{
 "metadata": {
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  "signature": "sha256:b5b5ffec4222396a2ae9f167350c51b7b08cee757bc1131613ec828ea64864fd"
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "# Some nice default configuration for plots\n",
      "plt.rcParams['figure.figsize'] = 10, 7.5\n",
      "plt.rcParams['axes.grid'] = True\n",
      "plt.gray()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x3755590>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Loading the training and test set\n",
      "\n",
      "The first step is to load the training and set that was saved when running the [Training and test feature set generation notebook][genotes].\n",
      "\n",
      "[genotes]: http://nbviewer.ipython.org/github/ggray1729/opencast-bio/blob/master/notebooks/Training%20and%20test%20featureset%20generation.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd ../../features/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/data/opencast/MRes/features\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "posdata = loadtxt(\"human.iRefIndex.positive.vectors.txt\", delimiter=\"\\t\", dtype=\"str\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Training with the full negative set is impossible on this computer, it is unable to load the file into RAM.\n",
      "To get around this, can use a negative training set only 10 times larger than the positive training set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "negdata = loadtxt(\"human.iRefIndex.negative.vectors.txt\", delimiter=\"\\t\", dtype=\"str\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are some conflicting entries in this notebook detected in the [training and test set generation][traintest].\n",
      "These can be removed using their indexes:\n",
      "\n",
      "[traintest]: http://nbviewer.ipython.org/github/ggray1729/opencast-bio/blob/master/notebooks/Classifier%20Training%20HIPPIE.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.concatenate((posdata,negdata))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plottablerows = X==\"missing\"\n",
      "plottablerows = where(plottablerows.max(axis=1)==False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unfortunately, some \"NA\" strings have sneaked in somehow, so we will have to zero these. Appears to be due to a feature in Gene_Ontology:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "NAinds = where(X==\"NA\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[NAinds] = 0.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "X[X==\"missing\"] = np.nan\n",
      "X = X.astype(np.float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally we can create the target vector y from what we know about the lengths of the positive and negative sets:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#create the target y vector\n",
      "y = array([1]*len(posdata)+[0]*len(negdata))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Creating a proportional training set\n",
      "\n",
      "We would like the training set we use to reflect our prior estimation that only one in every 600 combinations of protein identifiers is a true interaction. To ensure that our training set also reflects this we need to sample from it to maintain this proportion of positive to negative.\n",
      "\n",
      "An efficient way to do this is to create a modified y vector which is in proportion and maps back to the original y vector.\n",
      "Then, after calling StatifiedShuffleSplit the returned indices can be transformed back to the correct indices on the original y vector:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def propy(y,samplesize=20000):\n",
      "    \"\"\"Function to produce proportional samples of y from the full dataset.\n",
      "    Takes as an input: y - the vector of class labels, samplesize - size of sample \n",
      "    Outputs: mody - modified y vector, transformdict - dictionary to map between indices\"\"\"\n",
      "    enuy = list(enumerate(y))\n",
      "    shuffle(enuy)\n",
      "    nones = int(samplesize/600)\n",
      "    nzeros = int((samplesize*599)/600)\n",
      "    transformdict = {}\n",
      "    mody = []\n",
      "    #iterate over this list and pull out the required number of ones and zeros\n",
      "    for j,(i,label) in enumerate(enuy):\n",
      "        if label == 1 and nones > 0:\n",
      "            transformdict[j] = i\n",
      "            mody.append(label)\n",
      "            nones += -1\n",
      "        elif label == 0 and nzeros > 0:\n",
      "            transformdict[j] = i\n",
      "            mody.append(label)\n",
      "            nzeros += -1\n",
      "        if nzeros == 0 and nones == 0:\n",
      "            break\n",
      "    return mody, transformdict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then all that's required is to create a sub-sampled, but still large, version of the X and y vectors:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mody,tfdict = propy(y,samplesize=400000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "propindexes = array(tfdict.values())\n",
      "X,y = X[propindexes],y[propindexes]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(X),len(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(399999, 399999)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Choosing a classifier\n",
      "\n",
      "Each classifier will be tested in the following ways over :\n",
      "\n",
      "* Mean accuracy\n",
      "* Combined ROC curves\n",
      "    * AUC values\n",
      "    * Partial AUC values\n",
      "* Combined Precision vs. Recall curves\n",
      "* Informational loss function\n",
      "* Quadratic loss function\n",
      "* Kappa test?\n",
      "\n",
      "The importance of different features will also be tested through elimination and repeating the tests above.\n",
      "\n",
      "Finally, a sanity check looking at the model's prediction for some well-known protein interactions is the final test.\n",
      "\n",
      "## Logistic Regression\n",
      "\n",
      "Logistic regression is a simple classifier which uses a linear transformation of the input variables through a sigmoid function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd tmp/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/data/opencast/MRes/features/tmp\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.linear_model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys, os\n",
      "sys.path.append(os.path.abspath(\"../../opencast-bio/\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ocbio.model_selection, ocbio.mmap_utils\n",
      "reload(ocbio.model_selection)\n",
      "reload(ocbio.mmap_utils)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<module 'ocbio.mmap_utils' from '/data/opencast/MRes/opencast-bio/ocbio/mmap_utils.pyc'>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ipcluster stop"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-08-05 14:33:28.752 [IPClusterStop] Using existing profile dir: u'/home/fin/.ipython/profile_default'\r\n",
        "2014-08-05 14:33:28.753 [IPClusterStop] CRITICAL | Could not read pid file, cluster is probably not running.\r\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ipcluster start -n=10 --daemon"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-08-05 14:33:32.761 [IPClusterStart] Using existing profile dir: u'/home/fin/.ipython/profile_default'\r\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "client = Client(profile='default')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#initialise load balanced distributed processing\n",
      "lb_view = client.load_balanced_view()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Defining pipeline\n",
      "\n",
      "We have to define a pipeline for the data to include two transformations for the data:\n",
      "\n",
      "* Imputing missing values\n",
      "* Standard scaling of the feature vectors\n",
      "\n",
      "Both of these are built in operations of Scikit-learn.\n",
      "And they can be easily integrated to a pipeline in Scikit-learn."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.pipeline, sklearn.preprocessing"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imputer = sklearn.preprocessing.Imputer(strategy='mean',missing_values=\"NaN\")\n",
      "scaler = sklearn.preprocessing.StandardScaler()\n",
      "classifier = sklearn.linear_model.LogisticRegression()\n",
      "model = sklearn.pipeline.Pipeline([('imp',imputer),('scl',scaler),('cls',classifier)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Plotting a learning curve\n",
      "\n",
      "We would like to plot a learning curve to get an idea of the required size of our training set to train a model on k-fold validation steps to get valid results.\n",
      "Until we decide on a final classifier it is preferable to be able to run fast iterations of different parameters for more complex parameters than to absolutely maximimise performance.\n",
      "A learning curve plots the performance of the algorithm on the training and test set for different training set sizes.\n",
      "\n",
      "As with the imported code, much of this code is taken from the excellent [parallel machine learning tutorial][parml] by Oliver Grisel.\n",
      "\n",
      "[parml]: https://github.com/ogrisel/parallel_ml_tutorial"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainsizes=logspace(3,5,5)\n",
      "#initialise and start run\n",
      "lcsearch = ocbio.model_selection.LearningCurve(lb_view)\n",
      "lcsearch.launch_for_arrays(model,X,y,trainsizes,n_cv_iter=5,params={'cls__C':0.316}, name=\"lrlc\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "Progress: 00% (000/125)"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print lcsearch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Progress: 100% (125/125)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lrlc = lcsearch.plot_curve()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rVqxA27Zt4eLigrFjxyIrKwsAUFxcjPHjx8PFxQUqlQq9e/dGamoq5s+fj5Mn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\nkIiIiMgPMQkkIiIi8kNMAomIiIj80P8D+zDElzHG5awAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x171ec210>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/logistic.regression.learning.curve.npz\",lrlc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This shows that we can test the model on a grid search with only 40,000 to 60,000 samples and still get meaningful results.\n",
      "When the grid search returns a best possible combination of parameters we will have to plot this learning curve again to make sure that the results are still valid.\n",
      "In the mean time, this will speed up our grid search considerably."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Running parallel grid search"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ipcluster stop --profile=altcluster"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-08-05 16:49:25.606 [IPClusterStop] Using existing profile dir: u'/home/fin/.ipython/profile_altcluster'\r\n",
        "2014-08-05 16:49:25.608 [IPClusterStop] CRITICAL | Could not read pid file, cluster is probably not running.\r\n"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ipcluster start -n=10 --profile=altcluster --daemon"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-08-05 16:49:28.359 [IPClusterStart] Using existing profile dir: u'/home/fin/.ipython/profile_altcluster'\r\n"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "altclient = Client(profile='altcluster')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "alb_view = altclient.load_balanced_view()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#make sure the processors aren't busy doing anything else:\n",
      "alb_view.abort()\n",
      "#initialise parameters to test:\n",
      "lr_params = {\n",
      "'cls__C':logspace(-3,1,7)\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#initialise mmaped data -  had to increase CV iterations to reduce output variance\n",
