SVM+Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORING SVMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import loadmat\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1000025</th>\n",
       "      <th>5</th>\n",
       "      <th>1</th>\n",
       "      <th>1.1</th>\n",
       "      <th>1.2</th>\n",
       "      <th>2</th>\n",
       "      <th>1.3</th>\n",
       "      <th>3</th>\n",
       "      <th>1.4</th>\n",
       "      <th>1.5</th>\n",
       "      <th>2.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017122</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1018099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1018561</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1033078</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1033078</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1035283</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1000025  5   1  1.1  1.2  2  1.3  3  1.4  1.5  2.1\n",
       "0  1002945  5   4    4    5  7   10  3    2    1    2\n",
       "1  1015425  3   1    1    1  2    2  3    1    1    2\n",
       "2  1016277  6   8    8    1  3    4  3    7    1    2\n",
       "3  1017023  4   1    1    3  2    1  3    1    1    2\n",
       "4  1017122  8  10   10    8  7   10  9    7    1    4\n",
       "5  1018099  1   1    1    1  2   10  3    1    1    2\n",
       "6  1018561  2   1    2    1  2    1  3    1    1    2\n",
       "7  1033078  2   1    1    1  2    1  1    1    5    2\n",
       "8  1033078  4   2    1    1  2    1  2    1    1    2\n",
       "9  1035283  1   1    1    1  1    1  3    1    1    2"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('breast-cancer-wisconsin.data')\n",
    "raw_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "data = raw_data\n",
    "data['y'] = raw_data['2.1']\n",
    "#data.head(10)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], data['y'], test_size=0.30, random_state=42)\n",
    "\n",
    "#THIS Normalizes all the data\n",
    "x = X_train.values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "X_train = pd.DataFrame(x_scaled)\n",
    "\n",
    "x = X_test.values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "X_test = pd.DataFrame(x_scaled)\n",
    "X_test.columns = ['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']\n",
    "X_train.columns = ['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']\n",
    "##converting all lesion values to appropriate ones\n",
    "y_train[y_train==2] = 0\n",
    "y_train[y_train==4] = 1\n",
    "y_test[y_test==2] = 0\n",
    "y_test[y_test==4] = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#data.head(10)\n",
    "#positive = data[data['y'].isin([4])]\n",
    "#negative = data[data['y'].isin([2])]\n",
    "#print(data)\n",
    "#fig, ax = plt.subplots(figsize=(12,8))\n",
    "#ax.scatter(positive['1.2'], positive['1.1'], s=50, marker='x', label='Positive')\n",
    "#ax.scatter(negative['1.2'], negative['1.1'], s=50, marker='o', label='Negative')\n",
    "#ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=5000, multi_class='ovr',\n",
       "     penalty='l2', random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "svc = svm.LinearSVC(C=1, loss='hinge', max_iter=5000)\n",
    "svc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96097560975609753"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_train)\n",
    "svc.score(X_test[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97560975609756095"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc2 = svm.LinearSVC(C=1000, loss='hinge', max_iter=5000)\n",
    "svc2.fit(X_train[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_train)\n",
    "svc2.score(X_test[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1289ffda0>]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UhkVERETEt1SGRURERMS3VIZFRERExLdUhkVERETEt1SGRURERMS3VIZFRERE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\nHzTNNy65RJUsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1289c1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['SVM 1 Confidence'] = svc.decision_function(data[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.scatter(data['5'], data['1.1'], s=50, c=data['SVM 1 Confidence'], cmap='seismic')\n",
    "ax.set_title('SVM (C=1) Decision Confidence')\n",
    "plt.plot(data['5'], -(svc.coef_[0][0]*data['5']+svc.intercept_[0])/svc.coef_[0][1], c='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128c68208>]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GAQAA4FuUYQAAAPgWZRgAAAC+RRkGAACAb1GGAQAA4FuUYQAAAPgWZRgAAAC+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\nKMMAAAAIrP8HcusO5vPg7vkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1289c15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['SVM 1 Confidence'] = svc.decision_function(data[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.scatter(data['5'], data['1.1'], s=50, c=data['SVM 1 Confidence'], cmap='seismic')\n",
    "ax.set_title('SVM (C=1) Decision Confidence')\n",
    "plt.plot(data['5'], -(svc2.coef_[0][0]*data['5']+svc2.intercept_[0])/svc2.coef_[0][1], c='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm, grid_search\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "    gammas = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "    d = [1,2,3,4,5]\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas,'degree':d}\n",
    "    search = grid_search.GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n",
    "    search.fit(X, y)\n",
    "    search.best_params_\n",
    "    return search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1, 'degree': 1, 'gamma': 1}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_param_selection(X_train[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_train, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.66341463  0.66341463  0.66341463  0.66341463  0.66341463  0.66341463\n",
      "  0.66341463  0.66341463  0.66341463  0.66341463  0.95609756  0.95609756\n",
      "  0.95609756  0.95609756  0.95609756  0.97073171  0.97073171  0.97073171\n",
      "  0.97073171  0.97073171  0.97560976  0.97560976  0.97560976  0.97560976\n",
      "  0.97560976  0.97073171  0.97073171  0.97073171  0.97073171  0.97073171\n",
      "  0.96585366  0.96585366  0.96585366  0.96585366  0.96585366]\n"
     ]
    }
   ],
   "source": [
    "d = [1,2,3,4,5]\n",
    "accuracies = np.array([])\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "for i in Cs:\n",
    "    for j in d :\n",
    "        tempsvm = svm.SVC(C=i,degree=j, max_iter=5000)\n",
    "        tempsvm.fit(X_train[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']],y_train)\n",
    "        accuracy=tempsvm.score(X_test[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_test)\n",
    "        accuracies = np.append(accuracies,accuracy)\n",
    "print(accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.66341463  0.66341463  0.66341463  0.66341463  0.66341463  0.66341463\n",
      "   0.66341463]\n",
      " [ 0.66341463  0.66341463  0.66341463  0.95609756  0.95609756  0.95609756\n",
      "   0.95609756]\n",
      " [ 0.95609756  0.97073171  0.97073171  0.97073171  0.97073171  0.97073171\n",
      "   0.97560976]\n",
      " [ 0.97560976  0.97560976  0.97560976  0.97560976  0.97073171  0.97073171\n",
      "   0.97073171]\n",
      " [ 0.97073171  0.97073171  0.96585366  0.96585366  0.96585366  0.96585366\n",
      "   0.96585366]]\n"
     ]
    }
   ],
   "source": [
    "another = accuracies.reshape(5,7)\n",
    "print(another)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A4ChJas0+R0mStuGtHJIkdZ6ZoySpNZtVJUnq49yqkiQNqt6I1S6xz1GSpAFmjpKk1pwh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12885b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.subplots_adjust(left=.2, right=0.95, bottom=0.15, top=0.95)\n",
    "plt.imshow(another)\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('d')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(len(Cs)), Cs)\n",
    "plt.yticks(np.arange(len(d)), d)\n",
    "plt.title('Grid Search AUC Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The best parameters were C =1 and Degree = 1, which had an accuracy of 97.56!!!\n",
    "#The general trend I see is that as C goes up so does the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svc = svm.SVC(C=1, gamma=0.1, probability=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96585365853658534"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_train)\n",
    "svc.score(X_test[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "X.shape[1] = 10 should be equal to 9, the number of features at training time",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-168-c75ae2baa115>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Probability'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msvc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'1000025'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'1.1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'1.2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'1.3'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'3'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'1.4'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'1.5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'2.1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'5'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'1.1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Probability'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Reds'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_predict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_predict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 594\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    595\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobA_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobB_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    596\u001b[0m             raise NotFittedError(\"predict_proba is not available when fitted \"\n",
      "\u001b[0;32m/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    457\u001b[0m             raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[1;32m    458\u001b[0m                              \u001b[0;34m\"the number of features at training time\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m                              (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[1;32m    460\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: X.shape[1] = 10 should be equal to 9, the number of features at training time"
     ]
    }
   ],
   "source": [
    "data['Probability'] = svc.predict_proba(data[['1000025','5','1.1','1.2','2','1.3','3','1.4','1.5','2.1']])[:,0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.scatter(data['5'], data['1.1'], s=30, c=data['Probability'], cmap='Reds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# START OF TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient</th>\n",
       "      <th>Cumulus (DA) - unnormalized</th>\n",
       "      <th>Cumulus (DA) - normalized</th>\n",
       "      <th>Cumulus (PD) - unnormalized</th>\n",
       "      <th>Cumulus (PD) - normalized</th>\n",
       "      <th>Libra (DA) - unnormalized</th>\n",
       "      <th>Libraæ(DA) - normalized</th>\n",
       "      <th>Libra(PD) - unnormalized</th>\n",
       "      <th>Libra(PD) - normalized</th>\n",
       "      <th>Volpara (DA) - unnormalized</th>\n",
