eyn-df-lgbm-manyf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from tqdm import tqdm\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.float_format', '{:.3f}'.format)\n",
    "x_min, x_max = -1., 1.\n",
    "y_min, y_max = -.3, .3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(120656,), (120656,), (120656,), (120657,), (120657,), (120657,), (120657,), (120657,), (120657,), (120657,)]\n",
      "[(13407,), (13407,), (13407,), (13406,), (13406,), (13406,), (13406,), (13406,), (13406,), (13406,)]\n"
     ]
    }
   ],
   "source": [
    "trn_index_list = np.load(\"../input/eyn-folds/trn_index_list_10f.npy\", allow_pickle=True)\n",
    "val_index_list = np.load(\"../input/eyn-folds/val_index_list_10f.npy\", allow_pickle=True)\n",
    "print([np.shape(trn_index_list[i]) for i in range(np.shape(trn_index_list)[0])])\n",
    "print([np.shape(val_index_list[i]) for i in range(np.shape(val_index_list)[0])])\n",
    "train_targets_inside = np.load(\"../input/eyn-original/train_targets_inside.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2815323, 23) (703815, 23)\n"
     ]
    },
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-1.596</td>\n",
       "      <td>-0.429</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.041</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.038</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.670</td>\n",
       "      <td>-0.638</td>\n",
       "      <td>-1.602</td>\n",
       "      <td>-0.534</td>\n",
       "      <td>-1.609</td>\n",
       "      <td>-0.516</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.353</td>\n",
       "      <td>-0.367</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.711</td>\n",
       "      <td>-0.694</td>\n",
       "      <td>-1.603</td>\n",
       "      <td>-0.547</td>\n",
       "      <td>-1.609</td>\n",
       "      <td>-0.539</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.542</td>\n",
       "      <td>5.631</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>0.810</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.766</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>-1.696</td>\n",
       "      <td>-0.836</td>\n",
       "      <td>-1.593</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.107</td>\n",
       "      <td>1.968</td>\n",
       "      <td>12.562</td>\n",
       "      <td>98.121</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.261</td>\n",
       "      <td>-0.325</td>\n",
       "      <td>-0.946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792</td>\n",
       "      <td>-0.786</td>\n",
       "      <td>-0.989</td>\n",
       "      <td>1.449</td>\n",
       "      <td>-1.058</td>\n",
       "      <td>1.026</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.429</td>\n",
       "      <td>nan</td>\n",
       "      <td>64.104</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.160</td>\n",
       "      <td>-0.987</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          t_entry  t_exit  x_entry   ...     dir_y  dir_x_a  dir_y_a\n",
       "seq traj                             ...                            \n",
       "0   0       0.004   0.031   -1.596   ...       nan    0.095    0.996\n",
       "    1      -0.036  -0.036   -1.599   ...     0.000    0.200    0.980\n",
       "    2      -0.670  -0.638   -1.602   ...     0.930    0.760    0.649\n",
       "    3      -0.711  -0.694   -1.603   ...     0.810   -0.041    0.999\n",
       "    4      -0.766  -0.757   -1.696   ...     0.261   -0.325   -0.946\n",
       "    5      -0.792  -0.786   -0.989   ...    -0.987      nan      nan\n",
       "    6         nan     nan      nan   ...       nan      nan      nan\n",
       "\n",
       "[7 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_pickle(\"../input/eyn-pre-unravel-df/df_train.pickle\")\n",
    "df_test = pd.read_pickle(\"../input/eyn-pre-unravel-df/df_test.pickle\")\n",
    "print(df_train.shape, df_test.shape)\n",
    "df_train.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2815323, 2) (703815, 2)\n"
     ]
    },
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       "      <td>nan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.312</td>\n",
       "      <td>-1.312</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.008</td>\n",
       "      <td>-4.008</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.980</td>\n",
       "      <td>-0.980</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.161</td>\n",
       "      <td>-0.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
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      "text/plain": [
       "          sum_entry  sum_exit\n",
       "seq traj                     \n",
       "0   0        -0.350       nan\n",
       "    1        -1.312    -1.312\n",
       "    2        -4.008    -4.008\n",
       "    3        -0.980    -0.980\n",
       "    4        -0.000    -0.000\n",
       "    5        -0.161    -0.161\n",
       "    6           nan       nan"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_cluster = pd.read_pickle(\"../input/eyn-pre-cluster-calc/df_train_cluster_s.pickle\")\n",
    "df_test_cluster = pd.read_pickle(\"../input/eyn-pre-cluster-calc/df_test_cluster_s.pickle\")\n",
    "print(df_train_cluster.shape, df_test_cluster.shape)\n",
    "df_train_cluster = df_train_cluster.astype(float)\n",
    "df_test_cluster = df_test_cluster.astype(float)\n",
    "df_train_cluster.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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      "(2815323, 25)\n"
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       "      <th rowspan=\"7\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-1.596</td>\n",
       "      <td>-0.429</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.041</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.038</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.996</td>\n",
       "      <td>-0.350</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.980</td>\n",
       "      <td>-1.312</td>\n",
       "      <td>-1.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.670</td>\n",
       "      <td>-0.638</td>\n",
       "      <td>-1.602</td>\n",
       "      <td>-0.534</td>\n",
       "      <td>-1.609</td>\n",
       "      <td>-0.516</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.353</td>\n",
