4400project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "858"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scikitplot as skplt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Imputer\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn import svm\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "seed = 42\n",
    "\n",
    "df=pd.read_csv(\"kag_risk_factors_cervical_cancer.csv\")\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123cfcb70>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U1HD77bdzzTXXkJiYqFlNT4sBAwZw1VVXcddddyHLMiNHjqR///5hVQIUNDLC8I6IUGaP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\nEW9BEIQCRMRbEAShABHxFgRBKED+H9CIJXSREDvLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1231c9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.area()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace ?s with -1 to represent missing values\n",
    "df=df.replace(\"?\", 0)\n",
    "# imp = Imputer(missing_values=\"?\", strategy='most_frequent', axis=0)\n",
    "# imp.fit(df)\n",
    "# df = imp.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into training and testing sets\n",
    "y=df[\"Biopsy\"]\n",
    "X=df.drop([\"Biopsy\"], axis=1)\n",
    "#20% of dataset goes to test set\n",
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardize the data\n",
    "X_scaled = preprocessing.scale(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4389896095932891e-17"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9561828874675149"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=42,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the random forest classifier using training data\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(random_state=seed)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94186046511627908"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluating the classifier using training set\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred=rfc.predict(X_test)\n",
    "accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>feature</th>\n",
       "      <th>rfc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.108359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Number of sexual partners</td>\n",
       "      <td>0.043818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>First sexual intercourse</td>\n",
       "      <td>0.090490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Num of pregnancies</td>\n",
       "      <td>0.043802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Smokes</td>\n",
       "      <td>0.000732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Smokes (years)</td>\n",
       "      <td>0.008442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Smokes (packs/year)</td>\n",
       "      <td>0.012189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hormonal Contraceptives</td>\n",
       "      <td>0.018789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hormonal Contraceptives (years)</td>\n",
       "      <td>0.073255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IUD</td>\n",
       "      <td>0.007286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IUD (years)</td>\n",
       "      <td>0.014674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>STDs</td>\n",
       "      <td>0.002097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>STDs (number)</td>\n",
       "      <td>0.004735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>STDs:condylomatosis</td>\n",
       "      <td>0.002229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>STDs:cervical condylomatosis</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>STDs:vaginal condylomatosis</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>STDs:vulvo-perineal condylomatosis</td>\n",
       "      <td>0.005680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>STDs:syphilis</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>STDs:pelvic inflammatory disease</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>STDs:genital herpes</td>\n",
       "      <td>0.029498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>STDs:molluscum contagiosum</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>STDs:AIDS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>STDs:HIV</td>\n",