      "split_filenames = ocbio.mmap_utils.persist_cv_splits(X,y, test_size=10000, train_size=40000,\n",
      "                                            n_cv_iter=10, name='testfeatures',random_state=42)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#initialise and start run\n",
      "lrsearch = ocbio.model_selection.RandomizedGridSearch(alb_view)\n",
      "lrsearch.launch_for_splits(model,lr_params,split_filenames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "Progress: 00% (000/070)"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print lrsearch.report()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Progress: 100% (070/070)\n",
        "\n",
        "Rank 1: validation: 0.99840 (+/-0.00003) train: 0.99847 (+/-0.00002):\n",
        " {'cls__C': 0.021544346900318832}\n",
        "Rank 2: validation: 0.99839 (+/-0.00003) train: 0.99846 (+/-0.00001):\n",
        " {'cls__C': 0.0046415888336127772}\n",
        "Rank 3: validation: 0.99839 (+/-0.00003) train: 0.99845 (+/-0.00001):\n",
        " {'cls__C': 0.001}\n",
        "Rank 4: validation: 0.99836 (+/-0.00004) train: 0.99850 (+/-0.00002):\n",
        " {'cls__C': 0.10000000000000001}\n",
        "Rank 5: validation: 0.99836 (+/-0.00006) train: 0.99854 (+/-0.00002):\n",
        " {'cls__C': 0.46415888336127775}\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With this data only one in every 18 examples will be positive therefore the expected accuracy of a classifier that only predicts zeros is:\n",
      "\n",
      "$$\n",
      "1 - \\frac{1}{18} = \\frac{17}{18} = 0.9(4)\n",
      "$$\n",
      "\n",
      "So this model is outperforming the hypothetical all zeros classifier.\n",
      "\n",
      "We can implement this all zeros classifier and use it to create a comparative measure to make it easier to discriminate between classifiers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.dummy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dummycompare(X,y,train,test,best_score):\n",
      "    dummy = sklearn.dummy.DummyClassifier(strategy=\"most_frequent\")\n",
      "    dummy.fit(imputer.fit_transform(X[train]),y[train])\n",
      "    score = dummy.score(imputer.fit_transform(X[test]),y[test])\n",
      "    if score > best_score:\n",
      "        return 0.0\n",
      "    else:\n",
      "        #using a logit function here on a biased and scaled best score\n",
      "        best_score = (best_score-score)*(1.0/(1.0-score))\n",
      "        return best_score,log(best_score/(1.0-best_score))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = np.zeros([5,2])\n",
      "for i,(train,test) in enumerate(sklearn.cross_validation.StratifiedShuffleSplit(y,n_iter=5,\n",
      "                                                                  test_size=10000, train_size=40000)):\n",
      "    results[i,:] = dummycompare(X,y,train,test,lrsearch.find_bests()[0][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print mean(results,axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.05882353 -2.77258872]\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Plotting a ROC curve\n",
      "\n",
      "A ROC curve indicates the tradeoffs possible with this classifier in terms of true positive versus false positive rate when setting the decision threshold.\n",
      "In some applications the price of a high false positive rate is acceptable to obtain a high true positive rate.\n",
      "As the false positive rate grows to 1.0 though, the true positive rate also grows to 1.0 inevitably for any classifier as all samples are being classified as positive."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plotroc(model,Xtest,ytest):\n",
      "    estimates = model.predict_proba(Xtest)\n",
      "    fpr, tpr, thresholds = sklearn.metrics.roc_curve(ytest,estimates[:,1])\n",
      "    clf()\n",
      "    plot(fpr, tpr)\n",
      "    plot([0, 1], [0, 1], 'k--')\n",
      "    xlim([0.0, 1.0])\n",
      "    ylim([0.0, 1.0])\n",
      "    xlabel('False Positive Rate')\n",
      "    ylabel('True Positive Rate')\n",
      "    title('Receiver operating characteristic AUC: {0}'.format(sklearn.metrics.roc_auc_score(ytest,estimates[:,1])))\n",
      "    show()\n",
      "    return fpr,tpr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train,test = next(iter(sklearn.cross_validation.StratifiedShuffleSplit(y,\n",
      "                                    test_size=0.25)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bestparams = lrsearch.find_bests()[0][-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "model.set_params(**bestparams)\n",
      "model.fit(X[train],y[train])\n",
      "fpr,tpr = plotroc(model,X[test],y[test])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AyEjhikgAyMgQ/jx4sOTXaLVazJ8/H0uXLkV4eDgCAgJKfO7zjIxJsrQFERER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\niIiISCA2Y0REREQC/T+VljvI9jgM9gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17d3b0d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 1min 59s, sys: 3.19 s, total: 2min 2s\n",
        "Wall time: 2min 3s\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that the AUC score is positive, which means that the classifier is performing better than chance."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/logistic.regression.roc.npz\",fpr,tpr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print dummycompare(X,y,train,test,model.score(X[test],y[test]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(0.071856287425090787, -2.5585184671321297)\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Precision recall curve\n",
      "\n",
      "Precision recall curves plot precision against recall.\n",
      "\n",
      "* Precision is the [\"fraction of retrieved instances which are relevant\"][wkpda]\n",
      "* Recall is the [\"fraction of relevant instances which are retrieved\"][wkpda]\n",
      "\n",
      "These are [implemented][] in Scikit-learn.\n",
      "\n",
      "[wkpda]: http://en.wikipedia.org/wiki/Precision_and_recall\n",
      "[implemented]: http://scikit-learn.org/stable/auto_examples/plot_precision_recall.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def drawprecisionrecall(X,y,test,model):\n",
      "    try:\n",
      "        yscore = model.decision_function(X[test])\n",
      "    except AttributeError:\n",
      "        yscore = model.predict_proba(X[test])[:,1]\n",
      "    precision,recall,_ = sklearn.metrics.precision_recall_curve(y[test],yscore)\n",
      "    average_precision = sklearn.metrics.average_precision_score(y[test], yscore,\n",
      "                                                         average=\"micro\")\n",
      "    plt.clf()\n",
      "    plt.plot(recall,precision)\n",
      "    plt.xlim([0.0, 1.0])\n",
      "    plt.ylim([0.0, 1.05])\n",
      "    plt.xlabel('Recall')\n",
      "    plt.ylabel('Precision')\n",
      "    plt.title('Precision-Recall curve, AUC: {0:0.2f}'.format(average_precision))\n",
      "    plt.show()\n",
      "    return precision, recall"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "precision,recall = drawprecisionrecall(X,y,test,model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iZgwAAMBADvuVK3ziBgAAqMRi5aVbHLYZE9E+XAA1zZw5U6y4ODocANmpjfzURXZqM5ms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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2ebbf10>"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This shows that it is not possible, even with loss of precision, to recall all of the positive training examples.\n",
      "Unfortunately, it is only possible to recall less than 10% with perfect precision."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/lr_precisionrecall.npz\",precision,recall)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Classifier coefficients\n",
      "\n",
      "We can also plot the weightings which this model is using to achieve the above performance:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(model.named_steps['cls'].coef_.T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "[<matplotlib.lines.Line2D at 0x176de310>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vRKIwA8Thw9SUsJTEVypFdxni7qtYna/+fgqkx9FeD0pra/DMl2hTUQriq7yc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\nX3qp6fULAz1+/PEmJBJAOk3ns/x6Q0O0/YcOeT9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V3D9PqfIYj6d7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\n85cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2ebbf10>"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/logistic.regression.coef.npz\",model.named_steps['cls'].coef_.T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Which feature is the largest peak in the above plot?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "where(model.named_steps['cls'].coef_.T==amax(model.named_steps['cls'].coef_.T))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "(array([0]), array([0]))"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The largest peak in the above corresponds to a Gene Ontology feature.\n",
      "However, many of the features are almost as strongly weighted, showing that the classifier is not relying on a single feature."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Support Vector Machine\n",
      "\n",
      "A support vector machine is a kernel-based classifier which aims to fit a hyperplane between training examples to separate the examples into different classes.\n",
      "The algorithm aims to maximise the distance from any given training point to the hyperplane in the high-dimensional space of the training vectors.\n",
      "\n",
      "### Building the pipeline\n",
      "\n",
      "Optimal performance of a SVM with the default parameters depends on scaling of the data so we must build a pipeline for the model as before with logistic regression:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.svm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imputer = sklearn.preprocessing.Imputer(strategy='mean',missing_values=\"NaN\")\n",
      "scaler = sklearn.preprocessing.StandardScaler()\n",
      "classifier = sklearn.svm.SVC()\n",
      "svmodel = sklearn.pipeline.Pipeline([('imp',imputer),('scl',scaler),('cls',classifier)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Plotting a learning curve\n",
      "\n",
      "Again, it would be useful to get an idea of acceptable sample sizes prior to running the grid search to save processing time:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainsizes=logspace(3,4.5,5)\n",
      "#initialise and start run\n",
      "lcsearch = ocbio.model_selection.LearningCurve(lb_view)\n",
      "lcsearch.launch_for_arrays(svmodel,X,y,trainsizes,n_cv_iter=3,params={'cls__C':1.0}, name=\"lrlc\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "Progress: 00% (000/075)"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print lcsearch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Progress: 60% (045/075)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svmlc = lcsearch.plot_curve()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y5kM6mAuSOhZvJGlDWwyFdT1rrLmwRuxQiIiIJIE9byR5W/7egoUnF+Lah9fK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\nIiKyIGzeiIiIiCwImzciIiIiC/L/AcG7P9mFUOPrAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1aa7d850>"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/svmlc.npz\",svmlc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From this curve we can conclude that at least 20,000 training samples are going to be necessary for meaningful results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#initialise parameters to test:\n",
      "svm_params = {\n",
      "'cls__C':logspace(-1,1,3),\n",
      "'cls__kernel':['rbf','poly','sigmoid'],\n",
      "'cls__gamma':logspace(-1,1,3)\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#initialise mmaped data\n",
      "split_filenames = ocbio.mmap_utils.persist_cv_splits(X,y, test_size=2000, train_size=10000,\n",
      "                                                     n_cv_iter=3, name='testfeatures',random_state=42)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#initialise and start run\n",
      "search = ocbio.model_selection.RandomizedGridSearch(alb_view)\n",
      "search.launch_for_splits(svmodel,svm_params,split_filenames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "Progress: 00% (000/081)"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print search"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Progress: 98% (080/081)\n",
        "\n",
        "Rank 1: validation: 0.99850 (+/-0.00000) train: 0.99933 (+/-0.00012):\n",
        " {'cls__kernel': 'rbf', 'cls__gamma': 10.0, 'cls__C': 10.0}\n",
        "Rank 2: validation: 0.99850 (+/-0.00000) train: 0.99933 (+/-0.00012):\n",
        " {'cls__kernel': 'rbf', 'cls__gamma': 1.0, 'cls__C': 10.0}\n",
        "Rank 3: validation: 0.99850 (+/-0.00000) train: 0.99933 (+/-0.00012):\n",
        " {'cls__kernel': 'rbf', 'cls__gamma': 0.10000000000000001, 'cls__C': 10.0}\n",
        "Rank 4: validation: 0.99850 (+/-0.00000) train: 0.99933 (+/-0.00012):\n",
        " {'cls__kernel': 'rbf', 'cls__gamma': 10.0, 'cls__C': 1.0}\n",
        "Rank 5: validation: 0.99850 (+/-0.00000) train: 0.99933 (+/-0.00012):\n",
        " {'cls__kernel': 'rbf', 'cls__gamma': 1.0, 'cls__C': 1.0}\n"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is taking an _extremely_ long time to train, and is not feasible in the sample sizes above.\n",
      "Quote from Murphy textbook on SVMs:\n",
      "\n",
      "> SVMs also take $O(N^{3})$ time to train...\n",
      "\n",
      "Which is also repeated in the [documentation for the Scikit-learn SVM classifier][skdocs].\n",
      "\n",
      "Due to this problem, it does not seem to be well-suited to this data, where we will primarily be working with very large sample sizes.\n",
      "\n",
      "[skdocs]: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train,test = next(iter(sklearn.cross_validation.StratifiedShuffleSplit(y,\n",
      "                                    test_size=2000,train_size=10000)))\n",
      "print len(train),len(test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 2000\n"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svmparams = search.find_bests()[0][-1]\n",
      "svmparams['cls__probability'] = True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "svmodel.set_params(**svmparams)\n",
      "svmodel.fit(X[train],y[train])\n",
      "fpr,tpr = plotroc(svmodel,X[test],y[test])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iNDQUQUFBiIyMhFKpLPa4v/8GZs8G1q8HrK0rPw72jBEREZHFMVzENSUl5b+a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\nKej/Acozk3sZ5tTLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1f15c790>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 1min 39s, sys: 9.48 s, total: 1min 48s\n",
        "Wall time: 1min 49s\n"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That is an extremely poor ROC curve, probably due to the extremely small training set.\n",
      "It is unavoidable, though, due to the time it takes to train Support Vector Machines."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd tmp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/data/opencast/MRes/features/tmp\n"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/svm.roc.npz\",fpr,tpr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svmprecision,svmrecall = drawprecisionrecall(X,y,test,svmodel)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9D7MP1kXtrI36WRe1sy9eTWlDDzwgZWVJ3btLn31m7EsGAADMwcyYTRUWSk8+\nabzCctMmY+d+AABQOtybEqVSWCjFxUkHDxq3T6pSxexEAABYEwP8KBUPD+m996S6daUBA6TcXOc9\nNrMP1kXtrI36WRe1sy+aMZvz8pLmzTMasZEjpYICsxMBAGAvLFNCknT6tLHlRatW0rRpxlUzAABQ\nMixTosyqVTM2hf3yS+npp81OAwCAfdCM4T/8/aXERCkhQXrttbI9FrMP1kXtrI36WRe1sy/2GcNf\nXHWVtGGDFBZmNGejR5udCACAio2ZMRTrwAEpPFx64w3jlZYAAODiyjIzxpUxFCs0VFq71rhtkp+f\ndOedZicCAKBiYmYMF9WqlfTpp9LQoVJKypV9LbMP1kXtrI36WRe1sy+aMVzSbbdJH38s9e0r7dhh\ndhoAACoeZsZQIp98YtxgfMsWqVkzs9MAAOBemBmDy911l5SdLXXvbixZNmpkdiIAACoGlilRYsOG\nSY89Zgz1Hzt26XOZfbAuamdt1M+6qJ19cWUMV+Thh6WsLOMKWVKSVLOm2YkAALA2ZsZwxQoLpUcf\nlVJTjQ1iq1c3OxEAAOYqy8wYzRhKpaBAuu8+KSPDuKdl5cpmJwIAwDzcKBzlztNTmjXL2BD2nnuk\nvLy/fp7ZB+uidtZG/ayL2tkXzRhKzdtb+ugj6eRJ4x6WBQVmJwIAwHpYpkSZnTplDPTffLP05puS\nR6ku0gIAYF0sU8JU1atLq1cbr6587jmz0wAAYC00Y3CKgABp3Tpj2fLtt5l9sDJqZ23Uz7qonX3R\njMFp6tWTNm6U3nhDSkw0Ow0AANbAzBicbt8+qXNn6d13pdhYs9MAAOB63JsSbqVZM2nNGikyUqpR\nwxjuBwAAxWOZEi6RlZWkZcuke++VvvjC7DS4EsytWBv1sy5qZ180Y3CZO+6QPvxQuusuafdus9MA\nAOCemBmDyy1ZYtxg/LPPpNBQs9MAAOB8zIzBrfXvL2VlSRERUkqKFBxsdiIAANwHy5RwiQtnH+67\nT3roIaMh+/13czKhZJhbsTbqZ13Uzr64MoZy89hjUmam8SrLLVskf3+zEwEAYD5mxlCuCgul8eOl\nXbuMHfurVTM7EQAAZVeWmTGaMZS7ggJp+HDp+HHp00+lSpXMTgQAQNlwo3C4nUvNPnh6Su+/bzRh\nQ4ZI+fnllwuXx9yKtVE/66J29kUzBlN4e0sLFxpXx8aMMZYvAQCwI5YpYaqcHKlbN6ljR+mVVySP\nUl3gBQAQgFTiAAAgAElEQVTAXCxTwrJ8faW1a6XEROmll8xOAwBA+aMZg0tcyexDrVrS+vXGHNm7\n77ouE0qGuRVro37WRe3si33G4Bbq15c2bDCWK/39pcGDzU4EAED5YGYMbmXPHqlLF2nWLCkmxuw0\nAACUDDNjqDCuv15atcq4fdLmzWanAQDA9WjG4BJlmX1o105avFgaMEDats15mVAyzK1YG/WzLmpn\nXzRjcEvh4VJ8vLFU+f33ZqcBAMB1mBmDW1uwQHr8cemzz6QmTcxOAwBA8coyM8arKeHWBg2SsrKk\niAhp61apQQOzEwEA4FwsU8IlnDn7MGaMdP/9RkP2xx9Oe1hcBHMr1kb9rIva2RfNGCxh4kQpOlrq\n0UPKzjY7DQAAzsPMGCyjsFAaO1bav19as0aqWtXsRAAAGMoyM0YzBkvJzzd25z91SkpIkHx8zE4E\nAACbvsINuWr2wctLmjdPKiiQhg83/gvnYm7F2qifdVE7+6IZg+X4+EhLlkgZGdK4ccbyJQAAVsUy\nJSwrO9u4j2X37tKLL5qdBgBgZ+wzBlvy85MSE6WOHaWAAOmJJ8xOBADAlWOZEi5RXrMPdepIGzZI\n770nzZpVLk9Z4TG3Ym3Uz7qonX1xZQyWFxhoNGSdOhlXywYONDsRAAAlx8wYKozvvpO6dTNuMB4V\nZXYaAICdsLUFIOmGG6Tly40tL5KTzU4DAEDJ0IzBJcyafbj1VmnBAqlfP+mbb0yJYHnMrVgb9bMu\namdfNGOocLp2NYb5e/WS9u41Ow0AAJfGzBgqrHnzpL//XUpJkUJCzE4DAKjI3G5mLDExsUezZs32\nhYaGHpg6deqTxZ2TlJQUfuONN+5s2bLl9+Hh4UmuyAF7GzrU2HssIkL69Vez0wAAUDynN2P5+fle\n48aNm56YmNhjz5491y9YsGDQ3r17mxc9JzMzM+DBBx98d+XKldHff/99y6VLl/Zzdg6Yy11mHx56\nyGjKuneXTpwwO401uEvtUDrUz7qonX05vRnbtm3bLU2bNj0YEhKS5uPjkztw4MCFy5cv7130nI8/\n/vievn37JgQFBWVIUp06dY47Owfg8NRTxtWxqCjp5Emz0wAA8FdO3/T16NGjgcHBwemO46CgoIzU\n1NT2Rc85cOBAaG5urk/nzp235OTk+D788MNvDxky5MMLH2v48OEK+b9hn4CAALVp00bh4eGS/vsX\nBMfueez4mDvk8fCQevVK0r59Up8+4Vq1SvrqK/PyuPtxeHi4W+XhmPpxzLE7HjveT0tLU1k5fYA/\nISGhb2JiYo/Zs2ePlqT58+cPTk1NbT9t2rSHHOeMGzdu+o4dO9pu2rSp6+nTp6vddtttX65evbpn\naGjogf8EY4AfTpafb+zOn5cnLVkieXP/CQCAk7jVAH9gYODR9PT0YMdxenp6sGM50iE4ODi9e/fu\n66tWrXqmdu3af3Ts2DF59+7drZ2dBeYp+peDu/DykubPl86cke67TyooMDuRe3LH2qHkqJ91UTv7\ncnoz1q5du+0HDhwITUtLCzl//nylRYsWDYiJiVlR9JzevXsv37p16x35+flep0+frpaamtr++uuv\n3+PsLMCFKleWEhKkgwelCRMkLr4CAMzmkn3G1q5de+eECRPeys/P9xo1atScSZMmvTRz5sw4SYqL\ni5spSa+99trf4uPjR3h6ehaMHj169vjx49/5SzCWKeFCmZlS585STIz07LNmpwEAWF1ZlinZ9BW2\n9dtvUliYNGaM9MgjZqcBAFiZW82MAZI1Zh/q1pU2bJDeekt6/32z07gPK9QOF0f9rIva2RevJ4Ot\nNWxoNGTh4ZKfn3GDcQAAyhPLlICkXbukyEjjfpaRkWanAQBYDcuUQBm1aSMtWyYNHix9/rnZaQAA\ndnLZZmzr1q13REREbAgNDT3QuHHjQ40bNz50zTXX/FQe4WBdVpx96NDB2IcsNta4UmZXVqwd/ov6\nWRe1s6/LzoyNGjVqzltvvTWhbdu2O7y8vPLLIxRglshI6d13jftYJiVJ115rdiIAQEV32Zmx9u3b\np154b8nywMwYzPT++9Jzz0nJycaQPwAAl+LSfcYmTpz4cn5+vldsbOyyypUrn3N8vG3btjtK84Ql\nDkYzBpO9+aY0Y4aUkmJsgwEAwMW4tBkLDw9P8vDw+J+TtmzZ0rk0T1hSNGPWlpSU9J873FvZP/4h\nrVwpbdkiBQSYnaZ8VJTa2RX1sy5qZ21lacYuOzOWlJQUXpoHBiqCZ581bp3Uq5e0fr1UrZrZiQAA\nFc1lr4xlZmYGPPvss88kJyd3lIwrZf/4xz+e8/f3z3JpMK6MwU0UFEgjRkjHjkkrVkiVKpmdCADg\nbly6z9jIkSPf9/Pzy16yZEn/xYsX3+3r65szYsSI+NI8GWBFnp7SnDlS1arGPmT5vKYYAOBEl70y\n1rp16927d+9ufbmPOT0YV8YsrSLOPpw9ayxXhoRIs2dLHqX6+8f9VcTa2Qn1sy5qZ20uvTJWtWrV\nMykpKWGO461bt95RrVq106V5MsDKqlSRPv1U+v576W9/k/hbAQDgDJe9MrZr1642Q4cOnZeVleUv\nSTVr1jwxd+7cYa1bt97t0mBcGYOb+vNP48bid98tPfWU2WkAAO7ApVtbOGRnZ/tJkp+fX3ZpnuhK\n0YzBnf36q3THHdKECdK4cWanAQCYzSVbW3z44YdDhgwZ8uHrr7/+WNF9xgoLCz08PDwKH3300TdK\n84Swh4o++3D11dLGjVJYmOTnJw0danYi56notavoqJ91UTv7umgzdvr06WqSlJOT41tcM1Ye4QB3\nFhJi7D3WpYvRkPXpY3YiAIAVlXiZsryxTAmr+OYb6c47pQULpK5dzU4DADCDS19N+cQTT7ySnZ3t\nl5ub69O1a9dNderUOf7hhx8OKc2TARXRTTdJS5ZIgwZJqalmpwEAWM1lm7F169ZF+vn5Za9atapX\nSEhI2o8//tjk1Vdffbw8wsG6kpKSzI5Qrjp1kuLjpZgY6bvvzE5TNnarXUVD/ayL2tnXZZuxvLw8\nb0latWpVr379+i319/fPYmYM+F89e0pvvy316CEdPGh2GgCAVVx2ZmzixIkvf/rpp32qVKlydtu2\nbbdkZmYGREdHr0xNTW3v0mDMjMGiZs6Upk6VUlKkwECz0wAAyoPL9xn7448/agcEBGR6eXnlnzp1\nqnpOTo7v1Vdf/WtpnrDEwWjGYGGvvCJ98IGUnCzVqWN2GgCAq7mkGdu0aVPXrl27bkpISOjrWJZ0\nPImHh0dhbGzsslInLkkwmjFLY78cadIkacMGafNmY+sLq6B21kb9rIvaWZtLNn1NTk7u2LVr100r\nV66MLm5GzNXNGGB1L74oZWYaQ/1r10pVq5qdCADgjthnDHChggJpyBApK0v65BPJx8fsRAAAV3Dp\nPmOTJ09+MTMzM8BxfOLEiZpPPfXUC6V5MsBuPD2N2TEPD2nYMCk/3+xEAAB3c9lmbM2aNVEBAQGZ\njuOaNWueWL16dU/XxoLVsV/Of/n4SIsXSz//bNxU3N0v+FI7a6N+1kXt7OuyzVhBQYHn2bNnqziO\nz5w5U/X8+fOVXBsLqFiqVpVWrJC2b5cmTzY7DQDAnVx2Zmzq1KlPrlixImbkyJHvFxYWesTHx4+I\niYlZ8eSTT051aTBmxlABHT9u7NY/ZIg0caLZaQAAzuLyfcbWrl1756ZNm7pKUkRExIbIyMh1pXmy\nKwpGM4YK6uhRKSxMeuIJacwYs9MAAJzBpQP8ktS8efO9kZGR61577bW/hYWFpeTk5PiW5slgH8w+\nXFxgoLH/2AsvSAsWmJ3mf1E7a6N+1kXt7OuyzdisWbPu79+//5IxY8bMkKSMjIygPn36fOr6aEDF\n1aSJlJgoPfKItGqV2WkAAGa67DJl69atd2/btu2WW2+99audO3feKEk33HDDd999990NLg3GMiVs\nIDVV6tVLWrrUmCUDAFiTS5cpK1eufK5y5crnHMd5eXnexe3ID+DKtW8vLVok9e9vvNISAGA/l23G\nOnXq9Nk///nPv58+fbrahg0bIvr3778kOjp6ZXmEg3Ux+1ByXbpIs2cbV8j27DE7DbWzOupnXdTO\nvi7bjE2dOvXJq6666vcbbrjhu5kzZ8ZFRUWteeGFF54qj3CAXfTuLb32mhQZKR06ZHYaAEB5uuTM\nWF5ennfLli2/37dvX7NyzCSJmTHY07vvSm++KaWkSPXrm50GAFBSLpsZ8/b2zrvuuuv2Hz58uFHp\nogG4Eg8+KI0YIXXvLv35p9lpAADl4bLLlH/++WetFi1a/LtLly6bo6OjV0ZHR6+MiYlZUR7hYF3M\nPpTe5MlSjx5SVJR08mT5Pz+1szbqZ13Uzr68L3eCYz6s6KU3Xk0JuI6Hh/TKK9L990t9+hj7kFWp\ncvmvAwBY00Vnxs6cOVN1xowZYw4ePNi0VatW344cOfJ9Hx+f3HILxswYbC4/Xxo0SDp/3tiHzPuy\nfzoBAMzikntT3n333YsrVap0PiwsLGXNmjVRISEhaW+//fbDZUp6JcFoxgCdP2+80vKqq6QPPpA8\nS3QDMwBAeXPJAP/evXubz58/f3BcXNzMhISEvsnJyR1LHxF2w+yDc1SqJCUkGNtdPPywVB5/n1A7\na6N+1kXt7OuizZi3t3dece8DKF/VqhlzY59/Lj3zjNlpAADOdtFlSi8vr/xq1aqddhyfOXOmatWq\nVc9IxhJidna2n0uDsUwJ/MVvv0kdO0qjR0uPPWZ2GgBAUWVZprzoSHB+fr5X6SMBcLa6daUNG6Sw\nMCkgQBo1yuxEAABnYBwYLsHsg2sEB0vr10tPPy0tWeKa56B21kb9rIva2Rcvlgcs5tprpbVrjV36\nfX2NDWIBANZ1yXtTmomZMeDSvvjC2Pbik0+kO+4wOw0A2JvL7k0JwH3dfrv08cdSbKy0c6fZaQAA\npUUzBpdg9qF8RERI771n3Mdy/37nPCa1szbqZ13Uzr6YGQMsrm9fKTvbmCFLSZEaNjQ7EQDgSjAz\nBlQQb71lXCVLTpbq1TM7DQDYi0v2GQNgLRMmSJmZUmSklJRk7EUGAHB/zIzBJZh9MMczz0jh4VLP\nntKpU6V7DGpnbdTPuqidfdGMARWIh4f0xhvGXmSxsdK5c2YnAgBcDjNjQAWUlyfdfbfk6SktXCh5\nM5AAAC7FPmMA/sLbW1qwwHiV5f33SwUFZicCAFwMzRhcgtkH81WuLC1bJu3dK/3tb1JJLzRTO2uj\nftZF7eyLZgyowGrUkNaskTZulF54wew0AIDiMDMG2MCvv0phYdJDD0njx5udBgAqHvYZA3BJV19t\nXB0LC5P8/aVhw8xOBABwYJkSLsHsg/tp1Ehav16aOFH65JOLn0ftrI36WRe1sy+ujAE20qyZtHq1\n1KOH5OsrdetmdiIAADNjgA2lpBibwq5cKd16q9lpAMD62GcMwBUJC5PmzpV695a+/dbsNABgbzRj\ncAlmH9xfVJT0zjvSnXdKBw/+9+PUztqon3VRO/tiZgywsQEDjF36IyKMpcugILMTAYD9MDMGQK++\nKr3/vpScLF11ldlpAMB63G5mLDExsUezZs32hYaGHpg6deqTFzvv66+/vtnb2ztv2bJlsa7IAaBk\nHn/cGOjv0UPKyjI7DQDYi9Obsfz8fK9x48ZNT0xM7LFnz57rFyxYMGjv3r3NizvvySefnNqjR4/E\n0naScF/MPljPCy8Yr6wMC0vS6dNmp0Fp8bNnXdTOvpw+M7Zt27ZbmjZtejAkJCRNkgYOHLhw+fLl\nvZs3b7636HnTpk17qF+/fku//vrrmy/2WMOHD1dISIgkKSAgQG3atFF4eLik//6PlmP3PN61a5db\n5eG4ZMfTpoUrMlLq0iVJzz8vRUS4Vz6OOa7Ixw7ukofjSx873k9LS1NZOX1mbOnSpf3WrVsXOXv2\n7NGSNH/+/MGpqantp02b9pDjnKNHjwYOHjx4/ubNm7uMHDny/ejo6JWxsbHL/hKMmTHAFLm5Ut++\nUrVq0kcfSV5eZicCAPfnVjNjHh4el+2gJkyY8NbLL7888f8aLg+WKQH34eMjLV4s/fab9MADEn8T\nAYBrOb0ZCwwMPJqenh7sOE5PTw8OCgrKKHrON998c9PAgQMXNm7c+FBCQkLfBx544F8rVqyIcXYW\nmOfCy+6wjqSkJFWpIi1fLu3cadzLEtbBz551UTv7cvrMWLt27bYfOHAgNC0tLaRBgwY/L1q0aMCC\nBQsGFT3np59+usbx/ogRI+Kjo6NXxsTErHB2FgCl5+srrV0rdeok1axJUwYAruL0Zszb2ztv+vTp\n4yIjI9fl5+d7jRo1ak7z5s33zpw5M06S4uLiZjr7OeF+HIOOsJ6itatdW1q/3rh9kr+/NHaseblQ\nMvzsWRe1sy82fQVwWT/9JHXsKL3yinTPPWanAQD341YD/IDE7IOVFVe7a66R1q2THn1UWrmy/DOh\n5PjZsy5qZ180YwBKpEULacUKadQoid8ZAOA8LFMCuCJbthg3GF+9Wrr5ols2A4C9sEwJoNx07izN\nmSNFR0v//rfZaQDA+mjG4BLMPlhXSWoXHS29/rpxY/GffnJ9JpQcP3vWRe3sy+lbWwCwh3vvlbKy\npIgIKSVFatDA7EQAYE3MjAEokxdflD7+WPrsM2NfMgCwo7LMjNGMASiTwkLpySeNZmzjRmPnfgCw\nGwb44XaYfbCuK62dh4c0darUurXUu7d09qxrcqFk+NmzLmpnXzRjAMrMw0N67z2pbl1j24vcXLMT\nAYB1sEwJwGnOn5f69DFmx+bOlTz5cw+ATbBMCcAtVKokLV0qHT4sjR9vzJMBAC6NZgwuweyDdZW1\ndtWqGfev/PJL6emnnZMJJcfPnnVRO/tinzEATufvLyUmSh07SgEB0t/+ZnYiAHBfzIwBcJmMDCks\nTJo8WRo92uw0AOA6ZZkZ48oYAJcJCpLWr5fCwyU/P+OVlgCAv2JmDC7B7IN1Obt2oaHS2rXGQP/a\ntU59aBSDnz3ronb2RTMGwOVatZI+/VQaOtS4jyUA4L+YGQNQbjZsMG4wnpgotW1rdhoAcB72GQNg\nCRER0owZUs+e0r59ZqcBAPdAMwaXYPbBulxdu9hY6aWXpO7djc1h4Vz87FkXtbMvXk0JoNwNHy5l\nZRlXylJSpHr1zE4EAOZhZgyAaZ57TkpIkJKSpJo1zU4DAKVXlpkxmjEApikslB59VEpNNYb7q1c3\nOxEAlA4D/HA7zD5YV3nWzsNDev11qVkz6a67pHPnyu2pKyx+9qyL2tkXzRgAU3l6SrNmGTv033OP\nlJdndiIAKF8sUwJwC+fOSTExUoMG0pw5RpMGAFbBMiUAy6tcWVq2TPrhB2OOjL/FANgFzRhcgtkH\n6zKzdtWrS6tXG6+ufO4502JYGj971kXt7It9xgC4lYAAad06KSzMeP/hh81OBACuxcwYALd0+LDR\nkD33nLFJLAC4s7LMjHFlDIBbatRIWr9e6tzZeKVlbKzZiQDANZgZg0sw+2Bd7lS7Zs2kNWukMWOM\nTWFxee5UP1wZamdfNGMA3NqNNxq3TLrnHunLL81OAwDOx8wYAEtITJSGDTOWLlu3NjsNAPwV+4wB\nqPB69JCmT5fuvFM6cMDsNADgPDRjcAlmH6zLnWvXv7/x6sqICCk93ew07smd64dLo3b2xaspAVjK\nffdJWVlGQ5aSIl11ldmJAKBsmBkDYElPP23s1r9li+Tvb3YaAHZXlpkxmjEAllRYKI0fL+3ebQz3\nV6tmdiIAdsYAP9wOsw/WZZXaeXhIb79tbA7br590/rzZidyDVeqH/0Xt7ItmDIBleXpK8fFSpUrS\nkCFSfr7ZiQDgyrFMCcDyzp6VevaUrrlGmjXLuGoGAOWJZUoAtlalivTpp9K330pPPGHMkwGAVdCM\nwSWYfbAuq9bO11dau9YY5n/pJbPTmMeq9QO1szP2GQNQYdSqZdwu6Y47jO0uHnzQ7EQAcHnMjAGo\ncA4dkjp2NK6QDR5sdhoAdlCWmTGujAGocBo3ltatk7p0kfz8pJgYsxMBwMUxMwaXYPbBuipK7a6/\nXlq1yrh90pYtZqcpPxWlfnZE7eyLZgxAhdWunbR4sTRggLRtm9lpAKB4zIwBqPAcV8g2bpRatjQ7\nDYCKiH3GAOASevWS3nxT6tFD+ukns9MAwF/RjMElmH2wropau0GDpKeekiIipJ9/NjuN61TU+tkB\ntbMvXk0JwDbGjJEyM42GLDlZql3b7EQAwMwYABuaOFHatMl48/MzOw2AiqAsM2M0YwBsp7BQGjtW\n2r9fWrNGqlrV7EQArI4BfrgdZh+syw618/CQ3n1XuvpqY9uL3FyzEzmPHepXUVE7+6IZA2BLXl7S\nvHlSQYE0fLjxXwAwA8uUAGztzBljy4sWLYyrZR6lWmQAYHcsUwJAKVWtKq1caezQ/9RTZqcBYEc0\nY3AJZh+sy4618/OTEhOlTz6RXnnF7DRlY8f6VRTUzr7YZwwAJNWpI23YIN1xhxQQIN1/v9mJANgF\nM2MAUMTBg1KnTtIbbxivtASAkijLzBhXxgCgiKZNjSXLbt0kX18pKsrsRAAqOmbG4BLMPlgXtZNu\nuEFavtzY8iI52ew0V4b6WRe1sy+aMQAoxq23SgsWSP36Sd98Y3YaABUZM2MAcAmffCI98IC0ebPU\nvLnZaQC4K2bGAMBF7rpLysmRuneXUlKkkBCzEwGoaFimhEsw+2Bd1O5/DR0qPfGEFBEh/fqr2Wku\njfpZF7WzL66MAUAJPPSQlJlpXCH77DOpZk2zEwGoKFxyZSwxMbFHs2bN9oWGhh6YOnXqkxd+/qOP\nPrq3devWu1u1avVthw4dPv/2229buSIHzBMeHm52BJQStbu4p54yro5FRUknT5qdpnjUz7qonX05\nfYA/Pz/f67rrrtu/cePGboGBgUdvvvnmrxcsWDCoefPmex3nfPnll7ddf/31e/z9/bMSExN7TJky\nZcpXX31161+CMcAPwA0VFkr33ScdPiytXi1Vrmx2IgDuwK1uFL5t27ZbmjZtejAkJCTNx8cnd+DA\ngQuXL1/eu+g5t91225f+/v5ZktS+ffvUjIyMIGfngLmYfbAuandpHh7SrFnGMuWgQVJentmJ/or6\nWRe1sy+nz4wdPXo0MDg4ON1xHBQUlJGamtr+YufPmTNnVFRU1JriPjd8+HCF/N9LlwICAtSmTZv/\nXMZ1/I+WY/c83rVrl1vl4ZhjZx6npCTp/vul118P1333SUOHJsnT033ycWzNYwd3ycPxpY8d76el\npamsnL5MmZCQ0DcxMbHH7NmzR0vS/PnzB6emprafNm3aQxeeu2XLls4PPvjgu59//nmHmjVrnvhL\nMJYpAbi5U6ekyEjpppukt94yrpoBsCe3WqYMDAw8mp6eHuw4Tk9PDw4KCsq48Lxvv/221ejRo2ev\nWLEi5sJGDACsoHp1adUq45ZJzz5rdhoAVuX0Zqxdu3bbDxw4EJqWlhZy/vz5SosWLRoQExOzoug5\nR44caRgbG7ts/vz5g5s2bXrQ2Rlgvgsvu8M6qN2VCQiQ1q0zbp305ptmp6F+Vkbt7MvpM2Pe3t55\n06dPHxcZGbkuPz/fa9SoUXOaN2++d+bMmXGSFBcXN/O55577x4kTJ2qOHTv2PUny8fHJ3bZt2y3O\nzgIA5aFuXWnDBiksTPL3l0aONDsRACvh3pQA4CQ//CCFh0vTpkl9+5qdBkB54t6UAOAGrr3W2Hss\nMlKqUcP4LwBcjtNnxgCJ2Qcro3Zlc+ON0iefSIMHS59/Xv7PT/2si9rZF80YADhZhw7S/PnSXXdJ\n/7flHgBcFDNjAOAiS5ZIDz8sJSUZS5gAKi5mxgDADfXvL+XkSN27G3uRNWxodiIA7ohlSrgEsw/W\nRe2ca+RIafx4KSJC+u031z8f9bMuamdfXBkDABd79FEpM9N4deWWLcZGsQDgwMwYAJSDwkJjfmzH\nDmn9eqlaNbMTAXCmssyM0YwBQDkpKJBGjDCWK5cvlypVMjsRAGdxqxuFAxKzD1ZG7VzH01OaM0eq\nUsXYhyw/3/nPQf2si9rZF80YAJQjb2/jpuJ//inFxRnLlwDsjWVKADDByZNSt27SHXdIr74qeZRq\ncQOAu2CZEgAspkYNac0aad066cUXzU4DwEw0Y3AJZh+si9qVn1q1jFdWxsdL06c75zGpn3VRO/ti\nnzEAMFH9+tLGjVJYmOTnJw0danYiAOWNmTEAcAN790qdO0szZ0q9e5udBsCV4t6UAGBxzZtLq1ZJ\nUVHGPFnXrmYnAlBemBmDSzD7YF3Uzjzt2klLlkiDBkmpqaV7DOpnXdTOvmjGAMCNdOpkDPTHxEjf\nfWd2GgDlgZkxAHBDCxZIjz8uffaZ1KSJ2WkAXA4zYwBQwQwaJGVnSxERUkqKFBhodiIArsIyJVyC\n2QfronbuIy5OGjPGaMiOHy/Z11A/66J29kUzBgBu7IknjK0u7rzTuFIGoOJhZgwA3FxhofTAA8Ze\nZGvXSlWrmp0IwIXKMjNGMwYAFlBQIA0ZImVlSZ98Ivn4mJ0IQFHcKBxuh9kH66J27snTU/rgA8nD\nQxo2TMrPL/486mdd1M6+aMYAwCJ8fKTFi6Wff5bGjTOWLwFYH8uUAGAx2dnG7ZK6dZNeesnsNAAk\nlikBwFb8/IxB/hUrpKlTzU4DoKxoxuASzD5YF7Wzhjp1pPXrpZkzjTcH6mdd1M6+2IEfACwqMFDa\nsMG4n6Wfn7FrPwDrYWYMACzu+++NGbL335d69jQ7DWBPzIwBgI21bGnMj40YYdxYHIC10IzBJZh9\nsC5qZ03t20sLF0q9eydp+3az06A0+NmzL5oxAKggunSRHn9c6tVL2rPH7DQASoqZMQCoYObPlyZN\nkpKTpcaNzU4D2ENZZsZ4NSUAVDCDB0uZmVJEhJSSItWvb3YiAJfCMiVcgtkH66J21uao37hxxkB/\n984uSVMAAAzTSURBVO7Sn3+amwklw8+efdGMAUAFNXmyFBkpRUVJJ0+anQbAxTAzBgAVWGGhNHq0\nlJYmrVolValidiKgYirLzBjNGABUcPn5xu78589LS5dK3kwLA07Hpq9wO8w+WBe1s7bi6uflZbzC\n8tw5aeRIqaCg/HPh8vjZsy+aMQCwgUqVpIQE6dAhacIEY/kSgHtgmRIAbCQrS+rc2dgY9rnnzE4D\nVBzsMwYAKBF/fykxUQoLkwICpEcfNTsRAJYp4RLMPlgXtbO2ktSvbl1p40bpnXekOXNcnwklw8+e\nfXFlDABsKDhYWr9eCg+X/Pyk/v3NTgTYFzNjAGBju3cbu/TPnSv16GF2GsC62NoCAFAqrVtLn3wi\nDRkibd1qdhrAnmjG4BLMPlgXtbO20tTv9tuljz6SYmOlnTudnwklw8+efdGMAQDUvbv03nvGfSz3\n7zc7DWAvzIwBAP4jPl6aMkVKSZEaNjQ7DWAd7DMGAHCKESOMjWEjIqTkZKlePbMTARUfy5RwCWYf\nrIvaWZsz6jdhgnFj8chIKTOz7JlQMvzs2RfNGADgfzzzjNSpk9Szp3TqlNlpgIqNmTEAQLEKCqSR\nI6VffpFWrJAqVzY7EeC+yjIzRjMGALiovDzp7rslT09p4ULJm0ljoFhs+gq3w+yDdVE7a3N2/by9\npQULpOxsKS5O4m9k1+Fnz75oxgAAl1S5srRsmbRnj/TYYzRkgLOxTAkAKJETJ4yh/v79paefNjsN\n4F7YZwwA4HI1a0rr10thYZK/vzR+vNmJgIqBZUq4BLMP1kXtrM3V9bv6amnjRum116R581z6VLbD\nz559cWUMAHBFGjWS1q2TunSRfH2lu+4yOxFgbcyMAQBKZccOqUcP6eOPpW7dzE4DmIutLQAA5a5t\nW2npUuPWSV99ZXYawLpoxuASzD5YF7WztvKuX8eO0ty5Uu/e0rfflutTVzj87NkXzRgAoEyioqR3\n3pHuvFM6eNDsNID1MDMGAHCK2bOlF1+UUlKkoCCz0wDli33GAACmGz1aysyUIiKk5GTpqqvMTgRY\nA8uUcAlmH6yL2lmb2fV7/HEpNtZ4lWVWlqlRLMfs2sE8NGNwiV27dpkdAaVE7azNHer3wgvSrbdK\nMTHSmTNmp7EOd6gdzOGSZiwxMbFHs2bN9oWGhh6YOnXqk8WdM378+HdCQ0MPtG7devfOnTtvdEUO\nmCczM9PsCCglamdt7lA/Dw9p2jQpOFjq1086f97sRNbgDrWDOZzejOXn53uNGzduemJiYo89e/Zc\nv2DBgkF79+5tXvScNWvWRB08eLDpgQMHQmfNmnX/2LFj33N2DgCAeTw9pfh4yctLGjpUys83OxHg\nvpw+wL9t27ZbmjZtejAkJCRNkgYOHLhw+fLlvZs3b77Xcc6KFStihg0bNleS2rdvn5qZmRlw7Nix\nevXq1Tvm7DwwR1pamtkRUErUztrcqX4+PtLixVLPnpK3t1StmnHVrCRvkrnnmvH8u3enaccO9/k3\ncPd/L3c7tyyc3owdPXo0MDg4ON1xHBQUlJGamtr+cudkZGQEXdiMeZT1u4Op5s6da3YElBK1szZ3\nrd/p02YncH8ZGe5ZO7iW05sxDw+PEm0OduFeHBd+XWn36gAAALASp8+MBQYGHk1PTw92HKenpwcH\nBQVlXOqcjIyMoMDAwKPOzgIAAODunN6MtWvXbvuBAwdC09LSQs6fP19p0aJFA2JiYlYUPScmJmbF\nvHnzhkrSV199dWtAQEAm82IAAMCOnL5M6e3tnTd9+vRxkZGR6/Lz871GjRo1p3nz5ntnzpwZJ0lx\ncXEzo6Ki1qxZsyaqadOmB6tXr34qPj5+hLNzAAAAWEJhYaGpb2vXru1x3XXX7WvatOmBl19++cni\nznnooYfeadq06YFWrVrt3rFjx41mZ+at5PWbP3/+va1atdp9ww03fHv77bd/vnv37lZmZ+atZLVz\nvG3btu1mLy+vvISEhFizM/N2ZfXbsmVLeJs2bXa2aNHi+06dOiWZnZm3ktfv999/rxMZGZnYunXr\nXS1atPg+Pj5+uNmZeSvUiBEj3q9bt+6xli1bfnexc0rTs5j6TeXl5Xk1adLk4KFDh0LOnz/v07p1\n61179uxpXvSc1atXR915551rCgsL9dVXX7Vv3779V2YXg7eS1++LL764LTMz07+w0Pg/H+rnHm8l\nqZ3jvM6dO2/u2bPnqqVLl/Y1OzdvJa/fiRMnAq6//vp/p6enBxUWGr/czc7NW8nr98wzz0yZOHHi\nS47a1apV64/c3Fxvs7Pb/S05OTlsx44dN16sGSttz2Lq7ZCK7knm4+OT69iTrOg5F9uTzJzEKKok\n9bvtttu+9Pf3z5KM+mVkZASZkxZF/f/27i6mqTOMA/gDtIYSILZWjBKW1kJgWNvDPGHtFplN/QAv\n5lCy6JYlfqQuMxnqzTIhmWgiM14sA5MZR8ALo1sMG+LFYMym6KKtBCytViDYFOO6qStlKUjRFroL\n06QhqAfEvjb8f8l7QSinz8k/DU9O3/McIdkREZ08efLLioqK5qVLl/7Lok6YmZD8zp8//8m2bdt+\nid5AJZfLfWyqhemE5Ld8+fJ/AoFAJhFRIBDIXLJkybBIJAqzqRii1q5d+6dUKh153u/n2rMwbcZm\nmjfm9XqzX/Ya/EN/MwjJL1ZjY+OezZs3/xaf6uBFhH72Wltbt0SfkCF0bA28fkLyGxwczPP7/TKD\nwWDheb777Nmzn8W/UpiJkPxMJlODy+VatWLFir+1Wq2jrq5uf/wrhdmaa88y7xv4Z2O+ZpIBG7PJ\nwWKxGJqamnZfu3bt/ddZEwgjJLsDBw58f/z48a+TkpIikUgkafrnENgRkl8oFBLfvHnzHbPZbBwf\nH0/T6/VWnU5ny8vLG4xHjfB8QvKrra2t4jiut7Ozc53b7VZt2LDhD4fDoc3IyBiNR40wd3PpWZg2\nY5hJltiE5EdE5HQ6NSaTqaG9vb30RZd3IX6EZNfT07Nm+/btPxMR+Xw+eVtbW5lYLA5NH1UD8Sck\nv5ycnPtyudwnkUiCEokkWFJSctXhcGjRjLEnJL/r16+/V11dfYyISKVSuZVKpWdgYCCf5/nueNcL\nws25Z2G5ES4UColWrlzp9ng8iidPnix62QZ+q9WqwwbwN2cJye/evXtvqVSqu1arVce6XqzZZRe7\ndu7ceQZ3U745S0h+fX19BUaj8XI4HE55/PhxmlqtvuVyuQpZ144lLL+DBw9+V1NTczgSidCDBw+W\nZWdn/zU8PCxjXTtWhDwej0LIBv7Z9CxMr4xhJlliE5Lf0aNHvxkZGZFG9x2JxeJQV1dXMdvKQUh2\nrGuE5xOSX0FBQX9paWm7RqNxJicnT5lMpobCwsI7rGsHYflVVVXV7tq164xWq3VMTU0lnzhx4iuZ\nTOZnXftCt2PHjp+uXLnygc/nk+fk5Nw/cuTI4VAoJCZ6tZ4lKRLB9isAAAAAVpjeTQkAAACw0KEZ\nAwAAAGAIzRgAAAAAQ2jGAAAAABhCMwYACSklJWWyqKjIrtFonFu3bv11bGwsfT6Pr1Aohvx+v4yI\nKD09fWw+jw0AEAvNGAAkpLS0tHG73V7kdDo1mZmZgehYgPkSOzUbT/0AgNcJzRgAJDy9Xm91u90q\nIiK3260qKytr43m+u6Sk5OrAwEA+EdHDhw+XlZeXt3Ac18txXK/NZtMREZWXl7fwPN+tVqtvNzQ0\nmFieBwAsTEyHvgIAvKrJycmUjo6OjUaj0UxEtHfv3h9Pnz79eW5u7t0bN268u2/fvh/MZrOxsrKy\n3mAwWFpaWsqnpqaSo19rNjU17ZZKpSPBYFBSXFzcVVFR0YzHdgFAPGHoKwAkJJFIFF69evUtr9eb\nrVAohmw2m258fDwtKyvrUX5+/kD0dU+fPl3kcrlWZWVlPfJ6vdlisTgUe5yampqaixcvfkRENDQ0\npOjo6NhYXFzcpVQqPT09PWtkMpk/IyNjdHR0NCPe5wgACwOujAFAQpJIJEG73V4UDAYlmzZt+r21\ntXXL+vXrLy9evPg/u91eNNPfRCKRpNifOzs715nNZqPNZtOlpqZOGAwGy8TERGp8zgAA4BnsGQOA\nhCaRSIL19fWV1dXVx9LT08eUSqWnubm5guhZ8+V0OjVEREaj0Xzq1KkviJ59tRkIBDIDgUCmVCod\nSU1Nnejv7y+I7iMDAIgnNGMAkJBi73DkOK43Nzf37oULFz4+d+7cp42NjXs4jutVq9W3L1269CER\nUV1d3X6LxWLQaDROnue7+/r63i4tLW0Ph8OiwsLCO4cOHfpWr9dbX/ZeAADzDXvGAAAAABjClTEA\nAAAAhtCMAQAAADCEZgwAAACAITRjAAAAAAyhGQMAAABgCM0YAAAAAEP/A0HZI7amvLZYAAAAAElF\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1dd65290>"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/svm.precisionrecall.npz\",svmprecision,svmrecall)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Random forest classifier\n",
      "\n",
      "Random forest classifiers have proven to be effective in the task of protein interaction prediction in the past.\n",
      "A random forest classifier combines the results of many decision tree classifiers.\n",
      "In this way, it can react to correlations in the data in a way that simpler methods, such as logistic regression, cannot.\n",
      "\n",
      "The combination of decision trees is implemented in [Scikit-learn][rfsklearn] by averaging the results of the individual trees.\n",
      "\n",
      "### Initialising the pipeline\n",
      "\n",
      "As above we will define the pipeline with the same scaling and imputation:\n",
      "\n",
      "[rfsklearn]: http://scikit-learn.org/stable/modules/ensemble.html#random-forests"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.ensemble"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imputer = sklearn.preprocessing.Imputer(strategy='mean',missing_values=\"NaN\")\n",
      "scaler = sklearn.preprocessing.StandardScaler()\n",
      "classifier = sklearn.ensemble.RandomForestClassifier()\n",
      "rfmodel = sklearn.pipeline.Pipeline([('imp',imputer),('scl',scaler),('cls',classifier)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Plotting a learning curve\n",
      "\n",
      "As above, we would like to know how quickly this model will learn in a pipeline with the scaling and imputer defined as with previous models before trying to tune hyper-parameters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainsizes=logspace(3,5,7)\n",
      "#initialise and start run\n",
      "rflcsearch = ocbio.model_selection.LearningCurve(alb_view)\n",
      "rflcsearch.launch_for_arrays(rfmodel,X,y,trainsizes,n_cv_iter=3,\n",
      "                             params={'cls__n_estimators':100,'cls__bootstrap':False},\n",
      "                             name=\"lrlc\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "Progress: 00% (000/147)"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print rflcsearch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Progress: 100% (147/147)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rflc = rflcsearch.plot_curve()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L4U6ABsCtjVtZCcTFealDBEEQBOEBcr7Kweotq6FjdZAyUiy8fyHGjx7v0zY0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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x133563d0>"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Training a single model with 100,000 to see how long each one of these is going to take:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/random.forest.learning.curve.npz\",rflc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Running a grid search\n",
      "\n",
      "The major [parameters][] to vary with a Random Forest classifier are:\n",
      "\n",
      "* `n_estimators` - the number of trees in the forest\n",
      "* `max_features` - size of random subsets of features to consider when splitting nodes\n",
      "\n",
      "According the to the [documentation][parameters] a good value for `max_features` is the square root of the number of features, in our case approximately 15.\n",
