From 2f442abc5da19923852666b76f9e2115be8d6d33 Mon Sep 17 00:00:00 2001
From: Daniel Himmelstein <daniel.himmelstein@gmail.com>
Date: Wed, 14 Sep 2016 17:14:05 -0400
Subject: [PATCH 1/4] Evaluate performance of covariates on TP53

Creates an explore directory and README for this type of exploratory notebook.

See how well covariates (non-expression features) predict TP53 mutation.

Related to https://github.com/cognoma/machine-learning/issues/8:
General mutation-load does provide some ability to predict mutation status of
TP53.

Partially addresses https://github.com/cognoma/machine-learning/issues/21:
Covariates are extracted from samples.tsv.
---
 explore/README.md                     |    9 +
 explore/confounding/README.md         |    3 +
 explore/confounding/confounding.ipynb | 1293 +++++++++++++++++++++++++
 explore/confounding/confounding.py    |  280 ++++++
 4 files changed, 1585 insertions(+)
 create mode 100644 explore/README.md
 create mode 100644 explore/confounding/README.md
 create mode 100644 explore/confounding/confounding.ipynb
 create mode 100644 explore/confounding/confounding.py

diff --git a/explore/README.md b/explore/README.md
new file mode 100644
index 0000000..9389710
--- /dev/null
+++ b/explore/README.md
@@ -0,0 +1,9 @@
+# A directory for exploratory machine learning analyses
+
+This directory is home is exploratory analyses that help answer questions about how we should do machine learning. For algorithm implementations see the [`algorithms`](../algorithms) directory. For other types of analyses, place them here.
+
+Notebooks should be exported to scripts for review. For example, from the directory containing your scripts run:
+
+```sh
+jupyter nbconvert --to=script *.ipynb
+```
diff --git a/explore/confounding/README.md b/explore/confounding/README.md
new file mode 100644
index 0000000..701864d
--- /dev/null
+++ b/explore/confounding/README.md
@@ -0,0 +1,3 @@
+This analysis looks into covariates and their potential confounding effects.
+
+Specifically, we find that disease type, gender, and mutation burden predict _TP53_ mutation with AUROC = 84%.
\ No newline at end of file
diff --git a/explore/confounding/confounding.ipynb b/explore/confounding/confounding.ipynb
new file mode 100644
index 0000000..982c18e
--- /dev/null
+++ b/explore/confounding/confounding.ipynb
@@ -0,0 +1,1293 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create a logistic regression model to predict TP53 mutation from covariates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\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, 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\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from scipy.special import logit"
+   ]
+  },
+  {
+   "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": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Parameter Sweep for Hyperparameters\n",
+    "n_feature_kept = 500\n",
+    "param_fixed = {\n",
+    "    'loss': 'log',\n",
+    "    'penalty': 'elasticnet',\n",
+    "}\n",
+    "param_grid = {\n",
+    "    'alpha': [10 ** x for x in range(-6, 2)],\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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 37 s, sys: 1.07 s, total: 38 s\n",
+      "Wall time: 38 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": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender_Female</th>\n",
+       "      <th>gender_Male</th>\n",
+       "      <th>disease_adrenocortical cancer</th>\n",
+       "      <th>disease_bladder urothelial carcinoma</th>\n",
+       "      <th>disease_brain lower grade glioma</th>\n",
+       "      <th>disease_breast invasive carcinoma</th>\n",
+       "      <th>disease_cervical &amp; endocervical cancer</th>\n",
+       "      <th>disease_cholangiocarcinoma</th>\n",
+       "      <th>disease_colon adenocarcinoma</th>\n",
+       "      <th>disease_diffuse large B-cell lymphoma</th>\n",
+       "      <th>...</th>\n",
+       "      <th>disease_sarcoma</th>\n",
+       "      <th>disease_skin cutaneous melanoma</th>\n",
+       "      <th>disease_stomach adenocarcinoma</th>\n",
+       "      <th>disease_testicular germ cell tumor</th>\n",
+       "      <th>disease_thymoma</th>\n",
+       "      <th>disease_thyroid carcinoma</th>\n",
+       "      <th>disease_uterine carcinosarcoma</th>\n",
+       "      <th>disease_uterine corpus endometrioid carcinoma</th>\n",
+       "      <th>disease_uveal melanoma</th>\n",
+       "      <th>n_mutations_log10</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></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-02-0047-01</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.591065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-02-0055-01</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.518514</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 gender_Female  gender_Male  disease_adrenocortical cancer  \\\n",
+       "sample_id                                                                    \n",
+       "TCGA-02-0047-01            0.0          1.0                            0.0   \n",
+       "TCGA-02-0055-01            1.0          0.0                            0.0   \n",
+       "\n",
+       "                 disease_bladder urothelial carcinoma  \\\n",
+       "sample_id                                               \n",
+       "TCGA-02-0047-01                                   0.0   \n",
+       "TCGA-02-0055-01                                   0.0   \n",
+       "\n",
+       "                 disease_brain lower grade glioma  \\\n",
+       "sample_id                                           \n",
+       "TCGA-02-0047-01                               0.0   \n",
+       "TCGA-02-0055-01                               0.0   \n",
+       "\n",
+       "                 disease_breast invasive carcinoma  \\\n",
+       "sample_id                                            \n",
+       "TCGA-02-0047-01                                0.0   \n",
+       "TCGA-02-0055-01                                0.0   \n",
+       "\n",
+       "                 disease_cervical & endocervical cancer  \\\n",
+       "sample_id                                                 \n",
+       "TCGA-02-0047-01                                     0.0   \n",
+       "TCGA-02-0055-01                                     0.0   \n",
+       "\n",
+       "                 disease_cholangiocarcinoma  disease_colon adenocarcinoma  \\\n",
+       "sample_id                                                                   \n",
+       "TCGA-02-0047-01                         0.0                           0.0   \n",
+       "TCGA-02-0055-01                         0.0                           0.0   \n",
+       "\n",
+       "                 disease_diffuse large B-cell lymphoma        ...          \\\n",
+       "sample_id                                                     ...           \n",
+       "TCGA-02-0047-01                                    0.0        ...           \n",
+       "TCGA-02-0055-01                                    0.0        ...           \n",
+       "\n",
+       "                 disease_sarcoma  disease_skin cutaneous melanoma  \\\n",
+       "sample_id                                                           \n",
+       "TCGA-02-0047-01              0.0                              0.0   \n",
+       "TCGA-02-0055-01              0.0                              0.0   \n",
+       "\n",
+       "                 disease_stomach adenocarcinoma  \\\n",
+       "sample_id                                         \n",
+       "TCGA-02-0047-01                             0.0   \n",
+       "TCGA-02-0055-01                             0.0   \n",
+       "\n",
+       "                 disease_testicular germ cell tumor  disease_thymoma  \\\n",
+       "sample_id                                                              \n",
+       "TCGA-02-0047-01                                 0.0              0.0   \n",
+       "TCGA-02-0055-01                                 0.0              0.0   \n",
+       "\n",
+       "                 disease_thyroid carcinoma  disease_uterine carcinosarcoma  \\\n",
+       "sample_id                                                                    \n",
+       "TCGA-02-0047-01                        0.0                             0.0   \n",
+       "TCGA-02-0055-01                        0.0                             0.0   \n",
+       "\n",
+       "                 disease_uterine corpus endometrioid carcinoma  \\\n",
+       "sample_id                                                        \n",
+       "TCGA-02-0047-01                                            0.0   \n",
+       "TCGA-02-0055-01                                            0.0   \n",
+       "\n",
+       "                 disease_uveal melanoma  n_mutations_log10  \n",
+       "sample_id                                                   \n",
+       "TCGA-02-0047-01                     0.0           1.591065  \n",
+       "TCGA-02-0055-01                     0.0           1.518514  \n",
+       "\n",
+       "[2 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read sample information and create a covariate TSV\n",
+    "path = os.path.join('..', '..', 'download', 'samples.tsv')\n",
+    "covariate_df = (\n",
+    "    pd.read_table(path, index_col=0)\n",
+    "    .drop(['patient_id', 'sample_type', 'organ_of_origin'], axis='columns')\n",
+    "    [['gender', 'disease']]\n",
+    "    .pipe(pd.get_dummies, columns=['gender', 'disease'])\n",
+    ")\n",
+    "\n",
+    "n_mutations = Y.sum(axis='columns')\n",
+    "covariate_df['n_mutations_log10'] = np.log10(n_mutations)\n",
+    "\n",
+    "#covariate_df['n_mutations_log1p'] = np.log1p(n_mutations)\n",
+    "#covariate_df['n_mutations_log'] = np.log(n_mutations)\n",
+    "#covariate_df['mutations_freq_logit'] = np.log1p(n_mutations / len(Y.columns))\n",
+    "#covariate_df['n_mutations_ihs'] = np.arcsinh(n_mutations)\n",
+    "\n",
+    "covariate_df.head(2)"
+   ]
+  },
+  {
+   "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",
+       "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 = Y[gene]\n",
+    "y.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = covariate_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.645907\n",
+       "1    0.354093\n",
+       "Name: 7157, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "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": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Size: 35 features, 6,575 training samples, 731 testing samples'"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "## Define pipeline and Cross validation model fitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Include loss='log' in param_grid doesn't work with pipeline somehow\n",
+    "clf = SGDClassifier(random_state=0, class_weight='balanced',\n",
+    "                    loss=param_fixed['loss'], penalty=param_fixed['penalty'])\n",
+    "\n",
+    "# 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')\n",
+    "clf_grid = grid_search.GridSearchCV(estimator=clf, param_grid=param_grid, n_jobs=-1, scoring='roc_auc')\n",
+    "pipeline = make_pipeline(\n",
+    "    StandardScaler(),\n",
+    "    clf_grid\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 368 ms, sys: 112 ms, total: 480 ms\n",
+      "Wall time: 3.53 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Fit the model (the computationally intensive part)\n",
+    "pipeline.fit(X=X_train, y=y_train)\n",
+    "best_clf = clf_grid.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'alpha': 0.1, 'l1_ratio': 0}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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, learning_rate='optimal',\n",
+       "       loss='log', n_iter=5, n_jobs=1, penalty='elasticnet', power_t=0.5,\n",
+       "       random_state=0, shuffle=True, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_clf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize hyperparameters performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "## Process Mutation Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>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>0.736324</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>0.749135</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      alpha  fold  l1_ratio     score\n",
+       "0  0.000001     0       0.0  0.736324\n",
+       "1  0.000001     1       0.0  0.749135"
+      ]
+     },
+     "execution_count": 16,
+     "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": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W8/bsZ1izZXGl5Vq0aMnw4adz+umjDupns6KNGzcwZcp7fPjh+2zYsL7SvOMa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G4XhoeH5Cq/S4vWQaflrhCxegMWe6rrVlYeiyrHG7fbhdraapw8eQJtbeonk4eSxsbLAIB4nR7Z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\nL2EAs1tHokLAiB5L5jGzHN/lZf4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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbe1ffb7390>"
+      ]
+     },
+     "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": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WQH/94RvBQSHYO9px08uPilXLYm5uRs16VfDyvEm27M506NqSQ3uP07hFXbq3G8L1K970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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbe3bb82f28>"
+      ]
+     },
+     "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": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_train = pipeline.decision_function(X_train)\n",
