From 5bc43158b547804795d4655470bc3ad3bdf5eddf Mon Sep 17 00:00:00 2001
From: Greg Way <gregory.way@gmail.com>
Date: Thu, 1 Sep 2016 10:10:49 -0400
Subject: [PATCH] Machine Learning Pathway Classifier Example (#39)

* adding machine learning example

* updating ml example for pull request comments

* updating ml example 1

* add neo4j-driver to environment

* add neo4j pathway example

this commit creates an example pipeline to build a classifier to detect a Hippo signalinggene expression signature. It queries hetnet pathways and generates a Y matrix for any sample with a mutation for any gene in the hippo pathway

* nbconvert example to .py

* update pathway script

I address all pull request comments in the update

* update pathway example py

nbconvert updated pathway notebook

* remove download logic and rename

* remove hardcode tissue filter output
---
 3.TCGA-MLexample_Pathway.ipynb      | 2413 +++++++++++++++++++++++++++
 environment.yml                     |    2 +
 scripts/3.TCGA-MLexample_Pathway.py |  502 ++++++
 3 files changed, 2917 insertions(+)
 create mode 100644 3.TCGA-MLexample_Pathway.ipynb
 create mode 100644 scripts/3.TCGA-MLexample_Pathway.py

diff --git a/3.TCGA-MLexample_Pathway.ipynb b/3.TCGA-MLexample_Pathway.ipynb
new file mode 100644
index 0000000..29a6bf2
--- /dev/null
+++ b/3.TCGA-MLexample_Pathway.ipynb
@@ -0,0 +1,2413 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Predicting Aberrations in The Hippo Signaling Pathway"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To do this, we will use Hetnets to query \"Signaling by Hippo\"\n",
+    "\n",
+    "The query is built in the [Cypher Language](https://neo4j.com/developer/cypher-query-language/) and draws data from [Hetionet](https://neo4j.het.io/browser/)\n",
+    "\n",
+    "### How Cognoma could help with Hippo signaling\n",
+    "\n",
+    "The Hippo pathway is a highly conserved signaling cascade that controls organ size, cell growth, and cell death ([Zhao et al. 2010](http://doi.org/10.1101/gad.1909210)). It is one of the mechanisms that influences size diversity across eukaryotes; including different sizes across dog breeds ([Dong et al. 2007](http://doi.org/10.1016/j.cell.2007.07.019), [Crickmore and Mann 2008](http://doi.org/10.1002/bies.20806)). Recently, Hippo signaling has also been shown to be important for tumorigenesis, but there are shockingly few recurrent mutations of single genes within the pathway across tissues ([Harvey et al 2013](http://doi.org/10.1038/nrc3458)). Therefore, leveraging cancers from multiple tissues and combining genes associated with the same pathway could aid in the detection of a Hippo signaling specific gene expression signature. Cognoma is situated well to quickly query the list of all pathway associated genes, build a machine learning classifier to detect aberrant pathway activity, and output tissue and gene specific performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import urllib\n",
+    "import random\n",
+    "import warnings\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import preprocessing, 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 sklearn.feature_selection import SelectKBest\n",
+    "from statsmodels.robust.scale import mad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from neo4j.v1 import GraphDatabase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.style.use('seaborn-notebook')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specify model configuration - Generate genelist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "names = ('label', 'rel_type', 'node_id')\n",
+    "query_params = [\n",
+    "    ('Pathway', 'PARTICIPATES_GpPW', 'PC7_7459'),             # \"Signaling by Hippo\" - Reactome\n",
+    "    ('BiologicalProcess', 'PARTICIPATES_GpBP', 'GO:0035329'), # \"hippo signaling\" - Gene Ontology\n",
+    "    ('BiologicalProcess', 'PARTICIPATES_GpBP', 'GO:0035330')  # \"regulation of hippo signaling\" - Gene Ontology\n",
+    "]\n",
+    "\n",
+    "param_list = [dict(zip(names, qp)) for qp in query_params]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "query = '''\n",
+    "MATCH (node)-[rel]-(gene)\n",
+    "WHERE node.identifier = {node_id}\n",
+    "  AND {label} in labels(node)\n",
+    "  AND {rel_type} = type(rel)\n",
+    "RETURN\n",
+    "  gene.identifier as entrez_gene_id,\n",
+    "  gene.name as gene_symbol\n",
+    "ORDER BY gene_symbol\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "driver = GraphDatabase.driver(\"bolt://neo4j.het.io\")\n",
+    "full_results_df = pd.DataFrame()\n",
+    "with driver.session() as session:\n",
+    "    for parameters in param_list:\n",
+    "        result = session.run(query, parameters)\n",
+    "        result_df = pd.DataFrame((x.values() for x in result), columns=result.keys())\n",
+    "        full_results_df = full_results_df.append(result_df, ignore_index=True)\n",
+    "\n",
+    "classifier_genes_df = full_results_df.drop_duplicates().sort_values('gene_symbol').reset_index(drop=True)\n",
+    "classifier_genes_df['entrez_gene_id'] = classifier_genes_df['entrez_gene_id'].astype('str')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>entrez_gene_id</th>\n",
+       "      <th>gene_symbol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>84962</td>\n",
+       "      <td>AJUBA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>154796</td>\n",
+       "      <td>AMOT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>154810</td>\n",
+       "      <td>AMOTL1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>51421</td>\n",
+       "      <td>AMOTL2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>836</td>\n",
+       "      <td>CASP3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8642</td>\n",
+       "      <td>DCHS1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1856</td>\n",
+       "      <td>DVL2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>79633</td>\n",
+       "      <td>FAT4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9113</td>\n",
+       "      <td>LATS1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>26524</td>\n",
+       "      <td>LATS2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>8994</td>\n",
+       "      <td>LIMD1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>55233</td>\n",
+       "      <td>MOB1A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>92597</td>\n",
+       "      <td>MOB1B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>284086</td>\n",
+       "      <td>NEK8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>4771</td>\n",
+       "      <td>NF2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>261734</td>\n",
+       "      <td>NPHP4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>9867</td>\n",
+       "      <td>PJA2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60485</td>\n",
+       "      <td>SAV1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>6664</td>\n",
+       "      <td>SOX11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>6788</td>\n",
+       "      <td>STK3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>6789</td>\n",
+       "      <td>STK4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>7003</td>\n",
+       "      <td>TEAD1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>8463</td>\n",
+       "      <td>TEAD2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>7005</td>\n",
+       "      <td>TEAD3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>7004</td>\n",
+       "      <td>TEAD4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>7082</td>\n",
+       "      <td>TJP1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>9414</td>\n",
+       "      <td>TJP2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>126374</td>\n",
+       "      <td>WTIP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>23286</td>\n",
+       "      <td>WWC1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>80014</td>\n",
+       "      <td>WWC2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>55841</td>\n",
+       "      <td>WWC3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>25937</td>\n",
+       "      <td>WWTR1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>10413</td>\n",
+       "      <td>YAP1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>7529</td>\n",
+       "      <td>YWHAB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>7531</td>\n",
+       "      <td>YWHAE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   entrez_gene_id gene_symbol\n",
+       "0           84962       AJUBA\n",
+       "1          154796        AMOT\n",
+       "2          154810      AMOTL1\n",
+       "3           51421      AMOTL2\n",
+       "4             836       CASP3\n",
+       "5            8642       DCHS1\n",
+       "6            1856        DVL2\n",
+       "7           79633        FAT4\n",
+       "8            9113       LATS1\n",
+       "9           26524       LATS2\n",
+       "10           8994       LIMD1\n",
+       "11          55233       MOB1A\n",
+       "12          92597       MOB1B\n",
+       "13         284086        NEK8\n",
+       "14           4771         NF2\n",
+       "15         261734       NPHP4\n",
+       "16           9867        PJA2\n",
+       "17          60485        SAV1\n",
+       "18           6664       SOX11\n",
+       "19           6788        STK3\n",
+       "20           6789        STK4\n",
+       "21           7003       TEAD1\n",
+       "22           8463       TEAD2\n",
+       "23           7005       TEAD3\n",
+       "24           7004       TEAD4\n",
+       "25           7082        TJP1\n",
+       "26           9414        TJP2\n",
+       "27         126374        WTIP\n",
+       "28          23286        WWC1\n",
+       "29          80014        WWC2\n",
+       "30          55841        WWC3\n",
+       "31          25937       WWTR1\n",
+       "32          10413        YAP1\n",
+       "33           7529       YWHAB\n",
+       "34           7531       YWHAE"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here are the genes that participate in the Hippo signaling pathway\n",
+    "classifier_genes_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Parameter Sweep for Hyperparameters\n",
+    "n_feature_kept = 8000\n",
+    "param_fixed = {\n",
+    "    'loss': 'log',\n",
+    "    'penalty': 'elasticnet',\n",
+    "}\n",
+    "param_grid = {\n",
+    "    'alpha': [10 ** x for x in range(-6, 1)],\n",
+    "    'l1_ratio': [0, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 1],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Here is some [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) regarding the classifier and hyperparameters*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 6s, sys: 748 ms, total: 1min 7s\n",
+      "Wall time: 1min 7s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "path = os.path.join('data', 'expression-matrix.tsv.bz2')\n",
+    "X = pd.read_table(path, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 42.5 s, sys: 316 ms, total: 42.8 s\n",
+      "Wall time: 42.8 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "path = os.path.join('data', 'mutation-matrix.tsv.bz2')\n",
+    "Y = pd.read_table(path, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 8 ms, sys: 4 ms, total: 12 ms\n",
+      "Wall time: 11.9 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "path = os.path.join('data', 'samples.tsv')\n",
+    "clinical = pd.read_table(path, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>patient_id</th>\n",
+       "      <th>sample_type</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>organ_of_origin</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age_diagnosed</th>\n",
+       "      <th>dead</th>\n",
+       "      <th>days_survived</th>\n",
+       "      <th>recurred</th>\n",
