From 2b07eedcd652c6fc2c2d7433534448b4c82250f0 Mon Sep 17 00:00:00 2001
From: Patrick Miller <miller1417@gmail.com>
Date: Tue, 6 Jun 2017 11:32:06 -0400
Subject: [PATCH] Add covariate-only & combined models to main notebook (#93)

Refs https://github.com/cognoma/machine-learning/issues/8
---
 .gitignore                        |    3 +
 2.TCGA-MLexample-covariates.ipynb | 1149 +++++++
 2.TCGA-MLexample-covariates.py    |  355 ++
 environment.yml                   |    1 +
 jupyter_data/auc.csv              |    7 +
 jupyter_data/roc_output.csv       | 5347 +++++++++++++++++++++++++++++
 jupyter_data/roc_vega_spec.json   |  307 ++
 7 files changed, 7169 insertions(+)
 create mode 100644 2.TCGA-MLexample-covariates.ipynb
 create mode 100644 2.TCGA-MLexample-covariates.py
 create mode 100644 jupyter_data/auc.csv
 create mode 100644 jupyter_data/roc_output.csv
 create mode 100644 jupyter_data/roc_vega_spec.json

diff --git a/.gitignore b/.gitignore
index 640e558..c56f760 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,9 @@
 # Large downloaded data files
 download/
 
+# Temporary files
+job-lib/
+
 # Mac metadata
 .DS_Store
 
diff --git a/2.TCGA-MLexample-covariates.ipynb b/2.TCGA-MLexample-covariates.ipynb
new file mode 100644
index 0000000..24a87b9
--- /dev/null
+++ b/2.TCGA-MLexample-covariates.ipynb
@@ -0,0 +1,1149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create a logistic regression model to predict TP53 mutation from gene expression data in TCGA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": 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",
+    "import vega\n",
+    "import json\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV, ShuffleSplit\n",
+    "from sklearn.metrics import roc_auc_score, roc_curve\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.feature_selection import SelectKBest\n",
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.style.use('seaborn-notebook')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specify model configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# We're going to be building a 'TP53' classifier \n",
+    "GENE = '7157' # TP53"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Here is some [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) regarding the classifier and hyperparameters*\n",
+    "\n",
+    "*Here is some [information](https://ghr.nlm.nih.gov/gene/TP53) about TP53*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 19s, sys: 6.25 s, total: 1min 26s\n",
+      "Wall time: 1min 28s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "path = os.path.join('download', 'expression-matrix.tsv.bz2')\n",
+    "expression = pd.read_table(path, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 51.8 s, sys: 1.08 s, total: 52.9 s\n",
+      "Wall time: 53 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "path = os.path.join('download', 'mutation-matrix.tsv.bz2')\n",
+    "Y = pd.read_table(path, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 40 ms, sys: 0 ns, total: 40 ms\n",
+      "Wall time: 44.1 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "path = os.path.join('download', 'covariates.tsv')\n",
+    "covariates = pd.read_table(path, index_col=0)\n",
+    "\n",
+    "# Select acronym_x and n_mutations_log1p covariates only\n",
+    "selected_cols = [col for col in covariates.columns if 'acronym_' in col]\n",
+    "selected_cols.append('n_mutations_log1p')\n",
+    "covariates = covariates[selected_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y = Y[GENE]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_id\n",
+       "TCGA-02-0047-01    0\n",
+       "TCGA-02-0055-01    1\n",
+       "TCGA-02-2483-01    1\n",
+       "TCGA-02-2485-01    1\n",
+       "TCGA-02-2486-01    0\n",
+       "TCGA-04-1348-01    1\n",
+       "Name: 7157, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The Series now holds TP53 Mutation Status for each Sample\n",
+    "y.head(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.645907\n",
+       "1    0.354093\n",
+       "Name: 7157, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here are the percentage of tumors with NF1\n",
+    "y.value_counts(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process data set\n",
+    "TODO: currently running PCA on both train and test partitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Pre-process expression data for use later\n",
+    "n_components = 100\n",
+    "scaled_expression = StandardScaler().fit_transform(expression)\n",
+    "pca = PCA(n_components).fit(scaled_expression)\n",
+    "explained_variance = pca.explained_variance_\n",
+    "expression_pca = pca.transform(scaled_expression)\n",
+    "expression_pca = pd.DataFrame(expression_pca)\n",
+    "expression_pca = expression_pca.set_index(expression.index.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fraction of variance explained: 0.704812987245\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('fraction of variance explained: ' + str(pca.explained_variance_ratio_.sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gene expression matrix shape: 7306, 20468\n",
+      "Full feature matrix shape: 7306, 133\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create full feature matrix (expression + covariates)\n",
+    "X = pd.concat([covariates,expression_pca],axis=1)\n",
+    "print('Gene expression matrix shape: {0[0]}, {0[1]}'.format(expression.shape))\n",
+    "print('Full feature matrix shape: {0[0]}, {0[1]}'.format(X.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set aside 10% of the data for testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Size: 133 features, 6,575 training samples, 731 testing samples'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Typically, this can only be done where the number of mutations is large enough\n",
