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 "metadata": {
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  "signature": "sha256:4f61405bb27b98e242a8367697885ca24fabceb2a77edd32fd9ca7595ebbf7f9"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">HNSCC HPV- Cohort <small> <i class=\"icon-search\"></i> Predicting Clinical Variables</small></h1>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import Data and Packages  \n",
      "For full list of data and packages imported see the [Imports](../Analysis_Notebooks/Imports.ipynb) notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import NotebookImport\n",
      "from Imports import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "importing IPython notebook from Imports.ipynb\n",
        "Populating the interactive namespace from numpy and matplotlib"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "changing to source dirctory"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "populating namespace with data"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Clinical variables "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Need to do some scrubbing to make them interpretable \n",
      "* Tumor subdivision are larger sub-groups to make comparative analysis more feasable"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stage = clinical.stage.pathologicstage.ix[keepers_o].fillna('nx')\n",
      "stage = stage.dropna().map(lambda s: s.replace('a','').replace('b',''))\n",
      "stage = stage.map(lambda s: s.replace('stge','Stage'))\n",
      "\n",
      "lymph_stage = clinical.stage.pathologicn.ix[keepers_o]\n",
      "lymph_stage = lymph_stage.dropna().map(lambda s: s[:2])\n",
      "\n",
      "old_age = (age >= 75).map({True: 'Age > 75', False: 'Age < 75'})\n",
      "pack_years = py = clinical.clinical.numberpackyearssmoked.dropna().astype(float)\n",
      "\n",
      "group = [['oral tongue','oral cavity','floor of mouth','buccal mucosa','alveolar ridge','hard palate','lip'],\n",
      "         ['oropharynx','tonsil','base of tongue'],\n",
      "         #['hypopharynx'],\n",
      "         ['larynx']]\n",
      "groups = ['oral cavity','oropharynx','larynx']\n",
      "tumor_subdivision = pd.Series({idx: groups[i] for i,g in enumerate(group) for idx,j in \n",
      "                               clinical.clinical.anatomicneoplasmsubdivision.iteritems() \n",
      "                               if j.lower() in g})\n",
      "\n",
      "invasion = clinical.clinical.perineuralinvasionpresent.replace('nan', nan)\n",
      "invasion = invasion.str.lower()\n",
      "\n",
      "spread = clinical.clinical.presenceofpathologicalnodalextracapsularspread\n",
      "spread = spread.map(str.lower, na_action='ignore')\n",
      "spread = spread.map({'no extranodal extension': 'no', 'microscopic extension':'yes', \n",
      "                     'gross extension':'yes'}).dropna()\n",
      "\n",
      "year = clinical.clinical.yearofinitialpathologicdiagnosis\n",
      "year = year.replace('[Discrepancy]', nan).astype(float)\n",
      "\n",
      "lymph = lymph_stage.ix[keepers_o] != 'n0'\n",
      "lymph_status = combine(lymph, spread.ix[keepers_o]=='yes')\n",
      "lymph_status = lymph_status.map({'neither': 'n0', lymph.name: 'lymph_node', 'both': 'extra_capsular_spread'})\n",
      "#lymph_status = (lymph_status == 'extra_capsular_spread').astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Support Vector Inference "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Using [SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) function from [Scikit Learn Package](http://scikit-learn.org/stable/) \n",
      "* Features are the binarized differential expression vectors  \n",
      "  * Have high change in expression from tumor to normal \n",
      "  * Thresholded at 1 standard deviation over the mean to reduce overfitting\n",
      "* Parameters are fit using cross validation, optimizing for AUC score \n",
      "  * I try linear, RBF, and polynomial kernels under a variety of parameters \n",
      "  * The best model in cross validation is fit on the entire dataset \n",
      "* Missing values are filled in based on the model prediction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from Stats.Classification import SVC_fill"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Smoking"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* I'm fitting the model on non-smokers verses current smokers and filling in the rest \n",
      "* I use the background genes here because this should not be a cancer specific event \n",
      "* Much of the variation in the background is tissue specific so I drop the genes that expression changes across tissues \n",
      "* I use a small test set becuase we are fitting extreme cases and want to overfit a little"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smoker = clinical.clinical.tobaccosmokinghistory.str.lower().ix[keepers_o].dropna()\n",