       "      <td>0.005334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>STDs:Hepatitis B</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>STDs:HPV</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>STDs: Number of diagnosis</td>\n",
       "      <td>0.002572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>STDs: Time since first diagnosis</td>\n",
       "      <td>0.013310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>STDs: Time since last diagnosis</td>\n",
       "      <td>0.009971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Dx:Cancer</td>\n",
       "      <td>0.003834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Dx:CIN</td>\n",
       "      <td>0.015490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Dx:HPV</td>\n",
       "      <td>0.014051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Dx</td>\n",
       "      <td>0.015054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Hinselmann</td>\n",
       "      <td>0.123566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Schiller</td>\n",
       "      <td>0.278592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Citology</td>\n",
       "      <td>0.051970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               feature       rfc\n",
       "0                                  Age  0.108359\n",
       "1            Number of sexual partners  0.043818\n",
       "2             First sexual intercourse  0.090490\n",
       "3                   Num of pregnancies  0.043802\n",
       "4                               Smokes  0.000732\n",
       "5                       Smokes (years)  0.008442\n",
       "6                  Smokes (packs/year)  0.012189\n",
       "7              Hormonal Contraceptives  0.018789\n",
       "8      Hormonal Contraceptives (years)  0.073255\n",
       "9                                  IUD  0.007286\n",
       "10                         IUD (years)  0.014674\n",
       "11                                STDs  0.002097\n",
       "12                       STDs (number)  0.004735\n",
       "13                 STDs:condylomatosis  0.002229\n",
       "14        STDs:cervical condylomatosis  0.000000\n",
       "15         STDs:vaginal condylomatosis  0.000000\n",
       "16  STDs:vulvo-perineal condylomatosis  0.005680\n",
       "17                       STDs:syphilis  0.000113\n",
       "18    STDs:pelvic inflammatory disease  0.000000\n",
       "19                 STDs:genital herpes  0.029498\n",
       "20          STDs:molluscum contagiosum  0.000000\n",
       "21                           STDs:AIDS  0.000000\n",
       "22                            STDs:HIV  0.005334\n",
       "23                    STDs:Hepatitis B  0.000000\n",
       "24                            STDs:HPV  0.000068\n",
       "25           STDs: Number of diagnosis  0.002572\n",
       "26    STDs: Time since first diagnosis  0.013310\n",
       "27     STDs: Time since last diagnosis  0.009971\n",
       "28                           Dx:Cancer  0.003834\n",
       "29                              Dx:CIN  0.015490\n",
       "30                              Dx:HPV  0.014051\n",
       "31                                  Dx  0.015054\n",
       "32                          Hinselmann  0.123566\n",
       "33                            Schiller  0.278592\n",
       "34                            Citology  0.051970"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaulating which features the classifier finds important for making its decisions\n",
    "feats = rfc.feature_importances_\n",
    "\n",
    "#Create new instance of dataframe\n",
    "feat_importances=pd.DataFrame()\n",
    "\n",
    "#set columns in datafram to features and their importances\n",
    "feat_importances[\"feature\"]=X.columns\n",
    "feat_importances[\"rfc\"]=feats\n",
    "\n",
    "#Display data\n",
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wE3gbydyvX0iaWuvAzKxxKeNWb1l6cJ8BDoiI9QCSLgDuB75dy8DMrHE1wi0gWWRJcKva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\nheUEZ2aF5QRnZoX1v9HNSVsaxKJcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eee8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(y_true=y_test, y_pred=y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 161)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_test.values==1), np.sum(y_test.values==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gtmw5TqlSl66Wql+/Ai1aXN1NdGC9Ee/1118nJiaGhIQEXn/9dQAsFgtt27alS5cu9Ojh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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a2ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_precision_recall_curve(y_true=y_test, y_probas=rfc.predict_proba(X_test))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.99      0.97       161\n",
