allRD = 2.0 * ((DF[c1] - DF[c2]) / (DF[c1] + DF[c2]).abs())\n",
    "    assert(len(rd.loc[nonZero][\"relDiff\"]) == len(allRD[nonZero]))\n",
    "    rd[\"relDiff\"][nonZero] = allRD[nonZero]\n",
    "    if len(bothZero) > 0:\n",
    "        rd[\"relDiff\"][bothZero] = 0.0\n",
    "    return rd.fillna(0), nonZero\n",
    "\n",
    "def L1(c1, c2, DF, cutoff=0.01, verbose=False):\n",
    "    rd = pd.DataFrame(data = {\"Name\" : DF.index, \"absDiff\" : np.zeros(len(DF.index))*np.nan})\n",
    "    rd.set_index(\"Name\", inplace=True)\n",
    "    bothZero = DF.loc[(DF[c1] < cutoff) & (DF[c2] < cutoff)].index\n",
    "    nonZero = DF.index.difference(bothZero)\n",
    "    allRD = abs(DF[c1] - DF[c2])\n",
    "    assert(len(rd.loc[nonZero][\"absDiff\"]) == len(allRD[nonZero]))\n",
    "    rd[\"absDiff\"][nonZero] = allRD[nonZero]\n",
    "    if len(bothZero) > 0:\n",
    "        rd[\"absDiff\"][bothZero] = 0.0\n",
    "    return rd.fillna(0), nonZero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# around 40,000 taxids are duplicates!!!!\n",
    "partyp = {}\n",
    "repeated_ids = []\n",
    "with open('/mnt/scratch2/cedar/input_files/nodes.dmp') as f:\n",
    "    for line in f:\n",
    "        sp = line.split(\"|\")\n",
    "        if int(sp[0].strip()) in partyp:\n",
    "            repeated_ids += [int(sp[0].strip())]\n",
    "        else:\n",
    "            partyp[int(sp[0].strip())] = [int(sp[1].strip()), sp[2].strip()]\n",
    "taxaTree = pd.DataFrame.from_dict(partyp, orient='index', columns=['parent_id', 'level'])\n",
    "taxaTree=taxaTree.reset_index()\n",
    "taxaTree.columns = ['tax_id','parent_id', 'level']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aggregate(df, taxidID, cntID, level = 'species'):\n",
    "    agg = {}\n",
    "    notFoundCntr = 0\n",
    "    notFoundMap = {}\n",
    "    print('num rows: {}'.format(len(df)))\n",
    "    for idx, row in df.iterrows():\n",
    "        taxid = row[taxidID] # should be of type int (number)\n",
    "        cnt = row[cntID]\n",
    "        if taxid in partyp:\n",
    "            taxlevel = partyp[taxid][1]\n",
    "#             print('next:{}'.format(taxlevel))\n",
    "            while taxlevel != level and taxid != 1:\n",
    "#                 print('{}:{}'.format(taxid, taxlevel))\n",
    "                if taxid in partyp:    \n",
    "                    taxid = partyp[taxid][0]\n",
    "                    taxlevel = partyp[taxid][1]\n",
    "                else:\n",
    "                    if taxid not in notFoundMap:\n",
    "                        notFoundMap[taxid] = 0\n",
    "                        notFoundMap[taxid] += 1\n",
    "#                     print('{} in loop'.format(taxid))\n",
    "                    notFoundCntr+=1\n",
    "                    break\n",
    "            if taxlevel == level:\n",
    "                if taxid not in agg:\n",
    "                     agg[taxid] = 0\n",
    "                agg[taxid] += cnt\n",
    "        else:\n",
    "            if taxid not in notFoundMap:\n",
    "                notFoundMap[taxid] = 0\n",
    "            notFoundMap[taxid] += 1\n",
    "            notFoundCntr+=1\n",
    "    print('notFound: {}'.format(notFoundCntr))\n",
    "    agg_df = pd.DataFrame.from_dict(agg, orient='index').reset_index()\n",
    "    agg_df.columns = ['taxid', 'count']\n",
    "    for k, v in notFoundMap.items():\n",
    "        print('{}:{}'.format(k, v))\n",
    "    return agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Prepare the file with the accepted taxonomy format for Karp\n",
    "seq2taxa = pd.read_table('/mnt/scratch5/cedar/indices/bacteria_i100_cami_kraken_k31_Jan22/seqid2taxid.map', header=None, names=['ref', 'taxid'])\n",
    "lineagef = pd.read_table('/mnt/scratch5/cedar/indices/bacteria_i100_cami_kraken_k31_Jan22/taxonomy/lineage.dmp', header=None, names = ['taxid', 'lin'])\n",
    "karptaxonomyf = pd.merge(seq2taxa, lineagef, on='taxid', how='left')\n",
    "karptaxonomyf[['ref', 'lin']]. \\\n",
    "to_csv('/mnt/scratch5/cedar/indices/bacteria_i100_cami_kraken_k31_Jan22/seqid2lineage.map', \\\n",
    "       sep='\\t', index=None, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## i100 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### i100 dataset #####\n",
    "seq2taxid=pd.read_table('/mnt/scratch2/cedar/input_files/seqid2taxid.map', header=None, names=['ref', 'taxid'])\n",
    "seq2taxid['acc'] = seq2taxid['ref'].str.split('|').str[0]\n",
    "truth=pd.read_table('/mnt/scratch2/cedar/input_files/i100_ref.truth')\n",
    "truth_taxid = pd.merge(truth, seq2taxid, left_on='id', right_on='acc', how='left')\n",
    "puff=pd.read_table('/mnt/scratch2/cedar/results/puff/aligned_trimmed_i100_31_sr1.quant/quant.sf')\n",
    "sal=pd.read_table('/mnt/scratch2/cedar/results/salmon/aligned_trimmed_i100_31_sr1.salmon/quant.sf')\n",
    "cedar=pd.read_table('/mnt/scratch2/cedar/results/cedar/aligned_trimmed_i100_31_sr1_2.cedar')\n",
    "# cedar=pd.read_table('/mnt/scratch2/cedar/results/cedar/i100_31_mohsen_align_newPuffer_trimmed_itThresholding_setCover2_segmentSize100_sr0.65.cedar')\n",
    "bracken=pd.read_table('/mnt/scratch2/cedar/results/bracken/trimmed_i100_k31_bracken.kreport', header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## i100 over the complete database of bacteria, i100, and 4 cami toy datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq2taxid=pd.read_table('/mnt/scratch5/cedar/indices/bacteria_i100_cami_kraken_k31_Jan22/seqid2taxid.map', \\\n",
    "                        header=None, names=['ref', 'taxid'])\n",
    "for idx, row in seq2taxid.iterrows():\n",
    "    if row['ref'].split('|')[0] == \"kraken:taxid\":\n",
    "        seq2taxid.loc[idx, 'acc'] = row['ref'].split('|')[2].split('.')[0]\n",
    "    else:\n",
    "        seq2taxid.loc[idx, 'acc'] = row['ref'].split('|')[0].split('.')[0]\n",
    "\n",
    "truth=pd.read_table('/mnt/scratch2/cedar/input_files/i100_ref.truth')\n",
    "truth_taxid = pd.merge(truth, seq2taxid, left_on='id', right_on='acc', how='left')\n",
    "cedar=pd.read_table('/mnt/scratch2/cedar/results/cedar/i100_trimmed_BacteriaI100CAMI_top200_sr0.65_sparse_2.cedar')\n",
    "bracken=pd.read_table('/mnt/scratch2/cedar/results/bracken/i100_trimmed_BacteriaI100CAMI_p16_sr0.65_bracken.kreport', header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cami medium toy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Run once to generate the right format for the rest of analysis\n",
    "## generate the truth for cami\n",
    "readmappings= pd.read_table('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/M2_S001__insert_180_gs_read_mapping.tsv')\n",
    "readmappings['number_reads'] = 1\n",
    "truth_taxid = (readmappings[['tax_id', 'number_reads']].groupby('tax_id').sum().reset_index())\n",
    "truth_taxid.columns = ['taxid', 'cnt']\n",
    "truth_taxid.to_csv('/mnt/scratch2/cedar/results/cedar/truth_M2S1.map', sep='\\t', index=None)\n",
    "\n",
    "## generate seq2taxid file for cami\n",
    "fullseq2taxidmap= pd.read_table('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/M2_S001__insert_180_gsa_mapping.tsv')\n",
    "seq2taxid=fullseq2taxidmap[['#anonymous_gsa_id', 'tax_id']]\n",
    "seq2taxid.columns = ['ref', 'taxid']\n",
    "seq2taxid.to_csv('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/seqid2taxid_M2_S001__insert_180.map')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "## generate seq2taxid file for cami kraken_format ids\n",
    "refnames = pd.read_table('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/M1S2_krak_refnames.lst', header=None)\n",
    "uniqrefnames = np.unique(refnames)\n",
    "outf = open('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/seqid2taxid_M1S2_krak.map', 'w')\n",
    "for u in uniqrefnames:\n",
    "    outf.write('{}\\t{}\\n'.format(u[1:], u[1:].split('|')[2]))\n",
    "outf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### cami1 medium toy dataset #####\n",
    "seq2taxid = pd.read_table('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/seqid2taxid_M2S1_krak.map', header=None)\n",
    "seq2taxid.columns = ['ref', 'taxid']\n",
    "truth_taxid= pd.read_table('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/truth_M2S1.map')\n",
    "# puff=pd.read_table('/mnt/scratch2/cedar/results/puff/cami_aligned_31_sr1.quant/quant.sf')\n",
    "# sal=pd.read_table('/mnt/scratch2/cedar/results/salmon/cami_aligned_31_sr1.salmon/quant.sf')\n",
    "# cedar=pd.read_table('/mnt/scratch2/cedar/results/cedar/cami_aligned_31_sr1_2.cedar')\n",
    "cedar=pd.read_table('/mnt/scratch2/cedar/results/cedar/cami_M2S1_31_sr1_0.cedar')\n",
    "bracken=pd.read_table('/mnt/scratch2/cedar/results/bracken/cami_M2S1_31_sr1_bracken.kreport', header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cami M1S1 over complete dataset of bacteria, i100, and 4 cami toy datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {},
   "outputs": [],
   "source": [
    "# M1S1 full database\n",
    "seq2taxid=pd.read_table('/mnt/scratch5/cedar/indices/bacteria_i100_cami_kraken_k31_Jan22/seqid2taxid.map', \\\n",
    "                        header=None, names=['ref', 'taxid'])\n",
    "truth_taxid= pd.read_table('/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/truth_M1S1.map')\n",
    "cedar=pd.read_table('/mnt/scratch2/cedar/results/cedar/cami_S1M1_BacteriaI100CAMI_top200_sr0.65_2.cedar')\n",
    "bracken=pd.read_table('/mnt/scratch2/cedar/results/bracken/cami_S1M1_BacteriaI100CAMI_top200_sr0.65_bracken.kreport', header=None)\n",
    "karp=pd.read_table('/mnt/scratch2/cedar/results/karp/M1S1_fullbacteriamash.freqs')\n",
    "for idx, row in karp.iterrows():\n",
    "    a = row['Label'].split('|')\n",
    "    if a[0] == \"kraken:taxid\":\n",
    "        karp.loc[idx, 'taxid'] = int(a[1])\n",
    "    else:\n",
    "        karp.loc[idx, 'taxid'] = int(a[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1485, 'species']"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partyp[1042156]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1857237"
      ]
     },
     "execution_count": 684,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bracken[bracken['level'] == 'S']['count'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ditasic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ditasic = pd.read_table('/mnt/scratch2/cedar/results/ditasic/M2S1_bothends.res')\n",
    "file2ref = []\n",
    "parentDir = '/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes/ditasic_M2_S001__insert_180_gsa_anonymous/'\n",
    "for i, r in ditasic.iterrows():\n",
    "    with open(os.path.join(parentDir, r['taxa.name'])) as f:\n",
    "        ref = f.readline()\n",
    "        ref = ref.strip()[1:]\n",
    "        taxid = int(ref.split('|')[2])\n",
    "#         print('{}, {}, {}'.format(os.path.join(parentDir, r['taxa.name']), ref, taxid))\n",
    "        ditasic.loc[i,'ref'] = ref\n",
    "        ditasic.loc[i,'taxid'] = taxid\n",
    "        ditasic.loc[i, 'count'] = r['count.estimate']/2\n",
    "#/mnt/scratch2/cedar/datasets/cami1/medium/225_genomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# continue for any dataset from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {},
   "outputs": [],
   "source": [
    "bracken.columns = ['percentage', 'count', 'distinctCount', 'level', 'taxid', 'taxname']\n",
    "bracken = bracken[bracken['level'] == 'S'][['taxid', 'taxname', 'count']]\n",
    "bracken['taxname'] = bracken['taxname'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "puff_taxid = pd.merge(puff, seq2taxid, left_on='Name', right_on='ref', how='left')\n",
    "sal_taxid = pd.merge(sal, seq2taxid, left_on='Name', right_on='ref', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true cnt: 3719013\n",
      "cedar cnt: 3663490.6126899994\n",
      "bracken cnt: 3714608\n",
      "karp cnt: 3263.97\n"
     ]
    }
   ],
   "source": [
    "print('true cnt: {}'.format(truth_taxid['cnt'].sum()))\n",
    "# print('pufferfish cnt: {}'.format(puff['NumReads'].sum()))\n",
    "# print('salmon cnt: {}'.format(sal['NumReads'].sum()))\n",
    "print('cedar cnt: {}'.format(cedar['count'].sum()))\n",
    "print('bracken cnt: {}'.format(bracken['count'].sum()))\n",
    "print('karp cnt: {}'.format(karp['ExpectedCounts'].sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num rows: 225\n",
      "notFound: 2\n",
      "1240726:1\n",
      "1240783:1\n",
      "num rows: 710\n",
      "notFound: 39\n",
      "2500132:1\n",
      "2015795:1\n",
      "2496836:1\n",
      "2203895:1\n",
      "2005388:1\n",
      "2364787:1\n",
      "2320867:1\n",
      "2488567:1\n",
      "2220096:1\n",
      "2036206:1\n",
      "2259340:1\n",
      "2203210:1\n",
      "2109688:1\n",
      "2109691:1\n",
      "2109690:1\n",
      "2500543:1\n",
      "2203204:1\n",
      "2306583:1\n",
      "2382163:1\n",
      "2495589:1\n",
      "2126069:1\n",
      "2493678:1\n",
      "2493671:1\n",
      "2493673:1\n",
      "2493672:1\n",
      "2493675:1\n",
      "2493674:1\n",
      "2493677:1\n",
      "2109687:1\n",
      "2500532:1\n",
      "2493681:1\n",
      "2493669:1\n",
      "2493668:1\n",
      "2282309:1\n",
      "2282310:1\n",
      "2093856:1\n",
      "2341112:1\n",
      "2219057:1\n",
      "2218628:1\n",
      "num rows: 174\n",
      "notFound: 0\n"
     ]
    }
   ],
   "source": [
    "truth_agg = aggregate(truth_taxid, 'taxid', 'cnt')\n",
    "# sal_agg = aggregate(sal_taxid, 'taxid', 'NumReads')\n",
    "# puff_agg = aggregate(puff_taxid, 'taxid', 'NumReads')\n",
    "cedar_agg = aggregate(cedar, 'taxaId', 'count')\n",
    "karp_agg = aggregate(karp, 'taxid', 'ExpectedCounts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {},
   "outputs": [],
   "source": [
    "tru = pd.merge(truth_agg, cedar_agg, on = 'taxid', how='outer', suffixes=['', '_cedar']).fillna(0)\n",
    "tru = pd.merge(tru, bracken, on = 'taxid', how='outer', suffixes=['', '_brac']).fillna(0)\n",
    "tru = pd.merge(tru, karp_agg, on = 'taxid', how='outer', suffixes=['', '_karp']).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          3719011\n",
       "count_cedar    3662772\n",
       "count_brac     3714608\n",
       "count_karp        3263\n",
       "dtype: uint64"
      ]
     },
     "execution_count": 706,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tru[['count', 'count_cedar', 'count_brac', 'count_ditasic']].sum().astype(np.uint64)\n",
    "# tru[['count', 'count_cedar', 'count_brac']].sum().astype(np.uint64)\n",
    "tru[['count', 'count_cedar', 'count_brac', 'count_karp']].sum().astype(np.uint64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "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>taxid</th>\n",
