1.TCGA-MLexample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a logistic regression model to predict TP53 mutation from gene expression data in TCGA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib\n",
    "import random\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing, grid_search\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('seaborn-notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify model configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We're going to be building a 'TP53' classifier \n",
    "GENE = 'TP53'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Parameter Sweep for Hyperparameters\n",
    "param_grid = {\n",
    "    'loss': ['log'],\n",
    "    'penalty': ['elasticnet'],\n",
    "    'alpha': [10 ** x for x in range(-6, 1)],\n",
    "    'l1_ratio': [0, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 1],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Here is some [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) regarding the classifier and hyperparameters*\n",
    "\n",
    "*Here is some [information](https://ghr.nlm.nih.gov/gene/TP53) about TP53*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('data'):\n",
    "    os.makedirs('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url_to_path = {\n",
    "    # X matrix\n",
    "    'https://ndownloader.figshare.com/files/5514386':\n",
    "        os.path.join('data', 'expression.tsv.bz2'),\n",
    "    # Y Matrix\n",
    "    'https://ndownloader.figshare.com/files/5514389':\n",
    "        os.path.join('data', 'mutation-matrix.tsv.bz2'),\n",
    "}\n",
    "\n",
    "for url, path in url_to_path.items():\n",
    "    if not os.path.exists(path):\n",
    "        urllib.request.urlretrieve(url, path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 43s, sys: 1.13 s, total: 1min 45s\n",
      "Wall time: 1min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path = os.path.join('data', 'expression.tsv.bz2')\n",
    "X = pd.read_table(path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 19s, sys: 984 ms, total: 1min 20s\n",
      "Wall time: 1min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path = os.path.join('data', 'mutation-matrix.tsv.bz2')\n",
    "Y = pd.read_table(path, index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process Mutation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = Y[GENE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sample_id\n",
       "TCGA-02-0047-01    0\n",
       "TCGA-02-0055-01    1\n",
       "TCGA-02-2483-01    1\n",
       "TCGA-02-2485-01    1\n",
       "TCGA-02-2486-01    0\n",
       "TCGA-04-1348-01    1\n",
       "Name: TP53, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The Series now holds TP53 Mutation Status for each Sample\n",
    "y.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.655334\n",
       "1    0.344666\n",
       "Name: TP53, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here are the percentage of tumors with NF1\n",
    "y.value_counts(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set aside 10% of the data for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Size: 7,706 features, 6,935 training samples, 771 testing samples'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Typically, this can only be done where the number of mutations is large enough\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "'Size: {:,} features, {:,} training samples, {:,} testing samples'.format(len(X.index), len(X_train), len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transformation: Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scale_df(df):\n",
    "    \"\"\"Standardize colummns by scaling to z-scores\"\"\"\n",
    "    scaled_matrix = preprocessing.scale(df)\n",
    "    return pd.DataFrame(scaled_matrix, index=df.index, columns=df.columns)\n",
    "\n",
    "# Standardize X separately for the holdout dataset\n",
    "X_train, X_test = scale_df(X_train), scale_df(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross validation model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# joblib is used to cross-validate in parallel by setting `n_jobs=-1` in GridSearchCV\n",