       "      <th>...</th>\n",
       "      <th>race</th>\n",
       "      <th>age</th>\n",
       "      <th>mens_status</th>\n",
       "      <th>parity</th>\n",
       "      <th>bmi</th>\n",
       "      <th>Ground truth</th>\n",
       "      <th>our MIRS</th>\n",
       "      <th>Our binary MIRS</th>\n",
       "      <th>Patient Name</th>\n",
       "      <th>Unnamed: 23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>14.025080</td>\n",
       "      <td>0.064117</td>\n",
       "      <td>7.437008</td>\n",
       "      <td>0.120466</td>\n",
       "      <td>295.0</td>\n",
       "      <td>0.046964</td>\n",
       "      <td>272.0</td>\n",
       "      <td>0.046143</td>\n",
       "      <td>120712.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG11.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>356.0</td>\n",
       "      <td>0.048516</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.043058</td>\n",
       "      <td>92426.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.246667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG111_replacement.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>45.006323</td>\n",
       "      <td>0.094573</td>\n",
       "      <td>23.551121</td>\n",
       "      <td>0.214374</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.037864</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.041077</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.413333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG113.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.046474</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.029497</td>\n",
       "      <td>74772.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.873333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG121_replacement.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>92.764367</td>\n",
       "      <td>0.120356</td>\n",
       "      <td>80.408153</td>\n",
       "      <td>0.396111</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.030667</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.045112</td>\n",
       "      <td>193370.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.393333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG128.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>32.542222</td>\n",
       "      <td>0.084883</td>\n",
       "      <td>22.179090</td>\n",
       "      <td>0.208036</td>\n",
       "      <td>418.0</td>\n",
       "      <td>0.049842</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.032680</td>\n",
       "      <td>84615.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG131.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>83.889312</td>\n",
       "      <td>0.116388</td>\n",
       "      <td>54.677571</td>\n",
       "      <td>0.326642</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.031251</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.044974</td>\n",
       "      <td>222705.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.913333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG134.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>27.919842</td>\n",
       "      <td>0.080658</td>\n",
       "      <td>20.887662</td>\n",
       "      <td>0.201889</td>\n",
       "      <td>367.0</td>\n",
       "      <td>0.048768</td>\n",
       "      <td>376.0</td>\n",
       "      <td>0.048808</td>\n",
       "      <td>118084.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.493333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG140.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>100.911520</td>\n",
       "      <td>0.123781</td>\n",
       "      <td>35.955963</td>\n",
       "      <td>0.264882</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.024316</td>\n",
       "      <td>331.0</td>\n",
       "      <td>0.047759</td>\n",
       "      <td>238050.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG146.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>47.261385</td>\n",
       "      <td>0.096126</td>\n",
       "      <td>63.653678</td>\n",
       "      <td>0.352435</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.048745</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.044904</td>\n",
       "      <td>87342.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG158.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>394.0</td>\n",
       "      <td>0.049354</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.041775</td>\n",
       "      <td>192142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG162_replacement.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>129.782361</td>\n",
       "      <td>0.134611</td>\n",
       "      <td>85.697219</td>\n",
       "      <td>0.408931</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.035979</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.042078</td>\n",
       "      <td>257670.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.606667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG2.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>24.088575</td>\n",
       "      <td>0.076785</td>\n",
       "      <td>52.597242</td>\n",
       "      <td>0.320368</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.034473</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.041723</td>\n",
       "      <td>59814.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG205.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>92.702395</td>\n",
       "      <td>0.120329</td>\n",
       "      <td>61.243322</td>\n",
       "      <td>0.345698</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.031969</td>\n",
       "      <td>184.0</td>\n",
       "      <td>0.042925</td>\n",
       "      <td>180318.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.986667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG21.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>47.966534</td>\n",
       "      <td>0.096602</td>\n",
       "      <td>35.871932</td>\n",
       "      <td>0.264572</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.034486</td>\n",
       "      <td>154558.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG210.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>12.146098</td>\n",
       "      <td>0.061116</td>\n",
       "      <td>10.690149</td>\n",
       "      <td>0.144430</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.047075</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0.048960</td>\n",
       "      <td>60769.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG212.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>78.469356</td>\n",
       "      <td>0.113826</td>\n",
       "      <td>63.738648</td>\n",
       "      <td>0.352670</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.017172</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.042225</td>\n",
       "      <td>142966.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG216.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>46.319769</td>\n",
       "      <td>0.095484</td>\n",
       "      <td>51.978620</td>\n",
       "      <td>0.318478</td>\n",
       "      <td>413.0</td>\n",
       "      <td>0.049743</td>\n",
       "      <td>183.0</td>\n",
       "      <td>0.042881</td>\n",
       "      <td>162636.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.946667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG227.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>12.000553</td>\n",
       "      <td>0.060871</td>\n",
       "      <td>9.801474</td>\n",
       "      <td>0.138297</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.045329</td>\n",
       "      <td>326.0</td>\n",
       "      <td>0.047633</td>\n",
       "      <td>100663.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.453333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG236.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>11.143573</td>\n",
       "      <td>0.059386</td>\n",
       "      <td>12.927669</td>\n",
       "      <td>0.158828</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.033812</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.046293</td>\n",
       "      <td>30181.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.753333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG24.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.0</td>\n",
       "      <td>82.130289</td>\n",
       "      <td>0.115569</td>\n",
       "      <td>73.248705</td>\n",
       "      <td>0.378065</td>\n",
       "      <td>406.0</td>\n",
       "      <td>0.049602</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.042559</td>\n",
       "      <td>153924.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG240.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22.0</td>\n",
       "      <td>27.438233</td>\n",
       "      <td>0.080191</td>\n",
       "      <td>38.118355</td>\n",
       "      <td>0.272731</td>\n",
       "      <td>378.0</td>\n",
       "      <td>0.049012</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.042699</td>\n",
       "      <td>86524.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG251.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.0</td>\n",
       "      <td>13.395093</td>\n",
       "      <td>0.063143</td>\n",
       "      <td>30.018410</td>\n",
       "      <td>0.242025</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.046414</td>\n",
       "      <td>191.0</td>\n",
       "      <td>0.043233</td>\n",
       "      <td>55710.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.473333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG266.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.0</td>\n",
       "      <td>116.735638</td>\n",
       "      <td>0.129940</td>\n",
       "      <td>54.242066</td>\n",
       "      <td>0.325338</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.037342</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.039543</td>\n",
       "      <td>38367.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG37.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.0</td>\n",
       "      <td>31.983768</td>\n",
       "      <td>0.084395</td>\n",
       "      <td>20.253789</td>\n",
       "      <td>0.198802</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.045051</td>\n",
       "      <td>303.0</td>\n",
       "      <td>0.047031</td>\n",
       "      <td>16290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG390.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.0</td>\n",
       "      <td>55.279459</td>\n",
       "      <td>0.101281</td>\n",
       "      <td>52.061286</td>\n",
       "      <td>0.318731</td>\n",
       "      <td>281.0</td>\n",
       "      <td>0.046563</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.036069</td>\n",
       "      <td>142818.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG4.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.0</td>\n",
       "      <td>8.769526</td>\n",
       "      <td>0.054828</td>\n",
       "      <td>7.116927</td>\n",
       "      <td>0.117846</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0.042461</td>\n",
       "      <td>281.0</td>\n",
       "      <td>0.046411</td>\n",
       "      <td>11060.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG433.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.0</td>\n",
       "      <td>9.174290</td>\n",
       "      <td>0.055658</td>\n",
