envisionTuneTrainPredict.ipynb

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      "## We use graphlab (aka Turi) to tune hyperparameters and train models\n",
      "import graphlab as gl\n",
      "from graphlab.util import cloudpickle\n",
      "import graphlab.aggregate as agg\n",
      "import sys\n",
      "import pickle   ## Used for saving models\n",
      "\n",
      "## We use pandas, numpy and scipy for data analysis\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import scipy.stats.stats as stats\n",
      "%load_ext rpy2.ipython\n",
      "from math import sqrt\n",
      "\n",
      "## sklearn is used for setting up cross validation data sets\n",
      "import itertools\n",
      "from collections import Counter\n",
      "from sklearn import cross_validation, metrics, preprocessing, datasets   #Additional scklearn functions\n",
      "from sklearn.grid_search import GridSearchCV   #Performing grid search\n",
      "from sklearn.utils import shuffle\n",
      "from sklearn.metrics import mean_squared_error\n",
      "from sklearn.metrics import confusion_matrix\n",
      "from sklearn.cross_validation import LabelKFold\n",
      "from sklearn.cross_validation import LeaveOneLabelOut\n",
      "\n",
      "## Matplotlib for graphing\n",
      "import matplotlib.pylab as plt\n",
      "%matplotlib inline\n",
      "from matplotlib.pylab import rcParams\n",
      "rcParams['figure.figsize'] = 8, 8\n"
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        "/net/gs/vol3/software/modules-sw-python/2.7.3/requests/2.7/Linux/RHEL6/x86_64/lib/python2.7/site-packages/requests-2.7.0-py2.7.egg/requests/packages/urllib3/util/ssl_.py:90: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.\n",
        "  InsecurePlatformWarning\n"
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       "text": [
        "This non-commercial license of GraphLab Create for academic use is assigned to vegray@uw.edu and will expire on May 18, 2017.\n"
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        "\u001b[0;32m/net/gs/vol3/software/modules-sw/python/2.7.3-UCS4/Linux/RHEL6/x86_64/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2156\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2160\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/net/gs/vol3/software/modules-sw/python/2.7.3-UCS4/Linux/RHEL6/x86_64/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2077\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2078\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2080\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m<decorator-gen-62>\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
        "\u001b[0;32m/net/gs/vol3/software/modules-sw/python/2.7.3-UCS4/Linux/RHEL6/x86_64/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/net/gs/vol3/software/modules-sw/python/2.7.3-UCS4/Linux/RHEL6/x86_64/lib/python2.7/site-packages/IPython/core/magics/extension.pyc\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/net/gs/vol3/software/modules-sw/python/2.7.3-UCS4/Linux/RHEL6/x86_64/lib/python2.7/site-packages/IPython/core/extensions.pyc\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m                     \u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m             \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "## Read in data\n",
      "## Set your own working directory ## \n",
      "data_1per =  gl.SFrame.read_csv('~/data/dmsTraining_2017-02-20.csv',column_type_hints={'aa1_psic':float, 'aa2_psic':float, 'delta_psic':float, \n",
      "                                                        'accessibility': float, 'delta_solvent_accessibility':float,\n",
      "                                                        'mut_mut_msa_congruency':float})"
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        "<pre>Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/dmsTraining_2017-02-20.csv</pre>"
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        "Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/dmsTraining_2017-02-20.csv"
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       "html": [
        "<pre>Parsing completed. Parsed 100 lines in 2.91623 secs.</pre>"
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       "text": [
        "Parsing completed. Parsed 100 lines in 2.91623 secs."
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        "<pre>Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/dmsTraining_2017-02-20.csv</pre>"
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       "output_type": "display_data",
       "text": [
        "Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/dmsTraining_2017-02-20.csv"
       ]
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       "html": [
        "<pre>Parsing completed. Parsed 101460 lines in 1.46528 secs.</pre>"
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        "Parsing completed. Parsed 101460 lines in 1.46528 secs."
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     "input": [
      "## Filter data by dataset, missense only and assign IDs to each row\n",
      "data_1per = data_1per.filter_by([x for x in data_1per['dms_id'].unique() if x not in ['CcdB','beta-lactamase_2500', 'beta-lactamase_0', \n",
      "                                                                                      'kka2_1:1','kka2_1:4','kka2_1:8',\n",
      "                                                                     'beta-lactamase_39', 'gal4','hemagglutinin', 'np',\n",
      "                                                                                      'dbr1', 'beta-lactamase_156',\n",
      "                                                                                      'beta-lactamase_625', 'ERK2', \n",
      "                                                                                      'Brca1_HDR','GFP', 'Brca1_E3',\n",
      "                                                                                      'E3_ligase','Brca1_Y2H']], 'dms_id')\n",
      "data_1per2 = data_1per.filter_by([x for x in data_1per['mut_type'].unique() if x in ['missense']], 'mut_type')\n",
      "del data_1per2['id']\n",
      "data_1per2 = data_1per2.add_row_number('id',0)"
     ],
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     "input": [
      "## Define feature set\n",
      "predictors3 = ['aa1', 'aa2', 'wt_mut', 'aa1_polarity', \n",
      "               'aa2_polarity', 'aa1_PI', 'aa2_PI', 'deltaPI', \n",
      "               'Grantham', 'aa1_weight', 'aa2_weight', 'deltaWeight',\n",
      "               'aa1vol', 'aa2vol', 'deltavolume', 'aa1_psic',\n",
      "               'aa2_psic', 'delta_psic',  'accessibility',\n",
      "               'dssp_sec_str', 'phi_psi_reg', 'delta_solvent_accessibility',\n",
      "               'b_factor', 'mut_msa_congruency', 'mut_mut_msa_congruency', \n",
      "               'seq_ind_closest_mut', 'evolutionary_coupling_avg']"
     ],
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     "input": [
      "### Create Leave-one-protein-out folds\n",
      "folds = LeaveOneLabelOut(data_1per2['protein'])\n",
      "lopo_folds = []\n",
      "for train, test in folds:\n",
      "    lopo_folds.append((data_1per2.filter_by(train,'id'), data_1per2.filter_by(test,'id')))"
     ],
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     "input": [
      "### Define Tuning parameters\n",
      "params = dict([('target', 'scaled_effect1'), ('max_depth', 5), ('max_iterations' , [25,50,100]), \n",
      "               ('column_subsample' , .8), ('row_subsample' , .8), ('min_child_weight' , 1), \n",
      "               ('min_loss_reduction' , 0), ('step_size' , 0.1), ('features' , [predictors3])])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "params1 = dict([('target', 'scaled_effect1'), ('max_depth', [2, 6, 10, 25, 50] ), ('max_iterations' , 100), \n",
      "               ('column_subsample' , .8), ('row_subsample' , .8), ('min_child_weight' , [2, 6, 10, 25, 50]), \n",
      "               ('min_loss_reduction' , 0), ('step_size' , 0.1), ('features' , [predictors3])])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "params2 = dict([('target', 'scaled_effect1'), ('max_depth',6), ('max_iterations' , 100), \n",
      "               ('column_subsample' , .8), ('row_subsample' , .8), ('min_child_weight' ,50 ), \n",
      "               ('min_loss_reduction' , [0.5,0.6,0.7,0.8,0.9,1]), ('step_size' , 0.1), ('features' , [predictors3])])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "params3 = dict([('target', 'scaled_effect1'), ('max_depth',6), ('max_iterations' , 100), \n",
      "               ('column_subsample' , [0.6, 0.7, 0.8, 0.9]), ('row_subsample' , [0.6, 0.7, 0.8, 0.9]), ('min_child_weight' ,50 ), \n",
      "               ('min_loss_reduction' ,  0.5), ('step_size' , 0.1), ('features' , [predictors3])])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Parameter search with VEG selected features\n",
      "lopo_train_params1 = gl.model_parameter_search.grid_search.create(lopo_folds, gl.boosted_trees_regression.create, params3)"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true,
      "scrolled": true
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO] graphlab.deploy.job: Validating job.\n",
        "[INFO] graphlab.deploy.map_job: Validation complete. Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100000' ready for execution\n",
        "[INFO] graphlab.deploy.map_job: Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100000' scheduled.\n",
        "[INFO] graphlab.deploy.job: Validating job.\n",
        "[INFO] graphlab.deploy.map_job: A job with name 'Model-Parameter-Search-Feb-23-2017-13-11-2100000' already exists. Renaming the job to 'Model-Parameter-Search-Feb-23-2017-13-11-2100000-6e730'.\n",
        "[INFO] graphlab.deploy.map_job: Validation complete. Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100000-6e730' ready for execution\n",
        "[INFO] graphlab.deploy.map_job: Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100000-6e730' scheduled.\n",
        "[INFO] graphlab.deploy.job: Validating job.\n",
        "[INFO] graphlab.deploy.map_job: Validation complete. Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100001' ready for execution\n",
        "[INFO] graphlab.deploy.map_job: Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100001' scheduled.\n",
        "[INFO] graphlab.deploy.job: Validating job.\n",
        "[INFO] graphlab.deploy.map_job: Validation complete. Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100002' ready for execution\n",
        "[INFO] graphlab.deploy.map_job: Job: 'Model-Parameter-Search-Feb-23-2017-13-11-2100002' scheduled.\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Identify optimal tuning parameters\n",
      "lopo_train_params1.get_best_params()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true,
      "scrolled": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "{'column_subsample': 0.6,\n",
        " 'features': ['aa1',\n",
        "  'aa2',\n",
        "  'wt_mut',\n",
        "  'aa1_polarity',\n",
        "  'aa2_polarity',\n",
        "  'aa1_PI',\n",
        "  'aa2_PI',\n",
        "  'deltaPI',\n",
        "  'Grantham',\n",
        "  'aa1_weight',\n",
        "  'aa2_weight',\n",
        "  'deltaWeight',\n",
        "  'aa1vol',\n",
        "  'aa2vol',\n",
        "  'deltavolume',\n",
        "  'aa1_psic',\n",
        "  'aa2_psic',\n",
        "  'delta_psic',\n",
        "  'accessibility',\n",
        "  'dssp_sec_str',\n",
        "  'phi_psi_reg',\n",
        "  'delta_solvent_accessibility',\n",
        "  'b_factor',\n",
        "  'mut_msa_congruency',\n",
        "  'mut_mut_msa_congruency',\n",
        "  'seq_ind_closest_mut',\n",
        "  'evolutionary_coupling_avg'],\n",
        " 'max_depth': 2,\n",
        " 'max_iterations': 100,\n",
        " 'min_child_weight': 50,\n",
        " 'min_loss_reduction': 0.5,\n",
        " 'row_subsample': 0.9,\n",
        " 'step_size': 0.1,\n",
        " 'target': 'scaled_effect1'}"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Train final global model with 5-fold more trees and 0.01 learning rate\n",
      "global_regression_model_noSELCO = gl.boosted_trees_regression.create(data_1per2, 'scaled_effect1', \n",
      "                features = predictors3, max_iterations = 500, \n",
      "                column_subsample= 0.6, row_subsample =0.9,\n",
