Pathogen Prediction-gsea.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Pathogen from RNAseq data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from math import pow\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from sklearn.model_selection import train_test_split, ShuffleSplit, cross_val_score, GridSearchCV\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import RFE, SelectFromModel\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "random_state = 0xDEADCAFE\n",
    "use_gsea_genes = True\n",
    "rfe_step = 0.02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if use_gsea_genes:\n",
    "    with open(\"geneSet.txt\") as gsfile:\n",
    "        gsea_genes = list(gsfile.read().splitlines())[1:]\n",
    "    rfe_step = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete data set has 142 samples and 582 features.\n",
      "Training set has 99 samples. Testing set has 43 samples.\n"
     ]
    }
   ],
   "source": [
    "patient_groups=[\"control\", \"viral\", \"bacterial\", \"fungal\"]\n",
    "group_id = lambda name: patient_groups.index(name)\n",
    "\n",
    "X = pd.DataFrame.from_csv(\"combineSV_WTcpmtable_v2.txt\", sep=\"\\s+\").T\n",
    "y = [group_id(\"bacterial\")] * 29 \\\n",
    "    + [group_id(\"viral\")] * 42 \\\n",
    "    + [group_id(\"fungal\")] * 10 \\\n",
    "    + [group_id(\"control\")] * 61\n",
    "\n",
    "if use_gsea_genes:\n",
    "    X = X.filter(gsea_genes, axis=1)\n",
    "\n",
    "print \"Complete data set has %d samples and %d features.\" % (X.shape[0], X.shape[1])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=random_state)\n",
    "print \"Training set has %d samples. Testing set has %d samples.\" % (len(X_train), len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 29 candidates, totalling 58 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=8)]: Done  58 out of  58 | elapsed:  3.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.484848 using {'estimator__C': 2.384185791015625e-07}\n",
      "0.434343 (0.023274) with: {'estimator__C': 2.9802322387695312e-08}\n",
      "0.474747 (0.044710) with: {'estimator__C': 5.960464477539063e-08}\n",
      "0.444444 (0.013474) with: {'estimator__C': 1.1920928955078125e-07}\n",
      "0.484848 (0.014699) with: {'estimator__C': 2.384185791015625e-07}\n",
      "0.484848 (0.034910) with: {'estimator__C': 4.76837158203125e-07}\n",
      "0.434343 (0.023274) with: {'estimator__C': 9.5367431640625e-07}\n",
      "0.434343 (0.017149) with: {'estimator__C': 1.9073486328125e-06}\n",
      "0.393939 (0.022049) with: {'estimator__C': 3.814697265625e-06}\n",
      "0.393939 (0.038585) with: {'estimator__C': 7.62939453125e-06}\n",
      "0.414141 (0.058184) with: {'estimator__C': 1.52587890625e-05}\n",
      "0.454545 (0.023886) with: {'estimator__C': 3.0517578125e-05}\n",
      "0.454545 (0.044097) with: {'estimator__C': 6.103515625e-05}\n",
      "0.434343 (0.043485) with: {'estimator__C': 0.0001220703125}\n",
      "0.434343 (0.043485) with: {'estimator__C': 0.000244140625}\n",
      "0.383838 (0.048997) with: {'estimator__C': 0.00048828125}\n",
      "0.414141 (0.037973) with: {'estimator__C': 0.0009765625}\n",
      "0.404040 (0.007962) with: {'estimator__C': 0.001953125}\n",
      "0.363636 (0.132292) with: {'estimator__C': 0.00390625}\n",
      "0.434343 (0.017149) with: {'estimator__C': 0.0078125}\n",
      "0.434343 (0.043485) with: {'estimator__C': 0.015625}\n",
      "0.424242 (0.027561) with: {'estimator__C': 0.03125}\n",
      "0.414141 (0.017761) with: {'estimator__C': 0.0625}\n",
      "0.404040 (0.028173) with: {'estimator__C': 0.125}\n",
      "0.343434 (0.010412) with: {'estimator__C': 0.25}\n",
      "0.383838 (0.008574) with: {'estimator__C': 0.5}\n",
      "0.434343 (0.003062) with: {'estimator__C': 1.0}\n",
      "0.373737 (0.018986) with: {'estimator__C': 2.0}\n",
      "0.404040 (0.012249) with: {'estimator__C': 4.0}\n",
      "0.434343 (0.037360) with: {'estimator__C': 8.0}\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import RandomizedLogisticRegression\n",
    "\n",
    "parameters={'estimator__C': [pow(2, i) for i in xrange(-25, 4, 1)]}\n",
    "svc = LinearSVC(class_weight='balanced')\n",
    "rfe = RFECV(estimator=svc, step=rfe_step, cv=2, scoring='accuracy', n_jobs=1)\n",
    "clf = GridSearchCV(rfe, parameters, scoring='accuracy', n_jobs=8, cv=2, verbose=1)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (clf.best_score_, clf.best_params_))\n",
    "means = clf.cv_results_['mean_test_score']\n",
    "stds = clf.cv_results_['std_test_score']\n",
    "params = clf.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 248\n",
      "Recursive Feature Elimination (RFE) eliminated 334 features\n"
     ]
    },
    {
     "data": {
      "image/png": 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BT3KYbfy+a9cWjulqsJIOvPIqeTMbNy40X7W3h+dDh5YPVkqNXskiDj7cw2Nn6zklg5ta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\nlRXHcRzHcaoaV1Ycx3Ecx6lq/gLtCYIj7yT04gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e6c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_estimator = clf.best_estimator_\n",
    "\n",
    "print(\"Optimal number of features : %d\" % best_estimator.n_features_)\n",
    "print(\"Recursive Feature Elimination (RFE) eliminated %d features\" % (X.shape[1] - best_estimator.n_features_))\n",
    "\n",
    "# Plot number of features VS. cross-validation scores\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of feature subsets selected\")\n",
    "plt.ylabel(\"Cross validation score\")\n",