       "      <th>count</th>\n",
       "      <th>count_cedar</th>\n",
       "      <th>taxname</th>\n",
       "      <th>count_brac</th>\n",
       "      <th>count_karp</th>\n",
       "      <th>cedar_diff</th>\n",
       "      <th>brac_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1209372</td>\n",
       "      <td>131973.0</td>\n",
       "      <td>134761.0000</td>\n",
       "      <td>Bacillus sp. WBUNB009</td>\n",
       "      <td>72046.0</td>\n",
       "      <td>112.050</td>\n",
       "      <td>2788.0000</td>\n",
       "      <td>59927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4670.2300</td>\n",
       "      <td>Escherichia coli</td>\n",
       "      <td>41211.0</td>\n",
       "      <td>31.465</td>\n",
       "      <td>4670.2300</td>\n",
       "      <td>41211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>621</td>\n",
       "      <td>92910.0</td>\n",
       "      <td>67157.7000</td>\n",
       "      <td>Shigella boydii</td>\n",
       "      <td>52208.0</td>\n",
       "      <td>84.270</td>\n",
       "      <td>25752.3000</td>\n",
       "      <td>40702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1386080</td>\n",
       "      <td>100623.0</td>\n",
       "      <td>100404.0000</td>\n",
       "      <td>Bacillus sp. EGD-AK10</td>\n",
       "      <td>65218.0</td>\n",
       "      <td>104.700</td>\n",
       "      <td>219.0000</td>\n",
       "      <td>35405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1423</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.4390</td>\n",
       "      <td>Bacillus subtilis</td>\n",
       "      <td>32266.0</td>\n",
       "      <td>33.029</td>\n",
       "      <td>173.4390</td>\n",
       "      <td>32266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1428</td>\n",
       "      <td>27179.0</td>\n",
       "      <td>26623.1000</td>\n",
       "      <td>Bacillus thuringiensis</td>\n",
       "      <td>52897.0</td>\n",
       "      <td>42.392</td>\n",
       "      <td>555.9000</td>\n",
       "      <td>25718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1396</td>\n",
       "      <td>10545.0</td>\n",
       "      <td>8459.4500</td>\n",
       "      <td>Bacillus cereus</td>\n",
       "      <td>30104.0</td>\n",
       "      <td>63.598</td>\n",
       "      <td>2085.5500</td>\n",
       "      <td>19559.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>1392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.3159</td>\n",
       "      <td>Bacillus anthracis</td>\n",
       "      <td>6516.0</td>\n",
       "      <td>6.996</td>\n",
       "      <td>40.3159</td>\n",
       "      <td>6516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1203566</td>\n",
       "      <td>26706.0</td>\n",
       "      <td>26627.7000</td>\n",
       "      <td>Corynebacterium sp. KPL1859</td>\n",
       "      <td>20622.0</td>\n",
       "      <td>26.520</td>\n",
       "      <td>78.3000</td>\n",
       "      <td>6084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>640511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.2066</td>\n",
       "      <td>Burkholderia sp. CCGE1002</td>\n",
       "      <td>5985.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>25.2066</td>\n",
       "      <td>5985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1203562</td>\n",
       "      <td>11355.0</td>\n",
       "      <td>11309.9000</td>\n",
       "      <td>Corynebacterium sp. KPL1855</td>\n",
       "      <td>16528.0</td>\n",
       "      <td>27.620</td>\n",
       "      <td>45.1000</td>\n",
       "      <td>5173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>948107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.9505</td>\n",
       "      <td>Paraburkholderia sprentiae</td>\n",
       "      <td>4978.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>31.9505</td>\n",
       "      <td>4978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>134676</td>\n",
       "      <td>39680.0</td>\n",
       "      <td>39394.6000</td>\n",
       "      <td>Actinoplanes sp. SE50/110</td>\n",
       "      <td>37173.0</td>\n",
       "      <td>45.600</td>\n",
       "      <td>285.4000</td>\n",
       "      <td>2507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>2033844</td>\n",
       "      <td>0.0</td>\n",
       "      <td>227.7710</td>\n",
       "      <td>Actinoplanes sp. SE50</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>3.510</td>\n",
       "      <td>227.7710</td>\n",
       "      <td>2030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1292.1700</td>\n",
       "      <td>Shigella dysenteriae</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>38.400</td>\n",
       "      <td>1292.1700</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1156433</td>\n",
       "      <td>8309.0</td>\n",
       "      <td>8268.3500</td>\n",
       "      <td>Streptococcus sp. I-P16</td>\n",
       "      <td>6308.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>40.6500</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35793</td>\n",
       "      <td>10503.0</td>\n",
       "      <td>10296.1000</td>\n",
       "      <td>Rickettsia sibirica</td>\n",
       "      <td>8774.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>206.9000</td>\n",
       "      <td>1729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>66865</td>\n",
       "      <td>147905.0</td>\n",
       "      <td>146376.0000</td>\n",
       "      <td>Streptomyces afghaniensis</td>\n",
       "      <td>146375.0</td>\n",
       "      <td>140.354</td>\n",
       "      <td>1529.0000</td>\n",
       "      <td>1530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1209364</td>\n",
       "      <td>432.0</td>\n",
       "      <td>26.3998</td>\n",
       "      <td>Bacillus sp. WBUNB004</td>\n",
       "      <td>1857.0</td>\n",
       "      <td>1.492</td>\n",
       "      <td>405.6002</td>\n",
       "      <td>1425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>33934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0825</td>\n",
       "      <td>Anoxybacillus flavithermus</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>80.0825</td>\n",
       "      <td>1119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>198467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>653.3670</td>\n",
       "      <td>Anoxybacillus gonensis</td>\n",
       "      <td>988.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>653.3670</td>\n",
       "      <td>988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>195</td>\n",
       "      <td>109761.0</td>\n",
       "      <td>109526.0000</td>\n",
       "      <td>Campylobacter coli</td>\n",
       "      <td>108880.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>235.0000</td>\n",
       "      <td>881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>196180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315.9950</td>\n",
       "      <td>Anoxybacillus kamchatkensis</td>\n",
       "      <td>806.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>315.9950</td>\n",
       "      <td>806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1158758</td>\n",
       "      <td>15240.0</td>\n",
       "      <td>19601.1000</td>\n",
       "      <td>Thioalkalivibrio sp. ALJ9</td>\n",
       "      <td>14450.0</td>\n",
       "      <td>22.320</td>\n",
       "      <td>4361.1000</td>\n",
       "      <td>790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1169668</td>\n",
       "      <td>3440.0</td>\n",
       "      <td>365.1640</td>\n",
       "      <td>Enterococcus sp. GMD4E</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3074.8360</td>\n",
       "      <td>762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1304</td>\n",
       "      <td>10854.0</td>\n",
       "      <td>10826.2000</td>\n",
       "      <td>Streptococcus salivarius</td>\n",
       "      <td>11587.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.8000</td>\n",
       "      <td>733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0142</td>\n",
       "      <td>Campylobacter jejuni</td>\n",
       "      <td>703.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.0142</td>\n",
       "      <td>703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1314</td>\n",
       "      <td>5053.0</td>\n",
       "      <td>5050.2400</td>\n",
       "      <td>Streptococcus pyogenes</td>\n",
       "      <td>5749.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.7600</td>\n",
       "      <td>696.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>28901</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>411.2510</td>\n",
       "      <td>Salmonella enterica</td>\n",
       "      <td>1767.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>667.7490</td>\n",
       "      <td>688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1169322</td>\n",
       "      <td>14179.0</td>\n",
       "      <td>14115.4000</td>\n",
       "      <td>Citrobacter sp. KTE151</td>\n",
       "      <td>13512.0</td>\n",
       "      <td>10.026</td>\n",
       "      <td>63.6000</td>\n",
       "      <td>667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1263006</td>\n",
       "      <td>52102.0</td>\n",
       "      <td>52015.9000</td>\n",
       "      <td>Firmicutes bacterium CAG:170</td>\n",
       "      <td>52102.0</td>\n",
       "      <td>53.130</td>\n",
       "      <td>86.1000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1263062</td>\n",
       "      <td>5012.0</td>\n",
       "      <td>5007.0000</td>\n",
       "      <td>Butyrivibrio crossotus CAG:259</td>\n",
       "      <td>5012.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1263070</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0000</td>\n",
       "      <td>Coprococcus comes CAG:19</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1263099</td>\n",
       "      <td>13698.0</td>\n",
       "      <td>13692.0000</td>\n",
       "      <td>Parasutterella excrementihominis CAG:233</td>\n",
       "      <td>13698.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>168695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.8645</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>45.8645</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1332069</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>Pantoea sp. AS-PWVM4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2201356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.1852</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.1852</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>446</td>\n",
       "      <td>1062.0</td>\n",
       "      <td>1062.0000</td>\n",
       "      <td>Legionella pneumophila</td>\n",
       "      <td>1062.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>507626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.3407</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>17.3407</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>69223</td>\n",
       "      <td>914.0</td>\n",
       "      <td>911.8650</td>\n",
       "      <td>Dickeya paradisiaca</td>\n",
       "      <td>914.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.1350</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>40989</td>\n",
       "      <td>635.0</td>\n",
       "      <td>631.0000</td>\n",
       "      <td>Saccharomonospora cyanea</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1611770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.2314</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>15.2314</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>927677</td>\n",
       "      <td>194.0</td>\n",
       "      <td>194.0000</td>\n",
       "      <td>Synechocystis sp. PCC 7509</td>\n",
       "      <td>194.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>1500686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.4033</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>36.4033</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>1500687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.8648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.8648</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>939301</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>2120.4500</td>\n",
       "      <td>alpha proteobacterium SCGC AAA015-O19</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>15.5500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>265606</td>\n",
       "      <td>5073.0</td>\n",
       "      <td>5070.0000</td>\n",
       "      <td>Rhodopirellula baltica</td>\n",
       "      <td>5073.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1394711</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>Candidatus Saccharibacteria bacterium RAAC3_TM7_1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>405212</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>1211.0000</td>\n",
       "      <td>Alicyclobacillus acidocaldarius</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>174</td>\n",
       "      <td>7053.0</td>\n",
       "      <td>7064.6900</td>\n",
       "      <td>Leptospira borgpetersenii</td>\n",
       "      <td>7053.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.6900</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1054213</td>\n",
       "      <td>121.0</td>\n",
       "      <td>121.0000</td>\n",
       "      <td>Acetobacteraceae bacterium AT-5844</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1111069</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0000</td>\n",
       "      <td>Thermus sp. CCB_US3_UF1</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1131273</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0000</td>\n",
       "      <td>Marinimicrobia bacterium SCGC AB-629-J13</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1134055</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3256.0000</td>\n",
       "      <td>Verrucomicrobium sp. 3C</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1574141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.4798</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.4798</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1137281</td>\n",
       "      <td>10264.0</td>\n",
       "      <td>10255.6000</td>\n",
       "      <td>Xanthomarina gelatinilytica</td>\n",
       "      <td>10264.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.4000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1167637</td>\n",
       "      <td>616.0</td>\n",
       "      <td>616.0040</td>\n",
       "      <td>Gillisia marina</td>\n",
       "      <td>616.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>351187</td>\n",
       "      <td>389.0</td>\n",
       "      <td>335.0000</td>\n",