      "                min_child_weight = 50, min_loss_reduction = 0.5, step_size = 0.01,\n",
      "                max_depth = 6)\n"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true,
      "scrolled": true
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
        "          You can set ``validation_set=None`` to disable validation tracking.\n",
        "\n"
       ]
      },
      {
       "html": [
        "<pre>Boosted trees regression:</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Boosted trees regression:"
       ]
      },
      {
       "html": [
        "<pre>--------------------------------------------------------</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "--------------------------------------------------------"
       ]
      },
      {
       "html": [
        "<pre>Number of examples          : 20031</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Number of examples          : 20031"
       ]
      },
      {
       "html": [
        "<pre>Number of features          : 27</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Number of features          : 27"
       ]
      },
      {
       "html": [
        "<pre>Number of unpacked features : 27</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Number of unpacked features : 27"
       ]
      },
      {
       "html": [
        "<pre>+-----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "+-----------+--------------+--------------------+----------------------+---------------+-----------------+"
       ]
      },
      {
       "html": [
        "<pre>| Iteration | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| Iteration | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |"
       ]
      },
      {
       "html": [
        "<pre>+-----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "+-----------+--------------+--------------------+----------------------+---------------+-----------------+"
       ]
      },
      {
       "html": [
        "<pre>| 1         | 0.129369     | 1.089102           | 0.944266             | 0.427360      | 0.429951        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 1         | 0.129369     | 1.089102           | 0.944266             | 0.427360      | 0.429951        |"
       ]
      },
      {
       "html": [
        "<pre>| 2         | 0.263070     | 1.084508           | 0.939951             | 0.424626      | 0.427243        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 2         | 0.263070     | 1.084508           | 0.939951             | 0.424626      | 0.427243        |"
       ]
      },
      {
       "html": [
        "<pre>| 3         | 0.377345     | 1.079721           | 0.935946             | 0.421813      | 0.424496        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 3         | 0.377345     | 1.079721           | 0.935946             | 0.421813      | 0.424496        |"
       ]
      },
      {
       "html": [
        "<pre>| 4         | 0.490532     | 1.074890           | 0.933955             | 0.419116      | 0.421770        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 4         | 0.490532     | 1.074890           | 0.933955             | 0.419116      | 0.421770        |"
       ]
      },
      {
       "html": [
        "<pre>| 5         | 0.609968     | 1.070689           | 0.929730             | 0.416522      | 0.419169        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 5         | 0.609968     | 1.070689           | 0.929730             | 0.416522      | 0.419169        |"
       ]
      },
      {
       "html": [
        "<pre>| 6         | 0.719464     | 1.066361           | 0.929051             | 0.413802      | 0.416463        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 6         | 0.719464     | 1.066361           | 0.929051             | 0.413802      | 0.416463        |"
       ]
      },
      {
       "html": [
        "<pre>| 10        | 1.007702     | 1.048771           | 0.913654             | 0.403533      | 0.406173        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 10        | 1.007702     | 1.048771           | 0.913654             | 0.403533      | 0.406173        |"
       ]
      },
      {
       "html": [
        "<pre>| 11        | 1.053229     | 1.045307           | 0.909205             | 0.401055      | 0.403683        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 11        | 1.053229     | 1.045307           | 0.909205             | 0.401055      | 0.403683        |"
       ]
      },
      {
       "html": [
        "<pre>| 20        | 1.432511     | 1.010347           | 0.878731             | 0.380329      | 0.383070        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 20        | 1.432511     | 1.010347           | 0.878731             | 0.380329      | 0.383070        |"
       ]
      },
      {
       "html": [
        "<pre>| 30        | 1.858896     | 0.974853           | 0.852618             | 0.359632      | 0.362496        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 30        | 1.858896     | 0.974853           | 0.852618             | 0.359632      | 0.362496        |"
       ]
      },
      {
       "html": [
        "<pre>| 40        | 2.276365     | 0.999811           | 0.833114             | 0.341985      | 0.344969        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 40        | 2.276365     | 0.999811           | 0.833114             | 0.341985      | 0.344969        |"
       ]
      },
      {
       "html": [
        "<pre>| 50        | 2.704659     | 1.021310           | 0.818987             | 0.325868      | 0.329046        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 50        | 2.704659     | 1.021310           | 0.818987             | 0.325868      | 0.329046        |"
       ]
      },
      {
       "html": [
        "<pre>| 51        | 2.746579     | 1.023581           | 0.816344             | 0.324299      | 0.327505        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 51        | 2.746579     | 1.023581           | 0.816344             | 0.324299      | 0.327505        |"
       ]
      },
      {
       "html": [
        "<pre>| 60        | 3.120284     | 1.042232           | 0.803304             | 0.311745      | 0.315137        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 60        | 3.120284     | 1.042232           | 0.803304             | 0.311745      | 0.315137        |"
       ]
      },
      {
       "html": [
        "<pre>| 70        | 3.513023     | 1.063248           | 0.788583             | 0.299784      | 0.303360        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 70        | 3.513023     | 1.063248           | 0.788583             | 0.299784      | 0.303360        |"
       ]
      },
      {
       "html": [
        "<pre>| 80        | 3.927128     | 1.079755           | 0.775252             | 0.288998      | 0.292873        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 80        | 3.927128     | 1.079755           | 0.775252             | 0.288998      | 0.292873        |"
       ]
      },
      {
       "html": [
        "<pre>| 90        | 4.358433     | 1.093145           | 0.765358             | 0.279546      | 0.283719        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 90        | 4.358433     | 1.093145           | 0.765358             | 0.279546      | 0.283719        |"
       ]
      },
      {
       "html": [
        "<pre>| 100       | 4.796877     | 1.103446           | 0.755456             | 0.271136      | 0.275562        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 100       | 4.796877     | 1.103446           | 0.755456             | 0.271136      | 0.275562        |"
       ]
      },
      {
       "html": [
        "<pre>| 101       | 4.843679     | 1.104409           | 0.754960             | 0.270332      | 0.274778        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 101       | 4.843679     | 1.104409           | 0.754960             | 0.270332      | 0.274778        |"
       ]
      },
      {
       "html": [
        "<pre>| 110       | 5.226488     | 1.114081           | 0.744732             | 0.263882      | 0.268700        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 110       | 5.226488     | 1.114081           | 0.744732             | 0.263882      | 0.268700        |"
       ]
      },
      {
       "html": [
        "<pre>| 120       | 5.647821     | 1.124138           | 0.758161             | 0.257363      | 0.262474        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 120       | 5.647821     | 1.124138           | 0.758161             | 0.257363      | 0.262474        |"
       ]
      },
      {
       "html": [
        "<pre>| 130       | 6.051773     | 1.134616           | 0.772084             | 0.251845      | 0.257231        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 130       | 6.051773     | 1.134616           | 0.772084             | 0.251845      | 0.257231        |"
       ]
      },
      {
       "html": [
        "<pre>| 140       | 6.466225     | 1.143566           | 0.783780             | 0.247023      | 0.252646        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 140       | 6.466225     | 1.143566           | 0.783780             | 0.247023      | 0.252646        |"
       ]
      },
      {
       "html": [
        "<pre>| 150       | 6.895993     | 1.150356           | 0.793966             | 0.242441      | 0.248261        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 150       | 6.895993     | 1.150356           | 0.793966             | 0.242441      | 0.248261        |"
       ]
      },
      {
       "html": [
        "<pre>| 160       | 7.318301     | 1.157737           | 0.802383             | 0.238743      | 0.244770        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 160       | 7.318301     | 1.157737           | 0.802383             | 0.238743      | 0.244770        |"
       ]
      },
      {
       "html": [
        "<pre>| 170       | 7.728651     | 1.164126           | 0.812721             | 0.235205      | 0.241588        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 170       | 7.728651     | 1.164126           | 0.812721             | 0.235205      | 0.241588        |"
       ]
      },
      {
       "html": [
        "<pre>| 180       | 8.150051     | 1.169270           | 0.822784             | 0.232158      | 0.238735        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 180       | 8.150051     | 1.169270           | 0.822784             | 0.232158      | 0.238735        |"
       ]
      },
      {
       "html": [
        "<pre>| 190       | 8.575476     | 1.175537           | 0.826035             | 0.229459      | 0.236252        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 190       | 8.575476     | 1.175537           | 0.826035             | 0.229459      | 0.236252        |"
       ]
      },
      {
       "html": [
        "<pre>| 200       | 8.997420     | 1.179128           | 0.832899             | 0.226804      | 0.233767        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 200       | 8.997420     | 1.179128           | 0.832899             | 0.226804      | 0.233767        |"
       ]
      },
      {
       "html": [
        "<pre>| 210       | 9.418191     | 1.180765           | 0.835712             | 0.224586      | 0.231732        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 210       | 9.418191     | 1.180765           | 0.835712             | 0.224586      | 0.231732        |"
       ]
      },
      {
       "html": [
        "<pre>| 220       | 9.844801     | 1.183361           | 0.838933             | 0.222504      | 0.229890        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 220       | 9.844801     | 1.183361           | 0.838933             | 0.222504      | 0.229890        |"
       ]
      },
      {
       "html": [
        "<pre>| 230       | 10.244596    | 1.187216           | 0.839497             | 0.220611      | 0.228036        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 230       | 10.244596    | 1.187216           | 0.839497             | 0.220611      | 0.228036        |"
       ]
      },
      {
       "html": [
        "<pre>| 240       | 10.669386    | 1.189963           | 0.844646             | 0.218780      | 0.226461        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 240       | 10.669386    | 1.189963           | 0.844646             | 0.218780      | 0.226461        |"
