ensembling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s9aqrWOnOi12"
   },
   "source": [
    "# 8300 public score aka best result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fg2fhsoAjd9O"
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "from google.colab import drive\n",
    "import os\n",
    "import numpy as np\n",
    "drive.mount('/content/drive')\n",
    "os.chdir('/content/drive/My Drive/kaggle/finnet/last_try')\n",
    "!pip install catboost\n",
    "clear_output()\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter(\"ignore\")\n",
    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YKTIegLZpvvd"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "import os\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter(\"ignore\")\n",
    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from catboost import CatBoostClassifier, Pool\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "import networkx as nx\n",
    "from gensim.models import KeyedVectors\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SqBdUU0iFGKU"
   },
   "outputs": [],
   "source": [
    "# fix seeds\n",
    "s = 42\n",
    "from numpy.random import seed\n",
    "seed(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XO21lB0Ppvvh"
   },
   "source": [
    "## Загрузим данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5683,
     "status": "ok",
     "timestamp": 1590412163568,
     "user": {
      "displayName": "Даниил Цимерман",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjW801CjZAa4FtuOmLzD5v2cc1gUWuL8CGiGqcT=s64",
      "userId": "00628076829980203964"
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     "user_tz": -600
    },
    "id": "32YGBqUhpvvi",
    "outputId": "5367d530-4fc3-4f3a-fe69-165814c3029a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.07 s, sys: 191 ms, total: 2.26 s\n",
      "Wall time: 2.38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "edges = pd.read_csv('../data/edges.csv')\n",
    "ids = pd.read_csv('../data/ids.csv')\n",
    "vertices = pd.read_csv('../data/vertices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AIPwlGqsUNzq"
   },
   "outputs": [],
   "source": [
    "# replace old region codes with new\n",
    "old_new_regions = {\n",
    "    81: 59,\n",
    "    85: 38,\n",
    "    99: 77,   \n",
    "}\n",
    "vertices['region_code'] = vertices['region_code'].replace(old_new_regions)\n",
    "\n",
    "# encode categorical vars\n",
    "vertices['main_okved'] = vertices['main_okved'].astype(str)\n",
    "vertices['region_code'] = vertices['region_code'].astype('uint8')\n",
    "vertices['pre_main_okved'] = vertices['main_okved'].agg([lambda x: x.split(\".\")[0]])\n",
    "vertices['post_main_okved'] = vertices['main_okved'].agg([lambda x: x.split(\".\")[1]])\n",
    "edges['av_transaction'] = edges['value'] / edges['n_transactions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S-El68UUhDdk"
   },
   "outputs": [],
   "source": [
    "inv_edges = edges[['id_2', 'id_1'] + edges.columns[2:].to_list()]\n",
    "inv_edges.rename({'id_2': 'id_1', 'id_1': 'id_2'}, axis=1, inplace=True)\n",
    "bi_edges = pd.concat([edges, inv_edges])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dwwI2aS7mr3l"
   },
   "outputs": [],
   "source": [
    "if 'embeddings256.pkl' not in os.listdir():\n",
    "    model = KeyedVectors.load_word2vec_format(\"n2v256.bin\")\n",
    "    embs = pd.DataFrame(np.zeros((len(vertices), model.vector_size + 1)), columns=['id'] + [f\"e_{i}\" for i in range(model.vector_size)])\n",
    "\n",
    "    for i in tqdm_notebook(range(1, 1534750)):\n",
    "        embs.iloc[i-1, 0] = i\n",
    "        if str(i) in model:\n",
    "            embs.iloc[i-1, 1:] = model[str(i)]\n",
    "    embs.to_pickle(\"/embeddings256.pkl\")\n",
    "else:\n",
    "    embs = pd.read_pickle(\"embeddings256.pkl\")\n",
    "\n",
    "emb_features = list(embs.columns[embs.columns.str.startswith('e_')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "67VeFyMowicy"
   },
   "outputs": [],
   "source": [
    "ver_w_features = vertices.copy()\n",
    "ver_w_features = pd.merge(ver_w_features, embs, how='left', on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QLsVMABqUYIy"
   },
   "outputs": [],
   "source": [
    "feature_edges = bi_edges[~bi_edges['id_2'].isin(ids['id'])]\n",
    "\n",
    "agg_funcs = ['mean', 'max', 'size']\n",
    "agg_features = feature_edges.groupby('id_1')[['value', 'n_transactions', 'av_transaction']].agg(agg_funcs)\n",
    "agg_features.columns = ['_'.join(i) for i in agg_features.columns]\n",
    "agg_features.drop(['value_size', 'av_transaction_size'], axis=1, inplace=True)\n",
    "agg_features_cols = agg_features.columns.to_list()\n",
    "agg_features = agg_features.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z0H50jL7UMI4"
   },
   "outputs": [],
   "source": [
    "ver_w_features = pd.merge(ver_w_features, agg_features, how='left', left_on='id', right_on='id_1').drop('id_1', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 81778,
     "status": "ok",
     "timestamp": 1590412242823,
     "user": {
      "displayName": "Даниил Цимерман",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjW801CjZAa4FtuOmLzD5v2cc1gUWuL8CGiGqcT=s64",
      "userId": "00628076829980203964"
     },
     "user_tz": -600
    },
    "id": "MrNTSJstvnvl",
    "outputId": "f5073278-5aa1-46b6-e1ab-c07a93d655e6"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del embs, edges, vertices, inv_edges;\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Pacf8YFCpvvr"
