code/dna_prediction/non_attendance_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc811e30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_clustering.py:35: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _pt_shuffle_rec(i, indexes, index_mask, partition_tree, M, pos):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_clustering.py:54: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def delta_minimization_order(all_masks, max_swap_size=100, num_passes=2):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_clustering.py:63: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _reverse_window(order, start, length):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_clustering.py:69: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _reverse_window_score_gain(masks, order, start, length):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_clustering.py:77: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _mask_delta_score(m1, m2):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/links.py:5: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def identity(x):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/links.py:10: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _identity_inverse(x):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/links.py:15: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def logit(x):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/links.py:20: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _logit_inverse(x):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_masked_model.py:363: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_masked_model.py:385: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _build_fixed_multi_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_masked_model.py:428: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _init_masks(cluster_matrix, M, indices_row_pos, indptr):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/utils/_masked_model.py:439: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _rec_fill_masks(cluster_matrix, indices_row_pos, indptr, indices, M, ind):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/maskers/_tabular.py:186: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _single_delta_mask(dind, masked_inputs, last_mask, data, x, noop_code):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/maskers/_tabular.py:197: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _delta_masking(masks, x, curr_delta_inds, varying_rows_out,\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/maskers/_image.py:175: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def _jit_build_partition_tree(xmin, xmax, ymin, ymax, zmin, zmax, total_ywidth, total_zwidth, M, clustering, q):\n",
      "/home/amy/anaconda3/envs/phd/lib/python3.9/site-packages/shap/explainers/_partition.py:676: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  def lower_credit(i, value, M, values, clustering):\n",
      "The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n"
     ]
    }
   ],
   "source": [
    "import dna_predict\n",
    "import os\n",
    "import re\n",
    "import shap\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "031679fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Columns (17) have mixed types. Specify dtype option on import or set low_memory=False.\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join('..', 'simulated_data', 'simulated_data.csv'))\n",
    "df['DNA'] = 0 \n",
    "df.loc[df['Attended last appointment points: timepoint 0'] == 0, 'DNA'] = 1\n",
    "pred = dna_predict.feature_preprop(df)\n",