    "# Supress joblib warning. See https://github.com/scikit-learn/scikit-learn/issues/6370\n",
    "warnings.filterwarnings('ignore', message='Changing the shape of non-C contiguous array')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7min 42s, sys: 17.6 s, total: 7min 59s\n",
      "Wall time: 9min 57s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "clf = SGDClassifier(random_state=0, class_weight='balanced')\n",
    "clf_grid = grid_search.GridSearchCV(estimator=clf, param_grid=param_grid, n_jobs=-1, scoring='roc_auc')\n",
    "# Fit the model (the computationally intensive part)\n",
    "clf_grid.fit(X=X_train, y=y_train)\n",
    "best_clf = clf_grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.1, 'l1_ratio': 0.05, 'loss': 'log', 'penalty': 'elasticnet'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.1, average=False, class_weight='balanced', epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.05,\n",
       "       learning_rate='optimal', loss='log', n_iter=5, n_jobs=1,\n",
       "       penalty='elasticnet', power_t=0.5, random_state=0, shuffle=True,\n",
       "       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize hyperparameters performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def grid_scores_to_df(grid_scores):\n",
    "    \"\"\"\n",
    "    Convert a sklearn.grid_search.GridSearchCV.grid_scores_ attribute to \n",
    "    a tidy pandas DataFrame where each row is a hyperparameter-fold combinatination.\n",
    "    \"\"\"\n",
    "    rows = list()\n",
    "    for grid_score in grid_scores:\n",
    "        for fold, score in enumerate(grid_score.cv_validation_scores):\n",
    "            row = grid_score.parameters.copy()\n",
    "            row['fold'] = fold\n",
    "            row['score'] = score\n",
    "            rows.append(row)\n",
    "    df = pd.DataFrame(rows)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>alpha</th>\n",
       "      <th>fold</th>\n",
       "      <th>l1_ratio</th>\n",
       "      <th>loss</th>\n",
       "      <th>penalty</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>log</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>0.895547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>log</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>0.895994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      alpha  fold  l1_ratio loss     penalty     score\n",
       "0  0.000001     0       0.0  log  elasticnet  0.895547\n",
       "1  0.000001     1       0.0  log  elasticnet  0.895994"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_score_df = grid_scores_to_df(clf_grid.grid_scores_)\n",
    "cv_score_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TozKNvLLezXvvvUVNjYWbb/4pERH6zUpqK2ccFeVP9Dc2NvbYMoBLly5k+fKl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\nmC+p3DTNFyQ9oXASPijp56Zplp1IvNNOm6tnn12kZ774RNn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62538d0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cross-validated performance distribution\n",
    "facet_grid = sns.factorplot(x='l1_ratio', y='score', col='alpha',\n",
    "    data=cv_score_df, kind='violin', size=4, aspect=1)\n",
    "facet_grid.set_ylabels('AUROC');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N2zvx6+mrGdKUrVKKjr00VWpmFvkw+sqIZ/F/6eZ/N7gTW3017UK5DDTXHlZ2FooXpEVL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\n+MuhaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f624ebb4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cross-validated performance heatmap\n",
    "cv_score_mat = pd.pivot_table(cv_score_df, values='score', index='l1_ratio', columns='alpha')\n",
    "ax = sns.heatmap(cv_score_mat, annot=True, fmt='.1%')\n",
    "ax.set_xlabel('Regularization strength multiplier (alpha)')\n",
    "ax.set_ylabel('Elastic net mixing parameter (l1_ratio)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Optimal Hyperparameters to Output ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred_train = best_clf.decision_function(X_train)\n",
