solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-09T22:14:19.742356Z",
     "iopub.status.busy": "2020-10-09T22:14:19.741920Z",
     "iopub.status.idle": "2020-10-09T22:14:23.548323Z",
     "shell.execute_reply": "2020-10-09T22:14:23.547367Z",
     "shell.execute_reply.started": "2020-10-09T22:14:19.742318Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: plotly in /home/mnostrouhov/.local/lib/python3.6/site-packages (4.11.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from plotly) (1.14.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /home/mnostrouhov/.local/lib/python3.6/site-packages (from plotly) (1.3.3)\n",
      "\u001b[33mWARNING: You are using pip version 20.0.2; however, version 20.2.3 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# !pip install nb_black --user\n",
    "# !pip install xgboost --user\n",
    "# !pip install lightgbm --user\n",
    "# !pip install neptune-client --user\n",
    "# !pip install catboost --user\n",
    "# !pip install neptune-contrib\n",
    "# !pip install plotly -- user\n",
    "# !pip install --upgrade tables --user\n",
    "# !pip install optuna --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-09T22:56:05.364950Z",
     "iopub.status.busy": "2020-10-09T22:56:05.364561Z",
     "iopub.status.idle": "2020-10-09T22:57:09.649532Z",
     "shell.execute_reply": "2020-10-09T22:57:09.648290Z",
     "shell.execute_reply.started": "2020-10-09T22:56:05.364916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building jupyterlab assets (build:prod:minimize)\n"
     ]
    }
   ],
   "source": [
    "!jupyter labextension install jupyterlab-plotly@4.11.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:34:57.507637Z",
     "iopub.status.busy": "2020-10-19T16:34:57.506985Z",
     "iopub.status.idle": "2020-10-19T16:34:58.993586Z",
     "shell.execute_reply": "2020-10-19T16:34:58.992452Z",
     "shell.execute_reply.started": "2020-10-19T16:34:57.507575Z"
    }
   },
   "outputs": [],
   "source": [
    "import optuna\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import catboost as cb\n",
    "from sklearn.model_selection import (\n",
    "    train_test_split,\n",
    "    KFold,\n",
    "    cross_validate,\n",
    "    cross_val_score,\n",
    "    cross_val_predict,\n",
    ")\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# from plotly import tools\n",
    "import neptune\n",
    "from neptunecontrib.api import log_chart\n",
    "\n",
    "import scoring\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from tqdm.auto import tqdm as tqdm_auto\n",
    "\n",
    "tqdm_auto.pandas()\n",
    "from copy import deepcopy\n",
    "import os\n",
    "import re\n",
    "import pickle\n",
    "from itertools import chain\n",
    "from collections import Counter\n",
    "\n",
    "import time\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "%load_ext lab_black"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NEPTUNE logger "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:35:02.619429Z",
     "iopub.status.busy": "2020-10-19T16:35:02.618758Z",
     "iopub.status.idle": "2020-10-19T16:35:02.632104Z",
     "shell.execute_reply": "2020-10-19T16:35:02.631095Z",
     "shell.execute_reply.started": "2020-10-19T16:35:02.619365Z"
    }
   },
   "outputs": [],
   "source": [
    "os.environ[\n",
    "    \"NEPTUNE_API_TOKEN\"\n",
    "] = #token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:35:03.010265Z",
     "iopub.status.busy": "2020-10-19T16:35:03.009619Z",
     "iopub.status.idle": "2020-10-19T16:35:07.374196Z",
     "shell.execute_reply": "2020-10-19T16:35:07.372953Z",
     "shell.execute_reply.started": "2020-10-19T16:35:03.010208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Project(nrjjj/muon)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neptune.init(project_qualified_name=\"nrjjj/muon\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = neptune.create_experiment(\n",
    "    name=\"moar_features\",\n",
    "    upload_source_files=[\"sandbox.ipynb\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Чтение датки "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:35:13.108852Z",
     "iopub.status.busy": "2020-10-19T16:35:13.108205Z",
     "iopub.status.idle": "2020-10-19T16:35:13.117612Z",
     "shell.execute_reply": "2020-10-19T16:35:13.116664Z",
     "shell.execute_reply.started": "2020-10-19T16:35:13.108791Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_PATH = \"/data/mnostrouhov/pds/muon/\"\n",
    "GLOBAL_SEED = 333"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:35:17.727267Z",
     "iopub.status.busy": "2020-10-19T16:35:17.726643Z",
     "iopub.status.idle": "2020-10-19T16:35:17.750316Z",
     "shell.execute_reply": "2020-10-19T16:35:17.749008Z",
     "shell.execute_reply.started": "2020-10-19T16:35:17.727213Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_data_csv(path):\n",
    "    train = pd.concat(\n",
    "        [pd.read_hdf(os.path.join(path, f\"train_part_{i}_v2.hdf\")) for i in (1, 2)],\n",
    "        axis=0,\n",
    "        ignore_index=True,\n",
    "    )\n",
    "    test = pd.read_hdf(os.path.join(path, \"test_public_v2.hdf\"))\n",
    "    private_test = pd.read_hdf(os.path.join(path, \"test_private_v2_track_1.hdf\"))\n",
    "    return train, test, private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:35:17.752167Z",
     "iopub.status.busy": "2020-10-19T16:35:17.751859Z",
     "iopub.status.idle": "2020-10-19T16:37:36.091051Z",
     "shell.execute_reply": "2020-10-19T16:37:36.089890Z",
     "shell.execute_reply.started": "2020-10-19T16:35:17.752137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size:(5445705, 79)\n",
      "Test size:(726095, 74)\n"
     ]
    }
   ],
   "source": [
    "df_train, df_test, df_private_test = load_data_csv(DATA_PATH)\n",
    "print(f\"Train size:{df_train.shape}\")\n",
    "print(f\"Test size:{df_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:37:36.093374Z",
     "iopub.status.busy": "2020-10-19T16:37:36.092957Z",
     "iopub.status.idle": "2020-10-19T16:37:46.302626Z",
     "shell.execute_reply": "2020-10-19T16:37:46.301522Z",
     "shell.execute_reply.started": "2020-10-19T16:37:36.093343Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train = df_train.rename(columns=lambda x: re.sub(\"[^A-Za-z0-9_]+\", \"\", x))\n",
    "df_test = df_test.rename(columns=lambda x: re.sub(\"[^A-Za-z0-9_]+\", \"\", x))\n",
    "df_private_test = df_private_test.rename(\n",
    "    columns=lambda x: re.sub(\"[^A-Za-z0-9_]+\", \"\", x)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:38:00.899655Z",
     "iopub.status.busy": "2020-10-19T16:38:00.899037Z",
     "iopub.status.idle": "2020-10-19T16:38:00.976593Z",
     "shell.execute_reply": "2020-10-19T16:38:00.975664Z",
     "shell.execute_reply.started": "2020-10-19T16:38:00.899600Z"
    }
   },
   "outputs": [
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       "      <th>FOI_hits_DT</th>\n",
       "      <th>FOI_hits_S</th>\n",
       "      <th>PT</th>\n",
       "      <th>P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>2.127273</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[-1849.3373, -1826.5162, -1833.7968, -1982.797...</td>\n",
       "      <td>[15396.441, 16315.722, 16404.695, 16521.16, 17...</td>\n",
       "      <td>[12.75, 13.75, 13.75, 13.75, 59.0, 63.0]</td>\n",
       "      <td>[63.078957, 68.078926, 68.078926, 68.078926, 7...</td>\n",
       "      <td>[33.727364, 33.74537, 33.74537, 33.74537, 33.7...</td>\n",
       "      <td>[6, 13, 2, 2, 7, 3]</td>\n",
       "      <td>[-1, 0, -2, -1, 0, 0]</td>\n",
       "      <td>[0, 1, 1, 1, 2, 3]</td>\n",
       "      <td>1174.484530</td>\n",
       "      <td>6884.502272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2.697675</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[-383.5468, -222.24156, -240.82657, -240.82657...</td>\n",
       "      <td>[15401.72, 15117.299, 16321.432, 16321.432, 17...</td>\n",
       "      <td>[6.375, 6.375, 6.875, 82.5, 29.5, 29.5, 31.5]</td>\n",
       "      <td>[126.27855, 31.479162, 33.979145, 33.979145, 3...</td>\n",
       "      <td>[33.95495, 33.613575, 33.622578, 33.622578, 33...</td>\n",
       "      <td>[6, 4, 5, 9, 12, 6, 3]</td>\n",
       "      <td>[6, 0, -2, 9, -1, -1, 0]</td>\n",
       "      <td>[0, 0, 1, 1, 2, 2, 3]</td>\n",
       "      <td>4344.639775</td>\n",
       "      <td>48769.526891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.058824</td>\n",
       "      <td>1.210526</td>\n",
       "      <td>1.344828</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[-1628.1324, -1758.3168, -1887.1799, -2014.6083]</td>\n",
       "      <td>[15112.236, 16315.968, 17508.303, 18715.945]</td>\n",
       "      <td>[25.5, 27.5, 118.0, 126.0]</td>\n",
       "      <td>[126.27855, 136.27849, 146.27841, 156.27835]</td>\n",
       "      <td>[33.95495, 33.99096, 34.026966, 34.062977]</td>\n",
       "      <td>[3, 10, 7, 7]</td>\n",
       "      <td>[-1, 0, 0, 0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>4109.805750</td>\n",
       "      <td>20296.048412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141</td>\n",
       "      <td>43</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>4.609929</td>\n",
       "      <td>2.744186</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[372.85458, 537.1497, 483.45386, 483.45386, 52...</td>\n",
       "      <td>[15119.441, 15321.034, 15119.84, 15119.84, 163...</td>\n",
       "      <td>[3.2083333, 76.5, 3.2083333, 3.2083333, 3.4583...</td>\n",
       "      <td>[126.27855, 31.479162, 15.679264, 15.679264, 1...</td>\n",
       "      <td>[33.95495, 33.613575, 33.55668, 33.55668, 33.5...</td>\n",
       "      <td>[14, 13, 3, 4, 5, 6, 8, 7, 3]</td>\n",
       "      <td>[14, 13, 1, 2, 1, 0, 2, 7, 0]</td>\n",
       "      <td>[0, 0, 0, 0, 1, 2, 2, 2, 3]</td>\n",
       "      <td>940.242439</td>\n",
       "      <td>26062.046122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[264.6528, 367.2695, 284.41754, 305.50333, 328...</td>\n",
       "      <td>[15203.053, 16323.622, 16407.324, 17600.2, 188...</td>\n",
       "      <td>[3.2083333, 82.5, 3.4583333, 14.833333, 15.833...</td>\n",
       "      <td>[15.679264, 33.979145, 16.929256, 18.179249, 1...</td>\n",
       "      <td>[33.55668, 33.622578, 33.56118, 33.56568, 33.5...</td>\n",
       "      <td>[8, 5, 7, 5, 2, 13, 5, 6]</td>\n",
       "      <td>[1, 5, 0, 5, 2, 13, 5, 6]</td>\n",
       "      <td>[0, 1, 1, 2, 3, 3, 3, 3]</td>\n",
       "      <td>1386.377851</td>\n",
       "      <td>59872.343332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ncl0  ncl1  ncl2  ncl3   avg_cs0   avg_cs1   avg_cs2   avg_cs3  ndof  \\\n",
       "0    55    12    10    15  2.127273  1.166667  1.500000  1.600000     8   \n",
       "1    43    18    12    15  2.697675  1.111111  1.333333  1.600000     8   \n",
       "2    90    34    19    29  1.800000  2.058824  1.210526  1.344828     8   \n",
       "3   141    43    15    16  4.609929  2.744186  1.466667  1.125000     8   \n",
       "4    22    18     8     9  1.636364  2.444444  1.000000  1.555556     8   \n",
       "\n",
       "   MatchedHit_TYPE0  ...                                         FOI_hits_Y  \\\n",
       "0                 2  ...  [-1849.3373, -1826.5162, -1833.7968, -1982.797...   \n",
       "1                 2  ...  [-383.5468, -222.24156, -240.82657, -240.82657...   \n",
       "2                 2  ...   [-1628.1324, -1758.3168, -1887.1799, -2014.6083]   \n",
