Models_AllFeatures_20.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# To Plot matplotlib figures inline on the notebook\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# various options in pandas\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 25)\n",
    "pd.set_option('display.precision', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dlicht/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.model_selection import learning_curve, GridSearchCV\n",
    "from sklearn.cross_validation import cross_val_score, KFold, StratifiedKFold,train_test_split\n",
    "from sklearn.model_selection import GroupKFold,LeaveOneGroupOut\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Haar_Data.csv                    gestures_data_2D.csv\r\n",
      "Haar_Data_3D.csv                 gestures_data_3D+Haar.csv\r\n",
      "Haar_Data_3D_only.csv            gestures_data_3D.csv\r\n",
      "gestures_data.csv                gestures_data_3d_haar_no_g.csv\r\n",
      "gestures_data_2D+Haar.csv        gestures_data_3d_no_g.csv\r\n",
      "gestures_data_2D+Haar_minus3.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls *.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
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       "      <th>dim0-coef6</th>\n",
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       "      <th>dim0-coef8</th>\n",
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       "      <th>dim1-coef0</th>\n",
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       "      <th>dim1-coef2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>U01</td>\n",
       "      <td>gestures-dataset/U01/01/01.txt</td>\n",
       "      <td>14.210</td>\n",
       "      <td>1.490</td>\n",
       "      <td>4.377</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>4.668</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.396</td>\n",
       "      <td>0.762</td>\n",
       "      <td>-4.472</td>\n",
       "      <td>5.234</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.849</td>\n",
       "      <td>10.383</td>\n",
       "      <td>9.237</td>\n",
       "      <td>1.146</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>0.549</td>\n",
       "      <td>-0.890</td>\n",
       "      <td>1.439</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.836</td>\n",
       "      <td>2.143</td>\n",
       "      <td>0.094</td>\n",
       "      <td>2.049</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.779</td>\n",
       "      <td>11.353</td>\n",
       "      <td>0.358</td>\n",
       "      <td>10.995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.369</td>\n",
       "      <td>94.484</td>\n",
       "      <td>0.743</td>\n",
       "      <td>93.741</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>1.141</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>2.710</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>1.502</td>\n",
       "      <td>-2.093</td>\n",
       "      <td>3.595</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.288</td>\n",
       "      <td>-0.922</td>\n",
       "      <td>1.211</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>2.845</td>\n",
       "      <td>-1.823</td>\n",
       "      <td>4.668</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>1.681</td>\n",
       "      <td>-3.553</td>\n",
       "      <td>5.234</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.270</td>\n",
       "      <td>-0.877</td>\n",
       "      <td>1.146</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.272</td>\n",
       "      <td>9.039</td>\n",
       "      <td>-0.463</td>\n",
       "      <td>9.502</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.603</td>\n",
       "      <td>3.747</td>\n",
       "      <td>-6.890</td>\n",
       "      <td>10.638</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.510</td>\n",
       "      <td>0.573</td>\n",
       "      <td>-3.209</td>\n",
       "      <td>3.782</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.440</td>\n",
       "      <td>54.619</td>\n",
       "      <td>-0.362</td>\n",
       "      <td>54.982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.756</td>\n",
       "      <td>10.447</td>\n",
       "      <td>-30.563</td>\n",
       "      <td>41.009</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.793</td>\n",
       "      <td>1.378</td>\n",
       "      <td>-17.957</td>\n",
       "      <td>19.335</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.688</td>\n",
       "      <td>5.427</td>\n",
       "      <td>5.395</td>\n",
       "      <td>1.487</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>-0.521</td>\n",
       "      <td>-0.698</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>0.806</td>\n",
       "      <td>-3.537</td>\n",
       "      <td>-5.756</td>\n",
       "      <td>-2.244</td>\n",
       "      <td>-0.711</td>\n",
       "      <td>0.271</td>\n",
       "      <td>1.533</td>\n",
       "      <td>-0.568</td>\n",
       "      <td>-0.813</td>\n",
       "      <td>14.451</td>\n",
       "      <td>14.525</td>\n",
       "      <td>13.476</td>\n",
       "      <td>13.178</td>\n",
       "      <td>13.824</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.269</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>U01</td>\n",
       "      <td>gestures-dataset/U01/01/02.txt</td>\n",
       "      <td>14.242</td>\n",
       "      <td>1.057</td>\n",
       "      <td>3.992</td>\n",
       "      <td>-3.051</td>\n",
       "      <td>7.043</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.381</td>\n",
       "      <td>1.081</td>\n",
       "      <td>-4.462</td>\n",
       "      <td>5.543</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9.941</td>\n",
       "      <td>10.344</td>\n",
       "      <td>9.455</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.537</td>\n",
       "      <td>-0.601</td>\n",
       "      <td>1.138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.842</td>\n",
       "      <td>1.925</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.869</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.112</td>\n",
       "      <td>11.663</td>\n",
       "      <td>0.232</td>\n",
       "      <td>11.431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.435</td>\n",
       "      <td>86.033</td>\n",
       "      <td>0.529</td>\n",
       "      <td>85.504</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.406</td>\n",
       "      <td>1.028</td>\n",
       "      <td>-3.061</td>\n",
       "      <td>4.089</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.122</td>\n",
       "      <td>2.133</td>\n",
       "      <td>-2.041</td>\n",
       "      <td>4.175</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.291</td>\n",
       "      <td>3.226</td>\n",
       "      <td>-3.817</td>\n",
       "      <td>7.043</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.768</td>\n",
       "      <td>1.694</td>\n",
       "      <td>-3.849</td>\n",
       "      <td>5.543</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.537</td>\n",
       "      <td>-0.351</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.273</td>\n",
       "      <td>12.548</td>\n",
       "      <td>0.226</td>\n",
       "      <td>12.321</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.198</td>\n",
       "      <td>3.017</td>\n",
       "      <td>-12.599</td>\n",
       "      <td>15.616</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.439</td>\n",
       "      <td>2.453</td>\n",
       "      <td>0.154</td>\n",
       "      <td>2.299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.704</td>\n",
       "      <td>103.474</td>\n",
       "      <td>0.590</td>\n",
       "      <td>102.884</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-13.233</td>\n",
       "      <td>7.970</td>\n",
       "      <td>-62.085</td>\n",
       "      <td>70.055</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.294</td>\n",
       "      <td>17.451</td>\n",
       "      <td>0.352</td>\n",
       "      <td>17.100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.785</td>\n",
       "      <td>4.733</td>\n",
       "      <td>5.094</td>\n",
       "      <td>1.857</td>\n",
       "      <td>0.125</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-0.663</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-0.312</td>\n",
       "      <td>0.842</td>\n",
       "      <td>-2.847</td>\n",
       "      <td>-6.249</td>\n",
       "      <td>-4.028</td>\n",
       "      <td>-0.550</td>\n",
       "      <td>0.166</td>\n",
       "      <td>1.488</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>-0.728</td>\n",
       "      <td>14.056</td>\n",
       "      <td>14.573</td>\n",
       "      <td>14.268</td>\n",
       "      <td>13.466</td>\n",
       "      <td>13.822</td>\n",
       "      <td>-0.184</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.094</td>\n",
       "      <td>-0.220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label user                        filename  time_range  x_mean  x_max  \\\n",
       "0      1  U01  gestures-dataset/U01/01/01.txt      14.210   1.490  4.377   \n",
       "1      1  U01  gestures-dataset/U01/01/02.txt      14.242   1.057  3.992   \n",
       "\n",
       "   x_min  x_range  x_upturns  x_downturns  x_upcross  x_downcross  y_mean  \\\n",
       "0 -0.291    4.668          1            2          1            2  -1.396   \n",
       "1 -3.051    7.043          1            2          0            1  -1.381   \n",
       "\n",
       "   y_max  y_min  y_range  y_upturns  y_downturns  y_upcross  y_downcross  \\\n",
       "0  0.762 -4.472    5.234          1            2          1            1   \n",
       "1  1.081 -4.462    5.543          1            1          2            1   \n",
       "\n",
       "   z_mean   z_max  z_min  z_range  z_upturns  z_downturns  z_upcross  \\\n",
       "0   9.849  10.383  9.237    1.146          1            2          0   \n",
       "1   9.941  10.344  9.455    0.889          1            2          0   \n",
       "\n",
       "   z_downcross  Mjerk_mean  Mjerk_max  Mjerk_min  Mjerk_range  Mjerk_upturns  \\\n",
       "0            0      -0.147      0.549     -0.890        1.439              1   \n",
       "1            0       0.011      0.537     -0.601        1.138              1   \n",
       "\n",
       "   Mjerk_downturns  Mjerk_upcross  Mjerk_downcross  Macc_mean  Macc_max  \\\n",
       "0                2              2                2      0.836     2.143   \n",
       "1                2              1                2      0.842     1.925   \n",
       "\n",
       "   Macc_min  Macc_range  Macc_upturns  Macc_downturns  Macc_upcross  \\\n",
       "0     0.094       2.049             1               2             0   \n",
       "1     0.057       1.869             1               2             0   \n",
       "\n",
       "   Macc_downcross  Mvel_mean  Mvel_max  Mvel_min  Mvel_range  Mvel_upturns  \\\n",
       "0               0      7.779    11.353     0.358      10.995             0   \n",
       "1               0      7.112    11.663     0.232      11.431             0   \n",
       "\n",
       "   Mvel_downturns  Mvel_upcross  Mvel_downcross  Mdist_mean  Mdist_max  \\\n",
       "0               0             0               0      40.369     94.484   \n",
       "1               0             0               0      34.435     86.033   \n",
       "\n",
       "   Mdist_min  Mdist_range  Mdist_upturns  Mdist_downturns  Mdist_upcross  \\\n",
       "0      0.743       93.741              0                0              0   \n",
       "1      0.529       85.504              0                0              0   \n",
       "\n",
       "   Mdist_downcross  jerk0_mean  jerk0_max  jerk0_min  jerk0_range  \\\n",
       "0                0      -0.210      1.141     -1.569        2.710   \n",
       "1                0      -0.406      1.028     -3.061        4.089   \n",
       "\n",
       "   jerk0_upturns  jerk0_downturns  jerk0_upcross  jerk0_downcross  jerk1_mean  \\\n",
       "0              1                2              2                2      -0.123   \n",
       "1              1                2              1                2       0.122   \n",
       "\n",
       "   jerk1_max  jerk1_min  jerk1_range  jerk1_upturns  jerk1_downturns  \\\n",
       "0      1.502     -2.093        3.595              2                2   \n",
       "1      2.133     -2.041        4.175              2                2   \n",
       "\n",
       "   jerk1_upcross  jerk1_downcross  jerk2_mean  jerk2_max  jerk2_min  \\\n",
       "0              2                2      -0.133      0.288     -0.922   \n",
       "1              2                1      -0.034      0.333     -0.766   \n",
       "\n",
       "   jerk2_range  jerk2_upturns  jerk2_downturns  jerk2_upcross  \\\n",
       "0        1.211              1                2              1   \n",
       "1        1.099              1                1              1   \n",
       "\n",
       "   jerk2_downcross  acc_g0_mean  acc_g0_max  acc_g0_min  acc_g0_range  \\\n",
       "0                1       -0.043       2.845      -1.823         4.668   \n",
       "1                2        0.291       3.226      -3.817         7.043   \n",
       "\n",
       "   acc_g0_upturns  acc_g0_downturns  acc_g0_upcross  acc_g0_downcross  \\\n",
       "0               1                 2               0                 1   \n",
       "1               1                 2               0                 1   \n",
       "\n",
       "   acc_g1_mean  acc_g1_max  acc_g1_min  acc_g1_range  acc_g1_upturns  \\\n",
       "0       -0.477       1.681      -3.553         5.234               1   \n",
       "1       -0.768       1.694      -3.849         5.543               1   \n",
       "\n",
       "   acc_g1_downturns  acc_g1_upcross  acc_g1_downcross  acc_g2_mean  \\\n",
       "0                 2               1                 2       -0.264   \n",
       "1                 1               1                 1        0.135   \n",
       "\n",
       "   acc_g2_max  acc_g2_min  acc_g2_range  acc_g2_upturns  acc_g2_downturns  \\\n",
       "0       0.270      -0.877         1.146               1                 2   \n",
       "1       0.537      -0.351         0.889               1                 2   \n",
       "\n",
       "   acc_g2_upcross  acc_g2_downcross  vel_g0_mean  vel_g0_max  vel_g0_min  \\\n",
       "0               0                 1        4.272       9.039      -0.463   \n",
       "1               1                 2        8.273      12.548       0.226   \n",
       "\n",
       "   vel_g0_range  vel_g0_upturns  vel_g0_downturns  vel_g0_upcross  \\\n",
       "0         9.502               0                 1               1   \n",
       "1        12.321               0                 1               0   \n",
       "\n",
       "   vel_g0_downcross  vel_g1_mean  vel_g1_max  vel_g1_min  vel_g1_range  \\\n",
       "0                 1       -2.603       3.747      -6.890        10.638   \n",
       "1                 0       -5.198       3.017     -12.599        15.616   \n",
       "\n",
       "   vel_g1_upturns  vel_g1_downturns  vel_g1_upcross  vel_g1_downcross  \\\n",
       "0               1                 2               0                 1   \n",
       "1               1                 1               1                 1   \n",
       "\n",
       "   vel_g2_mean  vel_g2_max  vel_g2_min  vel_g2_range  vel_g2_upturns  \\\n",
       "0       -1.510       0.573      -3.209         3.782               0   \n",
       "1        1.439       2.453       0.154         2.299               1   \n",
       "\n",
       "   vel_g2_downturns  vel_g2_upcross  vel_g2_downcross  pos_g0_mean  \\\n",
       "0                 1               1                 1       30.440   \n",
       "1                 1               0                 0       48.704   \n",
       "\n",
       "   pos_g0_max  pos_g0_min  pos_g0_range  pos_g0_upturns  pos_g0_downturns  \\\n",
       "0      54.619      -0.362        54.982               0                 0   \n",
       "1     103.474       0.590       102.884               0                 0   \n",
       "\n",
       "   pos_g0_upcross  pos_g0_downcross  pos_g1_mean  pos_g1_max  pos_g1_min  \\\n",
       "0               1                 0       -3.756      10.447     -30.563   \n",
       "1               0                 0      -13.233       7.970     -62.085   \n",
       "\n",
       "   pos_g1_range  pos_g1_upturns  pos_g1_downturns  pos_g1_upcross  \\\n",
       "0        41.009               0                 1               0   \n",
       "1        70.055               0                 1               0   \n",
       "\n",
       "   pos_g1_downcross  pos_g2_mean  pos_g2_max  pos_g2_min  pos_g2_range  \\\n",
       "0                 1       -4.793       1.378     -17.957        19.335   \n",
       "1                 1        8.294      17.451       0.352        17.100   \n",
       "\n",
       "   pos_g2_upturns  pos_g2_downturns  pos_g2_upcross  pos_g2_downcross  \\\n",
       "0               0                 1               0                 1   \n",
       "1               0                 0               0                 0   \n",
       "\n",
       "   dim0-coef0  dim0-coef1  dim0-coef2  dim0-coef3  dim0-coef4  dim0-coef5  \\\n",
       "0       2.688       5.427       5.395       1.487      -0.154      -0.521   \n",
       "1       1.785       4.733       5.094       1.857       0.125      -0.700   \n",
       "\n",
       "   dim0-coef6  dim0-coef7  dim0-coef8  dim0-coef9  dim1-coef0  dim1-coef1  \\\n",
       "0      -0.698       0.795        0.86       0.024      -0.583       0.806   \n",
       "1      -0.663       0.551        0.83       0.045      -0.312       0.842   \n",
       "\n",
       "   dim1-coef2  dim1-coef3  dim1-coef4  dim1-coef5  dim1-coef6  dim1-coef7  \\\n",
       "0      -3.537      -5.756      -2.244      -0.711       0.271       1.533   \n",
       "1      -2.847      -6.249      -4.028      -0.550       0.166       1.488   \n",
       "\n",
       "   dim1-coef8  dim1-coef9  dim2-coef0  dim2-coef1  dim2-coef2  dim2-coef3  \\\n",
       "0      -0.568      -0.813      14.451      14.525      13.476      13.178   \n",
       "1      -0.061      -0.728      14.056      14.573      14.268      13.466   \n",
       "\n",
       "   dim2-coef4  dim2-coef5  dim2-coef6  dim2-coef7  dim2-coef8  dim2-coef9  \n",
       "0      13.824      -0.148       0.158       0.269      -0.115      -0.152  \n",
       "1      13.822      -0.184      -0.055       0.215       0.094      -0.220  "
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv('gestures_data_3D.csv',index_col=0)\n",
    "#df = pd.read_csv('gestures_data_2D.csv',index_col=0)\n",
    "#df = pd.read_csv('gestures_data_3d_haar_no_g.csv',index_col=0)\n",
    "#df = pd.read_csv('gestures_data_3d_no_g.csv',index_col=0)\n",
    "#df = pd.read_csv('gestures_data_2D+Haar.csv',index_col=0)\n",
    "#df = pd.read_csv('gestures_data_2D+Haar_minus3.csv',index_col=0)\n",
    "df = pd.read_csv('gestures_data_3D+Haar.csv',index_col=0)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#if limiting to looking only at certain gestures\n",
    "#df = df[df['label']<=12]\n",
    "#df = df[df['label']>=6]\n",
    "#df.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .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>label</th>\n",
       "      <th>user</th>\n",
       "      <th>filename</th>\n",
       "      <th>time_range</th>\n",
       "      <th>x_mean</th>\n",
       "      <th>x_max</th>\n",
       "      <th>x_min</th>\n",
       "      <th>x_range</th>\n",
       "      <th>x_upturns</th>\n",
       "      <th>x_downturns</th>\n",
       "      <th>x_upcross</th>\n",
       "      <th>x_downcross</th>\n",
       "      <th>y_mean</th>\n",
       "      <th>y_max</th>\n",
       "      <th>y_min</th>\n",
       "      <th>y_range</th>\n",
       "      <th>y_upturns</th>\n",
       "      <th>y_downturns</th>\n",
       "      <th>y_upcross</th>\n",
       "      <th>y_downcross</th>\n",
       "      <th>z_mean</th>\n",
       "      <th>z_max</th>\n",
       "      <th>z_min</th>\n",
       "      <th>z_range</th>\n",
       "      <th>z_upturns</th>\n",
       "      <th>z_downturns</th>\n",
       "      <th>z_upcross</th>\n",
       "      <th>z_downcross</th>\n",
       "      <th>Mjerk_mean</th>\n",