+    "y_pred_test = 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": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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csyGprv27ef/AtUTfYFxf7SPwH8ijlcejtfZ6/0fcd3DdWwLGmH8Dl9nEB7Ebtt8/uSzS\ntRTsSntFmvll2xpluJP3UTS2vsRbR2N3gIKkef1p3mZcQNieVrm2aAjEk8vW2pbHtlhlrV3a1oX8\nk/ynuG4F+K1PoxtujcaZjzvhj2Dnty/boknQaIwpbMVy3wSetNb+Im65A9q47YaLTEIrqB/c9aXz\nLkLbaLw1HK+hXKkCn11RS9Ix6Xcvig+kWvPda60yXMvZDTQNxGqbZgca9/2tuL71yWJBnjFmNK51\n99+4PqsPW2s/S8qfHCT2xX2+VJ8tvtxVuP6wqQLIlQDW2pW4B/x+YIzZy//7fmPMUmvtG9aNtvAb\nXB/vwbhj95e4Hyk3Jq2zoa5Ttcz3o/EHbXttozFwS5Z8rl7q9yu+3RjzUkMLpN/KPB/3A/vWVCvy\n+/keg9/6bq1db4xZiOvLekszy+wOTLX+w6spbMTdldiZ5Lrq04plwN3pu9DvqnMk7ofYs/68Vh+P\n1trHgcf9lv8TcQ+sPeGvs0HD9UmBbjejB9SkK7yHO3ENs9YubfgPd4Ko8m/PL8Y9FZsc6BxOM/zW\nFIv75R5jjLnYGBPfL3WXWiD9W4VluNuh8b5B5zyglsAYc5NJGvvS77fa8DAZuIeM/mGMGZK0+GRc\nq3zyrfb2iP/sDSf7+IejDmpmufi6yMH9aIl3bop8qaYbWBofHIo3Ddei16Qvbgd5H9gjxT4/DFjo\nB0vptBjYP6nF9iu4fdqgNd+91noPmGCtXZa0rmxc3+Em/NbZ+YCJX8ZfLo/EuyN/AT6w1p6C63Lw\nqHFjycY7Mml6Kq6LQks/EN/Dtdr1Sdp+Na7fddgYs6/fJ7Oh3AuttZfgWk8nG2OGmbgHxqy1G/y+\nwy/hvlPJn7sC12KecEwaY4biWkp39ZhcB2QZY0pbmf+HuB9GyUHtTcChxpgzmlnuBn+52+PSbgYO\n9Pu5JvCD4weAm1v4sTsPGGMaH/zDGDPaGPO2MabhRT/baPqgZXPnkwTW2ndwP2BOxXVneLbhOG/N\n8WiMyTfGnG7cKCFYa6ustU8Bt9G0rocCterC0P2oZVc6nbX2I+PGX7zDv3h9DIzGnTQBpltrK4x7\nKOxCY8x7uIeeTiDplnwKv8UNSfVj3C/6fWl8Kh0aW3yON8Zst9Z+3M6P8Q/cLdxv4x5K+CLuifSu\nEMR1RcjCtbT1wz10MYTGfXoPrnvAM8aYa3EXxxNwD4fcb1t4WroZqQLN+LQP/OmfGGNuxg3/dQOJ\nrZkNdXGkMWYO7qn6d4GvGmOewN3avBY30sOxwCH+sZBquRg/WLkF+D+/pep//vb/hAs4Wv3Eegta\n82PpQVz/9b8ZY67CffZv4UY5aE9/xJ35Oy64/b1xDwwa3P6LXXhb891rw/Z+hxtJ4E7c8VWN6wf+\nS1xg0dyDj78GHjNuGLe/4fbld3DH50HAHGPMD/yyNLwo5CJcUHK9v/4Gk/zj+RncsFGX4IKZlm6L\n/xs3jNrjxpgrcD8S9sYFcJ/gAqIDcfvxB7iRVsC1XhbiAu/+wBP+HZNHccfqVFx/+Pjyxfst8LAx\n5qe4bgSluNbBTez6i3jexO3Hw3H7okXW2jXGDd31e2PM49Z/+NRa+5gx5lDcOXQ/XP1swg1R913c\naCDnxbewW2sf8pe51xizD+4Bvy24ffp/uC4OJzXTDQXcj5qrcMfEpbg+wzfjHn5rOD9/AHzFGPMY\n7oG+b9M0+G3JX3GNEeNxo8rE29nxOM8vz1nGPSy4zt8f38KNIRzvCNz1QLoZtexKe7WlBTNV3q/i\nxhL9E+5i8zfcAxMnxOX5Hq6l5GF/3iGkfjgttn5r7aO4E9U3gUW4los/4sbLBDde7j9xD7u8ENcK\n1uxwZs18jqtwwcWtuBPxNNyFNoB7CKq10tESfA3uonIR7uLwOu5i/AVr7QwAa+1m3Il4He7zf4Lb\nv9fhRgZoqxb3j7X2PVxg8iXcxeIX/naq4/JsxD0JfhruobsBuH34uT/9P2CRtfb7uAv4NcDl/nJ3\nJS2XvP1f4p7m/iHuOHgSV/fH2MQB35v7HDurl+T5TZbx7wAciQtyX8b9YDsZONu/JdrS9lqbFr+9\nv+KGKDsT91DWlbin/MMkHpOt+e7tdB/4x9YJuID0Xf/znQp8w7YwwodfzjNxQ/rNwX1/pgDHWWvn\nGGP2wAUgNzTcfrdu7Nif4n7A7Bu3ultwP2Tex91Sfo3Ec0SqeqnHjUgxC/eD+HPc8F5P4wIYrLV/\nxo3heg3u+J3tf7avW2tnWzdayJdwweW7uGPsl8BN1trbkrbfsN3HcEFaw8NS/8W9/vsImzicWio7\nq/uPcH1uU73Bq7nl/oirs3tM3BBf1tqLcN+thqH4FuOCxRCwv7X2yeQVWWsvwNXpRNw5+1PcGwbf\nB/azjaMYpCp7w/ekCvdg4Ku4riBfiLv78QPcj51/4YJJD9ey2trP+rhfto3++uO33+Lx6N8xPJrG\nsd4X++t7H1efQGwYtv1oxY8N6XwBz+v0u64iPZ7filps44a+MsacgrtgTrfWJj9dL9LhjDFDrLXr\n46aLca1sV9vGIbp6PNP4cpGLbPtGW+l1jBta7y7cLfl2j8Et7ePf4TgJGGv1BrVuR90YRNrnBuAq\nY8x3cbc1x+Buj81WoCtdwRhzDG4g/N/iHrbpg2tR30HjcF7Sez2Ea2m8Fdd6L53Ev9twPm7ccgW6\n3ZC6MYi0zw24hzluxN3CfAR3W/QrLSwj0mGsezHGN3GvAf4Y9ya1AlzXjbUtLdtD6bZkHOuGDvwa\nMMUYk87xvqUF/vCNTwG/se617NINqRuDiIiIiPRaatkVERERkV5Lwa6IiIiI9FoZ94Ca53ne1q2V\nRKPqvtHbBYMBSkoKUX1nBtV3ZlF9ZxbVd2YJBgMMGNBnV18/37i+dK2opwgEAgSDadt/0o0FgwHV\ndwZRfWcW1XdmUX1nlnTXc8YFuyIiIiKSORTsioiIiEivpWBXRERERHotBbsiIiIi0msp2BURERGR\nXkvBroiIiIj0Wgp2RURERKTXUrArIiIiIr2Wgl0RERER6bUU7IqIiIhIr6VgV0RERER6LQW7IiIi\nItJrKdgVERERkV4rq6sLAGCMOR54GHjNWnvWTvJeBlwCDAHmAj+01n7U8aUUERERkZ6my1t2jTHX\nALcBn7Ui75eBG4BvAYOB/wD/Mcbkd2ghRURERKRH6vJgF6gGpgNLWpH3QuBBa+2H1tpa4GbAA77c\ngeUTERERkR6qy4Nda+0d1todrcy+PxDrsmCt9YCPgWkdUTYRERER6dm6RZ/dNhgAlCWlbQVK27KS\nUKjLY3zpBA31rPrODKrvzNLW+p69/hNeWPYq4Wi4I4slHSZAMBQgGvFwN3RbxwMiEQ8PiEY9IpEo\nkWjrl+/NPP9/Hh5RD7yoRzgaxesmu+evZ9+UtnX1tGAXILCrKygqUhffTKL6ziyq78xSVJTP8rLV\nrC5f12K+e+c+2kklEumBAkDI/68X6mnB7iZc6268AcC8tqykvLyaSCSatkJJ9xQKBSkqyld9ZwjV\nd2ZpqO+Fa5ZxwzttawE6bMSBHVSqbs7z2Ly9hm0VtSnbRsPhKLX1yd8dj8rqMHXhSGeUsFMEAwEC\ngV1uN+sVggEIBYOEQgFCwQBZoSC5OaFdb1XcVWmun54W7H6I67f7KIAxJghMAe5vy0oikSjhsC6G\nmUL1nVlU393L4rIlPL3431RHatO63gAQDAXZULGp1ctkB7O4YOLZTCzdK61lSQfP86gPR6mpi1BT\nF3a3lT2PtZur8Jq5rxyJelTVhqmqqac+6Zivqg2zo6o+9l3YUFbNjqo6tlfWdfhnSTagKI+8nFDK\n+7IBAgwoyiUvt+VwJBCAnJws6urCeB6U9M2lIK91IUz/Prn0LcihX2EOfQuyKe6bq2C3m8vKSm93\ntG4f7BpjFgLfsdbOBO4GnjTGPIkbY/caoAZ4vguLKCIizXhzzbusqljbKdu68cAfkZeV2+z83FAO\nOaGcFtfheR47quuJpujXGY16lFfVUVkddsFpJEpNbYRPlmwmKxRsEsu99+kGhpcWEggEWLHBPYcd\nCqYOsjwPop3UWbIgN4v8ZoLLqOdR2i8vYX5hXjZmVH9yWhmAuKDS7efS/nnk5ex6qJGVFaS4uJCy\nskr9mJU26/Jg1xhTjesnne1Pnwp41toCP8s4oA+AtfZFY8yPgb8DA4FZwIn+MGQiItLNRKLu9nf/\n3H7sO3Bi2tYbDAbIzc2itjZMNOoxoWQcAwuSe7lBVU09S9eWs3l7DZu311Bb78qzbUctlTX1rN5U\nmZC/PhyN5UmHlRsrEqY74uGo7Kwg2aFgwp3fvJws+hZkk5Md1wnT85i212AO33co2Vm9tHOmSAqB\n5m6P9GKefhlmBrUEZBbVd/cwb/OnPPv589RG3O3yyvpK6qNhxvXfg8unfG+X1l1RXc/CFWVEox6b\ntlUTyg5RU1PPwuVl5GYHWbC8jAFFebGgr7ImTHVt542+0Cc/m5KixJbllRsqmDJuILnZIerCEYKB\nAKOH9m2ybIAAuTkh8nNC5OVkEQo1Rq6D+ucnBq2+YAAK8rIyInDV9zuz+PWdtr4mXd6yKyIiPVvU\ni7Jw62LKasp40j6TMk9RbtMAryW19REqquqpqQvzyIuWxau3t2q5LeU1zc7rk59NQdzt+ajnMaSk\ngLzcLIaWuJuJwWCA0n55KYPLANC3IJs++dkEgwFCoSAFuVnk5YTI0pB3It2Wgl0REdklczct4L75\nTYf2OmbU4QDkhnI5eGjiu3/qw1Hq457wrwtHeXf+ep56Y0kbRlF1wWcAKK+qp6gwh2jU44C9BhPw\nY0/Pg92HFjF5bGm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ixMA+nVpWERGR7qyrW3anAMutteVxaR8BxhhTaK2Nb656HrjLGLMv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\nnnJ0dDROp5OMjIwC29at6902Lc398Kb9+/d57QsNDcWYVoVe8/DhwzR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co7QFJcX+9tBD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\nmZmVlpNdMzMzMystJ7tmZmZmVlr/ADHXXheBL6GgAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbe3b210358>"
+      ]
+     },
+     "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": 21,
+   "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": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.0% zero coefficients; 17 negative and 18 positive coefficients'"
+      ]
+     },
+     "execution_count": 22,
+     "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": 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>weight</th>\n",
+       "      <th>abs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>n_mutations_log10</th>\n",
+       "      <td>0.406853</td>\n",
+       "      <td>0.406853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_thyroid carcinoma</th>\n",
+       "      <td>-0.283209</td>\n",
+       "      <td>0.283209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_ovarian serous cystadenocarcinoma</th>\n",
+       "      <td>0.271586</td>\n",
+       "      <td>0.271586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_head &amp; neck squamous cell carcinoma</th>\n",
+       "      <td>0.255094</td>\n",
+       "      <td>0.255094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_esophageal carcinoma</th>\n",
+       "      <td>0.221305</td>\n",
+       "      <td>0.221305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_kidney clear cell carcinoma</th>\n",
+       "      <td>-0.206621</td>\n",
+       "      <td>0.206621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_cervical &amp; endocervical cancer</th>\n",
+       "      <td>-0.194747</td>\n",
+       "      <td>0.194747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_kidney papillary cell carcinoma</th>\n",
+       "      <td>-0.186020</td>\n",
+       "      <td>0.186020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_pheochromocytoma &amp; paraganglioma</th>\n",
+       "      <td>-0.170498</td>\n",
+       "      <td>0.170498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_testicular germ cell tumor</th>\n",
+       "      <td>-0.169789</td>\n",
+       "      <td>0.169789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_prostate adenocarcinoma</th>\n",
+       "      <td>-0.158974</td>\n",
+       "      <td>0.158974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_uterine carcinosarcoma</th>\n",
+       "      <td>0.157790</td>\n",
+       "      <td>0.157790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_pancreatic adenocarcinoma</th>\n",
+       "      <td>0.151359</td>\n",
+       "      <td>0.151359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_lung squamous cell carcinoma</th>\n",
+       "      <td>0.149765</td>\n",
+       "      <td>0.149765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_skin cutaneous melanoma</th>\n",
+       "      <td>-0.148936</td>\n",
+       "      <td>0.148936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_rectum adenocarcinoma</th>\n",
+       "      <td>0.137583</td>\n",
+       "      <td>0.137583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_thymoma</th>\n",
+       "      <td>-0.134407</td>\n",
+       "      <td>0.134407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_uveal melanoma</th>\n",
+       "      <td>-0.118979</td>\n",
+       "      <td>0.118979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_brain lower grade glioma</th>\n",
+       "      <td>0.107231</td>\n",
+       "      <td>0.107231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_colon adenocarcinoma</th>\n",
+       "      <td>0.091189</td>\n",
+       "      <td>0.091189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_lung adenocarcinoma</th>\n",
+       "      <td>0.088074</td>\n",
+       "      <td>0.088074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_mesothelioma</th>\n",
+       "      <td>-0.087272</td>\n",
+       "      <td>0.087272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_diffuse large B-cell lymphoma</th>\n",
+       "      <td>-0.071398</td>\n",
+       "      <td>0.071398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_bladder urothelial carcinoma</th>\n",
+       "      <td>0.065330</td>\n",
+       "      <td>0.065330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_stomach adenocarcinoma</th>\n",
+       "      <td>0.056392</td>\n",
+       "      <td>0.056392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_cholangiocarcinoma</th>\n",
+       "      <td>-0.053046</td>\n",
+       "      <td>0.053046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_adrenocortical cancer</th>\n",
+       "      <td>-0.047275</td>\n",
+       "      <td>0.047275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_breast invasive carcinoma</th>\n",
+       "      <td>0.034973</td>\n",
+       "      <td>0.034973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_sarcoma</th>\n",
+       "      <td>0.032583</td>\n",
+       "      <td>0.032583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_uterine corpus endometrioid carcinoma</th>\n",
+       "      <td>-0.024682</td>\n",
+       "      <td>0.024682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_liver hepatocellular carcinoma</th>\n",
+       "      <td>-0.016290</td>\n",
+       "      <td>0.016290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_kidney chromophobe</th>\n",
+       "      <td>0.012236</td>\n",
+       "      <td>0.012236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender_Male</th>\n",
+       "      <td>0.011323</td>\n",
+       "      <td>0.011323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender_Female</th>\n",
+       "      <td>-0.006353</td>\n",
+       "      <td>0.006353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disease_glioblastoma multiforme</th>\n",
+       "      <td>0.002017</td>\n",
+       "      <td>0.002017</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 weight       abs\n",
+       "n_mutations_log10                              0.406853  0.406853\n",
+       "disease_thyroid carcinoma                     -0.283209  0.283209\n",
+       "disease_ovarian serous cystadenocarcinoma      0.271586  0.271586\n",
+       "disease_head & neck squamous cell carcinoma    0.255094  0.255094\n",
+       "disease_esophageal carcinoma                   0.221305  0.221305\n",
+       "disease_kidney clear cell carcinoma           -0.206621  0.206621\n",
+       "disease_cervical & endocervical cancer        -0.194747  0.194747\n",
+       "disease_kidney papillary cell carcinoma       -0.186020  0.186020\n",
+       "disease_pheochromocytoma & paraganglioma      -0.170498  0.170498\n",
+       "disease_testicular germ cell tumor            -0.169789  0.169789\n",
+       "disease_prostate adenocarcinoma               -0.158974  0.158974\n",
+       "disease_uterine carcinosarcoma                 0.157790  0.157790\n",
+       "disease_pancreatic adenocarcinoma              0.151359  0.151359\n",
+       "disease_lung squamous cell carcinoma           0.149765  0.149765\n",
+       "disease_skin cutaneous melanoma               -0.148936  0.148936\n",
+       "disease_rectum adenocarcinoma                  0.137583  0.137583\n",
+       "disease_thymoma                               -0.134407  0.134407\n",
+       "disease_uveal melanoma                        -0.118979  0.118979\n",
+       "disease_brain lower grade glioma               0.107231  0.107231\n",
+       "disease_colon adenocarcinoma                   0.091189  0.091189\n",
+       "disease_lung adenocarcinoma                    0.088074  0.088074\n",
+       "disease_mesothelioma                          -0.087272  0.087272\n",
+       "disease_diffuse large B-cell lymphoma         -0.071398  0.071398\n",
+       "disease_bladder urothelial carcinoma           0.065330  0.065330\n",
+       "disease_stomach adenocarcinoma                 0.056392  0.056392\n",
+       "disease_cholangiocarcinoma                    -0.053046  0.053046\n",
+       "disease_adrenocortical cancer                 -0.047275  0.047275\n",
+       "disease_breast invasive carcinoma              0.034973  0.034973\n",
+       "disease_sarcoma                                0.032583  0.032583\n",
+       "disease_uterine corpus endometrioid carcinoma -0.024682  0.024682\n",
+       "disease_liver hepatocellular carcinoma        -0.016290  0.016290\n",
+       "disease_kidney chromophobe                     0.012236  0.012236\n",
+       "gender_Male                                    0.011323  0.011323\n",
+       "gender_Female                                 -0.006353  0.006353\n",