       "      <td>15.567067</td>\n",
       "      <td>0.174289</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.036492</td>\n",
       "      <td>406.0</td>\n",
       "      <td>0.049440</td>\n",
       "      <td>18214.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG449.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.040926</td>\n",
       "      <td>363.0</td>\n",
       "      <td>0.048518</td>\n",
       "      <td>65768.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.373333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG57_replacement.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.036975</td>\n",
       "      <td>273.0</td>\n",
       "      <td>0.046173</td>\n",
       "      <td>84612.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG60_replacement.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>378.0</td>\n",
       "      <td>43.293071</td>\n",
       "      <td>0.093357</td>\n",
       "      <td>36.215296</td>\n",
       "      <td>0.265836</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.038818</td>\n",
       "      <td>343.0</td>\n",
       "      <td>0.048052</td>\n",
       "      <td>66317.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG903.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>379.0</td>\n",
       "      <td>36.190850</td>\n",
       "      <td>0.087944</td>\n",
       "      <td>21.568347</td>\n",
       "      <td>0.205152</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.041434</td>\n",
       "      <td>230.0</td>\n",
       "      <td>0.044762</td>\n",
       "      <td>41744.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG904.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>380.0</td>\n",
       "      <td>11.859966</td>\n",
       "      <td>0.060632</td>\n",
       "      <td>36.444290</td>\n",
       "      <td>0.266675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38872.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG905.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>381.0</td>\n",
       "      <td>15.947265</td>\n",
       "      <td>0.066922</td>\n",
       "      <td>13.505293</td>\n",
       "      <td>0.162338</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.042605</td>\n",
       "      <td>325.0</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>81181.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG906.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>382.0</td>\n",
       "      <td>35.647181</td>\n",
       "      <td>0.087502</td>\n",
       "      <td>32.585068</td>\n",
       "      <td>0.252160</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.044118</td>\n",
       "      <td>408.0</td>\n",
       "      <td>0.049480</td>\n",
       "      <td>103496.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.306667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG907.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>383.0</td>\n",
       "      <td>41.704117</td>\n",
       "      <td>0.092201</td>\n",
       "      <td>34.354363</td>\n",
       "      <td>0.258915</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.040386</td>\n",
       "      <td>334.0</td>\n",
       "      <td>0.047833</td>\n",
       "      <td>50068.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG909.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>384.0</td>\n",
       "      <td>48.386791</td>\n",
       "      <td>0.096883</td>\n",
       "      <td>31.310044</td>\n",
       "      <td>0.247177</td>\n",
       "      <td>294.0</td>\n",
       "      <td>0.046936</td>\n",
       "      <td>388.0</td>\n",
       "      <td>0.049066</td>\n",
       "      <td>161176.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG911.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>385.0</td>\n",
       "      <td>7.611186</td>\n",
       "      <td>0.052299</td>\n",
       "      <td>7.914514</td>\n",
       "      <td>0.124274</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.036188</td>\n",
       "      <td>301.0</td>\n",
       "      <td>0.046977</td>\n",
       "      <td>41735.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG912.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>386.0</td>\n",
       "      <td>19.784198</td>\n",
       "      <td>0.071908</td>\n",
       "      <td>13.920412</td>\n",
       "      <td>0.164814</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.043546</td>\n",
       "      <td>280.0</td>\n",
       "      <td>0.046381</td>\n",
       "      <td>111839.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG914.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>387.0</td>\n",
       "      <td>98.711082</td>\n",
       "      <td>0.122875</td>\n",
       "      <td>53.895683</td>\n",
       "      <td>0.324298</td>\n",
       "      <td>330.0</td>\n",
       "      <td>0.047890</td>\n",
       "      <td>321.0</td>\n",
       "      <td>0.047506</td>\n",
       "      <td>137888.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG915.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>388.0</td>\n",
       "      <td>105.444483</td>\n",
       "      <td>0.125607</td>\n",
       "      <td>55.694899</td>\n",
       "      <td>0.329666</td>\n",
       "      <td>412.0</td>\n",
       "      <td>0.049723</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.038839</td>\n",
       "      <td>208866.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG916.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>389.0</td>\n",
       "      <td>100.008592</td>\n",
       "      <td>0.123411</td>\n",
       "      <td>52.732760</td>\n",
       "      <td>0.320780</td>\n",
       "      <td>343.0</td>\n",
       "      <td>0.048209</td>\n",
       "      <td>347.0</td>\n",
       "      <td>0.048147</td>\n",
       "      <td>120755.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG919.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>390.0</td>\n",
       "      <td>1.115845</td>\n",
       "      <td>0.027577</td>\n",
       "      <td>0.966990</td>\n",
       "      <td>0.043439</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.031436</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.043694</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.406667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG921.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>391.0</td>\n",
       "      <td>60.229404</td>\n",
       "      <td>0.104218</td>\n",
       "      <td>39.832465</td>\n",
       "      <td>0.278795</td>\n",
       "      <td>358.0</td>\n",
       "      <td>0.048563</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.016017</td>\n",
       "      <td>133225.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG922.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>392.0</td>\n",
       "      <td>41.113084</td>\n",
       "      <td>0.091763</td>\n",
       "      <td>45.506893</td>\n",
       "      <td>0.297993</td>\n",
       "      <td>257.0</td>\n",
       "      <td>0.045825</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.037656</td>\n",
       "      <td>94989.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.473333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG923.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>393.0</td>\n",
       "      <td>26.934315</td>\n",
       "      <td>0.079697</td>\n",
       "      <td>23.436751</td>\n",
       "      <td>0.213853</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.045696</td>\n",
       "      <td>411.0</td>\n",
       "      <td>0.049540</td>\n",
       "      <td>85329.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG924.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>394.0</td>\n",
       "      <td>35.038796</td>\n",
       "      <td>0.087001</td>\n",
       "      <td>33.358442</td>\n",
       "      <td>0.255135</td>\n",
       "      <td>265.0</td>\n",
       "      <td>0.046079</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.031149</td>\n",
       "      <td>66202.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG925.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>395.0</td>\n",
       "      <td>6.947204</td>\n",
       "      <td>0.050731</td>\n",
       "      <td>8.086431</td>\n",
       "      <td>0.125616</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.030464</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.044690</td>\n",
       "      <td>16143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.226667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG927.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>396.0</td>\n",
       "      <td>21.366424</td>\n",
       "      <td>0.073776</td>\n",
       "      <td>19.143172</td>\n",
       "      <td>0.193274</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.043713</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0.049109</td>\n",
       "      <td>48806.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG928.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>397.0</td>\n",
       "      <td>39.765997</td>\n",
       "      <td>0.090750</td>\n",
       "      <td>28.692220</td>\n",
       "      <td>0.236619</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.045663</td>\n",
       "      <td>353.0</td>\n",
       "      <td>0.048288</td>\n",
       "      <td>71510.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG929.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>398.0</td>\n",
       "      <td>37.419925</td>\n",
       "      <td>0.088929</td>\n",
       "      <td>20.654922</td>\n",
       "      <td>0.200761</td>\n",
       "      <td>269.0</td>\n",
       "      <td>0.046202</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.046585</td>\n",
       "      <td>72717.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG930.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>399.0</td>\n",
       "      <td>20.659946</td>\n",
       "      <td>0.072954</td>\n",
       "      <td>17.819970</td>\n",
       "      <td>0.186475</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.034345</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.044471</td>\n",
       "      <td>6828.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG931.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>400.0</td>\n",
       "      <td>62.709777</td>\n",
       "      <td>0.105630</td>\n",
       "      <td>25.902572</td>\n",
       "      <td>0.224822</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.037693</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.005705</td>\n",
       "      <td>4956.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.306667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG933.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>401.0</td>\n",
       "      <td>55.776738</td>\n",
       "      <td>0.101584</td>\n",
       "      <td>67.774782</td>\n",
       "      <td>0.363665</td>\n",
       "      <td>384.0</td>\n",
       "      <td>0.049142</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.043276</td>\n",
       "      <td>106446.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG935.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>402.0</td>\n",
       "      <td>39.482078</td>\n",
       "      <td>0.090533</td>\n",
       "      <td>37.860931</td>\n",
       "      <td>0.271808</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.041963</td>\n",