       "      <td>Salinispora pacifica</td>\n",
       "      <td>389.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>54.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>228006</td>\n",
       "      <td>22343.0</td>\n",
       "      <td>22311.0000</td>\n",
       "      <td>Nocardia tenerifensis</td>\n",
       "      <td>22343.0</td>\n",
       "      <td>28.080</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1262734</td>\n",
       "      <td>774.0</td>\n",
       "      <td>772.0000</td>\n",
       "      <td>Bacteroides sp. CAG:1060</td>\n",
       "      <td>774.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>298 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       taxid     count  count_cedar  \\\n",
       "139  1209372  131973.0  134761.0000   \n",
       "350      562       0.0    4670.2300   \n",
       "67       621   92910.0   67157.7000   \n",
       "192  1386080  100623.0  100404.0000   \n",
       "253     1423       0.0     173.4390   \n",
       "47      1428   27179.0   26623.1000   \n",
       "46      1396   10545.0    8459.4500   \n",
       "521     1392       0.0      40.3159   \n",
       "134  1203566   26706.0   26627.7000   \n",
       "220   640511       0.0      25.2066   \n",
       "133  1203562   11355.0   11309.9000   \n",
       "238   948107       0.0      31.9505   \n",
       "5     134676   39680.0   39394.6000   \n",
       "628  2033844       0.0     227.7710   \n",
       "443      622       0.0    1292.1700   \n",
       "112  1156433    8309.0    8268.3500   \n",
       "10     35793   10503.0   10296.1000   \n",
       "176    66865  147905.0  146376.0000   \n",
       "138  1209364     432.0      26.3998   \n",
       "631    33934       0.0      80.0825   \n",
       "472   198467       0.0     653.3670   \n",
       "72       195  109761.0  109526.0000   \n",
       "262   196180       0.0     315.9950   \n",
       "115  1158758   15240.0   19601.1000   \n",
       "126  1169668    3440.0     365.1640   \n",
       "91      1304   10854.0   10826.2000   \n",
       "239      197       0.0      10.0142   \n",
       "102     1314    5053.0    5050.2400   \n",
       "75     28901    1079.0     411.2510   \n",
       "125  1169322   14179.0   14115.4000   \n",
       "..       ...       ...          ...   \n",
       "167  1263006   52102.0   52015.9000   \n",
       "170  1263062    5012.0    5007.0000   \n",
       "171  1263070      63.0      63.0000   \n",
       "172  1263099   13698.0   13692.0000   \n",
       "214   168695       0.0      45.8645   \n",
       "184  1332069      28.0      28.0000   \n",
       "200  2201356       0.0      10.1852   \n",
       "188      446    1062.0    1062.0000   \n",
       "310   507626       0.0      17.3407   \n",
       "144    69223     914.0     911.8650   \n",
       "70     40989     635.0     631.0000   \n",
       "338  1611770       0.0      15.2314   \n",
       "76    927677     194.0     194.0000   \n",
       "459  1500686       0.0      36.4033   \n",
       "456  1500687       0.0      23.8648   \n",
       "79    939301    2136.0    2120.4500   \n",
       "83    265606    5073.0    5070.0000   \n",
       "193  1394711      98.0      98.0000   \n",
       "92    405212    1212.0    1211.0000   \n",
       "93       174    7053.0    7064.6900   \n",
       "96   1054213     121.0     121.0000   \n",
       "100  1111069      84.0      84.0000   \n",
       "105  1131273      82.0      82.0000   \n",
       "107  1134055    3272.0    3256.0000   \n",
       "373  1574141       0.0      27.4798   \n",
       "108  1137281   10264.0   10255.6000   \n",
       "120  1167637     616.0     616.0040   \n",
       "124   351187     389.0     335.0000   \n",
       "137   228006   22343.0   22311.0000   \n",
       "153  1262734     774.0     772.0000   \n",
       "\n",
       "                                               taxname  count_brac  \\\n",
       "139                              Bacillus sp. WBUNB009     72046.0   \n",
       "350                                   Escherichia coli     41211.0   \n",
       "67                                     Shigella boydii     52208.0   \n",
       "192                              Bacillus sp. EGD-AK10     65218.0   \n",
       "253                                  Bacillus subtilis     32266.0   \n",
       "47                              Bacillus thuringiensis     52897.0   \n",
       "46                                     Bacillus cereus     30104.0   \n",
       "521                                 Bacillus anthracis      6516.0   \n",
       "134                        Corynebacterium sp. KPL1859     20622.0   \n",
       "220                          Burkholderia sp. CCGE1002      5985.0   \n",
       "133                        Corynebacterium sp. KPL1855     16528.0   \n",
       "238                         Paraburkholderia sprentiae      4978.0   \n",
       "5                            Actinoplanes sp. SE50/110     37173.0   \n",
       "628                              Actinoplanes sp. SE50      2030.0   \n",
       "443                               Shigella dysenteriae      2001.0   \n",
       "112                            Streptococcus sp. I-P16      6308.0   \n",
       "10                                 Rickettsia sibirica      8774.0   \n",
       "176                          Streptomyces afghaniensis    146375.0   \n",
       "138                              Bacillus sp. WBUNB004      1857.0   \n",
       "631                         Anoxybacillus flavithermus      1119.0   \n",
       "472                             Anoxybacillus gonensis       988.0   \n",
       "72                                  Campylobacter coli    108880.0   \n",
       "262                        Anoxybacillus kamchatkensis       806.0   \n",
       "115                          Thioalkalivibrio sp. ALJ9     14450.0   \n",
       "126                             Enterococcus sp. GMD4E      2678.0   \n",
       "91                            Streptococcus salivarius     11587.0   \n",
       "239                               Campylobacter jejuni       703.0   \n",
       "102                             Streptococcus pyogenes      5749.0   \n",
       "75                                 Salmonella enterica      1767.0   \n",
       "125                             Citrobacter sp. KTE151     13512.0   \n",
       "..                                                 ...         ...   \n",
       "167                       Firmicutes bacterium CAG:170     52102.0   \n",
       "170                     Butyrivibrio crossotus CAG:259      5012.0   \n",
       "171                           Coprococcus comes CAG:19        63.0   \n",
       "172           Parasutterella excrementihominis CAG:233     13698.0   \n",
       "214                                                  0         0.0   \n",
       "184                               Pantoea sp. AS-PWVM4        28.0   \n",
       "200                                                  0         0.0   \n",
       "188                             Legionella pneumophila      1062.0   \n",
       "310                                                  0         0.0   \n",
       "144                                Dickeya paradisiaca       914.0   \n",
       "70                            Saccharomonospora cyanea       635.0   \n",
       "338                                                  0         0.0   \n",
       "76                          Synechocystis sp. PCC 7509       194.0   \n",
       "459                                                  0         0.0   \n",
       "456                                                  0         0.0   \n",
       "79               alpha proteobacterium SCGC AAA015-O19      2136.0   \n",
       "83                              Rhodopirellula baltica      5073.0   \n",
       "193  Candidatus Saccharibacteria bacterium RAAC3_TM7_1        98.0   \n",
       "92                     Alicyclobacillus acidocaldarius      1212.0   \n",
       "93                           Leptospira borgpetersenii      7053.0   \n",
       "96                  Acetobacteraceae bacterium AT-5844       121.0   \n",
       "100                            Thermus sp. CCB_US3_UF1        84.0   \n",
       "105           Marinimicrobia bacterium SCGC AB-629-J13        82.0   \n",
       "107                            Verrucomicrobium sp. 3C      3272.0   \n",
       "373                                                  0         0.0   \n",
       "108                        Xanthomarina gelatinilytica     10264.0   \n",
       "120                                    Gillisia marina       616.0   \n",
       "124                               Salinispora pacifica       389.0   \n",
       "137                              Nocardia tenerifensis     22343.0   \n",
       "153                           Bacteroides sp. CAG:1060       774.0   \n",
       "\n",
       "     count_karp  cedar_diff  brac_diff  \n",
       "139     112.050   2788.0000    59927.0  \n",
       "350      31.465   4670.2300    41211.0  \n",
       "67       84.270  25752.3000    40702.0  \n",
       "192     104.700    219.0000    35405.0  \n",
       "253      33.029    173.4390    32266.0  \n",
       "47       42.392    555.9000    25718.0  \n",
       "46       63.598   2085.5500    19559.0  \n",
       "521       6.996     40.3159     6516.0  \n",
       "134      26.520     78.3000     6084.0  \n",
       "220       0.000     25.2066     5985.0  \n",
       "133      27.620     45.1000     5173.0  \n",
       "238       0.000     31.9505     4978.0  \n",
       "5        45.600    285.4000     2507.0  \n",
       "628       3.510    227.7710     2030.0  \n",
       "443      38.400   1292.1700     2001.0  \n",
       "112       0.000     40.6500     2001.0  \n",
       "10        0.000    206.9000     1729.0  \n",
       "176     140.354   1529.0000     1530.0  \n",
       "138       1.492    405.6002     1425.0  \n",
       "631       0.000     80.0825     1119.0  \n",
       "472       0.000    653.3670      988.0  \n",
       "72        0.000    235.0000      881.0  \n",
       "262       0.000    315.9950      806.0  \n",
       "115      22.320   4361.1000      790.0  \n",
       "126       0.000   3074.8360      762.0  \n",
       "91        0.000     27.8000      733.0  \n",
       "239       0.000     10.0142      703.0  \n",
       "102       0.000      2.7600      696.0  \n",
       "75        0.000    667.7490      688.0  \n",
       "125      10.026     63.6000      667.0  \n",
       "..          ...         ...        ...  \n",
       "167      53.130     86.1000        0.0  \n",
       "170       0.000      5.0000        0.0  \n",
       "171       0.000      0.0000        0.0  \n",
       "172       0.000      6.0000        0.0  \n",
       "214       0.000     45.8645        0.0  \n",
       "184       0.000      0.0000        0.0  \n",
       "200       0.000     10.1852        0.0  \n",
       "188       0.000      0.0000        0.0  \n",
       "310       0.000     17.3407        0.0  \n",
       "144       0.000      2.1350        0.0  \n",
       "70        0.000      4.0000        0.0  \n",
       "338       0.000     15.2314        0.0  \n",
       "76        0.000      0.0000        0.0  \n",
       "459       0.000     36.4033        0.0  \n",
       "456       0.000     23.8648        0.0  \n",
       "79        0.000     15.5500        0.0  \n",
       "83        0.000      3.0000        0.0  \n",
       "193       0.000      0.0000        0.0  \n",
       "92        0.000      1.0000        0.0  \n",
       "93        0.000     11.6900        0.0  \n",
       "96        0.000      0.0000        0.0  \n",
       "100       0.000      0.0000        0.0  \n",
       "105       0.000      0.0000        0.0  \n",
       "107       0.000     16.0000        0.0  \n",
       "373       0.000     27.4798        0.0  \n",
       "108       0.000      8.4000        0.0  \n",
       "120       0.000      0.0040        0.0  \n",
       "124       0.000     54.0000        0.0  \n",
       "137      28.080     32.0000        0.0  \n",
       "153       0.000      2.0000        0.0  \n",
       "\n",
       "[298 rows x 8 columns]"
      ]
     },
     "execution_count": 717,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tru[tru['count_cedar'] > 10].sort_values(by='brac_diff', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "legends = ['count_cedar', 'count_brac']\n",
    "for col in legends:\n",
    "    precision = []\n",
    "    recall = []    \n",
    "    a = tru[(tru[col] > 0) | (tru['count'] != 0)]\n",
    "    i = 0\n",
    "    f1 = []\n",
    "    n = 500\n",
    "    for minCnt in range(0,n):\n",
    "        tp = len(a[(a[col] > minCnt) & (a['count'] > 0)])\n",
    "        fp = len(a[(a[col] > minCnt) & (a['count'] == 0)])\n",
    "        fn = len(a[(a[col] <= minCnt) & (a['count'] > 0)])\n",
    "        precision += [tp / (tp + fp)]\n",
    "        recall += [tp / (tp + fn)]\n",
    "        f1 += [2*(precision[i]*recall[i])/(precision[i]+recall[i])]\n",
    "#         print('{}, {}, {}'.format(tp, fp, fn))\n",
    "        i+=1\n",
    "    #plt.plot(range(0, 100), recall)\n",
    "    plt.plot(range(0, n), f1)\n",
    "plt.legend(legends, loc='lower right')\n",
    "plt.ylabel(\"f1\")\n",
    "plt.xlabel(\"cutoff\")\n",
    "plt.title(\"cami_M1S1\")\n",
    "plt.savefig('/mnt/scratch2/cedar/figs/camiM1S1_f1_{}.png'.format(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'cami')"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "legends = ['count_sal', 'count_puff', 'count_cedar', 'count_brac']\n",
    "for col in legends:\n",
    "    precision = []\n",
    "    recall = []    \n",
    "    a = trusalpufcedbrac[(trusalpufcedbrac[col] > 0) | (trusalpufcedbrac['count'] != 0)]\n",
    "    i = 0\n",
    "    f1 = []\n",
    "    n = 1000\n",
    "    for minCnt in range(0,n):\n",
    "        tp = len(a[(a[col] > minCnt) & (a['count'] > 0)])\n",
    "        fp = len(a[(a[col] > minCnt) & (a['count'] == 0)])\n",
    "        fn = len(a[(a[col] <= minCnt) & (a['count'] > 0)])\n",
    "#         if col == 'count_cedar':\n",
    "#             print('{},{},{}'.format(tp, fp, fn))\n",
    "        precision += [tp / (tp + fp)]\n",
    "        recall += [tp / (tp + fn)]\n",
    "        f1 += [(precision[i]*recall[i])/(2*(precision[i]+recall[i]))]\n",
    "        i+=1\n",
    "    #plt.plot(range(0, 100), recall)\n",
    "    plt.plot(range(0, n), f1)\n",
    "plt.legend(legends, loc='lower right')\n",
    "plt.ylabel(\"f1\")\n",
    "plt.xlabel(\"cutoff\")\n",
    "plt.title(\"cami\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "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>tool</th>\n",