    "y_pred_test = best_clf.decision_function(X_test)\n",
    "\n",
    "def get_threshold_metrics(y_true, y_pred):\n",
    "    roc_columns = ['fpr', 'tpr', 'threshold']\n",
    "    roc_items = zip(roc_columns, roc_curve(y_true, y_pred))\n",
    "    roc_df = pd.DataFrame.from_items(roc_items)\n",
    "    auroc = roc_auc_score(y_true, y_pred)\n",
    "    return {'auroc': auroc, 'roc_df': roc_df}\n",
    "\n",
    "metrics_train = get_threshold_metrics(y_train, y_pred_train)\n",
    "metrics_test = get_threshold_metrics(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TzAF8Z6llhwCjgQLgZXe/PtHxiIiIpLuE1tzNbCvgduCNcla5DTgOOBDoZ2ZdExmPiIhI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62477ccda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC\n",
    "plt.figure()\n",
    "for label, metrics in ('Training', metrics_train), ('Testing', metrics_test):\n",
    "    roc_df = metrics['roc_df']\n",
    "    plt.plot(roc_df.fpr, roc_df.tpr,\n",
    "        label='{} (AUROC = {:.1%})'.format(label, metrics['auroc']))\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Predicting TP53 mutation from gene expression (ROC curves)')\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the classifier coefficients?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coef_df = pd.DataFrame(best_clf.coef_.transpose(), index=X.columns, columns=['weight'])\n",
    "coef_df['abs'] = coef_df['weight'].abs()\n",
    "coef_df = coef_df.sort_values('abs', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'96.3% zero coefficients; 270 negative and 482 positive coefficients'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{:.1%} zero coefficients; {:,} negative and {:,} positive coefficients'.format(\n",
    "    (coef_df.weight == 0).mean(),\n",
    "    (coef_df.weight < 0).sum(),\n",
    "    (coef_df.weight > 0).sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>weight</th>\n",
       "      <th>abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MDM2</th>\n",
       "      <td>-0.187769</td>\n",
       "      <td>0.187769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA2R</th>\n",
       "      <td>-0.144938</td>\n",
       "      <td>0.144938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHLDA3</th>\n",
       "      <td>-0.130180</td>\n",
       "      <td>0.130180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AEN</th>\n",
       "      <td>-0.127819</td>\n",
       "      <td>0.127819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPS27L</th>\n",
       "      <td>-0.116172</td>\n",
       "      <td>0.116172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDB2</th>\n",
       "      <td>-0.114379</td>\n",
       "      <td>0.114379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPATA18</th>\n",
       "      <td>-0.100797</td>\n",
       "      <td>0.100797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLH</th>\n",
       "      <td>-0.097972</td>\n",
       "      <td>0.097972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN1A</th>\n",
       "      <td>-0.094824</td>\n",
       "      <td>0.094824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>0.087242</td>\n",
       "      <td>0.087242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           weight       abs\n",
       "MDM2    -0.187769  0.187769\n",
       "EDA2R   -0.144938  0.144938\n",
       "PHLDA3  -0.130180  0.130180\n",
       "AEN     -0.127819  0.127819\n",
       "RPS27L  -0.116172  0.116172\n",
       "DDB2    -0.114379  0.114379\n",
       "SPATA18 -0.100797  0.100797\n",
       "POLH    -0.097972  0.097972\n",
       "CDKN1A  -0.094824  0.094824\n",
       "CDKN2A   0.087242  0.087242"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are not surprising. TP53 is a transcription modulator and when it mutated in a tumor, the cell goes haywire. This makes finding a transcriptional signature fairly easy. Also, the genes that the classifier uses is interesting, but not necessarily novel.\n",