       "      <td>394.0</td>\n",
       "      <td>0.049193</td>\n",
       "      <td>82596.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG936.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>403.0</td>\n",
       "      <td>27.018596</td>\n",
       "      <td>0.079780</td>\n",
       "      <td>41.008400</td>\n",
       "      <td>0.282881</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0.042746</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.038765</td>\n",
       "      <td>68301.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG937.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>404.0</td>\n",
       "      <td>120.180290</td>\n",
       "      <td>0.131205</td>\n",
       "      <td>74.480257</td>\n",
       "      <td>0.381230</td>\n",
       "      <td>221.0</td>\n",
       "      <td>0.044579</td>\n",
       "      <td>369.0</td>\n",
       "      <td>0.048653</td>\n",
       "      <td>102889.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG938.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>405.0</td>\n",
       "      <td>45.186572</td>\n",
       "      <td>0.094699</td>\n",
       "      <td>28.744957</td>\n",
       "      <td>0.236836</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.036290</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.040498</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG95.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>406.0</td>\n",
       "      <td>195.836751</td>\n",
       "      <td>0.154397</td>\n",
       "      <td>96.334288</td>\n",
       "      <td>0.433568</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.037824</td>\n",
       "      <td>233852.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>'MG97.dcm'</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>407.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>327.0</td>\n",
       "      <td>0.047815</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.020454</td>\n",
       "      <td>134889.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.893333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>'MG9_replacement.dcm'</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>407 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patient  Cumulus (DA) - unnormalized  Cumulus (DA) - normalized  \\\n",
       "0        1.0                    14.025080                   0.064117   \n",
       "1        2.0                     0.000000                   0.000000   \n",
       "2        3.0                    45.006323                   0.094573   \n",
       "3        4.0                     0.000000                   0.000000   \n",
       "4        5.0                    92.764367                   0.120356   \n",
       "5        6.0                    32.542222                   0.084883   \n",
       "6        7.0                    83.889312                   0.116388   \n",
       "7        8.0                    27.919842                   0.080658   \n",
       "8        9.0                   100.911520                   0.123781   \n",
       "9       10.0                    47.261385                   0.096126   \n",
       "10      11.0                     0.000000                   0.000000   \n",
       "11      12.0                   129.782361                   0.134611   \n",
       "12      13.0                    24.088575                   0.076785   \n",
       "13      14.0                    92.702395                   0.120329   \n",
       "14      15.0                    47.966534                   0.096602   \n",
       "15      16.0                    12.146098                   0.061116   \n",
       "16      17.0                    78.469356                   0.113826   \n",
       "17      18.0                    46.319769                   0.095484   \n",
       "18      19.0                    12.000553                   0.060871   \n",
       "19      20.0                    11.143573                   0.059386   \n",
       "20      21.0                    82.130289                   0.115569   \n",
       "21      22.0                    27.438233                   0.080191   \n",
       "22      23.0                    13.395093                   0.063143   \n",
       "23      24.0                   116.735638                   0.129940   \n",
       "24      25.0                    31.983768                   0.084395   \n",
       "25      26.0                    55.279459                   0.101281   \n",
       "26      27.0                     8.769526                   0.054828   \n",
       "27      28.0                     9.174290                   0.055658   \n",
       "28      29.0                     0.000000                   0.000000   \n",
       "29      30.0                     0.000000                   0.000000   \n",
       "..       ...                          ...                        ...   \n",
       "377    378.0                    43.293071                   0.093357   \n",
       "378    379.0                    36.190850                   0.087944   \n",
       "379    380.0                    11.859966                   0.060632   \n",
       "380    381.0                    15.947265                   0.066922   \n",
       "381    382.0                    35.647181                   0.087502   \n",
       "382    383.0                    41.704117                   0.092201   \n",
       "383    384.0                    48.386791                   0.096883   \n",
       "384    385.0                     7.611186                   0.052299   \n",
       "385    386.0                    19.784198                   0.071908   \n",
       "386    387.0                    98.711082                   0.122875   \n",
       "387    388.0                   105.444483                   0.125607   \n",
       "388    389.0                   100.008592                   0.123411   \n",
       "389    390.0                     1.115845                   0.027577   \n",
       "390    391.0                    60.229404                   0.104218   \n",
       "391    392.0                    41.113084                   0.091763   \n",
       "392    393.0                    26.934315                   0.079697   \n",
       "393    394.0                    35.038796                   0.087001   \n",
       "394    395.0                     6.947204                   0.050731   \n",
       "395    396.0                    21.366424                   0.073776   \n",
       "396    397.0                    39.765997                   0.090750   \n",
       "397    398.0                    37.419925                   0.088929   \n",
       "398    399.0                    20.659946                   0.072954   \n",
       "399    400.0                    62.709777                   0.105630   \n",
       "400    401.0                    55.776738                   0.101584   \n",
       "401    402.0                    39.482078                   0.090533   \n",
       "402    403.0                    27.018596                   0.079780   \n",
       "403    404.0                   120.180290                   0.131205   \n",
       "404    405.0                    45.186572                   0.094699   \n",
       "405    406.0                   195.836751                   0.154397   \n",
       "406    407.0                     0.000000                   0.000000   \n",
       "\n",
       "     Cumulus (PD) - unnormalized  Cumulus (PD) - normalized  \\\n",
       "0                       7.437008                   0.120466   \n",
       "1                       0.000000                   0.000000   \n",
       "2                      23.551121                   0.214374   \n",
       "3                       0.000000                   0.000000   \n",
       "4                      80.408153                   0.396111   \n",
       "5                      22.179090                   0.208036   \n",
       "6                      54.677571                   0.326642   \n",
       "7                      20.887662                   0.201889   \n",
       "8                      35.955963                   0.264882   \n",
       "9                      63.653678                   0.352435   \n",
       "10                      0.000000                   0.000000   \n",
       "11                     85.697219                   0.408931   \n",
       "12                     52.597242                   0.320368   \n",
       "13                     61.243322                   0.345698   \n",
       "14                     35.871932                   0.264572   \n",
       "15                     10.690149                   0.144430   \n",
       "16                     63.738648                   0.352670   \n",
       "17                     51.978620                   0.318478   \n",
       "18                      9.801474                   0.138297   \n",
       "19                     12.927669                   0.158828   \n",
       "20                     73.248705                   0.378065   \n",
       "21                     38.118355                   0.272731   \n",
       "22                     30.018410                   0.242025   \n",
       "23                     54.242066                   0.325338   \n",
       "24                     20.253789                   0.198802   \n",
       "25                     52.061286                   0.318731   \n",
       "26                      7.116927                   0.117846   \n",
       "27                     15.567067                   0.174289   \n",
       "28                      0.000000                   0.000000   \n",
       "29                      0.000000                   0.000000   \n",
       "..                           ...                        ...   \n",
       "377                    36.215296                   0.265836   \n",
       "378                    21.568347                   0.205152   \n",
       "379                    36.444290                   0.266675   \n",
       "380                    13.505293                   0.162338   \n",
       "381                    32.585068                   0.252160   \n",
       "382                    34.354363                   0.258915   \n",
       "383                    31.310044                   0.247177   \n",
       "384                     7.914514                   0.124274   \n",
       "385                    13.920412                   0.164814   \n",
       "386                    53.895683                   0.324298   \n",
       "387                    55.694899                   0.329666   \n",
       "388                    52.732760                   0.320780   \n",
       "389                     0.966990                   0.043439   \n",
       "390                    39.832465                   0.278795   \n",
       "391                    45.506893                   0.297993   \n",
       "392                    23.436751                   0.213853   \n",
       "393                    33.358442                   0.255135   \n",
       "394                     8.086431                   0.125616   \n",
       "395                    19.143172                   0.193274   \n",
       "396                    28.692220                   0.236619   \n",
       "397                    20.654922                   0.200761   \n",
       "398                    17.819970                   0.186475   \n",