      "smoker_binary = smoker[smoker.isin(['current smoker','lifelong non-smoker'])] == 'current smoker'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smoker.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "current smoker                                 84\n",
        "current reformed smoker for < or = 15 years    72\n",
        "lifelong non-smoker                            44\n",
        "current reformed smoker for > 15 years         40\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ret = SVC_fill(smoker_binary, rna.features.ix['real'])\n",
      "ret['auc']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "0.953125"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(6,4)\n",
      "fun = ret['decision_function']\n",
      "o = ['current smoker','current reformed smoker for < or = 15 years',\n",
      "     'current reformed smoker for > 15 years', 'lifelong non-smoker']\n",
      "violin_plot_pandas(smoker, fun, order = o)\n",
      "ax = plt.gca()\n",
      "t = ax.set_xticklabels(o, rotation=20)\n",
      "prettify_ax(ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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Qbmwcg1isE52dnYhESquP/emnudxSb73VgUCg9BH63LkpbNkSxZIlIcydW1qN63Q6C0Xh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x753a110>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, smoker_binary.ix[keepers_o].fillna('Missing'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>    </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 7.54</td>\n",
        "      <td> 0.0231</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missing</th>\n",
        "      <td> 122</td>\n",
        "      <td> 50</td>\n",
        "      <td>    4</td>\n",
        "      <td>  2.3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.484</td>\n",
        "      <td> 0.387</td>\n",
        "      <td> 0.606</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True</th>\n",
        "      <td>  84</td>\n",
        "      <td> 38</td>\n",
        "      <td>  1.6</td>\n",
        "      <td> 1.35</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.284</td>\n",
        "      <td> 0.146</td>\n",
        "      <td> 0.553</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td>  44</td>\n",
        "      <td> 14</td>\n",
        "      <td> 4.71</td>\n",
        "      <td> 2.96</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.393</td>\n",
        "      <td>  0.19</td>\n",
        "      <td> 0.813</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "             Stats          Median Survival             5y Survival               Log-Rank        \n",
        "        # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2       p\n",
        "                                                                                      7.54  0.0231\n",
        "Missing        122       50               4   2.3   NaN       0.484  0.387  0.606                 \n",
        "True            84       38             1.6  1.35   NaN       0.284  0.146  0.553                 \n",
        "False           44       14            4.71  2.96   NaN       0.393   0.19  0.813                 \n",
        "\n",
        "[4 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smoker_inferred = 1.*smoker_binary.combine_first(ret['filled_feature'])\n",
      "smoker_inferred.name = 'smoker_inferred'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(smoker_inferred, smoker.ix[smoker_inferred.index].fillna('M')).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>smoker_inferred</th>\n",
        "      <th>0.0</th>\n",
        "      <th>1.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tobaccosmokinghistory</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>M</th>\n",
        "      <td>  8</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>current reformed smoker for &lt; or = 15 years</th>\n",
        "      <td> 18</td>\n",
        "      <td> 54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>current reformed smoker for &gt; 15 years</th>\n",
        "      <td> 23</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>current smoker</th>\n",
        "      <td>  0</td>\n",
        "      <td> 84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lifelong non-smoker</th>\n",
        "      <td> 44</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "smoker_inferred                               0   1\n",
        "tobaccosmokinghistory                              \n",
        "M                                             8  10\n",
        "current reformed smoker for < or = 15 years  18  54\n",
        "current reformed smoker for > 15 years       23  17\n",
        "current smoker                                0  84\n",
        "lifelong non-smoker                          44   0\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(clinical.survival.survival, smoker_inferred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>    </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 13</td>\n",
        "      <td> 0.000304</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 165</td>\n",