    "clean_df = pd.concat([pred, df['DNA']],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a034d450",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_val, y_val, X_train_all, y_train_all, X_test, y_test = dna_predict.split_scale_numeric(clean_df.iloc[:, :-1], clean_df['DNA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c61cda0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------CROSS-VALIDATING MODELS-------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "data": {
      "image/png": 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ta+/GYvIgIptnsTUPG8K7rYiISDaOPIjI5nHkIR+TBxHZvD8/5Gd2G3Lu030McNqKiIhk48iDiGyeNZ7zqO+YPIiILLDmYWvq/LTV9OnTK2yZ2KFDB2uHRURk0+rFyKNTp07YuXOn+LlBg3oRNhHVE7zbSr568VO4QYMGNd4ykYioKgqFYIEnzHm3VZ1z/vx5eHp6onXr1ggPD0d2dra1QyIisml1fuQRFBSE5ORktG/fHjk5OZgxYwZ69+6N06dPw9HRsUL90tLSCnsGN7hjgrpBvciTRGQFSsXdo0ZtWCaUeqPO93fw4MEYMWIE/P39ERISgq1btyI/Px/r1q2rtH5CQgK0Wq3kmPPDtVqOmojqk3trHjU9bEmdTx73c3JyQrt27ZCVlVXp+djYWBQUFEiOqb29ajlKIqLHW71LHkVFRbhw4QI8PDwqPa9WqyvuIcwpKyJ6AAUEixy2pM7/VP3ggw+wd+9eXLp0CQcPHsSwYcNgZ2eHUaNGWTs0InpMcNpKvjq/YH716lWMGjUKt27dQvPmzfHMM8/g0KFDaN68ubVDIyKyWXU+eaSkpFg7BCJ6zPEhQfnqfPIgInrU+Ep2+er8mgcREdU9HHkQkc3jtJV8TB5EZPO4n4d8nLYiIiLZOPIgIpvHt+rKx+RBRDaPax7ycdqKiIhk48iDiGyeEhZ4JbttzVoxeRARWWTNo4bfr284bUVERLJx5EFEBNtb8K4pJg8isnkWudvKxrIPp62IiEg2jjyIyOZxwVw+Jg8isnlKhQVu1eW0FRER0YNx5EFENo8L5vLZRPI49c9ca4dgEd8W2Vs7hBqLVpVZOwSLUL/6f9YOwSKKc43WDsEinE/U7PvWSh6JiYmYN28eDAYDAgICsHjxYvTo0aPSun379sXevXsrlD///PPYsmULAEAQBMTHx2PFihXIz89Hr169sGzZMjz55JNi/by8PEycOBHfffcdlEolQkND8emnn6JJkyayYue0FRGRFaxduxYxMTGIj4/HsWPHEBAQgJCQEFy/fr3S+hs2bEBOTo54nD59GnZ2dhgxYoRYZ+7cuVi0aBGWL1+Ow4cPo3HjxggJCUFJSYlYJzw8HD///DN27NiBzZs3Y9++fXj77bdlx68QBOGxv0XghGNra4dgEY/FyCPIZO0QLEKttbN2CBbx+Iw8fjXre4WFhdBqtfjZzROOypr9Lv27yYROub+hoKAAGo3mofWDgoLQvXt3LFmyBABgMpng7e2NiRMnYurUqQ/9/sKFCxEXF4ecnBw0btwYgiDA09MT77//Pj744AMAQEFBAdzc3JCcnIyRI0fizJkz6NixI44ePYpu3boBANLS0vD888/j6tWr8PT0rHZ/OfIgIpt3b9qqpkd1GY1G6PV6BAcHi2VKpRLBwcHIyMioVhtJSUkYOXIkGjduDAC4ePEiDAaDpE2tVougoCCxzYyMDDg5OYmJAwCCg4OhVCpx+PDh6ncANrLmQURUWwoLCyWf1Wo11Gq1pOzmzZsoLy+Hm5ubpNzNzQ1nz5596DWOHDmC06dPIykpSSwzGAxiG/e3ee+cwWCAq6ur5HyDBg3g7Ows1qkujjyIyOYplAqLHADg7e0NrVYrHgkJCRaPNykpCZ07d65ycb02cORBRDZPobx71KiN//7vlStXJGse9486AKBZs2aws7NDbq70TtDc3Fy4u7s/8DrFxcVISUnBzJkzJeX3vpebmwsPDw9Jm126dBHr3L8gf+fOHeTl5T30uvfjyIOIyII0Go3kqCx5qFQqBAYGIj09XSwzmUxIT0+HTqd7YPupqakoLS3Fa6+9Jilv1aoV3N3dJW0WFhbi8OHDYps6nQ75+fnQ6/VinV27dsFkMiEoKEhWPznyICKbZ43nPGJiYjBmzBh069YNPXr0wMKFC1FcXIyIiAgAwOjRo+Hl5VVh2ispKQlDhw6Fi4vLfddXYPLkyfj444/x5JNPolWrVpg2bRo8PT0xdOhQAICvry8GDRqEcePGYfny5SgrK0NUVBRGjhwp604rgMmDiMgyL7eSuSNIWFgYbty4gbi4OBgMBnTp0gVpaWnignd2djaU990+nJmZif379+P777+vtM0PP/wQxcXFePvtt5Gfn49nnnkGaWlpcHBwEOusWbMGUVFRGDBggPiQ4KJFi2T2lc951Ct8zqPu4HMedUtNn/PIbPmERZ7zaH/5arWf86jvOPIgIptnyQVzW8HkQUQ2T6FQQFHDRY+afr++4d1WREQkG0ceRGTzFAoLTFs99qvHUkweRETc0EM2TlsREZFsHHkQkc2zyN1WnLYiIrItf36xodltCJy2IiIieiCOPIjI5nG9XD6rjjz27duHF154AZ6enlAoFNi4caPkvCAIiIuLg4eHBxo2bIjg4GCcP3/eOsES0eNL+b91D3MPW5vHsWp3i4uLERAQgMTExErPV2czdyIiqn1WnbYaPHgwBg8eXOk5QRCwcOFCfPTRR3jppZcAAF988QXc3NywceNGjBw5sjZDJaLHmSXeqssF87qhOpu5V6a0tBSFhYWSw/j4vziYiGrg3ppHTQ9bUmeTR3U2c69MQkKCZP9grVaLlcb8RxkqEZHNqbPJw1yxsbEoKCiQHG+qnKwdFhHVYfee86jpYUvq7K261dnMvTJqtbrCnsEqWxtPEpEsfMJcvjo78qjOZu5ERGQdVh15FBUVISsrS/x88eJFnDhxAs7OzmjRosVDN3MnIrIEbgYln1WTx48//oh+/fqJn2NiYgAAY8aMQXJycrU2cyciqjEFaj4PY7JEIPWHVZNH3759ITzgNlqFQoGZM2di5syZtRgVERE9TJ1dMCciqi18t5V8TB5EZPPu3m1VwzWPOnv70aNhY90lIiJL4MiDiGyeRZ7zsLFfxZk8iIi46CGbjeVKIiKyBI48iMjmcdpKPiYPIrJ5lnixoa29GNHGciUREVkCRx5EZPO4Xi4fkwcR2TxOW8nHaSsiIpKNIw8iIsV/j5q2YUOYPIjI5vFWXflsrLtERGQJHHkQkc1TKCywYG5jt1sxeRCRzeOtuvLZRPLYX2Rv7RAs4vmGd6wdQo01+ZuvtUOwiMJZZ60dgkVoAxytHYJNS0xMxLx582AwGBAQEIDFixejR48eVdbPz8/H3//+d2zYsAF5eXlo2bIlFi5ciOeffx4A4OPjg8uXL1f43rvvvovExEQAd3dw3bt3r+T8+PHjsXz5clmx20TyICJ6EGs857F27VrExMRg+fLlCAoKwsKFCxESEoLMzEy4urpWqG80GjFw4EC4urpi/fr18PLywuXLl+Hk5CTWOXr0KMrLy8XPp0+fxsCBAzFixAhJW+PGjZNs792oUSNZsQNMHkREd28dquntQzK/P3/+fIwbNw4REREAgOXLl2PLli1YuXIlpk6dWqH+ypUrkZeXh4MHD8Le/u5sio+Pj6RO8+bNJZ/nzJmDNm3a4Nlnn5WUN2rUCO7u7vICvg/vtiIisqDCwkLJUVpaWqGO0WiEXq9HcHCwWKZUKhEcHIyMjIxK2920aRN0Oh0iIyPh5uYGPz8/zJ49WzLSuP8aX375Jd58880Ki/lr1qxBs2bN4Ofnh9jYWNy+fVt2PznyICJSKu4eNW0DgLe3t6Q4Pj4e06dPl5TdvHkT5eXlcHNzk5S7ubnh7NnK19N+/fVX7Nq1C+Hh4di6dSuysrLw7rvvoqysDPHx8RXqb9y4Efn5+XjjjTck5a+++ipatmwJT09P/PTTT5gyZQoyMzOxYcMGWd1l8iAisuC01ZUrV6DRaMRitVpdw4bvMplMcHV1xeeffw47OzsEBgbi2rVrmDdvXqXJIykpCYMHD4anp6ek/O233xb/3LlzZ3h4eGDAgAG4cOEC2rRpU+14mDyIiCxIo9FIkkdlmjVrBjs7O+Tm5krKc3Nzq1yL8PDwgL29Pezs7MQyX19fGAwGGI1GqFQqsfzy5cvYuXNntUYTQUFBAICsrCxZyYNrHkRE96atanpUk0qlQmBgINLT08Uyk8mE9PR06HS6Sr/Tq1cvZGVlwWQyiWXnzp2Dh4eHJHEAwKpVq+Dq6oohQ4Y8NJYTJ04AuJuc5GDyICJSWuiQISYmBitWrMDq1atx5swZTJgwAcXFxeLdV6NHj0ZsbKxYf8KECcjLy8OkSZNw7tw5bNmyBbNnz0ZkZKSkXZPJhFWrVmHMmDFo0EA6uXThwgXMmjULer0ely5dwqZNmzB69Gj06dMH/v7+suLntBURkRWEhYXhxo0biIuLg8FgQJcuXZCWliYuomdnZ0Op/F9G8vb2xvbt2xEdHQ1/f394eXlh0qRJmDJliqTdnTt3Ijs7G2+++WaFa6pUKuzcuRMLFy5EcXExvL29ERoaio8++kh2/ApBEATZ36pnlijaWzsEiwh6DJ4wD0zhE+Z1iWPHxtYOwSLsVp8063uFhYXQarXIe7kjNPZ2D//Cg9oqK4fz+l9QUFDw0DWPxwFHHkREFrxV11ZwzYOIiGTjyIOISIGa/yptWwMPJg8iIk5bycdpKyIiks2skUd5eTmSk5ORnp6O69evSx5aAYBdu3ZZJDgiolphhbfq1ndmJY9JkyYhOTkZQ4YMgZ+fn81tv0hEjxlOW8lmVvJISUnBunXrxN2riIjItpiVPFQqFdq2bWvpWIiIrEOBmt8tZVsDD/Nm6d5//318+umnsIGH04nIFtTyixEfB9UeeQwfPlzyedeuXdi2bRs6deokbol4j9xNRYiIqH6p9shDq9VKjmHDhuHZZ59Fs2bNKpyrroSEBHTv3h2Ojo5wdXXF0KFDkZmZKalTUlKCyMhIuLi4oEmTJggNDa3wDnwiohrhyEO2ao88Vq1aZfGL7927F5GRkejevTvu3LmDv/3tb3juuefwyy+/oHHjuy9si46OxpYtW5CamgqtVouoqCgMHz4cBw4csHg8RGSjeKuubGYtmPfv3x8bNmyAk5OTpLywsBBDhw6t9nMeaWlpks/JyclwdXWFXq9Hnz59UFBQgKSkJHz11Vfo378/gLtJzNfXF4cOHcLTTz9tTvhERFRDZiWPPXv2wGg0VigvKSnBDz/8YHYwBQUFAABnZ2cAgF6vR1lZGYKDg8U6HTp0QIsWLZCRkVFp8igtLUVpaamkrAwm2NvarwVEVH18zkM2Wcnjp59+Ev/8yy+/wGAwiJ/Ly8uRlpYGLy8vswIxmUyYPHkyevXqBT8/PwCAwWCASqWqMMJxc3OTXPvPEhISMGPGDEnZYDjjeTQzKy4ievwpFICihr9f2tqz0rKSR5cuXaBQKKBQKMRppD9r2LAhFi9ebFYgkZGROH36NPbv32/W9++JjY1FTEyMpCxJG1ijNomISEpW8rh48SIEQUDr1q1x5MgRNG/eXDynUqng6uoKOzv5u3FFRUVh8+bN2LdvH5544gmx3N3dHUajEfn5+ZLRR25uLtzd3SttS61WQ61WS8o4ZUVED8RpK9lkJY+WLVsCQIUXIZpLEARMnDgR33zzDfbs2YNWrVpJzgcGBsLe3h7p6ekIDQ0FAGRmZiI7Oxs6nc4iMRAR8W4r+czezyMzMxOLFy/GmTNnAAC+vr6IiopChw4dqt1GZGQkvvrqK3z77bdwdHQU1zG0Wi0aNmwIrVaLsWPHIiYmBs7OztBoNJg4cSJ0Oh3vtCIisiKzcuW///1v+Pn5Qa/XIyAgAAEBATh27Bg6d+6Mf//739VuZ9myZSgoKEDfvn3h4eEhHmvXrhXrLFiwAH/5y18QGhqKPn36wN3dnU+wE5Fl8SFB2cwaeXz44YeIjY3FzJkzJeXx8fH48MMPxSmmh6nOu7EcHByQmJiIxMREc0IlIno4rnnIZtbIIycnB6NHj65Q/tprryEnJ6fGQRERUd1mVvLo27dvpQ8D7t+/H717965xUEREtUppocOGmDVt9eKLL2LKlCnQ6/XiwvWhQ4eQmpqKGTNmYNOmTZK6RER1mhIWmLaySCT1hlnJ49133wUALF26FEuXLq30HAAoFAqUl5fXIDwiIqqLzEoelnrOg4ioTuBzHrKZ/ZzHPSUlJXBwcLBELERE1sG7rWQzK1eWl5dj1qxZ8PLyQpMmTfDrr78CAKZNm4akpCSLBkhERHWPWcnjH//4B5KTkzF37lyoVCqx3M/PD//6178sFhwRUa1QoOZ3WtnWwMO85PHFF1/g888/R3h4uORFiAEBATh79qzFgiMiqhV8wlw2s5LHtWvX0LZt2wrlJpMJZWVlNQ6KiIjqNrOSR8eOHSt9SHD9+vV46qmnahwUEVGt4kOCspl1t1VcXBzGjBmDa9euwWQyYcOGDcjMzMQXX3yBzZs3WzpGIqJHi3dbyWZWrnzppZfw3XffYefOnWjcuDHi4uJw5swZfPfddxg4cKClYyQiojrG7Oc8evfujR07dlgyFiIi6+DIQ7YaPyRIRFTv8Qlz2ard3aZNm8LZ2blaBxERPVxiYiJ8fHzg4OCAoKAgHDly5IH18/PzERkZCQ8PD6jVarRr1w5bt24Vz0+fPh0KhUJy3L+7a0lJCSIjI+Hi4oImTZogNDQUubm5smOv9shj4cKF4p9v3bqFjz/+GCEhIeJe4hkZGdi+fTumTZsmOwgiIquywrTV2