       "399                    25.902572                   0.224822   \n",
       "400                    67.774782                   0.363665   \n",
       "401                    37.860931                   0.271808   \n",
       "402                    41.008400                   0.282881   \n",
       "403                    74.480257                   0.381230   \n",
       "404                    28.744957                   0.236836   \n",
       "405                    96.334288                   0.433568   \n",
       "406                     0.000000                   0.000000   \n",
       "\n",
       "     Libra (DA) - unnormalized  Libraæ(DA) - normalized  \\\n",
       "0                        295.0                 0.046964   \n",
       "1                        356.0                 0.048516   \n",
       "2                         98.0                 0.037864   \n",
       "3                        278.0                 0.046474   \n",
       "4                         41.0                 0.030667   \n",
       "5                        418.0                 0.049842   \n",
       "6                         44.0                 0.031251   \n",
       "7                        367.0                 0.048768   \n",
       "8                         19.0                 0.024316   \n",
       "9                        366.0                 0.048745   \n",
       "10                       394.0                 0.049354   \n",
       "11                        78.0                 0.035979   \n",
       "12                        65.0                 0.034473   \n",
       "13                        48.0                 0.031969   \n",
       "14                        11.0                 0.019802   \n",
       "15                       299.0                 0.047075   \n",
       "16                         8.0                 0.017172   \n",
       "17                       413.0                 0.049743   \n",
       "18                       242.0                 0.045329   \n",
       "19                        60.0                 0.033812   \n",
       "20                       406.0                 0.049602   \n",
       "21                       378.0                 0.049012   \n",
       "22                       276.0                 0.046414   \n",
       "23                        92.0                 0.037342   \n",
       "24                       234.0                 0.045051   \n",
       "25                       281.0                 0.046563   \n",
       "26                       171.0                 0.042461   \n",
       "27                        83.0                 0.036492   \n",
       "28                       142.0                 0.040926   \n",
       "29                        88.0                 0.036975   \n",
       "..                         ...                      ...   \n",
       "377                      110.0                 0.038818   \n",
       "378                      151.0                 0.041434   \n",
       "379                        1.0                 0.000000   \n",
       "380                      174.0                 0.042605   \n",
       "381                      209.0                 0.044118   \n",
       "382                      133.0                 0.040386   \n",
       "383                      294.0                 0.046936   \n",
       "384                       80.0                 0.036188   \n",
       "385                      195.0                 0.043546   \n",
       "386                      330.0                 0.047890   \n",
       "387                      412.0                 0.049723   \n",
       "388                      343.0                 0.048209   \n",
       "389                       45.0                 0.031436   \n",
       "390                      358.0                 0.048563   \n",
       "391                      257.0                 0.045825   \n",
       "392                      253.0                 0.045696   \n",
       "393                      265.0                 0.046079   \n",
       "394                       40.0                 0.030464   \n",
       "395                      199.0                 0.043713   \n",
       "396                      252.0                 0.045663   \n",
       "397                      269.0                 0.046202   \n",
       "398                       64.0                 0.034345   \n",
       "399                       96.0                 0.037693   \n",
       "400                      384.0                 0.049142   \n",
       "401                      161.0                 0.041963   \n",
       "402                      177.0                 0.042746   \n",
       "403                      221.0                 0.044579   \n",
       "404                       81.0                 0.036290   \n",
       "405                       22.0                 0.025526   \n",
       "406                      327.0                 0.047815   \n",
       "\n",
       "     Libra(PD) - unnormalized  Libra(PD) - normalized  \\\n",
       "0                       272.0                0.046143   \n",
       "1                       187.0                0.043058   \n",
       "2                       147.0                0.041077   \n",
       "3                        36.0                0.029497   \n",
       "4                       240.0                0.045112   \n",
       "5                        53.0                0.032680   \n",
       "6                       236.0                0.044974   \n",
       "7                       376.0                0.048808   \n",
       "8                       331.0                0.047759   \n",
       "9                       234.0                0.044904   \n",
       "10                      160.0                0.041775   \n",
       "11                      166.0                0.042078   \n",
       "12                      159.0                0.041723   \n",
       "13                      184.0                0.042925   \n",
       "14                       66.0                0.034486   \n",
       "15                      383.0                0.048960   \n",
       "16                      169.0                0.042225   \n",
       "17                      183.0                0.042881   \n",
       "18                      326.0                0.047633   \n",
       "19                      277.0                0.046293   \n",
       "20                      176.0                0.042559   \n",
       "21                      179.0                0.042699   \n",
       "22                      191.0                0.043233   \n",
       "23                      122.0                0.039543   \n",
       "24                      303.0                0.047031   \n",
       "25                       80.0                0.036069   \n",
       "26                      281.0                0.046411   \n",
       "27                      406.0                0.049440   \n",
       "28                      363.0                0.048518   \n",
       "29                      273.0                0.046173   \n",
       "..                        ...                     ...   \n",
       "377                     343.0                0.048052   \n",
       "378                     230.0                0.044762   \n",
       "379                       1.0                0.000000   \n",
       "380                     325.0                0.047608   \n",
       "381                     408.0                0.049480   \n",
       "382                     334.0                0.047833   \n",
       "383                     388.0                0.049066   \n",
       "384                     301.0                0.046977   \n",
       "385                     280.0                0.046381   \n",
       "386                     321.0                0.047506   \n",
       "387                     112.0                0.038839   \n",
       "388                     347.0                0.048147   \n",
       "389                     202.0                0.043694   \n",
       "390                       7.0                0.016017   \n",
       "391                      97.0                0.037656   \n",
       "392                     411.0                0.049540   \n",
       "393                      44.0                0.031149   \n",
       "394                     228.0                0.044690   \n",
       "395                     390.0                0.049109   \n",
       "396                     353.0                0.048288   \n",
       "397                     287.0                0.046585   \n",
       "398                     222.0                0.044471   \n",
       "399                       2.0                0.005705   \n",
       "400                     192.0                0.043276   \n",
       "401                     394.0                0.049193   \n",
       "402                     111.0                0.038765   \n",
       "403                     369.0                0.048653   \n",
       "404                     137.0                0.040498   \n",
       "405                      99.0                0.037824   \n",
       "406                      12.0                0.020454   \n",
       "\n",
       "     Volpara (DA) - unnormalized     ...       race  age  mens_status  parity  \\\n",
       "0                       120712.0     ...        1.0  5.0          3.0     1.0   \n",
       "1                        92426.0     ...        3.0  1.0          1.0     1.0   \n",
       "2                        10100.0     ...        1.0  7.0          3.0     5.0   \n",
       "3                        74772.0     ...        1.0  3.0          3.0     5.0   \n",
       "4                       193370.0     ...        1.0  3.0          1.0     3.0   \n",
       "5                        84615.0     ...        1.0  1.0          1.0     1.0   \n",
       "6                       222705.0     ...        3.0  2.0          1.0     3.0   \n",
       "7                       118084.0     ...        3.0  4.0          3.0     1.0   \n",
       "8                       238050.0     ...        1.0  3.0          1.0     3.0   \n",
       "9                        87342.0     ...        3.0  3.0          3.0     2.0   \n",
       "10                      192142.0     ...        1.0  2.0          1.0     4.0   \n",
       "11                      257670.0     ...        1.0  3.0          1.0     1.0   \n",
       "12                       59814.0     ...        3.0  2.0          1.0     2.0   \n",
       "13                      180318.0     ...        4.0  3.0          3.0     5.0   \n",
       "14                      154558.0     ...        1.0  2.0          1.0     5.0   \n",
       "15                       60769.0     ...        1.0  2.0          1.0     5.0   \n",
       "16                      142966.0     ...        3.0  2.0          1.0     3.0   \n",
       "17                      162636.0     ...        1.0  2.0          2.0     2.0   \n",
       "18                      100663.0     ...        3.0  2.0          1.0     2.0   \n",
       "19                       30181.0     ...        3.0  1.0          1.0     1.0   \n",
       "20                      153924.0     ...        3.0  2.0          1.0     1.0   \n",
       "21                       86524.0     ...        3.0  3.0          1.0     1.0   \n",
