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atmaCup10
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032_more_FE.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import os\n",
"import time\n",
"import warnings\n",
"\n",
"import catboost as ctb\n",
"import lightgbm as lgb\n",
"import matplotlib.pyplot as plt\n",
"import nltk\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pycld2 as cld2\n",
"import seaborn as sns\n",
"import texthero as hero\n",
"import xgboost as xgb\n",
"\n",
"from contextlib import contextmanager\n",
"from pathlib import Path\n",
"from typing import Optional\n",
"\n",
"from fasttext import load_model\n",
"from gensim.models import word2vec, KeyedVectors\n",
"from geopy.geocoders import Nominatim\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.decomposition import TruncatedSVD\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /home/arai/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.download(\"stopwords\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"pd.set_option(\"max_columns\", 60)\n",
"tqdm.pandas()\n",
"\n",
"model = load_model(\"../bin/lid.176.bin\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"OUTDIR = Path(\"../out/032_more_FE\")\n",
"OUTDIR.mkdir(exist_ok=True, parents=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"model_dutch = load_model(\"../bin/cc.nl.300.bin\")\n",
"model_en = load_model(\"../bin/cc.en.300.bin\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utils"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"@contextmanager\n",
"def timer(name: str):\n",
" t0 = time.time()\n",
" print(f\"[{name}] start\")\n",
" yield\n",
" msg = f\"[{name}] done in {time.time() - t0:.0f} s\"\n",
" print(msg)\n",
" \n",
" \n",
"def set_seed(seed=42):\n",
" random.seed(seed)\n",
" os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
" np.random.seed(seed)\n",
" \n",
" \n",
"set_seed(1213)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Load train/test data] start\n",
"[Load train/test data] done in 0 s\n"
]
}
],
"source": [
"DATADIR = Path(\"../input/atmacup10_dataset/\")\n",
"\n",
"with timer(\"Load train/test data\"):\n",
" train = pd.read_csv(DATADIR / \"train.csv\")\n",
" test = pd.read_csv(DATADIR / \"test.csv\")\n",
" technique = pd.read_csv(DATADIR / \"technique.csv\")\n",
" place = pd.read_csv(DATADIR / \"production_place.csv\")\n",
" historical = pd.read_csv(DATADIR / \"historical_person.csv\")\n",
" collection = pd.read_csv(DATADIR / \"object_collection.csv\")\n",
" material = pd.read_csv(DATADIR / \"material.csv\")\n",
" principal = pd.read_csv(DATADIR / \"principal_maker.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Word2Vec"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"major_historical_person = historical[\"name\"].value_counts()[historical[\"name\"].value_counts() > 20].index.values"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(major_historical_person)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"historical = historical[historical[\"name\"].isin(major_historical_person)].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"material_collection = pd.concat([material, collection], axis=0).reset_index(drop=True)\n",
"material_technique = pd.concat([material, technique], axis=0).reset_index(drop=True)\n",
"collection_technique = pd.concat([collection, technique], axis=0).reset_index(drop=True)\n",
"material_collection_technique = pd.concat([material, collection, technique], axis=0).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for material] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 30%|██▉ | 7062/23586 [00:00<00:00, 70618.38it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for material] done in 3 s\n",
"[Getting document vector for material] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 23586/23586 [00:00<00:00, 71284.04it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Getting document vector for material] done in 0 s\n",
"[Creating w2v for collection] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 14160/14160 [00:00<00:00, 75510.97it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for collection] done in 2 s\n",
"[Getting document vector for collection] start\n",
"[Getting document vector for collection] done in 0 s\n",
"[Creating w2v for technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
" 49%|████▉ | 8464/17329 [00:00<00:00, 84630.40it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for technique] done in 2 s\n",
"[Getting document vector for technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 17329/17329 [00:00<00:00, 78232.60it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Getting document vector for technique] done in 0 s\n",
"[Creating w2v for material_collection] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 67%|██████▋ | 15836/23597 [00:00<00:00, 78780.00it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for material_collection] done in 3 s\n",
"[Getting document vector for material_collection] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 23597/23597 [00:00<00:00, 76941.31it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Getting document vector for material_collection] done in 0 s\n",
"[Creating w2v for material_technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 61%|██████ | 14514/23950 [00:00<00:00, 73918.37it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for material_technique] done in 3 s\n",
"[Getting document vector for material_technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 23950/23950 [00:00<00:00, 73477.23it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Getting document vector for material_technique] done in 0 s\n",
"[Creating w2v for collection_technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 35%|███▌ | 7650/21646 [00:00<00:00, 76498.61it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for collection_technique] done in 3 s\n",
"[Getting document vector for collection_technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 21646/21646 [00:00<00:00, 72720.06it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Getting document vector for collection_technique] done in 0 s\n",
"[Creating w2v for material_collection_technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 30%|███ | 7198/23953 [00:00<00:00, 71972.18it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for material_collection_technique] done in 4 s\n",
"[Getting document vector for material_collection_technique] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 23953/23953 [00:00<00:00, 73147.16it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Getting document vector for material_collection_technique] done in 0 s\n",
"[Creating w2v for historical] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1502/1502 [00:00<00:00, 66995.39it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Creating w2v for historical] done in 0 s\n",
"[Getting document vector for historical] start\n",
"[Getting document vector for historical] done in 0 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"model_size = {\n",
" \"material\": 30,\n",
" \"technique\": 15,\n",
" \"collection\": 6,\n",
" \"material_collection\": 35,\n",
" \"material_technique\": 35,\n",
" \"collection_technique\": 20,\n",
" \"material_collection_technique\": 40,\n",
" \"historical\": 10\n",
"}\n",
"n_iter = 100\n",
"w2v_dfs = []\n",
"for df, df_name in zip(\n",
" [material, collection, technique, material_collection, material_technique, collection_technique, material_collection_technique, historical], \n",
" [\"material\", \"collection\", \"technique\", \"material_collection\", \"material_technique\", \"collection_technique\", \"material_collection_technique\", \"historical\"]):\n",
" with timer(f\"Creating w2v for {df_name}\"):\n",
" df_group = df.groupby(\"object_id\")[\"name\"].apply(list).reset_index()\n",
" w2v_model = word2vec.Word2Vec(df_group[\"name\"].values.tolist(),\n",
" size=model_size[df_name],\n",
" min_count=1,\n",
" window=1,\n",
" iter=n_iter)\n",
" \n",
" with timer(f\"Getting document vector for {df_name}\"):\n",
" vec_for_docs = df_group[\"name\"].progress_apply(lambda x: np.mean([w2v_model.wv[e] for e in x], axis=0))\n",
" vec_for_docs = np.vstack([x for x in vec_for_docs])\n",
" doc_df = pd.DataFrame(vec_for_docs,\n",
" columns=[f\"{df_name}_w2v_{i}\" for i in range(model_size[df_name])])\n",
" doc_df[\"object_id\"] = df_group[\"object_id\"]\n",
" w2v_dfs.append(doc_df)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>material_collection_technique_w2v_0</th>\n",
" <th>material_collection_technique_w2v_1</th>\n",
" <th>material_collection_technique_w2v_2</th>\n",
" <th>material_collection_technique_w2v_3</th>\n",
" <th>material_collection_technique_w2v_4</th>\n",
" <th>material_collection_technique_w2v_5</th>\n",
" <th>material_collection_technique_w2v_6</th>\n",
" <th>material_collection_technique_w2v_7</th>\n",
" <th>material_collection_technique_w2v_8</th>\n",
" <th>material_collection_technique_w2v_9</th>\n",
" <th>material_collection_technique_w2v_10</th>\n",
" <th>material_collection_technique_w2v_11</th>\n",
" <th>material_collection_technique_w2v_12</th>\n",
" <th>material_collection_technique_w2v_13</th>\n",
" <th>material_collection_technique_w2v_14</th>\n",
" <th>material_collection_technique_w2v_15</th>\n",
" <th>material_collection_technique_w2v_16</th>\n",
" <th>material_collection_technique_w2v_17</th>\n",
" <th>material_collection_technique_w2v_18</th>\n",
" <th>material_collection_technique_w2v_19</th>\n",
" <th>material_collection_technique_w2v_20</th>\n",
" <th>material_collection_technique_w2v_21</th>\n",
" <th>material_collection_technique_w2v_22</th>\n",
" <th>material_collection_technique_w2v_23</th>\n",
" <th>material_collection_technique_w2v_24</th>\n",
" <th>material_collection_technique_w2v_25</th>\n",
" <th>material_collection_technique_w2v_26</th>\n",
" <th>material_collection_technique_w2v_27</th>\n",
" <th>material_collection_technique_w2v_28</th>\n",
" <th>material_collection_technique_w2v_29</th>\n",
" <th>material_collection_technique_w2v_30</th>\n",
" <th>material_collection_technique_w2v_31</th>\n",
" <th>material_collection_technique_w2v_32</th>\n",
" <th>material_collection_technique_w2v_33</th>\n",
" <th>material_collection_technique_w2v_34</th>\n",
" <th>material_collection_technique_w2v_35</th>\n",
" <th>material_collection_technique_w2v_36</th>\n",
" <th>material_collection_technique_w2v_37</th>\n",
" <th>material_collection_technique_w2v_38</th>\n",
" <th>material_collection_technique_w2v_39</th>\n",
" <th>object_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.214781</td>\n",
" <td>-1.254680</td>\n",
" <td>-0.079783</td>\n",
" <td>-0.169312</td>\n",
" <td>0.068885</td>\n",
" <td>-0.234959</td>\n",
" <td>0.025053</td>\n",
" <td>-0.099676</td>\n",
" <td>-0.539394</td>\n",
" <td>-0.474485</td>\n",
" <td>0.752900</td>\n",
" <td>-0.258753</td>\n",
" <td>0.070450</td>\n",
" <td>0.132246</td>\n",
" <td>0.541844</td>\n",
" <td>-0.458638</td>\n",
" <td>0.028730</td>\n",
" <td>0.549018</td>\n",
" <td>0.255246</td>\n",
" <td>-0.538175</td>\n",
" <td>0.674761</td>\n",
" <td>-0.703094</td>\n",
" <td>0.558242</td>\n",
" <td>-0.011356</td>\n",
" <td>-0.198824</td>\n",
" <td>-0.014135</td>\n",
" <td>-0.354850</td>\n",
" <td>0.305101</td>\n",
" <td>0.044536</td>\n",
" <td>-0.190434</td>\n",
" <td>-0.692245</td>\n",
" <td>0.555915</td>\n",
" <td>0.032849</td>\n",
" <td>0.264612</td>\n",
" <td>-0.522483</td>\n",
" <td>0.139394</td>\n",
" <td>-0.199406</td>\n",
" <td>-1.348141</td>\n",
" <td>0.017246</td>\n",
" <td>-0.507939</td>\n",
" <td>000405d9a5e3f49fc49d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.207667</td>\n",
" <td>0.797312</td>\n",
" <td>0.296588</td>\n",
" <td>-0.078258</td>\n",
" <td>-0.594985</td>\n",
" <td>-0.150879</td>\n",
" <td>0.244803</td>\n",
" <td>-0.566412</td>\n",
" <td>-0.017474</td>\n",
" <td>-0.036901</td>\n",
" <td>-0.070395</td>\n",
" <td>-0.066760</td>\n",
" <td>0.652603</td>\n",
" <td>0.658356</td>\n",
" <td>0.957716</td>\n",
" <td>0.600374</td>\n",
" <td>-0.390279</td>\n",
" <td>0.356860</td>\n",
" <td>0.163423</td>\n",
" <td>-0.818167</td>\n",
" <td>0.498726</td>\n",
" <td>-0.217250</td>\n",
" <td>0.217618</td>\n",
" <td>0.896405</td>\n",
" <td>-0.742672</td>\n",
" <td>0.718531</td>\n",
" <td>-0.285864</td>\n",
" <td>0.031793</td>\n",
" <td>-0.231084</td>\n",
" <td>0.835172</td>\n",
" <td>-0.677453</td>\n",
" <td>0.305730</td>\n",
" <td>-0.288015</td>\n",
" <td>0.611005</td>\n",
" <td>0.574827</td>\n",
" <td>0.000375</td>\n",
" <td>-0.384318</td>\n",
" <td>-0.281647</td>\n",
" <td>-0.421321</td>\n",
" <td>-0.046871</td>\n",
" <td>001020bd00b149970f78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.315981</td>\n",
" <td>0.876197</td>\n",
" <td>0.229323</td>\n",
" <td>-0.223103</td>\n",
" <td>-0.567071</td>\n",
" <td>-0.029581</td>\n",
" <td>0.376860</td>\n",
" <td>-0.612751</td>\n",
" <td>0.003634</td>\n",
" <td>0.037243</td>\n",
" <td>-0.159816</td>\n",
" <td>0.008764</td>\n",
" <td>0.743641</td>\n",
" <td>0.587642</td>\n",
" <td>0.799610</td>\n",
" <td>0.302909</td>\n",
" <td>-0.396049</td>\n",
" <td>0.308480</td>\n",
" <td>0.022015</td>\n",
" <td>-0.756692</td>\n",
" <td>0.531610</td>\n",
" <td>-0.149059</td>\n",
" <td>0.139371</td>\n",
" <td>0.788582</td>\n",
" <td>-0.800044</td>\n",
" <td>0.873661</td>\n",
" <td>-0.197883</td>\n",
" <td>0.031327</td>\n",
" <td>-0.468295</td>\n",
" <td>0.802160</td>\n",
" <td>-0.656822</td>\n",
" <td>0.267039</td>\n",
" <td>-0.434521</td>\n",
" <td>0.556768</td>\n",
" <td>0.412600</td>\n",
" <td>-0.002057</td>\n",
" <td>-0.391793</td>\n",
" <td>-0.141938</td>\n",
" <td>-0.608571</td>\n",
" <td>0.073993</td>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.079582</td>\n",
" <td>-1.510130</td>\n",
" <td>0.231202</td>\n",
" <td>-0.008185</td>\n",
" <td>0.148634</td>\n",
" <td>-0.270417</td>\n",
" <td>0.068426</td>\n",
" <td>-0.277470</td>\n",
" <td>-0.550247</td>\n",
" <td>-0.566974</td>\n",
" <td>0.805188</td>\n",
" <td>-0.210919</td>\n",
" <td>-0.120345</td>\n",
" <td>0.119551</td>\n",
" <td>0.167644</td>\n",
" <td>-0.529621</td>\n",
" <td>0.278141</td>\n",
" <td>0.587659</td>\n",
" <td>0.237474</td>\n",
" <td>-0.700840</td>\n",
" <td>0.958319</td>\n",
" <td>-0.559394</td>\n",
" <td>0.428427</td>\n",
" <td>-0.174426</td>\n",
" <td>-0.136385</td>\n",
" <td>-0.023561</td>\n",
" <td>-0.543559</td>\n",
" <td>0.327859</td>\n",
" <td>-0.302516</td>\n",
" <td>0.095558</td>\n",
" <td>-0.594819</td>\n",
" <td>0.345316</td>\n",
" <td>-0.001510</td>\n",
" <td>0.574482</td>\n",
" <td>-0.649272</td>\n",
" <td>0.021701</td>\n",
" <td>-0.004917</td>\n",
" <td>-1.381419</td>\n",
" <td>0.111944</td>\n",
" <td>-0.414844</td>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.100285</td>\n",
" <td>-0.368256</td>\n",
" <td>-0.076610</td>\n",
" <td>-0.963522</td>\n",
" <td>-0.101717</td>\n",
" <td>0.072880</td>\n",
" <td>-0.286649</td>\n",
" <td>-1.474145</td>\n",
" <td>-0.017153</td>\n",
" <td>0.297055</td>\n",
" <td>-0.090056</td>\n",
" <td>0.403131</td>\n",
" <td>-0.108870</td>\n",
" <td>0.017531</td>\n",
" <td>-0.098082</td>\n",
" <td>-1.280785</td>\n",
" <td>0.341703</td>\n",
" <td>0.733895</td>\n",
" <td>-0.172242</td>\n",
" <td>0.105333</td>\n",
" <td>0.626823</td>\n",
" <td>0.469284</td>\n",
" <td>-0.583471</td>\n",
" <td>0.517745</td>\n",
" <td>-0.245795</td>\n",
" <td>0.048766</td>\n",
" <td>0.048336</td>\n",
" <td>-0.026860</td>\n",
" <td>-0.622398</td>\n",
" <td>-0.483013</td>\n",
" <td>0.280285</td>\n",
" <td>-0.327359</td>\n",
" <td>0.215534</td>\n",
" <td>0.203802</td>\n",
" <td>-0.353433</td>\n",
" <td>0.161117</td>\n",
" <td>-0.858037</td>\n",
" <td>0.108541</td>\n",
" <td>0.585385</td>\n",
" <td>-0.016500</td>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" material_collection_technique_w2v_0 material_collection_technique_w2v_1 \\\n",
"0 -0.214781 -1.254680 \n",
"1 -0.207667 0.797312 \n",
"2 -0.315981 0.876197 \n",
"3 -0.079582 -1.510130 \n",
"4 0.100285 -0.368256 \n",
"\n",
" material_collection_technique_w2v_2 material_collection_technique_w2v_3 \\\n",
"0 -0.079783 -0.169312 \n",
"1 0.296588 -0.078258 \n",
"2 0.229323 -0.223103 \n",
"3 0.231202 -0.008185 \n",
"4 -0.076610 -0.963522 \n",
"\n",
" material_collection_technique_w2v_4 material_collection_technique_w2v_5 \\\n",
"0 0.068885 -0.234959 \n",
"1 -0.594985 -0.150879 \n",
"2 -0.567071 -0.029581 \n",
"3 0.148634 -0.270417 \n",
"4 -0.101717 0.072880 \n",
"\n",
" material_collection_technique_w2v_6 material_collection_technique_w2v_7 \\\n",
"0 0.025053 -0.099676 \n",
"1 0.244803 -0.566412 \n",
"2 0.376860 -0.612751 \n",
"3 0.068426 -0.277470 \n",
"4 -0.286649 -1.474145 \n",
"\n",
" material_collection_technique_w2v_8 material_collection_technique_w2v_9 \\\n",
"0 -0.539394 -0.474485 \n",
"1 -0.017474 -0.036901 \n",
"2 0.003634 0.037243 \n",
"3 -0.550247 -0.566974 \n",
"4 -0.017153 0.297055 \n",
"\n",
" material_collection_technique_w2v_10 material_collection_technique_w2v_11 \\\n",
"0 0.752900 -0.258753 \n",
"1 -0.070395 -0.066760 \n",
"2 -0.159816 0.008764 \n",
"3 0.805188 -0.210919 \n",
"4 -0.090056 0.403131 \n",
"\n",
" material_collection_technique_w2v_12 material_collection_technique_w2v_13 \\\n",
"0 0.070450 0.132246 \n",
"1 0.652603 0.658356 \n",
"2 0.743641 0.587642 \n",
"3 -0.120345 0.119551 \n",
"4 -0.108870 0.017531 \n",
"\n",
" material_collection_technique_w2v_14 material_collection_technique_w2v_15 \\\n",
"0 0.541844 -0.458638 \n",
"1 0.957716 0.600374 \n",
"2 0.799610 0.302909 \n",
"3 0.167644 -0.529621 \n",
"4 -0.098082 -1.280785 \n",
"\n",
" material_collection_technique_w2v_16 material_collection_technique_w2v_17 \\\n",
"0 0.028730 0.549018 \n",
"1 -0.390279 0.356860 \n",
"2 -0.396049 0.308480 \n",
"3 0.278141 0.587659 \n",
"4 0.341703 0.733895 \n",
"\n",
" material_collection_technique_w2v_18 material_collection_technique_w2v_19 \\\n",
"0 0.255246 -0.538175 \n",
"1 0.163423 -0.818167 \n",
"2 0.022015 -0.756692 \n",
"3 0.237474 -0.700840 \n",
"4 -0.172242 0.105333 \n",
"\n",
" material_collection_technique_w2v_20 material_collection_technique_w2v_21 \\\n",
"0 0.674761 -0.703094 \n",
"1 0.498726 -0.217250 \n",
"2 0.531610 -0.149059 \n",
"3 0.958319 -0.559394 \n",
"4 0.626823 0.469284 \n",
"\n",
" material_collection_technique_w2v_22 material_collection_technique_w2v_23 \\\n",
"0 0.558242 -0.011356 \n",
"1 0.217618 0.896405 \n",
"2 0.139371 0.788582 \n",
"3 0.428427 -0.174426 \n",
"4 -0.583471 0.517745 \n",
"\n",
" material_collection_technique_w2v_24 material_collection_technique_w2v_25 \\\n",
"0 -0.198824 -0.014135 \n",
"1 -0.742672 0.718531 \n",
"2 -0.800044 0.873661 \n",
"3 -0.136385 -0.023561 \n",
"4 -0.245795 0.048766 \n",
"\n",
" material_collection_technique_w2v_26 material_collection_technique_w2v_27 \\\n",
"0 -0.354850 0.305101 \n",
"1 -0.285864 0.031793 \n",
"2 -0.197883 0.031327 \n",
"3 -0.543559 0.327859 \n",
"4 0.048336 -0.026860 \n",
"\n",
" material_collection_technique_w2v_28 material_collection_technique_w2v_29 \\\n",
"0 0.044536 -0.190434 \n",
"1 -0.231084 0.835172 \n",
"2 -0.468295 0.802160 \n",
"3 -0.302516 0.095558 \n",
"4 -0.622398 -0.483013 \n",
"\n",
" material_collection_technique_w2v_30 material_collection_technique_w2v_31 \\\n",
"0 -0.692245 0.555915 \n",
"1 -0.677453 0.305730 \n",
"2 -0.656822 0.267039 \n",
"3 -0.594819 0.345316 \n",
"4 0.280285 -0.327359 \n",
"\n",
" material_collection_technique_w2v_32 material_collection_technique_w2v_33 \\\n",
"0 0.032849 0.264612 \n",
"1 -0.288015 0.611005 \n",
"2 -0.434521 0.556768 \n",
"3 -0.001510 0.574482 \n",
"4 0.215534 0.203802 \n",
"\n",
" material_collection_technique_w2v_34 material_collection_technique_w2v_35 \\\n",
"0 -0.522483 0.139394 \n",
"1 0.574827 0.000375 \n",
"2 0.412600 -0.002057 \n",
"3 -0.649272 0.021701 \n",
"4 -0.353433 0.161117 \n",
"\n",
" material_collection_technique_w2v_36 material_collection_technique_w2v_37 \\\n",
"0 -0.199406 -1.348141 \n",
"1 -0.384318 -0.281647 \n",
"2 -0.391793 -0.141938 \n",
"3 -0.004917 -1.381419 \n",
"4 -0.858037 0.108541 \n",
"\n",
" material_collection_technique_w2v_38 material_collection_technique_w2v_39 \\\n",
"0 0.017246 -0.507939 \n",
"1 -0.421321 -0.046871 \n",
"2 -0.608571 0.073993 \n",
"3 0.111944 -0.414844 \n",
"4 0.585385 -0.016500 \n",
"\n",
" object_id \n",
"0 000405d9a5e3f49fc49d \n",
"1 001020bd00b149970f78 \n",
"2 0011d6be41612ec9eae3 \n",
"3 0012765f7a97ccc3e9e9 \n",
"4 00133be3ff222c9b74b0 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2v_dfs[6].head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"dummy = pd.get_dummies(technique[\"name\"])\n",
"dummy.index = technique.object_id\n",
"technique = dummy.reset_index().groupby(\"object_id\").sum().reset_index()\n",
"technique.columns = [f\"technique_{c}\" if c != \"object_id\" else c for c in technique.columns]\n",
"\n",
"dummy = pd.get_dummies(collection[\"name\"])\n",
"dummy.index = collection.object_id\n",
"collection = dummy.reset_index().groupby(\"object_id\").sum().reset_index()\n",
"collection.columns = [f\"collection_{c}\" if c != \"object_id\" else c for c in collection.columns]\n",
"\n",
"dummy = pd.get_dummies(material[\"name\"])\n",
"dummy.index = material.object_id\n",
"material = dummy.reset_index().groupby(\"object_id\").sum().reset_index()\n",
"material.columns = [f\"material_{c}\" if c != \"object_id\" else c for c in material.columns]\n",
"\n",
"dummy = pd.get_dummies(historical[\"name\"])\n",
"dummy.index = historical.object_id\n",
"historical = dummy.reset_index().groupby(\"object_id\").sum().reset_index()\n",
"historical.columns = [f\"historical_{c}\" if c != \"object_id\" else c for c in historical.columns]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>object_id</th>\n",
" <th>historical_Djatiroto, Suikeronderneming</th>\n",
" <th>historical_Emma (koningin-regentes der Nederlanden)</th>\n",
" <th>historical_Farnese, Alessandro (landvoogd van de Nederlanden en hertog van Parma en Piacenza)</th>\n",
" <th>historical_Fraters van Tilburg</th>\n",
" <th>historical_Frederik Hendrik (prins van Oranje)</th>\n",
" <th>historical_Galen, Christoph Bernhard von, bisschop van Münster</th>\n",
" <th>historical_Handels Vereeniging Amsterdam</th>\n",
" <th>historical_Hendrik IV (koning van Frankrijk en Navarra)</th>\n",
" <th>historical_Jacobus II (koning van Engeland en Schotland)</th>\n",
" <th>historical_Kabouterbeweging</th>\n",
" <th>historical_Lodewijk XIII (koning van Frankrijk)</th>\n",
" <th>historical_Lodewijk XIV (koning van Frankrijk)</th>\n",
" <th>historical_Maria II Stuart (koningin van Engeland, Schotland en Ierland)</th>\n",
" <th>historical_Maurits (prins van Oranje)</th>\n",
" <th>historical_Musschenbroek, Sam van</th>\n",
" <th>historical_Oldenbarnevelt, Johan van</th>\n",
" <th>historical_Piek-Jolles, Lucia Wilhelmina</th>\n",
" <th>historical_Rabenhaupt, Karl (baron von Sucha)</th>\n",
" <th>historical_Ruyter, Michiel Adriaansz. de</th>\n",
" <th>historical_Staten-Generaal</th>\n",
" <th>historical_Stuart, Jacobus Frans Eduard (prins van Wales)</th>\n",
" <th>historical_Titzenthaler, Eckart</th>\n",
" <th>historical_Titzenthaler, Marba</th>\n",
" <th>historical_Tjomal Suikerfabriek</th>\n",
" <th>historical_Tromp, Cornelis</th>\n",
" <th>historical_Verenigde Oostindische Compagnie</th>\n",
" <th>historical_Wehrmacht</th>\n",
" <th>historical_West-Indische Compagnie</th>\n",
" <th>historical_Wilhelm II (keizer van Duitsland)</th>\n",
" <th>historical_Wilhelmina (koningin der Nederlanden)</th>\n",
" <th>historical_Willem I (prins van Oranje)</th>\n",
" <th>historical_Willem III (koning der Nederlanden)</th>\n",
" <th>historical_Willem III (prins van Oranje en koning van Engeland, Schotland en Ierland)</th>\n",
" <th>historical_Willem V (prins van Oranje-Nassau)</th>\n",
" <th>historical_Wirix, F.J.</th>\n",
" <th>historical_Witt, Cornelis de</th>\n",
" <th>historical_Witt, Johan de</th>\n",
" <th>historical_Álvarez de Toledo, Fernando (3e hertog van Alva)</th>\n",
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" </tbody>\n",
"</table>\n",
"<p>1502 rows × 39 columns</p>\n",
"</div>"
],
"text/plain": [
" object_id historical_Djatiroto, Suikeronderneming \\\n",
"0 00133be3ff222c9b74b0 0 \n",
"1 0026e030a0209b834b3e 0 \n",
"2 00440ec5a4783b4b6bdb 0 \n",
"3 009d44bd779a8722b00c 0 \n",
"4 00cd543929cbd97be573 0 \n",
"... ... ... \n",
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"1498 ffa4fd042461afe1c253 0 \n",
"1499 ffbe15cc751929195346 0 \n",
"1500 ffc97bdc4c3bab1d253a 0 \n",
"1501 fff08e76cbb969eaddc7 0 \n",
"\n",
" historical_Emma (koningin-regentes der Nederlanden) \\\n",
"0 0 \n",
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"2 0 \n",
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"4 0 \n",
"... ... \n",
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"1498 0 \n",
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"\n",
" historical_Farnese, Alessandro (landvoogd van de Nederlanden en hertog van Parma en Piacenza) \\\n",
"0 0 \n",
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"... ... \n",
"1497 0 \n",
"1498 0 \n",
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"\n",
" historical_Fraters van Tilburg \\\n",
"0 0 \n",
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"... ... \n",
"1497 0 \n",
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"1500 0 \n",
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"\n",
" historical_Frederik Hendrik (prins van Oranje) \\\n",
"0 0 \n",
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"... ... \n",
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"\n",
" historical_Galen, Christoph Bernhard von, bisschop van Münster \\\n",
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"... ... \n",
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"\n",
" historical_Handels Vereeniging Amsterdam \\\n",
"0 0 \n",
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"... ... \n",
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"1501 0 \n",
"\n",
" historical_Hendrik IV (koning van Frankrijk en Navarra) \\\n",
"0 0 \n",
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"4 0 \n",
"... ... \n",
"1497 0 \n",
"1498 0 \n",
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"1500 0 \n",
"1501 0 \n",
"\n",
" historical_Jacobus II (koning van Engeland en Schotland) \\\n",
"0 0 \n",
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"... ... \n",
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"1501 1 \n",
"\n",
" historical_Kabouterbeweging \\\n",
"0 0 \n",
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"\n",
" historical_Lodewijk XIII (koning van Frankrijk) \\\n",
"0 0 \n",
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"... ... \n",
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"1498 1 \n",
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"\n",
" historical_Lodewijk XIV (koning van Frankrijk) \\\n",
"0 0 \n",
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"... ... \n",
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"\n",
" historical_Maria II Stuart (koningin van Engeland, Schotland en Ierland) \\\n",
"0 0 \n",
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"... ... \n",
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"\n",
" historical_Maurits (prins van Oranje) \\\n",