+    "train_index, test_index = next(ShuffleSplit(n_splits=2, test_size=0.1, random_state=0).split(y))\n",
+    "\n",
+    "X_partitions = {\n",
+    "    'full': {\n",
+    "        'train': X.ix[train_index], \n",
+    "        'test': X.ix[test_index]\n",
+    "        },\n",
+    "    'expressions': {\n",
+    "        'train': expression_pca.ix[train_index], \n",
+    "        'test': expression_pca.ix[test_index]\n",
+    "        },\n",
+    "    'covariates': {\n",
+    "        'train': covariates.ix[train_index], \n",
+    "        'test': covariates.ix[test_index]\n",
+    "        }    \n",
+    "    } \n",
+    "\n",
+    "y_train = y[train_index]\n",
+    "y_test = y[test_index]\n",
+    "\n",
+    "'Size: {:,} features, {:,} training samples, {:,} testing samples'.format(\n",
+    "    len(X_partitions['full']['train'].columns), \n",
+    "    len(X_partitions['full']['train']), \n",
+    "    len(X_partitions['full']['test']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define pipeline and Cross validation model fitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Parameter Sweep for Hyperparameters\n",
+    "param_grid = {\n",
+    "    'classify__loss': ['log'],\n",
+    "    'classify__penalty': ['elasticnet'],\n",
+    "    'classify__alpha': [10 ** x for x in range(-3, 1)],\n",
+    "    'classify__l1_ratio': [0, 0.2, 0.8, 1],\n",
+    "}\n",
+    "\n",
+    "pipeline = Pipeline(steps=[\n",
+    "    ('standardize', StandardScaler()),\n",
+    "    ('classify', SGDClassifier(random_state=0, class_weight='balanced'))\n",
+    "])\n",
+    "\n",
+    "models = ['full', 'expressions', 'covariates']\n",
+    "\n",
+    "cv_pipelines = {mod: GridSearchCV(estimator=pipeline, \n",
+    "                             param_grid=param_grid, \n",
+    "                             n_jobs=1, \n",
+    "                             scoring='roc_auc') for mod in models}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting CV for model: full\n",
+      "Fitting CV for model: covariates\n",
+      "Fitting CV for model: expressions\n",
+      "CPU times: user 6.72 s, sys: 14.8 s, total: 21.5 s\n",
+      "Wall time: 5.43 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "for model, pipeline in cv_pipelines.items():\n",
+    "    print('Fitting CV for model: {0}'.format(model))\n",
+    "    pipeline.fit(X=X_partitions.get(model).get('train'), y=y_train)\n",
+    "# cv_pipeline_full.fit(X=X_train_full, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "full: 92.537%\n",
+      "{'classify__alpha': 0.1, 'classify__penalty': 'elasticnet', 'classify__l1_ratio': 0, 'classify__loss': 'log'}\n",
+      "covariates: 84.259%\n",
+      "{'classify__alpha': 0.1, 'classify__penalty': 'elasticnet', 'classify__l1_ratio': 0, 'classify__loss': 'log'}\n",
+      "expressions: 91.959%\n",
+      "{'classify__alpha': 0.1, 'classify__penalty': 'elasticnet', 'classify__l1_ratio': 0, 'classify__loss': 'log'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Best Params\n",
+    "for model, pipeline in cv_pipelines.items():\n",
+    "    print('{0}: {1:.3%}'.format(model, pipeline.best_score_))\n",
+    "\n",
+    "    # Best Params\n",
+    "    print(pipeline.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize hyperparameters performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>mean_train_score</th>\n",
+       "      <th>param_classify__alpha</th>\n",
+       "      <th>param_classify__l1_ratio</th>\n",
+       "      <th>param_classify__loss</th>\n",
+       "      <th>param_classify__penalty</th>\n",
+       "      <th>params</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "      <th>...</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split2_train_score</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>std_train_score</th>\n",
+       "      <th>classify__alpha</th>\n",
+       "      <th>classify__l1_ratio</th>\n",
+       "      <th>classify__loss</th>\n",
+       "      <th>classify__penalty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.032062</td>\n",
+       "      <td>0.003444</td>\n",
+       "      <td>0.886926</td>\n",
+       "      <td>0.903579</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>log</td>\n",
+       "      <td>elasticnet</td>\n",
+       "      <td>{'classify__alpha': 0.001, 'classify__penalty'...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.889509</td>\n",
+       "      <td>0.905814</td>\n",
+       "      <td>0.000558</td>\n",
+       "      <td>0.000247</td>\n",
+       "      <td>0.006383</td>\n",
+       "      <td>0.009607</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>log</td>\n",
+       "      <td>elasticnet</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.043467</td>\n",
+       "      <td>0.003128</td>\n",
+       "      <td>0.901912</td>\n",
+       "      <td>0.916187</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>log</td>\n",
+       "      <td>elasticnet</td>\n",
+       "      <td>{'classify__alpha': 0.001, 'classify__penalty'...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.904836</td>\n",
+       "      <td>0.920198</td>\n",
+       "      <td>0.004416</td>\n",
+       "      <td>0.000031</td>\n",
+       "      <td>0.002592</td>\n",
+       "      <td>0.005190</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>log</td>\n",
+       "      <td>elasticnet</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
+       "0       0.032062         0.003444         0.886926          0.903579   \n",
+       "1       0.043467         0.003128         0.901912          0.916187   \n",
+       "\n",
+       "  param_classify__alpha param_classify__l1_ratio param_classify__loss  \\\n",
+       "0                 0.001                        0                  log   \n",
+       "1                 0.001                      0.2                  log   \n",