+       "disease_glioblastoma multiforme                0.002017  0.002017"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coef_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Investigate the predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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', pipeline.decision_function(X)),\n",
+    "    ('probability', pipeline.predict_proba(X)[:, 1]),\n",
+    "])\n",
+    "predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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-IB-7651-01</th>\n",
+       "      <td>TCGA-IB-7651-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.662026</td>\n",
+       "      <td>0.934748</td>\n",
+       "      <td>93.5%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-L5-A4OI-01</th>\n",
+       "      <td>TCGA-L5-A4OI-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.483659</td>\n",
+       "      <td>0.922988</td>\n",
+       "      <td>92.3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-N7-A4Y0-01</th>\n",
+       "      <td>TCGA-N7-A4Y0-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.429504</td>\n",
+       "      <td>0.919050</td>\n",
+       "      <td>91.9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-L5-A8NM-01</th>\n",
+       "      <td>TCGA-L5-A8NM-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.385745</td>\n",
+       "      <td>0.915734</td>\n",
+       "      <td>91.6%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-CA-6717-01</th>\n",
+       "      <td>TCGA-CA-6717-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.890060</td>\n",
+       "      <td>0.868762</td>\n",
+       "      <td>86.9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-AZ-4315-01</th>\n",
+       "      <td>TCGA-AZ-4315-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.850787</td>\n",
+       "      <td>0.864219</td>\n",
+       "      <td>86.4%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-66-2785-01</th>\n",
+       "      <td>TCGA-66-2785-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.839677</td>\n",
+       "      <td>0.862910</td>\n",
+       "      <td>86.3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-L5-A8NE-01</th>\n",
+       "      <td>TCGA-L5-A8NE-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.761791</td>\n",
+       "      <td>0.853434</td>\n",
+       "      <td>85.3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-Q9-A6FW-01</th>\n",
+       "      <td>TCGA-Q9-A6FW-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.710566</td>\n",
+       "      <td>0.846910</td>\n",
+       "      <td>84.7%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-R6-A6XG-01</th>\n",
+       "      <td>TCGA-R6-A6XG-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.705193</td>\n",
+       "      <td>0.846212</td>\n",
+       "      <td>84.6%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       sample_id  testing  status  decision_function  \\\n",
+       "sample_id                                                              \n",
+       "TCGA-IB-7651-01  TCGA-IB-7651-01        0       0           2.662026   \n",
+       "TCGA-L5-A4OI-01  TCGA-L5-A4OI-01        1       0           2.483659   \n",
+       "TCGA-N7-A4Y0-01  TCGA-N7-A4Y0-01        0       0           2.429504   \n",
+       "TCGA-L5-A8NM-01  TCGA-L5-A8NM-01        0       0           2.385745   \n",
+       "TCGA-CA-6717-01  TCGA-CA-6717-01        1       0           1.890060   \n",
+       "TCGA-AZ-4315-01  TCGA-AZ-4315-01        0       0           1.850787   \n",
+       "TCGA-66-2785-01  TCGA-66-2785-01        0       0           1.839677   \n",
+       "TCGA-L5-A8NE-01  TCGA-L5-A8NE-01        0       0           1.761791   \n",
+       "TCGA-Q9-A6FW-01  TCGA-Q9-A6FW-01        0       0           1.710566   \n",
+       "TCGA-R6-A6XG-01  TCGA-R6-A6XG-01        0       0           1.705193   \n",
+       "\n",
+       "                 probability probability_str  \n",
+       "sample_id                                     \n",
+       "TCGA-IB-7651-01     0.934748           93.5%  \n",
+       "TCGA-L5-A4OI-01     0.922988           92.3%  \n",
+       "TCGA-N7-A4Y0-01     0.919050           91.9%  \n",
+       "TCGA-L5-A8NM-01     0.915734           91.6%  \n",
+       "TCGA-CA-6717-01     0.868762           86.9%  \n",
+       "TCGA-AZ-4315-01     0.864219           86.4%  \n",
+       "TCGA-66-2785-01     0.862910           86.3%  \n",
+       "TCGA-L5-A8NE-01     0.853434           85.3%  \n",
+       "TCGA-Q9-A6FW-01     0.846910           84.7%  \n",
+       "TCGA-R6-A6XG-01     0.846212           84.6%  "
+      ]
+     },
+     "execution_count": 25,
+     "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": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Qa9acBMDQ0BD9/X00Ny/H7/czOTnB2rUnsXbtSXzgAzfx/vffwNatL1FeXh7z/S+99Er+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\nJe84FdCCoJ+KkpDL0UgmhaOTDE+OAKqAZlplaQEffduFhIJ+wpEYX7p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b+0C1BC9eoQRU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fCsMebPEntCJYUx5ibgKuCDiQ+l/Qt2th9C+l2gAthujOkyxjhP1FuNMZ90Ma6sZYxZY4xpNcZU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\nZmZmlfoLaNabQ0jCRHYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbe3f4a78d0>"
+      ]
+     },
+     "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": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VBRXxx13D3VlciRDZ4Uj2A1RVnQM8AFwChIA/AB/x+Xx9Z1x3D/AvgDElWAE0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\nWL0GlfnmCUw3rCrlZy+2E4vH8XWOc9Xaxa+9yOnh09s+xT8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VM4O8/+1r0/4YuXx9syUb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o/z/Ou9PPB0K6HEWvKdNu7Y08AtO1csurwfiUX4f2d+ysGBI4D6mvvuptvZ17A343v2jXR9lysJ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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbe39c7a7f0>"
+      ]
+     },
+     "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": {
+  "anaconda-cloud": {},
+  "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
+}
diff --git a/explore/confounding/confounding.py b/explore/confounding/confounding.py
new file mode 100644
index 0000000..b224fa7
--- /dev/null
+++ b/explore/confounding/confounding.py
@@ -0,0 +1,280 @@
+
+# coding: utf-8
+
+# # Create a logistic regression model to predict TP53 mutation from covariates
+
+# In[1]:
+
+import os
+import warnings
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn import preprocessing, grid_search
+from sklearn.linear_model import SGDClassifier
+from sklearn.cross_validation import train_test_split
+from sklearn.metrics import roc_auc_score, roc_curve
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from scipy.special import logit
+
+
+# In[2]:
+
+get_ipython().magic('matplotlib inline')
+plt.style.use('seaborn-notebook')
+
+
+# ## Specify model configuration
+
+# In[3]:
+
+# We're going to be building a 'TP53' classifier 
+gene = '7157' # TP53
+
+
+# In[4]:
+
+# Parameter Sweep for Hyperparameters
+n_feature_kept = 500
+param_fixed = {
+    'loss': 'log',
+    'penalty': 'elasticnet',
+}
+param_grid = {
+    'alpha': [10 ** x for x in range(-6, 2)],
+    'l1_ratio': [0, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 1],
+}
+
+
+# *Here is some [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) regarding the classifier and hyperparameters*
+# 
+# *Here is some [information](https://ghr.nlm.nih.gov/gene/TP53) about TP53*
+
+# ## Load Data
+
+# In[5]:
+
+get_ipython().run_cell_magic('time', '', "path = os.path.join('..', '..', 'download', 'mutation-matrix.tsv.bz2')\nY = pd.read_table(path, index_col=0)")
+
+
+# In[6]:
+
+# Read sample information and create a covariate TSV
+path = os.path.join('..', '..', 'download', 'samples.tsv')
+covariate_df = (
+    pd.read_table(path, index_col=0)
+    .drop(['patient_id', 'sample_type', 'organ_of_origin'], axis='columns')
+    [['gender', 'disease']]
+    .pipe(pd.get_dummies, columns=['gender', 'disease'])
+)
+
+n_mutations = Y.sum(axis='columns')
+covariate_df['n_mutations_log10'] = np.log10(n_mutations)
+
+#covariate_df['n_mutations_log1p'] = np.log1p(n_mutations)
+#covariate_df['n_mutations_log'] = np.log(n_mutations)
+#covariate_df['mutations_freq_logit'] = np.log1p(n_mutations / len(Y.columns))
+#covariate_df['n_mutations_ihs'] = np.arcsinh(n_mutations)
+
+covariate_df.head(2)
+
+
+# In[7]:
+
+# The Series now holds TP53 Mutation Status for each Sample
+y = Y[gene]
+y.head(5)
+
+
+# In[8]:
+
+X = covariate_df
+
+
+# In[9]:
+
+# Here are the percentage of tumors with NF1
+y.value_counts(True)
+
+
+# ## Set aside 10% of the data for testing
+
+# In[10]:
+
+# Typically, this can only be done where the number of mutations is large enough
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
+'Size: {:,} features, {:,} training samples, {:,} testing samples'.format(len(X.columns), len(X_train), len(X_test))
+
+
+# ## Define pipeline and Cross validation model fitting
+
+# In[11]:
+
+# Include loss='log' in param_grid doesn't work with pipeline somehow
+clf = SGDClassifier(random_state=0, class_weight='balanced',
+                    loss=param_fixed['loss'], penalty=param_fixed['penalty'])
+
+# joblib is used to cross-validate in parallel by setting `n_jobs=-1` in GridSearchCV
+# Supress joblib warning. See https://github.com/scikit-learn/scikit-learn/issues/6370
+warnings.filterwarnings('ignore', message='Changing the shape of non-C contiguous array')
+clf_grid = grid_search.GridSearchCV(estimator=clf, param_grid=param_grid, n_jobs=-1, scoring='roc_auc')
+pipeline = make_pipeline(
+    StandardScaler(),
+    clf_grid
+)
+
+
+# In[12]:
+
+get_ipython().run_cell_magic('time', '', '# Fit the model (the computationally intensive part)\npipeline.fit(X=X_train, y=y_train)\nbest_clf = clf_grid.best_estimator_')
+
+
+# In[13]:
+
+clf_grid.best_params_
+
+
+# In[14]:
+
+best_clf
+
+
+# ## Visualize hyperparameters performance
+
+# In[15]:
+
+def grid_scores_to_df(grid_scores):
+    """
+    Convert a sklearn.grid_search.GridSearchCV.grid_scores_ attribute to 
+    a tidy pandas DataFrame where each row is a hyperparameter-fold combinatination.
+    """
+    rows = list()
+    for grid_score in grid_scores:
+        for fold, score in enumerate(grid_score.cv_validation_scores):
+            row = grid_score.parameters.copy()
+            row['fold'] = fold
+            row['score'] = score
+            rows.append(row)
+    df = pd.DataFrame(rows)
+    return df
+
+
+# ## Process Mutation Matrix
+
+# In[16]:
+
+cv_score_df = grid_scores_to_df(clf_grid.grid_scores_)
+cv_score_df.head(2)
+
+
+# In[17]:
+
+# Cross-validated performance distribution
+facet_grid = sns.factorplot(x='l1_ratio', y='score', col='alpha',
+    data=cv_score_df, kind='violin', size=4, aspect=1)
+facet_grid.set_ylabels('AUROC');
+
+
+# In[18]:
+
+# Cross-validated performance heatmap
+cv_score_mat = pd.pivot_table(cv_score_df, values='score', index='l1_ratio', columns='alpha')
+ax = sns.heatmap(cv_score_mat, annot=True, fmt='.1%')
+ax.set_xlabel('Regularization strength multiplier (alpha)')
+ax.set_ylabel('Elastic net mixing parameter (l1_ratio)');
+
+
+# ## Use Optimal Hyperparameters to Output ROC Curve
+
+# In[19]:
+
+y_pred_train = pipeline.decision_function(X_train)
+y_pred_test = pipeline.decision_function(X_test)
+
+def get_threshold_metrics(y_true, y_pred):
+    roc_columns = ['fpr', 'tpr', 'threshold']
+    roc_items = zip(roc_columns, roc_curve(y_true, y_pred))
+    roc_df = pd.DataFrame.from_items(roc_items)
+    auroc = roc_auc_score(y_true, y_pred)
+    return {'auroc': auroc, 'roc_df': roc_df}
+
+metrics_train = get_threshold_metrics(y_train, y_pred_train)
+metrics_test = get_threshold_metrics(y_test, y_pred_test)
+
+
+# In[20]:
+
+# Plot ROC
+plt.figure()
+for label, metrics in ('Training', metrics_train), ('Testing', metrics_test):
+    roc_df = metrics['roc_df']
+    plt.plot(roc_df.fpr, roc_df.tpr,
+        label='{} (AUROC = {:.1%})'.format(label, metrics['auroc']))
+plt.xlim([0.0, 1.0])
+plt.ylim([0.0, 1.05])
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('Predicting TP53 mutation from gene expression (ROC curves)')
+plt.legend(loc='lower right');
+
+
+# ## What are the classifier coefficients?
+
+# In[21]:
+
+coef_df = pd.DataFrame(best_clf.coef_.transpose(), index=X.columns, columns=['weight'])
+coef_df['abs'] = coef_df['weight'].abs()
+coef_df = coef_df.sort_values('abs', ascending=False)
+
+
+# In[22]:
+
+'{:.1%} zero coefficients; {:,} negative and {:,} positive coefficients'.format(
+    (coef_df.weight == 0).mean(),
+    (coef_df.weight < 0).sum(),
+    (coef_df.weight > 0).sum()
+)
+
+
+# In[23]:
+
+coef_df
+
+
+# ## Investigate the predictions
+
+# In[24]:
+
+predict_df = pd.DataFrame.from_items([
+    ('sample_id', X.index),
+    ('testing', X.index.isin(X_test.index).astype(int)),
+    ('status', y),
+    ('decision_function', pipeline.decision_function(X)),
+    ('probability', pipeline.predict_proba(X)[:, 1]),
+])
+predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)
+
+
+# In[25]:
+
+# Top predictions amongst negatives (potential hidden responders)
+predict_df.sort_values('decision_function', ascending=False).query("status == 0").head(10)
+
+
+# In[26]:
+
+# Ignore numpy warning caused by seaborn
+warnings.filterwarnings('ignore', 'using a non-integer number instead of an integer')
+
+ax = sns.distplot(predict_df.query("status == 0").decision_function, hist=False, label='Negatives')
+ax = sns.distplot(predict_df.query("status == 1").decision_function, hist=False, label='Positives')
+
+
+# In[27]:
+
+ax = sns.distplot(predict_df.query("status == 0").probability, hist=False, label='Negatives')
+ax = sns.distplot(predict_df.query("status == 1").probability, hist=False, label='Positives')
+