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"... ... \n",
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" historical_Musschenbroek, Sam van historical_Oldenbarnevelt, Johan van \\\n",
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"... ... ... \n",
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"\n",
" historical_Piek-Jolles, Lucia Wilhelmina \\\n",
"0 0 \n",
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"... ... \n",
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"\n",
" historical_Rabenhaupt, Karl (baron von Sucha) \\\n",
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"... ... \n",
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"\n",
" historical_Ruyter, Michiel Adriaansz. de historical_Staten-Generaal \\\n",
"0 0 1 \n",
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"... ... ... \n",
"1497 0 0 \n",
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"\n",
" historical_Stuart, Jacobus Frans Eduard (prins van Wales) \\\n",
"0 0 \n",
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"2 0 \n",
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"... ... \n",
"1497 0 \n",
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"\n",
" historical_Titzenthaler, Eckart historical_Titzenthaler, Marba \\\n",
"0 0 0 \n",
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"3 0 0 \n",
"4 0 0 \n",
"... ... ... \n",
"1497 0 0 \n",
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"1500 0 0 \n",
"1501 0 0 \n",
"\n",
" historical_Tjomal Suikerfabriek historical_Tromp, Cornelis \\\n",
"0 0 0 \n",
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"3 0 0 \n",
"4 0 0 \n",
"... ... ... \n",
"1497 0 0 \n",
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"1500 0 0 \n",
"1501 0 0 \n",
"\n",
" historical_Verenigde Oostindische Compagnie historical_Wehrmacht \\\n",
"0 0 0 \n",
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"4 0 0 \n",
"... ... ... \n",
"1497 0 0 \n",
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"1500 0 0 \n",
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"\n",
" historical_West-Indische Compagnie \\\n",
"0 0 \n",
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"4 0 \n",
"... ... \n",
"1497 0 \n",
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"1500 1 \n",
"1501 0 \n",
"\n",
" historical_Wilhelm II (keizer van Duitsland) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
"1497 0 \n",
"1498 0 \n",
"1499 0 \n",
"1500 0 \n",
"1501 0 \n",
"\n",
" historical_Wilhelmina (koningin der Nederlanden) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
"1497 0 \n",
"1498 0 \n",
"1499 0 \n",
"1500 0 \n",
"1501 0 \n",
"\n",
" historical_Willem I (prins van Oranje) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
"1497 0 \n",
"1498 0 \n",
"1499 0 \n",
"1500 0 \n",
"1501 0 \n",
"\n",
" historical_Willem III (koning der Nederlanden) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
"1497 0 \n",
"1498 0 \n",
"1499 0 \n",
"1500 0 \n",
"1501 0 \n",
"\n",
" historical_Willem III (prins van Oranje en koning van Engeland, Schotland en Ierland) \\\n",
"0 0 \n",
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"4 0 \n",
"... ... \n",
"1497 0 \n",
"1498 0 \n",
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"1500 0 \n",
"1501 0 \n",
"\n",
" historical_Willem V (prins van Oranje-Nassau) historical_Wirix, F.J. \\\n",
"0 0 0 \n",
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"4 0 0 \n",
"... ... ... \n",
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"1499 0 0 \n",
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"\n",
" historical_Witt, Cornelis de historical_Witt, Johan de \\\n",
"0 0 0 \n",
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"4 0 0 \n",
"... ... ... \n",
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"1500 0 0 \n",
"1501 0 0 \n",
"\n",
" historical_Álvarez de Toledo, Fernando (3e hertog van Alva) \n",
"0 1 \n",
"1 1 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
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"1500 0 \n",
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"\n",
"[1502 rows x 39 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"historical"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge] start\n",
"[Merge] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge\"):\n",
" train = train.merge(technique, on=\"object_id\", how=\"left\")\n",
" train = train.merge(collection, on=\"object_id\", how=\"left\")\n",
" train = train.merge(material, on=\"object_id\", how=\"left\")\n",
" train = train.merge(historical, on=\"object_id\", how=\"left\")\n",
" test = test.merge(technique, on=\"object_id\", how=\"left\")\n",
" test = test.merge(collection, on=\"object_id\", how=\"left\")\n",
" test = test.merge(material, on=\"object_id\", how=\"left\")\n",
" test = test.merge(historical, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge W2V] start\n",
"[Merge W2V] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge W2V\"):\n",
" train = train.merge(w2v_dfs[0], on=\"object_id\", how=\"left\")\n",
" train = train.merge(w2v_dfs[1], on=\"object_id\", how=\"left\")\n",
" train = train.merge(w2v_dfs[2], on=\"object_id\", how=\"left\")\n",
" train = train.merge(w2v_dfs[3], on=\"object_id\", how=\"left\")\n",
" train = train.merge(w2v_dfs[4], on=\"object_id\", how=\"left\")\n",
" train = train.merge(w2v_dfs[5], on=\"object_id\", how=\"left\")\n",
" train = train.merge(w2v_dfs[6], on=\"object_id\", how=\"left\")\n",
" # train = train.merge(w2v_dfs[7], on=\"object_id\", how=\"left\")\n",
" test = test.merge(w2v_dfs[0], on=\"object_id\", how=\"left\")\n",
" test = test.merge(w2v_dfs[1], on=\"object_id\", how=\"left\")\n",
" test = test.merge(w2v_dfs[2], on=\"object_id\", how=\"left\")\n",
" test = test.merge(w2v_dfs[3], on=\"object_id\", how=\"left\")\n",
" test = test.merge(w2v_dfs[4], on=\"object_id\", how=\"left\")\n",
" test = test.merge(w2v_dfs[5], on=\"object_id\", how=\"left\")\n",
" test = test.merge(w2v_dfs[6], on=\"object_id\", how=\"left\")\n",
" # test = test.merge(w2v_dfs[7], on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Principal Maker"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"principal = principal.drop_duplicates(subset=[\"object_id\"])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"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>id</th>\n",
" <th>object_id</th>\n",
" <th>qualification</th>\n",
" <th>roles</th>\n",
" <th>productionPlaces</th>\n",
" <th>maker_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>000405d9a5e3f49fc49d</td>\n",
" <td>mentioned on object</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Bernardus Bruining</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>001020bd00b149970f78</td>\n",
" <td>workshop of</td>\n",
" <td>painter</td>\n",
" <td>NaN</td>\n",
" <td>Jan Antonisz van Ravesteyn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>NaN</td>\n",
" <td>painter</td>\n",
" <td>NaN</td>\n",
" <td>Jan Hackaert</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>attributed to</td>\n",
" <td>NaN</td>\n",
" <td>Netherlands</td>\n",
" <td>Richard Tepe</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>NaN</td>\n",
" <td>print maker</td>\n",
" <td>Northern Netherlands</td>\n",
" <td>Theodoor Koning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24533</th>\n",
" <td>24534</td>\n",
" <td>fff4bbb55fd7702d294e</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Henry W. Taunt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24534</th>\n",
" <td>24535</td>\n",
" <td>fffbe07b997bec00e203</td>\n",
" <td>attributed to</td>\n",
" <td>NaN</td>\n",
" <td>Great Britain</td>\n",
" <td>John Jabez Edwin Mayall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24535</th>\n",
" <td>24536</td>\n",
" <td>fffd1675758205748d7f</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Francis Frith</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24536</th>\n",
" <td>24537</td>\n",
" <td>fffd43b134ba7197d890</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Henry W. Taunt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24537</th>\n",
" <td>24538</td>\n",
" <td>ffff22ea12d7f99cff31</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>England</td>\n",
" <td>anonymous</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>24034 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" id object_id qualification roles \\\n",
"0 1 000405d9a5e3f49fc49d mentioned on object NaN \n",
"1 2 001020bd00b149970f78 workshop of painter \n",
"2 3 0011d6be41612ec9eae3 NaN painter \n",
"3 4 0012765f7a97ccc3e9e9 attributed to NaN \n",
"4 5 00133be3ff222c9b74b0 NaN print maker \n",
"... ... ... ... ... \n",
"24533 24534 fff4bbb55fd7702d294e NaN NaN \n",
"24534 24535 fffbe07b997bec00e203 attributed to NaN \n",
"24535 24536 fffd1675758205748d7f NaN NaN \n",
"24536 24537 fffd43b134ba7197d890 NaN NaN \n",
"24537 24538 ffff22ea12d7f99cff31 NaN NaN \n",
"\n",
" productionPlaces maker_name \n",
"0 NaN Bernardus Bruining \n",
"1 NaN Jan Antonisz van Ravesteyn \n",
"2 NaN Jan Hackaert \n",
"3 Netherlands Richard Tepe \n",
"4 Northern Netherlands Theodoor Koning \n",
"... ... ... \n",
"24533 NaN Henry W. Taunt \n",
"24534 Great Britain John Jabez Edwin Mayall \n",
"24535 NaN Francis Frith \n",
"24536 NaN Henry W. Taunt \n",
"24537 England anonymous \n",
"\n",
"[24034 rows x 6 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"principal"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge principal] start\n",
"[Merge principal] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge principal\"):\n",
" train = train.merge(principal[[\"object_id\", \"qualification\", \"roles\", \"productionPlaces\", \"maker_name\"]],\n",
" on=\"object_id\", how=\"left\")\n",
" test = test.merge(principal[[\"object_id\", \"qualification\", \"roles\", \"productionPlaces\", \"maker_name\"]],\n",
" on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"y = np.log1p(train[\"likes\"])\n",
"train = train.drop(\"likes\", axis=1)\n",
"object_id = train[\"object_id\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### place to country"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def place2country(address):\n",
" geolocator = Nominatim(user_agent='sample', timeout=200)\n",
" loc = geolocator.geocode(address, language='en')\n",
" coordinates = (loc.latitude, loc.longitude)\n",
" location = geolocator.reverse(coordinates, language='en')\n",
" country = location.raw['address']['country']\n",
" return country"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"if not Path(\"../input/place.csv\").exists():\n",
" place_list = place[\"name\"].unique()\n",
" country_dict = {}\n",
" for place in tqdm(place_list):\n",
" try:\n",
" country = place2country(place)\n",
" country_dict[place] = country\n",
" except:\n",
" country_dict[place] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"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>object_id</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>Amsterdam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>Northern Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0017be8caa87206532cb</td>\n",
" <td>Amsterdam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001b2b8c9d3aa1534dfe</td>\n",
" <td>Suriname</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17681</th>\n",
" <td>fff08e76cbb969eaddc7</td>\n",
" <td>Northern Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17682</th>\n",
" <td>fff08e76cbb969eaddc7</td>\n",
" <td>Antwerp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17683</th>\n",
" <td>fffbe07b997bec00e203</td>\n",
" <td>Great Britain</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17684</th>\n",
" <td>fffd43b134ba7197d890</td>\n",
" <td>London</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17685</th>\n",
" <td>ffff22ea12d7f99cff31</td>\n",
" <td>England</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>17686 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" object_id name\n",
"0 0012765f7a97ccc3e9e9 Netherlands\n",
"1 00133be3ff222c9b74b0 Amsterdam\n",
"2 00133be3ff222c9b74b0 Northern Netherlands\n",
"3 0017be8caa87206532cb Amsterdam\n",
"4 001b2b8c9d3aa1534dfe Suriname\n",
"... ... ...\n",
"17681 fff08e76cbb969eaddc7 Northern Netherlands\n",
"17682 fff08e76cbb969eaddc7 Antwerp\n",
"17683 fffbe07b997bec00e203 Great Britain\n",
"17684 fffd43b134ba7197d890 London\n",
"17685 ffff22ea12d7f99cff31 England\n",
"\n",
"[17686 rows x 2 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"place"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"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>object_id</th>\n",
" <th>name</th>\n",
" <th>country_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>Netherlands</td>\n",
" <td>Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>Amsterdam</td>\n",
" <td>Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>Northern Netherlands</td>\n",
" <td>Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0017be8caa87206532cb</td>\n",
" <td>Amsterdam</td>\n",
" <td>Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001b2b8c9d3aa1534dfe</td>\n",
" <td>Suriname</td>\n",
" <td>Suriname</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" object_id name country_name\n",
"0 0012765f7a97ccc3e9e9 Netherlands Netherlands\n",
"1 00133be3ff222c9b74b0 Amsterdam Netherlands\n",
"2 00133be3ff222c9b74b0 Northern Netherlands Netherlands\n",
"3 0017be8caa87206532cb Amsterdam Netherlands\n",
"4 001b2b8c9d3aa1534dfe Suriname Suriname"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"if Path(\"../input/place.csv\").exists():\n",
" place = pd.read_csv(\"../input/place.csv\")\n",
"else:\n",
" place = pd.read_csv(DATADIR / \"production_place.csv\")\n",
" place[\"country_name\"] = place[\"name\"].map(lambda x: country_dict[x])\n",
" place.to_csv(\"../input/place.csv\", index=False)\n",
"place.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"place = place.drop_duplicates(subset=[\"object_id\"]).reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### place features"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge place features] start\n",
"[Merge place features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge place features\"):\n",
" train = train.merge(place[[\"object_id\", \"country_name\"]], on=\"object_id\", how=\"left\")\n",
" test = test.merge(place[[\"object_id\", \"country_name\"]], on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### color embedding features"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"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>object_id</th>\n",
" <th>color_embedding_0</th>\n",
" <th>color_embedding_1</th>\n",
" <th>color_embedding_2</th>\n",
" <th>color_embedding_3</th>\n",
" <th>color_embedding_4</th>\n",
" <th>color_embedding_5</th>\n",
" <th>color_embedding_6</th>\n",
" <th>color_embedding_7</th>\n",
" <th>color_embedding_8</th>\n",
" <th>color_embedding_9</th>\n",
" <th>color_embedding_10</th>\n",
" <th>color_embedding_11</th>\n",
" <th>color_embedding_12</th>\n",
" <th>color_embedding_13</th>\n",
" <th>color_embedding_14</th>\n",
" <th>color_embedding_15</th>\n",
" <th>color_embedding_16</th>\n",
" <th>color_embedding_17</th>\n",
" <th>color_embedding_18</th>\n",
" <th>color_embedding_19</th>\n",
" <th>color_embedding_20</th>\n",
" <th>color_embedding_21</th>\n",
" <th>color_embedding_22</th>\n",
" <th>color_embedding_23</th>\n",
" <th>color_embedding_24</th>\n",
" <th>color_embedding_25</th>\n",
" <th>color_embedding_26</th>\n",
" <th>color_embedding_27</th>\n",
" <th>color_embedding_28</th>\n",
" <th>...</th>\n",
" <th>color_embedding_34</th>\n",
" <th>color_embedding_35</th>\n",
" <th>color_embedding_36</th>\n",
" <th>color_embedding_37</th>\n",
" <th>color_embedding_38</th>\n",
" <th>color_embedding_39</th>\n",
" <th>color_embedding_40</th>\n",
" <th>color_embedding_41</th>\n",
" <th>color_embedding_42</th>\n",
" <th>color_embedding_43</th>\n",
" <th>color_embedding_44</th>\n",
" <th>color_embedding_45</th>\n",
" <th>color_embedding_46</th>\n",
" <th>color_embedding_47</th>\n",
" <th>color_embedding_48</th>\n",
" <th>color_embedding_49</th>\n",
" <th>color_embedding_50</th>\n",
" <th>color_embedding_51</th>\n",
" <th>color_embedding_52</th>\n",
" <th>color_embedding_53</th>\n",
" <th>color_embedding_54</th>\n",
" <th>color_embedding_55</th>\n",
" <th>color_embedding_56</th>\n",
" <th>color_embedding_57</th>\n",
" <th>color_embedding_58</th>\n",
" <th>color_embedding_59</th>\n",
" <th>color_embedding_60</th>\n",
" <th>color_embedding_61</th>\n",
" <th>color_embedding_62</th>\n",
" <th>color_embedding_63</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000405d9a5e3f49fc49d</td>\n",
" <td>1.109169</td>\n",
" <td>1.765781</td>\n",
" <td>-3.729540</td>\n",
" <td>-2.130067</td>\n",
" <td>-0.892975</td>\n",
" <td>-0.634692</td>\n",
" <td>-1.467840</td>\n",
" <td>2.002744</td>\n",
" <td>-2.264348</td>\n",
" <td>3.601904</td>\n",
" <td>1.584345</td>\n",
" <td>-1.417762</td>\n",
" <td>3.912406</td>\n",
" <td>2.451618</td>\n",
" <td>1.314947</td>\n",
" <td>-1.031864</td>\n",
" <td>-3.099328</td>\n",
" <td>-1.008405</td>\n",
" <td>1.840399</td>\n",
" <td>-0.213633</td>\n",
" <td>0.491547</td>\n",
" <td>0.272234</td>\n",
" <td>1.888176</td>\n",
" <td>-0.606439</td>\n",
" <td>0.726907</td>\n",
" <td>-1.634417</td>\n",
" <td>-1.984412</td>\n",
" <td>-0.213820</td>\n",
" <td>-2.296728</td>\n",
" <td>...</td>\n",
" <td>-0.887141</td>\n",
" <td>0.901435</td>\n",
" <td>-0.185311</td>\n",
" <td>1.674237</td>\n",
" <td>1.862397</td>\n",
" <td>-2.009387</td>\n",
" <td>-1.107112</td>\n",
" <td>-0.807389</td>\n",
" <td>-0.438670</td>\n",
" <td>2.213312</td>\n",
" <td>-1.836348</td>\n",
" <td>-4.649899</td>\n",
" <td>-0.763967</td>\n",
" <td>-2.242036</td>\n",
" <td>-1.447228</td>\n",
" <td>0.215193</td>\n",
" <td>2.041084</td>\n",
" <td>0.167956</td>\n",
" <td>-0.711776</td>\n",
" <td>-0.357251</td>\n",
" <td>-1.073913</td>\n",
" <td>3.188730</td>\n",
" <td>2.694779</td>\n",
" <td>2.222783</td>\n",
" <td>1.632237</td>\n",
" <td>-3.336735</td>\n",
" <td>2.266035</td>\n",
" <td>-0.472088</td>\n",
" <td>0.012621</td>\n",
" <td>-1.639241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>001020bd00b149970f78</td>\n",
" <td>-1.325283</td>\n",
" <td>5.759610</td>\n",
" <td>-6.312686</td>\n",
" <td>-3.341624</td>\n",
" <td>-0.210441</td>\n",
" <td>-4.561804</td>\n",
" <td>4.024418</td>\n",
" <td>-2.694325</td>\n",
" <td>-5.162290</td>\n",
" <td>5.951659</td>\n",
" <td>0.295313</td>\n",
" <td>1.723153</td>\n",
" <td>6.407045</td>\n",
" <td>2.661093</td>\n",
" <td>-2.275815</td>\n",
" <td>3.312970</td>\n",
" <td>-5.187991</td>\n",
" <td>-1.703394</td>\n",
" <td>5.152281</td>\n",
" <td>2.579139</td>\n",
" <td>6.190881</td>\n",
" <td>1.941474</td>\n",
" <td>-2.759844</td>\n",
" <td>0.633496</td>\n",
" <td>-2.958190</td>\n",
" <td>1.172203</td>\n",
" <td>2.045834</td>\n",
" <td>-3.566754</td>\n",
" <td>1.261892</td>\n",
" <td>...</td>\n",
" <td>3.525579</td>\n",
" <td>-0.999245</td>\n",
" <td>-3.278188</td>\n",
" <td>2.887467</td>\n",
" <td>4.697828</td>\n",
" <td>-0.327238</td>\n",
" <td>-2.011215</td>\n",
" <td>4.197240</td>\n",
" <td>-6.663392</td>\n",
" <td>-3.001937</td>\n",
" <td>-7.361915</td>\n",
" <td>-3.715696</td>\n",
" <td>4.372580</td>\n",
" <td>-6.865096</td>\n",
" <td>-3.275308</td>\n",
" <td>5.966186</td>\n",
" <td>-0.360063</td>\n",
" <td>5.211979</td>\n",
" <td>4.276104</td>\n",
" <td>-5.558449</td>\n",
" <td>-1.388145</td>\n",
" <td>5.929566</td>\n",
" <td>5.875044</td>\n",
" <td>5.887198</td>\n",
" <td>-1.854250</td>\n",
" <td>-7.330433</td>\n",
" <td>7.397486</td>\n",
" <td>2.430608</td>\n",
" <td>-0.394953</td>\n",
" <td>-1.538312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>-1.282135</td>\n",
" <td>6.476494</td>\n",
" <td>-7.027996</td>\n",
" <td>-4.019228</td>\n",
" <td>0.007505</td>\n",
" <td>-5.128618</td>\n",
" <td>3.666570</td>\n",
" <td>-2.469866</td>\n",
" <td>-5.874065</td>\n",
" <td>6.605112</td>\n",
" <td>0.830485</td>\n",
" <td>1.044410</td>\n",
" <td>7.685607</td>\n",
" <td>2.938847</td>\n",
" <td>-1.588355</td>\n",
" <td>2.924071</td>\n",
" <td>-6.274859</td>\n",
" <td>-1.543591</td>\n",
" <td>5.559781</td>\n",
" <td>2.072784</td>\n",
" <td>6.354873</td>\n",
" <td>2.213432</td>\n",
" <td>-2.521385</td>\n",
" <td>0.006141</td>\n",
" <td>-2.948494</td>\n",
" <td>0.904258</td>\n",
" <td>1.412667</td>\n",
" <td>-3.946771</td>\n",
" <td>0.415474</td>\n",
" <td>...</td>\n",
" <td>2.992260</td>\n",
" <td>-1.020496</td>\n",
" <td>-3.224357</td>\n",
" <td>3.650487</td>\n",
" <td>5.120438</td>\n",
" <td>-0.952266</td>\n",
" <td>-2.735146</td>\n",
" <td>3.883504</td>\n",
" <td>-6.897026</td>\n",
" <td>-2.406408</td>\n",
" <td>-8.059082</td>\n",
" <td>-4.701990</td>\n",
" <td>4.662065</td>\n",
" <td>-7.624742</td>\n",
" <td>-3.849058</td>\n",
" <td>6.357888</td>\n",
" <td>0.083933</td>\n",
" <td>5.110420</td>\n",
" <td>4.114912</td>\n",
" <td>-5.488976</td>\n",
" <td>-1.874853</td>\n",
" <td>6.681599</td>\n",
" <td>6.456271</td>\n",
" <td>6.491204</td>\n",
" <td>-1.537909</td>\n",
" <td>-8.316347</td>\n",
" <td>7.903818</td>\n",
" <td>2.009379</td>\n",
" <td>-0.536269</td>\n",
" <td>-1.940773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>-0.599223</td>\n",
" <td>2.487043</td>\n",
" <td>-2.890166</td>\n",
" <td>-1.376251</td>\n",
" <td>-0.281932</td>\n",
" <td>-1.877687</td>\n",
" <td>2.000291</td>\n",
" <td>-1.255109</td>\n",
" <td>-2.225118</td>\n",
" <td>2.715409</td>\n",
" <td>-0.029450</td>\n",
" <td>1.101797</td>\n",
" <td>2.638704</td>\n",
" <td>1.304331</td>\n",
" <td>-1.366105</td>\n",
" <td>1.710837</td>\n",
" <td>-2.100916</td>\n",
" <td>-0.965499</td>\n",
" <td>2.353144</td>\n",
" <td>1.493339</td>\n",
" <td>2.904616</td>\n",
" <td>0.791604</td>\n",
" <td>-1.300036</td>\n",
" <td>0.570807</td>\n",
" <td>-1.396788</td>\n",
" <td>0.598048</td>\n",
" <td>1.165924</td>\n",
" <td>-1.505575</td>\n",
" <td>0.856850</td>\n",
" <td>...</td>\n",
" <td>1.911809</td>\n",
" <td>-0.401379</td>\n",
" <td>-1.583402</td>\n",
" <td>1.089510</td>\n",
" <td>2.146107</td>\n",
" <td>0.038322</td>\n",
" <td>-0.657994</td>\n",
" <td>2.134323</td>\n",
" <td>-3.037192</td>\n",
" <td>-1.596861</td>\n",
" <td>-3.231900</td>\n",
" <td>-1.584695</td>\n",
" <td>1.898538</td>\n",
" <td>-3.006271</td>\n",
" <td>-1.355007</td>\n",
" <td>2.640723</td>\n",
" <td>-0.282981</td>\n",
" <td>2.540546</td>\n",
" <td>2.077469</td>\n",
" <td>-2.687122</td>\n",
" <td>-0.517146</td>\n",
" <td>2.665287</td>\n",
" <td>2.644320</td>\n",
" <td>2.620498</td>\n",
" <td>-0.948392</td>\n",
" <td>-3.161879</td>\n",
" <td>3.397576</td>\n",
" <td>1.315140</td>\n",
" <td>-0.126981</td>\n",
" <td>-0.633104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>-0.694053</td>\n",
" <td>-1.019006</td>\n",
" <td>2.290811</td>\n",
" <td>1.164565</td>\n",
" <td>0.796905</td>\n",
" <td>0.267057</td>\n",
" <td>0.658418</td>\n",
" <td>-1.117232</td>\n",
" <td>1.318180</td>\n",
" <td>-2.277381</td>\n",
" <td>-0.727138</td>\n",
" <td>0.438941</td>\n",
" <td>-2.159545</td>\n",
" <td>-1.643249</td>\n",
" <td>-0.327740</td>\n",
" <td>0.288620</td>\n",
" <td>1.658292</td>\n",
" <td>0.938048</td>\n",
" <td>-1.259391</td>\n",
" <td>-0.318345</td>\n",
" <td>-0.474019</td>\n",
" <td>-0.050483</td>\n",
" <td>-1.006135</td>\n",
" <td>-0.070519</td>\n",
" <td>-0.361474</td>\n",
" <td>0.926001</td>\n",
" <td>0.875599</td>\n",
" <td>-0.006165</td>\n",
" <td>1.003255</td>\n",
" <td>...</td>\n",
" <td>0.067842</td>\n",
" <td>-0.636095</td>\n",
" <td>0.278391</td>\n",
" <td>-0.766396</td>\n",
" <td>-1.252393</td>\n",
" <td>1.011956</td>\n",
" <td>0.348621</td>\n",
" <td>0.122227</td>\n",
" <td>0.408220</td>\n",
" <td>-1.010503</td>\n",
" <td>1.161177</td>\n",
" <td>2.711056</td>\n",
" <td>0.474396</td>\n",
" <td>1.419070</td>\n",
" <td>0.772869</td>\n",
" <td>-0.167899</td>\n",
" <td>-1.124388</td>\n",
" <td>-0.404170</td>\n",
" <td>0.207747</td>\n",
" <td>0.557539</td>\n",
" <td>0.415312</td>\n",
" <td>-1.995529</td>\n",
" <td>-1.693971</td>\n",
" <td>-1.423900</td>\n",
" <td>-0.811472</td>\n",
" <td>2.017273</td>\n",
" <td>-1.572711</td>\n",
" <td>-0.123904</td>\n",
" <td>-0.109974</td>\n",
" <td>0.932220</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 65 columns</p>\n",
"</div>"
],
"text/plain": [
" object_id color_embedding_0 color_embedding_1 \\\n",
"0 000405d9a5e3f49fc49d 1.109169 1.765781 \n",
"1 001020bd00b149970f78 -1.325283 5.759610 \n",
"2 0011d6be41612ec9eae3 -1.282135 6.476494 \n",
"3 0012765f7a97ccc3e9e9 -0.599223 2.487043 \n",
"4 00133be3ff222c9b74b0 -0.694053 -1.019006 \n",
"\n",
" color_embedding_2 color_embedding_3 color_embedding_4 color_embedding_5 \\\n",
"0 -3.729540 -2.130067 -0.892975 -0.634692 \n",
"1 -6.312686 -3.341624 -0.210441 -4.561804 \n",
"2 -7.027996 -4.019228 0.007505 -5.128618 \n",
"3 -2.890166 -1.376251 -0.281932 -1.877687 \n",
"4 2.290811 1.164565 0.796905 0.267057 \n",
"\n",
" color_embedding_6 color_embedding_7 color_embedding_8 color_embedding_9 \\\n",
"0 -1.467840 2.002744 -2.264348 3.601904 \n",
"1 4.024418 -2.694325 -5.162290 5.951659 \n",
"2 3.666570 -2.469866 -5.874065 6.605112 \n",
"3 2.000291 -1.255109 -2.225118 2.715409 \n",
"4 0.658418 -1.117232 1.318180 -2.277381 \n",
"\n",
" color_embedding_10 color_embedding_11 color_embedding_12 \\\n",
"0 1.584345 -1.417762 3.912406 \n",
"1 0.295313 1.723153 6.407045 \n",
"2 0.830485 1.044410 7.685607 \n",
"3 -0.029450 1.101797 2.638704 \n",
"4 -0.727138 0.438941 -2.159545 \n",
"\n",
" color_embedding_13 color_embedding_14 color_embedding_15 \\\n",
"0 2.451618 1.314947 -1.031864 \n",
"1 2.661093 -2.275815 3.312970 \n",
"2 2.938847 -1.588355 2.924071 \n",
"3 1.304331 -1.366105 1.710837 \n",
"4 -1.643249 -0.327740 0.288620 \n",
"\n",
" color_embedding_16 color_embedding_17 color_embedding_18 \\\n",
"0 -3.099328 -1.008405 1.840399 \n",
"1 -5.187991 -1.703394 5.152281 \n",
"2 -6.274859 -1.543591 5.559781 \n",
"3 -2.100916 -0.965499 2.353144 \n",
"4 1.658292 0.938048 -1.259391 \n",
"\n",
" color_embedding_19 color_embedding_20 color_embedding_21 \\\n",
"0 -0.213633 0.491547 0.272234 \n",
"1 2.579139 6.190881 1.941474 \n",
"2 2.072784 6.354873 2.213432 \n",
"3 1.493339 2.904616 0.791604 \n",
"4 -0.318345 -0.474019 -0.050483 \n",
"\n",
" color_embedding_22 color_embedding_23 color_embedding_24 \\\n",
"0 1.888176 -0.606439 0.726907 \n",
"1 -2.759844 0.633496 -2.958190 \n",
"2 -2.521385 0.006141 -2.948494 \n",
"3 -1.300036 0.570807 -1.396788 \n",
"4 -1.006135 -0.070519 -0.361474 \n",
"\n",
" color_embedding_25 color_embedding_26 color_embedding_27 \\\n",
"0 -1.634417 -1.984412 -0.213820 \n",
"1 1.172203 2.045834 -3.566754 \n",
"2 0.904258 1.412667 -3.946771 \n",
"3 0.598048 1.165924 -1.505575 \n",
"4 0.926001 0.875599 -0.006165 \n",
"\n",
" color_embedding_28 ... color_embedding_34 color_embedding_35 \\\n",
"0 -2.296728 ... -0.887141 0.901435 \n",
"1 1.261892 ... 3.525579 -0.999245 \n",
"2 0.415474 ... 2.992260 -1.020496 \n",
"3 0.856850 ... 1.911809 -0.401379 \n",
"4 1.003255 ... 0.067842 -0.636095 \n",
"\n",
" color_embedding_36 color_embedding_37 color_embedding_38 \\\n",
"0 -0.185311 1.674237 1.862397 \n",
"1 -3.278188 2.887467 4.697828 \n",
"2 -3.224357 3.650487 5.120438 \n",
"3 -1.583402 1.089510 2.146107 \n",
"4 0.278391 -0.766396 -1.252393 \n",
"\n",
