├── script.py
├── README.md
├── feature_extraction.py
├── preprocessing.py
└── summary.ipynb


/script.py:
--------------------------------------------------------------------------------
 1 | from preprocessing import normalize
 2 | from feature_extraction import vectorize
 3 | import pandas as pd
 4 | from gensim.models import FastText
 5 | import pickle
 6 | 
 7 | test = pd.read_parquet('data/task1_test_for_user.parquet')
 8 | pipe = pickle.load(open('clf_task1', 'rb'))
 9 | 
10 | test.item_name = normalize(test.item_name)
11 | X_wv = vectorize(test.item_name)
12 | pred = pipe.predict(X_wv)
13 | 
14 | res = pd.DataFrame(pred, columns=['pred'])
15 | res['id'] = test['id']
16 | 
17 | res[['id', 'pred']].to_csv('answers.csv', index=None)
18 | 


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/README.md:
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1 | # GoodsClassifier
2 | Всем привет, в этом проекте я покажу, как я решал первую задачу в соревновании [Data Fusion Contest](https://boosters.pro/championship/data_fusion/overview), в которой необходимо было классифицировать товары по данным из чека. Данные представляют собой набор размеченных и неразмеченных чеков. Каждый чек имеет такую информацию как описание товара, его категорию, цена товара, количество и пр. Для предсказания категории мы будем пользоваться только описанием товара (item_name). [Приятного ресёрча!](https://github.com/gorodion/GoodsClassifier/blob/master/summary.ipynb)
3 | 


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/feature_extraction.py:
--------------------------------------------------------------------------------
 1 | import gensim
 2 | import numpy as np
 3 | import pandas as pd
 4 | 
 5 | def word_averaging(wv, words):
 6 |     mean = np.zeros((wv.vector_size,))
 7 |     
 8 |     for word in words:
 9 |         mean += wv.get_vector(word)
10 | 
11 |     mean = gensim.matutils.unitvec(mean)
12 |     return mean
13 | 
14 | def word_averaging_list(wv, text_list):
15 |     return np.vstack([word_averaging(wv, review) for review in text_list])
16 |   
17 |   
18 | def vectorize(test: pd.Series):
19 |     test = test.apply(lambda x: [i for i in x.split() if len(i) > 1])
20 |     model = FastText.load('ft.model', mmap='r')
21 |     X_wv = word_averaging_list(model.wv, test)
22 |     X_wv = X_wv.astype('float16')
23 |     return X_wv
24 | 


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/preprocessing.py:
--------------------------------------------------------------------------------
 1 | import re
 2 | import pandas as pd
 3 | 
 4 | def make_trans():
 5 |     a = 'a b c d e f g h i j k l m n o p q r s t u v w x y z ё'.split()
 6 |     b = 'а в с д е ф г н и ж к л м н о р к р с т у в в х у з е'.split()
 7 |     trans_dict = dict(zip(a, b))
 8 |     trans_table = ''.join(a).maketrans(trans_dict)
 9 |     return trans_table
10 | 
11 | def normalize(ser: pd.Series):
12 | #   "СокДобрый" -> "Сок Добрый"
13 |     camel_case_pat = re.compile(r'([а-яa-z])([А-ЯA-Z])')
14 | #   "lmno" -> "лмно"
15 |     trans_table = make_trans()
16 | #   "14х15х30" -> "DxDxD"
17 |     dxdxd_pat = re.compile(r'((?:\d+\s*[х\*]\s*){2}\d+)')
18 | #   "1.2 15,5" -> "1p2 15p5"  
19 |     digit_pat = re.compile(r'(\d+)[\.,](\d+)')
20 | #   "15 мл" -> "15мл"
21 |     unit = 'мг|г|гр|кг|мл|л|шт'
22 |     unit_pat = re.compile(fr'((?:\d+p)?\d+)\s*({unit})\b')
23 | #   "ж/б ст/б" -> "жб стб"
24 |     w_w_pat = re.compile(r'\b([а-я]{1,2})/([а-я]{1,2})\b')
25 | #   "a b c d" -> "abcd"
26 |     glue_pat = re.compile(r'(?<=(?<!\w)\w) (?=\w(?!\w))', re.UNICODE)
27 |     
28 |     return ser \
29 |             .str.replace(camel_case_pat, r'\1 \2') \
30 |             .str.lower() \
31 |             .str.replace(r'ъ\b', '') \
32 |             .str.translate(trans_table) \
33 |             .str.replace(dxdxd_pat, ' DxDxD ') \
34 |             .str.replace('№', ' NUM ') \
35 |             .str.replace('%', ' PERC ') \
36 |             .str.replace(digit_pat, r' \1p\2 ') \
37 |             .str.replace(unit_pat, r' \1\2 ') \
38 |             .str.replace(w_w_pat, r' \1\2 ') \
39 |             .str.replace(r'[\W_]', ' ') \
40 |             .str.replace(glue_pat, '')


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/summary.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "requested-stranger",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "# DataFusionHack. Task &#8544;"
   9 |    ]
  10 |   },
  11 |   {
  12 |    "cell_type": "markdown",
  13 |    "id": "exotic-motorcycle",
  14 |    "metadata": {},
  15 |    "source": [
  16 |     "## Introduction"
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "markdown",
  21 |    "id": "running-firmware",
  22 |    "metadata": {},
  23 |    "source": [
  24 |     "Всем привет, в этом ноутбуке я покажу, как я решал первую задачу, в которой необходимо было классифицировать товары по данным из чека. Данные представляют собой набор размеченных и неразмеченных чеков. Каждый чек имеет такую информацию как описание товара, его категорию, цена товара, количество и пр. Для предсказания категории мы будем пользоваться только описанием товара (item_name). Приятного ресёрча!"