        "      <td> 89</td>\n",
        "      <td> 1.83</td>\n",
        "      <td>  1.5</td>\n",
        "      <td> 2.84</td>\n",
        "      <td> 0.335</td>\n",
        "      <td>  0.25</td>\n",
        "      <td> 0.447</td>\n",
        "      <td>   </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  93</td>\n",
        "      <td> 32</td>\n",
        "      <td> 5.48</td>\n",
        "      <td> 4.49</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0.533</td>\n",
        "      <td> 0.392</td>\n",
        "      <td> 0.724</td>\n",
        "      <td>   </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "       Stats          Median Survival             5y Survival               Log-Rank          \n",
        "  # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2         p\n",
        "                                                                                  13  0.000304\n",
        "1        165       89            1.83   1.5  2.84       0.335   0.25  0.447                   \n",
        "0         93       32            5.48  4.49   NaN       0.533  0.392  0.724                   \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(smoker_binary.ix[smoker_inferred.index], surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Uc6mu8bxULsWIPcNQ170uVvVaU+n5Io9Mxaxn30ZDz0Y61d0v/VraZv2fSP4d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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1eaac50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "si = smoker_inferred.map({1:'smoker_inf', 0:'non-smoker_inf'})\n",
      "s = smoker_binary.map({True:'smoker', False:'non-smoker'})\n",
      "survival_and_stats(s.combine_first(si), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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390dOTg4iIyMxZMgQhIaGIigoSBh0BwDDhw9H586dcfToUbz44ouYPXs2AgMD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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xafac490>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Drinking"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* This is a tough one to split but for now I'm fitting on patients that:\n",
      "  * Had less than 7 drinks per week\n",
      "  * Had more than 14 drinks per week\n",
      "* I use a small test set becuase we are fitting extreme cases and want to overfit a little"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(6,4)\n",
      "clinical.clinical.amountofalcoholconsumptionperday.astype(float).hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xaf76950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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efPFFsXTpUj22krZ161bx1FNPGZqhLxaLxfCzWW7cuCFGjBghdu7cKb799ltR\nUFAgnnzySUMzCSHEyZMnRXh4uPjss8/E3bt3xcaNG8WMGTMMyxMdHd3jbJbGxkb32SwHDhwwNFNi\nYqL48MMP/Z7hQfdnul9ZWZlITU01INE99+fasmWLiI2NFWfPnhUXL14Us2bNEhs2bDA00+uvvy5+\n8IMfiPPnz4tr166J5ORkUVxc7PHzdRnmQghRW1srYmNjxZAhQ0Rqaqq4fv26XltJ+cUvfiGCgoJE\nSEiI+yM0NFT85z//MTTX/VQ4NVEIIQ4fPiymTJkirFarePrpp8XZs2eNjiSEEGLnzp1i4sSJwmq1\nimeeeUacO3fOsCwPDqm1a9eKkSNHipEjR4p169YZnknTtG7HekhIiEhOTjY00/3KysoMOTWxy4O5\n8vLyxPDhw8Xw4cNFTk6O4Zna2tpEVlaWiIiIEMOGDRO//vWvRWdnp8fP9/pCW0REpA7jTuUgIiKf\n4TAnIgoAHOZERAGAw5yIKABwmBMRBQAOcyKiAPD/2jb4xPrQBh8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d90550>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical.clinical.alcoholhistorydocumented.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "yes    254\n",
        "no     118\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "freq = clinical.clinical.frequencyofalcoholconsumption.astype(float)\n",
      "count = clinical.clinical.amountofalcoholconsumptionperday.astype(float)\n",
      "drinker = (freq * count).ix[keepers_o].dropna()\n",
      "print len(drinker)\n",
      "drinker = drinker[(drinker < 8) + (drinker > 14)]\n",
      "drinker = drinker > 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "95\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "drinker.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "True     45\n",
        "False    35\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "ret = SVC_fill(drinker, rna.features.ix['real'])\n",
      "ret['auc']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0.83750000000000002"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fun = ret['decision_function']\n",