   },
   "source": [
    "## Обучим модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_EsXTSRtsDIM"
   },
   "outputs": [],
   "source": [
    "class WorldGreatestModel(object):\n",
    "    # basically, simple ensemble\n",
    "    def __init__(self, estimators=None):\n",
    "        self.estimators = estimators\n",
    "    \n",
    "    def fit(self, X, y, cat_features):\n",
    "        train = Pool(X, y, cat_features=cat_features)\n",
    "        for i in range(len(self.estimators)):\n",
    "            self.estimators[i].fit(X, y, cat_features=cat_features)\n",
    "        del train\n",
    "        gc.collect()\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        preds = [self.estimators[i].predict_proba(X) for i in range(len(self.estimators))]\n",
    "        preds = sum(preds) / len(self.estimators)\n",
    "        return preds\n",
    "\n",
    "\n",
    "def get_model(ver_id):\n",
    "    tt = 'GPU'\n",
    "    iters = 650\n",
    "    model = WorldGreatestModel(\n",
    "                    [\n",
    "                        CatBoostClassifier(\n",
    "                            iters, task_type=tt, random_state=s + 7, objective='Logloss',\n",
    "                            verbose=350,  border_count=32,\n",
    "                        ),\n",
    "                        CatBoostClassifier(\n",
    "                            iters, task_type=tt, random_state=s*2 + 6, objective='Logloss',\n",
    "                            verbose=350,  border_count=32\n",
    "                        ),\n",
    "                        CatBoostClassifier(\n",
    "                            iters, task_type=tt, random_state=s*3 + 5, objective='Logloss',\n",
    "                            verbose=350,  border_count=32\n",
    "                        ),\n",
    "                        CatBoostClassifier(\n",
    "                            iters, task_type=tt, random_state=s*4 + 4, objective='Logloss',\n",
    "                            verbose=350,  border_count=32\n",
    "                        ),\n",
    "                        CatBoostClassifier(\n",
    "                            iters, task_type=tt, random_state=s*5 + 3, objective='Logloss',\n",
    "                            verbose=350,  border_count=32\n",
    "                        ),\n",
    "                        CatBoostClassifier(\n",
    "                            iters, task_type=tt, random_state=s*6 + 2, objective='Logloss',\n",
    "                            verbose=350,  border_count=32\n",
    "                        ),\n",
    "                        CatBoostClassifier(\n",
    "                            iters, task_type=tt, random_state=s*7 + 1, objective='Logloss',\n",
    "                            verbose=350,  border_count=32\n",
    "                        ),\n",
    "                        ],\n",
    "                    )\n",
    "    return model"
   ]
  },
  {
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   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    "executionInfo": {
     "elapsed": 2208177,
     "status": "error",
     "timestamp": 1590414369987,
     "user": {
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      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjW801CjZAa4FtuOmLzD5v2cc1gUWuL8CGiGqcT=s64",
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      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0185313\ttotal: 34.6ms\tremaining: 0us\n",
      "Fitting vertice with id: 1297265\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0201911\ttotal: 32.9ms\tremaining: 0us\n",
      "Fitting vertice with id: 1292275\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0174756\ttotal: 35.7ms\tremaining: 0us\n",
      "Fitting vertice with id: 1356407\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0192217\ttotal: 35.7ms\tremaining: 0us\n",
      "Fitting vertice with id: 846476\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0185391\ttotal: 31.6ms\tremaining: 0us\n",
      "Fitting vertice with id: 567406\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0205608\ttotal: 35.1ms\tremaining: 0us\n",
      "Fitting vertice with id: 127685\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0144381\ttotal: 34.8ms\tremaining: 0us\n",
      "Fitting vertice with id: 1392226\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0174698\ttotal: 32.8ms\tremaining: 0us\n",
      "Fitting vertice with id: 434248\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0189284\ttotal: 31.3ms\tremaining: 0us\n",
      "Fitting vertice with id: 1452068\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0196885\ttotal: 33.3ms\tremaining: 0us\n",
      "Fitting vertice with id: 713665\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0201641\ttotal: 30.7ms\tremaining: 0us\n",
      "Fitting vertice with id: 391589\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0206361\ttotal: 31.9ms\tremaining: 0us\n",
      "Fitting vertice with id: 1487070\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0133514\ttotal: 32.8ms\tremaining: 0us\n",
      "Fitting vertice with id: 831789\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0169402\ttotal: 32.8ms\tremaining: 0us\n",
      "Fitting vertice with id: 800360\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0167206\ttotal: 33.6ms\tremaining: 0us\n",
      "Fitting vertice with id: 51755\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0208134\ttotal: 21.7ms\tremaining: 0us\n",