From 50aa17482dd1f9ebfae7d98fcf98a0209eb86487 Mon Sep 17 00:00:00 2001
From: Daniel Himmelstein <daniel.himmelstein@gmail.com>
Date: Mon, 19 Sep 2016 12:17:22 -0400
Subject: [PATCH 2/4] Evaluate more covariate/mutation combinations

Evaluate covariate-only classifiers for the interesting mutations compiled in
https://github.com/cognoma/cancer-data/pull/22#issuecomment-245294155.

Switches to an expand grid system for evaluating all possible covariate
combinations.

Plot performance of all covariates on each mutation.

Switches to `covariates.tsv` created in
https://github.com/cognoma/cancer-data/pull/24 for encoded covariates.
---
 explore/confounding/auroc.tsv         |  249 +++++
 explore/confounding/confounding.ipynb | 1300 +++++++------------------
 2 files changed, 595 insertions(+), 954 deletions(-)
 create mode 100644 explore/confounding/auroc.tsv

diff --git a/explore/confounding/auroc.tsv b/explore/confounding/auroc.tsv
new file mode 100644
index 0000000..6cd711c
--- /dev/null
+++ b/explore/confounding/auroc.tsv
@@ -0,0 +1,249 @@
+mutation	disease_covariate	organ_covariate	gender_covariate	mutation_covariate	survival_covariate	symbol	positive_prevalence	mean_cv_auroc	training_auroc	testing_auroc
+238	1	0	0	1	1	ALK	0.018889	0.85642	0.82764	0.84673
+238	1	0	1	1	0	ALK	0.018889	0.85762	0.82936	0.84633
+238	1	0	1	1	1	ALK	0.018889	0.85618	0.82939	0.84613
+238	1	1	1	1	0	ALK	0.018889	0.85385	0.82907	0.84604
+238	1	1	1	1	1	ALK	0.018889	0.85313	0.82821	0.84564
+238	0	1	1	1	1	ALK	0.018889	0.85275	0.82823	0.84135
+238	0	1	1	1	0	ALK	0.018889	0.85362	0.82817	0.84085
+238	0	0	1	1	0	ALK	0.018889	0.8477	0.82684	0.83622
+238	1	1	0	1	1	ALK	0.018889	0.85368	0.83335	0.83528
+238	1	1	0	1	0	ALK	0.018889	0.85243	0.83322	0.83508
+238	1	0	0	1	0	ALK	0.018889	0.85629	0.82957	0.83488
+238	0	0	1	1	1	ALK	0.018889	0.8476	0.82754	0.83274
+238	0	0	0	1	1	ALK	0.018889	0.84552	0.82696	0.82865
+238	0	1	0	1	1	ALK	0.018889	0.85331	0.83747	0.81236
+238	0	1	0	1	0	ALK	0.018889	0.85211	0.83829	0.80524
+238	0	1	0	0	1	ALK	0.018889	0.78255	0.76903	0.70129
+238	0	1	0	0	0	ALK	0.018889	0.77866	0.76335	0.69586
+238	0	1	1	0	0	ALK	0.018889	0.77725	0.76281	0.69506
+238	0	1	1	0	1	ALK	0.018889	0.78244	0.7626	0.69406
+238	1	0	1	0	0	ALK	0.018889	0.77592	0.77116	0.69327
+238	1	0	1	0	1	ALK	0.018889	0.78126	0.77554	0.68574
+238	1	0	0	0	1	ALK	0.018889	0.78142	0.77687	0.68405
+238	1	1	1	0	0	ALK	0.018889	0.78731	0.76632	0.68315
+238	1	1	1	0	1	ALK	0.018889	0.79123	0.76632	0.68315
+238	1	0	0	0	0	ALK	0.018889	0.77786	0.7709	0.68295
+238	1	1	0	0	0	ALK	0.018889	0.78553	0.7541	0.6829
+238	1	1	0	0	1	ALK	0.018889	0.79121	0.76059	0.67533
+238	0	0	0	0	1	ALK	0.018889	0.51956	0.50443	0.60007
+238	0	0	1	0	0	ALK	0.018889	0.57719	0.54399	0.565
+238	0	0	1	0	1	ALK	0.018889	0.57671	0.54399	0.565
+238	0	0	0	1	0	ALK	0.018889	0.84524	0.17316	0.16378
+672	1	0	0	1	0	BRCA1	0.018615	0.84055	0.8314	0.86103
+672	1	0	1	1	0	BRCA1	0.018615	0.83859	0.8314	0.86103
+672	0	1	1	1	0	BRCA1	0.018615	0.83636	0.83073	0.86006
+672	0	1	0	1	0	BRCA1	0.018615	0.83632	0.83074	0.86006
+672	0	1	0	1	1	BRCA1	0.018615	0.83624	0.83074	0.86006
+672	0	1	1	1	1	BRCA1	0.018615	0.83628	0.8309	0.86006
+672	1	1	0	1	0	BRCA1	0.018615	0.83635	0.83116	0.86006
+672	1	1	1	1	0	BRCA1	0.018615	0.83634	0.83113	0.86006
+672	1	0	0	1	1	BRCA1	0.018615	0.83862	0.82585	0.85291
+672	1	0	1	1	1	BRCA1	0.018615	0.83864	0.82585	0.85291
+672	1	1	0	1	1	BRCA1	0.018615	0.83638	0.82333	0.8475
+672	1	1	1	1	1	BRCA1	0.018615	0.8364	0.82333	0.8475
+672	0	0	1	1	0	BRCA1	0.018615	0.82859	0.81965	0.83918
+672	0	0	0	1	0	BRCA1	0.018615	0.82738	0.81799	0.83544
+672	0	0	0	1	1	BRCA1	0.018615	0.82738	0.81799	0.83544
+672	0	0	1	1	1	BRCA1	0.018615	0.82738	0.81799	0.83544
+672	1	0	0	0	1	BRCA1	0.018615	0.7033	0.75681	0.71179
+672	0	1	0	0	1	BRCA1	0.018615	0.71455	0.75257	0.7113
+672	1	0	1	0	0	BRCA1	0.018615	0.72292	0.75269	0.70943
+672	0	1	1	0	1	BRCA1	0.018615	0.71844	0.75269	0.70763
+672	1	0	1	0	1	BRCA1	0.018615	0.71953	0.75806	0.707
+672	1	0	0	0	0	BRCA1	0.018615	0.72229	0.75222	0.7052
+672	1	1	1	0	0	BRCA1	0.018615	0.72895	0.75386	0.7025
+672	1	1	0	0	0	BRCA1	0.018615	0.72504	0.75177	0.7016
+672	0	1	1	0	0	BRCA1	0.018615	0.72877	0.75015	0.69945
+672	0	1	0	0	0	BRCA1	0.018615	0.72111	0.7484	0.69861
+672	1	1	0	0	1	BRCA1	0.018615	0.72	0.71965	0.66907
+672	1	1	1	0	1	BRCA1	0.018615	0.72568	0.71965	0.66907
+672	0	0	1	0	1	BRCA1	0.018615	0.54317	0.53337	0.53564
+672	0	0	0	0	1	BRCA1	0.018615	0.53768	0.5	0.5
+672	0	0	1	0	0	BRCA1	0.018615	0.52635	0.50191	0.45798
+675	0	0	0	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	0	0	0	1	1	BRCA2	0.032439	0.8235	0.79603	0.88723
+675	0	0	1	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	0	0	1	1	1	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	0	1	0	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	0	1	0	1	1	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	0	1	1	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	0	1	1	1	1	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	0	0	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	0	0	1	1	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	0	1	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	0	1	1	1	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	1	0	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	1	0	1	1	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	1	1	1	0	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	1	1	1	1	1	BRCA2	0.032439	0.82343	0.79603	0.88723
+675	0	1	1	0	1	BRCA2	0.032439	0.69087	0.71246	0.80843
+675	1	1	1	0	1	BRCA2	0.032439	0.69176	0.71708	0.80458
+675	0	1	1	0	0	BRCA2	0.032439	0.69254	0.71312	0.80346
+675	0	1	0	0	1	BRCA2	0.032439	0.69183	0.71193	0.80284
+675	1	1	1	0	0	BRCA2	0.032439	0.69065	0.71562	0.80281
+675	1	1	0	0	1	BRCA2	0.032439	0.69176	0.71874	0.7994
+675	0	1	0	0	0	BRCA2	0.032439	0.69146	0.71288	0.79498
+675	1	0	1	0	1	BRCA2	0.032439	0.68641	0.7158	0.79288
+675	1	0	0	0	1	BRCA2	0.032439	0.68913	0.71485	0.79149
+675	1	0	1	0	0	BRCA2	0.032439	0.68932	0.71682	0.79034
+675	1	0	0	0	0	BRCA2	0.032439	0.69165	0.71541	0.78996
+675	1	1	0	0	0	BRCA2	0.032439	0.69176	0.71744	0.78448
+675	0	0	1	0	1	BRCA2	0.032439	0.58062	0.5581	0.61077
+675	0	0	0	0	1	BRCA2	0.032439	0.55403	0.52461	0.57545
+675	0	0	1	0	0	BRCA2	0.032439	0.55441	0.54702	0.57182
+29126	0	0	1	1	0	CD274	0.0026006	0.92574	0.82964	0.9219
+29126	0	0	1	1	1	CD274	0.0026006	0.93361	0.78557	0.89697
+29126	0	0	0	1	1	CD274	0.0026006	0.93557	0.86543	0.87713
+29126	0	0	0	1	0	CD274	0.0026006	0.92561	0.86562	0.8741
+29126	0	1	0	1	0	CD274	0.0026006	0.92561	0.87603	0.86708
+29126	0	1	0	1	1	CD274	0.0026006	0.92561	0.87603	0.86708
+29126	0	1	1	1	0	CD274	0.0026006	0.9269	0.87603	0.86708
+29126	0	1	1	1	1	CD274	0.0026006	0.92561	0.87603	0.86708
+29126	1	0	0	1	0	CD274	0.0026006	0.92561	0.87603	0.86708
+29126	1	0	0	1	1	CD274	0.0026006	0.92561	0.87603	0.86708
+29126	1	0	1	1	0	CD274	0.0026006	0.92655	0.87603	0.86708
+29126	1	0	1	1	1	CD274	0.0026006	0.92561	0.87603	0.86708
+29126	1	1	0	1	0	CD274	0.0026006	0.92561	0.87559	0.86598
+29126	1	1	0	1	1	CD274	0.0026006	0.92561	0.87559	0.86598
+29126	1	1	1	1	0	CD274	0.0026006	0.92561	0.87559	0.86598
+29126	1	1	1	1	1	CD274	0.0026006	0.92561	0.87559	0.86598
+29126	0	0	1	0	1	CD274	0.0026006	0.62654	0.59426	0.78388
+29126	0	0	1	0	0	CD274	0.0026006	0.64024	0.56949	0.76033
+29126	1	0	1	0	0	CD274	0.0026006	0.81727	0.88121	0.72824
+29126	1	1	1	0	0	CD274	0.0026006	0.76515	0.82536	0.70785
+29126	0	1	0	0	0	CD274	0.0026006	0.71433	0.6496	0.57287
+29126	0	1	0	0	1	CD274	0.0026006	0.742	0.6496	0.57287
+29126	1	0	0	0	0	CD274	0.0026006	0.71125	0.6496	0.57287
+29126	1	0	0	0	1	CD274	0.0026006	0.75034	0.6496	0.57287
+29126	1	1	0	0	0	CD274	0.0026006	0.71125	0.6496	0.57287
+29126	1	1	0	0	1	CD274	0.0026006	0.71125	0.6496	0.57287
+29126	1	1	1	0	1	CD274	0.0026006	0.79372	0.6496	0.57287
+29126	0	1	1	0	0	CD274	0.0026006	0.82562	0.6745	0.5646
+29126	0	1	1	0	1	CD274	0.0026006	0.79327	0.70564	0.55799
+29126	0	0	0	0	1	CD274	0.0026006	0.53146	0.55083	0.54504
+29126	1	0	1	0	1	CD274	0.0026006	0.79784	0.7308	0.54256
+4221	0	1	0	1	0	MEN1	0.0078018	0.83516	0.83394	0.90351
+4221	1	1	0	1	0	MEN1	0.0078018	0.85521	0.83546	0.84057
+4221	1	0	0	1	0	MEN1	0.0078018	0.85524	0.83897	0.83564
+4221	1	1	1	1	0	MEN1	0.0078018	0.85037	0.83299	0.82597
+4221	0	1	1	1	0	MEN1	0.0078018	0.83827	0.81897	0.82133
+4221	1	1	1	1	1	MEN1	0.0078018	0.85458	0.83504	0.81452
+4221	1	1	0	1	1	MEN1	0.0078018	0.8574	0.8345	0.81433
+4221	0	1	0	1	1	MEN1	0.0078018	0.84544	0.82366	0.81255
+4221	0	1	1	1	1	MEN1	0.0078018	0.854	0.82502	0.81146
+4221	1	0	0	1	1	MEN1	0.0078018	0.85072	0.83205	0.80979
+4221	1	0	1	1	1	MEN1	0.0078018	0.85552	0.8339	0.8087
+4221	1	0	1	1	0	MEN1	0.0078018	0.85487	0.833	0.80781
+4221	0	0	0	1	0	MEN1	0.0078018	0.75727	0.72028	0.78562
+4221	0	0	0	1	1	MEN1	0.0078018	0.76606	0.73202	0.77289
+4221	0	0	1	1	0	MEN1	0.0078018	0.75744	0.72204	0.76115
+4221	0	0	1	1	1	MEN1	0.0078018	0.76603	0.71694	0.7428
+4221	1	0	1	0	0	MEN1	0.0078018	0.72826	0.76812	0.69307
+4221	0	1	0	0	1	MEN1	0.0078018	0.78894	0.75859	0.6837
+4221	0	1	1	0	0	MEN1	0.0078018	0.70709	0.71926	0.63398
+4221	1	1	0	0	1	MEN1	0.0078018	0.75274	0.76115	0.62007
+4221	1	1	1	0	1	MEN1	0.0078018	0.74922	0.76524	0.60902
+4221	1	0	0	0	0	MEN1	0.0078018	0.72857	0.73738	0.60616
+4221	1	1	1	0	0	MEN1	0.0078018	0.72861	0.73261	0.59136
+4221	1	0	0	0	1	MEN1	0.0078018	0.75487	0.74982	0.57616
+4221	1	1	0	0	0	MEN1	0.0078018	0.72307	0.69823	0.56719
+4221	0	1	0	0	0	MEN1	0.0078018	0.71001	0.70745	0.55041
+4221	0	1	1	0	1	MEN1	0.0078018	0.78162	0.737	0.5219
+4221	1	0	1	0	1	MEN1	0.0078018	0.75782	0.75556	0.51944
+4221	0	0	1	0	0	MEN1	0.0078018	0.59436	0.53765	0.47178
+4221	0	0	0	0	1	MEN1	0.0078018	0.63918	0.60479	0.41604
+4221	0	0	1	0	1	MEN1	0.0078018	0.64165	0.61631	0.41298
+5979	0	1	1	1	0	RET	0.016014	0.84825	0.82611	0.88778
+5979	0	1	0	1	0	RET	0.016014	0.84804	0.82584	0.88767
+5979	0	1	0	1	1	RET	0.016014	0.84551	0.82589	0.88767
+5979	0	1	1	1	1	RET	0.016014	0.84586	0.82639	0.88767
+5979	0	0	0	1	1	RET	0.016014	0.80154	0.78132	0.88467
+5979	0	0	1	1	0	RET	0.016014	0.79772	0.78132	0.88467
+5979	0	0	1	1	1	RET	0.016014	0.80119	0.78036	0.88097
+5979	1	1	1	1	0	RET	0.016014	0.84965	0.83005	0.87626
+5979	1	1	1	1	1	RET	0.016014	0.84671	0.83035	0.87594
+5979	1	0	1	1	0	RET	0.016014	0.84605	0.82659	0.8709
+5979	1	0	0	1	0	RET	0.016014	0.84601	0.82627	0.87079
+5979	1	0	0	1	1	RET	0.016014	0.84289	0.82633	0.87079
+5979	1	0	1	1	1	RET	0.016014	0.8433	0.82669	0.87079
+5979	1	1	0	1	0	RET	0.016014	0.8476	0.82988	0.87037
+5979	1	1	0	1	1	RET	0.016014	0.84665	0.82979	0.87037
+5979	0	1	0	0	1	RET	0.016014	0.72391	0.74649	0.78996
+5979	0	1	0	0	0	RET	0.016014	0.73719	0.75251	0.78278
+5979	0	1	1	0	1	RET	0.016014	0.72498	0.75247	0.78027
+5979	0	1	1	0	0	RET	0.016014	0.73027	0.75576	0.77834
+5979	1	0	0	0	1	RET	0.016014	0.72909	0.75378	0.76387
+5979	1	1	0	0	0	RET	0.016014	0.74045	0.76007	0.76253
+5979	1	1	0	0	1	RET	0.016014	0.72952	0.75645	0.75777
+5979	1	0	1	0	1	RET	0.016014	0.72807	0.76008	0.75198
+5979	1	1	1	0	1	RET	0.016014	0.72902	0.75858	0.74963
+5979	1	0	0	0	0	RET	0.016014	0.73982	0.76069	0.74936
+5979	1	1	1	0	0	RET	0.016014	0.74002	0.75906	0.74325
+5979	1	0	1	0	0	RET	0.016014	0.73368	0.76267	0.74229
+5979	0	0	1	0	1	RET	0.016014	0.57853	0.54692	0.58501
+5979	0	0	0	0	1	RET	0.016014	0.55554	0.52569	0.54398
+5979	0	0	1	0	0	RET	0.016014	0.56051	0.46443	0.45082
+5979	0	0	0	1	0	RET	0.016014	0.79629	0.21868	0.11533
+7157	1	1	0	1	0	TP53	0.35409	0.84786	0.84534	0.85651
+7157	1	1	0	1	1	TP53	0.35409	0.84806	0.84484	0.85651
+7157	1	1	1	1	1	TP53	0.35409	0.84774	0.84489	0.85644
+7157	1	1	1	1	0	TP53	0.35409	0.8475	0.84537	0.85627
+7157	1	0	1	1	0	TP53	0.35409	0.84783	0.84529	0.85374
+7157	1	0	0	1	1	TP53	0.35409	0.84739	0.84391	0.85014
+7157	1	0	1	1	1	TP53	0.35409	0.84744	0.84322	0.85006
+7157	1	1	1	0	1	TP53	0.35409	0.82911	0.82597	0.84982
+7157	0	1	0	1	0	TP53	0.35409	0.84504	0.84027	0.84954
+7157	1	0	0	1	0	TP53	0.35409	0.84783	0.84421	0.84908
+7157	1	1	0	0	1	TP53	0.35409	0.82826	0.82576	0.84874
+7157	0	1	0	1	1	TP53	0.35409	0.84511	0.83979	0.84788
+7157	0	1	1	1	0	TP53	0.35409	0.84507	0.83896	0.84765
+7157	1	0	1	0	1	TP53	0.35409	0.83044	0.82512	0.84753
+7157	0	1	1	1	1	TP53	0.35409	0.84513	0.83901	0.84744
+7157	1	1	1	0	0	TP53	0.35409	0.82923	0.82674	0.84737
+7157	1	0	0	0	1	TP53	0.35409	0.83008	0.82507	0.84703
+7157	1	0	1	0	0	TP53	0.35409	0.83034	0.82702	0.84611
+7157	0	1	1	0	1	TP53	0.35409	0.8271	0.82209	0.84578
+7157	0	1	0	0	1	TP53	0.35409	0.82692	0.82182	0.84554
+7157	0	1	1	0	0	TP53	0.35409	0.82799	0.82325	0.84532
+7157	1	0	0	0	0	TP53	0.35409	0.82831	0.82592	0.8439
+7157	1	1	0	0	0	TP53	0.35409	0.82758	0.82536	0.84369
+7157	0	1	0	0	0	TP53	0.35409	0.82558	0.82191	0.84312
+7157	0	0	1	1	0	TP53	0.35409	0.72269	0.72046	0.73622
+7157	0	0	1	1	1	TP53	0.35409	0.72909	0.72524	0.73554
+7157	0	0	0	1	1	TP53	0.35409	0.72904	0.72525	0.73546
+7157	0	0	0	1	0	TP53	0.35409	0.72255	0.71984	0.73498
+7157	0	0	0	0	1	TP53	0.35409	0.57634	0.57419	0.58941
+7157	0	0	1	0	1	TP53	0.35409	0.59265	0.58341	0.58714
+7157	0	0	1	0	0	TP53	0.35409	0.53082	0.48264	0.4987
+7428	1	0	0	1	0	VHL	0.018478	0.98297	0.98363	0.99392
+7428	1	1	0	1	0	VHL	0.018478	0.98464	0.98335	0.99392
+7428	1	1	0	1	1	VHL	0.018478	0.98284	0.98095	0.99392
+7428	1	1	1	1	0	VHL	0.018478	0.98556	0.98297	0.99392
+7428	1	1	1	1	1	VHL	0.018478	0.98436	0.98306	0.99388
+7428	1	0	0	1	1	VHL	0.018478	0.98075	0.98375	0.99375
+7428	1	1	0	0	1	VHL	0.018478	0.98036	0.98134	0.99318
+7428	1	0	0	0	1	VHL	0.018478	0.974	0.98134	0.99318
+7428	1	0	1	1	0	VHL	0.018478	0.98078	0.98287	0.99305
+7428	1	0	1	1	1	VHL	0.018478	0.98097	0.98297	0.99301
+7428	1	0	1	0	1	VHL	0.018478	0.97814	0.98014	0.99161
+7428	1	1	0	0	0	VHL	0.018478	0.98076	0.97964	0.99161
+7428	1	0	1	0	0	VHL	0.018478	0.97937	0.96852	0.99126
+7428	1	1	1	0	0	VHL	0.018478	0.98076	0.97868	0.99126
+7428	1	1	1	0	1	VHL	0.018478	0.9796	0.97852	0.99047
+7428	0	1	0	1	1	VHL	0.018478	0.97458	0.97149	0.98488
+7428	0	1	1	1	1	VHL	0.018478	0.97516	0.97063	0.98217
+7428	0	1	0	1	0	VHL	0.018478	0.9757	0.97078	0.98212
+7428	0	1	0	0	1	VHL	0.018478	0.97056	0.96755	0.98204
+7428	0	1	1	0	1	VHL	0.018478	0.97085	0.96782	0.98081
+7428	0	1	1	1	0	VHL	0.018478	0.97554	0.96966	0.9795
+7428	0	1	0	0	0	VHL	0.018478	0.96924	0.9652	0.97552
+7428	0	1	1	0	0	VHL	0.018478	0.97062	0.96434	0.97264
+7428	1	0	0	0	0	VHL	0.018478	0.97722	0.91939	0.92727
+7428	0	0	1	1	0	VHL	0.018478	0.6471	0.60612	0.59161
+7428	0	0	0	1	0	VHL	0.018478	0.60565	0.57224	0.58099
+7428	0	0	1	0	0	VHL	0.018478	0.61571	0.57833	0.55656
+7428	0	0	1	1	1	VHL	0.018478	0.65996	0.61086	0.54301
+7428	0	0	1	0	1	VHL	0.018478	0.63026	0.58997	0.52146
+7428	0	0	0	1	1	VHL	0.018478	0.62362	0.58426	0.50175
+7428	0	0	0	0	1	VHL	0.018478	0.54628	0.52655	0.43684
diff --git a/explore/confounding/confounding.ipynb b/explore/confounding/confounding.ipynb
index 982c18e..45b464b 100644
--- a/explore/confounding/confounding.ipynb
+++ b/explore/confounding/confounding.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Create a logistic regression model to predict TP53 mutation from covariates"
+    "# Create a logistic regression model to several mutations from covariates"
    ]
   },
   {
@@ -16,7 +16,9 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "import itertools\n",
     "import warnings\n",
+    "import collections\n",
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -47,82 +49,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Specify model configuration"
+    "## Load Data"
    ]
   },
   {
    "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": "code",
-   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# Parameter Sweep for Hyperparameters\n",
-    "n_feature_kept = 500\n",
-    "param_fixed = {\n",
-    "    'loss': 'log',\n",
-    "    'penalty': 'elasticnet',\n",
-    "}\n",
-    "param_grid = {\n",
-    "    'alpha': [10 ** x for x in range(-6, 2)],\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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 37 s, sys: 1.07 s, total: 38 s\n",
-      "Wall time: 38 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,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -135,8 +79,9 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>gender_Female</th>\n",
-       "      <th>gender_Male</th>\n",
+       "      <th>age_diagnosed</th>\n",
+       "      <th>days_survived</th>\n",
+       "      <th>days_recurrence_free</th>\n",
        "      <th>disease_adrenocortical cancer</th>\n",
        "      <th>disease_bladder urothelial carcinoma</th>\n",
        "      <th>disease_brain lower grade glioma</th>\n",
@@ -144,18 +89,17 @@
        "      <th>disease_cervical &amp; endocervical cancer</th>\n",
        "      <th>disease_cholangiocarcinoma</th>\n",
        "      <th>disease_colon adenocarcinoma</th>\n",
-       "      <th>disease_diffuse large B-cell lymphoma</th>\n",
        "      <th>...</th>\n",
-       "      <th>disease_sarcoma</th>\n",
-       "      <th>disease_skin cutaneous melanoma</th>\n",
-       "      <th>disease_stomach adenocarcinoma</th>\n",
-       "      <th>disease_testicular germ cell tumor</th>\n",
-       "      <th>disease_thymoma</th>\n",
-       "      <th>disease_thyroid carcinoma</th>\n",
-       "      <th>disease_uterine carcinosarcoma</th>\n",
-       "      <th>disease_uterine corpus endometrioid carcinoma</th>\n",
-       "      <th>disease_uveal melanoma</th>\n",
-       "      <th>n_mutations_log10</th>\n",
+       "      <th>organ_of_origin_Thymus</th>\n",
+       "      <th>organ_of_origin_Thyroid Gland</th>\n",
+       "      <th>organ_of_origin_Uterus</th>\n",
+       "      <th>gender_Female</th>\n",
+       "      <th>gender_Male</th>\n",
+       "      <th>alive</th>\n",
+       "      <th>dead</th>\n",
+       "      <th>has_not_recurred</th>\n",
+       "      <th>has_recurred</th>\n",
+       "      <th>n_mutations_log1p</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sample_id</th>\n",
@@ -185,267 +129,154 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TCGA-02-0047-01</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>448.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.591065</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.6889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TCGA-02-0055-01</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.518514</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.5264</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2 rows × 35 columns</p>\n",
