1-pass-data-science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PySpark imports for ML and Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import *\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "from pyspark.ml.classification import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Isarn: Feature Importance Using Generative T-Digests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from isarnproject.pipelines.fi import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Tox21 data from CSV to Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def loadCSV(fname):\n",
    "    return spark.read \\\n",
    "        .format(\"com.databricks.spark.csv\") \\\n",
    "        .option(\"header\", \"true\") \\\n",
    "        .option(\"mode\", \"DROPMALFORMED\") \\\n",
    "        .load(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainCSV = loadCSV(\"./tox21/tox21_dense_train.csv\")\n",
    "testCSV = loadCSV(\"./tox21/tox21_dense_test.csv\")\n",
    "trainLabelsCSV = loadCSV(\"./tox21/tox21_labels_train.csv\")\n",
    "testLabelsCSV = loadCSV(\"./tox21/tox21_labels_test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are 12 toxicity assays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NR.AhR',\n",
       " 'NR.AR',\n",
       " 'NR.AR.LBD',\n",
       " 'NR.Aromatase',\n",
       " 'NR.ER',\n",
       " 'NR.ER.LBD',\n",
       " 'NR.PPAR.gamma',\n",
       " 'SR.ARE',\n",
       " 'SR.ATAD5',\n",
       " 'SR.HSE',\n",
       " 'SR.MMP',\n",
       " 'SR.p53']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depVars = trainLabelsCSV.columns[1:]\n",
    "depVars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labeled sub-sets are different for each assay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-----+---------+------------+-----+---------+-------------+------+--------+------+------+------+\n",
      "|NR.AhR|NR.AR|NR.AR.LBD|NR.Aromatase|NR.ER|NR.ER.LBD|NR.PPAR.gamma|SR.ARE|SR.ATAD5|SR.HSE|SR.MMP|SR.p53|\n",
      "+------+-----+---------+------------+-----+---------+-------------+------+--------+------+------+------+\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     0|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     0|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     0|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     1|    NA|    NA|\n",
      "|    NA|    0|       NA|          NA|   NA|       NA|           NA|    NA|      NA|    NA|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     1|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     1|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     1|    NA|    NA|\n",
      "|    NA|    0|       NA|          NA|   NA|       NA|           NA|    NA|      NA|    NA|    NA|    NA|\n",
      "|    NA|    0|       NA|          NA|   NA|       NA|           NA|    NA|      NA|    NA|    NA|    NA|\n",
      "|    NA|    0|       NA|          NA|   NA|       NA|           NA|    NA|      NA|    NA|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     0|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|     0|      NA|    NA|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|     0|      NA|    NA|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|     1|      NA|    NA|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     0|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|     1|      NA|    NA|    NA|    NA|\n",
      "|    NA|    0|       NA|          NA|   NA|       NA|           NA|    NA|      NA|    NA|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|     1|      NA|    NA|    NA|    NA|\n",
      "|    NA|   NA|       NA|          NA|   NA|       NA|           NA|    NA|      NA|     1|    NA|    NA|\n",
      "+------+-----+---------+------------+-----+---------+-------------+------+--------+------+------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainLabelsCSV.drop(\"_c0\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About 2/3 of samples are labeled for each assay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8441, 9654, 8852, 7440, 7962, 9040, 8451, 7401, 9363, 8417, 7558, 8903]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[trainLabelsCSV.select(\"`\"+c+\"`\").filter(col(\"`\"+c+\"`\") != \"NA\").count() for c in depVars]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert individual feature columns to ML Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vectorAssembler = VectorAssembler(inputCols=trainCSV.columns[1:], outputCol=\"features\")\n",
    "castToDouble = [col(c).cast(\"Double\").alias(c) for c in vectorAssembler.getInputCols()]\n",
    "\n",
    "trainFV = vectorAssembler.transform(trainCSV.select(\"_c0\", *castToDouble)).select(\"_c0\", \"features\").repartition(8).cache()\n",