      "Fitting vertice with id: 1500415\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0205989\ttotal: 31.2ms\tremaining: 0us\n",
      "Fitting vertice with id: 596221\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0179310\ttotal: 33.8ms\tremaining: 0us\n",
      "Fitting vertice with id: 641486\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0196065\ttotal: 32.8ms\tremaining: 0us\n",
      "Fitting vertice with id: 1331069\n",
      "Learning rate set to 0.5\n",
      "0:\tlearn: 0.0207584\ttotal: 30.2ms\tremaining: 0us\n",
      "Fitting vertice with id: 310694\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-a070f05bebc4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Fitting vertice with id: {i}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcat_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-87542e021514>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, cat_features)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcat_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcat_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcat_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, cat_features, text_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)\u001b[0m\n\u001b[1;32m   3995\u001b[0m         self._fit(X, y, cat_features, text_features, None, sample_weight, None, None, None, None, baseline, use_best_model,\n\u001b[1;32m   3996\u001b[0m                   \u001b[0meval_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogging_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_description\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric_period\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3997\u001b[0;31m                   silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)\n\u001b[0m\u001b[1;32m   3998\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3999\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, cat_features, text_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)\u001b[0m\n\u001b[1;32m   1729\u001b[0m             \u001b[0muse_best_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogging_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1730\u001b[0m             \u001b[0mcolumn_description\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric_period\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1731\u001b[0;31m             \u001b[0msave_snapshot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnapshot_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnapshot_interval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1732\u001b[0m         )\n\u001b[1;32m   1733\u001b[0m         \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"params\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m_prepare_train_params\u001b[0;34m(self, X, y, cat_features, text_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)\u001b[0m\n\u001b[1;32m   1617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1618\u001b[0m         train_pool = _build_train_pool(X, y, cat_features, text_features, pairs, sample_weight, group_id,\n\u001b[0;32m-> 1619\u001b[0;31m                                        group_weight, subgroup_id, pairs_weight, baseline, column_description)\n\u001b[0m\u001b[1;32m   1620\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtrain_pool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_empty_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1621\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCatBoostError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"X is empty.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m_build_train_pool\u001b[0;34m(X, y, cat_features, text_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, column_description)\u001b[0m\n\u001b[1;32m    955\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCatBoostError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y has not initialized in fit(): X is not catboost.Pool object, y must be not None in fit().\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    956\u001b[0m         train_pool = Pool(X, y, cat_features=cat_features, text_features=text_features, pairs=pairs, weight=sample_weight, group_id=group_id,\n\u001b[0;32m--> 957\u001b[0;31m                           group_weight=group_weight, subgroup_id=subgroup_id, pairs_weight=pairs_weight, baseline=baseline)\n\u001b[0m\u001b[1;32m    958\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtrain_pool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, label, cat_features, text_features, column_description, pairs, delimiter, has_header, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\u001b[0m\n\u001b[1;32m    432\u001b[0m                     )\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcat_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubgroup_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpairs_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbaseline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m_init\u001b[0;34m(self, data, label, cat_features, text_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\u001b[0m\n\u001b[1;32m    936\u001b[0m             \u001b[0mbaseline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbaseline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msamples_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_baseline_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbaseline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_pool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcat_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubgroup_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpairs_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbaseline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._PoolBase._init_pool\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._PoolBase._init_pool\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._PoolBase._init_features_order_layout_pool\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._set_features_order_data_pd_data_frame\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36miteritems\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    899\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"items\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 901\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    902\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mitems\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    892\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_item_cache\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "num_features = emb_features + agg_features_cols\n",
    "cat_features = ['main_okved', 'region_code', 'company_type', 'pre_main_okved', 'post_main_okved']\n",
    "features = num_features + cat_features\n",
    "\n",
    "n_left = [2000, 3000, 4000]\n",
    "results = {n: pd.DataFrame(columns=['id_1', 'id_2', 'goodness']) for n in n_left}\n",
    "\n",
    "\n",
    "for i in tqdm_notebook(ids[\"id\"]):\n",
    "    train = bi_edges[bi_edges['id_1'] == i]\n",
    "    train['target'] = 1\n",
    "    train = pd.merge(ver_w_features, train[['id_2', 'target']], how='left', left_on='id', right_on='id_2')\n",
    "    train.drop('id_2', inplace=True, axis=1)\n",
    "    train.rename({'id': 'id_2'}, axis=1, inplace=True)\n",
    "    train[\"target\"] = train[\"target\"].fillna(0)\n",
    "    \n",
    "    X = train[features]\n",
    "    y = train['target']\n",
    "    \n",
    "    model = get_model(i)\n",
    "    print(f\"Fitting vertice with id: {i}\")\n",
    "    model.fit(X, y, cat_features=cat_features)\n",
    "\n",
    "    preds = model.predict_proba(X)[:,1]\n",
    "\n",
    "    train['id_1'] = i\n",
    "    train['goodness'] = preds\n",
    "    \n",
    "    res = train[train['target'] != 1].sort_values(by='goodness', ascending=False)\n",
    "\n",
    "    for n in n_left:\n",
    "        results[n] = results[n].append(res.iloc[:n][['id_1', 'id_2', 'goodness']], ignore_index=True, sort=False)\n",
    "\n",
    "    del res, preds, train, y, X, model\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ML1Dke5upvvx"
   },
   "source": [
    "## Результат готов к отправке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2aS272Xz96RG"
   },
   "outputs": [],
   "source": [
    "def drop_same_edges(res):\n",
    "    res = nx.to_pandas_edgelist(nx.from_pandas_edgelist(res, 'id_1', 'id_2', 'goodness')).rename({'source':'id_1', 'target':'id_2'}, axis=1)\n",
    "    return res \n",
    "\n",
    "def write_sub(res, fp='subs/submission.csv'):\n",
    "    res.sort_values(by='goodness', ascending=False)[['id_1', 'id_2']].head(100000).to_csv(fp, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11910,
     "status": "ok",
     "timestamp": 1590414453255,
     "user": {
      "displayName": "Даниил Цимерман",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjW801CjZAa4FtuOmLzD5v2cc1gUWuL8CGiGqcT=s64",
      "userId": "00628076829980203964"
     },
     "user_tz": -600
    },
    "id": "OGdgZ1FVJEz_",
    "outputId": "02c85f10-7793-4d9e-eaee-cec8ead07e80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 left\n",
      "         id  edges_before  edges_added  edges_after  main_okved  region_code company_type\n",
      "0     41216           977         2001         2978       47.54           66   Individual\n",
      "1     55389           572            1          573       46.60           66      Limited\n",
      "2     61537          2246         2000         4246       46.66           66      Limited\n",
      "3     83777          1618         2000         3618       71.20           16      Limited\n",
      "4     83818          1255         2000         3255       63.11           77      Limited\n",
      "5    127685           655         2000         2655       63.11           55      Limited\n",
      "6    194554           723         2000         2723       63.11           77      Limited\n",
      "7    206473          1543         2000         3543       63.11           77      Limited\n",
      "8    214507           837         2000         2837       69.20           77   Individual\n",
      "9    227665           718         2000         2718       46.72           54      Limited\n",
      "10   258315           833         2000         2833       70.22           47   Individual\n",
      "11   300432          1709         2000         3709       63.11           66      Limited\n",
      "12   324065           830         2000         2830       18.12           77   Individual\n",
      "13   331678           708         2000         2708       17.20           78      Limited\n",
      "14   341670          1394         2000         3394       46.43           12   Individual\n",
      "15   361401          1463         2001         3464       49.40           59      Limited\n",
      "16   386565           567            1          568       62.00           59      Limited\n",