      "violin_plot_pandas(drinker, fun)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WaztisViqQ+oXXSWEiJDH40FX10lotRz0ehqHkA4YhkFOjh4KRRidnScRCARS\nHVKfqLWJEBGJxWJwOLrAcR6qLkpTGo0KCkUMbvdpBINmmEy5afV/Sp9ICCHD4vP50Nl5AnJ5ECZT\naquLMmEGaZZNXb2+TCaD2awHz7tw5kx6lRooKRCS4aLRKDo72+DztcFkUkKjSe0o2muuCWPjRjV8\nvvRtTD15Uop//EOBSZMiKY1Dr9fCaJTC7T4Nm60zLdZjoKRASIbieR5utxtW6wkolWGYTPq06G56\nzTURfO97kbit4hdvPA/cd58RS5f6MWpU6r+Ez5YaJBIfOjqOw+v1pjQeSgqEZKBQKISOjpOIRKww\nm9VQq9Nrjp0nn/Rg40Y1/v3v9Gu2fPttFVpapLj/fl+qQ+lBq1XDbFbC729HZ2crIpHUlGIoKRCS\nQViWhd1uhcNxEno9YDCkZ2OyxcJh5Uovli7NwTAW8os7r5fBsmVGrFnjhkKR6mh6k0qlMJn0UKki\nsFqPw+l0DGslxKFIv6uJENILz/PweDzo6DgOhvEiN1cPRTp+q51j/vwApFIeL76YPqN4V67U49pr\nw7j66tS2JQxEpVIhL08HlrUnvSGa4TN8WS6GYWhlMSJq4XAYTmcnGCYEg0GTliWD/hw+LMPUqbnY\nu7cLhYVDv+NtbW2DQqGExZI35GN88okcN9xgxv79XcjLS6PiywBisRg8niCkUgNMpry4zbra33dn\n5lxdhGQZjuPgcNhgs52ARsMiJ0eXUQkBAC6+OIb/9/8CePDB1C44w7LA3XcbsWqVJ6MSAvB/DdEK\nRQBW6wm4XK6E3ghn1hVGSJbw+/3o6DgBnnchN1eX0Wv+/uIXPvzrXwr8/e+pew/PP6+FVsvjttuC\nKYthuNRqNXJzNYhGu9DRcRKhUCgh56HqI0LSyNkRySzrgcGgFs0U1//7v0rcf78R+/dboVZf+O8P\np/qorU2CiRPz8d57dnz72+k759CFiEQi8HrDUCqHPiKaqo8ISXPnjkg2m/WiSQgAMHVqGBMmRPGr\nXyV/7MIDDxixaFFANAkBABQKBXJzEzMimkoKhKTYufMVGQziXe+gvb37jv3vf7/wO/ahlhSGW0LJ\nBLFYDG53AEpl7gWVGqikQEgaCgQC6Ow8KcxXJNaEAAAjRnD4xS98uOceI5JxHxcIMKitNWL1ardo\nEwLQ3RCdm2sA4MaZMyeHvcobJQVCUoDneTgcNrhcp5CTI0/5fEXJ8pOf+OH3M9iwIfHf0o8/rsOV\nV0bwox92J+NvAAAbz0lEQVRlzlKYw6HTaaDXM7DZTsLtdg/5OElPCvv370dVVRV0Oh1qamrgcDj6\n3K+srKzHmtANDQ1JjpSQxGBZFp2drWBZJ3JzDaJqOxiIVAqsXetGXZ0BNlvivn4OH5bh5Zc1+NWv\nPAk7RzrqbmvQIhg8A5utc0ijoZOaFFiWxaxZs7B06VK0t7fDYrGgtra2136hUAhSqRTBYFB41NfX\nJzNUQhKiezW0U1AqozAYsnPxm0svjeLmm4P4xS8SM3aB44C7787BI494hzVgLlMxDAOTSQ+G8cJq\nbb3gVd6SmhQ++ugjqNVqLFy4EAaDAQ0NDXjrrbcQjUZ77Hf8+HFUVFQkMzRCEi4UCqGr6xT0eiZr\nqov6s3y5Fzt3KvHBB/GfquPllzVgWWDhwvRZoyAVdDoNVKoYrNbTvb5jzyepSeHgwYOoqqoSnpeU\nlECtVuPIkSM99mtpaUF7ezsqKythsViwaNEi+P3+ZIZKSFyFw2HY7a0wGuVpP2dRMuj1PB5/3B33\nkc7RKPDLXxqwerULGTb4OyHUahW0Wh5dXYMvMSS1MtPj8cBg6HkRGAyGXo0ifr8f48aNQ2NjI5RK\nJebPn49ly5bh2Wef7fO4K1asEH6urq5GdXV1vEMnZMii0Shstu6EEK95a8Rg714F9h9QAoMoNI0e\n5DGVABwA1u51YsIE8YxLGA6VSgUghLfeegPNzZ+DYc6/+FFCxik888wzaGxs7PW61+vFD3/4Q2ze\nvFl4zWKxoKmpCRdddFG/x9u3bx+mTp0Km83WaxuNUyDpjOM4dHaehkbDfv3hJED35HQzZ5px4MDg\nJqe7kHEKhw51T8K3b9/wJuETG58vAJbVwmIpApDkcQr33nsvjh8/3uvx1ltv4dNPPxX2O336NILB\nIMaOHdvj9zdu3Ijm5mbheSwWg1abnY1yJLO53S7I5RFKCOdI9OR0l1ySHpPwpRudTgOO8wy4sltS\na90mTZqEYDCIdevWweVyob6+HjNnzuxVpP7qq69QW1uLzs5O2Gw2LF++HLNnz05mqIQMWzQaRSBg\ng06XPusJpIMXXtBAreYxb17iJqc7Ownfe+9l7kSCiWAwaODxWM/bVTWpSUEmk+HNN9/EmjVrUFxc\njM7OTqxevRoAsH79eqHEUFdXh4qKCowbNw5jx45FWVkZVq5cmcxQCRk2n88LtVo6YB1uNmlvl+Cx\nx/RYs8aNRP5ZtFoezzzjxj33GJGgyUQzklQqhULBnneuJJr7iJAEaWtrgdmsyLg1EBLp1ltNGDMm\nhpUrL2xx+qHOfTRnjgmVlTE88siFnU/MIpEIAgEZCgtLae4jQpIlFouBYVhKCOd4910lDhyQ46GH\nkvcF/ZvfuPHCCxp8+WX2jBofiEKhQCTSf9UdXbGEJADLspBIqAR7Vqompysu5vDwwz4sXZqcSfgy\nxfmuTUoKhCRAdzsCtSWc9eSTOlxxRRRTpiR/crrFi/3w+ZIzCV/m6P/apDIVIQkgk8nAsqmOIj18\n9pkMf/yjBnv3dqXk/FIpsGaNG7NmmTF9egi5udldZOhuR+h/inYqKRCSABKJBFKp8oLmnBEjjgOW\nLjWivt6LoqLUDSS77LIobropcZPwZZJQKASVStfvdkoKhCSITmdGIJAdc/n3Z/16DSIRJi0mp1ux\nwou//12FXbuye+6pYDAGrbb/5EhJgZAE0Wq1iMXkFzx1sVh0dUnwyCN6rF3rQjosKGcw8Pj1r924\n+24jIpFUR5MaoVAIEon2vCPsKSkQkiAMwyAnpwAeT+JG7qazhx82YM6cIKqq0icp3nhjCKNGsXjm\nmf6rT8SK4zj4fDGYTJbz7kcNzYQkkEajQTBohsfjhMGQPV9E77+vwPvvK/DJJ6lpXO4PwwCNjW5M\nnpyHm28Oorw8e3oDuN1+6HSFA07dTiOaCUkwnudhtbZDLg9BqxV/t0ittggs293lUaOJT+Ny92ec\nidvUGIFAdyXJtm12/PCH4m/38Xh8kEhMyM3NF17r77uTSgqEJBjDMMjPL4LV2gYgKPrEcPPNQbz2\nmgZ2e0fcjtnW1o4XXijBypXxSTLRKFBYWIQTJ9KgsSPBPB4feF4Ps3lwU4RQSYGQJGFZFl1d7ZDL\nw6KeOfXsBHTxnC28tbUNY8ZcgVCoPW7H9PkYqFQ8ZCK+NXa7fZBIjMjNtfSamDGp6ykQQnqTSqWw\nWIrBslq43T7R3syoVPFNCImi04k3IXAcB4fDC5nM3GdCOB9KCoQkkUQiQX5+IeTyPDgcXrA07JnE\nWTQahcMRgFZbBLM574KnbhdpniQkfTEMA5PJDIVCCaezAzqdhFZmI3Hh9wcRCkmQmztyyNcUlRQI\nSRGtVguLpQzBoFzU1Ukk8TiOg9PpRSymQUHB0BMCQEmBkJSSy+UoKCiBQpEPu92PcFj83SOH6r77\nOlMdQloKBoNwOELQaIpgsRRBOszh49T7iJA0EQ6H4XCcgVQahl6voQV6zjHUldfEjGVZeDwBMIwO\nZrOl11r3A6FxCoSkOaVSicLCkfB4PHA4rNBqpVCrqa2B9ObzBRAMAjk5xdDp4jtSnpICIWmEYRgY\njUZoNBo4nTYEgx4YDGrIxNp3klyQSCQCrzcMpdKEoqLcYVcV9YWuNELSkFwuh8VShEDACJerE3J5\nCDodVSllK5Zl4fUGwfMqmM1lCe2tRkmBkDSm0WigUpV9XaXUBbVaIvppMsj/4Xkefn8QwSBgNBZB\np9Nd8LiDC0W3HYSkOYlEgpycHBQUlIPn9bDZvAidnUsiizz9dEGqQ0iqQCAEu90PicSMoqJy6PX6\nhCcEgHofEZJxwuEwXK4ucJwfer3qgnudZKJEzH2UrsLhMHy+CORyI3JychP2/6XeR4SIhFKpREFB\nCQKBAFwuK6RSH/R6dUIaHUnyRKNReL3dK6OZzUUpG+VOSYGQDKXRaKBWl8Hv98PptEKhCFJjdAY6\n24jMsgrk5JRCo0ntDLqUFAjJYAzDQKfTQaPRwOv1wm7vgloNaLXqpNQ/k6HrXh4zgEhEBqNxBLRa\nbVr8z1J2S1FTU4M9e/b0u/3dd9/FmDFjoNfrMW/ePBr+T8h5SCQSGI1GFBWVQyIxw24PwOcLUHtb\nGuI4Dl6vH3Z7CApFAUaMKE9Kr6LBSnpSYFkWW7ZswY4dO/r9IzgcDsydOxeNjY04efIkrFYrVq1a\nleRICck8UqkUJpMZBQXlYJgc2O1+BALi6KmU6XMf8TwPny8Auz0ImSwPI0ZUwGg0pk0yOCvpvY8u\nueQSfPnll+A4Drt378bEiRN77fPKK6/g1Vdfxfbt2wEAu3btwoIFC/DVV1/12pd6HxHSv2g0Crfb\ngXDY/fW0GZk5xiHT5z7qHmvAQa02wWg0pUWngLTpfXTo0CEAQHl5eb/7HDx4EFVVVcLzSy+9FEeP\nHkUgEEh5IwwhmUQulyMvrwCRiAkulx2BgAcajSzjGqNZlkUsFsu4amSW5RAIsFCpclBQYM6I6UrS\nMkKv14uysjLhuVarhVQqhdvt7jMprFixQvi5uroa1dXVSYiSkMyhUChgsRQhFDLB7/cgGk2PFd8s\nlhEJOa7Vmh7jGaRSGfLzjVAoFKkOBU1NTWhqahpwv4QkhWeeeQaNjY29Xp8/fz6WL18+4O8bDAZ4\nPB7hud/vB8uyMBqNfe5/blIghPRPpVKl1SpvVPWbPN+8YV65cmWf+yUkKdx777249957h/z7EyZM\nwIYNG4TnBw4cwOjRo6nqiBBCEiwtKxanT5+OvXv3Ytu2bbDb7WhoaMAtt9yS6rAIIUT00iYprF+/\nHmPHjgUAmM1mbNiwAbW1tSgrK4PFYkF9fX2KIySEEPGjCfEIISQL9ffdmTYlBUIIIalHSYEQQoiA\nkgIhhBABJQVCCCECSgqEEEIElBQIIYQIKCkQQggRUFIghBAioKRACCFEQEmBEEKIgJICIYQQASUF\nQgghAkoKhBBCBJQUCCGECCgpEEIIEVBSIISQPnR1deGGG26ARqNBSUkJli1b1uf6A2fOnMGUKVOg\n0WgwduxYvPrqq0M+Z2dnJy6++OIerx07dgxTpkyBVqvFxRdfjK1btw75+INBSYEQQvpQX18Ps9kM\nh8OB3bt3Y8eOHdi0aVOv/ZYsWYLKykpYrVb88Y9/xOLFi3Hy5MkLPp/H48HDDz/cK/HcfPPN+P73\nvw+3243GxkbcdtttaGtrG/L7GgglBUJIxmtqasLEiRPxk5/8BHq9HhMmTMAXX3wxrGNKJBJEo1Fw\nHCf8bDAYeu23c+dOPPzww9DpdLj66qtRWVmJQ4cOAQD27t2L8ePHQ61W4/vf/z66urr6PNfHH3+M\n3NxcrF+/vsfrnZ2d+Oqrr/DQQw9BJpPhRz/6EcaPH4+PP/54WO/tfCgpEEJEYf/+/bj44ovhdrux\nYMEC3HLLLcM6XkNDA95//33odDqUlpaisLAQ1113Xa/9nE4nioqKAAB79uzBkSNHcOmll8Lr9aKm\npgarVq2C3W5HdXU1Fi9e3Oe5Jk6ciGg0ivfee69HScFoNOL999+HTCYDAAQCARw7dgwFBQXDem/n\nQ0mBECIKRqMR99xzDyQSCZYuXYrW1la0tLT02OcPf/gD1Gp1r8dtt93W63g//elPMXnyZDidTnz+\n+ec4depUrzt5oLtEAQBFRUW46qqr8IMf/AAFBQXYunUrJk2ahBkzZkCj0eCXv/wl/va3vyESifT7\nHr5ZdaRSqXDZZZcBAL744gtce+21GD9+PCZPnnzBf5/BoqRACBGFsrIy4WeGYWAymeBwOHrss3Dh\nQgSDwV6PDRs29Dre1q1b8eijj8JoNGLcuHFYvHgx/vrXv/Z7/o6ODhw5cgRffvklGhsbcfr0aWzf\nvl1IPEajEZFIBO3t7RgzZgzkcjkUCgVOnz593vfFcRweffRRXHnllZgyZQq2bNlygX+ZC0NJgRAi\nCmfOnBF+DofDOHPmTI9EcaFYlkU4HBaeSySSXm0Kp0+fxtSpU4Xno0ePxowZM3DixAnk5eVh1qxZ\nPZLP7t27UVpaiqNHjyIajSISiaC0tPS8cdx7773YsmUL9u/fj4aGBsjl8iG/p8GgpEAIEYXOzk6s\nXr0awWAQTz75JCZNmoT8/Pwe+7zwwguQy+W9HnPmzOl1vJkzZ2LlypWw2+1obW3Fn/70J9x44409\n9hkxYgSam5vx3HPPIRgM4vDhw/jzn/+MKVOmYPr06di5cyd27dqFSCSC559/HrfeeiukUumg39OJ\nEyewYcMGvPvuuxgzZszQ/jAXis9wIngLhJBh+sc//sFXVFTwN910E69SqfirrrqK7+joGNYxnU4n\nP3v2bF6r1fIWi4V/+umnhW1jxozhX3nlFZ7neX737t385ZdfzqvVan7s2LH8mjVrhP3eeecd/qKL\nLuIVCgV/+eWX84cPHx7wfVRWVgrP33zzTZ5hGF4mk/V4nD33cPT33cl8vTFjMQzT54ASQkj2aGpq\nwpIlS/D555+nOpSM0d93J1UfEUIIEVBSIISIAsMwqQ5BFFKWFGpqarBnz55+t5eVlfXoR9zQ0JDE\n6LJXU1NTqkMgpE/nuzarq6vx2WefJS8YEUt6UmBZFlu2bMGOHTv6zeyhUAhSqbRHV676+vokR5qd\nKCmQdEXXZnLIkn3CqqoqfPnll+A4rt99jh8/joqKiiRGRQghBEhBSeHQoUOIRqMYOXJkv/u0tLSg\nvb0dlZWVsFgsWLRoEfx+fxKjJISQLDXszq5DNGrUKH7Pnj19btu8eTN/ww038CdPnuTPnDnDT506\nlV+8eHGf+wKgBz3oQQ96DOHRl4SMU3jmmWfQ2NjY6/X58+dj+fLlAIDy8nJs3rwZEydOHPB4+/bt\nw9SpU2Gz2eIdKiGEkHMkpE3h3nvvxb333jvk39+4cSPGjx+P73znOwCAWCwGrVYbr/AIIYT0Iy3H\nKXz11Veora1FZ2cnbDYbli9fjtmzZ6c6LEIIEb20SQrr16/H2LFjAQB1dXWoqKjAuHHjMHbsWJSV\nlWHlypUpjpAQQsQv4+c+Iuc3ZswYYb1YlmWFGRoZhsGxY8cGnLaXkHiTSCS9ZgotLS3ttSDON3/n\nzJkzsFgsiQ4v6yV9nAJJrqNHjwo/SyQStLW10QeLpBxdh+krbaqPSHLwPI+XX34ZN954I2666SbM\nnDkT77//PiorK4V9mpqaejx/8sknYbFYYDQahd5jhMSb0+nEjBkzYDQakZeXh1/+8pe99gmHw7jt\ntttgNBpRUFCAp59+Wti2fft2jBkzBmq1GrNmzUIgEEhm+KJBSSHLnJ1aZOvWrbjjjjvw9ttvn3fq\n8Y0bN+K1117D/v37cfjwYWzduvW8SxISMhh9XXO//vWvUVJSgq6uLnz44YdYvXo1Dh8+3GOfdevW\nIRwOo6urCx988AEef/xxtLa24sSJE5g3bx7Wr18Pq9UKk8lEU+MMESWFLHX55ZfjuuuuG3C/9evX\n45FHHkFpaSlKSkqwdOlSvP7660mIkIjZqFGjekx4uXXrVsyfPx+PPfYYOI5DNBqFQqGA3W4Xfofn\nefh8PnR0dGDfvn2oqKjA0aNHUVRUhE2bNmHu3LmYPHky9Ho96uvr6TodImpTyFI5OTn9bjv3Lu7U\nqVOYO3euUMLgeR7f/e53Ex4fEbeTJ0/2alP429/+hhkzZkAul+OSSy7p1RjNMAzuuece2O123Hrr\nrXA6nZgzZw6efvppnDp1Ci+++CLWrVvX43c4joNEQve+F4L+WgQMw4BlWeH56dOnhZ/z8/Px1ltv\nCbPVdnR04IUXXkhFmETkFixYgN///vdobm7Gpk2boNFoeu2zd+9e/PznP8fx48fx8ccf48MPP8SG\nDRuQn5+P++67T7hOvV4vdu/eTQlhCOgvRlBSUoKTJ0/i8OHDaGtrw9q1a4WSwU033YTf/OY3sFqt\nsNlsmDNnDhXLSUKEw2H4/X4Eg0H87ne/Q2trK4LBoLCd53m88sorqK+vRyQSgdFoBM/zKCoqwqxZ\ns/Dqq6+iubkZoVAIK1aswIMPPpjCd5O5KClkkbNf9AzD9FjLYvTo0bj//vsxadIkfO9738Ott94q\nbPvpT38qTDlSXl6OkSNHYtmyZUmPnYhHf+uoPP3001i4cCFKS0vh8XhQW1uLO+64o8fvNTQ04MSJ\nE8jLy8Mll1yC6667DjU1NZgwYQIeffRR3HDDDcjJycHu3bvx0ksvJestiQoNXiOEECKgkgIhhBAB\nJQVCCCECSgqEEEIElBQIIYQIKCmI0O9//3ts2rRp2Mepq6vDtGnThOePPPIIVCqVMAr1W9/6FgDg\noYcewr/+9a9hn4+I33CuzWg0ip/+9KcwGo0oLCzEz3/+c2EbXZtxNIxllkka8ng8/GWXXcZzHDes\n4xw4cIBXq9X8tGnThNfmzJnDNzU19dq3tbWVv+aaa4Z1PiJ+w702n3nmGf6qq67iOzo6+BMnTvCV\nlZX8hg0beJ6nazOeqKQgMn/84x8xffp0/POf/8SVV16J2tpaGAwGXHzxxfj0008HdYxoNIqf/OQn\n+K//+q8eU160tLRg9OjRvfYvLi6GTqfDBx98ELf3QcRnuNfmzp07sXTpUhQWFqKsrAzXX389Dh48\nCICuzXiipCAyb7zxBq699lrwPI/9+/ejtLQUNpsNU6dOxX333TeoYzzxxBOYOnWqsEb2WS0tLVi0\naBHMZjMmT56Mffv2CdumT5+OzZs3x/W9EHEZ7rX5l7/8BXPmzAEAdHR04J133sGkSZMA0LUZT5QU\nRIRlWXz88ceYMGECgO5J75YtWwaFQoEZM2YIK7B9c4bKs48vvvgChw4dwv/8z/+gvr6+RykhFAqh\npKQEd999N9ra2jBv3jxcf/318Hg8AIBLL70Uu3btSv6bJhkhHtfm2XmM5syZg+LiYoTDYVx99dV0\nbcYZJQURsdvtYFlWmAE1Pz9f2KZUKsFxHADgxIkTwsRh5z7GjRuHO++8E2vXroVcLu9xbJVKhQMH\nDqCmpgZqtRpLlixBbm4u/vnPfwrnamtrS9I7JZlmuNfmt7/9bWH/TZs2wW6344orrsDChQvp2owz\nSgoiwjDMsGaF9Hg8+OSTTzBlyhSo1Wrcdddd2LFjBywWCw4fPoxXXnmlx/4sy0Kn0wGA8KEmpC/D\nvTYB4IYbbsCRI0cAACaTCbfffjtOnDhB12acUVIQkdzcXACAz+c7734lJSWQy+U9HgqFAu3t7YhE\nIsLd2bp16zBlyhRYrVbI5XIsXboU77//PsLhMJ577jl4vV5873vfAwBYrVaMGDEi4e+RZKbhXpuf\nffYZlEolHnvsMbhcLlitVqxduxZTpkyBQqGgazOOaJEdEZFIJLjyyivxySef9JoJFfi/2SlbW1sH\nfcyzv/Otb30Lzz33HBYsWID29nZMmDABf/3rXyGTdV9Czc3NtPgO6Vc8rs3GxkYsXrwYpaWlMBgM\nuPHGG7Fq1Sqo1Wq6NuOIZkkVmbVr16KtrQ1PPPFEUs/7n//5n1i8eDGuvfbapJ6XZA66NjMDJQWR\nCQQCuOqqq7B//37hTinRbDYbampqsGfPnqScj2QmujYzg3TFihUrUh0EiZ+zdbCHDx8Wuv8l2lNP\nPYWFCxeivLw8KecjmYmuzcxAJQVCCCEC6n1ECCFEQEmBEEKIgJICIYQQASUFQgghAkoKhAzCiRMn\nIJFIUFdX1+P1l19+ucdCRIRkOkoKhAySRCLB6tWrhfl3CBEjSgqEDJJcLsfixYtx991399rGcRwe\neOAB5OTkoLCwEGvXrhW2SSQSPP/88ygsLERhYSE2btwobNu+fTvGjBkDtVqNWbNmIRAIJOW9ENIf\nSgqEXIDly5fj888/xxtvvNHj9SeeeAK7d+/G8ePH8a9//Qu/+93v8Pbbbwvbd+7ciZaWFjz33HO4\n6667EIlEcOLECcybNw/r16+H1WqFyWRCfX19st8SIT3Q4DVCBuHEiROorKxEMBjEX/7yF9TW1uKL\nL77A66+/jtdeew3Hjh3Diy++iKuvvhoA8Oyzz2L79u3YsmULJBIJPv30U3znO98Bx3FQKpVoaWnB\nhg0b0NHRgdWrVwvnuOaaa3Dq1KlUvlWS5aikQMgFuvHGGzF+/HisXLkSAMDzPFpbW1FRUSHsU1FR\n0WPGz4KCAgDdVUkymQwcx+HUqVN47rnnhNXFKisr0dXVRfP/k5SipEDIEKxduxbPP/88PvvsMzAM\ng9LSUrS0tAjbW1paUFpaet5j5Ofn47777hPWr/B6vdi9e/ewF6MhZDjo6iNkCEaNGoWf//znaGxs\nBADcfvvtqKurg9PpxMmTJ/Hb3/4WCxYs6Pf3GYbBrFmz8Oqrr6K5uRmhUAgrVqzAgw8+mKy3QEif\nKCkQMkjfXBjmgQceQHl5ORiGwUMPPYTLLrsMo0aNwhVXXIGlS5di5syZff7e2ecTJkzAo48+ihtu\nuAE5OTnYvXs3XnrppeS8GUL6QQ3NhBBCBFRSIIQQIqCkQAghREBJgRBCiICSAiGEEAElBUIIIQJK\nCoQQQgT/H7yWqRxCRdEzAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1f090>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_scatter((freq * count).dropna(), fun)\n",