       "      <td>-0.367</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.649</td>\n",
       "      <td>-4.008</td>\n",
       "      <td>-4.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.711</td>\n",
       "      <td>-0.694</td>\n",
       "      <td>-1.603</td>\n",
       "      <td>-0.547</td>\n",
       "      <td>-1.609</td>\n",
       "      <td>-0.539</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.542</td>\n",
       "      <td>5.631</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>0.810</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-0.980</td>\n",
       "      <td>-0.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.766</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>-1.696</td>\n",
       "      <td>-0.836</td>\n",
       "      <td>-1.593</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.107</td>\n",
       "      <td>1.968</td>\n",
       "      <td>12.562</td>\n",
       "      <td>98.121</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.261</td>\n",
       "      <td>-0.325</td>\n",
       "      <td>-0.946</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792</td>\n",
       "      <td>-0.786</td>\n",
       "      <td>-0.989</td>\n",
       "      <td>1.449</td>\n",
       "      <td>-1.058</td>\n",
       "      <td>1.026</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.429</td>\n",
       "      <td>nan</td>\n",
       "      <td>64.104</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.160</td>\n",
       "      <td>-0.987</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>-0.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          t_entry  t_exit  x_entry    ...     dir_y_a  sum_entry  sum_exit\n",
       "seq traj                              ...                                 \n",
       "0   0       0.004   0.031   -1.596    ...       0.996     -0.350       nan\n",
       "    1      -0.036  -0.036   -1.599    ...       0.980     -1.312    -1.312\n",
       "    2      -0.670  -0.638   -1.602    ...       0.649     -4.008    -4.008\n",
       "    3      -0.711  -0.694   -1.603    ...       0.999     -0.980    -0.980\n",
       "    4      -0.766  -0.757   -1.696    ...      -0.946     -0.000    -0.000\n",
       "    5      -0.792  -0.786   -0.989    ...         nan     -0.161    -0.161\n",
       "    6         nan     nan      nan    ...         nan        nan       nan\n",
       "\n",
       "[7 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.concat([df_train, df_train_cluster], ignore_index=False, axis=1, sort=True)\n",
    "df_test = pd.concat([df_test, df_test_cluster], ignore_index=False, axis=1, sort=True)\n",
    "print(df_train.shape)\n",
    "df_train.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not using the individual data points\n",
    "# df_train_cluster = pd.read_pickle(\"../input/eyn-pre-cluster-calc/df_train_cluster_w.pickle\")\n",
    "# df_test_cluster = pd.read_pickle(\"../input/eyn-pre-cluster-calc/df_test_cluster_w.pickle\")\n",
    "# print(df_train_cluster.shape, df_test_cluster.shape)\n",
    "# df_train_cluster = df_train_cluster.astype(float)\n",
    "# df_test_cluster = df_test_cluster.astype(float)\n",
    "# df_train_cluster.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train = pd.concat([df_train, df_train_cluster], ignore_index=False, axis=1, sort=True)\n",
    "# df_test = pd.concat([df_test, df_test_cluster], ignore_index=False, axis=1, sort=True)\n",
    "# print(df_train.shape)\n",
    "# df_train.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "time_dummy_train = []\n",
    "for i in df_train[\"t_entry\"]:\n",
    "    entry = np.repeat(0, 16)\n",
    "    if math.isnan(i):\n",
    "        time_dummy_train.append([np.nan]*16)\n",
    "    else:\n",
    "        num = int((i + 1.5) // 0.1)\n",
    "        if num != 0:\n",
    "            entry[num-1] = 1\n",
    "        time_dummy_train.append(entry)\n",
    "        \n",
    "import math\n",
    "time_dummy_test = []\n",
    "for i in df_test[\"t_entry\"]:\n",
    "    entry = np.repeat(0, 16)\n",
    "    if math.isnan(i):\n",
    "        time_dummy_test.append([np.nan]*16)\n",
    "    else:\n",
    "        num = int((i + 1.5) // 0.1)\n",
    "        if num != 0:\n",
    "            entry[num-1] = 1\n",
    "        time_dummy_test.append(entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>t_entry</th>\n",
       "      <th>t_exit</th>\n",
       "      <th>x_entry</th>\n",
       "      <th>y_entry</th>\n",
       "      <th>x_exit</th>\n",
       "      <th>y_exit</th>\n",
       "      <th>vmax</th>\n",
       "      <th>vmin</th>\n",
       "      <th>vmean</th>\n",
       "      <th>tid_0</th>\n",
       "      <th>tid_1</th>\n",
       "      <th>entry_in</th>\n",
       "      <th>exit_in</th>\n",
       "      <th>dur</th>\n",
       "      <th>dur_a</th>\n",
       "      <th>dist</th>\n",
       "      <th>dist_a</th>\n",
       "      <th>speed</th>\n",
       "      <th>speed_a</th>\n",
       "      <th>dir_x</th>\n",
       "      <th>dir_y</th>\n",
       "      <th>dir_x_a</th>\n",
       "      <th>dir_y_a</th>\n",
       "      <th>sum_entry</th>\n",
       "      <th>sum_exit</th>\n",
       "      <th>td0</th>\n",
       "      <th>td1</th>\n",
       "      <th>td2</th>\n",
       "      <th>td3</th>\n",
       "      <th>td4</th>\n",
       "      <th>td5</th>\n",
       "      <th>td6</th>\n",
       "      <th>td7</th>\n",
       "      <th>td8</th>\n",
       "      <th>td9</th>\n",
       "      <th>td10</th>\n",
       "      <th>td11</th>\n",
       "      <th>td12</th>\n",
       "      <th>td13</th>\n",
       "      <th>td14</th>\n",
       "      <th>td15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th>traj</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-1.596</td>\n",
       "      <td>-0.429</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.041</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.038</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.996</td>\n",
       "      <td>-0.350</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.980</td>\n",
       "      <td>-1.312</td>\n",
       "      <td>-1.312</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.670</td>\n",
       "      <td>-0.638</td>\n",
       "      <td>-1.602</td>\n",