+       "\n",
+       "  param_classify__penalty                                             params  \\\n",
+       "0              elasticnet  {'classify__alpha': 0.001, 'classify__penalty'...   \n",
+       "1              elasticnet  {'classify__alpha': 0.001, 'classify__penalty'...   \n",
+       "\n",
+       "   rank_test_score        ...          split2_test_score  split2_train_score  \\\n",
+       "0               11        ...                   0.889509            0.905814   \n",
+       "1                9        ...                   0.904836            0.920198   \n",
+       "\n",
+       "   std_fit_time  std_score_time  std_test_score  std_train_score  \\\n",
+       "0      0.000558        0.000247        0.006383         0.009607   \n",
+       "1      0.004416        0.000031        0.002592         0.005190   \n",
+       "\n",
+       "   classify__alpha  classify__l1_ratio  classify__loss  classify__penalty  \n",
+       "0            0.001                 0.0             log         elasticnet  \n",
+       "1            0.001                 0.2             log         elasticnet  \n",
+       "\n",
+       "[2 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_results_df_dict = {model: \n",
+    "    pd.concat([\n",
+    "        pd.DataFrame(pipeline.cv_results_),\n",
+    "        pd.DataFrame.from_records(pipeline.cv_results_['params']),\n",
+    "    ], axis='columns') for model, pipeline in cv_pipelines.items()}\n",
+    "\n",
+    "model = 'full'\n",
+    "\n",
+    "cv_results_df_dict[model].head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iYd6PnxNxK4L+PYcz7tOhZM2ahRkLbXMkHDl0nBEDJzJm6mAG9x3NhtVbGDNl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\nn8wbRZuOzTh+5CSREZFx2+/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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f63aa4b0390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Cross-validated performance heatmap\n",
+    "model = 'full'\n",
+    "\n",
+    "cv_score_mat = pd.pivot_table(cv_results_df_dict[model],\n",
+    "                              values='mean_test_score', \n",
+    "                              index='classify__l1_ratio',\n",
+    "                              columns='classify__alpha')\n",
+    "ax = sns.heatmap(cv_score_mat, annot=True, fmt='.1%')\n",
+    "ax.set_xlabel('Regularization strength multiplier (alpha)')\n",
+    "ax.set_ylabel('Elastic net mixing parameter (l1_ratio)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use Optimal Hyperparameters to Output ROC Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_dict = {\n",
+    "    model: {\n",
+    "        'train': pipeline.decision_function(X_partitions[model]['train']),\n",
+    "        'test':  pipeline.decision_function(X_partitions[model]['test'])\n",
+    "    } for model, pipeline in cv_pipelines.items()\n",
+    "}\n",
+    "\n",
+    "def get_threshold_metrics(y_true, y_pred):\n",
+    "    roc_columns = ['fpr', 'tpr', 'threshold']\n",
+    "    roc_items = zip(roc_columns, roc_curve(y_true, y_pred))\n",
+    "    roc_df = pd.DataFrame.from_items(roc_items)\n",
+    "    auroc = roc_auc_score(y_true, y_pred)\n",
+    "    return {'auroc': auroc, 'roc_df': roc_df}\n",
+    "\n",
+    "metrics_dict = {    \n",
+    "    model: {\n",
+    "        'train': get_threshold_metrics(y_train, y_pred_dict[model]['train']),\n",
+    "        'test':  get_threshold_metrics(y_test, y_pred_dict[model]['test'])\n",
+    "    } for model in y_pred_dict.keys()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"vega-embed\" id=\"3f00e7a1-5d6c-4c92-9ae6-e57da7073f18\"></div>\n",
+       "\n",
+       "<style>\n",
+       ".vega-embed svg, .vega-embed canvas {\n",
+       "  border: 1px dotted gray;\n",
+       "}\n",
+       "\n",
+       ".vega-embed .vega-actions a {\n",
+       "  margin-right: 6px;\n",
+       "}\n",
+       "</style>\n"
+      ]
+     },
+     "metadata": {
+      "jupyter-vega": "#3f00e7a1-5d6c-4c92-9ae6-e57da7073f18"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "var spec = {\"marks\": [{\"properties\": {\"update\": {\"y\": {\"value\": 0}, \"stroke\": {\"value\": \"red\"}, \"y2\": {\"field\": {\"group\": \"height\"}}, \"x\": {\"signal\": \"index_fpr\", \"scale\": \"xscale\"}}}, \"type\": \"rule\"}, {\"marks\": [{\"properties\": {\"update\": {\"strokeOpacity\": {\"value\": 0.6}, \"stroke\": {\"field\": \"feature_set\", \"scale\": \"colorscale\"}, \"strokeDash\": {\"field\": \"partition\", \"scale\": \"dashscale\"}, \"strokeWidth\": {\"value\": 4}, \"y\": {\"field\": \"true_positive_rate\", \"scale\": \"yscale\"}, \"x\": {\"field\": \"false_positive_rate\", \"scale\": \"xscale\"}}}, \"type\": \"line\"}], \"type\": \"group\", \"from\": {\"data\": \"roc\", \"transform\": [{\"groupby\": \"legend\", \"type\": \"facet\"}]}}, {\"marks\": [{\"marks\": [{\"properties\": {\"update\": {\"fontSize\": {\"value\": 11}, \"y\": {\"value\": 5}, \"x\": {\"value\": 0}, \"fontWeight\": {\"value\": \"bold\"}, \"fill\": {\"value\": \"black\"}, \"text\": {\"template\": \"{{parent.legend}}\"}}}, \"type\": \"text\"}, {\"properties\": {\"update\": {\"y\": {\"value\": 15}, \"fill\": {\"value\": \"black\"}, \"fontSize\": {\"value\": 11}, \"x\": {\"value\": 0}, \"text\": {\"template\": \"TPR: {{parent.agg_true_positive_rate|number: '.3p'}}, FPR: {{index_fpr|number: '.3p'}}, AUC {{parent.lookup.auc|number: '.3p'}}\"}}}, \"type\": \"text\"}], \"properties\": {\"update\": {\"y\": {\"offset\": 5, \"field\": \"lookup.legend_index\", \"scale\": \"legendrow\"}, \"x\": {\"value\": 4}}}, \"type\": \"group\"}], \"properties\": {\"update\": {\"width\": {\"value\": 200}, \"height\": {\"value\": 140}, \"strokeWidth\": {\"value\": 0.5}, \"y\": {\"value\": 100}, \"x\": {\"signal\": \"index_fpr\", \"offset\": 