      "          1       0.60      0.27      0.37        11\n",
      "\n",
      "avg / total       0.93      0.94      0.93       172\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight={0: 20, 1: 0.5},\n",
       "            criterion='gini', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the random forest classifier using training data\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfcw=RandomForestClassifier(random_state=seed, class_weight={0:20, 1:0.5})\n",
    "rfcw.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.99      0.97       161\n",
      "          1       0.75      0.27      0.40        11\n",
      "\n",
      "avg / total       0.94      0.95      0.94       172\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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9/VvgamAXYHFEPJyWH0SywObvJAH0Ipl6tBfwQkQ8CyDpBuDMTZzjA8AnASJi\nPfCGpL7tjjk63R5L3/chSXjbArdGxKr0HFnm3u4t6Rsk3eA+JFPb2kyPiA3As5KeT7/D0cA+Jdfn\ntk/P/UyGc9lmxgmuuayOiP1KC9Ik9mZpEXBfRJzS7riNPtdFAr4ZEf/V7hyfr6Cua4ETImK+pNOA\ncSX72s8jjPTc50REaSJE0vAKzm0F5y5q8TwMHCppdwBJ20jaA3gKGC5pt/S4Uzr4/AzgrPSzLZK2\nB/5M0jprcw9wesm1vcGSBgAPACdI2krStiTd4c5sCyyTtAVwart9J0rqkcb8LuDp9NxnpccjaQ9J\n22Q4j22G3IIrmIhYmbaEpknqnRZfEhHPSDoTuFPSKpIu7rabqOJcYEq6isZ64KyIeEjS79JhGL9O\nr8O9G3gobUH+Bfh4RMyV9HNgPrCCZMmoznwJmAWsTH+WxvRH4BFgO+CzEbFG0o9Jrs3NVXLylcAJ\n2X47trnxaiJmVljuoppZYTnBmVlhOcGZWWE5wZlZYTnBmVlhOcGZWWE5wZlZYf0flo+NbYvFd/wA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ae76dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfcw_pred = rfcw.predict(X_test)\n",
    "print(classification_report(y_test, rfcw_pred))\n",
    "skplt.metrics.plot_confusion_matrix(y_true=y_test, y_pred=rfcw_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1282e9f60>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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43jTJeuCXwDZga1UtSnIE8D+AucB64Lyq+n/jyCdJGu8WxKlVtbCqFjXzVwL3VdVxwH3N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\nhCSp1f8H15LMhPdk7a8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1281ef518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(x=\"Biopsy\", y=\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12e7562e8>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x129623a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=\"Age\", by=\"Biopsy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12e5d72e8>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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43jTJeuCXwDZga1UtSnIE8D+AucB64Lyq+n/jyCdJGu8WxKlVtbCqFjXzVwL3VdVxwH3N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\nhCSp1f8H15LMhPdk7a8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b1a7550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(x=\"Biopsy\", y=\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11dd92240>]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PgWFge9W+Hbi12h4GRm2ftX0MGAduauusIyKiNtlu3alxRX4QeBfwadufkPSS7cXVcQEv\n2l4s6X5gv+2Hq2PbgMds75rynJuATQD9/f03jo6OzriIidNnOPXKjIfP2HXLFnX/pMDk5CR9fX09\nOXevpOb5ITVPz9DQ0EHbA636LazzZLbPAzdIWgx8TtJ7pxy3pNavGBeOGQFGAAYGBjw4ODid4Re4\nb8du7jlUq5S2Or5hsOvnBBgbG2M2P6+5KDXPD6m5M6b1rhvbLwF/RGPt/ZSkpQDV40TV7SSwomnY\n8qotIiJ6oM67bt5WXckj6c3Ah4BvAnuAjVW3jcDuansPsF7SlZJWAauBA+2eeERE1FNnvWMpsL1a\np38DsNP2FyT9CbBT0h3ACeA2ANuHJe0EjgDngDurpZ+IiOiBlkFv+xvA371I+wvA2kuM2QpsnfXs\nIiJi1vLJ2IiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIK\nl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwtX5ztgVkv5I0hFJhyXdVbVfLWmv\npGeqxyVNY7ZIGpd0VNItnSwgIiIur84V/Tng122vAW4G7pS0BtgM7LO9GthX7VMdWw9cC6wDHqi+\nbzYiInqgZdDb/rbtr1Xb/wd4GlgGDAPbq27bgVur7WFg1PZZ28eAceCmdk88IiLqke36naWVwJeB\n9wLP2V5ctQt40fZiSfcD+20/XB3bBjxme9eU59oEbALo7++/cXR0dMZFTJw+w6lXZjx8xq5btqj7\nJwUmJyfp6+vrybl7JTXPD6l5eoaGhg7aHmjVb2HdJ5TUB3wW+FXb32tke4NtS6r/itEYMwKMAAwM\nDHhwcHA6wy9w347d3HOodiltc3zDYNfPCTA2NsZsfl5zUWqeH1JzZ9R6142kN