       "      <td>-0.534</td>\n",
       "      <td>-1.609</td>\n",
       "      <td>-0.516</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.353</td>\n",
       "      <td>-0.367</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.649</td>\n",
       "      <td>-4.008</td>\n",
       "      <td>-4.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.711</td>\n",
       "      <td>-0.694</td>\n",
       "      <td>-1.603</td>\n",
       "      <td>-0.547</td>\n",
       "      <td>-1.609</td>\n",
       "      <td>-0.539</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.542</td>\n",
       "      <td>5.631</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>0.810</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-0.980</td>\n",
       "      <td>-0.980</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.766</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>-1.696</td>\n",
       "      <td>-0.836</td>\n",
       "      <td>-1.593</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.107</td>\n",
       "      <td>1.968</td>\n",
       "      <td>12.562</td>\n",
       "      <td>98.121</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.261</td>\n",
       "      <td>-0.325</td>\n",
       "      <td>-0.946</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792</td>\n",
       "      <td>-0.786</td>\n",
       "      <td>-0.989</td>\n",
       "      <td>1.449</td>\n",
       "      <td>-1.058</td>\n",
       "      <td>1.026</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.429</td>\n",
       "      <td>nan</td>\n",
       "      <td>64.104</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.160</td>\n",
       "      <td>-0.987</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          t_entry  t_exit  x_entry  y_entry  ...   td12  td13  td14  td15\n",
       "seq traj                                     ...                         \n",
       "0   0       0.004   0.031   -1.596   -0.429  ...  0.000 0.000 1.000 0.000\n",
       "    1      -0.036  -0.036   -1.599   -0.467  ...  0.000 1.000 0.000 0.000\n",
       "    2      -0.670  -0.638   -1.602   -0.534  ...  0.000 0.000 0.000 0.000\n",
       "    3      -0.711  -0.694   -1.603   -0.547  ...  0.000 0.000 0.000 0.000\n",
       "    4      -0.766  -0.757   -1.696   -0.836  ...  0.000 0.000 0.000 0.000\n",
       "    5      -0.792  -0.786   -0.989    1.449  ...  0.000 0.000 0.000 0.000\n",
       "    6         nan     nan      nan      nan  ...    nan   nan   nan   nan\n",
       "\n",
       "[7 rows x 41 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dummy_train = np.array(time_dummy_train)\n",
    "time_dummy_test = np.array(time_dummy_test)\n",
    "\n",
    "time_dummy_col_name = [\"td{}\".format(i) for i in range(16)]\n",
    "\n",
    "for i,col_name in enumerate(time_dummy_col_name):\n",
    "    df_train[col_name] = time_dummy_train[:,i]\n",
    "    df_test[col_name] = time_dummy_test[:,i]\n",
    "    \n",
    "df_train[time_dummy_col_name] = df_train[time_dummy_col_name].astype('category')\n",
    "df_test[time_dummy_col_name] = df_test[time_dummy_col_name].astype('category')\n",
    "df_train.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col = ['entry_in', 'exit_in', 'tid_0', 'tid_1']\n",
    "df_train[cat_col] = df_train[cat_col].astype('category')\n",
    "df_test[cat_col] = df_test[cat_col].astype('category')\n",
    "# cluster targets has been made categorical in eyn-pre-cluster-unif, but can be ensured categorical again here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col = ['x_exit_0', 'y_exit_0', 'vmean_0', 'vmax_0', 'vmin_0', \n",
    "             'exit_in_0', 'dist_0', 'speed_0', 'dir_x_0', 'dir_y_0']\n",
    "\n",
    "df_columns = df_train.columns\n",
    "flatten = lambda l: [item for sublist in l for item in sublist]\n",
    "drop_col += flatten([[name + \"_\" + str(i) for name in df_columns] for i in range(15,21)])\n",
    "# drop_col = []  # not dropping any columns, if you choose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>x_entry_1</th>\n",
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       "      <th>...</th>\n",
       "      <th>td13_5</th>\n",
       "      <th>td13_6</th>\n",
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       "      <th>td13_9</th>\n",
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       "      <th>td13_11</th>\n",
       "      <th>td13_12</th>\n",
       "      <th>td13_13</th>\n",
       "      <th>td13_14</th>\n",
       "      <th>td14_0</th>\n",
       "      <th>td14_1</th>\n",
       "      <th>td14_2</th>\n",
       "      <th>td14_3</th>\n",
       "      <th>td14_4</th>\n",
       "      <th>td14_5</th>\n",
       "      <th>td14_6</th>\n",
       "      <th>td14_7</th>\n",
       "      <th>td14_8</th>\n",
       "      <th>td14_9</th>\n",
       "      <th>td14_10</th>\n",
       "      <th>td14_11</th>\n",
       "      <th>td14_12</th>\n",
       "      <th>td14_13</th>\n",
       "      <th>td14_14</th>\n",
       "      <th>td15_0</th>\n",
       "      <th>td15_1</th>\n",
       "      <th>td15_2</th>\n",
       "      <th>td15_3</th>\n",
       "      <th>td15_4</th>\n",
       "      <th>td15_5</th>\n",
       "      <th>td15_6</th>\n",
       "      <th>td15_7</th>\n",
       "      <th>td15_8</th>\n",
       "      <th>td15_9</th>\n",
       "      <th>td15_10</th>\n",
       "      <th>td15_11</th>\n",
       "      <th>td15_12</th>\n",
       "      <th>td15_13</th>\n",
       "      <th>td15_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.670</td>\n",
       "      <td>-0.711</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>-0.792</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.638</td>\n",
       "      <td>-0.694</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>-0.786</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.596</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-1.602</td>\n",
       "      <td>-1.603</td>\n",
       "      <td>-1.696</td>\n",
       "      <td>-0.989</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.181</td>\n",
       "      <td>-1.186</td>\n",
       "      <td>-1.181</td>\n",
       "      <td>-1.145</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.042</td>\n",