+       "      <th>days_recurrence_free</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TCGA-ZS-A9CE-01</th>\n",
+       "      <td>TCGA-ZS-A9CE</td>\n",
+       "      <td>Primary Tumor</td>\n",
+       "      <td>liver hepatocellular carcinoma</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1241.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>892.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-ZS-A9CF-01</th>\n",
+       "      <td>TCGA-ZS-A9CF</td>\n",
+       "      <td>Primary Tumor</td>\n",
+       "      <td>liver hepatocellular carcinoma</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2412.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>636.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-ZS-A9CG-01</th>\n",
+       "      <td>TCGA-ZS-A9CG</td>\n",
+       "      <td>Primary Tumor</td>\n",
+       "      <td>liver hepatocellular carcinoma</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-ZT-A8OM-01</th>\n",
+       "      <td>TCGA-ZT-A8OM</td>\n",
+       "      <td>Primary Tumor</td>\n",
+       "      <td>thymoma</td>\n",
+       "      <td>Thymus</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1398.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1398.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-ZU-A8S4-01</th>\n",
+       "      <td>TCGA-ZU-A8S4</td>\n",
+       "      <td>Primary Tumor</td>\n",
+       "      <td>cholangiocarcinoma</td>\n",
+       "      <td>Bile duct</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   patient_id    sample_type                         disease  \\\n",
+       "sample_id                                                                      \n",
+       "TCGA-ZS-A9CE-01  TCGA-ZS-A9CE  Primary Tumor  liver hepatocellular carcinoma   \n",
+       "TCGA-ZS-A9CF-01  TCGA-ZS-A9CF  Primary Tumor  liver hepatocellular carcinoma   \n",
+       "TCGA-ZS-A9CG-01  TCGA-ZS-A9CG  Primary Tumor  liver hepatocellular carcinoma   \n",
+       "TCGA-ZT-A8OM-01  TCGA-ZT-A8OM  Primary Tumor                         thymoma   \n",
+       "TCGA-ZU-A8S4-01  TCGA-ZU-A8S4  Primary Tumor              cholangiocarcinoma   \n",
+       "\n",
+       "                organ_of_origin  gender  age_diagnosed  dead  days_survived  \\\n",
+       "sample_id                                                                     \n",
+       "TCGA-ZS-A9CE-01           Liver  Female           79.0   0.0         1241.0   \n",
+       "TCGA-ZS-A9CF-01           Liver    Male           64.0   0.0         2412.0   \n",
+       "TCGA-ZS-A9CG-01           Liver    Male           55.0   0.0          341.0   \n",
+       "TCGA-ZT-A8OM-01          Thymus  Female           73.0   0.0         1398.0   \n",
+       "TCGA-ZU-A8S4-01       Bile duct    Male           52.0   1.0           98.0   \n",
+       "\n",
+       "                 recurred  days_recurrence_free  \n",
+       "sample_id                                        \n",
+       "TCGA-ZS-A9CE-01       1.0                 892.0  \n",
+       "TCGA-ZS-A9CF-01       1.0                 636.0  \n",
+       "TCGA-ZS-A9CG-01       0.0                 341.0  \n",
+       "TCGA-ZT-A8OM-01       0.0                1398.0  \n",
+       "TCGA-ZU-A8S4-01       NaN                   NaN  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clinical.tail(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Subset the Y matrix to only the genes to be classified\n",
+    "y_full = Y[classifier_genes_df['entrez_gene_id']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y_full.columns = classifier_genes_df['gene_symbol']\n",
+    "y_full = y_full.assign(disease = clinical['disease'])\n",
+    "\n",
+    "# This matrix now stores the final y matrix for the classifier (y['indicator'])\n",
+    "y = y_full.assign(indicator = y_full.max(axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "unique_pos = y.groupby('disease').apply(lambda x: x['indicator'].sum())\n",
+    "heatmap_df = y_full.groupby('disease').sum().assign(TOTAL = unique_pos)\n",
+    "heatmap_df = heatmap_df.divide(y_full.disease.value_counts(sort=False).sort_index(), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UtR7FxIsTion5klM2L3OVHLsdC0xGAbcCvwjHn8QyUs+p6qeAYP6oYOLIe4vIk5gA7/OZ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\nSl0QBEEQBMFkwP8DNJYxbPFpbJMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45ca8b1e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# What is the percentage of different mutations across different cancer types?\n",
+    "sns.heatmap(heatmap_df);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualizing the input data here is key. The heterogeneity of the mutations across tissues is apparent for this particular pathway. In comparison with `TP53` mutations, it appears that Hippo signaling impacts different tissues with higher diversity.\n",
+    "\n",
+    "Looking closer at the plots above, it is evident that several tissues do not demonstrate aberrations (at least at the mutation level) in Hippo signaling. Specifically, it appears that cancers with gender specificity like testicular cancer and and prostate cancer are _not_ impacted. Therefore, because of this artificial imbalance, if Cognoma were to include these cancers in the classifier, it **will** key in on gender specific signal (i.e. genes that are only on the Y chromosome, or X inactivation genes).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>Negatives</th>\n",
+       "      <th>Positives</th>\n",
+       "      <th>Positive Prop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>diffuse large B-cell lymphoma</th>\n",
+       "      <td>25</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.479167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colon adenocarcinoma</th>\n",
+       "      <td>114</td>\n",
+       "      <td>96</td>\n",
+       "      <td>0.457143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>skin cutaneous melanoma</th>\n",
+       "      <td>56</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.456311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stomach adenocarcinoma</th>\n",
+       "      <td>151</td>\n",
+       "      <td>121</td>\n",
+       "      <td>0.444853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lung squamous cell carcinoma</th>\n",
+       "      <td>101</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0.432584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lung adenocarcinoma</th>\n",
+       "      <td>280</td>\n",
+       "      <td>207</td>\n",
+       "      <td>0.425051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bladder urothelial carcinoma</th>\n",
+       "      <td>227</td>\n",
+       "      <td>163</td>\n",
+       "      <td>0.417949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rectum adenocarcinoma</th>\n",
+       "      <td>49</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.395062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mesothelioma</th>\n",
+       "      <td>53</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.361446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cervical &amp; endocervical cancer</th>\n",
+       "      <td>132</td>\n",
+       "      <td>61</td>\n",
+       "      <td>0.316062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>head &amp; neck squamous cell carcinoma</th>\n",
+       "      <td>346</td>\n",
+       "      <td>156</td>\n",
+       "      <td>0.310757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uterine corpus endometrioid carcinoma</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>esophageal carcinoma</th>\n",
+       "      <td>130</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>liver hepatocellular carcinoma</th>\n",
+       "      <td>285</td>\n",
+       "      <td>82</td>\n",
+       "      <td>0.223433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kidney papillary cell carcinoma</th>\n",
+       "      <td>131</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0.220238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kidney clear cell carcinoma</th>\n",
+       "      <td>176</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.169811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pancreatic adenocarcinoma</th>\n",
+       "      <td>126</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uterine carcinosarcoma</th>\n",
+       "      <td>48</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.157895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>adrenocortical cancer</th>\n",
+       "      <td>66</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.153846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cholangiocarcinoma</th>\n",
+       "      <td>31</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.138889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ovarian serous cystadenocarcinoma</th>\n",
+       "      <td>164</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.122995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sarcoma</th>\n",
+       "      <td>223</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.118577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>breast invasive carcinoma</th>\n",
+       "      <td>869</td>\n",
+       "      <td>107</td>\n",
+       "      <td>0.109631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kidney chromophobe</th>\n",
+       "      <td>60</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0909091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>glioblastoma multiforme</th>\n",
+       "      <td>140</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0789474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prostate adenocarcinoma</th>\n",
+       "      <td>305</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0785498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>brain lower grade glioma</th>\n",
+       "      <td>483</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0639535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uveal melanoma</th>\n",
+       "      <td>75</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thymoma</th>\n",
+       "      <td>112</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pheochromocytoma &amp; paraganglioma</th>\n",
+       "      <td>170</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.0502793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>testicular germ cell tumor</th>\n",
+       "      <td>143</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0466667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thyroid carcinoma</th>\n",
+       "      <td>475</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0365112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      Negatives Positives Positive Prop\n",
+       "diffuse large B-cell lymphoma                25        23      0.479167\n",
+       "colon adenocarcinoma                        114        96      0.457143\n",
+       "skin cutaneous melanoma                      56        47      0.456311\n",
+       "stomach adenocarcinoma                      151       121      0.444853\n",
+       "lung squamous cell carcinoma                101        77      0.432584\n",
+       "lung adenocarcinoma                         280       207      0.425051\n",
+       "bladder urothelial carcinoma                227       163      0.417949\n",
+       "rectum adenocarcinoma                        49        32      0.395062\n",
+       "mesothelioma                                 53        30      0.361446\n",
+       "cervical & endocervical cancer              132        61      0.316062\n",
+       "head & neck squamous cell carcinoma         346       156      0.310757\n",
+       "uterine corpus endometrioid carcinoma         5         2      0.285714\n",
+       "esophageal carcinoma                        130        52      0.285714\n",
+       "liver hepatocellular carcinoma              285        82      0.223433\n",