       "22                       55710.0     ...        1.0  3.0          1.0     2.0   \n",
       "23                       38367.0     ...        2.0  5.0          3.0     5.0   \n",
       "24                       16290.0     ...        1.0  5.0          3.0     4.0   \n",
       "25                      142818.0     ...        2.0  3.0          3.0     3.0   \n",
       "26                       11060.0     ...        1.0  7.0          3.0     4.0   \n",
       "27                       18214.0     ...        3.0  7.0          3.0     4.0   \n",
       "28                       65768.0     ...        3.0  6.0          3.0     5.0   \n",
       "29                       84612.0     ...        1.0  5.0          3.0     5.0   \n",
       "..                           ...     ...        ...  ...          ...     ...   \n",
       "377                      66317.0     ...        1.0  6.0          3.0     2.0   \n",
       "378                      41744.0     ...        1.0  6.0          3.0     2.0   \n",
       "379                      38872.0     ...        1.0  6.0          3.0     2.0   \n",
       "380                      81181.0     ...        1.0  7.0          3.0     3.0   \n",
       "381                     103496.0     ...        1.0  7.0          3.0     4.0   \n",
       "382                      50068.0     ...        1.0  7.0          3.0     4.0   \n",
       "383                     161176.0     ...        1.0  7.0          3.0     4.0   \n",
       "384                      41735.0     ...        1.0  8.0          3.0     6.0   \n",
       "385                     111839.0     ...        1.0  8.0          3.0     5.0   \n",
       "386                     137888.0     ...        1.0  8.0          3.0     4.0   \n",
       "387                     208866.0     ...        1.0  8.0          3.0     4.0   \n",
       "388                     120755.0     ...        1.0  9.0          3.0     6.0   \n",
       "389                       5890.0     ...        1.0  7.0          3.0     3.0   \n",
       "390                     133225.0     ...        2.0  4.0          3.0     2.0   \n",
       "391                      94989.0     ...        2.0  5.0          2.0     2.0   \n",
       "392                      85329.0     ...        3.0  3.0          2.0     4.0   \n",
       "393                      66202.0     ...        3.0  3.0          3.0     2.0   \n",
       "394                      16143.0     ...        3.0  6.0          3.0     3.0   \n",
       "395                      48806.0     ...        3.0  9.0          3.0     4.0   \n",
       "396                      71510.0     ...        3.0  6.0          3.0     3.0   \n",
       "397                      72717.0     ...        4.0  4.0          3.0     1.0   \n",
       "398                       6828.0     ...        1.0  8.0          3.0     3.0   \n",
       "399                       4956.0     ...        1.0  9.0          3.0     5.0   \n",
       "400                     106446.0     ...        2.0  2.0          1.0     1.0   \n",
       "401                      82596.0     ...        4.0  7.0          3.0     4.0   \n",
       "402                      68301.0     ...        4.0  8.0          3.0     5.0   \n",
       "403                     102889.0     ...        1.0  8.0          3.0     4.0   \n",
       "404                       2950.0     ...        1.0  4.0          3.0     4.0   \n",
       "405                     233852.0     ...        1.0  1.0          1.0     2.0   \n",
       "406                     134889.0     ...        1.0  5.0          3.0     2.0   \n",
       "\n",
       "       bmi  Ground truth  our MIRS  Our binary MIRS             Patient Name  \\\n",
       "0    354.0           1.0  1.000000              1.0               'MG11.dcm'   \n",
       "1    147.0           1.0  0.246667              0.0  'MG111_replacement.dcm'   \n",
       "2    108.0           1.0  0.413333              0.0              'MG113.dcm'   \n",
       "3     79.0           1.0  0.873333              1.0  'MG121_replacement.dcm'   \n",
       "4     31.0           1.0  0.393333              0.0              'MG128.dcm'   \n",
       "5    206.0           1.0  0.426667              0.0              'MG131.dcm'   \n",
       "6    179.0           1.0  0.913333              1.0              'MG134.dcm'   \n",
       "7    277.0           1.0  0.493333              0.0              'MG140.dcm'   \n",
       "8    355.0           1.0  0.973333              1.0              'MG146.dcm'   \n",
       "9      5.0           1.0  0.286667              0.0              'MG158.dcm'   \n",
       "10   103.0           1.0  0.960000              1.0  'MG162_replacement.dcm'   \n",
       "11   193.0           1.0  0.606667              1.0                'MG2.dcm'   \n",
       "12     1.0           0.0  0.566667              1.0              'MG205.dcm'   \n",
       "13   282.0           1.0  0.986667              1.0               'MG21.dcm'   \n",
       "14     1.0           0.0  0.280000              0.0              'MG210.dcm'   \n",
       "15   242.0           0.0  0.093333              0.0              'MG212.dcm'   \n",
       "16   177.0           0.0  0.740000              1.0              'MG216.dcm'   \n",
       "17   129.0           0.0  0.946667              1.0              'MG227.dcm'   \n",
       "18   310.0           0.0  0.453333              0.0              'MG236.dcm'   \n",
       "19   213.0           1.0  0.753333              1.0               'MG24.dcm'   \n",
       "20    83.0           0.0  0.926667              1.0              'MG240.dcm'   \n",
       "21   142.0           0.0  0.286667              0.0              'MG251.dcm'   \n",
       "22    14.0           0.0  0.473333              0.0              'MG266.dcm'   \n",
       "23   211.0           1.0  0.960000              1.0               'MG37.dcm'   \n",
       "24   155.0           0.0  0.220000              0.0              'MG390.dcm'   \n",
       "25    42.0           1.0  0.920000              1.0                'MG4.dcm'   \n",
       "26   335.0           0.0  0.280000              0.0              'MG433.dcm'   \n",
       "27    56.0           0.0  0.540000              1.0              'MG449.dcm'   \n",
       "28   119.0           1.0  0.373333              0.0   'MG57_replacement.dcm'   \n",
       "29   290.0           1.0  0.266667              0.0   'MG60_replacement.dcm'   \n",
       "..     ...           ...       ...              ...                      ...   \n",
       "377  180.0           0.0  0.160000              0.0              'MG903.dcm'   \n",
       "378  264.0           0.0  0.280000              0.0              'MG904.dcm'   \n",
       "379  160.0           0.0  0.460000              0.0              'MG905.dcm'   \n",
       "380  271.0           0.0  0.260000              0.0              'MG906.dcm'   \n",
       "381   34.0           0.0  0.306667              0.0              'MG907.dcm'   \n",
       "382  149.0           0.0  0.286667              0.0              'MG909.dcm'   \n",
       "383    1.0           0.0  0.233333              0.0              'MG911.dcm'   \n",
       "384    1.0           0.0  0.186667              0.0              'MG912.dcm'   \n",
       "385  281.0           0.0  0.186667              0.0              'MG914.dcm'   \n",
       "386  298.0           0.0  0.446667              0.0              'MG915.dcm'   \n",
       "387   40.0           0.0  0.546667              1.0              'MG916.dcm'   \n",
       "388  168.0           0.0  0.160000              0.0              'MG919.dcm'   \n",
       "389  152.0           0.0  0.406667              0.0              'MG921.dcm'   \n",
       "390    1.0           0.0  0.446667              0.0              'MG922.dcm'   \n",
       "391  170.0           0.0  0.473333              0.0              'MG923.dcm'   \n",
       "392  256.0           0.0  0.366667              0.0              'MG924.dcm'   \n",
       "393   19.0           0.0  0.400000              0.0              'MG925.dcm'   \n",
       "394  144.0           0.0  0.226667              0.0              'MG927.dcm'   \n",
       "395   44.0           0.0  0.300000              0.0              'MG928.dcm'   \n",
       "396    1.0           0.0  0.340000              0.0              'MG929.dcm'   \n",
       "397  288.0           0.0  0.080000              0.0              'MG930.dcm'   \n",
       "398  224.0           0.0  0.260000              0.0              'MG931.dcm'   \n",
       "399  327.0           0.0  0.306667              0.0              'MG933.dcm'   \n",
       "400  153.0           0.0  0.466667              0.0              'MG935.dcm'   \n",
       "401  217.0           0.0  0.446667              0.0              'MG936.dcm'   \n",
       "402   92.0           0.0  0.253333              0.0              'MG937.dcm'   \n",
       "403   35.0           0.0  0.286667              0.0              'MG938.dcm'   \n",
       "404    1.0           1.0  0.760000              1.0               'MG95.dcm'   \n",
       "405   66.0           1.0  0.420000              0.0               'MG97.dcm'   \n",
       "406   76.0           1.0  0.893333              1.0    'MG9_replacement.dcm'   \n",
       "\n",
       "     Unnamed: 23  \n",
       "0            1.0  \n",
       "1            0.0  \n",
       "2            0.0  \n",
       "3            1.0  \n",
       "4            0.0  \n",
       "5            0.0  \n",
       "6            1.0  \n",
       "7            0.0  \n",
       "8            1.0  \n",
       "9            0.0  \n",
       "10           1.0  \n",
       "11           1.0  \n",
       "12           0.0  \n",
       "13           1.0  \n",
       "14           1.0  \n",
       "15           1.0  \n",
       "16           0.0  \n",
       "17           0.0  \n",
       "18           1.0  \n",
       "19           1.0  \n",
       "20           0.0  \n",
       "21           1.0  \n",
       "22           1.0  \n",
       "23           1.0  \n",
       "24           1.0  \n",
       "25           1.0  \n",
       "26           1.0  \n",
       "27           0.0  \n",
       "28           0.0  \n",
       "29           0.0  \n",
       "..           ...  \n",
       "377          1.0  \n",
       "378          1.0  \n",
       "379          1.0  \n",
       "380          1.0  \n",
       "381          1.0  \n",
       "382          1.0  \n",
       "383          1.0  \n",
       "384          1.0  \n",
       "385          1.0  \n",
       "386          1.0  \n",
       "387          0.0  \n",
       "388          1.0  \n",
       "389          1.0  \n",
       "390          1.0  \n",
       "391          1.0  \n",
       "392          1.0  \n",
       "393          1.0  \n",
       "394          1.0  \n",
       "395          1.0  \n",
       "396          1.0  \n",
       "397          1.0  \n",
       "398          1.0  \n",
       "399          1.0  \n",
       "400          1.0  \n",
       "401          1.0  \n",
       "402          1.0  \n",
       "403          1.0  \n",
       "404          1.0  \n",
       "405          0.0  \n",
       "406          1.0  \n",
       "\n",
       "[407 rows x 24 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_covar = pd.read_csv('PatientsData.xls - PatientsData.csv.csv')\n",
    "raw_covar.head(0)\n",
    "raw_covar.dropna()\n",