10, \"scale\": \"xscale\"}, \"stroke\": {\"value\": \"black\"}}}, \"type\": \"group\", \"from\": {\"data\": \"roc_index\", \"transform\": [{\"type\": \"lookup\", \"onKey\": \"legend\", \"on\": \"legend_auc\", \"default\": -1, \"keys\": [\"legend\"], \"as\": [\"lookup\"]}, {\"groupby\": \"legend\", \"type\": \"facet\"}]}}], \"scales\": [{\"round\": \"true\", \"type\": \"linear\", \"name\": \"xscale\", \"range\": \"width\", \"domain\": {\"field\": \"false_positive_rate\", \"data\": \"roc\"}, \"zero\": \"true\"}, {\"round\": \"true\", \"type\": \"linear\", \"nice\": \"true\", \"name\": \"yscale\", \"range\": \"height\", \"domain\": {\"field\": \"true_positive_rate\", \"data\": \"roc\"}, \"zero\": \"true\"}, {\"type\": \"ordinal\", \"range\": [\"#FF0000\", \"#5254a3\", \"#fd8d3c\"], \"domain\": {\"field\": \"feature_set\", \"data\": \"roc\"}, \"name\": \"colorscale\"}, {\"type\": \"ordinal\", \"range\": {\"field\": \"strokeDash\", \"data\": \"dash_data\"}, \"domain\": {\"field\": \"partition\", \"data\": \"roc\"}, \"name\": \"dashscale\"}, {\"type\": \"ordinal\", \"range\": {\"field\": \"strokeDash\", \"data\": \"dash_legend\"}, \"domain\": {\"field\": \"partition\", \"data\": \"roc\"}, \"name\": \"dashlegendscale\"}, {\"type\": \"linear\", \"rangeMax\": 110, \"domain\": {\"field\": \"legend_index\", \"data\": \"legend_auc\"}, \"name\": \"legendrow\", \"rangeMin\": 0}], \"data\": [{\"url\": \"jupyter_data/roc_output.csv\", \"name\": \"roc\", \"transform\": [{\"groupby\": [\"partition\", \"feature_set\", \"false_positive_rate\"], \"summarize\": [{\"ops\": [\"min\"], \"field\": \"true_positive_rate\", \"as\": [\"true_positive_rate\"]}], \"type\": \"aggregate\"}, {\"field\": \"legend\", \"expr\": \"datum.partition + ' (' + datum.feature_set + ')'\", \"type\": \"formula\"}], \"format\": {\"type\": \"csv\"}}, {\"source\": \"roc\", \"name\": \"roc_index_lookup\", \"transform\": [{\"test\": \"datum.false_positive_rate >= index_fpr\", \"type\": \"filter\"}, {\"groupby\": [\"legend\"], \"summarize\": [{\"ops\": [\"min\"], \"field\": \"false_positive_rate\", \"as\": [\"min_false_positive_rate\"]}], \"type\": \"aggregate\"}]}, {\"source\": \"roc\", \"name\": \"roc_index\", \"transform\": [{\"type\": \"lookup\", \"onKey\": \"legend\", \"on\": \"roc_index_lookup\", \"default\": -1, \"keys\": [\"legend\"], \"as\": [\"lookup\"]}, {\"test\": \"datum.false_positive_rate == datum.lookup.min_false_positive_rate\", \"type\": \"filter\"}, {\"groupby\": [\"legend\", \"partition\", \"feature_set\"], \"summarize\": [{\"ops\": [\"min\"], \"field\": \"true_positive_rate\", \"as\": [\"agg_true_positive_rate\"]}], \"type\": \"aggregate\"}]}, {\"name\": \"dash_data\", \"values\": [{\"strokeDash\": [0, 0]}, {\"strokeDash\": [5, 7]}]}, {\"name\": \"dash_legend\", \"values\": [{\"strokeDash\": [0, 0]}, {\"strokeDash\": [3, 3]}]}, {\"url\": \"jupyter_data/auc.csv\", \"name\": \"legend_auc\", \"transform\": [{\"field\": \"legend\", \"expr\": \"datum.partition + ' (' + datum.feature_set + ')'\", \"type\": \"formula\"}], \"format\": {\"type\": \"csv\"}}], \"padding\": {\"left\": 70, \"right\": 190, \"top\": 10, \"bottom\": 60}, \"width\": 600, \"legends\": [{\"title\": \"feature set\", \"properties\": {\"legend\": {\"y\": {\"value\": 250}, \"x\": {\"value\": 440}}, \"title\": {\"fontSize\": {\"value\": 16}}, \"symbols\": {\"stroke\": {\"field\": \"data\", \"scale\": \"colorscale\"}, \"shape\": {\"value\": \"M-0.5,-0.0L0.5,0\"}, \"strokeWidth\": {\"value\": 3}, \"size\": {\"value\": 250}}, \"labels\": {\"fontSize\": {\"value\": 15}}}, \"fill\": \"colorscale\"}, {\"stroke\": \"dashlegendscale\", \"title\": \"partition\", \"properties\": {\"legend\": {\"y\": {\"value\": 320}, \"x\": {\"value\": 440}}, \"title\": {\"fontSize\": {\"value\": 16}}, \"symbols\": {\"stroke\": {\"value\": \"black\"}, \"strokeDash\": {\"field\": \"data\", \"scale\": \"dashlegendscale\"}, \"shape\": {\"value\": \"M-0.5,-0.0L0.5,0\"}, \"strokeWidth\": {\"value\": 3}, \"size\": {\"value\": 250}}, \"labels\": {\"fontSize\": {\"value\": 15}, \"text\": {\"field\": \"data\"}}}}], \"signals\": [{\"name\": \"index_fpr\", \"streams\": [{\"expr\": \"clamp(eventX(), 0, eventGroup('root').width)\", \"type\": \"mousemove\", \"scale\": {\"invert\": true, \"name\": \"xscale\"}}], \"init\": 0.4}], \"height\": 400, \"axes\": [{\"title\": \"False positive rate\", \"type\": \"x\", \"scale\": \"xscale\", \"format\": \"%\"}, {\"title\": \"True positive rate\", \"type\": \"y\", \"scale\": \"yscale\", \"format\": \"%\"}]};\n",
+       "var selector = \"#3f00e7a1-5d6c-4c92-9ae6-e57da7073f18\";\n",
+       "var type = \"vega\";\n",
+       "\n",
+       "var output_area = this;\n",
+       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
+       "  vega.render(selector, spec, type, output_area);\n",
+       "}, function (err) {\n",
+       "  if (err.requireType !== 'scripterror') {\n",
+       "    throw(err);\n",
+       "  }\n",
+       "});\n"
+      ]
+     },
+     "metadata": {
+      "jupyter-vega": "#3f00e7a1-5d6c-4c92-9ae6-e57da7073f18"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {
+      "jupyter-vega": "#3f00e7a1-5d6c-4c92-9ae6-e57da7073f18"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TODO: do not save intermediate files?\n",
+    "# Assemble the data for ROC curves\n",
+    "model_order = ['full', 'expressions', 'covariates']\n",
+    "\n",
+    "auc_output = pd.DataFrame()\n",
+    "roc_output = pd.DataFrame()\n",
+    "\n",
+    "for model in model_order:\n",
+    "    metrics_partition = metrics_dict[model]\n",
+    "    for partition, metrics in metrics_partition.items():\n",
+    "        auc_output = auc_output.append(pd.DataFrame({\n",
+    "            'partition': [partition],\n",
+    "            'feature_set': [model],\n",
+    "            'auc': metrics['auroc']\n",
+    "        }))\n",
+    "        roc_df = metrics['roc_df']\n",