       "3                 2  ...  [372.85458, 537.1497, 483.45386, 483.45386, 52...   \n",
       "4                 2  ...  [264.6528, 367.2695, 284.41754, 305.50333, 328...   \n",
       "\n",
       "                                          FOI_hits_Z  \\\n",
       "0  [15396.441, 16315.722, 16404.695, 16521.16, 17...   \n",
       "1  [15401.72, 15117.299, 16321.432, 16321.432, 17...   \n",
       "2       [15112.236, 16315.968, 17508.303, 18715.945]   \n",
       "3  [15119.441, 15321.034, 15119.84, 15119.84, 163...   \n",
       "4  [15203.053, 16323.622, 16407.324, 17600.2, 188...   \n",
       "\n",
       "                                         FOI_hits_DX  \\\n",
       "0           [12.75, 13.75, 13.75, 13.75, 59.0, 63.0]   \n",
       "1      [6.375, 6.375, 6.875, 82.5, 29.5, 29.5, 31.5]   \n",
       "2                         [25.5, 27.5, 118.0, 126.0]   \n",
       "3  [3.2083333, 76.5, 3.2083333, 3.2083333, 3.4583...   \n",
       "4  [3.2083333, 82.5, 3.4583333, 14.833333, 15.833...   \n",
       "\n",
       "                                         FOI_hits_DY  \\\n",
       "0  [63.078957, 68.078926, 68.078926, 68.078926, 7...   \n",
       "1  [126.27855, 31.479162, 33.979145, 33.979145, 3...   \n",
       "2       [126.27855, 136.27849, 146.27841, 156.27835]   \n",
       "3  [126.27855, 31.479162, 15.679264, 15.679264, 1...   \n",
       "4  [15.679264, 33.979145, 16.929256, 18.179249, 1...   \n",
       "\n",
       "                                         FOI_hits_DZ  \\\n",
       "0  [33.727364, 33.74537, 33.74537, 33.74537, 33.7...   \n",
       "1  [33.95495, 33.613575, 33.622578, 33.622578, 33...   \n",
       "2         [33.95495, 33.99096, 34.026966, 34.062977]   \n",
       "3  [33.95495, 33.613575, 33.55668, 33.55668, 33.5...   \n",
       "4  [33.55668, 33.622578, 33.56118, 33.56568, 33.5...   \n",
       "\n",
       "                      FOI_hits_T                    FOI_hits_DT  \\\n",
       "0            [6, 13, 2, 2, 7, 3]          [-1, 0, -2, -1, 0, 0]   \n",
       "1         [6, 4, 5, 9, 12, 6, 3]       [6, 0, -2, 9, -1, -1, 0]   \n",
       "2                  [3, 10, 7, 7]                  [-1, 0, 0, 0]   \n",
       "3  [14, 13, 3, 4, 5, 6, 8, 7, 3]  [14, 13, 1, 2, 1, 0, 2, 7, 0]   \n",
       "4      [8, 5, 7, 5, 2, 13, 5, 6]      [1, 5, 0, 5, 2, 13, 5, 6]   \n",
       "\n",
       "                    FOI_hits_S           PT             P  \n",
       "0           [0, 1, 1, 1, 2, 3]  1174.484530   6884.502272  \n",
       "1        [0, 0, 1, 1, 2, 2, 3]  4344.639775  48769.526891  \n",
       "2                 [0, 1, 2, 3]  4109.805750  20296.048412  \n",
       "3  [0, 0, 0, 0, 1, 2, 2, 2, 3]   940.242439  26062.046122  \n",
       "4     [0, 1, 1, 2, 3, 3, 3, 3]  1386.377851  59872.343332  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_train.head())\n",
    "display(df_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:38:26.758078Z",
     "iopub.status.busy": "2020-10-19T16:38:26.757474Z",
     "iopub.status.idle": "2020-10-19T16:38:35.935226Z",
     "shell.execute_reply": "2020-10-19T16:38:35.934061Z",
     "shell.execute_reply.started": "2020-10-19T16:38:26.758024Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train, df_valid = train_test_split(\n",
    "    df_train,\n",
    "    train_size=0.4,\n",
    "    test_size=0.05,\n",
    "    random_state=GLOBAL_SEED,\n",
    "    shuffle=True,\n",
    "    stratify=df_train[\"label\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:38:35.937009Z",
     "iopub.status.busy": "2020-10-19T16:38:35.936738Z",
     "iopub.status.idle": "2020-10-19T16:38:35.988212Z",
     "shell.execute_reply": "2020-10-19T16:38:35.987484Z",
     "shell.execute_reply.started": "2020-10-19T16:38:35.936983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ncl0</th>\n",
       "      <th>ncl1</th>\n",
       "      <th>ncl2</th>\n",
       "      <th>ncl3</th>\n",
       "      <th>avg_cs0</th>\n",
       "      <th>avg_cs1</th>\n",
       "      <th>avg_cs2</th>\n",
       "      <th>avg_cs3</th>\n",
       "      <th>ndof</th>\n",
       "      <th>MatchedHit_TYPE0</th>\n",
       "      <th>...</th>\n",
       "      <th>FOI_hits_T</th>\n",
       "      <th>FOI_hits_DT</th>\n",
       "      <th>FOI_hits_S</th>\n",
       "      <th>PT</th>\n",
       "      <th>P</th>\n",
       "      <th>sWeight</th>\n",
       "      <th>particle_type</th>\n",
       "      <th>label</th>\n",
       "      <th>kinWeight</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5271239</th>\n",
       "      <td>13</td>\n",
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       "      <td>2209.385880</td>\n",
       "      <td>38006.332407</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>452195</th>\n",
       "      <td>71</td>\n",
       "      <td>17</td>\n",
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       "      <td>8</td>\n",
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       "      <td>[0, 1, 1, 2, 3, 3]</td>\n",
       "      <td>4957.291019</td>\n",
       "      <td>137046.777545</td>\n",
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       "      <td>3.00000</td>\n",
       "      <td>3.658273</td>\n",
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       "    <tr>\n",
       "      <th>5412008</th>\n",
       "      <td>81</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2.876543</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[10, 11, 10, 11, 6, 6, 5, 9]</td>\n",
       "      <td>[3, 4, 0, 1, 1, 2, 1, 1]</td>\n",
       "      <td>[0, 0, 0, 0, 1, 2, 2, 3]</td>\n",
       "      <td>1212.343683</td>\n",
       "      <td>6240.248635</td>\n",
       "      <td>1.203003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.85646</td>\n",
       "      <td>2.233327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3773018</th>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[3, 9, 7, 8]</td>\n",
       "      <td>[0, 1, -1, -1]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>3059.933560</td>\n",
       "      <td>33736.286948</td>\n",
       "      <td>1.209923</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.85646</td>\n",
       "      <td>2.246173</td>\n",
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       "    <tr>\n",
       "      <th>3207395</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1.545455</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>8</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.85646</td>\n",
       "      <td>2.006296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ncl0  ncl1  ncl2  ncl3   avg_cs0   avg_cs1   avg_cs2   avg_cs3  ndof  \\\n",
       "5271239    13    13     6     5  2.076923  2.153846  1.333333  1.400000     8   \n",
       "452195     71    17    14     8  3.816901  2.352941  1.642857  1.000000     8   \n",
       "5412008    81    10     7     9  2.876543  1.400000  1.000000  1.222222     8   \n",
       "3773018    25     6     3     6  2.080000  1.000000  1.333333  1.000000     8   \n",
       "3207395    22     2     2     5  1.545455  1.500000  1.000000  1.200000     8   \n",
       "\n",
       "         MatchedHit_TYPE0  ...                    FOI_hits_T  \\\n",
       "5271239                 2  ...   [3, 5, 5, 1, 3, 3, 4, 3, 8]   \n",
       "452195                  2  ...            [6, 8, 9, 6, 6, 7]   \n",
       "5412008                 2  ...  [10, 11, 10, 11, 6, 6, 5, 9]   \n",
       "3773018                 2  ...                  [3, 9, 7, 8]   \n",
       "3207395                 2  ...               [4, 3, 3, 7, 5]   \n",
       "\n",
       "                              FOI_hits_DT                   FOI_hits_S  \\\n",
       "5271239  [-1, 5, -10, -14, 1, 1, 2, 0, 0]  [0, 0, 1, 1, 1, 1, 1, 2, 3]   \n",
       "452195                 [0, 0, 1, 6, 6, 7]           [0, 1, 1, 2, 3, 3]   \n",
       "5412008          [3, 4, 0, 1, 1, 2, 1, 1]     [0, 0, 0, 0, 1, 2, 2, 3]   \n",
       "3773018                    [0, 1, -1, -1]                 [0, 1, 2, 3]   \n",
       "3207395                  [0, -4, 0, 0, 0]              [0, 1, 1, 2, 3]   \n",
       "\n",
       "                  PT              P   sWeight  particle_type  label  \\\n",
       "5271239  2209.385880   38006.332407  1.052345              1      1   \n",
       "452195   4957.291019  137046.777545  1.219424              1      1   \n",
       "5412008  1212.343683    6240.248635  1.203003              1      1   \n",
       "3773018  3059.933560   33736.286948  1.209923              1      1   \n",
       "3207395  1539.810222   40739.699970  1.080711              1      1   \n",
       "\n",
       "         kinWeight    weight  \n",
       "5271239    1.85646  1.953636  \n",
       "452195     3.00000  3.658273  \n",
       "5412008    1.85646  2.233327  \n",
       "3773018    1.85646  2.246173  \n",
       "3207395    1.85646  2.006296  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>ncl0</th>\n",
       "      <th>ncl1</th>\n",
       "      <th>ncl2</th>\n",
       "      <th>ncl3</th>\n",
       "      <th>avg_cs0</th>\n",
       "      <th>avg_cs1</th>\n",
       "      <th>avg_cs2</th>\n",
       "      <th>avg_cs3</th>\n",
       "      <th>ndof</th>\n",
       "      <th>MatchedHit_TYPE0</th>\n",
       "      <th>...</th>\n",
       "      <th>FOI_hits_T</th>\n",
       "      <th>FOI_hits_DT</th>\n",
       "      <th>FOI_hits_S</th>\n",
       "      <th>PT</th>\n",
       "      <th>P</th>\n",
       "      <th>sWeight</th>\n",
       "      <th>particle_type</th>\n",
       "      <th>label</th>\n",
       "      <th>kinWeight</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4517692</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>2.885714</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[7, 7, 2, 6, 11, 6, 1, 5, 8, 4]</td>\n",
       "      <td>[0, 0, 2, 3, 8, 3, -2, 1, -1, 0]</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 1, 1, 2, 3]</td>\n",
       "      <td>1091.295038</td>\n",
       "      <td>15777.107432</td>\n",
       "      <td>-0.425461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.85646</td>\n",
       "      <td>-0.789851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134032</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.476190</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[10, 4, 5, 7, 11, 11, 4, 4, 6, 6, 15, 15]</td>\n",
       "      <td>[10, -2, -7, 1, 7, -1, 0, -8, -1, 5, 8, 14]</td>\n",
       "      <td>[0, 0, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3]</td>\n",
       "      <td>8724.773419</td>\n",
       "      <td>155013.606986</td>\n",
       "      <td>-0.115114</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.80650</td>\n",
       "      <td>-0.323069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2143828</th>\n",
       "      <td>51</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2.196079</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[5, 8, 1, 2, 6, 7, 7, 6, 6, 6, 4]</td>\n",
       "      <td>[5, 7, 0, 1, 0, -2, 0, -6, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 3]</td>\n",
       "      <td>2578.549906</td>\n",
       "      <td>45351.833096</td>\n",
       "      <td>-0.418960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.85646</td>\n",
       "      <td>-0.777783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2754845</th>\n",
       "      <td>68</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2.514706</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[1, 9, 9, 5, 2, 9, 6]</td>\n",