       "      <th>Mjerk_max</th>\n",
       "      <th>Mjerk_min</th>\n",
       "      <th>Mjerk_range</th>\n",
       "      <th>Mjerk_upturns</th>\n",
       "      <th>Mjerk_downturns</th>\n",
       "      <th>Mjerk_upcross</th>\n",
       "      <th>Mjerk_downcross</th>\n",
       "      <th>Macc_mean</th>\n",
       "      <th>Macc_max</th>\n",
       "      <th>Macc_min</th>\n",
       "      <th>Macc_range</th>\n",
       "      <th>Macc_upturns</th>\n",
       "      <th>Macc_downturns</th>\n",
       "      <th>Macc_upcross</th>\n",
       "      <th>Macc_downcross</th>\n",
       "      <th>Mvel_mean</th>\n",
       "      <th>Mvel_max</th>\n",
       "      <th>Mvel_min</th>\n",
       "      <th>Mvel_range</th>\n",
       "      <th>Mvel_upturns</th>\n",
       "      <th>Mvel_downturns</th>\n",
       "      <th>Mvel_upcross</th>\n",
       "      <th>Mvel_downcross</th>\n",
       "      <th>Mdist_mean</th>\n",
       "      <th>Mdist_max</th>\n",
       "      <th>Mdist_min</th>\n",
       "      <th>Mdist_range</th>\n",
       "      <th>Mdist_upturns</th>\n",
       "      <th>Mdist_downturns</th>\n",
       "      <th>Mdist_upcross</th>\n",
       "      <th>Mdist_downcross</th>\n",
       "      <th>jerk0_mean</th>\n",
       "      <th>jerk0_max</th>\n",
       "      <th>jerk0_min</th>\n",
       "      <th>jerk0_range</th>\n",
       "      <th>jerk0_upturns</th>\n",
       "      <th>jerk0_downturns</th>\n",
       "      <th>jerk0_upcross</th>\n",
       "      <th>jerk0_downcross</th>\n",
       "      <th>jerk1_mean</th>\n",
       "      <th>jerk1_max</th>\n",
       "      <th>jerk1_min</th>\n",
       "      <th>jerk1_range</th>\n",
       "      <th>jerk1_upturns</th>\n",
       "      <th>jerk1_downturns</th>\n",
       "      <th>jerk1_upcross</th>\n",
       "      <th>jerk1_downcross</th>\n",
       "      <th>jerk2_mean</th>\n",
       "      <th>jerk2_max</th>\n",
       "      <th>jerk2_min</th>\n",
       "      <th>jerk2_range</th>\n",
       "      <th>jerk2_upturns</th>\n",
       "      <th>jerk2_downturns</th>\n",
       "      <th>jerk2_upcross</th>\n",
       "      <th>jerk2_downcross</th>\n",
       "      <th>acc_g0_mean</th>\n",
       "      <th>acc_g0_max</th>\n",
       "      <th>acc_g0_min</th>\n",
       "      <th>acc_g0_range</th>\n",
       "      <th>acc_g0_upturns</th>\n",
       "      <th>acc_g0_downturns</th>\n",
       "      <th>acc_g0_upcross</th>\n",
       "      <th>acc_g0_downcross</th>\n",
       "      <th>acc_g1_mean</th>\n",
       "      <th>acc_g1_max</th>\n",
       "      <th>acc_g1_min</th>\n",
       "      <th>acc_g1_range</th>\n",
       "      <th>acc_g1_upturns</th>\n",
       "      <th>acc_g1_downturns</th>\n",
       "      <th>acc_g1_upcross</th>\n",
       "      <th>acc_g1_downcross</th>\n",
       "      <th>acc_g2_mean</th>\n",
       "      <th>acc_g2_max</th>\n",
       "      <th>acc_g2_min</th>\n",
       "      <th>acc_g2_range</th>\n",
       "      <th>acc_g2_upturns</th>\n",
       "      <th>acc_g2_downturns</th>\n",
       "      <th>acc_g2_upcross</th>\n",
       "      <th>acc_g2_downcross</th>\n",
       "      <th>vel_g0_mean</th>\n",
       "      <th>vel_g0_max</th>\n",
       "      <th>vel_g0_min</th>\n",
       "      <th>vel_g0_range</th>\n",
       "      <th>vel_g0_upturns</th>\n",
       "      <th>vel_g0_downturns</th>\n",
       "      <th>vel_g0_upcross</th>\n",
       "      <th>vel_g0_downcross</th>\n",
       "      <th>vel_g1_mean</th>\n",
       "      <th>vel_g1_max</th>\n",
       "      <th>vel_g1_min</th>\n",
       "      <th>vel_g1_range</th>\n",
       "      <th>vel_g1_upturns</th>\n",
       "      <th>vel_g1_downturns</th>\n",
       "      <th>vel_g1_upcross</th>\n",
       "      <th>vel_g1_downcross</th>\n",
       "      <th>vel_g2_mean</th>\n",
       "      <th>vel_g2_max</th>\n",
       "      <th>vel_g2_min</th>\n",
       "      <th>vel_g2_range</th>\n",
       "      <th>vel_g2_upturns</th>\n",
       "      <th>vel_g2_downturns</th>\n",
       "      <th>vel_g2_upcross</th>\n",
       "      <th>vel_g2_downcross</th>\n",
       "      <th>pos_g0_mean</th>\n",
       "      <th>pos_g0_max</th>\n",
       "      <th>pos_g0_min</th>\n",
       "      <th>pos_g0_range</th>\n",
       "      <th>pos_g0_upturns</th>\n",
       "      <th>pos_g0_downturns</th>\n",
       "      <th>pos_g0_upcross</th>\n",
       "      <th>pos_g0_downcross</th>\n",
       "      <th>pos_g1_mean</th>\n",
       "      <th>pos_g1_max</th>\n",
       "      <th>pos_g1_min</th>\n",
       "      <th>pos_g1_range</th>\n",
       "      <th>pos_g1_upturns</th>\n",
       "      <th>pos_g1_downturns</th>\n",
       "      <th>pos_g1_upcross</th>\n",
       "      <th>pos_g1_downcross</th>\n",
       "      <th>pos_g2_mean</th>\n",
       "      <th>pos_g2_max</th>\n",
       "      <th>pos_g2_min</th>\n",
       "      <th>pos_g2_range</th>\n",
       "      <th>pos_g2_upturns</th>\n",
       "      <th>pos_g2_downturns</th>\n",
       "      <th>pos_g2_upcross</th>\n",
       "      <th>pos_g2_downcross</th>\n",
       "      <th>dim0-coef0</th>\n",
       "      <th>dim0-coef1</th>\n",
       "      <th>dim0-coef2</th>\n",
       "      <th>dim0-coef3</th>\n",
       "      <th>dim0-coef4</th>\n",
       "      <th>dim0-coef5</th>\n",
       "      <th>dim0-coef6</th>\n",
       "      <th>dim0-coef7</th>\n",
       "      <th>dim0-coef8</th>\n",
       "      <th>dim0-coef9</th>\n",
       "      <th>dim1-coef0</th>\n",
       "      <th>dim1-coef1</th>\n",
       "      <th>dim1-coef2</th>\n",
       "      <th>dim1-coef3</th>\n",
       "      <th>dim1-coef4</th>\n",
       "      <th>dim1-coef5</th>\n",
       "      <th>dim1-coef6</th>\n",
       "      <th>dim1-coef7</th>\n",
       "      <th>dim1-coef8</th>\n",
       "      <th>dim1-coef9</th>\n",
       "      <th>dim2-coef0</th>\n",
       "      <th>dim2-coef1</th>\n",
       "      <th>dim2-coef2</th>\n",
       "      <th>dim2-coef3</th>\n",
       "      <th>dim2-coef4</th>\n",
       "      <th>dim2-coef5</th>\n",
       "      <th>dim2-coef6</th>\n",
       "      <th>dim2-coef7</th>\n",
       "      <th>dim2-coef8</th>\n",
       "      <th>dim2-coef9</th>\n",
       "      <th>label2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>U01</td>\n",
       "      <td>gestures-dataset/U01/01/01.txt</td>\n",
       "      <td>14.210</td>\n",
       "      <td>1.490</td>\n",
       "      <td>4.377</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>4.668</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.396</td>\n",
       "      <td>0.762</td>\n",
       "      <td>-4.472</td>\n",
       "      <td>5.234</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.849</td>\n",
       "      <td>10.383</td>\n",
       "      <td>9.237</td>\n",
       "      <td>1.146</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>0.549</td>\n",
       "      <td>-0.890</td>\n",
       "      <td>1.439</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.836</td>\n",
       "      <td>2.143</td>\n",
       "      <td>0.094</td>\n",
       "      <td>2.049</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.779</td>\n",
       "      <td>11.353</td>\n",
       "      <td>0.358</td>\n",
       "      <td>10.995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.369</td>\n",
       "      <td>94.484</td>\n",
       "      <td>0.743</td>\n",
       "      <td>93.741</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>1.141</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>2.710</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>1.502</td>\n",
       "      <td>-2.093</td>\n",
       "      <td>3.595</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.288</td>\n",
       "      <td>-0.922</td>\n",
       "      <td>1.211</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>2.845</td>\n",
       "      <td>-1.823</td>\n",
       "      <td>4.668</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>1.681</td>\n",
       "      <td>-3.553</td>\n",
       "      <td>5.234</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.270</td>\n",
       "      <td>-0.877</td>\n",
       "      <td>1.146</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.272</td>\n",
       "      <td>9.039</td>\n",
       "      <td>-0.463</td>\n",
       "      <td>9.502</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.603</td>\n",
       "      <td>3.747</td>\n",
       "      <td>-6.890</td>\n",
       "      <td>10.638</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.510</td>\n",
       "      <td>0.573</td>\n",
       "      <td>-3.209</td>\n",
       "      <td>3.782</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.440</td>\n",
       "      <td>54.619</td>\n",
       "      <td>-0.362</td>\n",
       "      <td>54.982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.756</td>\n",
       "      <td>10.447</td>\n",
       "      <td>-30.563</td>\n",
       "      <td>41.009</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.793</td>\n",
       "      <td>1.378</td>\n",
       "      <td>-17.957</td>\n",
       "      <td>19.335</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.688</td>\n",
       "      <td>5.427</td>\n",
       "      <td>5.395</td>\n",
       "      <td>1.487</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>-0.521</td>\n",
       "      <td>-0.698</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>0.806</td>\n",
       "      <td>-3.537</td>\n",
       "      <td>-5.756</td>\n",
       "      <td>-2.244</td>\n",
       "      <td>-0.711</td>\n",
       "      <td>0.271</td>\n",
       "      <td>1.533</td>\n",
       "      <td>-0.568</td>\n",
       "      <td>-0.813</td>\n",
       "      <td>14.451</td>\n",
       "      <td>14.525</td>\n",
       "      <td>13.476</td>\n",
       "      <td>13.178</td>\n",
       "      <td>13.824</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.269</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.152</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>U01</td>\n",
       "      <td>gestures-dataset/U01/01/02.txt</td>\n",
       "      <td>14.242</td>\n",
       "      <td>1.057</td>\n",
       "      <td>3.992</td>\n",
       "      <td>-3.051</td>\n",
       "      <td>7.043</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.381</td>\n",
       "      <td>1.081</td>\n",
       "      <td>-4.462</td>\n",
       "      <td>5.543</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9.941</td>\n",
       "      <td>10.344</td>\n",
       "      <td>9.455</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.537</td>\n",
       "      <td>-0.601</td>\n",
       "      <td>1.138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.842</td>\n",
       "      <td>1.925</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.869</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.112</td>\n",
       "      <td>11.663</td>\n",
       "      <td>0.232</td>\n",
       "      <td>11.431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.435</td>\n",
       "      <td>86.033</td>\n",
       "      <td>0.529</td>\n",
       "      <td>85.504</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.406</td>\n",
       "      <td>1.028</td>\n",
       "      <td>-3.061</td>\n",
       "      <td>4.089</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.122</td>\n",
       "      <td>2.133</td>\n",
       "      <td>-2.041</td>\n",
       "      <td>4.175</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.291</td>\n",
       "      <td>3.226</td>\n",
       "      <td>-3.817</td>\n",
       "      <td>7.043</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.768</td>\n",
       "      <td>1.694</td>\n",
       "      <td>-3.849</td>\n",
       "      <td>5.543</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.537</td>\n",
       "      <td>-0.351</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.273</td>\n",
       "      <td>12.548</td>\n",
       "      <td>0.226</td>\n",
       "      <td>12.321</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.198</td>\n",
       "      <td>3.017</td>\n",
       "      <td>-12.599</td>\n",
       "      <td>15.616</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.439</td>\n",
       "      <td>2.453</td>\n",
       "      <td>0.154</td>\n",
       "      <td>2.299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.704</td>\n",
       "      <td>103.474</td>\n",
       "      <td>0.590</td>\n",
       "      <td>102.884</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-13.233</td>\n",
       "      <td>7.970</td>\n",
       "      <td>-62.085</td>\n",
       "      <td>70.055</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.294</td>\n",
       "      <td>17.451</td>\n",
       "      <td>0.352</td>\n",
       "      <td>17.100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.785</td>\n",
       "      <td>4.733</td>\n",
       "      <td>5.094</td>\n",
       "      <td>1.857</td>\n",
       "      <td>0.125</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-0.663</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-0.312</td>\n",
       "      <td>0.842</td>\n",
       "      <td>-2.847</td>\n",
       "      <td>-6.249</td>\n",
       "      <td>-4.028</td>\n",
       "      <td>-0.550</td>\n",
       "      <td>0.166</td>\n",
       "      <td>1.488</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>-0.728</td>\n",
       "      <td>14.056</td>\n",
       "      <td>14.573</td>\n",
       "      <td>14.268</td>\n",
       "      <td>13.466</td>\n",
       "      <td>13.822</td>\n",
       "      <td>-0.184</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.094</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label user                        filename  time_range  x_mean  x_max  \\\n",
       "0      1  U01  gestures-dataset/U01/01/01.txt      14.210   1.490  4.377   \n",
       "1      1  U01  gestures-dataset/U01/01/02.txt      14.242   1.057  3.992   \n",
       "\n",
       "   x_min  x_range  x_upturns  x_downturns  x_upcross  x_downcross  y_mean  \\\n",
       "0 -0.291    4.668          1            2          1            2  -1.396   \n",
       "1 -3.051    7.043          1            2          0            1  -1.381   \n",
       "\n",
       "   y_max  y_min  y_range  y_upturns  y_downturns  y_upcross  y_downcross  \\\n",
       "0  0.762 -4.472    5.234          1            2          1            1   \n",
       "1  1.081 -4.462    5.543          1            1          2            1   \n",
       "\n",
       "   z_mean   z_max  z_min  z_range  z_upturns  z_downturns  z_upcross  \\\n",
       "0   9.849  10.383  9.237    1.146          1            2          0   \n",
       "1   9.941  10.344  9.455    0.889          1            2          0   \n",
       "\n",
       "   z_downcross  Mjerk_mean  Mjerk_max  Mjerk_min  Mjerk_range  Mjerk_upturns  \\\n",
       "0            0      -0.147      0.549     -0.890        1.439              1   \n",
       "1            0       0.011      0.537     -0.601        1.138              1   \n",
       "\n",
       "   Mjerk_downturns  Mjerk_upcross  Mjerk_downcross  Macc_mean  Macc_max  \\\n",
       "0                2              2                2      0.836     2.143   \n",
       "1                2              1                2      0.842     1.925   \n",
       "\n",
       "   Macc_min  Macc_range  Macc_upturns  Macc_downturns  Macc_upcross  \\\n",
       "0     0.094       2.049             1               2             0   \n",
       "1     0.057       1.869             1               2             0   \n",
       "\n",
       "   Macc_downcross  Mvel_mean  Mvel_max  Mvel_min  Mvel_range  Mvel_upturns  \\\n",
       "0               0      7.779    11.353     0.358      10.995             0   \n",
       "1               0      7.112    11.663     0.232      11.431             0   \n",
       "\n",
       "   Mvel_downturns  Mvel_upcross  Mvel_downcross  Mdist_mean  Mdist_max  \\\n",
       "0               0             0               0      40.369     94.484   \n",
       "1               0             0               0      34.435     86.033   \n",
       "\n",
       "   Mdist_min  Mdist_range  Mdist_upturns  Mdist_downturns  Mdist_upcross  \\\n",
       "0      0.743       93.741              0                0              0   \n",
       "1      0.529       85.504              0                0              0   \n",
       "\n",
       "   Mdist_downcross  jerk0_mean  jerk0_max  jerk0_min  jerk0_range  \\\n",
       "0                0      -0.210      1.141     -1.569        2.710   \n",
       "1                0      -0.406      1.028     -3.061        4.089   \n",
       "\n",
       "   jerk0_upturns  jerk0_downturns  jerk0_upcross  jerk0_downcross  jerk1_mean  \\\n",
       "0              1                2              2                2      -0.123   \n",
       "1              1                2              1                2       0.122   \n",
       "\n",
       "   jerk1_max  jerk1_min  jerk1_range  jerk1_upturns  jerk1_downturns  \\\n",
       "0      1.502     -2.093        3.595              2                2   \n",
       "1      2.133     -2.041        4.175              2                2   \n",
       "\n",
       "   jerk1_upcross  jerk1_downcross  jerk2_mean  jerk2_max  jerk2_min  \\\n",
       "0              2                2      -0.133      0.288     -0.922   \n",
       "1              2                1      -0.034      0.333     -0.766   \n",
       "\n",
       "   jerk2_range  jerk2_upturns  jerk2_downturns  jerk2_upcross  \\\n",
       "0        1.211              1                2              1   \n",
       "1        1.099              1                1              1   \n",
       "\n",
       "   jerk2_downcross  acc_g0_mean  acc_g0_max  acc_g0_min  acc_g0_range  \\\n",
       "0                1       -0.043       2.845      -1.823         4.668   \n",
       "1                2        0.291       3.226      -3.817         7.043   \n",
       "\n",
       "   acc_g0_upturns  acc_g0_downturns  acc_g0_upcross  acc_g0_downcross  \\\n",
       "0               1                 2               0                 1   \n",
       "1               1                 2               0                 1   \n",
       "\n",
       "   acc_g1_mean  acc_g1_max  acc_g1_min  acc_g1_range  acc_g1_upturns  \\\n",
       "0       -0.477       1.681      -3.553         5.234               1   \n",
       "1       -0.768       1.694      -3.849         5.543               1   \n",
       "\n",
       "   acc_g1_downturns  acc_g1_upcross  acc_g1_downcross  acc_g2_mean  \\\n",
       "0                 2               1                 2       -0.264   \n",
       "1                 1               1                 1        0.135   \n",
       "\n",
       "   acc_g2_max  acc_g2_min  acc_g2_range  acc_g2_upturns  acc_g2_downturns  \\\n",
       "0       0.270      -0.877         1.146               1                 2   \n",
       "1       0.537      -0.351         0.889               1                 2   \n",
       "\n",
       "   acc_g2_upcross  acc_g2_downcross  vel_g0_mean  vel_g0_max  vel_g0_min  \\\n",
       "0               0                 1        4.272       9.039      -0.463   \n",
       "1               1                 2        8.273      12.548       0.226   \n",
       "\n",
       "   vel_g0_range  vel_g0_upturns  vel_g0_downturns  vel_g0_upcross  \\\n",
       "0         9.502               0                 1               1   \n",
       "1        12.321               0                 1               0   \n",
       "\n",
       "   vel_g0_downcross  vel_g1_mean  vel_g1_max  vel_g1_min  vel_g1_range  \\\n",
       "0                 1       -2.603       3.747      -6.890        10.638   \n",
       "1                 0       -5.198       3.017     -12.599        15.616   \n",
       "\n",
       "   vel_g1_upturns  vel_g1_downturns  vel_g1_upcross  vel_g1_downcross  \\\n",
       "0               1                 2               0                 1   \n",
       "1               1                 1               1                 1   \n",
       "\n",
       "   vel_g2_mean  vel_g2_max  vel_g2_min  vel_g2_range  vel_g2_upturns  \\\n",
       "0       -1.510       0.573      -3.209         3.782               0   \n",
       "1        1.439       2.453       0.154         2.299               1   \n",
       "\n",
       "   vel_g2_downturns  vel_g2_upcross  vel_g2_downcross  pos_g0_mean  \\\n",