    "\n",
    "1. TP53 is a [transcription factor](https://en.wikipedia.org/wiki/Transcription_factor \"TF wiki\") that regulates many genes including EDA2R. Studies have linked EDA2R (or XEDAR) to [increased survival in colon cancer patients](http://www.ncbi.nlm.nih.gov/pubmed/19543321) and [losing hair as a result of chemotherapy](http://onlinelibrary.wiley.com/doi/10.1016/j.febslet.2010.04.058/full)\n",
    "2. SPATA18 is a gene associated with spermatogenesis and is a transcription factor for TP53. It's association with TP53 was [recently discovered](http://www.ncbi.nlm.nih.gov/pubmed/21300779) in 2011.\n",
    "3. C6orf138 (or [PTCHD4](http://www.genecards.org/cgi-bin/carddisp.pl?gene=PTCHD4)) is also a transcriptional target for TP53 and was only recently discovered in [2014 to repress hedgehog signalling](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239647/).\n",
    "4. The list goes on and includes several other TP53 targets..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigate the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predict_df = pd.DataFrame.from_items([\n",
    "    ('sample_id', X.index),\n",
    "    ('testing', X.index.isin(X_test.index).astype(int)),\n",
    "    ('status', y),\n",
    "    ('decision_function', best_clf.decision_function(X)),\n",
    "    ('probability', best_clf.predict_proba(X)[:, 1]),\n",
    "])\n",
    "predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>sample_id</th>\n",
       "      <th>testing</th>\n",
       "      <th>status</th>\n",
       "      <th>decision_function</th>\n",
       "      <th>probability</th>\n",
       "      <th>probability_str</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-L5-A8NR-01</th>\n",
       "      <td>TCGA-L5-A8NR-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.022358</td>\n",
       "      <td>0.999879</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-E2-A1LI-01</th>\n",
       "      <td>TCGA-E2-A1LI-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.291229</td>\n",
       "      <td>0.999749</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-L5-A4OH-01</th>\n",
       "      <td>TCGA-L5-A4OH-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.278039</td>\n",
       "      <td>0.999746</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-CQ-A4CI-01</th>\n",
       "      <td>TCGA-CQ-A4CI-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.247578</td>\n",
       "      <td>0.999738</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-18-3412-01</th>\n",
       "      <td>TCGA-18-3412-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.982775</td>\n",
       "      <td>0.999659</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-CD-8527-01</th>\n",
       "      <td>TCGA-CD-8527-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.839696</td>\n",
       "      <td>0.999606</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-EI-6513-01</th>\n",
       "      <td>TCGA-EI-6513-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.723317</td>\n",
       "      <td>0.999558</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-13-1506-01</th>\n",
       "      <td>TCGA-13-1506-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.652425</td>\n",
       "      <td>0.999525</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-20-1684-01</th>\n",
       "      <td>TCGA-20-1684-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.608053</td>\n",
       "      <td>0.999504</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-E2-A574-01</th>\n",
       "      <td>TCGA-E2-A574-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.606733</td>\n",
       "      <td>0.999503</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       sample_id  testing  status  decision_function  \\\n",
       "sample_id                                                              \n",
       "TCGA-L5-A8NR-01  TCGA-L5-A8NR-01        0       0           9.022358   \n",
       "TCGA-E2-A1LI-01  TCGA-E2-A1LI-01        1       0           8.291229   \n",