  25 |    ]
  26 |   },
  27 |   {
  28 |    "cell_type": "code",
  29 |    "execution_count": 1,
  30 |    "id": "collaborative-content",
  31 |    "metadata": {},
  32 |    "outputs": [],
  33 |    "source": [
  34 |     "# путь до файла\n",
  35 |     "data_train_path = '../data_fusion_train.parquet'"
  36 |    ]
  37 |   },
  38 |   {
  39 |    "cell_type": "markdown",
  40 |    "id": "coordinated-cooling",
  41 |    "metadata": {},
  42 |    "source": [
  43 |     "## Importing"
  44 |    ]
  45 |   },
  46 |   {
  47 |    "cell_type": "markdown",
  48 |    "id": "coordinate-identifier",
  49 |    "metadata": {},
  50 |    "source": [
  51 |     "Сделаем все необходимые импорты"
  52 |    ]
  53 |   },
  54 |   {
  55 |    "cell_type": "code",
  56 |    "execution_count": null,
  57 |    "id": "scenic-breeding",
  58 |    "metadata": {},
  59 |    "outputs": [],
  60 |    "source": [
  61 |     "!pip install numpy==1.18.5\n",
  62 |     "!pip install pandas==1.1.5\n",
  63 |     "!pip install scikit-learn==0.23.2\n",
  64 |     "!pip install gensim==3.8.3\n",
  65 |     "!pip install xgboost==1.2.1\n",
  66 |     "!pip install nltk==3.2.4"
  67 |    ]
  68 |   },
  69 |   {
  70 |    "cell_type": "code",
  71 |    "execution_count": 2,
  72 |    "id": "governmental-aspect",
  73 |    "metadata": {},
  74 |    "outputs": [
  75 |     {
  76 |      "name": "stderr",
  77 |      "output_type": "stream",
  78 |      "text": [
  79 |       "c:\\users\\rodion\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tqdm\\std.py:702: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
  80 |       "  from pandas import Panel\n"
  81 |      ]
  82 |     },
  83 |     {
  84 |      "name": "stdout",
  85 |      "output_type": "stream",
  86 |      "text": [
  87 |       "[nltk_data] Downloading package stopwords to\n",
  88 |       "[nltk_data]     C:\\Users\\Rodion\\AppData\\Roaming\\nltk_data...\n",
  89 |       "[nltk_data]   Package stopwords is already up-to-date!\n"
  90 |      ]
  91 |     }
  92 |    ],
  93 |    "source": [
  94 |     "# Data Analysis\n",
  95 |     "import pandas as pd\n",
  96 |     "import numpy as np\n",
  97 |     "import scipy\n",
  98 |     "import matplotlib.pyplot as plt\n",
  99 |     "import seaborn as sns\n",
 100 |     "\n",
 101 |     "# Service tools\n",
 102 |     "from tqdm.notebook import tqdm, trange\n",
 103 |     "import logging, sys, os, pickle\n",
 104 |     "from pathlib import Path\n",
 105 |     "tqdm.pandas()\n",
 106 |     "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
 107 |     "\n",
 108 |     "# Work with text\n",
 109 |     "import re\n",
 110 |     "import nltk\n",
 111 |     "from nltk.corpus import stopwords\n",
 112 |     "import gensim\n",
 113 |     "from gensim.models import Word2Vec\n",
 114 |     "from gensim.models.fasttext import FastText \n",
 115 |     "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
 116 |     "nltk.download('stopwords')\n",
 117 |     "\n",
 118 |     "# Sklearn Pipelines & Preprocessing\n",
 119 |     "from sklearn.base import BaseEstimator, TransformerMixin\n",
 120 |     "from sklearn.pipeline import make_pipeline\n",
 121 |     "from sklearn.feature_extraction import DictVectorizer\n",
 122 |     "from sklearn.preprocessing import StandardScaler, LabelBinarizer\n",
 123 |     "from sklearn.decomposition import TruncatedSVD, PCA\n",
 124 |     "from sklearn.model_selection import *\n",
 125 |     "\n",
 126 |     "# Models & Metrics\n",
 127 |     "from sklearn.cluster import KMeans\n",
 128 |     "from sklearn.linear_model import RidgeClassifier, LogisticRegression\n",
 129 |     "from sklearn.neighbors import KNeighborsClassifier\n",
 130 |     "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
 131 |     "from sklearn.svm import LinearSVC, SVC\n",
 132 |     "from sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB\n",
 133 |     "from xgboost import XGBClassifier\n",
 134 |     "from sklearn.metrics import f1_score, confusion_matrix, classification_report\n",
 135 |     "\n",