       "0                 1               1                 1       30.440   \n",
       "1                 1               0                 0       48.704   \n",
       "\n",
       "   pos_g0_max  pos_g0_min  pos_g0_range  pos_g0_upturns  pos_g0_downturns  \\\n",
       "0      54.619      -0.362        54.982               0                 0   \n",
       "1     103.474       0.590       102.884               0                 0   \n",
       "\n",
       "   pos_g0_upcross  pos_g0_downcross  pos_g1_mean  pos_g1_max  pos_g1_min  \\\n",
       "0               1                 0       -3.756      10.447     -30.563   \n",
       "1               0                 0      -13.233       7.970     -62.085   \n",
       "\n",
       "   pos_g1_range  pos_g1_upturns  pos_g1_downturns  pos_g1_upcross  \\\n",
       "0        41.009               0                 1               0   \n",
       "1        70.055               0                 1               0   \n",
       "\n",
       "   pos_g1_downcross  pos_g2_mean  pos_g2_max  pos_g2_min  pos_g2_range  \\\n",
       "0                 1       -4.793       1.378     -17.957        19.335   \n",
       "1                 1        8.294      17.451       0.352        17.100   \n",
       "\n",
       "   pos_g2_upturns  pos_g2_downturns  pos_g2_upcross  pos_g2_downcross  \\\n",
       "0               0                 1               0                 1   \n",
       "1               0                 0               0                 0   \n",
       "\n",
       "   dim0-coef0  dim0-coef1  dim0-coef2  dim0-coef3  dim0-coef4  dim0-coef5  \\\n",
       "0       2.688       5.427       5.395       1.487      -0.154      -0.521   \n",
       "1       1.785       4.733       5.094       1.857       0.125      -0.700   \n",
       "\n",
       "   dim0-coef6  dim0-coef7  dim0-coef8  dim0-coef9  dim1-coef0  dim1-coef1  \\\n",
       "0      -0.698       0.795        0.86       0.024      -0.583       0.806   \n",
       "1      -0.663       0.551        0.83       0.045      -0.312       0.842   \n",
       "\n",
       "   dim1-coef2  dim1-coef3  dim1-coef4  dim1-coef5  dim1-coef6  dim1-coef7  \\\n",
       "0      -3.537      -5.756      -2.244      -0.711       0.271       1.533   \n",
       "1      -2.847      -6.249      -4.028      -0.550       0.166       1.488   \n",
       "\n",
       "   dim1-coef8  dim1-coef9  dim2-coef0  dim2-coef1  dim2-coef2  dim2-coef3  \\\n",
       "0      -0.568      -0.813      14.451      14.525      13.476      13.178   \n",
       "1      -0.061      -0.728      14.056      14.573      14.268      13.466   \n",
       "\n",
       "   dim2-coef4  dim2-coef5  dim2-coef6  dim2-coef7  dim2-coef8  dim2-coef9  \\\n",
       "0      13.824      -0.148       0.158       0.269      -0.115      -0.152   \n",
       "1      13.822      -0.184      -0.055       0.215       0.094      -0.220   \n",
       "\n",
       "   label2  \n",
       "0       1  \n",
       "1       1  "
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#secondary label column, based on pairs\n",
    "df['label2'] = (df['label']+1)//2\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "183\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(['label','label2','user','filename'],axis=1)\n",
    "#X = df[df.columns[28:60]]  #only Magnitude columns\n",
    "#X = df[['Macc_min','Macc_max','Mvel_mean','z_min']]\n",
    "print(len(X.columns))\n",
    "\n",
    "#select which set of labels to fit on\n",
    "#y = df['label2']  #<-- paired labels\n",
    "y = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_range</th>\n",
       "      <th>x_mean</th>\n",
       "      <th>x_max</th>\n",
       "      <th>x_min</th>\n",
       "      <th>x_range</th>\n",
       "      <th>x_upturns</th>\n",
       "      <th>x_downturns</th>\n",
       "      <th>x_upcross</th>\n",
       "      <th>x_downcross</th>\n",
       "      <th>y_mean</th>\n",
       "      <th>y_max</th>\n",
       "      <th>y_min</th>\n",
       "      <th>y_range</th>\n",
       "      <th>y_upturns</th>\n",
       "      <th>y_downturns</th>\n",
       "      <th>y_upcross</th>\n",
       "      <th>y_downcross</th>\n",
       "      <th>z_mean</th>\n",
       "      <th>z_max</th>\n",
       "      <th>z_min</th>\n",
       "      <th>z_range</th>\n",
       "      <th>z_upturns</th>\n",
       "      <th>z_downturns</th>\n",
       "      <th>z_upcross</th>\n",
       "      <th>z_downcross</th>\n",
       "      <th>Mjerk_mean</th>\n",
       "      <th>Mjerk_max</th>\n",
       "      <th>Mjerk_min</th>\n",
       "      <th>Mjerk_range</th>\n",
       "      <th>Mjerk_upturns</th>\n",
       "      <th>Mjerk_downturns</th>\n",
       "      <th>Mjerk_upcross</th>\n",
       "      <th>Mjerk_downcross</th>\n",
       "      <th>Macc_mean</th>\n",
       "      <th>Macc_max</th>\n",
       "      <th>Macc_min</th>\n",
       "      <th>Macc_range</th>\n",
       "      <th>Macc_upturns</th>\n",
       "      <th>Macc_downturns</th>\n",
       "      <th>Macc_upcross</th>\n",
       "      <th>Macc_downcross</th>\n",
       "      <th>Mvel_mean</th>\n",
       "      <th>Mvel_max</th>\n",
       "      <th>Mvel_min</th>\n",
       "      <th>Mvel_range</th>\n",
       "      <th>Mvel_upturns</th>\n",
       "      <th>Mvel_downturns</th>\n",
       "      <th>Mvel_upcross</th>\n",
       "      <th>Mvel_downcross</th>\n",
       "      <th>Mdist_mean</th>\n",
       "      <th>Mdist_max</th>\n",
       "      <th>Mdist_min</th>\n",
       "      <th>Mdist_range</th>\n",
       "      <th>Mdist_upturns</th>\n",
       "      <th>Mdist_downturns</th>\n",
       "      <th>Mdist_upcross</th>\n",
       "      <th>Mdist_downcross</th>\n",
       "      <th>jerk0_mean</th>\n",
       "      <th>jerk0_max</th>\n",
       "      <th>jerk0_min</th>\n",
       "      <th>jerk0_range</th>\n",
       "      <th>jerk0_upturns</th>\n",
       "      <th>jerk0_downturns</th>\n",
       "      <th>jerk0_upcross</th>\n",
       "      <th>jerk0_downcross</th>\n",
       "      <th>jerk1_mean</th>\n",
       "      <th>jerk1_max</th>\n",
       "      <th>jerk1_min</th>\n",
       "      <th>jerk1_range</th>\n",
       "      <th>jerk1_upturns</th>\n",
       "      <th>jerk1_downturns</th>\n",
       "      <th>jerk1_upcross</th>\n",
       "      <th>jerk1_downcross</th>\n",
       "      <th>jerk2_mean</th>\n",
       "      <th>jerk2_max</th>\n",
       "      <th>jerk2_min</th>\n",
       "      <th>jerk2_range</th>\n",
       "      <th>jerk2_upturns</th>\n",
       "      <th>jerk2_downturns</th>\n",
       "      <th>jerk2_upcross</th>\n",
       "      <th>jerk2_downcross</th>\n",
       "      <th>acc_g0_mean</th>\n",
       "      <th>acc_g0_max</th>\n",
       "      <th>acc_g0_min</th>\n",
       "      <th>acc_g0_range</th>\n",
       "      <th>acc_g0_upturns</th>\n",
       "      <th>acc_g0_downturns</th>\n",
       "      <th>acc_g0_upcross</th>\n",
       "      <th>acc_g0_downcross</th>\n",
       "      <th>acc_g1_mean</th>\n",
       "      <th>acc_g1_max</th>\n",
       "      <th>acc_g1_min</th>\n",
       "      <th>acc_g1_range</th>\n",
       "      <th>acc_g1_upturns</th>\n",
       "      <th>acc_g1_downturns</th>\n",
       "      <th>acc_g1_upcross</th>\n",
       "      <th>acc_g1_downcross</th>\n",
       "      <th>acc_g2_mean</th>\n",
       "      <th>acc_g2_max</th>\n",
       "      <th>acc_g2_min</th>\n",
       "      <th>acc_g2_range</th>\n",
       "      <th>acc_g2_upturns</th>\n",
       "      <th>acc_g2_downturns</th>\n",
       "      <th>acc_g2_upcross</th>\n",
       "      <th>acc_g2_downcross</th>\n",
       "      <th>vel_g0_mean</th>\n",
       "      <th>vel_g0_max</th>\n",
       "      <th>vel_g0_min</th>\n",
       "      <th>vel_g0_range</th>\n",
       "      <th>vel_g0_upturns</th>\n",
       "      <th>vel_g0_downturns</th>\n",
       "      <th>vel_g0_upcross</th>\n",
       "      <th>vel_g0_downcross</th>\n",
       "      <th>vel_g1_mean</th>\n",
       "      <th>vel_g1_max</th>\n",
       "      <th>vel_g1_min</th>\n",
       "      <th>vel_g1_range</th>\n",
       "      <th>vel_g1_upturns</th>\n",
       "      <th>vel_g1_downturns</th>\n",
       "      <th>vel_g1_upcross</th>\n",
       "      <th>vel_g1_downcross</th>\n",
       "      <th>vel_g2_mean</th>\n",
       "      <th>vel_g2_max</th>\n",
       "      <th>vel_g2_min</th>\n",
       "      <th>vel_g2_range</th>\n",
       "      <th>vel_g2_upturns</th>\n",
       "      <th>vel_g2_downturns</th>\n",
       "      <th>vel_g2_upcross</th>\n",
       "      <th>vel_g2_downcross</th>\n",
       "      <th>pos_g0_mean</th>\n",
       "      <th>pos_g0_max</th>\n",
       "      <th>pos_g0_min</th>\n",
       "      <th>pos_g0_range</th>\n",
       "      <th>pos_g0_upturns</th>\n",
       "      <th>pos_g0_downturns</th>\n",
       "      <th>pos_g0_upcross</th>\n",
       "      <th>pos_g0_downcross</th>\n",
       "      <th>pos_g1_mean</th>\n",
       "      <th>pos_g1_max</th>\n",
       "      <th>pos_g1_min</th>\n",
       "      <th>pos_g1_range</th>\n",
       "      <th>pos_g1_upturns</th>\n",
       "      <th>pos_g1_downturns</th>\n",
       "      <th>pos_g1_upcross</th>\n",
       "      <th>pos_g1_downcross</th>\n",
       "      <th>pos_g2_mean</th>\n",
       "      <th>pos_g2_max</th>\n",
       "      <th>pos_g2_min</th>\n",
       "      <th>pos_g2_range</th>\n",
       "      <th>pos_g2_upturns</th>\n",
       "      <th>pos_g2_downturns</th>\n",
       "      <th>pos_g2_upcross</th>\n",
       "      <th>pos_g2_downcross</th>\n",
       "      <th>dim0-coef0</th>\n",
       "      <th>dim0-coef1</th>\n",
       "      <th>dim0-coef2</th>\n",
       "      <th>dim0-coef3</th>\n",
       "      <th>dim0-coef4</th>\n",
       "      <th>dim0-coef5</th>\n",
       "      <th>dim0-coef6</th>\n",
       "      <th>dim0-coef7</th>\n",
       "      <th>dim0-coef8</th>\n",
       "      <th>dim0-coef9</th>\n",
       "      <th>dim1-coef0</th>\n",
       "      <th>dim1-coef1</th>\n",
       "      <th>dim1-coef2</th>\n",
       "      <th>dim1-coef3</th>\n",
       "      <th>dim1-coef4</th>\n",
       "      <th>dim1-coef5</th>\n",
       "      <th>dim1-coef6</th>\n",
       "      <th>dim1-coef7</th>\n",
       "      <th>dim1-coef8</th>\n",
       "      <th>dim1-coef9</th>\n",
       "      <th>dim2-coef0</th>\n",
       "      <th>dim2-coef1</th>\n",
       "      <th>dim2-coef2</th>\n",
       "      <th>dim2-coef3</th>\n",
       "      <th>dim2-coef4</th>\n",
       "      <th>dim2-coef5</th>\n",
       "      <th>dim2-coef6</th>\n",
       "      <th>dim2-coef7</th>\n",
       "      <th>dim2-coef8</th>\n",
       "      <th>dim2-coef9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>-0.193</td>\n",
       "      <td>-1.957</td>\n",
       "      <td>0.852</td>\n",
       "      <td>-2.026</td>\n",
       "      <td>2.011</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>0.162</td>\n",
       "      <td>-0.969</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.844</td>\n",
       "      <td>-0.224</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-0.454</td>\n",
       "      <td>-1.373</td>\n",
       "      <td>-1.217</td>\n",
       "      <td>0.016</td>\n",
       "      <td>-1.308</td>\n",
       "      <td>-1.922</td>\n",
       "      <td>0.844</td>\n",
       "      <td>-1.130</td>\n",
       "      <td>1.341</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.288</td>\n",
       "      <td>2.565</td>\n",
       "      <td>2.554</td>\n",
       "      <td>-0.434</td>\n",
       "      <td>-0.224</td>\n",
       "      <td>-0.683</td>\n",
       "      <td>0.367</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>-0.622</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>-0.449</td>\n",
       "      <td>-0.594</td>\n",
       "      <td>0.892</td>\n",
       "      <td>-1.225</td>\n",
       "      <td>1.225</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-0.331</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.883</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.514</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>-0.095</td>\n",
       "      <td>1.459</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.473</td>\n",
       "      <td>0.291</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.403</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>-0.601</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>-2.268</td>\n",
       "      <td>-0.687</td>\n",
       "      <td>-2.028</td>\n",
       "      <td>0.804</td>\n",
       "      <td>-1.189</td>\n",
       "      <td>-0.328</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.770</td>\n",
       "      <td>-0.354</td>\n",
       "      <td>0.956</td>\n",
       "      <td>-0.783</td>\n",
       "      <td>0.687</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.226</td>\n",
       "      <td>-0.932</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.204</td>\n",
       "      <td>-0.319</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>-0.437</td>\n",
       "      <td>-2.332</td>\n",
       "      <td>-0.482</td>\n",
       "      <td>-2.752</td>\n",
       "      <td>2.011</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>1.161</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.839</td>\n",
       "      <td>-0.454</td>\n",
       "      <td>-1.373</td>\n",
       "      <td>-1.217</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.193</td>\n",
       "      <td>-0.562</td>\n",
       "      <td>1.539</td>\n",
       "      <td>-0.528</td>\n",
       "      <td>1.341</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.288</td>\n",
       "      <td>-1.692</td>\n",
       "      <td>-0.466</td>\n",
       "      <td>-1.977</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-3.459</td>\n",
       "      <td>1.972</td>\n",
       "      <td>-0.953</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.961</td>\n",
       "      <td>1.153</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-1.217</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.337</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.432</td>\n",
       "      <td>-1.494</td>\n",
       "      <td>-0.267</td>\n",
       "      <td>-0.958</td>\n",
       "      <td>0.706</td>\n",
       "      <td>-1.380</td>\n",
       "      <td>-0.668</td>\n",
       "      <td>-2.433</td>\n",
       "      <td>0.829</td>\n",
       "      <td>-0.647</td>\n",
       "      <td>1.143</td>\n",
       "      <td>1.146</td>\n",
       "      <td>1.042</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.619</td>\n",
       "      <td>-0.177</td>\n",
       "      <td>1.132</td>\n",
       "      <td>-0.824</td>\n",
       "      <td>1.391</td>\n",
       "      <td>1.017</td>\n",
       "      <td>0.656</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.491</td>\n",
       "      <td>-0.404</td>\n",
       "      <td>-0.719</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>1.352</td>\n",
       "      <td>1.556</td>\n",
       "      <td>0.889</td>\n",
       "      <td>-1.166</td>\n",
       "      <td>-2.251</td>\n",
       "      <td>-0.942</td>\n",
       "      <td>-0.209</td>\n",
       "      <td>1.853</td>\n",
       "      <td>2.110</td>\n",
       "      <td>0.787</td>\n",
       "      <td>-0.628</td>\n",
       "      <td>-0.746</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>0.557</td>\n",
       "      <td>1.031</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.253</td>\n",
       "      <td>-0.761</td>\n",
       "      <td>-0.730</td>\n",
       "      <td>-0.207</td>\n",
       "      <td>-0.398</td>\n",
       "      <td>1.218</td>\n",
       "      <td>0.421</td>\n",
       "      <td>-1.353</td>\n",
       "      <td>-2.157</td>\n",
       "      <td>-1.604</td>\n",
       "      <td>-0.962</td>\n",
       "      <td>1.434</td>\n",
       "      <td>1.125e+00</td>\n",
       "      <td>-0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>1.550</td>\n",
       "      <td>-0.553</td>\n",
       "      <td>-0.594</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>0.415</td>\n",
       "      <td>1.496</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.691</td>\n",
       "      <td>-0.548</td>\n",
       "      <td>0.124</td>\n",
       "      <td>-0.727</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1.142</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.045</td>\n",
       "      <td>1.087</td>\n",
       "      <td>1.236</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.410</td>\n",
       "      <td>2.679</td>\n",
       "      <td>2.097</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.152</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.986</td>\n",
       "      <td>1.610</td>\n",
       "      <td>0.966</td>\n",
       "      <td>1.985</td>\n",
       "      <td>1.009</td>\n",
       "      <td>0.529</td>\n",
       "      <td>1.283</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>1.373</td>\n",
       "      <td>0.890</td>\n",
       "      <td>-1.722</td>\n",
       "      <td>-1.748</td>\n",
       "      <td>1.889</td>\n",
       "      <td>1.861</td>\n",
       "      <td>0.422</td>\n",
       "      <td>1.816</td>\n",
       "      <td>-1.598</td>\n",
       "      <td>-1.514</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.473</td>\n",
       "      <td>2.393</td>\n",
       "      <td>2.641</td>\n",
       "      <td>0.305</td>\n",
       "      <td>2.655</td>\n",
       "      <td>-0.601</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>-0.218</td>\n",
       "      <td>-0.141</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.221</td>\n",
       "      <td>1.433</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.126</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.082</td>\n",
       "      <td>3.045</td>\n",
       "      <td>2.060</td>\n",
       "      <td>1.005</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.359</td>\n",
       "      <td>-0.737</td>\n",
       "      <td>0.581</td>\n",
       "      <td>2.233</td>\n",
       "      <td>1.707</td>\n",
       "      <td>2.135</td>\n",
       "      <td>1.895</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>-0.202</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>0.415</td>\n",
       "      <td>1.496</td>\n",
       "      <td>0.029</td>\n",
       "      <td>1.361</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.436</td>\n",
       "      <td>-0.313</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1.142</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>1.054</td>\n",
       "      <td>1.203</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.254</td>\n",
       "      <td>-0.349</td>\n",
       "      <td>0.410</td>\n",
       "      <td>2.679</td>\n",
       "      <td>2.097</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.814</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>0.555</td>\n",
       "      <td>-0.847</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1.642</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.407</td>\n",
       "      <td>0.457</td>\n",
       "      <td>-0.792</td>\n",
       "      <td>1.487</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.747</td>\n",
       "      <td>2.202</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>-0.293</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-0.445</td>\n",
       "      <td>1.350</td>\n",
       "      <td>1.054</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.059</td>\n",
       "      <td>-0.137</td>\n",
       "      <td>0.485</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>1.413</td>\n",
       "      <td>1.143</td>\n",
       "      <td>-0.818</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.507</td>\n",
       "      <td>0.277</td>\n",
       "      <td>-0.626</td>\n",
       "      <td>1.132</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.652</td>\n",
       "      <td>1.017</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>-0.482</td>\n",
       "      <td>0.484</td>\n",
       "      <td>-0.821</td>\n",
       "      <td>1.265</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>-0.245</td>\n",
       "      <td>-0.208</td>\n",
       "      <td>-0.442</td>\n",
       "      <td>-1.217</td>\n",
       "      <td>-1.051</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.753</td>\n",
       "      <td>-0.920</td>\n",