" color_embedding_39 color_embedding_40 color_embedding_41 \\\n",
"0 -2.009387 -1.107112 -0.807389 \n",
"1 -0.327238 -2.011215 4.197240 \n",
"2 -0.952266 -2.735146 3.883504 \n",
"3 0.038322 -0.657994 2.134323 \n",
"4 1.011956 0.348621 0.122227 \n",
"\n",
" color_embedding_42 color_embedding_43 color_embedding_44 \\\n",
"0 -0.438670 2.213312 -1.836348 \n",
"1 -6.663392 -3.001937 -7.361915 \n",
"2 -6.897026 -2.406408 -8.059082 \n",
"3 -3.037192 -1.596861 -3.231900 \n",
"4 0.408220 -1.010503 1.161177 \n",
"\n",
" color_embedding_45 color_embedding_46 color_embedding_47 \\\n",
"0 -4.649899 -0.763967 -2.242036 \n",
"1 -3.715696 4.372580 -6.865096 \n",
"2 -4.701990 4.662065 -7.624742 \n",
"3 -1.584695 1.898538 -3.006271 \n",
"4 2.711056 0.474396 1.419070 \n",
"\n",
" color_embedding_48 color_embedding_49 color_embedding_50 \\\n",
"0 -1.447228 0.215193 2.041084 \n",
"1 -3.275308 5.966186 -0.360063 \n",
"2 -3.849058 6.357888 0.083933 \n",
"3 -1.355007 2.640723 -0.282981 \n",
"4 0.772869 -0.167899 -1.124388 \n",
"\n",
" color_embedding_51 color_embedding_52 color_embedding_53 \\\n",
"0 0.167956 -0.711776 -0.357251 \n",
"1 5.211979 4.276104 -5.558449 \n",
"2 5.110420 4.114912 -5.488976 \n",
"3 2.540546 2.077469 -2.687122 \n",
"4 -0.404170 0.207747 0.557539 \n",
"\n",
" color_embedding_54 color_embedding_55 color_embedding_56 \\\n",
"0 -1.073913 3.188730 2.694779 \n",
"1 -1.388145 5.929566 5.875044 \n",
"2 -1.874853 6.681599 6.456271 \n",
"3 -0.517146 2.665287 2.644320 \n",
"4 0.415312 -1.995529 -1.693971 \n",
"\n",
" color_embedding_57 color_embedding_58 color_embedding_59 \\\n",
"0 2.222783 1.632237 -3.336735 \n",
"1 5.887198 -1.854250 -7.330433 \n",
"2 6.491204 -1.537909 -8.316347 \n",
"3 2.620498 -0.948392 -3.161879 \n",
"4 -1.423900 -0.811472 2.017273 \n",
"\n",
" color_embedding_60 color_embedding_61 color_embedding_62 \\\n",
"0 2.266035 -0.472088 0.012621 \n",
"1 7.397486 2.430608 -0.394953 \n",
"2 7.903818 2.009379 -0.536269 \n",
"3 3.397576 1.315140 -0.126981 \n",
"4 -1.572711 -0.123904 -0.109974 \n",
"\n",
" color_embedding_63 \n",
"0 -1.639241 \n",
"1 -1.538312 \n",
"2 -1.940773 \n",
"3 -0.633104 \n",
"4 0.932220 \n",
"\n",
"[5 rows x 65 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"color = pd.read_csv(\"../input/color_embedding_3.csv\")\n",
"color.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge color features] start\n",
"[Merge color features] done in 0 s\n"
]
}
],
"source": [
"color.columns = [c.replace(\"color\", \"palette\") for c in color.columns]\n",
"with timer(\"Merge color features\"):\n",
" train = train.merge(color, on=\"object_id\", how=\"left\")\n",
" test = test.merge(color, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Color features"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>object_id</th>\n",
" <th>color_embedding_0</th>\n",
" <th>color_embedding_1</th>\n",
" <th>color_embedding_2</th>\n",
" <th>color_embedding_3</th>\n",
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" <th>color_embedding_7</th>\n",
" <th>color_embedding_8</th>\n",
" <th>color_embedding_9</th>\n",
" <th>color_embedding_10</th>\n",
" <th>color_embedding_11</th>\n",
" <th>color_embedding_12</th>\n",
" <th>color_embedding_13</th>\n",
" <th>color_embedding_14</th>\n",
" <th>color_embedding_15</th>\n",
" <th>color_embedding_16</th>\n",
" <th>color_embedding_17</th>\n",
" <th>color_embedding_18</th>\n",
" <th>color_embedding_19</th>\n",
" <th>color_embedding_20</th>\n",
" <th>color_embedding_21</th>\n",
" <th>color_embedding_22</th>\n",
" <th>color_embedding_23</th>\n",
" <th>color_embedding_24</th>\n",
" <th>color_embedding_25</th>\n",
" <th>color_embedding_26</th>\n",
" <th>color_embedding_27</th>\n",
" <th>color_embedding_28</th>\n",
" <th>...</th>\n",
" <th>color_embedding_34</th>\n",
" <th>color_embedding_35</th>\n",
" <th>color_embedding_36</th>\n",
" <th>color_embedding_37</th>\n",
" <th>color_embedding_38</th>\n",
" <th>color_embedding_39</th>\n",
" <th>color_embedding_40</th>\n",
" <th>color_embedding_41</th>\n",
" <th>color_embedding_42</th>\n",
" <th>color_embedding_43</th>\n",
" <th>color_embedding_44</th>\n",
" <th>color_embedding_45</th>\n",
" <th>color_embedding_46</th>\n",
" <th>color_embedding_47</th>\n",
" <th>color_embedding_48</th>\n",
" <th>color_embedding_49</th>\n",
" <th>color_embedding_50</th>\n",
" <th>color_embedding_51</th>\n",
" <th>color_embedding_52</th>\n",
" <th>color_embedding_53</th>\n",
" <th>color_embedding_54</th>\n",
" <th>color_embedding_55</th>\n",
" <th>color_embedding_56</th>\n",
" <th>color_embedding_57</th>\n",
" <th>color_embedding_58</th>\n",
" <th>color_embedding_59</th>\n",
" <th>color_embedding_60</th>\n",
" <th>color_embedding_61</th>\n",
" <th>color_embedding_62</th>\n",
" <th>color_embedding_63</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>001020bd00b149970f78</td>\n",
" <td>-1.963787</td>\n",
" <td>6.739867</td>\n",
" <td>-4.945860</td>\n",
" <td>-3.967160</td>\n",
" <td>-1.690328</td>\n",
" <td>-6.407980</td>\n",
" <td>5.437441</td>\n",
" <td>-1.996925</td>\n",
" <td>-6.550920</td>\n",
" <td>7.186263</td>\n",
" <td>-0.217964</td>\n",
" <td>2.846546</td>\n",
" <td>10.841927</td>\n",
" <td>8.638573</td>\n",
" <td>-2.015988</td>\n",
" <td>2.555417</td>\n",
" <td>-6.332300</td>\n",
" <td>2.330411</td>\n",
" <td>3.331545</td>\n",
" <td>3.230083</td>\n",
" <td>11.196459</td>\n",
" <td>2.800659</td>\n",
" <td>-9.511781</td>\n",
" <td>3.930397</td>\n",
" <td>-4.323146</td>\n",
" <td>1.884543</td>\n",
" <td>2.846198</td>\n",
" <td>-3.876047</td>\n",
" <td>0.915986</td>\n",
" <td>...</td>\n",
" <td>3.393429</td>\n",
" <td>-0.659834</td>\n",
" <td>-2.978736</td>\n",
" <td>2.558584</td>\n",
" <td>4.793808</td>\n",
" <td>-1.387312</td>\n",
" <td>-2.529040</td>\n",
" <td>0.400940</td>\n",
" <td>-9.265282</td>\n",
" <td>-3.550425</td>\n",
" <td>-9.613931</td>\n",
" <td>-10.660444</td>\n",
" <td>9.311360</td>\n",
" <td>-5.328705</td>\n",
" <td>-3.052422</td>\n",
" <td>10.773338</td>\n",
" <td>1.616294</td>\n",
" <td>3.584433</td>\n",
" <td>7.170421</td>\n",
" <td>-3.983306</td>\n",
" <td>-0.614538</td>\n",
" <td>5.443189</td>\n",
" <td>5.036304</td>\n",
" <td>7.646272</td>\n",
" <td>-1.914301</td>\n",
" <td>-10.535980</td>\n",
" <td>9.987000</td>\n",
" <td>-2.324170</td>\n",
" <td>0.241503</td>\n",
" <td>0.017426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>-1.468089</td>\n",
" <td>4.973768</td>\n",
" <td>-3.655201</td>\n",
" <td>-2.960394</td>\n",
" <td>-1.227628</td>\n",
" <td>-4.717051</td>\n",
" <td>4.078471</td>\n",
" <td>-1.524756</td>\n",
" <td>-4.842924</td>\n",
" <td>5.292057</td>\n",
" <td>-0.146573</td>\n",
" <td>2.154481</td>\n",
" <td>7.962188</td>\n",
" <td>6.396473</td>\n",
" <td>-1.522938</td>\n",
" <td>1.917820</td>\n",
" <td>-4.649628</td>\n",
" <td>1.672230</td>\n",
" <td>2.446416</td>\n",
" <td>2.428500</td>\n",
" <td>8.269217</td>\n",
" <td>2.070013</td>\n",
" <td>-7.052204</td>\n",
" <td>2.947555</td>\n",
" <td>-3.187481</td>\n",
" <td>1.385355</td>\n",
" <td>2.161577</td>\n",
" <td>-2.816521</td>\n",
" <td>0.691043</td>\n",
" <td>...</td>\n",
" <td>2.563083</td>\n",
" <td>-0.483035</td>\n",
" <td>-2.187777</td>\n",
" <td>1.858291</td>\n",
" <td>3.534853</td>\n",
" <td>-0.991585</td>\n",
" <td>-1.840313</td>\n",
" <td>0.325528</td>\n",
" <td>-6.878268</td>\n",
" <td>-2.663374</td>\n",
" <td>-7.117446</td>\n",
" <td>-7.849199</td>\n",
" <td>6.858797</td>\n",
" <td>-3.940537</td>\n",
" <td>-2.254805</td>\n",
" <td>7.954999</td>\n",
" <td>1.198593</td>\n",
" <td>2.683525</td>\n",
" <td>5.327009</td>\n",
" <td>-3.005433</td>\n",
" <td>-0.449433</td>\n",
" <td>4.027910</td>\n",
" <td>3.739817</td>\n",
" <td>5.687069</td>\n",
" <td>-1.434358</td>\n",
" <td>-7.761081</td>\n",
" <td>7.387713</td>\n",
" <td>-1.691173</td>\n",
" <td>0.144985</td>\n",
" <td>0.005981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>0.835040</td>\n",
" <td>-1.440082</td>\n",
" <td>1.693879</td>\n",
" <td>1.085231</td>\n",
" <td>0.645742</td>\n",
" <td>-0.179382</td>\n",
" <td>-3.276297</td>\n",
" <td>2.222496</td>\n",
" <td>0.068324</td>\n",
" <td>-1.783111</td>\n",
" <td>0.630436</td>\n",
" <td>-2.899952</td>\n",
" <td>-0.166959</td>\n",
" <td>-1.217550</td>\n",
" <td>3.371764</td>\n",
" <td>-1.816508</td>\n",
" <td>0.824687</td>\n",
" <td>1.864594</td>\n",
" <td>-1.455014</td>\n",
" <td>-2.739754</td>\n",
" <td>-2.240265</td>\n",
" <td>-0.210853</td>\n",
" <td>2.938945</td>\n",
" <td>-2.260092</td>\n",
" <td>0.985322</td>\n",
" <td>-0.689567</td>\n",
" <td>-2.290468</td>\n",
" <td>0.418533</td>\n",
" <td>-2.359352</td>\n",
" <td>...</td>\n",
" <td>-3.181385</td>\n",
" <td>0.460193</td>\n",
" <td>0.688111</td>\n",
" <td>1.042391</td>\n",
" <td>-1.497057</td>\n",
" <td>-1.181879</td>\n",
" <td>-0.990638</td>\n",
" <td>-3.150775</td>\n",
" <td>3.217519</td>\n",
" <td>3.036210</td>\n",
" <td>2.418263</td>\n",
" <td>0.030914</td>\n",
" <td>-1.250423</td>\n",
" <td>1.715267</td>\n",
" <td>-0.075782</td>\n",
" <td>-2.641023</td>\n",
" <td>-0.816092</td>\n",
" <td>-1.903244</td>\n",
" <td>-2.799172</td>\n",
" <td>2.415422</td>\n",
" <td>-0.726119</td>\n",
" <td>-0.716847</td>\n",
" <td>-1.390135</td>\n",
" <td>-0.721278</td>\n",
" <td>1.503996</td>\n",
" <td>1.498211</td>\n",
" <td>-2.617611</td>\n",
" <td>-1.345437</td>\n",
" <td>-0.132722</td>\n",
" <td>-0.088661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0017be8caa87206532cb</td>\n",
" <td>0.186556</td>\n",
" <td>-0.493242</td>\n",
" <td>0.443594</td>\n",
" <td>0.298499</td>\n",
" <td>0.139708</td>\n",
" <td>0.256174</td>\n",
" <td>-0.725417</td>\n",
" <td>0.404549</td>\n",
" <td>0.305140</td>\n",
" <td>-0.558968</td>\n",
" <td>0.104695</td>\n",
" <td>-0.546815</td>\n",
" <td>-0.516724</td>\n",
" <td>-0.563558</td>\n",
" <td>0.602942</td>\n",
" <td>-0.374829</td>\n",
" <td>0.379741</td>\n",
" <td>0.172530</td>\n",
" <td>-0.333174</td>\n",
" <td>-0.543990</td>\n",
" <td>-0.841584</td>\n",
" <td>-0.139324</td>\n",
" <td>0.848845</td>\n",
" <td>-0.493825</td>\n",
" <td>0.317602</td>\n",
" <td>-0.152756</td>\n",
" <td>-0.452909</td>\n",
" <td>0.202096</td>\n",
" <td>-0.414372</td>\n",
" <td>...</td>\n",
" <td>-0.617599</td>\n",
" <td>0.059563</td>\n",
" <td>0.208309</td>\n",
" <td>0.056513</td>\n",
" <td>-0.429294</td>\n",
" <td>-0.116655</td>\n",
" <td>-0.034902</td>\n",
" <td>-0.460157</td>\n",
" <td>0.906921</td>\n",
" <td>0.607322</td>\n",
" <td>0.793904</td>\n",
" <td>0.500648</td>\n",
" <td>-0.596060</td>\n",
" <td>0.448263</td>\n",
" <td>0.117029</td>\n",
" <td>-0.867537</td>\n",
" <td>-0.135116</td>\n",
" <td>-0.442059</td>\n",
" <td>-0.731791</td>\n",
" <td>0.503702</td>\n",
" <td>-0.083384</td>\n",
" <td>-0.323191</td>\n",
" <td>-0.396757</td>\n",
" <td>-0.445615</td>\n",
" <td>0.309000</td>\n",
" <td>0.705457</td>\n",
" <td>-0.817697</td>\n",
" <td>-0.088170</td>\n",
" <td>-0.009688</td>\n",
" <td>-0.041770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001fa7a1c48acb8a2ec1</td>\n",
" <td>-1.890014</td>\n",
" <td>6.466066</td>\n",
" <td>-4.717495</td>\n",
" <td>-3.797523</td>\n",
" <td>-1.618052</td>\n",
" <td>-6.219454</td>\n",
" <td>5.190354</td>\n",
" <td>-1.861356</td>\n",
" <td>-6.374613</td>\n",
" <td>6.879678</td>\n",
" <td>-0.168298</td>\n",
" <td>2.661820</td>\n",
" <td>10.486411</td>\n",
" <td>8.330012</td>\n",
" <td>-1.856157</td>\n",
" <td>2.409953</td>\n",
" <td>-6.089809</td>\n",
" <td>2.310973</td>\n",
" <td>3.172939</td>\n",
" <td>3.058480</td>\n",
" <td>10.768784</td>\n",
" <td>2.722937</td>\n",
" <td>-9.138641</td>\n",
" <td>3.725910</td>\n",
" <td>-4.144100</td>\n",
" <td>1.779364</td>\n",
" <td>2.656900</td>\n",
" <td>-3.688718</td>\n",
" <td>0.796525</td>\n",
" <td>...</td>\n",
" <td>3.186186</td>\n",
" <td>-0.626362</td>\n",
" <td>-2.840724</td>\n",
" <td>2.482171</td>\n",
" <td>4.591260</td>\n",
" <td>-1.389367</td>\n",
" <td>-2.465328</td>\n",
" <td>0.300723</td>\n",
" <td>-8.877924</td>\n",
" <td>-3.361639</td>\n",
" <td>-9.246294</td>\n",
" <td>-10.311655</td>\n",
" <td>8.980938</td>\n",
" <td>-5.096307</td>\n",
" <td>-2.933834</td>\n",
" <td>10.317692</td>\n",
" <td>1.548571</td>\n",
" <td>3.425790</td>\n",
" <td>6.892689</td>\n",
" <td>-3.781508</td>\n",
" <td>-0.606475</td>\n",
" <td>5.234817</td>\n",
" <td>4.823212</td>\n",
" <td>7.422957</td>\n",
" <td>-1.789327</td>\n",
" <td>-10.127080</td>\n",
" <td>9.621022</td>\n",
" <td>-2.308365</td>\n",
" <td>0.181772</td>\n",
" <td>0.033027</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 65 columns</p>\n",
"</div>"
],
"text/plain": [
" object_id color_embedding_0 color_embedding_1 \\\n",
"0 001020bd00b149970f78 -1.963787 6.739867 \n",
"1 0011d6be41612ec9eae3 -1.468089 4.973768 \n",
"2 00133be3ff222c9b74b0 0.835040 -1.440082 \n",
"3 0017be8caa87206532cb 0.186556 -0.493242 \n",
"4 001fa7a1c48acb8a2ec1 -1.890014 6.466066 \n",
"\n",
" color_embedding_2 color_embedding_3 color_embedding_4 color_embedding_5 \\\n",
"0 -4.945860 -3.967160 -1.690328 -6.407980 \n",
"1 -3.655201 -2.960394 -1.227628 -4.717051 \n",
"2 1.693879 1.085231 0.645742 -0.179382 \n",
"3 0.443594 0.298499 0.139708 0.256174 \n",
"4 -4.717495 -3.797523 -1.618052 -6.219454 \n",
"\n",
" color_embedding_6 color_embedding_7 color_embedding_8 color_embedding_9 \\\n",
"0 5.437441 -1.996925 -6.550920 7.186263 \n",
"1 4.078471 -1.524756 -4.842924 5.292057 \n",
"2 -3.276297 2.222496 0.068324 -1.783111 \n",
"3 -0.725417 0.404549 0.305140 -0.558968 \n",
"4 5.190354 -1.861356 -6.374613 6.879678 \n",
"\n",
" color_embedding_10 color_embedding_11 color_embedding_12 \\\n",
"0 -0.217964 2.846546 10.841927 \n",
"1 -0.146573 2.154481 7.962188 \n",
"2 0.630436 -2.899952 -0.166959 \n",
"3 0.104695 -0.546815 -0.516724 \n",
"4 -0.168298 2.661820 10.486411 \n",
"\n",
" color_embedding_13 color_embedding_14 color_embedding_15 \\\n",
"0 8.638573 -2.015988 2.555417 \n",
"1 6.396473 -1.522938 1.917820 \n",
"2 -1.217550 3.371764 -1.816508 \n",
"3 -0.563558 0.602942 -0.374829 \n",
"4 8.330012 -1.856157 2.409953 \n",
"\n",
" color_embedding_16 color_embedding_17 color_embedding_18 \\\n",
"0 -6.332300 2.330411 3.331545 \n",
"1 -4.649628 1.672230 2.446416 \n",
"2 0.824687 1.864594 -1.455014 \n",
"3 0.379741 0.172530 -0.333174 \n",
"4 -6.089809 2.310973 3.172939 \n",
"\n",
" color_embedding_19 color_embedding_20 color_embedding_21 \\\n",
"0 3.230083 11.196459 2.800659 \n",
"1 2.428500 8.269217 2.070013 \n",
"2 -2.739754 -2.240265 -0.210853 \n",
"3 -0.543990 -0.841584 -0.139324 \n",
"4 3.058480 10.768784 2.722937 \n",
"\n",
" color_embedding_22 color_embedding_23 color_embedding_24 \\\n",
"0 -9.511781 3.930397 -4.323146 \n",
"1 -7.052204 2.947555 -3.187481 \n",
"2 2.938945 -2.260092 0.985322 \n",
"3 0.848845 -0.493825 0.317602 \n",
"4 -9.138641 3.725910 -4.144100 \n",
"\n",
" color_embedding_25 color_embedding_26 color_embedding_27 \\\n",
"0 1.884543 2.846198 -3.876047 \n",
"1 1.385355 2.161577 -2.816521 \n",
"2 -0.689567 -2.290468 0.418533 \n",
"3 -0.152756 -0.452909 0.202096 \n",
"4 1.779364 2.656900 -3.688718 \n",
"\n",
" color_embedding_28 ... color_embedding_34 color_embedding_35 \\\n",
"0 0.915986 ... 3.393429 -0.659834 \n",
"1 0.691043 ... 2.563083 -0.483035 \n",
"2 -2.359352 ... -3.181385 0.460193 \n",
"3 -0.414372 ... -0.617599 0.059563 \n",
"4 0.796525 ... 3.186186 -0.626362 \n",
"\n",
" color_embedding_36 color_embedding_37 color_embedding_38 \\\n",
"0 -2.978736 2.558584 4.793808 \n",
"1 -2.187777 1.858291 3.534853 \n",
"2 0.688111 1.042391 -1.497057 \n",
"3 0.208309 0.056513 -0.429294 \n",
"4 -2.840724 2.482171 4.591260 \n",
"\n",
" color_embedding_39 color_embedding_40 color_embedding_41 \\\n",
"0 -1.387312 -2.529040 0.400940 \n",
"1 -0.991585 -1.840313 0.325528 \n",
"2 -1.181879 -0.990638 -3.150775 \n",
"3 -0.116655 -0.034902 -0.460157 \n",
"4 -1.389367 -2.465328 0.300723 \n",
"\n",
" color_embedding_42 color_embedding_43 color_embedding_44 \\\n",
"0 -9.265282 -3.550425 -9.613931 \n",
"1 -6.878268 -2.663374 -7.117446 \n",
"2 3.217519 3.036210 2.418263 \n",
"3 0.906921 0.607322 0.793904 \n",
"4 -8.877924 -3.361639 -9.246294 \n",
"\n",
" color_embedding_45 color_embedding_46 color_embedding_47 \\\n",
"0 -10.660444 9.311360 -5.328705 \n",
"1 -7.849199 6.858797 -3.940537 \n",
"2 0.030914 -1.250423 1.715267 \n",
"3 0.500648 -0.596060 0.448263 \n",
"4 -10.311655 8.980938 -5.096307 \n",
"\n",
" color_embedding_48 color_embedding_49 color_embedding_50 \\\n",
"0 -3.052422 10.773338 1.616294 \n",
"1 -2.254805 7.954999 1.198593 \n",
"2 -0.075782 -2.641023 -0.816092 \n",
"3 0.117029 -0.867537 -0.135116 \n",
"4 -2.933834 10.317692 1.548571 \n",
"\n",
" color_embedding_51 color_embedding_52 color_embedding_53 \\\n",
"0 3.584433 7.170421 -3.983306 \n",
"1 2.683525 5.327009 -3.005433 \n",
"2 -1.903244 -2.799172 2.415422 \n",
"3 -0.442059 -0.731791 0.503702 \n",
"4 3.425790 6.892689 -3.781508 \n",
"\n",
" color_embedding_54 color_embedding_55 color_embedding_56 \\\n",
"0 -0.614538 5.443189 5.036304 \n",
"1 -0.449433 4.027910 3.739817 \n",
"2 -0.726119 -0.716847 -1.390135 \n",
"3 -0.083384 -0.323191 -0.396757 \n",
"4 -0.606475 5.234817 4.823212 \n",
"\n",
" color_embedding_57 color_embedding_58 color_embedding_59 \\\n",
"0 7.646272 -1.914301 -10.535980 \n",
"1 5.687069 -1.434358 -7.761081 \n",
"2 -0.721278 1.503996 1.498211 \n",
"3 -0.445615 0.309000 0.705457 \n",
"4 7.422957 -1.789327 -10.127080 \n",
"\n",
" color_embedding_60 color_embedding_61 color_embedding_62 \\\n",
"0 9.987000 -2.324170 0.241503 \n",
"1 7.387713 -1.691173 0.144985 \n",
"2 -2.617611 -1.345437 -0.132722 \n",
"3 -0.817697 -0.088170 -0.009688 \n",
"4 9.621022 -2.308365 0.181772 \n",
"\n",
" color_embedding_63 \n",
"0 0.017426 \n",
"1 0.005981 \n",
"2 -0.088661 \n",
"3 -0.041770 \n",
"4 0.033027 \n",
"\n",
"[5 rows x 65 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"color2 = pd.read_csv(\"../input/color_ssl.csv\")\n",
"color2.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge color ssl features] start\n",
"[Merge color ssl features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge color ssl features\"):\n",
" train = train.merge(color2, on=\"object_id\", how=\"left\")\n",
" test = test.merge(color2, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Color and Palette statistical features"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"color_feats = pd.read_csv(\"../input/color_features.csv\")\n",
"palette_feats = pd.read_csv(\"../input/palette_features.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge color and palette features] start\n",
"[Merge color and palette features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge color and palette features\"):\n",
" train = train.merge(color_feats, on=\"object_id\", how=\"left\")\n",
" train = train.merge(palette_feats, on=\"object_id\", how=\"left\")\n",
" test = test.merge(color_feats, on=\"object_id\", how=\"left\")\n",
" test = test.merge(palette_feats, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Effnet Color and palette features"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"eff_color_feats = pd.read_csv(\"../input/effnet_color_features.csv\")\n",
"eff_palette_feats = pd.read_csv(\"../input/effnet_palette_features.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge color and palette features] start\n",
"[Merge color and palette features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge color and palette features\"):\n",
" train = train.merge(eff_color_feats, on=\"object_id\", how=\"left\")\n",
" train = train.merge(eff_palette_feats, on=\"object_id\", how=\"left\")\n",
" test = test.merge(eff_color_feats, on=\"object_id\", how=\"left\")\n",
" test = test.merge(eff_palette_feats, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>object_id</th>\n",
" <th>effnet_color_feature_0</th>\n",
" <th>effnet_color_feature_1</th>\n",
" <th>effnet_color_feature_2</th>\n",
" <th>effnet_color_feature_3</th>\n",
" <th>effnet_color_feature_4</th>\n",
" <th>effnet_color_feature_5</th>\n",
" <th>effnet_color_feature_6</th>\n",
" <th>effnet_color_feature_7</th>\n",
" <th>effnet_color_feature_8</th>\n",
" <th>effnet_color_feature_9</th>\n",
" <th>effnet_color_feature_10</th>\n",
" <th>effnet_color_feature_11</th>\n",
" <th>effnet_color_feature_12</th>\n",
" <th>effnet_color_feature_13</th>\n",
" <th>effnet_color_feature_14</th>\n",
" <th>effnet_color_feature_15</th>\n",
" <th>effnet_color_feature_16</th>\n",
" <th>effnet_color_feature_17</th>\n",
" <th>effnet_color_feature_18</th>\n",
" <th>effnet_color_feature_19</th>\n",
" <th>effnet_color_feature_20</th>\n",
" <th>effnet_color_feature_21</th>\n",
" <th>effnet_color_feature_22</th>\n",
" <th>effnet_color_feature_23</th>\n",
" <th>effnet_color_feature_24</th>\n",
" <th>effnet_color_feature_25</th>\n",
" <th>effnet_color_feature_26</th>\n",
" <th>effnet_color_feature_27</th>\n",
" <th>effnet_color_feature_28</th>\n",
" <th>effnet_color_feature_29</th>\n",
" <th>effnet_color_feature_30</th>\n",
" <th>effnet_color_feature_31</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>001020bd00b149970f78</td>\n",
" <td>5.308928</td>\n",
" <td>-0.048716</td>\n",
" <td>0.448416</td>\n",
" <td>-0.859684</td>\n",
" <td>0.087989</td>\n",
" <td>-0.022889</td>\n",
" <td>0.011907</td>\n",
" <td>-0.153170</td>\n",
" <td>0.020790</td>\n",
" <td>-0.084053</td>\n",
" <td>-0.323996</td>\n",
" <td>0.012112</td>\n",
" <td>-0.036985</td>\n",
" <td>-0.058924</td>\n",
" <td>-0.046914</td>\n",
" <td>0.010690</td>\n",
" <td>-0.209429</td>\n",
" <td>0.075280</td>\n",
" <td>-0.114444</td>\n",
" <td>0.099619</td>\n",
" <td>-0.040123</td>\n",
" <td>0.059834</td>\n",
" <td>0.011719</td>\n",
" <td>0.127615</td>\n",
" <td>-0.133527</td>\n",
" <td>0.031139</td>\n",
" <td>0.048042</td>\n",
" <td>0.007558</td>\n",
" <td>0.027995</td>\n",
" <td>0.012560</td>\n",
" <td>-0.046884</td>\n",
" <td>-0.024548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>5.392905</td>\n",
" <td>0.900492</td>\n",
" <td>0.117759</td>\n",
" <td>-0.347856</td>\n",
" <td>-0.091621</td>\n",
" <td>0.086296</td>\n",
" <td>-0.024224</td>\n",
" <td>-0.227354</td>\n",
" <td>0.011076</td>\n",
" <td>-0.246319</td>\n",
" <td>-0.289187</td>\n",
" <td>0.045066</td>\n",
" <td>-0.068980</td>\n",
" <td>0.013968</td>\n",
" <td>-0.083364</td>\n",
" <td>-0.098511</td>\n",
" <td>-0.008693</td>\n",
" <td>-0.150669</td>\n",
" <td>-0.049827</td>\n",
" <td>0.003575</td>\n",
" <td>-0.072542</td>\n",
" <td>-0.000379</td>\n",
" <td>0.048759</td>\n",
" <td>-0.017132</td>\n",
" <td>-0.034970</td>\n",
" <td>0.124584</td>\n",
" <td>0.072210</td>\n",
" <td>-0.023683</td>\n",
" <td>0.066760</td>\n",
" <td>0.002699</td>\n",
" <td>-0.025725</td>\n",
" <td>0.052961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>5.194559</td>\n",
" <td>-0.691896</td>\n",
" <td>1.755499</td>\n",
" <td>0.830203</td>\n",
" <td>0.298997</td>\n",
" <td>-0.012634</td>\n",
" <td>0.344659</td>\n",
" <td>-0.779312</td>\n",
" <td>-0.029927</td>\n",
" <td>-0.024853</td>\n",
" <td>-0.267017</td>\n",
" <td>0.411207</td>\n",
" <td>-0.145490</td>\n",
" <td>0.021918</td>\n",
" <td>0.091574</td>\n",
" <td>-0.174193</td>\n",
" <td>0.022807</td>\n",
" <td>-0.228027</td>\n",
" <td>0.313458</td>\n",
" <td>0.108157</td>\n",
" <td>-0.164162</td>\n",
" <td>-0.020098</td>\n",
" <td>-0.290919</td>\n",
" <td>0.070812</td>\n",
" <td>-0.132334</td>\n",
" <td>0.069684</td>\n",
" <td>0.211994</td>\n",
" <td>-0.162664</td>\n",
" <td>0.041206</td>\n",
" <td>-0.281869</td>\n",
" <td>-0.021932</td>\n",
" <td>-0.005808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0017be8caa87206532cb</td>\n",
" <td>5.397009</td>\n",
" <td>-0.809881</td>\n",
" <td>-0.376945</td>\n",
" <td>-0.095838</td>\n",
" <td>-0.412060</td>\n",
" <td>-0.117470</td>\n",
" <td>-0.195414</td>\n",
" <td>0.014143</td>\n",
" <td>-0.045279</td>\n",
" <td>-0.000688</td>\n",
" <td>0.047522</td>\n",
" <td>0.224581</td>\n",
" <td>0.086319</td>\n",
" <td>0.274721</td>\n",
" <td>-0.110875</td>\n",
" <td>-0.056615</td>\n",
" <td>-0.011617</td>\n",
" <td>0.043577</td>\n",
" <td>0.066723</td>\n",
" <td>0.009942</td>\n",
" <td>0.123603</td>\n",
" <td>0.133110</td>\n",
" <td>0.054180</td>\n",
" <td>0.029677</td>\n",
" <td>-0.061681</td>\n",
" <td>0.021514</td>\n",
" <td>-0.072290</td>\n",
" <td>-0.016571</td>\n",
" <td>0.039604</td>\n",
" <td>-0.012253</td>\n",
" <td>-0.032268</td>\n",
" <td>0.093992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001fa7a1c48acb8a2ec1</td>\n",
" <td>5.372493</td>\n",
" <td>0.670626</td>\n",
" <td>-0.124998</td>\n",
" <td>-0.525216</td>\n",
" <td>0.185670</td>\n",
" <td>0.141341</td>\n",
" <td>-0.156707</td>\n",
" <td>0.010996</td>\n",
" <td>0.019624</td>\n",
" <td>-0.044919</td>\n",
" <td>-0.268803</td>\n",
" <td>0.138157</td>\n",
" <td>-0.054350</td>\n",
" <td>-0.098968</td>\n",
" <td>-0.024339</td>\n",
" <td>-0.040127</td>\n",
" <td>0.027755</td>\n",
" <td>-0.117163</td>\n",
" <td>-0.006763</td>\n",
" <td>0.032814</td>\n",
" <td>0.011342</td>\n",
" <td>0.033009</td>\n",
" <td>-0.074540</td>\n",
" <td>-0.095215</td>\n",
" <td>0.044278</td>\n",
" <td>0.065873</td>\n",
" <td>0.027893</td>\n",
" <td>-0.077643</td>\n",
" <td>0.055516</td>\n",
" <td>0.072680</td>\n",
" <td>0.042924</td>\n",
" <td>-0.084771</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" object_id effnet_color_feature_0 effnet_color_feature_1 \\\n",
"0 001020bd00b149970f78 5.308928 -0.048716 \n",
"1 0011d6be41612ec9eae3 5.392905 0.900492 \n",
"2 00133be3ff222c9b74b0 5.194559 -0.691896 \n",
"3 0017be8caa87206532cb 5.397009 -0.809881 \n",
"4 001fa7a1c48acb8a2ec1 5.372493 0.670626 \n",
"\n",
" effnet_color_feature_2 effnet_color_feature_3 effnet_color_feature_4 \\\n",
"0 0.448416 -0.859684 0.087989 \n",
"1 0.117759 -0.347856 -0.091621 \n",
"2 1.755499 0.830203 0.298997 \n",
"3 -0.376945 -0.095838 -0.412060 \n",
"4 -0.124998 -0.525216 0.185670 \n",
"\n",
" effnet_color_feature_5 effnet_color_feature_6 effnet_color_feature_7 \\\n",
"0 -0.022889 0.011907 -0.153170 \n",
"1 0.086296 -0.024224 -0.227354 \n",
"2 -0.012634 0.344659 -0.779312 \n",
"3 -0.117470 -0.195414 0.014143 \n",
"4 0.141341 -0.156707 0.010996 \n",
"\n",
" effnet_color_feature_8 effnet_color_feature_9 effnet_color_feature_10 \\\n",
"0 0.020790 -0.084053 -0.323996 \n",
"1 0.011076 -0.246319 -0.289187 \n",
"2 -0.029927 -0.024853 -0.267017 \n",
"3 -0.045279 -0.000688 0.047522 \n",
"4 0.019624 -0.044919 -0.268803 \n",
"\n",
" effnet_color_feature_11 effnet_color_feature_12 effnet_color_feature_13 \\\n",
"0 0.012112 -0.036985 -0.058924 \n",
"1 0.045066 -0.068980 0.013968 \n",
"2 0.411207 -0.145490 0.021918 \n",
"3 0.224581 0.086319 0.274721 \n",
"4 0.138157 -0.054350 -0.098968 \n",
"\n",
" effnet_color_feature_14 effnet_color_feature_15 effnet_color_feature_16 \\\n",
"0 -0.046914 0.010690 -0.209429 \n",
"1 -0.083364 -0.098511 -0.008693 \n",
"2 0.091574 -0.174193 0.022807 \n",
"3 -0.110875 -0.056615 -0.011617 \n",
"4 -0.024339 -0.040127 0.027755 \n",
"\n",
" effnet_color_feature_17 effnet_color_feature_18 effnet_color_feature_19 \\\n",
"0 0.075280 -0.114444 0.099619 \n",
"1 -0.150669 -0.049827 0.003575 \n",
"2 -0.228027 0.313458 0.108157 \n",
"3 0.043577 0.066723 0.009942 \n",
"4 -0.117163 -0.006763 0.032814 \n",
"\n",
" effnet_color_feature_20 effnet_color_feature_21 effnet_color_feature_22 \\\n",
"0 -0.040123 0.059834 0.011719 \n",
"1 -0.072542 -0.000379 0.048759 \n",
"2 -0.164162 -0.020098 -0.290919 \n",
"3 0.123603 0.133110 0.054180 \n",
"4 0.011342 0.033009 -0.074540 \n",