       "      <td>-0.338</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-0.313</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>1.056</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>-0.085</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>-0.236</td>\n",
       "      <td>-0.472</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>-0.085</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>-0.236</td>\n",
       "      <td>-0.472</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.696</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>-0.431</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.464</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.387</td>\n",
       "      <td>-0.388</td>\n",
       "      <td>-0.906</td>\n",
       "      <td>-1.432</td>\n",
       "      <td>-1.477</td>\n",
       "      <td>-1.495</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.335</td>\n",
       "      <td>-0.387</td>\n",
       "      <td>-0.901</td>\n",
       "      <td>-1.432</td>\n",
       "      <td>-1.445</td>\n",
       "      <td>-1.488</td>\n",
       "      <td>-1.495</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.326</td>\n",
       "      <td>-1.326</td>\n",
       "      <td>-1.336</td>\n",
       "      <td>-1.340</td>\n",
       "      <td>-1.341</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t_entry_0  t_entry_1  t_entry_2   ...     td15_12  td15_13  td15_14\n",
       "seq                                    ...                              \n",
       "0        0.004     -0.036     -0.670   ...         nan      nan      nan\n",
       "1        0.001     -0.016     -0.034   ...         nan      nan      nan\n",
       "2       -0.042     -0.338        nan   ...         nan      nan      nan\n",
       "3        0.048     -0.034     -0.051   ...         nan      nan      nan\n",
       "4        0.013     -0.387     -0.388   ...         nan      nan      nan\n",
       "\n",
       "[5 rows x 605 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_unstack = df_train.unstack()\n",
    "unstack_col_names = [\"_\".join([tup[0],str(tup[1])]) for tup in df_train_unstack.columns.values]\n",
    "df_train_unstack.columns = unstack_col_names\n",
    "df_train_unstack = df_train_unstack.drop(drop_col, axis=1)\n",
    "df_train_unstack.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>td13_12</th>\n",
       "      <th>td13_13</th>\n",
       "      <th>td13_14</th>\n",
       "      <th>td14_0</th>\n",
       "      <th>td14_1</th>\n",
       "      <th>td14_2</th>\n",
       "      <th>td14_3</th>\n",
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       "      <th>td14_5</th>\n",
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       "      <th>td14_7</th>\n",
       "      <th>td14_8</th>\n",
       "      <th>td14_9</th>\n",
       "      <th>td14_10</th>\n",
       "      <th>td14_11</th>\n",
       "      <th>td14_12</th>\n",
       "      <th>td14_13</th>\n",
       "      <th>td14_14</th>\n",
       "      <th>td15_0</th>\n",
       "      <th>td15_1</th>\n",
       "      <th>td15_2</th>\n",
       "      <th>td15_3</th>\n",
       "      <th>td15_4</th>\n",
       "      <th>td15_5</th>\n",
       "      <th>td15_6</th>\n",
       "      <th>td15_7</th>\n",
       "      <th>td15_8</th>\n",
       "      <th>td15_9</th>\n",
       "      <th>td15_10</th>\n",
       "      <th>td15_11</th>\n",
       "      <th>td15_12</th>\n",
       "      <th>td15_13</th>\n",
       "      <th>td15_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.157</td>\n",
       "      <td>-0.243</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>-0.328</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.018</td>\n",
       "      <td>-0.128</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>-0.316</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.222</td>\n",
       "      <td>1.248</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1.230</td>\n",
       "      <td>1.251</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.286</td>\n",
       "      <td>-0.686</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.278</td>\n",
       "      <td>-0.666</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.908</td>\n",
       "      <td>1.048</td>\n",
       "      <td>1.048</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.385</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.385</td>\n",
       "      <td>-0.616</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.343</td>\n",
       "      <td>-0.555</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.059</td>\n",
       "      <td>-0.537</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.537</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>-0.955</td>\n",
       "      <td>-0.957</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.010</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.619</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.042</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>-0.543</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.542</td>\n",
       "      <td>1.536</td>\n",
       "      <td>1.537</td>\n",
       "      <td>-1.354</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t_entry_0  t_entry_1  t_entry_2   ...     td15_12  td15_13  td15_14\n",
       "seq                                    ...                              \n",
       "0        0.006     -0.157     -0.243   ...         nan      nan      nan\n",
       "1        0.049     -0.286     -0.686   ...         nan      nan      nan\n",
       "2        0.044     -0.385     -0.625   ...         nan      nan      nan\n",
       "3        0.059     -0.537     -0.583   ...         nan      nan      nan\n",
       "4       -0.010     -0.069     -0.211   ...         nan      nan      nan\n",
       "\n",
       "[5 rows x 605 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_unstack = df_test.unstack()\n",
    "unstack_col_names = [\"_\".join([tup[0],str(tup[1])]) for tup in df_test_unstack.columns.values]\n",
    "df_test_unstack.columns = unstack_col_names\n",
    "df_test_unstack = df_test_unstack.drop(drop_col, axis=1)\n",
    "df_test_unstack.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_entry_0</th>\n",
       "      <th>t_entry_1</th>\n",
       "      <th>t_entry_2</th>\n",
       "      <th>t_entry_3</th>\n",