    "#raw_covar.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant_frame = raw_covar[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Ground truth','Our binary MIRS']]\n",
    "relevant_frame = relevant_frame.dropna()\n",
    "relevant_frame\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(relevant_frame)\n",
    "relevant_frame = pd.DataFrame(x_scaled)\n",
    "relevant_frame.columns = [['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Ground truth','Our binary MIRS']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(relevant_frame[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']], relevant_frame['Ground truth'], test_size=0.20, random_state=42)\n",
    "\n",
    "#THIS Normalizes all the data\n",
    "x = X_train.values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "X_train = pd.DataFrame(x_scaled)\n",
    "\n",
    "x = X_test.values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "X_test = pd.DataFrame(x_scaled)\n",
    "X_test.columns = ['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']\n",
    "X_train.columns = ['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cumulus (DA) - normalized</th>\n",
       "      <th>Cumulus (PD) - normalized</th>\n",
       "      <th>Libraæ(DA) - normalized</th>\n",
       "      <th>Libra(PD) - normalized</th>\n",
       "      <th>Volpara(PD) - normalized</th>\n",
       "      <th>BIRADS - unnormalized</th>\n",
       "      <th>race</th>\n",
       "      <th>age</th>\n",
       "      <th>mens_status</th>\n",
       "      <th>parity</th>\n",
       "      <th>bmi</th>\n",
       "      <th>Our binary MIRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.363651</td>\n",
       "      <td>0.277849</td>\n",
       "      <td>0.942257</td>\n",
       "      <td>0.928808</td>\n",
       "      <td>0.602955</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.964481</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.973399</td>\n",
       "      <td>0.866726</td>\n",
       "      <td>0.798931</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.398907</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.536382</td>\n",
       "      <td>0.494442</td>\n",
       "      <td>0.759669</td>\n",
       "      <td>0.826849</td>\n",
       "      <td>0.143962</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.292350</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.932423</td>\n",
       "      <td>0.593742</td>\n",
       "      <td>0.650729</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.213115</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.682616</td>\n",
       "      <td>0.913607</td>\n",
       "      <td>0.615290</td>\n",
       "      <td>0.908070</td>\n",
       "      <td>0.915419</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.081967</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.481429</td>\n",
       "      <td>0.479823</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.657825</td>\n",
       "      <td>0.593670</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.560109</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.660113</td>\n",
       "      <td>0.753380</td>\n",
       "      <td>0.626991</td>\n",
       "      <td>0.905285</td>\n",
       "      <td>0.865957</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.486339</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.457461</td>\n",
       "      <td>0.465644</td>\n",
       "      <td>0.978441</td>\n",
       "      <td>0.982455</td>\n",
       "      <td>0.699146</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.754098</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.702042</td>\n",
       "      <td>0.610935</td>\n",
       "      <td>0.487855</td>\n",
       "      <td>0.961335</td>\n",
       "      <td>0.684914</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.967213</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.545195</td>\n",
       "      <td>0.812870</td>\n",
       "      <td>0.977989</td>\n",
       "      <td>0.903875</td>\n",
       "      <td>0.855001</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.010929</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cumulus (DA) - normalized  Cumulus (PD) - normalized  \\\n",
       "0                   0.363651                   0.277849   \n",
       "1                   0.000000                   0.000000   \n",
       "2                   0.536382                   0.494442   \n",
       "3                   0.000000                   0.000000   \n",
       "4                   0.682616                   0.913607   \n",
       "5                   0.481429                   0.479823   \n",
       "6                   0.660113                   0.753380   \n",
       "7                   0.457461                   0.465644   \n",
       "8                   0.702042                   0.610935   \n",
       "9                   0.545195                   0.812870   \n",
       "\n",
       "   Libraæ(DA) - normalized  Libra(PD) - normalized  Volpara(PD) - normalized  \\\n",
       "0                 0.942257                0.928808                  0.602955   \n",
       "1                 0.973399                0.866726                  0.798931   \n",
       "2                 0.759669                0.826849                  0.143962   \n",
       "3                 0.932423                0.593742                  0.650729   \n",
       "4                 0.615290                0.908070                  0.915419   \n",
       "5                 1.000000                0.657825                  0.593670   \n",
       "6                 0.626991                0.905285                  0.865957   \n",
       "7                 0.978441                0.982455                  0.699146   \n",
       "8                 0.487855                0.961335                  0.684914   \n",
       "9                 0.977989                0.903875                  0.855001   \n",
       "\n",
       "   BIRADS - unnormalized      race    age  mens_status  parity       bmi  \\\n",
       "0                   0.50  0.000000  0.500          1.0   0.000  0.964481   \n",
       "1                   0.75  0.666667  0.000          0.0   0.000  0.398907   \n",
       "2                   0.50  0.000000  0.750          1.0   0.500  0.292350   \n",
       "3                   0.50  0.000000  0.250          1.0   0.500  0.213115   \n",
       "4                   0.75  0.000000  0.250          0.0   0.250  0.081967   \n",
       "5                   0.25  0.000000  0.000          0.0   0.000  0.560109   \n",
       "6                   0.75  0.666667  0.125          0.0   0.250  0.486339   \n",
       "7                   0.75  0.666667  0.375          1.0   0.000  0.754098   \n",
       "8                   0.50  0.000000  0.250          0.0   0.250  0.967213   \n",
       "9                   1.00  0.666667  0.250          1.0   0.125  0.010929   \n",
       "\n",
       "   Our binary MIRS  \n",
       "0              1.0  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              1.0  \n",
       "4              0.0  \n",
       "5              0.0  \n",
       "6              1.0  \n",
       "7              0.0  \n",
       "8              1.0  \n",
       "9              0.0  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = relevant_frame['Ground truth']\n",
    "relevant_frame = relevant_frame.drop('Ground truth',axis=1)\n",
    "relevant_frame.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cumulus (DA) - normalized</th>\n",
       "      <th>Cumulus (PD) - normalized</th>\n",
       "      <th>Libraæ(DA) - normalized</th>\n",
       "      <th>Libra(PD) - normalized</th>\n",
       "      <th>Volpara(PD) - normalized</th>\n",
       "      <th>BIRADS - unnormalized</th>\n",
       "      <th>race</th>\n",
       "      <th>age</th>\n",
       "      <th>mens_status</th>\n",
       "      <th>parity</th>\n",
       "      <th>bmi</th>\n",
       "      <th>Our binary MIRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.363651</td>\n",
       "      <td>0.277849</td>\n",
       "      <td>0.942257</td>\n",
       "      <td>0.928808</td>\n",
       "      <td>0.602955</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.964481</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.973399</td>\n",
       "      <td>0.866726</td>\n",
       "      <td>0.798931</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.398907</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.536382</td>\n",
       "      <td>0.494442</td>\n",
       "      <td>0.759669</td>\n",
       "      <td>0.826849</td>\n",
       "      <td>0.143962</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.292350</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.932423</td>\n",
       "      <td>0.593742</td>\n",
       "      <td>0.650729</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.213115</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.682616</td>\n",
       "      <td>0.913607</td>\n",
       "      <td>0.615290</td>\n",
       "      <td>0.908070</td>\n",
       "      <td>0.915419</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.081967</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.481429</td>\n",
       "      <td>0.479823</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.657825</td>\n",
       "      <td>0.593670</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.560109</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.660113</td>\n",
       "      <td>0.753380</td>\n",
       "      <td>0.626991</td>\n",
       "      <td>0.905285</td>\n",
       "      <td>0.865957</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.486339</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.457461</td>\n",
       "      <td>0.465644</td>\n",
       "      <td>0.978441</td>\n",
       "      <td>0.982455</td>\n",
       "      <td>0.699146</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.754098</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.702042</td>\n",
       "      <td>0.610935</td>\n",
       "      <td>0.487855</td>\n",
       "      <td>0.961335</td>\n",
       "      <td>0.684914</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.967213</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.545195</td>\n",
       "      <td>0.812870</td>\n",
       "      <td>0.977989</td>\n",
       "      <td>0.903875</td>\n",
       "      <td>0.855001</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.010929</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.990203</td>\n",
       "      <td>0.840890</td>\n",
       "      <td>0.881613</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.278689</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.763463</td>\n",
       "      <td>0.943176</td>\n",
       "      <td>0.721849</td>\n",
       "      <td>0.846989</td>\n",
       "      <td>0.919999</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.435499</td>\n",
       "      <td>0.738909</td>\n",
       "      <td>0.691641</td>\n",
       "      <td>0.839851</td>\n",
       "      <td>0.834669</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.682464</td>\n",
       "      <td>0.797332</td>\n",
       "      <td>0.641407</td>\n",
       "      <td>0.864046</td>\n",
       "      <td>0.808840</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.767760</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.547893</td>\n",