+    "        roc_output = roc_output.append(pd.DataFrame({\n",
+    "            'false_positive_rate': roc_df.fpr,\n",
+    "            'true_positive_rate': roc_df.tpr,\n",
+    "            'partition': partition,\n",
+    "            'feature_set': model\n",
+    "        }))\n",
+    "auc_output['legend_index'] = range(len(auc_output.index))\n",
+    "\n",
+    "roc_output.to_csv('jupyter_data/roc_output.csv', index = False)\n",
+    "auc_output.to_csv('jupyter_data/auc.csv', index = False)\n",
+    "\n",
+    "with open('jupyter_data/roc_vega_spec.json', 'r') as fp:\n",
+    "    vega_spec = json.load(fp)\n",
+    "    \n",
+    "vega.Vega(vega_spec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What are the classifier coefficients?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "final_pipelines = {\n",
+    "    model: pipeline.best_estimator_\n",
+    "    for model, pipeline in cv_pipelines.items()\n",
+    "}\n",
+    "final_classifiers = {\n",
+    "    model: pipeline.named_steps['classify']\n",
+    "    for model, pipeline in final_pipelines.items()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_coefficients(classifier, X_mat):\n",
+    "    coef_df = pd.DataFrame.from_items([\n",
+    "        ('feature', X_mat.columns),\n",
+    "        ('weight', classifier.coef_[0]),\n",
+    "    ])\n",
+    "\n",
+    "    coef_df['abs'] = coef_df['weight'].abs()\n",
+    "    coef_df = coef_df.sort_values('abs', ascending=False)\n",
+    "    \n",
+    "    return coef_df\n",
+    "\n",
+    "coef_df_dict = {\n",
+    "    model: get_coefficients(classifier, X_partitions[model]['train'])\n",
+    "    for model, classifier in final_classifiers.items()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0% zero coefficients; 63 negative and 70 positive coefficients\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>abs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.339458</td>\n",
+       "      <td>0.339458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>n_mutations_log1p</td>\n",
+       "      <td>0.324050</td>\n",
+       "      <td>0.324050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>11</td>\n",
+       "      <td>-0.277003</td>\n",
+       "      <td>0.277003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>24</td>\n",
+       "      <td>0.257535</td>\n",
+       "      <td>0.257535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>38</td>\n",
+       "      <td>0.246583</td>\n",
+       "      <td>0.246583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>29</td>\n",
+       "      <td>0.235441</td>\n",
+       "      <td>0.235441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.206559</td>\n",
+       "      <td>0.206559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>34</td>\n",
+       "      <td>-0.206115</td>\n",
+       "      <td>0.206115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>31</td>\n",
+       "      <td>-0.195329</td>\n",
+       "      <td>0.195329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>acronym_THCA</td>\n",
+       "      <td>-0.193927</td>\n",
+       "      <td>0.193927</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              feature    weight       abs\n",
+       "35                  2 -0.339458  0.339458\n",
+       "32  n_mutations_log1p  0.324050  0.324050\n",
+       "44                 11 -0.277003  0.277003\n",
+       "57                 24  0.257535  0.257535\n",
+       "71                 38  0.246583  0.246583\n",
+       "62                 29  0.235441  0.235441\n",
+       "33                  0 -0.206559  0.206559\n",
+       "67                 34 -0.206115  0.206115\n",
+       "64                 31 -0.195329  0.195329\n",
+       "27       acronym_THCA -0.193927  0.193927"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = 'full'\n",
+    "\n",
+    "print('{:.1%} zero coefficients; {:,} negative and {:,} positive coefficients'.format(\n",
+    "    (coef_df_dict[model].weight == 0).mean(),\n",
+    "    (coef_df_dict[model].weight < 0).sum(),\n",
+    "    (coef_df_dict[model].weight > 0).sum()\n",
+    "))\n",
+    "coef_df_dict[model].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Investigate the predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "model = 'full'\n",
+    "\n",
+    "X_all = X_partitions[model]['train'].append(X_partitions[model]['test'])\n",
+    "X_test_index = X_partitions[model]['test'].index\n",
+    "y_all = y_train.append(y_test)\n",
+    "\n",
+    "predict_df = pd.DataFrame.from_items([\n",
+    "    ('sample_id', X_all.index),\n",
+    "    ('testing', X_all.index.isin(X_test_index).astype(int)),\n",
+    "    ('status', y_all),\n",
+    "    ('decision_function', final_pipelines[model].decision_function(X_all)),\n",
+    "    ('probability', final_pipelines[model].predict_proba(X_all)[:, 1])\n",
+    "])\n",
+    "\n",
+    "predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sample_id</th>\n",
+       "      <th>testing</th>\n",
+       "      <th>status</th>\n",
+       "      <th>decision_function</th>\n",
+       "      <th>probability</th>\n",
+       "      <th>probability_str</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sample_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TCGA-L5-A4OH-01</th>\n",
+       "      <td>TCGA-L5-A4OH-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.761191</td>\n",
+       "      <td>0.977273</td>\n",
+       "      <td>97.7%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-EI-6513-01</th>\n",
+       "      <td>TCGA-EI-6513-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.537008</td>\n",
+       "      <td>0.971723</td>\n",
+       "      <td>97.2%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-22-4591-01</th>\n",
+       "      <td>TCGA-22-4591-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.196160</td>\n",