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       "      <th>t_exit_0</th>\n",
       "      <th>t_exit_1</th>\n",
       "      <th>t_exit_2</th>\n",
       "      <th>t_exit_3</th>\n",
       "      <th>t_exit_4</th>\n",
       "      <th>t_exit_5</th>\n",
       "      <th>t_exit_6</th>\n",
       "      <th>t_exit_7</th>\n",
       "      <th>t_exit_8</th>\n",
       "      <th>t_exit_9</th>\n",
       "      <th>t_exit_10</th>\n",
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       "      <th>t_exit_12</th>\n",
       "      <th>t_exit_13</th>\n",
       "      <th>t_exit_14</th>\n",
       "      <th>x_entry_0</th>\n",
       "      <th>x_entry_1</th>\n",
       "      <th>x_entry_2</th>\n",
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       "      <th>x_entry_4</th>\n",
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       "      <th>x_entry_6</th>\n",
       "      <th>x_entry_7</th>\n",
       "      <th>x_entry_8</th>\n",
       "      <th>x_entry_9</th>\n",
       "      <th>...</th>\n",
       "      <th>td14_5</th>\n",
       "      <th>td14_6</th>\n",
       "      <th>td14_7</th>\n",
       "      <th>td14_8</th>\n",
       "      <th>td14_9</th>\n",
       "      <th>td14_10</th>\n",
       "      <th>td14_11</th>\n",
       "      <th>td14_12</th>\n",
       "      <th>td14_13</th>\n",
       "      <th>td14_14</th>\n",
       "      <th>td15_0</th>\n",
       "      <th>td15_1</th>\n",
       "      <th>td15_2</th>\n",
       "      <th>td15_3</th>\n",
       "      <th>td15_4</th>\n",
       "      <th>td15_5</th>\n",
       "      <th>td15_6</th>\n",
       "      <th>td15_7</th>\n",
       "      <th>td15_8</th>\n",
       "      <th>td15_9</th>\n",
       "      <th>td15_10</th>\n",
       "      <th>td15_11</th>\n",
       "      <th>td15_12</th>\n",
       "      <th>td15_13</th>\n",
       "      <th>td15_14</th>\n",
       "      <th>e0</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "      <th>e3</th>\n",
       "      <th>e4</th>\n",
       "      <th>e5</th>\n",
       "      <th>e6</th>\n",
       "      <th>e7</th>\n",
       "      <th>e8</th>\n",
       "      <th>e9</th>\n",
       "      <th>e10</th>\n",
       "      <th>e11</th>\n",
       "      <th>e12</th>\n",
       "      <th>e13</th>\n",
       "      <th>e14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.670</td>\n",
       "      <td>-0.711</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>-0.792</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.638</td>\n",
       "      <td>-0.694</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>-0.786</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.596</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>-1.602</td>\n",
       "      <td>-1.603</td>\n",
       "      <td>-1.696</td>\n",
       "      <td>-0.989</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.142</td>\n",
       "      <td>1.698</td>\n",
       "      <td>-0.517</td>\n",
       "      <td>-0.173</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>1.439</td>\n",
       "      <td>0.711</td>\n",
       "      <td>-2.463</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.834</td>\n",
       "      <td>-1.530</td>\n",
       "      <td>-0.452</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.181</td>\n",
       "      <td>-1.186</td>\n",
       "      <td>-1.181</td>\n",
       "      <td>-1.145</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.232</td>\n",
       "      <td>-1.077</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>-1.781</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.384</td>\n",
       "      <td>1.584</td>\n",
       "      <td>1.816</td>\n",
       "      <td>1.049</td>\n",
       "      <td>-0.695</td>\n",
       "      <td>-1.246</td>\n",
       "      <td>-0.252</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.042</td>\n",
       "      <td>-0.338</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-0.313</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>1.056</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.341</td>\n",
       "      <td>0.856</td>\n",
       "      <td>4.215</td>\n",
       "      <td>0.207</td>\n",
       "      <td>-2.637</td>\n",
       "      <td>-3.969</td>\n",
       "      <td>-4.036</td>\n",
       "      <td>-4.312</td>\n",
       "      <td>-1.407</td>\n",
       "      <td>-3.851</td>\n",
       "      <td>0.464</td>\n",
       "      <td>1.481</td>\n",
       "      <td>3.623</td>\n",
       "      <td>0.044</td>\n",
       "      <td>1.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>-0.085</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>-0.236</td>\n",
       "      <td>-0.472</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>-0.085</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>-0.236</td>\n",
       "      <td>-0.472</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.696</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>-0.431</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.464</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.541</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.325</td>\n",
       "      <td>-0.644</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>-0.296</td>\n",
       "      <td>-0.792</td>\n",
       "      <td>-0.852</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.387</td>\n",
       "      <td>-0.388</td>\n",
       "      <td>-0.906</td>\n",
       "      <td>-1.432</td>\n",
       "      <td>-1.477</td>\n",
       "      <td>-1.495</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.335</td>\n",
       "      <td>-0.387</td>\n",
       "      <td>-0.901</td>\n",
       "      <td>-1.432</td>\n",
       "      <td>-1.445</td>\n",
       "      <td>-1.488</td>\n",
       "      <td>-1.495</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.326</td>\n",
       "      <td>-1.326</td>\n",
       "      <td>-1.336</td>\n",
       "      <td>-1.340</td>\n",
       "      <td>-1.341</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.760</td>\n",
       "      <td>1.861</td>\n",
       "      <td>-0.322</td>\n",
       "      <td>-0.360</td>\n",
       "      <td>-0.991</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.124</td>\n",
       "      <td>-1.858</td>\n",
       "      <td>0.799</td>\n",
       "      <td>1.480</td>\n",