      "xlim(-1,20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(-1, 20)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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D0IbTaUer1Xe5j6o2YzTKnDzuoNVq0esNTJ78E8rLv6K4eAOtrVbCwkIYOnQSYWHDKSt7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\nmurpEIUYNGTFvR5yuVx88cUXHDlyBIP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       "text": [
        "<matplotlib.figure.Figure at 0xb82f6d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, drinker*1.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>    </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 1.73</td>\n",
        "      <td> 0.189</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 45</td>\n",
        "      <td> 16</td>\n",
        "      <td>   4</td>\n",
        "      <td> 1.79</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.393</td>\n",
        "      <td> 0.221</td>\n",
        "      <td> 0.696</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 35</td>\n",
        "      <td>  7</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.739</td>\n",
        "      <td> 0.583</td>\n",
        "      <td> 0.935</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "       Stats          Median Survival             5y Survival               Log-Rank       \n",
        "  # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                1.73  0.189\n",
        "1         45       16               4  1.79   NaN       0.393  0.221  0.696                \n",
        "0         35        7             NaN   NaN   NaN       0.739  0.583  0.935                \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "drinker_inferred = 1.*drinker.combine_first(ret['filled_feature'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, drinker_inferred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>    </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 6.27</td>\n",
        "      <td> 0.0122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 183</td>\n",
        "      <td> 87</td>\n",
        "      <td> 2.16</td>\n",
        "      <td>  1.6</td>\n",
        "      <td> 3.53</td>\n",
        "      <td> 0.358</td>\n",
        "      <td> 0.276</td>\n",
        "      <td> 0.464</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  75</td>\n",
        "      <td> 22</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 4.49</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  0.56</td>\n",
        "      <td> 0.405</td>\n",
        "      <td> 0.774</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "       Stats          Median Survival             5y Survival               Log-Rank        \n",
        "  # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2       p\n",
        "                                                                                6.27  0.0122\n",
        "1        183       87            2.16   1.6  3.53       0.358  0.276  0.464                 \n",
        "0         75       22             NaN  4.49   NaN        0.56  0.405  0.774                 \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "si = drinker_inferred.map({1:'drinker_inf', 0:'non-drinker_inf'})\n",
      "s = drinker.map({True:'drinker', False:'non-drinker'})\n",
      "survival_and_stats(s.combine_first(si), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uHC5pXjjjnUCuXr0KALh16xYAsLqciuQybiwyNm2GQ88eSF/5DYDcJ/p4z9yl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\nIqf+H8ilXftDhMtLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7e84290>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(drinker_inferred, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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n+aFDh2Lo0KEW+yV1/MIjckcSqe2NaFK1Gv4jhpu2VVMmQzVlMq4NGQrvyHuh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d1ec50>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Perineural Invasion "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* This is part of the pathological evaluation, we are missing these for about 100 patients \n",
      "* We expect this to effect tumor specific signatures, so we use differentially expressed genes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "invasion = invasion.ix[keepers_o].dropna()\n",
      "invasion.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "yes    95\n",
        "no     89\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ret = SVC_fill(invasion[invasion.isin(['yes','no'])]=='yes', \n",
      "               rna.features.ix['real'])\n",