       "      <th>count_cedar</th>\n",
       "      <th>count_brac</th>\n",
       "      <th>count_karp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>#rows</th>\n",
       "      <td>228</td>\n",
       "      <td>255</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#reads</th>\n",
       "      <td>3.65928e+06</td>\n",
       "      <td>3.70475e+06</td>\n",
       "      <td>3142.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp</th>\n",
       "      <td>0.888793</td>\n",
       "      <td>0.861382</td>\n",
       "      <td>0.713806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pr</th>\n",
       "      <td>0.998372</td>\n",
       "      <td>0.985925</td>\n",
       "      <td>0.967784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained_var</th>\n",
       "      <td>0.996712</td>\n",
       "      <td>0.970423</td>\n",
       "      <td>-996230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kendalltau</th>\n",
       "      <td>0.809165</td>\n",
       "      <td>0.799062</td>\n",
       "      <td>0.600896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARD</th>\n",
       "      <td>0.197494</td>\n",
       "      <td>0.253972</td>\n",
       "      <td>0.948762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>578.68</td>\n",
       "      <td>1395.66</td>\n",
       "      <td>18767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>5.52067e+06</td>\n",
       "      <td>4.26936e+07</td>\n",
       "      <td>2.22739e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSLE</th>\n",
       "      <td>6.23148</td>\n",
       "      <td>9.36145</td>\n",
       "      <td>52.9841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MedAE</th>\n",
       "      <td>31.65</td>\n",
       "      <td>22</td>\n",
       "      <td>4472.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAE</th>\n",
       "      <td>131935</td>\n",
       "      <td>355889</td>\n",
       "      <td>3.71526e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSE</th>\n",
       "      <td>1.25871e+09</td>\n",
       "      <td>1.08869e+10</td>\n",
       "      <td>4.41023e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>braycurtisDist</th>\n",
       "      <td>0.0178821</td>\n",
       "      <td>0.0479397</td>\n",
       "      <td>0.998312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cosineDist</th>\n",
       "      <td>0.00141564</td>\n",
       "      <td>0.01234</td>\n",
       "      <td>0.0294575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canberraDist</th>\n",
       "      <td>45.0621</td>\n",
       "      <td>64.7933</td>\n",
       "      <td>197.855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tool            count_cedar   count_brac   count_karp\n",
       "#rows                   228          255          198\n",
       "#reads          3.65928e+06  3.70475e+06      3142.01\n",
       "sp                 0.888793     0.861382     0.713806\n",
       "pr                 0.998372     0.985925     0.967784\n",
       "explained_var      0.996712     0.970423      -996230\n",
       "Kendalltau         0.809165     0.799062     0.600896\n",
       "MARD               0.197494     0.253972     0.948762\n",
       "MAE                  578.68      1395.66        18767\n",
       "MSE             5.52067e+06  4.26936e+07  2.22739e+09\n",
       "MSLE                6.23148      9.36145      52.9841\n",
       "MedAE                 31.65           22       4472.5\n",
       "SAE                  131935       355889  3.71526e+06\n",
       "SSE             1.25871e+09  1.08869e+10  4.41023e+11\n",
       "braycurtisDist    0.0178821    0.0479397     0.998312\n",
       "cosineDist       0.00141564      0.01234    0.0294575\n",
       "canberraDist        45.0621      64.7933      197.855"
      ]
     },
     "execution_count": 704,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minCnt = 100\n",
    "res = []\n",
    "for col in ['count_cedar', 'count_brac', 'count_karp']:\n",
    "    #, 'count_karp']:\n",
    "    df = tru[(tru[col] > minCnt) | (tru['count'] != 0)]\n",
    "    df[df[col] <= minCnt][col] = 0\n",
    "    rd, _ = relDiff('count', col, df, cutoff=minCnt)\n",
    "    rd1, _ = L1('count', col, df, cutoff=minCnt) \n",
    "    res += [[\n",
    "        col, len(df), df[col].sum(),\n",
    "        df[['count', col]].corr(method='spearman')['count'][col],\n",
    "        df[['count', col]].corr(method='pearson')['count'][col],\n",
    "        metrics.explained_variance_score(df[col], df['count']),\n",
    "        scipy.stats.kendalltau(df[col], df['count']).correlation,\n",
    "        (0.5*rd['relDiff'].abs()).mean(),\n",
    "        metrics.mean_absolute_error(df[col], df['count']),\n",
    "        metrics.mean_squared_error(df[col], df['count']),\n",
    "        metrics.mean_squared_log_error(df[col], df['count']),\n",
    "        metrics.median_absolute_error(df[col], df['count']),\n",
    "        rd1['absDiff'].sum(),\n",
    "        metrics.mean_squared_error(df[col], df['count'])*len(df),\n",
    "        scipy.spatial.distance.braycurtis(df[col], df['count']),\n",
    "        scipy.spatial.distance.cosine(df[col], df['count']),\n",
    "        scipy.spatial.distance.canberra(df[col], df['count'])\n",
    "    ]]\n",
    "final_metrics = pd.DataFrame.from_records(res, columns=['tool', '#rows', '#reads',\n",
    "                                                       'sp', 'pr', 'explained_var', 'Kendalltau',\n",
    "                                                        'MARD',  \n",
    "                                                        'MAE', 'MSE', 'MSLE', 'MedAE', 'SAE', 'SSE',\n",
    "                                                       'braycurtisDist', 'cosineDist', 'canberraDist'])\n",
    "final_metrics = final_metrics.transpose()\n",
    "final_metrics.columns = final_metrics.iloc[0]\n",
    "final_metrics = final_metrics[1:]\n",
    "final_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.load('/home/fatemeh/others_projects/ditasic/similarity_matrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[249871.]])"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "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>taxid</th>\n",
       "      <th>count</th>\n",
       "      <th>count_sal</th>\n",
       "      <th>count_puff</th>\n",
       "      <th>count_cedar</th>\n",
       "      <th>taxname</th>\n",
       "      <th>count_brac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>562</td>\n",
       "      <td>1493685.0</td>\n",
       "      <td>1430201.159</td>\n",
       "      <td>1406064.613</td>\n",
       "      <td>1.430560e+06</td>\n",
       "      <td>Escherichia coli</td>\n",
       "      <td>1481960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>727</td>\n",
       "      <td>120507.0</td>\n",
       "      <td>114640.865</td>\n",
       "      <td>116610.975</td>\n",
       "      <td>1.169680e+05</td>\n",
       "      <td>Haemophilus influenzae</td>\n",
       "      <td>116880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83558</td>\n",
       "      <td>79974.0</td>\n",
       "      <td>78213.465</td>\n",
       "      <td>78142.175</td>\n",
       "      <td>7.863590e+04</td>\n",
       "      <td>Chlamydia pneumoniae</td>\n",
       "      <td>79090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>782</td>\n",
       "      <td>73307.0</td>\n",
       "      <td>71813.471</td>\n",
       "      <td>71767.715</td>\n",
       "      <td>7.216460e+04</td>\n",
       "      <td>Rickettsia prowazekii</td>\n",
       "      <td>71893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1423</td>\n",
       "      <td>272560.0</td>\n",
       "      <td>263965.741</td>\n",
       "      <td>265027.262</td>\n",
       "      <td>2.656240e+05</td>\n",
       "      <td>Bacillus subtilis</td>\n",
       "      <td>269266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>134821</td>\n",
       "      <td>48374.0</td>\n",
       "      <td>47346.000</td>\n",
       "      <td>47274.007</td>\n",
       "      <td>4.757350e+04</td>\n",
       "      <td>Ureaplasma parvum</td>\n",
       "      <td>45798.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>86665</td>\n",
       "      <td>274294.0</td>\n",
       "      <td>262720.352</td>\n",
       "      <td>267824.339</td>\n",
       "      <td>2.690430e+05</td>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>271726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1358</td>\n",
       "      <td>414349.0</td>\n",
       "      <td>393029.611</td>\n",
       "      <td>402925.997</td>\n",
       "      <td>4.048260e+05</td>\n",
       "      <td>Lactococcus lactis</td>\n",
       "      <td>407743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>518</td>\n",
       "      <td>466134.0</td>\n",
       "      <td>450130.078</td>\n",
       "      <td>455432.643</td>\n",
       "      <td>4.577070e+05</td>\n",
       "      <td>Bordetella bronchiseptica</td>\n",
       "      <td>458333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35554</td>\n",
       "      <td>248267.0</td>\n",
       "      <td>241612.939</td>\n",
       "      <td>242379.657</td>\n",
       "      <td>2.439730e+05</td>\n",
       "      <td>Geobacter sulfurreducens</td>\n",
       "      <td>246125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1764</td>\n",
       "      <td>316134.0</td>\n",
       "      <td>305375.577</td>\n",
       "      <td>309109.109</td>\n",
       "      <td>3.107140e+05</td>\n",
       "      <td>Mycobacterium avium</td>\n",
       "      <td>312192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1765</td>\n",
       "      <td>288400.0</td>\n",
       "      <td>293805.981</td>\n",
       "      <td>288747.195</td>\n",
       "      <td>3.219790e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>485</td>\n",
       "      <td>156640.0</td>\n",
       "      <td>148258.938</td>\n",
       "      <td>151681.927</td>\n",
       "      <td>1.539320e+05</td>\n",
       "      <td>Neisseria gonorrhoeae</td>\n",
       "      <td>152745.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1280</td>\n",
       "      <td>1231949.0</td>\n",
       "      <td>1202372.878</td>\n",
       "      <td>1204424.516</td>\n",
       "      <td>1.208780e+06</td>\n",
       "      <td>Staphylococcus aureus</td>\n",
       "      <td>1217861.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>158</td>\n",
       "      <td>186640.0</td>\n",
       "      <td>182306.575</td>\n",
       "      <td>182500.471</td>\n",
       "      <td>1.835390e+05</td>\n",
       "      <td>Treponema denticola</td>\n",
       "      <td>183442.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>777</td>\n",
       "      <td>137654.0</td>\n",
       "      <td>132881.178</td>\n",
       "      <td>134620.927</td>\n",
       "      <td>1.354210e+05</td>\n",
       "      <td>Coxiella burnetii</td>\n",
       "      <td>136044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>382</td>\n",
       "      <td>431681.0</td>\n",
       "      <td>418774.236</td>\n",
       "      <td>422097.332</td>\n",
       "      <td>4.241300e+05</td>\n",
       "      <td>Sinorhizobium meliloti</td>\n",
       "      <td>428153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1313</td>\n",
       "      <td>181280.0</td>\n",
       "      <td>175151.335</td>\n",
       "      <td>176708.936</td>\n",
       "      <td>1.775340e+05</td>\n",
       "      <td>Streptococcus pneumoniae</td>\n",
       "      <td>181034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>487</td>\n",
       "      <td>155920.0</td>\n",
       "      <td>146319.955</td>\n",
       "      <td>151227.551</td>\n",
       "      <td>1.507910e+05</td>\n",
       "      <td>Neisseria meningitidis</td>\n",
       "      <td>154368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>632</td>\n",
       "      <td>319921.0</td>\n",
       "      <td>300699.074</td>\n",
       "      <td>307000.903</td>\n",
       "      <td>3.079740e+05</td>\n",
       "      <td>Yersinia pestis</td>\n",
       "      <td>315032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>83557</td>\n",
       "      <td>75947.0</td>\n",
       "      <td>74120.513</td>\n",
       "      <td>74306.408</td>\n",
       "      <td>7.470810e+04</td>\n",
       "      <td>Chlamydia caviae</td>\n",
       "      <td>75097.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51229</td>\n",
       "      <td>67361.0</td>\n",
       "      <td>65147.175</td>\n",
       "      <td>65838.626</td>\n",
       "      <td>6.623740e+04</td>\n",
       "      <td>Wigglesworthia glossinidia</td>\n",
       "      <td>55353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1396</td>\n",
       "      <td>1320777.0</td>\n",
       "      <td>1272693.134</td>\n",
       "      <td>1275963.785</td>\n",
       "      <td>1.278230e+06</td>\n",
       "      <td>Bacillus cereus</td>\n",
       "      <td>1313587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1392</td>\n",
       "      <td>408054.0</td>\n",
       "      <td>395175.543</td>\n",
       "      <td>396389.137</td>\n",
       "      <td>4.015480e+05</td>\n",
       "      <td>Bacillus anthracis</td>\n",
       "      <td>394928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1314</td>\n",
       "      <td>260614.0</td>\n",
       "      <td>253181.758</td>\n",
       "      <td>253347.103</td>\n",
       "      <td>2.548820e+05</td>\n",
       "      <td>Streptococcus pyogenes</td>\n",
       "      <td>256829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1311</td>\n",
       "      <td>167414.0</td>\n",
       "      <td>162795.025</td>\n",
       "      <td>162214.388</td>\n",
       "      <td>1.635070e+05</td>\n",
       "      <td>Streptococcus agalactiae</td>\n",
       "      <td>161219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>152794</td>\n",
       "      <td>237814.0</td>\n",
       "      <td>231429.944</td>\n",
       "      <td>232647.687</td>\n",
       "      <td>2.338720e+05</td>\n",
       "      <td>Corynebacterium efficiens</td>\n",
       "      <td>235789.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>818</td>\n",
       "      <td>402214.0</td>\n",
       "      <td>383468.884</td>\n",
       "      <td>368871.257</td>\n",
       "      <td>3.902820e+05</td>\n",
       "      <td>Bacteroides thetaiotaomicron</td>\n",
       "      <td>392498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1219</td>\n",
       "      <td>312374.0</td>\n",
       "      <td>304280.382</td>\n",
       "      <td>304982.514</td>\n",
       "      <td>3.068680e+05</td>\n",
       "      <td>Prochlorococcus marinus</td>\n",