       "      <td>-1.504</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.291</td>\n",
       "      <td>-0.214</td>\n",
       "      <td>-0.021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t_entry_0  t_entry_1  t_entry_2  ...     e12    e13    e14\n",
       "seq                                   ...                      \n",
       "0        0.004     -0.036     -0.670  ...   0.247  0.125  0.048\n",
       "1        0.001     -0.016     -0.034  ...   0.183  0.052  0.008\n",
       "2       -0.042     -0.338        nan  ...   3.623  0.044  1.390\n",
       "3        0.048     -0.034     -0.051  ...   0.028 -0.037  0.034\n",
       "4        0.013     -0.387     -0.388  ...   0.291 -0.214 -0.021\n",
       "\n",
       "[5 rows x 620 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn_embedding = np.load(\"../input/eynembedding/train_lstm.npy\")\n",
    "test_embedding = np.load(\"../input/eynembedding/test_lstm.npy\")\n",
    "for e in range(len(trn_embedding[0])):\n",
    "    df_train_unstack[\"e{}\".format(e)] = trn_embedding[:,e]\n",
    "    df_test_unstack[\"e{}\".format(e)] = test_embedding[:,e]\n",
    "df_train_unstack.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "# pylint: disable = invalid-name, C0111\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error, log_loss\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, roc_auc_score\n",
    "\n",
    "def scoring_package(y_true, y_pred, plotting=False):\n",
    "    threshold_search = np.arange(0, 1., 0.01)\n",
    "    f1_arr = [f1_score(y_true, [k > threshold for k in y_pred]) for threshold in threshold_search]\n",
    "    precision_arr = [precision_score(y_true, [k > threshold for k in y_pred]) for threshold in threshold_search]\n",
    "    recall_arr = [recall_score(y_true, [k > threshold for k in y_pred]) for threshold in threshold_search]\n",
    "    \n",
    "    if plotting:\n",
    "        plt.figure(figsize=(24,3))\n",
    "        plt.plot(threshold_search, f1_arr)\n",
    "        plt.plot(threshold_search, precision_arr)\n",
    "        plt.plot(threshold_search, recall_arr)\n",
    "        plt.show()\n",
    "    \n",
    "    threshold = threshold_search[np.argmax(f1_arr)]\n",
    "    y_pred_class = np.array([k > threshold for k in y_pred])\n",
    "    f1 = f1_score(y_true, y_pred_class)\n",
    "    precision = precision_score(y_true, y_pred_class)\n",
    "    recall = recall_score(y_true, y_pred_class)\n",
    "    roc = roc_auc_score(y_true, y_pred)\n",
    "    return f1, precision, recall, roc, threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(66998,) (67065,)\n",
      "(16841,) (16674,)\n",
      "(38449,) (95614,)\n",
      "(9544,) (23971,)\n"
     ]
    }
   ],
   "source": [
    "df_train_values = df_train.values\n",
    "df_train_columns = list(df_train.columns)\n",
    "df_test_values = df_test.values\n",
    "df_test_columns = list(df_test.columns)\n",
    "train_last_is_stationary = np.argwhere(df_train_values[::21,df_train_columns.index(\"dur\")] == 0)[:,0]\n",
    "train_last_not_stationary = np.argwhere(df_train_values[::21,df_train_columns.index(\"dur\")] != 0)[:,0]\n",
    "test_last_is_stationary = np.argwhere(df_test_values[::21,df_test_columns.index(\"dur\")] == 0)[:,0]\n",
    "test_last_not_stationary = np.argwhere(df_test_values[::21,df_test_columns.index(\"dur\")] != 0)[:,0]\n",
    "print(train_last_is_stationary.shape, train_last_not_stationary.shape)\n",
    "print(test_last_is_stationary.shape, test_last_not_stationary.shape)\n",
    "\n",
    "train_last_seen_is_inside = np.argwhere(df_train_values[::21,df_train_columns.index(\"entry_in\")] == 1)[:,0]\n",
    "train_last_seen_not_inside = np.argwhere(df_train_values[::21,df_train_columns.index(\"entry_in\")] == 0)[:,0]\n",
    "test_last_seen_is_inside = np.argwhere(df_test_values[::21,df_test_columns.index(\"entry_in\")] == 1)[:,0]\n",
    "test_last_seen_not_inside = np.argwhere(df_test_values[::21,df_test_columns.index(\"entry_in\")] == 0)[:,0]\n",
    "print(train_last_seen_is_inside.shape, train_last_seen_not_inside.shape)\n",
    "print(test_last_seen_is_inside.shape, test_last_seen_not_inside.shape)\n",
    "\n",
    "y_pred_full = np.zeros(np.shape(train_targets_inside))\n",
    "y_pred_full[np.intersect1d(train_last_is_stationary, train_last_seen_is_inside)] = 1\n",
    "y_pred_full[np.intersect1d(train_last_is_stationary, train_last_seen_not_inside)] = 0\n",
    "# print('The F1-PC-RC-ROC of full prediction is: {:.5f}-{:.5f}-{:.5f}-{:.5f} at threshold {:.3f}'\n",
    "#       .format(*scoring_package(train_targets_inside, y_pred_full)))\n",
    "test_preds = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify your configurations as a dict\n",
    "params = {\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': 'binary_logloss',\n",
    "    'num_leaves': 63,\n",
    "    'learning_rate': 0.05,  # dynamic one below\n",
    "    'feature_fraction': 0.9,\n",
    "    'bagging_fraction': 0.9,\n",
    "    'bagging_freq': 5,\n",
    "    'verbose': 0,\n",
    "    'lambda': 0.1,\n",
    "    'num_threads': 4,\n",
    "    'seed' : 42,\n",
    "#     'histogram_pool_size' : 2048  # to restrict memory usage\n",
    "#     'boost_from_average' : False  # as per warning message\n",
    "}\n",
    "num_boost_round = 3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training fold 0, hash: 847\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.12059\tvalid_1's binary_logloss: 0.133055\n",
      "[100]\ttraining's binary_logloss: 0.100946\tvalid_1's binary_logloss: 0.129155\n",
      "Early stopping, best iteration is:\n",
      "[104]\ttraining's binary_logloss: 0.0998953\tvalid_1's binary_logloss: 0.129064\n",
      "The F1-PC-RC-ROC score of fold 0 is: 0.89935-0.90347-0.89527-0.98637 at threshold 0.430\n",
      "Training fold 1, hash: 662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.119067\tvalid_1's binary_logloss: 0.144333\n",
      "[100]\ttraining's binary_logloss: 0.0997482\tvalid_1's binary_logloss: 0.141947\n",
      "Early stopping, best iteration is:\n",
      "[93]\ttraining's binary_logloss: 0.101595\tvalid_1's binary_logloss: 0.141732\n",
      "The F1-PC-RC-ROC score of fold 1 is: 0.88784-0.92831-0.85074-0.98321 at threshold 0.550\n",
      "Training fold 2, hash: 437\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.119328\tvalid_1's binary_logloss: 0.143667\n",