      "ret['auc']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "0.88586956521739135"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fun = ret['decision_function']\n",
      "violin_plot_pandas(invasion, fun)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JhQXWN4heFackyZQUgtSqyrY1a9Zg5syZuPzyy7Fnz54G3VNJ/FCpVOC46HdN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\n8ndeXh7y8vKiECUhzWNZFjp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uv/56dOjQAVqtFrt370Z+fj54nsfUqVORlpaGzZs3K8c6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\nQghR/H/ntKeHdOwR2gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc15bdd0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "invasion_inferred = 1.*(invasion.dropna()=='yes').combine_first(ret['filled_feature'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, invasion)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>    </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 8.73</td>\n",
        "      <td> 0.00314</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td> 95</td>\n",
        "      <td> 45</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 1.38</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.398</td>\n",
        "      <td> 0.289</td>\n",
        "      <td> 0.548</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td> 89</td>\n",
        "      <td> 23</td>\n",
        "      <td> NaN</td>\n",
        "      <td>    4</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.598</td>\n",
        "      <td> 0.455</td>\n",
        "      <td> 0.786</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "         Stats          Median Survival             5y Survival               Log-Rank         \n",
        "    # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2        p\n",
        "                                                                                  8.73  0.00314\n",
        "yes         95       45             2.5  1.38   NaN       0.398  0.289  0.548                  \n",
        "no          89       23             NaN     4   NaN       0.598  0.455  0.786                  \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, invasion_inferred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>    </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 3.76</td>\n",
        "      <td> 0.0524</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 141</td>\n",
        "      <td> 52</td>\n",
        "      <td>    4</td>\n",
        "      <td> 2.25</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0.455</td>\n",
        "      <td> 0.348</td>\n",
        "      <td> 0.596</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 117</td>\n",
        "      <td> 57</td>\n",
        "      <td> 1.71</td>\n",
        "      <td> 1.42</td>\n",
        "      <td> 4.49</td>\n",
        "      <td> 0.364</td>\n",
        "      <td> 0.265</td>\n",
        "      <td>   0.5</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "       Stats          Median Survival             5y Survival               Log-Rank        \n",
        "  # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2       p\n",
        "                                                                                3.76  0.0524\n",
        "0        141       52               4  2.25   NaN       0.455  0.348  0.596                 \n",
        "1        117       57            1.71  1.42  4.49       0.364  0.265    0.5                 \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(invasion_inferred, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j5gP27MnYYEFXIq808PFGfim2n8yx6PW//eOq3gZFW09mI/ve/f0M1h++jvxi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\nKvrAAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x80d6a90>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(invasion.ix[invasion_inferred.index].combine_first(invasion_inferred), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3scSNtFwUFHO8nsjazoopbY1EbGwsYmNjq30fownyXbt2xZdffok5c+YgJycH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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb48eed0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extra-Capsular Spread "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Similar to perineural invasion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spread = spread.ix[keepers_o].dropna()\n",
      "spread.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "no     118\n",
        "yes     51\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ret = SVC_fill(spread=='yes', rna.features.ix['real'])\n",
      "ret['auc']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "0.89349112426035504"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fun = ret['decision_function']\n",