+       "<p>2 rows × 70 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 gender_Female  gender_Male  disease_adrenocortical cancer  \\\n",
-       "sample_id                                                                    \n",
-       "TCGA-02-0047-01            0.0          1.0                            0.0   \n",
-       "TCGA-02-0055-01            1.0          0.0                            0.0   \n",
+       "                 age_diagnosed  days_survived  days_recurrence_free  \\\n",
+       "sample_id                                                             \n",
+       "TCGA-02-0047-01           78.0          448.0                   NaN   \n",
+       "TCGA-02-0055-01           62.0           76.0                   NaN   \n",
+       "\n",
+       "                 disease_adrenocortical cancer  \\\n",
+       "sample_id                                        \n",
+       "TCGA-02-0047-01                              0   \n",
+       "TCGA-02-0055-01                              0   \n",
        "\n",
        "                 disease_bladder urothelial carcinoma  \\\n",
        "sample_id                                               \n",
-       "TCGA-02-0047-01                                   0.0   \n",
-       "TCGA-02-0055-01                                   0.0   \n",
+       "TCGA-02-0047-01                                     0   \n",
+       "TCGA-02-0055-01                                     0   \n",
        "\n",
        "                 disease_brain lower grade glioma  \\\n",
        "sample_id                                           \n",
-       "TCGA-02-0047-01                               0.0   \n",
-       "TCGA-02-0055-01                               0.0   \n",
+       "TCGA-02-0047-01                                 0   \n",
+       "TCGA-02-0055-01                                 0   \n",
        "\n",
        "                 disease_breast invasive carcinoma  \\\n",
        "sample_id                                            \n",
-       "TCGA-02-0047-01                                0.0   \n",
-       "TCGA-02-0055-01                                0.0   \n",
+       "TCGA-02-0047-01                                  0   \n",
+       "TCGA-02-0055-01                                  0   \n",
        "\n",
        "                 disease_cervical & endocervical cancer  \\\n",
        "sample_id                                                 \n",
-       "TCGA-02-0047-01                                     0.0   \n",
-       "TCGA-02-0055-01                                     0.0   \n",
+       "TCGA-02-0047-01                                       0   \n",
+       "TCGA-02-0055-01                                       0   \n",
        "\n",
        "                 disease_cholangiocarcinoma  disease_colon adenocarcinoma  \\\n",
        "sample_id                                                                   \n",
-       "TCGA-02-0047-01                         0.0                           0.0   \n",
-       "TCGA-02-0055-01                         0.0                           0.0   \n",
-       "\n",
-       "                 disease_diffuse large B-cell lymphoma        ...          \\\n",
-       "sample_id                                                     ...           \n",
-       "TCGA-02-0047-01                                    0.0        ...           \n",
-       "TCGA-02-0055-01                                    0.0        ...           \n",
-       "\n",
-       "                 disease_sarcoma  disease_skin cutaneous melanoma  \\\n",
-       "sample_id                                                           \n",
-       "TCGA-02-0047-01              0.0                              0.0   \n",
-       "TCGA-02-0055-01              0.0                              0.0   \n",
+       "TCGA-02-0047-01                           0                             0   \n",
+       "TCGA-02-0055-01                           0                             0   \n",
        "\n",
-       "                 disease_stomach adenocarcinoma  \\\n",
-       "sample_id                                         \n",
-       "TCGA-02-0047-01                             0.0   \n",
-       "TCGA-02-0055-01                             0.0   \n",
+       "                       ...          organ_of_origin_Thymus  \\\n",
+       "sample_id              ...                                   \n",
+       "TCGA-02-0047-01        ...                               0   \n",
+       "TCGA-02-0055-01        ...                               0   \n",
        "\n",
-       "                 disease_testicular germ cell tumor  disease_thymoma  \\\n",
-       "sample_id                                                              \n",
-       "TCGA-02-0047-01                                 0.0              0.0   \n",
-       "TCGA-02-0055-01                                 0.0              0.0   \n",
+       "                 organ_of_origin_Thyroid Gland  organ_of_origin_Uterus  \\\n",
+       "sample_id                                                                \n",
+       "TCGA-02-0047-01                              0                       0   \n",
+       "TCGA-02-0055-01                              0                       0   \n",
        "\n",
-       "                 disease_thyroid carcinoma  disease_uterine carcinosarcoma  \\\n",
+       "                 gender_Female  gender_Male  alive  dead  has_not_recurred  \\\n",
        "sample_id                                                                    \n",
-       "TCGA-02-0047-01                        0.0                             0.0   \n",
-       "TCGA-02-0055-01                        0.0                             0.0   \n",
-       "\n",
-       "                 disease_uterine corpus endometrioid carcinoma  \\\n",
-       "sample_id                                                        \n",
-       "TCGA-02-0047-01                                            0.0   \n",
-       "TCGA-02-0055-01                                            0.0   \n",
+       "TCGA-02-0047-01              0            1      0     1                 0   \n",
+       "TCGA-02-0055-01              1            0      0     1                 0   \n",
        "\n",
-       "                 disease_uveal melanoma  n_mutations_log10  \n",
-       "sample_id                                                   \n",
-       "TCGA-02-0047-01                     0.0           1.591065  \n",
-       "TCGA-02-0055-01                     0.0           1.518514  \n",
+       "                 has_recurred  n_mutations_log1p  \n",
+       "sample_id                                         \n",
+       "TCGA-02-0047-01             0             3.6889  \n",
+       "TCGA-02-0055-01             0             3.5264  \n",
        "\n",
-       "[2 rows x 35 columns]"
+       "[2 rows x 70 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Read sample information and create a covariate TSV\n",
-    "path = os.path.join('..', '..', 'download', 'samples.tsv')\n",
-    "covariate_df = (\n",
-    "    pd.read_table(path, index_col=0)\n",
-    "    .drop(['patient_id', 'sample_type', 'organ_of_origin'], axis='columns')\n",
-    "    [['gender', 'disease']]\n",
-    "    .pipe(pd.get_dummies, columns=['gender', 'disease'])\n",
-    ")\n",
-    "\n",
-    "n_mutations = Y.sum(axis='columns')\n",
-    "covariate_df['n_mutations_log10'] = np.log10(n_mutations)\n",
-    "\n",
-    "#covariate_df['n_mutations_log1p'] = np.log1p(n_mutations)\n",
-    "#covariate_df['n_mutations_log'] = np.log(n_mutations)\n",
-    "#covariate_df['mutations_freq_logit'] = np.log1p(n_mutations / len(Y.columns))\n",
-    "#covariate_df['n_mutations_ihs'] = np.arcsinh(n_mutations)\n",
-    "\n",
+    "url = 'https://github.com/cognoma/cancer-data/raw/54140cf6addc48260c9723213c40b628d7c861da/data/covariates.tsv'\n",
+    "covariate_df = pd.read_table(url, index_col=0)\n",
     "covariate_df.head(2)"
    ]
   },
-  {
-   "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",
-       "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 = Y[gene]\n",
-    "y.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "X = covariate_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0.645907\n",
-       "1    0.354093\n",
-       "Name: 7157, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "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": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Size: 35 features, 6,575 training samples, 731 testing samples'"
-      ]
-     },
-     "execution_count": 10,
-     "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": [
-    "## Define pipeline and Cross validation model fitting"
+    "## Specify the type of classifier"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# Include loss='log' in param_grid doesn't work with pipeline somehow\n",
-    "clf = SGDClassifier(random_state=0, class_weight='balanced',\n",
-    "                    loss=param_fixed['loss'], penalty=param_fixed['penalty'])\n",
+    "param_grid = {\n",
+    "    'alpha': [10 ** x for x in range(-4, 2)],\n",
+    "    'l1_ratio': [0, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 1],\n",
+    "}\n",
+    "\n",
+    "clf = SGDClassifier(\n",
+    "    random_state=0,\n",
+    "    class_weight='balanced',\n",
+    "    loss='log',\n",
+    "    penalty='elasticnet'\n",
+    ")\n",
     "\n",
     "# 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",
@@ -458,90 +289,157 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 368 ms, sys: 112 ms, total: 480 ms\n",
-      "Wall time: 3.53 s\n"
-     ]
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "%%time\n",
-    "# Fit the model (the computationally intensive part)\n",
-    "pipeline.fit(X=X_train, y=y_train)\n",
-    "best_clf = clf_grid.best_estimator_"
+    "## Specify covariates and outcomes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "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>mutation</th>\n",
+       "      <th>disease_covariate</th>\n",
+       "      <th>organ_covariate</th>\n",
+       "      <th>gender_covariate</th>\n",
+       "      <th>mutation_covariate</th>\n",
+       "      <th>survival_covariate</th>\n",
+       "      <th>symbol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>238</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ALK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>238</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ALK</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "{'alpha': 0.1, 'l1_ratio': 0}"
+       "  mutation  disease_covariate  organ_covariate  gender_covariate  \\\n",
+       "0      238                  0                0                 0   \n",
+       "1      238                  0                0                 0   \n",
+       "\n",
+       "   mutation_covariate  survival_covariate symbol  \n",
+       "0                   0                   0    ALK  \n",
+       "1                   0                   1    ALK  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "clf_grid.best_params_"
+    "def expand_grid(data_dict):\n",
+    "    \"\"\"Create a dataframe from every combination of given values.\"\"\"\n",
+    "    rows = itertools.product(*data_dict.values())\n",
+    "    return pd.DataFrame.from_records(rows, columns=data_dict.keys())\n",
+    "\n",
+    "mutations = {\n",
+    "    '7157': 'TP53', # tumor protein p53\n",
+    "    '7428': 'VHL', # von Hippel-Lindau tumor suppressor\n",
+    "    '29126': 'CD274', # CD274 molecule\n",
+    "    '672': 'BRCA1', # BRCA1, DNA repair associated\n",
+    "    '675': 'BRCA2', # BRCA2, DNA repair associated\n",
+    "    '238': 'ALK', # anaplastic lymphoma receptor tyrosine kinase\n",
+    "    '4221': 'MEN1', # menin 1\n",
+    "    '5979': 'RET', # ret proto-oncogene\n",
+    "}\n",
+    "\n",
+    "options = collections.OrderedDict()\n",
+    "\n",
+    "options['mutation'] = list(mutations)\n",
+    "\n",
+    "binary_options = [\n",
+    "    'disease_covariate',\n",
+    "    'organ_covariate',\n",
+    "    'gender_covariate',\n",
+    "    'mutation_covariate',\n",
+    "    'survival_covariate'\n",
+    "]\n",
+    "\n",
+    "for opt in binary_options:\n",
+    "    options[opt] = [0, 1]\n",
+    "\n",
+    "option_df = expand_grid(options)\n",
+    "option_df['symbol'] = option_df.mutation.map(mutations)\n",
+    "option_df.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "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, learning_rate='optimal',\n",
-       "       loss='log', n_iter=5, n_jobs=1, penalty='elasticnet', power_t=0.5,\n",
-       "       random_state=0, shuffle=True, verbose=0, warm_start=False)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "best_clf"
+    "covariate_to_columns = {\n",
+    "    'gender': covariate_df.columns[covariate_df.columns.str.startswith('gender')].tolist(),\n",
+    "    'disease': covariate_df.columns[covariate_df.columns.str.startswith('disease')].tolist(),\n",
+    "    'organ': covariate_df.columns[covariate_df.columns.str.contains('organ')].tolist(),\n",
+    "    'mutation': covariate_df.columns[covariate_df.columns.str.contains('n_mutations')].tolist(),\n",
+    "    'survival': ['alive', 'dead'],\n",
+    "}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Visualize hyperparameters performance"
+    "## Compute performance"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
+    "def get_aurocs(X, y, series):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
+    "    series['positive_prevalence'] = np.mean(y)\n",
+    "    pipeline.fit(X=X_train, y=y_train)\n",
+    "    y_pred_train = pipeline.decision_function(X_train)\n",
+    "    y_pred_test = pipeline.decision_function(X_test)\n",
+    "    cv_score_df = grid_scores_to_df(clf_grid.grid_scores_)\n",
+    "    series['mean_cv_auroc'] = cv_score_df.score.max()\n",
+    "    series['training_auroc'] = roc_auc_score(y_train, y_pred_train)\n",
+    "    series['testing_auroc'] = roc_auc_score(y_test, y_pred_test)\n",
+    "    return series\n",
+    "\n",
     "def grid_scores_to_df(grid_scores):\n",
     "    \"\"\"\n",
     "    Convert a sklearn.grid_search.GridSearchCV.grid_scores_ attribute to \n",
@@ -559,15 +457,31 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
-    "## Process Mutation Matrix"
+    "rows = list()\n",
+    "for i, series in option_df.iterrows():\n",
+    "    columns = list()\n",
+    "    for name, add_columns in covariate_to_columns.items():\n",
+    "        if series[name + '_covariate']:\n",
+    "            columns.extend(add_columns)\n",
+    "    if not columns:\n",
+    "        continue\n",
+    "    X = covariate_df[columns]\n",
+    "    y = Y[series.mutation]\n",
+    "    rows.append(get_aurocs(X, y, series))\n",
+    "auroc_df = pd.DataFrame(rows)\n",
+    "auroc_df.sort_values(['symbol', 'testing_auroc'], ascending=[True, False], inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -580,682 +494,157 @@
        "  <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>score</th>\n",
+       "      <th>mutation</th>\n",
+       "      <th>disease_covariate</th>\n",
+       "      <th>organ_covariate</th>\n",
+       "      <th>gender_covariate</th>\n",
+       "      <th>mutation_covariate</th>\n",
+       "      <th>survival_covariate</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>positive_prevalence</th>\n",
+       "      <th>mean_cv_auroc</th>\n",
+       "      <th>training_auroc</th>\n",
+       "      <th>testing_auroc</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.000001</td>\n",
+       "      <th>19</th>\n",
+       "      <td>238</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.736324</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ALK</td>\n",
+       "      <td>0.018889</td>\n",
+       "      <td>0.856415</td>\n",
+       "      <td>0.827638</td>\n",
+       "      <td>0.846732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.000001</td>\n",
+       "      <th>22</th>\n",
+       "      <td>238</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.749135</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      alpha  fold  l1_ratio     score\n",
-       "0  0.000001     0       0.0  0.736324\n",
-       "1  0.000001     1       0.0  0.749135"
-      ]
-     },
-     "execution_count": 16,
-     "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": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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W8/bsZ1izZXGl5Vq0aMnw4adz+umjDupns6KNGzcwZcp7fPjh+2zYsL7SvOMa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G4XhoeH5Cq/S4vWQaflrhCxegMWe6rrVlYeiyrHG7fbhdraapw8eQJtbeonk4eSxsbLAIB4nR7Z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\nL2EAs1tHokLAiB5L5jGzHN/lZf4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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbe1ffb7390>"
-      ]
-     },
-     "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": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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WQH/94RvBQSHYO9px08uPilXLYm5uRs16VfDyvEm27M506NqSQ3uP07hFXbq3G8L1K970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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbe3bb82f28>"
-      ]
-     },
-     "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": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "y_pred_train = pipeline.decision_function(X_train)\n",
-    "y_pred_test = 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": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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csyGprv27ef/AtUTfYFxf7SPwH8ijlcejtfZ6/0fcd3DdWwLGmH8Dl9nEB7Ebtt8/uSzS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\nY0/Pg92HFjF5bGm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ixMA+nVpWERGR7qyrW3anAMutteVxaR8BxhhTaK2Nb656HrjLGLMv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\nnnJ0dDROp5OMjIwC29at6902Lc398Kb9+/d57QsNDcWYVoVe8/DhwzR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co7QFJcX+9tBD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\nmZmVlpNdMzMzMystJ7tmZmZmVlr/ADHXXheBL6GgAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbe3b210358>"