 136 |     "# метрика задачи\n",
 137 |     "def score(y_true, y_val):\n",
 138 |     "    return f1_score(y_true, y_val, average='weighted')\n",
 139 |     "\n",
 140 |     "# датафрейм, содержащий категорию n\n",
 141 |     "def samples(df, n):\n",
 142 |     "    return df[df.category_id == n]"
 143 |    ]
 144 |   },
 145 |   {
 146 |    "cell_type": "markdown",
 147 |    "id": "discrete-material",
 148 |    "metadata": {},
 149 |    "source": [
 150 |     "## Reading data"
 151 |    ]
 152 |   },
 153 |   {
 154 |    "cell_type": "markdown",
 155 |    "id": "minus-duplicate",
 156 |    "metadata": {},
 157 |    "source": [
 158 |     "Прочитаем данные и сохраним размеченную и неразмеченную части в разных файлах"
 159 |    ]
 160 |   },
 161 |   {
 162 |    "cell_type": "code",
 163 |    "execution_count": 4,
 164 |    "id": "incorporated-jurisdiction",
 165 |    "metadata": {},
 166 |    "outputs": [
 167 |     {
 168 |      "name": "stderr",
 169 |      "output_type": "stream",
 170 |      "text": [
 171 |       "2021-03-22 13:51:19,430 : WARNING : To save RAM restart Notebook and then skip this cell\n"
 172 |      ]
 173 |     }
 174 |    ],
 175 |    "source": [
 176 |     "def read_save(read_path):\n",
 177 |     "    df = pd.read_parquet(read_path)[['item_name', 'category_id']]\n",
 178 |     "    df = df.drop_duplicates('item_name')\n",
 179 |     "    os.makedirs('data', exist_ok=True)\n",
 180 |     "    df[df.category_id == -1]['item_name'].to_csv('data/backup.csv', index=False)\n",
 181 |     "    df[df.category_id != -1].to_csv('data/backup_label.csv', index=False)\n",
 182 |     "    logging.warning('To save RAM restart Notebook and then skip this cell')\n",
 183 |     "\n",
 184 |     "df = read_save(data_train_path)"
 185 |    ]
 186 |   },
 187 |   {
 188 |    "cell_type": "markdown",
 189 |    "id": "pregnant-communications",
 190 |    "metadata": {},
 191 |    "source": [
 192 |     "Теперь посмотрим на данные"
 193 |    ]
 194 |   },
 195 |   {
 196 |    "cell_type": "code",
 197 |    "execution_count": 5,
 198 |    "id": "differential-triple",
 199 |    "metadata": {},
 200 |    "outputs": [],
 201 |    "source": [
 202 |     "df = pd.read_csv('data/backup_label.csv')\n",
 203 |     "df_full = pd.read_csv('data/backup.csv')\n",
 204 |     "\n",
 205 |     "for spam_df in [df, df_full]:\n",
 206 |     "    spam_df.dropna(inplace=True)"
 207 |    ]
 208 |   },
 209 |   {
 210 |    "cell_type": "code",
 211 |    "execution_count": 6,
 212 |    "id": "useful-johnston",
 213 |    "metadata": {},
 214 |    "outputs": [
 215 |     {
 216 |      "data": {
 217 |       "text/html": [
 218 |        "<div>\n",
 219 |        "<style scoped>\n",
 220 |        "    .dataframe tbody tr th:only-of-type {\n",
 221 |        "        vertical-align: middle;\n",
 222 |        "    }\n",
 223 |        "\n",
 224 |        "    .dataframe tbody tr th {\n",
 225 |        "        vertical-align: top;\n",
 226 |        "    }\n",
 227 |        "\n",
 228 |        "    .dataframe thead th {\n",
 229 |        "        text-align: right;\n",
 230 |        "    }\n",
 231 |        "</style>\n",
 232 |        "<table border=\"1\" class=\"dataframe\">\n",
 233 |        "  <thead>\n",
 234 |        "    <tr style=\"text-align: right;\">\n",
 235 |        "      <th></th>\n",
 236 |        "      <th>item_name</th>\n",
 237 |        "    </tr>\n",
 238 |        "  </thead>\n",
 239 |        "  <tbody>\n",
 240 |        "    <tr>\n",
 241 |        "      <th>2437976</th>\n",
 242 |        "      <td>Сапоги школьные д/д Flois-Kids (Размер: 34)</td>\n",
 243 |        "    </tr>\n",
 244 |        "    <tr>\n",
 245 |        "      <th>882416</th>\n",
 246 |        "      <td>Шок.Озера Т/У 100г Молочный /малин шт</td>\n",
 247 |        "    </tr>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>2297689</th>\n",
 250 |        "      <td>Кальмар в масле Щупальца</td>\n",
 251 |        "    </tr>\n",
 252 |        "    <tr>\n",
 253 |        "      <th>1742849</th>\n",
 254 |        "      <td>pellesana масло косметическое 100мл</td>\n",