       "      <td>[1, 2, 9, 1, 0, 0, -1]</td>\n",
       "      <td>[0, 0, 0, 1, 2, 2, 3]</td>\n",
       "      <td>1267.846267</td>\n",
       "      <td>7957.915083</td>\n",
       "      <td>1.235896</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.85646</td>\n",
       "      <td>2.294392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4419965</th>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>1.766667</td>\n",
       "      <td>2.235294</td>\n",
       "      <td>1.826087</td>\n",
       "      <td>1.055556</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>[8, 6, 5, 5, 9, 9, 3, 5]</td>\n",
       "      <td>[0, 0, 0, -4, 4, 0, -1, 0]</td>\n",
       "      <td>[0, 1, 2, 2, 2, 2, 3, 3]</td>\n",
       "      <td>2493.213864</td>\n",
       "      <td>10350.507597</td>\n",
       "      <td>1.144913</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.85646</td>\n",
       "      <td>2.125485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ncl0  ncl1  ncl2  ncl3   avg_cs0   avg_cs1   avg_cs2   avg_cs3  ndof  \\\n",
       "4517692    35     6     8    11  2.885714  3.666667  1.375000  1.000000     8   \n",
       "134032     21     4     3     6  1.476190  1.250000  2.000000  2.500000     8   \n",
       "2143828    51    12     7    12  2.196079  4.666667  2.428571  1.333333     8   \n",
       "2754845    68     7     6    12  2.514706  1.428571  1.166667  1.416667     8   \n",
       "4419965    30    17    23    18  1.766667  2.235294  1.826087  1.055556     8   \n",
       "\n",
       "         MatchedHit_TYPE0  ...                                 FOI_hits_T  \\\n",
       "4517692                 2  ...            [7, 7, 2, 6, 11, 6, 1, 5, 8, 4]   \n",
       "134032                  2  ...  [10, 4, 5, 7, 11, 11, 4, 4, 6, 6, 15, 15]   \n",
       "2143828                 2  ...          [5, 8, 1, 2, 6, 7, 7, 6, 6, 6, 4]   \n",
       "2754845                 2  ...                      [1, 9, 9, 5, 2, 9, 6]   \n",
       "4419965                 2  ...                   [8, 6, 5, 5, 9, 9, 3, 5]   \n",
       "\n",
       "                                         FOI_hits_DT  \\\n",
       "4517692             [0, 0, 2, 3, 8, 3, -2, 1, -1, 0]   \n",
       "134032   [10, -2, -7, 1, 7, -1, 0, -8, -1, 5, 8, 14]   \n",
       "2143828          [5, 7, 0, 1, 0, -2, 0, -6, 0, 0, 0]   \n",
       "2754845                       [1, 2, 9, 1, 0, 0, -1]   \n",
       "4419965                   [0, 0, 0, -4, 4, 0, -1, 0]   \n",
       "\n",
       "                                   FOI_hits_S           PT              P  \\\n",
       "4517692        [0, 0, 1, 1, 1, 1, 1, 1, 2, 3]  1091.295038   15777.107432   \n",
       "134032   [0, 0, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3]  8724.773419  155013.606986   \n",
       "2143828     [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 3]  2578.549906   45351.833096   \n",
       "2754845                 [0, 0, 0, 1, 2, 2, 3]  1267.846267    7957.915083   \n",
       "4419965              [0, 1, 2, 2, 2, 2, 3, 3]  2493.213864   10350.507597   \n",
       "\n",
       "          sWeight  particle_type  label  kinWeight    weight  \n",
       "4517692 -0.425461              1      1    1.85646 -0.789851  \n",
       "134032  -0.115114              1      1    2.80650 -0.323069  \n",
       "2143828 -0.418960              1      1    1.85646 -0.777783  \n",
       "2754845  1.235896              1      1    1.85646  2.294392  \n",
       "4419965  1.144913              1      1    1.85646  2.125485  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_train.head())\n",
    "display(df_valid.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Фичи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:53.211113Z",
     "iopub.status.busy": "2020-10-19T16:39:53.210423Z",
     "iopub.status.idle": "2020-10-19T16:39:53.222594Z",
     "shell.execute_reply": "2020-10-19T16:39:53.221427Z",
     "shell.execute_reply.started": "2020-10-19T16:39:53.211052Z"
    }
   },
   "outputs": [],
   "source": [
    "TARGET_COLS = df_train.columns.difference(df_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:53.372475Z",
     "iopub.status.busy": "2020-10-19T16:39:53.371874Z",
     "iopub.status.idle": "2020-10-19T16:39:53.383039Z",
     "shell.execute_reply": "2020-10-19T16:39:53.382221Z",
     "shell.execute_reply.started": "2020-10-19T16:39:53.372422Z"
    }
   },
   "outputs": [],
   "source": [
    "FOI_COLS = df_train.columns[[\"FOI\" in col for col in df_train.columns]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:53.572358Z",
     "iopub.status.busy": "2020-10-19T16:39:53.571786Z",
     "iopub.status.idle": "2020-10-19T16:39:53.585397Z",
     "shell.execute_reply": "2020-10-19T16:39:53.584468Z",
     "shell.execute_reply.started": "2020-10-19T16:39:53.572305Z"
    }
   },
   "outputs": [],
   "source": [
    "BASIC_FEATURES = df_train.columns.difference(\n",
    "    list(FOI_COLS) + [\"id\"] + list(TARGET_COLS)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:53.886259Z",
     "iopub.status.busy": "2020-10-19T16:39:53.885655Z",
     "iopub.status.idle": "2020-10-19T16:39:53.931350Z",
     "shell.execute_reply": "2020-10-19T16:39:53.930308Z",
     "shell.execute_reply.started": "2020-10-19T16:39:53.886203Z"
    }
   },
   "outputs": [],
   "source": [
    "def fillna(df_train, df_test=None, df_private_test=None):\n",
    "    match_coords = {\n",
    "        0: [\n",
    "            \"MatchedHit_DX0\",\n",
    "            \"MatchedHit_DY0\",\n",
    "            \"MatchedHit_DZ0\",\n",
    "        ],\n",
    "        1: [\n",
    "            \"MatchedHit_DX1\",\n",
    "            \"MatchedHit_DY1\",\n",
    "            \"MatchedHit_DZ1\",\n",
    "        ],\n",
    "        2: [\n",
    "            \"MatchedHit_DX2\",\n",
    "            \"MatchedHit_DY2\",\n",
    "            \"MatchedHit_DZ2\",\n",
    "        ],\n",
    "        3: [\n",
    "            \"MatchedHit_DX3\",\n",
    "            \"MatchedHit_DY3\",\n",
    "            \"MatchedHit_DZ3\",\n",
    "        ],\n",
    "    }\n",
    "    t_cols = [\n",
    "        \"MatchedHit_T0\",\n",
    "        \"MatchedHit_T1\",\n",
    "        \"MatchedHit_T2\",\n",
    "        \"MatchedHit_T3\",\n",
    "    ]\n",
    "    for df in [df_train, df_test, df_private_test]:\n",
    "        if df is not None:\n",
    "            all_d_columns = list(chain.from_iterable(match_coords.values()))\n",
    "            all_columns = [col.replace(\"D\", \"\") for col in all_d_columns]\n",
    "            for cols in [all_d_columns, all_columns]:\n",
    "                df[cols] = df[cols].replace(-9999, np.nan)\n",
    "                df[cols] = df[cols].fillna(df[cols].median())\n",
    "\n",
    "            df[t_cols] = df[t_cols].replace(255, np.nan)\n",
    "            df[t_cols] = df[t_cols].fillna(df[t_cols].median())\n",
    "\n",
    "    return df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:54.191392Z",
     "iopub.status.busy": "2020-10-19T16:39:54.190832Z",
     "iopub.status.idle": "2020-10-19T16:39:54.246098Z",
     "shell.execute_reply": "2020-10-19T16:39:54.244948Z",
     "shell.execute_reply.started": "2020-10-19T16:39:54.191340Z"
    }
   },
   "outputs": [],
   "source": [
    "def ncl_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/ncl_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        ncl_features = [\"ncl0\", \"ncl1\", \"ncl2\", \"ncl3\"]\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                df[\"ncl_max\"] = df[ncl_features].max(axis=1)\n",
    "                df[\"ncl_sum\"] = df[ncl_features].sum(axis=1)\n",
    "                df[\"ncl_min\"] = df[ncl_features].min(axis=1)\n",
    "                df[\"ncl_mean\"] = df[ncl_features].mean(axis=1)\n",
    "                df[\"ncl_std\"] = df[ncl_features].std(axis=1)\n",
    "\n",
    "        new_columns = [\"ncl_max\", \"ncl_sum\", \"ncl_min\", \"ncl_mean\", \"ncl_std\"]\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:54.480856Z",
     "iopub.status.busy": "2020-10-19T16:39:54.480298Z",
     "iopub.status.idle": "2020-10-19T16:39:54.547232Z",
     "shell.execute_reply": "2020-10-19T16:39:54.546208Z",
     "shell.execute_reply.started": "2020-10-19T16:39:54.480802Z"
    }
   },
   "outputs": [],
   "source": [
    "def avg_cs(df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"):\n",
    "    _name = f\"{DATA_PATH}features/avg_cs_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        avg_cs_features = [\"avg_cs0\", \"avg_cs1\", \"avg_cs2\", \"avg_cs3\"]\n",
    "        ncl_features = [\"ncl0\", \"ncl1\", \"ncl2\", \"ncl3\"]\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                df[\"avg_cs_max\"] = df[avg_cs_features].max(axis=1)\n",
    "                df[\"avg_cs_sum\"] = df[avg_cs_features].sum(axis=1)\n",
    "                df[\"avg_cs_min\"] = df[avg_cs_features].min(axis=1)\n",
    "                df[\"avg_cs_mean\"] = df[avg_cs_features].mean(axis=1)\n",
    "                df[\"avg_cs_std\"] = df[avg_cs_features].std(axis=1)\n",
    "                avg_total_points = df[ncl_features].values * df[avg_cs_features].values\n",
    "                new_column_names = [\"total_cs0\", \"total_cs1\", \"total_cs2\", \"total_cs3\"]\n",
    "                df[new_column_names] = pd.DataFrame(avg_total_points, index=df.index)\n",
    "                df[\"total_cs_max\"] = df[new_column_names].max(axis=1)\n",
    "                df[\"total_cs_sum\"] = df[new_column_names].sum(axis=1)\n",
    "                df[\"total_cs_min\"] = df[new_column_names].min(axis=1)\n",
    "                df[\"total_cs_mean\"] = df[new_column_names].mean(axis=1)\n",
    "                df[\"total_cs_std\"] = df[new_column_names].std(axis=1)\n",
    "        new_columns = (\n",
    "            new_column_names\n",
    "            + [\n",
    "                \"total_cs_max\",\n",
    "                \"total_cs_sum\",\n",
    "                \"total_cs_min\",\n",
    "                \"total_cs_mean\",\n",
    "                \"total_cs_std\",\n",
    "            ]\n",
    "            + [\n",
    "                \"avg_cs_max\",\n",
    "                \"avg_cs_sum\",\n",
    "                \"avg_cs_min\",\n",
    "                \"avg_cs_mean\",\n",
    "                \"avg_cs_std\",\n",
    "            ]\n",
    "        )\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:54.772146Z",
     "iopub.status.busy": "2020-10-19T16:39:54.771612Z",
     "iopub.status.idle": "2020-10-19T16:39:54.826209Z",
     "shell.execute_reply": "2020-10-19T16:39:54.825088Z",
     "shell.execute_reply.started": "2020-10-19T16:39:54.772094Z"
    }
   },
   "outputs": [],
   "source": [
    "def matchhit_type_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/matchhit_type_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        match_type_features = [\n",
    "            \"MatchedHit_TYPE0\",\n",
    "            \"MatchedHit_TYPE1\",\n",
    "            \"MatchedHit_TYPE2\",\n",
    "            \"MatchedHit_TYPE3\",\n",
    "        ]\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                for c in range(3):\n",
    "                    df[f\"MatchedHit_count_{c}\"] = (\n",
    "                        df[match_type_features].apply(lambda x: x == c).sum(axis=1)\n",
    "                    )\n",
    "                    cumsum_aggs = (\n",
    "                        df[match_type_features]\n",
    "                        .apply(lambda x: x == c)\n",