+       "kidney papillary cell carcinoma             131        37      0.220238\n",
+       "kidney clear cell carcinoma                 176        36      0.169811\n",
+       "pancreatic adenocarcinoma                   126        24          0.16\n",
+       "uterine carcinosarcoma                       48         9      0.157895\n",
+       "adrenocortical cancer                        66        12      0.153846\n",
+       "cholangiocarcinoma                           31         5      0.138889\n",
+       "ovarian serous cystadenocarcinoma           164        23      0.122995\n",
+       "sarcoma                                     223        30      0.118577\n",
+       "breast invasive carcinoma                   869       107      0.109631\n",
+       "kidney chromophobe                           60         6     0.0909091\n",
+       "glioblastoma multiforme                     140        12     0.0789474\n",
+       "prostate adenocarcinoma                     305        26     0.0785498\n",
+       "brain lower grade glioma                    483        33     0.0639535\n",
+       "uveal melanoma                               75         5        0.0625\n",
+       "thymoma                                     112         7     0.0588235\n",
+       "pheochromocytoma & paraganglioma            170         9     0.0502793\n",
+       "testicular germ cell tumor                  143         7     0.0466667\n",
+       "thyroid carcinoma                           475        18     0.0365112"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How many samples in each tissue that have Hippo signaling aberrations\n",
+    "ind = ['Negatives', 'Positives', 'Positive Prop']\n",
+    "percent = heatmap_df['TOTAL']\n",
+    "neg = y.disease.value_counts() - unique_pos\n",
+    "tissue_summary_df = pd.DataFrame([neg, unique_pos, percent], index=ind, \n",
+    "                                 dtype='object').T.sort_values('Positive Prop', ascending=False)\n",
+    "tissue_summary_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filter Data by Tissue\n",
+    "\n",
+    "This is a crucial step that is different from previous classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Technically, these are hyper parameters, but for simplicity, set here\n",
+    "filter_prop = 0.10\n",
+    "filter_count = 15\n",
+    "\n",
+    "tissue_prop_decision = tissue_summary_df['Positive Prop'] >= filter_prop\n",
+    "tissue_count_decision = tissue_summary_df['Positives'] >= filter_count\n",
+    "\n",
+    "tissue_decision = tissue_prop_decision & tissue_count_decision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                     adrenocortical cancer\n",
+       "1                  brain lower grade glioma\n",
+       "2                        cholangiocarcinoma\n",
+       "3                   glioblastoma multiforme\n",
+       "4                        kidney chromophobe\n",
+       "5          pheochromocytoma & paraganglioma\n",
+       "6                   prostate adenocarcinoma\n",
+       "7                testicular germ cell tumor\n",
+       "8                                   thymoma\n",
+       "9                         thyroid carcinoma\n",
+       "10                   uterine carcinosarcoma\n",
+       "11    uterine corpus endometrioid carcinoma\n",
+       "12                           uveal melanoma\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This criteria filters out the following tissues\n",
+    "pd.Series(tissue_summary_df.index[~tissue_decision].sort_values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>Negatives</th>\n",
+       "      <th>Positives</th>\n",
+       "      <th>Positive Prop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>diffuse large B-cell lymphoma</th>\n",
+       "      <td>25</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.479167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colon adenocarcinoma</th>\n",
+       "      <td>114</td>\n",
+       "      <td>96</td>\n",
+       "      <td>0.457143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>skin cutaneous melanoma</th>\n",
+       "      <td>56</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.456311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stomach adenocarcinoma</th>\n",
+       "      <td>151</td>\n",
+       "      <td>121</td>\n",
+       "      <td>0.444853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lung squamous cell carcinoma</th>\n",
+       "      <td>101</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0.432584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lung adenocarcinoma</th>\n",
+       "      <td>280</td>\n",
+       "      <td>207</td>\n",
+       "      <td>0.425051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bladder urothelial carcinoma</th>\n",
+       "      <td>227</td>\n",
+       "      <td>163</td>\n",
+       "      <td>0.417949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rectum adenocarcinoma</th>\n",
+       "      <td>49</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.395062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mesothelioma</th>\n",
+       "      <td>53</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.361446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cervical &amp; endocervical cancer</th>\n",
+       "      <td>132</td>\n",
+       "      <td>61</td>\n",
+       "      <td>0.316062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>head &amp; neck squamous cell carcinoma</th>\n",
+       "      <td>346</td>\n",
+       "      <td>156</td>\n",
+       "      <td>0.310757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>esophageal carcinoma</th>\n",
+       "      <td>130</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>liver hepatocellular carcinoma</th>\n",
+       "      <td>285</td>\n",
+       "      <td>82</td>\n",
+       "      <td>0.223433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kidney papillary cell carcinoma</th>\n",
+       "      <td>131</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0.220238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kidney clear cell carcinoma</th>\n",
+       "      <td>176</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.169811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pancreatic adenocarcinoma</th>\n",
+       "      <td>126</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ovarian serous cystadenocarcinoma</th>\n",
+       "      <td>164</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.122995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sarcoma</th>\n",
+       "      <td>223</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.118577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>breast invasive carcinoma</th>\n",
+       "      <td>869</td>\n",
+       "      <td>107</td>\n",
+       "      <td>0.109631</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    Negatives Positives Positive Prop\n",
+       "diffuse large B-cell lymphoma              25        23      0.479167\n",
+       "colon adenocarcinoma                      114        96      0.457143\n",
+       "skin cutaneous melanoma                    56        47      0.456311\n",
+       "stomach adenocarcinoma                    151       121      0.444853\n",
+       "lung squamous cell carcinoma              101        77      0.432584\n",
+       "lung adenocarcinoma                       280       207      0.425051\n",
+       "bladder urothelial carcinoma              227       163      0.417949\n",
+       "rectum adenocarcinoma                      49        32      0.395062\n",
+       "mesothelioma                               53        30      0.361446\n",
+       "cervical & endocervical cancer            132        61      0.316062\n",
+       "head & neck squamous cell carcinoma       346       156      0.310757\n",
+       "esophageal carcinoma                      130        52      0.285714\n",
+       "liver hepatocellular carcinoma            285        82      0.223433\n",
+       "kidney papillary cell carcinoma           131        37      0.220238\n",
+       "kidney clear cell carcinoma               176        36      0.169811\n",
+       "pancreatic adenocarcinoma                 126        24          0.16\n",
+       "ovarian serous cystadenocarcinoma         164        23      0.122995\n",
+       "sarcoma                                   223        30      0.118577\n",
+       "breast invasive carcinoma                 869       107      0.109631"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What are the tissues remaining?\n",
+    "tissue_summary_df = tissue_summary_df[tissue_decision]\n",
+    "tissue_summary_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YX7A9HNgsG2gAA/J4jsovgMWBq2xvDCwuaW3SPN+fPY8/YOb8rgScnefqYOAK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45b28630b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Distribution of mutation counts after filtering\n",
+    "sns.heatmap(heatmap_df.loc[tissue_decision]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Subset data\n",
+    "clinical_sub = clinical[clinical['disease'].isin(tissue_summary_df.index)]\n",
+    "X_sub = X.ix[clinical_sub.index]\n",
+    "y_sub = y['indicator'].ix[clinical_sub.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.721539\n",
+       "1    0.278461\n",
+       "Name: indicator, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Total distribution of positives/negatives\n",
+    "y_sub.value_counts(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_id\n",
+       "TCGA-04-1348-01    0\n",
+       "TCGA-04-1357-01    0\n",
+       "TCGA-04-1362-01    0\n",
+       "TCGA-05-4244-01    0\n",
+       "TCGA-05-4249-01    1\n",
+       "TCGA-05-4250-01    0\n",
+       "TCGA-05-4382-01    1\n",
+       "Name: indicator, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_sub.head(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set aside 10% of the data for testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_id\n",
+       "TCGA-04-1348-01    ovarian serous cystadenocarcinoma0\n",
+       "TCGA-04-1357-01    ovarian serous cystadenocarcinoma0\n",
+       "TCGA-04-1362-01    ovarian serous cystadenocarcinoma0\n",
+       "TCGA-05-4244-01                  lung adenocarcinoma0\n",
+       "TCGA-05-4249-01                  lung adenocarcinoma1\n",
+       "TCGA-05-4250-01                  lung adenocarcinoma0\n",
+       "Name: disease, dtype: object"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strat = clinical_sub['disease'].str.cat(y_sub.astype(str))\n",
+    "strat.head(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Size: 20,530 features, 4,536 training samples, 506 testing samples'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Make sure the splits have equal tissue and label partitions\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_sub, y_sub, test_size=0.1, random_state=0,\n",
+    "                                                    stratify=strat)\n",
+    "'Size: {:,} features, {:,} training samples, {:,} testing samples'.format(len(X_sub.columns),\n",
+    "                                                                          len(X_train), len(X_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Median absolute deviation feature selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def fs_mad(x, y):\n",
+    "    \"\"\"    \n",
+    "    Get the median absolute deviation (MAD) for each column of x\n",
+    "    \"\"\"\n",
+    "    scores = mad(x) \n",
+    "    return scores, np.array([np.NaN]*len(scores))\n",
+    "\n",
+    "# select the top features with the highest MAD\n",
+    "feature_select = SelectKBest(fs_mad, k=n_feature_kept)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define pipeline and Cross validation model fitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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",