-      ]
-     },
-     "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": 21,
-   "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": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.0% zero coefficients; 17 negative and 18 positive coefficients'"
-      ]
-     },
-     "execution_count": 22,
-     "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": 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>weight</th>\n",
-       "      <th>abs</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>n_mutations_log10</th>\n",
-       "      <td>0.406853</td>\n",
-       "      <td>0.406853</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_thyroid carcinoma</th>\n",
-       "      <td>-0.283209</td>\n",
-       "      <td>0.283209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_ovarian serous cystadenocarcinoma</th>\n",
-       "      <td>0.271586</td>\n",
-       "      <td>0.271586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_head &amp; neck squamous cell carcinoma</th>\n",
-       "      <td>0.255094</td>\n",
-       "      <td>0.255094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_esophageal carcinoma</th>\n",
-       "      <td>0.221305</td>\n",
-       "      <td>0.221305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_kidney clear cell carcinoma</th>\n",
-       "      <td>-0.206621</td>\n",
-       "      <td>0.206621</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_cervical &amp; endocervical cancer</th>\n",
-       "      <td>-0.194747</td>\n",
-       "      <td>0.194747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_kidney papillary cell carcinoma</th>\n",
-       "      <td>-0.186020</td>\n",
-       "      <td>0.186020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_pheochromocytoma &amp; paraganglioma</th>\n",
-       "      <td>-0.170498</td>\n",
-       "      <td>0.170498</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ALK</td>\n",
+       "      <td>0.018889</td>\n",
+       "      <td>0.857618</td>\n",
+       "      <td>0.829357</td>\n",
+       "      <td>0.846334</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>disease_testicular germ cell tumor</th>\n",
-       "      <td>-0.169789</td>\n",
-       "      <td>0.169789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_prostate adenocarcinoma</th>\n",
-       "      <td>-0.158974</td>\n",
-       "      <td>0.158974</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_uterine carcinosarcoma</th>\n",
-       "      <td>0.157790</td>\n",
-       "      <td>0.157790</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_pancreatic adenocarcinoma</th>\n",
-       "      <td>0.151359</td>\n",
-       "      <td>0.151359</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_lung squamous cell carcinoma</th>\n",
-       "      <td>0.149765</td>\n",
-       "      <td>0.149765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_skin cutaneous melanoma</th>\n",
-       "      <td>-0.148936</td>\n",
-       "      <td>0.148936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_rectum adenocarcinoma</th>\n",
-       "      <td>0.137583</td>\n",
-       "      <td>0.137583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_thymoma</th>\n",
-       "      <td>-0.134407</td>\n",
-       "      <td>0.134407</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_uveal melanoma</th>\n",
-       "      <td>-0.118979</td>\n",
-       "      <td>0.118979</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_brain lower grade glioma</th>\n",
-       "      <td>0.107231</td>\n",
-       "      <td>0.107231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_colon adenocarcinoma</th>\n",
-       "      <td>0.091189</td>\n",
-       "      <td>0.091189</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_lung adenocarcinoma</th>\n",
-       "      <td>0.088074</td>\n",
-       "      <td>0.088074</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_mesothelioma</th>\n",
-       "      <td>-0.087272</td>\n",
-       "      <td>0.087272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_diffuse large B-cell lymphoma</th>\n",
-       "      <td>-0.071398</td>\n",
-       "      <td>0.071398</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_bladder urothelial carcinoma</th>\n",
-       "      <td>0.065330</td>\n",
-       "      <td>0.065330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_stomach adenocarcinoma</th>\n",
-       "      <td>0.056392</td>\n",
-       "      <td>0.056392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_cholangiocarcinoma</th>\n",
-       "      <td>-0.053046</td>\n",
-       "      <td>0.053046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_adrenocortical cancer</th>\n",
-       "      <td>-0.047275</td>\n",
-       "      <td>0.047275</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_breast invasive carcinoma</th>\n",
-       "      <td>0.034973</td>\n",
-       "      <td>0.034973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_sarcoma</th>\n",
-       "      <td>0.032583</td>\n",
-       "      <td>0.032583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_uterine corpus endometrioid carcinoma</th>\n",
-       "      <td>-0.024682</td>\n",
-       "      <td>0.024682</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_liver hepatocellular carcinoma</th>\n",
-       "      <td>-0.016290</td>\n",
-       "      <td>0.016290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_kidney chromophobe</th>\n",
-       "      <td>0.012236</td>\n",
-       "      <td>0.012236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gender_Male</th>\n",
-       "      <td>0.011323</td>\n",
-       "      <td>0.011323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gender_Female</th>\n",
-       "      <td>-0.006353</td>\n",
-       "      <td>0.006353</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>disease_glioblastoma multiforme</th>\n",
-       "      <td>0.002017</td>\n",
-       "      <td>0.002017</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                 weight       abs\n",
-       "n_mutations_log10                              0.406853  0.406853\n",
-       "disease_thyroid carcinoma                     -0.283209  0.283209\n",
-       "disease_ovarian serous cystadenocarcinoma      0.271586  0.271586\n",
-       "disease_head & neck squamous cell carcinoma    0.255094  0.255094\n",
-       "disease_esophageal carcinoma                   0.221305  0.221305\n",
-       "disease_kidney clear cell carcinoma           -0.206621  0.206621\n",
-       "disease_cervical & endocervical cancer        -0.194747  0.194747\n",
-       "disease_kidney papillary cell carcinoma       -0.186020  0.186020\n",
-       "disease_pheochromocytoma & paraganglioma      -0.170498  0.170498\n",
-       "disease_testicular germ cell tumor            -0.169789  0.169789\n",
-       "disease_prostate adenocarcinoma               -0.158974  0.158974\n",
-       "disease_uterine carcinosarcoma                 0.157790  0.157790\n",
-       "disease_pancreatic adenocarcinoma              0.151359  0.151359\n",
-       "disease_lung squamous cell carcinoma           0.149765  0.149765\n",
-       "disease_skin cutaneous melanoma               -0.148936  0.148936\n",
-       "disease_rectum adenocarcinoma                  0.137583  0.137583\n",
-       "disease_thymoma                               -0.134407  0.134407\n",
-       "disease_uveal melanoma                        -0.118979  0.118979\n",
-       "disease_brain lower grade glioma               0.107231  0.107231\n",
-       "disease_colon adenocarcinoma                   0.091189  0.091189\n",
-       "disease_lung adenocarcinoma                    0.088074  0.088074\n",
-       "disease_mesothelioma                          -0.087272  0.087272\n",
-       "disease_diffuse large B-cell lymphoma         -0.071398  0.071398\n",
-       "disease_bladder urothelial carcinoma           0.065330  0.065330\n",
-       "disease_stomach adenocarcinoma                 0.056392  0.056392\n",
-       "disease_cholangiocarcinoma                    -0.053046  0.053046\n",
-       "disease_adrenocortical cancer                 -0.047275  0.047275\n",
-       "disease_breast invasive carcinoma              0.034973  0.034973\n",
-       "disease_sarcoma                                0.032583  0.032583\n",
-       "disease_uterine corpus endometrioid carcinoma -0.024682  0.024682\n",
-       "disease_liver hepatocellular carcinoma        -0.016290  0.016290\n",
-       "disease_kidney chromophobe                     0.012236  0.012236\n",
-       "gender_Male                                    0.011323  0.011323\n",
-       "gender_Female                                 -0.006353  0.006353\n",
-       "disease_glioblastoma multiforme                0.002017  0.002017"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coef_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Investigate the predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "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', pipeline.decision_function(X)),\n",
-    "    ('probability', pipeline.predict_proba(X)[:, 1]),\n",
-    "])\n",
-    "predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "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-IB-7651-01</th>\n",
-       "      <td>TCGA-IB-7651-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.662026</td>\n",
-       "      <td>0.934748</td>\n",
-       "      <td>93.5%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TCGA-L5-A4OI-01</th>\n",
-       "      <td>TCGA-L5-A4OI-01</td>\n",
+       "      <th>23</th>\n",
+       "      <td>238</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>2.483659</td>\n",
-       "      <td>0.922988</td>\n",
-       "      <td>92.3%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TCGA-N7-A4Y0-01</th>\n",
-       "      <td>TCGA-N7-A4Y0-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.429504</td>\n",
-       "      <td>0.919050</td>\n",
-       "      <td>91.9%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TCGA-L5-A8NM-01</th>\n",
-       "      <td>TCGA-L5-A8NM-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.385745</td>\n",
-       "      <td>0.915734</td>\n",
-       "      <td>91.6%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TCGA-CA-6717-01</th>\n",
-       "      <td>TCGA-CA-6717-01</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.890060</td>\n",
-       "      <td>0.868762</td>\n",
-       "      <td>86.9%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TCGA-AZ-4315-01</th>\n",
-       "      <td>TCGA-AZ-4315-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.850787</td>\n",
-       "      <td>0.864219</td>\n",
-       "      <td>86.4%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TCGA-66-2785-01</th>\n",
-       "      <td>TCGA-66-2785-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.839677</td>\n",
-       "      <td>0.862910</td>\n",
-       "      <td>86.3%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TCGA-L5-A8NE-01</th>\n",
-       "      <td>TCGA-L5-A8NE-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.761791</td>\n",
-       "      <td>0.853434</td>\n",
-       "      <td>85.3%</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ALK</td>\n",
+       "      <td>0.018889</td>\n",
+       "      <td>0.856177</td>\n",
+       "      <td>0.829393</td>\n",
+       "      <td>0.846135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TCGA-Q9-A6FW-01</th>\n",
-       "      <td>TCGA-Q9-A6FW-01</td>\n",
-       "      <td>0</td>\n",
+       "      <th>30</th>\n",
+       "      <td>238</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>1.710566</td>\n",
-       "      <td>0.846910</td>\n",
-       "      <td>84.7%</td>\n",
+       "      <td>ALK</td>\n",
+       "      <td>0.018889</td>\n",
+       "      <td>0.853851</td>\n",
+       "      <td>0.829066</td>\n",
+       "      <td>0.846035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TCGA-R6-A6XG-01</th>\n",
-       "      <td>TCGA-R6-A6XG-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.705193</td>\n",
-       "      <td>0.846212</td>\n",
-       "      <td>84.6%</td>\n",
+       "      <th>31</th>\n",
+       "      <td>238</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ALK</td>\n",
+       "      <td>0.018889</td>\n",
+       "      <td>0.853125</td>\n",
+       "      <td>0.828207</td>\n",
+       "      <td>0.845637</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                       sample_id  testing  status  decision_function  \\\n",
-       "sample_id                                                              \n",
-       "TCGA-IB-7651-01  TCGA-IB-7651-01        0       0           2.662026   \n",
-       "TCGA-L5-A4OI-01  TCGA-L5-A4OI-01        1       0           2.483659   \n",
-       "TCGA-N7-A4Y0-01  TCGA-N7-A4Y0-01        0       0           2.429504   \n",
-       "TCGA-L5-A8NM-01  TCGA-L5-A8NM-01        0       0           2.385745   \n",
-       "TCGA-CA-6717-01  TCGA-CA-6717-01        1       0           1.890060   \n",
-       "TCGA-AZ-4315-01  TCGA-AZ-4315-01        0       0           1.850787   \n",
-       "TCGA-66-2785-01  TCGA-66-2785-01        0       0           1.839677   \n",
-       "TCGA-L5-A8NE-01  TCGA-L5-A8NE-01        0       0           1.761791   \n",
-       "TCGA-Q9-A6FW-01  TCGA-Q9-A6FW-01        0       0           1.710566   \n",
-       "TCGA-R6-A6XG-01  TCGA-R6-A6XG-01        0       0           1.705193   \n",
+       "   mutation  disease_covariate  organ_covariate  gender_covariate  \\\n",
+       "19      238                  1                0                 0   \n",
+       "22      238                  1                0                 1   \n",
+       "23      238                  1                0                 1   \n",
+       "30      238                  1                1                 1   \n",
+       "31      238                  1                1                 1   \n",
+       "\n",
+       "    mutation_covariate  survival_covariate symbol  positive_prevalence  \\\n",
+       "19                   1                   1    ALK             0.018889   \n",
+       "22                   1                   0    ALK             0.018889   \n",
+       "23                   1                   1    ALK             0.018889   \n",
+       "30                   1                   0    ALK             0.018889   \n",
+       "31                   1                   1    ALK             0.018889   \n",
        "\n",
-       "                 probability probability_str  \n",
-       "sample_id                                     \n",
-       "TCGA-IB-7651-01     0.934748           93.5%  \n",
-       "TCGA-L5-A4OI-01     0.922988           92.3%  \n",
-       "TCGA-N7-A4Y0-01     0.919050           91.9%  \n",
-       "TCGA-L5-A8NM-01     0.915734           91.6%  \n",
-       "TCGA-CA-6717-01     0.868762           86.9%  \n",
-       "TCGA-AZ-4315-01     0.864219           86.4%  \n",
-       "TCGA-66-2785-01     0.862910           86.3%  \n",
-       "TCGA-L5-A8NE-01     0.853434           85.3%  \n",
-       "TCGA-Q9-A6FW-01     0.846910           84.7%  \n",
-       "TCGA-R6-A6XG-01     0.846212           84.6%  "
+       "    mean_cv_auroc  training_auroc  testing_auroc  \n",
+       "19       0.856415        0.827638       0.846732  \n",
+       "22       0.857618        0.829357       0.846334  \n",
+       "23       0.856177        0.829393       0.846135  \n",
+       "30       0.853851        0.829066       0.846035  \n",
+       "31       0.853125        0.828207       0.845637  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 10,
      "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)"
+    "auroc_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Qa9acBMDQ0BD9/X00Ny/H7/czOTnB2rUnsXbtSXzgAzfx/vffwNatL1FeXh7z/S+99Er+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\nJe84FdCCoJ+KkpDL0UgmhaOTDE+OAKqAZlplaQEffduFhIJ+wpEYX7p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b+0C1BC9eoQRU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\nZmZmlfoLaNabQ0jCRHYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbe3f4a78d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "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')"
+    "auroc_df.to_csv('auroc.tsv', index=False, sep='\\t', float_format='%.5g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Covariate performance by mutation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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VBRXxx13D3VlciRDZ4Uj2A1RVnQM8AFwChIA/AB/x+Xx9Z1x3D/AvgDElWAE0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\nWL0GlfnmCUw3rCrlZy+2E4vH8XWOc9Xaxa+9yOnh09s+xT8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VM4O8/+1r0/4YuXx9syUb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o/z/Ou9PPB0K6HEWvKdNu7Y08AtO1csurwfiUX4f2d+ysGBI4D6mvvuptvZ17A343v2jXR9lysJ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\nj5ia2YgQE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dgCkA0ZzPaxQrsGzu/glhnOZ+hFaGHo8wcPe5wM8IC0BoMiMREUmMQm56M8zs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\nIiIiCaQEQEREJIH+H4ZEUQFgO51UAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbe39c7a7f0>"
+       "<matplotlib.figure.Figure at 0x7f777946c7b8>"
       ]
      },
      "metadata": {},
@@ -1263,8 +652,11 @@
     }
    ],
    "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')"
+    "# Filter for models which include all covariates\n",
+    "plot_df = auroc_df[auroc_df[binary_options].all(axis='columns')]\n",
+    "plot_df = pd.melt(plot_df, id_vars='symbol', value_vars=['mean_cv_auroc', 'training_auroc', 'testing_auroc'], var_name='kind', value_name='auroc')\n",
+    "grid = sns.factorplot(y='symbol', x='auroc', hue='kind', data=plot_df, kind=\"bar\")\n",
+    "xlimits = grid.ax.set_xlim(0.5, 1)"
    ]
   }
  ],