    "plt.plot(range(1, len(best_estimator.grid_scores_) + 1), best_estimator.grid_scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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zZdGiRdxxxx0sWrSIJUuW0LNnT1555RVOP/30Y96x9JZbbmHhwoXMmDGDw4cP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\nASKrD/lTvNVqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111daac90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xpqfz5sSJx3V5bSTrLq1gX3K9evXiiSee4C9/+Qvt27fnu+++Y/78+QHnOMRC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\nxBhjjDGu+V8q0+zBphGseAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11207b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from learning_curves import plot_learning_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def create_learning_curve(title, model):\n",
    "    cv = ShuffleSplit(n_splits=2, test_size=0.3, random_state=random_state)\n",
    "    plot_learning_curve(model, title, X, y, (0.2, 1.01), cv=cv, n_jobs=1)\n",
    "    \n",
    "create_learning_curve(\"Learning Curves Full Feature Set\", svc)\n",
    "create_learning_curve(\"Learning Curves Limited Feature Set\", best_estimator.estimator_)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions based on the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy was 55.81%\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "    control       0.50      0.61      0.55        18\n",
      "      viral       0.83      0.31      0.45        16\n",
      "  bacterial       0.64      0.88      0.74         8\n",
      "     fungal       0.25      1.00      0.40         1\n",
      "\n",
      "avg / total       0.64      0.56      0.55        43\n",
      "\n",
      "Confusion Matrix: cols = predictions, rows = actual\n",
      "\n",
      "                       control          viral      bacterial         fungal\n",
      "        control             11              1              3              3\n",
      "          viral             10              5              1              0\n",
      "      bacterial              1              0              7              0\n",
      "         fungal              0              0              0              1\n"
     ]
    }
   ],
   "source": [
    "from classification_metrics import classification_metrics\n",
    "\n",
    "# make predictions\n",
    "predicted = best_estimator.predict(X_test)\n",
    "\n",
    "classification_metrics(y_test, predicted, patient_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review model predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MNC.473</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_223</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_208</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.557</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_363</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_304</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.214</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_307</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.611</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_366</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.633</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.151</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.595</th>\n",
       "      <td>control</td>\n",
       "      <td>fungal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.571</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.452.y</th>\n",
       "      <td>control</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.294</th>\n",
       "      <td>control</td>\n",
       "      <td>fungal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.172</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.612</th>\n",
       "      <td>control</td>\n",
       "      <td>viral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.392</th>\n",
       "      <td>control</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.631</th>\n",
       "      <td>control</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.173</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.676</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.213</th>\n",
       "      <td>control</td>\n",
       "      <td>fungal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.533</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.354</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.675</th>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_323</th>\n",
       "      <td>fungal</td>\n",
       "      <td>fungal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_382</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.033</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_207</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.091</th>\n",
       "      <td>viral</td>\n",
       "      <td>viral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_097.y</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.215</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.474</th>\n",
       "      <td>viral</td>\n",
       "      <td>viral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.012.y</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_100</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.534</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.674</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_282</th>\n",
       "      <td>viral</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_171</th>\n",
       "      <td>viral</td>\n",