+    "    feature_select,  # Feature selection\n",
+    "    StandardScaler(),  # Feature scaling\n",
+    "    clf_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 51s, sys: 4.04 s, total: 1min 55s\n",
+      "Wall time: 2min 54s\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_\n",
+    "feature_mask = feature_select.get_support()  # Get a boolean array indicating the selected features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'alpha': 0.1, 'l1_ratio': 0.1}"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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=0.1, 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": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_clf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize hyperparameters performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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": 33,
+   "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>0.617272</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>0.619249</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      alpha  fold  l1_ratio     score\n",
+       "0  0.000001     0       0.0  0.617272\n",
+       "1  0.000001     1       0.0  0.619249"
+      ]
+     },
+     "execution_count": 33,
+     "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": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FHmsBrjFNMyd2/ybgjMvtERERERERERERERERERERSYqrK9gty9prmma9aZqvA2HgT03T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\nmKShaGTli4mJyZnDBResYvbsueY15giSPw+r1Upur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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4563fe1e48>"
+      ]
+     },
+     "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": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vdn6/Dp+AJmTFpdWJloDh3Ql7ZzG2GjsCRgSh1mvRONpTfJ1z+QQ2w1arZvPH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\nLdlGyGPD2Ds/nKY92tB+RBB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31XNk/SFSL/17VlC6E9xMy/R5SZJuZcm1R4GmQIYsy5er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\nRqOpI/8fWImZITcl5SAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45cd3fa278>"
+      ]
+     },
+     "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='.2%')\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": 36,
+   "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, tissue='all'):\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, 'tissue': tissue}\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": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s3kw9JjqKcpdbWbqIBIWCu0S8snIXi5dvZW9xOQBvz/2t1vPHj+jGBceZoNTF\nm6l3TEoFlKWLSHAouEtEcrncTP9iDdFRUXy0eEO157RJiKVtUizjhnUlo30ivTqn0CElocHPr9e2\nnrqXN1N/9pT7m3zJVxERLwV3iTilZa4Ki7p4nTq2Nz07peB2u+nfrT3tm3gu+NrWU/dSpi4ioaDg\nLhGl8mptk8b3p3+39vTukkJsTHTQy1f/uYi0BAruEhHcbjf3v7aUtZsLfPsuP3UIhwzpHJLy3189\nm9yiPNISO4SkPBGR2ii4S1hzud3M+GJthX71pIRYLj5xEAcPygxZPbx97WpyF5GWQMFdwtbe4jKm\nPfFFhX1Xnb4fB4UwqEPFrH1i/wkhLVtEpDoK7hJ2SstcXPu3LykuLfftO+XQ3hx7cA+Sk+JCXh9l\n7SLS0ii4S9h44PWlrP59Z4V9bRNjufSkwQwfmNFMtXIoaxeRlkTBXVo8uzGPh/ymhQXo3709hw7t\nzJHDuzVTrUREWi4Fd2mxXG430574guKSfc3v5xw9gOMO7tGMtRIRafkU3KXFcLncFOwpAWBb7p4K\n2XqfLu249qxhtG8b31zVExEJGwru0uzcbjfX/u1LdheVVXs8lM+rewUylaxXXbPSiYiEmoK7NCu3\n283kh+ZX2DdqsPMo2+6iMq44dWizjYAPNGhrSlkRaWkCCu7GmI5AH2vtEmNMtLXWFeR6SSsw+6v1\nvP/FWt/2MSO7c96xA5uxRhVpKlkRCVd1TrZtjDkX+Bp4xbPraWPM5GBWSiLfrS8srhDYjx/Vo0UF\ndhGRcBZI5n4DcADwkWf7JmAB8FKQ6iQR6sHXl/Jb1k7c7n37BvVM5eZzhxMVFdV8FRMRiTCBLJO1\n01q7x7thrd0LlASvShJptufv5dIH57HqdyewJyXEktImjtPH9eH/nTdCgV1EpIkFkrnnGGMuApKM\nMSOAPwDZwa2WRAq3282tzy/2bR/YP51rzxrWjDWqnXeUvEbAi0g4CyS4TwXuA1KAfwILAfW5S50K\n9pRw/VMLfdv/d/HB9Oqc0ow1qpt/YNcIeBEJV4EE9xOstVf77zDGTAWeD06VJBJUnjL2+rOHtfjA\n7r+6m0bJi0g4qzG4G2OGAyOAm4wxbfwOxQF3ouAuNcjK2V0hsN8zeRTdM5KbsUaB0epuIhIpasvc\ni4BOQCpwmN9+F3BzMCsl4eunNTk8+e5Pvu2nrjusWSahqU1Ns8/lF+/U6m4iEhFqDO7W2hXACmPM\nPGvt1/7HxTq2AAAgAElEQVTHjDFnBr1mEnbe/2INs7/a4Nt+9KpDW1xgh5pnn1M/u4hEikD63Dcb\nYx4G0j3bCcBRwPSg1UrCzqUPzquw/eS142jXpuUu8qLZ50QkkgXynPtrQC4wBlgKZAAXBLNSEl7+\n9u4y3+vBvTrw0i3jW3RgFxGJdIEE9zJr7YPANmvts8CpwLTgVkvCgcvt5u25v7FszQ4ADjIZLX62\nOe+IeBGRSBZIcE8yxnQHXMaYvkAp0DuotZKw8NHiDXz63Sbf9lVntPz+ao2IF5HWIJDg/jBwNPAI\n8COQA3wVzEpJyzf/+9+Z4Vn4pVt6W/55y/hmrlHd/J9j14h4EYlkdQ6os9bO9L42xqQBKdZatWu2\nYr+s3cFrn67ybd89eRTRLbgp3ktZu4i0FrVNYhMNTAH2A76y1r5lrS0zxhQbY5611qrfvRXatbeU\nx9/ZN4DupVvGt5g+9pqeX/fSc+wi0lrUlrk/DaQBi4Gpxph0YDnwIjAjBHWTFuiLZZt9r1tSYIea\nn1/30nPsItJa1BbcD7TWjgUwxrwEbADWA3+w1i4NtABjzOPAITgz211vrV3id6w78BbOlLbfW2uv\nqvcnkJB6b8EaACYe3rdFBXYvPb8uIlL7gDrfmu3W2t2ABUbXM7AfDvS31h4KXAY8VemUx4BHrLWH\nAOWeYC8t1KNv75sv/vhRPZqxJiIiUpvagru70naxtba8ntc/GpgJYK1dCaQaY5IBjDFRwDjgQ8/x\na6y1v9fz+hIir/5vJb+ud8ZRjhyYQVxsTDPXSEREalJbs3xXY8ylfttd/LettS8HcP3OwBK/7RzP\nvtU4M93tAp40xowAvrTW3hZwzSVkdu0t5fMfnb72qCiYNrF5+q0DGTBXU3+7iEhrUltwX0zF1eC+\n9tt2A4EE98qiKr3uBjwBbAQ+MsacaK39uLYLZGS07DXBI4H/Pd66YzfX/u1L3/asR09rjioB8NPX\nv5BfvJOOSanVHu+YlMohPUaEzXckXOoZznSPg0/3uGWqbVW4S5rg+ptxMnWvrsAWz+scYL21dj2A\nMWYuMBSoNbhnZxc2QbWkJhkZKb57nJW9izte+tZ37P4po0N+//2zdW9mftcht9b6nnD4jvjfZwkO\n3ePg0z0OjYb8gApkhrrG+BQ4C8DT9J7lGZyHp/9+rTGmn+fckTiD9qQFcLvdFQL741ePpUvHtiGv\nh/fxNtCjbCIigQpkydcGs9YuNsYsNcYsAsqBacaYi4B8a+0HwJ+AVzyD63621n4YzPpIYEpKy5n6\n2Oe+7cemjSU1OaHZ6qPH20RE6ieowR2gmkFyP/sdW0PFfn1pZm63u0Jgn3zyYDqkNF9gFxGR+qsz\nuBtjDgBeApKttYOMMXcAn1prvwl67SSklq/P5bEH5/m277l0FN0zk0NWfnWj4TUCXkSk/gLpc38G\nuJR9A+H+AzwetBpJs3ns7R99r6edsX9IAztU7F/3Uj+7iEj9BdIsX2qt/ckYA4C1dpUxpiy41ZJQ\nKthTwvVPLfRtP3fjESTEBW+SmpqeV/dm6epfFxFpnEAy9zJjTB88M9YZY06k4vPqEsYKKwX2ow/u\nEdTADtVn6KAsXUSkqQSSud8IfAAYY8xOnMVjLgxmpST4ikrKuOrxLyrse+DyQ9jfdArqc6vvr55N\nblEeaYkdlKGLiARJIMG9xFo7zBiTgTO/fEGwKyXB5Xa7KwT2Lh3bcNM5w0MyKt7bHK8MXUQkeAIJ\n7h8aY/KB13GWZ5Uw9/eZv/hePzh1DJmpSUEpp6bR72mJHZjYf0JQyhQRkQD63K21A4ErceaB/8oY\nM9sY84eg10yCIit7F0ttNgCnjesTtMAOGv0uItJcAprExrOG+1JjzBPAHcBrOI/ESZjxTinbJiGW\n08b1CVo56lsXEWk+gUxi0wU4EzgbZ5nWt4EhQa6XBMGin7f4Xv/fJQcHtSz1rYuINJ9AMvclOFn6\njdbaJXWdLC1LucvFPz78lW9XbPftG9avIxlBaI6vvIKb+tZFRJpHjcHdGNPFWrsFGA+Uefb19R63\n1q4NfvWkMdxuN699YisE9qF90ph62tCglOftY09NaK++dRGRZlRb5v4YcB7wCc4ENv4T17iBvtW9\nSVoGl8vNZQ/P920PH5DONWcOC0pZ3oxdM8yJiLQMNQZ3a+15npcnWWtX+B8zxowJaq2kwQp2l3D9\n0wsr7Bs/ohvnHj0gaGX6B3Zl6yIiza+2ZvlUoCPwsjHmPPZl7nHAq8DA4FdP6muh36C55KQ4Lj9l\nCPv17diga9U0B3xlythFRFqW2prlxwB/Ag4E5vntd+E01UsLUlJazr3/XkJW9m4Arpm4P8MHZjTq\nmv4ZeW2UsYuItCy1Nct/DHxsjJlqrX0+hHWSenK73Ux97PMK+4b2SWuSaysjFxEJP7U1y19irf0X\n0M0Yc0/l49baO4NaMwnYZQ/tGzh3/dkHMKxfw5rhRUQkMtTWLO/y/F9rt7dgZeUuZy1eYNL4/k0S\n2CuPfhcRkfBSW7P8q57/322MSbHWFhpjOuEMpFsUqgpKzcrKXfx9xr5FYE4Y3bNJrqvR7yIi4S2Q\n6WefBn40xswAvsKZse584Iog103qcPkjC3yvJxzau0mvrb52EZHwFcj0s8OttdcYY6YCr1hr7zXG\nzA12xaR2b8xZ5Xs9aXx/jh7ZvVHX8zbFx0RHqTleRCTM1bnkK/ueb58AfOh5nRCc6kgg9hSVMnfp\n7wDs1yeNE0b3JC42kD/Kmvkvz6rmeBGR8BZI5r7KGPMrkG2t/dEYcyGQG+R6SQ3KXS6ufvJL3/af\nJh3Q6Gv6L8/67Cn3k51d2OhriohI8wkkuF8G7A/86tleDswKWo2kVvOWZvle3zt5FFFRUbWcHRgt\nzyoiElkCCe5JwCnAPcYYN/A18GRQayXVKit38dbc3wA47uAedMtIbtT1/B950/KsIiKRI5CO2n8A\n7YAXPK87ef4vIVRW7qowOv6sI/s1+pp65E1EJDIFkrl3stae67c92xizIEj1kUpKy1zc9uJidhQU\n+/bddM6BxMY0fACdlmgVEYlsgQT3tsaYNtbaPQDGmLZAYnCr1bot/GkLv67PJX9XMSs35vv2x0RH\n8efzR9K3a7tGXV8Zu4hIZAskuL8ArDTGLPFsjwTuCF6VWrdbn1/M9vy9VfZfdfp+HDQos8nKUcYu\nIhK56gzu1tqXjTFzgBGAG7jGWptVx9ukAd6cs8oX2DNTk5hyyhC6ZyQTHR3V6OfYRUSk9ag1uBtj\nTgIGAQuttR+Epkqt0/a8PXzmmZima3pb7rtsdDPXSEREwlWN6aAx5i7gL0BX4B/GmD+GqlKtzVKb\nza0vfA1Ah5QEBXYREWmU2jL344HDrLVlxpj2wHTgjdBUq/W49MF5Fbb/33nDm/T63pHx/jR3vIhI\nZKutI7fIWlsGYK3dCcSEpkqtQ07+Xu586Vvfds/MZF68+Ug6dWjTpOX4zxnvpVHyIiKRrbbM3V3H\ntjTCszN/4ffsXQD069qOv1x4UNDK0sh4EZHWpbbgPsQY8++atq21FwavWpGvYHcJALecNxzTs0Mz\n10ZERCJJbcH9lkrbWsO9CeUVOjPO9esWnL7vyrPQiYhI61FjcLfWvhrKirQm363c7nvdmGlka6NZ\n6EREWq9AZqiTJvbczF8A6NU5JajlqK9dRKR10rRnIbanqMz3+uZzDmzGmoiISKQKKLgbYzoaYw7y\nvNYPgkb40zMLAUhpE0ebxLhmro2IiESiOgO1MeZc4GvgFc+up40xk4NZqUj13683UFrmAuC6sw5o\n5tqIiEikCiQLvwE4AMj2bN8EXB60GkWgsnIX1/7tS95bsAaAwb06NHrZ1tq8v3o2uUV5Qbu+iIi0\nbIEE953etdwBrLV7gZLgVSny/LYpn117SwEYu39nbgxyX7t3ulmNkhcRaZ0CGS2fY4y5CEgyxowA\n/sC+LF4C8Nh/lgEwrF9HJp88JCRlpiV2YGL/CSEpS0REWpZAMvepwMFACvBPIAm4LJiViiSLft6C\ny+3M3HvSIb2auTYiItIa1Jm5W2vzgatDUJeI88Nv2bz00Qrf9sAeqU1ehlZ9ExGRyuoM7saYTVSz\naIy1tmdQahQh9hSV8fR0J+i2TYzl4SsPDUo51U0xq1npRERat0D63Mf5vY4HjsZpmg+IMeZx4BDA\nBVxvrV1SzTl/BQ6x1o4P9LotWcHuEq5/eqFv+4HLDyEpIXiTAWomOhER8RdIs/yGSrt+M8Z8AjxR\n13uNMYcD/a21hxpjBgEvA4dWOmcwcBgRNAL/P/NW+14/fOUYUtrEN+n1/Zvi1QQvIiKVBdIsf1Sl\nXT2AfgFe/2hgJoC1dqUxJtUYk2yt3eV3zmPAbcBdAV6zxftpTQ4Afz5/BOntA27kCJh/U7ya4EVE\npLJA2orv8HvtBgpwRtAHojPg3wyf49m3GsDziN18oHLrQNhyud3s9swf36dL8CaqUVO8iIjUJJDg\nfqO19vsmKi/K+8IY0wG4BCe77+F/LFy9M381//tmo287WMu5ioiI1CaQ4P4oULlpPlCbcTJ1r67A\nFs/ro4B04EsgEehrjHnMWntjbRfMyAjuMqkN4Xa7OfWmWRX2PXDl2KDU9bUfp5NblEdGm7Sg3YuW\neI8jke5z8OkeB5/uccsUSHDfaIxZgLN4jG/Qm7X2zgDe+ylOX/o/PLPbZVlrd3vePx2YDmCM6QX8\nq67ADpCdXRhAsaF137/39TycOLonZ4/vDwSnros2LAVgWPp+Qbl+RkZKi7zHkUb3Ofh0j4NP9zg0\nGvIDKpDgvs7zX71ZaxcbY5YaYxYB5cA0Tz97vrX2g4Zcs6Vxu92s3VwAwIUnGI48sFvQy9TUsiIi\nUpsag7sx5o/W2jestXc3pgBr7W2Vdv1czTkbaHjTf7P6dsV23+tQBHYREZG61Ja5TwbeCFVFwk3h\nnhIeevMHNufsBqBX5+D3O3mXck1L7BD0skREJHxpOHcDvfjhr77AnhAXw63njQh6mVrKVUREAlFb\n5n6oMWZjNfujAHdrn1t+naefffLJgxm7f5eQlav+dhERqUttwf0H4JxQVSScvPLxSvYUOxPVhDKw\ni4iIBKK24F5Uzbzyrd5Su50vlm0GoHNam6CWVXk5V80jLyIigaitz/3bkNUijKzJcprje3dO4YHL\nDwlqWd455L00j7yIiASixszdWntLKCsSLv73rTMMYeIRfYNWhjdj92bqmkNeRETqQ6Pl62H+D1m+\n14N6Bu9xNP/ArkxdRETqK5AZ6gS4/Z/f+B59S2kT1+SLwlS3RrsydhERaQhl7gFwudy+wN6xXQKP\nXz22ycvw719Xxi4iIo2hzD0AM75c63v9yFVNG9jVvy4iIk1NmXsdSstcfLTYeSLwuIN7NPn11b8u\nIiJNTZl7Ha54dIHv9VlH9gtKGcrYRUSkKSlzr8WfX/za9/rmcw5s8kF0IiIiwaBoVQOX28223D0A\nnDi6J4N7pzV5Gd5V3kRERJqSgnsNLntovu/12eP7B6UMrfImIiLBoD73arz52Srf68knD2709SrP\nEe+VX7xTq7yJiEiTU+ZeyY+/5fDZkt8BGDO0U5Os+lZ5jngvjZAXEZFgUOZeyVPTfwKgZ2YyU04Z\n2mTX1Yh4EREJFWXuNbjz4oObuwoiIiINouDu5/tV2QAkJcQSHR3VzLURERFpGAV3j7JyF8+87wx6\n65CS0My1ERERaTgFd4+H3vje9/reyaOasSYiIiKNo+DusS1vLwDnHj2AqCg1yYuISPhScAc+/noD\nu/aWAjB+RLdmro2IiEjjtPrg/tmSTby7YA0A8bHRmj9eRETCXqt+zv3Tbzfy9rzVvu3nbzqy+Soj\nIiLSRFp1mrpszQ7f65dvPaoZayIiItJ0WnVwX7HBWZHtqesOa+aaiIiINJ1WG9zLXS7f6+SkuGas\niYiISNNqtcH97bmr6z5JREQkDLXaAXVzlzorv508plfQyvAu9ZpfvJPUhPZBK0dERMRfq83cvSYc\n2jto1/YP7FraVUREQqVVZu6zFq0DICoKEuJiglqWlnoVEZFQa5WZ++c/bgZg+ICMZq6JiIhI02t1\nmXtZuYu8wmLAmUe+sbz96tVRX7uIiDSHVpe5f7lss+91x/aJjb6et1+9OuprFxGR5tDqMvfs/CIA\nRg3ObLJrql9dRERaklaVubvdbv737UYAjj24RzPXRkREJDhaVXD/buV23+uuHds2Y01ERESCp1UF\n9+c/WA7AkcO7kZTQ6nokRESklWg1wd3lcvte//HYxo+SB2ekfG5RXpNcS0REpKm0muC+PX+v73VM\ndNN8bO8jcBoRLyIiLUmraZt+evpPQONHyfs/155fvJO0xA5M7D+h0fUTERFpKq0ic99bXMaWHXsA\nOHpk90Zdy/+5dj3HLiIiLVGryNznfLfJ93pA99RGX0/PtYuISEvWKjL3mQudhWKG9evYzDUREREJ\nvogP7v4D6a49a1gz1kRERCQ0Ir5Z/oMvnaw9NTme6KioBl/HO5BOi8GIiEhLF/GZe26BM5f8GYf3\nbdR1/AO7BtGJiEhLFvTM3RjzOHAI4AKut9Yu8Ts2HngAKAOstfaypiw7t6AIuykfgME9O9TrvZWX\ncvUGdg2kExGRli6ombsx5nCgv7X2UOAy4KlKpzwPTLTWHga0M8ac0JTl3/aPr32v67u8a+WlXJWx\ni4hIuAh25n40MBPAWrvSGJNqjEm21u7yHB/p9zobaLLh7GXlLkpKXQA8cc04ogLob688QY0ydRER\nCUfB7nPvjBO0vXI8+wDwBnZjTBfgWOC/TVXwk+8u871u3zY+oPdoghoREYkEoR4tXyV9NsZkArOA\nK621da7CkpGRUmch23P38Ot651J/PGFQne957cfpfL3pe/KLd9IxKZVnT7m/zjIiWSD3WBpP9zn4\ndI+DT/e4ZQp2cN+MX6YOdAW2eDeMMSk42fqfrbVzA7lgdnZhnef866MVvtdHH9i1zvcs2rDU1ww/\nLH2/gMqIVBkZKa3684eK7nPw6R4Hn+5xaDTkB1Swg/unwF3AP4wxI4Asa+1uv+OPA49ba+c0VYF7\ni8tY+LPz++HE0T0Dfp/610VEJFIENbhbaxcbY5YaYxYB5cA0Y8xFQD5O4D8f6GeMmQK4gTettf9s\nTJnPzfzF9/rMI/s15lIiIiJhKeh97tba2yrt+tnvdVJTl/fLulzAmWq2MTPSiYiIhKuInaHuAC0S\nIyIirVREBff1WwsAyOyQFNBz7SIiIpEoooL7yg3OVLNtE+OauSYiIiLNJ6JWhftpTQ4Ao4d0Cuh8\nrfQmIiKRKLIy941O5t4jo21A52ulNxERiUQRk7m/+OFy3+vBvdNqPbdyxq7n20VEJJJETOb+9fJt\nAPTvXnfzujJ2ERGJZBGTucdER1HucnPb+SMDOl8Zu4iIRKqIyNzdbjflLnfVVWlERERaoYjI3Lfl\n7QUgNrbqbxX/Ndq9NDpeREQiWURk7jk7neA+cmBGlWP+a7R7qa9dREQiWURk7m/M+Q2AnbtLqj2u\n/nUREWlNwj5z37W3lG25ewC46ATTzLURERFpfmGfuX+/Ktv3OiN13yJzmn1ORERaq7DO3F1uN698\nvBKAyScPrrBYjJ5lFxGR1iqsM/ef1uzwve7csU2V4+prFxGR1ihsM3e3281T7/0EwKCeqfTrqqZ3\nERERCOPgfufL3/peXzZhSDPWREREpGUJ22b5rOzdAFx+6hDS2iX69msgnYiItHZhmbm73W7f60OG\ndK5wTAPpRESktQvLzH391kIAEuNjqj2ugXQiEu6eeeZJrF1Bbu4O9u7dS/fuPWjXrh333fdwne/9\n+OPZJCcnc9hhR1Z7/OmnH+fss8+lc+cuDa7f119/xbJl33HFFdcBMGfO/7j//ruYNesT2rVzWk0f\neOBuxo8/mjFjxvned/bZp/Laa++wYsVy7rjjVvr27Yfb7aa4uJjRo8cwefIVAOTn5/Pkk4+wadNG\noqOj6NmzN9dddxPt2rUDYMWK5Tz33NOUlpZQWlrG2LGHccklU+r1GVwuF9dddyVRUVG43W5ycrI5\n6aRT6d+/P2+++Zpv/6pVK3nzzel07Jhe5RozZ07njTde5d13Z7FzZz63334LZWVl3H773XTr1p3y\n8nJuuOEaHnrocRITE5k+/R3Ky8uZNOncBt33QIVlcM/ZWQTAoJ4dmrkmIiLBcfXV1wNOoF63bg1X\nXXVdwO898cQJtR6/5pobGlW30tJSnnvuad5//z0KC0sB+OyzT+jevQfz58/ltNMm1vLufY8sDx8+\nknvvfdC3fd11V/HTTz8ybNiB3HvvnRx//Ikcd9yJACxYMJfbbruJZ555kT17dnPPPXfw178+Ru/e\nfSgvL+fOO29l9uyZTJhwesCfIzo6mqeffsG3ffPN13HCCSeRkZHp+0GSlfU7zz77t2oDe15eHl98\nscD3mebN+4xTTz2DzMzOfPjhTKZOvZoPP5zBccedQGKi03185pmTuOKKSzjqqGNJT696zaYSlsF9\nS47T356emljHmSIikeWHH5by1luvU1S0l6uvvp7vv1/CggXzcLvdjBkzlosvvoyXX36R9u1T6du3\nH9Onv0NUVBQbN65n/PhjuPjiy7jmmiu44YZbmD//M3btKmTjxg1s2bKZa6+9kdGjx/D6668wd+6n\ndO3ajbKyMs499wIOPHCErw7z53/GQQcdTGJiIoWFpRQUFLBy5a/ceuudvPHGq3UE95oNGjSY33/f\nRGpqKrt2FfoCO8CRRx7NjBnTsXYlK1Ys5/DDx9O7dx8AYmJiuP32e3wB1Ovf/36Z7777xpeBR0VF\nceONt9KrV+8qZS9Z8i09evQkIyOzwv6XX36hxhaB5557iilTpnLnnbcBUFhYQK9evenYsSOFhQXs\n2bOHL7/8gscee6rC+0455XRmzHiXKVOurPc9ClRYBvfcQidz75GR3Mw1EZHW4J15q/lu5fYmvebB\ngzKZdFT/Br133bo1vPXW+8TGxvLDD0t57rmXiIqK4uyzT2PSpPMAfJN6rVz5K2++OZ3y8nLOPvtU\nLr74sgrXys7O5tFHn+KbbxbzwQfvM3jwUN5//13+85+Z7NpVyDnnnMG5515Q4T1Ll37H2LGH+7bn\nz/+MQw89nNGjx/Dww/eTk5MTUFbqP35qz549fPvtYo499gQ2bFjPgAEDq5zfv/8ANmxYz8aN6xky\nZL8Kx5KSkqqcf+GFl3LhhZfWWQ+Ad999i+uuu6nCvpycHHJzc6utyw8/LCUhIZHBg4f69mVmduL3\n3zdRXFxE585deeONV5k06Vz+9rfHKCoq4qKLJtO5c2cOOGA4H300K6B6NVRYDqj7YtkWALqkt23m\nmoiIhF7//gOIjXVys4SEBKZNm8I111xBQUE+BQUFFc4dOHAQ8fHx1QY/gGHDDgQgIyOT3bt3kZW1\nif79BxAXF0eHDmlVgig4QS8zc1+GO2fO/zjmmOOIjo7miCOOYt68T2upvRvvZKI//vg91147lWnT\npnDuuWcwadJ59O8/gKioKMrLXdW+NyYmhqioKFyu6o43TE5ONkVFxXTt2q3C/o8/nl2h9cCrrKyM\nl156gSuumOarF8ARRxzFN998xYwZ7zFq1CFs3pxFQcFOBgwYyAUXXMy///0SAJmZmWRnN+2PxcrC\nMnNPiI+huKScXp2UuYtI8E06qn+Ds+xgiI2NA2Dr1q28/fabvPrqmyQkJHLhhX+ocm5MTPUDj6s7\nvi+Tjqr+5Aqcc7Kzt/Prr8t55pknACguLiY5OYVJk84jNTWVwsJdFd5VXl5OQoLTfO7f5z516qX0\n6+fc4549e/Pyyy9WKfG331Zx8smnsmtXIb/++kuFwLtzZz579xbRufO+J6gCbZZfvHgRI0ceVKW8\nxYsXcs89f62yf9WqleTl5XLTTdfidrvZsWMHd931F+66637uv/8RAB566H4mT76CefPmsN9+w8jM\n7MSWLZtrv6VNKOyCe3FpOcUl5fTMTCYutuKX9v3Vs8ktyiMtUQPtRCTy7dyZT1paGgkJiVi7km3b\ntlJaWv3S14Hq3LkL69atpby83NOXvqLKOenp6WRnbwOcrP3MMycxbdq+AX/nnDORzZuzGDlyFNOn\nv8PRRx9LTEwMc+b8j/33P6Dacq+++noee+whXnjhX/Ts2Yv09AxmzZrBqaeeATgD6mJiYujbtz9d\nunTj4ovP5YQTTmbQoCGUlpbyyCN/ZcyYsZx88qm+awbaLL9ixXLGjTuiyv7Nm7NIT8+osn/IkP14\n4433fNtnn30qd911v2/7t98sbdu2pXv3HqSlpbF16xY6d+7i68/Pzs6u0rff1MIuuL84azkAG7fv\nqnLsh+0/A+j5dhFpFQYMGEhiYhJXXXUZw4YdyGmnTeTxxx/yNbXXxNsf77/YlleHDmkcc8xxTJly\nEb1792HIkP2Ijq7YgztixEEsW/YjEyeewty5c7j99rsrHD/xxJOZO/dTLrjgEtavX8u0aVOIj48n\nLa0jN9zw/6qt0377DaNbt+58+OFMTjnldO6++6888cTDzJw5nZiYaLp27c6dd94HOP3rjz76FA8/\nfD8lJSVER0dz3HEnVgjs9ZGbu4MOHSomhQUFO0lOTqmw75tvFrNly2ZOP/3MSleoeB9ff/0VbrrJ\nGWR3+OFH8Ze/3MysWTO4/vqbAac7YsSIqi0FTSnKf0BDGHDf/eJXLLHZnDauD6eN6+ObkQ7wTV6j\nZ9wbLiMjhezswuauRsTTfQ4+3eOG+/jj2Rx77AnExMRw4YV/4Iknnq2QwZaUlHD55Rczffq7vkfh\nJHBTp17Kvfc+GHD2npGREkg/SQVhN6Auf5fT5DR2f6dfxTsjHaBZ6UREmsCOHTlcfvlFXHnlZI4/\n/qQqTdPx8fFceeU1PProo81Uw/D1/vvvctRRx6hZvrKikjIAEuP3VV3ZuohI0zn//Is5//yLaz1n\n9FCT0/kAABGvSURBVOgxTJhwnFpH6mnixLNDUk7YZe6xMU6Vk5PimrkmIiIiLVPYBXfvvPKwb3S8\niIiI7BN2wd2fRseLiIhUFVbBffdeZ1Smt2keIC2xAxP7175IgoiISGsSVgPqflyVDUD7tupvF5HI\n1pglX722bt3Czp07MWYQTz75KOeddwGZmZ0aXKdFi75k6dJvufbaGwGYOXMmt99+Ox9+OIfkZGfG\n0HvvvYPjjz+ZUaMO8b1v4sST+c9/ZrJs2Q/cffft9OnT17fM65gxY30Ls+Tl5fLkk4+QlZVFVBT0\n7t2X6667yXft5ct/4fnnn6asrJSSklIOO+yIKnPl16WsrIw//Wmab9a67OxsTj31DM477wK2bt3C\nbbfdzOjRY/ymlq1q+vR3ePfdt3j77Rnk5eVxxx234HK5uP32u32L7dx44zU8/PCTJCQk8M47bxET\nE8OZZ06qV10bI6yC+4atzpzJPTJT6jhTRCS8NWbJV68lS76hrKwcYwZx/fU31f2GWhQXF/Pii3/n\nxRdf8e376KOP6NatOwsWzGXChNMCus7IkQf7ZnNzuVxce+1URo0aw9Ch+3H33bczYcJpHHPM8QDM\nnTuH2267iaeeep7du3dx33138tBDT9CzZy/Kysq4445b+Pjj2XUucesvNja2wjKvN954LSeccBIA\nf/3rvRx00Kha35+bu4OFCz/HO3HN3LmfMHHiJFJTU/noo1lMmXIlH3wwnRNPnEBCQgIAkyady5Qp\nF3HUUcfQoUNawHVtjLAK7vOWbALg0P0613GmiEjkeu65p1m+/GdcLhdnn30O48cfw+LFi3j55RdJ\nSEggPT2DadOu45VXXiI+Pp5OnTrx2muvcOutd/DJJ/+lqGgvGzasZ8uWzVx//c0cfPBo/v3vl5k/\n/zO6detOSUkJF1xwSYWpYufNm8Po0Yf4AlZ+fj4rVqzgpptu45133go4uPuLjo5m0KAh/P77RpKS\nkiguLvYFdoCjjz6WGTPeZfXq31i27AfGjz+Gnj17AU6QvvPO+6os8/rKK/9k6dLvKswnf/PNt9Gj\nR88q5X/zzWL69u1HWlpHAB588DHmzv2ErKysGuv87LN/Y8qUK7nnnjsBKCwsZMAAQ2pqBwoLC9i9\nexeLFy/i0UcrLvM6YcJpzJjxHpdeenm971NDhFVw39H2exJ6bOX97MV8kBvtm5FORCSY/GfCbCrD\nM/dv0Hih779fQl5eLs888yLFxcVcdtkFjBt3BO+//w7XX38zQ4fux+efzyMuLp7jjz+JzMxOjBkz\njtdff9V3jZycHB599Cm++mohs2bNYMAAw6xZM3j77Rnk5+dz7rkTueCCSyqUu3Tpd4wff4xve968\nORxzzDEccsihPPzw/eTl5dY7K92zZzdLlnzLSSedwsaN6xk40FQ5p3//gb5lXv3XlIfql3m9+OLL\nAm6qf/fdt7j55ttqvZ6/JUu+JSUlBWMG+/Z5l3ktLCygc+f/3969R0dZmHkc/yaEUFQMHENU8IaA\nD4hsFlAXxUoBRS5Si6AUZKms3AxHoHS79ajUU25BBETduuC6lUqRegW8IC6iLRd1d2txlZPyoBXo\nYuUW4By1J+GS7B/vmzC5zSSBzGSG3+cfknnfed8nzxnmmfcyz9OG5cuXMWLEXSxevIDi4mLGjh1H\nTs755OZ2Y8GC/LgV96S6oS4zez9pmcU0CW+oU0c6ETnTbNv2CZ9++r9MmTIpnEoWnCru2/dmHnlk\nFitW/BqzK2nZsmWF50W2Gs/N7QacHPO6Z89f6NjxCjIyMsjOzqZz5yur7PfgwQNVxrwOHjyYJk2a\ncOONfXj33fVR4y7rY//RR//DlCmTyMsbx8iRwxg5cjSXX94+6pjXjIzTP+Z13769lJSUcP75tTsT\nfOzYMZYte4YJE/LCXAb57NOnH1u2bOK111bTvfvVHDiwj8LCg3TufCWjRv0jy5cvA+Iz5jVSUh25\nlzb9G2lHmzOrn7rRiUj83N7h1kbzrZyMjKZ8//u3M3Lk6AqPDxx4Kz179mLjxvf42c+mlY8erc6p\njnndu/cr3LczZ84cjh8/QVFREZ9/voNhw0aEp6erdq0rmz9fds29tLSUiRPH0r59RyAY81pWCCN9\n9tkOhg69g8LCQgoKttGvX//yZUeOHKG4uKhCga7tafkPPthMjx7X1OJvDmzfXsDhw4eYPv2+8Ea8\n/cyaNYMZM2Yxd26Q6/z8mdxzzyTefnst3btfHY55rfkUf0NKqiN3gGOF9b/TU0Qk2XXpchVbtmyk\ntLSUoqIiHn98IRAUtczMptx22+307t2X3bt3kZ6ezokTJ2Ju84IL2rBz558pKSnh0KFCduzYXmWd\n7OzW5Uee69ev4847R7J69WqeffZ5Vq58lcLCg+zbt5cePa7lnXfWlR9lr1v3Jt26da+yvbS0NCZP\nnsrChcE893btLufcc7N4883XytfZsGE9zZs359JLL2PAgEFs2vR73IPYjh49yvz5c9i69aMK2737\n7nE8+eRSnnhiSfm/1V1v/9OfCujQ4Ypq81HdQLWuXXNZseJlliz5FU899QytW+cwY8as8uXbtxeQ\nldWSNm3a0qpVMOZ1//59cR3zGimpjtxLir9Du9J/SHQYIiIJk5vbja5dc5k4MbgmPnz4CCA4xT5l\nyr20aNGCrKyWjB59NxkZGcybN4usrJZRx7xmZ2fTu3ffcMzrZeGY1yYV1gnGvG7l+utvYMOG9cyc\nmV9h+YABwZjXUaPGsGvXTvLyxpGZmUl2duvyUafV/S05OTmsXfs6gwYNYfbseSxaNJ9XXnmR9PR0\nLr74EmbMmAnAWWedzYIFjzN//lyOHTtGeno6AwYMZsCAwfXKY+Uxr/v27WX27Ic5dKiQ4uJiCgq2\n8dOfPsCePX/h4MGDDBnyg0pbqJjHFSue4/77HwKCU/UPPvgvrFr1cvmI23iMea0QXTKNfB3+3I9L\ns3YPZN6k6xIdSsrSmMz4UJ4bnnJcN2+99Qb9+w8kLS2NMWN+yJNPLqlwg1xxcTETJ47l6aeXkZmZ\nCSjHdTFhwt3k5y/gvPOy6/zcM2Lka9vWZyc6BBGRlHPgwH7Gjx9DXt44Bg0aUuXO92bNmjFhQh5L\nl/4yQREmr5de+i233DKwXoW9vpLuyL1Xk7sY3b/q1yXk9NAn8fhQnhuectzwlOP4OCOO3LPOzkx0\nCCIiIo1a0hV3ERERiS7pivu3RccTHYKIiEij1uBfhTOzRUBPoASY5u5/iFh2EzAHOA685e6zY22v\n40VqNysiIhJNgx65m9mNQAd3vx4YBzxRaZXHgaHADUB/M+sUc5uXtIq1ioiIyBmtoU/L9wNWA3jQ\nVqilmZ0DYGbtgEJ3/6u7lwJrw/VFRETkFDR0cb8AOBDx+8HwseqW7QcujLaxE4c06lVERCSWeN9Q\nF+27ejG/x1eypxNN0uv8dT8REZEzSkPfUPdXTh6pA7QBvopYFnmk3jZ8rEZrFtymyh4HrVu3SHQI\nZwTlueEpxw1POW6cGvrI/T+B4QBm1h340t2/BXD33UALM7vEzDKAW8P1RURE5BQ0ePtZM5sL9AZO\nAJOB7sARd19jZjcA8wmm3r/s7o81aDAiIiJngKTqLS8iIiKxJV2HOhEREYlOxV1ERCTFqLiLiIik\nmAbvLV9fp7snvVQVI8d9gLkEOXZ3H5eYKJNbtBxHrJMP9HT3PvGOLxXEeB1fBKwEmgJ/dPe8xESZ\n/GLkeTJwF8H7xR/cfXpiokxuZnYVQVfXRe7+VKVldap7jfLIvSF60ktFtcjxEuB2d/8ucK6ZDYh3\njMmuFjnGzDoD3yX4xojUUS1yvBB41N17AifCYi91FC3PZtYC+Gegl7vfCHQxs2sTE2nyMrOzCPL6\nTg2r1KnuNcrijnrSx0ONOQ71cPeyhkMHgPPiHF8qiJVjCIrPA/EOLIVEe69II3gjfD1cfp+770lU\noEku2mv5KFBMcBCQATQHDiUkyuRWBAzkZKO3cvWpe421uJ/WnvRSrWg5xt2/ATCzC4GbCV5MUjdR\nc2xmPwLeA3bHOa5UEi3HrYFvgMVmtinsuSH1U2Oe3b0YmAl8AewE/svdP497hEnO3UvCXFanznWv\nsRb3yk6pJ73USpU8mlkO8Bpwr7sfjn9IKac8x2bWChgLLAof1+v49Eir9HNb4DGCRlrdzGxgQqJK\nPZGv5RYEZ586AO2AnmbWNVGBnSFivl801uJ+WnvSS7Wi5bjsP+xa4AF33xDn2FJFtBz3BbKBTcCr\nBIVnYXzDSwnRcnwQ2OXuu9y9BNgAdIlzfKkiWp47A39298PufpzgNd0jzvGlujrXvcZa3NWTvuHV\nmOPQIoI7NtcnIrgUEe11/Iq7XxXeoDSU4E7unyQu1KQVLccngC/MrH24bg/AExJl8ov2frEL6Gxm\nzcLfrwY+i3uEqaXCkXl96l6jbT+rnvQNr6YcE7xoDgEfELzISoHn3f2ZBIWatKK9jiPWuRR41t37\nJibK5BbjvaI9sIzgdfypu9+bsECTXIw8jwf+CTgGvO/u9ycu0uQUfmhaCFxKkMcvCS6L7qxP3Wu0\nxV1ERETqp7GelhcREZF6UnEXERFJMSruIiIiKUbFXUREJMWouIuIiKQYFXcREZEU02hHvoqkmvD7\n7A68Hz5U1kNgmrt/UsNzHgaauPvPT2G/vYE1wB/DfTYLf54aNnqpy7ZuAbq7e76ZXQd85e67zOwx\n4Dl333oKcT5M0JL3izDODOD/gInu/nWU510IdHL39+q7b5FUo+IuEl/7E9Ss5pPI/ZrZb4GJwFM1\nP6Uqd38beDv8dSzwAkGL1x+fpjifi/wgY2bzgAeBaE1R+hC0QFVxFwmpuIs0AmZmwFKCzlTnAg9F\ntv41sybAfwAdCY72t7r7fWbWFPgl0B5oAaysZcfGzUCncNuDgRnAt8DfgAnu/lVYWL9HMM7zS+BH\nwCjgJuAV4A7gGjObDvwcmA3kA1Pc/cNw2+uBBUABwQeJ5sA5wIO1nFnwPjA+3FYv4BGC0ZhnAXkE\nHRXnhMsLw1zUJx8iKUXX3EUahwsICvrNwFSg8njSrsC17t7L3W8APg6H+0wl6PPdD+gJjDSzq6Lt\nyMy+AwwBNppZc+DfgaHhNtYBs82sJUHxvM7dexMMtzk/3ESpu68GPgamR5wOLwV+Q1D0y6YKdiJo\nZ/xvwAJ3vwm4DXjGzKK+/4Q9tEdx8jJGNjAp3MYTBEONdhG0l13u7ovrkw+RVKQjd5H4yjGzd8Of\ny66530EwYevRsH93JnBepecVAAfM7A3gDeBFd//azPoAbc3se+F6zQhGb26r9Py/C/dbts/X3f1l\nM8sF9rp72YSv3xFc4z5iZusIPgCsAl5w9y+DEwwVVB49+QLBWYGfAMOAl9y9NIzzHDMr63ddDOQA\neys9f0zYQzsd6AYsJjhaJ1x3YfjhJItg/kFltc2HSEpTcReJr2qvuZvZSmCFu//azLoAr0cud/ej\nQG8z+3uCo+7/DotgMTDT3V+Nsd9PqtsvQaGvPAO9NNznnWZ2BcEEqt+Z2bBYf5y77zOzL8zsGmAE\nMC1cVERwduBwjE2UX3M3szXA7nBcK8ByYLy7/z68lFDdFL3a5kMkpem0vEh8VT7SLZNDcHQOQVFs\nFrnQzHqY2Rh3/9jdZwEfEVx/3xyuj5mlm9nC8JR6be0AWpvZReHvNwEfmtllZjbN3Xe4+yJgFZBb\n6bklQNNqtrkCuAdo5e4fh49tBn4Yxpkd3l0fy2TgF2bWJvw9BygI7z+4g5M5iozjVPMhkhJU3EXi\nq6YxjIuA5Wb2FrAJOGRmj0as/zkw3Mw2m9kGghvJthDcPPa1mb1PcG36sLsfqW0w7l5EUIhfDE/b\n9wUeIriBrpuZfWhm7wCXEdxEF2k9sNTMflDp71oFjASej3hsKjDUzDYSXFaIeTOdu+8B5gFPhw/N\nJ7gjfg3wLHCxmU0hyNdYM/sF8K/AN/XNh0iq0MhXERGRFKMjdxERkRSj4i4iIpJiVNxFRERSjIq7\niIhIilFxFxERSTEq7iIiIilGxV1ERCTFqLiLiIikmP8HIgq3segloOEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45cc081da0>"
+      ]
+     },
+     "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 Hippo Signaling Pathway Aberations')\n",
+    "plt.legend(loc='lower right');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tissue specific performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "tissue_metrics = {}\n",
+    "for tissue in clinical_sub.disease.unique():\n",
+    "    sample_sub = clinical_sub[clinical_sub['disease'] == tissue].index.values\n",
+    "\n",
+    "    y_tissue_train = y_train[y_train.index.isin(sample_sub)]\n",
+    "    y_tissue_pred_train = y_pred_train[y_train.index.isin(sample_sub)]\n",
+    "    y_tissue_test = y_test[y_test.index.isin(sample_sub)]\n",
+    "    y_tissue_pred_test = y_pred_test[y_test.index.isin(sample_sub)]\n",
+    "\n",
+    "    metrics_train = get_threshold_metrics(y_tissue_train, y_tissue_pred_train, tissue=tissue)\n",
+    "    metrics_test = get_threshold_metrics(y_tissue_test, y_tissue_pred_test, tissue=tissue) \n",
+    "    \n",