From 7ef10dd77b57f35866d246590d13d8293ab5199b Mon Sep 17 00:00:00 2001
From: Daniel Himmelstein <daniel.himmelstein@gmail.com>
Date: Wed, 21 Sep 2016 12:39:17 -0400
Subject: [PATCH 3/4] Export clean notebook to script

---
 explore/confounding/confounding.ipynb |   2 +-
 explore/confounding/confounding.py    | 289 +++++++++-----------------
 2 files changed, 102 insertions(+), 189 deletions(-)

diff --git a/explore/confounding/confounding.ipynb b/explore/confounding/confounding.ipynb
index 45b464b..8f90449 100644
--- a/explore/confounding/confounding.ipynb
+++ b/explore/confounding/confounding.ipynb
@@ -634,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "scrolled": false
diff --git a/explore/confounding/confounding.py b/explore/confounding/confounding.py
index b224fa7..f7fd2a0 100644
--- a/explore/confounding/confounding.py
+++ b/explore/confounding/confounding.py
@@ -1,12 +1,14 @@
 
 # coding: utf-8
 
-# # Create a logistic regression model to predict TP53 mutation from covariates
+# # Create a logistic regression model to several mutations from covariates
 
 # In[1]:
 
 import os
+import itertools
 import warnings
+import collections
 
 import pandas as pd
 import numpy as np
@@ -27,95 +29,37 @@
 plt.style.use('seaborn-notebook')
 
 
-# ## Specify model configuration
+# ## Load Data
 
 # In[3]:
 
-# We're going to be building a 'TP53' classifier 
-gene = '7157' # TP53
+path = os.path.join('..', '..', 'download', 'mutation-matrix.tsv.bz2')
+Y = pd.read_table(path, index_col=0)
 
 
 # In[4]:
 
-# Parameter Sweep for Hyperparameters
-n_feature_kept = 500
-param_fixed = {
-    'loss': 'log',
-    'penalty': 'elasticnet',
-}
-param_grid = {
-    'alpha': [10 ** x for x in range(-6, 2)],
-    'l1_ratio': [0, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 1],
-}
-
-
-# *Here is some [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) regarding the classifier and hyperparameters*
-# 
-# *Here is some [information](https://ghr.nlm.nih.gov/gene/TP53) about TP53*
-
-# ## Load Data
-
-# In[5]:
-
-get_ipython().run_cell_magic('time', '', "path = os.path.join('..', '..', 'download', 'mutation-matrix.tsv.bz2')\nY = pd.read_table(path, index_col=0)")
-
-
-# In[6]:
-
 # Read sample information and create a covariate TSV
-path = os.path.join('..', '..', 'download', 'samples.tsv')
-covariate_df = (
-    pd.read_table(path, index_col=0)
-    .drop(['patient_id', 'sample_type', 'organ_of_origin'], axis='columns')
-    [['gender', 'disease']]
-    .pipe(pd.get_dummies, columns=['gender', 'disease'])
-)
-
-n_mutations = Y.sum(axis='columns')
-covariate_df['n_mutations_log10'] = np.log10(n_mutations)
-
-#covariate_df['n_mutations_log1p'] = np.log1p(n_mutations)
-#covariate_df['n_mutations_log'] = np.log(n_mutations)
-#covariate_df['mutations_freq_logit'] = np.log1p(n_mutations / len(Y.columns))
-#covariate_df['n_mutations_ihs'] = np.arcsinh(n_mutations)
-
+url = 'https://github.com/cognoma/cancer-data/raw/54140cf6addc48260c9723213c40b628d7c861da/data/covariates.tsv'
+covariate_df = pd.read_table(url, index_col=0)
 covariate_df.head(2)
 