      "17   391589           647         1365         2012       46.74           77      Limited\n",
      "18   409925           793          355         1148       46.73           78      Limited\n",
      "19   434248           636         2000         2636       61.10           77      Limited\n",
      "20   524354          2971         2000         4971       62.01           77      Limited\n",
      "21   567406           686          160          846       62.01           77      Limited\n",
      "22   596221           630         2000         2630       46.72           52      Limited\n",
      "23   640944           805         2000         2805       46.75           66      Limited\n",
      "24   641486           620          782         1402       53.20           77      Limited\n",
      "25   676805           712          137          849       46.73           74      Limited\n",
      "26   713665           646          138          784       47.91           78      Limited\n",
      "27   785334           843         2000         2843       73.11           77      Limited\n",
      "28   800360           650         2000         2650       46.43           78      Limited\n",
      "29   831789           672         2000         2672       86.10           77      Limited\n",
      "30   846476           669           48          717       13.30           50   Individual\n",
      "31   929264          1039         2000         3039       46.69           77      Limited\n",
      "32   955991           762         2001         2763       79.11           77      Limited\n",
      "33   966969           546            1          547       46.90           66      Limited\n",
      "34   983180           912         1488         2400       46.90           77      Limited\n",
      "35  1025839           541            2          543       49.40           77      Limited\n",
      "36  1036225           534            1          535       45.31           26      Limited\n",
      "37  1063763          1001          919         1920       46.73           77   Individual\n",
      "38  1136487           766         2002         2768       46.51           66      Limited\n",
      "39  1142564          1930         2000         3930       63.11           77      Limited\n",
      "40  1147550           775         2001         2776       62.09           39      Limited\n",
      "41  1154568           801         2000         2801       46.74           66      Limited\n",
      "42  1189202          1255         2000         3255       46.65           78      Limited\n",
      "43  1203323          1439         2000         3439       62.01           66      Limited\n",
      "44  1209828          1480         2000         3480       63.11           66      Limited\n",
      "45  1227912           925         2000         2925       62.01           77      Limited\n",
      "46  1263393          1043         2000         3043       47.91           78      Limited\n",
      "47  1292275           678         2000         2678       73.11           66      Limited\n",
      "48  1292407          1523         2000         3523       62.01           78      Limited\n",
      "49  1297265           683         2000         2683       31.00           77      Limited\n",
      "50  1301544          1530         2000         3530       52.29           66      Limited\n",
      "51  1319172           840         2000         2840       52.29           77      Limited\n",
      "52  1331069           586           88          674       71.20           78      Limited\n",
      "53  1342003          1100         2001         3101       62.09           26   Individual\n",
      "54  1356407           649         2000         2649       46.72           77      Limited\n",
      "55  1378192           753         2000         2753       46.69           54   Individual\n",
      "56  1380777           673         2000         2673       49.42           66      Limited\n",
      "57  1392199           510            1          511       46.40           59      Limited\n",
      "58  1392226           645         2000         2645        2.10           59      Limited\n",
      "59  1408687           756         2000         2756       52.29           74      Limited\n",
      "60  1434836          1225         2000         3225       47.59           77      Limited\n",
      "61  1443535           865         2000         2865       25.61           33      Limited\n",
      "62  1452068           659           84          743       62.01           77      Limited\n",
      "63  1487070           629         2000         2629       53.20           66      Limited\n",
      "64  1500415           612          436         1048       46.69           50      Limited\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "3000 left\n",
      "         id  edges_before  edges_added  edges_after  main_okved  region_code company_type\n",
      "0     41216           977         3001         3978       47.54           66   Individual\n",
      "1     55389           572            1          573       46.60           66      Limited\n",
      "2     61537          2246         3000         5246       46.66           66      Limited\n",
      "3     83777          1618         3000         4618       71.20           16      Limited\n",
      "4     83818          1255         3000         4255       63.11           77      Limited\n",