+    "    tissue_metrics[tissue] = [metrics_train, metrics_test]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UiERERKRWEmmWPzDqdhPgUKBZoicws1HA/sB6YJi7T69gn1uA/d29R6LHFRER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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45ca9fa6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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EGq5k5paXCIoNaasuDYETEWn4kqq5m9mWwI7uPsPMMt29OMVxSRpUNKRNy2SK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c9fb5748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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6XkSk/sXN3M3sQKAH8IG7P18/RZJ0pBHxIiKNR7WZu5n9A/g7sAnwgJn9pb4K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c92e3b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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OSpO7uz+WykBSTZ3oREQkqhIZfjaS1IlORESiKpHhZyNLnehERCSKEqq5m1lb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\nJ44jIyOdDh224tprbwKC++E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XYZxNgG+B89x9dZz9tgR2dPc3anpukahRchdJraV1NFjN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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c88fd5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JlniS+3IzOw9obGZdgDPZ3IoXERGRWiaebvlBwMFAKvAI0BgYkMigREREpPLK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\nDRs20KBBA3r3Pr5IYo/XgAG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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c840f198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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BrVrqDyZSlcm90F/C3ReZWUMz24UgqR8HnFbaTrKz16QoPIHgjao2Tj21c+qpjVOvsts4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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c7c4cfd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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QXvVy/IQmdzPrDowD2gObzKw/MAVY5O4vAxcDzwGFwLPuvjCR8YiIiGwJEprc3f1joHcF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\nzz8/mc8++4Trrx/JLrt0LB7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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c7260898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RO2b2G6BJ4kKSZKFyvIhIaoqn5/4gMN/MZof3DwBuSFxIkkxUjhcRST1VJnd3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\ntL1mli/PZ8OGAtq2bVfq8ZE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QFVjl7i+bWQ9gFMH6eS+5+90JDUZERKQBSKlV4URERKRq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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c67f9438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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LVxvn3Pxa0f3qmkw9xYmISDLFS+5dzWxBKdPTgIKq6Ft+2er1NKpX85OieooT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\nSu4iIiIRo+QuIiISMUruIiIiEfP/MYuhaOmEBqwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c5e12048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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vHD4WV25uVtICkoDKODVUzsmnMk6+2pbxR1/ms3JdATntWtdzRFUr2mIxJS3Wk1bYJmXn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\ndOjAjz8uYenS/5GbG7R25Of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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c564ab38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Clrr7r+G+RcAeZpYaTtEfCLxaXoOZmVkJClUg+B9VfZx4sf2cm5cP1I3Pdm5u3YmlqvRZ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\nchcREYmY/wfMNT57VRGh5gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c515d6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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EpE6qyPjwDb13fEmquYuISJ1UkfHhG3rv+JJUcxcRkTpLPeArRzV3ERGRiFFyFxERiRgl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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45cb412860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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bvI3g5s0DzeyC2H2G9xC9RlBvMwnu1n8/Xv3Eqa9VBBfhfw/r6xyqvxmuuu9yO3Bu+O9/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\n/QeE07xuG07zmlNlnWCa11n07t2XKVM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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c681ad30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ewN1nm1mmmW3l7iuBtUABsLWZrQKaAUsSHI+INABZGekM699tk8rIyckgLy+/liISaVgS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\nKLmLiIhEjJK7iIh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r6NEPM3Pm9HLzyV955WC2375t6TEZGRmsWLGc9PR0Vq7Mp3nzTD7+\neAZdux5EamoqWVktyM7OYd68ubRt2w4IJu7Jy1tYulpdt26HMGPGRzRr1owOHYzMzCzy81ewatVK\npk17n+HDyy/zevzxJzFu3Auce+4F1a6nmlByFxGpwotzXuaThV/Uapn75u7JKe2r1+IE+PjjGSxd\nuoR77nmQgoIC+vU7k4MPPowXXxzDwIFXsvvue/Df/06hceMmHH30seTmbkPXrgfz5JOPlZaxaNEi\nhg+/m6lT32PChHF06GBMmDCOZ58dx7Jly+jT5xTOPPOcctedOXM63bv3KH0+ZcokevToQZcu3Rg6\n9BaWLl1S7Vbp6tWrmDHjI4499gTmzZvLLrvYBse0b79L6TKvZdeUh8qXXT377H5VdtVfdtkgzjqr\nDxkZGey66+7ss08nPv30YzIzs0qPyczMYtGiRaXJfenSJWy9dfPS/VlZLVi8+EM6ddqfn376kfz8\nFbRq1ZonnhjN6af/mTvvHE5BQQHnnNOP3Nxt2HvvfRk+/LakJXcNqBMRaUC+/PJzvvjiMwYMuIhB\ngwZQXAxLlizm8MOP5Pbbb+Kppx7DbDcyMzPLnVd2wrK9994X+H2Z159+mkeHDrvQqFEjsrOz2XXX\n3Ta47qJFeRss83rccceRlpbGoYd2Z8qUSTHjLpnHfubM6QwYcBH9+/ejT59T6dPnL+y0084xl3lt\n1Kj2lnktKipi1KiRPPro04wZ8xKFheuZNu39Da9axQRvJb0Cf/jDEbz//rtMmDCeTp32Jy/vVxYv\nXsSuu+7GGWecyRNPjAaSs8xrWWq5i4hU4ZT2x9eolZ0IjRo15sQTT6FPn7+U296z5/F06XIQ77zz\nFn//+8DSpUcrs6nLvP7yywLcZ3PLLbewfn0ha9asYc6cbzj11NPD7ukNZ60rWX++5J57cXExF154\nDjvv3AEIlnktSYRlffvtN5x8ci8WL17MrFlfcsQRR5XuW7ZsGQUFa9hmm9+Xea2qW37JksWkpTUq\nXRq2U6fOuH9NdnYOv/yyoLScRYvyyk2726JFS5YtW1r6PC9vIdnZOWyxxRbcemtQ17fddiPnnXcR\nr7/+Kp067R8u8/pzHPVa+9RyFxFpQHbffQ/ef/8diouLWbNmDXfdNQIIklqTJo056aRTOOyww/nh\nh7mkpqaWLrcaS6tWrfn+++8oKipiyZLFfPPN7A2Oyc7OKW15Tpr0H3r37sP48eN59NGneeaZF1m8\neBG//vpLuMzrf0pb2f/5zyvsu2+nDcpLSUnhkksuY8SIYD33HXfcia23bs4rr0woPWby5Ek0a9aM\nHXZoxzHHHMu77/4X9yC2tWvXMnToLXzyycxy5Z59dr8Nlnkte789MzOLZcuWsmLFCgBmz57F9tu3\nZb/9OjN16nsUFhaycOGvLFu2tPT+PkDjxo1p3Xq70qVg33nnrXKj6WfPnkXz5pm0bt2GrKy6Wea1\nLLXcRUQakL333pc999ybCy8M7omfdtrpQNDFPmDAxWRkZNC8eSZ/+cvZNGrUiH/+8yaaN8+Mucxr\ndnY2hx12eLjMa7twmde0cscEy7x+QrduBzN58iRuvPG2cvuPOSZY5vWMM/oyd+739O/fjyZNmpCd\nnVO61GllryU3N5dXX53IsceewM03/5ORI4cyduwYUlNT2X77tgwZciMAW2yxJcOH38XQobeybt06\nUlNTOeaY4zjmmOOqVX+NGjVi4MArufLKYBnXNm22o3v3HqXl9e/fj9TUoLUPMG3aeyxatIgTTvgj\nl112BcOH30ZxcTF77rl3uTEATz31OFdddQ0A3bsfwdVX/x/jxr1QusRtMpZ5LatBLRzzyMSvik/o\n0rbqA6XGtBBEcqieE091XD2vvfYyRx3Vk5SUFPr2/ROjRt1fboBcQUEBF154Dg8+OJomTZoAquPq\nuOCCs7nttuG0bJld7XMjv3DMuSfsXtchiIhEUl7eQs4/vy/9+/fj2GNP2GDke3p6Ohdc0J8HHvhX\nHUXYcD3//LMcfXTPGiX2mmpQLXegWN8SE0vfxJND9Zx4quPEUx0nR+Rb7iIiIlI1JXcREZGIUXIX\nERGJmIT/FM7MRgJdgCJgoLvPKLOvB3ALsB54zd1vTnQ8IiIiUZfQlruZHQq0d/duQD/g7gqH3AWc\nDBwMHGVmHRMZj4iIyOYg0d3yRwDjATyYVijTzLYCMLMdgcXuPt/di4FXw+NFRERkEyQ6ubcC8so8\nXxRuq2zfQmDbBMcjIiISeckeUBfrt3rV/h2fiIiIbCjRA+rm83tLHaA1sKDMvrIt9TbhtlhScnIy\nai86qZTqODlUz4mnOk481XH9lOiW+xvAaQBm1gn42d1XAbj7D0CGmbU1s0bA8eHxIiIisgkSPv2s\nmd0KHAYUApcAnYBl7v6SmR0MDAWKgRfc/Y6EBiMiIrIZaGhzy4uIiEgVNEOdiIhIxCi5i4iIRIyS\nu4iISMQkfG75mtKc9IlXRR13B24lqGN39351E2XDFquOyxxzG9DF3bsnO74oqOJ9vB3wDNAY+Njd\n+9dNlA1fFfV8CfBngs+LGe5+ed1E2bCZ2R4Es7qOdPd7K+yrVt6rly13zUmfeHHU8f3AKe5+CLC1\nmR2T7BgbujjqGDPbFTiE4BcjUk1x1PEIYJi7dwEKw2Qv1RSrns0sAxgEHOTuhwK7m9kBdRNpw2Vm\nWxDU65sbOaRaea9eJnc0J30ybLSOQ/u5e8mEQ3lAyyTHFwVV1TEEyWdwsgOLkFifFSkEH4QTw/2X\nuvtPdRVoAxfrvbwWKCBoBDQCmgFL6iTKhm0N0JPfJ3orVZO8V1+Tu+akT7xYdYy7rwQws22BIwne\nTFI9MevYzM4C3gJ+SHJcURKrjnOAlcCdZvZuOOeG1MxG69ndC4Abgf8B3wMfuvucpEfYwLl7UViX\nlal23quvyb0izUmfeBvUo5nlAhOAi919afJDipzSOjazLOAcYGS4Xe/j2pFS4XEb4A6CibT2NbOe\ndRJV9JR9L2cQ9D61B3YEupjZnnUV2Gaiys+L+prca3tOetlQrDou+Qf7KjDY3ScnObaoiFXHhwPZ\nwLvAiwSJZ0Ryw4uEWHW8CJjr7nPdvQiYDOye5PiiIlY97wp85+5L3X09wXt6vyTHF3XVznv1Nblr\nTvrE22gdh0YSjNicVBfBRUSs9/FYd98jHKB0MsFI7ivqLtQGK1YdFwL/M7Odw2P3A7xOomz4Yn1e\nzAV2NbP08Pn+wLdJjzBayrXMa5L36u30s5qTPvE2VscEb5olwDSCN1kx8LS7P1xHoTZYsd7HZY7Z\nAXjU3Q+vmygbtio+K3YGRhO8j79w94vrLNAGrop6Ph84F1gHTHX3q+ou0oYp/NI0AtiBoB5/Jrgt\n+n1N8l69Te4iIiJSM/W1W15ERERqSMldREQkYpTcRUREIkbJXUREJGKU3EVERCJGyV1ERCRi6u2S\nryJRE/6e3YGp4aaSOQQGuvvnGznnOiDN3a/dhOseBrwEfBxeMz18fFk40Ut1yjoa6OTut5lZV2CB\nu881szuAx939k02I8zqCKXn/F8bZCPgRuNDd82Octy3Q0d3fqum1RaJGyV0kuRbW0WQ1n5e9rpk9\nC1wI3LvxUzbk7q8Dr4dPzwGeI5ji9W+1FOfjZb/ImNk/gauBWJOidCeYAlXJXSSk5C5SD5iZAQ8Q\nzEy1NXBN2al/zSwN+DfQgaC1/4m7X2pmjYF/ATsDGcAzcc7Y+B7QMSz7OGAIsApYDVzg7gvCxPoH\nguU8fwbOAs4AegBjgV5AZzO7HLgWuBm4DRjg7h+EZU8ChgOzCL5INAO2Aq6Oc82CqcD5YVkHAbcT\nLI25BdCfYEbFW8L9i8O6qEl9iESK7rmL1A+tCBL6kcBlQMXlSfcEDnD3g9z9YODTcHGfywjm+T4C\n6AL0MbM9Yl3IzJoCJwDvmFkz4CHg5LCM/wA3m1kmQfLs6u6HESxus01YRLG7jwc+BS4v0x1eDDxJ\nkPRLVhXsSDCd8X3AcHfvAZwEPGxmMT9/wjm0z+D32xjZwEVhGXcTLGo0l2B62Sfc/c6a1IdIFKnl\nLpJcuWY2JXxccs+9F8EKW8PC+bubAC0rnDcLyDOzl4GXgTHunm9m3YE2ZvaH8Lh0gqU3v6xw/l7h\ndUuuOdHdXzCzvYFf3L1kha+3Ce5xLzOz/xB8ARgHPOfuPwcdDOVUXHryOYJegSuAU4Hn3b04jHMr\nMyuZ77oAyAV+qXB+33AO7VRgX+BOgtY64bEjwi8nzQnWP6go3voQiTQld5HkqvSeu5k9Azzl7o+Z\n2e7AxLL73X0tcJiZ7UPQ6v4oTIIFwI3u/mIV1/28susSJPqKa6AXh9fsbWa7EKxA9baZnVrVi3P3\nX83sf2bWGTgdGBjuWkPQO7C0iiJK77mb2UvAD+FyrQBPAOe7+3/DWwmVraIXb32IRJq65UWSq2JL\nt0QuQescgqSYXnanme1nZn3d/VN3vwmYSXD//b3weMws1cxGhF3q8foGyDGz7cLnPYAPzKydmQ10\n92/cfSQwDti7wrlFQONKynwKOA/IcvdPw23vAX8K48wOR9dX5RLgBjNrHT7PBWaF4w968XsdlY1j\nU+tDJBKU3EWSa2PLMI4EnjCz14B3gSVmNqzM8XOA08zsPTObTDCQ7H2CwWP5ZjaV4N70UndfFm8w\n7r6GIBGPCbvtDweuIRhAt6+ZfWBmbwLtCAbRlTUJeMDM/ljhdY0D+gBPl9l2GXCymb1DcFuhysF0\n7v4T8E/gwXDTUIIR8S8BjwLbm9kAgvo6x8xuAO4BVta0PkSiQku+ioiIRIxa7iIiIhGj5C4iIhIx\nSu4iIiIRo+QuIiISMUruIiIiEaPkLiIiEjFK7iIiIhGj5C4iIhIx/w+ppirxgC3hewAAAABJRU5E\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c891d0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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9n5LMQERERKRibDG5u/uTlRmIiIiIVIxEZoUTERGRGkTJXUREJGISSu5m1sjM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\n6VV++WVxkQuJtLQ0brvtRh5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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45b76cd4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JwGXA3xLcpoiIiMSRSLP86JjXpcBPBHfQJ6I9ENsMvzycthAgfMTudaBy64CI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\nxdKlS/jDH45nypRn2HLLDuU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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c67fe400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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LiIg0QYlMHCMiIiJNSLx77t3M7NtKXk8DSjS2vIiISOMUL7nPAU6tr0BERESk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\nKmLMmH8wZszYstdefvlltt1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m9pWZ7QucAowIF60maB0oqGYXZffczexF4JtwulaAJ4Bz\n3P3f4a2EymbRS7Q8RCJNzfIi9atiTbdUHkHtHIKkmBm70Mz2NrP+7v6Ru18PzCa4//5uuD5mlm5m\no8Im9UR9DuSa2Xbh857Af8ysnZmNcPfP3X00MAHYs8K264FNKtnnU8DZQLa7fxS+9i5wahhnTti7\nvjpDgWvNrE34PA+YG/Y/6MNvZRQbR23LQyQSlNxF6ldV0zCOBp4ws1eAd4Cfzez2mPXnA380s3fN\nbCpBR7L3CDqPFZrZdIJ70wXuvizRYNx9NUEiHhc22/cAriToQLeXmf3HzN4A2hF0oos1BXjAzE6o\n8L4mAP2Ap2NeGw6caGZvE9xWqLYznbt/D9wCjAlfuo2gR/yLwKPA9mY2jKC8BpjZtcC9wMpky0Mk\nKjTlq4iISMSo5i4iIhIxSu4iIiIRo+QuIiISMUruIiIiEaPkLiIiEjFK7iIiIhGj5C4iIhIxSu4i\nIiIR8/8JnAaA3+se4wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c7c26908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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F0+H9917AHWFsAyn5YbiSzqXxwKCwPAYBlwL9zOz0EtaPtCSN5y4VZWZjCL7MLqvEPnYl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\nxeeffwrAO++8ySGHbByw7Jt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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c725c4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JK/JX0rZlViTHrw1tW2bRfftukV9rupajpzJumFJJ7t+Z2esEk8eU3vTm7lem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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c7739fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tissue_auroc = {}\n",
+    "plt.figure()\n",
+    "for tissue, metrics_val in tissue_metrics.items():\n",
+    "    metrics_train, metrics_test = metrics_val\n",
+    "    plt.subplot()\n",
+    "    auroc = []\n",
+    "    for label, metrics in ('Training', metrics_train), ('Testing', metrics_test):\n",
+    "        roc_df = metrics['roc_df']\n",
+    "        auroc.append(metrics['auroc'])\n",
+    "        plt.plot(roc_df.fpr, roc_df.tpr,\n",
+    "            label='{} (AUROC = {:.1%})'.format(label, metrics['auroc']))\n",
+    "    tissue_auroc[tissue] = 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 Hippo Signaling in {}'.format(tissue))\n",
+    "    plt.legend(loc='lower right'); \n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "tissue_results = pd.DataFrame(tissue_auroc, index=['Train', 'Test']).T\n",
+    "tissue_results = tissue_results.sort_values('Test', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wERGRNmn2HPjOw5iHiIiIDEGzx8iOqZvUBzwFXOzuD4ZmJSIiIk01a8S2fN2/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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45cc090c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = tissue_results.plot(kind='bar', title='Tissue Specific Prediction of Hippo Signaling')\n",