    "                        .cumsum(axis=1)\n",
    "                        .values\n",
    "                    )\n",
    "                    cumsum_aggs_names = [\n",
    "                        f\"{col}_cumsum_{c}\" for col in match_type_features\n",
    "                    ]\n",
    "                    df[cumsum_aggs_names] = pd.DataFrame(\n",
    "                        cumsum_aggs, index=df.index, columns=cumsum_aggs_names\n",
    "                    )\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:55.066361Z",
     "iopub.status.busy": "2020-10-19T16:39:55.065831Z",
     "iopub.status.idle": "2020-10-19T16:39:55.164369Z",
     "shell.execute_reply": "2020-10-19T16:39:55.163254Z",
     "shell.execute_reply.started": "2020-10-19T16:39:55.066312Z"
    }
   },
   "outputs": [],
   "source": [
    "def matchhit_coords_features(\n",
    "    df_train,\n",
    "    df_test=None,\n",
    "    df_private_test=None,\n",
    "    match_coords=None,\n",
    "    load_saved=False,\n",
    "    data_tag=\"\",\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/matchhit_coords_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        if not match_coords:\n",
    "            match_coords = {\n",
    "                0: [\n",
    "                    \"MatchedHit_X0\",\n",
    "                    \"MatchedHit_Y0\",\n",
    "                    \"MatchedHit_Z0\",\n",
    "                ],\n",
    "                1: [\n",
    "                    \"MatchedHit_X1\",\n",
    "                    \"MatchedHit_Y1\",\n",
    "                    \"MatchedHit_Z1\",\n",
    "                ],\n",
    "                2: [\n",
    "                    \"MatchedHit_X2\",\n",
    "                    \"MatchedHit_Y2\",\n",
    "                    \"MatchedHit_Z2\",\n",
    "                ],\n",
    "                3: [\n",
    "                    \"MatchedHit_X3\",\n",
    "                    \"MatchedHit_Y3\",\n",
    "                    \"MatchedHit_Z3\",\n",
    "                ],\n",
    "            }\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                for type_ in range(4):\n",
    "                    df[f\"XYZdistance_from_center_type_{type_}\"] = np.sqrt(\n",
    "                        df[match_coords[type_]].apply(lambda x: x ** 2).sum(axis=1)\n",
    "                    )\n",
    "                    # to polar\n",
    "                    df[f\"lat_angle_type_{type_}\"] = np.arccos(\n",
    "                        df[f\"MatchedHit_Z{type_}\"]\n",
    "                        / df[f\"XYZdistance_from_center_type_{type_}\"]\n",
    "                    )\n",
    "                    df[f\"long_angle_type_{type_}\"] = np.arctan2(\n",
    "                        df[f\"MatchedHit_Y{type_}\"], df[f\"MatchedHit_X{type_}\"]\n",
    "                    )\n",
    "\n",
    "                    df[f\"XYdistance_from_center_type_{type_}\"] = np.sqrt(\n",
    "                        df[match_coords[type_][:-1]].apply(lambda x: x ** 2).sum(axis=1)\n",
    "                    )\n",
    "\n",
    "                dist_cols = [f\"XYZdistance_from_center_type_{i}\" for i in range(4)] + [\n",
    "                    f\"XYdistance_from_center_type_{i}\" for i in range(4)\n",
    "                ]\n",
    "                lat_cols = [f\"lat_angle_type_{i}\" for i in range(4)]\n",
    "                long_cols = [f\"long_angle_type_{i}\" for i in range(4)]\n",
    "                for polar_cols in [dist_cols, lat_cols, long_cols]:\n",
    "                    polar_prefix = polar_cols[0].rsplit(\"_\", 2)[0]\n",
    "                    df[f\"{polar_prefix}_max\"] = df[polar_cols].max(axis=1)\n",
    "                    df[f\"{polar_prefix}_sum\"] = df[polar_cols].sum(axis=1)\n",
    "                    df[f\"{polar_prefix}_min\"] = df[polar_cols].min(axis=1)\n",
    "                    df[f\"{polar_prefix}_mean\"] = df[polar_cols].mean(axis=1)\n",
    "                    df[f\"{polar_prefix}_std\"] = df[polar_cols].std(axis=1)\n",
    "\n",
    "                for col_coord in [\"X\", \"Y\", \"Z\"]:\n",
    "                    coord_cols = [f\"MatchedHit_{col_coord}{i}\" for i in range(4)]\n",
    "                    df[f\"{col_coord}_coord_max\"] = df[coord_cols].max(axis=1)\n",
    "                    df[f\"{col_coord}_coord_sum\"] = df[coord_cols].sum(axis=1)\n",
    "                    df[f\"{col_coord}_coord_min\"] = df[coord_cols].min(axis=1)\n",
    "                    df[f\"{col_coord}_coord_mean\"] = df[coord_cols].mean(axis=1)\n",
    "                    df[f\"{col_coord}_coord_std\"] = df[coord_cols].std(axis=1)\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:55.429239Z",
     "iopub.status.busy": "2020-10-19T16:39:55.428675Z",
     "iopub.status.idle": "2020-10-19T16:39:55.532629Z",
     "shell.execute_reply": "2020-10-19T16:39:55.531332Z",
     "shell.execute_reply.started": "2020-10-19T16:39:55.429184Z"
    }
   },
   "outputs": [],
   "source": [
    "def matchhit_relative_coords_features(\n",
    "    df_train,\n",
    "    df_test=None,\n",
    "    df_private_test=None,\n",
    "    match_coords=None,\n",
    "    load_saved=False,\n",
    "    data_tag=\"\",\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/matchhit_relative_coords_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        if not match_coords:\n",
    "            match_coords = {\n",
    "                \"X\": [\n",
    "                    \"MatchedHit_X0\",\n",
    "                    \"MatchedHit_X1\",\n",
    "                    \"MatchedHit_X2\",\n",
    "                    \"MatchedHit_X3\",\n",
    "                ],\n",
    "                \"Y\": [\n",
    "                    \"MatchedHit_Y0\",\n",
    "                    \"MatchedHit_Y1\",\n",
    "                    \"MatchedHit_Y2\",\n",
    "                    \"MatchedHit_Y3\",\n",
    "                ],\n",
    "                \"Z\": [\n",
    "                    \"MatchedHit_Z0\",\n",
    "                    \"MatchedHit_Z1\",\n",
    "                    \"MatchedHit_Z2\",\n",
    "                    \"MatchedHit_Z3\",\n",
    "                ],\n",
    "            }\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                for coord in \"XYZ\":\n",
    "                    relative_diff_cols_names = [\n",
    "                        col + \"diff_with_prev\" for col in match_coords[coord]\n",
    "                    ]\n",
    "                    relative_div_cols_names = [\n",
    "                        col + \"div_with_prev\" for col in match_coords[coord]\n",
    "                    ]\n",
    "                    min_val = abs(df[match_coords[coord]].min().min())\n",
    "                    df[relative_diff_cols_names[1:]] = (\n",
    "                        df[match_coords[coord]]\n",
    "                        .diff(axis=1)\n",
    "                        .rename(\n",
    "                            dict(zip(match_coords[coord], relative_diff_cols_names)),\n",
    "                            axis=1,\n",
    "                        )\n",
    "                        .iloc[:, 1:]\n",
    "                    )\n",
    "                    df[relative_div_cols_names[1:]] = (\n",
    "                        np.exp(np.log1p(df[match_coords[coord]] + min_val).diff(axis=1))\n",
    "                        .rename(\n",
    "                            dict(zip(match_coords[coord], relative_diff_cols_names)),\n",
    "                            axis=1,\n",
    "                        )\n",
    "                        .iloc[:, 1:]\n",
    "                    )\n",
    "\n",
    "                    df[f\"{coord}_relative_diff_max\"] = df[\n",
    "                        relative_diff_cols_names[1:]\n",
    "                    ].max(axis=1)\n",
    "                    df[f\"{coord}_relative_diff_sum\"] = df[\n",
    "                        relative_diff_cols_names[1:]\n",
    "                    ].sum(axis=1)\n",
    "                    df[f\"{coord}_relative_diff_min\"] = df[\n",
    "                        relative_diff_cols_names[1:]\n",
    "                    ].min(axis=1)\n",
    "                    df[f\"{coord}_relative_diff_mean\"] = df[\n",
    "                        relative_diff_cols_names[1:]\n",
    "                    ].mean(axis=1)\n",
    "                    df[f\"{coord}_relative_diff_std\"] = df[\n",
    "                        relative_diff_cols_names[1:]\n",
    "                    ].std(axis=1)\n",
    "\n",
    "                    df[f\"{coord}_relative_div_max\"] = df[\n",
    "                        relative_div_cols_names[1:]\n",
    "                    ].max(axis=1)\n",
    "                    df[f\"{coord}_relative_div_sum\"] = df[\n",
    "                        relative_div_cols_names[1:]\n",
    "                    ].sum(axis=1)\n",
    "                    df[f\"{coord}_relative_div_min\"] = df[\n",
    "                        relative_div_cols_names[1:]\n",
    "                    ].min(axis=1)\n",
    "                    df[f\"{coord}_relative_div_mean\"] = df[\n",
    "                        relative_div_cols_names[1:]\n",
    "                    ].mean(axis=1)\n",
    "                    df[f\"{coord}_relative_div_std\"] = df[\n",
    "                        relative_div_cols_names[1:]\n",
    "                    ].std(axis=1)\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:55.978511Z",
     "iopub.status.busy": "2020-10-19T16:39:55.977939Z",
     "iopub.status.idle": "2020-10-19T16:39:56.089514Z",
     "shell.execute_reply": "2020-10-19T16:39:56.088262Z",
     "shell.execute_reply.started": "2020-10-19T16:39:55.978458Z"
    }
   },
   "outputs": [],
   "source": [
    "def matchhit_dcoords_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/matchhit_dcoords_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        match_coords = {\n",
    "            0: [\n",
    "                \"MatchedHit_DX0\",\n",
    "                \"MatchedHit_DY0\",\n",
    "                \"MatchedHit_DZ0\",\n",
    "            ],\n",
    "            1: [\n",
    "                \"MatchedHit_DX1\",\n",
    "                \"MatchedHit_DY1\",\n",
    "                \"MatchedHit_DZ1\",\n",
    "            ],\n",
    "            2: [\n",
    "                \"MatchedHit_DX2\",\n",
    "                \"MatchedHit_DY2\",\n",
    "                \"MatchedHit_DZ2\",\n",
    "            ],\n",
    "            3: [\n",
    "                \"MatchedHit_DX3\",\n",
    "                \"MatchedHit_DY3\",\n",
    "                \"MatchedHit_DZ3\",\n",
    "            ],\n",
    "        }\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                all_columns = list(chain.from_iterable(match_coords.values()))\n",
    "                for type_ in range(4):\n",
    "                    absolute_cols = [\n",
    "                        col.replace(\"D\", \"\") for col in match_coords[type_]\n",
    "                    ]\n",
    "                    relative_cols = df[match_coords[type_]] / df[absolute_cols].values\n",
    "\n",
    "                    relative_cols_names = [\n",
    "                        f\"relative_{col}\" for col in match_coords[type_]\n",
    "                    ]\n",
    "                    df[relative_cols_names] = relative_cols.rename(\n",
    "                        dict(zip(relative_cols, relative_cols_names)), axis=1\n",
    "                    )\n",
    "                    relative_prefix = f\"relative_MatchedHit_D{type_}\"\n",
