2.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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from statsmodels.robust.scale import mad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "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 = '7157' # TP53"
   ]
  },
  {
   "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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 25s, sys: 1.27 s, total: 1min 26s\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path = os.path.join('download', 'expression-matrix.tsv.bz2')\n",
    "X = pd.read_table(path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 45.2 s, sys: 496 ms, total: 45.7 s\n",
      "Wall time: 45.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path = os.path.join('download', 'mutation-matrix.tsv.bz2')\n",
    "Y = pd.read_table(path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = Y[GENE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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: 7157, dtype: int64"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.645907\n",
       "1    0.354093\n",
       "Name: 7157, dtype: float64"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Size: 20,530 features, 6,575 training samples, 731 testing samples'"
      ]
     },
     "execution_count": 9,
     "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.columns), len(X_train), len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Median absolute deviation feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def fs_mad(x, y):\n",
    "    \"\"\"    \n",
    "    Get the median absolute deviation (MAD) for each column of x\n",
    "    \"\"\"\n",
    "    scores = mad(x) \n",
    "    return scores, np.array([np.NaN]*len(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define pipeline and Cross validation model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Parameter Sweep for Hyperparameters\n",
    "param_grid = {\n",
    "    'select__k': [2000],\n",
    "    'classify__loss': ['log'],\n",
    "    'classify__penalty': ['elasticnet'],\n",
    "    'classify__alpha': [10 ** x for x in range(-3, 1)],\n",
    "    'classify__l1_ratio': [0, 0.2, 0.8, 1],\n",
    "}\n",
    "\n",
    "pipeline = Pipeline(steps=[\n",
    "    ('select', SelectKBest(fs_mad)),\n",
    "    ('standardize', StandardScaler()),\n",
    "    ('classify', SGDClassifier(random_state=0, class_weight='balanced'))\n",
    "])\n",
    "\n",
    "cv_pipeline = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 24s, sys: 12.6 s, total: 1min 37s\n",
      "Wall time: 4min 6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('select', SelectKBest(k=10, score_func=<function fs_mad at 0x7f37cd93dd08>)), ('standardize', StandardScaler(copy=True, with_mean=True, with_std=True)), ('classify', SGDClassifier(alpha=0.0001, average=False, class_weight='balanced',\n",
       "       epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,\n",
       "       penalty='l2', power_t=0.5, random_state=0, shuffle=True, verbose=0,\n",
       "       warm_start=False))]),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'classify__alpha': [0.001, 0.01, 0.1, 1], 'classify__loss': ['log'], 'classify__l1_ratio': [0, 0.2, 0.8, 1], 'classify__penalty': ['elasticnet'], 'select__k': [2000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "cv_pipeline.fit(X=X_train, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.724%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'classify__alpha': 0.1,\n",
       " 'classify__l1_ratio': 0,\n",
       " 'classify__loss': 'log',\n",
       " 'classify__penalty': 'elasticnet',\n",
       " 'select__k': 2000}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best Params\n",
    "print('{:.3%}'.format(cv_pipeline.best_score_))\n",
    "\n",
    "# Best Params\n",
    "cv_pipeline.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize hyperparameters performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_classify__alpha</th>\n",
       "      <th>param_classify__l1_ratio</th>\n",
       "      <th>param_classify__loss</th>\n",
       "      <th>param_classify__penalty</th>\n",
       "      <th>param_select__k</th>\n",
       "      <th>params</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "      <th>classify__alpha</th>\n",
       "      <th>classify__l1_ratio</th>\n",
       "      <th>classify__loss</th>\n",
       "      <th>classify__penalty</th>\n",
       "      <th>select__k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.848116</td>\n",
       "      <td>0.690111</td>\n",
       "      <td>0.900288</td>\n",
       "      <td>0.959226</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>log</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>2000</td>\n",
       "      <td>{'classify__alpha': 0.001, 'classify__loss': '...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.957316</td>\n",
       "      <td>0.507874</td>\n",
       "      <td>0.166336</td>\n",
       "      <td>0.002217</td>\n",
       "      <td>0.007118</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>log</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.370757</td>\n",
       "      <td>0.797598</td>\n",
       "      <td>0.899193</td>\n",