"\n",
" effnet_color_feature_23 effnet_color_feature_24 effnet_color_feature_25 \\\n",
"0 0.127615 -0.133527 0.031139 \n",
"1 -0.017132 -0.034970 0.124584 \n",
"2 0.070812 -0.132334 0.069684 \n",
"3 0.029677 -0.061681 0.021514 \n",
"4 -0.095215 0.044278 0.065873 \n",
"\n",
" effnet_color_feature_26 effnet_color_feature_27 effnet_color_feature_28 \\\n",
"0 0.048042 0.007558 0.027995 \n",
"1 0.072210 -0.023683 0.066760 \n",
"2 0.211994 -0.162664 0.041206 \n",
"3 -0.072290 -0.016571 0.039604 \n",
"4 0.027893 -0.077643 0.055516 \n",
"\n",
" effnet_color_feature_29 effnet_color_feature_30 effnet_color_feature_31 \n",
"0 0.012560 -0.046884 -0.024548 \n",
"1 0.002699 -0.025725 0.052961 \n",
"2 -0.281869 -0.021932 -0.005808 \n",
"3 -0.012253 -0.032268 0.093992 \n",
"4 0.072680 0.042924 -0.084771 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eff_color_feats.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>object_id</th>\n",
" <th>effnet_palette_feature_0</th>\n",
" <th>effnet_palette_feature_1</th>\n",
" <th>effnet_palette_feature_2</th>\n",
" <th>effnet_palette_feature_3</th>\n",
" <th>effnet_palette_feature_4</th>\n",
" <th>effnet_palette_feature_5</th>\n",
" <th>effnet_palette_feature_6</th>\n",
" <th>effnet_palette_feature_7</th>\n",
" <th>effnet_palette_feature_8</th>\n",
" <th>effnet_palette_feature_9</th>\n",
" <th>effnet_palette_feature_10</th>\n",
" <th>effnet_palette_feature_11</th>\n",
" <th>effnet_palette_feature_12</th>\n",
" <th>effnet_palette_feature_13</th>\n",
" <th>effnet_palette_feature_14</th>\n",
" <th>effnet_palette_feature_15</th>\n",
" <th>effnet_palette_feature_16</th>\n",
" <th>effnet_palette_feature_17</th>\n",
" <th>effnet_palette_feature_18</th>\n",
" <th>effnet_palette_feature_19</th>\n",
" <th>effnet_palette_feature_20</th>\n",
" <th>effnet_palette_feature_21</th>\n",
" <th>effnet_palette_feature_22</th>\n",
" <th>effnet_palette_feature_23</th>\n",
" <th>effnet_palette_feature_24</th>\n",
" <th>effnet_palette_feature_25</th>\n",
" <th>effnet_palette_feature_26</th>\n",
" <th>effnet_palette_feature_27</th>\n",
" <th>effnet_palette_feature_28</th>\n",
" <th>effnet_palette_feature_29</th>\n",
" <th>effnet_palette_feature_30</th>\n",
" <th>effnet_palette_feature_31</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000405d9a5e3f49fc49d</td>\n",
" <td>7.888149</td>\n",
" <td>-0.479236</td>\n",
" <td>-1.563714</td>\n",
" <td>-1.263327</td>\n",
" <td>1.778493</td>\n",
" <td>0.008482</td>\n",
" <td>0.429855</td>\n",
" <td>0.430928</td>\n",
" <td>0.163529</td>\n",
" <td>-0.387839</td>\n",
" <td>-0.370306</td>\n",
" <td>0.085108</td>\n",
" <td>0.001966</td>\n",
" <td>-0.067542</td>\n",
" <td>-0.284877</td>\n",
" <td>-0.042564</td>\n",
" <td>-0.082956</td>\n",
" <td>0.052644</td>\n",
" <td>-0.416452</td>\n",
" <td>-0.206487</td>\n",
" <td>-0.196915</td>\n",
" <td>-0.243273</td>\n",
" <td>-0.229650</td>\n",
" <td>0.106454</td>\n",
" <td>-0.045358</td>\n",
" <td>-0.076802</td>\n",
" <td>0.039286</td>\n",
" <td>0.039126</td>\n",
" <td>0.063913</td>\n",
" <td>0.288671</td>\n",
" <td>-0.178367</td>\n",
" <td>-0.183591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>001020bd00b149970f78</td>\n",
" <td>6.142496</td>\n",
" <td>2.277995</td>\n",
" <td>0.753009</td>\n",
" <td>0.338663</td>\n",
" <td>0.746789</td>\n",
" <td>0.415324</td>\n",
" <td>0.965877</td>\n",
" <td>-0.427607</td>\n",
" <td>0.724034</td>\n",
" <td>0.402549</td>\n",
" <td>-0.129145</td>\n",
" <td>-0.169462</td>\n",
" <td>0.138272</td>\n",
" <td>-0.076085</td>\n",
" <td>0.087607</td>\n",
" <td>-0.255276</td>\n",
" <td>-0.275005</td>\n",
" <td>0.017401</td>\n",
" <td>-0.224762</td>\n",
" <td>-0.186871</td>\n",
" <td>-0.238786</td>\n",
" <td>0.071414</td>\n",
" <td>0.131836</td>\n",
" <td>0.108882</td>\n",
" <td>0.150931</td>\n",
" <td>-0.154351</td>\n",
" <td>0.119911</td>\n",
" <td>0.217546</td>\n",
" <td>-0.026026</td>\n",
" <td>-0.046474</td>\n",
" <td>0.099300</td>\n",
" <td>-0.080136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>6.499674</td>\n",
" <td>1.258664</td>\n",
" <td>-0.175817</td>\n",
" <td>-1.125722</td>\n",
" <td>-0.237378</td>\n",
" <td>-0.631817</td>\n",
" <td>0.187825</td>\n",
" <td>-0.917450</td>\n",
" <td>-0.338768</td>\n",
" <td>0.304730</td>\n",
" <td>0.405196</td>\n",
" <td>-0.171761</td>\n",
" <td>0.291008</td>\n",
" <td>0.151136</td>\n",
" <td>0.239540</td>\n",
" <td>0.102965</td>\n",
" <td>0.010207</td>\n",
" <td>0.187114</td>\n",
" <td>0.015587</td>\n",
" <td>0.029224</td>\n",
" <td>0.045018</td>\n",
" <td>-0.332866</td>\n",
" <td>-0.013759</td>\n",
" <td>0.028253</td>\n",
" <td>-0.050373</td>\n",
" <td>0.054650</td>\n",
" <td>-0.024990</td>\n",
" <td>-0.151303</td>\n",
" <td>0.186750</td>\n",
" <td>0.073367</td>\n",
" <td>-0.044664</td>\n",
" <td>0.067142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>7.258843</td>\n",
" <td>-0.993872</td>\n",
" <td>-0.418950</td>\n",
" <td>-1.040979</td>\n",
" <td>-0.792940</td>\n",
" <td>-1.008525</td>\n",
" <td>-0.088784</td>\n",
" <td>-0.196457</td>\n",
" <td>1.259171</td>\n",
" <td>0.073634</td>\n",
" <td>0.180975</td>\n",
" <td>0.207530</td>\n",
" <td>-0.292060</td>\n",
" <td>-0.519410</td>\n",
" <td>0.289046</td>\n",
" <td>-0.282460</td>\n",
" <td>0.255951</td>\n",
" <td>0.114261</td>\n",
" <td>0.479267</td>\n",
" <td>0.066932</td>\n",
" <td>-0.139462</td>\n",
" <td>-0.207957</td>\n",
" <td>-0.039616</td>\n",
" <td>-0.145943</td>\n",
" <td>0.184430</td>\n",
" <td>-0.105777</td>\n",
" <td>-0.001878</td>\n",
" <td>0.063489</td>\n",
" <td>0.160352</td>\n",
" <td>-0.047408</td>\n",
" <td>0.062623</td>\n",
" <td>-0.076383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>00133be3ff222c9b74b0</td>\n",
" <td>7.701722</td>\n",
" <td>-0.838611</td>\n",
" <td>0.787433</td>\n",
" <td>0.134209</td>\n",
" <td>0.484461</td>\n",
" <td>-0.643303</td>\n",
" <td>0.566374</td>\n",
" <td>0.203065</td>\n",
" <td>-0.564324</td>\n",
" <td>-0.453384</td>\n",
" <td>-0.360608</td>\n",
" <td>-0.064390</td>\n",
" <td>0.373687</td>\n",
" <td>0.019220</td>\n",
" <td>-0.149771</td>\n",
" <td>-0.120188</td>\n",
" <td>0.100312</td>\n",
" <td>0.161614</td>\n",
" <td>0.017972</td>\n",
" <td>-0.159480</td>\n",
" <td>-0.125305</td>\n",
" <td>-0.190432</td>\n",
" <td>0.103902</td>\n",
" <td>-0.023744</td>\n",
" <td>-0.288569</td>\n",
" <td>-0.061910</td>\n",
" <td>0.167167</td>\n",
" <td>0.095168</td>\n",
" <td>0.056186</td>\n",
" <td>0.035410</td>\n",
" <td>-0.129768</td>\n",
" <td>0.077833</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" object_id effnet_palette_feature_0 effnet_palette_feature_1 \\\n",
"0 000405d9a5e3f49fc49d 7.888149 -0.479236 \n",
"1 001020bd00b149970f78 6.142496 2.277995 \n",
"2 0011d6be41612ec9eae3 6.499674 1.258664 \n",
"3 0012765f7a97ccc3e9e9 7.258843 -0.993872 \n",
"4 00133be3ff222c9b74b0 7.701722 -0.838611 \n",
"\n",
" effnet_palette_feature_2 effnet_palette_feature_3 \\\n",
"0 -1.563714 -1.263327 \n",
"1 0.753009 0.338663 \n",
"2 -0.175817 -1.125722 \n",
"3 -0.418950 -1.040979 \n",
"4 0.787433 0.134209 \n",
"\n",
" effnet_palette_feature_4 effnet_palette_feature_5 \\\n",
"0 1.778493 0.008482 \n",
"1 0.746789 0.415324 \n",
"2 -0.237378 -0.631817 \n",
"3 -0.792940 -1.008525 \n",
"4 0.484461 -0.643303 \n",
"\n",
" effnet_palette_feature_6 effnet_palette_feature_7 \\\n",
"0 0.429855 0.430928 \n",
"1 0.965877 -0.427607 \n",
"2 0.187825 -0.917450 \n",
"3 -0.088784 -0.196457 \n",
"4 0.566374 0.203065 \n",
"\n",
" effnet_palette_feature_8 effnet_palette_feature_9 \\\n",
"0 0.163529 -0.387839 \n",
"1 0.724034 0.402549 \n",
"2 -0.338768 0.304730 \n",
"3 1.259171 0.073634 \n",
"4 -0.564324 -0.453384 \n",
"\n",
" effnet_palette_feature_10 effnet_palette_feature_11 \\\n",
"0 -0.370306 0.085108 \n",
"1 -0.129145 -0.169462 \n",
"2 0.405196 -0.171761 \n",
"3 0.180975 0.207530 \n",
"4 -0.360608 -0.064390 \n",
"\n",
" effnet_palette_feature_12 effnet_palette_feature_13 \\\n",
"0 0.001966 -0.067542 \n",
"1 0.138272 -0.076085 \n",
"2 0.291008 0.151136 \n",
"3 -0.292060 -0.519410 \n",
"4 0.373687 0.019220 \n",
"\n",
" effnet_palette_feature_14 effnet_palette_feature_15 \\\n",
"0 -0.284877 -0.042564 \n",
"1 0.087607 -0.255276 \n",
"2 0.239540 0.102965 \n",
"3 0.289046 -0.282460 \n",
"4 -0.149771 -0.120188 \n",
"\n",
" effnet_palette_feature_16 effnet_palette_feature_17 \\\n",
"0 -0.082956 0.052644 \n",
"1 -0.275005 0.017401 \n",
"2 0.010207 0.187114 \n",
"3 0.255951 0.114261 \n",
"4 0.100312 0.161614 \n",
"\n",
" effnet_palette_feature_18 effnet_palette_feature_19 \\\n",
"0 -0.416452 -0.206487 \n",
"1 -0.224762 -0.186871 \n",
"2 0.015587 0.029224 \n",
"3 0.479267 0.066932 \n",
"4 0.017972 -0.159480 \n",
"\n",
" effnet_palette_feature_20 effnet_palette_feature_21 \\\n",
"0 -0.196915 -0.243273 \n",
"1 -0.238786 0.071414 \n",
"2 0.045018 -0.332866 \n",
"3 -0.139462 -0.207957 \n",
"4 -0.125305 -0.190432 \n",
"\n",
" effnet_palette_feature_22 effnet_palette_feature_23 \\\n",
"0 -0.229650 0.106454 \n",
"1 0.131836 0.108882 \n",
"2 -0.013759 0.028253 \n",
"3 -0.039616 -0.145943 \n",
"4 0.103902 -0.023744 \n",
"\n",
" effnet_palette_feature_24 effnet_palette_feature_25 \\\n",
"0 -0.045358 -0.076802 \n",
"1 0.150931 -0.154351 \n",
"2 -0.050373 0.054650 \n",
"3 0.184430 -0.105777 \n",
"4 -0.288569 -0.061910 \n",
"\n",
" effnet_palette_feature_26 effnet_palette_feature_27 \\\n",
"0 0.039286 0.039126 \n",
"1 0.119911 0.217546 \n",
"2 -0.024990 -0.151303 \n",
"3 -0.001878 0.063489 \n",
"4 0.167167 0.095168 \n",
"\n",
" effnet_palette_feature_28 effnet_palette_feature_29 \\\n",
"0 0.063913 0.288671 \n",
"1 -0.026026 -0.046474 \n",
"2 0.186750 0.073367 \n",
"3 0.160352 -0.047408 \n",
"4 0.056186 0.035410 \n",
"\n",
" effnet_palette_feature_30 effnet_palette_feature_31 \n",
"0 -0.178367 -0.183591 \n",
"1 0.099300 -0.080136 \n",
"2 -0.044664 0.067142 \n",
"3 0.062623 -0.076383 \n",
"4 -0.129768 0.077833 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eff_palette_feats.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BERT features"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
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" 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>object_id</th>\n",
" <th>title_bert_0</th>\n",
" <th>title_bert_1</th>\n",
" <th>title_bert_2</th>\n",
" <th>title_bert_3</th>\n",
" <th>title_bert_4</th>\n",
" <th>title_bert_5</th>\n",
" <th>title_bert_6</th>\n",
" <th>title_bert_7</th>\n",
" <th>title_bert_8</th>\n",
" <th>title_bert_9</th>\n",
" <th>title_bert_10</th>\n",
" <th>title_bert_11</th>\n",
" <th>title_bert_12</th>\n",
" <th>title_bert_13</th>\n",
" <th>title_bert_14</th>\n",
" <th>title_bert_15</th>\n",
" <th>title_bert_16</th>\n",
" <th>title_bert_17</th>\n",
" <th>title_bert_18</th>\n",
" <th>title_bert_19</th>\n",
" <th>title_bert_20</th>\n",
" <th>title_bert_21</th>\n",
" <th>title_bert_22</th>\n",
" <th>title_bert_23</th>\n",
" <th>title_bert_24</th>\n",
" <th>title_bert_25</th>\n",
" <th>title_bert_26</th>\n",
" <th>title_bert_27</th>\n",
" <th>title_bert_28</th>\n",
" <th>...</th>\n",
" <th>more_title_bert_20</th>\n",
" <th>more_title_bert_21</th>\n",
" <th>more_title_bert_22</th>\n",
" <th>more_title_bert_23</th>\n",
" <th>more_title_bert_24</th>\n",
" <th>more_title_bert_25</th>\n",
" <th>more_title_bert_26</th>\n",
" <th>more_title_bert_27</th>\n",
" <th>more_title_bert_28</th>\n",
" <th>more_title_bert_29</th>\n",
" <th>more_title_bert_30</th>\n",
" <th>more_title_bert_31</th>\n",
" <th>more_title_bert_32</th>\n",
" <th>more_title_bert_33</th>\n",
" <th>more_title_bert_34</th>\n",
" <th>more_title_bert_35</th>\n",
" <th>more_title_bert_36</th>\n",
" <th>more_title_bert_37</th>\n",
" <th>more_title_bert_38</th>\n",
" <th>more_title_bert_39</th>\n",
" <th>more_title_bert_40</th>\n",
" <th>more_title_bert_41</th>\n",
" <th>more_title_bert_42</th>\n",
" <th>more_title_bert_43</th>\n",
" <th>more_title_bert_44</th>\n",
" <th>more_title_bert_45</th>\n",
" <th>more_title_bert_46</th>\n",
" <th>more_title_bert_47</th>\n",
" <th>more_title_bert_48</th>\n",
" <th>more_title_bert_49</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>13.093434</td>\n",
" <td>-1.057206</td>\n",
" <td>0.071615</td>\n",
" <td>0.030268</td>\n",
" <td>-0.414510</td>\n",
" <td>0.337979</td>\n",
" <td>-0.171632</td>\n",
" <td>-0.000123</td>\n",
" <td>0.003349</td>\n",
" <td>-0.107935</td>\n",
" <td>0.097728</td>\n",
" <td>-0.145241</td>\n",
" <td>0.077461</td>\n",
" <td>-0.076985</td>\n",
" <td>-0.077982</td>\n",
" <td>-0.024289</td>\n",
" <td>0.018438</td>\n",
" <td>-0.003868</td>\n",
" <td>-0.123224</td>\n",
" <td>0.115881</td>\n",
" <td>0.089613</td>\n",
" <td>-0.058230</td>\n",
" <td>-0.055514</td>\n",
" <td>-0.234716</td>\n",
" <td>-0.020724</td>\n",
" <td>0.062652</td>\n",
" <td>-0.126006</td>\n",
" <td>-0.123680</td>\n",
" <td>0.039238</td>\n",
" <td>...</td>\n",
" <td>0.161441</td>\n",
" <td>-0.083282</td>\n",
" <td>-0.026504</td>\n",
" <td>0.017277</td>\n",
" <td>-0.111532</td>\n",
" <td>0.095647</td>\n",
" <td>-0.186469</td>\n",
" <td>0.024969</td>\n",
" <td>-0.118570</td>\n",
" <td>-0.062508</td>\n",
" <td>0.104949</td>\n",
" <td>-0.074168</td>\n",
" <td>0.078220</td>\n",
" <td>-0.028623</td>\n",
" <td>0.076360</td>\n",
" <td>-0.041911</td>\n",
" <td>-0.056168</td>\n",
" <td>-0.067740</td>\n",
" <td>0.083393</td>\n",
" <td>0.024094</td>\n",
" <td>0.025429</td>\n",
" <td>0.061643</td>\n",
" <td>-0.008178</td>\n",
" <td>0.020161</td>\n",
" <td>-0.058973</td>\n",
" <td>0.036097</td>\n",
" <td>-0.010490</td>\n",
" <td>0.017007</td>\n",
" <td>-0.095909</td>\n",
" <td>0.002736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>13.128937</td>\n",
" <td>-0.291956</td>\n",
" <td>0.055698</td>\n",
" <td>-0.563165</td>\n",
" <td>0.052786</td>\n",
" <td>-0.328120</td>\n",
" <td>-0.343186</td>\n",
" <td>0.186108</td>\n",
" <td>0.004301</td>\n",
" <td>0.054837</td>\n",
" <td>-0.050160</td>\n",
" <td>0.146499</td>\n",
" <td>0.074501</td>\n",
" <td>-0.024675</td>\n",
" <td>0.007150</td>\n",
" <td>0.201268</td>\n",
" <td>-0.114928</td>\n",
" <td>0.130149</td>\n",
" <td>-0.051505</td>\n",
" <td>0.213410</td>\n",
" <td>0.044856</td>\n",
" <td>0.009658</td>\n",
" <td>-0.124087</td>\n",
" <td>0.102803</td>\n",
" <td>0.009484</td>\n",
" <td>0.248023</td>\n",
" <td>-0.273675</td>\n",
" <td>0.128581</td>\n",
" <td>0.091128</td>\n",
" <td>...</td>\n",
" <td>-0.054734</td>\n",
" <td>0.094992</td>\n",
" <td>-0.039231</td>\n",
" <td>0.044065</td>\n",
" <td>-0.107927</td>\n",
" <td>0.359355</td>\n",
" <td>-0.000377</td>\n",
" <td>0.064099</td>\n",
" <td>0.069063</td>\n",
" <td>0.061332</td>\n",
" <td>-0.110232</td>\n",
" <td>-0.103445</td>\n",
" <td>-0.172749</td>\n",
" <td>-0.159382</td>\n",
" <td>0.191283</td>\n",
" <td>-0.171140</td>\n",
" <td>-0.007101</td>\n",
" <td>0.093422</td>\n",
" <td>-0.104650</td>\n",
" <td>-0.115908</td>\n",
" <td>0.049000</td>\n",
" <td>0.090353</td>\n",
" <td>0.094541</td>\n",
" <td>-0.110688</td>\n",
" <td>0.060315</td>\n",
" <td>0.028220</td>\n",
" <td>0.030488</td>\n",
" <td>-0.024356</td>\n",
" <td>-0.034394</td>\n",
" <td>-0.210189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0017be8caa87206532cb</td>\n",
" <td>12.623429</td>\n",
" <td>0.904595</td>\n",
" <td>0.151828</td>\n",
" <td>0.827763</td>\n",
" <td>-0.503424</td>\n",
" <td>-0.491851</td>\n",
" <td>0.076383</td>\n",
" <td>-0.061716</td>\n",
" <td>-0.286657</td>\n",
" <td>0.387295</td>\n",
" <td>0.018516</td>\n",
" <td>-0.036808</td>\n",
" <td>0.055457</td>\n",
" <td>0.099706</td>\n",
" <td>0.198765</td>\n",
" <td>0.080165</td>\n",
" <td>0.074970</td>\n",
" <td>-0.036158</td>\n",
" <td>0.133168</td>\n",
" <td>0.032372</td>\n",
" <td>-0.054698</td>\n",
" <td>-0.091800</td>\n",
" <td>0.025958</td>\n",
" <td>-0.056871</td>\n",
" <td>0.001807</td>\n",
" <td>-0.000524</td>\n",
" <td>-0.061090</td>\n",
" <td>-0.104671</td>\n",
" <td>0.068429</td>\n",
" <td>...</td>\n",
" <td>0.017104</td>\n",
" <td>-0.008960</td>\n",
" <td>-0.183665</td>\n",
" <td>-0.060701</td>\n",
" <td>-0.030025</td>\n",
" <td>0.019310</td>\n",
" <td>-0.002861</td>\n",
" <td>-0.107933</td>\n",
" <td>-0.026692</td>\n",
" <td>-0.132148</td>\n",
" <td>0.013119</td>\n",
" <td>-0.110854</td>\n",
" <td>-0.001000</td>\n",
" <td>-0.064942</td>\n",
" <td>-0.005321</td>\n",
" <td>-0.006769</td>\n",
" <td>0.066144</td>\n",
" <td>-0.052615</td>\n",
" <td>0.100249</td>\n",
" <td>0.028990</td>\n",
" <td>-0.001544</td>\n",
" <td>-0.078290</td>\n",
" <td>0.020592</td>\n",
" <td>0.018093</td>\n",
" <td>0.025220</td>\n",
" <td>0.043490</td>\n",
" <td>-0.042478</td>\n",
" <td>-0.022460</td>\n",
" <td>0.024410</td>\n",
" <td>0.033054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00181d86ff1a7b95864e</td>\n",
" <td>13.027927</td>\n",
" <td>-0.998395</td>\n",
" <td>-0.375197</td>\n",
" <td>-0.367709</td>\n",
" <td>0.052783</td>\n",
" <td>-0.009629</td>\n",
" <td>-0.039803</td>\n",
" <td>-0.203232</td>\n",
" <td>-0.320942</td>\n",
" <td>0.058042</td>\n",
" <td>0.073204</td>\n",
" <td>-0.031175</td>\n",
" <td>-0.011282</td>\n",
" <td>-0.137414</td>\n",
" <td>-0.140918</td>\n",
" <td>0.049948</td>\n",
" <td>-0.058703</td>\n",
" <td>-0.022045</td>\n",
" <td>-0.324661</td>\n",
" <td>-0.055913</td>\n",
" <td>-0.070795</td>\n",
" <td>-0.165176</td>\n",
" <td>-0.244123</td>\n",
" <td>0.002521</td>\n",
" <td>0.160899</td>\n",
" <td>0.027965</td>\n",
" <td>-0.023463</td>\n",
" <td>-0.060920</td>\n",
" <td>0.081746</td>\n",
" <td>...</td>\n",
" <td>0.195529</td>\n",
" <td>-0.026854</td>\n",
" <td>0.011605</td>\n",
" <td>-0.077954</td>\n",
" <td>-0.189911</td>\n",
" <td>-0.067083</td>\n",
" <td>-0.013927</td>\n",
" <td>0.308586</td>\n",
" <td>-0.014910</td>\n",
" <td>-0.075798</td>\n",
" <td>-0.078143</td>\n",
" <td>-0.024187</td>\n",
" <td>-0.169573</td>\n",
" <td>-0.022178</td>\n",
" <td>-0.072628</td>\n",
" <td>-0.096904</td>\n",
" <td>-0.024075</td>\n",
" <td>0.052756</td>\n",
" <td>0.203551</td>\n",
" <td>0.138745</td>\n",
" <td>0.056759</td>\n",
" <td>0.107687</td>\n",
" <td>-0.089123</td>\n",
" <td>-0.011761</td>\n",
" <td>0.104768</td>\n",
" <td>-0.030140</td>\n",
" <td>-0.179201</td>\n",
" <td>0.088626</td>\n",
" <td>0.084967</td>\n",
" <td>0.009828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001c52ae28ec106d9cd5</td>\n",
" <td>12.971839</td>\n",
" <td>0.522216</td>\n",
" <td>0.127247</td>\n",
" <td>-0.374960</td>\n",
" <td>-0.388506</td>\n",
" <td>-0.135914</td>\n",
" <td>0.101020</td>\n",
" <td>0.360851</td>\n",
" <td>0.441328</td>\n",
" <td>-0.104278</td>\n",
" <td>-0.010496</td>\n",
" <td>-0.013252</td>\n",
" <td>0.081729</td>\n",
" <td>-0.059149</td>\n",
" <td>0.097535</td>\n",
" <td>-0.191583</td>\n",
" <td>-0.111842</td>\n",
" <td>-0.053894</td>\n",
" <td>0.085811</td>\n",
" <td>-0.058494</td>\n",
" <td>-0.058677</td>\n",
" <td>0.161453</td>\n",
" <td>0.136026</td>\n",
" <td>0.168437</td>\n",
" <td>0.081166</td>\n",
" <td>0.033552</td>\n",
" <td>-0.060623</td>\n",
" <td>-0.038450</td>\n",
" <td>-0.144136</td>\n",
" <td>...</td>\n",
" <td>-0.095776</td>\n",
" <td>0.124192</td>\n",
" <td>0.095961</td>\n",
" <td>0.061257</td>\n",
" <td>0.096010</td>\n",
" <td>-0.085046</td>\n",
" <td>0.115364</td>\n",
" <td>-0.083319</td>\n",
" <td>0.209798</td>\n",
" <td>0.039755</td>\n",
" <td>-0.106417</td>\n",
" <td>0.007407</td>\n",
" <td>-0.002341</td>\n",
" <td>-0.008190</td>\n",
" <td>0.085018</td>\n",
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" <td>0.040202</td>\n",
" <td>-0.051751</td>\n",
" <td>-0.082851</td>\n",
" <td>0.121171</td>\n",
" <td>0.117718</td>\n",
" <td>0.056207</td>\n",
" <td>-0.047530</td>\n",
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" <td>-0.029032</td>\n",
" <td>0.010124</td>\n",
" <td>-0.093171</td>\n",
" <td>0.038483</td>\n",
" <td>0.114827</td>\n",
" <td>-0.003462</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 201 columns</p>\n",
"</div>"
],
"text/plain": [
" object_id title_bert_0 title_bert_1 title_bert_2 \\\n",
"0 0011d6be41612ec9eae3 13.093434 -1.057206 0.071615 \n",
"1 0012765f7a97ccc3e9e9 13.128937 -0.291956 0.055698 \n",
"2 0017be8caa87206532cb 12.623429 0.904595 0.151828 \n",
"3 00181d86ff1a7b95864e 13.027927 -0.998395 -0.375197 \n",
"4 001c52ae28ec106d9cd5 12.971839 0.522216 0.127247 \n",
"\n",
" title_bert_3 title_bert_4 title_bert_5 title_bert_6 title_bert_7 \\\n",
"0 0.030268 -0.414510 0.337979 -0.171632 -0.000123 \n",
"1 -0.563165 0.052786 -0.328120 -0.343186 0.186108 \n",
"2 0.827763 -0.503424 -0.491851 0.076383 -0.061716 \n",
"3 -0.367709 0.052783 -0.009629 -0.039803 -0.203232 \n",
"4 -0.374960 -0.388506 -0.135914 0.101020 0.360851 \n",
"\n",
" title_bert_8 title_bert_9 title_bert_10 title_bert_11 title_bert_12 \\\n",
"0 0.003349 -0.107935 0.097728 -0.145241 0.077461 \n",
"1 0.004301 0.054837 -0.050160 0.146499 0.074501 \n",
"2 -0.286657 0.387295 0.018516 -0.036808 0.055457 \n",
"3 -0.320942 0.058042 0.073204 -0.031175 -0.011282 \n",
"4 0.441328 -0.104278 -0.010496 -0.013252 0.081729 \n",
"\n",
" title_bert_13 title_bert_14 title_bert_15 title_bert_16 title_bert_17 \\\n",
"0 -0.076985 -0.077982 -0.024289 0.018438 -0.003868 \n",
"1 -0.024675 0.007150 0.201268 -0.114928 0.130149 \n",
"2 0.099706 0.198765 0.080165 0.074970 -0.036158 \n",
"3 -0.137414 -0.140918 0.049948 -0.058703 -0.022045 \n",
"4 -0.059149 0.097535 -0.191583 -0.111842 -0.053894 \n",
"\n",
" title_bert_18 title_bert_19 title_bert_20 title_bert_21 title_bert_22 \\\n",
"0 -0.123224 0.115881 0.089613 -0.058230 -0.055514 \n",
"1 -0.051505 0.213410 0.044856 0.009658 -0.124087 \n",
"2 0.133168 0.032372 -0.054698 -0.091800 0.025958 \n",
"3 -0.324661 -0.055913 -0.070795 -0.165176 -0.244123 \n",
"4 0.085811 -0.058494 -0.058677 0.161453 0.136026 \n",
"\n",
" title_bert_23 title_bert_24 title_bert_25 title_bert_26 title_bert_27 \\\n",
"0 -0.234716 -0.020724 0.062652 -0.126006 -0.123680 \n",
"1 0.102803 0.009484 0.248023 -0.273675 0.128581 \n",
"2 -0.056871 0.001807 -0.000524 -0.061090 -0.104671 \n",
"3 0.002521 0.160899 0.027965 -0.023463 -0.060920 \n",
"4 0.168437 0.081166 0.033552 -0.060623 -0.038450 \n",
"\n",
" title_bert_28 ... more_title_bert_20 more_title_bert_21 \\\n",
"0 0.039238 ... 0.161441 -0.083282 \n",
"1 0.091128 ... -0.054734 0.094992 \n",
"2 0.068429 ... 0.017104 -0.008960 \n",
"3 0.081746 ... 0.195529 -0.026854 \n",
"4 -0.144136 ... -0.095776 0.124192 \n",
"\n",
" more_title_bert_22 more_title_bert_23 more_title_bert_24 \\\n",
"0 -0.026504 0.017277 -0.111532 \n",
"1 -0.039231 0.044065 -0.107927 \n",
"2 -0.183665 -0.060701 -0.030025 \n",
"3 0.011605 -0.077954 -0.189911 \n",
"4 0.095961 0.061257 0.096010 \n",
"\n",
" more_title_bert_25 more_title_bert_26 more_title_bert_27 \\\n",
"0 0.095647 -0.186469 0.024969 \n",
"1 0.359355 -0.000377 0.064099 \n",
"2 0.019310 -0.002861 -0.107933 \n",
"3 -0.067083 -0.013927 0.308586 \n",
"4 -0.085046 0.115364 -0.083319 \n",
"\n",
" more_title_bert_28 more_title_bert_29 more_title_bert_30 \\\n",
"0 -0.118570 -0.062508 0.104949 \n",
"1 0.069063 0.061332 -0.110232 \n",
"2 -0.026692 -0.132148 0.013119 \n",
"3 -0.014910 -0.075798 -0.078143 \n",
"4 0.209798 0.039755 -0.106417 \n",
"\n",
" more_title_bert_31 more_title_bert_32 more_title_bert_33 \\\n",
"0 -0.074168 0.078220 -0.028623 \n",
"1 -0.103445 -0.172749 -0.159382 \n",
"2 -0.110854 -0.001000 -0.064942 \n",
"3 -0.024187 -0.169573 -0.022178 \n",
"4 0.007407 -0.002341 -0.008190 \n",
"\n",
" more_title_bert_34 more_title_bert_35 more_title_bert_36 \\\n",
"0 0.076360 -0.041911 -0.056168 \n",
"1 0.191283 -0.171140 -0.007101 \n",
"2 -0.005321 -0.006769 0.066144 \n",
"3 -0.072628 -0.096904 -0.024075 \n",
"4 0.085018 -0.095352 0.040202 \n",
"\n",
" more_title_bert_37 more_title_bert_38 more_title_bert_39 \\\n",
"0 -0.067740 0.083393 0.024094 \n",
"1 0.093422 -0.104650 -0.115908 \n",
"2 -0.052615 0.100249 0.028990 \n",
"3 0.052756 0.203551 0.138745 \n",
"4 -0.051751 -0.082851 0.121171 \n",
"\n",
" more_title_bert_40 more_title_bert_41 more_title_bert_42 \\\n",
"0 0.025429 0.061643 -0.008178 \n",
"1 0.049000 0.090353 0.094541 \n",
"2 -0.001544 -0.078290 0.020592 \n",
"3 0.056759 0.107687 -0.089123 \n",
"4 0.117718 0.056207 -0.047530 \n",
"\n",
" more_title_bert_43 more_title_bert_44 more_title_bert_45 \\\n",
"0 0.020161 -0.058973 0.036097 \n",
"1 -0.110688 0.060315 0.028220 \n",
"2 0.018093 0.025220 0.043490 \n",
"3 -0.011761 0.104768 -0.030140 \n",
"4 -0.084619 -0.029032 0.010124 \n",
"\n",
" more_title_bert_46 more_title_bert_47 more_title_bert_48 \\\n",
"0 -0.010490 0.017007 -0.095909 \n",
"1 0.030488 -0.024356 -0.034394 \n",
"2 -0.042478 -0.022460 0.024410 \n",
"3 -0.179201 0.088626 0.084967 \n",
"4 -0.093171 0.038483 0.114827 \n",
"\n",
" more_title_bert_49 \n",
"0 0.002736 \n",
"1 -0.210189 \n",
"2 0.033054 \n",
"3 0.009828 \n",
"4 -0.003462 \n",
"\n",
"[5 rows x 201 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bert = pd.read_csv(\"../input/bert.csv\")\n",
"bert.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge BERT features] start\n",
"[Merge BERT features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge BERT features\"):\n",
" train = train.merge(bert, on=\"object_id\", how=\"left\")\n",
" test = test.merge(bert, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>object_id</th>\n",
" <th>nl_title_bert_0</th>\n",
" <th>nl_title_bert_1</th>\n",
" <th>nl_title_bert_2</th>\n",
" <th>nl_title_bert_3</th>\n",
" <th>nl_title_bert_4</th>\n",
" <th>nl_title_bert_5</th>\n",
" <th>nl_title_bert_6</th>\n",
" <th>nl_title_bert_7</th>\n",
" <th>nl_title_bert_8</th>\n",
" <th>nl_title_bert_9</th>\n",
" <th>nl_title_bert_10</th>\n",
" <th>nl_title_bert_11</th>\n",
" <th>nl_title_bert_12</th>\n",
" <th>nl_title_bert_13</th>\n",
" <th>nl_title_bert_14</th>\n",
" <th>nl_title_bert_15</th>\n",
" <th>nl_title_bert_16</th>\n",
" <th>nl_title_bert_17</th>\n",
" <th>nl_title_bert_18</th>\n",
" <th>nl_title_bert_19</th>\n",
" <th>nl_title_bert_20</th>\n",
" <th>nl_title_bert_21</th>\n",
" <th>nl_title_bert_22</th>\n",
" <th>nl_title_bert_23</th>\n",
" <th>nl_title_bert_24</th>\n",
" <th>nl_title_bert_25</th>\n",
" <th>nl_title_bert_26</th>\n",
" <th>nl_title_bert_27</th>\n",
" <th>nl_title_bert_28</th>\n",
" <th>...</th>\n",
" <th>nl_more_title_bert_20</th>\n",
" <th>nl_more_title_bert_21</th>\n",
" <th>nl_more_title_bert_22</th>\n",
" <th>nl_more_title_bert_23</th>\n",
" <th>nl_more_title_bert_24</th>\n",
" <th>nl_more_title_bert_25</th>\n",
" <th>nl_more_title_bert_26</th>\n",
" <th>nl_more_title_bert_27</th>\n",
" <th>nl_more_title_bert_28</th>\n",
" <th>nl_more_title_bert_29</th>\n",
" <th>nl_more_title_bert_30</th>\n",
" <th>nl_more_title_bert_31</th>\n",
" <th>nl_more_title_bert_32</th>\n",
" <th>nl_more_title_bert_33</th>\n",
" <th>nl_more_title_bert_34</th>\n",
" <th>nl_more_title_bert_35</th>\n",
" <th>nl_more_title_bert_36</th>\n",
" <th>nl_more_title_bert_37</th>\n",
" <th>nl_more_title_bert_38</th>\n",
" <th>nl_more_title_bert_39</th>\n",
" <th>nl_more_title_bert_40</th>\n",
" <th>nl_more_title_bert_41</th>\n",
" <th>nl_more_title_bert_42</th>\n",
" <th>nl_more_title_bert_43</th>\n",
" <th>nl_more_title_bert_44</th>\n",
" <th>nl_more_title_bert_45</th>\n",
" <th>nl_more_title_bert_46</th>\n",
" <th>nl_more_title_bert_47</th>\n",
" <th>nl_more_title_bert_48</th>\n",
" <th>nl_more_title_bert_49</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>17.086653</td>\n",
" <td>5.462179</td>\n",
" <td>2.484311</td>\n",
" <td>3.556112</td>\n",