9II+R22H62aT0la\nWh1fCkxU7SeBFU3Dl1dtERHRA3XedSNgG/C07X/bdGgPsLHa3gjsbmpfL+lKSauA1cCB9k05IiKm\no856x48DPwsckvRk1fabwN3ATkl3ACeA2wBsH5a0EzhC4x07d9o+3/aZR0RELS2D3vYfA7rE4bWX\nGLMV2DqLeUVERJvkk7EREYVL0EdEFC5BHxFRuAR9REThEvQREYVL0EdEFC5BHxFRuAR9REThEvQR\nEYVL0EdEFC5BHxFRuAR9REThEvQREYVL0EdEFC5BHxFRuAR9REThEvQREYWr852xD0qakPRUU9vV\nkvZKeqZ6XNJ0bIukcUlHJd3SqYlHREQ9da7oHwLWTWnbDOyzvRrYV+0jaQ2wHri2GvOApAVtm21E\nRExby6C3/WXg9JTmYWB7tb0duLWpfdT2WdvHgHHgpjbNNSIiZkC2W3eSVgJfsP3eav8l24urbQEv\n2l4s6X5gv+2Hq2PbgMds77rIc24CNgH09/ffODo6OuMiJk6f4dQrMx4+Y9ctW9T9kwKTk5P09fX1\n5Ny9kprnh9Q8PUNDQwdtD7Tqt3BGz97EtiW1frX4/8eNACMAAwMDHhwcnPEc7tuxm3sOzbqUaTu+\nYbDr5wQYGxtjNj+vuSg1zw+puTNm+q6bU5KWAlSPE1X7SWBFU7/lVVtERPTITIN+D7Cx2t4I7G5q\nXy/pSkmrgNXAgdlNMSIiZqPleoekR4BB4BpJzwOfBO4Gdkq6AzgB3AZg+7CkncAR4Bxwp+3zHZp7\nRETU0DLobd9+iUNrL9F/K7B1NpOKiIj2ySdjIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChc\n9+8bEBHxOrNy83/t2bkfWndVx8+RK/qIiMIl6CMiCpegj4goXII+IqJwCfqIiMIl6CMiCpegj4go\nXII+IqJwCfqIiMIl6CMiCtexoJe0TtJRSeOSNnfqPBERcXkdCXpJC4BPAx8G1gC3S1rTiXNFRMTl\ndeqK/iZg3Paztv8KGAWGO3SuiIi4jE7dvXIZ8K2m/eeBv9fcQdImYFO1Oynp6CzOdw3w3VmMnxF9\nqttn/Gs9qbfHUvP8MO9qHvrUrGp+Z51OPbtNse0RYKQdzyXpCdsD7XiuuWC+1Qupeb5IzZ3RqaWb\nk8CKpv3lVVtERHRZp4L+q8BqSaskXQGsB/Z06FwREXEZHVm6sX1O0i8D/x1YADxo+3AnzlVpyxLQ\nHDLf6oXUPF+k5g6Q7U6fIyIieiifjI2IKFyCPiKicHMm6FvdUkEN/646/g1J7+vFPNupRs0bqloP\nSfqKpOt7Mc92qnvrDEk/IumcpJ/p5vw6oU7NkgYlPSnpsKQvdXuO7Vbj3/YiSb8v6etVzR/rxTzb\nRdKDkiYkPXWJ453NL9uv+z80fqH7Z8APAVcAXwfWTOnzEeAxQMDNwOO9nncXav4xYEm1/eH5UHNT\nvz8E/hvwM72edxf+nhcDR4B3VPs/2Ot5d6Hm3wQ+VW2/DTgNXNHruc+i5g8A7wOeusTxjubXXLmi\nr3NLhWHgP7phP7BY0tJuT7SNWtZs+yu2X6x299P4vMJcVvfWGR8HPgtMdHNyHVKn5n8CPGr7OQDb\nc73uOjUb+AFJAvpoBP257k6zfWx/mUYNl9LR/JorQX+xWyosm0GfuWS69dxB44pgLmtZs6RlwE8D\n/76L8+qkOn/PfxtYImlM0kFJP9e12XVGnZrvB94D/DlwCLjL9qvdmV5PdDS/enYLhGgfSUM0gv79\nvZ5LF9wLfML2q42LvXlhIXAjsBZ4M/Ankvbb/l+9nVZH3QI8CXwQ+FvAXkn/0/b3ejutuWmuBH2d\nWyqUdtuFWvVI+jvAZ4AP236hS3PrlDo1DwCjVchfA3xE0jnbn+/OFNuuTs3PAy/Yfhl4WdKXgeuB\nuRr0dWr+GHC3GwvY45KOAT8MHOjOFLuuo/k1V5Zu6txSYQ/wc9Vvr28Gztj+drcn2kYta5b0DuBR\n4GcLubprWbPtVbZX2l4J7AL+6RwOeaj3b3s38H5JCyW9hcadYJ/u8jzbqU7Nz9H4Pxgk9QPvBp7t\n6iy7q6P5NSeu6H2JWypI+qXq+O/SeAfGR4Bx4C9oXBHMWTVr/hfAW4EHqivcc57Dd/6rWXNR6tRs\n+2lJXwS+AbwKfMb2Rd+mNxfU/Hv+LeAhSYdovBPlE7bn7O2LJT0CDALXSHoe+CTwRuhOfuUWCBER\nhZsrSzcRETFDCfqIiMIl6CMiCpegj4goXII+IqJwCfqIiMIl6CMiCvd/Ad9Mxyhmg5V8AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc9f588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=\"Biopsy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qW0+f1rf1aFVzpx5s7VG3x6fNnX3a2NmrHT0e9fr86vZaZf3+gPZ6fMMehjVn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\nExVBJBKJfMGJiiASiUS+4Pw/9uBUDOR/g24AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c2cd748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNC 0.93023255814\n",
      "Linear SVC 0.936046511628\n",
      "SVC 0.936046511628\n",
      "RFC 0.941860465116\n",
      "MLP 0.953488372093\n",
      "GBC 0.953488372093\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "import numpy as np\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1, random_state=42),\n",
    "    MLPClassifier(alpha=1),\n",
    "    GradientBoostingClassifier(random_state=42)\n",
    "    ]\n",
    "\n",
    "names = [\"KNC\", \"Linear SVC\", \"SVC\", \"RFC\", \"MLP\", \"GBC\"]\n",
    "\n",
    "\n",
    "clfs = list(zip(classifiers, names))\n",
    "\n",
    "for clf, n in clfs:\n",
    "    clf.fit(X_train, y_train)\n",
    "    preds = np.rint(clf.predict(X_test))\n",
    "    print(n, accuracy_score(y_test, preds))\n",
    "\n",
    "vclf = VotingClassifier(estimators=clfs, voting=\"hard\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'classifiers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-692794c2e98b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m for idx, clf, tt in zip(product([0, 1], [0, 1]),\n\u001b[0;32m---> 20\u001b[0;31m                         \u001b[0mclassifiers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m                         names):\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'classifiers' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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3vzaXCQEAFti83iD/h0meTvIbSX4ryVer6tc2Lqqqo1V1pqrOXLhwYU5PDXBtOMOArZgl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143ce128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.gaussian_process.kernels import Product\n",