 255 |        "    </tr>\n",
 256 |        "    <tr>\n",
 257 |        "      <th>780364</th>\n",
 258 |        "      <td>Обезжириватель Полихим 0,5л шт</td>\n",
 259 |        "    </tr>\n",
 260 |        "    <tr>\n",
 261 |        "      <th>1745151</th>\n",
 262 |        "      <td>1 864 055 Обувь весна-лето-осень ABRICOT</td>\n",
 263 |        "    </tr>\n",
 264 |        "    <tr>\n",
 265 |        "      <th>1905276</th>\n",
 266 |        "      <td>Огурцы Луховицкие Россия     2.456кг*97.00</td>\n",
 267 |        "    </tr>\n",
 268 |        "    <tr>\n",
 269 |        "      <th>1159080</th>\n",
 270 |        "      <td>Штуцер для шланга VALTEC 3/4 внутр. *20 мм</td>\n",
 271 |        "    </tr>\n",
 272 |        "    <tr>\n",
 273 |        "      <th>1733705</th>\n",
 274 |        "      <td>Лизун № \"Роза\"</td>\n",
 275 |        "    </tr>\n",
 276 |        "    <tr>\n",
 277 |        "      <th>583559</th>\n",
 278 |        "      <td>БЗМЖ Молоко пастеризов 3.7% 1400мл ПЭТ Домик в...</td>\n",
 279 |        "    </tr>\n",
 280 |        "  </tbody>\n",
 281 |        "</table>\n",
 282 |        "</div>"
 283 |       ],
 284 |       "text/plain": [
 285 |        "                                                 item_name\n",
 286 |        "2437976        Сапоги школьные д/д Flois-Kids (Размер: 34)\n",
 287 |        "882416               Шок.Озера Т/У 100г Молочный /малин шт\n",
 288 |        "2297689                           Кальмар в масле Щупальца\n",
 289 |        "1742849                pellesana масло косметическое 100мл\n",
 290 |        "780364                      Обезжириватель Полихим 0,5л шт\n",
 291 |        "1745151           1 864 055 Обувь весна-лето-осень ABRICOT\n",
 292 |        "1905276         Огурцы Луховицкие Россия     2.456кг*97.00\n",
 293 |        "1159080         Штуцер для шланга VALTEC 3/4 внутр. *20 мм\n",
 294 |        "1733705                                     Лизун № \"Роза\"\n",
 295 |        "583559   БЗМЖ Молоко пастеризов 3.7% 1400мл ПЭТ Домик в..."
 296 |       ]
 297 |      },
 298 |      "execution_count": 6,
 299 |      "metadata": {},
 300 |      "output_type": "execute_result"
 301 |     }
 302 |    ],
 303 |    "source": [
 304 |     "df_full.sample(10)"
 305 |    ]
 306 |   },
 307 |   {
 308 |    "cell_type": "markdown",
 309 |    "id": "interstate-familiar",
 310 |    "metadata": {},
 311 |    "source": [
 312 |     "Посмотрим распределение классов"
 313 |    ]
 314 |   },
 315 |   {
 316 |    "cell_type": "code",
 317 |    "execution_count": 7,
 318 |    "id": "featured-smell",
 319 |    "metadata": {},
 320 |    "outputs": [
 321 |     {
 322 |      "data": {
 323 |       "text/plain": [
 324 |        "84     7070\n",
 325 |        "71     4760\n",
 326 |        "78     2866\n",
 327 |        "83     2856\n",
 328 |        "0      2352\n",
 329 |        "       ... \n",
 330 |        "102      19\n",
 331 |        "101      16\n",
 332 |        "46       15\n",
 333 |        "100      14\n",
 334 |        "97       13\n",
 335 |        "Name: category_id, Length: 96, dtype: int64"
 336 |       ]
 337 |      },
 338 |      "execution_count": 7,
 339 |      "metadata": {},
 340 |      "output_type": "execute_result"
 341 |     }
 342 |    ],
 343 |    "source": [
 344 |     "df.category_id.value_counts()"
 345 |    ]
 346 |   },
 347 |   {
 348 |    "cell_type": "code",
 349 |    "execution_count": 8,
 350 |    "id": "adult-species",
 351 |    "metadata": {},
 352 |    "outputs": [
 353 |     {
 354 |      "data": {
 355 |       "image/png": 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\n",
 356 |       "text/plain": [
 357 |        "<Figure size 1440x288 with 1 Axes>"
 358 |       ]
 359 |      },
 360 |      "metadata": {
 361 |       "needs_background": "light"
 362 |      },
 363 |      "output_type": "display_data"
 364 |     }
 365 |    ],
 366 |    "source": [
 367 |     "df.category_id.value_counts().plot.bar(figsize=(20, 4))\n",
 368 |     "plt.title('Распределение по классам');"
 369 |    ]