" <td>1.905520</td>\n",
" <td>-0.739742</td>\n",
" <td>1.043704</td>\n",
" <td>0.661390</td>\n",
" <td>-0.252872</td>\n",
" <td>-0.364386</td>\n",
" <td>-1.407573</td>\n",
" <td>1.825758</td>\n",
" <td>1.193802</td>\n",
" <td>1.120849</td>\n",
" <td>-1.116895</td>\n",
" <td>-0.570539</td>\n",
" <td>-0.416349</td>\n",
" <td>-0.551504</td>\n",
" <td>-0.865151</td>\n",
" <td>-1.165568</td>\n",
" <td>-1.215268</td>\n",
" <td>0.233961</td>\n",
" <td>-0.701217</td>\n",
" <td>0.412688</td>\n",
" <td>-0.511935</td>\n",
" <td>0.830174</td>\n",
" <td>-0.004883</td>\n",
" <td>2.071927</td>\n",
" <td>0.524657</td>\n",
" <td>...</td>\n",
" <td>-0.310783</td>\n",
" <td>-0.605841</td>\n",
" <td>-0.040312</td>\n",
" <td>1.965202</td>\n",
" <td>-0.314602</td>\n",
" <td>-1.047065</td>\n",
" <td>1.399202</td>\n",
" <td>0.365861</td>\n",
" <td>1.513174</td>\n",
" <td>1.465677</td>\n",
" <td>0.423610</td>\n",
" <td>-0.514279</td>\n",
" <td>0.402504</td>\n",
" <td>-0.812419</td>\n",
" <td>0.198304</td>\n",
" <td>1.065463</td>\n",
" <td>0.204852</td>\n",
" <td>-0.465791</td>\n",
" <td>0.139698</td>\n",
" <td>-0.727962</td>\n",
" <td>-0.559707</td>\n",
" <td>0.221358</td>\n",
" <td>0.283059</td>\n",
" <td>-0.193876</td>\n",
" <td>-0.445904</td>\n",
" <td>-0.442122</td>\n",
" <td>-1.634989</td>\n",
" <td>0.022952</td>\n",
" <td>-0.918300</td>\n",
" <td>0.169348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>16.720003</td>\n",
" <td>0.748178</td>\n",
" <td>2.144553</td>\n",
" <td>-1.398578</td>\n",
" <td>-3.459243</td>\n",
" <td>1.828367</td>\n",
" <td>1.304625</td>\n",
" <td>1.119050</td>\n",
" <td>-1.662916</td>\n",
" <td>-1.565539</td>\n",
" <td>-0.386612</td>\n",
" <td>1.023918</td>\n",
" <td>-0.578937</td>\n",
" <td>-0.372179</td>\n",
" <td>2.313214</td>\n",
" <td>-0.047521</td>\n",
" <td>-1.026860</td>\n",
" <td>-0.548970</td>\n",
" <td>0.330061</td>\n",
" <td>-0.585369</td>\n",
" <td>1.394068</td>\n",
" <td>-0.812095</td>\n",
" <td>1.061626</td>\n",
" <td>-0.083886</td>\n",
" <td>-0.222217</td>\n",
" <td>-1.430895</td>\n",
" <td>1.076464</td>\n",
" <td>-0.476976</td>\n",
" <td>0.849329</td>\n",
" <td>...</td>\n",
" <td>2.202819</td>\n",
" <td>1.094500</td>\n",
" <td>-1.136409</td>\n",
" <td>-0.499728</td>\n",
" <td>-1.629292</td>\n",
" <td>0.584902</td>\n",
" <td>-0.252251</td>\n",
" <td>0.025710</td>\n",
" <td>0.211144</td>\n",
" <td>-0.451096</td>\n",
" <td>1.650787</td>\n",
" <td>0.124306</td>\n",
" <td>0.414121</td>\n",
" <td>-0.180383</td>\n",
" <td>-0.742119</td>\n",
" <td>0.712480</td>\n",
" <td>-0.231429</td>\n",
" <td>0.134121</td>\n",
" <td>-0.184914</td>\n",
" <td>0.591913</td>\n",
" <td>1.238697</td>\n",
" <td>1.485766</td>\n",
" <td>0.556680</td>\n",
" <td>-0.479492</td>\n",
" <td>0.447146</td>\n",
" <td>-0.692313</td>\n",
" <td>-0.622953</td>\n",
" <td>0.576057</td>\n",
" <td>1.205097</td>\n",
" <td>-0.811840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0017be8caa87206532cb</td>\n",
" <td>16.565134</td>\n",
" <td>-7.346473</td>\n",
" <td>7.288344</td>\n",
" <td>-0.749761</td>\n",
" <td>1.358727</td>\n",
" <td>0.623737</td>\n",
" <td>1.173736</td>\n",
" <td>1.432733</td>\n",
" <td>-0.068259</td>\n",
" <td>1.515634</td>\n",
" <td>-1.387685</td>\n",
" <td>0.136828</td>\n",
" <td>0.568498</td>\n",
" <td>0.140849</td>\n",
" <td>-1.159987</td>\n",
" <td>0.551415</td>\n",
" <td>-1.098633</td>\n",
" <td>-1.071198</td>\n",
" <td>0.275495</td>\n",
" <td>-0.592860</td>\n",
" <td>1.055716</td>\n",
" <td>0.165850</td>\n",
" <td>0.508027</td>\n",
" <td>-1.705222</td>\n",
" <td>-0.959297</td>\n",
" <td>0.109723</td>\n",
" <td>-1.516574</td>\n",
" <td>1.080722</td>\n",
" <td>-1.044419</td>\n",
" <td>...</td>\n",
" <td>0.330231</td>\n",
" <td>1.136284</td>\n",
" <td>-0.757491</td>\n",
" <td>-0.877948</td>\n",
" <td>1.202687</td>\n",
" <td>0.715536</td>\n",
" <td>0.724170</td>\n",
" <td>-0.345074</td>\n",
" <td>0.811388</td>\n",
" <td>0.993080</td>\n",
" <td>-1.288310</td>\n",
" <td>-0.292499</td>\n",
" <td>0.950790</td>\n",
" <td>0.233917</td>\n",
" <td>0.037219</td>\n",
" <td>-0.054370</td>\n",
" <td>0.650280</td>\n",
" <td>-0.073138</td>\n",
" <td>0.057888</td>\n",
" <td>-0.995966</td>\n",
" <td>-0.376492</td>\n",
" <td>0.105374</td>\n",
" <td>-0.319583</td>\n",
" <td>0.029552</td>\n",
" <td>-0.595680</td>\n",
" <td>0.336928</td>\n",
" <td>0.410975</td>\n",
" <td>0.744889</td>\n",
" <td>0.713155</td>\n",
" <td>-0.416886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00181d86ff1a7b95864e</td>\n",
" <td>15.649596</td>\n",
" <td>2.150249</td>\n",
" <td>-0.116159</td>\n",
" <td>2.196240</td>\n",
" <td>-2.898489</td>\n",
" <td>0.142807</td>\n",
" <td>3.385924</td>\n",
" <td>-1.237366</td>\n",
" <td>0.593879</td>\n",
" <td>1.896893</td>\n",
" <td>0.477891</td>\n",
" <td>0.313860</td>\n",
" <td>1.188280</td>\n",
" <td>-1.180292</td>\n",
" <td>-1.004540</td>\n",
" <td>1.256428</td>\n",
" <td>-0.539962</td>\n",
" <td>1.430337</td>\n",
" <td>-1.680860</td>\n",
" <td>1.747333</td>\n",
" <td>-0.092615</td>\n",
" <td>-0.484734</td>\n",
" <td>-0.563418</td>\n",
" <td>0.491212</td>\n",
" <td>0.408397</td>\n",
" <td>0.875512</td>\n",
" <td>1.192523</td>\n",
" <td>0.831208</td>\n",
" <td>1.511446</td>\n",
" <td>...</td>\n",
" <td>0.161551</td>\n",
" <td>-1.482677</td>\n",
" <td>2.007757</td>\n",
" <td>0.143304</td>\n",
" <td>-0.221446</td>\n",
" <td>0.154958</td>\n",
" <td>0.572058</td>\n",
" <td>0.309980</td>\n",
" <td>-0.241704</td>\n",
" <td>1.260777</td>\n",
" <td>0.580652</td>\n",
" <td>-0.395595</td>\n",
" <td>-1.578569</td>\n",
" <td>-0.782938</td>\n",
" <td>-2.671234</td>\n",
" <td>-0.907781</td>\n",
" <td>-1.768683</td>\n",
" <td>0.385995</td>\n",
" <td>0.309524</td>\n",
" <td>1.014485</td>\n",
" <td>-0.365519</td>\n",
" <td>-1.257162</td>\n",
" <td>-0.127858</td>\n",
" <td>0.511038</td>\n",
" <td>0.317116</td>\n",
" <td>1.051697</td>\n",
" <td>-1.151952</td>\n",
" <td>0.597712</td>\n",
" <td>0.844708</td>\n",
" <td>-0.300780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001c52ae28ec106d9cd5</td>\n",
" <td>16.556509</td>\n",
" <td>-0.088197</td>\n",
" <td>0.660743</td>\n",
" <td>-2.648931</td>\n",
" <td>-3.271605</td>\n",
" <td>-0.019639</td>\n",
" <td>-1.211676</td>\n",
" <td>0.024765</td>\n",
" <td>-2.124913</td>\n",
" <td>0.364198</td>\n",
" <td>-2.498878</td>\n",
" <td>0.573200</td>\n",
" <td>1.025608</td>\n",
" <td>2.587207</td>\n",
" <td>-0.173727</td>\n",
" <td>-0.683091</td>\n",
" <td>-0.851177</td>\n",
" <td>2.153705</td>\n",
" <td>0.170051</td>\n",
" <td>0.673793</td>\n",
" <td>-0.378365</td>\n",
" <td>-1.191594</td>\n",
" <td>1.915591</td>\n",
" <td>1.414150</td>\n",
" <td>-0.244728</td>\n",
" <td>1.459237</td>\n",
" <td>0.270478</td>\n",
" <td>-1.114143</td>\n",
" <td>0.723756</td>\n",
" <td>...</td>\n",
" <td>-0.610063</td>\n",
" <td>-0.776461</td>\n",
" <td>0.451073</td>\n",
" <td>0.643782</td>\n",
" <td>-1.579037</td>\n",
" <td>1.375537</td>\n",
" <td>-1.181763</td>\n",
" <td>-0.611174</td>\n",
" <td>1.519368</td>\n",
" <td>0.143409</td>\n",
" <td>1.560338</td>\n",
" <td>1.211996</td>\n",
" <td>-1.487426</td>\n",
" <td>-0.928530</td>\n",
" <td>0.800599</td>\n",
" <td>1.354951</td>\n",
" <td>0.470412</td>\n",
" <td>-0.464241</td>\n",
" <td>0.412142</td>\n",
" <td>0.126665</td>\n",
" <td>0.675875</td>\n",
" <td>0.093925</td>\n",
" <td>-1.251163</td>\n",
" <td>0.669070</td>\n",
" <td>-0.835437</td>\n",
" <td>-0.082205</td>\n",
" <td>2.749352</td>\n",
" <td>-0.081496</td>\n",
" <td>-0.148613</td>\n",
" <td>-0.938420</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 201 columns</p>\n",
"</div>"
],
"text/plain": [
" object_id nl_title_bert_0 nl_title_bert_1 nl_title_bert_2 \\\n",
"0 0011d6be41612ec9eae3 17.086653 5.462179 2.484311 \n",
"1 0012765f7a97ccc3e9e9 16.720003 0.748178 2.144553 \n",
"2 0017be8caa87206532cb 16.565134 -7.346473 7.288344 \n",
"3 00181d86ff1a7b95864e 15.649596 2.150249 -0.116159 \n",
"4 001c52ae28ec106d9cd5 16.556509 -0.088197 0.660743 \n",
"\n",
" nl_title_bert_3 nl_title_bert_4 nl_title_bert_5 nl_title_bert_6 \\\n",
"0 3.556112 1.905520 -0.739742 1.043704 \n",
"1 -1.398578 -3.459243 1.828367 1.304625 \n",
"2 -0.749761 1.358727 0.623737 1.173736 \n",
"3 2.196240 -2.898489 0.142807 3.385924 \n",
"4 -2.648931 -3.271605 -0.019639 -1.211676 \n",
"\n",
" nl_title_bert_7 nl_title_bert_8 nl_title_bert_9 nl_title_bert_10 \\\n",
"0 0.661390 -0.252872 -0.364386 -1.407573 \n",
"1 1.119050 -1.662916 -1.565539 -0.386612 \n",
"2 1.432733 -0.068259 1.515634 -1.387685 \n",
"3 -1.237366 0.593879 1.896893 0.477891 \n",
"4 0.024765 -2.124913 0.364198 -2.498878 \n",
"\n",
" nl_title_bert_11 nl_title_bert_12 nl_title_bert_13 nl_title_bert_14 \\\n",
"0 1.825758 1.193802 1.120849 -1.116895 \n",
"1 1.023918 -0.578937 -0.372179 2.313214 \n",
"2 0.136828 0.568498 0.140849 -1.159987 \n",
"3 0.313860 1.188280 -1.180292 -1.004540 \n",
"4 0.573200 1.025608 2.587207 -0.173727 \n",
"\n",
" nl_title_bert_15 nl_title_bert_16 nl_title_bert_17 nl_title_bert_18 \\\n",
"0 -0.570539 -0.416349 -0.551504 -0.865151 \n",
"1 -0.047521 -1.026860 -0.548970 0.330061 \n",
"2 0.551415 -1.098633 -1.071198 0.275495 \n",
"3 1.256428 -0.539962 1.430337 -1.680860 \n",
"4 -0.683091 -0.851177 2.153705 0.170051 \n",
"\n",
" nl_title_bert_19 nl_title_bert_20 nl_title_bert_21 nl_title_bert_22 \\\n",
"0 -1.165568 -1.215268 0.233961 -0.701217 \n",
"1 -0.585369 1.394068 -0.812095 1.061626 \n",
"2 -0.592860 1.055716 0.165850 0.508027 \n",
"3 1.747333 -0.092615 -0.484734 -0.563418 \n",
"4 0.673793 -0.378365 -1.191594 1.915591 \n",
"\n",
" nl_title_bert_23 nl_title_bert_24 nl_title_bert_25 nl_title_bert_26 \\\n",
"0 0.412688 -0.511935 0.830174 -0.004883 \n",
"1 -0.083886 -0.222217 -1.430895 1.076464 \n",
"2 -1.705222 -0.959297 0.109723 -1.516574 \n",
"3 0.491212 0.408397 0.875512 1.192523 \n",
"4 1.414150 -0.244728 1.459237 0.270478 \n",
"\n",
" nl_title_bert_27 nl_title_bert_28 ... nl_more_title_bert_20 \\\n",
"0 2.071927 0.524657 ... -0.310783 \n",
"1 -0.476976 0.849329 ... 2.202819 \n",
"2 1.080722 -1.044419 ... 0.330231 \n",
"3 0.831208 1.511446 ... 0.161551 \n",
"4 -1.114143 0.723756 ... -0.610063 \n",
"\n",
" nl_more_title_bert_21 nl_more_title_bert_22 nl_more_title_bert_23 \\\n",
"0 -0.605841 -0.040312 1.965202 \n",
"1 1.094500 -1.136409 -0.499728 \n",
"2 1.136284 -0.757491 -0.877948 \n",
"3 -1.482677 2.007757 0.143304 \n",
"4 -0.776461 0.451073 0.643782 \n",
"\n",
" nl_more_title_bert_24 nl_more_title_bert_25 nl_more_title_bert_26 \\\n",
"0 -0.314602 -1.047065 1.399202 \n",
"1 -1.629292 0.584902 -0.252251 \n",
"2 1.202687 0.715536 0.724170 \n",
"3 -0.221446 0.154958 0.572058 \n",
"4 -1.579037 1.375537 -1.181763 \n",
"\n",
" nl_more_title_bert_27 nl_more_title_bert_28 nl_more_title_bert_29 \\\n",
"0 0.365861 1.513174 1.465677 \n",
"1 0.025710 0.211144 -0.451096 \n",
"2 -0.345074 0.811388 0.993080 \n",
"3 0.309980 -0.241704 1.260777 \n",
"4 -0.611174 1.519368 0.143409 \n",
"\n",
" nl_more_title_bert_30 nl_more_title_bert_31 nl_more_title_bert_32 \\\n",
"0 0.423610 -0.514279 0.402504 \n",
"1 1.650787 0.124306 0.414121 \n",
"2 -1.288310 -0.292499 0.950790 \n",
"3 0.580652 -0.395595 -1.578569 \n",
"4 1.560338 1.211996 -1.487426 \n",
"\n",
" nl_more_title_bert_33 nl_more_title_bert_34 nl_more_title_bert_35 \\\n",
"0 -0.812419 0.198304 1.065463 \n",
"1 -0.180383 -0.742119 0.712480 \n",
"2 0.233917 0.037219 -0.054370 \n",
"3 -0.782938 -2.671234 -0.907781 \n",
"4 -0.928530 0.800599 1.354951 \n",
"\n",
" nl_more_title_bert_36 nl_more_title_bert_37 nl_more_title_bert_38 \\\n",
"0 0.204852 -0.465791 0.139698 \n",
"1 -0.231429 0.134121 -0.184914 \n",
"2 0.650280 -0.073138 0.057888 \n",
"3 -1.768683 0.385995 0.309524 \n",
"4 0.470412 -0.464241 0.412142 \n",
"\n",
" nl_more_title_bert_39 nl_more_title_bert_40 nl_more_title_bert_41 \\\n",
"0 -0.727962 -0.559707 0.221358 \n",
"1 0.591913 1.238697 1.485766 \n",
"2 -0.995966 -0.376492 0.105374 \n",
"3 1.014485 -0.365519 -1.257162 \n",
"4 0.126665 0.675875 0.093925 \n",
"\n",
" nl_more_title_bert_42 nl_more_title_bert_43 nl_more_title_bert_44 \\\n",
"0 0.283059 -0.193876 -0.445904 \n",
"1 0.556680 -0.479492 0.447146 \n",
"2 -0.319583 0.029552 -0.595680 \n",
"3 -0.127858 0.511038 0.317116 \n",
"4 -1.251163 0.669070 -0.835437 \n",
"\n",
" nl_more_title_bert_45 nl_more_title_bert_46 nl_more_title_bert_47 \\\n",
"0 -0.442122 -1.634989 0.022952 \n",
"1 -0.692313 -0.622953 0.576057 \n",
"2 0.336928 0.410975 0.744889 \n",
"3 1.051697 -1.151952 0.597712 \n",
"4 -0.082205 2.749352 -0.081496 \n",
"\n",
" nl_more_title_bert_48 nl_more_title_bert_49 \n",
"0 -0.918300 0.169348 \n",
"1 1.205097 -0.811840 \n",
"2 0.713155 -0.416886 \n",
"3 0.844708 -0.300780 \n",
"4 -0.148613 -0.938420 \n",
"\n",
"[5 rows x 201 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nl_bert = pd.read_csv(\"../input/nl_bert.csv\")\n",
"nl_bert.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge BERT features] start\n",
"[Merge BERT features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge BERT features\"):\n",
" train = train.merge(nl_bert, on=\"object_id\", how=\"left\")\n",
" test = test.merge(nl_bert, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>object_id</th>\n",
" <th>en_title_bert_0</th>\n",
" <th>en_title_bert_1</th>\n",
" <th>en_title_bert_2</th>\n",
" <th>en_title_bert_3</th>\n",
" <th>en_title_bert_4</th>\n",
" <th>en_title_bert_5</th>\n",
" <th>en_title_bert_6</th>\n",
" <th>en_title_bert_7</th>\n",
" <th>en_title_bert_8</th>\n",
" <th>en_title_bert_9</th>\n",
" <th>en_title_bert_10</th>\n",
" <th>en_title_bert_11</th>\n",
" <th>en_title_bert_12</th>\n",
" <th>en_title_bert_13</th>\n",
" <th>en_title_bert_14</th>\n",
" <th>en_title_bert_15</th>\n",
" <th>en_title_bert_16</th>\n",
" <th>en_title_bert_17</th>\n",
" <th>en_title_bert_18</th>\n",
" <th>en_title_bert_19</th>\n",
" <th>en_title_bert_20</th>\n",
" <th>en_title_bert_21</th>\n",
" <th>en_title_bert_22</th>\n",
" <th>en_title_bert_23</th>\n",
" <th>en_title_bert_24</th>\n",
" <th>en_title_bert_25</th>\n",
" <th>en_title_bert_26</th>\n",
" <th>en_title_bert_27</th>\n",
" <th>en_title_bert_28</th>\n",
" <th>...</th>\n",
" <th>en_more_title_bert_20</th>\n",
" <th>en_more_title_bert_21</th>\n",
" <th>en_more_title_bert_22</th>\n",
" <th>en_more_title_bert_23</th>\n",
" <th>en_more_title_bert_24</th>\n",
" <th>en_more_title_bert_25</th>\n",
" <th>en_more_title_bert_26</th>\n",
" <th>en_more_title_bert_27</th>\n",
" <th>en_more_title_bert_28</th>\n",
" <th>en_more_title_bert_29</th>\n",
" <th>en_more_title_bert_30</th>\n",
" <th>en_more_title_bert_31</th>\n",
" <th>en_more_title_bert_32</th>\n",
" <th>en_more_title_bert_33</th>\n",
" <th>en_more_title_bert_34</th>\n",
" <th>en_more_title_bert_35</th>\n",
" <th>en_more_title_bert_36</th>\n",
" <th>en_more_title_bert_37</th>\n",
" <th>en_more_title_bert_38</th>\n",
" <th>en_more_title_bert_39</th>\n",
" <th>en_more_title_bert_40</th>\n",
" <th>en_more_title_bert_41</th>\n",
" <th>en_more_title_bert_42</th>\n",
" <th>en_more_title_bert_43</th>\n",
" <th>en_more_title_bert_44</th>\n",
" <th>en_more_title_bert_45</th>\n",
" <th>en_more_title_bert_46</th>\n",
" <th>en_more_title_bert_47</th>\n",
" <th>en_more_title_bert_48</th>\n",
" <th>en_more_title_bert_49</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0011d6be41612ec9eae3</td>\n",
" <td>12.626774</td>\n",
" <td>-4.833184</td>\n",
" <td>0.703586</td>\n",
" <td>-0.717203</td>\n",
" <td>0.212656</td>\n",
" <td>-0.268172</td>\n",
" <td>0.611709</td>\n",
" <td>0.383786</td>\n",
" <td>0.086742</td>\n",
" <td>0.024708</td>\n",
" <td>0.451744</td>\n",
" <td>0.537851</td>\n",
" <td>-0.333360</td>\n",
" <td>0.808009</td>\n",
" <td>-0.698687</td>\n",
" <td>-0.018808</td>\n",
" <td>0.443671</td>\n",
" <td>-0.112474</td>\n",
" <td>-0.818108</td>\n",
" <td>0.235574</td>\n",
" <td>0.309111</td>\n",
" <td>-0.106155</td>\n",
" <td>0.414505</td>\n",
" <td>-0.244120</td>\n",
" <td>0.266933</td>\n",
" <td>-0.039635</td>\n",
" <td>-0.065880</td>\n",
" <td>-0.411748</td>\n",
" <td>-0.279252</td>\n",
" <td>...</td>\n",
" <td>0.389561</td>\n",
" <td>-0.181384</td>\n",
" <td>0.563113</td>\n",
" <td>0.003839</td>\n",
" <td>0.201002</td>\n",
" <td>-0.236561</td>\n",
" <td>-0.198972</td>\n",
" <td>-0.356772</td>\n",
" <td>-0.187985</td>\n",
" <td>-0.229615</td>\n",
" <td>-0.995489</td>\n",
" <td>-0.122398</td>\n",
" <td>0.292693</td>\n",
" <td>0.509988</td>\n",
" <td>0.333678</td>\n",
" <td>-0.087523</td>\n",
" <td>-0.706237</td>\n",
" <td>-0.057135</td>\n",
" <td>0.113895</td>\n",
" <td>0.118731</td>\n",
" <td>0.085320</td>\n",
" <td>0.039879</td>\n",
" <td>-0.500713</td>\n",
" <td>0.077829</td>\n",
" <td>0.115718</td>\n",
" <td>0.078613</td>\n",
" <td>0.129753</td>\n",
" <td>-0.103177</td>\n",
" <td>-0.072235</td>\n",
" <td>0.000548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0012765f7a97ccc3e9e9</td>\n",
" <td>13.027542</td>\n",
" <td>-2.667802</td>\n",
" <td>-0.234941</td>\n",
" <td>-1.635778</td>\n",
" <td>-1.118525</td>\n",
" <td>0.380276</td>\n",
" <td>-0.568013</td>\n",
" <td>1.695839</td>\n",
" <td>0.809001</td>\n",
" <td>0.404228</td>\n",
" <td>0.069177</td>\n",
" <td>1.426772</td>\n",
" <td>0.010097</td>\n",
" <td>-0.195974</td>\n",
" <td>-0.168713</td>\n",
" <td>1.008560</td>\n",
" <td>0.184678</td>\n",
" <td>-0.115377</td>\n",
" <td>-0.486872</td>\n",
" <td>-1.041480</td>\n",
" <td>0.495145</td>\n",
" <td>0.054872</td>\n",
" <td>-0.305403</td>\n",
" <td>0.183333</td>\n",
" <td>-0.283043</td>\n",
" <td>-0.064235</td>\n",
" <td>0.614472</td>\n",
" <td>0.463003</td>\n",
" <td>0.118260</td>\n",
" <td>...</td>\n",
" <td>1.094407</td>\n",
" <td>-0.130241</td>\n",
" <td>-0.766464</td>\n",
" <td>-0.076177</td>\n",
" <td>0.058236</td>\n",
" <td>-0.104342</td>\n",
" <td>0.165517</td>\n",
" <td>-0.552768</td>\n",
" <td>-0.375126</td>\n",
" <td>0.160216</td>\n",
" <td>0.023187</td>\n",
" <td>-0.111831</td>\n",
" <td>-0.133776</td>\n",
" <td>-0.098487</td>\n",
" <td>-0.470474</td>\n",
" <td>0.869783</td>\n",
" <td>0.017964</td>\n",
" <td>-0.816890</td>\n",
" <td>-0.154945</td>\n",
" <td>-0.225445</td>\n",
" <td>-0.598515</td>\n",
" <td>0.039904</td>\n",
" <td>0.551894</td>\n",
" <td>-0.274307</td>\n",
" <td>-0.295357</td>\n",
" <td>0.154704</td>\n",
" <td>-0.468305</td>\n",
" <td>-0.610486</td>\n",
" <td>-0.170374</td>\n",
" <td>0.889165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0017be8caa87206532cb</td>\n",
" <td>13.840333</td>\n",
" <td>-1.199563</td>\n",
" <td>-0.796609</td>\n",
" <td>-1.915899</td>\n",
" <td>-0.342460</td>\n",
" <td>0.585084</td>\n",
" <td>1.000436</td>\n",
" <td>-1.147781</td>\n",
" <td>0.252824</td>\n",
" <td>0.234175</td>\n",
" <td>-0.573946</td>\n",
" <td>-0.960809</td>\n",
" <td>-0.112373</td>\n",
" <td>0.203365</td>\n",
" <td>-0.516822</td>\n",
" <td>-0.215131</td>\n",
" <td>-0.383390</td>\n",
" <td>-0.013532</td>\n",
" <td>0.373336</td>\n",
" <td>0.423558</td>\n",
" <td>0.356423</td>\n",
" <td>-0.137554</td>\n",
" <td>-0.398903</td>\n",
" <td>-0.262933</td>\n",
" <td>-0.458420</td>\n",
" <td>0.319028</td>\n",
" <td>0.289413</td>\n",
" <td>-0.309064</td>\n",
" <td>-0.043147</td>\n",
" <td>...</td>\n",
" <td>0.228491</td>\n",
" <td>0.570181</td>\n",
" <td>-0.360011</td>\n",
" <td>0.347662</td>\n",
" <td>-0.499598</td>\n",
" <td>0.060707</td>\n",
" <td>-0.405925</td>\n",
" <td>0.272866</td>\n",
" <td>0.497490</td>\n",
" <td>-0.051781</td>\n",
" <td>-0.189797</td>\n",
" <td>0.147104</td>\n",
" <td>0.267020</td>\n",
" <td>0.232547</td>\n",
" <td>0.101529</td>\n",
" <td>-0.072668</td>\n",
" <td>0.378593</td>\n",
" <td>0.380505</td>\n",
" <td>-0.140734</td>\n",
" <td>0.007491</td>\n",
" <td>0.283877</td>\n",
" <td>0.140491</td>\n",
" <td>0.069903</td>\n",
" <td>-0.107196</td>\n",
" <td>-0.132137</td>\n",
" <td>-0.306891</td>\n",
" <td>-0.017558</td>\n",
" <td>0.064488</td>\n",
" <td>0.028032</td>\n",
" <td>-0.061843</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00181d86ff1a7b95864e</td>\n",
" <td>12.378110</td>\n",
" <td>-4.076839</td>\n",
" <td>1.762742</td>\n",
" <td>0.614974</td>\n",
" <td>1.511671</td>\n",
" <td>0.180478</td>\n",
" <td>0.584412</td>\n",
" <td>0.345281</td>\n",
" <td>1.446714</td>\n",
" <td>0.317193</td>\n",
" <td>0.971661</td>\n",
" <td>0.347300</td>\n",
" <td>0.325254</td>\n",
" <td>-0.865336</td>\n",
" <td>-0.132366</td>\n",
" <td>0.469634</td>\n",
" <td>0.206561</td>\n",
" <td>0.455690</td>\n",
" <td>-0.486754</td>\n",
" <td>0.554845</td>\n",
" <td>-0.611138</td>\n",
" <td>-0.887757</td>\n",
" <td>-0.650567</td>\n",
" <td>0.619774</td>\n",
" <td>-0.093453</td>\n",
" <td>0.007300</td>\n",
" <td>-0.203065</td>\n",
" <td>-0.517235</td>\n",
" <td>0.027389</td>\n",
" <td>...</td>\n",
" <td>-0.480090</td>\n",
" <td>-0.142411</td>\n",
" <td>0.146459</td>\n",
" <td>0.106265</td>\n",
" <td>-0.887618</td>\n",
" <td>-0.777156</td>\n",
" <td>0.573504</td>\n",
" <td>0.398247</td>\n",
" <td>0.136697</td>\n",
" <td>0.048429</td>\n",
" <td>0.811383</td>\n",
" <td>0.044471</td>\n",
" <td>-0.142168</td>\n",
" <td>0.203729</td>\n",
" <td>0.884818</td>\n",
" <td>-0.207317</td>\n",
" <td>-0.416153</td>\n",
" <td>-0.030251</td>\n",
" <td>0.236761</td>\n",
" <td>0.113535</td>\n",
" <td>-0.520574</td>\n",
" <td>-0.386547</td>\n",
" <td>-1.160239</td>\n",
" <td>-0.050893</td>\n",
" <td>-0.497966</td>\n",
" <td>-0.020273</td>\n",
" <td>0.162922</td>\n",
" <td>0.263952</td>\n",
" <td>0.026662</td>\n",
" <td>-0.192525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001c52ae28ec106d9cd5</td>\n",
" <td>13.528032</td>\n",
" <td>-0.627604</td>\n",
" <td>-2.035810</td>\n",
" <td>2.102370</td>\n",
" <td>1.266722</td>\n",
" <td>-0.335750</td>\n",
" <td>-0.073869</td>\n",
" <td>-0.394667</td>\n",
" <td>-0.089133</td>\n",
" <td>0.066500</td>\n",
" <td>-0.460797</td>\n",
" <td>0.562902</td>\n",
" <td>0.022582</td>\n",
" <td>-0.278319</td>\n",
" <td>0.052553</td>\n",
" <td>0.925680</td>\n",
" <td>-0.381899</td>\n",
" <td>0.044695</td>\n",
" <td>0.199438</td>\n",
" <td>0.389632</td>\n",
" <td>0.168568</td>\n",
" <td>0.580023</td>\n",
" <td>0.174159</td>\n",
" <td>-0.350844</td>\n",
" <td>-0.521142</td>\n",
" <td>0.297349</td>\n",
" <td>-0.486753</td>\n",
" <td>0.320408</td>\n",
" <td>0.123543</td>\n",
" <td>...</td>\n",
" <td>0.193384</td>\n",
" <td>-0.358411</td>\n",
" <td>-0.509234</td>\n",
" <td>0.038638</td>\n",
" <td>0.334755</td>\n",
" <td>0.281736</td>\n",
" <td>-0.539662</td>\n",
" <td>0.327682</td>\n",
" <td>-0.192763</td>\n",
" <td>0.038868</td>\n",
" <td>0.114521</td>\n",
" <td>0.215306</td>\n",
" <td>-0.020394</td>\n",
" <td>-0.093313</td>\n",
" <td>-0.305974</td>\n",
" <td>0.061545</td>\n",
" <td>0.593608</td>\n",
" <td>-0.332009</td>\n",
" <td>-0.013412</td>\n",
" <td>0.182370</td>\n",
" <td>0.307056</td>\n",
" <td>0.109259</td>\n",
" <td>-0.244337</td>\n",
" <td>0.524686</td>\n",
" <td>-0.135363</td>\n",
" <td>-0.156572</td>\n",
" <td>0.076641</td>\n",
" <td>0.083989</td>\n",
" <td>-0.191296</td>\n",
" <td>-0.222544</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 201 columns</p>\n",
"</div>"
],
"text/plain": [
" object_id en_title_bert_0 en_title_bert_1 en_title_bert_2 \\\n",
"0 0011d6be41612ec9eae3 12.626774 -4.833184 0.703586 \n",
"1 0012765f7a97ccc3e9e9 13.027542 -2.667802 -0.234941 \n",
"2 0017be8caa87206532cb 13.840333 -1.199563 -0.796609 \n",
"3 00181d86ff1a7b95864e 12.378110 -4.076839 1.762742 \n",
"4 001c52ae28ec106d9cd5 13.528032 -0.627604 -2.035810 \n",
"\n",
" en_title_bert_3 en_title_bert_4 en_title_bert_5 en_title_bert_6 \\\n",
"0 -0.717203 0.212656 -0.268172 0.611709 \n",
"1 -1.635778 -1.118525 0.380276 -0.568013 \n",
"2 -1.915899 -0.342460 0.585084 1.000436 \n",
"3 0.614974 1.511671 0.180478 0.584412 \n",
"4 2.102370 1.266722 -0.335750 -0.073869 \n",
"\n",
" en_title_bert_7 en_title_bert_8 en_title_bert_9 en_title_bert_10 \\\n",
"0 0.383786 0.086742 0.024708 0.451744 \n",
"1 1.695839 0.809001 0.404228 0.069177 \n",
"2 -1.147781 0.252824 0.234175 -0.573946 \n",
"3 0.345281 1.446714 0.317193 0.971661 \n",
"4 -0.394667 -0.089133 0.066500 -0.460797 \n",
"\n",
" en_title_bert_11 en_title_bert_12 en_title_bert_13 en_title_bert_14 \\\n",
"0 0.537851 -0.333360 0.808009 -0.698687 \n",
"1 1.426772 0.010097 -0.195974 -0.168713 \n",
"2 -0.960809 -0.112373 0.203365 -0.516822 \n",
"3 0.347300 0.325254 -0.865336 -0.132366 \n",
"4 0.562902 0.022582 -0.278319 0.052553 \n",
"\n",
" en_title_bert_15 en_title_bert_16 en_title_bert_17 en_title_bert_18 \\\n",
"0 -0.018808 0.443671 -0.112474 -0.818108 \n",
"1 1.008560 0.184678 -0.115377 -0.486872 \n",
"2 -0.215131 -0.383390 -0.013532 0.373336 \n",
"3 0.469634 0.206561 0.455690 -0.486754 \n",
"4 0.925680 -0.381899 0.044695 0.199438 \n",
"\n",
" en_title_bert_19 en_title_bert_20 en_title_bert_21 en_title_bert_22 \\\n",
"0 0.235574 0.309111 -0.106155 0.414505 \n",
"1 -1.041480 0.495145 0.054872 -0.305403 \n",
"2 0.423558 0.356423 -0.137554 -0.398903 \n",
"3 0.554845 -0.611138 -0.887757 -0.650567 \n",
"4 0.389632 0.168568 0.580023 0.174159 \n",
"\n",
" en_title_bert_23 en_title_bert_24 en_title_bert_25 en_title_bert_26 \\\n",
"0 -0.244120 0.266933 -0.039635 -0.065880 \n",
"1 0.183333 -0.283043 -0.064235 0.614472 \n",
"2 -0.262933 -0.458420 0.319028 0.289413 \n",
"3 0.619774 -0.093453 0.007300 -0.203065 \n",
"4 -0.350844 -0.521142 0.297349 -0.486753 \n",
"\n",
" en_title_bert_27 en_title_bert_28 ... en_more_title_bert_20 \\\n",
"0 -0.411748 -0.279252 ... 0.389561 \n",
"1 0.463003 0.118260 ... 1.094407 \n",
"2 -0.309064 -0.043147 ... 0.228491 \n",
"3 -0.517235 0.027389 ... -0.480090 \n",
"4 0.320408 0.123543 ... 0.193384 \n",
"\n",
" en_more_title_bert_21 en_more_title_bert_22 en_more_title_bert_23 \\\n",
"0 -0.181384 0.563113 0.003839 \n",
"1 -0.130241 -0.766464 -0.076177 \n",
"2 0.570181 -0.360011 0.347662 \n",
"3 -0.142411 0.146459 0.106265 \n",
"4 -0.358411 -0.509234 0.038638 \n",
"\n",
" en_more_title_bert_24 en_more_title_bert_25 en_more_title_bert_26 \\\n",
"0 0.201002 -0.236561 -0.198972 \n",
"1 0.058236 -0.104342 0.165517 \n",
"2 -0.499598 0.060707 -0.405925 \n",
"3 -0.887618 -0.777156 0.573504 \n",
"4 0.334755 0.281736 -0.539662 \n",
"\n",
" en_more_title_bert_27 en_more_title_bert_28 en_more_title_bert_29 \\\n",
"0 -0.356772 -0.187985 -0.229615 \n",
"1 -0.552768 -0.375126 0.160216 \n",
"2 0.272866 0.497490 -0.051781 \n",
"3 0.398247 0.136697 0.048429 \n",
"4 0.327682 -0.192763 0.038868 \n",
"\n",
" en_more_title_bert_30 en_more_title_bert_31 en_more_title_bert_32 \\\n",
"0 -0.995489 -0.122398 0.292693 \n",
"1 0.023187 -0.111831 -0.133776 \n",
"2 -0.189797 0.147104 0.267020 \n",
"3 0.811383 0.044471 -0.142168 \n",
"4 0.114521 0.215306 -0.020394 \n",
"\n",
" en_more_title_bert_33 en_more_title_bert_34 en_more_title_bert_35 \\\n",
"0 0.509988 0.333678 -0.087523 \n",
"1 -0.098487 -0.470474 0.869783 \n",
"2 0.232547 0.101529 -0.072668 \n",
"3 0.203729 0.884818 -0.207317 \n",
"4 -0.093313 -0.305974 0.061545 \n",
"\n",
" en_more_title_bert_36 en_more_title_bert_37 en_more_title_bert_38 \\\n",
"0 -0.706237 -0.057135 0.113895 \n",
"1 0.017964 -0.816890 -0.154945 \n",
"2 0.378593 0.380505 -0.140734 \n",
"3 -0.416153 -0.030251 0.236761 \n",
"4 0.593608 -0.332009 -0.013412 \n",
"\n",
" en_more_title_bert_39 en_more_title_bert_40 en_more_title_bert_41 \\\n",
"0 0.118731 0.085320 0.039879 \n",
"1 -0.225445 -0.598515 0.039904 \n",
"2 0.007491 0.283877 0.140491 \n",
"3 0.113535 -0.520574 -0.386547 \n",
"4 0.182370 0.307056 0.109259 \n",
"\n",