+       "      <td>0.960690</td>\n",
+       "      <td>96.1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-L5-A8NR-01</th>\n",
+       "      <td>TCGA-L5-A8NR-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.962345</td>\n",
+       "      <td>0.950844</td>\n",
+       "      <td>95.1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-RE-A7BO-01</th>\n",
+       "      <td>TCGA-RE-A7BO-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.664928</td>\n",
+       "      <td>0.934925</td>\n",
+       "      <td>93.5%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-46-3765-01</th>\n",
+       "      <td>TCGA-46-3765-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.654768</td>\n",
+       "      <td>0.934304</td>\n",
+       "      <td>93.4%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-BA-5149-01</th>\n",
+       "      <td>TCGA-BA-5149-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.602380</td>\n",
+       "      <td>0.931015</td>\n",
+       "      <td>93.1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-24-2298-01</th>\n",
+       "      <td>TCGA-24-2298-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.561030</td>\n",
+       "      <td>0.928311</td>\n",
+       "      <td>92.8%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-L5-A4OS-01</th>\n",
+       "      <td>TCGA-L5-A4OS-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.534246</td>\n",
+       "      <td>0.926508</td>\n",
+       "      <td>92.7%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGA-21-1081-01</th>\n",
+       "      <td>TCGA-21-1081-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.529144</td>\n",
+       "      <td>0.926160</td>\n",
+       "      <td>92.6%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       sample_id  testing  status  decision_function  \\\n",
+       "sample_id                                                              \n",
+       "TCGA-L5-A4OH-01  TCGA-L5-A4OH-01        0       0           3.761191   \n",
+       "TCGA-EI-6513-01  TCGA-EI-6513-01        1       0           3.537008   \n",
+       "TCGA-22-4591-01  TCGA-22-4591-01        0       0           3.196160   \n",
+       "TCGA-L5-A8NR-01  TCGA-L5-A8NR-01        0       0           2.962345   \n",
+       "TCGA-RE-A7BO-01  TCGA-RE-A7BO-01        0       0           2.664928   \n",
+       "TCGA-46-3765-01  TCGA-46-3765-01        0       0           2.654768   \n",
+       "TCGA-BA-5149-01  TCGA-BA-5149-01        0       0           2.602380   \n",
+       "TCGA-24-2298-01  TCGA-24-2298-01        0       0           2.561030   \n",
+       "TCGA-L5-A4OS-01  TCGA-L5-A4OS-01        0       0           2.534246   \n",
+       "TCGA-21-1081-01  TCGA-21-1081-01        0       0           2.529144   \n",
+       "\n",
+       "                 probability probability_str  \n",
+       "sample_id                                     \n",
+       "TCGA-L5-A4OH-01     0.977273           97.7%  \n",
+       "TCGA-EI-6513-01     0.971723           97.2%  \n",
+       "TCGA-22-4591-01     0.960690           96.1%  \n",
+       "TCGA-L5-A8NR-01     0.950844           95.1%  \n",
+       "TCGA-RE-A7BO-01     0.934925           93.5%  \n",
+       "TCGA-46-3765-01     0.934304           93.4%  \n",
+       "TCGA-BA-5149-01     0.931015           93.1%  \n",
+       "TCGA-24-2298-01     0.928311           92.8%  \n",
+       "TCGA-L5-A4OS-01     0.926508           92.7%  \n",
+       "TCGA-21-1081-01     0.926160           92.6%  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Top predictions amongst negatives (potential hidden responders)\n",
+    "predict_df.sort_values('decision_function', ascending=False).query(\"status == 0\").head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LAG4GLnI4HOuAKmBtkLKKMNQXY5OyTlVdbCclMY6dx7rxeGNzGUqXu4dRz5iM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\nQthEkrAQQghhk/8HmL5fDRFc0CUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6352ca92b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ignore numpy warning caused by seaborn\n",
+    "warnings.filterwarnings('ignore', 'using a non-integer number instead of an integer')\n",
+    "\n",
+    "ax = sns.distplot(predict_df.query(\"status == 0\").decision_function, hist=False, label='Negatives')\n",
+    "ax = sns.distplot(predict_df.query(\"status == 1\").decision_function, hist=False, label='Positives')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cMVaGo0NMknAYc0bI9qQLzZ2cBcAeGZIWUczv9/Pa8VX4/D4+M/YOzEaz3iEN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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f62feeb5c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.distplot(predict_df.query(\"status == 0\").probability, hist=False, label='Negatives')\n",
+    "ax = sns.distplot(predict_df.query(\"status == 1\").probability, hist=False, label='Positives')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/2.TCGA-MLexample-covariates.py b/2.TCGA-MLexample-covariates.py
new file mode 100644
index 0000000..fdc4ac9
--- /dev/null
+++ b/2.TCGA-MLexample-covariates.py
@@ -0,0 +1,355 @@
+
+# coding: utf-8
+
+# # Create a logistic regression model to predict TP53 mutation from gene expression data in TCGA
+
+# 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
+import vega
+import json
+from sklearn import preprocessing
+from sklearn.linear_model import SGDClassifier
+from sklearn.model_selection import train_test_split, GridSearchCV, ShuffleSplit
+from sklearn.metrics import roc_auc_score, roc_curve
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.feature_selection import SelectKBest
+from sklearn.decomposition import PCA
+
+
+# In[2]:
+
+get_ipython().magic('matplotlib inline')
+plt.style.use('seaborn-notebook')
+
+
+# ## Specify model configuration
+
+# In[3]:
+
+# We're going to be building a 'TP53' classifier 