      "5    127685           655         2432         3087       63.11           55      Limited\n",
      "6    206473          1543         3000         4543       63.11           77      Limited\n",
      "7    227665           718         3000         3718       46.72           54      Limited\n",
      "8    258315           833         2877         3710       70.22           47   Individual\n",
      "9    300432          1709         3000         4709       63.11           66      Limited\n",
      "10   331678           708         3000         3708       17.20           78      Limited\n",
      "11   341670          1394         1429         2823       46.43           12   Individual\n",
      "12   361401          1463         3001         4464       49.40           59      Limited\n",
      "13   386565           567            1          568       62.00           59      Limited\n",
      "14   391589           647           24          671       46.74           77      Limited\n",
      "15   409925           793          194          987       46.73           78      Limited\n",
      "16   434248           636          385         1021       61.10           77      Limited\n",
      "17   524354          2971         3000         5971       62.01           77      Limited\n",
      "18   596221           630         2437         3067       46.72           52      Limited\n",
      "19   640944           805         3000         3805       46.75           66      Limited\n",
      "20   641486           620          617         1237       53.20           77      Limited\n",
      "21   676805           712           46          758       46.73           74      Limited\n",
      "22   785334           843         3000         3843       73.11           77      Limited\n",
      "23   800360           650         3000         3650       46.43           78      Limited\n",
      "24   831789           672         3000         3672       86.10           77      Limited\n",
      "25   846476           669            7          676       13.30           50   Individual\n",
      "26   929264          1039         3000         4039       46.69           77      Limited\n",
      "27   955991           762           40          802       79.11           77      Limited\n",
      "28   966969           546            1          547       46.90           66      Limited\n",
      "29   983180           912           50          962       46.90           77      Limited\n",
      "30  1025839           541            2          543       49.40           77      Limited\n",
      "31  1036225           534            1          535       45.31           26      Limited\n",
      "32  1063763          1001           22         1023       46.73           77   Individual\n",
      "33  1136487           766         3002         3768       46.51           66      Limited\n",
      "34  1142564          1930         3000         4930       63.11           77      Limited\n",
      "35  1147550           775         3001         3776       62.09           39      Limited\n",
      "36  1154568           801         3000         3801       46.74           66      Limited\n",
      "37  1189202          1255         1012         2267       46.65           78      Limited\n",
      "38  1203323          1439         3000         4439       62.01           66      Limited\n",
      "39  1209828          1480         3000         4480       63.11           66      Limited\n",
      "40  1292275           678         3000         3678       73.11           66      Limited\n",
      "41  1292407          1523         3000         4523       62.01           78      Limited\n",
      "42  1301544          1530         2860         4390       52.29           66      Limited\n",
      "43  1319172           840         3000         3840       52.29           77      Limited\n",
      "44  1342003          1100         3001         4101       62.09           26   Individual\n",
      "45  1356407           649          787         1436       46.72           77      Limited\n",
      "46  1378192           753          188          941       46.69           54   Individual\n",
      "47  1392199           510            1          511       46.40           59      Limited\n",
      "48  1392226           645         2252         2897        2.10           59      Limited\n",
      "49  1408687           756         3000         3756       52.29           74      Limited\n",
      "50  1443535           865         1218         2083       25.61           33      Limited\n",
      "51  1487070           629         3000         3629       53.20           66      Limited\n",
      "52  1500415           612          126          738       46.69           50      Limited\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "4000 left\n",
      "         id  edges_before  edges_added  edges_after  main_okved  region_code company_type\n",
      "0     41216           977         4001         4978       47.54           66   Individual\n",
      "1     55389           572            1          573       46.60           66      Limited\n",
      "2     61537          2246         4000         6246       46.66           66      Limited\n",
      "3     83777          1618         4000         5618       71.20           16      Limited\n",
      "4     83818          1255          871         2126       63.11           77      Limited\n",
      "5    127685           655         2432         3087       63.11           55      Limited\n",