rVrERMTg+XLlyMoKAgLFy5ESEgIMjMz4erqWqG+0WjEwIED4erqivXr18PLywuXL1+Gk5OTpF6nTp2wc+dO8XODBtIf89HR0diyZQtSU1Oh1WoRFRWF4cOH48CBA7LiVwjV2c7vPqGhoejXrx+ioqIk5UuWLMHOnTuxceNGuU0+UuUJvawdgkUcnSH/t4O6pseK1tYOwSKEXwqsHYJFCKd/t3YIFtHgu1/M+l5hYSG0Wi3+8/HT0DjUbBa/sOQOmn50CAUFBdBoNA+tHxQUhO7du2PJkiUA7j4n5+3tjYkTJ2Lq1KkV6i9fvhzz5s3D2bNnYW9vX2mb06dPx8aNG3HixIlKzxcUFKB58+b46quv8PLLLwMAzp49C19fX2RkZIhbbFSHWbN027dvx6BBgyqUDxo0SJLxiIjqBQs+51FYWCg5SktLK1zOaDRCr9cjODj4fyEolQgODkZGRkalIW7atAk6nQ6RkZFwc3ODn58fZs+eXWHbi/Pnz8PT0xOtW7dGeHg4srOzxXN6vR5lZWWS63bo0AEtWrSo8rpVMSt5uLi44Ntvv61Q/u2338LFxcWcJomIrEehsMwBwNvbG1qtVjwSEhIqXO7mzZsoLy+Hm5ubpNzNzQ0Gg6HSEH/99VesX78e5eXl2Lp1K6ZNm4ZPPvkEH3/8sVgnKCgIycnJSEtLw7Jly3Dx4kX07t0bv/9+d4RpMBigUqkqTHU96LpVMWucNmPGDLz11lvYs2cPgoKCAACHDx9GWloaVqxYYU6TRESPhStXrkimrdRqtUXaNZlMcHV1xeeffw47OzsEBgbi2rVrmDdvHuLj4wEAgwcPFuv7+/sjKCgILVu2xLp16zB27FiLxHGPWcnjjTfegK+vLxYtWoQNGzYAAHx9fbF//34xmRAR1RsK1PytuP/9vkajeeiaR7NmzWBnZ1fhLqfc3Fy4u7tX+h0PDw/Y29tLXkbr6+sLg8EAo9EoecP5PU5OTmjXrh2ysrIAAO7u7jAajcjPz5eMPh503aqYfWdyUFAQ1qxZg2PHjuHYsWNYs2ZNhcQxZ84c5Ofnm3sJIqLaYcFpq+pQqVQIDAxEenq6WGYymZCeni7ewXq/Xr16ISsrS7KT67lz5+Dh4VFp4gCAoqIiXLhwAR4eHgCAwMBA2NvbS66bmZmJ7OzsKq9blUf6WMvs2bORl5f3KC9BRFQvxcTEYMWKFVi9ejXOnDmDCRMmoLi4GBEREQCA0aNHIzY2Vqw/YcIE5OXlYdKkSTh37hy2bNmC2bNnIzIyUqzzwQcfYO/evbh06RIOHjyIYcOGwc7ODqNGjQIAaLVajB07FjExMdi9ezf0ej0iIiKg0+lk3WkFPOInzM24C5iIyDpq+e0iYWFhuHHjBuLi4mAwGNClSxekpaWJi+jZ2dlQKv/3+723tze2b9+O6Oho+Pv7w8vLC5MmTcKUKVPEOlevXsWoUaNw69YtNG/eHM888wwOHTqE5s2bi3UWLFgApVKJ0NBQlJaWIiQkBEuXLpUdv1nPeVSXo6MjTp48idatrXtvP5/zqDv4nEfdwuc8/vucx9xe0DSs4XMef9xB0w8PVPs5j/rOxt7GQkRElsAXIxIR8cWIsjF5EBHJvFuqyjZsyCPNlb1790bDhg0f5SWIiMgKzEoeycnJlZbfuXNHcmvZ1q1bxfuLiYjqLIWFDhtiVvJ47733MGLECPznP/8RyzIzMxEUFISvv/7aYsEREdWKWn5I8HFgVvI4fvw4rl69is6dO2PHjh1ITExE165d0aFDB5w8edLSMRIRUR1j1oJ5mzZtcODAAUyePBmDBg2CnZ0dVq9eLT7FSERUr1jw3Va2wuwF8y1btiAlJQU6nQ5OTk5ISkrCb7/9ZsnYiIhqB6etZDMreYwfPx4jRozAlClT8MMPP+Cnn36CSqVC586dsW7dOkvHSEREdYxZ01YHDhzA4cOHERAQAODua363bt2KxMREvPnmm3jllVcsGiQR0SPFhwRlMyt56PX6Sjc4iYyMlGxvSERUL/AhQdnMypUP2hmrffv21W5n2bJl8Pf3FzdP0el02LZtm3i+pKQEkZGRcHFxQZMmTRAaGlph8xQiIqp9Zr+eZP369Vi3bh2ys7NhNBol544dO1atNp544gnMmTMHTz75JARBwOrVq/HSSy/h+PHj6NSpE6Kjo7FlyxakpqZCq9UiKioKw4cPx4EDB8wNm4ioIt5tJZtZI49FixYhIiICbm5uOH78OHr06AEXFxf8+uuvkj10H+aFF17A888/jyeffBLt2rXDP/7xDzRp0gSHDh1CQUEBkpKSMH/+fPTv3x+BgYFYtWoVDh48iEOHDpkTNhFR5Xi3lWxmJY+lS5fi888/x+LFi6FSqfDhhx9ix44deO+991BQYN4+B+Xl5UhJSUFxcTF0Oh30ej3KysokaygdOnRAixYtkJGRUWU7paWlKCwslByld0xV1iciIvnMSh7Z2dno2bMnAKBhw4b4/fe7G8q8/vrrsl9PcurUKTRp0gRqtRrvvPMOvvnmG3Ts2BEGgwEqlUqySTsAuLm5wWAwVNleQkICtFqt5Jiz56q8DhKRTeHAQz6zkoe7u7u4N3mLFi3EaaSLFy/K3nq2ffv2OHHiBA4fPowJEyZgzJgx+OUX83YFA4DY2FgUFBRIjql9nzC7PSKyAcwespm1YN6/f39s2rQJTz31FCIiIhAdHY3169fjxx9/xPDhw2W1pVKp0LZtWwBAYGAgjh49ik8//RRhYWEwGo3Iz8+XjD5yc3Ph7u5eZXtqtbrC3WDlDWzsBmwiokfMrOTx+eefw2S6u44QGRmJZs2a4cCBA3jxxRfxzjvv1Cggk8mE0tJSBAYGwt7eHunp6QgNDQVw98292dnZ0Ol0NboGEZEE77aSzazkoVQqYTQacezYMVy/fh0NGzYUF7bT0tLwwgsvVKud2NhYDB48GC1atMDvv/+Or776Cnv27MH27duh1WoxduxYxMTEwNnZGRqNBhMnToROp8PTTz9tTthERJVTKAAlHxKUw6zkkZaWhtdffx23bt2qcE6hUKC8vLxa7Vy/fh2jR49GTk4OtFot/P39sX37dgwcOBAAsGDBAiiVSoSGhqK0tBQhISFYunSpOSETEZEFmZU8Jk6ciFdeeQVxcXFwc3Mz++JJSUkPPO/g4IDExEQkJiaafQ0ioofitJVsZiWP3NxcxMTE1ChxEBHVGXy3lWxm3Yb08ssvY8+ePRYOhYiI6guzRh5LlizBiBEj8MMPP6Bz586wt7eXnH/vvfcsEhwRUa3gtJVsZiWPr7/+Gt9//z0cHBywZ88eKP40XFMoFEweRFS/cNpKNrOSx9///nfMmDEDU6dOhVLJB/CIiGyNWcnDaDQiLCyMiYOIHg+ctpLNrJ/+Y8aMwdq1ay0dCxGRdShx9yHBGh3W7kTtMmvkUV5ejrlz52L79u3w9/evsGA+f/58iwRHRER1k1nJ49SpU3jqqacAAKdPn5acU9jYohERPQY4bSWbWclj9+7dlo6DiMh6eLeVbDY2S0dERJZg1siDiOixwmkr2Zg8iIg4bSUbp62IiEg2Jg8iIoWFDpkSExPh4+MDBwcHBAUF