       ]
      },
      {
       "html": [
        "<pre>| 250       | 11.073809    | 1.192696           | 0.846957             | 0.217311      | 0.225116        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 250       | 11.073809    | 1.192696           | 0.846957             | 0.217311      | 0.225116        |"
       ]
      },
      {
       "html": [
        "<pre>| 260       | 11.464681    | 1.192680           | 0.849393             | 0.215875      | 0.223837        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 260       | 11.464681    | 1.192680           | 0.849393             | 0.215875      | 0.223837        |"
       ]
      },
      {
       "html": [
        "<pre>| 270       | 11.877307    | 1.194606           | 0.847445             | 0.214694      | 0.222813        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 270       | 11.877307    | 1.194606           | 0.847445             | 0.214694      | 0.222813        |"
       ]
      },
      {
       "html": [
        "<pre>| 280       | 12.268762    | 1.198358           | 0.847516             | 0.213492      | 0.221725        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 280       | 12.268762    | 1.198358           | 0.847516             | 0.213492      | 0.221725        |"
       ]
      },
      {
       "html": [
        "<pre>| 290       | 12.650919    | 1.200189           | 0.847987             | 0.212458      | 0.220813        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 290       | 12.650919    | 1.200189           | 0.847987             | 0.212458      | 0.220813        |"
       ]
      },
      {
       "html": [
        "<pre>| 300       | 13.045173    | 1.202171           | 0.849310             | 0.211458      | 0.219980        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 300       | 13.045173    | 1.202171           | 0.849310             | 0.211458      | 0.219980        |"
       ]
      },
      {
       "html": [
        "<pre>| 310       | 13.437198    | 1.203270           | 0.849636             | 0.210605      | 0.219311        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 310       | 13.437198    | 1.203270           | 0.849636             | 0.210605      | 0.219311        |"
       ]
      },
      {
       "html": [
        "<pre>| 320       | 13.844853    | 1.204414           | 0.850048             | 0.209705      | 0.218565        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 320       | 13.844853    | 1.204414           | 0.850048             | 0.209705      | 0.218565        |"
       ]
      },
      {
       "html": [
        "<pre>| 330       | 14.227862    | 1.205289           | 0.851897             | 0.208872      | 0.217897        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 330       | 14.227862    | 1.205289           | 0.851897             | 0.208872      | 0.217897        |"
       ]
      },
      {
       "html": [
        "<pre>| 340       | 14.643773    | 1.207198           | 0.852396             | 0.208102      | 0.217211        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 340       | 14.643773    | 1.207198           | 0.852396             | 0.208102      | 0.217211        |"
       ]
      },
      {
       "html": [
        "<pre>| 350       | 15.041182    | 1.206627           | 0.852754             | 0.207331      | 0.216472        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 350       | 15.041182    | 1.206627           | 0.852754             | 0.207331      | 0.216472        |"
       ]
      },
      {
       "html": [
        "<pre>| 360       | 15.453744    | 1.207692           | 0.852040             | 0.206677      | 0.215883        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 360       | 15.453744    | 1.207692           | 0.852040             | 0.206677      | 0.215883        |"
       ]
      },
      {
       "html": [
        "<pre>| 370       | 15.833840    | 1.207959           | 0.851955             | 0.206080      | 0.215363        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 370       | 15.833840    | 1.207959           | 0.851955             | 0.206080      | 0.215363        |"
       ]
      },
      {
       "html": [
        "<pre>| 380       | 16.247065    | 1.209424           | 0.852884             | 0.205467      | 0.214911        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 380       | 16.247065    | 1.209424           | 0.852884             | 0.205467      | 0.214911        |"
       ]
      },
      {
       "html": [
        "<pre>| 390       | 16.644127    | 1.210496           | 0.851402             | 0.204911      | 0.214465        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 390       | 16.644127    | 1.210496           | 0.851402             | 0.204911      | 0.214465        |"
       ]
      },
      {
       "html": [
        "<pre>| 400       | 17.039906    | 1.210687           | 0.850933             | 0.204412      | 0.214071        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 400       | 17.039906    | 1.210687           | 0.850933             | 0.204412      | 0.214071        |"
       ]
      },
      {
       "html": [
        "<pre>| 410       | 17.432937    | 1.211789           | 0.850823             | 0.203931      | 0.213647        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 410       | 17.432937    | 1.211789           | 0.850823             | 0.203931      | 0.213647        |"
       ]
      },
      {
       "html": [
        "<pre>| 420       | 17.826823    | 1.210087           | 0.851648             | 0.203411      | 0.213180        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 420       | 17.826823    | 1.210087           | 0.851648             | 0.203411      | 0.213180        |"
       ]
      },
      {
       "html": [
        "<pre>| 430       | 18.190144    | 1.210516           | 0.852000             | 0.203041      | 0.212945        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 430       | 18.190144    | 1.210516           | 0.852000             | 0.203041      | 0.212945        |"
       ]
      },
      {
       "html": [
        "<pre>| 440       | 18.584065    | 1.210290           | 0.852215             | 0.202687      | 0.212730        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 440       | 18.584065    | 1.210290           | 0.852215             | 0.202687      | 0.212730        |"
       ]
      },
      {
       "html": [
        "<pre>| 450       | 18.980320    | 1.211403           | 0.851098             | 0.202272      | 0.212386        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 450       | 18.980320    | 1.211403           | 0.851098             | 0.202272      | 0.212386        |"
       ]
      },
      {
       "html": [
        "<pre>| 460       | 19.374508    | 1.211712           | 0.850256             | 0.201855      | 0.212070        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 460       | 19.374508    | 1.211712           | 0.850256             | 0.201855      | 0.212070        |"
       ]
      },
      {
       "html": [
        "<pre>| 470       | 19.749036    | 1.212200           | 0.847555             | 0.201503      | 0.211771        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 470       | 19.749036    | 1.212200           | 0.847555             | 0.201503      | 0.211771        |"
       ]
      },
      {
       "html": [
        "<pre>| 480       | 20.149092    | 1.213602           | 0.848153             | 0.201194      | 0.211562        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 480       | 20.149092    | 1.213602           | 0.848153             | 0.201194      | 0.211562        |"
       ]
      },
      {
       "html": [
        "<pre>| 490       | 20.525091    | 1.214104           | 0.845877             | 0.200758      | 0.211199        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 490       | 20.525091    | 1.214104           | 0.845877             | 0.200758      | 0.211199        |"
       ]
      },
      {
       "html": [
        "<pre>| 500       | 20.910689    | 1.213419           | 0.844907             | 0.200391      | 0.210877        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "| 500       | 20.910689    | 1.213419           | 0.844907             | 0.200391      | 0.210877        |"
       ]
      },
      {
       "html": [
        "<pre>+-----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "+-----------+--------------+--------------------+----------------------+---------------+-----------------+"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Save the models w/ pickle\n",
      "pickler = gl._gl_pickle.GLPickler(filename = '/net/fowler/vol1/home/vegray/metaDMS/dato/scaled/models/global_regression_model_NOSELCO_selectDMS_update_2017-02-09')\n",
      "pickler.dump(global_regression_model_noSELCO)\n",
      "pickler.close()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use unpickle to re-open models \n",
      "unpickler = gl._gl_pickle.GLUnpickler(filename = '/net/fowler/vol1/home/vegray/metaDMS/dato/scaled/models/global_regression_model_NOSELCO_selectDMS_update_2017-02-09')\n",
      "global_regression_model_noSELCO = unpickler.load()\n",
      "unpickler.close()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use model to predict training data w/ Pearson and Spearman methods\n",
      "predictions = global_regression_model_noSELCO.predict(data_1per2)\n",
      "stats.pearsonr(predictions, data_1per2['scaled_effect1'])\n",
      "#stats.spearmanr(predictions, data_1per2['scaled_effect1'])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(0.79439166462378064, 0.0)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predictions = global_regression_model_noSELCO.predict(data_1per2)\n",
      "stats.pearsonr(predictions,data_1per2['scaled_effect1'])\n",
      "fig = plt.figure(figsize=(11,8))\n",
      "ax = fig.add_subplot(111)\n",
      "ax.set_xlabel('Observed score')\n",
      "ax.set_ylabel('Predicted score')\n",
      "ax.set_ylim([-0.2,1.6])\n",
      "ax.set_xlim([0,1.6])\n",
      "pic = ax.hexbin(data_1per2['scaled_effect1'],predictions,bins='log',cmap='YlGnBu_r',gridsize=45,mincnt= 5, vmax = 3)\n",
      "font = {'family' : 'normal',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 18}\n",
      "cb = plt.colorbar(pic)\n",
      "cb.set_label('mutation count (log10)')\n",
      "plt.rc('font', **font)\n",
      "ax.axis('tight')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FfOqzznSozeQEdHMFqSYdd9iIKPGugzCYgw5LHESV2nW3QGXyKV5NY51QKC4l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       "text": [
        "<matplotlib.figure.Figure at 0x2b81f0296c90>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "source": [
      "Validation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Use Envision to predict TP53\n",
      "mutagenesisSets = gl.SFrame.read_csv('~/data/mutagenesisSet_annotated.csv', \n",
      "                                     column_type_hints={'aa1_psic':float, 'aa2_psic':float, 'delta_psic':float, \n",
      "                                                        'accessibility': float, 'delta_solvent_accessibility':float,\n",
      "                                                        'mut_mut_msa_congruency':float})"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<pre>Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/annotation/r/mutagenesisSet_annotated.csv</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/annotation/r/mutagenesisSet_annotated.csv"
       ]
      },
      {
       "html": [
        "<pre>Parsing completed. Parsed 100 lines in 0.165186 secs.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Parsing completed. Parsed 100 lines in 0.165186 secs."
       ]
      },
      {
       "html": [
        "<pre>These column type hints were not used: X1</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "These column type hints were not used: X1"
       ]
      },
      {
       "html": [
        "<pre>Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/annotation/r/mutagenesisSet_annotated.csv</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/annotation/r/mutagenesisSet_annotated.csv"
       ]
      },
      {
       "html": [
        "<pre>Parsing completed. Parsed 8704 lines in 0.095831 secs.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Parsing completed. Parsed 8704 lines in 0.095831 secs."