    "                    df[f\"{relative_prefix}_max\"] = df[relative_cols_names].max(axis=1)\n",
    "                    df[f\"{relative_prefix}_sum\"] = df[relative_cols_names].sum(axis=1)\n",
    "                    df[f\"{relative_prefix}_min\"] = df[relative_cols_names].min(axis=1)\n",
    "                    df[f\"{relative_prefix}_mean\"] = df[relative_cols_names].mean(axis=1)\n",
    "                    df[f\"{relative_prefix}_std\"] = df[relative_cols_names].std(axis=1)\n",
    "\n",
    "                    df[f\"Ddistance_from_center_type_{type_}\"] = np.sqrt(\n",
    "                        df[match_coords[type_]].apply(lambda x: x ** 2).sum(axis=1)\n",
    "                    )\n",
    "                    # to polar\n",
    "                    df[f\"Dlat_angle_type_{type_}\"] = np.arccos(\n",
    "                        df[f\"MatchedHit_DZ{type_}\"]\n",
    "                        / df[f\"Ddistance_from_center_type_{type_}\"]\n",
    "                    )\n",
    "                    df[f\"Dlong_angle_type_{type_}\"] = np.arctan2(\n",
    "                        df[f\"MatchedHit_DY{type_}\"], df[f\"MatchedHit_DX{type_}\"]\n",
    "                    )\n",
    "\n",
    "                dist_cols = [f\"Ddistance_from_center_type_{i}\" for i in range(4)]\n",
    "                lat_cols = [f\"Dlat_angle_type_{i}\" for i in range(4)]\n",
    "                long_cols = [f\"Dlong_angle_type_{i}\" for i in range(4)]\n",
    "                for polar_cols in [dist_cols, lat_cols, long_cols]:\n",
    "                    polar_prefix = polar_cols[0].rsplit(\"_\", 2)[0]\n",
    "                    df[f\"{polar_prefix}_max\"] = df[polar_cols].max(axis=1)\n",
    "                    df[f\"{polar_prefix}_sum\"] = df[polar_cols].sum(axis=1)\n",
    "                    df[f\"{polar_prefix}_min\"] = df[polar_cols].min(axis=1)\n",
    "                    df[f\"{polar_prefix}_mean\"] = df[polar_cols].mean(axis=1)\n",
    "                    df[f\"{polar_prefix}_std\"] = df[polar_cols].std(axis=1)\n",
    "\n",
    "                for col_coord in [\"DX\", \"DY\", \"DZ\"]:\n",
    "                    coord_cols = [f\"MatchedHit_{col_coord}{i}\" for i in range(4)]\n",
    "                    df[f\"{col_coord}_coord_max\"] = df[coord_cols].max(axis=1)\n",
    "                    df[f\"{col_coord}_coord_sum\"] = df[coord_cols].sum(axis=1)\n",
    "                    df[f\"{col_coord}_coord_min\"] = df[coord_cols].min(axis=1)\n",
    "                    df[f\"{col_coord}_coord_mean\"] = df[coord_cols].mean(axis=1)\n",
    "                    df[f\"{col_coord}_coord_std\"] = df[coord_cols].std(axis=1)\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:56.363276Z",
     "iopub.status.busy": "2020-10-19T16:39:56.362667Z",
     "iopub.status.idle": "2020-10-19T16:39:56.446767Z",
     "shell.execute_reply": "2020-10-19T16:39:56.445699Z",
     "shell.execute_reply.started": "2020-10-19T16:39:56.363218Z"
    }
   },
   "outputs": [],
   "source": [
    "def matchhit_t_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/matchhit_t_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        t_cols = [\n",
    "            \"MatchedHit_T0\",\n",
    "            \"MatchedHit_T1\",\n",
    "            \"MatchedHit_T2\",\n",
    "            \"MatchedHit_T3\",\n",
    "        ]\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                df[t_cols] = df[t_cols].astype(\"int\")\n",
    "                df[\"MatchedHit_T_idxmax\"] = df[t_cols].idxmax(axis=1)\n",
    "                df[\"MatchedHit_T_idxmin\"] = df[t_cols].idxmin(axis=1)\n",
    "                valid_lt_hits = df[t_cols[:-1]].lt(df[t_cols[1:]].values, axis=\"index\")\n",
    "                df[\"MatchedHit_T_valid_hits_cat\"] = (\n",
    "                    valid_lt_hits.iloc[:, 0].map(str)\n",
    "                    + valid_lt_hits.iloc[:, 1].map(str)\n",
    "                    + valid_lt_hits.iloc[:, 2].map(str)\n",
    "                )\n",
    "                df[\"MatchedHit_T_conseq_valid_hits\"] = valid_lt_hits.cumsum(\n",
    "                    axis=1\n",
    "                ).iloc[:, -1]\n",
    "                diff_t_cols_names = [f\"{col}_diff\" for col in t_cols]\n",
    "                abs_diff_t_cols_names = [f\"{col}_diff_abs\" for col in t_cols]\n",
    "                diff_t_cols = (\n",
    "                    df[t_cols]\n",
    "                    .diff(axis=1)\n",
    "                    .rename(dict(zip(t_cols, diff_t_cols_names)), axis=1)\n",
    "                )\n",
    "                df[diff_t_cols_names[1:]] = diff_t_cols.iloc[:, 1:]\n",
    "                df[abs_diff_t_cols_names[1:]] = diff_t_cols.abs().iloc[:, 1:]\n",
    "\n",
    "                df[f\"MatchedHit_T_max\"] = df[abs_diff_t_cols_names[1:]].max(axis=1)\n",
    "                df[f\"MatchedHit_T_sum\"] = df[abs_diff_t_cols_names[1:]].sum(axis=1)\n",
    "                df[f\"MatchedHit_T_min\"] = df[abs_diff_t_cols_names[1:]].min(axis=1)\n",
    "                df[f\"MatchedHit_T_mean\"] = df[abs_diff_t_cols_names[1:]].mean(axis=1)\n",
    "                df[f\"MatchedHit_T_std\"] = df[abs_diff_t_cols_names[1:]].std(axis=1)\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:57.089092Z",
     "iopub.status.busy": "2020-10-19T16:39:57.088532Z",
     "iopub.status.idle": "2020-10-19T16:39:57.197737Z",
     "shell.execute_reply": "2020-10-19T16:39:57.196495Z",
     "shell.execute_reply.started": "2020-10-19T16:39:57.089041Z"
    }
   },
   "outputs": [],
   "source": [
    "def lextra_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/lextra_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        lextra_coords = {\n",
    "            0: [\"Lextra_X0\", \"Lextra_Y0\"],\n",
    "            1: [\"Lextra_X1\", \"Lextra_Y1\"],\n",
    "            2: [\"Lextra_X2\", \"Lextra_Y2\"],\n",
    "            3: [\"Lextra_X3\", \"Lextra_Y3\"],\n",
    "        }\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                for type_ in range(4):\n",
    "                    df[f\"Lextra_XYdistance_from_center_type_{type_}\"] = np.sqrt(\n",
    "                        df[lextra_coords[type_]].apply(lambda x: x ** 2).sum(axis=1)\n",
    "                    )\n",
    "\n",
    "                    lextra_cols = [f\"Lextra_X{type_}\", f\"Lextra_Y{type_}\"]\n",
    "                    hit_cols = [f\"MatchedHit_X{type_}\", f\"MatchedHit_Y{type_}\"]\n",
    "                    df[f\"mse_XYdistance_Hit_Lextra{type_}\"] = np.sqrt(\n",
    "                        np.power(df[hit_cols].values - df[lextra_cols].values, 2).sum(\n",
    "                            axis=1\n",
    "                        )\n",
    "                    )\n",
    "                    df[f\"mae_XYdistance_Hit_Lextra{type_}\"] = (\n",
    "                        df[hit_cols].values - df[lextra_cols].values\n",
    "                    ).sum(axis=1)\n",
    "\n",
    "                    for coord in \"XY\":\n",
    "                        df[f\"diff_Hit_Lextra_{coord}{type_}\"] = (\n",
    "                            df[f\"MatchedHit_{coord}{type_}\"]\n",
    "                            - df[f\"Lextra_{coord}{type_}\"]\n",
    "                        )\n",
    "                        df[f\"div_Hit_Lextra_{coord}{type_}\"] = (\n",
    "                            df[f\"MatchedHit_{coord}{type_}\"]\n",
    "                            / df[f\"Lextra_{coord}{type_}\"]\n",
    "                        )\n",
    "\n",
    "                for coord in \"XY\":\n",
    "                    for type_ in range(3):\n",
    "                        col_first = f\"Lextra_{coord}{type_}\"\n",
    "                        col_second = f\"Lextra_{coord}{type_+1}\"\n",
    "                        df[f\"diff_{col_first}_{col_second}\"] = (\n",
    "                            df[col_first] - df[col_second]\n",
    "                        )\n",
    "                        df[f\"div_{col_first}_{col_second}\"] = (\n",
    "                            df[col_first] / df[col_second].values\n",
    "                        )\n",
    "\n",
    "                        df[f\"diff_Lextra_XYdistance_from_center_type_{type_}\"] = (\n",
    "                            df[f\"Lextra_XYdistance_from_center_type_{type_}\"]\n",
    "                            / df[f\"Lextra_XYdistance_from_center_type_{type_+1}\"]\n",
    "                        )\n",
    "                        df[f\"div_Lextra_XYdistance_from_center_type_{type_}\"] = (\n",
    "                            df[f\"Lextra_XYdistance_from_center_type_{type_}\"]\n",
    "                            / df[f\"Lextra_XYdistance_from_center_type_{type_+1}\"].values\n",
    "                        )\n",
    "\n",
    "                    for prefix in [\n",
    "                        \"diff_Lextra_\",\n",
    "                        \"diff_Hit_Lextra_\",\n",
    "                        \"div_Hit_Lextra_\",\n",
    "                        \"div_Lextra_\",\n",
    "                    ]:\n",
    "                        coord_cols = df.columns[\n",
    "                            df.columns.str.startswith(f\"{prefix}{coord}\")\n",
    "                        ]\n",
    "                        df[f\"{prefix}{coord}_max\"] = df[coord_cols].max(axis=1)\n",
    "                        df[f\"{prefix}{coord}_sum\"] = df[coord_cols].sum(axis=1)\n",
    "                        df[f\"{prefix}{coord}_min\"] = df[coord_cols].min(axis=1)\n",
    "                        df[f\"{prefix}{coord}_mean\"] = df[coord_cols].mean(axis=1)\n",
    "                        df[f\"{prefix}{coord}_std\"] = df[coord_cols].std(axis=1)\n",
    "\n",
    "                for prefix in [\n",
    "                    \"diff_Lextra_XYdistance\",\n",
    "                    \"div_Lextra_XYdistance\",\n",
    "                    \"mse_XYdistance_Hit_Lextra\",\n",
    "                    \"mae_XYdistance_Hit_Lextra\",\n",
    "                ]:\n",
    "                    coord_cols = df.columns[df.columns.str.startswith(f\"{prefix}\")]\n",
    "                    df[f\"{prefix}_max\"] = df[coord_cols].max(axis=1)\n",
    "                    df[f\"{prefix}_sum\"] = df[coord_cols].sum(axis=1)\n",
    "                    df[f\"{prefix}_min\"] = df[coord_cols].min(axis=1)\n",
    "                    df[f\"{prefix}_mean\"] = df[coord_cols].mean(axis=1)\n",
    "                    df[f\"{prefix}_std\"] = df[coord_cols].std(axis=1)\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:39:57.735286Z",
     "iopub.status.busy": "2020-10-19T16:39:57.734674Z",
     "iopub.status.idle": "2020-10-19T16:39:57.778620Z",
     "shell.execute_reply": "2020-10-19T16:39:57.777497Z",
     "shell.execute_reply.started": "2020-10-19T16:39:57.735231Z"
    }
   },
   "outputs": [],
   "source": [
    "def pt_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/pt_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                df[\"PO\"] = np.power(df[\"P\"], 2.0) - np.power(df[\"PT\"], 2.0)\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:41:41.972637Z",
     "iopub.status.busy": "2020-10-19T16:41:41.971946Z",
     "iopub.status.idle": "2020-10-19T16:41:41.998275Z",
     "shell.execute_reply": "2020-10-19T16:41:41.997214Z",
     "shell.execute_reply.started": "2020-10-19T16:41:41.972552Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_closest_hit(df_train, df_test, df_private_test):\n",
    "    _name = f\"{DATA_PATH}features/foi_closest_hit_full\"\n",