      "[100]\ttraining's binary_logloss: 0.099922\tvalid_1's binary_logloss: 0.140525\n",
      "Early stopping, best iteration is:\n",
      "[91]\ttraining's binary_logloss: 0.102315\tvalid_1's binary_logloss: 0.140376\n",
      "The F1-PC-RC-ROC score of fold 2 is: 0.89286-0.88669-0.89912-0.98396 at threshold 0.350\n",
      "Training fold 3, hash: 804\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.12024\tvalid_1's binary_logloss: 0.138931\n",
      "[100]\ttraining's binary_logloss: 0.10076\tvalid_1's binary_logloss: 0.136343\n",
      "Early stopping, best iteration is:\n",
      "[93]\ttraining's binary_logloss: 0.102572\tvalid_1's binary_logloss: 0.136158\n",
      "The F1-PC-RC-ROC score of fold 3 is: 0.89325-0.91206-0.87521-0.98457 at threshold 0.480\n",
      "Training fold 4, hash: 689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.119694\tvalid_1's binary_logloss: 0.141624\n",
      "[100]\ttraining's binary_logloss: 0.100245\tvalid_1's binary_logloss: 0.13778\n",
      "Early stopping, best iteration is:\n",
      "[108]\ttraining's binary_logloss: 0.0983936\tvalid_1's binary_logloss: 0.13762\n",
      "The F1-PC-RC-ROC score of fold 4 is: 0.89045-0.89168-0.88922-0.98445 at threshold 0.390\n",
      "Training fold 5, hash: 351\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.119023\tvalid_1's binary_logloss: 0.144531\n",
      "[100]\ttraining's binary_logloss: 0.0998593\tvalid_1's binary_logloss: 0.141314\n",
      "Early stopping, best iteration is:\n",
      "[102]\ttraining's binary_logloss: 0.0994365\tvalid_1's binary_logloss: 0.141272\n",
      "The F1-PC-RC-ROC score of fold 5 is: 0.89068-0.90262-0.87905-0.98347 at threshold 0.430\n",
      "Training fold 6, hash: 284\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.119601\tvalid_1's binary_logloss: 0.141736\n",
      "[100]\ttraining's binary_logloss: 0.100299\tvalid_1's binary_logloss: 0.138715\n",
      "Early stopping, best iteration is:\n",
      "[112]\ttraining's binary_logloss: 0.0974693\tvalid_1's binary_logloss: 0.138577\n",
      "The F1-PC-RC-ROC score of fold 6 is: 0.88746-0.90535-0.87026-0.98425 at threshold 0.470\n",
      "Training fold 7, hash: 456\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.118909\tvalid_1's binary_logloss: 0.14575\n",
      "[100]\ttraining's binary_logloss: 0.0993728\tvalid_1's binary_logloss: 0.142928\n",
      "Early stopping, best iteration is:\n",
      "[123]\ttraining's binary_logloss: 0.0943716\tvalid_1's binary_logloss: 0.142502\n",
      "The F1-PC-RC-ROC score of fold 7 is: 0.88727-0.90202-0.87301-0.98316 at threshold 0.450\n",
      "Training fold 8, hash: 920\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.120172\tvalid_1's binary_logloss: 0.136761\n",
      "[100]\ttraining's binary_logloss: 0.100464\tvalid_1's binary_logloss: 0.132959\n",
      "Early stopping, best iteration is:\n",
      "[104]\ttraining's binary_logloss: 0.0994667\tvalid_1's binary_logloss: 0.132854\n",
      "The F1-PC-RC-ROC score of fold 8 is: 0.89299-0.91542-0.87163-0.98547 at threshold 0.470\n",
      "Training fold 9, hash: 869\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:762: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds.\n",
      "[50]\ttraining's binary_logloss: 0.119431\tvalid_1's binary_logloss: 0.141752\n",
      "[100]\ttraining's binary_logloss: 0.100169\tvalid_1's binary_logloss: 0.138269\n",
      "Early stopping, best iteration is:\n",
      "[118]\ttraining's binary_logloss: 0.0960903\tvalid_1's binary_logloss: 0.137947\n",
      "The F1-PC-RC-ROC score of fold 9 is: 0.88990-0.91715-0.86421-0.98418 at threshold 0.460\n"
     ]
    }
   ],
   "source": [
    "# inputs: train_pd, train_targets_inside, and test_pd\n",
    "# probably try https://lightgbm.readthedocs.io/en/latest/Python-API.html#lightgbm.cv, but cluster complications\n",
    "for fold_num, (trn_index, val_index) in enumerate(zip(trn_index_list, val_index_list)):\n",
    "    print(\"Training fold {}, hash: {}\".format(fold_num, np.sum(val_index)%999))\n",
    "    \n",
    "    x_trn = df_train_unstack.iloc[trn_index]\n",
    "    x_val = df_train_unstack.iloc[val_index]\n",
    "    y_trn = pd.DataFrame(train_targets_inside).astype('bool').iloc[trn_index]\n",
    "    y_val = pd.DataFrame(train_targets_inside).astype('bool').iloc[val_index]\n",
    "    \n",
    "    # create dataset for lightgbm\n",
    "    lgb_train = lgb.Dataset(x_trn, y_trn)\n",
    "    lgb_eval = lgb.Dataset(x_val, y_val, reference=lgb_train)\n",
    "    \n",
    "    gbm = lgb.train(params,\n",
    "                    lgb_train,\n",
    "                    valid_sets=[lgb_train, lgb_eval],\n",
    "                    num_boost_round=num_boost_round,\n",
    "                    early_stopping_rounds=25,\n",
    "                    learning_rates=lambda iter: 0.1 * (0.995 ** iter),\n",
    "                    verbose_eval=50)\n",
    "\n",
    "    # eval\n",
    "    y_pred = gbm.predict(x_val, num_iteration=gbm.best_iteration)\n",
    "    print('The F1-PC-RC-ROC score of fold {} is: {:.5f}-{:.5f}-{:.5f}-{:.5f} at threshold {:.3f}'\n",
    "          .format(fold_num, *scoring_package(y_val, y_pred)))\n",
    "    y_pred_full[val_index] = y_pred\n",
    "    \n",
    "    # testing\n",
    "    test_pred = gbm.predict(df_test_unstack, num_iteration=gbm.best_iteration)\n",
    "    test_preds.append(test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The F1-PC-RC-ROC of full prediction is: 0.89001-0.90852-0.87224-0.98429 at threshold 0.460\n"
     ]
    }
   ],
   "source": [
    "scoring_results = scoring_package(train_targets_inside, y_pred_full, plotting=True)\n",
    "print('The F1-PC-RC-ROC of full prediction is: {:.5f}-{:.5f}-{:.5f}-{:.5f} at threshold {:.3f}'\n",
    "      .format(*scoring_results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (30,5))\n",
    "for i in range(gbm.num_trees())[:2]:\n",
    "    plt.plot([gbm.get_leaf_output(i,j) for j in range(gbm.params['num_leaves'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# very blur cannot see, don't know how to plot just a small part of it\n",
    "# lags when asked to plot everything\n",
    "# ax = lgb.plot_tree(gbm, tree_index=0, figsize=(30, 10), \n",
    "#               show_info=['split_gain', 'internal_value', 'internal_count', 'leaf_count'])\n",