       "      <td>308752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>536</td>\n",
       "      <td>336880.0</td>\n",
       "      <td>326148.743</td>\n",
       "      <td>328659.574</td>\n",
       "      <td>3.304290e+05</td>\n",
       "      <td>Chromobacterium violaceum</td>\n",
       "      <td>333153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.841</td>\n",
       "      <td>7.287</td>\n",
       "      <td>3.004700e+00</td>\n",
       "      <td>Haemophilus parahaemolyticus</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.852</td>\n",
       "      <td>3.011</td>\n",
       "      <td>1.248360e+01</td>\n",
       "      <td>Pasteurella bettyae</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>817.193</td>\n",
       "      <td>921.976</td>\n",
       "      <td>2.245550e+03</td>\n",
       "      <td>Haemophilus haemolyticus</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.022</td>\n",
       "      <td>2.946</td>\n",
       "      <td>1.966890e+01</td>\n",
       "      <td>Haemophilus paraphrohaemolyticus</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>1095726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.607</td>\n",
       "      <td>5.621</td>\n",
       "      <td>3.037550e+00</td>\n",
       "      <td>Streptococcus sp. SK140</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>1095727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.436</td>\n",
       "      <td>12.750</td>\n",
       "      <td>4.711550e+00</td>\n",
       "      <td>Streptococcus sp. SK643</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>1105030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Actinomyces sp. ICM58</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>936554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082</td>\n",
       "      <td>2.207</td>\n",
       "      <td>1.000190e+00</td>\n",
       "      <td>Campylobacter sp. FOBRC14</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>936594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.001</td>\n",
       "      <td>1.003</td>\n",
       "      <td>1.000020e+00</td>\n",
       "      <td>Lachnoanaerobaculum sp. ICM7</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>936565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.227</td>\n",
       "      <td>6.920</td>\n",
       "      <td>6.972580e+00</td>\n",
       "      <td>Klebsiella sp. OBRC7</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>936572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.195290e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>1105032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.684</td>\n",
       "      <td>1.801</td>\n",
       "      <td>2.905900e+00</td>\n",
       "      <td>Streptococcus sp. BS35b</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>936375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.017</td>\n",
       "      <td>4.064</td>\n",
       "      <td>4.229020e+00</td>\n",
       "      <td>Mogibacterium sp. CM50</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>936381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>6.307260e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1111137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.870</td>\n",
       "      <td>1.598630e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>461393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Actinomyces massiliensis</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>172042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.929</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.858540e-08</td>\n",
       "      <td>Rothia aeria</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>936595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.000020e+00</td>\n",
       "      <td>Lachnoanaerobaculum sp. OBRC5-5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>598660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.650</td>\n",
       "      <td>6.290</td>\n",
       "      <td>5.489680e+00</td>\n",
       "      <td>Corynebacterium pyruviciproducens</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>712213</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.011</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Capnocytophaga sp. oral taxon 332</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>712401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.598</td>\n",
       "      <td>5.437</td>\n",
       "      <td>3.359930e+00</td>\n",
       "      <td>Neisseria sp. oral taxon 020</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>633701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.002750e+00</td>\n",
       "      <td>Prevotella saccharolytica</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>712532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.002</td>\n",
       "      <td>4.732370e-65</td>\n",
       "      <td>Selenomonas sp. oral taxon 138</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>1078480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.228</td>\n",
       "      <td>2.471</td>\n",
       "      <td>3.003500e+00</td>\n",
       "      <td>Haemophilus sputorum</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>1111121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.829</td>\n",
       "      <td>9.374450e-88</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>1111135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.222880e-164</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>1111126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.902</td>\n",
       "      <td>5.150</td>\n",
       "      <td>1.086130e-102</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>1111133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1.434260e-240</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>1111134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.415</td>\n",
       "      <td>1.083</td>\n",
       "      <td>4.707650e-205</td>\n",
       "      <td>Peptoniphilus sp. BV3C26</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>35518</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.231</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.691890e-297</td>\n",
       "      <td>[Eubacterium] nodatum</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>523 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       taxid      count    count_sal   count_puff    count_cedar  \\\n",
       "0        562  1493685.0  1430201.159  1406064.613   1.430560e+06   \n",
       "1        727   120507.0   114640.865   116610.975   1.169680e+05   \n",
       "2      83558    79974.0    78213.465    78142.175   7.863590e+04   \n",
       "3        782    73307.0    71813.471    71767.715   7.216460e+04   \n",
       "4       1423   272560.0   263965.741   265027.262   2.656240e+05   \n",
       "5     134821    48374.0    47346.000    47274.007   4.757350e+04   \n",
       "6      86665   274294.0   262720.352   267824.339   2.690430e+05   \n",
       "7       1358   414349.0   393029.611   402925.997   4.048260e+05   \n",
       "8        518   466134.0   450130.078   455432.643   4.577070e+05   \n",
       "9      35554   248267.0   241612.939   242379.657   2.439730e+05   \n",
       "10      1764   316134.0   305375.577   309109.109   3.107140e+05   \n",
       "11      1765   288400.0   293805.981   288747.195   3.219790e+05   \n",
       "12       485   156640.0   148258.938   151681.927   1.539320e+05   \n",
       "13      1280  1231949.0  1202372.878  1204424.516   1.208780e+06   \n",
       "14       158   186640.0   182306.575   182500.471   1.835390e+05   \n",
       "15       777   137654.0   132881.178   134620.927   1.354210e+05   \n",
       "16       382   431681.0   418774.236   422097.332   4.241300e+05   \n",
       "17      1313   181280.0   175151.335   176708.936   1.775340e+05   \n",
       "18       487   155920.0   146319.955   151227.551   1.507910e+05   \n",
       "19       632   319921.0   300699.074   307000.903   3.079740e+05   \n",
       "20     83557    75947.0    74120.513    74306.408   7.470810e+04   \n",
       "21     51229    67361.0    65147.175    65838.626   6.623740e+04   \n",
       "22      1396  1320777.0  1272693.134  1275963.785   1.278230e+06   \n",
       "23      1392   408054.0   395175.543   396389.137   4.015480e+05   \n",
       "24      1314   260614.0   253181.758   253347.103   2.548820e+05   \n",
       "25      1311   167414.0   162795.025   162214.388   1.635070e+05   \n",
       "26    152794   237814.0   231429.944   232647.687   2.338720e+05   \n",
       "27       818   402214.0   383468.884   368871.257   3.902820e+05   \n",
       "28      1219   312374.0   304280.382   304982.514   3.068680e+05   \n",
       "29       536   336880.0   326148.743   328659.574   3.304290e+05   \n",
       "..       ...        ...          ...          ...            ...   \n",
       "698      735        0.0       16.841        7.287   3.004700e+00   \n",
       "699      752        0.0        1.852        3.011   1.248360e+01   \n",
       "700      726        0.0      817.193      921.976   2.245550e+03   \n",
       "701      736        0.0       11.022        2.946   1.966890e+01   \n",
       "703  1095726        0.0        0.607        5.621   3.037550e+00   \n",
       "704  1095727        0.0       11.436       12.750   4.711550e+00   \n",
       "708  1105030        0.0        1.000        1.000   1.000000e+00   \n",
       "709   936554        0.0        0.082        2.207   1.000190e+00   \n",
       "710   936594        0.0        1.001        1.003   1.000020e+00   \n",
       "713   936565        0.0        5.227        6.920   6.972580e+00   \n",
       "716   936572        0.0        0.000        0.000   1.195290e+00   \n",
       "717  1105032        0.0        2.684        1.801   2.905900e+00   \n",
       "720   936375        0.0        3.017        4.064   4.229020e+00   \n",
       "721   936381        0.0        0.000        0.001   6.307260e+00   \n",
       "723  1111137        0.0        0.000        1.870   1.598630e+00   \n",
       "725   461393        0.0        0.000        0.000   1.000000e+00   \n",
       "728   172042        0.0       74.929        0.000   3.858540e-08   \n",
       "729   936595        0.0        0.005        0.001   1.000020e+00   \n",
       "730   598660        0.0        1.650        6.290   5.489680e+00   \n",
       "733   712213        0.0        1.000        1.011   1.000000e+00   \n",
       "735   712401        0.0        3.598        5.437   3.359930e+00   \n",
       "737   633701        0.0        0.000        1.000   2.002750e+00   \n",
       "738   712532        0.0        0.516        0.002   4.732370e-65   \n",
       "739  1078480        0.0        1.228        2.471   3.003500e+00   \n",
       "741  1111121        0.0        0.000        2.829   9.374450e-88   \n",
       "742  1111135        0.0        0.000        0.000  4.222880e-164   \n",
       "743  1111126        0.0        1.902        5.150  1.086130e-102   \n",
       "745  1111133        0.0        0.013        0.013  1.434260e-240   \n",
       "746  1111134        0.0        5.415        1.083  4.707650e-205   \n",
       "749    35518        0.0       11.231        0.000  2.691890e-297   \n",
       "\n",
       "                               taxname  count_brac  \n",
       "0                     Escherichia coli   1481960.0  \n",
       "1               Haemophilus influenzae    116880.0  \n",
       "2                 Chlamydia pneumoniae     79090.0  \n",
       "3                Rickettsia prowazekii     71893.0  \n",
       "4                    Bacillus subtilis    269266.0  \n",
       "5                    Ureaplasma parvum     45798.0  \n",
       "6                  Bacillus halodurans    271726.0  \n",
       "7                   Lactococcus lactis    407743.0  \n",
       "8            Bordetella bronchiseptica    458333.0  \n",
       "9             Geobacter sulfurreducens    246125.0  \n",
       "10                 Mycobacterium avium    312192.0  \n",
       "11                                   0         0.0  \n",
       "12               Neisseria gonorrhoeae    152745.0  \n",
       "13               Staphylococcus aureus   1217861.0  \n",
       "14                 Treponema denticola    183442.0  \n",
       "15                   Coxiella burnetii    136044.0  \n",
       "16              Sinorhizobium meliloti    428153.0  \n",
       "17            Streptococcus pneumoniae    181034.0  \n",
       "18              Neisseria meningitidis    154368.0  \n",
       "19                     Yersinia pestis    315032.0  \n",
       "20                    Chlamydia caviae     75097.0  \n",
       "21          Wigglesworthia glossinidia     55353.0  \n",
       "22                     Bacillus cereus   1313587.0  \n",
       "23                  Bacillus anthracis    394928.0  \n",
       "24              Streptococcus pyogenes    256829.0  \n",
       "25            Streptococcus agalactiae    161219.0  \n",
       "26           Corynebacterium efficiens    235789.0  \n",
       "27        Bacteroides thetaiotaomicron    392498.0  \n",
       "28             Prochlorococcus marinus    308752.0  \n",
       "29           Chromobacterium violaceum    333153.0  \n",
       "..                                 ...         ...  \n",
       "698       Haemophilus parahaemolyticus        26.0  \n",
       "699                Pasteurella bettyae         8.0  \n",
       "700           Haemophilus haemolyticus       351.0  \n",
       "701   Haemophilus paraphrohaemolyticus        15.0  \n",
       "703            Streptococcus sp. SK140        43.0  \n",
       "704            Streptococcus sp. SK643       106.0  \n",
       "708              Actinomyces sp. ICM58         8.0  \n",
       "709          Campylobacter sp. FOBRC14        35.0  \n",
       "710       Lachnoanaerobaculum sp. ICM7         8.0  \n",
       "713               Klebsiella sp. OBRC7        20.0  \n",
       "716                                  0         0.0  \n",
       "717            Streptococcus sp. BS35b        40.0  \n",
       "720             Mogibacterium sp. CM50         8.0  \n",
       "721                                  0         0.0  \n",
       "723                                  0         0.0  \n",
       "725           Actinomyces massiliensis         9.0  \n",
       "728                       Rothia aeria         3.0  \n",
       "729    Lachnoanaerobaculum sp. OBRC5-5         3.0  \n",
       "730  Corynebacterium pyruviciproducens         6.0  \n",
       "733  Capnocytophaga sp. oral taxon 332         1.0  \n",
       "735       Neisseria sp. oral taxon 020        39.0  \n",
       "737          Prevotella saccharolytica         1.0  \n",
       "738     Selenomonas sp. oral taxon 138         2.0  \n",
       "739               Haemophilus sputorum        24.0  \n",
       "741                                  0         0.0  \n",
       "742                                  0         0.0  \n",
       "743                                  0         0.0  \n",
       "745                                  0         0.0  \n",
       "746           Peptoniphilus sp. BV3C26         4.0  \n",
       "749              [Eubacterium] nodatum         7.0  \n",
       "\n",
       "[523 rows x 7 columns]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trusalpufcedbrac[(trusalpufcedbrac['count_cedar'] > minCnt)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for minCnt in range(0, 100):\n",
    "    print(minCnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "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>tool</th>\n",
       "      <th>count_sal</th>\n",
       "      <th>count_puff</th>\n",
       "      <th>count_cedar</th>\n",
       "      <th>count_brac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>#rows</th>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#reads</th>\n",
       "      <td>1.31571e+06</td>\n",
       "      <td>3.66231e+06</td>\n",
       "      <td>3.7131e+06</td>\n",
       "      <td>1.84504e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp</th>\n",
       "      <td>0.983664</td>\n",
       "      <td>0.996942</td>\n",
       "      <td>0.998035</td>\n",
       "      <td>0.959301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explained_var</th>\n",
       "      <td>0.586569</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>0.999509</td>\n",
       "      <td>0.00541485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kendalltau</th>\n",
       "      <td>0.903421</td>\n",
       "      <td>0.983345</td>\n",
       "      <td>0.986739</td>\n",
       "      <td>0.946392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARD</th>\n",
       "      <td>0.938505</td>\n",
       "      <td>0.0134296</td>\n",
       "      <td>0.0108701</td>\n",
       "      <td>0.352377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>12173.1</td>\n",
       "      <td>421.531</td>\n",
       "      <td>164.905</td>\n",
       "      <td>9486.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>6.14174e+08</td>\n",
       "      <td>2.47306e+06</td>\n",
       "      <td>935798</td>\n",
       "      <td>5.61102e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSLE</th>\n",
       "      <td>23.9163</td>\n",
       "      <td>0.0209179</td>\n",
       "      <td>0.0125635</td>\n",
       "      <td>2.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MedAE</th>\n",
       "      <td>4421</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>braycurtisDist</th>\n",
       "      <td>0.476804</td>\n",
       "      <td>0.0112582</td>\n",
       "      <td>0.00437414</td>\n",
       "      <td>0.33618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cosineDist</th>\n",
       "      <td>0.136276</td>\n",
       "      <td>0.000338293</td>\n",
       "      <td>0.000200758</td>\n",
       "      <td>0.000485658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canberraDist</th>\n",
       "      <td>184.886</td>\n",
       "      <td>2.64564</td>\n",
       "      <td>2.14141</td>\n",
       "      <td>69.4182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tool              count_sal   count_puff  count_cedar   count_brac\n",
       "#rows                   197          197          197          197\n",
       "#reads          1.31571e+06  3.66231e+06   3.7131e+06  1.84504e+06\n",
       "sp                 0.983664     0.996942     0.998035     0.959301\n",
       "explained_var      0.586569     0.998667     0.999509   0.00541485\n",
       "Kendalltau         0.903421     0.983345     0.986739     0.946392\n",
       "MARD               0.938505    0.0134296    0.0108701     0.352377\n",
       "MAE                 12173.1      421.531      164.905      9486.17\n",
       "MSE             6.14174e+08  2.47306e+06       935798  5.61102e+08\n",
       "MSLE                23.9163    0.0209179    0.0125635       2.0001\n",
       "MedAE                  4421            1          2.2         2280\n",
       "braycurtisDist     0.476804    0.0112582   0.00437414      0.33618\n",
       "cosineDist         0.136276  0.000338293  0.000200758  0.000485658\n",
       "canberraDist        184.886      2.64564      2.14141      69.4182"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minCnt = 0\n",
    "res = []\n",
    "for col in ['count_sal', 'count_puff', 'count_cedar', 'count_brac']:\n",
    "    df = trusalpufcedbrac[(trusalpufcedbrac[col] > minCnt) | (trusalpufcedbrac['count'] != 0)]\n",
    "    df[df[col] <= minCnt][col] = 0\n",
    "    rd, _ = relDiff('count', col, df, cutoff=minCnt)\n",
    "    res += [[\n",
    "        col, len(df), df[col].sum(),\n",
    "        df[['count', col]].corr(method='spearman')['count'][col],\n",
    "        metrics.explained_variance_score(df[col], df['count']),\n",
    "        scipy.stats.kendalltau(df[col], df['count']).correlation,\n",
    "        (0.5*rd['relDiff'].abs()).mean(),\n",
    "        metrics.mean_absolute_error(df[col], df['count']),\n",
    "        metrics.mean_squared_error(df[col], df['count']),\n",
    "        metrics.mean_squared_log_error(df[col], df['count']),\n",
    "        metrics.median_absolute_error(df[col], df['count']),\n",
    "        scipy.spatial.distance.braycurtis(df[col], df['count']),\n",
    "        scipy.spatial.distance.cosine(df[col], df['count']),\n",
    "        scipy.spatial.distance.canberra(df[col], df['count'])\n",
    "    ]]\n",
    "final_metrics = pd.DataFrame.from_records(res, columns=['tool', '#rows', '#reads',\n",
    "                                                       'sp', 'explained_var', 'Kendalltau',\n",
    "                                                        'MARD',  \n",
    "                                                        'MAE', 'MSE', 'MSLE', 'MedAE',\n",
    "                                                       'braycurtisDist', 'cosineDist', 'canberraDist'])\n",
    "final_metrics = final_metrics.transpose()\n",
    "final_metrics.columns = final_metrics.iloc[0]\n",
    "final_metrics = final_metrics[1:]\n",
    "final_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#rows                     272\n",
       "#reads            2.60648e+07\n",
       "sp                    0.82132\n",
       "explained_var        0.998164\n",
       "Kendalltau           0.725386\n",
       "MARD                  0.69163\n",
       "MAE                   3227.83\n",
       "MSE               7.61004e+07\n",
       "MSLE                  10.6949\n",
       "MedAE                 57.4371\n",
       "braycurtisDist      0.0166497\n",
       "cosineDist        0.000246832\n",
       "canberraDist          188.123\n",
       "Name: count_cedar, dtype: object"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_metrics['count_cedar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247012\n"
     ]
    }
   ],
   "source": [
    "kraken=pd.read_table('/mnt/scratch2/cedar/results/bracken/i100_31.kraken', header=None)\n",
    "kraken.columns = ['status', 'read', 'taxid', 'tmp', 'tmp2']\n",
    "kraken = kraken[kraken['status'] == 'C'][['read', 'taxid']]\n",
    "kraken['count'] = 1\n",
    "kraken = kraken[['taxid', 'count']].groupby('taxid').sum().reset_index()\n",
    "print(kraken['count'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notFound: 0\n"
     ]
    }
   ],
   "source": [
    "kraken_agg = aggregate(kraken, 'taxid', 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count_krak ==> # of unique species: 189\n",
      "\tcorr: 0.9273881782943014\n",
      "\tMARD : 0.6429138449054718\n"
     ]
    }
   ],
   "source": [
    "trukrak = pd.merge(truth_agg, kraken_agg, on = 'taxid', how='outer', suffixes=['', '_krak']).fillna(0)\n",
    "col = 'count_krak'\n",
    "print('{} ==> # of unique species: {}'.format(col, len(trukrak[trukrak[col] != 0])))\n",
    "print('\\tcorr: {}'.format(trukrak[(trukrak[col] != 0) | (trukrak['count'] != 0)]\n",
    "                          [['count', col]].corr(method='spearman')['count'][col]))\n",
    "rd, _ = relDiff('count', col, trukrak, cutoff=2)\n",
    "print(\"\\tMARD : {}\".format((0.5*rd['relDiff'].abs()).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = 'count_cedar'\n",
    "a = trusalpufcedbrac[(trusalpufcedbrac['count'] != 0) | (trusalpufcedbrac[col] > 0.001)][['taxid','count',col]]\n",
    "a.sort_values(by='count', ascending=False, inplace=True)\n",
    "a=a.reset_index()[['taxid','count', col]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iFTI5urpMRCSjYhUyuQoZEZGMilXI5GjhX0Qko2IVMqdHMjUKGRGRTIhXyCRyyEsYJ6pqst0VEZFYaFPImNnfmtl6M1tnZr80s25mNsTMlptZqZn9h5nlh7oF4XFp2D84cp77QvkmM7s6Ul4SykrNbEakvME2mqN7XoKKUwoZEZFMaHXImNlA4G5gnLuPBBLAjcCPgIfd/ULgIHBbOOQ24GAofzjUw8xGhOMuAkqAfzGzhJklgMeAa4ARwJdCXZpoI6UeBblUnKpu7dMWEZEWaOt0WS7Q3cxygUJgFzAJmB/2zwWuDdtTwmPC/slmZqF8nrufdPetQCkwPtxK3X2Lu58C5gFTwjGNtZFSYX6C4xrJiIhkRKtDxt13Ag8C20mGy2FgFXDI3euGCmXAwLA9ENgRjq0O9ftFy+sd01h5vybaSKkwP5eKkxrJiIhkQlumy/qSHIUMAS4AepCc7uowzGy6ma00s5U1FYeB5EhGazIiIpnRlumyTwNb3X2fu1cBvwIuB/qE6TOAYmBn2N4JDAII+3sDB6Ll9Y5prPxAE22cxd1nu/s4dx+XKOwNKGRERDKpLSGzHZhgZoVhnWQysAF4Fbg+1JkGPB+2F4THhP2vuLuH8hvD1WdDgKHAW8AKYGi4kiyf5MUBC8IxjbWRUmFBLse18C8ikhFtWZNZTnLx/W1gbTjXbOBe4B4zKyW5fvJEOOQJoF8ovweYEc6zHniGZEC9BNzl7jVhzeXrwEJgI/BMqEsTbaTUIz/BCY1kREQywjwmH7FScP5QP7lrM3+/YD2/eruMNX9/deqDRERizsxWufu41h4fq3f8w5k1mbiEq4hINsUyZKprnVM1tdnuiohIlxfDkElelKZ1GRGR9ItdyPQoSADoXf8iIhkQu5DpHkYyete/iEj6xS5keuQnRzLHFDIiImkXu5Dp17MAgAPHTmW5JyIiXV/sQua8XsmQ2XfsZJZ7IiLS9cUuZPqHkczeIwoZEZF0i13I5Ofm0L0W9h2rzHZXRES6vNiFDEAPN/Yd1UhGRCTdYhkyhbUKGRGRTIhlyPR0Y69CRkQk7WIZMr1qjT1HKqnW55eJiKRVLEOmqNaoqnF2HDyR7a6IiHRpsQyZvjUGwJZ9x7LcExGRri2WIVNUm3za7ylkRETSKpYh092N/j3z2bLveLa7IiLSpcUyZAA+2r+nRjIiImnWppAxsz5mNt/M/mBmG83sz8ysyMwWmdnmcN831DUzm2VmpWa2xszGRs4zLdTfbGbTIuWXmtnacMwsM7NQ3mAbLfGx83rwnkYyIiJp1daRzD8BL7n7MGA0sBGYASx296HA4vAY4BpgaLhNBx6HZGAAM4HLgPHAzEhoPA7cHjmuJJQ31kazfezcnpQfP8XB4/o0ZhGRdGl1yJhZb+BK4AkAdz/l7oeAKcDcUG0ucG3YngI85UnLgD5mdj5wNbDI3cvd/SCwCCgJ+85x92Xu7sBT9c7VUBvN9tFzewCwZb+mzERE0qUtI5khwD7gZ2a22sx+amY9gAHuvivU2Q0MCNsDgR2R48tCWVPlZQ2U00Qbze98/54AbN1f0dJDRUSkmdoSMrnAWOBxdx8DHKfetFUYgXgb2kipqTbMbLqZrTSzlTUVh8/a179nPoCmy0RE0qgtIVMGlLn78vB4PsnQ2ROmugj3e8P+ncCgyPHFoayp8uIGymmijbO4+2x3H+fu4xKFvc/a17MgF8M5dEIhIyKSLq0OGXffDewws4+HosnABmABUHeF2DTg+bC9ALg5XGU2ATgcprwWAleZWd+w4H8VsDDsO2JmE8JVZTfXO1dDbTSbmZFPNYdPVLX0UBERaabcNh7/DeBpM8sHtgC3kgyuZ8zsNuB94IZQ9wXgM0ApUBHq4u7lZvYDYEWo9313Lw/bdwJPAt2BF8MN4IFG2miRAqvmUIVCRkQkXdoUMu7+n8C4BnZNbqCuA3c1cp45wJwGylcCIxsoP9BQGy1VQI1GMiIiaRTbd/wD5Jumy0RE0inWIVNAjabLRETSKN4ho5GMiEhaxTpk8qnmSGUVNbVpfSuPiEhsxTpkCqwGdzhaqdGMiEg6xDpkulkyXPYf0xsyRUTSIdYh051kyOw9UpnlnoiIdE2xDpkelhzB7FbIiIikRaxDpnuYLlPIiIikR6xDJs9q6Z5bwZ7DChkRkXSIdcgA9C04zJ4jJ7PdDRGRLin2IdOn4DAfHD6R7W6IiHRJsQ+ZP+n7HmvKDvPwoneprqnNdndERLqU2IdMyeDFjPnjPvzT4s08+ftt2e6OiEiXEvuQyUtU8+s7Lwfgide3UquPmBERaTexD5moXYcr2bDrSLa7ISLSZShk6nlt8/5sd0FEpMtQyEQMP/8cHnx5E1+du4It+45luzsiIp2eQibi0S9dwpTRF7B8Szklj7zGz9/clu0uiYh0am0OGTNLmNlqM/tteDzEzJabWamZ/YeZ5YfygvC4NOwfHDnHfaF8k5ldHSkvCWWlZjYjUt5gG2114Xm9eOiLl3DT3gQjB57Dj17axMHj+oRmEZHWao+RzN8AGyOPfwQ87O4XAgeB20L5bcDBUP5wqIeZjQBuBC4CSoB/CcGVAB4DrgFGAF8KdZtqo12c4zk88PlRnKiq4b/Nf0f/1ExEpJXaFDJmVgz8JfDT8NiAScD8UGUucG3YnhIeE/ZPDvWnAPPc/aS7bwVKgfHhVuruW9z9FDAPmJKijXbzJwN68b2/GsHvNu7lwZc3tffpRURioa0jmUeAbwN1b5XvBxxy9+rwuAwYGLYHAjsAwv7Dof7p8nrHNFbeVBtnMbPpZrbSzFbWVBxu8ZPbsvBJPln8Oo8veY/frN7Z4uNFROKu1SFjZn8F7HX3Ve3Yn3bl7rPdfZy7j0sU9m7x8WZw07D5TPhoEd9+bg2b9xxNQy9FRLqutoxkLgc+a2bbSE5lTQL+CehjZrmhTjFQNwTYCQwCCPt7Awei5fWOaaz8QBNttLvcnFoeu2kseTnGw797F3etz4iINFerQ8bd73P3YncfTHLh/hV3/zLwKnB9qDYNeD5sLwiPCftf8eQr9gLgxnD12RBgKPAWsAIYGq4kyw9tLAjHNNZGWvTrWcDxUzW8sHY3U594i/3H9K8BRESaIx3vk7kXuMfMSkmunzwRyp8A+oXye4AZAO6+HngG2AC8BNzl7jVhzeXrwEKSV689E+o21UZa/Y+/HM7rpfuZ99b2TDQnItLp5aaukpq7LwGWhO0tJK8Mq1+nEvhCI8ffD9zfQPkLwAsNlDfYRrp99YqP8tPXtrJl//FMNy0i0inpHf8tNOC9dWxVyIiINItCpoUGHtunkBERaSaFTAsNPLaPQxVVHNDiv4hISgqZFrp4/3sAzH5tS5Z7IiLS8SlkWmjo4Z1cfDLBE69tZf0HLf8UARGROFHItMKVlXn07p7HlH9+g5vnvMXijXuorKo5vf+xr72Sxd6JiHQcCplWKHRjwTf+nK9e8VHe2XGI2+au5IEX/5DtbomIdDgKmVYa2Kc7lcvnseTvJnLpR/ryi7e2s2Jbeba7JSLSoShk2qhvj3z+1+cupltuDl/41zf5u2ff4YTp881EREAh0y7+ZEAvjlRWc8fEj/Gb1Tv5yTmV3PKzt3h98/5sd01EJKsUMu3o3pJhzPrdj7mwKsGSTfv4wW836FObRSTWFDLtbMiR3XymIp9//PwoNu05ym1zV3K0sirb3RIRyQqFTJpcN3YgIxO7WPruPj77z2/w3Koyjp+sTn2giEgXopBJk7xEDuPyyvjZrX9KQW4O33r2Hf7Lo69TVVOb+mARkS5CIZNmVww9lxfuvgKALfuP85PXtmidRkRiQyGTATk5BsCwP+rFP760iTlvbMtuh0REMkQhk0Hz7/gEI/e/xyOL3uWDQyey3R0RkbRTyGRQz4Jc/nb1M1RWVjPxwSXc96s1nDhVk/pAEZFOqtUhY2aDzOxVM9tgZuvN7G9CeZGZLTKzzeG+byg3M5tlZqVmtsbMxkbONS3U32xm0yLll5rZ2nDMLDOzptroDC44foDrj+XzFyMG8Mu3dvDzZduy3SURkbRpy0imGviWu48AJgB3mdkIYAaw2N2HAovDY4BrgKHhNh14HJKBAcwELgPGAzMjofE4cHvkuJJQ3lgbnUJxTYLHbhrLADvC3N+/T22tLgQQka6p1SHj7rvc/e2wfRTYCAwEpgBzQ7W5wLVhewrwlCctA/qY2fnA1cAidy9394PAIqAk7DvH3Zd58nKsp+qdq6E2OpULE/vZeegEG3YdyXZXRETSol3WZMxsMDAGWA4McPddYdduYEDYHgjsiBxWFsqaKi9roJwm2uhUBiaS4bJ0874s90REJD3aHDJm1hN4Dvimu5/1J3kYgaR1LqipNsxsupmtNLOVNRUd779YFloVIweew9PLtrP3SGW2uyMi0u7aFDJmlkcyYJ5291+F4j1hqotwvzeU7wQGRQ4vDmVNlRc3UN5UG2dx99nuPs7dxyUKe7fuSabZ/7zuYsqPnzr90TM1Wp8RkS6kLVeXGfAEsNHdH4rsWgDUXSE2DXg+Un5zuMpsAnA4THktBK4ys75hwf8qYGHYd8TMJoS2bq53roba6HRGFffh2a/9GbuPVPKtZ9/hjn9fpQsBRKTLaMtI5nJgKjDJzP4z3D4DPAD8hZltBj4dHgO8AGwBSoGfAHcCuHs58ANgRbh9P5QR6vw0HPMe8GIob6yNTmnkwOQo696SYby8YQ///GpplnskItI+clt7oLu/Dlgjuyc3UN+Buxo51xxgTgPlK4GRDZQfaKiNzu5rn/woK37yNA//DgacU8BVI/6Ivj3ys90tEZFW0zv+OxAz4xv/OZ+RF/Tm3ufWcukPF/Fff76SRxdv5pj+TYCIdEIKmQ6mW00Vz37tz/jFVy9jWM5u1u08wv9e9C4PvfxutrsmItJiCpkOqFtegk9c2J/xeTt4Y8YkAH69uozSvUf1bwJEpFNRyHQSByuq+PRDS/nu8+uy3RURkWZTyHQinx9bzL8v285zq8pSVxYR6QAUMp3Ijz5/MRft38K3nn2Hqx7+f7y6qcH3oIqIdBgKmU4kN5HD95Y/yYxrhlHr8JUnV1C692i2uyUi0iiFTCdzTlUFX/vkx/jpzeNwh1XvH8x2l0REGqWQ6aT+uKiQPGrY8IH+TYCIdFwKmU4qJ8foaxWsV8iISAemkOnE+uccZ03ZYd7ZcSjbXRERaZBCphMbmbubnt1ymfLYG3zv+XVs239cb9YUkQ5FIdOJFVoV/+cbfw7AU2++z8QHl3Dn02/rf9KISIehkOnkBvbpfnp76oSP8OK63fx2zQdZ7JGIyBkKmS7kHz57EedVlDNfnwggIh2EQqYLyckxrnp/Ba9t3s+/L3s/290REVHIdDU3vPsK4wcX8fiS93QRgIhknUKmi8nzGqaMuYCdh07w3r7j2e6OiMScQqYLunLouQD8/YL1nKyuyXJvRCTOOnXImFmJmW0ys1Izm5Ht/nQUg4oK+XbJx3m9dD/Lt5RnuzsiEmOdNmTMLAE8BlwDjAC+ZGYjsturjuPLl30EgLU7D2e5J+3n4rkXZ7sLItJCnTZkgPFAqbtvcfdTwDxgSpb71GH07p5HL6tk/QddJ2REpPPJzXYH2mAgsCPyuAy4LEt96ZCKrIIX1+3mou+9lO2utIvj1f/QZZ6LdE2nTtaQX5DIdjc6FOusl7ma2fVAibt/NTyeClzm7l+P1JkOTA8PPw6cAo4CvcI9jWy3Zn9nOaeeR8c6Z1za1PPoWG225Jh+7t6LVurM02U7gUGRx8Wh7DR3n+3u48KtF9AN2B+5b2y7Nfs7yzn1PDrWOePSpp5Hx2qzJcdsog06c8isAIaa2RAzywduBBZkuU8iIhLRaddk3L3azL4OLAQSwBx3X5/lbomISESnXZNpDTP7OfAacEW4p5Ht1uzvLOfU8+hY54xLm3oeHavNlhyDu8+mlWIVMiIiklmdeU1GREQ6uE67JtMSZrYQmExy7aaamDxvEZE0esTd/zZVpS4/kgkfPzMS+AqwFdgA1AC1oUp1eOyhzIFK4EjkNJXh3iPH1c0z1gInIo/rzz96I2V19zWROhX19lWFshrgQKRP0X40paF0UrVTAAADR0lEQVS50OYcF1XTwvrplGput7HvgXR8Lfm5bOv3t6W/A+3RZltlqv0aku8nrAGOh1vd681rwB/Cvt3A55pzwi4fMiQ/fmYdsJTkC/ZikiOaU2H/KZIv5hb2A5wk+Wak6A+jR/bDmRffHCA/HO/hPsoaKKuJ7It+Dwojdev2VQKHgX4NnLPqw0+3wXbgw79Ybf2hbe4Lfntr6rx1/9ug/tc7mxrrb20T+yD5x09r22rs56LuBSTbL5gNaei1qLF+tvX729TrXnUj7abzZ6o5349M/UznkHz9qyX5ulbAmdeObiR/x8pDeQ8zO785J+zq6n/8zB3hviDcF5L8YgLkkfxmdufsAOjGmRCpK4tOuSXCfXN/EKLHGg0HUd15i8IN4Jx6x+XQ9ItRtJ2cevfN7WtjPyOpjk/HL0VjX6c6PdPQZls11t+cJvZB66Z0686X18j+BGf+IOoMstHP3Cy025G+H0byD+y8cMsl+XOTA/wpcCnwCNCH5B/dA1OdMA4hE9ULqHsvzYFIeXTqC86ERv2pom6R7RPhPtVook50JHEist2avyrrjklw9otR/XOdbMW5m9Nue9cVaQ79TDUtHV+fWpJ/yNZGbt8hOV3WrPbiEDLRj58pBEaT/OLUjQrq1kXgTLjUv4cPXzBQ99dHXVmqL3j0ax0Nq7q1l5aoa7v+KMbrbUf7W3+6rLnz0tFztuQvrtbMe8dNZ/4atWStrrXretH1zEwGTP3fo9Yclw3pGBHlkHwdyQm3XcAWkn+wF1Lvo7waO0FXtwIYSnLxPx84BKwlOa8IyV/0unnqvEhZ3YIX4T7BmR/63wGPR/ZF12JOcCbxqbddHdqKfv5+NAyaWjdxzoRK3Xb0+3ey3uPo1FJV5Fx1bTT3ex+t15Jforq2G7rwoSWy/YubTtEXhdaswUD2LsxoyfclkbpKg+p+htP5M5Rq/aUlL9zRuq0Np5b2Fc6+cKk17Td2XC3J18voxUl9gI8A+4AT7r4r1clj8WZMM1sC/Dmt/2EXEZEzf1DXklyTOQyMcPcjjR0Qi5AREZHsiMN0mYiIZIlCRkRE0kYhIyIiaaOQERGRtFHIiIhI2ihkREQkbRQyIiKSNgoZERFJm/8PJ6gWmxpCSIMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a.plot(kind='bar', y=col)\n",