 370 |   },
 371 |   {
 372 |    "cell_type": "markdown",
 373 |    "id": "freelance-capture",
 374 |    "metadata": {},
 375 |    "source": [
 376 |     "Посмотрим самые частые категории"
 377 |    ]
 378 |   },
 379 |   {
 380 |    "cell_type": "code",
 381 |    "execution_count": 9,
 382 |    "id": "aggressive-anthropology",
 383 |    "metadata": {},
 384 |    "outputs": [
 385 |     {
 386 |      "data": {
 387 |       "text/html": [
 388 |        "<div>\n",
 389 |        "<style scoped>\n",
 390 |        "    .dataframe tbody tr th:only-of-type {\n",
 391 |        "        vertical-align: middle;\n",
 392 |        "    }\n",
 393 |        "\n",
 394 |        "    .dataframe tbody tr th {\n",
 395 |        "        vertical-align: top;\n",
 396 |        "    }\n",
 397 |        "\n",
 398 |        "    .dataframe thead th {\n",
 399 |        "        text-align: right;\n",
 400 |        "    }\n",
 401 |        "</style>\n",
 402 |        "<table border=\"1\" class=\"dataframe\">\n",
 403 |        "  <thead>\n",
 404 |        "    <tr style=\"text-align: right;\">\n",
 405 |        "      <th></th>\n",
 406 |        "      <th>item_name</th>\n",
 407 |        "      <th>category_id</th>\n",
 408 |        "    </tr>\n",
 409 |        "  </thead>\n",
 410 |        "  <tbody>\n",
 411 |        "    <tr>\n",
 412 |        "      <th>4</th>\n",
 413 |        "      <td>Хлеб на СЫВОРОТКЕ 350г</td>\n",
 414 |        "      <td>84</td>\n",
 415 |        "    </tr>\n",
 416 |        "    <tr>\n",
 417 |        "      <th>5</th>\n",
 418 |        "      <td>Сосиска в тесте с сыром 1шт ГЕ</td>\n",
 419 |        "      <td>84</td>\n",
 420 |        "    </tr>\n",
 421 |        "    <tr>\n",
 422 |        "      <th>13</th>\n",
 423 |        "      <td>Курник 1 шт.</td>\n",
 424 |        "      <td>84</td>\n",
 425 |        "    </tr>\n",
 426 |        "    <tr>\n",
 427 |        "      <th>19</th>\n",
 428 |        "      <td>Вафли с топленым молоком вес. 1кг Тортугалия</td>\n",
 429 |        "      <td>84</td>\n",
 430 |        "    </tr>\n",
 431 |        "    <tr>\n",
 432 |        "      <th>28</th>\n",
 433 |        "      <td>Кф.Золотой Степ 50г с орехом</td>\n",
 434 |        "      <td>84</td>\n",
 435 |        "    </tr>\n",
 436 |        "  </tbody>\n",
 437 |        "</table>\n",
 438 |        "</div>"
 439 |       ],
 440 |       "text/plain": [
 441 |        "                                       item_name  category_id\n",
 442 |        "4                         Хлеб на СЫВОРОТКЕ 350г           84\n",
 443 |        "5                 Сосиска в тесте с сыром 1шт ГЕ           84\n",
 444 |        "13                                  Курник 1 шт.           84\n",
 445 |        "19  Вафли с топленым молоком вес. 1кг Тортугалия           84\n",
 446 |        "28                  Кф.Золотой Степ 50г с орехом           84"
 447 |       ]
 448 |      },
 449 |      "execution_count": 9,
 450 |      "metadata": {},
 451 |      "output_type": "execute_result"
 452 |     }
 453 |    ],
 454 |    "source": [
 455 |     "samples(df, 84).head()"
 456 |    ]
 457 |   },
 458 |   {
 459 |    "cell_type": "code",
 460 |    "execution_count": 10,
 461 |    "id": "binary-velvet",
 462 |    "metadata": {},
 463 |    "outputs": [
 464 |     {
 465 |      "data": {
 466 |       "text/html": [
 467 |        "<div>\n",
 468 |        "<style scoped>\n",
 469 |        "    .dataframe tbody tr th:only-of-type {\n",
 470 |        "        vertical-align: middle;\n",
 471 |        "    }\n",
 472 |        "\n",
 473 |        "    .dataframe tbody tr th {\n",
 474 |        "        vertical-align: top;\n",
 475 |        "    }\n",
 476 |        "\n",
 477 |        "    .dataframe thead th {\n",
 478 |        "        text-align: right;\n",
 479 |        "    }\n",
 480 |        "</style>\n",
 481 |        "<table border=\"1\" class=\"dataframe\">\n",
 482 |        "  <thead>\n",
 483 |        "    <tr style=\"text-align: right;\">\n",
 484 |        "      <th></th>\n",
 485 |        "      <th>item_name</th>\n",