 
-# In[7]:
-
-# The Series now holds TP53 Mutation Status for each Sample
-y = Y[gene]
-y.head(5)
-
-
-# In[8]:
-
-X = covariate_df
-
-
-# In[9]:
-
-# Here are the percentage of tumors with NF1
-y.value_counts(True)
-
-
-# ## Set aside 10% of the data for testing
-
-# In[10]:
-
-# Typically, this can only be done where the number of mutations is large enough
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
-'Size: {:,} features, {:,} training samples, {:,} testing samples'.format(len(X.columns), len(X_train), len(X_test))
-
+# ## Specify the type of classifier
 
-# ## Define pipeline and Cross validation model fitting
+# In[5]:
 
-# In[11]:
+param_grid = {
+    'alpha': [10 ** x for x in range(-4, 2)],
+    'l1_ratio': [0, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 1],
+}
 
-# Include loss='log' in param_grid doesn't work with pipeline somehow
-clf = SGDClassifier(random_state=0, class_weight='balanced',
-                    loss=param_fixed['loss'], penalty=param_fixed['penalty'])
+clf = SGDClassifier(
+    random_state=0,
+    class_weight='balanced',
+    loss='log',
+    penalty='elasticnet'
+)
 
 # joblib is used to cross-validate in parallel by setting `n_jobs=-1` in GridSearchCV
 # Supress joblib warning. See https://github.com/scikit-learn/scikit-learn/issues/6370
@@ -127,24 +71,72 @@
 )
 
 
-# In[12]:
+# ## Specify covariates and outcomes
 
-get_ipython().run_cell_magic('time', '', '# Fit the model (the computationally intensive part)\npipeline.fit(X=X_train, y=y_train)\nbest_clf = clf_grid.best_estimator_')
+# In[6]:
 
+def expand_grid(data_dict):
+    """Create a dataframe from every combination of given values."""
+    rows = itertools.product(*data_dict.values())
+    return pd.DataFrame.from_records(rows, columns=data_dict.keys())
+
+mutations = {
+    '7157': 'TP53', # tumor protein p53
+    '7428': 'VHL', # von Hippel-Lindau tumor suppressor
+    '29126': 'CD274', # CD274 molecule
+    '672': 'BRCA1', # BRCA1, DNA repair associated
+    '675': 'BRCA2', # BRCA2, DNA repair associated
+    '238': 'ALK', # anaplastic lymphoma receptor tyrosine kinase
+    '4221': 'MEN1', # menin 1
+    '5979': 'RET', # ret proto-oncogene
+}
+
+options = collections.OrderedDict()
+
+options['mutation'] = list(mutations)
 
-# In[13]:
+binary_options = [
+    'disease_covariate',
+    'organ_covariate',
+    'gender_covariate',
+    'mutation_covariate',
+    'survival_covariate'
+]
 
-clf_grid.best_params_
+for opt in binary_options:
+    options[opt] = [0, 1]
 
+option_df = expand_grid(options)
+option_df['symbol'] = option_df.mutation.map(mutations)
+option_df.head(2)
 
-# In[14]:
 
-best_clf
+# In[7]:
 
+covariate_to_columns = {
+    'gender': covariate_df.columns[covariate_df.columns.str.startswith('gender')].tolist(),
+    'disease': covariate_df.columns[covariate_df.columns.str.startswith('disease')].tolist(),
+    'organ': covariate_df.columns[covariate_df.columns.str.contains('organ')].tolist(),
+    'mutation': covariate_df.columns[covariate_df.columns.str.contains('n_mutations')].tolist(),
+    'survival': ['alive', 'dead'],
+}
 
-# ## Visualize hyperparameters performance
 
-# In[15]:
+# ## Compute performance
+
+# In[8]:
+
+def get_aurocs(X, y, series):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
+    series['positive_prevalence'] = np.mean(y)
+    pipeline.fit(X=X_train, y=y_train)
+    y_pred_train = pipeline.decision_function(X_train)
+    y_pred_test = pipeline.decision_function(X_test)
+    cv_score_df = grid_scores_to_df(clf_grid.grid_scores_)
+    series['mean_cv_auroc'] = cv_score_df.score.max()
+    series['training_auroc'] = roc_auc_score(y_train, y_pred_train)
+    series['testing_auroc'] = roc_auc_score(y_test, y_pred_test)
+    return series
 
 def grid_scores_to_df(grid_scores):
     """
@@ -162,119 +154,40 @@ def grid_scores_to_df(grid_scores):
     return df
 
 
-# ## Process Mutation Matrix
-
-# In[16]:
-
-cv_score_df = grid_scores_to_df(clf_grid.grid_scores_)
-cv_score_df.head(2)
-
-
-# In[17]:
-
-# Cross-validated performance distribution
-facet_grid = sns.factorplot(x='l1_ratio', y='score', col='alpha',
-    data=cv_score_df, kind='violin', size=4, aspect=1)
-facet_grid.set_ylabels('AUROC');
-
-
-# In[18]:
-
-# Cross-validated performance heatmap
-cv_score_mat = pd.pivot_table(cv_score_df, values='score', index='l1_ratio', columns='alpha')
-ax = sns.heatmap(cv_score_mat, annot=True, fmt='.1%')
-ax.set_xlabel('Regularization strength multiplier (alpha)')
-ax.set_ylabel('Elastic net mixing parameter (l1_ratio)');
-
-
-# ## Use Optimal Hyperparameters to Output ROC Curve
-
-# In[19]:
-
-y_pred_train = pipeline.decision_function(X_train)
-y_pred_test = pipeline.decision_function(X_test)
-
-def get_threshold_metrics(y_true, y_pred):
-    roc_columns = ['fpr', 'tpr', 'threshold']
-    roc_items = zip(roc_columns, roc_curve(y_true, y_pred))
-    roc_df = pd.DataFrame.from_items(roc_items)
-    auroc = roc_auc_score(y_true, y_pred)
-    return {'auroc': auroc, 'roc_df': roc_df}
-
-metrics_train = get_threshold_metrics(y_train, y_pred_train)
-metrics_test = get_threshold_metrics(y_test, y_pred_test)
-
-
-# In[20]:
-
-# Plot ROC
-plt.figure()
-for label, metrics in ('Training', metrics_train), ('Testing', metrics_test):
-    roc_df = metrics['roc_df']
-    plt.plot(roc_df.fpr, roc_df.tpr,
-        label='{} (AUROC = {:.1%})'.format(label, metrics['auroc']))
-plt.xlim([0.0, 1.0])
-plt.ylim([0.0, 1.05])
-plt.xlabel('False Positive Rate')
-plt.ylabel('True Positive Rate')
-plt.title('Predicting TP53 mutation from gene expression (ROC curves)')
-plt.legend(loc='lower right');
-
-
-# ## What are the classifier coefficients?
-
-# In[21]:
-
-coef_df = pd.DataFrame(best_clf.coef_.transpose(), index=X.columns, columns=['weight'])
-coef_df['abs'] = coef_df['weight'].abs()
-coef_df = coef_df.sort_values('abs', ascending=False)
-
-
-# In[22]:
-
-'{:.1%} zero coefficients; {:,} negative and {:,} positive coefficients'.format(
-    (coef_df.weight == 0).mean(),
-    (coef_df.weight < 0).sum(),
-    (coef_df.weight > 0).sum()
-)
-
-
-# In[23]:
-
-coef_df
-
-
-# ## Investigate the predictions
-
-# In[24]:
+# In[9]:
 
-predict_df = pd.DataFrame.from_items([
-    ('sample_id', X.index),
-    ('testing', X.index.isin(X_test.index).astype(int)),
-    ('status', y),
-    ('decision_function', pipeline.decision_function(X)),
-    ('probability', pipeline.predict_proba(X)[:, 1]),
-])
-predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)
+rows = list()
+for i, series in option_df.iterrows():
+    columns = list()
+    for name, add_columns in covariate_to_columns.items():
+        if series[name + '_covariate']:
+            columns.extend(add_columns)
+    if not columns:
+        continue
+    X = covariate_df[columns]
+    y = Y[series.mutation]
+    rows.append(get_aurocs(X, y, series))
+auroc_df = pd.DataFrame(rows)
+auroc_df.sort_values(['symbol', 'testing_auroc'], ascending=[True, False], inplace=True)
 
 
-# In[25]:
+# In[10]:
 
-# Top predictions amongst negatives (potential hidden responders)
-predict_df.sort_values('decision_function', ascending=False).query("status == 0").head(10)
+auroc_df.head()
 
 
-# In[26]:
+# In[11]:
 
-# Ignore numpy warning caused by seaborn
-warnings.filterwarnings('ignore', 'using a non-integer number instead of an integer')
+auroc_df.to_csv('auroc.tsv', index=False, sep='\t', float_format='%.5g')
 
-ax = sns.distplot(predict_df.query("status == 0").decision_function, hist=False, label='Negatives')
-ax = sns.distplot(predict_df.query("status == 1").decision_function, hist=False, label='Positives')
 
+# ## Covariate performance by mutation
 
-# In[27]:
+# In[12]:
 
-ax = sns.distplot(predict_df.query("status == 0").probability, hist=False, label='Negatives')
-ax = sns.distplot(predict_df.query("status == 1").probability, hist=False, label='Positives')
+# Filter for models which include all covariates
+plot_df = auroc_df[auroc_df[binary_options].all(axis='columns')]
+plot_df = pd.melt(plot_df, id_vars='symbol', value_vars=['mean_cv_auroc', 'training_auroc', 'testing_auroc'], var_name='kind', value_name='auroc')
+grid = sns.factorplot(y='symbol', x='auroc', hue='kind', data=plot_df, kind="bar")
+xlimits = grid.ax.set_xlim(0.5, 1)
 

From b7d2fad0b58e895119cae0ccd6cb2240464ff057 Mon Sep 17 00:00:00 2001
From: Daniel Himmelstein <daniel.himmelstein@gmail.com>
Date: Wed, 21 Sep 2016 17:22:47 -0400
Subject: [PATCH 4/4] Address review comments

---
 explore/README.md                     |  2 +-
 explore/confounding/confounding.ipynb | 21 +++++++++++++--------
 explore/confounding/confounding.py    | 21 +++++++++++++--------
 3 files changed, 27 insertions(+), 17 deletions(-)

diff --git a/explore/README.md b/explore/README.md
index 9389710..62f5503 100644
--- a/explore/README.md
+++ b/explore/README.md
@@ -1,6 +1,6 @@
 # A directory for exploratory machine learning analyses
 
-This directory is home is exploratory analyses that help answer questions about how we should do machine learning. For algorithm implementations see the [`algorithms`](../algorithms) directory. For other types of analyses, place them here.
+This directory is home to exploratory analyses that help answer questions about how we should do machine learning. For algorithm implementations see the [`algorithms`](../algorithms) directory. For other types of analyses, place them here.
 
 Notebooks should be exported to scripts for review. For example, from the directory containing your scripts run:
 
diff --git a/explore/confounding/confounding.ipynb b/explore/confounding/confounding.ipynb
index 8f90449..e828a65 100644
--- a/explore/confounding/confounding.ipynb
+++ b/explore/confounding/confounding.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Create a logistic regression model to several mutations from covariates"
+    "# Create a logistic regression model to predict several mutations from covariates"
    ]
   },
   {
@@ -366,14 +366,14 @@
     "    return pd.DataFrame.from_records(rows, columns=data_dict.keys())\n",
     "\n",
     "mutations = {\n",
-    "    '7157': 'TP53', # tumor protein p53\n",
-    "    '7428': 'VHL', # von Hippel-Lindau tumor suppressor\n",
+    "    '7157': 'TP53',   # tumor protein p53\n",
+    "    '7428': 'VHL',    # von Hippel-Lindau tumor suppressor\n",
     "    '29126': 'CD274', # CD274 molecule\n",
-    "    '672': 'BRCA1', # BRCA1, DNA repair associated\n",
-    "    '675': 'BRCA2', # BRCA2, DNA repair associated\n",
-    "    '238': 'ALK', # anaplastic lymphoma receptor tyrosine kinase\n",
-    "    '4221': 'MEN1', # menin 1\n",
-    "    '5979': 'RET', # ret proto-oncogene\n",
+    "    '672': 'BRCA1',   # BRCA1, DNA repair associated\n",
+    "    '675': 'BRCA2',   # BRCA2, DNA repair associated\n",
+    "    '238': 'ALK',     # anaplastic lymphoma receptor tyrosine kinase\n",
+    "    '4221': 'MEN1',   # menin 1\n",
+    "    '5979': 'RET',    # ret proto-oncogene\n",
     "}\n",
     "\n",
     "options = collections.OrderedDict()\n",
@@ -429,6 +429,11 @@
    "outputs": [],
    "source": [
     "def get_aurocs(X, y, series):\n",
+    "    \"\"\"\n",
+    "    Fit the classifier specified by series and add the cv, training, and testing AUROCs.\n",
+    "    series is a row of option_df, which specificies the which covariates and mutation\n",
+    "    status to use in the classifier.\n",
+    "    \"\"\"\n",
     "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
     "    series['positive_prevalence'] = np.mean(y)\n",
     "    pipeline.fit(X=X_train, y=y_train)\n",
diff --git a/explore/confounding/confounding.py b/explore/confounding/confounding.py
index f7fd2a0..ab80975 100644
--- a/explore/confounding/confounding.py
+++ b/explore/confounding/confounding.py
@@ -1,7 +1,7 @@
 
 # coding: utf-8
 
-# # Create a logistic regression model to several mutations from covariates
+# # Create a logistic regression model to predict several mutations from covariates
 
 # In[1]:
 
@@ -81,14 +81,14 @@ def expand_grid(data_dict):
     return pd.DataFrame.from_records(rows, columns=data_dict.keys())
 
 mutations = {
-    '7157': 'TP53', # tumor protein p53
-    '7428': 'VHL', # von Hippel-Lindau tumor suppressor
+    '7157': 'TP53',   # tumor protein p53
+    '7428': 'VHL',    # von Hippel-Lindau tumor suppressor
     '29126': 'CD274', # CD274 molecule
-    '672': 'BRCA1', # BRCA1, DNA repair associated
-    '675': 'BRCA2', # BRCA2, DNA repair associated
-    '238': 'ALK', # anaplastic lymphoma receptor tyrosine kinase
-    '4221': 'MEN1', # menin 1
-    '5979': 'RET', # ret proto-oncogene
+    '672': 'BRCA1',   # BRCA1, DNA repair associated
+    '675': 'BRCA2',   # BRCA2, DNA repair associated
+    '238': 'ALK',     # anaplastic lymphoma receptor tyrosine kinase
+    '4221': 'MEN1',   # menin 1
+    '5979': 'RET',    # ret proto-oncogene
 }
 
 options = collections.OrderedDict()
@@ -127,6 +127,11 @@ def expand_grid(data_dict):
 # In[8]:
 
 def get_aurocs(X, y, series):
+    """
+    Fit the classifier specified by series and add the cv, training, and testing AUROCs.
+    series is a row of option_df, which specificies the which covariates and mutation
+    status to use in the classifier.
+    """
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
     series['positive_prevalence'] = np.mean(y)
     pipeline.fit(X=X_train, y=y_train)