" en_more_title_bert_42 en_more_title_bert_43 en_more_title_bert_44 \\\n",
"0 -0.500713 0.077829 0.115718 \n",
"1 0.551894 -0.274307 -0.295357 \n",
"2 0.069903 -0.107196 -0.132137 \n",
"3 -1.160239 -0.050893 -0.497966 \n",
"4 -0.244337 0.524686 -0.135363 \n",
"\n",
" en_more_title_bert_45 en_more_title_bert_46 en_more_title_bert_47 \\\n",
"0 0.078613 0.129753 -0.103177 \n",
"1 0.154704 -0.468305 -0.610486 \n",
"2 -0.306891 -0.017558 0.064488 \n",
"3 -0.020273 0.162922 0.263952 \n",
"4 -0.156572 0.076641 0.083989 \n",
"\n",
" en_more_title_bert_48 en_more_title_bert_49 \n",
"0 -0.072235 0.000548 \n",
"1 -0.170374 0.889165 \n",
"2 0.028032 -0.061843 \n",
"3 0.026662 -0.192525 \n",
"4 -0.191296 -0.222544 \n",
"\n",
"[5 rows x 201 columns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"en_bert = pd.read_csv(\"../input/en_bert.csv\")\n",
"en_bert.head()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Merge BERT features] start\n",
"[Merge BERT features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Merge BERT features\"):\n",
" train = train.merge(en_bert, on=\"object_id\", how=\"left\")\n",
" test = test.merge(en_bert, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Language Features"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def language_pred(x):\n",
" if isinstance(x, str):\n",
" return model.predict(x.replace(\"\\n\", \".\"))[0][0]\n",
" else:\n",
" return x\n",
" \n",
" \n",
"def language_pred_cld2(x):\n",
" if isinstance(x, str):\n",
" return cld2.detect(x)[2][0][1]\n",
" else:\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Title Language Prediction] start\n",
"[Title Language Prediction] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Title Language Prediction\"):\n",
" train[\"Title_language\"] = train[\"title\"].map(lambda x: language_pred(x))\n",
" test[\"Title_language\"] = test[\"title\"].map(lambda x: language_pred(x))\n",
" train[\"Title_language_cld2\"] = train[\"title\"].map(lambda x: language_pred_cld2(x))\n",
" test[\"Title_language_cld2\"] = test[\"title\"].map(lambda x: language_pred_cld2(x))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Description Language Prediction] start\n",
"[Description Language Prediction] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Description Language Prediction\"):\n",
" train[\"Description_language\"] = train[\"description\"].map(\n",
" lambda x: language_pred(x))\n",
" test[\"Description_language\"] = test[\"description\"].map(\n",
" lambda x: language_pred(x))\n",
" train[\"Description_language_cld2\"] = train[\"description\"].map(\n",
" lambda x: language_pred_cld2(x))\n",
" test[\"Description_language_cld2\"] = test[\"description\"].map(\n",
" lambda x: language_pred_cld2(x))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Long title language prediction] start\n",
"[Long title language prediction] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Long title language prediction\"):\n",
" train[\"Long_title_language\"] = train[\"long_title\"].map(\n",
" lambda x: language_pred(x))\n",
" test[\"Long_title_language\"] = test[\"long_title\"].map(\n",
" lambda x: language_pred(x))\n",
" train[\"Long_title_language_cld2\"] = train[\"long_title\"].map(\n",
" lambda x: language_pred_cld2(x))\n",
" test[\"Long_title_language_cld2\"] = test[\"long_title\"].map(\n",
" lambda x: language_pred_cld2(x))"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[More title language prediction] start\n",
"[More title language prediction] done in 0 s\n"
]
}
],
"source": [
"with timer(\"More title language prediction\"):\n",
" train[\"More_title_language\"] = train[\"more_title\"].map(\n",
" lambda x: language_pred(x))\n",
" test[\"More_title_language\"] = test[\"more_title\"].map(\n",
" lambda x: language_pred(x))\n",
" train[\"More_title_language_cld2\"] = train[\"more_title\"].map(\n",
" lambda x: language_pred_cld2(x))\n",
" test[\"More_title_language_cld2\"] = test[\"more_title\"].map(\n",
" lambda x: language_pred_cld2(x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## fastText features"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"FT_COMPONENTS = 30"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 60320.90it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Title fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"100%|██████████| 12008/12008 [00:00<00:00, 62776.18it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Title fastText] done in 1 s\n"
]
}
],
"source": [
"with timer(\"Title fastText\"):\n",
" X = train[\"title\"].progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"title_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"title\"].progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"title_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 24%|██▍ | 2867/12026 [00:00<00:00, 28660.46it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[description fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 30667.07it/s]\n",
"100%|██████████| 12008/12008 [00:00<00:00, 32320.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[description fastText] done in 1 s\n"
]
}
],
"source": [
"with timer(\"description fastText\"):\n",
" X = train[\"description\"].fillna(\"\").progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"description_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"description\"].fillna(\"\").progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"description_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 33%|███▎ | 3946/12026 [00:00<00:00, 39455.80it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[long title fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 42789.80it/s]\n",
"100%|██████████| 12008/12008 [00:00<00:00, 42440.71it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[long title fastText] done in 1 s\n"
]
}
],
"source": [
"with timer(\"long title fastText\"):\n",
" X = train[\"long_title\"].fillna(\"\").progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"long_title_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"long_title\"].fillna(\"\").progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"long_title_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 79%|███████▊ | 9457/12026 [00:00<00:00, 47198.53it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[more title fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 47226.11it/s]\n",
"100%|██████████| 12008/12008 [00:00<00:00, 45697.69it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[more title fastText] done in 1 s\n"
]
}
],
"source": [
"with timer(\"more title fastText\"):\n",
" X = train[\"more_title\"].fillna(\"\").progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"more_title_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"more_title\"].fillna(\"\").progress_apply(lambda x: model_dutch.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"more_title_fastText_dutch_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 96%|█████████▌| 11499/12026 [00:00<00:00, 56285.68it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Title fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 57245.99it/s]\n",
"100%|██████████| 12008/12008 [00:00<00:00, 56581.72it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Title fastText] done in 1 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"with timer(\"Title fastText\"):\n",
" X = train[\"title\"].progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"title_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"title\"].progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"title_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 24%|██▍ | 2858/12026 [00:00<00:00, 28578.05it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[description fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 31135.54it/s]\n",
"100%|██████████| 12008/12008 [00:00<00:00, 31763.73it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[description fastText] done in 1 s\n"
]
}
],
"source": [
"with timer(\"description fastText\"):\n",
" X = train[\"description\"].fillna(\"\").progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"description_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"description\"].fillna(\"\").progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"description_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 74%|███████▍ | 8924/12026 [00:00<00:00, 44442.96it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[long title fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 45475.09it/s]\n",
"100%|██████████| 12008/12008 [00:00<00:00, 44142.87it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[long title fastText] done in 1 s\n"
]
}
],
"source": [
"with timer(\"long title fastText\"):\n",
" X = train[\"long_title\"].fillna(\"\").progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"long_title_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"long_title\"].fillna(\"\").progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"long_title_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 77%|███████▋ | 9239/12026 [00:00<00:00, 45382.96it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[more title fastText] start\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12026/12026 [00:00<00:00, 46666.11it/s]\n",
"100%|██████████| 12008/12008 [00:00<00:00, 46184.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[more title fastText] done in 1 s\n"
]
}
],
"source": [
"with timer(\"more title fastText\"):\n",
" X = train[\"more_title\"].fillna(\"\").progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" svd = TruncatedSVD(n_components=FT_COMPONENTS)\n",
" X = svd.fit_transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"more_title_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = train[\"object_id\"]\n",
" train = train.merge(X_df, on=\"object_id\", how=\"left\")\n",
" \n",
" X = test[\"more_title\"].fillna(\"\").progress_apply(lambda x: model_en.get_sentence_vector(x.replace(\"\\n\", \"\")))\n",
" X = np.stack(X.values)\n",
" X = svd.transform(X)\n",
" X_df = pd.DataFrame(X, columns=[f\"more_title_fastText_en_{i}\" for i in range(X.shape[1])])\n",
" X_df[\"object_id\"] = test[\"object_id\"]\n",
" test = test.merge(X_df, on=\"object_id\", how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Acquisition date feature"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Acquisition date feature] start\n",
"[Acquisition date feature] done in 0 s\n"
]
}
],
"source": [
"train[\"acquisition_date\"] = pd.to_datetime(train[\"acquisition_date\"])\n",
"test[\"acquisition_date\"] = pd.to_datetime(test[\"acquisition_date\"])\n",
"\n",
"with timer(\"Acquisition date feature\"):\n",
" train[\"acquisition_year\"] = train[\"acquisition_date\"].dt.year\n",
" test[\"acquisition_year\"] = test[\"acquisition_date\"].dt.year"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Year Diff features"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[years_to_create] start\n",
"[years_to_create] done in 0 s\n",
"[years_to_acquisite] start\n",
"[years_to_acquisite] done in 0 s\n"
]
}
],
"source": [
"with timer(\"years_to_create\"):\n",
" train[\"years_to_create\"] = train[\"dating_year_late\"] - train[\"dating_year_early\"]\n",
" test[\"years_to_create\"] = test[\"dating_year_late\"] - test[\"dating_year_early\"]\n",
" \n",
"with timer(\"years_to_acquisite\"):\n",
" train[\"years_to_acquisite\"] = train[\"acquisition_year\"] - train[\"dating_year_late\"]\n",
" test[\"years_to_acquisite\"] = test[\"acquisition_year\"] - test[\"dating_year_late\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Size features"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"for df in [train, test]:\n",
" for axis in [\"h\", \"w\", \"t\", \"d\"]:\n",
" column_name = f\"size_{axis}\"\n",
" size_info = df[\"sub_title\"].str.extract(r\"{} (\\d*|\\d*\\.\\d*)(cm|mm)\".format(axis))\n",
" size_info = size_info.rename(columns={0: column_name, 1: \"unit\"})\n",
" size_info[column_name] = size_info[column_name].replace('', np.nan).astype(float) # dtypeがobjectになってるのでfloatに直す\n",
" size_info[column_name] = size_info.apply(lambda row: row[column_name] * 10 if row['unit'] == 'cm' else row[column_name], axis=1) # 　単位をmmに統一する\n",
" df[column_name] = size_info[column_name] # trainにくっつける"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"train[\"area\"] = train[\"size_h\"] * train[\"size_w\"]\n",
"test[\"area\"] = test[\"size_h\"] * test[\"size_w\"]\n",
"\n",
"train[\"ar\"] = train[\"size_h\"] / train[\"size_w\"]\n",
"test[\"ar\"] = test[\"size_h\"] / test[\"size_w\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Title length features"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Title letter number] start\n",
"[Title letter number] done in 0 s\n",
"[Title word number] start\n",
"[Title word number] done in 0 s\n"
]
}
],
"source": [
"with timer(\"Title letter number\"):\n",
" train[\"title_letter_number\"] = train[\"title\"].map(lambda x: len(x))\n",
" test[\"title_letter_number\"] = test[\"title\"].map(lambda x: len(x))\n",
" \n",
"with timer(\"Title word number\"):\n",
" train[\"title_word_number\"] = train[\"title\"].map(lambda x: len(x.split(\" \")))\n",
" test[\"title_word_number\"] = test[\"title\"].map(lambda x: len(x.split(\" \")))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goto features"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[str length] start\n",
"[str length] done in 0 s\n"
]
}
],
"source": [
"with timer(\"str length\"):\n",
" train[\"sub_title_length\"] = train[\"sub_title\"].fillna(\"\").map(lambda x: len(x))\n",
" test[\"sub_title_length\"] = test[\"sub_title\"].fillna(\"\").map(lambda x: len(x))\n",
" train[\"more_title_length\"] = train[\"more_title\"].fillna(\"\").map(lambda x: len(x))\n",
" test[\"more_title_length\"] = test[\"more_title\"].fillna(\"\").map(lambda x: len(x))"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CE_principal_maker] start\n",
"[CE_principal_maker] done in 0 s\n"
]
}
],
"source": [
"concatenated = pd.concat([train, test]).reset_index(drop=True)\n",
"vc = concatenated[\"principal_maker\"].value_counts()\n",
"with timer(\"CE_principal_maker\"):\n",
" train[\"CE_principal_maker\"] = train[\"principal_maker\"].map(vc)\n",
" test[\"CE_principal_maker\"] = test[\"principal_maker\"].map(vc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tfidf features"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"def preprocess(text):\n",
" custom_stopwords = nltk.corpus.stopwords.words(\"dutch\") + nltk.corpus.stopwords.words(\"english\")\n",
" x = hero.clean(text, pipeline=[\n",
" hero.preprocessing.fillna,\n",
" hero.preprocessing.lowercase,\n",
" hero.preprocessing.remove_digits,\n",
" hero.preprocessing.remove_punctuation,\n",
" hero.preprocessing.remove_diacritics,\n",
" lambda x: hero.preprocessing.remove_stopwords(x, stopwords=custom_stopwords)\n",
" ])\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[working on title] start\n",
"[working on title] done in 1 s\n",
"[working on description] start\n",
"[working on description] done in 3 s\n",
"[working on long_title] start\n",
"[working on long_title] done in 2 s\n",
"[working on more_title] start\n",
"[working on more_title] done in 2 s\n"
]
}
],
"source": [
"tfidf_dfs = []\n",
"VOCAB_SIZE = 10000\n",
"COMPONENT_SIZE = 50\n",
"text_columns = [\"title\", \"description\", \"long_title\", \"more_title\"]\n",
"concatenated = pd.concat([train, test]).reset_index(drop=True)\n",
"concatenated = concatenated.set_index(\"object_id\")\n",
"for col in text_columns:\n",
" with timer(f\"working on {col}\"):\n",
" docs = preprocess(concatenated[col])\n",
" tv = TfidfVectorizer(max_features=VOCAB_SIZE, analyzer=\"word\", ngram_range=(1, 3))\n",
" X = tv.fit_transform(docs)\n",
"\n",
" svd = TruncatedSVD(n_components=COMPONENT_SIZE)\n",
" X = svd.fit_transform(X)\n",
" df = pd.DataFrame(X, columns=[f\"tfidf_{col}_{i}\" for i in range(COMPONENT_SIZE)])\n",
" df.index = docs.index\n",
" tfidf_dfs.append(df)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[merge tfidf features] start\n",
"[merge tfidf features] done in 0 s\n"
]
}
],
"source": [
"with timer(\"merge tfidf features\"):\n",
" train = train.merge(tfidf_dfs[0], left_on=\"object_id\", right_index=True, how=\"left\")\n",
" train = train.merge(tfidf_dfs[1], left_on=\"object_id\", right_index=True, how=\"left\")\n",
" train = train.merge(tfidf_dfs[2], left_on=\"object_id\", right_index=True, how=\"left\")\n",
" train = train.merge(tfidf_dfs[3], left_on=\"object_id\", right_index=True, how=\"left\")\n",
" test = test.merge(tfidf_dfs[0], left_on=\"object_id\", right_index=True, how=\"left\")\n",
" test = test.merge(tfidf_dfs[1], left_on=\"object_id\", right_index=True, how=\"left\")\n",
" test = test.merge(tfidf_dfs[2], left_on=\"object_id\", right_index=True, how=\"left\")\n",
" test = test.merge(tfidf_dfs[3], left_on=\"object_id\", right_index=True, how=\"left\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Embedding mean diff groupby principal_maker"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"def get_mean_diff_embedding(train: pd.DataFrame, test: pd.DataFrame, groupby_key: str, columns: list):\n",
" concatenated = pd.concat([\n",
" train[[groupby_key] + columns],\n",
" test[[groupby_key] + columns]\n",
" ], axis=0).reset_index(drop=True)\n",
" \n",
" groupby_mean = concatenated.groupby(groupby_key)[columns].mean()\n",
" groupby_mean = concatenated[[groupby_key]].merge(groupby_mean, on=groupby_key, how=\"left\")\n",
" mean_diff = concatenated[columns] - groupby_mean[columns]\n",
" mean_diff.columns = [c + \"_mean_diff\" for c in mean_diff.columns]\n",
" return mean_diff.iloc[:len(train)].reset_index(drop=True), mean_diff.iloc[len(train):].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\ncolor_feats_cols = color_feats.columns.tolist()\\ncolor_feats_cols.remove(\"object_id\")\\nall_emb_cols.extend(color_feats_cols)\\n\\npalette_feats_cols = palette_feats.columns.tolist()\\npalette_feats_cols.remove(\"object_id\")\\nall_emb_cols.extend(palette_feats_cols)\\n\\neff_color_cols = eff_color_feats.columns.tolist()\\neff_color_cols.remove(\"object_id\")\\nall_emb_cols.extend(eff_color_cols)\\n\\neff_palette_cols = eff_palette_feats.columns.tolist()\\neff_palette_cols.remove(\"object_id\")\\nall_emb_cols.extend(eff_palette_cols)\\n'"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_emb_cols = []\n",
"\n",
"all_emb_cols.extend([f\"tfidf_title_{i}\" for i in range(COMPONENT_SIZE)])\n",
"all_emb_cols.extend([f\"tfidf_description_{i}\" for i in range(COMPONENT_SIZE)])\n",
"all_emb_cols.extend([f\"tfidf_more_title_{i}\" for i in range(COMPONENT_SIZE)])\n",
"all_emb_cols.extend([f\"tfidf_long_title_{i}\" for i in range(COMPONENT_SIZE)])\n",
"\n",
"all_emb_cols.extend([f\"title_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)])\n",
"all_emb_cols.extend([f\"description_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)])\n",
"all_emb_cols.extend([f\"more_title_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)])\n",
"all_emb_cols.extend([f\"long_title_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)])\n",
"\n",
"all_emb_cols.extend([f\"title_fastText_en_{i}\" for i in range(FT_COMPONENTS)])\n",
"all_emb_cols.extend([f\"description_fastText_en_{i}\" for i in range(FT_COMPONENTS)])\n",
"all_emb_cols.extend([f\"long_title_fastText_en_{i}\" for i in range(FT_COMPONENTS)])\n",
"all_emb_cols.extend([f\"more_title_fastText_en_{i}\" for i in range(FT_COMPONENTS)])\n",
"\n",
"# all_emb_cols.extend([f\"palette_embedding_{i}\" for i in range(64)])\n",
"# all_emb_cols.extend([f\"color_embedding_{i}\" for i in range(64)])\n",
"\n",
"w2v_materials = w2v_dfs[0].columns.tolist()\n",
"w2v_materials.remove(\"object_id\")\n",
"all_emb_cols.extend(w2v_materials)\n",
"\n",
"w2v_collection = w2v_dfs[1].columns.tolist()\n",
"w2v_collection.remove(\"object_id\")\n",
"all_emb_cols.extend(w2v_collection)\n",
"\n",
"w2v_technique = w2v_dfs[2].columns.tolist()\n",
"w2v_technique.remove(\"object_id\")\n",
"all_emb_cols.extend(w2v_technique)\n",
"\n",
"w2v_material_collection = w2v_dfs[3].columns.tolist()\n",
"w2v_material_collection.remove(\"object_id\")\n",
"all_emb_cols.extend(w2v_material_collection)\n",
"\n",
"w2v_material_technique = w2v_dfs[4].columns.tolist()\n",
"w2v_material_technique.remove(\"object_id\")\n",
"all_emb_cols.extend(w2v_material_technique)\n",
"\n",
"w2v_collection_technique = w2v_dfs[5].columns.tolist()\n",
"w2v_collection_technique.remove(\"object_id\")\n",
"all_emb_cols.extend(w2v_collection_technique)\n",
"\n",
"w2v_material_collection_technique = w2v_dfs[6].columns.tolist()\n",
"w2v_material_collection_technique.remove(\"object_id\")\n",
"all_emb_cols.extend(w2v_material_collection_technique)\n",
"\n",
"bert_cols = bert.columns.tolist()\n",
"bert_cols.remove(\"object_id\")\n",
"all_emb_cols.extend(bert_cols)\n",
"\n",
"nl_bert_cols = nl_bert.columns.tolist()\n",
"nl_bert_cols.remove(\"object_id\")\n",
"all_emb_cols.extend(nl_bert_cols)\n",
"\n",
"en_bert_cols = en_bert.columns.tolist()\n",
"en_bert_cols.remove(\"object_id\")\n",
"all_emb_cols.extend(en_bert_cols)\n",
"\n",
"\"\"\"\n",
"color_feats_cols = color_feats.columns.tolist()\n",
"color_feats_cols.remove(\"object_id\")\n",
"all_emb_cols.extend(color_feats_cols)\n",
"\n",
"palette_feats_cols = palette_feats.columns.tolist()\n",
"palette_feats_cols.remove(\"object_id\")\n",
"all_emb_cols.extend(palette_feats_cols)\n",
"\n",
"eff_color_cols = eff_color_feats.columns.tolist()\n",
"eff_color_cols.remove(\"object_id\")\n",
"all_emb_cols.extend(eff_color_cols)\n",
"\n",
"eff_palette_cols = eff_palette_feats.columns.tolist()\n",
"eff_palette_cols.remove(\"object_id\")\n",
"all_emb_cols.extend(eff_palette_cols)\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"mean_diff_all_emb_trn, mean_diff_all_emb_tst = get_mean_diff_embedding(train, test, \"principal_maker\", all_emb_cols)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"train = pd.concat([train, mean_diff_all_emb_trn], axis=1)\n",
"test = pd.concat([test, mean_diff_all_emb_tst], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Final"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"cols = [\"principal_maker\", \"principal_or_first_maker\",\n",
" \"copyright_holder\", \"acquisition_method\",\n",
" \"acquisition_credit_line\", \"dating_period\", \"dating_year_early\", \"dating_year_late\",\n",
" \"Title_language\", \"Description_language\", \"Long_title_language\",\n",
" \"acquisition_year\", \"years_to_create\", \"years_to_acquisite\",\n",
" \"size_w\", \"size_h\", \"size_t\", \"size_d\", \"area\", \"ar\",\n",
" \"title_letter_number\", \"title_word_number\",\n",
" \"More_title_language\", \"CE_principal_maker\", \"sub_title_length\", \"more_title_length\",\n",
" \"country_name\", \"maker_name\", \"roles\", \"productionPlaces\", \"qualification\"]\n",
"\n",
"cols += [f\"tfidf_title_{i}\" for i in range(COMPONENT_SIZE)]\n",
"cols += [f\"tfidf_description_{i}\" for i in range(COMPONENT_SIZE)]\n",
"cols += [f\"tfidf_more_title_{i}\" for i in range(COMPONENT_SIZE)]\n",
"cols += [f\"tfidf_long_title_{i}\" for i in range(COMPONENT_SIZE)]\n",
"\n",
"cols += [f\"title_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)]\n",
"cols += [f\"description_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)]\n",
"cols += [f\"long_title_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)]\n",
"cols += [f\"more_title_fastText_dutch_{i}\" for i in range(FT_COMPONENTS)]\n",
"\n",
"cols += [f\"title_fastText_en_{i}\" for i in range(FT_COMPONENTS)]\n",
"cols += [f\"description_fastText_en_{i}\" for i in range(FT_COMPONENTS)]\n",
"cols += [f\"long_title_fastText_en_{i}\" for i in range(FT_COMPONENTS)]\n",
"cols += [f\"more_title_fastText_en_{i}\" for i in range(FT_COMPONENTS)]\n",
"\n",
"cols += [f\"palette_embedding_{i}\" for i in range(64)]\n",
"cols += [f\"color_embedding_{i}\" for i in range(64)]\n",
"\n",
"technique_columns = technique.columns.tolist()\n",
"technique_columns.remove(\"object_id\")\n",
"\n",
"collection_columns = collection.columns.tolist()\n",
"collection_columns.remove(\"object_id\")\n",
"\n",
"material_columns = material.columns.tolist()\n",
"material_columns.remove(\"object_id\")\n",
"\n",
"historical_columns = historical.columns.tolist()\n",
"historical_columns.remove(\"object_id\")\n",
"\n",
"cols += technique_columns\n",
"cols += collection_columns\n",
"cols += material_columns\n",
"cols += historical_columns\n",
"\n",
"w2v_materials = w2v_dfs[0].columns.tolist()\n",
"w2v_materials.remove(\"object_id\")\n",
"\n",
"w2v_collection = w2v_dfs[1].columns.tolist()\n",
"w2v_collection.remove(\"object_id\")\n",
"\n",
"w2v_technique = w2v_dfs[2].columns.tolist()\n",
"w2v_technique.remove(\"object_id\")\n",
"\n",
"w2v_material_collection = w2v_dfs[3].columns.tolist()\n",
"w2v_material_collection.remove(\"object_id\")\n",
"\n",
"w2v_material_technique = w2v_dfs[4].columns.tolist()\n",
"w2v_material_technique.remove(\"object_id\")\n",
"\n",
"w2v_collection_technique = w2v_dfs[5].columns.tolist()\n",
"w2v_collection_technique.remove(\"object_id\")\n",
"\n",
"w2v_material_collection_technique = w2v_dfs[6].columns.tolist()\n",
"w2v_material_collection_technique.remove(\"object_id\")\n",
"\n",
"w2v_historical = w2v_dfs[7].columns.tolist()\n",
"w2v_historical.remove(\"object_id\")\n",
"\n",
"cols += w2v_materials\n",
"cols += w2v_collection\n",
"cols += w2v_technique\n",
"cols += w2v_material_collection\n",
"cols += w2v_material_technique\n",
"cols += w2v_collection_technique\n",
"cols += w2v_material_collection_technique\n",
"# cols += w2v_historical\n",
"\n",
"bert_cols = bert.columns.tolist()\n",
"bert_cols.remove(\"object_id\")\n",
"cols += bert_cols\n",
"\n",
"nl_bert_cols = nl_bert.columns.tolist()\n",
"nl_bert_cols.remove(\"object_id\")\n",
"cols += nl_bert_cols\n",
"\n",
"en_bert_cols = en_bert.columns.tolist()\n",
"en_bert_cols.remove(\"object_id\")\n",
"cols += en_bert_cols\n",
"\n",
"color_feats_cols = color_feats.columns.tolist()\n",
"color_feats_cols.remove(\"object_id\")\n",
"cols += color_feats_cols\n",
"\n",
"palette_feats_cols = palette_feats.columns.tolist()\n",
"palette_feats_cols.remove(\"object_id\")\n",
"cols += palette_feats_cols\n",
"\n",
"eff_color_cols = eff_color_feats.columns.tolist()\n",
"eff_color_cols.remove(\"object_id\")\n",
"cols += eff_color_cols\n",
"\n",
"eff_palette_cols = eff_palette_feats.columns.tolist()\n",
"eff_palette_cols.remove(\"object_id\")\n",
"cols += eff_palette_cols\n",
"\n",
"cols += mean_diff_all_emb_trn.columns.tolist()\n",
"\n",
"cat_cols = [\"principal_maker\", \"principal_or_first_maker\", \"copyright_holder\", \"acquisition_method\",\n",
" \"acquisition_credit_line\", \"Title_language\", \"Description_language\", \"Long_title_language\",\n",
" \"More_title_language\", \"country_name\", \"maker_name\", \"qualification\", \"roles\", \"productionPlaces\"]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"for c in cat_cols:\n",
" train.loc[~train[c].isin(test[c].unique()), c] = np.nan\n",
" test.loc[~test[c].isin(train[c].unique()), c] = np.nan\n",
" \n",
"concatenated = pd.concat([train[cols], test[cols]]).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"for c in cat_cols:\n",
" concatenated[c] = concatenated[c].astype(str)\n",
" le = LabelEncoder()\n",