 486 |        "      <th>category_id</th>\n",
 487 |        "    </tr>\n",
 488 |        "  </thead>\n",
 489 |        "  <tbody>\n",
 490 |        "    <tr>\n",
 491 |        "      <th>1</th>\n",
 492 |        "      <td>Компот из изюма, 114 ккал</td>\n",
 493 |        "      <td>71</td>\n",
 494 |        "    </tr>\n",
 495 |        "    <tr>\n",
 496 |        "      <th>2</th>\n",
 497 |        "      <td>Макаронные изделия отварные (масло сливочное),...</td>\n",
 498 |        "      <td>71</td>\n",
 499 |        "    </tr>\n",
 500 |        "    <tr>\n",
 501 |        "      <th>15</th>\n",
 502 |        "      <td>Филе бедра куриного жареное 3</td>\n",
 503 |        "      <td>71</td>\n",
 504 |        "    </tr>\n",
 505 |        "    <tr>\n",
 506 |        "      <th>30</th>\n",
 507 |        "      <td>МОРС 200  мл</td>\n",
 508 |        "      <td>71</td>\n",
 509 |        "    </tr>\n",
 510 |        "    <tr>\n",
 511 |        "      <th>41</th>\n",
 512 |        "      <td>Kотлета Kуриная Домашняя 100 г</td>\n",
 513 |        "      <td>71</td>\n",
 514 |        "    </tr>\n",
 515 |        "  </tbody>\n",
 516 |        "</table>\n",
 517 |        "</div>"
 518 |       ],
 519 |       "text/plain": [
 520 |        "                                            item_name  category_id\n",
 521 |        "1                           Компот из изюма, 114 ккал           71\n",
 522 |        "2   Макаронные изделия отварные (масло сливочное),...           71\n",
 523 |        "15                      Филе бедра куриного жареное 3           71\n",
 524 |        "30                                       МОРС 200  мл           71\n",
 525 |        "41                     Kотлета Kуриная Домашняя 100 г           71"
 526 |       ]
 527 |      },
 528 |      "execution_count": 10,
 529 |      "metadata": {},
 530 |      "output_type": "execute_result"
 531 |     }
 532 |    ],
 533 |    "source": [
 534 |     "samples(df, 71).head()"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "markdown",
 539 |    "id": "infrared-eight",
 540 |    "metadata": {},
 541 |    "source": [
 542 |     "## Preprocessing"
 543 |    ]
 544 |   },
 545 |   {
 546 |    "cell_type": "markdown",
 547 |    "id": "proud-dragon",
 548 |    "metadata": {},
 549 |    "source": [
 550 |     "Препроцессинг включает следующие шаги:\n",
 551 |     "* CamelCase сплиттинг: \"СокДобрый\" -> \"Сок Добрый\"\n",
 552 |     "* Нижний регистр\n",
 553 |     "* Транслитерация: \"lmnoё\" -> \"лмное\"\n",
 554 |     "* 50x30x45 -> DxDxD\n",
 555 |     "* [\"№\", \"%\"] -> ['NUM', 'PERC']\n",
 556 |     "* Замены величин: [\"1.5\", \"1,3 кг\" 3,5г -> [\"1p5\", \"1p3кг\" \"3p5г\"]\n",
 557 |     "* сокращения со слешами заменяются: [\"ж/б\", \"тп/р]->[\"жб\", тпр\"]\n",
 558 |     "* Не буквы и цифры заменяются на пробелы\n",
 559 |     "* Склейка одиночных символов: \"а б в\" -> \"абв\""
 560 |    ]
 561 |   },
 562 |   {
 563 |    "cell_type": "code",
 564 |    "execution_count": 11,
 565 |    "id": "magnetic-burke",
 566 |    "metadata": {},
 567 |    "outputs": [],
 568 |    "source": [
 569 |     "# функция для создания словаря транслитераций\n",
 570 |     "def make_trans():\n",
 571 |     "    a = 'a b c d e f g h i j k l m n o p q r s t u v w x y z ё'.split()\n",
 572 |     "    b = 'а в с д е ф г н и ж к л м н о р к р с т у в в х у з е'.split()\n",
 573 |     "    trans_dict = dict(zip(a, b))\n",
 574 |     "    trans_table = ''.join(a).maketrans(trans_dict)\n",
 575 |     "    return trans_table\n",
 576 |     "\n",
 577 |     "def normalize(ser: pd.Series):\n",
 578 |     "#   \"СокДобрый\" -> \"Сок Добрый\"\n",
 579 |     "    camel_case_pat = re.compile(r'([а-яa-z])([А-ЯA-Z])')\n",
 580 |     "#   \"lmno\" -> \"лмно\"\n",
 581 |     "    trans_table = make_trans()\n",
 582 |     "#   \"14х15х30\" -> \"DxDxD\"\n",
 583 |     "    dxdxd_pat = re.compile(r'((?:\\d+\\s*[х\\*]\\s*){2}\\d+)')\n",
 584 |     "#   \"1.2 15,5\" -> \"1p2 15p5\"  \n",
 585 |     "    digit_pat = re.compile(r'(\\d+)[\\.,](\\d+)')\n",
 586 |     "#   \"15 мл\" -> \"15мл\"\n",