       "      <td>0.958899</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.2</td>\n",
       "      <td>log</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>2000</td>\n",
       "      <td>{'classify__alpha': 0.001, 'classify__loss': '...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.955539</td>\n",
       "      <td>0.364284</td>\n",
       "      <td>0.134266</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>0.006859</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.2</td>\n",
       "      <td>log</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "0      15.848116         0.690111         0.900288          0.959226   \n",
       "1      16.370757         0.797598         0.899193          0.958899   \n",
       "\n",
       "  param_classify__alpha param_classify__l1_ratio param_classify__loss  \\\n",
       "0                 0.001                        0                  log   \n",
       "1                 0.001                      0.2                  log   \n",
       "\n",
       "  param_classify__penalty param_select__k  \\\n",
       "0              elasticnet            2000   \n",
       "1              elasticnet            2000   \n",
       "\n",
       "                                              params    ...      \\\n",
       "0  {'classify__alpha': 0.001, 'classify__loss': '...    ...       \n",
       "1  {'classify__alpha': 0.001, 'classify__loss': '...    ...       \n",
       "\n",
       "   split2_train_score  std_fit_time  std_score_time  std_test_score  \\\n",
       "0            0.957316      0.507874        0.166336        0.002217   \n",
       "1            0.955539      0.364284        0.134266        0.001441   \n",
       "\n",
       "   std_train_score  classify__alpha  classify__l1_ratio  classify__loss  \\\n",
       "0         0.007118            0.001                 0.0             log   \n",
       "1         0.006859            0.001                 0.2             log   \n",
       "\n",
       "   classify__penalty  select__k  \n",
       "0         elasticnet       2000  \n",
       "1         elasticnet       2000  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result_df = pd.concat([\n",
    "    pd.DataFrame(cv_pipeline.cv_results_),\n",
    "    pd.DataFrame.from_records(cv_pipeline.cv_results_['params']),\n",
    "], axis='columns')\n",
    "cv_result_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAokAAAHqCAYAAABoX9RIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIgEUZAoIiIiIkEUJIqIiIhIkP8H3G/bMx8/OXkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f37cd3a69e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cross-validated performance heatmap\n",
    "cv_score_mat = pd.pivot_table(cv_result_df, values='mean_test_score', index='classify__l1_ratio', columns='classify__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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred_train = cv_pipeline.decision_function(X_train)\n",
    "y_pred_test = cv_pipeline.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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArsAAAH9CAYAAAAairRuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njTU4JfkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f37bd1d17f0>"
      ]
     },
     "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_pipeline = cv_pipeline.best_estimator_\n",
    "final_classifier = final_pipeline.named_steps['classify']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "select_indices = final_pipeline.named_steps['select'].transform(\n",
    "    np.arange(len(X.columns)).reshape(1, -1)\n",
    ").tolist()\n",
    "\n",
    "coef_df = pd.DataFrame.from_items([\n",
    "    ('feature', X.columns[select_indices]),\n",
    "    ('weight', final_classifier.coef_[0]),\n",
    "])\n",
    "\n",
    "coef_df['abs'] = coef_df['weight'].abs()\n",
    "coef_df = coef_df.sort_values('abs', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0% zero coefficients; 945 negative and 1,055 positive coefficients'"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "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>feature</th>\n",
       "      <th>weight</th>\n",
       "      <th>abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>60401</td>\n",
       "      <td>-0.435706</td>\n",
       "      <td>0.435706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>9518</td>\n",
       "      <td>-0.177989</td>\n",
       "      <td>0.177989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>55065</td>\n",
       "      <td>-0.155145</td>\n",
       "      <td>0.155145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1029</td>\n",
       "      <td>0.154317</td>\n",
       "      <td>0.154317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>56164</td>\n",
       "      <td>0.116513</td>\n",
       "      <td>0.116513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>80704</td>\n",
       "      <td>0.111716</td>\n",
       "      <td>0.111716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>11065</td>\n",
       "      <td>0.105740</td>\n",
       "      <td>0.105740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>84417</td>\n",
       "      <td>-0.104588</td>\n",