    "testFV = vectorAssembler.transform(testCSV.select(\"_c0\", *castToDouble)).select(\"_c0\", \"features\").repartition(8).cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def labDef(lc, fData, lData):\n",
    "    lcol = \"`\" + lc + \"`\"\n",
    "    ldef = lData.filter(col(lcol) != \"NA\").select(\"_c0\", *[(col(lcol).cast(\"Double\").alias(\"label\"))])\n",
    "    return ldef.join(fData, \"_c0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For each assay: identify the labeled sub-set and sync with the feature vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trainSets = dict([(y, labDef(y, trainFV, trainLabelsCSV)) for y in depVars])\n",
    "testSets = dict([(y, labDef(y, testFV, testLabelsCSV)) for y in depVars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For each assay: Fit a toxicity model using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression(maxIter=10)\n",
    "lrModels = dict([(y, lr.fit(trainSets[y])) for y in depVars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit a Feature Importance model to the training data\n",
    "### A T-Digest sketch of the value distribution for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi = TDigestFI() \\\n",
    "    .setDelta(0.75) \\\n",
    "    .setMaxDiscrete(35)\n",
    "\n",
    "fiModel = fi.fit(trainFV) \\\n",
    "    .setFeatureNames(trainCSV.columns[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For each Toxicity Model: Apply the Feature Importance model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imps = dict([(y, \\\n",
    "              fiModel.setTargetModel(lrModels[y]) \\\n",
    "                  .transform(trainSets[y]) \\\n",
    "                  .sort(-col(\"importance\"))) \\\n",
    "             for y in depVars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features and their Importance, sorted descending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------------------+\n",
      "|      name|          importance|\n",
      "+----------+--------------------+\n",
      "| slogPVSA7|0.011017651936974291|\n",
      "|    MRVSA9|0.005686530031986731|\n",
      "| slogPVSA9| 0.00521265252932117|\n",
      "|    nnitro| 0.00509418315365478|\n",
      "|       P3u|   0.004857244402322|\n",
      "|  PEOEVSA7| 0.00473877502665561|\n",
      "|       P3e|0.004620305650989219|\n",
      "|EstateVSA9|0.004264897523990...|\n",
      "| PEOEVSA12|0.004146428148323659|\n",
      "|       P3v|0.004146428148323659|\n",
      "|    MATSm1|0.003909489396990878|\n",
      "|      naro|0.003909489396990878|\n",
      "|slogPVSA10|0.003672550645658...|\n",
      "|       P3p|0.003554081269991707|\n",
      "|     PNSA3|0.003554081269991707|\n",
      "|     FNSA3|0.003435611894325317|\n",
      "|     mChi1|0.003435611894325317|\n",
      "|    GATSm1|0.003435611894325317|\n",
      "|EstateVSA8|0.003317142518658...|\n",
      "|       ndb|0.003198673142992...|\n",
      "+----------+--------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "imps['NR.AhR'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract importance values to a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rawImps = pd.DataFrame(dict([(y, imps[y].select(\"importance\").rdd.map(lambda r: r[0]).collect()) for y in depVars]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram the Feature Importances for a Toxicity Model\n",
    "## Distribution is Zipfian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8dca2fa150>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WXl9Vj0nyXa21/zJmcQAAADBpiz01+h1JTktyXpLXJ5lJ8s4kTz7SoKo6M8kVSU7P3GOX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8dc9ff7310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16, 8)\n",
    "ax.axes.xaxis.set_label_text(\"importance\")\n",
    "ax.axes.yaxis.set_label_text(\"frequency\")\n",
    "sns.distplot(rawImps['NR.AhR'], kde=False, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the Feature Importances for all Toxicity Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8dc9ff44d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VexUKynHvsdb2GmNajDETwtdfjbpvX/hah6QjxphHFBolfkOhqdXtyfkWAAAAAACZyu1Q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\nnR4IxWlo+fIVysvLc9SI74MPDjlqr6x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DR+AcIv+uYU5x9crPiJgcfK1VJyycsAFDCRt6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8dc9b19c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16, 8)\n",
    "ax.axes.xaxis.set_label_text(\"toxicity measure\")\n",
    "ax.axes.yaxis.set_label_text(\"importance\")\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(10)\n",
    "sns.violinplot(data=rawImps, inner=\"points\", scale=\"width\", cut=0, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}