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Make predictions\n",
      "predictions = global_regression_model_noSELCO.predict(mutagenesisSets)"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Add predictions to dataframe\n",
      "predictions1 = gl.SFrame(predictions)\n",
      "predictions1.rename({'X1': 'global_model_predictions'})\n",
      "predictions1\n",
      "mutagenesisSets_predictions = mutagenesisSets.add_column(predictions1['global_model_predictions'])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Filter out TP53 data set\n",
      "tp53 = mutagenesisSets_predictions.filter_by([x for x in mutagenesisSets_predictions['Protein'].unique() if x in ['TP53']], 'Protein')\n",
      "tp53['Activity']=pd.to_numeric(tp53['Activity'])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'pd' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-12-de850a0f9d4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtp53\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmutagenesisSets_predictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter_by\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmutagenesisSets_predictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Protein'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'TP53'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Protein'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtp53\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Activity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtp53\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Activity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Use SNAP2 to predict TP53\n",
      "stats.spearmanr(tp53['Activity'], tp53['SNAP2_score'])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "SpearmanrResult(correlation=-0.50363618596238757, pvalue=5.8404671136292302e-149)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Look at Envision predictive performance for each mutant amino acid (aa2)\n",
      "## Predictive performance is measured with Pearson CC here\n",
      "tp53.to_dataframe().groupby(['aa2'])['Activity', 'X45'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true,
      "scrolled": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Activity</th>\n",
        "      <th>X46</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aa2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.458451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.458451</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.668085</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.668085</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.670926</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.670926</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.591126</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.591126</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.576038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.576038</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.625037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.625037</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.534299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.534299</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.497033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.497033</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.589743</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.589743</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.469768</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.469768</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.593828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.593828</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.536923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.536923</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.656797</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.656797</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.519858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.519858</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.619087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.619087</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.615039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.615039</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.521372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.521372</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.638289</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.638289</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.640218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.640218</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.623067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X46</th>\n",
        "      <td>0.623067</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "              Activity       X46\n",
        "aa2                             \n",
        "A   Activity  1.000000  0.458451\n",
        "    X46       0.458451  1.000000\n",
        "C   Activity  1.000000  0.668085\n",
        "    X46       0.668085  1.000000\n",
        "D   Activity  1.000000  0.670926\n",
        "    X46       0.670926  1.000000\n",
        "E   Activity  1.000000  0.591126\n",
        "    X46       0.591126  1.000000\n",
        "F   Activity  1.000000  0.576038\n",
        "    X46       0.576038  1.000000\n",
        "G   Activity  1.000000  0.625037\n",
        "    X46       0.625037  1.000000\n",
        "H   Activity  1.000000  0.534299\n",
        "    X46       0.534299  1.000000\n",
        "I   Activity  1.000000  0.497033\n",
        "    X46       0.497033  1.000000\n",
        "K   Activity  1.000000  0.589743\n",
        "    X46       0.589743  1.000000\n",
        "L   Activity  1.000000  0.469768\n",
        "    X46       0.469768  1.000000\n",
        "M   Activity  1.000000  0.593828\n",
        "    X46       0.593828  1.000000\n",
        "N   Activity  1.000000  0.536923\n",
        "    X46       0.536923  1.000000\n",
        "P   Activity  1.000000  0.656797\n",
        "    X46       0.656797  1.000000\n",
        "Q   Activity  1.000000  0.519858\n",
        "    X46       0.519858  1.000000\n",
        "R   Activity  1.000000  0.619087\n",
        "    X46       0.619087  1.000000\n",
        "S   Activity  1.000000  0.615039\n",
        "    X46       0.615039  1.000000\n",
        "T   Activity  1.000000  0.521372\n",
        "    X46       0.521372  1.000000\n",
        "V   Activity  1.000000  0.638289\n",
        "    X46       0.638289  1.000000\n",
        "W   Activity  1.000000  0.640218\n",
        "    X46       0.640218  1.000000\n",
        "Y   Activity  1.000000  0.623067\n",
        "    X46       0.623067  1.000000"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Look at Envision predictive performance for each original amino acid (aa1)\n",
      "## Predictive performance is measured with Pearson CC here\n",
      "tp53.to_dataframe().groupby(['aa1'])['Activity', 'X45'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Activity</th>\n",
        "      <th>X45</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aa1</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.607232</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.607232</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.655468</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.655468</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.458470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.458470</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.599111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.599111</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.635470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.635470</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.554071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.554071</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.524761</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.524761</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.704648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.704648</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.211173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.211173</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.611399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.611399</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.530880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.530880</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.543638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.543638</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.493607</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.493607</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.376568</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.376568</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.530586</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.530586</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.499731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.499731</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.551405</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.551405</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.529796</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.529796</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.391787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.391787</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.585797</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X45</th>\n",
        "      <td>0.585797</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "              Activity       X45\n",
        "aa1                             \n",
        "A   Activity  1.000000  0.607232\n",
        "    X45       0.607232  1.000000\n",
        "C   Activity  1.000000  0.655468\n",
        "    X45       0.655468  1.000000\n",
        "D   Activity  1.000000  0.458470\n",
        "    X45       0.458470  1.000000\n",
        "E   Activity  1.000000  0.599111\n",
        "    X45       0.599111  1.000000\n",
        "F   Activity  1.000000  0.635470\n",
        "    X45       0.635470  1.000000\n",
        "G   Activity  1.000000  0.554071\n",
        "    X45       0.554071  1.000000\n",
        "H   Activity  1.000000  0.524761\n",
        "    X45       0.524761  1.000000\n",
        "I   Activity  1.000000  0.704648\n",
        "    X45       0.704648  1.000000\n",
        "K   Activity  1.000000  0.211173\n",
        "    X45       0.211173  1.000000\n",
        "L   Activity  1.000000  0.611399\n",
        "    X45       0.611399  1.000000\n",
        "M   Activity  1.000000  0.530880\n",
        "    X45       0.530880  1.000000\n",
        "N   Activity  1.000000  0.543638\n",
        "    X45       0.543638  1.000000\n",
        "P   Activity  1.000000  0.493607\n",
        "    X45       0.493607  1.000000\n",
        "Q   Activity  1.000000  0.376568\n",
        "    X45       0.376568  1.000000\n",
        "R   Activity  1.000000  0.530586\n",
        "    X45       0.530586  1.000000\n",
        "S   Activity  1.000000  0.499731\n",
        "    X45       0.499731  1.000000\n",
        "T   Activity  1.000000  0.551405\n",
        "    X45       0.551405  1.000000\n",
        "V   Activity  1.000000  0.529796\n",
        "    X45       0.529796  1.000000\n",
        "W   Activity  1.000000  0.391787\n",
        "    X45       0.391787  1.000000\n",
        "Y   Activity  1.000000  0.585797\n",
        "    X45       0.585797  1.000000"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Look at SNAP2 predictive performance for each mutant amino acid (aa2)\n",
      "## Predictive performance is measured with Pearson CC here\n",
      "tp53.to_dataframe().groupby(['aa2'])['Activity', 'SNAP2_score'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Activity</th>\n",
        "      <th>SNAP2_score</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aa2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.362330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.362330</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.489923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.489923</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.542501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.542501</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.576881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.576881</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.574711</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.574711</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.588331</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.588331</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.430176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.430176</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.441082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.441082</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.497597</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.497597</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.452548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.452548</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.697203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.697203</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.496682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.496682</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.677102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.677102</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.514609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.514609</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.615525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.615525</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.577310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.577310</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.520268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.520268</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.488174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.488174</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.804879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.804879</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.495518</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.495518</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "                 Activity  SNAP2_score\n",
        "aa2                                   \n",
        "A   Activity     1.000000    -0.362330\n",
        "    SNAP2_score -0.362330     1.000000\n",
        "C   Activity     1.000000    -0.489923\n",
        "    SNAP2_score -0.489923     1.000000\n",
        "D   Activity     1.000000    -0.542501\n",
        "    SNAP2_score -0.542501     1.000000\n",
        "E   Activity     1.000000    -0.576881\n",
        "    SNAP2_score -0.576881     1.000000\n",
        "F   Activity     1.000000    -0.574711\n",
        "    SNAP2_score -0.574711     1.000000\n",
        "G   Activity     1.000000    -0.588331\n",
        "    SNAP2_score -0.588331     1.000000\n",
        "H   Activity     1.000000    -0.430176\n",
        "    SNAP2_score -0.430176     1.000000\n",
        "I   Activity     1.000000    -0.441082\n",
        "    SNAP2_score -0.441082     1.000000\n",
        "K   Activity     1.000000    -0.497597\n",
        "    SNAP2_score -0.497597     1.000000\n",
        "L   Activity     1.000000    -0.452548\n",
        "    SNAP2_score -0.452548     1.000000\n",
        "M   Activity     1.000000    -0.697203\n",
        "    SNAP2_score -0.697203     1.000000\n",
        "N   Activity     1.000000    -0.496682\n",
        "    SNAP2_score -0.496682     1.000000\n",
        "P   Activity     1.000000    -0.677102\n",
        "    SNAP2_score -0.677102     1.000000\n",
        "Q   Activity     1.000000    -0.514609\n",
        "    SNAP2_score -0.514609     1.000000\n",
        "R   Activity     1.000000    -0.615525\n",
        "    SNAP2_score -0.615525     1.000000\n",
        "S   Activity     1.000000    -0.577310\n",
        "    SNAP2_score -0.577310     1.000000\n",
        "T   Activity     1.000000    -0.520268\n",
        "    SNAP2_score -0.520268     1.000000\n",
        "V   Activity     1.000000    -0.488174\n",
        "    SNAP2_score -0.488174     1.000000\n",
        "W   Activity     1.000000    -0.804879\n",
        "    SNAP2_score -0.804879     1.000000\n",
        "Y   Activity     1.000000    -0.495518\n",
        "    SNAP2_score -0.495518     1.000000"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Look at Envision predictive performance for each original amino acid (aa1)\n",
      "## Predictive performance is measured with Pearson CC here\n",
      "tp53.to_dataframe().groupby(['aa1'])['Activity', 'SNAP2_score'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Activity</th>\n",
        "      <th>SNAP2_score</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aa1</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.584662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.584662</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.659132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.659132</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.174237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.174237</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.324304</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.324304</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.402375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.402375</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.586574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.586574</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.673306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.673306</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.652662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.652662</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.400473</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.400473</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.634903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.634903</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.517142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.517142</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.561879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.561879</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.455158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.455158</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.311251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.311251</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.455114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.455114</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.412732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.412732</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.428978</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.428978</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.597756</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.597756</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.391187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.391187</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>Activity</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.719325</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAP2_score</th>\n",