       "      <td>0.610221</td>\n",
       "      <td>0.397300</td>\n",
       "      <td>0.694171</td>\n",
       "      <td>0.797823</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cumulus (DA) - normalized  Cumulus (PD) - normalized  \\\n",
       "0                    0.363651                   0.277849   \n",
       "1                    0.000000                   0.000000   \n",
       "2                    0.536382                   0.494442   \n",
       "3                    0.000000                   0.000000   \n",
       "4                    0.682616                   0.913607   \n",
       "5                    0.481429                   0.479823   \n",
       "6                    0.660113                   0.753380   \n",
       "7                    0.457461                   0.465644   \n",
       "8                    0.702042                   0.610935   \n",
       "9                    0.545195                   0.812870   \n",
       "10                   0.000000                   0.000000   \n",
       "11                   0.763463                   0.943176   \n",
       "12                   0.435499                   0.738909   \n",
       "13                   0.682464                   0.797332   \n",
       "14                   0.547893                   0.610221   \n",
       "\n",
       "    Libraæ(DA) - normalized  Libra(PD) - normalized  Volpara(PD) - normalized  \\\n",
       "0                  0.942257                0.928808                  0.602955   \n",
       "1                  0.973399                0.866726                  0.798931   \n",
       "2                  0.759669                0.826849                  0.143962   \n",
       "3                  0.932423                0.593742                  0.650729   \n",
       "4                  0.615290                0.908070                  0.915419   \n",
       "5                  1.000000                0.657825                  0.593670   \n",
       "6                  0.626991                0.905285                  0.865957   \n",
       "7                  0.978441                0.982455                  0.699146   \n",
       "8                  0.487855                0.961335                  0.684914   \n",
       "9                  0.977989                0.903875                  0.855001   \n",
       "10                 0.990203                0.840890                  0.881613   \n",
       "11                 0.721849                0.846989                  0.919999   \n",
       "12                 0.691641                0.839851                  0.834669   \n",
       "13                 0.641407                0.864046                  0.808840   \n",
       "14                 0.397300                0.694171                  0.797823   \n",
       "\n",
       "    BIRADS - unnormalized      race    age  mens_status  parity       bmi  \\\n",
       "0                    0.50  0.000000  0.500          1.0   0.000  0.964481   \n",
       "1                    0.75  0.666667  0.000          0.0   0.000  0.398907   \n",
       "2                    0.50  0.000000  0.750          1.0   0.500  0.292350   \n",
       "3                    0.50  0.000000  0.250          1.0   0.500  0.213115   \n",
       "4                    0.75  0.000000  0.250          0.0   0.250  0.081967   \n",
       "5                    0.25  0.000000  0.000          0.0   0.000  0.560109   \n",
       "6                    0.75  0.666667  0.125          0.0   0.250  0.486339   \n",
       "7                    0.75  0.666667  0.375          1.0   0.000  0.754098   \n",
       "8                    0.50  0.000000  0.250          0.0   0.250  0.967213   \n",
       "9                    1.00  0.666667  0.250          1.0   0.125  0.010929   \n",
       "10                   0.75  0.000000  0.125          0.0   0.375  0.278689   \n",
       "11                   1.00  0.000000  0.250          0.0   0.000  0.524590   \n",
       "12                   0.75  0.666667  0.125          0.0   0.125  0.000000   \n",
       "13                   0.75  1.000000  0.250          1.0   0.500  0.767760   \n",
       "14                   0.75  0.000000  0.125          0.0   0.500  0.000000   \n",
       "\n",
       "    Our binary MIRS  \n",
       "0               1.0  \n",
       "1               0.0  \n",
       "2               0.0  \n",
       "3               1.0  \n",
       "4               0.0  \n",
       "5               0.0  \n",
       "6               1.0  \n",
       "7               0.0  \n",
       "8               1.0  \n",
       "9               0.0  \n",
       "10              1.0  \n",
       "11              1.0  \n",
       "12              1.0  \n",
       "13              1.0  \n",
       "14              0.0  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relevant_frame.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm, grid_search\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "    gammas = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "    d = [1,2,3,4,5]\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas,'degree':d}\n",
    "    search = grid_search.GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n",
    "    search.fit(X, y)\n",
    "    search.best_params_\n",
    "    return search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'degree': 1, 'gamma': 0.1}"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_param_selection(X_train[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']], y_train, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svc = svm.SVC(C=100, gamma=0.1, probability=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78048780487804881"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']], y_train)\n",
    "svc.score(X_test[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.76 (+/- 0.09)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(tempsvm, relevant_frame, y, cv=5)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.67850877  0.67850877  0.67850877  0.67850877  0.67850877  0.67850877\n",
      "  0.67850877  0.67850877  0.67850877  0.67850877  0.67850877  0.67850877\n",
      "  0.67850877  0.67850877  0.67850877  0.74070175  0.74070175  0.74070175\n",
      "  0.74070175  0.74070175  0.80321429  0.80321429  0.80321429  0.80321429\n",
      "  0.80321429  0.78919173  0.78919173  0.78919173  0.78919173  0.78919173\n",
      "  0.78670426  0.78670426  0.78670426  0.78670426  0.78670426]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "d = [1,2,3,4,5]\n",
    "accuracies = np.array([])\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "for i in Cs:\n",
    "    for j in d :\n",
    "        tempsvm = svm.SVC(C=i,degree=j, max_iter=5000)\n",
    "        tempsvm.fit(X_train[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']],y_train)\n",
    "        accuracy= cross_val_score(tempsvm, relevant_frame, y, cv=20).mean()\n",
    "        accuracies = np.append(accuracies,accuracy)\n",
    "print(accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.67850877  0.67850877  0.67850877  0.67850877  0.67850877  0.67850877\n",
      "   0.67850877]\n",
      " [ 0.67850877  0.67850877  0.67850877  0.67850877  0.67850877  0.67850877\n",
      "   0.67850877]\n",
      " [ 0.67850877  0.74070175  0.74070175  0.74070175  0.74070175  0.74070175\n",
      "   0.80321429]\n",
      " [ 0.80321429  0.80321429  0.80321429  0.80321429  0.78919173  0.78919173\n",
      "   0.78919173]\n",
      " [ 0.78919173  0.78919173  0.78670426  0.78670426  0.78670426  0.78670426\n",
      "   0.78670426]]\n"
     ]
    }
   ],
   "source": [
    "another = accuracies.reshape(5,7)\n",
    "print(another)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SL0XYNmYPATA4SpJ6G7duVYOjJKmXYvwePD5eVyNJ0hwwOEqSegpbey4zqiVZnuTqJJuS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\nJUkaYHCUJGmAwVGSpAH/HzY0VAMgje/fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1276e12b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.subplots_adjust(left=.2, right=0.95, bottom=0.15, top=0.95)\n",
    "plt.imshow(another)\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('d')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(len(Cs)), Cs)\n",
    "plt.yticks(np.arange(len(d)), d)\n",
    "plt.title('Grid Search CROSSVAL AUC Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.67073171  0.67073171  0.67073171  0.67073171  0.67073171  0.67073171\n",
      "  0.67073171  0.67073171  0.67073171  0.67073171  0.67073171  0.67073171\n",
      "  0.67073171  0.67073171  0.67073171  0.80487805  0.80487805  0.80487805\n",
      "  0.80487805  0.80487805  0.82926829  0.82926829  0.82926829  0.82926829\n",
      "  0.82926829  0.7804878   0.7804878   0.7804878   0.7804878   0.7804878\n",
      "  0.80487805  0.80487805  0.80487805  0.80487805  0.80487805]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=5000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "d = [1,2,3,4,5]\n",
    "accuracies = np.array([])\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "for i in Cs:\n",
    "    for j in d :\n",
    "        tempsvm = svm.SVC(C=i,degree=j, max_iter=5000)\n",
    "        tempsvm.fit(X_train[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']],y_train)\n",
    "        accuracy= tempsvm.score(X_test[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']], y_test)\n",
    "        accuracies = np.append(accuracies,accuracy)\n",
    "print(accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80487804878048785"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempsvm = svm.SVC(C=1,degree=4,gamma=0.1, max_iter=5000)\n",
    "tempsvm.fit(X_train[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']],y_train)\n",
    "tempsvm.score(X_test[['Cumulus (DA) - normalized','Cumulus (PD) - normalized','Libraæ(DA) - normalized','Libra(PD) - normalized','Volpara(PD) - normalized','BIRADS - unnormalized','race','age','mens_status','parity','bmi','Our binary MIRS']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.67073171  0.67073171  0.67073171  0.67073171  0.67073171  0.67073171\n",
      "   0.67073171]\n",
      " [ 0.67073171  0.67073171  0.67073171  0.67073171  0.67073171  0.67073171\n",
      "   0.67073171]\n",
      " [ 0.67073171  0.80487805  0.80487805  0.80487805  0.80487805  0.80487805\n",
      "   0.82926829]\n",
      " [ 0.82926829  0.82926829  0.82926829  0.82926829  0.7804878   0.7804878\n",
      "   0.7804878 ]\n",
      " [ 0.7804878   0.7804878   0.80487805  0.80487805  0.80487805  0.80487805\n",
      "   0.80487805]]\n"
     ]
    }
   ],
   "source": [
    "another = accuracies.reshape(5,7)\n",
    "print(another)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GLDg65ihJUo+hyhzv+dYP7rj8uPd+d4BVLALuGOD1Z8vA7+PyQV68YxT+W4zC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1276cbcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.subplots_adjust(left=.2, right=0.95, bottom=0.15, top=0.95)\n",
    "plt.imshow(another)\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('d')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(len(Cs)), Cs)\n",
    "plt.yticks(np.arange(len(d)), d)\n",
    "plt.title('Grid Search AUC Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.73 (+/- 0.18)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "relevant_frame.head(10)\n",
    "scores = cross_val_score(tempsvm, relevant_frame, y, cv=5)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}