       "TCGA-L5-A4OH-01  TCGA-L5-A4OH-01        0       0           8.278039   \n",
       "TCGA-CQ-A4CI-01  TCGA-CQ-A4CI-01        0       0           8.247578   \n",
       "TCGA-18-3412-01  TCGA-18-3412-01        1       0           7.982775   \n",
       "TCGA-CD-8527-01  TCGA-CD-8527-01        0       0           7.839696   \n",
       "TCGA-EI-6513-01  TCGA-EI-6513-01        0       0           7.723317   \n",
       "TCGA-13-1506-01  TCGA-13-1506-01        0       0           7.652425   \n",
       "TCGA-20-1684-01  TCGA-20-1684-01        0       0           7.608053   \n",
       "TCGA-E2-A574-01  TCGA-E2-A574-01        0       0           7.606733   \n",
       "\n",
       "                 probability probability_str  \n",
       "sample_id                                     \n",
       "TCGA-L5-A8NR-01     0.999879          100.0%  \n",
       "TCGA-E2-A1LI-01     0.999749          100.0%  \n",
       "TCGA-L5-A4OH-01     0.999746          100.0%  \n",
       "TCGA-CQ-A4CI-01     0.999738          100.0%  \n",
       "TCGA-18-3412-01     0.999659          100.0%  \n",
       "TCGA-CD-8527-01     0.999606          100.0%  \n",
       "TCGA-EI-6513-01     0.999558          100.0%  \n",
       "TCGA-13-1506-01     0.999525          100.0%  \n",
       "TCGA-20-1684-01     0.999504          100.0%  \n",
       "TCGA-E2-A574-01     0.999503          100.0%  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top predictions amongst negatives (potential hidden responders)\n",
    "predict_df.sort_values('decision_function', ascending=False).query(\"status == 0\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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qs8rpnOxmPuCL+/WEWA4JbSGEofkCPppGTlDk9FDoLEjINauyKwiGgnRN9iTkekJES0Jb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\nQ6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6252cad4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ignore numpy warning caused by seaborn\n",
    "warnings.filterwarnings('ignore', 'using a non-integer number instead of an integer')\n",
    "\n",
    "ax = sns.distplot(predict_df.query(\"status == 0\").decision_function, hist=False, label='Negatives')\n",
    "ax = sns.distplot(predict_df.query(\"status == 1\").decision_function, hist=False, label='Positives')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B1wEnAreP9TybNnVX2dSpq62tUf1X/ye6GROmlvtfi33vTA7tb3d3f839DPIq/UBSyjHm\n3wLXG2PeCcSAM4EngP8xxnwMWAVcU9Gri4jItJIfyo66UTJeRrOyK1DKUHYP8I4Rbjou/OaIiMhU\nlp/8FXUiZMjoGHMFtPKXiIiEJltUMYNW/qqEgllEREKTP4854kQAzcquhIJZRERCk/VUMVdLwSwi\nIqHJV8xRVxVzpRTMIiISmu2OMSuYy6ZgFhGR0GSHHWPW6VLlUzCLiEhoPFXMVVMwi4hIaIrPYwZt\nYlEJBbOIiIRmu1nZqpjLpmAWEZHQDM7K1ulSlVIwi4hIaAqTv3S6VMUUzCIiEprC5C/Nyq6YgllE\nREIzvGKW8imYRUQkNJr8VT0Fs4iIhMYbdrqUp9OlyqZgFhGR0GhJzuopmEVEJDTbLzCiYC6XgllE\nREKjirl6CmYREQmNN/w8ZlXMZVMwi4hIaLLesJW/VDGXTcEsIiKh0QIj1VMwi4hIaLLD18pWxVw2\nBbOIiISmcIzZ0VrZlVIwi4hIaLabla2h7LIpmEVEJDSDQ9mqmCulYBYRkdCoYq6egllERELjeUOP\nMXuqmMumYBYRkdBsXzFrE4tyKZhFRCQ0no4xV03BLCIioSlUzI6OMVdKwSwiIqFRxVw9BbOIiIRG\ns7Krp2AWEZHQZLXyV9UUzCIiEhpv2O5S2sSifApmEREJzeBQtirmSkVLuZMxpg74B3A+8CfgWoJQ\nXw+cZq1Nj1sLRURkysj6Hg4Obn4oWxVz2UqtmL8MbMldPh/4gbV2OfAScPp4NExERKYez/eIOC4O\nDqCKuRJjBrMxxgD7AL8DHGA5cEvu5luAlePWOhERmVKyfhbXjeRiWRVzJUqpmL8NfBoKP+eGoqHr\ndmDBeDRMRESmnkLF7KhirtSox5iNMacBD1prVwWF83acka4cSVtbY5lNm17Uf/W/ltVy/2uu765P\nNBJlblsTALG4W3s/gyqNNfnrX4Alxpi3AwuBFNBjjElYa5O569aV8kKbNnVX1dCprK2tUf1X/ye6\nGROmlvtfi31PpzM4vsPmzT0AJJOZmvsZ5FX6gWTUYLbWvjd/2RjzFeBV4HDgJOA64ETg9opeWURE\npp2snyXiRAYnf+kYc9nKOY85P2x9HvAhY8y9QAtwTeitEhGRKSnre7i5Y8wOjvZjrkBJ5zEDWGv/\nq+jb48ahLSIiMsVl/SzxSAwAx3FUMVdAK3+JiEhoglnZweIijuNoVnYFFMwiIhKarBcMZQO4qGKu\nhIJZRERC4+Umf4Eq5kopmEVEJDTZ3AIjAI7j4uNNcIumHgWziIiExvO9wgYWLqqYK6FgFhGRUHi+\nh49fVDE72o+5AgpmEREJRdYPhq0jro4xV0PBLCIioch6WQDNyq6SgllERELh5StmzcquioJZRERC\nkfWHVszBrGwFc7kUzCIiEoqMlwEg6mpWdjUUzCIiEop8xRx1g20YNCu7MgpmEREJRSY3+SuqY8xV\nUTCLiEgo8hVzJFcxa1Z2ZRTMIiISisIxZlXMVVEwi4hIKPJD2YMLjGhWdiUUzCIiEoqsn5+VXTSU\n7WsTi3IpmEVEJBQjTf7SrOzyKZhFRCQU+WPMWiu7OgpmEREJRSZ/HrOTO49Zs7IromAWEZFQ5Dex\niKpiroqCWUREQpHRecyhUDCLiEgoRj6PeSJbNDUpmEVEJBTZ7c5jdvDR6