    "from itertools import product\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "pca.fit(X_train)\n",
    "\n",
    "X_train2d = pca.transform(X_train)\n",
    "\n",
    "# Plotting decision regions\n",
    "x_min, x_max = X_train2d[:, 0].min() - 1, X_train2d[:, 0].max() + 1\n",
    "y_min, y_max = X_train2d[:, 1].min() - 1, X_train2d[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8))\n",
    "\n",
    "for idx, clf, tt in zip(product([0, 1], [0, 1]),\n",
    "                        classifiers,\n",
    "                        names):\n",
    "\n",
    "    clf.fit(X_train2d, y)\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4)\n",
    "    axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y,\n",
    "                                  s=20, edgecolor='k')\n",
    "    axarr[idx[0], idx[1]].set_title(tt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the SVM using training data\n",
    "svmc=svm.SVC()\n",
    "svmc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lauramann/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      1.00      0.97       161\n",
      "          1       0.00      0.00      0.00        11\n",
      "\n",
      "avg / total       0.88      0.94      0.91       172\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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WAUi6Fjizi2v8D+BUgIhoB16VNKDTOUek22Pp/rYkCa8/cFNEvJZeI8vY2/dJ\n+i5JN3hbkqFtHWZFxJvAIknPp9/hCGCfkvtz26fXfi7DtWwz4wTXXF6PiP1KC9Ik9o/SIuCeiDip\n03kb/V4vCfheRPy00zW+UEFd04HjI+JxSacBh5Uc6zyOMNJrnxMRpYkQSUMquLYVnLuoxfMQcLCk\nYQCStpG0F/AMMETSnul5J3Xz+3OAs9Lf7SNpe+BvJK2zDrOB00vu7bVJ2hm4Dzhe0laS+pN0h3vS\nH1ghaQvg5E7HTpDUksa8B/Bseu2z0vORtJekbTJcxzZDbsEVTESsSltCMyX1S4sviIjnJJ0J3CHp\nNZIubv8uqjgXmJrOotEOnBURD0p6IH0N4zfpfbj3Ag+mLci/A6dExHxJ1wOPAytJpozqyf8CHgZW\npT9LY/ozMBfYDvhMRLwh6UqSe3PzlVx8FXB8tj8d29x4NhEzKyx3Uc2ssJzgzKywnODMrLCc4Mys\nsJzgzKywnODMrLCc4MyssP4/c590Likr2gEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140911d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.94186046511627908"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svmc_pred = svmc.predict(X_test)\n",
    "print(classification_report(y_test, svmc_pred))\n",
    "skplt.metrics.plot_confusion_matrix(y_true=y_test, y_pred=svmc_pred)\n",
    "plt.show()#Evaluating the classifier using training set\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred=rfc.predict(X_test)\n",
    "accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94186046511627908"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluating the classifier using training set\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred=rfc.predict(X_test)\n",
    "accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'slice'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-f267b0639167>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0mX0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\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[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_meshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2060\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2067\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2068\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2069\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2071\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1530\u001b[0m         \u001b[0;34m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'slice'"
     ]
    },
    {
     "data": {
      "image/png": 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4tAbMBc28VT99VFXXkhwFzgBzwPGqupTkSNP/TDP0z4B/qqr/Gpn+eeBkkuuP9cOq+kmf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\nkloWBUlSy6IgSWr9H9VV9GoL1Ld8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112add208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_meshgrid(x, y, h=.02):\n",
    "    \"\"\"Create a mesh of points to plot in\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: data to base x-axis meshgrid on\n",
    "    y: data to base y-axis meshgrid on\n",
    "    h: stepsize for meshgrid, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    xx, yy : ndarray\n",
    "    \"\"\"\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    return xx, yy\n",