4ciRIw+sn5+fj8jISHh4eECtVqNdu3bYunWreD4hIQHdu3eHo6MjXF1dMXToUGRmZkra6Nu3LxQKheQwZwdYJg8ioho/ICh/J8K1a9ciJiYG8fHxOHbsGAICAhASEoLr169XWt9oNGLgwIG4dOkS1q9fj8zMTKxYsQJeXl5inb179yIyMhKHDh3Cjh07UFZWhueeew7FxcWStsaNG4ecnBzxmDt3ruy/Mq55EBFZwfz58zFu3DhEREQAAJYvX44tW7Zg5cqVmDp1aoX6K1euRF5eHg4ePCg+mO3j4yOpk5aWJvmcnJwMV1dX6PV69OnTRyxv1KgR3N3daxQ/Rx5ERPcWzGt6ACgsLJQcpaWlFS5nNBqh1+sRHBwslimVSgQHByMjI6PSEDdt2gSdTofIyEi4ubnBz88Ps2fPfuC23wUFBQAAZ2dnSfmaNWvQrFkz+Pn5ITY2Frdv35b9V2YTIw/Tgf9YOwSL6DHbw9oh1Jhi0Ahrh2AR2X+dae0QLMJ7Ugtrh1A3WPBuK29vb0lxfHw8pk+fLim7efMmysvLK+zG6ubmhrNnz1ba/K+//opdu3YhPDwcW7duRVZWFt59912UlZUhPj6+Qn2TyYTJkyejV69e8PPzE8tfffVVtGzZEp6envjpp58wZcoUZGZmYsOGDbK6axPJg4iotly5cgUajUb8rFarLdKuyWSCq6srPv/8c9jZ2SEwMBDXrl3DvHnzKk0ekZGROH36NPbv3y8pf/vtt8U/d+7cGR4eHhgwYAAuXLiANm3aVDseJg8iIguOPDQajSR5VKZZs2aws7NDbm6upDw3N7fKtQgPDw/Y29vDzs5OLPP19YXBYIDRaIRKpRLLo6KisHnzZuzbtw9PPPHEA2MJCgoCAGRlZclKHlzzICJSKC1zVJNKpUJgYCDS09PFMpPJhPT0dOh0ukq/06tXL2RlZcFkMoll586dg4eHh5g4BEFAVFQUvvnmG+zatQutWrV6aCwnTpwAcDc5ycHkQURkBTExMVixYgVWr16NM2fOYMKECSguLhbvvho9ejRiY2PF+hMmTEBeXh4mTZqEc+fOYcuWLZg9ezYiIyPFOpGRkfjyyy/x1VdfwdHREQaDAQaDAX/88QcA4MKFC5g1axb0ej0uXbqETZs2YfTo0ejTpw/8/f1lxc9pKyIihfznNCptQ4awsDDcuHEDcXFxMBgM6NKlC9LS0sRF9OzsbMlurd7e3ti+fTuio6Ph7+8PLy8vTJo0CVOmTBHrLFu2DMDdBwH/bNWqVXjjjTegUqmwc+dOLFy4EMXFxfD29kZoaCg++ugj2d1l8iAistK7raKiohAVFVXpuT179lQo0+l0OHToUJXtCYLwwOt5e3tj7969smKsCqetiIhINo48iIhkLnhX2YYNYfIgIuIr2WWzrVRJREQWwZEHEZEZb8WttA0bwuRBRMQ1D9lsq7dERGQRHHkQEXHBXDYmDyIiJg/ZOG1FRESyceRBRKRQWGDB3LZGHkweRES8VVc2TlsREZFsHHkQEXHBXLY6NfKYM2cOFAoFJk+eLJaVlJQgMjISLi4uaNKkCUJDQyts3UhEVCO1vJPg46DO9Pbo0aP47LPPKuxmFR0dje+++w6pqanYu3cvfvvtNwwfPtxKURIREVBHkkdRURHCw8OxYsUKNG3aVCwvKChAUlIS5s+fj/79+yMwMBCrVq3CwYMHH7ghChGRLPemrWp62JA6kTwiIyMxZMgQBAcHS8r1ej3Kysok5R06dECLFi2QkZFRaVulpaUoLCyUHKXlpkrrEhEB+N/dVjU9bIjVk0dKSgqOHTuGhISECucMBgNUKhWcnJwk5W5ubjAYDJW2l5CQAK1WKzn+78KtRxE6EZHNsmryuHLlCiZNmoQ1a9bAwcHBIm3GxsaioKBAckxp42KRtonocWWJxXKr/y5eq6x6q65er8f169fRtWtXsay8vBz79u3DkiVLsH37dhiNRuTn50tGH7m5uXB3d6+0TbVaDbVaLSkrs7Otf1Qikom36spm1eQxYMAAnDp1SlIWERGBDh06YMqUKfD29oa9vT3S09MRGhoKAMjMzER2djZ0Op01QiYiIlg5eTg6OsLPz09S1rhxY7i4uIjlY8eORUxMDJydnaHRaDBx4kTodDo8/fTT1giZiB5HHHnIVuefMF+wYAGUSiVCQ0NRWlqKkJAQLF261NphEdHjhMlDtjqXPPbs2SP57ODggMTERCQmJlonICIiqqDOJQ8iolqnVN49atqGDWHyICLitJVstpUqiYjIIjjyICLiyEM2Jg8iIm5DKxunrYiISDaOPIiIuIe5bEweRERc85CN01ZERCQbRx5ERJbYg9zG9jBn8iAi4rSVbLaVKomIyCKYPIiI7o08anrIlJiYCB8fHzg4OCAoKAhHjhx5YP38/HxERkbCw8MDarUa7dq1w9atW2W1WVJSgsjISLi4uKBJkyYIDQ1Fbm6u7NiZPIiI7r0YsaaHDGvXrkVMTAzi4+Nx7NgxBAQEICQkBNevX6+0vtFoxMCBA3Hp0iWsX78emZmZWLFiBby8vGS1GR0dje+++w6pqanYu3cvfvvtNwwfPlz+X5nsbxARUY3Nnz8f48aNQ0REBDp27Ijly5ejUaNGWLlyZaX1V65ciby8PGzcuBG9evWCj48Pnn32WQQEBFS7zYKCAiQlJWH+/Pno378/AgMDsWrVKhw8eBCHDh2SFT+TBxERFBY6qsdoNEKv1yM4OFgsUyqVCA4ORkZGRqXf2bRpE3Q6HSIjI+Hm5gY/Pz/Mnj0b5eXl1W5Tr9ejrKxMUqdDhw5o0aJFldetCu+2IiKy4N1WhYWFkmK1Wg21Wi0pu3nzJsrLy+Hm5iYpd3Nzw9mzZytt/tdff8WuXbsQHh6OrVu3IisrC++++y7KysoQHx9frTYNBgNUKhWcnJwq1DEYDLK6axPJ4+rRImuHYBGtNv8/a4dQY/9P8aq1Q7CI8K87WTsEi1C08rZ2CI8db2/p32l8fDymT59e43ZNJhNcXV3x+eefw87ODoGBgbh27RrmzZuH+Pj4Grcvl00kDyKiB7PAQ4L/XQW4cuUKNBqNWHr/qAMAmjVrBjs7uwp3OeXm5sLd3b3S1j08PGBvbw87OzuxzNfXFwaDAUajsVpturu7w2g0Ij8/XzL6eNB1H9xbIiKbZrk1D41GIzkqSx4qlQqBgYFIT08Xy0wmE9LT06HT6SqNsFevXsjKyoLJZBLLzp07Bw8PD6hUqmq1GRgYCHt7e0mdzMxMZGdnV3ndqjB5EBFZQUxMDFasWIHVq1fjzJkzmDBhAoqLixEREQEAGD16NGJjY8X6EyZMQF5eHiZNmoRz585hy5YtmD17NiIjI6vdplarxdixYxETE4Pdu3dDr9cjIiICOp0OTz/9tKz4OW1FRGSF15OEhYXhxo0biIuLg8FgQJcuXZCWliYueGdnZ0P5p2dHvL29sX37dkRHR8Pf3x9eXl6YNGkSpkyZUu02AWDBggVQKpUIDQ1FaWkpQkJCsHTpUvndFQRBkP2teuaiWwtrh2ARrXK/sXYINfb4LJi3snYIFvG4LJgrglaY9b3CwkJotVrkn5gCjWPF6SVZbf1eCqcu/4eCggLJmsfjitNWREQkG6etiIhkPuRXdRu2g8mDiEgBC6x5WCSSeoPTVkREJBtHHkREUKLmv0vb1u/iTB5ERNxJUDbbSpVERGQRHHkQEXHkIRuTBxERb9WVjdNWREQkG0ceREQKC7ySvcavdK9fmDyIiLjmIZttpUoiIrIIjjyIiLhgLhuTBxER1zxks63eEhGRRXDkQUQ2T6FQQFHDBe+afr++YfIgIuKah2yctiIiItkeu5FHaWkpSktLpWWCALWNDSmJSAYumMv22PU2ISEBWq1WciwrLrR2WERUpyksdNiOxy55xMbGoqCgQHJMaKyxdlhERI+Vx27aSq1WQ61WS8puccqKiB6EryeR7bFLHkREsikUFljzsK3k8dhNWxER0aPHkQcREZ/zkI3Jg4iIax6ycdqKiIhk48iDiIgPCcrG5EFExDUP2WwrVRIRkUVw5EFExAVz2Zg8iIigRM0nYmxrIse2ektERBbBkQcREaetZGPyICJi8pCN01ZERCQbRx5ERFwwl822ektEVCnF/6auzD3MeEgwMTERPj4+cHBwQFBQEI4cOVJl3eTkZCgUCsnh4OAg7cV95+8d8+bNE+v4+PhUOD9nzhzZsXPkQURkBWvXrkVMTAyWL1+OoKAgLFy4ECEhIcjMzISrq2ul39FoNMjMzBQ/K+5bZ8nJyZF83rZtG8aOHYvQ0FBJ+cyZMzFu3Djxs6Ojo+z4mTyIiKzwepL58+dj3LhxiIiIAAAsX74cW7ZswcqVKzF16tTKr6BQwN3dvco27z/37bffol+/fmjdurWk3NHR8YHtVAenrYiI7r0YsaYHgMLCQslRWlpa4XJGoxF6vR7BwcFimVKpRHBwMDIyMqoMs6ioCC1btoS3tzdeeukl/Pzzz1XWzc3NxZYtWzB27NgK5+bMmQMXFxc89dRTmDdvHu7cuSPnb+tuvLK/QUREVfL29oZWqxWPhISECnVu3ryJ8vJyuLm5Scrd3NxgMBgqbbd9+/ZYuXIlvv32W3z55ZcwmUzo2bMnrl69Wmn91atXw9HREcOHD5eUv/fee0hJScHu3bsxfvx4zJ49Gx9++KHsfnLaiohIAQs853H3f65cuQKNRiMWq9XqmrX7XzqdDjqdTvzcs2dP+Pr64rPPPsOsWbMq1F+5ciXCw8MrLKrHxMSIf/b394dKpcL48eORkJAgK1abSB6tcrMfafulpaVISEhAbGysxf5DqW211YfXhcyHV6qBx+HfAng8+lG/+mC5NQ+NRiNJHpVp1qwZ7OzskJubKynPzc2t9lqEvb09nnrqKWRlZVU498MPPyAzMxNr1659aDtBQUG4c+cOLl26hPbt21fr2gCnrSyitLQUM2bMqHRus754HPoAsB91yePQh0dFpVIhMDAQ6enpYpnJZEJ6erpkdPEg5eXlOHXqFDw8PCqcS0pKQmBgIAICAh7azokTJ6BUKqu8w6sqNjHyICJ6ICvsJBgTE4MxY8agW7du6NGjBxYuXIji4mLx7qvRo0fDy8tLXDOZOXMmnn76abRt2xb5+fmYN28eLl++jLfeekvSbmFhIVJTU/HJJ59UuGZGRgYOHz6Mfv36wdHRERkZGYiOjsZrr72Gpk2byoqfyYOIyAq36oaFheHGjRuIi4uDwWBAly5dkJaWJi6iZ2dnQ6n8X0L6z3/+g3HjxsFgMKBp06YIDAzEwYMH0bFjR0m7KSkpEAQBo0aNqnBNtVqNlJQUTJ8+HaWlpWjVqhWio6Ml6yDV7q0gCILsb5FEYWEhtFotCgoKHjrXWVc9Dn0A2I+6pD70QYwxeyU0mkY1bOs2tC3erNP9tSSOPCxArVYjPj6+HiwKVu1x6APAftQl9aoPfKuubBx5EJHNEkceV1ZbZuThPcZmRh6824qIiGTjtBUREaetZGPyICKywq269Z1t9ZaIiCyCyaMK+/btwwsvvABPT08oFAps3LhRcl4QBMTFxcHDwwMNGzZEcHAwzp8/L6mTl5eH8PBwaDQaODk5YezYsSgqKqq1PiQkJKB79+5wdHSEq6srhg4dKtkLAABKSkoQGRkJFxcXNGnSBKGhoRVemZCdnY0hQ4agUaNGcHV1xV//+lez3sJprmXLlsHf31987YNOp8O2bdvqVR/uN2fOHCgUCkyePFksqw/9mD59eoWNhDp06FCv+lA5hYUO28HkUYXi4mIEBAQgMTGx0vNz587FokWLsHz5chw+fBiNGzdGSEgISkpKxDrh4eH4+eefsWPHDmzevBn79u3D22+/XVtdwN69exEZGYlDhw5hx44dKCsrw3PPPYfi4mKxTnR0NL777jukpqZi7969+O233yRv4SwvL8eQIUNgNBpx8OBBrF69GsnJyYiLi6u1fjzxxBOYM2cO9Ho9fvzxR/Tv31/yOur60Ic/O3r0KD777DP4+/tLyutLPzp16oScnBzx2L9/f73rQwU13UXQEmsm9Y1ADwVA+Oabb8TPJpNJcHd3F+bNmyeW5efnC2q1Wvj6668FQRCEX375RQAgHD16VKyzbds2QaFQCNeuXau12P/s+vXrAgBh7969Ysz29vZCamqqWOfMmTMCACEjI0MQBEHYunWroFQqBYPBINZZtmyZoNFohNLS0trtwJ80bdpU+Ne//lXv+vD7778LTz75pLBjxw7h2WefFSZNmiQIQv35t4iPjxcCAgIqPVdf+vBnBQUFAgCh4LevBKFoY42Ogt++uttWQUGt98MaOPIww8WLF2EwGCQbuWi1WgQFBYkbuWRkZMDJyQndunUT6wQHB0OpVOLw4cO1HjMAFBQUAACcnZ0BAHq9HmVlZZJ+dOjQAS1atJD0o3PnzpJ9B0JCQlBYWPjAjWgelfLycqSkpKC4uBg6na7e9SEyMhJDhgyRxAvUr3+L8+fPw9PTE61bt0Z4eDiys7PrXR8qUlrosB2828oM9zZredBGLgaDocJbKhs0aABnZ+cqN3t5lEwmEyZPnoxevXrBz89PjFGlUsHJyUlS9/5+VNbPe+dqy6lTp6DT6VBSUoImTZrgm2++QceOHXHixIl604eUlBQcO3YMR48erXCuvvxbBAUFITk5Ge3bt0dOTg5mzJiB3r174/Tp0/WmD5XirbqyMXnYiMjISJw+fVoyP12ftG/fHidOnEBBQQHWr1+PMWPGYO/evdYOq9quXLmCSZMmYceOHRU256lPBg8eLP7Z398fQUFBaNmyJdatW4eGDRtaMTKqbbY1zrKQe5u1PGgjF3d3d1y/fl1y/s6dO8jLy6vxxvNyRUVFYfPmzdi9ezeeeOIJsdzd3R1GoxH5+fmS+vf3o7J+3jtXW1QqFdq2bYvAwEAkJCQgICAAn376ab3pg16vx/Xr19G1a1c0aNAADRo0wN69e7Fo0SI0aNAAbm5u9aIf93NyckK7du2QlZVVb/4tKsdpK7lsq7cW0qpVK7i7u0s2ciksLMThw4fFjVx0Oh3y8/Oh1+vFOrt27YLJZEJQUFCtxCkIAqKiovDNN99g165daNWqleR8YGAg7O3tJf3IzMxEdna2pB+nTp2SJMIdO3ZAo9FUeBV0bTKZTCgtLa03fRgwYABOnTqFEydOiEe3bt0QHh4u/rk+9ON+RUVFuHDhAjw8POrNv0WleLeVfNZesa+rfv/9d+H48ePC8ePHBQDC/PnzhePHjwuXL18WBEEQ5syZIzg5OQnffvut8NNPPwkvvfSS0KpVK+GPP/4Q2xg0aJDw1FNPCYcPHxb2798vPPnkk8KoUaNqrQ8TJkwQtFqtsGfPHiEnJ0c8bt++LdZ55513hBYtWgi7du0SfvzxR0Gn0wk6nU48f+fOHcHPz0947rnnhBMnTghpaWlC8+bNhdjY2Frrx9SpU4W9e/cKFy9eFH766Sdh6tSpgkKhEL7//vt604fK/PluK0GoH/14//33hT179ggXL14UDhw4IAQHBwvNmjUTrl+/Xm/68Gfi3VaG9YJwe2uNjgLDepu624rJowq7d+8WAFQ4xowZIwjC3dt1p02bJri5uQlqtVoYMGCAkJmZKWnj1q1bwqhRo4QmTZoIGo1GiIiIEH7//fda60Nl8QMQVq1aJdb5448/hHfffVdo2rSp0KhRI2HYsGFCTk6OpJ1Lly4JgwcPFho2bCg0a9ZMeP/994WysrJa68ebb74ptGzZUlCpVELz5s2FAQMGiImjvvShMvcnj/rQj7CwMMHDw0NQqVSCl5eXEBYWJmRlZdWrPvyZmDxy1wvCH9tqdBTk2lby4CvZichmia9kz90AjaZxDdsqhtZtOF/JTkREVBXeqktExOc8ZGPyICKyyIsNbSt5cNqKiIhk48iDiIibQcnG5EFExGkr2WwrVRIRkUVw5EFExGkr2Wyrt/RY8PHxwcKFC60dBj1WuA2tXEweVGclJydX2BsCuLuNa21s58skRVQ1TltRvdO8eXNrhyCL0WiESqWydhj0IHxIUDaOPOih+vbti/feew8ffvghnJ2d4e7ujunTp1fru/n5+XjrrbfQvHlzaDQa9O/fHydPnhTPnzx5Ev369YOjoyM0Gg0CAwPx448/Ys+ePYiIiEBBQQEUCgUUCoV4zftHBAqFAp999hn+8pe/oFGjRvD19UVGRgaysrLQt29fNG7cGD179sSFCxfE71y4cAEvvfQS3Nzc0KRJE3Tv3h07d+6U9Pny5cuIjo4Wr3/Pv//9b3Tq1AlqtRo+Pj745JNPJH328fHBrFmzMHr0aGg0Grz99tswGo2IioqCh4cHHBwc0LJlSyQkJMj4V6BH6t6aR00PG2JbvSWzrV69Go0bN8bhw4cxd+5czJw5Ezt27Hjo90aMGIHr169j27Zt0Ov16Nq1KwYMGIC8vDwAQHh4OJ544gkcPXoUer0eU6dOhb29PXr27ImFCxdCo9EgJycHOTk5+OCDD6q8zr0f1idOnECHDh3w6quvYvz48YiNjcWPP/4o7m1yT1FREZ5//nmkp6fj+PHjGDRoEF544QVxP+4NGzbgiSeewMyZM8XrA3c3dXrllVcwcuRInDp1CtOnT8e0adOQnJwsieef//wnAgICcPz4cUybNg2LFi3Cpk2bsG7dOmRmZmLNmjXw8fGR+a9AVIdY+a2+VA88++yzwjPPPCMp6969uzBlypQHfu+HH34QNBqNUFJSIilv06aN8NlnnwmCIAiOjo5CcnJypd9ftWqVoNVqK5S3bNlSWLBggfgZgPDRRx+JnzMyMgQAQlJSklj29ddfCw4ODg+Mt1OnTsLixYurvI4gCMKrr74qDBw4UFL217/+VejYsaPke0OHDpXUmThxotC/f3/BZDI9MAaqXeIr2fO+F4Q7B2p0FOR9b1OvZOfIg6rF399f8tnDw6PCNrv3O3nyJIqKiuDi4oImTZqIx8WLF8UppJiYGLz11lsIDg7GnDlzJFNL5sbn5uYGAOjcubOkrKSkBIWFhQDujjw++OAD+Pr6wsnJCU2aNMGZM2fEkUdVzpw5g169eknKevXqhfPnz6O8vFws69atm6TOG2+8gRMnTqB9+/Z477338P3335vVT3pEuJOgbFwwp2qxt7eXfFYoFDCZTA/8TlFRETw8PLBnz54K5+7dRTV9+nS8+uqr2LJlC7Zt24b4+HikpKRg2LBhZsd3b32isrJ7MX/wwQfYsWMH/vnPf6Jt27Zo2LAhXn75ZRiNRlnXrUrjxtK9Ibp27YqLFy9i27Zt2LlzJ1555RUEBwdj/fr1FrkeUW1j8qBHpmvXrjAYDGjQoMED5/fbtWuHdu3aITo6GqNGjcKqVaswbNgwqFQqyW/zlnTgwAG88cYbYpIqKirCpUuXJHUqu76vry8OHDhQoa127drBzs7ugdfUaDQICwtDWFgYXn75ZQwaNAh5eXlwdnaueYeohpSo+RKwbU3k2FZvqVYFBwdDp9Nh6NCh+P7773Hp0iUcPHgQf//73/Hjjz/ijz/+QFRUFPbs2YPLly/jwIEDOHr0KHx9fQHcvWupqKgI6enpuHnzJm7fvm2x2J588kls2LABJ06cwMmTJ/Hqq69WGEn5+Phg3759uHbtGm7evAkAeP/995Geno5Zs2bh3LlzWL16NZYsWfLAxXwAmD9/Pr7++mucPXsW586dQ2pqKtzd3St9joWsQAELTFtZuxO1i8mDHhmFQoGtW7eiT58+iIiIQLt27TBy5EhcvnwZbm5usLOzw61btzB69Gi0a9cOr7zyCgYPHowZM2YAAHr27Il33nkHYWFhaN68OebOnWux2ObPn4+mTZuiZ8+eeOGFFxASEoKuXbtK6sycOROXLl1CmzZtxGdLunbtinXr1iElJQV+fn6Ii4vDzJkz8cYbbzzweo6Ojpg7dy66deuG7t2749KlS9i6dSuUSv5fkOon7mFORDZL3MM8fzc0miY1bKsIWqd+NrOHOdc8iIj4hLlsTB5ktjVr1mD8+PGVnmvZsiV+/vnnWo6IyDyFhcV1oo36hNNWZLbff/8dubm5lZ6zt7dHy5YtazkiInlKSkrQqlUrGAwGi7Tn7u6OixcvwsHBwSLt1WVMHkRk00pKSiz2fI9KpbKJxAEweRARkRl4nyAREcnG5EFERLIxeRARkWxMHkREJBuTBxERycbkQUREsjF5EBGRbP8fAf/qTsWt2IsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest best score is: 0.7505650213983548\n",