    "a['count'].plot()\n",
    "plt.savefig('/mnt/scratch2/cedar/results/figs/i100_cedar_0.001.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "228964"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trukrak['count_krak'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff5934ae898>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trukrak.sort_values(by='count', ascending=False, inplace=True)\n",
    "b=trukrak.reset_index()[['taxid','count', 'count_krak']]\n",
    "b.plot(kind='bar', y='count_krak')\n",
    "b['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Species    TaxID  NumCounts\n",
      "0  Acinetobacter baumannii  1018343   262164.0\n",
      "             Species   TaxID  NumCounts\n",
      "0  Absiella dolichum  428127      1.274\n",
      "             Species   TaxID  NumCounts\n",
      "0  Absiella dolichum  428127      1.005\n",
      "             Species  TaxID  NumCounts\n",
      "0  Absiella dolichum  31971      662.0\n"
     ]
    }
   ],
   "source": [
    "truth=pd.read_table('/mnt/scratch2/cedar/input_files/i100_taxonomy.truth')\n",
    "truth = truth.groupby('Species').sum().reset_index()\n",
    "print(truth.head(1))\n",
    "puff=pd.read_table('/mnt/scratch2/cedar/results/script_results/noalnmnt_i100_31_sr1_puff/out.tab')\n",
    "puff = puff.groupby('Species').sum().reset_index()\n",
    "print(puff.head(1))\n",
    "salmon=pd.read_table('/mnt/scratch2/cedar/results/script_results/i100_31_sr1_salmon/out.tab')\n",
    "salmon = salmon.groupby('Species').sum().reset_index()\n",
    "print(salmon.head(1))\n",
    "cedar=pd.read_table('/mnt/scratch2/cedar/results/script_results/i100_31_sr1_cedar/out.tab')\n",
    "cedar = cedar.groupby('Species').sum().reset_index()\n",
    "print(cedar.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "bracken=pd.read_table('/mnt/scratch2/cedar/results/bracken/i100_31.kreport_bracken', header=None)\n",
    "bracken.columns = ['percentage', 'aggrCount', 'NumCounts', 'level', 'taxid', 'taxname']\n",
    "bracken.head(1)\n",
    "bracken = bracken[bracken['level'] == 'S'][['taxid', 'taxname', 'NumCounts']]\n",
    "bracken['taxname'] = bracken['taxname'].str.strip()\n",
    "bracken.columns = ['taxid', 'taxname', 'count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "truth=pd.read_table('/mnt/scratch2/cedar/input_files/i100_taxonomy.truth')\n",
    "truth = truth.groupby('Species').sum().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.merge(truth, bracken, left_on='Species', right_on='taxname', how='outer').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bracken ==> # of unique species: 85\n",
      "\tcorr: 0.8808748841859805\n",
      "\tMARD : 0.6445672673488408\n"
     ]
    }
   ],
   "source": [
    "print('{} ==> # of unique species: {}'.format('bracken', len(res[res['Species'] != 0]['Species'].unique())))\n",
    "print('\\tcorr: {}'.format(res[['NumCounts', 'count']].corr(method='spearman')['NumCounts']['count']))\n",
    "rd, _ = relDiff('NumCounts', 'count', res, cutoff=2)\n",
    "print(\"\\tMARD : {}\".format((0.5*rd['relDiff'].abs()).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "al=pd.merge(truth, puff, on='Species', how='outer')[['Species', 'NumCounts_x', 'NumCounts_y']].fillna(0)\n",
    "al.columns = ['Species', 'truth', 'puff']\n",
    "al=pd.merge(al, salmon, on='Species', how='outer')[['Species', 'truth', 'puff', 'NumCounts']].fillna(0)\n",
    "al.columns = ['Species', 'truth', 'puff', 'salmon']\n",
    "al=pd.merge(al, cedar, on='Species', how='outer')[['Species', 'truth', 'puff', 'salmon', 'NumCounts']].fillna(0)\n",
    "al.columns = ['Species', 'truth', 'puff', 'salmon', 'cedar']\n",
    "al=pd.merge(al, bracken, on='Species', how='outer')[['Species', 'truth', 'puff', 'salmon', 'cedar', 'NumCounts']].fillna(0)\n",
    "al.columns = ['Species', 'truth', 'puff', 'salmon', 'cedar', 'bracken']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "truth      26667004\n",
       "puff       26206795\n",
       "salmon     25893411\n",
       "cedar      26336914\n",
       "bracken      246970\n",
       "dtype: uint64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#26667004\n",
    "pd.set_option('display.width', 500)\n",
    "al[['truth', 'puff', 'salmon', 'cedar', 'bracken']].sum().astype(np.uint64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of distinct species:\n",
      "puff ==> # of unique species: 673\n",
      "\tcorr: 0.5771148730419895\n",
      "\tMARD : 0.44326697994503655\n",
      "salmon ==> # of unique species: 485\n",
      "\tcorr: 0.6627743789409638\n",
      "\tMARD : 0.3759827143824895\n",
      "cedar ==> # of unique species: 750\n",
      "\tcorr: 0.5363935842728118\n",
      "\tMARD : 0.9442527780267141\n",
      "bracken ==> # of unique species: 188\n",
      "\tcorr: 0.9220709392084507\n",
      "\tMARD : 0.16307294380330192\n"
     ]
    }
   ],
   "source": [
    "print('number of distinct species:')\n",
    "for col in ['puff', 'salmon', 'cedar', 'bracken']:\n",
    "    print('{} ==> # of unique species: {}'.format(col, len(al[al[col] != 0]['Species'].unique())))\n",
    "    print('\\tcorr: {}'.format(al[al[col] != 0][['truth', col]].corr(method='spearman')['truth'][col]))\n",
    "    rd, _ = relDiff('truth', col, al, cutoff=2)\n",
    "    print(\"\\tMARD : {}\".format((0.5*rd['relDiff'].abs()).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "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>Species</th>\n",
       "      <th>truth</th>\n",
       "      <th>puff</th>\n",
       "      <th>salmon</th>\n",
       "      <th>cedar</th>\n",
       "      <th>bracken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acinetobacter baumannii</td>\n",
       "      <td>262164.0</td>\n",
       "      <td>59875.158</td>\n",
       "      <td>243181.077</td>\n",
       "      <td>938622.0</td>\n",
       "      <td>2395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alkalilimnicola ehrlichii</td>\n",
       "      <td>230160.0</td>\n",
       "      <td>52339.024</td>\n",
       "      <td>224504.034</td>\n",
       "      <td>56202.0</td>\n",
       "      <td>2234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alkaliphilus metalliredigens</td>\n",
       "      <td>374667.0</td>\n",
       "      <td>85591.446</td>\n",
       "      <td>352826.229</td>\n",
       "      <td>109518.0</td>\n",
       "      <td>3465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bacillus anthracis</td>\n",
       "      <td>408054.0</td>\n",
       "      <td>61818.911</td>\n",
       "      <td>394338.602</td>\n",
       "      <td>311929.0</td>\n",
       "      <td>3684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bacillus cereus</td>\n",
       "      <td>1320777.0</td>\n",
       "      <td>248185.262</td>\n",
       "      <td>1271628.751</td>\n",
       "      <td>823304.0</td>\n",
       "      <td>12241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacillus clausii</td>\n",
       "      <td>286614.0</td>\n",
       "      <td>64841.676</td>\n",
       "      <td>280708.654</td>\n",
       "      <td>78647.0</td>\n",
       "      <td>2651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>274294.0</td>\n",
       "      <td>63007.950</td>\n",
       "      <td>263758.940</td>\n",
       "      <td>93128.0</td>\n",
       "      <td>2512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bacillus subtilis</td>\n",
       "      <td>272560.0</td>\n",
       "      <td>62709.799</td>\n",
       "      <td>265348.603</td>\n",
       "      <td>84908.0</td>\n",
       "      <td>2543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bacillus thuringiensis</td>\n",
       "      <td>372187.0</td>\n",
       "      <td>161021.430</td>\n",
       "      <td>356551.975</td>\n",
       "      <td>374846.0</td>\n",
       "      <td>3272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bacteroides thetaiotaomicron</td>\n",
       "      <td>402214.0</td>\n",
       "      <td>89738.833</td>\n",
       "      <td>384015.731</td>\n",
       "      <td>94806.0</td>\n",
       "      <td>3641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bordetella bronchiseptica</td>\n",
       "      <td>466134.0</td>\n",
       "      <td>104465.286</td>\n",
       "      <td>451959.368</td>\n",
       "      <td>108951.0</td>\n",
       "      <td>4281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Borreliella bavariensis</td>\n",
       "      <td>81441.0</td>\n",
       "      <td>18255.070</td>\n",
       "      <td>80065.082</td>\n",
       "      <td>18528.0</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Buchnera aphidicola</td>\n",
       "      <td>26587.0</td>\n",
       "      <td>5936.762</td>\n",
       "      <td>26133.113</td>\n",
       "      <td>6262.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Burkholderia pseudomallei</td>\n",
       "      <td>464987.0</td>\n",
       "      <td>103699.123</td>\n",
       "      <td>454981.077</td>\n",
       "      <td>113630.0</td>\n",
       "      <td>4140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Campylobacter concisus</td>\n",
       "      <td>160562.0</td>\n",
       "      <td>35972.756</td>\n",
       "      <td>155658.329</td>\n",
       "      <td>38295.0</td>\n",
       "      <td>1444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Candidatus Blochmannia pennsylvanicus</td>\n",
       "      <td>53307.0</td>\n",
       "      <td>11919.096</td>\n",
       "      <td>52494.570</td>\n",
       "      <td>12361.0</td>\n",
       "      <td>436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chlamydia caviae</td>\n",
       "      <td>75947.0</td>\n",
       "      <td>16800.230</td>\n",
       "      <td>74536.269</td>\n",
       "      <td>17017.0</td>\n",
       "      <td>723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Chlamydia pneumoniae</td>\n",
       "      <td>79974.0</td>\n",
       "      <td>17808.445</td>\n",
       "      <td>78638.059</td>\n",
       "      <td>18160.0</td>\n",
       "      <td>736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chlamydia trachomatis</td>\n",
       "      <td>73440.0</td>\n",
       "      <td>16459.796</td>\n",
       "      <td>72177.840</td>\n",
       "      <td>16978.0</td>\n",
       "      <td>757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Chromobacterium violaceum</td>\n",
       "      <td>336880.0</td>\n",
       "      <td>75431.730</td>\n",
       "      <td>327284.134</td>\n",
       "      <td>86047.0</td>\n",
       "      <td>3047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Clostridium beijerinckii</td>\n",
       "      <td>426880.0</td>\n",
       "      <td>98435.618</td>\n",
       "      <td>413267.137</td>\n",
       "      <td>127115.0</td>\n",
       "      <td>4027.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Clostridium botulinum</td>\n",
       "      <td>286907.0</td>\n",
       "      <td>64838.124</td>\n",
       "      <td>278477.357</td>\n",
       "      <td>81941.0</td>\n",
       "      <td>2533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Clostridium novyi</td>\n",
       "      <td>215627.0</td>\n",
       "      <td>49180.381</td>\n",
       "      <td>206259.549</td>\n",
       "      <td>75079.0</td>\n",
       "      <td>1966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Corynebacterium efficiens</td>\n",
       "      <td>237814.0</td>\n",
       "      <td>53553.524</td>\n",
       "      <td>232071.884</td>\n",
       "      <td>134223.0</td>\n",
       "      <td>2226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Corynebacterium urealyticum</td>\n",
       "      <td>174534.0</td>\n",
       "      <td>39848.420</td>\n",
       "      <td>167490.816</td>\n",
       "      <td>45034.0</td>\n",
       "      <td>1648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Coxiella burnetii</td>\n",
       "      <td>137654.0</td>\n",
       "      <td>31320.021</td>\n",
       "      <td>133613.902</td>\n",
       "      <td>38636.0</td>\n",
       "      <td>1192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Cupriavidus necator</td>\n",
       "      <td>29627.0</td>\n",
       "      <td>6559.670</td>\n",
       "      <td>28333.421</td>\n",
       "      <td>6710.0</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Cyanothece sp. ATCC 51142</td>\n",
       "      <td>378402.0</td>\n",
       "      <td>85593.847</td>\n",
       "      <td>370256.105</td>\n",
       "      <td>93053.0</td>\n",
       "      <td>3371.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cytophaga hutchinsonii</td>\n",
       "      <td>312400.0</td>\n",
       "      <td>70580.015</td>\n",
       "      <td>304636.293</td>\n",
       "      <td>73869.0</td>\n",
       "      <td>2876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Erwinia tasmaniensis</td>\n",
       "      <td>328615.0</td>\n",
       "      <td>73803.114</td>\n",
       "      <td>319475.090</td>\n",
       "      <td>90212.0</td>\n",
       "      <td>3061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>Myroides odoratimimus</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>507.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Neisseria elongata</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.471</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>Neisseria subflava</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.298</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>Paenisporosarcina sp. HGH0030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.430</td>\n",