      "With the number of trees in the forests, the more trees the better the performance will be, but it will take longer to train the model.\n",
      "We will try up to a maximum of 200 estimators to see at which point there are no longer significant gains from increasing the number of estimators.\n",
      "\n",
      "Also the random trees are using bootstrapping by default when training, which is unnecessary on a dataset as large as this.\n",
      "Therefore, we should set `bootstrap=False`.\n",
      "\n",
      "[parameters]: http://scikit-learn.org/stable/modules/ensemble.html#parameters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "split_filenames = ocbio.mmap_utils.persist_cv_splits(X,y, test_size=20000, train_size=100000,\n",
      "                                                     n_cv_iter=3, name='rffeatures',random_state=42)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rfparams = {'cls__n_estimators':100,'cls__bootstrap':False}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "train,test = next(iter(sklearn.cross_validation.StratifiedShuffleSplit(y,\n",
      "                                    test_size=20000,train_size=100000)))\n",
      "rfmodel.set_params(**rfparams)\n",
      "rfmodel.fit(X[train],y[train])\n",
      "print \"Training score: {0}\".format(rfmodel.score(X[train],y[train]))\n",
      "print \"Test score: {0}\".format(rfmodel.score(X[test],y[test]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training score: 0.99925\n",
        "Test score: 0.9984"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CPU times: user 2min 38s, sys: 2min 21s, total: 5min\n",
        "Wall time: 5min 52s\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rf_params = {\n",
      "'cls__n_estimators':logspace(1,2.3,5).astype(np.int),\n",
      "'cls__max_features':arange(5,30,5).astype(np.int),\n",
      "'cls__bootstrap':[False]\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rfsearch = ocbio.model_selection.RandomizedGridSearch(lb_view)\n",
      "rfsearch.launch_for_splits(rfmodel,rf_params,split_filenames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "Progress: 00% (000/075)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print rfsearch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Progress: 100% (075/075)\n",
        "\n",
        "Rank 1: validation: 0.99837 (+/-0.00003) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 44, 'cls__max_features': 25, 'cls__bootstrap': False}\n",
        "Rank 2: validation: 0.99835 (+/-0.00008) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 94, 'cls__max_features': 25, 'cls__bootstrap': False}\n",
        "Rank 3: validation: 0.99835 (+/-0.00003) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 94, 'cls__max_features': 10, 'cls__bootstrap': False}\n",
        "Rank 4: validation: 0.99833 (+/-0.00003) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 10, 'cls__max_features': 10, 'cls__bootstrap': False}\n",
        "Rank 5: validation: 0.99833 (+/-0.00004) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 199, 'cls__max_features': 25, 'cls__bootstrap': False}\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Training a the model we would like to use on a large training set to get the best possible performance:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rfparams = rfsearch.find_bests()[0][-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "train,test = next(iter(sklearn.cross_validation.StratifiedShuffleSplit(y,\n",
      "                                    test_size=20000,train_size=200000)))\n",
      "rfmodel.set_params(**rfparams)\n",
      "rfmodel.fit(X[train],y[train])\n",
      "print \"Training score: {0}\".format(rfmodel.score(X[train],y[train]))\n",
      "print \"Test score: {0}\".format(rfmodel.score(X[test],y[test]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training score: 0.999295\n",
        "Test score: 0.99845"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CPU times: user 8min 38s, sys: 2min 56s, total: 11min 35s\n",
        "Wall time: 11min 56s\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rfroc = plotroc(rfmodel,X[test],y[test])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rm1arhbm5OerXr1+hxz/LyJgs2yERERERSR49EouvDz4AOnUCPD2Bn38GmjcH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\nIjRjAAAAACL6f8TIDmulOsPcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1a276150>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/random.forest.roc.npz\",rfroc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rfprec,rfrecall = drawprecisionrecall(X,y,test,rfmodel)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HsaSkpAgLrAMAAMAu1dgZu3z5sscbb7zx+r59+/qIiERERCS99tprbzZu3PiK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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x18c02850>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!git annex unlock ../../plots/bayes/random.forest.precisionrecall.npz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "unlock ../../plots/bayes/random.forest.precisionrecall.npz (copying...) ok\r\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/random.forest.precisionrecall.npz\",rfprec,rfrecall)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, we can compare accuracy to the dummy case:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dummycompare(X,y,train,test,rfsearch.find_bests()[0][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "(0.012121212121223947, -4.4006030202458293)"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Extremely Randomized Trees\n",
      "\n",
      "In Scikit-learn an alternative random forest in which the thresholds chosen for candidate features are randomly selected.\n",
      "This reduces the variance of the model at the cost of an increase in bias.\n",
      "These are called [Extremely Randomized Trees][etsklearn].\n",
      "\n",
      "[etsklearn]: http://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imputer = sklearn.preprocessing.Imputer(strategy='mean',missing_values=\"NaN\")\n",
      "scaler = sklearn.preprocessing.StandardScaler()\n",
      "classifier = sklearn.ensemble.ExtraTreesClassifier()\n",
      "erfmodel = sklearn.pipeline.Pipeline([('imp',imputer),('scl',scaler),('cls',classifier)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "extrasearch = ocbio.model_selection.RandomizedGridSearch(alb_view)\n",
      "extrasearch.launch_for_splits(erfmodel,rf_params,split_filenames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "Progress: 00% (000/075)"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print extrasearch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Progress: 100% (075/075)\n",
        "\n",
        "Rank 1: validation: 0.99837 (+/-0.00003) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 94, 'cls__max_features': 25, 'cls__bootstrap': False}\n",
        "Rank 2: validation: 0.99837 (+/-0.00003) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 21, 'cls__max_features': 20, 'cls__bootstrap': False}\n",
        "Rank 3: validation: 0.99835 (+/-0.00005) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 44, 'cls__max_features': 25, 'cls__bootstrap': False}\n",
        "Rank 4: validation: 0.99835 (+/-0.00003) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 21, 'cls__max_features': 25, 'cls__bootstrap': False}\n",
        "Rank 5: validation: 0.99835 (+/-0.00003) train: 0.99921 (+/-0.00003):\n",
        " {'cls__n_estimators': 10, 'cls__max_features': 25, 'cls__bootstrap': False}\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The performance does not appear to be much better than the random forest.\n",
      "For this reason, it is probably not worth using this classifier over a simple random forest."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Feature Importances\n",
      "\n",
      "We can find the feature importances for a random forest model simply by querying the model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "importances = rfmodel.named_steps['cls'].feature_importances_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(importances)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1dd54250>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QGRwvZBTHCqk5uuzFpYw9ZERERCSxQuYQVsjiB/s8yAyOFzKKY4XUuOyFQ9hD\nRkRERBIrZA6Rc8WskLkf+zzIDI4XMopjhdQYyBwiS5OskBEREREDmUPYQxY/2OdBZnC8kFEcK6TG\nQOYQ9pARERGRxEDmEPnAs0LmfuzzIDM4XsgojhVS4zpkDmGFjIiIiCQue+EQ9pDFD/Z5kBkcL2QU\nxwqpccrSIayQERERkcRA5hD2kMUP9nmQGRwvZBTHCqkxkDmEFTIiIiKSGMgcwh6y+ME+DzKD44WM\n4lghNQYyh8iPt7JCRkRERFz2wiHy462skLkf+zzIDI4XMopjhdS47IVD2ENGREREEqcsHcIesvjB\nPg8yg+OFjOJYITUGMoewh4ys8OqrwEMPOX0UREQULQYyh7CHLH64uc+jowM4c8bpoyA1N48XcheO\nFVJjIHMIe8jICgMD4ouIiOIbA5lD2EMWP9zc58FA5j5uHi/kLhwrpMZlLxwikzArZBQNBjIiosTA\nZS8cwgpZ/HBznwcDmfu4ebyQu3CskBqnLB3CHjKyAgMZEVFiYCBzCCtk8cPNfR4DAxw/buPm8ULu\nwrFCagxkDmEPGVmBFTIiosTAQOYQVsjih5v7PBjI3MfN44XchWOF1BjIHMIeMrICAxkRUWLgshcO\nkQ88K2Tu5+Y+DwYy93HzeCF34VghNS574RD5wLNCRtFgICMiSgycsnSIuofM7wcUxekjomDc3OfB\nQOY+bh4v5C4cK6TGQOYQGcg8HrHNQEaRYCAjIkoMDGQOUTfvsY/M3dzc58FA5j5uHi/kLhwrpMZA\n5hB18x77yChS/f0MZEREiYCBzCFyyhJghczt3NznwQqZ+7h5vJC7cKyQGpe9cIg2kLFCRpFgICMi\nSgxc9sIh6tJkUhIrZG7m5j4PBjL3cfN4IXfhWCE1x6csKyoq5k+ZMuVobm5u/YYNGx7T22fNmjVP\n5ebm1s+YMeNITU1Ngbx8xYoV/5ment42bdq099T7r1u3bl1mZmZzQUFBTUFBQU1FRcX86H8Ua7FC\nRlZgICMiSgyOBjKfz5dcVla2saKiYn5tbW3+jh077q2rq8tT77N79+6FDQ0NOfX19bnPPffcg6tX\nr35WXvfAAw9s0QtbHo9HeeSRR35SU1NTUFNTUzB//vwK634ka6gDGStk7ubmPg8GMvdx83ghd+FY\nITVHA9nBgwdvzsnJacjOzm5MTU3tX7p06Yu7du26S71PeXl5ybJly7YCQGFh4YHOzk7viRMnJgDA\n5z//+b9UsuytAAAgAElEQVSMGzfujN73VhTFY9UPYQdWyMgKTgay0lLgjTecuW8iokRjdyBLCXVl\nS0tLRlZWVpPczszMbD5w4EBhuH1aWloyJkyYcCLU93766ae/sW3btvtnzZp16Mc//vGjXq+3U7vP\n8uXLkZ2dDQDwer2YOXPmP96xyLl9u7a7uqpw+DCQn1+EpCTgL3+pwhVX2Hd/3I58W93n4YbjUW8P\nDBRhYMD87f/whyr09QF33RX5/b/3HjBnjrseDzdsu3m8cNtd2/IytxwPt53d9vmKkJw8eHxUVVWh\nsbERllAUJejXyy+/fPeqVauel9vbt2+/r6ys7Gn1PosWLfrd3r17b5XbxcXFrx8+fPizcvv48ePZ\nU6dOfU99m7a2tjS/3+/x+/2e7373uz9YsWLFZu19i0NzTk6Oohw7Jv4/aZKiNDc7ejgUQmVlpdOH\nEFRRkaIAiuLzmbvdM88oysMPR3ff8+Ypyvbt0X2PROTm8ULuwrFCatnZivLhh8Gv/zS3hMxVob6S\nQoW1jIyMlqampiy53dTUlJWZmdkcap/m5ubMjIyMllDfNy0trd3j8Sgej0dZtWrVCwcPHrw5ojRp\nI/aQxQ/5LsaN5HSl2WnL7m6gtze6++7rEwvT0mBuHi/kLhwrpOboshezZs06VF9fn9vY2Jjd19c3\nbOfOnUtKSkrK1fuUlJSUb9u27X4AqK6unu31ejvT09PbQn3f1tbWifL/r7zyyj9pP4XpBtpTJ7GH\njCIRaSCzIkzxLAFERNZxtKk/JSVlYOPGjWXz5s37Y35+fu2SJUt25uXl1W3atKl006ZNpQCwcOHC\n3dddd91HOTk5DaWlpZueeeaZh+Tt77333h233HLL/mPHjk3Oyspq2rJlywMA8Nhjj22YPn36uzNm\nzDjy1ltvfeGJJ574ln0/YmS0p05ihcy91PP5buN0IGOFbCg3jxdyF44VUnO0qR8AFixY8IcFCxb8\nQX1ZaWnpJvX2xo0by/Ruu2PHjnv1LpcVNTdLpE9ZKoqY/ho50ukjufREGsguXmQgIyJyE8cXhr1U\nJVIP2d/+Bixc6PRR2MfNfR5OV8g4ZTmUm8cLuQvHCqkxkDkkkXrITp4ETp1y+iguTU4GMjb1ExFZ\nh4HMIYnUQ9bVBVy44PRR2MfNfR4yiJkdPxcvRl/d4pSlPjePF3IXjhVSYyBzSCL1kJ0/L74o9jhl\nSUSUGNS5wA4MZEEkUg9ZolfI3NznMTAAjBjBT1m6iZvHSyy0tcX3G8xYutTHCg3GCplD1A98vFfI\nurrEQqPx/DPEq0gDmRWfsmQPWWJbuRLYv9/87RYvBvbts/54iBIdA5lDEqlCJqcru7udPQ67uLnP\nw+kKGacsh3LzeDHjo4+ATz4xf7uGBvEmjcJLlLFC1mAgc0gi9ZDJJ99EnrZ0K6cDGStkiau7G+jp\nMXeb8+fFJ67N3o7oUifOSsweMkckYoUsURv73dzn4eSUJStk+tw8XsyIJJAdPy7+ZSAzJlHGCkXP\n7uoYwEAWVKL1kAGskDnBqQqZ3y/GMCtkiSuSQNbYKP5lICMyh4HMQYlUIZOBLFErZG7u8+jvB4YP\njyyQRVPdkkGMgWwoN48XM1ghs1+ijBWKnt1LXgAMZEElUg/Z+fPiPJaskMWWooggH0kgi3bKUt6W\nU5aJq7tbnKPWjOPHgdRU87cjutSxQuYQGb48HvFvIlTIJkxI3AqZW/s85B9waqq9U5aHDwN33z34\nMlbIgnPreDEr0gpZbi4rZEYlylgJ5qOPgLNnnT6K+MBA5hD1aZOAxKiQTZzIClmsDQwAKSniy85A\nduoUcOLE4MsYyBKb/MBGJD1k+fkMZCR873vA1q1OH0V8YCBziHauOFEqZIkayNza5xFNIDMzZdnf\nLwKc9jJ5DDSYW8eLGXJNQTPBSlFEhYyBzLhEGCuhnDsH1Nc7fRTxgYHMIdpAFu8VskSfsnSrWFXI\n+vqGBjK5zQpZYookkJ05I/6dNImBjITz5xnIjIpFIEux99vHJ58vcSpkfX0iTI4fn7gVMrf2ecQq\nkIWqkDGQDeXW8WJGJIHs+HHg2mvFB3wYyIxJhLESyvnzQEeH00cRH1ghc0gi9ZCdPw+MGSO+WCGL\nrWinLOWnNMPhlOWlJ5JA1tgIZGczkFFAV5cYF3zjFh6XvXBIIvWQnT8PjB0LjB6duBUyt/Z5RBrI\nFEUErORkY0+UrJCZ49bxYgYrZLGRCGMllPPnxafA5fp0FBwrZA5JpB6yrq5AhSxRA5lbRRrI5JS5\n0fXLWCG79HR3i/HBQEbR6OoCpk0TJ5yn0BjIHKJ94OO5QtbVFaiQJeqUpVv7PCINZBcvihfb1NTI\nK2R9feKUTayQDeXW8WJGdzdwxRXmpywZyMxJhLESjKKI14SCAnc29