        "      <td>-0.719325</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "                 Activity  SNAP2_score\n",
        "aa1                                   \n",
        "A   Activity     1.000000    -0.584662\n",
        "    SNAP2_score -0.584662     1.000000\n",
        "C   Activity     1.000000    -0.659132\n",
        "    SNAP2_score -0.659132     1.000000\n",
        "D   Activity     1.000000    -0.174237\n",
        "    SNAP2_score -0.174237     1.000000\n",
        "E   Activity     1.000000    -0.324304\n",
        "    SNAP2_score -0.324304     1.000000\n",
        "F   Activity     1.000000    -0.402375\n",
        "    SNAP2_score -0.402375     1.000000\n",
        "G   Activity     1.000000    -0.586574\n",
        "    SNAP2_score -0.586574     1.000000\n",
        "H   Activity     1.000000    -0.673306\n",
        "    SNAP2_score -0.673306     1.000000\n",
        "I   Activity     1.000000    -0.652662\n",
        "    SNAP2_score -0.652662     1.000000\n",
        "K   Activity     1.000000    -0.400473\n",
        "    SNAP2_score -0.400473     1.000000\n",
        "L   Activity     1.000000    -0.634903\n",
        "    SNAP2_score -0.634903     1.000000\n",
        "M   Activity     1.000000    -0.517142\n",
        "    SNAP2_score -0.517142     1.000000\n",
        "N   Activity     1.000000    -0.561879\n",
        "    SNAP2_score -0.561879     1.000000\n",
        "P   Activity     1.000000    -0.455158\n",
        "    SNAP2_score -0.455158     1.000000\n",
        "Q   Activity     1.000000    -0.311251\n",
        "    SNAP2_score -0.311251     1.000000\n",
        "R   Activity     1.000000    -0.455114\n",
        "    SNAP2_score -0.455114     1.000000\n",
        "S   Activity     1.000000    -0.412732\n",
        "    SNAP2_score -0.412732     1.000000\n",
        "T   Activity     1.000000    -0.428978\n",
        "    SNAP2_score -0.428978     1.000000\n",
        "V   Activity     1.000000    -0.597756\n",
        "    SNAP2_score -0.597756     1.000000\n",
        "W   Activity     1.000000    -0.391187\n",
        "    SNAP2_score -0.391187     1.000000\n",
        "Y   Activity     1.000000    -0.719325\n",
        "    SNAP2_score -0.719325     1.000000"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Figure 3D: Hexbin plot showing correlation between TP53 varian effects and \n",
      "## Envision predictions (y-axis). \n",
      "\n",
      "## Build the plot\n",
      "ax = plt.hexbin(tp53['Activity'],tp53['X45'],\n",
      "           cmap=plt.cm.YlGnBu_r,\n",
      "           gridsize=20,\n",
      "           bins=None,\n",
      "           mincnt=5,\n",
      "        vmin= 1,\n",
      "               vmax = 100)        #hexbinning\n",
      "                          #necessary for correct aspect ratio\n",
      "## Formatting code\n",
      "    plt.axis([-10, 200, 0.3, 1.05])\n",
      "cb = plt.colorbar()\n",
      "cb.set_label('mutation count')\n",
      "font = {'family' : 'normal',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 18}\n",
      "\n",
      "plt.rc('font', **font)\n",
      "plt.show()\n",
      "\n",
      "## Measure correlation between predictions and TP523 with pearson and spearman CC\n",
      "stats.pearsonr(tp53['Activity'], tp53['X45'])\n",
      "stats.spearmanr(tp53['Activity'], tp53['X45'])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZYxthXZjMOxlyvpJaV+aRJ5S+vmQawhIpurqAv74IhRYmPtbCxEsSzeoCVOwY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\n99ReL01xjMPhcDhmMS6rf93QjvD3AM3Kz5VHtRlo54SSVDY5utb+J+0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SAAAg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\n+EVERBqIAr+IiEgDUeAXERFpIP8fz6OZOezsYbEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2b9176f13290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "SpearmanrResult(correlation=0.53325394738743614, pvalue=4.6250047987755086e-170)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Feature importance analysis\n",
      "## Use get_feature_importance function from graphlab\n",
      "importance = global_regression_model_noSELCO.get_feature_importance()\n",
      "importance"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
        "    <tr>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">accessibility</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">b_factor</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">seq_ind_closest_mut</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1559</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">aa1_psic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">evolutionary_coupling_avg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">delta_psic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">aa2_psic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">655</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">delta_solvent_accessibili<br>ty ...</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">633</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Grantham</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">468</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">aa1_weight</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">445</td>\n",
        "    </tr>\n",
        "</table>\n",
        "[472 rows x 3 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "Columns:\n",
        "\tname\tstr\n",
        "\tindex\tstr\n",
        "\tcount\tint\n",
        "\n",
        "Rows: 472\n",
        "\n",
        "Data:\n",
        "+-----------------------------+-------+-------+\n",
        "|             name            | index | count |\n",
        "+-----------------------------+-------+-------+\n",
        "|        accessibility        |  None |  1721 |\n",
        "|           b_factor          |  None |  1690 |\n",
        "|     seq_ind_closest_mut     |  None |  1559 |\n",
        "|           aa1_psic          |  None |  1330 |\n",
        "|  evolutionary_coupling_avg  |  None |  1319 |\n",
        "|          delta_psic         |  None |  710  |\n",
        "|           aa2_psic          |  None |  655  |\n",
        "| delta_solvent_accessibility |  None |  633  |\n",
        "|           Grantham          |  None |  468  |\n",
        "|          aa1_weight         |  None |  445  |\n",
        "+-----------------------------+-------+-------+\n",
        "[472 rows x 3 columns]\n",
        "Note: Only the head of the SFrame is printed.\n",
        "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Treat categorical features as dummy variables\n",
      "importance.groupby(key_columns=['name', 'index'], operations={'count': agg.SUM('count')}).sort('count', ascending=False).print_rows(num_rows = 25)"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+----------------+-----------------------------+-------+\n",
        "|     index      |             name            | count |\n",
        "+----------------+-----------------------------+-------+\n",
        "|      None      |        accessibility        |  1721 |\n",
        "|      None      |           b_factor          |  1690 |\n",
        "|      None      |     seq_ind_closest_mut     |  1559 |\n",
        "|      None      |           aa1_psic          |  1330 |\n",
        "|      None      |  evolutionary_coupling_avg  |  1319 |\n",
        "|      None      |          delta_psic         |  710  |\n",
        "|      None      |           aa2_psic          |  655  |\n",
        "|      None      | delta_solvent_accessibility |  633  |\n",
        "|      None      |           Grantham          |  468  |\n",
        "|      None      |          aa1_weight         |  445  |\n",
        "|      None      |      mut_msa_congruency     |  399  |\n",
        "|      None      |            aa1_PI           |  379  |\n",
        "|      None      |            aa1vol           |  376  |\n",
        "|       P        |             aa2             |  325  |\n",
        "|      None      |         deltavolume         |  318  |\n",
        "|      None      |         deltaWeight         |  304  |\n",
        "|      None      |            aa2vol           |  212  |\n",
        "|      None      |          aa2_weight         |  199  |\n",
        "|       E        |         dssp_sec_str        |  172  |\n",
        "|      None      |           deltaPI           |  171  |\n",
        "|       C        |             aa2             |  162  |\n",
        "|      None      |            aa2_PI           |  148  |\n",
        "| negativeCharge |         aa2_polarity        |  143  |\n",
        "|  Hydrophobic   |         aa1_polarity        |  132  |\n",
        "|       H        |         dssp_sec_str        |  130  |\n",
        "+----------------+-----------------------------+-------+\n",
        "[470 rows x 3 columns]\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Ignore index when assessing feature importance\n",
      "importance.groupby(key_columns=['name'], operations={'count': agg.SUM('count')}).sort('count', ascending=False).print_rows(num_rows = 25)"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+-----------------------------+-------+\n",
        "|             name            | count |\n",
        "+-----------------------------+-------+\n",
        "|        accessibility        |  1721 |\n",
        "|           b_factor          |  1690 |\n",
        "|     seq_ind_closest_mut     |  1559 |\n",
        "|           aa1_psic          |  1330 |\n",
        "|  evolutionary_coupling_avg  |  1319 |\n",
        "|             aa2             |  713  |\n",
        "|          delta_psic         |  710  |\n",
        "|           aa2_psic          |  655  |\n",
        "| delta_solvent_accessibility |  633  |\n",
        "|         dssp_sec_str        |  530  |\n",
        "|             aa1             |  499  |\n",
        "|           Grantham          |  468  |\n",
        "|          aa1_weight         |  445  |\n",
        "|      mut_msa_congruency     |  399  |\n",
        "|            aa1_PI           |  379  |\n",
        "|            aa1vol           |  376  |\n",
        "|         deltavolume         |  318  |\n",
        "|         deltaWeight         |  304  |\n",
        "|         aa2_polarity        |  297  |\n",
        "|         aa1_polarity        |  295  |\n",
        "|         phi_psi_reg         |  260  |\n",
        "|            aa2vol           |  212  |\n",
        "|          aa2_weight         |  199  |\n",
        "|           deltaPI           |  171  |\n",
        "|            aa2_PI           |  148  |\n",
        "+-----------------------------+-------+\n",
        "[27 rows x 2 columns]\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Use T-test to determine whether Proline mutations are significantly \n",
      "# more damaging that other amino acid variants.\n",
      "result = data_1per2.to_dataframe().groupby([ 'aa2','protein'])['scaled_effect1'].mean()\n",
      "result_AA = data_1per2.to_dataframe().groupby([ 'protein','aa2'])['scaled_effect1'].mean()\n",
      "result2 = result.unstack()\n",
      "result_AA2 = result_AA.unstack()\n",
      "ave = (result2).mean()\n",
      "P = result_AA2.P\n",
      "stats.ttest_ind(P, ave, equal_var = False)"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "Ttest_indResult(statistic=-5.2010943074610978, pvalue=0.00017970122481086742)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Mean variant effect of all amino acids\n",
      "ave.mean()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "0.8088730659170187"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Heatmap showing the mean variant effect of each amino acid for each data set\n",
      "fig, ax = plt.subplots()\n",
      "plt.pcolor(result2, cmap='YlGnBu',vmin=0, vmax=1)\n",
      "ax.set_xticklabels(result2.axes[1], minor=False)\n",
      "ax.set_yticklabels(result2.axes[0], minor=False)\n",
      "ax.set_yticks(np.arange(result2.shape[0]) + 0.5, minor=False)\n",
      "ax.set_xticks(np.arange(result2.shape[1]) + 0.5, minor=False)\n",
      "plt.xticks(rotation='vertical')\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Lkm7i4ZLPx4EdgH8uc+wy4CDgXSRdxBW5xWpmZi2vZ+Op9Gw8dej9kj9fWa7YPOD9wF7A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\nrkoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2ae9d06245d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Predict proline mutations from each DMS and calc Pearson's R\n",
      "P = data_1per2.filter_by([x for x in data_1per2['aa2'].unique() if x in ['P']], 'aa2')\n",
      "predictions = global_regression_model_noSELCO.predict(P)\n",
      "predictions1 = gl.SFrame(predictions)\n",
      "predictions1.rename({'X1': 'Envision_predictions'})\n",
      "P2 = P.add_column(predictions1['Envision_predictions'])\n",
      "P2.rename({'X55': 'Envision_predictions'})"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
        "    <tr>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id3</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">variant_id</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dms_id</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">first_author</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">protein</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">snap2score</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">protein_size</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">106</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_237</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_E237P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-29</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">128</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_54</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_I54P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-38</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">130</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_199</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_L199P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-74</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">225</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_225</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_A225P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-93</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">288</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_175</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_E175P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-86</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">306</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_102</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_E102P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-57</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">331</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_9</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_A9P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-19</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">381</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_50</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_N50P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-38</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">427</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_267</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_T267P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-60</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">469</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase_I3P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">beta-lactamase</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Firnberg</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TEM-1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-58</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">286</td>\n",
        "    </tr>\n",
        "</table>\n",
        "<table frame=\"box\" rules=\"cols\">\n",
        "    <tr>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">assay_type</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">organism</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">uniprot_id</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">pdb_id</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">pdb_chain_id</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reported_fitness</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa1</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa2</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">wt_mut</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">mut_type</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">position</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.422332</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">EP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.391388</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.390653</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">L</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">LP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">199</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.308118</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">AP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">225</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.270935</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">EP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.268011</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">EP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.254497</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">AP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.234678</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">N</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">NP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1xpb</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.214613</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">T</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">267</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in_vivo</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E. coli</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.201973</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">missense</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