      "6    206473          1543         4000         5543       63.11           77      Limited\n",
      "7    227665           718         3581         4299       46.72           54      Limited\n",
      "8    258315           833         2877         3710       70.22           47   Individual\n",
      "9    300432          1709         3980         5689       63.11           66      Limited\n",
      "10   331678           708         3461         4169       17.20           78      Limited\n",
      "11   341670          1394          638         2032       46.43           12   Individual\n",
      "12   361401          1463         3187         4650       49.40           59      Limited\n",
      "13   386565           567            1          568       62.00           59      Limited\n",
      "14   391589           647           12          659       46.74           77      Limited\n",
      "15   409925           793          194          987       46.73           78      Limited\n",
      "16   434248           636          385         1021       61.10           77      Limited\n",
      "17   524354          2971         4000         6971       62.01           77      Limited\n",
      "18   596221           630         2383         3013       46.72           52      Limited\n",
      "19   640944           805         4001         4806       46.75           66      Limited\n",
      "20   641486           620          153          773       53.20           77      Limited\n",
      "21   785334           843         4000         4843       73.11           77      Limited\n",
      "22   800360           650         1582         2232       46.43           78      Limited\n",
      "23   831789           672         1539         2211       86.10           77      Limited\n",
      "24   846476           669            7          676       13.30           50   Individual\n",
      "25   929264          1039          985         2024       46.69           77      Limited\n",
      "26   955991           762           39          801       79.11           77      Limited\n",
      "27   966969           546            1          547       46.90           66      Limited\n",
      "28   983180           912           50          962       46.90           77      Limited\n",
      "29  1025839           541            1          542       49.40           77      Limited\n",
      "30  1036225           534            1          535       45.31           26      Limited\n",
      "31  1063763          1001           22         1023       46.73           77   Individual\n",
      "32  1136487           766         4002         4768       46.51           66      Limited\n",
      "33  1142564          1930         4000         5930       63.11           77      Limited\n",
      "34  1147550           775         4001         4776       62.09           39      Limited\n",
      "35  1154568           801         4002         4803       46.74           66      Limited\n",
      "36  1209828          1480         4000         5480       63.11           66      Limited\n",
      "37  1292275           678         4000         4678       73.11           66      Limited\n",
      "38  1292407          1523         4000         5523       62.01           78      Limited\n",
      "39  1301544          1530          331         1861       52.29           66      Limited\n",
      "40  1319172           840         4000         4840       52.29           77      Limited\n",
      "41  1342003          1100         4001         5101       62.09           26   Individual\n",
      "42  1356407           649          787         1436       46.72           77      Limited\n",
      "43  1378192           753          188          941       46.69           54   Individual\n",
      "44  1392199           510            1          511       46.40           59      Limited\n",
      "45  1392226           645         2045         2690        2.10           59      Limited\n",
      "46  1408687           756          148          904       52.29           74      Limited\n",
      "47  1487070           629         4000         4629       53.20           66      Limited\n",
      "48  1500415           612          126          738       46.69           50      Limited\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fp = 'subs/ensemble4_700iters_iloc{}.csv'\n",
    "vertices = pd.read_csv('../data/vertices.csv')\n",
    "\n",
    "for n in n_left:\n",
    "    cfp = fp.format(n)\n",
    "    write_sub(drop_same_edges(results[n]), cfp)\n",
    "    s = pd.read_csv(cfp)\n",
    "    ie = s[['id_2', 'id_1'] + s.columns[2:].to_list()]\n",
    "    ie.rename({'id_2': 'id_1', 'id_1': 'id_2'}, axis=1, inplace=True)\n",
    "    be = pd.concat([s, ie])\n",
    "    b = be[be['id_1'].isin(ids['id'])]['id_1'].value_counts().reset_index().rename({'index': 'id', 'id_1' :'edges_added'}, axis=1)\n",
    "    a = bi_edges.groupby('id_1')['n_transactions'].size().reset_index().rename({'id_1' :'id', 'n_transactions': 'edges_before'}, axis=1)\n",
    "    a = a[a['id'].isin(ids['id'])]\n",
    "    df = pd.merge(a, b, on='id')\n",
    "    df['edges_after'] = df['edges_before'] + df['edges_added']\n",
    "    df = pd.merge(df, vertices, on='id')\n",
    "    print(n, 'left')\n",
    "    print(df.to_string())\n",
    "    print('\\n\\n\\n')"
   ]
  }
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