       "      <td>-0.398</td>\n",
       "      <td>-0.557</td>\n",
       "      <td>-0.649</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.397</td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-1.469</td>\n",
       "      <td>1.016</td>\n",
       "      <td>1.470</td>\n",
       "      <td>0.942</td>\n",
       "      <td>-0.521</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.421</td>\n",
       "      <td>-0.968</td>\n",
       "      <td>1.368</td>\n",
       "      <td>2.444e-04</td>\n",
       "      <td>-0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>-0.582</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>-1.156</td>\n",
       "      <td>0.425</td>\n",
       "      <td>-1.175</td>\n",
       "      <td>0.415</td>\n",
       "      <td>-1.172</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.216</td>\n",
       "      <td>-1.175</td>\n",
       "      <td>1.332</td>\n",
       "      <td>-1.774</td>\n",
       "      <td>1.142</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-1.360</td>\n",
       "      <td>-1.308</td>\n",
       "      <td>1.085</td>\n",
       "      <td>1.378</td>\n",
       "      <td>-0.438</td>\n",
       "      <td>1.034</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.288</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>-1.552</td>\n",
       "      <td>0.264</td>\n",
       "      <td>-0.368</td>\n",
       "      <td>0.368</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>0.494</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>-0.449</td>\n",
       "      <td>-0.436</td>\n",
       "      <td>-0.208</td>\n",
       "      <td>-1.250</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-0.331</td>\n",
       "      <td>-0.379</td>\n",
       "      <td>0.883</td>\n",
       "      <td>-0.378</td>\n",
       "      <td>-0.642</td>\n",
       "      <td>0.445</td>\n",
       "      <td>-0.722</td>\n",
       "      <td>1.409</td>\n",
       "      <td>1.459</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.473</td>\n",
       "      <td>-0.340</td>\n",
       "      <td>-0.511</td>\n",
       "      <td>0.452</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.601</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.851</td>\n",
       "      <td>0.543</td>\n",
       "      <td>-0.822</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.328</td>\n",
       "      <td>1.362</td>\n",
       "      <td>0.125</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>-1.464</td>\n",
       "      <td>1.096</td>\n",
       "      <td>-1.474</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>1.005</td>\n",
       "      <td>-0.226</td>\n",
       "      <td>-1.221</td>\n",
       "      <td>0.636</td>\n",
       "      <td>-1.027</td>\n",
       "      <td>0.900</td>\n",
       "      <td>-0.319</td>\n",
       "      <td>0.581</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>-0.437</td>\n",
       "      <td>-0.758</td>\n",
       "      <td>-1.250</td>\n",
       "      <td>-0.226</td>\n",
       "      <td>-1.175</td>\n",
       "      <td>0.415</td>\n",
       "      <td>-1.172</td>\n",
       "      <td>-1.416</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.178</td>\n",
       "      <td>-0.983</td>\n",
       "      <td>1.164</td>\n",
       "      <td>-1.774</td>\n",
       "      <td>1.142</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>1.203</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.592</td>\n",
       "      <td>-0.924</td>\n",
       "      <td>1.034</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.288</td>\n",
       "      <td>-0.414</td>\n",
       "      <td>-0.466</td>\n",
       "      <td>-0.869</td>\n",
       "      <td>-0.832</td>\n",
       "      <td>-0.296</td>\n",
       "      <td>-0.643</td>\n",
       "      <td>-0.953</td>\n",
       "      <td>-1.215</td>\n",
       "      <td>-0.970</td>\n",
       "      <td>-0.969</td>\n",
       "      <td>0.304</td>\n",
       "      <td>-0.559</td>\n",
       "      <td>0.812</td>\n",
       "      <td>-1.272</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.747</td>\n",
       "      <td>-1.156</td>\n",
       "      <td>0.323</td>\n",
       "      <td>-0.140</td>\n",
       "      <td>0.566</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>-0.267</td>\n",
       "      <td>2.080</td>\n",
       "      <td>0.706</td>\n",
       "      <td>-0.781</td>\n",
       "      <td>-0.723</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.522</td>\n",
       "      <td>-0.647</td>\n",
       "      <td>-0.841</td>\n",
       "      <td>-0.818</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>0.280</td>\n",
       "      <td>-0.432</td>\n",
       "      <td>0.664</td>\n",
       "      <td>-0.944</td>\n",
       "      <td>1.132</td>\n",
       "      <td>-0.824</td>\n",
       "      <td>1.391</td>\n",
       "      <td>-0.891</td>\n",
       "      <td>0.312</td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.492</td>\n",
       "      <td>-0.496</td>\n",
       "      <td>1.265</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.916</td>\n",
       "      <td>-0.661</td>\n",
       "      <td>1.049</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.683</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.233</td>\n",
       "      <td>-0.191</td>\n",
       "      <td>1.193</td>\n",
       "      <td>1.640</td>\n",
       "      <td>-0.393</td>\n",
       "      <td>-0.823</td>\n",
       "      <td>1.106</td>\n",
       "      <td>-1.553</td>\n",
       "      <td>0.720</td>\n",
       "      <td>1.663</td>\n",
       "      <td>-1.271e+00</td>\n",
       "      <td>-1.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>-1.203</td>\n",
       "      <td>-0.951</td>\n",
       "      <td>-0.407</td>\n",
       "      <td>-0.385</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.415</td>\n",
       "      <td>-1.172</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.691</td>\n",
       "      <td>1.647</td>\n",
       "      <td>1.121</td>\n",
       "      <td>1.458</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.714</td>\n",
       "      <td>3.113</td>\n",
       "      <td>0.219</td>\n",
       "      <td>1.345</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-1.480</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>1.186</td>\n",
       "      <td>1.386</td>\n",
       "      <td>-0.936</td>\n",
       "      <td>1.250</td>\n",
       "      <td>-1.503</td>\n",
       "      <td>-0.622</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>-0.449</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>1.233</td>\n",
       "      <td>-0.749</td>\n",
       "      <td>1.300</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-1.552</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.883</td>\n",
       "      <td>-0.616</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>-1.889</td>\n",
       "      <td>-0.124</td>\n",
       "      <td>1.409</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>1.282</td>\n",
       "      <td>2.115</td>\n",
       "      <td>-0.917</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>-1.334</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>1.654</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>1.918</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.646</td>\n",
       "      <td>-0.292</td>\n",
       "      <td>0.552</td>\n",
       "      <td>-1.189</td>\n",
       "      <td>-1.485</td>\n",
       "      <td>-0.919</td>\n",
       "      <td>-1.182</td>\n",
       "      <td>1.460</td>\n",
       "      <td>-0.284</td>\n",
       "      <td>0.599</td>\n",
       "      <td>-0.525</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.226</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.889</td>\n",
       "      <td>-1.326</td>\n",
       "      <td>1.208</td>\n",
       "      <td>-0.319</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.825</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.415</td>\n",
       "      <td>-1.172</td>\n",
       "      <td>1.474</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.771</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.193</td>\n",
       "      <td>-0.213</td>\n",
       "      <td>1.841</td>\n",
       "      <td>-0.272</td>\n",
       "      <td>1.345</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-1.480</td>\n",
       "      <td>-0.414</td>\n",
       "      <td>-0.466</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.607</td>\n",
       "      <td>-0.455</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.343</td>\n",
       "      <td>0.519</td>\n",
       "      <td>-0.801</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-1.217</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.523</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>-0.190</td>\n",
       "      <td>0.150</td>\n",
       "      <td>-0.309</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>-0.267</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.147</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.483</td>\n",
       "      <td>-0.393</td>\n",
       "      <td>1.413</td>\n",
       "      <td>-0.841</td>\n",
       "      <td>1.146</td>\n",
       "      <td>1.042</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.513</td>\n",
       "      <td>0.458</td>\n",
       "      <td>-0.804</td>\n",
       "      <td>1.132</td>\n",
       "      <td>-0.824</td>\n",
       "      <td>-0.652</td>\n",
       "      <td>-0.891</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>-0.584</td>\n",
       "      <td>0.330</td>\n",
       "      <td>-0.789</td>\n",
       "      <td>1.265</td>\n",
       "      <td>0.975</td>\n",
       "      <td>1.403</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>-1.162</td>\n",
       "      <td>-0.992</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.656</td>\n",
       "      <td>-0.156</td>\n",
       "      <td>0.789</td>\n",
       "      <td>-1.297</td>\n",
       "      <td>-1.304</td>\n",
       "      <td>0.156</td>\n",
       "      <td>1.632</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.926</td>\n",
       "      <td>-0.331</td>\n",
       "      <td>-0.342</td>\n",
       "      <td>0.215</td>\n",
       "      <td>-0.860</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>-1.116</td>\n",
       "      <td>2.221</td>\n",
       "      <td>2.492</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>2.081</td>\n",
       "      <td>-1.142</td>\n",
       "      <td>-2.644</td>\n",
       "      <td>1.953e+00</td>\n",
       "      <td>1.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>-0.388</td>\n",
       "      <td>0.235</td>\n",
       "      <td>2.310</td>\n",
       "      <td>-1.370</td>\n",
       "      <td>2.696</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>-1.172</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.397</td>\n",
       "      <td>1.380</td>\n",
       "      <td>-0.720</td>\n",
       "      <td>1.470</td>\n",
       "      <td>-1.373</td>\n",
       "      <td>-2.393</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-1.626</td>\n",
       "      <td>-0.680</td>\n",
       "      <td>-0.810</td>\n",
       "      <td>0.332</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-1.480</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>1.435</td>\n",
       "      <td>0.851</td>\n",
       "      <td>-1.059</td>\n",
       "      <td>1.098</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>-1.737</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>-0.449</td>\n",
       "      <td>0.948</td>\n",
       "      <td>1.990</td>\n",
       "      <td>-0.919</td>\n",
       "      <td>1.977</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-1.552</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.493</td>\n",
       "      <td>1.128</td>\n",
       "      <td>-2.597</td>\n",
       "      <td>1.564</td>\n",
       "      <td>-0.095</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>1.282</td>\n",
       "      <td>2.115</td>\n",
       "      <td>-0.261</td>\n",
       "      <td>0.183</td>\n",
       "      <td>-2.334</td>\n",
       "      <td>0.280</td>\n",
       "      <td>1.654</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>1.918</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>0.281</td>\n",
       "      <td>2.043</td>\n",
       "      <td>-2.948</td>\n",
       "      <td>2.951</td>\n",
       "      <td>-1.189</td>\n",
       "      <td>-1.485</td>\n",
       "      <td>0.221</td>\n",
       "      <td>-1.182</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.687</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>-1.390</td>\n",
       "      <td>-1.373</td>\n",
       "      <td>1.637</td>\n",
       "      <td>0.802</td>\n",
       "      <td>-0.357</td>\n",
       "      <td>0.701</td>\n",
       "      <td>-0.319</td>\n",
       "      <td>-1.671</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>-1.603</td>\n",
       "      <td>1.979</td>\n",
       "      <td>3.213</td>\n",
       "      <td>0.931</td>\n",
       "      <td>2.696</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>-1.172</td>\n",
       "      <td>-1.416</td>\n",
       "      <td>-1.372</td>\n",
       "      <td>-1.289</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>-1.810</td>\n",
       "      <td>1.470</td>\n",
       "      <td>-1.373</td>\n",
       "      <td>-2.393</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-1.588</td>\n",
       "      <td>-0.703</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-0.410</td>\n",
       "      <td>0.332</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-1.480</td>\n",
       "      <td>-0.414</td>\n",
       "      <td>0.814</td>\n",
       "      <td>2.472</td>\n",
       "      <td>2.286</td>\n",
       "      <td>0.609</td>\n",
       "      <td>1.923</td>\n",
       "      <td>-0.953</td>\n",
       "      <td>-1.215</td>\n",
       "      <td>0.797</td>\n",
       "      <td>-0.969</td>\n",
       "      <td>-1.807</td>\n",
       "      <td>-0.677</td>\n",
       "      <td>-1.557</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-1.217</td>\n",
       "      <td>-0.944</td>\n",
       "      <td>0.523</td>\n",
       "      <td>-0.630</td>\n",
       "      <td>-0.701</td>\n",
       "      <td>-0.360</td>\n",
       "      <td>-0.327</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>-0.267</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.706</td>\n",
       "      <td>2.109</td>\n",
       "      <td>2.328</td>\n",
       "      <td>0.486</td>\n",
       "      <td>2.061</td>\n",
       "      <td>-0.647</td>\n",
       "      <td>-0.841</td>\n",
       "      <td>-0.818</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>-1.542</td>\n",
       "      <td>-0.509</td>\n",
       "      <td>-1.640</td>\n",
       "      <td>1.211</td>\n",
       "      <td>-0.771</td>\n",
       "      <td>-0.824</td>\n",
       "      <td>-0.652</td>\n",
       "      <td>-0.891</td>\n",
       "      <td>-0.693</td>\n",
       "      <td>-0.689</td>\n",
       "      <td>-0.145</td>\n",
       "      <td>-0.503</td>\n",
       "      <td>1.265</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>-1.901</td>\n",
       "      <td>-1.313</td>\n",
       "      <td>0.535</td>\n",
       "      <td>2.381</td>\n",
       "      <td>2.875</td>\n",
       "      <td>0.113</td>\n",
       "      <td>-2.451</td>\n",
       "      <td>-2.478</td>\n",
       "      <td>-1.222</td>\n",
       "      <td>-0.502</td>\n",
       "      <td>1.308</td>\n",
       "      <td>0.498</td>\n",
       "      <td>-0.299</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>-1.612</td>\n",
       "      <td>0.570</td>\n",
       "      <td>1.239</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.946</td>\n",
       "      <td>-1.362</td>\n",
       "      <td>-2.254</td>\n",
       "      <td>-0.977</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.816</td>\n",
       "      <td>1.709</td>\n",
       "      <td>0.317</td>\n",
       "      <td>-1.660</td>\n",
       "      <td>-6.245e-01</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>-0.158</td>\n",
       "      <td>-0.798</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>-1.112</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>1.673</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.739</td>\n",
       "      <td>-1.007</td>\n",
       "      <td>0.350</td>\n",
       "      <td>1.761</td>\n",
       "      <td>-1.075</td>\n",
       "      <td>1.989</td>\n",
       "      <td>1.142</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.016</td>\n",
       "      <td>1.399</td>\n",
       "      <td>0.261</td>\n",
       "      <td>-0.855</td>\n",
       "      <td>0.632</td>\n",
       "      <td>-0.938</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.288</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>-0.205</td>\n",
       "      <td>-0.945</td>\n",
       "      <td>0.352</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.494</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>-0.449</td>\n",
       "      <td>0.020</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>1.059</td>\n",
       "      <td>-0.582</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-0.331</td>\n",
       "      <td>0.965</td>\n",
       "      <td>2.199</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.535</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>1.409</td>\n",
       "      <td>1.459</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.473</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>-0.201</td>\n",
       "      <td>0.409</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.601</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>1.093</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.850</td>\n",
       "      <td>-0.217</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.829</td>\n",
       "      <td>1.362</td>\n",
       "      <td>0.125</td>\n",
       "      <td>-0.896</td>\n",
       "      <td>0.739</td>\n",
       "      <td>-2.713</td>\n",
       "      <td>2.079</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>1.005</td>\n",
       "      <td>-0.226</td>\n",
       "      <td>0.231</td>\n",
       "      <td>-0.516</td>\n",
       "      <td>1.102</td>\n",
       "      <td>-0.855</td>\n",
       "      <td>-0.319</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>-0.437</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.803</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>1.673</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-1.372</td>\n",
       "      <td>-1.436</td>\n",
       "      <td>0.134</td>\n",
       "      <td>-2.203</td>\n",
       "      <td>1.989</td>\n",
       "      <td>1.142</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.193</td>\n",
       "      <td>-0.140</td>\n",
       "      <td>-0.932</td>\n",
       "      <td>0.495</td>\n",
       "      <td>-0.938</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.288</td>\n",
       "      <td>-0.414</td>\n",
       "      <td>-0.466</td>\n",
       "      <td>0.996</td>\n",
       "      <td>1.112</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.640</td>\n",
       "      <td>-1.215</td>\n",
       "      <td>0.797</td>\n",
       "      <td>-0.969</td>\n",
       "      <td>-1.205</td>\n",
       "      <td>-0.727</td>\n",
       "      <td>-1.592</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.171</td>\n",
       "      <td>-0.944</td>\n",
       "      <td>-1.156</td>\n",
       "      <td>-0.462</td>\n",
       "      <td>-0.867</td>\n",
       "      <td>0.141</td>\n",
       "      <td>-0.925</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>-1.587</td>\n",
       "      <td>-0.958</td>\n",
       "      <td>-1.034</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.526</td>\n",
       "      <td>1.413</td>\n",
       "      <td>-0.841</td>\n",
       "      <td>1.146</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>-0.893</td>\n",
       "      <td>-0.522</td>\n",
       "      <td>-1.022</td>\n",
       "      <td>0.610</td>\n",
       "      <td>-0.771</td>\n",
       "      <td>-0.824</td>\n",
       "      <td>-0.652</td>\n",
       "      <td>-0.891</td>\n",
       "      <td>-0.615</td>\n",
       "      <td>-0.686</td>\n",
       "      <td>0.026</td>\n",
       "      <td>-0.637</td>\n",