       "      <td>4596.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Parabacteroides goldsteinii</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.022</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>Parabacteroides johnsonii</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.749</td>\n",
       "      <td>509.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>Paraprevotella xylaniphila</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.114</td>\n",
       "      <td>304.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Parascardovia denticolens</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.218</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>Pasteurella bettyae</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.289</td>\n",
       "      <td>684.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Peptoniphilus lacrimalis</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.472</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Peptoniphilus sp. BV3C26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.007</td>\n",
       "      <td>245.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Peptoniphilus sp. oral taxon 836</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>524.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Porphyromonas asaccharolytica</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.001</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Porphyromonas endodontalis</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>Prevotella buccae</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>73.109</td>\n",
       "      <td>306.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Shuttleworthia satelles</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>Simonsiella muelleri</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.347</td>\n",
       "      <td>5075.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Staphylococcus caprae</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.734</td>\n",
       "      <td>4476.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>Streptococcus pseudoporcinus</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.174</td>\n",
       "      <td>2072.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>Streptococcus sp. 2_1_36FAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.095</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>Streptococcus sp. SK643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16.198</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>Streptomyces sp. HGB0020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>Streptomyces sp. HPH0547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Subdoligranulum sp. 4_3_54A2FAA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>93.868</td>\n",
       "      <td>556.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Synergistes sp. 3_1_syn1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.028</td>\n",
       "      <td>336.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Treponema socranskii</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.015</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Veillonella atypica</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.145</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>[Clostridium] asparagiforme</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>[Clostridium] leptum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.008</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>[Propionibacterium] humerusii</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>485 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Species      truth        puff       salmon     cedar  bracken\n",
       "0                  Acinetobacter baumannii   262164.0   59875.158   243181.077  938622.0   2395.0\n",
       "1                Alkalilimnicola ehrlichii   230160.0   52339.024   224504.034   56202.0   2234.0\n",
       "2             Alkaliphilus metalliredigens   374667.0   85591.446   352826.229  109518.0   3465.0\n",
       "3                       Bacillus anthracis   408054.0   61818.911   394338.602  311929.0   3684.0\n",
       "4                          Bacillus cereus  1320777.0  248185.262  1271628.751  823304.0  12241.0\n",
       "5                         Bacillus clausii   286614.0   64841.676   280708.654   78647.0   2651.0\n",
       "6                      Bacillus halodurans   274294.0   63007.950   263758.940   93128.0   2512.0\n",
       "7                        Bacillus subtilis   272560.0   62709.799   265348.603   84908.0   2543.0\n",
       "8                   Bacillus thuringiensis   372187.0  161021.430   356551.975  374846.0   3272.0\n",
       "9             Bacteroides thetaiotaomicron   402214.0   89738.833   384015.731   94806.0   3641.0\n",
       "10               Bordetella bronchiseptica   466134.0  104465.286   451959.368  108951.0   4281.0\n",
       "11                 Borreliella bavariensis    81441.0   18255.070    80065.082   18528.0    750.0\n",
       "12                     Buchnera aphidicola    26587.0    5936.762    26133.113    6262.0    185.0\n",
       "13               Burkholderia pseudomallei   464987.0  103699.123   454981.077  113630.0   4140.0\n",
       "14                  Campylobacter concisus   160562.0   35972.756   155658.329   38295.0   1444.0\n",
       "15   Candidatus Blochmannia pennsylvanicus    53307.0   11919.096    52494.570   12361.0    436.0\n",
       "16                        Chlamydia caviae    75947.0   16800.230    74536.269   17017.0    723.0\n",
       "17                    Chlamydia pneumoniae    79974.0   17808.445    78638.059   18160.0    736.0\n",
       "18                   Chlamydia trachomatis    73440.0   16459.796    72177.840   16978.0    757.0\n",
       "19               Chromobacterium violaceum   336880.0   75431.730   327284.134   86047.0   3047.0\n",
       "20                Clostridium beijerinckii   426880.0   98435.618   413267.137  127115.0   4027.0\n",
       "21                   Clostridium botulinum   286907.0   64838.124   278477.357   81941.0   2533.0\n",
       "22                       Clostridium novyi   215627.0   49180.381   206259.549   75079.0   1966.0\n",
       "23               Corynebacterium efficiens   237814.0   53553.524   232071.884  134223.0   2226.0\n",
       "24             Corynebacterium urealyticum   174534.0   39848.420   167490.816   45034.0   1648.0\n",
       "25                       Coxiella burnetii   137654.0   31320.021   133613.902   38636.0   1192.0\n",
       "26                     Cupriavidus necator    29627.0    6559.670    28333.421    6710.0    312.0\n",
       "27               Cyanothece sp. ATCC 51142   378402.0   85593.847   370256.105   93053.0   3371.0\n",
       "28                  Cytophaga hutchinsonii   312400.0   70580.015   304636.293   73869.0   2876.0\n",
       "29                    Erwinia tasmaniensis   328615.0   73803.114   319475.090   90212.0   3061.0\n",
       "..                                     ...        ...         ...          ...       ...      ...\n",
       "512                  Myroides odoratimimus        0.0       0.000        1.000     507.0      0.0\n",
       "513                     Neisseria elongata        0.0       0.000        9.471    1891.0      0.0\n",
       "514                     Neisseria subflava        0.0       0.000        8.298    1975.0      2.0\n",
       "515          Paenisporosarcina sp. HGH0030        0.0       0.000        5.430    4596.0      0.0\n",
       "516            Parabacteroides goldsteinii        0.0       0.000        9.022     800.0      0.0\n",
       "517              Parabacteroides johnsonii        0.0       0.000        4.749     509.0      0.0\n",
       "518             Paraprevotella xylaniphila        0.0       0.000        1.114     304.0      0.0\n",
       "519              Parascardovia denticolens        0.0       0.000        2.218     354.0      0.0\n",
       "520                    Pasteurella bettyae        0.0       0.000        4.289     684.0      0.0\n",
       "521               Peptoniphilus lacrimalis        0.0       0.000        6.472     277.0      0.0\n",
       "522               Peptoniphilus sp. BV3C26        0.0       0.000        1.007     245.0      0.0\n",
       "523       Peptoniphilus sp. oral taxon 836        0.0       0.000        0.012     524.0      0.0\n",
       "524          Porphyromonas asaccharolytica        0.0       0.000        1.001      22.0      0.0\n",
       "525             Porphyromonas endodontalis        0.0       0.000        1.000      33.0      0.0\n",
       "526                      Prevotella buccae        0.0       0.000       73.109     306.0      0.0\n",
       "527                Shuttleworthia satelles        0.0       0.000        2.000     233.0      0.0\n",
       "528                   Simonsiella muelleri        0.0       0.000        4.347    5075.0      0.0\n",
       "529                  Staphylococcus caprae        0.0       0.000        5.734    4476.0      2.0\n",
       "530           Streptococcus pseudoporcinus        0.0       0.000       14.174    2072.0      0.0\n",
       "531            Streptococcus sp. 2_1_36FAA        0.0       0.000        4.095    1578.0      0.0\n",
       "532                Streptococcus sp. SK643        0.0       0.000       16.198    1585.0      2.0\n",
       "533               Streptomyces sp. HGB0020        0.0       0.000        3.000    1564.0      0.0\n",
       "534               Streptomyces sp. HPH0547        0.0       0.000        1.000    1737.0      0.0\n",
       "535        Subdoligranulum sp. 4_3_54A2FAA        0.0       0.000       93.868     556.0      0.0\n",
       "536               Synergistes sp. 3_1_syn1        0.0       0.000        2.028     336.0      0.0\n",
       "537                   Treponema socranskii        0.0       0.000        1.015     486.0      0.0\n",
       "538                    Veillonella atypica        0.0       0.000        2.145    1289.0      0.0\n",
       "539            [Clostridium] asparagiforme        0.0       0.000        1.000     131.0      0.0\n",
       "540                   [Clostridium] leptum        0.0       0.000        1.008    2032.0      0.0\n",
       "541          [Propionibacterium] humerusii        0.0       0.000        1.000     253.0      0.0\n",
       "\n",
       "[485 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al[al['salmon'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of distinct shared species with truth:\n",
      "puff: 85\n",
      "salmon: 85\n",
      "cedar: 85\n",
      "bracken: 0\n"
     ]
    }
   ],
   "source": [
    "print('number of distinct shared species with truth:')\n",
    "for col in ['puff', 'salmon', 'cedar', 'bracken']:\n",
    "    print('{}: {}'.format(col, len(al[(al['truth'] != 0) & (al[col] != 0)]['Species'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pufmap=pd.read_csv('/home/mohsen/pufferfish-test/out', sep='\\t', header=None)\n",
    "pufmap.columns = ['origread', 'puf']\n",
    "pufmap['read'] = pufmap['origread'].str.split(';').str[0].str.split('/').str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "salmap=pd.read_csv('/mnt/scratch4/mohsen/puffalign_test/out', sep='\\t', header=None)\n",
    "salmap.columns = ['origread', 'sal']\n",
    "salmap['read'] = salmap['origread'].str.split(';').str[0].str.split('/').str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(len(salmap))\n",
    "print(len(pufmap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "salmap.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Puff average hits over read: 1535.147682745575\n",
      "Puff average hits over read and ref: 169.01331248918123\n",
      "Sal average hits over read: 1524.5548226218552\n",
      "Sal average hits over read and ref: 168.46238910425558\n"
     ]
    }
   ],
   "source": [
    "print('Puff average hits over read: {}'.format(pufmap.groupby('read')['puf'].count().mean()))\n",
    "print('Puff average hits over read and ref: {}'.format(pufmap.groupby(['read', 'puf'])['puf'].count().mean()))\n",
    "print('Sal average hits over read: {}'.format(salmap.groupby('read')['sal'].count().mean()))\n",
    "print('Sal average hits over read and ref: {}'.format(salmap.groupby(['read', 'sal'])['sal'].count().mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pufmap['count'] = 1\n",
    "puf_uniqreadref = (pufmap.groupby(['read', 'puf'])['count'].count()).reset_index()\n",
    "salmap['count'] = 1\n",
    "sal_uniqreadref = (salmap.groupby(['read', 'sal'])['count'].count()).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "merged = pd.merge(sal_uniqreadref, puf_uniqreadref, left_on=['read', 'sal'], right_on=['read', 'puf'], how = 'outer').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "740800"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pufbetter=merged[(merged['count_x'] == 0) & (merged['read'] == merged['puf'])]\n",
    "salbetter=merged[(merged['count_y'] == 0) & (merged['read'] == merged['sal'])]\n",
    "pufworse=merged[(merged['count_x'] == 0) & (merged['read'] != merged['puf'])]\n",
    "salworse=merged[(merged['count_y'] == 0) & (merged['read'] != merged['sal'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of references found by puf but not sal: 164\n",
      "# of references found by sal but not puf: 0\n",
      "# of invalid references found by puf but not sal: 3908\n",
      "# of invalid references found by sal but not puf: 1344\n",
      "sum of valid hits found by puf but not sal: 52565.0\n",
      "sum of invalid hits found by puf but not sal: 265940.0\n",
      "sum of valid hits found by sal but not puf: 0\n",
      "sum of invalid hits found by sal but not puf: 3677.0\n"
     ]
    }
   ],
   "source": [
    "print('# of references found by puf but not sal: {}'.format(len(pufbetter)))\n",
    "print('# of references found by sal but not puf: {}'.format(len(salbetter)))\n",
    "print('# of invalid references found by puf but not sal: {}'.format(len(pufworse)))\n",
    "print('# of invalid references found by sal but not puf: {}'.format(len(salworse)))\n",
    "print('sum of valid hits found by puf but not sal: {}'.format(pufbetter['count_y'].sum()))\n",
    "print('sum of invalid hits found by puf but not sal: {}'.format(pufworse['count_y'].sum()))\n",
    "print('sum of valid hits found by sal but not puf: {}'.format(salbetter['count_x'].sum()))\n",
    "print('sum of invalid hits found by sal but not puf: {}'.format(salworse['count_x'].sum()))"
   ]
  }
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