    "\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out\n",
    "\n",
    "\n",
    "\n",
    "# we create an instance of SVM and fit out data. We do not scale our\n",
    "# data since we want to plot the support vectors\n",
    "C = 1.0  # SVM regularization parameter\n",
    "models = (svm.SVC(kernel='linear', C=C),\n",
    "          svm.LinearSVC(C=C),\n",
    "          svm.SVC(kernel='rbf', gamma=0.7, C=C),\n",
    "          svm.SVC(kernel='poly', degree=3, C=C))\n",
    "models = (clf.fit(X_train, y_train) for clf in models)\n",
    "\n",
    "# title for the plots\n",
    "titles = ('SVC with linear kernel',\n",
    "          'LinearSVC (linear kernel)',\n",
    "          'SVC with RBF kernel',\n",
    "          'SVC with polynomial (degree 3) kernel')\n",
    "\n",
    "# Set-up 2x2 grid for plotting.\n",
    "fig, sub = plt.subplots(2, 2)\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "X0, X1 = X_train[:, 0], X_train[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "for clf, title, ax in zip(models, titles, sub.flatten()):\n",
    "    plot_contours(ax, clf, xx, yy,\n",
    "                  cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "    ax.scatter(X0, X1, c=y_test, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xlabel('Sepal length')\n",
    "    ax.set_ylabel('Sepal width')\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=1,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "              min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "              n_estimators=100, presort='auto', random_state=None,\n",
       "              subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbc = GradientBoostingClassifier(max_depth=1)\n",
    "gbc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95930232558139539"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluating the classifier using training set\n",
    "from sklearn.metrics import accuracy_score\n",
    "gbcy_pred=gbc.predict(X_test)\n",
    "accuracy_score(gbcy_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1JkMv4F2SbqpXIDFbzTVLFzXLXdSzganAZiTrL/1K0lm1DszMGpcybvWWpQV3LLBTRLwF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\nCc7MCssJzswKywnOzArr/wAs1T1piAeoQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148b5b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(y_true=y_test, y_pred=gbcy_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XXnQ9uiSUTHVk/qMRnIyv6bNhmsuNTRDzmFrI4/Lpf0bh2e9BPb9iu5DXc400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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124b43048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<hypertools.datageometry.DataGeometry at 0x122788550>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import hypertools as hyp\n",
    "\n",
    "hyp.plot(X, '.', reduce='TSNE', group=y, ndims=2)\n",
    "# hyp.plot(X, '.', reduce='TSNE', n_clusters=10, ndims=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-4dae74ab4dae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mhyp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/hypertools/plot/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(x, fmt, marker, markers, linestyle, linestyles, color, colors, palette, group, labels, legend, title, elev, azim, ndims, model, model_params, reduce, cluster, align, normalize, n_clusters, save_path, animate, duration, tail_duration, rotations, zoom, chemtrails, precog, bullettime, frame_rate, explore, show, transform)\u001b[0m\n\u001b[1;32m    333\u001b[0m                             \u001b[0mazim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mazim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m                             \u001b[0mexplore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexplore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m                             show=show)\n\u001b[0m\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[0;31m# tighten layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/hypertools/plot/draw.