    "\n",
    "# doesn't work on Kaggle\n",
    "# graph = lgb.create_tree_digraph(gbm, tree_index=0, name='Tree54')\n",
    "# graph.render(view=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_features = len(gbm.feature_importance())\n",
    "plt.figure(figsize=(100,3))\n",
    "plt.bar(np.arange(num_features), gbm.feature_importance(importance_type='split'), align='center', width=0.4)\n",
    "plt.xticks(np.arange(0,num_features,21), gbm.feature_name()[::21], rotation='vertical', fontsize=20)\n",
    "plt.title('Feature Importance - Split')\n",
    "plt.yscale('log') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(100,3))\n",
    "plt.bar(np.arange(num_features), gbm.feature_importance(importance_type='gain'), align='center', width=0.4)\n",
    "plt.xticks(np.arange(0,num_features,21), gbm.feature_name()[::21], rotation='vertical', fontsize=20)\n",
    "plt.title('Feature Importance - Gain')\n",
    "plt.yscale('log') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9951151729112788, 0.9947909850156448, 0.9962024013662732, 0.9966943057429614, 0.9943111618252496, 0.9954163378804939, 0.9950389163686705, 0.9952393542855197, 0.9961203479520033, 0.9963511101623577]\n",
      "[0.5413926825861083, 0.33253292102283444, 0.5809931702284716, 0.7299946229101183, 0.6588920283337872, 0.6314855856526359, 0.4206875390391561, 0.6475909826823661, 0.5554743225691164, 0.4127731605445966]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([7.03192511e-03, 3.45729994e-03, 9.95115173e-01, ...,\n",
       "        1.79041168e-03, 5.94756865e-02, 9.39074197e-04]),\n",
       " array([0.01032718, 0.00521852, 0.99479099, ..., 0.00230081, 0.01156682,\n",
       "        0.00113209]),\n",
       " array([0.00701802, 0.00444367, 0.9962024 , ..., 0.00212679, 0.05761751,\n",
       "        0.00158387]),\n",
       " array([6.83438619e-03, 3.92997675e-03, 9.96694306e-01, ...,\n",
       "        1.78288991e-03, 3.92845863e-02, 9.90686815e-04]),\n",
       " array([0.00895426, 0.00355128, 0.99431116, ..., 0.00280552, 0.04422036,\n",
       "        0.00148237]),\n",
       " array([0.0061719 , 0.00363834, 0.99541634, ..., 0.00172217, 0.02200799,\n",
       "        0.00116359]),\n",
       " array([0.00451007, 0.00513486, 0.99503892, ..., 0.00193352, 0.01936588,\n",
       "        0.00125531]),\n",
       " array([0.00579989, 0.00454218, 0.99523935, ..., 0.00237791, 0.01143469,\n",
       "        0.00121839]),\n",
       " array([0.00710384, 0.00545247, 0.99612035, ..., 0.00199993, 0.02475987,\n",
       "        0.00133683]),\n",
       " array([0.00501119, 0.00542137, 0.99635111, ..., 0.00236351, 0.02970859,\n",
       "        0.00166595])]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print([i[2] for i in test_preds])\n",
    "print([i[-4] for i in test_preds])\n",
    "test_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"train_preds\", y_pred_full)\n",
    "np.save(\"test_preds\", test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8722\n"
     ]
    }
   ],
   "source": [
    "test_preds_mean = np.mean(test_preds, axis=0)\n",
    "test_preds_mean = np.array([1 if pred>scoring_results[-1] else 0 for pred in test_preds_mean])\n",
    "print(np.sum(test_preds_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8722\n"
     ]
    }
   ],
   "source": [
    "test_preds_mean[np.intersect1d(test_last_is_stationary, test_last_seen_is_inside)] = 1.\n",
    "test_preds_mean[np.intersect1d(test_last_is_stationary, test_last_seen_not_inside)] = 0.\n",
    "print(np.sum(test_preds_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submit = pd.read_csv(\"../input/ey-nextwave/data_test/data_test.csv\")\n",
    "df_submit = df_submit[df_submit['x_exit'].isnull()]\n",
    "df_submit = df_submit[['trajectory_id']].copy()\n",
    "df_submit = df_submit.rename(columns = {'trajectory_id':'id'})\n",
    "df_submit['target'] = test_preds_mean\n",
    "df_submit.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>traj_00032f51796fd5437b238e3a9823d13d_31_5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>traj_000479418b5561ab694a2870cc04fd43_25_10</td>\n",
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       "      <th>2</th>\n",
       "      <td>traj_000506a39775e5bca661ac80e3f466eb_29_5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>traj_00063a4f6c12e1e4de7d876580620667_3_4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            id  target\n",
       "0   traj_00032f51796fd5437b238e3a9823d13d_31_5       0\n",
       "1  traj_000479418b5561ab694a2870cc04fd43_25_10       0\n",
       "2   traj_000506a39775e5bca661ac80e3f466eb_29_5       1\n",
       "3    traj_0005401ceddaf27a9b7f0d42ef1fbe95_1_4       0\n",
       "4    traj_00063a4f6c12e1e4de7d876580620667_3_4       0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submit = pd.read_csv(\"submission.csv\")\n",
    "df_submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "    <tr>\n",
       "      <th>33512</th>\n",
       "      <td>traj_fff813b56230c2f026f783f5b9f9ca90_19_0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33513</th>\n",
       "      <td>traj_fff9400843a88c3bfe52e7ce8bf97316_19_17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33514</th>\n",
       "      <td>traj_fff9552047b095e8242b4913f3289a26_25_7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                id  target\n",
       "33510  traj_ffe98f6e0adf12f9c7b51c4e9607a87a_15_13       0\n",
       "33511   traj_fff607ecd3f8d3dcb65791e8b4c22a5f_3_25       1\n",
       "33512   traj_fff813b56230c2f026f783f5b9f9ca90_19_0       0\n",
       "33513  traj_fff9400843a88c3bfe52e7ce8bf97316_19_17       0\n",
       "33514   traj_fff9552047b095e8242b4913f3289a26_25_7       0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submit.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8900107983788408, 0.9085234918544393, 0.8722374931280924, 0.9842948446455445, 0.46)\n",
      "{'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'binary_logloss', 'num_leaves': 63, 'learning_rate': 0.05, 'feature_fraction': 0.9, 'bagging_fraction': 0.9, 'bagging_freq': 5, 'verbose': 0, 'lambda': 0.1, 'num_threads': 4, 'seed': 42}\n"
     ]
    }
   ],
   "source": [
    "print(scoring_results)\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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