        "    </tr>\n",
        "</table>\n",
        "<table frame=\"box\" rules=\"cols\">\n",
        "    <tr>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id2</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">WT_Mut</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Variant</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa1_polarity</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa2_polarity</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa1_PI</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa2_PI</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">deltaPI</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa1_weight</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa2_weight</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_E237P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">EP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E237P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">negativeCharge</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.22</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.08</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">147</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_I54P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I54P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Hydrophobic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.02</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.28</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">131</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_L199P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">LP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">L199P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Hydrophobic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.71</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.41</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">131</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_A225P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">AP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A225P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Hydrophobic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.0</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">89</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_E175P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">EP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E175P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">negativeCharge</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.22</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.08</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">147</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_E102P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">EP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E102P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">negativeCharge</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.22</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.08</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">147</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_A9P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">AP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A9P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Hydrophobic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.0</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">89</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_N50P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">NP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">N50P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Polar</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.47</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.83</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">132</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_T267P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">TP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">T267P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Polar</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.6</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">119</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">P62593_I3P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IP</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I3P</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Hydrophobic</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Special</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.02</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.3</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.28</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">131</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">115</td>\n",
        "    </tr>\n",
        "</table>\n",
        "<table frame=\"box\" rules=\"cols\">\n",
        "    <tr>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">deltaWeight</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa1vol</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa2vol</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">deltavolume</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Grantham</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa1_psic</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">aa2_psic</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">delta_psic</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">accessibility</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dssp_sec_str</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-32</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">138.4</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-25.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">93</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.058</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.985</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.927</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.583</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-16</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">166.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-54.0</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">95</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.838</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-4.045</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.207</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.669</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-16</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">166.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-54.0</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.942</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.099</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.843</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.832</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">H</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">88.6</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24.1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">27</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.845</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.112</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.267</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-32</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">138.4</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-25.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">93</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.336</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.972</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.364</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.551</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-32</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">138.4</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-25.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">93</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.118</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.88</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.762</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.722</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">88.6</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24.1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">27</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.147</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.864</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.717</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-17</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">114.1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.4</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.979</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.95</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.971</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.912</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-4</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">116.1</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.4</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.308</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.021</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.713</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.54</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-16</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">166.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">112.7</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-54.0</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">95</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.143</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.857</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.714</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "    </tr>\n",
        "</table>\n",
        "<table frame=\"box\" rules=\"cols\">\n",
        "    <tr>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">phi_psi_reg</th>\n",
        "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">...</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">B</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">B</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">B</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">B</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">B</td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
        "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
        "    </tr>\n",
        "</table>\n",
        "[1003 rows x 55 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "Columns:\n",
        "\tid\tint\n",
        "\tid3\tstr\n",
        "\tvariant_id\tstr\n",
        "\tdms_id\tstr\n",
        "\tfirst_author\tstr\n",
        "\tprotein\tstr\n",
        "\tsnap2score\tint\n",
        "\tprotein_size\tint\n",
        "\tassay_type\tstr\n",
        "\torganism\tstr\n",
        "\tuniprot_id\tstr\n",
        "\tpdb_id\tstr\n",
        "\tpdb_chain_id\tstr\n",
        "\treported_fitness\tfloat\n",
        "\taa1\tstr\n",
        "\taa2\tstr\n",
        "\twt_mut\tstr\n",
        "\tmut_type\tstr\n",
        "\tposition\tint\n",
        "\tid2\tstr\n",
        "\tWT_Mut\tstr\n",
        "\tVariant\tstr\n",
        "\taa1_polarity\tstr\n",
        "\taa2_polarity\tstr\n",
        "\taa1_PI\tfloat\n",
        "\taa2_PI\tfloat\n",
        "\tdeltaPI\tfloat\n",
        "\taa1_weight\tint\n",
        "\taa2_weight\tint\n",
        "\tdeltaWeight\tint\n",
        "\taa1vol\tfloat\n",
        "\taa2vol\tfloat\n",
        "\tdeltavolume\tfloat\n",
        "\tGrantham\tint\n",
        "\taa1_psic\tfloat\n",
        "\taa2_psic\tfloat\n",
        "\tdelta_psic\tfloat\n",
        "\taccessibility\tfloat\n",
        "\tdssp_sec_str\tstr\n",
        "\tphi_psi_reg\tstr\n",
        "\tdelta_solvent_accessibility\tfloat\n",
        "\tb_factor\tfloat\n",
        "\tmut_msa_congruency\tfloat\n",
        "\tmut_mut_msa_congruency\tfloat\n",
        "\tseq_ind_closest_mut\tfloat\n",
        "\tscaled_effect1\tfloat\n",
        "\tselection_coefficient\tfloat\n",
        "\tevolutionary_coupling_avg\tfloat\n",
        "\tevolutionary_coupling_prop\tint\n",
        "\tevolutionary_coupling_avg_norm\tfloat\n",
        "\tdiscrete\tstr\n",
        "\tdiscrete_dam\tstr\n",
        "\tdiscrete_neutral\tstr\n",
        "\tdiscrete_enhancing\tstr\n",
        "\tEnvision_predictions\tfloat\n",
        "\n",
        "Rows: 1003\n",
        "\n",
        "Data:\n",
        "+-----+------------+----------------------+----------------+--------------+\n",
        "|  id |    id3     |      variant_id      |     dms_id     | first_author |\n",
        "+-----+------------+----------------------+----------------+--------------+\n",
        "| 106 | P62593_237 | beta-lactamase_E237P | beta-lactamase |   Firnberg   |\n",
        "| 128 | P62593_54  | beta-lactamase_I54P  | beta-lactamase |   Firnberg   |\n",
        "| 130 | P62593_199 | beta-lactamase_L199P | beta-lactamase |   Firnberg   |\n",
        "| 225 | P62593_225 | beta-lactamase_A225P | beta-lactamase |   Firnberg   |\n",
        "| 288 | P62593_175 | beta-lactamase_E175P | beta-lactamase |   Firnberg   |\n",
        "| 306 | P62593_102 | beta-lactamase_E102P | beta-lactamase |   Firnberg   |\n",
        "| 331 |  P62593_9  |  beta-lactamase_A9P  | beta-lactamase |   Firnberg   |\n",
        "| 381 | P62593_50  | beta-lactamase_N50P  | beta-lactamase |   Firnberg   |\n",
        "| 427 | P62593_267 | beta-lactamase_T267P | beta-lactamase |   Firnberg   |\n",
        "| 469 |  P62593_3  |  beta-lactamase_I3P  | beta-lactamase |   Firnberg   |\n",
        "+-----+------------+----------------------+----------------+--------------+\n",
        "+---------+------------+--------------+------------+----------+------------+\n",
        "| protein | snap2score | protein_size | assay_type | organism | uniprot_id |\n",
        "+---------+------------+--------------+------------+----------+------------+\n",
        "|  TEM-1  |    -29     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -38     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -74     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -93     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -86     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -57     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -19     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -38     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -60     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "|  TEM-1  |    -58     |     286      |  in_vivo   | E. coli  |   P62593   |\n",
        "+---------+------------+--------------+------------+----------+------------+\n",
        "+--------+--------------+------------------+-----+-----+--------+----------+----------+\n",
        "| pdb_id | pdb_chain_id | reported_fitness | aa1 | aa2 | wt_mut | mut_type | position |\n",
        "+--------+--------------+------------------+-----+-----+--------+----------+----------+\n",
        "|  1xpb  |      A       |     0.422332     |  E  |  P  |   EP   | missense |   237    |\n",
        "|  1xpb  |      A       |     0.391388     |  I  |  P  |   IP   | missense |    54    |\n",
        "|  1xpb  |      A       |     0.390653     |  L  |  P  |   LP   | missense |   199    |\n",
        "|  1xpb  |      A       |     0.308118     |  A  |  P  |   AP   | missense |   225    |\n",
        "|  1xpb  |      A       |     0.270935     |  E  |  P  |   EP   | missense |   175    |\n",
        "|  1xpb  |      A       |     0.268011     |  E  |  P  |   EP   | missense |   102    |\n",
        "|        |              |     0.254497     |  A  |  P  |   AP   | missense |    9     |\n",
        "|  1xpb  |      A       |     0.234678     |  N  |  P  |   NP   | missense |    50    |\n",
        "|  1xpb  |      A       |     0.214613     |  T  |  P  |   TP   | missense |   267    |\n",
        "|        |              |     0.201973     |  I  |  P  |   IP   | missense |    3     |\n",
        "+--------+--------------+------------------+-----+-----+--------+----------+----------+\n",
        "+--------------+-----+\n",
        "|     id2      | ... |\n",
        "+--------------+-----+\n",
        "| P62593_E237P | ... |\n",
        "| P62593_I54P  | ... |\n",
        "| P62593_L199P | ... |\n",
        "| P62593_A225P | ... |\n",
        "| P62593_E175P | ... |\n",
        "| P62593_E102P | ... |\n",
        "|  P62593_A9P  | ... |\n",
        "| P62593_N50P  | ... |\n",
        "| P62593_T267P | ... |\n",
        "|  P62593_I3P  | ... |\n",
        "+--------------+-----+\n",
        "[1003 rows x 55 columns]\n",
        "Note: Only the head of the SFrame is printed.\n",
        "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Measure Envision predictive performance for each mutant amino acid (aa2) type separately\n",
      "## Method  =  Spearman\n",
      "data_1per2.to_dataframe().groupby(['aa2'])['scaled_effect1', 'Envision_predictions'].corr(method = 'spearman')"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true,