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(x, return_data, legend, title, save_path, labels, show, kwargs_list, fmt, group, animate, tail_duration, rotations, zoom, chemtrails, precog, bullettime, frame_rate, elev, azim, duration, explore)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# add labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0madd_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexplore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexplore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;31m# add title\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/hypertools/plot/draw.py\u001b[0m in \u001b[0;36madd_labels\u001b[0;34m(x, labels, explore)\u001b[0m\n\u001b[1;32m    161\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m                 \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mannotate_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m             \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmpl_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'button_press_event'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhide_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmpl_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'button_release_event'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate_position\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/hypertools/plot/draw.py\u001b[0m in \u001b[0;36mannotate_plot\u001b[0;34m(data, labels)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m                     \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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pVNEexpRE2BQxgoiMo4+ICsUFaSKJRCLCpogFDAaDiIyjjBAiMQcTjYiMYwAhxtFFpVIJ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\nA4ABCGMAMABhDAAGIIwBwACEMQAY4B88HJKBBu+uwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b14d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# simulate labels\n",
    "labels=[]\n",
    "for idx,i in enumerate(df):\n",
    "    tmp=[]\n",
    "    for iidx,ii in enumerate(i):\n",
    "        if iidx==0:\n",
    "            tmp.append('Subject ' + str(idx))\n",
    "        else:\n",
    "            tmp.append(None)\n",
    "    labels.append(tmp)\n",
    "\n",
    "# plot\n",
    "hyp.plot(df, '.', labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "scale_type must be across, within, row or none.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-159aa4d11b2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# process the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m X = hyp.analyze(X, normalize='false', reduce='TSNE', ndims=10,\n\u001b[0;32m----> 3\u001b[0;31m                 align='hyper')\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# plot it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/hypertools/tools/analyze.py\u001b[0m in \u001b[0;36manalyze\u001b[0;34m(data, normalize, reduce, ndims, align, internal)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0;31m# return processed data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m     return aligner(reducer(normalizer(data, normalize=normalize, internal=internal),\n\u001b[0m\u001b[1;32m     54\u001b[0m                    reduce=reduce, ndims=ndims, internal=internal), align=align)\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/hypertools/_shared/helpers.py\u001b[0m in \u001b[0;36mmemoizer\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmemoizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lauramann/anaconda/lib/python3.6/site-packages/hypertools/tools/normalize.py\u001b[0m in \u001b[0;36mnormalize\u001b[0;34m(x, normalize, internal)\u001b[0m\n\u001b[1;32m     40\u001b[0m     \"\"\"\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mnormalize\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'across'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'within'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'row'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"scale_type must be across, within, row or none.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnormalize\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: scale_type must be across, within, row or none."
     ]
    }
   ],
   "source": [
    "# process the data\n",
    "X = hyp.analyze(X, normalize='false', reduce='TSNE', ndims=10,\n",
    "                align='hyper')\n",
    "\n",
    "# plot it\n",
    "hyp.plot(X, '.', legend=['Group A', 'Group B', 'Group C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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