      "violin_plot_pandas(spread, fun)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ll+rjU6dOQRRFDBkyRN22du1a7N+/X30syzLMZnNMy0kIiQ6NRoNevXpDq01Hba0npq/d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\nQkLwL+MF2n75DVATAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x80f3e50>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spread_inferred = 1.*(spread.dropna()=='yes').combine_first(ret['filled_feature'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, spread)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>    </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 13.1</td>\n",
        "      <td> 0.000299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td> 118</td>\n",
        "      <td> 40</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 2.96</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0.523</td>\n",
        "      <td> 0.409</td>\n",
        "      <td> 0.668</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td>  51</td>\n",
        "      <td> 29</td>\n",
        "      <td> 1.42</td>\n",
        "      <td> 1.24</td>\n",
        "      <td> 2.08</td>\n",
        "      <td> 0.258</td>\n",
        "      <td> 0.145</td>\n",
        "      <td>  0.46</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "         Stats          Median Survival             5y Survival               Log-Rank          \n",
        "    # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2         p\n",
        "                                                                                  13.1  0.000299\n",
        "no         118       40             NaN  2.96   NaN       0.523  0.409  0.668                   \n",
        "yes         51       29            1.42  1.24  2.08       0.258  0.145   0.46                   \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, spread_inferred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>    </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 12.8</td>\n",
        "      <td> 0.000341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 197</td>\n",
        "      <td> 74</td>\n",
        "      <td> 4.36</td>\n",
        "      <td> 2.58</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.457</td>\n",
        "      <td> 0.365</td>\n",
        "      <td> 0.572</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  61</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1.42</td>\n",
        "      <td> 1.25</td>\n",
        "      <td> 1.9</td>\n",
        "      <td> 0.251</td>\n",
        "      <td> 0.146</td>\n",
        "      <td>  0.43</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "       Stats          Median Survival             5y Survival               Log-Rank          \n",
        "  # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2         p\n",
        "                                                                                12.8  0.000341\n",
        "0        197       74            4.36  2.58   NaN       0.457  0.365  0.572                   \n",
        "1         61       35            1.42  1.25   1.9       0.251  0.146   0.43                   \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(spread, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rqa7XaDS4ceOGbjstLQ0HDhzAoEGD0KdPn3qLg9X10lW+Mx473xGRLbHp6vpz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\nCXkhhBDCTknICyGEEHbqXxitPHPnqUboAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf83510>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(spread_inferred, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bc6HR6estU1OFRofZqw4Zj4e9+3O9Zav7/Kt9kXkWX2SerVNu9Z4L+L9+Da+U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\nXBQHecYYY8xF/Q84jywVQ0Y6CgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8107f50>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Save processed clinical variables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical_processed = pd.concat({'spread': spread, \n",
      "                                'spread_inferred': spread_inferred,\n",
      "                                'invasion': invasion, \n",
      "                                'invasion_inferred': invasion_inferred,\n",
      "                                'hpv': clinical.hpv, \n",
      "                                'smoker': smoker, \n",
      "                                'smoker_inferred': smoker_inferred,\n",
      "                                'drinker': drinker, \n",
      "                                'drinker_inferred': drinker_inferred,\n",
      "                                'stage': stage,\n",
      "                                'lymph_stage': lymph_stage,\n",
      "                                'age': age, \n",
      "                                'old_age': old_age,\n",
      "                                'pack_years': pack_years,\n",
      "                                'year': (year < 2000).map({True: 'pre_2000', False: 'post_2000'}),\n",
      "                                'lymph_status': lymph_status,\n",
      "                                'tumor_subdivision': tumor_subdivision}, axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical.processed = clinical_processed\n",
      "clinical.save()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}