p89G6gEuwEDmUMSqUImpyxZ\nIYu9SANZXx8wbFh0gay/Hxg1ioEsUclAZmbFfVkhGzGCK/WTeOOXkgLk5bkzkP3rvwI//7nTRxHA\nQOaQROohk1OWiVwhc2ufx8CACFVOBjJOWQ7l1vFihtkKmaKwqT8SiTBWgpGvDbm57gxkra1AS4vT\nRxHAQOYQ7bIXiVAhS+SmfreK1ZSlPBG5eoyyQpbYurvFUjZGg1Vbm3gOGDOGgYwE+dqQm+vOHrL2\n9qFnIHESA5lDtMteJEKFLJGXvXBrn0e0U5YpKcYrZOp/5f8ZyPS5dbyYYbZCJvvHAAYyMxJhrAQj\nXxuys4Hm5qFVdqedPCmqZG7BZS8ckog9ZPFQIdu/3109A9FSBzIzgT6SKUt5O/X34JRl4lIHMkUJ\nv7/sHwMYyEiQa1QOGwZkZrpv6YuTJ1khIyRmD1k8NPV/8AGwd6/527m1zyOWn7IEBgey/n7xwssK\n2VBuHS9mdHcDl10mnpvC/Y67u4F9+0QlBGAgMyMRxkow8s064L4+Mp8POH2agYyQWD1k2mUvjLyb\ndoreORnjmRWfsjS6Dpm8nfoyTlkmru5u8fsNFa4aGoCFC8V5bN9/H1i6VFyemir+5di4tMk364D7\n+sg6OgCvV7xmXbzo9NEIDGQOSaQeMvkuKDVV/BxuDjyRBjK39nnEctkLeTv1ZZyy1OfW8WKGkUD2\nwx+KF9q//x2oqhLrTUmskhmTCGMlGDllCbivQnbyJJCeLr7cUiVjIIuhmprA/xOph0z9Lsjtjf16\nyzfEM6enLFkhS1zhAllHB/Dqq8D/+l+i0qDldCBraQG2b3fu/ikwewIED2SnTwO7dsX2uAARyK66\nSlR3GcguMT09wM03B7YTqYdM3Sfg9sb+vr7IAoRb+zycrJD19YkXXVbIhnLreDEjXCD7xS+ARYvE\ni5oepwPZ/v3Ac885d/9GJcJYCUZdIcvJ0Q9kb78N/OhHsT0ugIHsktbbO3gdp0TrIVNXyNweyFgh\nMx/I5GOmrZANHw54PPH7ZiLRDAxY16ejDmTaVff9fvFp5dWrg9/e6dX629vFqXHIOeo369nZYokJ\nbb/WhQvOzKq4MZBx2YsYkYNQvvglUg+Zuizt9tX62UMmWDVlaWYts0uJU+Oluhr4yles+V6hKmRv\nvCGu+9zngt/e6QpZezvQ2enc/Rvl1ucWK6jfrKemiqUvPv548D7d3c68iZeBbOJE96xFxgpZBDZv\nNv9JQhnI5AtaIvWQqd8Fub1Clmg9ZP39sV0YVhvIUlONf1KT7NfdDRw7Zs1ziQxkI0YMDVbPPiuq\nYx5P8Nu7IZCxQuYs9ZQlIHoNz50bvM+FC84GMjdVyBjITFIU4J//2fwTTbhAFu8VMvlHl6gVMrf2\neTj9KUsZyFghG8yp8XLxoghSn3wS/fcKViE7fRp4803ga18LfXs3BLJz59z/vOrW5xYrqGdPAP0e\nYzcGMqfeYPb2ijdAdnJ1IDP7DsrnE6HM7Iu6NpBpk3CiVMgStanfrWI5ZenxDG3qlyc2T6THNJ7J\n55ljx4Lv87e/GavwBwtkra1ARsbgyocepwPZyZPi364u547hUqetkOm9Ye/udmb9ymCBrLcXmDTJ\nmcJCd7d4jOzk6kB2+rS5/eUTntmF5BK1Qub3iyfdUaPEdqIue+HWPo9YLgw7apR+DxmnLIdyarwY\nCWTz5xtr/A8WyM6dEyv4h+N0IGtvF/+6vY/Mrc8tVtALZHoVMkWJ/QdAggWyujpxXV1dbI8HCPzN\n2cnVgayjw9z+ep82MyJRe8jOnxcDSP4s8VAhS6QeslhOWY4ePfiNiJyyZIXMPcIFsp4e8WJj5E1T\nIgSyjAx39pHV1opgnOiMTlmq/40VbSCTFbojR8S/778f2+MBxGPAQGaCfDGPtkKmXfYiXitk2ndA\nbm/qT9QesuRk+6csR49mU79RTvaQTZwYPJA1N4t/wwUyRRFhauTI+AxkfX0iDGRnu7NC9sknYp00\nRXHvc4sVjFbI1P/GgqKI2bErrxQBaPjwwDh5910R1D74IHbHI13yFTKzU5ZWVsgSoYdM7x1QsCf7\nFSuAU6dic1zBsEImRLIOWahAxgqZO/T1AdOmBQ9kf/+7+Dfci19vrxgfSUnxGchOnQKuuAIYN86d\nFbLz58VzpxUfvnAz7euD3hv27m7xbyxbXTo7RfAZNkxsq6ctjxwB7rnH/grZihVD3yxc8oEs1hUy\n9TpkiVIhC/cHJ5WXA8ePx+a4gunvjyw8uLXPI9ZTlmzqN8bJHrK8PBG89H4nRgOZui80HgPZyZNA\nWppYZsGtgQwAjh5173OLFdxaIWtvH3yGCfW05ZEj4hPEdgeyioqhgfySb+o3G8jsauqP5wpZuE/R\nACJsdnSYf7ytxgqZIKcszaxDplchY1O/u1y8KN4gZWYCH3009HqjgUz9Tl27Ur/RQObkSv3t7SKQ\nXX65O6cs5Sc/jx519jjs5PcPDRhuCWSyf0ySi8O2topPkxcWiiB/5ox9x3Dxon61kBUyE6yaskyU\nHjIjTZuAeJwVxR2BTH0KK6Pc2ufhZIWMU5bBOdlDNnw4MHmy/rTl3/8ufu/hpoe0gcyNFTJFCb5U\nggxkbq6QjRghAplbn1uiJRvU1a9zwZa9GD/e2UAmK2TvvgvMmCGO+YYb7O0j6+vTD6eXdCCLtIfM\nigpZIvSQGW3ql71jbghkQOIEiFgGsjFj9AMZpyzdI1wga2oCcnPNVci0K/W7JZA99BCwdav+dXJK\nyq0VsvPngZkzE7tCpn1tAIJXyNLSYttDJqe0JRnIjhwBpk8Xl02dau+0pWsrZBUVFfOnTJlyNDc3\nt37Dhg2P6e2zZs2ap3Jzc+tnzJhxpKampkBevmLFiv9MT09vmzZt2nvq/Ts6OsbPnTt3z+TJk4/d\ncccdf+rs7PTqfV+3LHuRSBUyvT8stwQyvRXnjXBrn4cVC8MaXYcsWA8ZpyyHcrKHbNiw0BWy/Hzz\nU5ZuDGQtLcDGjfrXxUOFbNasxO4h0742AKEDmfbyf/1X4M9/tufYwlXIAHsDmaKI51TXBTKfz5dc\nVla2saKiYn5tbW3+jh077q2rq8tT77N79+6FDQ0NOfX19bnPPffcg6tXr35WXvfAAw9sqaioGLKi\ny/r169fOnTt3z7FjxyYXFxe/sX79+rV6988esuiwQuYsVshILVSFTFFEIMvLS4xAduECcPgwUFMz\n9DpZAXFzhSw/XzwfJuqZBIxWyLq79QNZdTXw29/ac2zBApm6QmbnlKV8HtV7LOxu6k8JdeXBgwdv\nzsnJacjOzm4EgKVLl764a9euu/Ly8v6xTm55eXnJsmXLtgJAYWHhgc7OTu+JEycmTJgw4cTnP//5\nvzQ2NmZrv295eXnJW2+99QUAWLZs2daioqIqvVD2wQfLsW6duLnX68XMmTP/8Y5Fzu2rtw8dAoAi\n9PXpXx9sWwSyKrzzDvDlLxd92uRehaoqcX1SEtDcHNgO9/3csv3ee0BWVmD7+HHg/Pmh+588CaSm\nVn06wJ07XvHkbP73p+7zcNPjf/w4kJZWhJQU4MwZ4+Onrw84dqwKJ04Aqanh9+/vB06dqvr0iURc\nf/JkFWprxe37+93xeLhl26nx8vHHwOzZRZg8GXjvvcHjoby8CsnJQHp6Ed57L/T36+4GurvF7UeO\nLEJPT+D6c+eKcNll4Y/no4+qPv0QgT0/b2trFW66CXjhhSL87GeDr29vB9rbxd/72bP23H802+fP\nAy0tVZg0Cfj1r4HS0iJXHZ8V23/5S9Wnsz6B6+vrgQsXBu9/4UIR0tKGjtemJvH89tOfWn98J08C\no0cH7m/iRKC2tgonTwJ5eWL/c+eqPj3NWBE8HmvvXzyPijygfnza2oBRowbvL//f2NgISyiKEvTr\nN7/5zZdXrVr1vNzevn37fWVlZU+r91m0aNHv9u3bd4vcLi4ufv3QoUM3yu3jx49nT5069T31bbxe\n7xn5f7/f71Fvyy8AylVXKaa8+qpoJf3FL8zd7nvfE7fbskVs//a3irJ4ceD6F15QlBUrzH1PN3j0\nUUX50Y8C2x99pCjXXDN0vx/+UFFuuEFR7rwzZoem67rrxO/h44/N3a6ystKW44nWmjWK8uSTivK3\nvynKjBnGb7dggaK89pqiPP20ojz0UPj9R45UlH//d0X5xjcCl82erSj79inKokWKUl5u/tgTmVPj\nZelSRfnVrxTF5xO/s66uwHV/+5uiTJ8unru+/vXQ32fHDkVZskT8v7JSUf7bfwtcd8MNivLuu+GP\n5bXXFGX+fNM/gmFTpyrK73+vKOPHK8qFC4OvKyxUlP37FaW6WlFuusm+Y4jUl74kXkuWLlWUf/mX\nSqcPxxa//714nlGrqRn8POX3K0pSkqJ8//uK8i//Mnjf669XFI9HUU6dMne/P/iBotTVhd5n7lxF\nqagIbJ84IV4Xbrhh8LGNHy+us9rJk+L+Hn988OXXXx/+2EWkCp6pwn2FnLL0eDyGTimqKIonktvJ\nfYPtLz/9Z5SVTf1JqkcmKSk+e8j0piyD9ZBdf707piyHDUvMHjIz48fKT1lyynIop8aLnLJMSgJy\ncoD6+sB1f/87cPXVxs6mES9TltdfD8yeDbz88uDr1MteuLWHbMwYYMoUwOMpcvpwbGFkyrK3VzyH\nXH750DF59qxY5Pitt8zdb3k5cOBA6H20U5ZXXin+ZuR0JSCWv7CrjyzUlKWjPWQZGRktTU1NWXK7\nqakpKzMzsznUPs3NzZkZGRktob5venp624kTJyYAQGtr68S0tLR2vf1GjhRPMEZF29QfbGHYeO0h\nM7rsxalToq/FDYFMGyziWSx6yBRFfG829bufDGTA0D4yGciMnG82XgLZ6NHAqlXA888Pvk5+ytLN\nTf1jx4pAlqiftNSuUQkM/dCX7JnSG5NnzwJ33QWoZu4M6egAws3uaQNZcrII8LKhX7IrkMk84LpA\nNmvWrEP19fW5jY2N2X19fcN27ty5pKSkpFy9T0lJSfm2bdvuB4Dq6urZXq+3Mz09vS3U9y0pKSnf\nunXrMgDYunXrssWLF7+qt98VV5gLCdE09aemDl6HTL3sRaJUyEaOFD+j9mdxWyAzW9GpMvusECOx\nWBh2YECM1eHDuQ6ZUU6NF20g+6//ClynDmSRrkOmKPqfntOjXVDWajKQLVokKoFyIdzubjFmx451\nd1O/rJAdOlTl9OHYQnsWF2Bo8JLrbmkv7+8XzzV33glUVpq733CBTFGGBjJAnIg+VoHMyab+kIEs\nJSVlYOPGjWXz5s37Y35+fu2SJUt25uXl1W3atKl006ZNpQCwcOHC3dddd91HOTk5DaWlpZueeeaZ\nh+Tt77333h233HLL/mPHjk3Oyspq2rJlywMAsHbt2vV79uyZO3ny5GNvvvnmF9euXbte7/7HjzcX\nEqKpkI0dm3ifstQ+OXs84g9MO9BOnhQvEGfOmJsitprepwXjWSwqZHJFfu1Ur7ycU5bu0dcXCGSz\nZw+e7mlqCl4hGxgInMsPCL5S/4ULYl2ylJAf1RK065dZSVECL16pqUBRUWCJBPkJS49HHIPf79wZ\nA4KRgWzyZLE6fCJWmENNWcrXABmqtW8SZBX2xhvFG4mTJ43dp88nXmM+/jj4Pl1d4nlrxIjBl//m\nN8DcuYMvy88HamuN3bcZehUyv19crj0uq4X9012wYMEfFixY8Af1ZaWlpZvU2xs3bizTu+2OHTvu\n1bt8/PjxHa+//vrt4e470kAWSYUsVCBLlAoZEOhRUU9rnDoFTJokBltXl7EpDztEOmUZDz1kdgay\n1FT9QMYpS31O95ABwO23A/fdJ16gxo0TL2xZWfo9ZH/6E/CTnwCvvy62u7tFXw0wuEJmdLpSezur\n9fSI8ShnGW67Ddi3D1i+PNA/BohQJqct7X6hM0M+b44cCWRkFOH4cbFgr5vt3SvGxJQpxvbv6hLj\nTk0+V8ngIUO1dkzKcZaSIn63VVXAV74S/j7PnhVhL1SFTK86BgDXXjv0svHj7Zny1quQ9fSIx8Tj\n0b+NVUJWyJw2fry51fqtqpBpT52UKBUyQH9K5NQp8cds9vG2klyMjz1k5haGDRfIWCFzD3UgGzVK\nVI4qKsR2qB6y06cHVxWCrdTvlkAmKyvSrbeKQAYMDmSAOxv71W9kY91H5vdHNkvx+OPAzp3G99d7\nsw4MDl/BpizPnhW/NwCYM8d4H1lHhziPa0tL8AJHsECmZ/hw88UXI/QCWSz6x4A4CGRme8iGDbOm\nQpYIPWRGVmPu7RU/95gx5nv2rCTDy/Dh7CGzokKmbupnIBvMDT1kAFBSIj511t8vgsqkSfpvmM6e\nBZqbAy/UelOWiuLeQDZtmjj+06eHBjKv1119ZPK1Y9gw8e/o0VUxDWSPPgrcfbe55wtFEQu1Hj9u\n/DZ6Tf3A4NeHYFOW2kBmtI/s9GlxovArrwQ++UR/HzcEsosXxXM2A5mG2YAgg4XVU5bxWiELNWUp\nnT4t/kA8HvMB2EoyhESy7IVbxSKQyeDFKUv3k28YpUWLRIWssVGsRp6SMrSPBxAvgL29gTNqqF8c\nkpLE7/jixcgCmR09o9pAlpIieub27x/6guu2Cpn2OTMzE2hoiM199/QA27YBbW3Aww8b/93U14vn\nbTOBTK+pHwgeyPSmLAFxzs+WlsDYDKWjQ7ymZ2cHn7ZsbRV/C0YMH25P/2Ffn5jO1fvEqd1cHcgi\n6SG77DLrm/rjsUImP3EV7qPNcroScD6Q6QULI9zaQ9bfH/2UZTQ9ZGzq1+eGHjJAvPBcfz3wq1+J\n6UpA/C6Tkwf/LmVgaWoS/2rfrctwZSaQJSeLsWHHmx9tIAMC05Zur5Bpg8oXvlCE1tbY3Perr4pG\n+YoK4NAh4H//76H7nDwpwppadTVw003hl5NQCzZlqX59kONM+yZeXSFLThZVLyON/R0d4jXmmmuC\nH+vHH4vrjRgxwr4K2fjxrJANEUkgGzs28gqZfOFKhB6yvj5x3Kmpgy/X/nGdPOmeQMYKmWB1Uz8D\nmTtoAxkgpi03bRIN/ZJezw5gbSBT385qwQLZ3r3u7yHTBpWJE4NPr1ltyxbggQfEa9Hu3cB//ifw\n178O3ufgQeCZZ/Dpaa+E6mrRVN/aavxv3eyUZbBABoj/G1kv9PRp8RqTnR38k5ZmApmdPWTaQCb7\n6ezm+kBmtqk/kmUTErGHrLdX/5NLehUyOYXgZCCTFR31enBGsYdscCCTi8VyylKfW3rIAOBLXxJL\nWsgKGTD0b7SzU1SSmj9dkjseApn2xb6wEHjnHREq4ymQNTZWxaRC9ve/i5OxL14sttPTxdjQrmov\nl3nYtStwWXU18PnPi/AoQ3s4ZqYsR40S40QWJbTj7LLLjP0OZYUs1JRlY6P5QGb1tDsrZEFEsjBs\nNBWyROohCxbItBUy9ZSlk0396inLRKnoyECWnCz+b+SJw+8X4V8GuUgCWX+/uE+Ph1OWbqIXyKZO\nFS9Q2kCmrUhMnRrfFbKxY8X07L597p+yVAey8ePFLILdb8i3bQOWLBG/E0lv4dPaWrEgqwxkFy6I\nBYYLCsTSEEb7yEJNWcqxJ/umkpICy2AAQytkl11mrEIme8iuuSZ0hSw729jPkJRkz7S77CFT93Iy\nkCGyKctIK2Tq2yVCD5nekz8QvofMqWUvopmydGsPmQxkSUniy0iol4+DxxP5wrAypAGskOlxYrz4\n/YHflZrHA2zYMHjRS72eHbsCmR1N0XqBDBDTlv397m7q107lzZ1bhMsvN9a0Him/PzBdqTZ1KvDe\ne4Mv++AD4JvfFFOXZ86IXrNp08RzvZlAFuyMDnrLXmgv15uyNPI7VE9Z6lXI5KeNMzKM/QyAPX1k\nFy+Kn1uuyQawqR9A7HvIEmkdslBTlsEqZG6YskzEHjLA+LSlDGRA5OuQqV/42UPmDuqgrXXPPWJV\neCmSCllvr/lAZtdq/cEC2W23iX/VgcztFTJATAXaOW1ZXS1+h7NmDb586lQRwORrj6IAdXWi8X/O\nHOC118RtP/c5cX2oqUAtIxUy9e9RfbnelKXRCtn48aIa3NQ09DW1uTnwaWOj7Ogjk2fU0FYLL/kK\n2bhx4pdodI7YqkCWCD1kwSpkY8YMrpAlQlO/23vIAOOBTP17i7SHTF0h45TlUE6MF/Vpk8LRW/fp\nhhvie8oSEIFs0qTB03JOV8j+678GP7drg0pVVZXtgayxUVS5tGHd6xWvgXJ6r6lJvE6NGyd6zV59\nVQSy2bPF9UYrZP394rkoXI+xuiqkvjzSpn45ZTlypPjZ1KcDA8w19Et2BDK5PI36Z2ZTP8SDPXy4\nKK8aoQ1WRl1KPWTXXTd45Wm3NPWrFzJlhUz8P9J1yDhl6T7B3iDp0auQ5eeLT/v5/UMDmax0mT3t\nWawD2aRJgZOMS05XyL72tcGN83rN7nYHslDVl2nTAtOWtbUimANiDbs9e0RPnjqQGamQydCpV63V\nVsjkcYWrkJmZsgT0q3lmGvolO9YiY4UsBDON5lZWyBKhh0wvkN16q1igUQZMNzX1ywqZ2YqO23vI\nAFTn/xsAACAASURBVPsCmV6FTIY0eb+skA2mN17a2oZ+os1KZgKZul9nYECEpiuvFJWI1tah1bZ4\nqZABQx8Dpytk3d3idy9pK2RFRUW2B7JQj5e6sf+DD0QwB8Sb6BkzRLFAfiAkO9tYhSzYdCUQfMoy\nVA+Z2SlLQH8tskgqZHb1kA0bNvhnZiD7lJmqjQxkZiosihL4MIB6HTL1lGW8Vsj0XgAmTBAl77o6\nsa0OZGaniK3EHjIh0ilLWVmU5wRVV8gYyML705/E+QDtol2lPxRtNWLsWPGmMCsLOHZMvDCoqxvq\nQKbXqB2ME4FM6/LLna2Q9faKRnLJiR6yUA3j6sb+2tpAIAOA//7fxRtsORYmTRJVqHAVo2BrkAHG\nesgiaer3+cT49HrFtt5aZG6ZspRvivU+cWq3hAxkZn5BcjX1ESMujQoZEFg1W1FEILviCnH58OFi\nIGrPpaf11FOB9XCskug9ZHLpi3AirZAlJwfGqbapn1OWg+mNl54e8ak1u5idstTr18nKEj1P2nfq\n8VQh0/J6na2Q9fQMXmVeG1Zi0UMWqj9p2rRAhUwbyMrKxKczpeRkMUaCLSkhBVuDDAgeQtRjMpKm\n/s5OsZ8sdOhNWZpZ8kKyq4eMU5ZBmA1kZpe9kA++OggkSg9ZsBcAGcjOnxc/tzq4GXm8X3vN+umd\naE6d5FbRTlmaWYcMCDx2bOo3r6fH3un6SHvIzp4NVBWcDmStrcaq52YCmXwxd+r51Q0VslCP15Qp\n4lyafX1DA1lKytBgZaSxP1SFLNiyF+pzrEYSyNTTlYD+WmSRTlna0UOmrZCxqf9TZtbGimRhWPV5\nA4Mte5GoFTL1JywlI4Gsp8f604lEs1J/IvWQRTJlKQOcXiCL1ynL6mpxWiE76I2X3l73BLJg/TqZ\nmc4GsvnzgbffDr+fmUCWkiJ+nnBVebv09g6ukDnRQxaq+jJypAgplZXi/3I2Ixgjjf1GK2R6PWQX\nLgReLyUjU5byE5aStkLm94tlL9QLJBvBClmM2d3UH6xClqg9ZIB4l3X6tGgSVa8JBBgLZL291gey\naJr63cqKpn6fL3RVQq9Cpm7qj9cpy0OHjJ8GxgpurpCppyxlD5nayJFiSkhRjN+HvJ2ZQPbJJ8Y+\nxWcmkAHONfYrivEK2YkT9vXWhnu8pk4Fdu4cXB0LJlhj/7lzQHk50NISvqk/2LIXFy7oh34jFTL1\nJyyBQIVMPqatraISHKyIEIzdPWTaE63bzfWBzO6mfiNTlvFYIQu27AUgfp7PfU6cfkNbITMSgO2o\nkEUzZelkD1l7e/CwHm0g83jC955pA9nFi4kxZVlba3y5G7OC9ZD19trTUwVE3kPW2Tk4kB0/rh/I\n2tvFC6PeUgbBmFmpf2BAvKiqT2odjNlA5tTSF/J5RhvI1NWjqqoqjBol/rbsOsZwDeNTpwKvvBJY\n8iIUvQrZkSNi0dkNG8QnM1evDl5JDbXsxfnzQxv6AWPrkGmnLEePFqfQkr3IkUxXAvYsexGsQsam\nftjf1J+oPWThXgBuvRX43e8im7K0u0IWTz1kX/uaWA9IjzzBNxDZlCUQfspRXQ0bPjwwZRnvTf12\nBjI9MojZVSWzqkLm9+sHsrY2c9OVgLmV+k+fFtUMI1XLeKmQyRfyUFOWkp3TluH6k6ZNE2HQSIVM\n20O2eTNw++3A978v2lTa20U7wPe+p397da+YuiokpyzPnRsayOR1oYoW2kAGiDNU/PrX4v+RBjI7\nlr0I9ilLVshgvodMrgJttKKlF8gSoYcsVIUMEIGsvT3yHrKWluiPUS1ee8jOng3+qSZthczIGFJX\nyIDwgSxRm/rr6uwLZMF6yAB3BLJgPWQZGaICpn1hGDEiskBmZspSrtVlVyCT1af6eutfYIPp7Q28\nvsg33Ho9ZID9gSxchQwwN2WpKCJ0/ehHwJ//LN44AuK1LD9fLJGhR4YQ2fIiW3fk5WfPDh1nSUlD\nzy6hdfr00P63r30N+NWvxGMfTYXMrh6yYB9wsJPrA5nZHrJhw8z9ktSBTL5wJUIPWbgXgJtvFi/W\neoEsXADu6RE9FVaG1HitkF24IJpR9UQ7ZQlEH8jisan/1CnxgnDxYuyqezKY2LX0RaSnTlIHstRU\nsY6gVRUyM4GsvV28QNkRyOTSF/v3AzNnAi++aPy20ejpETMqY8cGfu/BPoE4caL1swJSuOpLTo54\nXjYyZZmeLh7///E/xAzIX/4C5OUZPxYZvLS/Q3Ug01bIgPBVTr0K2YwZ4rHevz+yJS+A2K5DxkAG\ncxWySF7UtZ+yVJTE7yEDxOAqKIi8qX/48MGl/mipe8jMBggne8jCBTIZ7O2askzEpv66OvEufuxY\ne6pkwXrIAHdUyEItwpmZqR/ITp60P5B99rPhe8gUJbIK2VtvAf/0T0BRkeh5igX5HHnVVYE+Mr1z\nWQLOVsiSk8Xjrn3zrMfjEcHm/ffFJzPT0swdy+jRInxoK0LyTUKwT/KGa+zXC2Qej6iS/fKXkZ02\nCYhtD1ksApmJ86o7IyNDvOApSviGVfkuNJIKmXphzUTpIQv3iZUf/hCYPHnwZeEqkooinsRvuEG8\nY5wwIfpjBRKvQub3i/Eqx5GdFTI5TZ8oU5ZyvaXmZhHIxo2z/z7l6YncHsiysvQDmc9n/5Tl9Omi\n/yjUz9LXJ8a8elmEcLxeMbX24osihP/f/2v8ttGQgWzsWBHIPvMZ8fymd0aFiROtb9OQjDSMmwm4\nO3eKcxZH0oSekiK+Tp8efHs5fReqQhYqkOlNWQLAV78K3HijGAPsIYuDCpn8KKz6fGN6ZAUrOTnw\naTMj1E8u8gUtUXrIwr0AzJ079I8gXIWsvz9w/jQrS/jRnDrJyR6yCxf0p3HU05VA5IEsXKAKtg6Z\nmdX+3aauTkyz2FUhC9ZDlpFhbyAzeuqkUOcNDBbIAOsCmZwlUGtvF2++Jk0KXhEGzFfHAHGi7Jde\nAr7yFRH6jhyJzenbenvFY5CWJiqMeifdjlUPmZUv9tOmRfeJwNGjxe/bzJRluBOM61XIAPH6k58P\nfPihe3rItCcXl8ujyL8zO7k+kAFiDr2hIfQ+6ic8+WkzI7QLcfb1JU4Pmdk1XYDwgaynRwzMSZOs\nDWRymi2Spn6n+P3i8WhqGvoCEmkgs2LKUttDFm9TlrJCZlcg09PTY38gi7aHDACWLxfBRc2qQNbd\nDfz