    "    #предпосчитаны отдельно\n",
    "    df_train_closest_hit = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "    if df_test is not None:\n",
    "        df_test_closest_hit = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "        df_private_test_closest_hit = pd.read_hdf(f\"{_name}_private_test.hdf\")\n",
    "    new_columns = list(df_train_closest_hit.columns)\n",
    "    df_train = df_train.join(df_train_closest_hit, how=\"left\")\n",
    "    if df_test is not None:\n",
    "        df_test = df_test.join(df_test_closest_hit, how=\"left\")\n",
    "        df_private_test = df_private_test.join(df_private_test_closest_hit, how=\"left\")\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T14:04:05.566090Z",
     "iopub.status.busy": "2020-10-16T14:04:05.565512Z",
     "iopub.status.idle": "2020-10-16T14:04:05.611170Z",
     "shell.execute_reply": "2020-10-16T14:04:05.610446Z",
     "shell.execute_reply.started": "2020-10-16T14:04:05.566037Z"
    }
   },
   "outputs": [],
   "source": [
    "def foi_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/foi_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                station_counts = pd.DataFrame(\n",
    "                    df[\"FOI_hits_S\"].progress_apply(func=lambda x: Counter(x)).tolist(),\n",
    "                    index=df.index,\n",
    "                ).rename(\n",
    "                    dict(zip(range(4), [f\"Station_{i}\" for i in range(4)])), axis=1\n",
    "                )\n",
    "                station_counts = station_counts.fillna(0)\n",
    "                df[list(station_counts.columns)] = station_counts\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T14:04:13.091164Z",
     "iopub.status.busy": "2020-10-16T14:04:13.090632Z",
     "iopub.status.idle": "2020-10-16T14:04:13.131459Z",
     "shell.execute_reply": "2020-10-16T14:04:13.130737Z",
     "shell.execute_reply.started": "2020-10-16T14:04:13.091115Z"
    }
   },
   "outputs": [],
   "source": [
    "def nshared_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/nshared_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        for df in [df_train, df_test, df_private_test]:\n",
    "            if df is not None:\n",
    "                df[\"NShared_div_FOI_hits_N\"] = (\n",
    "                    df.loc[:, \"NShared\"] / df.loc[:, \"FOI_hits_N\"]\n",
    "                )\n",
    "                df[\"NShared_div_FOI_hits_N\"] = df.loc[\n",
    "                    :, \"NShared_div_FOI_hits_N\"\n",
    "                ].fillna(-1)\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T14:04:35.015879Z",
     "iopub.status.busy": "2020-10-16T14:04:35.015202Z",
     "iopub.status.idle": "2020-10-16T14:04:35.071209Z",
     "shell.execute_reply": "2020-10-16T14:04:35.070129Z",
     "shell.execute_reply.started": "2020-10-16T14:04:35.015824Z"
    }
   },
   "outputs": [],
   "source": [
    "def kmeans_features(\n",
    "    df_train, df_test=None, df_private_test=None, load_saved=False, data_tag=\"\"\n",
    "):\n",
    "    _name = f\"{DATA_PATH}features/kmeans_features_{data_tag}\"\n",
    "    if load_saved:\n",
    "        features_train = pd.read_hdf(f\"{_name}_train.hdf\")\n",
    "        df_train = pd.concat([df_train, features_train], axis=1)\n",
    "        if df_test is not None:\n",
    "            features_test = pd.read_hdf(f\"{_name}_test.hdf\")\n",
    "            df_test = pd.concat([df_test, features_test], axis=1)\n",
    "        new_columns = list(features_train.columns)\n",
    "    else:\n",
    "        old_columns = df_train.columns\n",
    "        match_coords = {\n",
    "            0: [\n",
    "                \"MatchedHit_X0\",\n",
    "                \"MatchedHit_Y0\",\n",
    "                \"MatchedHit_Z0\",\n",
    "            ],\n",
    "            1: [\n",
    "                \"MatchedHit_X1\",\n",
    "                \"MatchedHit_Y1\",\n",
    "                \"MatchedHit_Z1\",\n",
    "            ],\n",
    "            2: [\n",
    "                \"MatchedHit_X2\",\n",
    "                \"MatchedHit_Y2\",\n",
    "                \"MatchedHit_Z2\",\n",
    "            ],\n",
    "            3: [\n",
    "                \"MatchedHit_X3\",\n",
    "                \"MatchedHit_Y3\",\n",
    "                \"MatchedHit_Z3\",\n",
    "            ],\n",
    "        }\n",
    "        km = KMeans(n_clusters=6, n_jobs=30)\n",
    "        tmp_full_df = pd.concat([df_train, df_test, df_private_test], axis=0)\n",
    "        for type_ in range(4):\n",
    "            clusters = km.fit(tmp_full_df[match_coords[type_]])\n",
    "            df_train[f\"cluster_type_{type_}\"] = km.predict(\n",
    "                df_train[match_coords[type_]]\n",
    "            )\n",
    "            df_test[f\"cluster_type_{type_}\"] = km.predict(df_test[match_coords[type_]])\n",
    "            df_private_test[f\"cluster_type_{type_}\"] = km.predict(\n",
    "                df_private_test[match_coords[type_]]\n",
    "            )\n",
    "\n",
    "        new_columns = list(df_train.columns.difference(old_columns))\n",
    "        df_train[new_columns].to_hdf(f\"{_name}_train.hdf\", \"df\", mode=\"w\")\n",
    "        if df_test is not None:\n",
    "            df_test[new_columns].to_hdf(f\"{_name}_test.hdf\", \"df\", mode=\"w\")\n",
    "            df_private_test[new_columns].to_hdf(\n",
    "                f\"{_name}_private_test.hdf\", \"df\", mode=\"w\"\n",
    "            )\n",
    "    return new_columns, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-19T16:47:59.471427Z",
     "iopub.status.busy": "2020-10-19T16:47:59.470618Z",
     "iopub.status.idle": "2020-10-19T16:47:59.567731Z",
     "shell.execute_reply": "2020-10-19T16:47:59.566695Z",
     "shell.execute_reply.started": "2020-10-19T16:47:59.471365Z"
    }
   },
   "outputs": [],
   "source": [
    "def build_features(df_train, df_test=None, df_private_test=None, data_tag=\"\"):\n",
    "    df_train, df_test, df_private_test = fillna(df_train, df_test, df_private_test)\n",
    "    start = time.time()\n",
    "    ncl_cols, df_train, df_test, df_private_test = ncl_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"ncl finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "\n",
    "    (\n",
    "        matchhit_relative_cols,\n",
    "        df_train,\n",
    "        df_test,\n",
    "        df_private_test,\n",
    "    ) = matchhit_relative_coords_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"matchhit_relative finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "\n",
    "    avg_cs_cols, df_train, df_test, df_private_test = avg_cs(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"avg_cs finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "    matchhit_type_cols, df_train, df_test, df_private_test = matchhit_type_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"matchhit_type finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "    matchhit_coords_cols, df_train, df_test, df_private_test = matchhit_coords_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"matchhit_coords finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "    (\n",
    "        matchhit_dcoords_cols,\n",
    "        df_train,\n",
    "        df_test,\n",
    "        df_private_test,\n",
    "    ) = matchhit_dcoords_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"matchhit_dcoords finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "    matchhit_t_cols, df_train, df_test, df_private_test = matchhit_t_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"matchhit_t finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "    lextra_cols, df_train, df_test, df_private_test = lextra_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"lextra finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "    pt_cols, df_train, df_test, df_private_test = pt_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"pt finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "\n",
    "    closest_hit_cols, df_train, df_test, df_private_test = load_closest_hit(\n",
    "        df_train, df_test, df_private_test\n",
    "    )\n",
    "    print(f\"closest_hit finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "\n",
    "    foi_cols, df_train, df_test, df_private_test = foi_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"foi finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "\n",
    "    nshared_cols, df_train, df_test, df_private_test = nshared_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"nshared finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "\n",
    "    kmeans_cols, df_train, df_test, df_private_test = nshared_features(\n",
    "        df_train, df_test, df_private_test, load_saved=False, data_tag=data_tag\n",
    "    )\n",
    "    print(f\"kmeans finished, elapsed: {(time.time()-start):.3f}\")\n",
    "    start = time.time()\n",
    "\n",
    "    FEATURES = (\n",
    "        list(BASIC_FEATURES)\n",
    "        + ncl_cols\n",
    "        + avg_cs_cols\n",
    "        + matchhit_type_cols\n",
    "        + matchhit_coords_cols\n",
    "        + matchhit_dcoords_cols\n",
    "        + matchhit_t_cols\n",
    "        + lextra_cols\n",
    "        + pt_cols\n",
    "        + closest_hit_cols\n",
    "        + foi_cols\n",
    "        + nshared_cols\n",
    "        + matchhit_relative_cols\n",
    "        + kmeans_cols\n",
    "    )\n",
    "\n",
    "    return FEATURES, df_train, df_test, df_private_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T15:14:42.332099Z",
     "iopub.status.busy": "2020-10-16T15:14:42.331607Z",
     "iopub.status.idle": "2020-10-16T15:36:55.349693Z",
     "shell.execute_reply": "2020-10-16T15:36:55.349009Z",
     "shell.execute_reply.started": "2020-10-16T15:14:42.332054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ncl finished, elapsed: 2.050\n",
      "matchhit_relative finished, elapsed: 74.565\n",
      "avg_cs finished, elapsed: 29.157\n",
      "matchhit_type finished, elapsed: 7.122\n",
      "matchhit_coords finished, elapsed: 130.364\n",
      "matchhit_dcoords finished, elapsed: 267.991\n",
      "matchhit_t finished, elapsed: 53.930\n",
      "lextra finished, elapsed: 505.344\n",
      "pt finished, elapsed: 0.947\n",
      "closest_hit finished, elapsed: 160.920\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1104525a324d42ac821fd4c38734cebe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5445705.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffca30a1b74d4eb1b672f2857c61f99c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=726095.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4be4f2aa60ea4d3d97368b18d89234e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1452188.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "foi finished, elapsed: 91.952\n",
      "nshared finished, elapsed: 1.101\n",
      "kmeans finished, elapsed: 0.853\n"
     ]
    }
   ],
   "source": [
    "# full train и test\n",
    "FEATURES, df_train, df_test, df_private_test = build_features(\n",
    "    df_train, df_test, df_private_test, data_tag=\"full_aftermath\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T15:49:11.120717Z",
     "iopub.status.busy": "2020-10-16T15:49:11.120079Z",
     "iopub.status.idle": "2020-10-16T15:49:11.131056Z",
     "shell.execute_reply": "2020-10-16T15:49:11.129841Z",
     "shell.execute_reply.started": "2020-10-16T15:49:11.120661Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1452188, 447)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T15:49:27.327770Z",