 587 |     "    unit = 'мг|г|гр|кг|мл|л|шт'\n",
 588 |     "    unit_pat = re.compile(fr'((?:\\d+p)?\\d+)\\s*({unit})\\b')\n",
 589 |     "#   \"ж/б ст/б\" -> \"жб стб\"\n",
 590 |     "    w_w_pat = re.compile(r'\\b([а-я]{1,2})/([а-я]{1,2})\\b')\n",
 591 |     "#   \"a b c d\" -> \"abcd\"\n",
 592 |     "    glue_pat = re.compile(r'(?<=(?<!\\w)\\w) (?=\\w(?!\\w))', re.UNICODE)\n",
 593 |     "    \n",
 594 |     "    return ser \\\n",
 595 |     "            .str.replace(camel_case_pat, r'\\1 \\2') \\\n",
 596 |     "            .str.lower() \\\n",
 597 |     "            .str.replace(r'ъ\\b', '') \\\n",
 598 |     "            .str.translate(trans_table) \\\n",
 599 |     "            .str.replace(dxdxd_pat, ' DxDxD ') \\\n",
 600 |     "            .str.replace('№', ' NUM ') \\\n",
 601 |     "            .str.replace('%', ' PERC ') \\\n",
 602 |     "            .str.replace(digit_pat, r' \\1p\\2 ') \\\n",
 603 |     "            .str.replace(unit_pat, r' \\1\\2 ') \\\n",
 604 |     "            .str.replace(w_w_pat, r' \\1\\2 ') \\\n",
 605 |     "            .str.replace(r'[\\W_]', ' ') \\\n",
 606 |     "            .str.replace(glue_pat, '')"
 607 |    ]
 608 |   },
 609 |   {
 610 |    "cell_type": "code",
 611 |    "execution_count": 12,
 612 |    "id": "limiting-briefs",
 613 |    "metadata": {},
 614 |    "outputs": [],
 615 |    "source": [
 616 |     "for spam_df in [df, df_full]:\n",
 617 |     "    spam_df['item_name'] = normalize(spam_df['item_name'])"
 618 |    ]
 619 |   },
 620 |   {
 621 |    "cell_type": "markdown",
 622 |    "id": "southeast-steel",
 623 |    "metadata": {},
 624 |    "source": [
 625 |     "Посмотрим на результат"
 626 |    ]
 627 |   },
 628 |   {
 629 |    "cell_type": "code",
 630 |    "execution_count": 14,
 631 |    "id": "developed-payroll",
 632 |    "metadata": {},
 633 |    "outputs": [
 634 |     {
 635 |      "data": {
 636 |       "text/html": [
 637 |        "<div>\n",
 638 |        "<style scoped>\n",
 639 |        "    .dataframe tbody tr th:only-of-type {\n",
 640 |        "        vertical-align: middle;\n",
 641 |        "    }\n",
 642 |        "\n",
 643 |        "    .dataframe tbody tr th {\n",
 644 |        "        vertical-align: top;\n",
 645 |        "    }\n",
 646 |        "\n",
 647 |        "    .dataframe thead th {\n",
 648 |        "        text-align: right;\n",
 649 |        "    }\n",
 650 |        "</style>\n",
 651 |        "<table border=\"1\" class=\"dataframe\">\n",
 652 |        "  <thead>\n",
 653 |        "    <tr style=\"text-align: right;\">\n",
 654 |        "      <th></th>\n",
 655 |        "      <th>item_name</th>\n",
 656 |        "    </tr>\n",
 657 |        "  </thead>\n",
 658 |        "  <tbody>\n",
 659 |        "    <tr>\n",
 660 |        "      <th>2408405</th>\n",
 661 |        "      <td>мор эскимо мм  63г</td>\n",
 662 |        "    </tr>\n",
 663 |        "    <tr>\n",
 664 |        "      <th>461664</th>\n",
 665 |        "      <td>эфес пилснер  0p3</td>\n",
 666 |        "    </tr>\n",
 667 |        "    <tr>\n",
 668 |        "      <th>2316442</th>\n",
 669 |        "      <td>хек тушка  1кг</td>\n",
 670 |        "    </tr>\n",
 671 |        "    <tr>\n",
 672 |        "      <th>344862</th>\n",
 673 |        "      <td>капучино бл</td>\n",
 674 |        "    </tr>\n",
 675 |        "    <tr>\n",
 676 |        "      <th>1178767</th>\n",
 677 |        "      <td>килька черноморская неразделанная в том  соусе...</td>\n",
 678 |        "    </tr>\n",
 679 |        "    <tr>\n",
 680 |        "      <th>2832448</th>\n",
 681 |        "      <td>ментос  лимонад   1шт</td>\n",
 682 |        "    </tr>\n",
 683 |        "    <tr>\n",
 684 |        "      <th>2710457</th>\n",
 685 |        "      <td>саморез универс  4p5 х20 оцинк</td>\n",
 686 |        "    </tr>\n",
 687 |        "    <tr>\n",
 688 |        "      <th>51850</th>\n",
 689 |        "      <td>фромилид уно таб  пролонг  дя  по  плен   500м...</td>\n",
 690 |        "    </tr>\n",
 691 |        "    <tr>\n",