      "scrolled": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <th>Envision_predictions</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aa2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.734201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.734201</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.680933</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.680933</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.813409</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.813409</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.832467</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.832467</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.718502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.718502</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.762876</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.762876</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.770144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.770144</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.754084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.754084</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.795015</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.795015</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.699727</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.699727</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.673998</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.673998</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.815928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.815928</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.717130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.717130</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.787524</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.787524</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.775479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.775479</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.808171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.808171</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.748349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.748349</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.706356</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.706356</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.745065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.745065</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.737982</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.737982</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "                          scaled_effect1  Envision_predictions\n",
        "aa2                                                           \n",
        "A   scaled_effect1              1.000000              0.734201\n",
        "    Envision_predictions        0.734201              1.000000\n",
        "C   scaled_effect1              1.000000              0.680933\n",
        "    Envision_predictions        0.680933              1.000000\n",
        "D   scaled_effect1              1.000000              0.813409\n",
        "    Envision_predictions        0.813409              1.000000\n",
        "E   scaled_effect1              1.000000              0.832467\n",
        "    Envision_predictions        0.832467              1.000000\n",
        "F   scaled_effect1              1.000000              0.718502\n",
        "    Envision_predictions        0.718502              1.000000\n",
        "G   scaled_effect1              1.000000              0.762876\n",
        "    Envision_predictions        0.762876              1.000000\n",
        "H   scaled_effect1              1.000000              0.770144\n",
        "    Envision_predictions        0.770144              1.000000\n",
        "I   scaled_effect1              1.000000              0.754084\n",
        "    Envision_predictions        0.754084              1.000000\n",
        "K   scaled_effect1              1.000000              0.795015\n",
        "    Envision_predictions        0.795015              1.000000\n",
        "L   scaled_effect1              1.000000              0.699727\n",
        "    Envision_predictions        0.699727              1.000000\n",
        "M   scaled_effect1              1.000000              0.673998\n",
        "    Envision_predictions        0.673998              1.000000\n",
        "N   scaled_effect1              1.000000              0.815928\n",
        "    Envision_predictions        0.815928              1.000000\n",
        "P   scaled_effect1              1.000000              0.717130\n",
        "    Envision_predictions        0.717130              1.000000\n",
        "Q   scaled_effect1              1.000000              0.787524\n",
        "    Envision_predictions        0.787524              1.000000\n",
        "R   scaled_effect1              1.000000              0.775479\n",
        "    Envision_predictions        0.775479              1.000000\n",
        "S   scaled_effect1              1.000000              0.808171\n",
        "    Envision_predictions        0.808171              1.000000\n",
        "T   scaled_effect1              1.000000              0.748349\n",
        "    Envision_predictions        0.748349              1.000000\n",
        "V   scaled_effect1              1.000000              0.706356\n",
        "    Envision_predictions        0.706356              1.000000\n",
        "W   scaled_effect1              1.000000              0.745065\n",
        "    Envision_predictions        0.745065              1.000000\n",
        "Y   scaled_effect1              1.000000              0.737982\n",
        "    Envision_predictions        0.737982              1.000000"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Measure Envision predictive performance for each original amino acid (aa1) type separately\n",
      "data_1per2.to_dataframe().groupby(['aa1'])['scaled_effect1', 'Envision_predictions'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <th>Envision_predictions</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aa1</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.770246</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.770246</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.581098</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.581098</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.810309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.810309</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.695640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.695640</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.745287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.745287</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.826985</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.826985</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.788999</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.788999</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.793939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.793939</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.830277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.830277</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.756331</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.756331</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.776330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.776330</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.765502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.765502</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.769665</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.769665</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.626793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.626793</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.766867</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.766867</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.829676</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.829676</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.765472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.765472</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.808896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.808896</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.649992</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.649992</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.759264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.759264</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "                          scaled_effect1  Envision_predictions\n",
        "aa1                                                           \n",
        "A   scaled_effect1              1.000000              0.770246\n",
        "    Envision_predictions        0.770246              1.000000\n",
        "C   scaled_effect1              1.000000              0.581098\n",
        "    Envision_predictions        0.581098              1.000000\n",
        "D   scaled_effect1              1.000000              0.810309\n",
        "    Envision_predictions        0.810309              1.000000\n",
        "E   scaled_effect1              1.000000              0.695640\n",
        "    Envision_predictions        0.695640              1.000000\n",
        "F   scaled_effect1              1.000000              0.745287\n",
        "    Envision_predictions        0.745287              1.000000\n",
        "G   scaled_effect1              1.000000              0.826985\n",
        "    Envision_predictions        0.826985              1.000000\n",
        "H   scaled_effect1              1.000000              0.788999\n",
        "    Envision_predictions        0.788999              1.000000\n",
        "I   scaled_effect1              1.000000              0.793939\n",
        "    Envision_predictions        0.793939              1.000000\n",
        "K   scaled_effect1              1.000000              0.830277\n",
        "    Envision_predictions        0.830277              1.000000\n",
        "L   scaled_effect1              1.000000              0.756331\n",
        "    Envision_predictions        0.756331              1.000000\n",
        "M   scaled_effect1              1.000000              0.776330\n",
        "    Envision_predictions        0.776330              1.000000\n",
        "N   scaled_effect1              1.000000              0.765502\n",
        "    Envision_predictions        0.765502              1.000000\n",
        "P   scaled_effect1              1.000000              0.769665\n",
        "    Envision_predictions        0.769665              1.000000\n",
        "Q   scaled_effect1              1.000000              0.626793\n",
        "    Envision_predictions        0.626793              1.000000\n",
        "R   scaled_effect1              1.000000              0.766867\n",
        "    Envision_predictions        0.766867              1.000000\n",
        "S   scaled_effect1              1.000000              0.829676\n",
        "    Envision_predictions        0.829676              1.000000\n",
        "T   scaled_effect1              1.000000              0.765472\n",
        "    Envision_predictions        0.765472              1.000000\n",
        "V   scaled_effect1              1.000000              0.808896\n",
        "    Envision_predictions        0.808896              1.000000\n",
        "W   scaled_effect1              1.000000              0.649992\n",
        "    Envision_predictions        0.649992              1.000000\n",
        "Y   scaled_effect1              1.000000              0.759264\n",
        "    Envision_predictions        0.759264              1.000000"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Measure Envision performance on proline mutations from each data set\n",
      "## Pearson correlation\n",
      "P2.to_dataframe().groupby(['dms_id'])['scaled_effect1', 'Envision_predictions'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <th>Envision_predictions</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dms_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E1_Ubiquitin</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.687919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.687919</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">PSD95pdz3</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.579800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.579800</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Pab1</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.708311</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.708311</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Ubiquitin</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.569076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.569076</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">WW_domain</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.544811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.544811</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">beta-lactamase</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.787117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.787117</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">gb1</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.604428</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.604428</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">hsp90</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.633606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.633606</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">kka2_1:2</th>\n",
        "      <th>scaled_effect1</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.736964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Envision_predictions</th>\n",
        "      <td>0.736964</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "                                     scaled_effect1  Envision_predictions\n",
        "dms_id                                                                   \n",
        "E1_Ubiquitin   scaled_effect1              1.000000              0.687919\n",
        "               Envision_predictions        0.687919              1.000000\n",
        "PSD95pdz3      scaled_effect1              1.000000              0.579800\n",
        "               Envision_predictions        0.579800              1.000000\n",
        "Pab1           scaled_effect1              1.000000              0.708311\n",
        "               Envision_predictions        0.708311              1.000000\n",
        "Ubiquitin      scaled_effect1              1.000000              0.569076\n",
        "               Envision_predictions        0.569076              1.000000\n",
        "WW_domain      scaled_effect1              1.000000              0.544811\n",
        "               Envision_predictions        0.544811              1.000000\n",
        "beta-lactamase scaled_effect1              1.000000              0.787117\n",
        "               Envision_predictions        0.787117              1.000000\n",
        "gb1            scaled_effect1              1.000000              0.604428\n",
        "               Envision_predictions        0.604428              1.000000\n",
        "hsp90          scaled_effect1              1.000000              0.633606\n",
        "               Envision_predictions        0.633606              1.000000\n",
        "kka2_1:2       scaled_effect1              1.000000              0.736964\n",
        "               Envision_predictions        0.736964              1.000000"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Measure Envision performance on proline mutations across all data sets\n",
      "predictions = global_regression_model_noSELCO.predict(P2)\n",
      "stats.pearsonr(predictions,P2['scaled_effect1'])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "(0.71456245648508943, 1.6756951724508062e-157)"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Plot Envision predictions vs. observed variant effects for proline variants\n",
      "\n",
      "fig = plt.figure(figsize=(4,4))\n",
      "ax = fig.add_subplot(111)\n",
      "ax.set_xlabel('Predicted proline effect')\n",
      "ax.set_ylabel('Observed proline effect')\n",
      "ax.set_ylim([-0.2,1.6])\n",
      "ax.set_xlim([-0.2,1.6])\n",
      "im = ax.scatter(predictions,P2['scaled_effect1'])\n",
      "ax.axis('tight')\n",
      "#cb =fig.colorbar(im, ax=ax)\n",
      "#cb.set_label('Number of proline mutations (log)')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5pnvEuqfIk3wOivNJQpLrOiLWNCzOE5SuJsiyVPuSXkrtlr0uiKSEE9cI77eJ6GbG2Coi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\ngYFBx/D/A9iz/CrVr2KEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2abc841a2550>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Read in EVmutation predictions for tp53 mutatations and make a hexbinplot\n",
      "evMut_TP53 =  gl.SFrame.read_csv('~/data/EVmutation_TP53.csv')"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<pre>Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/EVmutation_TP53.csv</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/EVmutation_TP53.csv"
       ]
      },
      {
       "html": [
        "<pre>Parsing completed. Parsed 100 lines in 0.085121 secs.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Parsing completed. Parsed 100 lines in 0.085121 secs."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "------------------------------------------------------\n",
        "Inferred types from first 100 line(s) of file as \n",
        "column_type_hints=[str,int,str,str,float,float,float,float,int]\n",
        "If parsing fails due to incorrect types, you can correct\n",
        "the inferred type list above and pass it to read_csv in\n",
        "the column_type_hints argument\n",
        "------------------------------------------------------\n"
       ]
      },
      {
       "html": [
        "<pre>Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/EVmutation_TP53.csv</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Finished parsing file /net/fowler/vol1/home/vegray/metaDMS/dato/scaled/data/EVmutation_TP53.csv"
       ]
      },
      {
       "html": [
        "<pre>Parsing completed. Parsed 4880 lines in 0.063486 secs.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Parsing completed. Parsed 4880 lines in 0.063486 secs."