     "iopub.status.busy": "2020-10-16T15:49:27.327419Z",
     "iopub.status.idle": "2020-10-16T15:49:53.846356Z",
     "shell.execute_reply": "2020-10-16T15:49:53.845244Z",
     "shell.execute_reply.started": "2020-10-16T15:49:27.327738Z"
    }
   },
   "outputs": [],
   "source": [
    "categorical_columns = list(\n",
    "    df_train[FEATURES].dtypes[df_train[FEATURES].dtypes == \"object\"].index\n",
    ")\n",
    "oe = OrdinalEncoder()\n",
    "oe.fit(df_train[categorical_columns])\n",
    "df_train[categorical_columns] = oe.transform(df_train[categorical_columns])\n",
    "df_test[categorical_columns] = oe.transform(df_test[categorical_columns])\n",
    "df_private_test[categorical_columns] = oe.transform(\n",
    "    df_private_test[categorical_columns]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Моделька "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:34:14.562034Z",
     "iopub.status.busy": "2020-10-14T15:34:14.561475Z",
     "iopub.status.idle": "2020-10-14T15:35:03.894353Z",
     "shell.execute_reply": "2020-10-14T15:35:03.893316Z",
     "shell.execute_reply.started": "2020-10-14T15:34:14.561992Z"
    }
   },
   "outputs": [],
   "source": [
    "#предпосчитанные признаки на диске, чтобы не считать кажый раз\n",
    "df_train = pd.read_hdf(f\"{DATA_PATH}_df_train_full_all_features.hdf\")\n",
    "df_test = pd.read_hdf(f\"{DATA_PATH}_df_test_full_all_features.hdf\")\n",
    "FEATURES = list(df_train.columns.difference([\"weight\", \"label\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-09T00:09:08.786885Z",
     "iopub.status.busy": "2020-10-09T00:09:08.786420Z",
     "iopub.status.idle": "2020-10-09T00:09:08.795618Z",
     "shell.execute_reply": "2020-10-09T00:09:08.794453Z",
     "shell.execute_reply.started": "2020-10-09T00:09:08.786831Z"
    }
   },
   "outputs": [],
   "source": [
    "# model = lgb.LGBMClassifier(max_depth=5, n_jobs=20, random_state=GLOBAL_SEED)\n",
    "\n",
    "params_dict = {\n",
    "    \"objective\": \"CrossEntropy\",\n",
    "    \"iterations\": 5366,\n",
    "    \"depth\": 4,\n",
    "    \"learning_rate\": 0.04555975348215824,\n",
    "    \"random_strength\": 13,\n",
    "    \"bagging_temperature\": 0.19139760726080166,\n",
    "    \"od_type\": \"Iter\",\n",
    "    \"od_wait\": 34,\n",
    "}\n",
    "model = cb.CatBoostClassifier(\n",
    "    **params_dict,\n",
    "    thread_count=20,\n",
    "    #     gpu_ram_part=0.15,\n",
    "    task_type=\"GPU\",\n",
    "    random_state=GLOBAL_SEED,\n",
    "    devices=\"3\",\n",
    ")\n",
    "\n",
    "# model = cb.CatBoostClassifier(\n",
    "#     iterations=7000,\n",
    "#     max_depth=8,\n",
    "#     thread_count=20,\n",
    "#     task_type=\"GPU\",\n",
    "#     random_state=GLOBAL_SEED,\n",
    "#     devices=\"6\",\n",
    "# )\n",
    "\n",
    "# model = xgb.XGBClassifier(n_jobs=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Кросс-валидация (какая-то)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:26:04.704259Z",
     "iopub.status.busy": "2020-10-14T15:26:04.703788Z",
     "iopub.status.idle": "2020-10-14T15:26:05.000378Z",
     "shell.execute_reply": "2020-10-14T15:26:04.999064Z",
     "shell.execute_reply.started": "2020-10-14T15:26:04.704222Z"
    }
   },
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=GLOBAL_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:26:05.003292Z",
     "iopub.status.busy": "2020-10-14T15:26:05.002701Z",
     "iopub.status.idle": "2020-10-14T15:26:05.132908Z",
     "shell.execute_reply": "2020-10-14T15:26:05.131573Z",
     "shell.execute_reply.started": "2020-10-14T15:26:05.003242Z"
    }
   },
   "outputs": [],
   "source": [
    "def custom_cross_validation(estimator, X, y, cv, scoring, scoring_weights):\n",
    "    scores_train = []\n",
    "    scores_test = []\n",
    "    for train_index, test_index in tqdm(cv.split(X), total=cv.n_splits):\n",
    "        model_clone = deepcopy(estimator)\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "        weights_train, weights_test = (\n",
    "            scoring_weights.iloc[train_index],\n",
    "            scoring_weights.iloc[test_index],\n",
    "        )\n",
    "        if isinstance(model_clone, cb.CatBoostClassifier):\n",
    "            model_clone.fit(X_train, y_train, sample_weight=np.abs(weights_train))\n",
    "        else:\n",
    "            model_clone.fit(X_train, y_train, sample_weight=weights_train)\n",
    "\n",
    "        y_pred_train = model_clone.predict_proba(X_train)[:, 1]\n",
    "        y_pred_test = model_clone.predict_proba(X_test)[:, 1]\n",
    "        score_train = scoring(y_train.values, y_pred_train, weights_train.values)\n",
    "        score_test = scoring(y_test.values, y_pred_test, weights_test.values)\n",
    "        scores_train.append(score_train)\n",
    "        scores_test.append(score_test)\n",
    "\n",
    "        experiment.log_metric(\"cross_val_score_train\", score_train)\n",
    "        experiment.log_metric(\"cross_val_score_test\", score_test)\n",
    "\n",
    "    experiment.log_metric(\"mean_cross_val_score_train\", np.mean(scores_train))\n",
    "    experiment.log_metric(\"mean_cross_val_score_test\", np.mean(scores_test))\n",
    "    return {\"scores_train\": scores_train, \"scores_test\": scores_test}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "scores = custom_cross_validation(\n",
    "    estimator=model,\n",
    "    X=df_train.loc[:, FEATURES],\n",
    "    y=df_train[\"label\"],\n",
    "    cv=kf,\n",
    "    scoring=scoring.rejection90,\n",
    "    scoring_weights=df_train[\"weight\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optuna_cat(trial):\n",
    "    param = {\n",
    "        \"random_state\": GLOBAL_SEED,\n",
    "        \"objective\": trial.suggest_categorical(\"objective\", [\"Logloss\", \"CrossEntropy\"]),\n",
    "        \"iterations\": trial.suggest_int(\"iterations\", 500, 6000),\n",
    "        \"depth\": trial.suggest_int(\"depth\", 4, 10),\n",
    "        \"learning_rate\": trial.suggest_loguniform(\"learning_rate\", 0.001, 0.5),\n",
    "        \"random_strength\": trial.suggest_int(\"random_strength\", 0, 100),\n",
    "        \"bagging_temperature\": trial.suggest_loguniform(\n",
    "            \"bagging_temperature\", 0.01, 100.00\n",
    "        ),\n",
    "        \n",
    "        \"od_type\": trial.suggest_categorical(\"od_type\", [\"IncToDec\", \"Iter\"]),\n",
    "        \"od_wait\": trial.suggest_int(\"od_wait\", 10, 50),\n",
    "        \"thread_count\":20,\n",
    "        \"task_type\":\"GPU\",\n",
    "        \"random_state\":GLOBAL_SEED,\n",
    "        \"devices\":\"5\",\n",
    "        \"verbose\":False\n",
    "    }\n",
    "    \n",
    "    model = cb.CatBoostClassifier(**param)\n",
    "    scores = custom_cross_validation(\n",
    "    estimator=model,\n",
    "    X=df_valid.loc[:, FEATURES],\n",
    "    y=df_valid[\"label\"],\n",
    "    cv=kf,\n",
    "    scoring=scoring.rejection90,\n",
    "    scoring_weights=df_valid[\"weight\"])\n",
    "    \n",
    "\n",
    "    return np.mean(scores['scores_test'])\n",
    "\n",
    "\n",
    "study_cat = optuna.create_study(direction=\"maximize\")\n",
    "study_cat.optimize(optuna_cat, n_trials=100)\n",
    "print(\"Number of finished trials: {}\".format(len(study_cat.trials)))\n",
    "print(\"Best trial:\")\n",
    "trial = study_cat.best_trial\n",
    "print(\"  Value: {}\".format(trial.value))\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rejection90scorer + custom_classifier_to_support_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-11T22:35:11.467902Z",
     "iopub.status.busy": "2020-10-11T22:35:11.467406Z",
     "iopub.status.idle": "2020-10-11T22:35:11.502637Z",
     "shell.execute_reply": "2020-10-11T22:35:11.501553Z",
     "shell.execute_reply.started": "2020-10-11T22:35:11.467859Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
    "from sklearn.utils.validation import check_X_y, check_array, check_is_fitted\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "from sklearn.metrics import euclidean_distances\n",
    "\n",
    "\n",
    "class ClassifierWithWeights(BaseEstimator, ClassifierMixin):\n",
    "    def __init__(self, estimator):\n",
    "        self.estimator = estimator\n",
    "\n",
    "    def fit(self, X, y):\n",
    "\n",
    "        self.classes_ = unique_labels(y)\n",
    "\n",
    "        self.weights = X[:, -1]\n",
    "        self.X_ = X[:, :-1]\n",
    "        self.y_ = y\n",
    "        self.estimator.fit(self.X_, self.y_, sample_weight=self.weights)\n",
    "\n",
    "        return self\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "\n",
    "        check_is_fitted(self)\n",
    "        return self.estimator.predict_proba(X[:, :-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-11T22:37:12.700004Z",
     "iopub.status.busy": "2020-10-11T22:37:12.699578Z",
     "iopub.status.idle": "2020-10-11T22:37:12.712242Z",
     "shell.execute_reply": "2020-10-11T22:37:12.711444Z",
     "shell.execute_reply.started": "2020-10-11T22:37:12.699968Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "\n",
    "def rejection90_scorer(estimator, X, y):\n",
    "    weights = X[:, -1]\n",
    "    predictions = estimator.predict_proba(X)[:, 1]\n",
    "    metric = scoring.rejection90(y, predictions, weights)\n",
    "    return metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-11T22:36:06.730849Z",
     "iopub.status.busy": "2020-10-11T22:36:06.730283Z",
     "iopub.status.idle": "2020-10-11T22:36:06.737800Z",
     "shell.execute_reply": "2020-10-11T22:36:06.736855Z",
     "shell.execute_reply.started": "2020-10-11T22:36:06.730799Z"
    }
   },
   "outputs": [],
   "source": [
    "cww = ClassifierWithWeights(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-11T22:36:06.938166Z",
     "iopub.status.busy": "2020-10-11T22:36:06.937598Z",
     "iopub.status.idle": "2020-10-11T22:36:51.689356Z",
     "shell.execute_reply": "2020-10-11T22:36:51.688588Z",
     "shell.execute_reply.started": "2020-10-11T22:36:06.938114Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ClassifierWithWeights(estimator=LGBMClassifier(boosting_type='gbdt',\n",
       "                                               class_weight=None,\n",
       "                                               colsample_bytree=1.0,\n",
       "                                               importance_type='split',\n",
       "                                               learning_rate=0.1, max_depth=5,\n",
       "                                               min_child_samples=20,\n",
       "                                               min_child_weight=0.001,\n",
       "                                               min_split_gain=0.0,\n",
       "                                               n_estimators=100, n_jobs=20,\n",
       "                                               num_leaves=31, objective=None,\n",
       "                                               random_state=333, reg_alpha=0.0,\n",
       "                                               reg_lambda=0.0, silent=True,\n",
       "                                               subsample=1.0,\n",
       "                                               subsample_for_bin=200000,\n",
       "                                               subsample_freq=0))"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cww.fit(df_valid.loc[:, FEATURES + [\"weight\"]].values, df_valid[\"label\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-11T22:37:18.706455Z",