85yIMP/TQ4H3b20V/UlZW6D6y8+fNh4FbbwXuvlv8f+JE8a+dC7FK2inLUAumOjllGWujR4uA\nqp2yDLYOGRDZlKV0332i8GJ2/AL2NfWr1yHr6RH3kxSDtBQXgSw3V3z6JhR1ZSGSKUsg8IKWKD1k\nZhaJlML17MknMTsCWaQLwzrVQ9bTEzjRs/bJyKoKWbhAlYifsrQ7kAXrIXNLIBs1SoQjRRkayGbO\nFL2falYEsoYGsWTCH/4AfP3rwNGjg/dtbxeVpKys0H1k0YYLj0c0e6v7yAYGgIqKyL9nMPK5LC0t\nEMi0Df1yrEyaZE8gGxgQX5E8V9slWIUs2DpkQPim/mBTlgCwdCnwgx9Edqx29JBpK2Sxmq4E4iSQ\nGamQqT/FFElTv/p22inLS6lCFu6djl0VsmimLJ0iX3wyM4dO48iT1kt29ZCpG/gToan/3DmxBMLV\nV8e+QjZpkjsCWXJyYI2wYC+AalYEsp/9DFixQiwWvWTJ0KVc2tpEcLn66tAVMiuqPdOnA+++G9iu\nrAQeeCC676lHHchOngx9jke7KmTyxd7Mgr520wtkodYhA0K/bvh84rENNo4vuwx4+OHIjtWOHjJ1\nhYyBTIfRQGZlhUw9ZXkpVchGjRJP0sF6OGJRIYuXHjLZ+6EXyJyesjRzgnI3qasDrr9e/M050UNm\n17IXZgIZEL5nRy2aQNbbK15wtm8HSkvF5VlZ4m9bPV6NTllaEci0FbLf/14co9V6eoZOWWoDmRwr\nl10mXgOsPudmrFaAN0NOWQbrITM7ZdnZKa5Xv6Zaxc4eMhlCGcg0jExZqnvIoq2QJcKnLMMtexFM\nUlLoFbzt7iFLlAqZlVOWl9I6ZHK6Erh0K2SAGFedneK4glVtpEgDmfw737kTmD07sA7U8OFiekn+\nfSvK4CnLWAYyRRHradkRyLQVslDnePR47KmSxWrBUTPGjBG/b/VxjRghnkc6OsxPWYaaroyW3T1k\nMpDFKjTHRSCTFbJQn7yxqkLW358YPWRmXwDURo0KfMpLy+4KmdGm/k8+Af7t38T/neohi5dAFk9T\nlupAZuSkxZFwew8ZIMZVa6t4DMI1E8s3XpFOWf7852IhUTV58mdABBWPRxyT3T1kgPhQwUcfieea\no0cD49fqN2p6Tf3BesgA+wKZGytk2ilLeYaITz4xP2UZqqE/WrHoIYtlaI6LQDZunHhhUZ8EVkvb\nQ2blsheXUoUMCCwOqEdWyNLSxB+aVdUXdQ+Zke957Bjw299ac9+RsiOQWf0py3icspQri8eyQtbb\n664K2ZgxwV/8tKIJZGfOiP6wBQsGX5edHQhksjoGxKaHbPhw8Sa8tlZMVy5aFPo5KVLqZS+CBTI1\nOwJZLKfDjNILZECgcqY3zkKtQ2ZnILOzh0yuydbRwUA2RG5u6D4ybYUs2inLeO8hi6ZCpl4QT0sG\nveRk8c4y3PpwRpmdsuzuDjxBO9lDZneFLJJ1yLRN/fEUyGIxZakdLz6f+N1ceaUYU3Y8XpFUyFpa\njAUyj0f8TUYyZQkADz44tL9HXSGT/WOAeHPc3x/892JVxUdOW8pAFqpqHyn5XHbFFWK6rbNz6LIX\n6rFyqVXItCFk9Gjx2mh2HbJ4mrKUr/PyuTvYY2GXuAlk4Rr77Vz24lKrkIV68pMVMsDaaUuzTf3q\nQOYU2VvgxilL9cKw8TJl2dsrxtN114ntWFXI5JhOShLrIckTXVvJzLksATGujFbIAFG9MLqvlJQk\nFhFduXLoddpAJitkHk/oPjKrAsb06UBVFVBTA8yZY1+FTL659HrFz+tEhcyNgaynZ+hxjR4tHiu9\ncOLklKWVgUy7gLPemmx2iqtAFqqx34qmfvXCsInQQ2bXlKX8vlYGMhkiZAAJt1K3nNsHEquHTG/K\nMpp1yJKSxHiOhzcULS3iRU8+brFah0z9JsPIuVwjYWalfsB8IPvLXwKLqprx7ruB6peaOpDJJS+k\nUH1kVlbIduwAvvAF8buxs0IGiJ/vo4+c6SFz45Sl+l/15Zddpr9ER6im/pMn7auQjRhhbQ+Z9o2T\n3idO7RQ3gczslKXVp04CYnM6D6tEuuwFEH7K0s4KmcdjrO/JDRUy+WQ6frx4XNQfiY9VhUxvHTJ1\nIPN44mfasrlZNNZLsaqQqV+Yx42zL5DZ1UMGAFOmRHZcwWh7yNShLVQfmZWBrL9fTFcC9lTI1G8u\ngwUytUtpyhIYGhRDVWFDVcj+9jdR8bQDK2QOMTJlGcnCsOrgEqyHDIi/Klk0FTKnpizVwcJIIOvv\nF19O95B5PKJKpj75sJNTlurH0sx9O62lRTyOUqx6yLQVMjvWIrOzh8wO11wjqmDqJS+kWExZpqcD\nN90UCGTy7AVWUgfxq64CGhuDr0MGXDpN/fIxCFYh0xOsqd/vB/76V+Bzn7P2GCWrA5lehYw9ZDrk\nlGWwKpWdPWRA/PWRRVshC/bkp34Ss6NCBhgL1DIwOlklU7/4aKct3fIpSyPfwy1aWpypkMVqytLO\nHjKrjR4tXoTa2/WnLO0OZB4PcPBgYDyEqtpHSjtl2d8fvkJm5VI/gLsrZHqBLNh4HDVKjHHt88zR\no+JvasIE648TsH7ZC22FbMwYVsh0XXGFCEWnTulfr+0hs3IdMiC+KmSKIn4OO6Ys7aqQ6X1aMBQZ\nxLq7ne8hA0QgU79IuaWpX36PeKiQaacsY7UOmVsDWU+Pc4EMCPSRaStkV19tfw+Zll0VMvl7v+oq\n8W+oHrIrrhBtCVYGALc29av/VV8ebDx6PPpvoOysjgHWL3uhff7llGUIoaYtrVz2QttDBsRXhezi\nRfEiHOn50YwsDAvYVyEzsjisOpA5xY4KmdULw8r7jpcKWSymLLXUY9otgUwGAycDmewj0/aQhauQ\nhTuzQCRi0dQPDF32Qi0pSTwOJ05YdwxuburX6yELtbSKXmP//v3ALbdYe3xqdvSQ6U1ZsqlfR6hP\nWqrnfq0+lyUQXxWyaPrHAGMLwwL29pAZnbK8cMG5HjL1u1tOWUZPb8ry/HnrP0wTrofMDYFMjis3\nVshkINP7vdhVIbNz2Qsg8POF6iEDrO8jS5QpS0C/om13hcyOHjJthay/nxUyXaE+aWm2B0lKxB6y\naPrHAGMLwwKihN/VZc0fhHbK0khTv/pfJ8SiQhbtwrBA/ExZagNZSoo49mDnVbWKOpC55VOWbglk\nH34oPuSgXrZg9OjAVI6WnVOWdlbIgk1ZalkdyNzY1B8skE2bBnz2s8Fvpw1kHR0iuE+bZv0xSrKH\nzKo3bXoVMoCBTNdnPiOeIPTYuewFEF8VsmgWhQWMf8oyKUmsbq73xGyW2UAtg9iFC872kMk/1HCB\nLDmZU5ah+HxiKmjSpMGX2zFtGS89ZIDzgezwYfGYaGcMsrPFpxK14qlCpl32AgjdQwZcWhUybQj5\nyleAVauC3047ZVldDdx88+DnQaulpIjXIavecOpVyAAGMl0TJgQ/VY/VC8Nqn4DiqUIWzWmTAOOf\nsgQC00rRimTKcvhw91TIsrKGBjJtKDIS6M0uDBtsHTIzoc4N2ttFdUq7eGos+si0PWRuWPZCBgOv\n1/pjMeqaa8Tpi/QWjo11ILN72YtggUzLjgqZ2wKZfAzMhhBthczu6UrJymlL11fIKioq5k+ZMuVo\nbm5u/YYNGx7T22fNmjVP5ebm1s+YMeNITU1NQbjbrlu3bl1mZmZzQUFBTUFBQU1FRcV8Iwd71VXB\nqzFW95BdyhUyo5+yBMQfrxUvmJE09V91lfjX6XXIAFEp7OoKTK850dQvP8yiVyFz+5SldrpSsiOQ\nOdFDFsmpkwDnm/r7+gb3j6mvi3Ugs3PK0usFvvGNob+jWPSQuW3KcuxY8XegfQ0MR7sWmd0N/ZKV\ngSxYhcwVTf0+ny+5rKxsY0VFxfza2tr8HTt23FtXV5en3mf37t0LGxoacurr63Ofe+65B1evXv1s\nuNt6PB7lkUce+UlNTU1BTU1Nwfz58yuMHGy4QGbFsheJ0EMWbVO/0U9ZAtZVyMwue3HhgghB2uN8\n6SXg/fejPx4j1C8+2vMguulTlrGqkH372/ov0kZol7yQxo61Z+kLNbsDmaIM7esLxw2BzOsVj79e\nIFOfWkktnqYs1cteJCUBTz0V/pPpl8KU5ZgxwLFj5m+nPsH4wADw9tvA7NnWHpseK9ci01bIIq0W\nRirk7O7BgwdvzsnJacjOzm4EgKVLl764a9euu/Ly8urkPuXl5SXLli3bCgCFhYUHOjs7vSdOnJhw\n/Pjxa0PdVlGUsIsyLF++HNnZ2QAAr9eLG26YiVOniqAowFtvVQEIvIP58MOqT09gWoThw4GWlipU\nVQWul70A2u2LF8X+VVVVaGoCxo4tgs8HHDlSBb8/sH9/fxX27gXuuSf093PDdm8v0Ntr7OfX2xaL\nUurfvqenCCNHBrbHjClCV1d0x+v3AwMD4vGdM6cIw4YBhw6Fvv3p01WYMAHo7i4a1Ofx0ktFuP12\n4NQp6x7PYNsdHcDo0YHt1FTg3LkiTJwIvP9+1adr5onrP/qo6tO1m0J/f/V4BIDU1CL09wffv7+/\nCKmpYvv994G+viL09YnxK6uHqanAgQNVgz6Rasfj8dJL4veXnR3Z7cWboMHXjx0b/fjSbqvHS1FR\nEXp6gPZ2Md5vu60IZ84Ab74pjseK++vrA5KTq/DnPxu//XvvVcHjEX9f0d5/NNvXXFOE9PSh13d1\nVeHwYUD9+xoYAPx+8fdr9fEcP171adi37ufr6gJGjAi9v7xMbk+aVITWVut+vu7uIowa5a7XD0CM\nP7O3P31aPF8BwC9+UYVx44Dx4+0/3hEjRB6YNCn679fXN3j8yuf3d9+tQnu7/vioqqpCY6TvRLUU\nRQn69Zvf/ObLq1atel5ub9++/b6ysrKn1fssWrTod/v27btFbhcXF79+6NChG19++eW7g9123bp1\n37/mmmsap0+ffmTFihWbz5w549Xetzi0ocaOVZQzZ4ZeXlamKE8+Kf7/+98rysKFujcfYtw4RTl1\nSvz/pz9VlDVrFGX2bEXZv3/wfldfrSiNjca+p9P27FGU4uLIb3/ggKLcdJP+dTfdpCjV1YHte+9V\nlF/9KvL7UhRF6e1VlNTUwPbCheJ3GMqkSYry1a8qyoYNilJZWfmPy++4Q1Eefzy64zHK61WUjo7A\n9mc/qyhvvy3+/8ILirJyZeC6F15QlBUrQn8/v19RAEXx+QKXPfmkGNuh9vf7xfbbbyvKrFmKkpen\nKO+9F9jvttsU5a23jP9ckUpPV5Rt2yK77Xe+oyj/5/8MvXzJEkX59a+jOy4t9XiR9/2DHwS2L7tM\nUTo7rbu/s2cVZcwYc7c5c0ZRsrKsO4ZI3Xmnovzwh0Mvf/ddRcnPH3zZmTPisbPD7t2KMn++td9z\nzBhF6eoKvY92rLS0iHFulays+HldCeeppxTl4YfF/7/9bUV55JHY3O/11ytKba013+v55wc/b1dU\niOfYtjZjt/80t4TMVaG+Qk5ZejweQx8mVQxUu9RWr1797PHjx6995513Zk6cOLH10Ucf/bHR26al\niQZgLbuXvUiKsx6yaJr6jX7KErCmh0zbhG7k96eespTvWgAxvSWnDe2mnW5QN7VGMmUpb6Mee6Gm\nGwcGxFS6nGaRj5sTTf2KIqb6gp1JIxy39JAB1i99EcmHbLzeyKaNrLZggfiknNY114jpafVyA3ZO\nv9ndQxaMdqykpQGnT1vXk+nGpv5Iyee/ri5g82bRkxcLVjf1q587XdXUn5GR0dLU1JQlt5uamrIy\nMzObQ+3T3NycmZmZ2Rzqtmlpae0ej0fxeDzKqlWrXjh48KDOn7y+YH1kdjf1X0o9ZEYXhgWs6SHT\n65sy09Sv1tUVWSB74AHgjTeM7y9PsaXuC1IHsv5+84FM74U7VJgKFmT1esjsbuqXJ3uPNJA1Nw9e\npV+KxacstWPa6j6ySD/1HM3fsFUefhi4/fahl192mfiZTp8OXGZnILO6h2xgQIRJs0sypKSINdn0\nigKRcGNTf6RkU/+WLUBxsfjgRyxY2UOmd3JxwCWBbNasWYfq6+tzGxsb/3971x4dRZWnv046QJ4E\nEUIgkWAeGCAkjgjsKMr7pQZYXEedZVgEZVXG9TEuOOs5juPgwMy4R1bcARRckFHxMUB0AgKuDAxz\nhEESEEEhQiAJEA15EEAISXr/+O2drq6+VXWr6/Yjzf3OqRO66Kqurq6696vv++7vZrW0tHRat27d\nj4qLi0u07ykuLi5Zs2bNTwDgs88+G56amtqYlpZWa7bt6dOn09n269evn1ZQUPCF6AGbETK7Cllb\nG3Wq7KZk20VDHbJQFIYF5Chk+sBzp07WQfb2dlIR9HNZNjcHVragstK3bIUVWOejDQE7Vcj0xJRt\nZ0bI9OctXHXIGIHpCAqZ9noBfOtRAfJLXzgtQxOp0I+0DLZCJpOQiY5E118rgLxgf1sbXRvah4GO\njJQUagdefhl48snQfa7M+Sx5ClmXLvZHnAYK0+cDt9vdunTp0nkTJkz4uK2tLXb27Nkr8/PzDy9f\nvnwuAMydO3f55MmTS0tLSyfn5ORUJCYmXnjjjTdmmW0LAPPnz19cXl5e5HK5PP369TvO9icCEUIm\nqpCxhlJv+URDHbJQFIYFqMN0OrebnohYEWpW3ZpHHANVyM6d8x/N19wMPPEE8Prr/u/ndT7BIGRm\n6haPyF6+zK/UHypCplVMROHxmI+ylDmqjQftaDsgchSySAcjZEOG0OuOZFk6KQ0ki5B9/z19r0Dn\nHI40pKRQqYuhQ0MzupJBdtkL7b2anBxaS9lSsJ00adKmSZMmbdKumzt37nLt66VLl84T3RYAmKIW\nCIwImb4wrB1CxqAyZISEBGosPB7/xiIYCpndDBkjZOypmeU8PJ7AM2Q8QlZTQ2U0QkXIArEsRRQy\nLanzeIC9e4GbbzY/Fruor6eHlkAUMnbOeBMXJyfLz1JZZciuuSYwYmmEaCVk+tIXHcmyFCVk+msF\nkEfIosmuBMiybGsLrToGyM+QaduC9HRgxw45+xZBiIQ4eRDNkIlYlryq6NGQIXNaGDYmhrbXzyHo\n8fjbO0lJoc+QsSCsvpG+dIkaBFmErKmJyCaPiIdSIQuEkGn3o7UsT57kZ4Kcor4e6NcvMEJWU0P5\nMZ5SEOo6ZAAwaBBVGTfC2bPAwoXi+49WQhZqy1K2QhaoVZieDpw65fwYoinQD9BMOsXFwNSpof3c\nLl2ClyEDgAED5OxbBFFFyAK1LBmiJUMmowPgNYBXrhAx1RINGRkfnvVmRsjYkyU7RpbzaG6mTl0m\nIWP71YPXmEYSITOyLBsa6BhlF9msrwfy8gJTlozyY0B4MmT/+I/Ahx8atyEHDwKvvCL+eYqQOUd8\nPHW6sh6KIyFDFm0KWXIysHFjcOeu5CGYoyxDjQ5HyHr2lBfq5xEyFhjvyBkypwoZwM9n6ZUEIDgK\nmVWoX29ZMjQ3U2PZ0OA7HF/k8y9d8p0YF/C+1q8H+J2PljwESsicWpaXL5tPncTC6rJzWQ0NQG4u\nETI75x4wJ2QpKaGZy1J7XffuTSrZ1q3899fV0Zy6ok/ldqdN6ihgpS8YgknIjFT7QBEJGbJIrNLf\nERHMDFmo0eEIWY8e/CHHWmbrVCHr6Bkyp6F+gJ/Z4DViMhSMQDJkWsvSWz2cro/YWHsNN1O1jBQy\nI0Kmf7p1qpA1NdH51MIOIYuNJTIUE2Ncy4wRMhmWixb19WRZdOli32I0KnkBhKcOGQD80z8BJGGH\nKgAAIABJREFU773H357ZsjTzgjXC/dQdLGRlUYaMEfDycrKtgwWZIy31qqgRgpkhizbLMlyQPXWS\nUshsQCRD5kQhM7IsjRQyWcxcJpyG+gG+ZRlMhcyJZclw7hx14No5JUUQKCGTbVmeOUONvRZ26pC5\nXPRaP2cij5DJVsjq66mg6rXX2s+Rhdqy1IN3XU+fbmxbsu/Hm8uRh2i1LFNTqZ1saKA24N13gRkz\ngvd5MoP9kaKQRZNlGS7ILHuhFDKbYIRMb4vIzJDxLEueQsZyM5EGWQqZnpAZKWThCPVrLUtthixQ\nQhYT40/I2OtQErJevXzXmdUQ401Y3akTv5ZZsC3L+noandi9u/0cWaRlyAA6ngEDgG3b/LdnD4RX\nOyEDvLblO+8At91m/DvKgMxgv5MMWa9eZFk7jbAohUwOVIYsjIiPp05I30hrO3VGpuyWGbBbqf/b\nb8m2CHYVdLuQoZDxnkaNFLJwW5YMzc1EigIhZL17G2fIePsKFSGzY1kC1gpZYyN1bMEiZB1RITMa\ncWdkW9bVAdnZipAB3mD/ihXAQw8F97MiRSHr0oXaPaelUZRCJgcqQxZm8GxLvcoiUvrCaYaMFY+U\nWdVbBmQoZLynUV4jxixLu0FuLeyG+llDFh9P/7799pEAvApZt272CFlTE2WYeJZl587hVcjMCsPa\nIWRahSw/P7IIWV0dDdbhISGBrmeZDz0iGTKAbMuSEv92pK4OuOkm30C7GaKdkG3YQIrRhAnB/SzZ\nCplI2QtehgygASxfCM8vw4cK9cuBypCFGTxCpj+RIqzZaYbM6XQxwYIshUwkQxYXR2TDyQ1hN0PG\nLMu4OPpd2Hu1GTI7JPncOVJozp3zJZZNTcB11/EJWTDKXgRLIdPanpFIyFj+jAeXS44Ka4T2duMH\nmIwMUsJ27/ZdzwiZUsiIkK1dC8yZ4x/zkI1IUcgAYMoU4I9/9F3X1kZqryiUZSkHKkMWZvBKX+hP\npEiw36wwrGiGDJBb1VsGgjXK0khJcJojC2TqJNaQJSYCW7ZsB+AsQ3bttf7EsqkJyMwUH2UZH0/E\n58qVyCJk+lD/gAHBKXsRSIaMlRzRjy7VQrZtqc0F6adP0+P66/3nOP3uO5ouSBEyypDFxAAPPBD8\nz4qUDBkA3H03ETLtQ/qrrwKjR4u7BcqylAOVIQszRC1LEYVMe1Nq65AphUzMsgScKxj6DJlVqF/b\nkCUkeEmUE0KWkuKrcAHmChnPbnC5vHWzwknIrEL9sgkZI1VJSfYVsoYGUsfM5vMLZi0yo4cMht69\n/UuE1NUBRUV0DkWs1GgmZMOHA7/6VXDD/AzhKHthhLw8utbZjA7t7cDSpUBVFXD4sNg+lEImB7Iz\nZIqQ2YQIIQtEIQs0QxZpCpmMwrCiZS8A56UvAp1cnB1nYeFIAM5C/V270rZa8nXunD1CBnhJnV1C\n1tZG11OPHr7rgxHqz8mhYxQpDSMCLamyS8iY1WkG2QqZNhdkRcj0JQ4uXqTfqls3UupFLKpoJmS9\newMLFoTms8JhWRplyABSyd5/n/69dSu1RbNnU6ZOBEohkwPZGTJlWdqEvjhsW5u/zRiKDFlDAzXm\nkaaQybioRAvDAs47TF6GzKpSv9ayZMcZaB2ypiYiUl27OlPIAO/ci3YJ2XffETHRW+V26pABYpZl\n9+5EJs6cMT4eO9CSqu7d7StkoSZkWthVyM6eJdLpcvlPrm2EcDfy0YJwWJZmmD4d+OADsiiXLgXm\nzQOmTQPWrxfbXilkciA7Q6YUMpvQK2QsP6a1Pdg0MmbQN5Ss02xtFc+Q5eREp0ImGuoHnCtkdste\n6C3Lv/xlO4DAR1maWZZ9+8pTyMxmeuDZlYA5ITOqQ8YL9be2UsfR0ECEVVZxS8CXkF17rb37IRwK\nmTYXZHWv6AnZd9/RdwS8leqtEO5cSrQgHAqZUYYMIOs/MRFYt46sy/vvp1psx4+TdWkFNcpSDmRn\nyJRCZhM8QqZv8AIpe8EqnV++LJ4hC3RC5WAilGUvADkKWSCFYdlx8jJkdkdZ6gnZ5cv0e6elhcay\nNCJkZoVh7VqWFy/SddylCxGNYBEyu5al0QhLhkhSyOrqvLayqEIW7pFb0YJwlL0wg8tFtuVDDwGz\nZtHxud3AnXfSJNtWUJalHKgMWZihH2VpRMjsKmQA7ae1VTxDlpsbeZZlKAvDAnIyZHbLXmgty9zc\nkQCcZcj0hKypiSzMrl35+zKyG7SEzGiCbx7MFDK7dcj09wLbB8t6AXIVMq3tyEZZio40E1XI7M6P\naQY7GTJGyNj3qavzKmTKsgwtZIb6ZWTIACJk588DjzziXTd1qphtqSxLOVAZsjBDRCELJNTPtgPE\nFbJItCxDOXUSIF8hEy0MC/g+NTuZy1If6tfmykTLXgDyFTK7of7OnY3rkAWLkGlVrk6diOCIEqhw\nWJZaWBEyVo6Dfb4iZOEDr00KFDJiHQBQWAgcOeI7qfr48cDevdb9glLI5EBlyMIMPSHjNXhOFDLA\nP0OWmOjfKTDLMhoVslCOsgx06iSA/paVbQcQeNkLRr60ChkjacnJ3pF1WphZlryyF7GxgREydh3y\n8md2LcvGxuARMi2psmNbihCylBS5Dz12MmQul++50mbI2DyOVlCETA7CUfbCLEPGkJPj+zohARgz\nBvjoI/PtlEImB2rqpDCDXcSMMMhUyFhnplfI9Jmb9nbqyKNZIQvlKEsnZS+M6pCJ2mbMstSOsmSW\nZUwMEU694hOqDBlgrJLZqdQfTMvSCSETGWV5113AmjXihVjtwEohA3xzZNoM2XXXUXjbapJpRcjk\nIJIq9Vvh/vuBlSvN36NC/XIgi5B5PPyBUqFEhyRkgK9KZidD1tTkXW/HskxP9w33NjVRR92jB3X+\nVo1yqNDeLkd2DeUoS/1NYLcwbO/eI/9eIZ9NPt+li7i9YZYhA4jgaW1Lj8eXFGrB9nHlir9C1tZm\nTBKDScjY9npCpi94Gij0hMxO6QsRhaygAHjqKQpOy7jP7GTIAH9CxhSyxES69rUleHhQhEwOwlH2\nwipDZoSpU0k93bfP+D3KspSDLl3kZMhYe6rv+0OJDk3IWENopJDxCNkTTwAvvED/NiJkLpd/5XC9\nosA6ErebGmU7Fll7O/DZZ+LvtwNeCZBAEMpRlk4ty4sXveoY+96iIy1bWrxEzoiQ6XNk339P55g3\nd5+RQuZyeUkZDzIJGS/UzwhZaiqtC7ZCJqoai4yyBICnn6b79ZVXAjtGIzghZIBYjkwRMjkIR6g/\nULjdwKOPAkuWGL9HWZZyIEshi4TyNB2akDGFjHcijcpenDgBrFhB2xgRMh5D1g9/1xfDtGNbHj5M\nNkwwICM/BoR+lKVoqJ+pU+w4EhKAr7/e/ndCxiCaI2tuJsLFpj3ShvqNCJlZQ2pEyABz2zIQQmb0\nICJiWaalEbkwq40mimBnyAAis6tX0zQ9a9d6ibPHA3zxBfCnP4kfr50MGeB772szZIAiZKGE7FC/\nSNkLkQyZER58ECgpAWprvZ/5y1/S9dLeLvYwoGANWYQs3PkxoAMTMm3pC96JNPqRamroxn7vPXuE\nzEghA+zXXqqupvfLGqqrhYz8GGA8ytJocnHZlfqNFLKWFvp92PtZhixQQsYC/YC/QsbW6wmZWfbD\nqFI/YEzIvv+evgMjgHoY1SJzEup3u+n6tbLbRKDPgQWDkAGU13zrLeCdd4CMDGDkSJpDcdo04Cc/\nAQ4csH3owgoZu/e1GTJAEbJQoiMpZABd1/fcAyxbRu3THXcAmzYBw4ZR4L9Ll/DaY9ECWWUvlELm\nAGlp3kbSTqi/pgZ4/nma6sIuIaut9WZYeLWXRFFd7T0W2ZClkMXHU2elzewYjUwSVciefBI4dMh/\nvZ1Qv16dSkwEUlNHBkzIWH4M8A31s1GWbL2ekBllPwJRyGprSR0zspmDkSED5NmWettRNEPGBsYw\nG1UE48ZRZ1ZdTTbmzp1ARQXw8597owhWsJshY3k7j4fu8+7dvf+XlQUcO2a+vSJkchCOsheBZsgY\nHnuMCNnYsfRA8Ze/AG+/TeqZsivlQFbZC6WQOcD113sbQtFQf3MzNao//jF1RPv28QkZLxvUuTMR\nD0a8nCpk2r8yIevJLybGPyxpZlmKKGQffkj1efTQZ8jMQv36MD17atYSKyAwQmaWIdPuy0whC4SQ\nmdmVgHFxWNEMGc+yBORU6+eRKtEMGRsYoz9PIkhJIcUhO5te/+u/Ejk7eNDefuxkyJqa6HrTnt9h\nw6iTNUMkPHlHA8JR9sIpBg4EbrkFGD2aiFlsLDBqFLWDog8QCuZwu6kdchq/iIT7tMMSsuxs4Jtv\n6N+iCllNDTWusbHAww8TiRJVyABfRcFJhqy6mtSQYBAyWZYl4P9Eahbqt1LI2tuBkydpnjc9jKZO\n4o1I1KtTCQlAVZV/hqxbN7FQvyghE7UsjeqQAc4ImahClpDgTzB4oX5AjkLGI1WiDygiJS9EkZhI\nCqxIJ2c3Q8YUsu++87UrAeCmm+g+NpuoPRKevKMBkTaXpSjefx/49a99FfDMTHqIUHAOl0tOjiwS\n7tMOS8hycryETLQwbE0NZU4AYPZsIgF2CJk23OuUkOXnB08hk3VR6UdaOgn119bSBW9EyLTEIjbW\neEQiz7J0kiHTWpPJyUQwPB5nhCwQhSwtzfgY7RCyuXPJyuN9bjAsS14GTJSQ2cmPieCRR4Dt2/m2\nuBFEFLLkZDqHFRW+gX6A1o8aBWzbZry9sizlIC6OHuzMZvEQRSgyZAqhg4wcmVLIHCAzkzr5y5fF\ny15oCVmPHjQCZuBA3/eY1SExUsgCsSyHD+8YCpn2idRJ2YsTJ+i88giZ3rIEjHNkPMuyUyc5GTJW\nyuLSJfM6ZFah/uZm/zpkgHOFzOMBRozwnkMeIUtJ8S8jwQv1A8EjZKIZMtGSF6JISqKyNr/5jfn7\n7GbIAHoYO3DAn5ABlA9ShCz4cLnk2JasAKjIb+I0Q6YQGsjIkSmFzAHcbiJlx48bZ8h4liUjZAAw\nYYJ4hgyQZ1nW1FD2JNIVMr1ladR5JSTQzWDm4Z84Adx4o5hlCRjnyHiW5YULgWfItKMpAa/CZTbK\n0qzsRWwsnaOmJvmE7PBhyivt30/reYTMbHu9QtanD1WadwKe7di9O90fVkVcZStkAE32/Mkn4u+3\nQ8i++MKckBkV/VWETB5kBPvZ7+G0VqNC5ECGZakUModgOTLRUL+ekPEQiGVpRyG7cIE6gcGDI18h\n01uWRgqZy0UNpZltefIkhVuZdamFnVGyPMuyvj7wOmR6IsdGWlqNsjQbIZWSQscum5Bt3Ejn+uhR\nWi86I4PbTcfc2upLZn/wA2DPHvEppnjgqVydOtH3sSoHEQxClp1N95fZCGa7GTKA7v39+/0zZACQ\nm0tE/Kuv/P+PqTHhbuijBTIUMjt2pYwMmULwoTJkEQBGyIzKV1gpZDzYCfWzjsiOQlZTQzWUMjM7\nhkLGGj+Px3xkklWO7MQJ+r169yZypgVv/jA7lqWdDFl7u2/YX0/ItApZIGUv2D6A4BCyqVO9hMyO\nQlZXR9erVhW47jq6VioqrPdhBCNSNWiQ9YjHYBAyl4viALt3i71fVCFLTyfCxVPIXC5j2/LgQe+c\nqArOISPYr/Jj0Qc7GbIzZ2huXD2UQuYQOTnUmYRKIdPO/6e3LEUVsupqImS9ehGJs5oA3S6CNcry\nyhVSAYxKFFjlyE6cIALQr5+/bWmUIeOFd3mW5ZUr/hkyo1GWpaVAcbH3tVYJA4hM1dfTzZ2UROsC\nUcgAeYTM7SYSe+QIMGdOYISstZVf7+uWW4Bdu6z3YQQnhEzmKEsthg83n5os0AxZayufkAFUH23r\nVt91Fy8C994LvPyy9f4VxCBjPks7JS9UhqxjwE6GbMkS4F/+BSgv912vFDKHMLMsnShkRhkyVrfJ\n4/FXyOrrxayf6mo6hthY3+K2shCsUZZWHZeIQta3LxXS1BMyJ5ZlQgIdm2iGbN8+uhFZvomnkFVV\n+c6LqSdkVVVEzo3AiKEIIauqIhvXapTlH/8ITJwIDBhgn5Cx4+AF6G+91bqOlhnq6vikqqAgPAoZ\nYE3ItLBDyABjQjZ6NPDnP/s+RDzxBOUmZ84UOxYFaxhZln/7m7dkjRWUQhZ9MLIsf/YzXwegpQVY\ntQp4/HFatH22Usgcwk6GrLWV6giZKRGAmGV5/jx1hOym7tyZFpHiqEwhA4JjWwZrlKVVIyaikPXt\nSwpZZaXv//EsS6NQv96ypGmUtuPbb8UsywMH6Pdjx8AL9VdV+apm+sKwe/dS/SkjsP3pryM9Idu7\nF/iHfwAWLjS3QOPigC1byK7MzCRl9eJFewoZwCdkt9zijJAdPEglXPQYNIhC8Fps20bFMRlkj7Jk\nuPlmIt48hdXjAZ59dvvf/89OhgzgZ8gAmsqtXz+aJ3fnTuD3vwf+93/prwqPy4NRqP+++/zLvRhB\nZciiDzxCdvo08NJLwPz53nXr11Pb9NvfkkL/wQfe/1MKmUNcfz11rN9/b132oraWlCyrDsyMkMXH\nU8f5zTeB115iGTKA/somZMEaZelEIWtqIkWqWze+ZWlHIePltzp3JttPS8hYFkw/0m//frpu2EhF\nnkJWXe27LimJvn9rKxGhigpSgIyQkkLkS98RawnZxo3ApEk0hdeTTxrvC6Br1uMhhSw2ls5hRYUc\nhayggBouNi+sHXg8RHx45DQ/n5Q8LSl66y3f7EawFLKuXUmJ1RNCgCzrhQuJ4ALyFDIAeOYZmo3i\nmWeA//kfmnNTe00qOAdPIautpba3pIQGqVhBKWTRB16G7KOPgClT6MGXxTKWLaOCvLGxFCX42c+o\nDQCUQuYY8fHUQB47xi8Mq+3QRexKwNyyBEgl+/JL/lB/kWC/ViELFiELxihLo4nFGcymT2LqmMtl\nL0MmYlkCNJdlba1v5+d20/u0x3T+PF0Hd9/tnYiaN8pSr5DFxHgJ3v79RDbMSC8jZHpoCdljj5EN\nOXWq8X4Y4uKAMWO8x5mb6yU7ThWy2Fiy+P76V+v96FFZSdcEz26Njyc1j9mrABVt3bfP+7sGi5AB\n/GB/ayupKHfeORJ/+AOtsxPqB8wJ2Y9+BGzeTIrj7t3mKqpCYOApZH/9K/DDHwKLF1NxYKspdOy0\nkSpD1jHAy5CVlNA9+atf0X1/+DAtU6bQ/48aRffoq6/Sa6WQSUB2NlXmtrIsRQmZWWFYgBrmgwcD\nL4YZbEIWLMvSKghrNn0SC/QD9hQynuWktywBb45Mr0akpvoG+w8eBG64gco9aAmZPtR/8qTvOsCb\nI9u7FxgyhP89tfswI2QnTtDvdOut5vth6NmTGhYGu4SMHYvRJN6B5sg+/9ycdGhzZFVVRI5zcrzK\nVbAJmT5H9vrrdP+uWkVK2fnz4oQsIYHsyGBYrAri4Clku3aR9T5jhvd3MoPVw6VCx4O+v79wgTKd\nkybR3NWXLlEb+sADvn3NT39Kk70DSiGTApYj05/I+HhfdcSOQmZGyHr35itkohMqs1A/4E/IvvqK\nVASrgppmCKdlaaWQAZThO3fO9ynXTtkLnmXZ1rYdgK/SBQD9+/uOpDlwACgspBpwRgoZL0MGeAnZ\n5587I2RtbcCOHVRxXzRb9PLLvsFwRshE61u5XKSEGZGJW28NbKSlFSHT5sj+/Gfg9tupIPLu3WR3\n6gvVysSwYb6E7Nw54Pnngd/9Dvjyy+245RZgwwZ7asmDD6o8WLjBK3vBCJnLBfz3fwPPPWf+oKsy\nZNEHPSHbuhUYOpQeQmNiKDN28CDdw1rceisp/dXVkVEvMCoIWXu7/4nMz6dcEStTIYuQmVmWVgrZ\n5cvUCfXsSa/1hOzJJ+kpr18/4D/+I7B6O8EqDCsS6jdTyBghi4mhf7NQfVsbdc56m9gs1K+3LBlR\nZGUqGCZPJiWEYf9+ImO5uXQ9NDbSZ2iJZkoKfQ8zhczKirJSyHbuJEJmB1oiYFchA+h9RuRn6FA6\nNyxLIQqj/BiDtvSFlpDt2UOfFRMTPKViwADKxrHSMvPnA+PH06hHgJ6a33iDfhOziIJCZIE3v+6B\nAzSQA6Br7vHHgfvv9x1A85//CTz7rHcblSGLLnTp4pshKynxLW80ZgxFm/r1893O7aZ+4sMPI2NG\njaggZID/iYyLI7nyo4/otcwM2bFjgSlkp07R9mz/WkLW2Ei20aFDdHHs2kXWil0EqzCsLIUM8LUt\nWX5MrzyIFoYFgPT0kUhM9CfSkycDmzZ5hzYzhcztJsK+axeRJ+1na6dL0qJrV/r9jh2jRt8MKSl8\nosQI2Y4dwG23me/DDLIJWWIizen6t7+JH4PHQwrZD35g/B4eIRs6lBSyYI2wZIiNpU76hRfot66s\npIwRQLmgKVPo+yrrqmNBb1l+/jmRb+1D2oIF1EE//zy9/t3vSDlbupQetlSGLPqgVcja2qjfv+su\n3/dkZfG3LS6mQVZKIZOAnBz6yzuR7EQDci1LILBQv3aEJUDkrLaWOukPP6RaRsnJpOIsWAC8+ab1\n8eohO0PGyj04UchOnjQmZDy7EhAvDAvQa95otrw82vfBg0QgDhzwjo4sLCQCrLc52Wv9+q5diUgN\nHGh905opZKdOkXJrNkrTCn36kFrX2ChOyNxucwI0YoT5BNl6nDxJn83uBx6YEvnNN3RvDBpES1UV\n/f7Byo8xjBtHge/XXiNiri15k5hI4V6llHQs6EP9zK7UIiaG2s5VqygztGwZPRDcdhvw3ntqlGU0\nQkvIdu+me12vhhlh4kS6jurqlELmGEwh43WSEyeSPcRG18myLIHALEttfgygDu3aa6mDfv99Gv3H\nMHYsKUtHjlgfsxYyFbLhw8nK2rfPuULGQv2APyHj/XZ2RlmeP7/dj0ABpHwx2/LECdqO1ZEaPJgI\nGU8J0/7Vrv/kE+v8GGBOyD79lDoQJzZZTAxd95cu2VPIjEL9AHVcK1aITz9iZVcC9H3z8qgW14gR\ndNxuN6lqH38cfEK2YAHZo6NH+65nuaAf/9h8xgWFyINeIeMRMoBG/q5dSxnGTz+ldveBB4ikqQxZ\n9EFb9uKtt3ztSiskJ9M19Kc/KYXMMbp1o4V3Irt2pczK1q2kTASTkBUV0VOYmUqmHWHJkJFBNuX2\n7b4Sq9tNxQ7XrrU+Zi1kKmTduwOLFgFz59JTaSAK2aVLZE9pK9vzLEs9RAvDAsD58+WG9Z6Ybcns\nSobBg6mzNlLI9IQsNZVsQhFClpxsTMi2b7efH+MhN5f+ihKy1FTz2QAGDiSixMpBWMEq0M8waBB1\ngrff7l03dCiVhwg2ITNC+f+P9JgwgbImCh0H3brRvXzhAqnerOQFD6NGkS2dmUmv77iDHnAPHBBv\nI8v18+soRCRY2Ys//IHcpkcftbd9cTEJIxGvkG3evHniDTfc8FVubu7RxYsXz+e957HHHvuv3Nzc\no4WFhfvLysputNq2vr7+mnHjxm3Ny8s7Mn78+C2NjY0mz+7WGDjQvwNlKC6mH6m93b/z5UEkQwb4\ndyY33EDDap95xnhbI0K2fDl10vrvMGMGETKRKZkYZCpkAM35FR9P+YtAFLKqKu9UUQzaav1mChmP\n4PEsS4+n0ZCQjRxJas6OHUTCGAoK6LNFCRl7LUJCcnKAWbP817vdVIDVSX6MwS4hKysztxcB4Kmn\nqLK1dpTvmTN8YmyVH2MYNIgGsmijOMOG0fbhImSN/+/Du1zWeUCFyMIdd9DD77hxZE0lJYk9aAN0\nr8yYAaxbJ54dbORN96EQcejcmR52n3iCHBGzqe14YGJIRCtkbW1tsfPmzVu6efPmiYcOHRrw9ttv\n33f48GGfiVJKS0snV1RU5Bw9ejR3xYoVDz388MO/t9p20aJFC8aNG7f1yJEjeWPGjPlk0aJFC5x8\niS1bjDvKu+6iHFmfPmJD1q0UsqQkUkB4nckLL1CY0GgePSNCtmGDr13JcOON1HDYKUkgUyED6Jwt\nWwZ8/XVgCpk+0A8QITt2DPj2W1K8eKRi2jQ6n7wJm/U2U6dOxhXRExJoaPNrr/kqZD160E1rh5B1\n7kzk3wopKXxi7nbTORRR2axgl5CZTc3EMHo0fcfNm+n1smVkOXbrRlM8Pfss/cYs0C9CTgsK6Nxp\nyfDQofQ3XIRMoePC7QZWrqQ4xdixfLvSDLNmqQxZNKJzZxoBv26dWButR2YmPWBGtEK2Z8+eoTk5\nORVZWVmVcXFxV+699953Nm7cOEX7npKSkuKZM2euBoBhw4btbmxsTD1z5kwvs22128ycOXP1hg0b\nBOqVGyM+3phsZWXRKBzRpygrQgZQNWheYLBrV6p38sgjFNT/+mtSv0pKSD3SZ8gAImQxMXzP2+Wi\nJzqzcH97O+2XWaWyFTKAzt+vf20eRDdSyPSBfoA6+IICunEKCvgd86RJNM/YjBlkeTGVkG9ZVppO\nUTN5MtWh0pICgF7rCVnnznQN8AhZYaE4AeLB7SZ1SMZTWF4eqY4y62K5XDSVyG9/S2VXXnqJlLXa\nWrKuq6vpnL31Fr1X5J66/Xb6/bQK6XXXkX0ariKrlfrJVBU6FGJi6NpcuND+xO0DB9I9KNpGqmul\nY+COO+hBctSowPexdi31FWGFx+MxXN57772758yZ8xp7/eabb/7zvHnzXtG+58477/xw165dP2Sv\nx4wZs23v3r03vf/++9ONtk1NTW1g69vb213a12wB4FGLWtSiFrWoRS1q6SiLGaeyWjjRYy9cLpfH\n7P8ZPB6P5XO6x+Nx8fbncrk8vPUi+1RQUFBQUFBQiAaYmnN9+vSpqaqqymSvq6qqMjMyMqrN3lNd\nXZ2RkZFRzVvfp0+fGgBIS0urPXPmTC8AOH36dHrPnj2/lfWFFBQUFBQUFBQ6GkwJ2ZAhQ/YePXo0\nt7KyMqulpaXTunXrflRcXOwzULy4uLhkzZo1PwGAzz77bHhqampjWlpardm2xcXFJasi92ftAAAE\n1UlEQVRXr54JAKtXr545derUDcH6ggoKCgoKCgoKkQ5Ty9LtdrcuXbp03oQJEz5ua2uLnT179sr8\n/PzDy5cvnwsAc+fOXT558uTS0tLSyTk5ORWJiYkX3njjjVlm2wLAggULFt1zzz3vrly5cnZWVlbl\nu+++e0/wv6qCgoKCgoKCQoTCSQAtWMumTZsm9u/f/6ucnJyjixYtmh/u41FLZC19+/atLCgoOFBU\nVFR288037/F4PDh79uw1Y8eO3Zqbm3tk3LhxWxoaGlLDfZxqCf0ya9asVT179qwdNGjQF2yd2bXx\n4osvPpOTk3O0f//+X3388cfjw338agn/9fLcc8/9ok+fPtVFRUVlRUVFZaWlpZPU9aKWkydPZo4c\nOfLTAQMGfDlw4MCDS5Yseczjkdu+hP1L6pfW1tbY7OzsiuPHj2e1tLTEFRYWlh86dCg/3MellshZ\nsrKyjp89e/Ya7bqnn376N4sXL/53j8eDRYsWzZ8/f/6icB+nWkK/7NixY8S+fftu1HawRtfGl19+\nOaCwsLC8paUl7vjx41nZ2dkVbW1tMeH+DmoJ7/Xyi1/84rmXXnrpSf171fVydS+nT5/uVVZWVuTx\neNDc3JyUl5f39aFDh/Jlti8RN3WSSO0zBQWPbhSu7Np2Ch0TI0aM2NmtW7cG7Tqja2Pjxo1T7rvv\nvrfj4uKuZGVlVebk5FTs2bNnaDiOWyE84F0vAH+Uv7perm706tXrTFFRUTkAJCUlnc/Pzz9cU1PT\nR2b7EnGErKampk9mZmYVe52RkVFdU1MjWNZV4WqAy+XyjB07dtuQIUP2vvbaaw8CQG1tbVpaWlot\nQKN4a2trTWZuVLiaYHRtnDp1qrd21LhqaxQYXnnllZ8WFhbunz179ko2tZ+6XhQYKisrs8rKym4c\nNmzYbpntS8QRMtHaZwpXL3bt2nVLWVnZjZs2bZr06quvPrpz506f6bqNatspKFhdG+q6UXj44Yd/\nf/z48X7l5eVF6enpp5966qmXjN6rrperD+fPn0+aPn36B0uWLPm35ORkn/lpnLYvEUfIRGqfKVzd\nSE9PPw0APXr0+G7atGnr9+zZM1TVtlMwgtG1YVYrUeHqRc+ePb9lHeucOXNeZzaTul4Urly5Ejd9\n+vQPZsyY8SYr1yWzfYk4QiZS+0zh6sXFixcTmpubkwHgwoULiVu2bBlfUFDwhaptp2AEo2ujuLi4\n5J133rm3paWl0/Hjx/sdPXo0d+jQoXvCe7QK4cbp06fT2b/Xr18/raCg4AtAXS9XOzwej2v27Nkr\nBwwYcOjxxx9/ma2X2r6Ee+QCbyktLZ2Ul5f3dXZ2dsWLL774TLiPRy2Rsxw7dqxfYWFheWFhYfnA\ngQMPsuvj7Nmz14wZM2abKntxdS/33nvv2+np6afi4uJaMjIyqlatWjXL7NpYuHDhz7Ozsyv69+//\n1ebNmyeE+/jVEt7rZeXKlQ/MmDFjTUFBwYHBgwfvnzJlyoYzZ86kqetFLTt37rzV5XK1FxYWlrOS\nKJs2bZoos31xeTzKAldQUFBQUFBQCCcizrJUUFBQUFBQULjaoAiZgoKCgoKCgkKYoQiZgoKCgoKC\ngkKYoQiZgoKCgoKCgkKYoQiZgoKCgoKCgkKYoQiZgoKCgoKCgkKY8X/urmDO+6Wf+QAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1dd6e110>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savez(\"../../plots/bayes/random.forest.importances.npz\",importances)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Testing example interaction\n",
      "\n",
      "Using a well-known interaction from the STRING database the classifier can be tested to ensure that it is able to detect some well-known interactions."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Pickling the classifier\n",
      "\n",
      "The classifier with the best performance by looking at the ROC curve appears to be the Random Forest classifier, as expected."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd .."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/data/opencast/MRes/features\n"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"random.forest.bayes.classifier.pickle\",\"wb\")\n",
      "pickle.dump(rfmodel,f)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Classifying active zone network\n",
      "\n",
      "A prepared network that is currently used at Edinburgh is available.\n",
      "To make the task of processing weighted edges easier we can apply our classifier to only these feature vectors and write the resulting feature vectors."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "edgelist = loadtxt(\"human.activezone.txt\",dtype=str)\n",
      "NAinds = where(edgelist==\"NA\")\n",
      "edgelist[NAinds] = 0.0\n",
      "edgelist[edgelist==\"missing\"] = np.nan\n",
      "edgelist = edgelist.astype(np.float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "estimates = rfmodel.predict_proba(edgelist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print estimates[5,1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00623885918004\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f,fw = open(\"../forGAVIN/mergecode/OUT/edgelist.txt\"), open(\"../forGAVIN/mergecode/OUT/edgelist_weighted.txt\",\"w\")\n",
      "c,cw = csv.reader(f, delimiter=\"\\t\"), csv.writer(fw, delimiter=\"\\t\")\n",
      "c.next()\n",
      "for i,l in enumerate(c):\n",
      "    cw.writerow(l + [estimates[i,1]])\n",
      "f.close(),fw.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "(None, None)"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Pickling the class-conditional distributions\n",
      "\n",
      "We would like to be able to update our Bayesian probability of interaction using these predictions,\n",
      "so we will pickle the ingredients needed to build a class-conditional beta distribution for the results of this classifier."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "oneindexes = where(y==1)\n",
      "zeroindexes = where(y==0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "onesamples = rfmodel.predict_proba(X[oneindexes])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zerosamples = rfmodel.predict_proba(X[zeroindexes])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"random.forest.bayes.dist.samples.pickle\",\"wb\")\n",
      "pickle.dump({1:onesamples,0:zerosamples},f)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 101
    }
   ],
   "metadata": {}
  }
 ]
}