+GENE = '7157' # TP53
+
+
+# *Here is some [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) regarding the classifier and hyperparameters*
+# 
+# *Here is some [information](https://ghr.nlm.nih.gov/gene/TP53) about TP53*
+
+# ## Load Data
+
+# In[4]:
+
+get_ipython().run_cell_magic('time', '', "path = os.path.join('download', 'expression-matrix.tsv.bz2')\nexpression = pd.read_table(path, index_col=0)")
+
+
+# In[5]:
+
+get_ipython().run_cell_magic('time', '', "path = os.path.join('download', 'mutation-matrix.tsv.bz2')\nY = pd.read_table(path, index_col=0)")
+
+
+# In[6]:
+
+get_ipython().run_cell_magic('time', '', "path = os.path.join('download', 'covariates.tsv')\ncovariates = pd.read_table(path, index_col=0)\n\n# Select acronym_x and n_mutations_log1p covariates only\nselected_cols = [col for col in covariates.columns if 'acronym_' in col]\nselected_cols.append('n_mutations_log1p')\ncovariates = covariates[selected_cols]")
+
+
+# In[7]:
+
+y = Y[GENE]
+
+
+# In[8]:
+
+# The Series now holds TP53 Mutation Status for each Sample
+y.head(6)
+
+
+# In[9]:
+
+# Here are the percentage of tumors with NF1
+y.value_counts(True)
+
+
+# ## Pre-process data set
+# TODO: currently running PCA on both train and test partitions
+
+# In[10]:
+
+# Pre-process expression data for use later
+n_components = 100
+scaled_expression = StandardScaler().fit_transform(expression)
+pca = PCA(n_components).fit(scaled_expression)
+explained_variance = pca.explained_variance_
+expression_pca = pca.transform(scaled_expression)
+expression_pca = pd.DataFrame(expression_pca)
+expression_pca = expression_pca.set_index(expression.index.values)
+
+
+# In[11]:
+
+print('fraction of variance explained: ' + str(pca.explained_variance_ratio_.sum()))
+
+
+# In[12]:
+
+# Create full feature matrix (expression + covariates)
+X = pd.concat([covariates,expression_pca],axis=1)
+print('Gene expression matrix shape: {0[0]}, {0[1]}'.format(expression.shape))
+print('Full feature matrix shape: {0[0]}, {0[1]}'.format(X.shape))
+
+
+# ## Set aside 10% of the data for testing
+
+# In[13]:
+
+# Typically, this can only be done where the number of mutations is large enough
+train_index, test_index = next(ShuffleSplit(n_splits=2, test_size=0.1, random_state=0).split(y))
+
+X_partitions = {
+    'full': {
+        'train': X.ix[train_index], 
+        'test': X.ix[test_index]
+        },
+    'expressions': {
+        'train': expression_pca.ix[train_index], 
+        'test': expression_pca.ix[test_index]
+        },
+    'covariates': {
+        'train': covariates.ix[train_index], 
+        'test': covariates.ix[test_index]
+        }    
+    } 
+
+y_train = y[train_index]
+y_test = y[test_index]
+
+'Size: {:,} features, {:,} training samples, {:,} testing samples'.format(
+    len(X_partitions['full']['train'].columns), 
+    len(X_partitions['full']['train']), 
+    len(X_partitions['full']['test']))
+
+
+# ## Define pipeline and Cross validation model fitting
+
+# In[14]:
+
+# Parameter Sweep for Hyperparameters
+param_grid = {
+    'classify__loss': ['log'],
+    'classify__penalty': ['elasticnet'],
+    'classify__alpha': [10 ** x for x in range(-3, 1)],
+    'classify__l1_ratio': [0, 0.2, 0.8, 1],
+}
+
+pipeline = Pipeline(steps=[
+    ('standardize', StandardScaler()),
+    ('classify', SGDClassifier(random_state=0, class_weight='balanced'))
+])
+
+models = ['full', 'expressions', 'covariates']
+
+cv_pipelines = {mod: GridSearchCV(estimator=pipeline, 
+                             param_grid=param_grid, 
+                             n_jobs=1, 
+                             scoring='roc_auc') for mod in models}
+
+
+# In[15]:
+
+get_ipython().run_cell_magic('time', '', "for model, pipeline in cv_pipelines.items():\n    print('Fitting CV for model: {0}'.format(model))\n    pipeline.fit(X=X_partitions.get(model).get('train'), y=y_train)\n# cv_pipeline_full.fit(X=X_train_full, y=y_train)")
+
+
+# In[16]:
+
+# Best Params
+for model, pipeline in cv_pipelines.items():
+    print('{0}: {1:.3%}'.format(model, pipeline.best_score_))
+
+    # Best Params
+    print(pipeline.best_params_)
+
+
+# ## Visualize hyperparameters performance
+
+# In[17]:
+
+cv_results_df_dict = {model: 
+    pd.concat([
+        pd.DataFrame(pipeline.cv_results_),
+        pd.DataFrame.from_records(pipeline.cv_results_['params']),
+    ], axis='columns') for model, pipeline in cv_pipelines.items()}
+
+model = 'full'
+
+cv_results_df_dict[model].head(2)
+
+
+# In[18]:
+
+# Cross-validated performance heatmap
+model = 'full'
+
+cv_score_mat = pd.pivot_table(cv_results_df_dict[model],
+                              values='mean_test_score', 
+                              index='classify__l1_ratio',
+                              columns='classify__alpha')
+ax = sns.heatmap(cv_score_mat, annot=True, fmt='.1%')
+ax.set_xlabel('Regularization strength multiplier (alpha)')
+ax.set_ylabel('Elastic net mixing parameter (l1_ratio)');
+
+
+# ## Use Optimal Hyperparameters to Output ROC Curve
+
+# In[19]:
+
+y_pred_dict = {
+    model: {
+        'train': pipeline.decision_function(X_partitions[model]['train']),
+        'test':  pipeline.decision_function(X_partitions[model]['test'])
+    } for model, pipeline in cv_pipelines.items()
+}
+
+def get_threshold_metrics(y_true, y_pred):
+    roc_columns = ['fpr', 'tpr', 'threshold']
+    roc_items = zip(roc_columns, roc_curve(y_true, y_pred))
+    roc_df = pd.DataFrame.from_items(roc_items)
+    auroc = roc_auc_score(y_true, y_pred)
+    return {'auroc': auroc, 'roc_df': roc_df}
+
+metrics_dict = {    
+    model: {
+        'train': get_threshold_metrics(y_train, y_pred_dict[model]['train']),
+        'test':  get_threshold_metrics(y_test, y_pred_dict[model]['test'])
+    } for model in y_pred_dict.keys()
+}
+
+
+# In[20]:
+
+# TODO: do not save intermediate files?