       "      <td>viral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_284</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.155</th>\n",
       "      <td>viral</td>\n",
       "      <td>viral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.014</th>\n",
       "      <td>viral</td>\n",
       "      <td>viral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Actual  Predicted\n",
       "MNC.473    bacterial  bacterial\n",
       "MN_223     bacterial  bacterial\n",
       "MN_208     bacterial  bacterial\n",
       "MNC.557    bacterial  bacterial\n",
       "MN_363     bacterial  bacterial\n",
       "MN_304     bacterial  bacterial\n",
       "MNC.214    bacterial  bacterial\n",
       "MN_307     bacterial    control\n",
       "MNC.611      control    control\n",
       "MN_366       control    control\n",
       "MNC.633      control    control\n",
       "MNC.151      control    control\n",
       "MNC.595      control     fungal\n",
       "MNC.571      control    control\n",
       "MNC.452.y    control  bacterial\n",
       "MNC.294      control     fungal\n",
       "MNC.172      control    control\n",
       "MNC.612      control      viral\n",
       "MNC.392      control  bacterial\n",
       "MNC.631      control  bacterial\n",
       "MNC.173      control    control\n",
       "MNC.676      control    control\n",
       "MNC.213      control     fungal\n",
       "MNC.533      control    control\n",
       "MNC.354      control    control\n",
       "MNC.675      control    control\n",
       "MN_323        fungal     fungal\n",
       "MN_382         viral    control\n",
       "MNC.033        viral    control\n",
       "MN_207         viral    control\n",
       "MNC.091        viral      viral\n",
       "MN_097.y       viral    control\n",
       "MNC.215        viral    control\n",
       "MNC.474        viral      viral\n",
       "MNC.012.y      viral    control\n",
       "MN_100         viral    control\n",
       "MNC.534        viral    control\n",
       "MNC.674        viral    control\n",
       "MN_282         viral  bacterial\n",
       "MN_171         viral      viral\n",
       "MN_284         viral    control\n",
       "MNC.155        viral      viral\n",
       "MNC.014        viral      viral"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = np.array(best_estimator.predict(X_test))\n",
    "\n",
    "d = {\"Predicted\": [patient_groups[i] for i in best_estimator.predict(X_test)],\n",
    "     \"Actual\": [patient_groups[i] for i in y_test]}\n",
    "\n",
    "patient_df = pd.DataFrame(d, index=X_test.index)\n",
    "patient_df.sort_values(by=\"Actual\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review patients the model misclassified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MN_097.y</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_284</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_282</th>\n",
       "      <td>viral</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.674</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.534</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.631</th>\n",
       "      <td>control</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.392</th>\n",
       "      <td>control</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.612</th>\n",
       "      <td>control</td>\n",
       "      <td>viral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_307</th>\n",
       "      <td>bacterial</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_100</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.595</th>\n",
       "      <td>control</td>\n",
       "      <td>fungal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.012.y</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.215</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_207</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.452.y</th>\n",
       "      <td>control</td>\n",
       "      <td>bacterial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.294</th>\n",
       "      <td>control</td>\n",
       "      <td>fungal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN_382</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.213</th>\n",
       "      <td>control</td>\n",
       "      <td>fungal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNC.033</th>\n",
       "      <td>viral</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Actual  Predicted\n",
       "MN_097.y       viral    control\n",
       "MN_284         viral    control\n",
       "MN_282         viral  bacterial\n",
       "MNC.674        viral    control\n",
       "MNC.534        viral    control\n",
       "MNC.631      control  bacterial\n",
       "MNC.392      control  bacterial\n",
       "MNC.612      control      viral\n",
       "MN_307     bacterial    control\n",
       "MN_100         viral    control\n",
       "MNC.595      control     fungal\n",
       "MNC.012.y      viral    control\n",
       "MNC.215        viral    control\n",
       "MN_207         viral    control\n",
       "MNC.452.y    control  bacterial\n",
       "MNC.294      control     fungal\n",
       "MN_382         viral    control\n",
       "MNC.213      control     fungal\n",
       "MNC.033        viral    control"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_df[patient_df[\"Predicted\"] != patient_df[\"Actual\"]]"
   ]
  }
 ],
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   "codemirror_mode": {
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