       "      <td>-0.719</td>\n",
       "      <td>-0.859</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>-1.705</td>\n",
       "      <td>-1.578</td>\n",
       "      <td>-0.782</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>-0.320</td>\n",
       "      <td>1.316</td>\n",
       "      <td>-1.268</td>\n",
       "      <td>-1.040</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.159</td>\n",
       "      <td>1.058</td>\n",
       "      <td>0.480</td>\n",
       "      <td>2.627</td>\n",
       "      <td>2.323</td>\n",
       "      <td>-1.477</td>\n",
       "      <td>2.278</td>\n",
       "      <td>-1.578</td>\n",
       "      <td>-1.990</td>\n",
       "      <td>2.638</td>\n",
       "      <td>2.528</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-0.544</td>\n",
       "      <td>-0.673</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.661</td>\n",
       "      <td>-0.296</td>\n",
       "      <td>-5.706e-01</td>\n",
       "      <td>-0.251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      time_range  x_mean  x_max  x_min  x_range  x_upturns  x_downturns  \\\n",
       "2101      -0.193  -1.957  0.852 -2.026    2.011     -0.843        0.162   \n",
       "2207       1.550  -0.553 -0.594 -0.169   -0.345      0.415        1.496   \n",
       "300       -0.582  -0.440 -1.156  0.425   -1.175      0.415       -1.172   \n",
       "1515      -1.203  -0.951 -0.407 -0.385   -0.056      0.415       -1.172   \n",
       "674       -0.388   0.235  2.310 -1.370    2.696     -0.843       -1.172   \n",
       "339       -0.158  -0.798 -0.985 -1.112   -0.016      1.673        0.162   \n",
       "\n",
       "      x_upcross  x_downcross  y_mean  y_max  y_min  y_range  y_upturns  \\\n",
       "2101     -0.969        0.691   0.844 -0.224  0.414   -0.454     -1.373   \n",
       "2207      0.739        0.691  -0.548  0.124 -0.727    0.613      1.142   \n",
       "300       0.739        0.691   0.216 -1.175  1.332   -1.774      1.142   \n",
       "1515      0.739        0.691   1.647  1.121  1.458   -0.291     -0.116   \n",
       "674       0.739        0.691   0.397  1.380 -0.720    1.470     -1.373   \n",
       "339       0.739       -1.007   0.350  1.761 -1.075    1.989      1.142   \n",
       "\n",
       "      y_downturns  y_upcross  y_downcross  z_mean  z_max  z_min  z_range  \\\n",
       "2101       -1.217      0.016       -1.308  -1.922  0.844 -1.130    1.341   \n",
       "2207       -0.041      0.016        0.045   1.087  1.236  0.238    0.410   \n",
       "300        -0.041     -1.360       -1.308   1.085  1.378 -0.438    1.034   \n",
       "1515       -0.041      0.016        0.045   0.714  3.113  0.219    1.345   \n",
       "674        -2.393      0.016        0.045  -1.626 -0.680 -0.810    0.332   \n",
       "339        -0.041      0.016        1.399   0.261 -0.855  0.632   -0.938   \n",
       "\n",
       "      z_upturns  z_downturns  z_upcross  z_downcross  Mjerk_mean  Mjerk_max  \\\n",
       "2101     -0.094       -0.288      2.565        2.554      -0.434     -0.224   \n",
       "2207      2.679        2.097     -0.373       -0.375       0.182      0.063   \n",
       "300      -0.094       -0.288     -0.373       -0.375      -1.552      0.264   \n",
       "1515     -0.094       -1.480     -0.373       -0.375       1.186      1.386   \n",
       "674      -0.094       -1.480     -0.373       -0.375       1.435      0.851   \n",
       "339      -0.094       -0.288     -0.373       -0.375      -0.205     -0.945   \n",
       "\n",
       "      Mjerk_min  Mjerk_range  Mjerk_upturns  Mjerk_downturns  Mjerk_upcross  \\\n",
       "2101     -0.683        0.367         -0.258           -0.622         -0.221   \n",
       "2207      0.152       -0.076          0.986            1.610          0.966   \n",
       "300      -0.368        0.368         -0.258            0.494         -0.221   \n",
       "1515     -0.936        1.250         -1.503           -0.622         -0.221   \n",
       "674      -1.059        1.098         -0.258           -1.737         -0.221   \n",
       "339       0.352       -0.657          0.986            0.494         -0.221   \n",
       "\n",
       "      Mjerk_downcross  Macc_mean  Macc_max  Macc_min  Macc_range  \\\n",
       "2101           -0.449     -0.594     0.892    -1.225       1.225   \n",
       "2207            1.985      1.009     0.529     1.283      -0.110   \n",
       "300            -0.449     -0.436    -0.208    -1.250       0.354   \n",
       "1515           -0.449     -0.216     1.233    -0.749       1.300   \n",
       "674            -0.449      0.948     1.990    -0.919       1.977   \n",
       "339            -0.449      0.020    -0.176     1.059      -0.582   \n",
       "\n",
       "      Macc_upturns  Macc_downturns  Macc_upcross  Macc_downcross  Mvel_mean  \\\n",
       "2101         0.057          -0.331         0.965           0.883     -0.048   \n",
       "2207         1.373           0.890        -1.722          -1.748      1.889   \n",
       "300          0.057          -0.331        -0.379           0.883     -0.378   \n",
       "1515         0.057          -1.552         0.965           0.883     -0.616   \n",
       "674          0.057          -1.552         0.965           0.883      0.493   \n",
       "339          0.057          -0.331         0.965           2.199     -0.112   \n",
       "\n",
       "      Mvel_max  Mvel_min  Mvel_range  Mvel_upturns  Mvel_downturns  \\\n",
       "2101    -0.205     0.514      -0.291        -0.095           1.459   \n",
       "2207     1.861     0.422       1.816        -1.598          -1.514   \n",
       "300     -0.642     0.445      -0.722         1.409           1.459   \n",
       "1515    -0.425    -1.889      -0.124         1.409          -0.027   \n",
       "674      1.128    -2.597       1.564        -0.095          -0.027   \n",
       "339     -0.060     0.535      -0.148         1.409           1.459   \n",
       "\n",
       "      Mvel_upcross  Mvel_downcross  Mdist_mean  Mdist_max  Mdist_min  \\\n",
       "2101        -0.675          -0.473       0.291     -0.136      0.403   \n",
       "2207        -0.675          -0.473       2.393      2.641      0.305   \n",
       "300         -0.675          -0.473      -0.340     -0.511      0.452   \n",
       "1515         1.282           2.115      -0.917     -0.812     -1.334   \n",
       "674          1.282           2.115      -0.261      0.183     -2.334   \n",
       "339         -0.675          -0.473      -0.345     -0.201      0.409   \n",
       "\n",
       "      Mdist_range  Mdist_upturns  Mdist_downturns  Mdist_upcross  \\\n",
       "2101       -0.154         -0.601           -0.295         -0.506   \n",
       "2207        2.655         -0.601           -0.295         -0.506   \n",
       "300        -0.535         -0.601           -0.295         -0.506   \n",
       "1515       -0.766          1.654           -0.295          1.918   \n",
       "674         0.280          1.654           -0.295          1.918   \n",
       "339        -0.220         -0.601           -0.295         -0.506   \n",
       "\n",
       "      Mdist_downcross  jerk0_mean  jerk0_max  jerk0_min  jerk0_range  \\\n",
       "2101           -0.273      -2.268     -0.687     -2.028        0.804   \n",
       "2207           -0.273      -0.047     -0.218     -0.141       -0.044   \n",
       "300            -0.273      -0.220     -0.851      0.543       -0.822   \n",
       "1515           -0.273       0.058      0.646     -0.292        0.552   \n",
       "674            -0.273       0.281      2.043     -2.948        2.951   \n",
       "339            -0.273       1.093      0.493      0.850       -0.217   \n",
       "\n",
       "      jerk0_upturns  jerk0_downturns  jerk0_upcross  jerk0_downcross  \\\n",
       "2101         -1.189           -0.328          0.221            0.125   \n",
       "2207          0.049            0.829          0.221            1.433   \n",
       "300           0.049           -0.328          1.362            0.125   \n",
       "1515         -1.189           -1.485         -0.919           -1.182   \n",
       "674          -1.189           -1.485          0.221           -1.182   \n",
       "339           0.049            0.829          1.362            0.125   \n",
       "\n",
       "      jerk1_mean  jerk1_max  jerk1_min  jerk1_range  jerk1_upturns  \\\n",
       "2101       0.770     -0.354      0.956       -0.783          0.687   \n",
       "2207       0.237      0.126     -0.022        0.082          3.045   \n",
       "300       -0.077     -1.464      1.096       -1.474         -0.492   \n",
       "1515       1.460     -0.284      0.599       -0.525         -0.492   \n",
       "674        0.983      0.915      0.245        0.346          0.687   \n",
       "339       -0.896      0.739     -2.713        2.079         -0.492   \n",
       "\n",
       "      jerk1_downturns  jerk1_upcross  jerk1_downcross  jerk2_mean  jerk2_max  \\\n",
       "2101           -0.231         -0.192           -0.226      -0.932     -0.215   \n",
       "2207            2.060          1.005            0.922       0.179      0.359   \n",
       "300            -0.231          1.005           -0.226      -1.221      0.636   \n",
       "1515           -0.231         -0.192           -0.226       0.577      0.889   \n",
       "674            -0.231         -1.390           -1.373       1.637      0.802   \n",
       "339            -0.231          1.005           -0.226       0.231     -0.516   \n",
       "\n",
       "      jerk2_min  jerk2_range  jerk2_upturns  jerk2_downturns  jerk2_upcross  \\\n",
       "2101     -0.751        0.204         -0.319           -0.545         -0.253   \n",
       "2207     -0.737        0.581          2.233            1.707          2.135   \n",
       "300      -1.027        0.900         -0.319            0.581         -0.253   \n",
       "1515     -1.326        1.208         -0.319           -0.545         -0.253   \n",
       "674      -0.357        0.701         -0.319           -1.671         -0.253   \n",
       "339       1.102       -0.855         -0.319           -0.545         -0.253   \n",
       "\n",
       "      jerk2_downcross  acc_g0_mean  acc_g0_max  acc_g0_min  acc_g0_range  \\\n",
       "2101           -0.437       -2.332      -0.482      -2.752         2.011   \n",
       "2207            1.895       -0.065      -0.202       0.132        -0.345   \n",
       "300            -0.437       -0.758      -1.250      -0.226        -1.175   \n",
       "1515            0.729        0.530       0.638       0.825        -0.056   \n",
       "674            -1.603        1.979       3.213       0.931         2.696   \n",
       "339            -0.437        1.100       0.657       0.803        -0.016   \n",
       "\n",
       "      acc_g0_upturns  acc_g0_downturns  acc_g0_upcross  acc_g0_downcross  \\\n",
       "2101          -0.843             0.162           0.029            -0.005   \n",
       "2207           0.415             1.496           0.029             1.361   \n",
       "300            0.415            -1.172          -1.416            -0.005   \n",
       "1515           0.415            -1.172           1.474            -0.005   \n",
       "674           -0.843            -1.172          -1.416            -1.372   \n",
       "339            1.673             0.162           0.029            -1.372   \n",
       "\n",
       "      acc_g1_mean  acc_g1_max  acc_g1_min  acc_g1_range  acc_g1_upturns  \\\n",
       "2101        1.161       0.332       0.839        -0.454          -1.373   \n",
       "2207        0.045       0.436      -0.313         0.613           1.142   \n",
       "300         0.178      -0.983       1.164        -1.774           1.142   \n",
       "1515        0.440       0.465       0.771        -0.291          -0.116   \n",
       "674        -1.289      -0.098      -1.810         1.470          -1.373   \n",
       "339        -1.436       0.134      -2.203         1.989           1.142   \n",
       "\n",
       "      acc_g1_downturns  acc_g1_upcross  acc_g1_downcross  acc_g2_mean  \\\n",
       "2101            -1.217          -0.200            -0.193       -0.562   \n",
       "2207            -0.041           1.054             1.203       -0.018   \n",
       "300             -0.041          -0.200             1.203        0.327   \n",
       "1515            -0.041          -0.200            -0.193       -0.213   \n",
       "674             -2.393          -0.200            -1.588       -0.703   \n",
       "339             -0.041          -0.200            -0.193       -0.140   \n",
       "\n",
       "      acc_g2_max  acc_g2_min  acc_g2_range  acc_g2_upturns  acc_g2_downturns  \\\n",
       "2101       1.539      -0.528         1.341          -0.094            -0.288   \n",
       "2207       0.254      -0.349         0.410           2.679             2.097   \n",
       "300        0.592      -0.924         1.034          -0.094            -0.288   \n",
       "1515       1.841      -0.272         1.345          -0.094            -1.480   \n",
       "674        0.058      -0.410         0.332          -0.094            -1.480   \n",
       "339       -0.932       0.495        -0.938          -0.094            -0.288   \n",
       "\n",
       "      acc_g2_upcross  acc_g2_downcross  vel_g0_mean  vel_g0_max  vel_g0_min  \\\n",
       "2101          -1.692            -0.466       -1.977      -0.667      -3.459   \n",
       "2207           0.863             0.814       -0.015      -0.400       0.555   \n",
       "300           -0.414            -0.466       -0.869      -0.832      -0.296   \n",
       "1515          -0.414            -0.466        0.415       0.013       0.607   \n",
       "674           -0.414             0.814        2.472       2.286       0.609   \n",
       "339           -0.414            -0.466        0.996       1.112       0.578   \n",
       "\n",
       "      vel_g0_range  vel_g0_upturns  vel_g0_downturns  vel_g0_upcross  \\\n",
       "2101         1.972          -0.953             0.213           0.797   \n",
       "2207        -0.847           0.640             1.642           0.797   \n",
       "300         -0.643          -0.953            -1.215          -0.970   \n",
       "1515        -0.455           0.640             0.213           0.797   \n",
       "674          1.923          -0.953            -1.215           0.797   \n",
       "339          0.718           0.640            -1.215           0.797   \n",
       "\n",
       "      vel_g0_downcross  vel_g1_mean  vel_g1_max  vel_g1_min  vel_g1_range  \\\n",
       "2101             0.837        0.961       1.153       0.677         0.271   \n",
       "2207             0.837        0.024      -0.407       0.457        -0.792   \n",
       "300             -0.969        0.304      -0.559       0.812        -1.272   \n",
       "1515             0.837        0.047      -0.343       0.519        -0.801   \n",
       "674             -0.969       -1.807      -0.677      -1.557         1.000   \n",
       "339             -0.969       -1.205      -0.727      -1.592         0.993   \n",
       "\n",
       "      vel_g1_upturns  vel_g1_downturns  vel_g1_upcross  vel_g1_downcross  \\\n",
       "2101           0.088            -1.217           0.747             0.523   \n",
       "2207           1.487             0.171           0.747             2.202   \n",
       "300            0.088             0.171           0.747            -1.156   \n",
       "1515           0.088            -1.217           0.747             0.523   \n",
       "674            0.088            -1.217          -0.944             0.523   \n",
       "339            0.088             0.171          -0.944            -1.156   \n",
       "\n",
       "      vel_g2_mean  vel_g2_max  vel_g2_min  vel_g2_range  vel_g2_upturns  \\\n",
       "2101        0.138       0.337      -0.136         0.432          -1.494   \n",
       "2207       -0.098      -0.293       0.196        -0.445           1.350   \n",
       "300         0.323      -0.140       0.566        -0.632          -0.072   \n",
       "1515       -0.067      -0.190       0.150        -0.309          -0.072   \n",
       "674        -0.630      -0.701      -0.360        -0.327          -0.072   \n",
       "339        -0.462      -0.867       0.141        -0.925          -0.072   \n",
       "\n",
       "      vel_g2_downturns  vel_g2_upcross  vel_g2_downcross  pos_g0_mean  \\\n",
       "2101            -0.267          -0.958             0.706       -1.380   \n",
       "2207             1.054           0.561             0.706        0.059   \n",
       "300             -0.267           2.080             0.706       -0.781   \n",
       "1515            -0.267           0.561             0.706        0.147   \n",
       "674             -0.267           0.561             0.706        2.109   \n",
       "339             -1.587          -0.958            -1.034        0.753   \n",
       "\n",
       "      pos_g0_max  pos_g0_min  pos_g0_range  pos_g0_upturns  pos_g0_downturns  \\\n",
       "2101      -0.668      -2.433         0.829          -0.647             1.143   \n",
       "2207      -0.137       0.485        -0.440           1.413             1.143   \n",
       "300       -0.723      -0.341        -0.522          -0.647            -0.841   \n",
       "1515      -0.093       0.483        -0.393           1.413            -0.841   \n",
       "674        2.328       0.486         2.061          -0.647            -0.841   \n",
       "339        0.807       0.473         0.526           1.413            -0.841   \n",
       "\n",
       "      pos_g0_upcross  pos_g0_downcross  pos_g1_mean  pos_g1_max  pos_g1_min  \\\n",
       "2101           1.146             1.042        0.605       0.586       0.619   \n",
       "2207          -0.818            -0.854       -0.113      -0.507       0.277   \n",
       "300           -0.818            -0.854        0.280      -0.432       0.664   \n",
       "1515           1.146             1.042       -0.020      -0.513       0.458   \n",
       "674           -0.818            -0.854       -1.542      -0.509      -1.640   \n",
       "339            1.146            -0.854       -0.893      -0.522      -1.022   \n",
       "\n",
       "      pos_g1_range  pos_g1_upturns  pos_g1_downturns  pos_g1_upcross  \\\n",
       "2101        -0.177           1.132            -0.824           1.391   \n",
       "2207        -0.626           1.132             1.000          -0.652   \n",
       "300         -0.944           1.132            -0.824           1.391   \n",
       "1515        -0.804           1.132            -0.824          -0.652   \n",
       "674          1.211          -0.771            -0.824          -0.652   \n",
       "339          0.610          -0.771            -0.824          -0.652   \n",
       "\n",
       "      pos_g1_downcross  pos_g2_mean  pos_g2_max  pos_g2_min  pos_g2_range  \\\n",
       "2101             1.017        0.656      -0.012       0.491        -0.404   \n",
       "2207             1.017       -0.030      -0.482       0.484        -0.821   \n",
       "300             -0.891        0.312      -0.114       0.492        -0.496   \n",
       "1515            -0.891       -0.242      -0.584       0.330        -0.789   \n",
       "674             -0.891       -0.693      -0.689      -0.145        -0.503   \n",
       "339             -0.891       -0.615      -0.686       0.026        -0.637   \n",
       "\n",
       "      pos_g2_upturns  pos_g2_downturns  pos_g2_upcross  pos_g2_downcross  \\\n",
       "2101          -0.719             0.975          -0.660            -0.657   \n",
       "2207           1.265             0.975          -0.660            -0.657   \n",
       "300            1.265             0.975          -0.660            -0.657   \n",
       "1515           1.265             0.975           1.403            -0.657   \n",
       "674            1.265             0.975          -0.660            -0.657   \n",
       "339           -0.719            -0.859          -0.660            -0.657   \n",
       "\n",
       "      dim0-coef0  dim0-coef1  dim0-coef2  dim0-coef3  dim0-coef4  dim0-coef5  \\\n",
       "2101       1.352       1.556       0.889      -1.166      -2.251      -0.942   \n",
       "2207      -0.245      -0.208      -0.442      -1.217      -1.051      -0.012   \n",