+    "ax.set_ylabel('AUROC');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hippo signaling prediction has highly variable predictions across different tissues. The classifier performs spectacularly in several tissues, but also appears to oppositely predict Hippo signaling in others. In three tissues the `test` set performance is actually _greater_ than the `train` set performance. This is likely a result of variance in samples across tissues and a happenstance in stratified `train_test_split`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What are the classifier coefficients?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "coef_df = pd.DataFrame(best_clf.coef_.transpose(), index=X_sub.columns[feature_mask], 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": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'97.4% zero coefficients; 118 negative and 92 positive coefficients'"
+      ]
+     },
+     "execution_count": 43,
+     "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": 44,
+   "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>399671</th>\n",
+       "      <td>-0.064672</td>\n",
+       "      <td>0.064672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29126</th>\n",
+       "      <td>0.061237</td>\n",
+       "      <td>0.061237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2852</th>\n",
+       "      <td>-0.060303</td>\n",
+       "      <td>0.060303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140730</th>\n",
+       "      <td>-0.057160</td>\n",
+       "      <td>0.057160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84688</th>\n",
+       "      <td>0.056350</td>\n",
+       "      <td>0.056350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387628</th>\n",
+       "      <td>0.048214</td>\n",
+       "      <td>0.048214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4438</th>\n",
+       "      <td>0.047826</td>\n",
+       "      <td>0.047826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80350</th>\n",
+       "      <td>-0.047799</td>\n",
+       "      <td>0.047799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56892</th>\n",
+       "      <td>-0.047692</td>\n",
+       "      <td>0.047692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22943</th>\n",
+       "      <td>-0.045938</td>\n",
+       "      <td>0.045938</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          weight       abs\n",
+       "399671 -0.064672  0.064672\n",
+       "29126   0.061237  0.061237\n",
+       "2852   -0.060303  0.060303\n",
+       "140730 -0.057160  0.057160\n",
+       "84688   0.056350  0.056350\n",
+       "387628  0.048214  0.048214\n",
+       "4438    0.047826  0.047826\n",
+       "80350  -0.047799  0.047799\n",
+       "56892  -0.047692  0.047692\n",
+       "22943  -0.045938  0.045938"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coef_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are very interesting. First, only 200 genes are used to build a fairly successful classifier. Biologists like sparsity! Second, the genes that fall out at the top are informative:\n",
+    "\n",
+    "| Entrez | Symbol | Comments |\n",
+    "| ------ | ---- | -------- |\n",
+    "| 399671 | [HEATR4](http://www.ncbi.nlm.nih.gov/gene/399671) | Relatively unstudied gene |\n",
+    "| 29126 | [CD274](http://www.ncbi.nlm.nih.gov/gene/29126) | Immune cell receptor - inhibits Tcell activation and cytokine production |\n",
+    "| 2852 | [GPER1](http://www.ncbi.nlm.nih.gov/gene/2852) | Estrogen receptor - implicated in lymphoma |\n",
+    "| 140730 | [RIMS4](http://www.ncbi.nlm.nih.gov/gene/140730) | Synaptic regulatory protein |\n",
+    "| 84688 | [C9orf24](http://www.ncbi.nlm.nih.gov/gene/84688) | relatively unknown gene - important for differentiation of bronchial cells |\n",
+    "| 387628 | [FGF7P6](http://www.ncbi.nlm.nih.gov/gene/387628) | Fibroblast growth factor - implicated in ovarian cancer |\n",
+    "| 4438 | [MSH4](http://www.ncbi.nlm.nih.gov/gene/4438) | Involved in DNA mismatch repair |\n",
+    "| 80350 | [LPAL2](http://www.ncbi.nlm.nih.gov/gene/157777) | Pseudogene involved with elevated risk for atherosclerosis |\n",
+    "| 56892 | [C8orf4](http://www.ncbi.nlm.nih.gov/gene/56892) | Relatively uknown gene product - evidence it is important in WNT signaling and proliferation across cancer types |\n",
+    "| 22943 | [DKK1](http://www.ncbi.nlm.nih.gov/gene/22943) | Inhibits WNT signaling pathway - implicated in myeloma |\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Investigate the predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "predict_df = pd.DataFrame.from_items([\n",
+    "    ('sample_id', X_sub.index),\n",
+    "    ('testing', X_sub.index.isin(X_test.index).astype(int)),\n",
+    "    ('status', y_sub),\n",
+    "    ('decision_function', pipeline.decision_function(X_sub)),\n",
+    "    ('probability', pipeline.predict_proba(X_sub)[:, 1]),\n",
+    "])\n",
+    "predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "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-37-4133-01</th>\n",
+       "      <td>TCGA-37-4133-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.679050</td>\n",
+       "      <td>0.842779</td>\n",
+       "      <td>84.3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-BR-7707-01</th>\n",
+       "      <td>TCGA-BR-7707-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.674772</td>\n",
+       "      <td>0.842211</td>\n",
+       "      <td>84.2%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-DK-A1A6-01</th>\n",
+       "      <td>TCGA-DK-A1A6-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.632387</td>\n",
+       "      <td>0.836496</td>\n",
+       "      <td>83.6%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-55-7570-01</th>\n",
+       "      <td>TCGA-55-7570-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.460996</td>\n",
+       "      <td>0.811685</td>\n",
+       "      <td>81.2%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-86-8672-01</th>\n",
+       "      <td>TCGA-86-8672-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.420436</td>\n",
+       "      <td>0.805407</td>\n",
+       "      <td>80.5%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-BR-4253-01</th>\n",
+       "      <td>TCGA-BR-4253-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.380836</td>\n",
+       "      <td>0.799125</td>\n",
+       "      <td>79.9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-05-4418-01</th>\n",
+       "      <td>TCGA-05-4418-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.354113</td>\n",
+       "      <td>0.794801</td>\n",
+       "      <td>79.5%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-HU-8604-01</th>\n",
+       "      <td>TCGA-HU-8604-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.321669</td>\n",
+       "      <td>0.789459</td>\n",
+       "      <td>78.9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-69-8255-01</th>\n",
+       "      <td>TCGA-69-8255-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.309655</td>\n",
+       "      <td>0.787455</td>\n",
+       "      <td>78.7%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-FD-A3B6-01</th>\n",
+       "      <td>TCGA-FD-A3B6-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.288549</td>\n",
+       "      <td>0.783901</td>\n",
+       "      <td>78.4%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       sample_id  testing  status  decision_function  \\\n",
+       "sample_id                                                              \n",
+       "TCGA-37-4133-01  TCGA-37-4133-01        0       0           1.679050   \n",
+       "TCGA-BR-7707-01  TCGA-BR-7707-01        0       0           1.674772   \n",
+       "TCGA-DK-A1A6-01  TCGA-DK-A1A6-01        0       0           1.632387   \n",
+       "TCGA-55-7570-01  TCGA-55-7570-01        0       0           1.460996   \n",
+       "TCGA-86-8672-01  TCGA-86-8672-01        0       0           1.420436   \n",
+       "TCGA-BR-4253-01  TCGA-BR-4253-01        0       0           1.380836   \n",
+       "TCGA-05-4418-01  TCGA-05-4418-01        1       0           1.354113   \n",
+       "TCGA-HU-8604-01  TCGA-HU-8604-01        0       0           1.321669   \n",
+       "TCGA-69-8255-01  TCGA-69-8255-01        0       0           1.309655   \n",
+       "TCGA-FD-A3B6-01  TCGA-FD-A3B6-01        0       0           1.288549   \n",
+       "\n",
+       "                 probability probability_str  \n",
+       "sample_id                                     \n",
+       "TCGA-37-4133-01     0.842779           84.3%  \n",
+       "TCGA-BR-7707-01     0.842211           84.2%  \n",
+       "TCGA-DK-A1A6-01     0.836496           83.6%  \n",
+       "TCGA-55-7570-01     0.811685           81.2%  \n",
+       "TCGA-86-8672-01     0.805407           80.5%  \n",
+       "TCGA-BR-4253-01     0.799125           79.9%  \n",
+       "TCGA-05-4418-01     0.794801           79.5%  \n",
+       "TCGA-HU-8604-01     0.789459           78.9%  \n",
+       "TCGA-69-8255-01     0.787455           78.7%  \n",
+       "TCGA-FD-A3B6-01     0.783901           78.4%  "
+      ]
+     },
+     "execution_count": 46,
+     "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": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CLIQYU0VXNdXdNcyLnU1sUMykvveNmetRUHi3cge6rk/qewsxmaQgCyHG9FH9YQBWp66Y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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c7f01eb8>"
+      ]
+     },
+     "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": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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L5vR9g0H6eKNYnVxBCxOyGR1AzB8Hj4yd8rQsd37tHHYmnOE2PvfpQs5ZlsbD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\nm+ntxv5mnjryPFH2SD5bcJ0sqQox5yw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MXDcXOHjGEiLL546QR8j+r8fZzg4M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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f45c8dfc978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.distplot(predict_df.query(\"status == 0\").probability, hist=False, label='Negatives')\n",
+    "ax = sns.distplot(predict_df.query(\"status == 1\").probability, hist=False, label='Positives')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/environment.yml b/environment.yml
index bdff148..93ec0f7 100644
--- a/environment.yml
+++ b/environment.yml
@@ -10,3 +10,5 @@ dependencies:
 - scipy=0.17.1
 - seaborn=0.7.0
 - statsmodels=0.6.1
+- pip:
+  - neo4j-driver==1.0.2
diff --git a/scripts/3.TCGA-MLexample_Pathway.py b/scripts/3.TCGA-MLexample_Pathway.py
new file mode 100644
index 0000000..488da63
--- /dev/null
+++ b/scripts/3.TCGA-MLexample_Pathway.py
@@ -0,0 +1,502 @@
+
+# coding: utf-8
+
+# # Predicting Aberrations in The Hippo Signaling Pathway
+
+# ## To do this, we will use Hetnets to query "Signaling by Hippo"
+# 
+# The query is built in the [Cypher Language](https://neo4j.com/developer/cypher-query-language/) and draws data from [Hetionet](https://neo4j.het.io/browser/)
+# 
+# ### How Cognoma could help with Hippo signaling
+# 
+# The Hippo pathway is a highly conserved signaling cascade that controls organ size, cell growth, and cell death ([Zhao et al. 2010](http://doi.org/10.1101/gad.1909210)). It is one of the mechanisms that influences size diversity across eukaryotes; including different sizes across dog breeds ([Dong et al. 2007](http://doi.org/10.1016/j.cell.2007.07.019), [Crickmore and Mann 2008](http://doi.org/10.1002/bies.20806)). Recently, Hippo signaling has also been shown to be important for tumorigenesis, but there are shockingly few recurrent mutations of single genes within the pathway across tissues ([Harvey et al 2013](http://doi.org/10.1038/nrc3458)). Therefore, leveraging cancers from multiple tissues and combining genes associated with the same pathway could aid in the detection of a Hippo signaling specific gene expression signature. Cognoma is situated well to quickly query the list of all pathway associated genes, build a machine learning classifier to detect aberrant pathway activity, and output tissue and gene specific performance.