       "      <td>0.104588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>283417</td>\n",
       "      <td>-0.102990</td>\n",
       "      <td>0.102990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>8744</td>\n",
       "      <td>-0.101395</td>\n",
       "      <td>0.101395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     feature    weight       abs\n",
       "1349   60401 -0.435706  0.435706\n",
       "902     9518 -0.177989  0.177989\n",
       "1224   55065 -0.155145  0.155145\n",
       "121     1029  0.154317  0.154317\n",
       "1283   56164  0.116513  0.116513\n",
       "1462   80704  0.111716  0.111716\n",
       "997    11065  0.105740  0.105740\n",
       "1502   84417 -0.104588  0.104588\n",
       "1843  283417 -0.102990  0.102990\n",
       "833     8744 -0.101395  0.101395"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigate the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": 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', cv_pipeline.decision_function(X)),\n",
    "    ('probability', cv_pipeline.predict_proba(X)[:, 1]),\n",
    "])\n",
    "predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>4.826262</td>\n",
       "      <td>0.992047</td>\n",
       "      <td>99.2%</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>4.801436</td>\n",
       "      <td>0.991849</td>\n",
       "      <td>99.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-09-2051-01</th>\n",
       "      <td>TCGA-09-2051-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.696496</td>\n",
       "      <td>0.990955</td>\n",
       "      <td>99.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0I6-01</th>\n",
       "      <td>TCGA-B6-A0I6-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.407778</td>\n",
       "      <td>0.987964</td>\n",
       "      <td>98.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-55-6981-01</th>\n",
       "      <td>TCGA-55-6981-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.145561</td>\n",
       "      <td>0.984412</td>\n",
       "      <td>98.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-L5-A4OO-01</th>\n",
       "      <td>TCGA-L5-A4OO-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.128203</td>\n",
       "      <td>0.984144</td>\n",
       "      <td>98.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-EI-6513-01</th>\n",
       "      <td>TCGA-EI-6513-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.993585</td>\n",
       "      <td>0.981900</td>\n",
       "      <td>98.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-46-3765-01</th>\n",
       "      <td>TCGA-46-3765-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.902791</td>\n",
       "      <td>0.980214</td>\n",
       "      <td>98.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-55-6982-01</th>\n",
       "      <td>TCGA-55-6982-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.849445</td>\n",
       "      <td>0.979152</td>\n",
       "      <td>97.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-18-3412-01</th>\n",
       "      <td>TCGA-18-3412-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.769527</td>\n",
       "      <td>0.977457</td>\n",
       "      <td>97.7%</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           4.826262   \n",
       "TCGA-L5-A4OH-01  TCGA-L5-A4OH-01        0       0           4.801436   \n",
       "TCGA-09-2051-01  TCGA-09-2051-01        0       0           4.696496   \n",
       "TCGA-B6-A0I6-01  TCGA-B6-A0I6-01        1       0           4.407778   \n",
       "TCGA-55-6981-01  TCGA-55-6981-01        0       0           4.145561   \n",
       "TCGA-L5-A4OO-01  TCGA-L5-A4OO-01        0       0           4.128203   \n",
       "TCGA-EI-6513-01  TCGA-EI-6513-01        1       0           3.993585   \n",
       "TCGA-46-3765-01  TCGA-46-3765-01        0       0           3.902791   \n",
       "TCGA-55-6982-01  TCGA-55-6982-01        0       0           3.849445   \n",
       "TCGA-18-3412-01  TCGA-18-3412-01        0       0           3.769527   \n",
       "\n",
       "                 probability probability_str  \n",
       "sample_id                                     \n",
       "TCGA-L5-A8NR-01     0.992047           99.2%  \n",
       "TCGA-L5-A4OH-01     0.991849           99.2%  \n",
       "TCGA-09-2051-01     0.990955           99.1%  \n",
       "TCGA-B6-A0I6-01     0.987964           98.8%  \n",
       "TCGA-55-6981-01     0.984412           98.4%  \n",
       "TCGA-L5-A4OO-01     0.984144           98.4%  \n",
       "TCGA-EI-6513-01     0.981900           98.2%  \n",
       "TCGA-46-3765-01     0.980214           98.0%  \n",
       "TCGA-55-6982-01     0.979152           97.9%  \n",
       "TCGA-18-3412-01     0.977457           97.7%  "
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAHxCAYAAACoOu6EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f37b7f0ce48>"
      ]
     },
     "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApsAAAHxCAYAAAAiFXfFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f37bbfe12e8>"
      ]
     },
     "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 [default]",
   "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
}