" concatenated[c] = le.fit_transform(concatenated[c])"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"test = concatenated.iloc[len(train):].reset_index(drop=True)\n",
"train = concatenated.iloc[:len(train)].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"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>principal_maker</th>\n",
" <th>principal_or_first_maker</th>\n",
" <th>copyright_holder</th>\n",
" <th>acquisition_method</th>\n",
" <th>acquisition_credit_line</th>\n",
" <th>dating_period</th>\n",
" <th>dating_year_early</th>\n",
" <th>dating_year_late</th>\n",
" <th>Title_language</th>\n",
" <th>Description_language</th>\n",
" <th>Long_title_language</th>\n",
" <th>acquisition_year</th>\n",
" <th>years_to_create</th>\n",
" <th>years_to_acquisite</th>\n",
" <th>size_w</th>\n",
" <th>size_h</th>\n",
" <th>size_t</th>\n",
" <th>size_d</th>\n",
" <th>area</th>\n",
" <th>ar</th>\n",
" <th>title_letter_number</th>\n",
" <th>title_word_number</th>\n",
" <th>More_title_language</th>\n",
" <th>CE_principal_maker</th>\n",
" <th>sub_title_length</th>\n",
" <th>more_title_length</th>\n",
" <th>country_name</th>\n",
" <th>maker_name</th>\n",
" <th>roles</th>\n",
" <th>productionPlaces</th>\n",
" <th>...</th>\n",
" <th>en_more_title_bert_20_mean_diff</th>\n",
" <th>en_more_title_bert_21_mean_diff</th>\n",
" <th>en_more_title_bert_22_mean_diff</th>\n",
" <th>en_more_title_bert_23_mean_diff</th>\n",
" <th>en_more_title_bert_24_mean_diff</th>\n",
" <th>en_more_title_bert_25_mean_diff</th>\n",
" <th>en_more_title_bert_26_mean_diff</th>\n",
" <th>en_more_title_bert_27_mean_diff</th>\n",
" <th>en_more_title_bert_28_mean_diff</th>\n",
" <th>en_more_title_bert_29_mean_diff</th>\n",
" <th>en_more_title_bert_30_mean_diff</th>\n",
" <th>en_more_title_bert_31_mean_diff</th>\n",
" <th>en_more_title_bert_32_mean_diff</th>\n",
" <th>en_more_title_bert_33_mean_diff</th>\n",
" <th>en_more_title_bert_34_mean_diff</th>\n",
" <th>en_more_title_bert_35_mean_diff</th>\n",
" <th>en_more_title_bert_36_mean_diff</th>\n",
" <th>en_more_title_bert_37_mean_diff</th>\n",
" <th>en_more_title_bert_38_mean_diff</th>\n",
" <th>en_more_title_bert_39_mean_diff</th>\n",
" <th>en_more_title_bert_40_mean_diff</th>\n",
" <th>en_more_title_bert_41_mean_diff</th>\n",
" <th>en_more_title_bert_42_mean_diff</th>\n",
" <th>en_more_title_bert_43_mean_diff</th>\n",
" <th>en_more_title_bert_44_mean_diff</th>\n",
" <th>en_more_title_bert_45_mean_diff</th>\n",
" <th>en_more_title_bert_46_mean_diff</th>\n",
" <th>en_more_title_bert_47_mean_diff</th>\n",
" <th>en_more_title_bert_48_mean_diff</th>\n",
" <th>en_more_title_bert_49_mean_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>638</td>\n",
" <td>638</td>\n",
" <td>25</td>\n",
" <td>6</td>\n",
" <td>217</td>\n",
" <td>17</td>\n",
" <td>1660.0</td>\n",
" <td>1685.0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>1808.0</td>\n",
" <td>25.0</td>\n",
" <td>123.0</td>\n",
" <td>537.0</td>\n",
" <td>665.0</td>\n",
" <td>25.0</td>\n",
" <td>47.0</td>\n",
" <td>357105.0</td>\n",
" <td>1.238361</td>\n",
" <td>21</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>39</td>\n",
" <td>21</td>\n",
" <td>19</td>\n",
" <td>636</td>\n",
" <td>3</td>\n",
" <td>84</td>\n",
" <td>...</td>\n",
" <td>0.233520</td>\n",
" <td>-0.053699</td>\n",
" <td>0.216418</td>\n",
" <td>0.277177</td>\n",
" <td>0.152929</td>\n",
" <td>-0.510699</td>\n",
" <td>-0.252252</td>\n",
" <td>-0.054081</td>\n",
" <td>0.000083</td>\n",
" <td>-0.168244</td>\n",
" <td>-0.984614</td>\n",
" <td>-0.467308</td>\n",
" <td>0.373471</td>\n",
" <td>0.671023</td>\n",
" <td>0.443482</td>\n",
" <td>-0.066743</td>\n",
" <td>-0.437611</td>\n",
" <td>-0.314361</td>\n",
" <td>0.040608</td>\n",
" <td>0.032341</td>\n",
" <td>0.064885</td>\n",
" <td>0.363810</td>\n",
" <td>-0.393186</td>\n",
" <td>-0.083509</td>\n",
" <td>0.101576</td>\n",
" <td>0.118520</td>\n",
" <td>0.226153</td>\n",
" <td>-0.134525</td>\n",
" <td>0.293020</td>\n",
" <td>-0.075682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1042</td>\n",
" <td>1041</td>\n",
" <td>23</td>\n",
" <td>6</td>\n",
" <td>217</td>\n",
" <td>19</td>\n",
" <td>1900.0</td>\n",
" <td>1930.0</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>2000.0</td>\n",
" <td>30.0</td>\n",
" <td>70.0</td>\n",
" <td>223.0</td>\n",
" <td>165.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>36795.0</td>\n",
" <td>0.739910</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>17</td>\n",
" <td>364</td>\n",
" <td>17</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>1044</td>\n",
" <td>2</td>\n",
" <td>54</td>\n",
" <td>...</td>\n",
" <td>0.928264</td>\n",
" <td>0.110160</td>\n",
" <td>-0.616900</td>\n",
" <td>-0.090577</td>\n",
" <td>0.067893</td>\n",
" <td>-0.190434</td>\n",
" <td>0.088411</td>\n",
" <td>-0.579660</td>\n",
" <td>-0.325908</td>\n",
" <td>0.161717</td>\n",
" <td>-0.095955</td>\n",
" <td>-0.086360</td>\n",
" <td>-0.177854</td>\n",
" <td>-0.149381</td>\n",
" <td>-0.532390</td>\n",
" <td>0.878381</td>\n",
" <td>-0.028265</td>\n",
" <td>-0.730058</td>\n",
" <td>-0.102459</td>\n",
" <td>-0.189515</td>\n",
" <td>-0.566161</td>\n",
" <td>0.078401</td>\n",
" <td>0.582363</td>\n",
" <td>-0.237948</td>\n",
" <td>-0.274214</td>\n",
" <td>0.178810</td>\n",
" <td>-0.500350</td>\n",
" <td>-0.599625</td>\n",
" <td>-0.130224</td>\n",
" <td>0.861052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1202</td>\n",
" <td>1211</td>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>104</td>\n",
" <td>19</td>\n",
" <td>1860.0</td>\n",
" <td>1880.0</td>\n",
" <td>20</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>2007.0</td>\n",
" <td>20.0</td>\n",
" <td>127.0</td>\n",
" <td>56.0</td>\n",
" <td>87.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4872.0</td>\n",
" <td>1.553571</td>\n",
" <td>21</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>21</td>\n",
" <td>11</td>\n",
" <td>1204</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1202</td>\n",
" <td>1211</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>217</td>\n",
" <td>19</td>\n",
" <td>1850.0</td>\n",
" <td>1879.0</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1881.0</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>2480.0</td>\n",
" <td>1790.0</td>\n",
" <td>40.0</td>\n",
" <td>NaN</td>\n",
" <td>4439200.0</td>\n",
" <td>0.721774</td>\n",
" <td>16</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" <td>16</td>\n",
" <td>19</td>\n",
" <td>1204</td>\n",
" <td>3</td>\n",
" <td>84</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1194</td>\n",
" <td>1194</td>\n",
" <td>25</td>\n",
" <td>8</td>\n",
" <td>217</td>\n",
" <td>19</td>\n",
" <td>1825.0</td>\n",
" <td>1874.0</td>\n",
" <td>20</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>1971.0</td>\n",
" <td>49.0</td>\n",
" <td>97.0</td>\n",
" <td>175.0</td>\n",
" <td>130.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>22750.0</td>\n",
" <td>0.742857</td>\n",
" <td>27</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>3457</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>11</td>\n",
" <td>1196</td>\n",
" <td>3</td>\n",
" <td>56</td>\n",
" <td>...</td>\n",
" <td>0.155748</td>\n",
" <td>-0.384273</td>\n",
" <td>-0.455481</td>\n",
" <td>0.071493</td>\n",
" <td>0.370948</td>\n",
" <td>0.298991</td>\n",
" <td>-0.549726</td>\n",
" <td>0.362264</td>\n",
" <td>-0.180307</td>\n",
" <td>0.011580</td>\n",
" <td>0.131026</td>\n",
" <td>0.215252</td>\n",
" <td>-0.015708</td>\n",
" <td>-0.081261</td>\n",
" <td>-0.296537</td>\n",
" <td>0.044759</td>\n",
" <td>0.543852</td>\n",
" <td>-0.293110</td>\n",
" <td>-0.008591</td>\n",
" <td>0.162720</td>\n",
" <td>0.303028</td>\n",
" <td>0.114431</td>\n",
" <td>-0.261534</td>\n",
" <td>0.525517</td>\n",
" <td>-0.118563</td>\n",
" <td>-0.167105</td>\n",
" <td>0.079006</td>\n",
" <td>0.098872</td>\n",
" <td>-0.189534</td>\n",
" <td>-0.205957</td>\n",
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],
"text/plain": [
" principal_maker principal_or_first_maker copyright_holder \\\n",
"0 638 638 25 \n",
"1 1042 1041 23 \n",
"2 1202 1211 25 \n",
"3 1202 1211 25 \n",
"4 1194 1194 25 \n",
"\n",
" acquisition_method acquisition_credit_line dating_period \\\n",
"0 6 217 17 \n",
"1 6 217 19 \n",
"2 2 104 19 \n",
"3 0 217 19 \n",
"4 8 217 19 \n",
"\n",
" dating_year_early dating_year_late Title_language Description_language \\\n",
"0 1660.0 1685.0 6 6 \n",
"1 1900.0 1930.0 20 6 \n",
"2 1860.0 1880.0 20 5 \n",
"3 1850.0 1879.0 6 5 \n",
"4 1825.0 1874.0 20 5 \n",
"\n",
" Long_title_language acquisition_year years_to_create years_to_acquisite \\\n",
"0 3 1808.0 25.0 123.0 \n",
"1 3 2000.0 30.0 70.0 \n",
"2 9 2007.0 20.0 127.0 \n",
"3 3 1881.0 29.0 2.0 \n",
"4 9 1971.0 49.0 97.0 \n",
"\n",
" size_w size_h size_t size_d area ar title_letter_number \\\n",
"0 537.0 665.0 25.0 47.0 357105.0 1.238361 21 \n",
"1 223.0 165.0 NaN NaN 36795.0 0.739910 15 \n",
"2 56.0 87.0 NaN NaN 4872.0 1.553571 21 \n",
"3 2480.0 1790.0 40.0 NaN 4439200.0 0.721774 16 \n",
"4 175.0 130.0 NaN 7.0 22750.0 0.742857 27 \n",
"\n",
" title_word_number More_title_language CE_principal_maker \\\n",
"0 4 6 5 \n",
"1 3 17 364 \n",
"2 4 17 1 \n",
"3 4 6 1 \n",
"4 4 17 3457 \n",
"\n",
" sub_title_length more_title_length country_name maker_name roles \\\n",
"0 39 21 19 636 3 \n",
"1 17 15 11 1044 2 \n",
"2 15 21 11 1204 2 \n",
"3 25 16 19 1204 3 \n",
"4 27 27 11 1196 3 \n",
"\n",
" productionPlaces ... en_more_title_bert_20_mean_diff \\\n",
"0 84 ... 0.233520 \n",
"1 54 ... 0.928264 \n",
"2 0 ... 0.000000 \n",
"3 84 ... 0.000000 \n",
"4 56 ... 0.155748 \n",
"\n",
" en_more_title_bert_21_mean_diff en_more_title_bert_22_mean_diff \\\n",
"0 -0.053699 0.216418 \n",
"1 0.110160 -0.616900 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 -0.384273 -0.455481 \n",
"\n",
" en_more_title_bert_23_mean_diff en_more_title_bert_24_mean_diff \\\n",
"0 0.277177 0.152929 \n",
"1 -0.090577 0.067893 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.071493 0.370948 \n",
"\n",
" en_more_title_bert_25_mean_diff en_more_title_bert_26_mean_diff \\\n",
"0 -0.510699 -0.252252 \n",
"1 -0.190434 0.088411 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.298991 -0.549726 \n",
"\n",
" en_more_title_bert_27_mean_diff en_more_title_bert_28_mean_diff \\\n",
"0 -0.054081 0.000083 \n",
"1 -0.579660 -0.325908 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.362264 -0.180307 \n",
"\n",
" en_more_title_bert_29_mean_diff en_more_title_bert_30_mean_diff \\\n",
"0 -0.168244 -0.984614 \n",
"1 0.161717 -0.095955 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.011580 0.131026 \n",
"\n",
" en_more_title_bert_31_mean_diff en_more_title_bert_32_mean_diff \\\n",
"0 -0.467308 0.373471 \n",
"1 -0.086360 -0.177854 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.215252 -0.015708 \n",
"\n",
" en_more_title_bert_33_mean_diff en_more_title_bert_34_mean_diff \\\n",
"0 0.671023 0.443482 \n",
"1 -0.149381 -0.532390 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 -0.081261 -0.296537 \n",
"\n",
" en_more_title_bert_35_mean_diff en_more_title_bert_36_mean_diff \\\n",
"0 -0.066743 -0.437611 \n",
"1 0.878381 -0.028265 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.044759 0.543852 \n",
"\n",
" en_more_title_bert_37_mean_diff en_more_title_bert_38_mean_diff \\\n",
"0 -0.314361 0.040608 \n",
"1 -0.730058 -0.102459 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 -0.293110 -0.008591 \n",
"\n",
" en_more_title_bert_39_mean_diff en_more_title_bert_40_mean_diff \\\n",
"0 0.032341 0.064885 \n",
"1 -0.189515 -0.566161 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.162720 0.303028 \n",
"\n",
" en_more_title_bert_41_mean_diff en_more_title_bert_42_mean_diff \\\n",
"0 0.363810 -0.393186 \n",
"1 0.078401 0.582363 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.114431 -0.261534 \n",
"\n",
" en_more_title_bert_43_mean_diff en_more_title_bert_44_mean_diff \\\n",
"0 -0.083509 0.101576 \n",
"1 -0.237948 -0.274214 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.525517 -0.118563 \n",
"\n",
" en_more_title_bert_45_mean_diff en_more_title_bert_46_mean_diff \\\n",
"0 0.118520 0.226153 \n",
"1 0.178810 -0.500350 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 -0.167105 0.079006 \n",
"\n",
" en_more_title_bert_47_mean_diff en_more_title_bert_48_mean_diff \\\n",
"0 -0.134525 0.293020 \n",
"1 -0.599625 -0.130224 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.098872 -0.189534 \n",
"\n",
" en_more_title_bert_49_mean_diff \n",
"0 -0.075682 \n",
"1 0.861052 \n",
"2 0.000000 \n",
"3 0.000000 \n",
"4 -0.205957 \n",
"\n",
"[5 rows x 2852 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>principal_maker</th>\n",
" <th>principal_or_first_maker</th>\n",
" <th>copyright_holder</th>\n",
" <th>acquisition_method</th>\n",
" <th>acquisition_credit_line</th>\n",
" <th>dating_period</th>\n",
" <th>dating_year_early</th>\n",
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" <th>Title_language</th>\n",
" <th>Description_language</th>\n",
" <th>Long_title_language</th>\n",
" <th>acquisition_year</th>\n",
" <th>years_to_create</th>\n",
" <th>years_to_acquisite</th>\n",
" <th>size_w</th>\n",
" <th>size_h</th>\n",
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" <th>area</th>\n",
" <th>ar</th>\n",
" <th>title_letter_number</th>\n",
" <th>title_word_number</th>\n",
" <th>More_title_language</th>\n",
" <th>CE_principal_maker</th>\n",
" <th>sub_title_length</th>\n",
" <th>more_title_length</th>\n",
" <th>country_name</th>\n",
" <th>maker_name</th>\n",
" <th>roles</th>\n",
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" <th>en_more_title_bert_20_mean_diff</th>\n",
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" <th>en_more_title_bert_23_mean_diff</th>\n",
" <th>en_more_title_bert_24_mean_diff</th>\n",
" <th>en_more_title_bert_25_mean_diff</th>\n",
" <th>en_more_title_bert_26_mean_diff</th>\n",
" <th>en_more_title_bert_27_mean_diff</th>\n",
" <th>en_more_title_bert_28_mean_diff</th>\n",
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" <th>en_more_title_bert_30_mean_diff</th>\n",
" <th>en_more_title_bert_31_mean_diff</th>\n",
" <th>en_more_title_bert_32_mean_diff</th>\n",
" <th>en_more_title_bert_33_mean_diff</th>\n",
" <th>en_more_title_bert_34_mean_diff</th>\n",
" <th>en_more_title_bert_35_mean_diff</th>\n",
" <th>en_more_title_bert_36_mean_diff</th>\n",
" <th>en_more_title_bert_37_mean_diff</th>\n",
" <th>en_more_title_bert_38_mean_diff</th>\n",
" <th>en_more_title_bert_39_mean_diff</th>\n",
" <th>en_more_title_bert_40_mean_diff</th>\n",
" <th>en_more_title_bert_41_mean_diff</th>\n",
" <th>en_more_title_bert_42_mean_diff</th>\n",
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" <th>en_more_title_bert_44_mean_diff</th>\n",
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" <th>en_more_title_bert_46_mean_diff</th>\n",
" <th>en_more_title_bert_47_mean_diff</th>\n",
" <th>en_more_title_bert_48_mean_diff</th>\n",
" <th>en_more_title_bert_49_mean_diff</th>\n",
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" <td>217</td>\n",
" <td>19</td>\n",
" <td>1850.0</td>\n",
" <td>1900.0</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>1994.0</td>\n",
" <td>50.0</td>\n",
" <td>94.0</td>\n",
" <td>108.0</td>\n",
" <td>167.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18036.0</td>\n",
" <td>1.546296</td>\n",
" <td>51</td>\n",
" <td>8</td>\n",
" <td>17</td>\n",
" <td>14</td>\n",
" <td>17</td>\n",
" <td>51</td>\n",
" <td>19</td>\n",
" <td>156</td>\n",
" <td>2</td>\n",
" <td>84</td>\n",
" <td>...</td>\n",
" <td>0.238445</td>\n",
" <td>-0.737660</td>\n",
" <td>-0.507756</td>\n",
" <td>-0.082622</td>\n",
" <td>0.512820</td>\n",
" <td>-0.881895</td>\n",
" <td>0.201240</td>\n",
" <td>0.333383</td>\n",
" <td>-0.196919</td>\n",
" <td>-0.198307</td>\n",
" <td>0.163790</td>\n",
" <td>0.109100</td>\n",
" <td>-0.089420</td>\n",
" <td>-0.037724</td>\n",
" <td>-0.397585</td>\n",
" <td>0.025229</td>\n",
" <td>-0.462651</td>\n",
" <td>-0.038014</td>\n",
" <td>0.295584</td>\n",
" <td>-0.404498</td>\n",
" <td>0.118537</td>\n",
" <td>-0.171953</td>\n",
" <td>-0.055267</td>\n",
" <td>-0.073008</td>\n",
" <td>-0.403933</td>\n",
" <td>-0.263048</td>\n",
" <td>-0.380959</td>\n",
" <td>0.275352</td>\n",
" <td>0.129177</td>\n",
" <td>0.027217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>623</td>\n",
" <td>623</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>217</td>\n",
" <td>17</td>\n",
" <td>1609.0</td>\n",
" <td>1633.0</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1798.0</td>\n",
" <td>24.0</td>\n",
" <td>165.0</td>\n",
" <td>241.0</td>\n",
" <td>297.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>71577.0</td>\n",
" <td>1.232365</td>\n",
" <td>59</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>31</td>\n",
" <td>19</td>\n",
" <td>59</td>\n",
" <td>19</td>\n",
" <td>621</td>\n",
" <td>3</td>\n",
" <td>84</td>\n",
" <td>...</td>\n",
" <td>0.628315</td>\n",
" <td>0.269612</td>\n",
" <td>0.434108</td>\n",
" <td>0.789628</td>\n",
" <td>-0.167794</td>\n",
" <td>-0.221561</td>\n",
" <td>0.082386</td>\n",
" <td>-0.097810</td>\n",
" <td>-0.231660</td>\n",
" <td>0.514047</td>\n",
" <td>0.195922</td>\n",
" <td>-0.090007</td>\n",
" <td>-0.530405</td>\n",
" <td>-0.113854</td>\n",
" <td>-0.135597</td>\n",
" <td>0.012477</td>\n",
" <td>-0.090950</td>\n",
" <td>0.110446</td>\n",
" <td>0.150932</td>\n",
" <td>-0.381709</td>\n",
" <td>0.491066</td>\n",
" <td>-0.249866</td>\n",
" <td>-0.136790</td>\n",
" <td>0.268029</td>\n",
" <td>0.055939</td>\n",
" <td>-0.105957</td>\n",
" <td>0.012994</td>\n",
" <td>-0.262101</td>\n",
" <td>-0.019628</td>\n",
" <td>-0.146686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1097</td>\n",
" <td>1096</td>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>84</td>\n",
" <td>18</td>\n",
" <td>1779.0</td>\n",
" <td>1779.0</td>\n",
" <td>20</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>2002.0</td>\n",
" <td>0.0</td>\n",
" <td>223.0</td>\n",
" <td>215.0</td>\n",
" <td>270.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>58050.0</td>\n",
" <td>1.255814</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>262</td>\n",
" <td>11</td>\n",
" <td>1098</td>\n",
" <td>4</td>\n",
" <td>56</td>\n",
" <td>...</td>\n",
" <td>0.380194</td>\n",
" <td>0.067271</td>\n",
" <td>0.231846</td>\n",
" <td>0.020618</td>\n",
" <td>-0.488165</td>\n",
" <td>-0.508553</td>\n",
" <td>0.476026</td>\n",
" <td>0.033216</td>\n",
" <td>-0.010701</td>\n",
" <td>-0.433889</td>\n",
" <td>-0.512983</td>\n",
" <td>0.122594</td>\n",
" <td>0.113122</td>\n",
" <td>-0.282127</td>\n",
" <td>-0.363452</td>\n",
" <td>-0.248944</td>\n",
" <td>0.118981</td>\n",
" <td>-0.219124</td>\n",
" <td>-0.363030</td>\n",
" <td>0.222623</td>\n",
" <td>0.062438</td>\n",
" <td>-0.117897</td>\n",
" <td>0.408852</td>\n",
" <td>-0.892897</td>\n",
" <td>0.612290</td>\n",
" <td>-0.357699</td>\n",
" <td>-0.108913</td>\n",
" <td>-0.263014</td>\n",
" <td>-0.043216</td>\n",
" <td>-0.202586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1114</td>\n",
" <td>1113</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>217</td>\n",
" <td>19</td>\n",
" <td>1895.0</td>\n",
" <td>1898.0</td>\n",
" <td>20</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>2009.0</td>\n",
" <td>3.0</td>\n",
" <td>111.0</td>\n",
" <td>159.0</td>\n",
" <td>116.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18444.0</td>\n",
" <td>0.729560</td>\n",
" <td>64</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>127</td>\n",
" <td>17</td>\n",
" <td>64</td>\n",
" <td>15</td>\n",
" <td>1115</td>\n",
" <td>2</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>-0.076455</td>\n",
" <td>-0.084601</td>\n",
" <td>0.263475</td>\n",
" <td>-0.929086</td>\n",
" <td>0.645861</td>\n",
" <td>0.680632</td>\n",
" <td>0.528578</td>\n",
" <td>-0.093705</td>\n",
" <td>-0.237921</td>\n",
" <td>0.153844</td>\n",
" <td>0.255587</td>\n",
" <td>0.056234</td>\n",
" <td>0.323003</td>\n",
" <td>0.109104</td>\n",
" <td>-0.241651</td>\n",
" <td>-0.010488</td>\n",
" <td>0.045534</td>\n",
" <td>0.103212</td>\n",
" <td>0.049097</td>\n",
" <td>0.332585</td>\n",
" <td>-0.084924</td>\n",
" <td>0.082461</td>\n",
" <td>0.070685</td>\n",
" <td>-0.005874</td>\n",
" <td>-0.083915</td>\n",
" <td>-0.467602</td>\n",
" <td>-0.312873</td>\n",
" <td>-0.657591</td>\n",
" <td>0.204300</td>\n",
" <td>-0.026818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>648</td>\n",
" <td>648</td>\n",
" <td>25</td>\n",
" <td>8</td>\n",
" <td>217</td>\n",
" <td>17</td>\n",
" <td>1700.0</td>\n",
" <td>1700.0</td>\n",
" <td>20</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>184.0</td>\n",
" <td>108.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19872.0</td>\n",
" <td>0.586957</td>\n",
" <td>52</td>\n",
" <td>8</td>\n",
" <td>17</td>\n",
" <td>284</td>\n",
" <td>17</td>\n",
" <td>52</td>\n",
" <td>11</td>\n",
" <td>646</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.072396</td>\n",
" <td>0.091404</td>\n",
" <td>-0.015105</td>\n",
" <td>0.429435</td>\n",
" <td>0.173962</td>\n",
" <td>0.013271</td>\n",
" <td>-0.074892</td>\n",
" <td>-0.482758</td>\n",
" <td>-0.245305</td>\n",
" <td>0.382167</td>\n",
" <td>0.020931</td>\n",
" <td>0.233962</td>\n",
" <td>0.244637</td>\n",
" <td>-0.047020</td>\n",
" <td>-0.027855</td>\n",
" <td>-0.114314</td>\n",
" <td>0.277663</td>\n",
" <td>-0.035372</td>\n",
" <td>0.119876</td>\n",
" <td>0.238165</td>\n",
" <td>-0.339771</td>\n",
" <td>-0.249214</td>\n",
" <td>-0.122470</td>\n",
" <td>0.167080</td>\n",
" <td>0.066360</td>\n",
" <td>0.246803</td>\n",
" <td>0.166796</td>\n",
" <td>0.117629</td>\n",
" <td>0.016187</td>\n",
" <td>-0.267358</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 2852 columns</p>\n",
"</div>"
],
"text/plain": [
" principal_maker principal_or_first_maker copyright_holder \\\n",
"0 157 158 25 \n",
"1 623 623 25 \n",
"2 1097 1096 25 \n",
"3 1114 1113 24 \n",
"4 648 648 25 \n",
"\n",
" acquisition_method acquisition_credit_line dating_period \\\n",
"0 7 217 19 \n",
"1 5 217 17 \n",
"2 2 84 18 \n",
"3 6 217 19 \n",
"4 8 217 17 \n",
"\n",
" dating_year_early dating_year_late Title_language Description_language \\\n",
"0 1850.0 1900.0 20 6 \n",
"1 1609.0 1633.0 6 5 \n",
"2 1779.0 1779.0 20 5 \n",
"3 1895.0 1898.0 20 5 \n",
"4 1700.0 1700.0 20 5 \n",
"\n",
" Long_title_language acquisition_year years_to_create years_to_acquisite \\\n",
"0 9 1994.0 50.0 94.0 \n",
"1 3 1798.0 24.0 165.0 \n",
"2 9 2002.0 0.0 223.0 \n",
"3 9 2009.0 3.0 111.0 \n",
"4 9 NaN 0.0 NaN \n",
"\n",
" size_w size_h size_t size_d area ar title_letter_number \\\n",
"0 108.0 167.0 NaN NaN 18036.0 1.546296 51 \n",
"1 241.0 297.0 NaN NaN 71577.0 1.232365 59 \n",
"2 215.0 270.0 NaN NaN 58050.0 1.255814 36 \n",
"3 159.0 116.0 NaN NaN 18444.0 0.729560 64 \n",
"4 184.0 108.0 NaN NaN 19872.0 0.586957 52 \n",
"\n",
" title_word_number More_title_language CE_principal_maker \\\n",
"0 8 17 14 \n",
"1 9 6 31 \n",
"2 7 17 66 \n",
"3 11 17 127 \n",
"4 8 17 284 \n",
"\n",
" sub_title_length more_title_length country_name maker_name roles \\\n",
"0 17 51 19 156 2 \n",
"1 19 59 19 621 3 \n",
"2 17 262 11 1098 4 \n",
"3 17 64 15 1115 2 \n",
"4 17 52 11 646 4 \n",
"\n",
" productionPlaces ... en_more_title_bert_20_mean_diff \\\n",
"0 84 ... 0.238445 \n",
"1 84 ... 0.628315 \n",
"2 56 ... 0.380194 \n",
"3 71 ... -0.076455 \n",
"4 0 ... -0.072396 \n",
"\n",
" en_more_title_bert_21_mean_diff en_more_title_bert_22_mean_diff \\\n",
"0 -0.737660 -0.507756 \n",
"1 0.269612 0.434108 \n",
"2 0.067271 0.231846 \n",
"3 -0.084601 0.263475 \n",
"4 0.091404 -0.015105 \n",
"\n",
" en_more_title_bert_23_mean_diff en_more_title_bert_24_mean_diff \\\n",
"0 -0.082622 0.512820 \n",
"1 0.789628 -0.167794 \n",
"2 0.020618 -0.488165 \n",
"3 -0.929086 0.645861 \n",
"4 0.429435 0.173962 \n",
"\n",
" en_more_title_bert_25_mean_diff en_more_title_bert_26_mean_diff \\\n",
"0 -0.881895 0.201240 \n",
"1 -0.221561 0.082386 \n",
"2 -0.508553 0.476026 \n",
"3 0.680632 0.528578 \n",
"4 0.013271 -0.074892 \n",
"\n",
" en_more_title_bert_27_mean_diff en_more_title_bert_28_mean_diff \\\n",
"0 0.333383 -0.196919 \n",
"1 -0.097810 -0.231660 \n",
"2 0.033216 -0.010701 \n",
"3 -0.093705 -0.237921 \n",
"4 -0.482758 -0.245305 \n",
"\n",
" en_more_title_bert_29_mean_diff en_more_title_bert_30_mean_diff \\\n",
"0 -0.198307 0.163790 \n",
"1 0.514047 0.195922 \n",
"2 -0.433889 -0.512983 \n",
"3 0.153844 0.255587 \n",
"4 0.382167 0.020931 \n",
"\n",
" en_more_title_bert_31_mean_diff en_more_title_bert_32_mean_diff \\\n",
"0 0.109100 -0.089420 \n",
"1 -0.090007 -0.530405 \n",
"2 0.122594 0.113122 \n",
"3 0.056234 0.323003 \n",
"4 0.233962 0.244637 \n",
"\n",
" en_more_title_bert_33_mean_diff en_more_title_bert_34_mean_diff \\\n",
"0 -0.037724 -0.397585 \n",
"1 -0.113854 -0.135597 \n",
"2 -0.282127 -0.363452 \n",
"3 0.109104 -0.241651 \n",
"4 -0.047020 -0.027855 \n",
"\n",
" en_more_title_bert_35_mean_diff en_more_title_bert_36_mean_diff \\\n",
"0 0.025229 -0.462651 \n",
"1 0.012477 -0.090950 \n",
"2 -0.248944 0.118981 \n",
"3 -0.010488 0.045534 \n",
"4 -0.114314 0.277663 \n",
"\n",
" en_more_title_bert_37_mean_diff en_more_title_bert_38_mean_diff \\\n",
"0 -0.038014 0.295584 \n",
"1 0.110446 0.150932 \n",
"2 -0.219124 -0.363030 \n",
"3 0.103212 0.049097 \n",
"4 -0.035372 0.119876 \n",
"\n",
" en_more_title_bert_39_mean_diff en_more_title_bert_40_mean_diff \\\n",
"0 -0.404498 0.118537 \n",
"1 -0.381709 0.491066 \n",
"2 0.222623 0.062438 \n",
"3 0.332585 -0.084924 \n",
"4 0.238165 -0.339771 \n",
"\n",
" en_more_title_bert_41_mean_diff en_more_title_bert_42_mean_diff \\\n",
"0 -0.171953 -0.055267 \n",
"1 -0.249866 -0.136790 \n",
"2 -0.117897 0.408852 \n",
"3 0.082461 0.070685 \n",
"4 -0.249214 -0.122470 \n",
"\n",
" en_more_title_bert_43_mean_diff en_more_title_bert_44_mean_diff \\\n",
"0 -0.073008 -0.403933 \n",
"1 0.268029 0.055939 \n",
"2 -0.892897 0.612290 \n",
"3 -0.005874 -0.083915 \n",
"4 0.167080 0.066360 \n",
"\n",
" en_more_title_bert_45_mean_diff en_more_title_bert_46_mean_diff \\\n",
"0 -0.263048 -0.380959 \n",
"1 -0.105957 0.012994 \n",
"2 -0.357699 -0.108913 \n",
"3 -0.467602 -0.312873 \n",
"4 0.246803 0.166796 \n",
"\n",
" en_more_title_bert_47_mean_diff en_more_title_bert_48_mean_diff \\\n",
"0 0.275352 0.129177 \n",
"1 -0.262101 -0.019628 \n",
"2 -0.263014 -0.043216 \n",
"3 -0.657591 0.204300 \n",
"4 0.117629 0.016187 \n",
"\n",
" en_more_title_bert_49_mean_diff \n",
"0 0.027217 \n",
"1 -0.146686 \n",
"2 -0.202586 \n",
"3 -0.026818 \n",
"4 -0.267358 \n",
"\n",
"[5 rows x 2852 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"columns = train.columns.tolist()\n",
"train.columns = [c.replace(\" \", \"_\").replace(\",\", \"\") for c in columns]\n",
"test.columns = [c.replace(\" \", \"_\").replace(\",\", \"\") for c in columns]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training Utilities"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"class TreeModel:\n",
" \"\"\"LGB/XGB/CatBoostのAPIを統一したwrapper\"\"\"\n",
" def __init__(self, model_type: str):\n",
" self.model_type = model_type\n",
" self.trn_data = None\n",
" self.val_data = None\n",
" self.model = None\n",
"\n",
" def train(self,\n",
" params: dict,\n",
" X_train: pd.DataFrame,\n",
" y_train: np.ndarray,\n",
" X_val: pd.DataFrame,\n",
" y_val: np.ndarray,\n",
" train_weight: Optional[np.ndarray] = None,\n",