       "300        0.430       0.024      -0.500      -0.916      -0.661       1.049   \n",
       "1515      -1.162      -0.992       0.018       0.656      -0.156       0.789   \n",
       "674       -1.901      -1.313       0.535       2.381       2.875       0.113   \n",
       "339       -1.705      -1.578      -0.782      -0.253      -0.320       1.316   \n",
       "\n",
       "      dim0-coef6  dim0-coef7  dim0-coef8  dim0-coef9  dim1-coef0  dim1-coef1  \\\n",
       "2101      -0.209       1.853       2.110       0.787      -0.628      -0.746   \n",
       "2207      -0.077       0.597       0.753      -0.920      -0.398      -0.557   \n",
       "300        0.888       0.584       0.196      -0.623      -0.048       0.167   \n",
       "1515      -1.297      -1.304       0.156       1.632       0.379       0.069   \n",
       "674       -2.451      -2.478      -1.222      -0.502       1.308       0.498   \n",
       "339       -1.268      -1.040       0.038      -0.159       1.058       0.480   \n",
       "\n",
       "      dim1-coef2  dim1-coef3  dim1-coef4  dim1-coef5  dim1-coef6  dim1-coef7  \\\n",
       "2101      -0.460       0.557       1.031       0.231       0.253      -0.761   \n",
       "2207      -0.649      -0.611       0.146       0.172       0.397      -0.128   \n",
       "300        0.529       0.322       0.299       0.062      -0.683      -0.054   \n",
       "1515       0.520       0.492       0.760       0.926      -0.331      -0.342   \n",
       "674       -0.299      -0.808      -1.612       0.570       1.239       0.572   \n",
       "339        2.627       2.323      -1.477       2.278      -1.578      -1.990   \n",
       "\n",
       "      dim1-coef8  dim1-coef9  dim2-coef0  dim2-coef1  dim2-coef2  dim2-coef3  \\\n",
       "2101      -0.730      -0.207      -0.398       1.218       0.421      -1.353   \n",
       "2207       0.039      -1.469       1.016       1.470       0.942      -0.521   \n",
       "300        0.233      -0.191       1.193       1.640      -0.393      -0.823   \n",
       "1515       0.215      -0.860      -0.123      -1.116       2.221       2.492   \n",
       "674        0.415       0.946      -1.362      -2.254      -0.977       0.816   \n",
       "339        2.638       2.528       0.100      -0.544      -0.673       0.036   \n",
       "\n",
       "      dim2-coef4  dim2-coef5  dim2-coef6  dim2-coef7  dim2-coef8  dim2-coef9  \n",
       "2101      -2.157      -1.604      -0.962       1.434   1.125e+00      -0.056  \n",
       "2207       0.050      -0.421      -0.968       1.368   2.444e-04      -0.277  \n",
       "300        1.106      -1.553       0.720       1.663  -1.271e+00      -1.343  \n",
       "1515      -0.107       2.081      -1.142      -2.644   1.953e+00       1.996  \n",
       "674        0.816       1.709       0.317      -1.660  -6.245e-01       0.760  \n",
       "339        0.591       0.537       0.661      -0.296  -5.706e-01      -0.251  "
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#option to Standardize the data\n",
    "\n",
    "X = (X - X.mean()) / X.std()   #giving X standard scaling to allow coefficient comparision\n",
    "#X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)\n",
    "X.sample(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accuracy(predict,y_test):\n",
    "    total = 0\n",
    "    for guess,ans in zip(predict,y_test):\n",
    "        if guess == ans: total +=1\n",
    "    return total/len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.959221311475 0.00683947510995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('jerk0_mean', 9.2482950599025777),\n",
       " ('jerk0_max', 9.0782479793281023),\n",
       " ('Macc_min', 8.7374201887424459),\n",
       " ('Mjerk_max', 8.0469041302154842),\n",
       " ('dim1-coef5', 8.0367645314525671),\n",
       " ('jerk1_min', 7.9348259840513924),\n",
       " ('jerk1_mean', 7.9210692146728672),\n",
       " ('y_mean', 7.9207292312337758),\n",
       " ('dim0-coef5', 7.9114550919821038),\n",
       " ('jerk0_min', 7.7830142417401502),\n",
       " ('vel_g0_range', 7.3063787370324276),\n",
       " ('dim1-coef9', 7.2616960562797885),\n",
       " ('dim1-coef4', 7.0345486947206179),\n",
       " ('Mdist_min', 6.9260089654158437),\n",
       " ('Mvel_range', 6.919630614524424),\n",
       " ('vel_g1_upturns', 6.8772043786943007),\n",
       " ('Mvel_max', 6.8581360145538639),\n",
       " ('jerk1_max', 6.8419024269352136),\n",
       " ('time_range', 6.7685924547449412),\n",
       " ('x_min', 6.7229829357811655),\n",
       " ('Macc_max', 6.636212786147464),\n",
       " ('jerk2_min', 6.5585830466228758),\n",
       " ('y_min', 6.4993095465159012),\n",
       " ('x_mean', 6.4349973847620925),\n",
       " ('x_downcross', 6.1958816922269886),\n",
       " ('acc_g0_downcross', 6.1849670671022139),\n",
       " ('dim1-coef3', 6.0715828036373756),\n",
       " ('dim0-coef6', 5.703503478574981),\n",
       " ('y_max', 5.5159712817264888),\n",
       " ('acc_g1_upcross', 5.4806679083210046),\n",
       " ('y_upcross', 5.4088198496547299),\n",
       " ('Macc_mean', 5.3809742142380035),\n",
       " ('dim0-coef9', 5.3378977066038775),\n",
       " ('vel_g1_range', 5.3350683246002255),\n",
       " ('z_mean', 5.3199877341683912),\n",
       " ('dim1-coef7', 5.3120517457042853),\n",
       " ('vel_g0_downturns', 5.3070588492304891),\n",
       " ('Mvel_mean', 5.2590474094280371),\n",
       " ('acc_g0_min', 5.2226924648222743),\n",
       " ('y_downcross', 5.1877399371844151),\n",
       " ('pos_g1_max', 5.0777078712503174),\n",
       " ('dim0-coef3', 5.0232493390265045),\n",
       " ('pos_g1_downturns', 5.0135974506574108),\n",
       " ('Mjerk_mean', 5.0134223565164779),\n",
       " ('Macc_range', 4.9998367791336786),\n",
       " ('x_max', 4.995451725038091),\n",
       " ('jerk2_mean', 4.9928868085196356),\n",
       " ('acc_g1_min', 4.9108155439160059),\n",
       " ('jerk0_range', 4.8826076751996625),\n",
       " ('dim2-coef6', 4.8582949001824822),\n",
       " ('Mvel_min', 4.8426952548183086),\n",
       " ('Mjerk_min', 4.7345791779366451),\n",
       " ('vel_g1_downturns', 4.733840673346327),\n",
       " ('pos_g0_downturns', 4.6801026178967753),\n",
       " ('Mdist_upturns', 4.6733835422992485),\n",
       " ('dim0-coef4', 4.6651387305999927),\n",
       " ('vel_g1_max', 4.6535735328259538),\n",
       " ('acc_g1_mean', 4.6186899210140284),\n",
       " ('dim1-coef2', 4.6002591130716093),\n",
       " ('vel_g0_max', 4.59650439266386),\n",
       " ('dim0-coef2', 4.5755924694426433),\n",
       " ('jerk2_max', 4.5585543586289008),\n",
       " ('pos_g0_downcross', 4.5319291269183042),\n",
       " ('pos_g0_mean', 4.4030120196757174),\n",
       " ('pos_g1_range', 4.3777410413608528),\n",
       " ('vel_g0_min', 4.339538773507102),\n",
       " ('pos_g1_upturns', 4.3106510032666447),\n",
       " ('vel_g0_downcross', 4.2906621133334086),\n",
       " ('vel_g1_mean', 4.2544764228843333),\n",
       " ('dim1-coef8', 4.2432225638963494),\n",
       " ('acc_g1_max', 4.2427105376777048),\n",
       " ('pos_g0_range', 4.2105739303378709),\n",
       " ('pos_g0_min', 4.1903072266865937),\n",
       " ('acc_g1_downcross', 4.1798838929167932),\n",
       " ('x_upcross', 4.1640443180742208),\n",
       " ('jerk1_range', 4.1056701767810129),\n",
       " ('y_range', 4.0908953469833378),\n",
       " ('acc_g1_range', 4.0908953469833351),\n",
       " ('acc_g2_upcross', 4.0634014116456898),\n",
       " ('z_max', 4.0607887489640735),\n",
       " ('jerk1_upturns', 4.0480023146649042),\n",
       " ('jerk1_downturns', 4.0352193724134295),\n",
       " ('acc_g2_min', 4.014277339711982),\n",
       " ('vel_g0_upturns', 4.008391093076531),\n",
       " ('pos_g1_mean', 3.9592697770154537),\n",
       " ('dim2-coef5', 3.9525414421946659),\n",
       " ('pos_g0_upturns', 3.9479950951501008),\n",
       " ('Mvel_upturns', 3.8899925073127872),\n",
       " ('dim0-coef7', 3.8874478793788132),\n",
       " ('vel_g0_mean', 3.8789767538222515),\n",
       " ('dim1-coef6', 3.8742289295394992),\n",
       " ('jerk2_downturns', 3.8603806859515912),\n",
       " ('vel_g1_min', 3.8293633073337032),\n",
       " ('dim0-coef8', 3.8204127011583351),\n",
       " ('z_min', 3.8068525055558267),\n",
       " ('acc_g0_max', 3.8019857708746891),\n",
       " ('acc_g0_mean', 3.755145832699526),\n",
       " ('dim2-coef4', 3.7484701667774778),\n",
       " ('Mjerk_range', 3.7449215365373152),\n",
       " ('Mjerk_downturns', 3.7292260648090254),\n",
       " ('Mvel_downturns', 3.7285660091598705),\n",
       " ('dim1-coef0', 3.7260460902327033),\n",
       " ('dim1-coef1', 3.723658275696907),\n",
       " ('acc_g2_downcross', 3.7156718017948518),\n",
       " ('jerk0_downturns', 3.7115369429034626),\n",
       " ('vel_g2_downturns', 3.6981023837567428),\n",
       " ('dim2-coef9', 3.6596190553293697),\n",
       " ('dim0-coef0', 3.6408803606693856),\n",
       " ('Mdist_upcross', 3.6074880848278563),\n",
       " ('pos_g1_upcross', 3.5816649793459883),\n",
       " ('acc_g0_upcross', 3.576168215850525),\n",
       " ('Mjerk_upturns', 3.5702671612839074),\n",
       " ('pos_g2_min', 3.5557303988004478),\n",
       " ('Mdist_mean', 3.5498089077512076),\n",
       " ('dim0-coef1', 3.4568423379581596),\n",
       " ('Mvel_upcross', 3.4140605665493213),\n",
       " ('Mdist_range', 3.3762271150844669),\n",
       " ('acc_g0_range', 3.3752180657648303),\n",
       " ('x_range', 3.3752180657648223),\n",
       " ('acc_g2_mean', 3.3562153548871128),\n",
       " ('pos_g2_downturns', 3.3442005289091972),\n",
       " ('jerk2_range', 3.3328135386082245),\n",
       " ('acc_g2_max', 3.324725810993721),\n",
       " ('dim2-coef3', 3.3104195157477623),\n",
       " ('vel_g2_max', 3.3094972258824336),\n",
       " ('Mdist_max', 3.307909957863441),\n",
       " ('pos_g2_mean', 3.2764711149335048),\n",
       " ('pos_g1_downcross', 3.2730936960593762),\n",
       " ('pos_g2_upturns', 3.2701045716614745),\n",
       " ('jerk2_downcross', 3.2658428184604777),\n",
       " ('vel_g2_upcross', 3.22123599732359),\n",
       " ('Mdist_downturns', 3.2179975514671368),\n",
       " ('vel_g2_upturns', 3.2073699759790841),\n",
       " ('vel_g1_upcross', 3.1906259921177522),\n",
       " ('vel_g2_range', 3.1875159086017981),\n",
       " ('Macc_downturns', 3.1858951080412941),\n",
       " ('dim2-coef0', 3.1647897496672859),\n",
       " ('jerk1_upcross', 3.160604880160903),\n",
       " ('jerk0_upturns', 3.1290547554225645),\n",
       " ('vel_g1_downcross', 3.1154742203374384),\n",
       " ('jerk2_upturns', 3.0808599565576094),\n",
       " ('pos_g2_upcross', 3.0273991598608738),\n",
       " ('z_range', 3.0263044268284167),\n",
       " ('acc_g2_range', 3.0263044268284056),\n",
       " ('dim2-coef2', 2.9901957257384311),\n",
       " ('dim2-coef7', 2.9710850919686722),\n",
       " ('z_downcross', 2.9460572045567286),\n",
       " ('vel_g0_upcross', 2.9150996953008357),\n",
       " ('Mjerk_downcross', 2.9094706337191409),\n",
       " ('pos_g2_downcross', 2.9088535322718219),\n",
       " ('Macc_downcross', 2.8971571120521098),\n",
       " ('vel_g2_min', 2.8688932572018429),\n",
       " ('Mdist_downcross', 2.8652440974081257),\n",
       " ('jerk2_upcross', 2.8464185536220139),\n",
       " ('z_upcross', 2.8200963829304295),\n",
       " ('pos_g0_max', 2.7943592667761084),\n",
       " ('Mjerk_upcross', 2.7887825349529192),\n",
       " ('dim2-coef8', 2.7335552801082197),\n",
       " ('Macc_upturns', 2.5487833772072039),\n",
       " ('Macc_upcross', 2.5120969701679856),\n",
       " ('jerk1_downcross', 2.5069830336163315),\n",
       " ('pos_g2_max', 2.5048707385657405),\n",
       " ('jerk0_upcross', 2.5027217533260586),\n",
       " ('dim2-coef1', 2.3845199601121334),\n",
       " ('pos_g1_min', 2.2809643648155404),\n",
       " ('y_upturns', 2.270347227280523),\n",
       " ('acc_g1_upturns', 2.270347227280523),\n",
       " ('pos_g0_upcross', 2.2106729866336003),\n",
       " ('x_downturns', 2.1754654239993276),\n",
       " ('acc_g0_downturns', 2.1754654239993276),\n",
       " ('pos_g2_range', 2.1750376801728848),\n",
       " ('vel_g2_downcross', 2.1269129809134557),\n",
       " ('Mvel_downcross', 1.9387131199213228),\n",
       " ('vel_g2_mean', 1.8727396301325459),\n",
       " ('y_downturns', 1.8659356373231542),\n",
       " ('acc_g1_downturns', 1.8659356373231542),\n",
       " ('jerk0_downcross', 1.8302088117361908),\n",
       " ('z_downturns', 1.6460239300524839),\n",
       " ('acc_g2_downturns', 1.6460239300524839),\n",
       " ('acc_g2_upturns', 1.4593164299074606),\n",
       " ('z_upturns', 1.4593164299074606),\n",
       " ('acc_g0_upturns', 0.95755884101768429),\n",
       " ('x_upturns', 0.95755884101768429)]"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_list = []\n",
    "for i in range(5):\n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)\n",
    "    LRmodel = LogisticRegression(n_jobs=-1)\n",
    "    LRmodel.fit(X_train,y_train)\n",
    "    predict = LRmodel.predict(X_test)\n",
    "    acc = accuracy(predict,y_test)\n",
    "    acc_list.append(acc)\n",
    "print(np.mean(acc_list), np.std(acc_list))#, LRmodel.coef_\n",
    "\n",
    "guesture_number = 1 - 1\n",
    "report = list(zip(X.columns,LRmodel.coef_[guesture_number]))  # importances FOR gesture 1 only\n",
    "\n",
    "order_indexes = np.argsort(np.abs(LRmodel.coef_[guesture_number]))\n",
    "sorted_report = []\n",
    "for i in order_indexes[::-1]:\n",
    "    sorted_report.append(report[i])\n",
    "#sorted_report\n",
    "\n",
    "# combined coefficent total to find the most important variables, and any irrelevant ones\n",
    "coef_total = 0\n",
    "for gesture in range(len(y.unique())):\n",
    "    coef_total += np.abs(LRmodel.coef_[gesture])\n",
    "    \n",
    "report = list(zip(X.columns,coef_total))  # importances FOR gesture 1 only\n",
    "\n",
    "order_indexes = np.argsort(np.abs(coef_total))\n",
    "              \n",
    "sorted_report = []\n",
    "for i in order_indexes[::-1]:\n",
    "    sorted_report.append(report[i])\n",
    "\n",
    "sorted_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAuByELzdmP+mGJG3Zx5TzAAAAgKcifLmxetV8FRTgay5v3se4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\nTED4AgAAAAATEL4AAAAAwAT/H7zvirXnERK/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143b21198>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 426,
       "width": 431
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.plot(sorted(coef_total,reverse=True))\n",
    "\n",
    "plt.title(\"Linear Regression Sorted Coefficient Weights\")\n",
    "plt.ylabel(\"Standardized Coefficient\")\n",
    "plt.ylabel(\"Sorted Feature\")\n",
    "plt.savefig(\"Coef_Weights_LR.png\",transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.976639344262 0.00357286798651\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('jerk0_mean', 9.2482950599025777),\n",
       " ('jerk0_max', 9.0782479793281023),\n",
       " ('Macc_min', 8.7374201887424459),\n",
       " ('Mjerk_max', 8.0469041302154842),\n",
       " ('dim1-coef5', 8.0367645314525671),\n",
       " ('jerk1_min', 7.9348259840513924),\n",
       " ('jerk1_mean', 7.9210692146728672),\n",
       " ('y_mean', 7.9207292312337758),\n",
       " ('dim0-coef5', 7.9114550919821038),\n",
       " ('jerk0_min', 7.7830142417401502),\n",
       " ('vel_g0_range', 7.3063787370324276),\n",
       " ('dim1-coef9', 7.2616960562797885),\n",
       " ('dim1-coef4', 7.0345486947206179),\n",
       " ('Mdist_min', 6.9260089654158437),\n",
       " ('Mvel_range', 6.919630614524424),\n",
       " ('vel_g1_upturns', 6.8772043786943007),\n",
       " ('Mvel_max', 6.8581360145538639),\n",
       " ('jerk1_max', 6.8419024269352136),\n",
       " ('time_range', 6.7685924547449412),\n",
       " ('x_min', 6.7229829357811655),\n",
       " ('Macc_max', 6.636212786147464),\n",
       " ('jerk2_min', 6.5585830466228758),\n",
       " ('y_min', 6.4993095465159012),\n",
       " ('x_mean', 6.4349973847620925),\n",
       " ('x_downcross', 6.1958816922269886),\n",
       " ('acc_g0_downcross', 6.1849670671022139),\n",
       " ('dim1-coef3', 6.0715828036373756),\n",
       " ('dim0-coef6', 5.703503478574981),\n",
       " ('y_max', 5.5159712817264888),\n",
       " ('acc_g1_upcross', 5.4806679083210046),\n",
       " ('y_upcross', 5.4088198496547299),\n",
       " ('Macc_mean', 5.3809742142380035),\n",
       " ('dim0-coef9', 5.3378977066038775),\n",
       " ('vel_g1_range', 5.3350683246002255),\n",
       " ('z_mean', 5.3199877341683912),\n",
       " ('dim1-coef7', 5.3120517457042853),\n",
       " ('vel_g0_downturns', 5.3070588492304891),\n",
       " ('Mvel_mean', 5.2590474094280371),\n",
       " ('acc_g0_min', 5.2226924648222743),\n",
       " ('y_downcross', 5.1877399371844151),\n",
       " ('pos_g1_max', 5.0777078712503174),\n",
       " ('dim0-coef3', 5.0232493390265045),\n",
       " ('pos_g1_downturns', 5.0135974506574108),\n",
       " ('Mjerk_mean', 5.0134223565164779),\n",
       " ('Macc_range', 4.9998367791336786),\n",
       " ('x_max', 4.995451725038091),\n",
       " ('jerk2_mean', 4.9928868085196356),\n",
       " ('acc_g1_min', 4.9108155439160059),\n",
       " ('jerk0_range', 4.8826076751996625),\n",
       " ('dim2-coef6', 4.8582949001824822),\n",
       " ('Mvel_min', 4.8426952548183086),\n",
       " ('Mjerk_min', 4.7345791779366451),\n",
       " ('vel_g1_downturns', 4.733840673346327),\n",
       " ('pos_g0_downturns', 4.6801026178967753),\n",
       " ('Mdist_upturns', 4.6733835422992485),\n",
       " ('dim0-coef4', 4.6651387305999927),\n",
       " ('vel_g1_max', 4.6535735328259538),\n",
       " ('acc_g1_mean', 4.6186899210140284),\n",
       " ('dim1-coef2', 4.6002591130716093),\n",
       " ('vel_g0_max', 4.59650439266386),\n",
       " ('dim0-coef2', 4.5755924694426433),\n",
       " ('jerk2_max', 4.5585543586289008),\n",
       " ('pos_g0_downcross', 4.5319291269183042),\n",
       " ('pos_g0_mean', 4.4030120196757174),\n",
       " ('pos_g1_range', 4.3777410413608528),\n",
       " ('vel_g0_min', 4.339538773507102),\n",
       " ('pos_g1_upturns', 4.3106510032666447),\n",
       " ('vel_g0_downcross', 4.2906621133334086),\n",
       " ('vel_g1_mean', 4.2544764228843333),\n",
       " ('dim1-coef8', 4.2432225638963494),\n",
       " ('acc_g1_max', 4.2427105376777048),\n",
       " ('pos_g0_range', 4.2105739303378709),\n",
       " ('pos_g0_min', 4.1903072266865937),\n",
       " ('acc_g1_downcross', 4.1798838929167932),\n",
       " ('x_upcross', 4.1640443180742208),\n",
       " ('jerk1_range', 4.1056701767810129),\n",
       " ('y_range', 4.0908953469833378),\n",
       " ('acc_g1_range', 4.0908953469833351),\n",
       " ('acc_g2_upcross', 4.0634014116456898),\n",
       " ('z_max', 4.0607887489640735),\n",
       " ('jerk1_upturns', 4.0480023146649042),\n",
       " ('jerk1_downturns', 4.0352193724134295),\n",
       " ('acc_g2_min', 4.014277339711982),\n",
       " ('vel_g0_upturns', 4.008391093076531),\n",
       " ('pos_g1_mean', 3.9592697770154537),\n",
       " ('dim2-coef5', 3.9525414421946659),\n",
       " ('pos_g0_upturns', 3.9479950951501008),\n",
       " ('Mvel_upturns', 3.8899925073127872),\n",
       " ('dim0-coef7', 3.8874478793788132),\n",
       " ('vel_g0_mean', 3.8789767538222515),\n",
       " ('dim1-coef6', 3.8742289295394992),\n",
       " ('jerk2_downturns', 3.8603806859515912),\n",
       " ('vel_g1_min', 3.8293633073337032),\n",
       " ('dim0-coef8', 3.8204127011583351),\n",
       " ('z_min', 3.8068525055558267),\n",
       " ('acc_g0_max', 3.8019857708746891),\n",
       " ('acc_g0_mean', 3.755145832699526),\n",
       " ('dim2-coef4', 3.7484701667774778),\n",
       " ('Mjerk_range', 3.7449215365373152),\n",
       " ('Mjerk_downturns', 3.7292260648090254),\n",
       " ('Mvel_downturns', 3.7285660091598705),\n",
       " ('dim1-coef0', 3.7260460902327033),\n",
       " ('dim1-coef1', 3.723658275696907),\n",
       " ('acc_g2_downcross', 3.7156718017948518),\n",
       " ('jerk0_downturns', 3.7115369429034626),\n",
       " ('vel_g2_downturns', 3.6981023837567428),\n",
       " ('dim2-coef9', 3.6596190553293697),\n",
       " ('dim0-coef0', 3.6408803606693856),\n",
       " ('Mdist_upcross', 3.6074880848278563),\n",
       " ('pos_g1_upcross', 3.5816649793459883),\n",
       " ('acc_g0_upcross', 3.576168215850525),\n",
       " ('Mjerk_upturns', 3.5702671612839074),\n",
       " ('pos_g2_min', 3.5557303988004478),\n",
       " ('Mdist_mean', 3.5498089077512076),\n",
       " ('dim0-coef1', 3.4568423379581596),\n",
       " ('Mvel_upcross', 3.4140605665493213),\n",