      "-------------CROSS-VALIDATING MODELS-------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done  21 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.9s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  49 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:    3.3s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  81 tasks      | elapsed:    3.9s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.9s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  98 tasks      | elapsed:    4.6s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 117 tasks      | elapsed:    5.3s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 136 tasks      | elapsed:    6.1s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done 157 tasks      | elapsed:    7.1s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done 178 tasks      | elapsed:    7.9s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Done 201 tasks      | elapsed:    9.0s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Done 224 tasks      | elapsed:    9.8s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.8s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.3s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.4s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 268 out of 300 | elapsed:   11.1s remaining:    1.3s\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.2s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
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      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   12.2s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1100x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting best score is: 0.8499540582873917\n"
     ]
    }
   ],
   "source": [
    "classifiers, models, scores = dna_predict.gridsearch_classifier_panel(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b581bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_max = np.argmax(scores)\n",
    "best_model_params = models[index_max]\n",
    "best_model_class = classifiers[index_max]\n",
    "best_model = best_model_class.set_params(**best_model_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d37bf93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dna_predict.plot_ROC(X_train, y_train, best_model, 5, 'training_ROC.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ff4bae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get test performance after final best model has been selected (do not change after this point)\n",
    "X_train, y_train, X_test, y_test = dna_predict.train_test_process(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "259086f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = best_model_class.set_params(**best_model_params)\n",
    "best_model.fit(X_train, y_train)\n",
    "X_train = pd.DataFrame(X_train)\n",
    "X_train.columns = clean_df.iloc[:,:-1].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60a1cadd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.initjs()\n",
    "# compute SHAP values\n",
    "explainer = shap.Explainer(best_model, X_train)\n",
    "shap_values = explainer(X_train)\n",
    "###remove 'shap_values[:,:,1]' if not random forest\n",
    "#summary_plot\n",
    "shap.plots.beeswarm(shap_values, max_display=20, show=False)\n",
    "plt.savefig(os.path.join('outputs','shap_values.pdf'), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93a79e71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Columns (17) have mixed types. Specify dtype option on import or set low_memory=False.\n"
     ]
    }
   ],
   "source": [
    "#take a new data input and predict non-attendance\n",
    "new_df = pd.read_csv(os.path.join('..', 'simulated_data', 'simulated_data.csv'))\n",
    "pred_new_df = dna_predict.feature_preprop(new_df)\n",
    "pred_new_df.fillna(X_train.median(), inplace=True)\n",
    "prob_DNA = best_model.predict_proba(pred_new_df)[:, 1]\n",
    "new_df['pred_DNA'] = prob_DNA\n",
    "new_df.to_csv('outputs/simulated_data_preds.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}