" val_weight: Optional[np.ndarray] = None,\n",
" train_params: dict = {}):\n",
" if self.model_type == \"lgb\":\n",
" self.trn_data = lgb.Dataset(X_train, label=y_train, weight=train_weight)\n",
" self.val_data = lgb.Dataset(X_val, label=y_val, weight=val_weight)\n",
" self.model = lgb.train(params=params,\n",
" train_set=self.trn_data,\n",
" valid_sets=[self.trn_data, self.val_data],\n",
" **train_params)\n",
" elif self.model_type == \"xgb\":\n",
" self.trn_data = xgb.DMatrix(X_train, y_train, weight=train_weight)\n",
" self.val_data = xgb.DMatrix(X_val, y_val, weight=val_weight)\n",
" self.model = xgb.train(params=params,\n",
" dtrain=self.trn_data,\n",
" evals=[(self.trn_data, \"train\"), (self.val_data, \"val\")],\n",
" **train_params)\n",
" elif self.model_type == \"cat\":\n",
" self.trn_data = ctb.Pool(X_train, y_train, weight=train_weight)\n",
" self.val_data = ctb.Pool(X_val, y_val, weight=val_weight)\n",
" self.model = ctb.CatBoostRegressor(**params)\n",
" self.model.fit(self.trn_data,\n",
" eval_set=self.val_data,\n",
" **train_params)\n",
" else:\n",
" raise NotImplementedError\n",
" return self.model\n",
"\n",
" def predict(self, X: pd.DataFrame):\n",
" if self.model_type == \"lgb\":\n",
" return self.model.predict(X, num_iteration=self.model.best_iteration) # type: ignore\n",
" elif self.model_type == \"xgb\":\n",
" X_DM = xgb.DMatrix(X)\n",
" return self.model.predict(X_DM) # type: ignore\n",
" elif self.model_type == \"cat\":\n",
" return self.model.predict(X)\n",
" else:\n",
" raise NotImplementedError\n",
"\n",
" @property\n",
" def feature_names_(self):\n",
" if self.model_type == \"lgb\":\n",
" return self.model.feature_name()\n",
" elif self.model_type == \"xgb\":\n",
" return list(self.model.get_score(importance_type=\"gain\").keys())\n",
" elif self.model_type == \"cat\":\n",
" return self.model.feature_names_\n",
" else:\n",
" raise NotImplementedError\n",
"\n",
" @property\n",
" def feature_importances_(self):\n",
" if self.model_type == \"lgb\":\n",
" return self.model.feature_importance(importance_type=\"gain\")\n",
" elif self.model_type == \"xgb\":\n",
" return list(self.model.get_score(importance_type=\"gain\").values())\n",
" elif self.model_type == \"cat\":\n",
" return self.model.feature_importances_\n",
" else:\n",
" raise NotImplementedError"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LightGBMの学習"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************\n",
"Fold: 0\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.168144 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 652886\n",
"[LightGBM] [Info] Number of data points in the train set: 9620, number of used features: 2852\n",
"[LightGBM] [Info] Start training from score 1.652514\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.961238\tvalid_1's rmse: 1.10364\n",
"[400]\ttraining's rmse: 0.799989\tvalid_1's rmse: 1.04844\n",
"[600]\ttraining's rmse: 0.696931\tvalid_1's rmse: 1.0277\n",
"[800]\ttraining's rmse: 0.617296\tvalid_1's rmse: 1.01688\n",
"[1000]\ttraining's rmse: 0.552826\tvalid_1's rmse: 1.00951\n",
"[1200]\ttraining's rmse: 0.498445\tvalid_1's rmse: 1.00515\n",
"[1400]\ttraining's rmse: 0.452105\tvalid_1's rmse: 1.00305\n",
"[1600]\ttraining's rmse: 0.411127\tvalid_1's rmse: 1.00086\n",
"[1800]\ttraining's rmse: 0.375265\tvalid_1's rmse: 0.99895\n",
"[2000]\ttraining's rmse: 0.34367\tvalid_1's rmse: 0.997784\n",
"[2200]\ttraining's rmse: 0.31502\tvalid_1's rmse: 0.996861\n",
"[2400]\ttraining's rmse: 0.28972\tvalid_1's rmse: 0.996458\n",
"[2600]\ttraining's rmse: 0.267003\tvalid_1's rmse: 0.995831\n",
"[2800]\ttraining's rmse: 0.24618\tvalid_1's rmse: 0.995386\n",
"[3000]\ttraining's rmse: 0.227238\tvalid_1's rmse: 0.995018\n",
"[3200]\ttraining's rmse: 0.21034\tvalid_1's rmse: 0.994775\n",
"[3400]\ttraining's rmse: 0.194916\tvalid_1's rmse: 0.994531\n",
"[3600]\ttraining's rmse: 0.180951\tvalid_1's rmse: 0.994466\n",
"[3800]\ttraining's rmse: 0.168089\tvalid_1's rmse: 0.994378\n",
"Early stopping, best iteration is:\n",
"[3721]\ttraining's rmse: 0.173176\tvalid_1's rmse: 0.994293\n",
"[Model training] done in 211 s\n",
"score: 0.99429\n",
"****************************************************************************************************\n",
"Fold: 1\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.157250 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 652485\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 2852\n",
"[LightGBM] [Info] Start training from score 1.650154\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.963859\tvalid_1's rmse: 1.09513\n",
"[400]\ttraining's rmse: 0.80217\tvalid_1's rmse: 1.03868\n",
"[600]\ttraining's rmse: 0.69871\tvalid_1's rmse: 1.02065\n",
"[800]\ttraining's rmse: 0.619338\tvalid_1's rmse: 1.01021\n",
"[1000]\ttraining's rmse: 0.554343\tvalid_1's rmse: 1.0047\n",
"[1200]\ttraining's rmse: 0.499759\tvalid_1's rmse: 1.00033\n",
"[1400]\ttraining's rmse: 0.453382\tvalid_1's rmse: 0.997572\n",
"[1600]\ttraining's rmse: 0.412708\tvalid_1's rmse: 0.995029\n",
"[1800]\ttraining's rmse: 0.376851\tvalid_1's rmse: 0.993342\n",
"[2000]\ttraining's rmse: 0.344979\tvalid_1's rmse: 0.992095\n",
"[2200]\ttraining's rmse: 0.316454\tvalid_1's rmse: 0.991912\n",
"[2400]\ttraining's rmse: 0.291119\tvalid_1's rmse: 0.991138\n",
"[2600]\ttraining's rmse: 0.268286\tvalid_1's rmse: 0.990751\n",
"[2800]\ttraining's rmse: 0.247642\tvalid_1's rmse: 0.990075\n",
"[3000]\ttraining's rmse: 0.229089\tvalid_1's rmse: 0.989719\n",
"[3200]\ttraining's rmse: 0.212054\tvalid_1's rmse: 0.989338\n",
"[3400]\ttraining's rmse: 0.196752\tvalid_1's rmse: 0.989111\n",
"Early stopping, best iteration is:\n",
"[3370]\ttraining's rmse: 0.199027\tvalid_1's rmse: 0.988978\n",
"[Model training] done in 192 s\n",
"score: 0.98898\n",
"****************************************************************************************************\n",
"Fold: 2\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.169727 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 652818\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 2852\n",
"[LightGBM] [Info] Start training from score 1.660950\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.968771\tvalid_1's rmse: 1.08063\n",
"[400]\ttraining's rmse: 0.807212\tvalid_1's rmse: 1.01911\n",
"[600]\ttraining's rmse: 0.703562\tvalid_1's rmse: 0.997274\n",
"[800]\ttraining's rmse: 0.623507\tvalid_1's rmse: 0.986749\n",
"[1000]\ttraining's rmse: 0.558045\tvalid_1's rmse: 0.980347\n",
"[1200]\ttraining's rmse: 0.502857\tvalid_1's rmse: 0.976691\n",
"[1400]\ttraining's rmse: 0.455692\tvalid_1's rmse: 0.974088\n",
"[1600]\ttraining's rmse: 0.41474\tvalid_1's rmse: 0.971913\n",
"[1800]\ttraining's rmse: 0.378852\tvalid_1's rmse: 0.97033\n",
"[2000]\ttraining's rmse: 0.346881\tvalid_1's rmse: 0.969094\n",
"[2200]\ttraining's rmse: 0.318587\tvalid_1's rmse: 0.968341\n",
"[2400]\ttraining's rmse: 0.293054\tvalid_1's rmse: 0.967672\n",
"[2600]\ttraining's rmse: 0.270117\tvalid_1's rmse: 0.967332\n",
"[2800]\ttraining's rmse: 0.249322\tvalid_1's rmse: 0.966883\n",
"[3000]\ttraining's rmse: 0.230728\tvalid_1's rmse: 0.967021\n",
"[3200]\ttraining's rmse: 0.21372\tvalid_1's rmse: 0.966768\n",
"[3400]\ttraining's rmse: 0.198355\tvalid_1's rmse: 0.966548\n",
"[3600]\ttraining's rmse: 0.184348\tvalid_1's rmse: 0.966439\n",
"[3800]\ttraining's rmse: 0.17141\tvalid_1's rmse: 0.966364\n",
"Early stopping, best iteration is:\n",
"[3682]\ttraining's rmse: 0.178907\tvalid_1's rmse: 0.96628\n",
"[Model training] done in 209 s\n",
"score: 0.96628\n",
"****************************************************************************************************\n",
"Fold: 3\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.154524 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 652845\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 2852\n",
"[LightGBM] [Info] Start training from score 1.649983\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.963021\tvalid_1's rmse: 1.09616\n",
"[400]\ttraining's rmse: 0.799888\tvalid_1's rmse: 1.04314\n",
"[600]\ttraining's rmse: 0.695567\tvalid_1's rmse: 1.02629\n",
"[800]\ttraining's rmse: 0.615188\tvalid_1's rmse: 1.01724\n",
"[1000]\ttraining's rmse: 0.549904\tvalid_1's rmse: 1.011\n",
"[1200]\ttraining's rmse: 0.495222\tvalid_1's rmse: 1.00873\n",
"[1400]\ttraining's rmse: 0.448634\tvalid_1's rmse: 1.00697\n",
"[1600]\ttraining's rmse: 0.408156\tvalid_1's rmse: 1.00588\n",
"[1800]\ttraining's rmse: 0.372544\tvalid_1's rmse: 1.005\n",
"[2000]\ttraining's rmse: 0.341284\tvalid_1's rmse: 1.00432\n",
"[2200]\ttraining's rmse: 0.313364\tvalid_1's rmse: 1.00377\n",
"[2400]\ttraining's rmse: 0.288194\tvalid_1's rmse: 1.00335\n",
"Early stopping, best iteration is:\n",
"[2333]\ttraining's rmse: 0.296253\tvalid_1's rmse: 1.00326\n",
"[Model training] done in 140 s\n",
"score: 1.00326\n",
"****************************************************************************************************\n",
"Fold: 4\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.160723 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 652899\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 2852\n",
"[LightGBM] [Info] Start training from score 1.661089\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.967775\tvalid_1's rmse: 1.08996\n",
"[400]\ttraining's rmse: 0.806633\tvalid_1's rmse: 1.02299\n",
"[600]\ttraining's rmse: 0.702949\tvalid_1's rmse: 1.00043\n",
"[800]\ttraining's rmse: 0.623056\tvalid_1's rmse: 0.989787\n",
"[1000]\ttraining's rmse: 0.55784\tvalid_1's rmse: 0.982877\n",
"[1200]\ttraining's rmse: 0.503415\tvalid_1's rmse: 0.979167\n",
"[1400]\ttraining's rmse: 0.456296\tvalid_1's rmse: 0.976719\n",
"[1600]\ttraining's rmse: 0.415392\tvalid_1's rmse: 0.974697\n",
"[1800]\ttraining's rmse: 0.379089\tvalid_1's rmse: 0.973769\n",
"[2000]\ttraining's rmse: 0.347238\tvalid_1's rmse: 0.972628\n",
"[2200]\ttraining's rmse: 0.318646\tvalid_1's rmse: 0.971826\n",
"[2400]\ttraining's rmse: 0.292671\tvalid_1's rmse: 0.971548\n",
"[2600]\ttraining's rmse: 0.269654\tvalid_1's rmse: 0.971173\n",
"[2800]\ttraining's rmse: 0.24894\tvalid_1's rmse: 0.970853\n",
"[3000]\ttraining's rmse: 0.229915\tvalid_1's rmse: 0.970897\n",
"Early stopping, best iteration is:\n",
"[2942]\ttraining's rmse: 0.235306\tvalid_1's rmse: 0.97074\n",
"[Model training] done in 171 s\n",
"score: 0.97074\n"
]
}
],
"source": [
"feature_importances_lgb = pd.DataFrame()\n",
"oof_lgb = pd.DataFrame()\n",
"preds_lgb = np.zeros(len(test))\n",
"scores = 0.0\n",
"\n",
"kf = KFold(n_splits=5, random_state=1213, shuffle=True)\n",
"for fold, (trn_idx, val_idx) in enumerate(kf.split(train)):\n",
" print(\"*\" * 100)\n",
" print(f\"Fold: {fold}\")\n",
" \n",
" X_trn = train.loc[trn_idx, :].reset_index(drop=True)\n",
" X_val = train.loc[val_idx, :].reset_index(drop=True)\n",
" \n",
" y_trn = y.loc[trn_idx].reset_index(drop=True).values\n",
" y_val = y.loc[val_idx].reset_index(drop=True).values\n",
" \n",
" obj_id_trn = object_id.loc[trn_idx].values\n",
" obj_id_val = object_id.loc[val_idx].values\n",
"\n",
" model = TreeModel(model_type=\"lgb\")\n",
" params = {\n",
" \"objective\": \"regression\",\n",
" \"num_leaves\": 32,\n",
" \"max_depth\": -1,\n",
" \"learning_rate\": 0.01,\n",
" \"boosting\": \"gbdt\",\n",
" \"bagging_freq\": 1,\n",
" \"bagging_fraction\": 0.8,\n",
" \"bagging_seed\": 1213,\n",
" \"reg_alpha\": 0.1,\n",
" \"reg_lambda\": 0.3,\n",
" \"colsample_bytree\": 0.7,\n",
" \"metric\": \"rmse\",\n",
" \"num_threads\": 6,\n",
" \"deterministic\": \"true\"\n",
" }\n",
" \n",
" with timer(\"Model training\"):\n",
" model.train(params=params,\n",
" X_train=X_trn,\n",
" y_train=y_trn,\n",
" X_val=X_val,\n",
" y_val=y_val,\n",
" train_params={\n",
" \"num_boost_round\": 20000,\n",
" \"early_stopping_rounds\": 200,\n",
" \"verbose_eval\": 200\n",
" })\n",
" fi_tmp = pd.DataFrame()\n",
" fi_tmp[\"feature\"] = model.feature_names_\n",
" fi_tmp[\"importance\"] = model.feature_importances_\n",
" fi_tmp[\"fold\"] = fold\n",
" feature_importances_lgb = feature_importances_lgb.append(fi_tmp)\n",
" \n",
" val_pred = model.predict(X_val)\n",
" score = np.sqrt(mean_squared_error(y_true=y_val, y_pred=val_pred))\n",
" scores += score / 5\n",
" \n",
" print(f\"score: {score:.5f}\")\n",
" \n",
" oof_lgb = oof_lgb.append(pd.DataFrame({\n",
" \"object_id\": obj_id_val,\n",
" \"preds\": val_pred\n",
" }))\n",
" \n",
" pred = model.predict(test)\n",
" preds_lgb += pred / 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check performance"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"score: 0.98481\n"
]
}
],
"source": [
"target_df = pd.DataFrame({\n",
" \"object_id\": object_id,\n",
" \"target\": y\n",
"})\n",
"target_df = target_df.merge(oof_lgb, on=\"object_id\")\n",
"score = np.sqrt(mean_squared_error(y_true=target_df[\"target\"], y_pred=target_df[\"preds\"]))\n",
"print(f\"score: {score:.5f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Second stage"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"fi_lgb = feature_importances_lgb.copy()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"n_features = fi_lgb[\"feature\"].nunique()\n",
"fi_top = fi_lgb.groupby(\"feature\")[\"importance\"].mean().sort_values(ascending=False).head(\n",
" 400).index.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************\n",
"Fold: 0\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007441 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 94656\n",
"[LightGBM] [Info] Number of data points in the train set: 9620, number of used features: 400\n",
"[LightGBM] [Info] Start training from score 1.652514\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.991085\tvalid_1's rmse: 1.09898\n",
"[400]\ttraining's rmse: 0.854828\tvalid_1's rmse: 1.04034\n",
"[600]\ttraining's rmse: 0.770183\tvalid_1's rmse: 1.0189\n",
"[800]\ttraining's rmse: 0.703707\tvalid_1's rmse: 1.00658\n",
"[1000]\ttraining's rmse: 0.648139\tvalid_1's rmse: 0.999598\n",
"[1200]\ttraining's rmse: 0.599781\tvalid_1's rmse: 0.994088\n",
"[1400]\ttraining's rmse: 0.557218\tvalid_1's rmse: 0.990081\n",
"[1600]\ttraining's rmse: 0.51863\tvalid_1's rmse: 0.988212\n",
"[1800]\ttraining's rmse: 0.483945\tvalid_1's rmse: 0.986904\n",
"[2000]\ttraining's rmse: 0.452432\tvalid_1's rmse: 0.984879\n",
"[2200]\ttraining's rmse: 0.423264\tvalid_1's rmse: 0.983774\n",
"[2400]\ttraining's rmse: 0.397082\tvalid_1's rmse: 0.983181\n",
"[2600]\ttraining's rmse: 0.372941\tvalid_1's rmse: 0.982748\n",
"[2800]\ttraining's rmse: 0.35032\tvalid_1's rmse: 0.981812\n",
"[3000]\ttraining's rmse: 0.329687\tvalid_1's rmse: 0.981094\n",
"[3200]\ttraining's rmse: 0.310425\tvalid_1's rmse: 0.9809\n",
"[3400]\ttraining's rmse: 0.292724\tvalid_1's rmse: 0.980637\n",
"[3600]\ttraining's rmse: 0.276384\tvalid_1's rmse: 0.980179\n",
"Early stopping, best iteration is:\n",
"[3567]\ttraining's rmse: 0.278982\tvalid_1's rmse: 0.980094\n",
"[Model training] done in 12 s\n",
"score: 0.98009\n",
"****************************************************************************************************\n",
"Fold: 1\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007606 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 94624\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 400\n",
"[LightGBM] [Info] Start training from score 1.650154\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.994836\tvalid_1's rmse: 1.09068\n",
"[400]\ttraining's rmse: 0.859133\tvalid_1's rmse: 1.03184\n",
"[600]\ttraining's rmse: 0.77444\tvalid_1's rmse: 1.01038\n",
"[800]\ttraining's rmse: 0.708295\tvalid_1's rmse: 0.998557\n",
"[1000]\ttraining's rmse: 0.652683\tvalid_1's rmse: 0.991432\n",
"[1200]\ttraining's rmse: 0.603998\tvalid_1's rmse: 0.986135\n",
"[1400]\ttraining's rmse: 0.561004\tvalid_1's rmse: 0.98359\n",
"[1600]\ttraining's rmse: 0.522767\tvalid_1's rmse: 0.980278\n",
"[1800]\ttraining's rmse: 0.487502\tvalid_1's rmse: 0.978411\n",
"[2000]\ttraining's rmse: 0.455821\tvalid_1's rmse: 0.976709\n",
"[2200]\ttraining's rmse: 0.426772\tvalid_1's rmse: 0.975419\n",
"[2400]\ttraining's rmse: 0.400272\tvalid_1's rmse: 0.97532\n",
"[2600]\ttraining's rmse: 0.375746\tvalid_1's rmse: 0.974718\n",
"[2800]\ttraining's rmse: 0.353136\tvalid_1's rmse: 0.974072\n",
"[3000]\ttraining's rmse: 0.332195\tvalid_1's rmse: 0.973375\n",
"[3200]\ttraining's rmse: 0.312943\tvalid_1's rmse: 0.973224\n",
"[3400]\ttraining's rmse: 0.294994\tvalid_1's rmse: 0.972674\n",
"[3600]\ttraining's rmse: 0.278326\tvalid_1's rmse: 0.972302\n",
"[3800]\ttraining's rmse: 0.262747\tvalid_1's rmse: 0.972\n",
"[4000]\ttraining's rmse: 0.2483\tvalid_1's rmse: 0.971849\n",
"[4200]\ttraining's rmse: 0.235018\tvalid_1's rmse: 0.97174\n",
"[4400]\ttraining's rmse: 0.22257\tvalid_1's rmse: 0.971289\n",
"[4600]\ttraining's rmse: 0.211\tvalid_1's rmse: 0.97124\n",
"Early stopping, best iteration is:\n",
"[4569]\ttraining's rmse: 0.212729\tvalid_1's rmse: 0.971139\n",
"[Model training] done in 15 s\n",
"score: 0.97114\n",
"****************************************************************************************************\n",
"Fold: 2\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009623 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 94668\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 400\n",
"[LightGBM] [Info] Start training from score 1.660950\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 1.00049\tvalid_1's rmse: 1.06935\n",
"[400]\ttraining's rmse: 0.864346\tvalid_1's rmse: 1.00603\n",
"[600]\ttraining's rmse: 0.779123\tvalid_1's rmse: 0.982226\n",
"[800]\ttraining's rmse: 0.712264\tvalid_1's rmse: 0.969089\n",
"[1000]\ttraining's rmse: 0.656162\tvalid_1's rmse: 0.962094\n",
"[1200]\ttraining's rmse: 0.607207\tvalid_1's rmse: 0.956842\n",
"[1400]\ttraining's rmse: 0.564117\tvalid_1's rmse: 0.953962\n",
"[1600]\ttraining's rmse: 0.525361\tvalid_1's rmse: 0.951459\n",
"[1800]\ttraining's rmse: 0.489884\tvalid_1's rmse: 0.949813\n",
"[2000]\ttraining's rmse: 0.458221\tvalid_1's rmse: 0.948718\n",
"[2200]\ttraining's rmse: 0.429063\tvalid_1's rmse: 0.947627\n",
"[2400]\ttraining's rmse: 0.402275\tvalid_1's rmse: 0.94687\n",
"[2600]\ttraining's rmse: 0.377516\tvalid_1's rmse: 0.946118\n",
"[2800]\ttraining's rmse: 0.354916\tvalid_1's rmse: 0.945402\n",
"[3000]\ttraining's rmse: 0.333836\tvalid_1's rmse: 0.944914\n",
"[3200]\ttraining's rmse: 0.314305\tvalid_1's rmse: 0.944394\n",
"[3400]\ttraining's rmse: 0.296296\tvalid_1's rmse: 0.943865\n",
"[3600]\ttraining's rmse: 0.279596\tvalid_1's rmse: 0.943644\n",
"[3800]\ttraining's rmse: 0.264006\tvalid_1's rmse: 0.943592\n",
"Early stopping, best iteration is:\n",
"[3684]\ttraining's rmse: 0.272861\tvalid_1's rmse: 0.943461\n",
"[Model training] done in 12 s\n",
"score: 0.94346\n",
"****************************************************************************************************\n",
"Fold: 3\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007819 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 94669\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 400\n",
"[LightGBM] [Info] Start training from score 1.649983\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.994708\tvalid_1's rmse: 1.09121\n",
"[400]\ttraining's rmse: 0.857482\tvalid_1's rmse: 1.03525\n",
"[600]\ttraining's rmse: 0.772586\tvalid_1's rmse: 1.01551\n",
"[800]\ttraining's rmse: 0.70552\tvalid_1's rmse: 1.0031\n",
"[1000]\ttraining's rmse: 0.649751\tvalid_1's rmse: 0.996501\n",
"[1200]\ttraining's rmse: 0.600847\tvalid_1's rmse: 0.991449\n",
"[1400]\ttraining's rmse: 0.557916\tvalid_1's rmse: 0.988551\n",
"[1600]\ttraining's rmse: 0.519596\tvalid_1's rmse: 0.986645\n",
"[1800]\ttraining's rmse: 0.484457\tvalid_1's rmse: 0.984932\n",
"[2000]\ttraining's rmse: 0.452624\tvalid_1's rmse: 0.984136\n",
"[2200]\ttraining's rmse: 0.423617\tvalid_1's rmse: 0.983081\n",
"[2400]\ttraining's rmse: 0.39693\tvalid_1's rmse: 0.982716\n",
"[2600]\ttraining's rmse: 0.372334\tvalid_1's rmse: 0.98235\n",
"[2800]\ttraining's rmse: 0.349759\tvalid_1's rmse: 0.98179\n",
"[3000]\ttraining's rmse: 0.328872\tvalid_1's rmse: 0.981609\n",
"[3200]\ttraining's rmse: 0.309528\tvalid_1's rmse: 0.981163\n",
"[3400]\ttraining's rmse: 0.291626\tvalid_1's rmse: 0.98072\n",
"[3600]\ttraining's rmse: 0.275113\tvalid_1's rmse: 0.980402\n",
"[3800]\ttraining's rmse: 0.259832\tvalid_1's rmse: 0.980214\n",
"[4000]\ttraining's rmse: 0.245614\tvalid_1's rmse: 0.980064\n",
"[4200]\ttraining's rmse: 0.232428\tvalid_1's rmse: 0.979815\n",
"[4400]\ttraining's rmse: 0.220144\tvalid_1's rmse: 0.979842\n",
"[4600]\ttraining's rmse: 0.208499\tvalid_1's rmse: 0.979763\n",
"[4800]\ttraining's rmse: 0.197671\tvalid_1's rmse: 0.979327\n",
"[5000]\ttraining's rmse: 0.187592\tvalid_1's rmse: 0.979303\n",
"[5200]\ttraining's rmse: 0.17815\tvalid_1's rmse: 0.979228\n",
"Early stopping, best iteration is:\n",
"[5108]\ttraining's rmse: 0.182403\tvalid_1's rmse: 0.979084\n",
"[Model training] done in 17 s\n",
"score: 0.97908\n",
"****************************************************************************************************\n",
"Fold: 4\n",
"[Model training] start\n",
"[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009582 seconds.\n",
"You can set `force_col_wise=true` to remove the overhead.\n",
"[LightGBM] [Info] Total Bins 94686\n",
"[LightGBM] [Info] Number of data points in the train set: 9621, number of used features: 400\n",
"[LightGBM] [Info] Start training from score 1.661089\n",
"Training until validation scores don't improve for 200 rounds\n",
"[200]\ttraining's rmse: 0.999935\tvalid_1's rmse: 1.08213\n",
"[400]\ttraining's rmse: 0.864884\tvalid_1's rmse: 1.0111\n",
"[600]\ttraining's rmse: 0.780765\tvalid_1's rmse: 0.984234\n",
"[800]\ttraining's rmse: 0.713983\tvalid_1's rmse: 0.969316\n",
"[1000]\ttraining's rmse: 0.657564\tvalid_1's rmse: 0.959639\n",
"[1200]\ttraining's rmse: 0.60914\tvalid_1's rmse: 0.95452\n",
"[1400]\ttraining's rmse: 0.565978\tvalid_1's rmse: 0.951225\n",
"[1600]\ttraining's rmse: 0.52701\tvalid_1's rmse: 0.946661\n",
"[1800]\ttraining's rmse: 0.492001\tvalid_1's rmse: 0.944216\n",
"[2000]\ttraining's rmse: 0.459955\tvalid_1's rmse: 0.942718\n",
"[2200]\ttraining's rmse: 0.430782\tvalid_1's rmse: 0.941782\n",
"[2400]\ttraining's rmse: 0.403717\tvalid_1's rmse: 0.941461\n",
"[2600]\ttraining's rmse: 0.378993\tvalid_1's rmse: 0.94034\n",
"[2800]\ttraining's rmse: 0.356384\tvalid_1's rmse: 0.94002\n",
"Early stopping, best iteration is:\n",
"[2708]\ttraining's rmse: 0.366715\tvalid_1's rmse: 0.939826\n",
"[Model training] done in 9 s\n",
"score: 0.93983\n"
]
}
],
"source": [
"use_cols = fi_top\n",
"\n",
"feature_importances_lgb = pd.DataFrame()\n",
"oof_lgb = pd.DataFrame()\n",
"preds_lgb = np.zeros(len(test))\n",
"scores = 0.0\n",
"\n",
"kf = KFold(n_splits=5, random_state=1213, shuffle=True)\n",
"for fold, (trn_idx, val_idx) in enumerate(kf.split(train)):\n",
" print(\"*\" * 100)\n",
" print(f\"Fold: {fold}\")\n",
" \n",
" X_trn = train.loc[trn_idx, use_cols].reset_index(drop=True)\n",
" X_val = train.loc[val_idx, use_cols].reset_index(drop=True)\n",
" \n",
" y_trn = y.loc[trn_idx].reset_index(drop=True).values\n",
" y_val = y.loc[val_idx].reset_index(drop=True).values\n",
" \n",
" obj_id_trn = object_id.loc[trn_idx].values\n",
" obj_id_val = object_id.loc[val_idx].values\n",
"\n",
" model = TreeModel(model_type=\"lgb\")\n",
" params = {\n",
" \"objective\": \"regression\",\n",
" \"num_leaves\": 32,\n",
" \"min_data_in_leaf\": 64,\n",
" \"max_depth\": -1,\n",
" \"learning_rate\": 0.01,\n",
" \"boosting\": \"gbdt\",\n",
" \"bagging_freq\": 1,\n",
" \"bagging_fraction\": 0.6,\n",
" \"bagging_seed\": 1213,\n",
" \"reg_alpha\": 0.3,\n",
" # \"reg_lambda\": 0.1,\n",
" \"colsample_bytree\": 0.5,\n",
" \"metric\": \"rmse\",\n",
" \"num_threads\": 20,\n",
" \"deterministic\": \"true\"\n",
" }\n",
" \n",
" with timer(\"Model training\"):\n",
" model.train(params=params,\n",
" X_train=X_trn,\n",
" y_train=y_trn,\n",
" X_val=X_val,\n",
" y_val=y_val,\n",
" train_params={\n",
" \"num_boost_round\": 20000,\n",
" \"early_stopping_rounds\": 200,\n",
" \"verbose_eval\": 200\n",
" })\n",
" fi_tmp = pd.DataFrame()\n",
" fi_tmp[\"feature\"] = model.feature_names_\n",
" fi_tmp[\"importance\"] = model.feature_importances_\n",
" fi_tmp[\"fold\"] = fold\n",
" feature_importances_lgb = feature_importances_lgb.append(fi_tmp)\n",
" \n",
" val_pred = model.predict(X_val)\n",
" score = np.sqrt(mean_squared_error(y_true=y_val, y_pred=val_pred))\n",
" scores += score / 5\n",
" \n",
" print(f\"score: {score:.5f}\")\n",
" \n",
" oof_lgb = oof_lgb.append(pd.DataFrame({\n",
" \"object_id\": obj_id_val,\n",
" \"preds\": val_pred\n",
" }))\n",
" \n",
" pred = model.predict(test[use_cols])\n",
" preds_lgb += pred / 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check Performance"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"score: 0.96288\n"
]
}
],
"source": [
"target_df = pd.DataFrame({\n",
" \"object_id\": object_id,\n",
" \"target\": y\n",
"})\n",
"target_df = target_df.merge(oof_lgb, on=\"object_id\")\n",
"score = np.sqrt(mean_squared_error(y_true=target_df[\"target\"], y_pred=target_df[\"preds\"]))\n",
"print(f\"score: {score:.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x3240 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"order = list(\n",
" feature_importances_lgb.groupby(\"feature\").mean().sort_values(\"importance\", ascending=False).head(300).index)\n",
"\n",
"plt.figure(figsize=(15, 45))\n",
"sns.barplot(x=\"importance\", y=\"feature\", data=feature_importances_lgb, order=order)\n",
"plt.title(\"LGBM importance\")\n",
"plt.tight_layout()\n",
"plt.savefig(OUTDIR / f\"feature_importance_lgb.png\")"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"oof_lgb.to_csv(OUTDIR / \"oof.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"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>likes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>68.500677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9.184876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>22.090073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>68.801811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>58.470058</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" likes\n",
"0 68.500677\n",
"1 9.184876\n",
"2 22.090073\n",
"3 68.801811\n",
"4 58.470058"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub = pd.read_csv(\"../input/atmacup10_dataset/atmacup10__sample_submission.csv\")\n",
"sub.head()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"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>likes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.437075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>27.290293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.876768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.149143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.998179</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" likes\n",
"0 0.437075\n",
"1 27.290293\n",
"2 0.876768\n",
"3 2.149143\n",
"4 0.998179"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub[\"likes\"] = np.expm1(preds_lgb)\n",
"sub.loc[sub[\"likes\"] < 0, \"likes\"] = 0\n",
"sub.to_csv(OUTDIR / \"submission.csv\", index=False)\n",
"sub.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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