       " ('Mdist_range', 3.3762271150844669),\n",
       " ('acc_g0_range', 3.3752180657648303),\n",
       " ('x_range', 3.3752180657648223),\n",
       " ('acc_g2_mean', 3.3562153548871128),\n",
       " ('pos_g2_downturns', 3.3442005289091972),\n",
       " ('jerk2_range', 3.3328135386082245),\n",
       " ('acc_g2_max', 3.324725810993721),\n",
       " ('dim2-coef3', 3.3104195157477623),\n",
       " ('vel_g2_max', 3.3094972258824336),\n",
       " ('Mdist_max', 3.307909957863441),\n",
       " ('pos_g2_mean', 3.2764711149335048),\n",
       " ('pos_g1_downcross', 3.2730936960593762),\n",
       " ('pos_g2_upturns', 3.2701045716614745),\n",
       " ('jerk2_downcross', 3.2658428184604777),\n",
       " ('vel_g2_upcross', 3.22123599732359),\n",
       " ('Mdist_downturns', 3.2179975514671368),\n",
       " ('vel_g2_upturns', 3.2073699759790841),\n",
       " ('vel_g1_upcross', 3.1906259921177522),\n",
       " ('vel_g2_range', 3.1875159086017981),\n",
       " ('Macc_downturns', 3.1858951080412941),\n",
       " ('dim2-coef0', 3.1647897496672859),\n",
       " ('jerk1_upcross', 3.160604880160903),\n",
       " ('jerk0_upturns', 3.1290547554225645),\n",
       " ('vel_g1_downcross', 3.1154742203374384),\n",
       " ('jerk2_upturns', 3.0808599565576094),\n",
       " ('pos_g2_upcross', 3.0273991598608738),\n",
       " ('z_range', 3.0263044268284167),\n",
       " ('acc_g2_range', 3.0263044268284056),\n",
       " ('dim2-coef2', 2.9901957257384311),\n",
       " ('dim2-coef7', 2.9710850919686722),\n",
       " ('z_downcross', 2.9460572045567286),\n",
       " ('vel_g0_upcross', 2.9150996953008357),\n",
       " ('Mjerk_downcross', 2.9094706337191409),\n",
       " ('pos_g2_downcross', 2.9088535322718219),\n",
       " ('Macc_downcross', 2.8971571120521098),\n",
       " ('vel_g2_min', 2.8688932572018429),\n",
       " ('Mdist_downcross', 2.8652440974081257),\n",
       " ('jerk2_upcross', 2.8464185536220139),\n",
       " ('z_upcross', 2.8200963829304295),\n",
       " ('pos_g0_max', 2.7943592667761084),\n",
       " ('Mjerk_upcross', 2.7887825349529192),\n",
       " ('dim2-coef8', 2.7335552801082197),\n",
       " ('Macc_upturns', 2.5487833772072039),\n",
       " ('Macc_upcross', 2.5120969701679856),\n",
       " ('jerk1_downcross', 2.5069830336163315),\n",
       " ('pos_g2_max', 2.5048707385657405),\n",
       " ('jerk0_upcross', 2.5027217533260586),\n",
       " ('dim2-coef1', 2.3845199601121334),\n",
       " ('pos_g1_min', 2.2809643648155404),\n",
       " ('y_upturns', 2.270347227280523),\n",
       " ('acc_g1_upturns', 2.270347227280523),\n",
       " ('pos_g0_upcross', 2.2106729866336003),\n",
       " ('x_downturns', 2.1754654239993276),\n",
       " ('acc_g0_downturns', 2.1754654239993276),\n",
       " ('pos_g2_range', 2.1750376801728848),\n",
       " ('vel_g2_downcross', 2.1269129809134557),\n",
       " ('Mvel_downcross', 1.9387131199213228),\n",
       " ('vel_g2_mean', 1.8727396301325459),\n",
       " ('y_downturns', 1.8659356373231542),\n",
       " ('acc_g1_downturns', 1.8659356373231542),\n",
       " ('jerk0_downcross', 1.8302088117361908),\n",
       " ('z_downturns', 1.6460239300524839),\n",
       " ('acc_g2_downturns', 1.6460239300524839),\n",
       " ('acc_g2_upturns', 1.4593164299074606),\n",
       " ('z_upturns', 1.4593164299074606),\n",
       " ('acc_g0_upturns', 0.95755884101768429),\n",
       " ('x_upturns', 0.95755884101768429)]"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#same thing for random forrest\n",
    "\n",
    "acc_list = []\n",
    "for i in range(5):\n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)\n",
    "    RFmodel = RandomForestClassifier(n_estimators=150,n_jobs=-1)\n",
    "    RFmodel.fit(X_train,y_train)\n",
    "    predict = RFmodel.predict(X_test)\n",
    "    acc = accuracy(predict,y_test)\n",
    "    acc_list.append(acc)\n",
    "print(np.mean(acc_list), np.std(acc_list))#, LRmodel.coef_\n",
    "\n",
    "guesture_number = 1 - 1\n",
    "report = list(zip(X.columns,LRmodel.coef_[guesture_number]))  # importances FOR gesture 1 only\n",
    "\n",
    "order_indexes = np.argsort(np.abs(LRmodel.coef_[guesture_number]))\n",
    "sorted_report = []\n",
    "for i in order_indexes[::-1]:\n",
    "    sorted_report.append(report[i])\n",
    "#sorted_report\n",
    "\n",
    "# combined coefficent total to find the most important variables, and any irrelevant ones\n",
    "coef_total = 0\n",
    "for gesture in range(len(y.unique())):\n",
    "    coef_total += np.abs(LRmodel.coef_[gesture])\n",
    "    \n",
    "report = list(zip(X.columns,coef_total))  # importances FOR gesture 1 only\n",
    "\n",
    "order_indexes = np.argsort(np.abs(coef_total))\n",
    "              \n",
    "sorted_report = []\n",
    "for i in order_indexes[::-1]:\n",
    "    sorted_report.append(report[i])\n",
    "\n",
    "sorted_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take aways:\n",
    "### Last used features:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Use the coefficients to deterimine good and poor features..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression did pretty well\n",
    "## will now try a battery of models, using their default parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ModelIt(LearningAlgorithm,X,y,N_times = 12,multi=False):\n",
    "    acc_list = []\n",
    "    LL_list = []\n",
    "    for i in range(N_times):\n",
    "        X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
    "        if multi == True: model = LearningAlgorithm(n_jobs=-1)\n",
    "        else: model = LearningAlgorithm()\n",
    "        model.fit(X_train,y_train)\n",
    "        predict = model.predict(X_test)\n",
    "        #predict_prob = model.predict_proba(X_test)\n",
    "        acc = accuracy(predict,y_test)\n",
    "        #LL = log_loss(y_test,predict_prob)\n",
    "        #LL_list.append(LL)\n",
    "        acc_list.append(acc)\n",
    "    return np.mean(acc_list), np.std(acc_list)#,np.mean(LL_list), np.std(LL_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9567332309267792, 0.0057075004632892914)"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt(KNeighborsClassifier,X,y,multi=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9709421402969789, 0.0063477093027226985)"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt(LogisticRegression,X,y,multi=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.97094214029697901, 0.0048760872900002472)"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt(SVC,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.94841269841269826, 0.0058646754679750664)"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt(RandomForestClassifier,X,y,multi=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ModelIt(GradientBoostingClassifier,X,y,N_times=5,multi=False)  #this one has to run in series, and is VERY slow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OR, using the built in Cross_val_score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.94986033321517205, 0.0066507580293853783) Individual scores: [ 0.95391705  0.95846154  0.95230769  0.94461538  0.94      ]\n",
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.41291058471337239, 0.085879715013581395) Individual scores: [-0.47205276 -0.31914078 -0.31233093 -0.42889408 -0.53213438]\n"
     ]
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=5)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1)\n",
    "print(\"(Accuracy Cross_Val_Score,Standard Deviation) =\",(np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1,scoring='neg_log_loss')\n",
    "print(\"(Log-Loss Cross_Val_Score,Standard Deviation) =\",(-np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.96708779392650368, 0.0071860463653587673) Individual scores: [ 0.96466974  0.96        0.98        0.96153846  0.96923077]\n",
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.22281453180265509, 0.018630850181213385) Individual scores: [-0.21833679 -0.24335156 -0.19918133 -0.2453966  -0.20780637]\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(C=1.15)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1)\n",
    "print(\"(Accuracy Cross_Val_Score,Standard Deviation) =\",(np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1,scoring='neg_log_loss')\n",
    "print(\"(Log-Loss Cross_Val_Score,Standard Deviation) =\",(-np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.96308779392650357, 0.006156601656322213) Individual scores: [ 0.96466974  0.95538462  0.96153846  0.97384615  0.96      ]\n",
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.18081541968490919, 0.0081016671583881636) Individual scores: [-0.16862953 -0.18326258 -0.19185755 -0.17511781 -0.18520963]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(kernel='linear',probability=True,C=0.01176)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1)\n",
    "print(\"(Accuracy Cross_Val_Score,Standard Deviation) =\",(np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1,scoring='neg_log_loss')\n",
    "print(\"(Log-Loss Cross_Val_Score,Standard Deviation) =\",(-np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.97447051872858326, 0.0057176396539404444) Individual scores: [ 0.97081413  0.97384615  0.98461538  0.97538462  0.96769231]\n",
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.1542820594500276, 0.013211320351770153) Individual scores: [-0.1615123  -0.15658    -0.13400252 -0.14650322 -0.17281225]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(kernel='rbf',probability=True,C=4)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1)\n",
    "print(\"(Accuracy Cross_Val_Score,Standard Deviation) =\",(np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1,scoring='neg_log_loss')\n",
    "print(\"(Log-Loss Cross_Val_Score,Standard Deviation) =\",(-np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.96062625546496516, 0.01095195999188303) Individual scores: [ 0.96466974  0.94769231  0.97384615  0.96923077  0.94769231]\n"
     ]
    }
   ],
   "source": [
    "model = LinearSVC(C=0.715)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1)\n",
    "print(\"(Accuracy Cross_Val_Score,Standard Deviation) =\",(np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)\n",
    "#scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1,scoring='neg_log_loss')\n",
    "#print(\"Log-Loss Cross_Val_Score =\",np.mean(scores),\"Individual scores:\",scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.98154082476663118, 0.007087202369858504) Individual scores: [ 0.99231951  0.98461538  0.97230769  0.98307692  0.97538462]\n",
      "(Log-Loss Cross_Val_Score,Standard Deviation) = (0.30689991573917436, 0.015889727589520508) Individual scores: [-0.28357451 -0.3013328  -0.30499976 -0.33253454 -0.31205797]\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=750)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1)\n",
    "print(\"(Accuracy Cross_Val_Score,Standard Deviation) =\",(np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1,scoring='neg_log_loss')\n",
    "print(\"(Log-Loss Cross_Val_Score,Standard Deviation) =\",(-np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.95878199220134697, 0.006627351832402175) Individual scores: [ 0.95852535  0.94615385  0.96461538  0.96307692  0.96153846]\n",
      "(Accuracy Cross_Val_Score,Standard Deviation) = (0.16237771675020671, 0.03029856760469498) Individual scores: [-0.16673469 -0.21584899 -0.12486331 -0.14521369 -0.1592279 ]\n"
     ]
    }
   ],
   "source": [
    "model = GradientBoostingClassifier(learning_rate=0.019,n_estimators=600,max_depth=4)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1)\n",
    "print(\"(Accuracy Cross_Val_Score,Standard Deviation) =\",(np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)\n",
    "scores = cross_val_score(model,X,y,cv=shufflekfold,n_jobs=-1,scoring='neg_log_loss')\n",
    "print(\"(Log-Loss Cross_Val_Score,Standard Deviation) =\",(-np.mean(scores),np.std(scores)),\"Individual scores:\",scores,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use GridSearch to optimize the above algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': [4, 5, 6], 'learning_rate': [0.03], 'n_estimators': [600]}\n",
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:  9.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.03, loss='deviance', max_depth=4,\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=600, presort='auto', random_state=None,\n",
       "              subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'max_depth':[4],\n",
    "              'learning_rate':[0.03],\n",
    "              'n_estimators':[600]}\n",
    "print(parameters)\n",
    "shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "model = GradientBoostingClassifier()\n",
    "grid_search = GridSearchCV(model, parameters, cv=shufflekfold,n_jobs=-1, verbose=1)   #note n_jobs = -2 means all but one core\n",
    "#grid_search.fit(X_train,y_train)\n",
    "grid_search.fit(X,y)\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.geomspace(0.01,0.015,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, without looking at the same user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ModelIt_Groups(LearningAlgorithmSTRING,X,y,groups = 8):\n",
    "    acc_list = []\n",
    "    LL_list = []\n",
    "    new_user = GroupKFold(groups)\n",
    "    new_user.get_n_splits(X, y, df['user'])\n",
    "    for train_index, test_index in new_user.split(X, y, df['user']):\n",
    "        #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "        X_train, X_test = X.loc[train_index,:], X.loc[test_index,:]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        #print(len(y_test))\n",
    "        model = LearningAlgorithmSTRING\n",
    "\n",
    "        model.fit(X_train,y_train)\n",
    "        predict = model.predict(X_test)\n",
    "        #predict_prob = model.predict_proba(X_test)\n",
    "        \n",
    "        acc = accuracy(predict,y_test)\n",
    "        #LL = log_loss(y_test,predict_prob)\n",
    "        #LL_list.append(LL)\n",
    "        acc_list.append(acc)\n",
    "    return np.mean(acc_list), np.std(acc_list)#,np.mean(LL_list), np.std(LL_list)#,acc_list\n",
    "\n",
    "def ModelIt_Groups_LL(LearningAlgorithmSTRING,X,y,groups = 8):\n",
    "    acc_list = []\n",
    "    LL_list = []\n",
    "    new_user = GroupKFold(groups)\n",
    "    new_user.get_n_splits(X, y, df['user'])\n",
    "    for train_index, test_index in new_user.split(X, y, df['user']):\n",
    "        #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "        X_train, X_test = X.loc[train_index,:], X.loc[test_index,:]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        #print(len(y_test))\n",
    "        model = LearningAlgorithmSTRING\n",
    "\n",
    "        model.fit(X_train,y_train)\n",
    "        #predict = model.predict(X_test)\n",
    "        predict_prob = model.predict_proba(X_test)\n",
    "        \n",
    "        #acc = accuracy(predict,y_test)\n",
    "        LL = log_loss(y_test,predict_prob)\n",
    "        LL_list.append(LL)\n",
    "        #acc_list.append(acc)\n",
    "    return np.mean(LL_list), np.std(LL_list)#,acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LAZVwS0eGBfdl7M23X/Xzqp4LPXqNd87oN23ZwwfrMjhXFoBG0dM/voqpqXcQ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11888aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confirming groups are being selected properly\n",
    "new_user = GroupKFold(8)\n",
    "new_user.get_n_splits(X, y, df['user'])\n",
    "for train_index, test_index in new_user.split(X, y, df['user']):\n",
    "    #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X.loc[train_index,:], X.loc[test_index,:]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "    plt.plot(sorted(train_index))\n",
    "    #plt.plot(sorted(test_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.79737921172248483, 0.050705823370305138)"
      ]
     },
     "execution_count": 811,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups(KNeighborsClassifier(n_neighbors=5,n_jobs=-1),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.4003215675341747, 0.88638415194919729)"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups_LL(KNeighborsClassifier(n_neighbors=5,n_jobs=-1),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.86344279093984277, 0.042336544459753607)"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups(LogisticRegression(C=0.3,n_jobs=-1),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.68464691242102726, 0.12213866200609753)"
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups_LL(LogisticRegression(C=0.3,n_jobs=-1),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.87695393562335888, 0.043057154016082518)"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups(SVC(kernel='linear',C=0.01,probability=True),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.44089015160811768, 0.14391361384111731)"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups_LL(SVC(kernel='linear',C=0.01,probability=True),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.87392480722833088, 0.041029093871839457)"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups(SVC(kernel='rbf',C=2.25,probability=True),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.49226516617371224, 0.14671777433081537)"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups_LL(SVC(kernel='rbf',C=2.25,probability=True),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.87150850023623305, 0.062211341107501629)"
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups(RandomForestClassifier(n_estimators=750,n_jobs=-1),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.70709358635256425, 0.16242483482985481)"
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups_LL(RandomForestClassifier(n_estimators=750,n_jobs=-1),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.78969273474213586, 0.062297432199416969)"
      ]
     },
     "execution_count": 813,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups(GradientBoostingClassifier(learning_rate=0.019,n_estimators=600,max_depth=4),X,y)  #this one has to run in series, and is VERY slow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.76545602544355174, 0.24858500437949219)"
      ]
     },
     "execution_count": 814,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelIt_Groups_LL(GradientBoostingClassifier(learning_rate=0.019,n_estimators=600,max_depth=4),X,y)  #this one has to run in series, and is VERY slow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Optimized the above algorithms using GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [50, 250, 750]}\n",
      "Fitting 8 folds for each of 3 candidates, totalling 24 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:   33.8s finished\n"
     ]
    },
    {
     "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=750, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 820,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'n_estimators':[50,250,750]}\n",
    "print(parameters)\n",
    "#shufflekfold = KFold(len(X),n_folds=5,shuffle=True)\n",
    "model = RandomForestClassifier()\n",
    "grid_search = GridSearchCV(model, parameters, cv=GroupKFold(8),n_jobs=-1, verbose=1)   #note n_jobs = -2 means all but one core\n",
    "#grid_search.fit(X_train,y_train)\n",
    "grid_search.fit(X,y,df['user'])     #note groups and GroupKFold(8) above\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensamble Model using best models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classify_from_multi_probs(probs):\n",
    "    length, N_classes = probs.shape\n",
    "    \n",
    "    arg_list = []\n",
    "    for i in range(length):\n",
    "        arg_list.append( probs[i,:].argmax() + 1 )  #I have to zero class, classes start at 1\n",
    "    return arg_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ModelIt_Groups_ENSAMBLE(model_list,X,y,groups = 8):\n",
    "    n=0\n",
    "    acc_list = []\n",
    "    LL_list = []\n",
    "    acc_list2 = []\n",
    "    LL_list2 = []\n",
    "    new_user = GroupKFold(groups)\n",
    "    new_user.get_n_splits(X, y, df['user'])\n",
    "    for train_index, test_index in new_user.split(X, y, df['user']):\n",
    "        #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "        X_train, X_test = X.loc[train_index,:], X.loc[test_index,:]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        \n",
    "        ensable_prob_list = []\n",
    "        if 'lr' in model_list:\n",
    "            LRmodel = LogisticRegression(C=0.3,n_jobs=-1)\n",