+
+# In[1]:
+
+import os
+import urllib
+import random
+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 sklearn.feature_selection import SelectKBest
+from statsmodels.robust.scale import mad
+
+
+# In[2]:
+
+from neo4j.v1 import GraphDatabase
+
+
+# In[3]:
+
+get_ipython().magic('matplotlib inline')
+plt.style.use('seaborn-notebook')
+
+
+# ## Specify model configuration - Generate genelist
+
+# In[4]:
+
+names = ('label', 'rel_type', 'node_id')
+query_params = [
+    ('Pathway', 'PARTICIPATES_GpPW', 'PC7_7459'),             # "Signaling by Hippo" - Reactome
+    ('BiologicalProcess', 'PARTICIPATES_GpBP', 'GO:0035329'), # "hippo signaling" - Gene Ontology
+    ('BiologicalProcess', 'PARTICIPATES_GpBP', 'GO:0035330')  # "regulation of hippo signaling" - Gene Ontology
+]
+
+param_list = [dict(zip(names, qp)) for qp in query_params]
+
+
+# In[5]:
+
+query = '''
+MATCH (node)-[rel]-(gene)
+WHERE node.identifier = {node_id}
+  AND {label} in labels(node)
+  AND {rel_type} = type(rel)
+RETURN
+  gene.identifier as entrez_gene_id,
+  gene.name as gene_symbol
+ORDER BY gene_symbol
+'''
+
+
+# In[6]:
+
+driver = GraphDatabase.driver("bolt://neo4j.het.io")
+full_results_df = pd.DataFrame()
+with driver.session() as session:
+    for parameters in param_list:
+        result = session.run(query, parameters)
+        result_df = pd.DataFrame((x.values() for x in result), columns=result.keys())
+        full_results_df = full_results_df.append(result_df, ignore_index=True)
+
+classifier_genes_df = full_results_df.drop_duplicates().sort_values('gene_symbol').reset_index(drop=True)
+classifier_genes_df['entrez_gene_id'] = classifier_genes_df['entrez_gene_id'].astype('str')
+
+
+# In[7]:
+
+# Here are the genes that participate in the Hippo signaling pathway
+classifier_genes_df
+
+
+# In[8]:
+
+# Parameter Sweep for Hyperparameters
+n_feature_kept = 8000
+param_fixed = {
+    'loss': 'log',
+    'penalty': 'elasticnet',
+}
+param_grid = {
+    'alpha': [10 ** x for x in range(-6, 1)],
+    '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*
+
+# ## Load Data
+
+# In[9]:
+
+get_ipython().run_cell_magic('time', '', "path = os.path.join('data', 'expression-matrix.tsv.bz2')\nX = pd.read_table(path, index_col=0)")
+
+
+# In[10]:
+
+get_ipython().run_cell_magic('time', '', "path = os.path.join('data', 'mutation-matrix.tsv.bz2')\nY = pd.read_table(path, index_col=0)")
+
+
+# In[11]:
+
+get_ipython().run_cell_magic('time', '', "path = os.path.join('data', 'samples.tsv')\nclinical = pd.read_table(path, index_col=0)")
+
+
+# In[12]:
+
+clinical.tail(5)
+
+
+# In[13]:
+
+# Subset the Y matrix to only the genes to be classified
+y_full = Y[classifier_genes_df['entrez_gene_id']]
+
+
+# In[14]:
+
+y_full.columns = classifier_genes_df['gene_symbol']
+y_full = y_full.assign(disease = clinical['disease'])
+
+# This matrix now stores the final y matrix for the classifier (y['indicator'])
+y = y_full.assign(indicator = y_full.max(axis=1))
+
+
+# In[15]:
+
+unique_pos = y.groupby('disease').apply(lambda x: x['indicator'].sum())
+heatmap_df = y_full.groupby('disease').sum().assign(TOTAL = unique_pos)
+heatmap_df = heatmap_df.divide(y_full.disease.value_counts(sort=False).sort_index(), axis=0)
+
+
+# In[16]:
+
+# What is the percentage of different mutations across different cancer types?
+sns.heatmap(heatmap_df);
+
+
+# Visualizing the input data here is key. The heterogeneity of the mutations across tissues is apparent for this particular pathway. In comparison with `TP53` mutations, it appears that Hippo signaling impacts different tissues with higher diversity.
+# 
+# Looking closer at the plots above, it is evident that several tissues do not demonstrate aberrations (at least at the mutation level) in Hippo signaling. Specifically, it appears that cancers with gender specificity like testicular cancer and and prostate cancer are _not_ impacted. Therefore, because of this artificial imbalance, if Cognoma were to include these cancers in the classifier, it **will** key in on gender specific signal (i.e. genes that are only on the Y chromosome, or X inactivation genes).  
+
+# In[17]:
+
+# How many samples in each tissue that have Hippo signaling aberrations
+ind = ['Negatives', 'Positives', 'Positive Prop']
+percent = heatmap_df['TOTAL']
+neg = y.disease.value_counts() - unique_pos
+tissue_summary_df = pd.DataFrame([neg, unique_pos, percent], index=ind, 
+                                 dtype='object').T.sort_values('Positive Prop', ascending=False)
+tissue_summary_df
+
+
+# ## Filter Data by Tissue
+# 
+# This is a crucial step that is different from previous classifiers
+
+# In[18]:
+
+# Technically, these are hyper parameters, but for simplicity, set here
+filter_prop = 0.10
+filter_count = 15
+
+tissue_prop_decision = tissue_summary_df['Positive Prop'] >= filter_prop
+tissue_count_decision = tissue_summary_df['Positives'] >= filter_count
+
+tissue_decision = tissue_prop_decision & tissue_count_decision
+
+
+# In[19]:
+
+# This criteria filters out the following tissues
+pd.Series(tissue_summary_df.index[~tissue_decision].sort_values())
+
+
+# In[20]:
+
+# What are the tissues remaining?
+tissue_summary_df = tissue_summary_df[tissue_decision]
+tissue_summary_df
+
+
+# In[21]:
+
+# Distribution of mutation counts after filtering
+sns.heatmap(heatmap_df.loc[tissue_decision]);
+
+
+# In[22]:
+
+# Subset data
+clinical_sub = clinical[clinical['disease'].isin(tissue_summary_df.index)]
+X_sub = X.ix[clinical_sub.index]
+y_sub = y['indicator'].ix[clinical_sub.index]
+
+
+# In[23]:
+
+# Total distribution of positives/negatives
+y_sub.value_counts(True)
+
+
+# In[24]:
+
+y_sub.head(7)
+
+
+# ## Set aside 10% of the data for testing
+
+# In[25]:
+
+strat = clinical_sub['disease'].str.cat(y_sub.astype(str))
+strat.head(6)
+
+
+# In[26]:
+
+# Make sure the splits have equal tissue and label partitions
+X_train, X_test, y_train, y_test = train_test_split(X_sub, y_sub, test_size=0.1, random_state=0,
+                                                    stratify=strat)
+'Size: {:,} features, {:,} training samples, {:,} testing samples'.format(len(X_sub.columns),
+                                                                          len(X_train), len(X_test))
+
+
+# ## Median absolute deviation feature selection
+
+# In[27]:
+
+def fs_mad(x, y):
+    """    
+    Get the median absolute deviation (MAD) for each column of x
+    """
+    scores = mad(x) 
+    return scores, np.array([np.NaN]*len(scores))
+
+# select the top features with the highest MAD
+feature_select = SelectKBest(fs_mad, k=n_feature_kept)
+
+
+# ## Define pipeline and Cross validation model fitting
+
+# In[28]:
+
+# 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(
+    feature_select,  # Feature selection
+    StandardScaler(),  # Feature scaling
+    clf_grid)
+
+
+# In[29]:
+
+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_\nfeature_mask = feature_select.get_support()  # Get a boolean array indicating the selected features')
+
+
+# In[30]:
+
+clf_grid.best_params_
+
+
+# In[31]:
+
+best_clf
+
+
+# ## Visualize hyperparameters performance
+
+# In[32]:
+
+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[33]:
+
+cv_score_df = grid_scores_to_df(clf_grid.grid_scores_)
+cv_score_df.head(2)
+
+
+# In[34]:
+
+# 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[35]:
+
+# 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='.2%')
+ax.set_xlabel('Regularization strength multiplier (alpha)')
+ax.set_ylabel('Elastic net mixing parameter (l1_ratio)');
+
+
+# ## Use Optimal Hyperparameters to Output ROC Curve
+
+# In[36]:
+
+y_pred_train = pipeline.decision_function(X_train)
+y_pred_test = pipeline.decision_function(X_test)
+
+def get_threshold_metrics(y_true, y_pred, tissue='all'):
+    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, 'tissue': tissue}
+
+metrics_train = get_threshold_metrics(y_train, y_pred_train)
+metrics_test = get_threshold_metrics(y_test, y_pred_test)
+
+
+# In[37]:
+
+# 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 Hippo Signaling Pathway Aberations')
+plt.legend(loc='lower right');
+
+
+# ## Tissue specific performance
+
+# In[38]:
+
+tissue_metrics = {}
+for tissue in clinical_sub.disease.unique():
+    sample_sub = clinical_sub[clinical_sub['disease'] == tissue].index.values
+
+    y_tissue_train = y_train[y_train.index.isin(sample_sub)]
+    y_tissue_pred_train = y_pred_train[y_train.index.isin(sample_sub)]
+    y_tissue_test = y_test[y_test.index.isin(sample_sub)]
+    y_tissue_pred_test = y_pred_test[y_test.index.isin(sample_sub)]
+
+    metrics_train = get_threshold_metrics(y_tissue_train, y_tissue_pred_train, tissue=tissue)
+    metrics_test = get_threshold_metrics(y_tissue_test, y_tissue_pred_test, tissue=tissue) 
+    
+    tissue_metrics[tissue] = [metrics_train, metrics_test]
+
+
+# In[39]:
+
+tissue_auroc = {}
+plt.figure()
+for tissue, metrics_val in tissue_metrics.items():
+    metrics_train, metrics_test = metrics_val
+    plt.subplot()
+    auroc = []
+    for label, metrics in ('Training', metrics_train), ('Testing', metrics_test):
+        roc_df = metrics['roc_df']
+        auroc.append(metrics['auroc'])
+        plt.plot(roc_df.fpr, roc_df.tpr,
+            label='{} (AUROC = {:.1%})'.format(label, metrics['auroc']))
+    tissue_auroc[tissue] = 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 Hippo Signaling in {}'.format(tissue))
+    plt.legend(loc='lower right'); 
+    plt.show()
+
+
+# In[40]:
+
+tissue_results = pd.DataFrame(tissue_auroc, index=['Train', 'Test']).T
+tissue_results = tissue_results.sort_values('Test', ascending=False)
+
+
+# In[41]:
+
+ax = tissue_results.plot(kind='bar', title='Tissue Specific Prediction of Hippo Signaling')
+ax.set_ylabel('AUROC');
+
+
+# Hippo signaling prediction has highly variable predictions across different tissues. The classifier performs spectacularly in several tissues, but also appears to oppositely predict Hippo signaling in others. In three tissues the `test` set performance is actually _greater_ than the `train` set performance. This is likely a result of variance in samples across tissues and a happenstance in stratified `train_test_split`.
+
+# ## What are the classifier coefficients?
+
+# In[42]:
+
+coef_df = pd.DataFrame(best_clf.coef_.transpose(), index=X_sub.columns[feature_mask], columns=['weight'])
+coef_df['abs'] = coef_df['weight'].abs()
+coef_df = coef_df.sort_values('abs', ascending=False)
+
+
+# In[43]:
+
+'{:.1%} zero coefficients; {:,} negative and {:,} positive coefficients'.format(
+    (coef_df.weight == 0).mean(),
+    (coef_df.weight < 0).sum(),
+    (coef_df.weight > 0).sum()
+)
+
+
+# In[44]:
+
+coef_df.head(10)
+
+
+# The results are very interesting. First, only 200 genes are used to build a fairly successful classifier. Biologists like sparsity! Second, the genes that fall out at the top are informative:
+# 
+# | Entrez | Symbol | Comments |
+# | ------ | ---- | -------- |
+# | 399671 | [HEATR4](http://www.ncbi.nlm.nih.gov/gene/399671) | Relatively unstudied gene |
+# | 29126 | [CD274](http://www.ncbi.nlm.nih.gov/gene/29126) | Immune cell receptor - inhibits Tcell activation and cytokine production |
+# | 2852 | [GPER1](http://www.ncbi.nlm.nih.gov/gene/2852) | Estrogen receptor - implicated in lymphoma |
+# | 140730 | [RIMS4](http://www.ncbi.nlm.nih.gov/gene/140730) | Synaptic regulatory protein |
+# | 84688 | [C9orf24](http://www.ncbi.nlm.nih.gov/gene/84688) | relatively unknown gene - important for differentiation of bronchial cells |
+# | 387628 | [FGF7P6](http://www.ncbi.nlm.nih.gov/gene/387628) | Fibroblast growth factor - implicated in ovarian cancer |
+# | 4438 | [MSH4](http://www.ncbi.nlm.nih.gov/gene/4438) | Involved in DNA mismatch repair |
+# | 80350 | [LPAL2](http://www.ncbi.nlm.nih.gov/gene/157777) | Pseudogene involved with elevated risk for atherosclerosis |
+# | 56892 | [C8orf4](http://www.ncbi.nlm.nih.gov/gene/56892) | Relatively uknown gene product - evidence it is important in WNT signaling and proliferation across cancer types |
+# | 22943 | [DKK1](http://www.ncbi.nlm.nih.gov/gene/22943) | Inhibits WNT signaling pathway - implicated in myeloma |
+# 
+# 
+
+# ## Investigate the predictions
+
+# In[45]:
+
+predict_df = pd.DataFrame.from_items([
+    ('sample_id', X_sub.index),
+    ('testing', X_sub.index.isin(X_test.index).astype(int)),
+    ('status', y_sub),
+    ('decision_function', pipeline.decision_function(X_sub)),
+    ('probability', pipeline.predict_proba(X_sub)[:, 1]),
+])
+predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)
+
+
+# In[46]:
+
+# Top predictions amongst negatives (potential hidden responders)
+predict_df.sort_values('decision_function', ascending=False).query("status == 0").head(10)
+
+
+# In[47]:
+
+# 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[48]:
+
+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')
+