+# Assemble the data for ROC curves
+model_order = ['full', 'expressions', 'covariates']
+
+auc_output = pd.DataFrame()
+roc_output = pd.DataFrame()
+
+for model in model_order:
+    metrics_partition = metrics_dict[model]
+    for partition, metrics in metrics_partition.items():
+        auc_output = auc_output.append(pd.DataFrame({
+            'partition': [partition],
+            'feature_set': [model],
+            'auc': metrics['auroc']
+        }))
+        roc_df = metrics['roc_df']
+        roc_output = roc_output.append(pd.DataFrame({
+            'false_positive_rate': roc_df.fpr,
+            'true_positive_rate': roc_df.tpr,
+            'partition': partition,
+            'feature_set': model
+        }))
+auc_output['legend_index'] = range(len(auc_output.index))
+
+roc_output.to_csv('jupyter_data/roc_output.csv', index = False)
+auc_output.to_csv('jupyter_data/auc.csv', index = False)
+
+with open('jupyter_data/roc_vega_spec.json', 'r') as fp:
+    vega_spec = json.load(fp)
+    
+vega.Vega(vega_spec)
+
+
+# ## What are the classifier coefficients?
+
+# In[21]:
+
+final_pipelines = {
+    model: pipeline.best_estimator_
+    for model, pipeline in cv_pipelines.items()
+}
+final_classifiers = {
+    model: pipeline.named_steps['classify']
+    for model, pipeline in final_pipelines.items()
+}
+
+
+# In[22]:
+
+def get_coefficients(classifier, X_mat):
+    coef_df = pd.DataFrame.from_items([
+        ('feature', X_mat.columns),
+        ('weight', classifier.coef_[0]),
+    ])
+
+    coef_df['abs'] = coef_df['weight'].abs()
+    coef_df = coef_df.sort_values('abs', ascending=False)
+    
+    return coef_df
+
+coef_df_dict = {
+    model: get_coefficients(classifier, X_partitions[model]['train'])
+    for model, classifier in final_classifiers.items()
+}
+
+
+# In[23]:
+
+model = 'full'
+
+print('{:.1%} zero coefficients; {:,} negative and {:,} positive coefficients'.format(
+    (coef_df_dict[model].weight == 0).mean(),
+    (coef_df_dict[model].weight < 0).sum(),
+    (coef_df_dict[model].weight > 0).sum()
+))
+coef_df_dict[model].head(10)
+
+
+# ## Investigate the predictions
+
+# In[24]:
+
+model = 'full'
+
+X_all = X_partitions[model]['train'].append(X_partitions[model]['test'])
+X_test_index = X_partitions[model]['test'].index
+y_all = y_train.append(y_test)
+
+predict_df = pd.DataFrame.from_items([
+    ('sample_id', X_all.index),
+    ('testing', X_all.index.isin(X_test_index).astype(int)),
+    ('status', y_all),
+    ('decision_function', final_pipelines[model].decision_function(X_all)),
+    ('probability', final_pipelines[model].predict_proba(X_all)[:, 1])
+])
+
+predict_df['probability_str'] = predict_df['probability'].apply('{:.1%}'.format)
+
+
+# In[25]:
+
+# Top predictions amongst negatives (potential hidden responders)
+predict_df.sort_values('decision_function', ascending=False).query("status == 0").head(10)
+
+
+# In[26]:
+
+# Ignore numpy warning caused by seaborn
+warnings.filterwarnings('ignore', 'using a non-integer number instead of an integer')
+
+ax = sns.distplot(predict_df.query("status == 0").decision_function, hist=False, label='Negatives')
+ax = sns.distplot(predict_df.query("status == 1").decision_function, hist=False, label='Positives')
+
+
+# In[27]:
+
+ax = sns.distplot(predict_df.query("status == 0").probability, hist=False, label='Negatives')
+ax = sns.distplot(predict_df.query("status == 1").probability, hist=False, label='Positives')
+
diff --git a/environment.yml b/environment.yml
index df0a70c..56f3bc6 100644
--- a/environment.yml
+++ b/environment.yml
@@ -11,6 +11,7 @@ dependencies:
 - seaborn=0.7.0
 - setuptools=27.2.0
 - statsmodels=0.6.1
+- vega=0.4.4
 - pip:
   - neo4j-driver==1.0.2
   - altair==1.2.0
diff --git a/jupyter_data/auc.csv b/jupyter_data/auc.csv
new file mode 100644
index 0000000..5d5d9cf
--- /dev/null
+++ b/jupyter_data/auc.csv
@@ -0,0 +1,7 @@
+auc,feature_set,partition,legend_index
+0.9305868803685398,full,test,0
+0.9303816224700439,full,train,1
+0.9243085880640466,expressions,test,2
+0.9250938696062881,expressions,train,3
+0.853991714253723,covariates,test,4
+0.8452932437615834,covariates,train,5
diff --git a/jupyter_data/roc_output.csv b/jupyter_data/roc_output.csv
new file mode 100644
index 0000000..2eaa734
--- /dev/null
+++ b/jupyter_data/roc_output.csv
@@ -0,0 +1,5347 @@
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diff --git a/jupyter_data/roc_vega_spec.json b/jupyter_data/roc_vega_spec.json
new file mode 100644
index 0000000..83b4e2c
--- /dev/null
+++ b/jupyter_data/roc_vega_spec.json
@@ -0,0 +1,307 @@
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+    }
+  ]
+}
\ No newline at end of file