Baseline - simple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import xgboost\n",
    "import utils\n",
    "import scoring\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test\n",
    "# ! wget --content-disposition https://codalab.coresearch.club/my/datasets/download/dd6255a1-a14b-4276-9a2b-db7f360e01c7\n",
    "# Train\n",
    "# ! wget --content-disposition https://codalab.coresearch.club/my/datasets/download/3a5e940c-2382-4716-9ff7-8fbc269b98ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nkazeev/.conda/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:569: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "columns = utils.SIMPLE_FEATURE_COLUMNS + [\"id\", \"label\", \"weight\", \"sWeight\", \"kinWeight\"]\n",
    "DATA_PATH = \".\"\n",
    "train = pd.read_csv(os.path.join(DATA_PATH, \"train.csv.gz\"), index_col=\"id\", usecols=columns)\n",
    "test = pd.read_csv(os.path.join(DATA_PATH, \"test-features.csv.gz\"), index_col=\"id\", usecols=utils.SIMPLE_FEATURE_COLUMNS + [\"id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>1497.54480</td>\n",
       "      <td>2901.06470</td>\n",
       "      <td>4</td>\n",
       "      <td>2360.763287</td>\n",
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       "      <td>-0.414881</td>\n",
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       "      <td>28</td>\n",
       "      <td>8</td>\n",
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       "      <td>15</td>\n",
       "      <td>2.964286</td>\n",
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       "      <td>25.208195</td>\n",
       "      <td>55.87817</td>\n",
       "      <td>106.25816</td>\n",
       "      <td>7</td>\n",
       "      <td>6015.405116</td>\n",
       "      <td>74550.106103</td>\n",
       "      <td>1.218314</td>\n",
       "      <td>1</td>\n",
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       "      <td>1507.013894</td>\n",
       "      <td>19206.813287</td>\n",
       "      <td>-0.387133</td>\n",
       "      <td>1</td>\n",
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       "      <td>-0.718697</td>\n",
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       "    ncl[0]  ncl[1]  ncl[2]  ncl[3]  avg_cs[0]  avg_cs[1]  avg_cs[2]  \\\n",
       "id                                                                    \n",
       "0       79      18      22      10   2.392405   2.055556   1.590909   \n",
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       "4       50       8       4      13   2.400000   1.250000   1.500000   \n",
       "\n",
       "    avg_cs[3]  ndof  MatchedHit_TYPE[0]  ...  Mextra_DY2[1]  Mextra_DY2[2]  \\\n",
       "id                                       ...                                 \n",
       "0    1.200000     8                   2  ...    5501.422000    12634.98400   \n",
       "1    1.400000     8                   2  ...     671.103200     1497.54480   \n",
       "2    1.280000     8                   2  ...     917.825600     2065.64230   \n",
       "3    1.000000     8                   2  ...      25.208195       55.87817   \n",
       "4    1.076923     8                   2  ...     408.524720      917.87640   \n",
       "\n",
       "    Mextra_DY2[3]  FOI_hits_N           PT             P   sWeight  label  \\\n",
       "id                                                                          \n",
       "0     25386.30500           8  1230.391860   5488.168681  1.190017      1   \n",
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <td>266.41522</td>\n",
       "      <td>21.983400</td>\n",
       "      <td>63.552402</td>\n",
       "      <td>140.25285</td>\n",
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       "      <td>7</td>\n",
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       "      <td>471.151430</td>\n",
       "      <td>900.27890</td>\n",
       "      <td>72.982890</td>\n",
       "      <td>211.607040</td>\n",
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       "      <td>900.28613</td>\n",
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       "      <th>4</th>\n",
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       "      <td>39.018433</td>\n",
       "      <td>85.89129</td>\n",
       "      <td>162.87330</td>\n",
       "      <td>8</td>\n",
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       "    ncl[0]  ncl[1]  ncl[2]  ncl[3]  avg_cs[0]  avg_cs[1]  avg_cs[2]  \\\n",
       "id                                                                    \n",
       "0       55      12      10      15   2.127273   1.166667   1.500000   \n",
       "1       43      18      12      15   2.697675   1.111111   1.333333   \n",
       "2       90      34      19      29   1.800000   2.058824   1.210526   \n",
       "3      141      43      15      16   4.609929   2.744186   1.466667   \n",
       "4       22      18       8       9   1.636364   2.444444   1.000000   \n",
       "\n",
       "    avg_cs[3]  ndof  MatchedHit_TYPE[0]  ...  Mextra_DX2[1]  Mextra_DX2[2]  \\\n",
       "id                                       ...                                 \n",
       "0    1.600000     8                   2  ...    3431.287800    7550.342300   \n",
       "1    1.600000     8                   2  ...      63.777058     140.748630   \n",
       "2    1.344828     8                   2  ...     401.787050     903.642400   \n",
       "3    1.125000     8                   2  ...     211.605320     471.151430   \n",
       "4    1.555556     8                   2  ...      39.059680      85.982086   \n",
       "\n",
       "    Mextra_DX2[3]  Mextra_DY2[0]  Mextra_DY2[1]  Mextra_DY2[2]  Mextra_DY2[3]  \\\n",
       "id                                                                              \n",
       "0     14739.10600    1210.756600    3462.087200     7618.11100    14871.39550   \n",
       "1       266.41522      21.983400      63.552402      140.25285      265.47680   \n",
       "2      1749.45590     133.357620     383.406950      862.30440     1669.42520   \n",
       "3       900.27890      72.982890     211.607040      471.15527      900.28613   \n",
       "4       163.04549      13.728162      39.018433       85.89129      162.87330   \n",
       "\n",
       "    FOI_hits_N           PT             P  \n",
       "id                                         \n",
       "0            6  1174.484530   6884.502272  \n",
       "1            7  4344.639775  48769.526891  \n",
       "2            4  4109.805750  20296.048412  \n",
       "3            9   940.242439  26062.046122  \n",
       "4            8  1386.377851  59872.343332  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_part, validation = train_test_split(train, test_size=0.25, shuffle=True, random_state=2342234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgboost.XGBClassifier(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train_part.loc[:, utils.SIMPLE_FEATURE_COLUMNS].values,\n",
    "          train_part.label.values,\n",
    "          sample_weight=train_part.kinWeight.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_predictions = model.predict_proba(validation.loc[:, utils.SIMPLE_FEATURE_COLUMNS].values)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring.rejection90(validation.label.values, validation_predictions, sample_weight=validation.weight.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train.loc[:, utils.SIMPLE_FEATURE_COLUMNS].values, train.label, sample_weight=train.kinWeight.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_proba(test.loc[:, utils.SIMPLE_FEATURE_COLUMNS].values)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(data={\"prediction\": predictions}, index=test.index).to_csv(\n",
    "    \"submission.csv.gz\", index_label=utils.ID_COLUMN)"
   ]
  }
 ],
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