VLlUjCLiEgoMrnzmGOF\noWxXx5groGAWEZFQFCrm4qFsHWMum4JZRERCMXw/Zs3KroyCWUREQlGYle0MzsrWyl/lUzCLiEgo\ndB5zOBTMIiISisLKX7nJX9pdqjIKZhERCUVhrezc5C/XcQBUNZdJwSwiIqHYbiibXDCrai6LgllE\nREKRGbYfs6OKuSIKZhERCcXw85jzQ9mamV0eBbOIiIRisGIeNpStirks0bHuYIyZAVwNzAMSwIXA\nE8C1BMG+HjjNWpsev2aKiMhkl/GGnsfsOEHtp2PM5SmlYn478LC1dgXwHuAS4Hzgh9ba5cBLwOnj\n1kIREZkSRjqPGcD3tZFFOcasmK21NxZ9uxvwGrAcODN33S3AZ4ArQm+diIhMGRk/i+u4uLlK2dWs\n7IqMGcx5xpg/AwsJKug/Fg1dtwMLxqFtIiIyhWS8TGHiF2hWdqVKDmZr7ZuNMa8DroPcx6CAs4OH\nDNHW1lhm06YX9V/9r2W13P9a6rvj+sQi0UKfneeDeGid3UBjYuZENm1KKWXy18FAu7V2jbX2SWNM\nBOg2xiSstUmCKnrdWM+zaVN39a2dotraGtV/9X+imzFharn/tdb3gXSaCJFCn93cNKZNm7sZiNde\n1Vzph7JSJn8dRXAMGWPMPGAmcCdwUu72E4HbK3p1ERGZNrJehog7wlC2jjGXpZSh7MuBq4wx9wF1\nwCeAR4FrjTEfA1YB14xfE0VEZCrI+FmiOsZctVJmZQ8Ap45w03HhN0dERKaqjJchEUsUvtes7Mpo\n5S8REQlF1s8WzmEGVcyVUjCLiEgoMl6WqDM4EJsPZk/BXBYFs4iIhCIzfPKXhrIromAWEZGqeb6H\nj6/JXyFQMIuISNUyhXWyB4eyNfmrMgpmERGpWja35ePQ85i1u1QlFMwiIlK1QsWsoeyqKZhFRKRq\nGW/7illD2ZVRMIuISNWy/vbHmFUxV0bBLCIiVRt1KFsVc1kUzCIiUrXBoeztZ2VrgZHyKJhFRKRq\nhaFsZ6RZ2d6EtGmqUjCLiEjVRjqPWceYK6NgFhGRqo10HrOrY8wVUTCLiEjV0iNN/kIVcyUUzCIi\nUrXsCOcxa1Z2ZRTMIiJStcwI5zG72vaxIgpmERGpWnbEoezcrGwFc1kUzCIiUrWRzmPWUHZlFMwi\nIlK1zAjnMbs6XaoiCmYREaladqTzmLWJRUUUzCIiUrVM7jzm6EizslUxl0XBLCIiVcuv/BUZMpQd\nRIynirksCmYREala/jzmIRVzYYERrZVdDgWziIhUbaTzmDUruzIKZhERqVp2xKFsHWOuhIJZRESq\nNjj5S7Oyq6VgFhGRqo00+UuzsiujYBYRkaoNnsesWdnVUjCLiEjVRhrKzlPFXB4Fs4iIVG3k85h1\njLkSCmYREaladqSVv/IRo4q5LApmERGp2mDFvP15zDrGXB4Fs4iIVG3kyV+alV2J7Y/Sj8AYczFw\nBBABvg48DFxLEOzrgdOstenxaqSIiExuGT+D67iFmdig85grNWbFbIxZAexnrT0cOB74LnA+8ENr\n7XLgJeD08WykiIhMbhkvO2TiF+g85kqVMpR9L3By7nIH0AAsB36bu+4WYGX4TRMRkaki62eHDGPD\n4HnMqpjLM+ZQtrXWB/pz354B/A54a9HQdTuwYHyaJyIiU0HGyxJ1hkbK4O5SCuZylHSMGcAY806C\nIevjgBeLbnLCbpSIiEwtGS9DxB15KFuzsstT6uSvtwLnElTK3caYbmNMwlqbBBYC68Z6jra2xupa\nOsWp/+p/Lavl/tdK333HIxGJDenvC6uDYJ45M14zP4cwjBnMxpgm4GLgLdbaztzVdwInAtfnvt4+\n1vNs2tRdRTOntra2RvVf/Z/oZkyYWu5/LfU9lUkTi8WH9DdfMXd1D9TMz6FYpR9GSqmY3wPMBm40\nxjiAD3wIuMoYcyawCrimolcXEZFpYaTJXzrGXJlSJn/9GPjxCDcdF35zRERkKsp4me0mf2lWdmW0\n8peIiFQt42V3OPnL972JaNKUpWAWEZGqeL6Hj090+AIjaFZ2JRTMIiJSlUxhnezhQ9k6xlwJBbOI\niFQl4wVbPu5wKFsVc1kUzCIiUpWsn6uYtxvKzk3+UsVcFgWziIhUZUcVs6uKuSIKZhERqUqhYh52\njFm7S1VGwSwiIlXJV8yalR0OBbOIiFQlPys7olnZoVAwi4hIVXY4+UvHmCuiYBYRkars6Dxmzcqu\njIJZRESqolnZ4VIwi4hIVdJeGoCYZmWHQsEsIiJVGcgmAaiLJIZcPzgrW5tYlEPBLCIiVUlmgmBO\nDA9mVcwVUTCLiEhVChVzdOSKWcqjYBYRkarkg3l4xazzmCujYBYRkarkh7K3q5gdrfxVCQWziIhU\nZazJX6qYy6NgFhGRqiR3OJSdW2BEFXNZFMwiIlKVgTGGsn1fp0uVQ8EsIiJV2VHFrLWyK6NgFhGR\nqgxkk8TdWGHoOs/VMeaKKJhFRKQqyUySxLBhbNCs7EopmEVEpCoD2SQzInXbXe842l2qEgpmERGp\nykB25Iq5MJStirksCmYREamY53uksqntzmEGrZVdKQWziIhUbEczskGzsiulYBYRkYrt6Bxm0Kzs\nSimYRUSkYqVUzJqVXR4Fs4iIVGxH62SDZmVXSsEsIiIVyw9la1Z2eBTMIiJSseSoFbOOMVdCwSwi\nIhUrTP4adVa2NrEoh4JZREQqVpj8pVnZoYmWcidjzDLgN8Al1trLjDGLgGsJgn09cJq1Nj1+zRQR\nkcmolMlfmpVdnjErZmNMPfB94M6iq88HfmCtXQ68BJw+Ps0TEZHJLJkpYYERVcxlKWUoewA4nqAy\nzlsB3JK7fAuwMtxmiYjIVFComDWUHZoxg9la61lrk8Oubigaum4HFoTeMhERmfQGh7JH2l1Kp0tV\nIozJX04IzyEiIlNQcpQlOTWUXZmSJn+NoNsYk8hV0guBdWM9oK2tscKXmh7Uf/W/ltVy/6d7371I\nFoBF82YTj8aH3JYP5GjMnfY/hzBVGsx3AicC1+e+3j7WAzZt6q7wpaa+trZG9V/9n+hmTJha7n8t\n9L2rrxfXcenYOoDjDD3qmQ/jZCoz7X8OI6n0w8iYwWyMORj4NrAYSBtjTgJOBa4xxpwJrAKuqejV\nRURkSktmkyQiicKw9XAODugYc1nGDGZr7WPA0SPcdFz4zRERkalkIJsc8RzmPMdxNPmrTFr5S0RE\nKpbMJEdc9SvPxdHkrzIpmEVEpGKlVMxa+as8CmYREalI2suQ9bOjB7Mq5rIpmEVEpCLJUfZiztMx\n5vIpmEVEpCKjbWCR5+CqYi6TgllERCpS2PJRs7JDpWAWEZGKDIyyHGeeZmWXT8EsIiIVGSixYtas\n7PIomEVEpCLJUbZ8zNOs7PIpmEVEpCKFoewxjzF7O6tJ04KCWUREKjKQHQDGmpWtirlcCmYREanI\nmu5gx9/mRPMO7+M4CuZyKZhFRKRsyWyKxzc9xey6FhY3Ldrh/Rx0ulS5FMwiIlK2v7c/RSqb4g3z\nD8F1dhwlruPgqWIui4JZRETK9tcNjwLwxvmHjHo/VczlUzCLiEhZtg108Py2l9ijeXfa6mePel8d\nYy6fglkAWNuznq5U90Q3Q0SmgL9teAwfn8PGqJZBS3JWQsEseL7HNx/5If9nfzPRTRGRKeC5rS8A\n8Pq5rxvzvtrEonwKZsF1XFzHYVP/5oluiohMAet6NzCnrpX62Iwx76uKuXwKZgGgOdFEZ7Jropsh\nIpNcV6qbnnQvC2bOL+n+2sSifApmAaA53kRPupesl53opojIJLauZwMACxtKC2ZtYlE+BbMA0BRv\nBNAEMBEZ1breIJh3KbFi1pKc5VMwCxAMZYOCWUS2t22gg2Q2BQxWzAvKqJi1iUV5FMwCDAZzh44z\ni0iR/kw/F/z1W/zvszcCQTBHnAjz6ttKerwDqpjLpGAWoHgoW8EsIoNe615HMpvi75v+QXeqh/W9\nG5hX30bEjZT0eAdXs7LLpGAWIJj8BdCZ1FC2iAxa0xPsIOX5HneuvpeUly75+DLkh7KlHApmAYqP\nMatiFpFB+a0dAe5d82cAFjYsKPnxmvxVPgWzANCcCIaydS6ziBRb07OOeCTO0llLSHsZABbMnFfy\n47XASPkUzAJAXaSOmBujMzcrO+tl6c8MTHCrRGQipb0M63s3srBhwZBdpHZRxTyuFMwCBJ9qmxNN\ndOUq5t++fDtffvC/6Un1TnDLRGRnynpZXuteC8D63g14vseixl04qO0Aom6UukiC1rpZJT+fm6uY\nFc6li050A2TyaI438nLnKjzf4x9bnqM/M8DLna/yurb9J7ppIrKT3PXafdz80m18dNlp9Gf6AVg0\ncwH1sRmcus9JQPBBvlQOwX19/MJlGZ2CWQqaEk34+LT3bWZD70YAXularWAWqSEPb3gcgD+8ehdL\nmncHYNdkfUzjAAAU8ElEQVTGhQC8Yf7BZT9fPsR930e5XBoFsxQ0585lfnLz04XrXu1cPVHNEZGd\nbENve2HJzdd61rF5YCsOTsmrfI2kuGKW0iiYpxDf9+lLZujqTdHTn6Y/maEvmaE/maU/mSl8n0pn\nyWZ9MlmPTOGrR8YbepynMKyU+9Lb2AON8Af7cOEv48Vtq7j69mcgkuaFyD0scQ+mLbqIeNQlGnWJ\nRV3iUZdYNEIsEnxf/C8edXHjUXr608F1ERfX1cdmkcnA93360ylcxyHiRHlkw98BWL7wCO5d+wD9\nmQHmN8wjHolV/BpDKmYpiYJ5kkhnPLZ2D7ClM/eva4Ct3Um6elN09qbo6k3R3Zcik638j9txgokY\nxfL/V3x83FaPeCMMRIN9mbMdbTBrE/fbF4jMaie261oe6egj9dwbK24DQMR1igLdJRqNFC6PHO6R\n7a7L3y9/W7To+WJRl2gk2GM64jo4rkPEcXBdB9ch+Oo6uE7x1+BnU86xM5Gdwfd9MlmfZDpLMpUN\nvg6/POR7r3A5lbttIDV4efC+Hqlsivj+D4KbJfnMYSTMIzh1LrffXEd86RwiszazdrXLJ/98b+ED\neDzmEo9FaKiLMnNGjMYZcRrrY8ysjwXf18dpbUzQ2pQAioJZFXPJKg5mY8wlwGGAB/y7tfaR0Fo1\nDfUnM2zpHGBz12DwFr52DdDZk9rhY2NRl6b6OLvObaS5IU5TQ5yZM2LMSESoT0SZUfSvPhElHo8Q\ndR2iUZeo6xKNOEFQjVGpPrPZcumTTwHQkpjFUQcdzs2v3sy7j2/hwfZn6EhBpGkb/3riYhrcZu7f\nfBcD2QEOrV9JJuuTznikM1nSWY9U2iOd9UhnPNyIS09vavD2THB9/n79yQxdvcFt1XzwCIPjBB8c\nXGfsQHec3PWOg+MEb0COMxjwbu66RCJKJp0t3Md1wMm/BrnrXGfI7WM95/Db8213cs+JE4yIDLlt\nyPfF9xv+PNs/h5N7kpEes8Pbct83Nc6gpyc49c7NPWCkNuY/NDq5G53i+xV+VkNvc4qer7gd+Z/F\n4POV+Psv4SCoj4/n+WS94Gsm9zW47BVuy3o+9fUJOjv7c997JNPB33kq45FOe6Qy2eD/Su66VO66\ndMbLfR/c7pVcbfr5n8R2Iq5DIu4Sa+xiRqSVlqY6+ltfpntGcOZF4+seJhPppT65kKW7z6Wf17PO\nv5PZ7iJis2YE7clk6e5Lk0oPkMqMvTFF88w4LOmCGfB/d7/A3KZGZjfV0dKUYHZTHU0N8e2KBakw\nmI0xRwFLrbWHG2P2AX4KHB5qy6YQz/Pp6EmytSsZVLq5sC18352ktz894mMjrkNrU4J9dpvF7OY6\nZjfVMbu5jjlNdbQ01dHcEKcuHtkpldysuubC5T2aF7P/3D24+VV4bOvf6EhtoyneSFeqm83uC7S0\n7Mkzqx4FYMWeB3HIvANZ17OBe9c+yD8vPoaWotMp2toa2bSptKU+Pd8nUxTa+XAfEujF/7LBm9fg\n/Qav9/Nvnr6P55H76g9+9Xw8HzzPw/Mp3HeHj8tf5/uk0x74wW2+H1Q1XuH7ous8cqeKhPqrkmnI\nAWKxYBQoHnOZkYjS3BAnEYuQiLmQ6CMRc2mKthCPRUjEHDLRXlrirdTFo/Q72/jTtptZULeAf1n0\nTuriUe5vv4c1fa/xPvNudpk5j1++8FvuWfNnZjfM4/jdV3L1M5bWRAv7te7NA+v+CsDJrz+SN8w/\nCICBzJEkIokR33/SmSw9/Rm6+1J096fp6UvT05+mqzfFtu7gva+zN8WWZBZ3Btz92FrwhkZONOLQ\n2jT0fS//PjinuY5ZjQmikdo7q7fSivktwG8ArLXPGWNmGWNmWmt7wmvaxPJ9n1TGo7c/XRhKLh5W\n7upL0dGdZEtXkm3dyR1+ok3EIsxtncGSBY1D/ujyX2fNTEyaY675ZTkBljQvZkHDPBKROBv62gE4\ndZ+T+NnTP+ehDY/yxOancXBwHZdfv/g7Fjct4tInrqIj2ckrnav49MGfpCPZydVPX88ec3bjXYvf\nTjwSY23PetZ0r+PQeQcVFsHvTvXQEKvHdYLh53gskhsqm5AfQ+ja2hppb+/CJ/gQlw9tvyjIve2u\ny4f90Nv83IeEIfcrCv7CcwNsd1v+/sEdfdjufn7uisH7DT5f8W25m4qe0889z/bP19iYoKtroDCQ\n6fnF9y1+7tFvG2yHP9jX/PfFl4fftgNJP6gUE05DWb9PoHCYJBIJRjsijkMkNyoVyf1zXYeWWfX0\n9gzgusGoVTzq4kZ8nut+ivkNbSydtSeJmMu21Fa2JDezbM6+uI6L7/ts7GtnVqKZumgdD677G//3\n/O/wfZ937PnPLJu9L//77I280rWa/RoMRyx4I7997pf0Zvro6tmGvzHFjEiCJ3ITOS957DL2bd2L\nxzc9RWNsJut7N/LTp68D4L3mBPZt3Zv+zAAvdrzMAXP2K/SzLrrj/4SxaISWxggtjYkd3qetrZEL\n73qWJze38/lTD6K3xykqXAZHDp9dtW3ExzsOtDQmaG2qY1ZDnOaGBM0z4zTPjDNrZqIwgthQFyUW\nLW1Tjamg0mCeDxQPXW/OXfdiqU/g+T5dvanCGwmw3X/w4v+co/3Hy2aDIaRs1i8MGQ1e9slmPbKe\nnzvGElRYydwQUv54S38yQ+9AbjLVQJregQxZb/QyJ/9Hs8cuTbTmhmbyn/5amxLMbq6jPhFl7tym\nkivGiVQfnUHUjZLxMuzetBuu47K4cVee73iJ2XWt7DfbcMi8A/nzur/Skezkzbu8gYZYA3esupuL\n/vZdBrJJdmmYz9qe9fzoyZ+ytmcD/Zl+XutZx+qt69hj1u7c/doDeL7HXa/dx7G7reCvGx7l2a3P\nM7d+DsfvvpJkNsVD6x8h62d54/xD2K1xEf/Y8iyvda9l6awl7Ndq2NDXjt32Ig3RevabbUhE4rzY\n8QpdqW6WNC9ml4b5bOxrZ31vOy2JZnZtXEgym2RNzzo832fhzAU0xWeysW8THcku5tS10lY/m20D\nHazv3YjruLTWtRCPxElmk/i+T3OimZmxerpS3XQmu6iP1dOSaKY/M0B7/2Z836etfjbpbIZH2//O\n6q417NWyJ6+fewAdbh0vbHyNeCTO3Po5JLNJXu54le50L4ubdmV+fRtrutexpmc9rXWzWNy0K8ls\nktW5RR4WNy5i9oxWulM99GX6mVnXQGN8JslMkp50H1E3QmOsgZSXZmPfJjqTXXi+h4NDS90sWuta\ncIC0l8Z1XKJulJgbJebGyPpZtg100JPuoznRRFO8kQ297azuXsOM6AyWNO9GOpvm6S2WrlQ3e7fs\nya6NC3mtey2ru9eQiCSYlWgmlU2xdaCDRCTOXi170JKYxZqedWzsbae/LkZPdoDOZDcdyQ4cHOpj\n9UTdCBkvSyISZ++WPdm9aTe2DmyjvX8zsxLNzK9vY31vO09veQ7f99l/zj7Mq29jdfdaNvdvYZeG\neSxqXEhfuo/2vs04jsPMWEPwN9e9jmQ2ydLm3dm1aRFb+reysW8TruMScVye3PwMz2yxABwwZz8O\nmLMfWwa20p3qZnHjruzauJBntj7PY+1PUB+dwbI5++L7Pk/kdlg6dN5BHDzvQLpTPWzq30JjrIHW\nGS283PkqD2/8O1nP43Vt+9PUPJcH2//Cqu7X2LtlKUubd+eeVQ+yZWArAIfOO4j66AweWPdXPN9j\n96bdOGrhm7h/7V94pWt1YXvFdb0bqI/OIOJG+PWLv+M3L/4eH5+2GbN5ZovlmS0WB4f37H0Cz219\nvhDIe7cs5dC5B/KLF27m8U1PMb9hHv/++jN5seMVfv7crziwbX/2n70PAB/Z//3BXBMn3ArVyT3f\nrnNn0rCwfsT7pNLZQkgPP9y3uXOAl9Z2jjnqFI+61NdFaZgRoyERfK2LR0nEgxGHYOQhkvvgH8xT\niURcorkPWBHXLXzYGno5KJwKh2GKD5cweEipLh5hRiKcaVtOJTPljDFXALdaa2/JfX8/8BFr7Y6C\n2R8eTJfe9BSPPr+p7NceT9GIQ31djPpElIa6KDPqosysi9GU+1SW/3TWVJ/72hAj4o79R1zOUO5E\n+8qDF9GZ7OJbyy8g5ka5+aXbuGPV3fzLkmP5f0uO5ZXO1Xzr0R9SF6njq2/6PDE3yvkPfZPOVDdv\nnH8Ip+5zEj984iqe3/YiESfCe827Wd2/ivtX/Q2A2XUtLGlezCMb/154zV1n7sLa3ApDAK7j4uCQ\n9bMT8jOQ2rCkaTc832dV92s7vE/UiZD1vcLEJddxiblRktkdzwkJ3qydwt8zBLu3deY2iHEdlyMX\nHsarna8VXntu/RwWNMzniU3/KDxmv1ZDb7qP1d1r2LVxIR9d9gHikTjXP/crVnW9xsl7v5OD2pbx\nWPuT3P3a/azY9QgOnXcQWS/Lra/cQdbP8o49/pmoG2V19xoe2fB3Vi5eXtjiNf/hbTwPk7W1NXLR\n3T/i8fYnufjIr9IQGzmYx+J5Pt19wYhlR0+Kzp4knb0pOntSdPen6B3I0NufpncgTd9Ahr6BzE6f\nahZxHc4/4w0smD04AtPW1ljRD7fSYD4PWGet/XHu+5eA11lrtX6jiIhIFSods7gDOAnAGHMwsFah\nLCIiUr2KKmYAY8x/A8uBLHCWtfapMBsmIiJSiyoOZhEREQlf7Z0gJiIiMokpmEVERCYRBbOIiMgk\nMi6bWBhjosDVwGIgQ3CO86vD7vMe4NMEk8f+ZK390ni0ZWcbbQ1xY8xK4GsEP5PbrLUXTkwrx8cY\nfT8a+G+Cvltr7UcnppXjp5T1440xFwGHWWuP3tntG29j/P4XAT8HYsBj1tpPTkwrx88Y/T8LOJXg\n7/8Ra+2nJ6aV48cYs4xgRchLrLWXDbttWr/3wZj9L+v9b7wq5vcD26y1R+Ya8/VhjZwBXAQcba09\nHFiZW3N7SiteQxz4KPD9YXf5HnACcARw3HToc14Jfb8ceHfub6LJGPPPO7uN46mE/mOM2Rc4Eqbf\nNjsl9P/bwDettYcB2VxQTxuj9d8Y0wh8FniztfYoYH9jzBsmpqXjwxhTT9DnO3dwl2n73gcl9b+s\n97/xCua3AL/OXb4TeHPxjdbafuAAa21f7qotwOxxasvONGQNcWCWMWYmgDFmCbDFWrvOWusDv8/d\nf7rYYd9zDrHWrs9d3sT0+H0XG6v/EITTF3Z2w3aS0f72HYI35Ftyt/9/1to1E9XQcTLa7z8FJAne\nkKPADGDrhLRy/AwAxwPrh99QA+99MEr/c8p6/xuvYJ6fe3Fyvwgv9wdZkF+QxBhzAMGQ90Pj1Jad\nqdDvnPwa4iPd1g4s2Ent2hlG6zv5DU6MMQuAYwn+c04no/bfGPMh4G5g1U5u184yWv/bgB7gu8aY\n+3NrIEw3O+y/tTYJnA+8DLwC/HWU5YunJGutl+vnSKb7e99Y/S/7/a/qY8zGmDMIhm7yw3MOMHyY\nZsQPAMaYvYDrgPdZa6fjwsijrZM6ObaUGj/b9c8YMxf4LfAJa+3I28lMH4X+G2NagI8QVAm7Mv1/\n9zC0jw6wEPgOsBr4nTHmeGvtbRPSsp2j+PffSDBSshToBu42xhxQw4sy1cLf/3bKef+rOpittVcB\nVw1rwE8JPiU9la+UrbWZYfdZBNwEfGAa/YGuo6hKAnZhcGhjHUM/JS7MXTddjNb3/JvT74FzrbV3\n7eS27Qyj9f8YYA5wP1AH7GGM+ba19jM7t4njarT+bwZezU8ANcbcBewPTKdgHq3/+wIv5d+Mc5v+\nHAJMl/e9sUz3974xlfv+N15D2X8ETs5dfgfBEN5wPyH45PDEOLVhIuxwDXFr7Sqg0RizW+7Dytty\n958uxlo//RKC2Yp/nIjG7QSj/e5/Za1dlpsYdALBrOTpFMowev+zwMvGmD1z9z0EsBPSyvEz2t//\nq8C+xpj8xsWHAi/s9BbuPEMq4hp47xtupBGBst7/xmVJTmOMSxC8exEcFP+wtXatMeY/gXsIJj48\nDvyNoBN+rtG3ht6YnWz4GuLAwUCHtfZmY8wRwMUE/f2ltfY7E9fS8O2o7wT/CbcCf2Hw9329tfYn\nE9TUcTHa777oPouBn1lrj5mYVo6fMf729yQ4hdIBnrLWfmLCGjpOxuj/vwKnA2ngQWvtORPX0vDl\nPox8m2C+UBpYSzBs+0qNvPftsP9U8P6ntbJFREQmEa38JSIiMokomEVERCYRBbOIiMgkomAWERGZ\nRBTMIiIik4iCWUREZBJRMItMUcaY5blVpMp5jJdbZ2D49dcbYxYYYz5kjPmf4utyl08Np9UiMpZx\n2Y9ZRHaachciGPH+1tr3AxhjRrpuIfBxgnXtRWScKZhFJpAxZjlwIcGuU0uAbcC5BCH4JPAPghWT\nvkuwlKUH3G2t/UruKeqMMVcTrLLXBZxkre01xvwXwRrdAGsI1qTPEqw89IXcxvUzgQ9aa58xxrzC\nsK34iq77KbAs9zpLgS9Ya+/L3ef3wPettbeH+oMRqWEayhaZeAcDn7XWvplgb/IVwD7AV621XwdO\nAXbP3b6cYKP5I3OPXUawMP6bCbbW+5AxJgL0WmuPzG3M3gK8tej1nrHWrgAuA75aQvvOI1hG88PA\nFQQ7ZeV3zdpboSwSLgWzyMR72lq7IXf5QeD/AVuL9ux9I3AnBPu+EuxS9U+5254r2oD9QWD/XGXs\nGWMeMMbcAxxIsLtV3p3F9y+zrTcCRxtj6gk25NDwtkjIFMwiE6/4/2F+kftU0XXDjws7Rdd5w683\nxhxOsGHCylxl/MCwx3vF9y+nobnN4G8C3k2wm9JPy3m8iIxNwSwy8fYxxszLXT4CuHnY7Q8BxwLk\nts1bnrsOgu0E8/sAv5lgj995BLv6DOR2szoMSBQ9X/5Y8hGUtiewB8SLvv8x8EkobOknIiFSMItM\nvGeAi3KnPs0kGKou9gvgRWPMA8B9wE3W2r/kbnsU+Jox5j6gGbiWYJu55tx15xAcI/6iMWYvIAPs\nb4y5HfhY7jYYuXLOX/c0MM8Y8wcAa+2zQAT4WXXdFpGRaNtHkQmUm5V9gbX2qIluS6mMMbsDtwIH\n5o5ni0iIVDGLSMmMMecCvwY+qlAWGR+qmEVERCYRVcwiIiKTiIJZRERkElEwi4iITCIKZhERkUlE\nwSwiIjKJKJhFREQmkf8f5sEOfCIW+UUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6251bd5978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(predict_df.query(\"status == 0\").probability, hist=False, label='Negatives')\n",
    "ax = sns.distplot(predict_df.query(\"status == 1\").probability, hist=False, label='Positives')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}