    "            LRmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [LRmodel.predict_proba(X_test)]\n",
    "            \n",
    "        if 'rf' in model_list:\n",
    "            RFmodel = RandomForestClassifier(n_estimators=750, n_jobs=-1)\n",
    "            RFmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [RFmodel.predict_proba(X_test)]\n",
    "\n",
    "        if 'svc' in model_list:\n",
    "            SVmodel = SVC(kernel='linear',C=0.01,probability=True)\n",
    "            SVmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [SVmodel.predict_proba(X_test)]\n",
    "        \n",
    "        if 'rbf' in model_list:\n",
    "            RBFmodel = SVC(kernel='rbf',C=2.25,probability=True)\n",
    "            RBFmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [RBFmodel.predict_proba(X_test)]\n",
    "\n",
    "        n+=1\n",
    "        #print(len(ensable_prob_list),\"models.  Fold number \",n)\n",
    "        ensable_predict = sum([x*x for x in ensable_prob_list])**len(ensable_prob_list)  # combine the models\n",
    "        ensable_predict2 = sum(ensable_prob_list)/len(ensable_prob_list)  # combine the models   \n",
    "        \n",
    "        ensable_classified = classify_from_multi_probs(ensable_predict)\n",
    "        ensable_classified2 = classify_from_multi_probs(ensable_predict2)\n",
    "        \n",
    "        acc = accuracy(ensable_classified,y_test)\n",
    "        LL = log_loss(y_test,ensable_predict)\n",
    "        LL_list.append(LL)\n",
    "        acc_list.append(acc)\n",
    "        acc2 = accuracy(ensable_classified2,y_test)\n",
    "        LL2 = log_loss(y_test,ensable_predict2)\n",
    "        LL_list2.append(LL2)\n",
    "        acc_list2.append(acc2)\n",
    "        \n",
    "    print(\"Geom Sum:\",np.mean(acc_list), np.std(acc_list),\"LL:\",np.mean(LL_list), np.std(LL_list))\n",
    "    print(\"Arth Sum:\",np.mean(acc_list2), np.std(acc_list2),\"LL:\",np.mean(LL_list2), np.std(LL_list2))\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 877,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.897977690853 0.0399705075168 0.33768280066 0.15818805694\n",
      "Arth Sum: 0.901374629646 0.0386762766991 0.660824905059 0.125191373128\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rf','lr'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 880,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.887649215382 0.0449984424484 0.393790799425 0.217082154685\n",
      "Arth Sum: 0.887065689375 0.0456422325789 0.534005382175 0.144276792526\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rf','svc'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 881,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.88804048064 0.045437622274 0.391405813847 0.221342282741\n",
      "Arth Sum: 0.887447790431 0.0461703539639 0.562840094023 0.148606958788\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rf','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 882,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.888239633253 0.0391522371331 0.489345786839 0.20392493992\n",
      "Arth Sum: 0.887309746606 0.0387556087155 0.528795216044 0.126086530227\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['lr','svc'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 883,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.891633454839 0.0299020200632 0.446955566699 0.165537289102\n",
      "Arth Sum: 0.892268394466 0.0307863733372 0.549290157601 0.110726796055\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['lr','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 884,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.883372099843 0.0404082721837 0.556128423275 0.220057043969\n",
      "Arth Sum: 0.884293742828 0.0432647128757 0.447898431975 0.13542836854\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rbf','svc'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 885,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.896302893403 0.0359296325937 0.603092989038 0.328347286033\n",
      "Arth Sum: 0.897234479892 0.036198813309 0.532304120636 0.126555224946\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rf','lr','svc','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 886,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.902198418843 0.0364251265632 0.44977215186 0.243148664689\n",
      "Arth Sum: 0.903741490099 0.0372228121466 0.581125876392 0.123611187859\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rf','lr','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.900001632653 0.0365075139961 0.476364489254 0.258646033272\n",
      "Arth Sum: 0.900936794748 0.0361702820366 0.56309057375 0.128418076891\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rf','lr','svc'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.886720562559 0.0464686812944 0.553793659119 0.316902060938\n",
      "Arth Sum: 0.887640047303 0.0454244551167 0.5068083785 0.143167532965\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['rf','svc','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.893495707277 0.0331226656211 0.612753202009 0.260812047296\n",
      "Arth Sum: 0.893183207277 0.0334684362143 0.499323281992 0.120199840015\n"
     ]
    }
   ],
   "source": [
    "ModelIt_Groups_ENSAMBLE(['lr','svc','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 879,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ModelIt_shuffle_ENSAMBLE(model_list,X,y,N_times = 5):\n",
    "    n=0\n",
    "    acc_list = []\n",
    "    LL_list = []\n",
    "    acc_list2 = []\n",
    "    LL_list2 = []\n",
    "    for i in range(N_times):\n",
    "        X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
    "     \n",
    "        ensable_prob_list = []\n",
    "        if 'lr' in model_list:\n",
    "            LRmodel = LogisticRegression(C=0.3,n_jobs=-1)\n",
    "            LRmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [LRmodel.predict_proba(X_test)]\n",
    "            \n",
    "        if 'rf' in model_list:\n",
    "            RFmodel = RandomForestClassifier(n_estimators=750, n_jobs=-1)\n",
    "            RFmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [RFmodel.predict_proba(X_test)]\n",
    "\n",
    "        if 'svc' in model_list:\n",
    "            SVmodel = SVC(kernel='linear',C=0.01,probability=True)\n",
    "            SVmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [SVmodel.predict_proba(X_test)]\n",
    "        \n",
    "        if 'rbf' in model_list:\n",
    "            RBFmodel = SVC(kernel='rbf',C=2.25,probability=True)\n",
    "            RBFmodel.fit(X_train,y_train)\n",
    "            ensable_prob_list += [RBFmodel.predict_proba(X_test)]\n",
    "\n",
    "        n+=1\n",
    "        #print(len(ensable_prob_list),\"models.  Fold number \",n)\n",
    "        ensable_predict = sum([x*x for x in ensable_prob_list])**len(ensable_prob_list)  # combine the models\n",
    "        ensable_predict2 = sum(ensable_prob_list)/len(ensable_prob_list)  # combine the models   \n",
    "        \n",
    "        ensable_classified = classify_from_multi_probs(ensable_predict)\n",
    "        ensable_classified2 = classify_from_multi_probs(ensable_predict2)\n",
    "        \n",
    "        acc = accuracy(ensable_classified,y_test)\n",
    "        LL = log_loss(y_test,ensable_predict)\n",
    "        LL_list.append(LL)\n",
    "        acc_list.append(acc)\n",
    "        acc2 = accuracy(ensable_classified2,y_test)\n",
    "        LL2 = log_loss(y_test,ensable_predict2)\n",
    "        LL_list2.append(LL2)\n",
    "        acc_list2.append(acc2)\n",
    "        \n",
    "    print(\"Geom Sum:\",np.mean(acc_list), np.std(acc_list),\"LL:\",np.mean(LL_list), np.std(LL_list))\n",
    "    print(\"Arth Sum:\",np.mean(acc_list2), np.std(acc_list2),\"LL:\",np.mean(LL_list2), np.std(LL_list2))\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.983102918587 0.0055809223117 LL: 0.0762011063858 0.0147685476221\n",
      "Arth Sum: 0.983410138249 0.00569807588049 LL: 0.222723881529 0.00697749444187\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['rf','lr','svc','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.977265745008 0.00552995391705 LL: 0.104657501894 0.0267901860155\n",
      "Arth Sum: 0.976344086022 0.00501060105386 LL: 0.262724984148 0.00918703519687\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['rf','lr','svc'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.975422427035 0.00363507206335 LL: 0.105453001064 0.0207984468936\n",
      "Arth Sum: 0.975422427035 0.00400565431963 LL: 0.260006711497 0.00623554443486\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['rf','lr','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 893,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.975422427035 0.00322214699899 LL: 0.0867893294662 0.0105666021983\n",
      "Arth Sum: 0.975422427035 0.0025703841061 LL: 0.201397237645 0.0088412579297\n"
     ]
    }
   ],
   "source": [
    "#tiny Log-Loss! (is it still?)\n",
    "ModelIt_shuffle_ENSAMBLE(['rf','svc','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 894,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.976651305684 0.00508539027872 LL: 0.133313692189 0.0522593078901\n",
      "Arth Sum: 0.974807987711 0.00537414921184 LL: 0.211691619281 0.0116018355841\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['lr','svc','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 895,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.977880184332 0.00383717234925 LL: 0.067012529116 0.00832679657697\n",
      "Arth Sum: 0.978494623656 0.00412178429032 LL: 0.313966672222 0.00936171773124\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['rf','lr'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 896,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.974500768049 0.00451518846596 LL: 0.122744161547 0.0268784371924\n",
      "Arth Sum: 0.973886328725 0.00485756937046 LL: 0.154794060324 0.0115933143807\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['svc','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 897,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.969892473118 0.00501060105386 LL: 0.113956203119 0.0201218071476\n",
      "Arth Sum: 0.969585253456 0.00561464420353 LL: 0.250304059575 0.00699934934399\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['lr','svc'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 898,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.980952380952 0.00724413279419 LL: 0.0664391337206 0.0288939757065\n",
      "Arth Sum: 0.981259600614 0.00682829823991 LL: 0.215640595646 0.00940454017444\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['rf','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 899,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.973271889401 0.00418991757204 LL: 0.097463419793 0.0269543955416\n",
      "Arth Sum: 0.973886328725 0.00423473079941 LL: 0.237888947393 0.00881953116494\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['lr','rbf'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 900,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geom Sum: 0.975115207373 0.00544394628162 LL: 0.0755158844095 0.0122415569988\n",
      "Arth Sum: 0.976036866359 0.0055469954179 LL: 0.216800382695 0.00738909292263\n"
     ]
    }
   ],
   "source": [
    "ModelIt_shuffle_ENSAMBLE(['rf','svc'],X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Creating a composite results graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 915,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Model_names = ['KNeighborsClassifier','LogisticRegression',\"SVC(kernel='linear')\",\n",
    "              \"SVC(kernel='rbf')\",'RandomForestClassifier','GradientBoostingClassifier',\n",
    "             'Ensemble (LR*SVlinear)',\"Ensemble (LR*RF)\"]\n",
    "Model_names_LL = ['KNeighborsClassifier','LogisticRegression',\"SVC(kernel='linear')\",\n",
    "              \"SVC(kernel='rbf')\",'RandomForestClassifier','GradientBoostingClassifier',\n",
    "             'Ensemble (LR*SVlinear)',\"Ensemble (LR*RF)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 921,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Kfold_results = [(0.95416802552286428, 0.0085598005211190258),\n",
    "                (0.96708779392650368, 0.0071860463653587673),\n",
    "                (0.96308779392650357, 0.006156601656322213),\n",
    "                (0.97447051872858326, 0.0057176396539404444),\n",
    "                (0.981543187994801, 0.0059210439148464295),\n",
    "                (0.95878199220134697, 0.006627351832402175),\n",
    "                (0.969892473118, 0.00501060105386),\n",
    "                (0.977880184332, 0.00383717234925) \n",
    "                ]\n",
    "Kfold_resultsLL = [(0.41291058471337239, 0.085879715013581395),\n",
    "                (0.22281453180265509, 0.018630850181213385),\n",
    "                (0.18081541968490919, 0.0081016671583881636),\n",
    "                (0.1542820594500276, 0.013211320351770153),\n",
    "                (0.29929186283170517, 0.0087525527527885089),\n",
    "                (0.16237771675020671, 0.03029856760469498),\n",
    "                (0.113956203119, 0.0201218071476),\n",
    "                 (0.067012529116, 0.00832679657697) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 917,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Group_results = [(0.80135487110562598, 0.054763026861536626),\n",
    "                (0.86344279093984277, 0.042336544459753607),\n",
    "                (0.87695393562335888, 0.043057154016082518),\n",
    "                (0.87392480722833088, 0.041029093871839457),\n",
    "                (0.87150850023623305, 0.062211341107501629),\n",
    "                (0.7470546088872132, 0.068920931018564807),\n",
    "                (0.888239633253, 0.0391522371331),\n",
    "                (0.897977690853, 0.0399705075168) ]\n",
    "Group_resultsLL = [(1.8427991375253261, 0.76660724454896301),\n",
    "                  (0.68464691242102726, 0.12213866200609753),\n",
    "                  (0.44089015160811768, 0.14391361384111731),\n",
    "                  (0.49226516617371224, 0.14671777433081537),\n",
    "                  (0.70709358635256425, 0.16242483482985481),\n",
    "                  (0.95930042825736106, 0.271705429690692),\n",
    "                  (0.489345786839, 0.20392493992),\n",
    "                   (0.33768280066, 0.15818805694)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 918,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NVPR+qA0VFRVMnDgRly9fxp49e+Di4sJ63FQlMjISLi4uvEofLmWXu7RKCNE5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\nFutLTk6W0+kstv/1lRRu2xUVFVVif05OjpxOp7Kzs7VixYoSx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3hc48rBda4cXOfKwXWu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n373rMFzMmTJ1m5ciWVlZVMmjSJKVOmtCvxrPr6epYsWcLy5cupqKjAy8uLJ598koCAADw8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\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F7t27OXnyJF5eXjzwwAPCAbyZUVBQwKZNmyguLiYnJ4f8/Hwxr67T6cRc5ahRo0hJSaGk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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14324b668>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 467,
       "width": 491
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "## the data\n",
    "\n",
    "set1 =      [Kfold_results[i][0] for i in range(len(Kfold_results))]\n",
    "set1Std =   [Kfold_results[i][1] for i in range(len(Kfold_results))]\n",
    "set2 =      [Group_results[i][0] for i in range(len(Group_results))]\n",
    "set2Std =   [Group_results[i][1] for i in range(len(Group_results))]\n",
    "N = len(set1)\n",
    "\n",
    "## necessary variables\n",
    "ind = np.arange(N)                # the x locations for the groups\n",
    "width = 0.35                      # the width of the bars\n",
    "\n",
    "## the bars\n",
    "rects1 = ax.bar(ind+0.5*width, set1, width,\n",
    "                #color='black',\n",
    "                yerr=set1Std,\n",
    "                error_kw=dict(elinewidth=2))#,ecolor='red'))\n",
    "\n",
    "rects2 = ax.bar(ind+1.5*width, set2, width,\n",
    "                    #color='red',\n",
    "                    yerr=set2Std,\n",
    "                    error_kw=dict(elinewidth=2))#,ecolor='black'))\n",
    "\n",
    "\n",
    "\n",
    "# axes and labels\n",
    "ax.set_xlim(-width,len(ind)+width)\n",
    "ax.set_ylim(0.6,1.05)\n",
    "ax.set_ylabel('Accuracy (note scale starts at 60%)', fontsize=14)\n",
    "ax.set_title('Accuracy Scores by Model and Sampling Method', fontsize=18)\n",
    "xTickMarks = Model_names\n",
    "ax.set_xticks(ind+width)\n",
    "xtickNames = ax.set_xticklabels(xTickMarks,ha='right')\n",
    "plt.setp(xtickNames, rotation=33, fontsize=14)\n",
    "\n",
    "## add a legend\n",
    "ax.legend( (rects1[0], rects2[0]), ('Random Sample', 'GroupedKfold Sample') )\n",
    "\n",
    "\n",
    "def autolabel(rects, ax):\n",
    "    # Get y-axis height to calculate label position from.\n",
    "    (y_bottom, y_top) = ax.get_ylim()\n",
    "    y_height = y_top - y_bottom\n",
    "\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        label_position = height + (y_height * 0.01)\n",
    "\n",
    "        ax.text(rect.get_x() +0.30+ rect.get_width()/2., label_position,\n",
    "                '%.3f' % height, fontsize=11,\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1, ax)\n",
    "autolabel(rects2, ax)\n",
    "\n",
    "plt.savefig(\"Model_Accuracy.png\",transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 919,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tbc2CdHDzEE+DAaDSaOJinTp1kmit+1JwC6zm5uZyXeiBsi70jx8/BiA5p/Pk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\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hYaSkpKBWq0lPTxeifVKV+91332X37t10d3dz5513Mm3aNJ3op/7uu++45557uHDhApMm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\n2L9/Py+88AITJ05s2gv/izBfu3lFNTAwkISEBC5fvkxhYSFt27blgQc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ioqI4efIkbdq0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143b62940>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 467,
       "width": 491
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "## the data\n",
    "\n",
    "set1 = [Kfold_resultsLL[i][0] for i in range(len(Kfold_resultsLL))]\n",
    "set1Std =   [Kfold_resultsLL[i][1] for i in range(len(Kfold_resultsLL))]\n",
    "set2 = [Group_resultsLL[i][0] for i in range(len(Group_resultsLL))]\n",
    "set2Std =   [Group_resultsLL[i][1] for i in range(len(Group_resultsLL))]\n",
    "N = len(set1)\n",
    "\n",
    "## necessary variables\n",
    "ind = np.arange(N)                # the x locations for the groups\n",
    "width = 0.35                      # the width of the bars\n",
    "\n",
    "## the bars\n",
    "rects1 = ax.bar(ind+0.5*width, set1, width,\n",
    "                #color='black',\n",
    "                yerr=set1Std,\n",
    "                error_kw=dict(elinewidth=2))#,ecolor='red'))\n",
    "\n",
    "rects2 = ax.bar(ind+1.5*width, set2, width,\n",
    "                    #color='red',\n",
    "                    yerr=set2Std,\n",
    "                    error_kw=dict(elinewidth=2))#,ecolor='black'))\n",
    "\n",
    "# axes and labels\n",
    "ax.set_xlim(-width,len(ind)+width)\n",
    "ax.set_ylim(0.0,2.0)\n",
    "ax.set_ylabel('Log-Loss (Small is better)', fontsize=14)\n",
    "ax.set_title('Log-Loss Scores by Model and Sampling Method', fontsize=18)\n",
    "xTickMarks = Model_names_LL\n",
    "ax.set_xticks(ind+width)\n",
    "xtickNames = ax.set_xticklabels(xTickMarks,ha='right')\n",
    "plt.setp(xtickNames, rotation=33, fontsize=14)\n",
    "\n",
    "## add a legend\n",
    "ax.legend( (rects1[0], rects2[0]), ('Random Sample', 'GroupedKfold Sample') )\n",
    "\n",
    "def autolabel(rects, ax):\n",
    "    # Get y-axis height to calculate label position from.\n",
    "    (y_bottom, y_top) = ax.get_ylim()\n",
    "    y_height = y_top - y_bottom\n",
    "\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        label_position = height + (y_height * 0.01)\n",
    "\n",
    "        ax.text(rect.get_x() +0.30+ rect.get_width()/2., label_position,\n",
    "                '%.3f' % height, fontsize=11,\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1, ax)\n",
    "autolabel(rects2, ax)\n",
    "\n",
    "plt.savefig(\"Model_LogLoss.png\",transparent=True)\n",
    "plt.show()"
   ]
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