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "## Use pandas to format data structure\n",
      "evMut_TP53['tp53']=pd.to_numeric(evMut_TP53['tp53'])"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Measure correlation between TP53 observed variant effects and EVmutation - epistatic \n",
      "# predictions w/ Spearman algorithm\n",
      "stats.spearmanr(evMut_TP53['tp53'], evMut_TP53['prediction_epistatic'], nan_policy = 'omit' )"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "SpearmanrResult(correlation=0.48883021488640388, pvalue=masked_array(data = 1.37945684633e-86,\n",
        "             mask = False,\n",
        "       fill_value = 1e+20)\n",
        ")"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Measure correlation between TP53 observed variant effects and EVmutation - independent \n",
      "# predictions w/ Spearman algorithm\n",
      "stats.spearmanr(evMut_TP53['tp53'], evMut_TP53['prediction_independent'], nan_policy = 'omit' )"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "SpearmanrResult(correlation=0.51661690907601687, pvalue=masked_array(data = 3.48892290919e-98,\n",
        "             mask = False,\n",
        "       fill_value = 1e+20)\n",
        ")"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## For each original amino acid (wt) type, measure correlation between TP53 observed \n",
      "#variant effects and EVmutation - independent predictions w/ Spearman algorithm\n",
      "evMut_TP53.to_dataframe().groupby(['wt'])['tp53', 'prediction_independent'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>tp53</th>\n",
        "      <th>prediction_independent</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>wt</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.593657</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.593657</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.817984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.817984</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.162227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.162227</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.282812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.282812</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.368373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.368373</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.426384</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.426384</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.535693</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.535693</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.432960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.432960</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.364028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.364028</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.616263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.616263</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.459548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.459548</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.460510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.460510</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.514257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.514257</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.144747</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.144747</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.455915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.455915</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.569506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.569506</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.573574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.573574</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.488625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.488625</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.449593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.449593</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.641594</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.641594</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                               tp53  prediction_independent\n",
        "wt                                                         \n",
        "A  tp53                    1.000000                0.593657\n",
        "   prediction_independent  0.593657                1.000000\n",
        "C  tp53                    1.000000                0.817984\n",
        "   prediction_independent  0.817984                1.000000\n",
        "D  tp53                    1.000000                0.162227\n",
        "   prediction_independent  0.162227                1.000000\n",
        "E  tp53                    1.000000                0.282812\n",
        "   prediction_independent  0.282812                1.000000\n",
        "F  tp53                    1.000000                0.368373\n",
        "   prediction_independent  0.368373                1.000000\n",
        "G  tp53                    1.000000                0.426384\n",
        "   prediction_independent  0.426384                1.000000\n",
        "H  tp53                    1.000000                0.535693\n",
        "   prediction_independent  0.535693                1.000000\n",
        "I  tp53                    1.000000                0.432960\n",
        "   prediction_independent  0.432960                1.000000\n",
        "K  tp53                    1.000000                0.364028\n",
        "   prediction_independent  0.364028                1.000000\n",
        "L  tp53                    1.000000                0.616263\n",
        "   prediction_independent  0.616263                1.000000\n",
        "M  tp53                    1.000000                0.459548\n",
        "   prediction_independent  0.459548                1.000000\n",
        "N  tp53                    1.000000                0.460510\n",
        "   prediction_independent  0.460510                1.000000\n",
        "P  tp53                    1.000000                0.514257\n",
        "   prediction_independent  0.514257                1.000000\n",
        "Q  tp53                    1.000000                0.144747\n",
        "   prediction_independent  0.144747                1.000000\n",
        "R  tp53                    1.000000                0.455915\n",
        "   prediction_independent  0.455915                1.000000\n",
        "S  tp53                    1.000000                0.569506\n",
        "   prediction_independent  0.569506                1.000000\n",
        "T  tp53                    1.000000                0.573574\n",
        "   prediction_independent  0.573574                1.000000\n",
        "V  tp53                    1.000000                0.488625\n",
        "   prediction_independent  0.488625                1.000000\n",
        "W  tp53                    1.000000                0.449593\n",
        "   prediction_independent  0.449593                1.000000\n",
        "Y  tp53                    1.000000                0.641594\n",
        "   prediction_independent  0.641594                1.000000"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## For each mutant amino acid (subs) type, measure correlation between TP53 observed \n",
      "#variant effects and EVmutation - independent predictions w/ Pearson algorithm\n",
      "evMut_TP53.to_dataframe().groupby(['subs'])['tp53', 'prediction_independent'].corr()"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>tp53</th>\n",
        "      <th>prediction_independent</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>subs</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.361323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.361323</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.393132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.393132</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.535817</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.535817</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.612588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.612588</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.627835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.627835</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.410820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.410820</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.357564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.357564</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.480585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.480585</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.501071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.501071</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.229980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.229980</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.235719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.235719</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.508937</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.508937</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.607852</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.607852</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.343555</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.343555</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.619480</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.619480</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.603498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.603498</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.547621</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.547621</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.458286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.458286</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.611082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.611082</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.505325</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_independent</th>\n",
        "      <td>0.505325</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "                                 tp53  prediction_independent\n",
        "subs                                                         \n",
        "A    tp53                    1.000000                0.361323\n",
        "     prediction_independent  0.361323                1.000000\n",
        "C    tp53                    1.000000                0.393132\n",
        "     prediction_independent  0.393132                1.000000\n",
        "D    tp53                    1.000000                0.535817\n",
        "     prediction_independent  0.535817                1.000000\n",
        "E    tp53                    1.000000                0.612588\n",
        "     prediction_independent  0.612588                1.000000\n",
        "F    tp53                    1.000000                0.627835\n",
        "     prediction_independent  0.627835                1.000000\n",
        "G    tp53                    1.000000                0.410820\n",
        "     prediction_independent  0.410820                1.000000\n",
        "H    tp53                    1.000000                0.357564\n",
        "     prediction_independent  0.357564                1.000000\n",
        "I    tp53                    1.000000                0.480585\n",
        "     prediction_independent  0.480585                1.000000\n",
        "K    tp53                    1.000000                0.501071\n",
        "     prediction_independent  0.501071                1.000000\n",
        "L    tp53                    1.000000                0.229980\n",
        "     prediction_independent  0.229980                1.000000\n",
        "M    tp53                    1.000000                0.235719\n",
        "     prediction_independent  0.235719                1.000000\n",
        "N    tp53                    1.000000                0.508937\n",
        "     prediction_independent  0.508937                1.000000\n",
        "P    tp53                    1.000000                0.607852\n",
        "     prediction_independent  0.607852                1.000000\n",
        "Q    tp53                    1.000000                0.343555\n",
        "     prediction_independent  0.343555                1.000000\n",
        "R    tp53                    1.000000                0.619480\n",
        "     prediction_independent  0.619480                1.000000\n",
        "S    tp53                    1.000000                0.603498\n",
        "     prediction_independent  0.603498                1.000000\n",
        "T    tp53                    1.000000                0.547621\n",
        "     prediction_independent  0.547621                1.000000\n",
        "V    tp53                    1.000000                0.458286\n",
        "     prediction_independent  0.458286                1.000000\n",
        "W    tp53                    1.000000                0.611082\n",
        "     prediction_independent  0.611082                1.000000\n",
        "Y    tp53                    1.000000                0.505325\n",
        "     prediction_independent  0.505325                1.000000"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## For each mutant amino acid (subs) type, measure correlation between TP53 observed \n",
      "#variant effects and EVmutation - epistatic predictions w/ Spearman algorithm\n",
      "evMut_TP53.to_dataframe().groupby(['subs'])['tp53', 'prediction_epistatic'].corr(method='spearman')"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>tp53</th>\n",
        "      <th>prediction_epistatic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>subs</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.343238</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.343238</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.420851</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.420851</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.545758</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.545758</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.616030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.616030</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.703529</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.703529</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.397712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.397712</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.269719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.269719</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.573503</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.573503</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.415502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.415502</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.334370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.334370</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.362694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.362694</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.534275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.534275</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.602419</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.602419</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.375494</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.375494</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.620185</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.620185</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.606786</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.606786</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.511819</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.511819</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.437981</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.437981</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.355138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.355138</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.465668</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.465668</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                               tp53  prediction_epistatic\n",
        "subs                                                     \n",
        "A    tp53                  1.000000              0.343238\n",
        "     prediction_epistatic  0.343238              1.000000\n",
        "C    tp53                  1.000000              0.420851\n",
        "     prediction_epistatic  0.420851              1.000000\n",
        "D    tp53                  1.000000              0.545758\n",
        "     prediction_epistatic  0.545758              1.000000\n",
        "E    tp53                  1.000000              0.616030\n",
        "     prediction_epistatic  0.616030              1.000000\n",
        "F    tp53                  1.000000              0.703529\n",
        "     prediction_epistatic  0.703529              1.000000\n",
        "G    tp53                  1.000000              0.397712\n",
        "     prediction_epistatic  0.397712              1.000000\n",
        "H    tp53                  1.000000              0.269719\n",
        "     prediction_epistatic  0.269719              1.000000\n",
        "I    tp53                  1.000000              0.573503\n",
        "     prediction_epistatic  0.573503              1.000000\n",
        "K    tp53                  1.000000              0.415502\n",
        "     prediction_epistatic  0.415502              1.000000\n",
        "L    tp53                  1.000000              0.334370\n",
        "     prediction_epistatic  0.334370              1.000000\n",
        "M    tp53                  1.000000              0.362694\n",
        "     prediction_epistatic  0.362694              1.000000\n",
        "N    tp53                  1.000000              0.534275\n",
        "     prediction_epistatic  0.534275              1.000000\n",
        "P    tp53                  1.000000              0.602419\n",
        "     prediction_epistatic  0.602419              1.000000\n",
        "Q    tp53                  1.000000              0.375494\n",
        "     prediction_epistatic  0.375494              1.000000\n",
        "R    tp53                  1.000000              0.620185\n",
        "     prediction_epistatic  0.620185              1.000000\n",
        "S    tp53                  1.000000              0.606786\n",
        "     prediction_epistatic  0.606786              1.000000\n",
        "T    tp53                  1.000000              0.511819\n",
        "     prediction_epistatic  0.511819              1.000000\n",
        "V    tp53                  1.000000              0.437981\n",
        "     prediction_epistatic  0.437981              1.000000\n",
        "W    tp53                  1.000000              0.355138\n",
        "     prediction_epistatic  0.355138              1.000000\n",
        "Y    tp53                  1.000000              0.465668\n",
        "     prediction_epistatic  0.465668              1.000000"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## For each original amino acid (wt) type, measure correlation between TP53 observed \n",
      "#variant effects and EVmutation - epistatic predictions w/ Spearman algorithm\n",
      "evMut_TP53.to_dataframe().groupby(['wt'])['tp53', 'prediction_epistatic'].corr(method='spearman')"
     ],
     "language": "python",
     "metadata": {
      "deletable": true,
      "editable": true
     },
     "outputs": [
      {
       "html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>tp53</th>\n",
        "      <th>prediction_epistatic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>wt</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.520741</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.520741</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.719795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.719795</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.050213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.050213</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">E</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.434480</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.434480</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.294702</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.294702</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.484080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.484080</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">H</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.562343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.562343</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.467060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.467060</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">K</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.290033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.290033</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">L</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.697199</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.697199</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.430905</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.430905</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.443748</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.443748</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">P</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.484143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.484143</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.062564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.062564</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">R</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.403977</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.403977</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.518171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.518171</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">T</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.580042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.580042</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">V</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.506079</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.506079</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">W</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.526316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.526316</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
        "      <th>tp53</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.512487</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prediction_epistatic</th>\n",
        "      <td>0.512487</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                             tp53  prediction_epistatic\n",
        "wt                                                     \n",
        "A  tp53                  1.000000              0.520741\n",
        "   prediction_epistatic  0.520741              1.000000\n",
        "C  tp53                  1.000000              0.719795\n",
        "   prediction_epistatic  0.719795              1.000000\n",
        "D  tp53                  1.000000              0.050213\n",
        "   prediction_epistatic  0.050213              1.000000\n",
        "E  tp53                  1.000000              0.434480\n",
        "   prediction_epistatic  0.434480              1.000000\n",
        "F  tp53                  1.000000              0.294702\n",
        "   prediction_epistatic  0.294702              1.000000\n",
        "G  tp53                  1.000000              0.484080\n",
        "   prediction_epistatic  0.484080              1.000000\n",
        "H  tp53                  1.000000              0.562343\n",
        "   prediction_epistatic  0.562343              1.000000\n",
        "I  tp53                  1.000000              0.467060\n",
        "   prediction_epistatic  0.467060              1.000000\n",
        "K  tp53                  1.000000              0.290033\n",
        "   prediction_epistatic  0.290033              1.000000\n",
        "L  tp53                  1.000000              0.697199\n",
        "   prediction_epistatic  0.697199              1.000000\n",
        "M  tp53                  1.000000              0.430905\n",
        "   prediction_epistatic  0.430905              1.000000\n",
        "N  tp53                  1.000000              0.443748\n",
        "   prediction_epistatic  0.443748              1.000000\n",
        "P  tp53                  1.000000              0.484143\n",
        "   prediction_epistatic  0.484143              1.000000\n",
        "Q  tp53                  1.000000              0.062564\n",
        "   prediction_epistatic  0.062564              1.000000\n",
        "R  tp53                  1.000000              0.403977\n",
        "   prediction_epistatic  0.403977              1.000000\n",
        "S  tp53                  1.000000              0.518171\n",
        "   prediction_epistatic  0.518171              1.000000\n",
        "T  tp53                  1.000000              0.580042\n",
        "   prediction_epistatic  0.580042              1.000000\n",
        "V  tp53                  1.000000              0.506079\n",
        "   prediction_epistatic  0.506079              1.000000\n",
        "W  tp53                  1.000000              0.526316\n",
        "   prediction_epistatic  0.526316              1.000000\n",
        "Y  tp53                  1.000000              0.512487\n",
        "   prediction_epistatic  0.512487              1.000000"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}