     "iopub.status.busy": "2020-10-11T22:37:18.705955Z",
     "iopub.status.idle": "2020-10-11T22:37:26.156476Z",
     "shell.execute_reply": "2020-10-11T22:37:26.155355Z",
     "shell.execute_reply.started": "2020-10-11T22:37:18.706411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7760997838011379"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rejection90_scorer(\n",
    "    cww, df_valid.loc[:, FEATURES + [\"weight\"]].values, df_valid[\"label\"].values\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permutation importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-12T00:37:00.183592Z",
     "iopub.status.busy": "2020-10-12T00:37:00.183173Z",
     "iopub.status.idle": "2020-10-12T00:37:00.194938Z",
     "shell.execute_reply": "2020-10-12T00:37:00.193899Z",
     "shell.execute_reply.started": "2020-10-12T00:37:00.183555Z"
    }
   },
   "outputs": [],
   "source": [
    "from eli5.sklearn import PermutationImportance\n",
    "from eli5 import show_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-11T22:37:47.083837Z",
     "iopub.status.busy": "2020-10-11T22:37:47.083345Z",
     "iopub.status.idle": "2020-10-12T00:31:00.166774Z",
     "shell.execute_reply": "2020-10-12T00:31:00.165765Z",
     "shell.execute_reply.started": "2020-10-11T22:37:47.083792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PermutationImportance(cv=KFold(n_splits=5, random_state=333, shuffle=True),\n",
       "                      estimator=ClassifierWithWeights(estimator=LGBMClassifier(boosting_type='gbdt',\n",
       "                                                                               class_weight=None,\n",
       "                                                                               colsample_bytree=1.0,\n",
       "                                                                               importance_type='split',\n",
       "                                                                               learning_rate=0.1,\n",
       "                                                                               max_depth=5,\n",
       "                                                                               min_child_samples=20,\n",
       "                                                                               min_child_weight=0.001,\n",
       "                                                                               min_split_gain=0.0,\n",
       "                                                                               n_estimators=100,\n",
       "                                                                               n_jobs=20,\n",
       "                                                                               num_leaves=31,\n",
       "                                                                               objective=None,\n",
       "                                                                               random_state=333,\n",
       "                                                                               reg_alpha=0.0,\n",
       "                                                                               reg_lambda=0.0,\n",
       "                                                                               silent=True,\n",
       "                                                                               subsample=1.0,\n",
       "                                                                               subsample_for_bin=200000,\n",
       "                                                                               subsample_freq=0)),\n",
       "                      n_iter=5, random_state=None, refit=True,\n",
       "                      scoring=<function rejection90_scorer at 0x7ff1b2a061e0>)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm = PermutationImportance(\n",
    "    ClassifierWithWeights(model), cv=kf, scoring=rejection90_scorer\n",
    ")\n",
    "perm.fit(df_valid.loc[:, FEATURES + [\"weight\"]].values, df_valid[\"label\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-12T05:03:34.567104Z",
     "iopub.status.busy": "2020-10-12T05:03:34.566497Z",
     "iopub.status.idle": "2020-10-12T05:03:34.690417Z",
     "shell.execute_reply": "2020-10-12T05:03:34.689106Z",
     "shell.execute_reply.started": "2020-10-12T05:03:34.567049Z"
    }
   },
   "outputs": [],
   "source": [
    "with open(f\"{DATA_PATH}feature_importance_0.4_valid.pkl\", \"wb\") as f:\n",
    "    pickle.dump([perm.feature_importances_, FEATURES + [\"weight\"]], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Финальный фит пайплайна"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T15:52:11.038796Z",
     "iopub.status.busy": "2020-10-16T15:52:11.038219Z",
     "iopub.status.idle": "2020-10-16T15:52:11.170939Z",
     "shell.execute_reply": "2020-10-16T15:52:11.169943Z",
     "shell.execute_reply.started": "2020-10-16T15:52:11.038743Z"
    }
   },
   "outputs": [],
   "source": [
    "model_path = f\"{DATA_PATH}model_MUON-49.pkl\"\n",
    "with open(model_path, \"rb\") as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T15:50:07.189913Z",
     "iopub.status.busy": "2020-10-16T15:50:07.189659Z",
     "iopub.status.idle": "2020-10-16T15:50:07.197393Z",
     "shell.execute_reply": "2020-10-16T15:50:07.196721Z",
     "shell.execute_reply.started": "2020-10-16T15:50:07.189890Z"
    }
   },
   "outputs": [],
   "source": [
    "cat_idx = [\n",
    "    FEATURES.index(\"MatchedHit_T_idxmax\"),\n",
    "    FEATURES.index(\"MatchedHit_T_idxmin\"),\n",
    "    FEATURES.index(\"MatchedHit_T_valid_hits_cat\"),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T15:50:03.667889Z",
     "iopub.status.busy": "2020-10-16T15:50:03.667531Z",
     "iopub.status.idle": "2020-10-16T15:50:06.313753Z",
     "shell.execute_reply": "2020-10-16T15:50:06.312977Z",
     "shell.execute_reply.started": "2020-10-16T15:50:03.667857Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train[\n",
    "    [\"MatchedHit_T_idxmax\", \"MatchedHit_T_idxmin\", \"MatchedHit_T_valid_hits_cat\"]\n",
    "] = df_train[\n",
    "    [\"MatchedHit_T_idxmax\", \"MatchedHit_T_idxmin\", \"MatchedHit_T_valid_hits_cat\"]\n",
    "].astype(\n",
    "    int\n",
    ")\n",
    "df_test[\n",
    "    [\"MatchedHit_T_idxmax\", \"MatchedHit_T_idxmin\", \"MatchedHit_T_valid_hits_cat\"]\n",
    "] = df_test[\n",
    "    [\"MatchedHit_T_idxmax\", \"MatchedHit_T_idxmin\", \"MatchedHit_T_valid_hits_cat\"]\n",
    "].astype(\n",
    "    int\n",
    ")\n",
    "df_private_test[\n",
    "    [\"MatchedHit_T_idxmax\", \"MatchedHit_T_idxmin\", \"MatchedHit_T_valid_hits_cat\"]\n",
    "] = df_private_test[\n",
    "    [\"MatchedHit_T_idxmax\", \"MatchedHit_T_idxmin\", \"MatchedHit_T_valid_hits_cat\"]\n",
    "].astype(\n",
    "    int\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заменяем weights по формуле $weights = max(weights,min(weights))$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T16:02:49.955105Z",
     "iopub.status.busy": "2020-10-14T16:02:49.954543Z",
     "iopub.status.idle": "2020-10-14T16:02:49.996361Z",
     "shell.execute_reply": "2020-10-14T16:02:49.995566Z",
     "shell.execute_reply.started": "2020-10-14T16:02:49.955057Z"
    }
   },
   "outputs": [],
   "source": [
    "idx = df_train[\"weight\"].loc[df_train[\"weight\"] < 0].index\n",
    "df_train[\"weight\"].loc[idx] = df_train[\"weight\"].loc[df_train[\"weight\"] > 0].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T16:15:40.622105Z",
     "iopub.status.busy": "2020-10-14T16:15:40.621406Z",
     "iopub.status.idle": "2020-10-14T16:15:40.817126Z",
     "shell.execute_reply": "2020-10-14T16:15:40.815733Z",
     "shell.execute_reply.started": "2020-10-14T16:15:40.622049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18         0.00006\n",
       "20         0.00006\n",
       "26         0.00006\n",
       "34         0.00006\n",
       "49         0.00006\n",
       "            ...   \n",
       "5445653    0.00006\n",
       "5445658    0.00006\n",
       "5445659    0.00006\n",
       "5445675    0.00006\n",
       "5445687    0.00006\n",
       "Name: weight, Length: 776386, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_train[\"weight\"].loc[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.fit(\n",
    "    df_train.loc[:, FEATURES],\n",
    "    df_train[\"label\"],\n",
    "    sample_weight=np.abs(df_train[\"weight\"]),\n",
    ")\n",
    "\n",
    "model_path = f\"{DATA_PATH}model_{experiment.id}.pkl\"\n",
    "with open(model_path, \"wb\") as f:\n",
    "    pickle.dump(model, f)\n",
    "\n",
    "experiment.log_artifact(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T16:25:47.764084Z",
     "iopub.status.busy": "2020-10-14T16:25:47.763780Z",
     "iopub.status.idle": "2020-10-14T16:25:47.788800Z",
     "shell.execute_reply": "2020-10-14T16:25:47.787919Z",
     "shell.execute_reply.started": "2020-10-14T16:25:47.764054Z"
    }
   },
   "outputs": [],
   "source": [
    "def log_feature_importance(model):\n",
    "    if isinstance(model, lgb.LGBMClassifier):\n",
    "        ax_split = lgb.plot_importance(\n",
    "            model, importance_type=\"split\", figsize=(15, 15), max_num_features=30\n",
    "        )\n",
    "        ax_gain = lgb.plot_importance(\n",
    "            model, importance_type=\"gain\", figsize=(15, 15), max_num_features=30\n",
    "        )\n",
    "        experiment.log_image(\"Feature importance: split\", ax_split.get_figure())\n",
    "        experiment.log_image(\"Feature importance: gain\", ax_gain.get_figure())\n",
    "    else:\n",
    "        fig = plt.figure(figsize=(15, 15))\n",
    "        ff = model.get_feature_importance(prettified=True).head(30)\n",
    "        plt.barh(\n",
    "            ff[\"Feature Id\"].values[::-1],\n",
    "            ff[\"Importances\"].values[::-1],\n",
    "        )\n",
    "        experiment.log_image(\"Feature importance\", fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T16:25:47.790720Z",
     "iopub.status.busy": "2020-10-14T16:25:47.790419Z",
     "iopub.status.idle": "2020-10-14T16:25:49.069758Z",
     "shell.execute_reply": "2020-10-14T16:25:49.068792Z",
     "shell.execute_reply.started": "2020-10-14T16:25:47.790690Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_feature_importance(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T15:52:59.982097Z",
     "iopub.status.busy": "2020-10-16T15:52:59.981526Z",
     "iopub.status.idle": "2020-10-16T15:53:08.665674Z",
     "shell.execute_reply": "2020-10-16T15:53:08.664511Z",
     "shell.execute_reply.started": "2020-10-16T15:52:59.982047Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions = model.predict_proba(df_test.loc[:, FEATURES])[:, 1]\n",
    "\n",
    "submit_name = \"sanity_check_test\"\n",
    "pd.DataFrame(data={\"prediction\": predictions}, index=df_test.index).to_csv(\n",
    "    f\"{DATA_PATH}{submit_name}.csv\", index_label=\"id\"\n",
    ")\n",
    "experiment.log_artifact(f\"{DATA_PATH}{submit_name}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-11T20:14:25.313185Z",
     "iopub.status.busy": "2020-10-11T20:14:25.312554Z",
     "iopub.status.idle": "2020-10-11T20:14:29.181164Z",
     "shell.execute_reply": "2020-10-11T20:14:29.179817Z",
     "shell.execute_reply.started": "2020-10-11T20:14:25.313121Z"
    }
   },
   "outputs": [],
   "source": [
    "experiment.stop()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}