 692 |        "      <th>513585</th>\n",
 693 |        "      <td>пивной нап амстердам навигатор  6p8  PERC    0...</td>\n",
 694 |        "    </tr>\n",
 695 |        "    <tr>\n",
 696 |        "      <th>2446381</th>\n",
 697 |        "      <td>семена укроп мамонт   2г</td>\n",
 698 |        "    </tr>\n",
 699 |        "  </tbody>\n",
 700 |        "</table>\n",
 701 |        "</div>"
 702 |       ],
 703 |       "text/plain": [
 704 |        "                                                 item_name\n",
 705 |        "2408405                                мор эскимо мм  63г \n",
 706 |        "461664                                  эфес пилснер  0p3 \n",
 707 |        "2316442                                    хек тушка  1кг \n",
 708 |        "344862                                         капучино бл\n",
 709 |        "1178767  килька черноморская неразделанная в том  соусе...\n",
 710 |        "2832448                             ментос  лимонад   1шт \n",
 711 |        "2710457                    саморез универс  4p5 х20 оцинк \n",
 712 |        "51850    фромилид уно таб  пролонг  дя  по  плен   500м...\n",
 713 |        "513585   пивной нап амстердам навигатор  6p8  PERC    0...\n",
 714 |        "2446381                         семена укроп мамонт   2г  "
 715 |       ]
 716 |      },
 717 |      "execution_count": 14,
 718 |      "metadata": {},
 719 |      "output_type": "execute_result"
 720 |     }
 721 |    ],
 722 |    "source": [
 723 |     "df_full.sample(10)"
 724 |    ]
 725 |   },
 726 |   {
 727 |    "cell_type": "markdown",
 728 |    "id": "pediatric-accordance",
 729 |    "metadata": {},
 730 |    "source": [
 731 |     "## Fasttext"
 732 |    ]
 733 |   },
 734 |   {
 735 |    "cell_type": "markdown",
 736 |    "id": "thermal-preparation",
 737 |    "metadata": {},
 738 |    "source": [
 739 |     "### Fitting"
 740 |    ]
 741 |   },
 742 |   {
 743 |    "cell_type": "markdown",
 744 |    "id": "southeast-model",
 745 |    "metadata": {},
 746 |    "source": [
 747 |     "Для извлечения фичей воспользуемся FastText. Изначально на его месте был Word2Vec, но позже благодаря [@dremovd](https://github.com/dremovd) осознал, что FastText справится здесь лучше. \n",
 748 |     "\n",
 749 |     "Обучать будем на всех неразмеченных данных, чтобы не было лишних утечек. Для начала сделаем сплиттинг строки на список слов, избавившися от однобуквенных слов"
 750 |    ]
 751 |   },
 752 |   {
 753 |    "cell_type": "code",
 754 |    "execution_count": 15,
 755 |    "id": "direct-heavy",
 756 |    "metadata": {},
 757 |    "outputs": [],
 758 |    "source": [
 759 |     "df_full.item_name = df_full.item_name.apply(lambda x: [i for i in x.split() if len(i) > 1])"
 760 |    ]
 761 |   },
 762 |   {
 763 |    "cell_type": "markdown",
 764 |    "id": "stainless-kelly",
 765 |    "metadata": {},
 766 |    "source": [
 767 |     "Метод построения векторов (sg), размер вектора (size) и минимальное количество слов в словаре (min_count) были подобраны эмпирическим путём. Стоит отметить, что sg=1 или Skip-Grams mode, когда fasttext (а изначально word2vec) пытается предсказать по слову остальной контент, работает значительно лучше, чем sg=0 или CBOW mode. В качестве аргумента window я поставил максимальную длину списка в корпусе. Для демонстрации я поставил количество эпох (iter) равным 10, хотя оптимальным является 30. Такой bucket выставил для экономии памяти на жёстком диске (и чтобы вместился в сабмит)). Последний параметр workers - это допустимое количество потоков, у меня это число 12. Из-за него кстати нет смысла выставлять seed, так как всё сбивается и воспроизвести решение можно только с workers=1 и seed=КАКОЕ-ТО_ЧИСЛО (конкретно в этой реализации от gensim)."
 768 |    ]
 769 |   },
 770 |   {
 771 |    "cell_type": "code",
 772 |    "execution_count": null,
 773 |    "id": "checked-triumph",
 774 |    "metadata": {
 775 |     "scrolled": true,
 776 |     "tags": []
 777 |    },
 778 |    "outputs": [],
 779 |    "source": [
 780 |     "model = FastText(df_full.item_name, size=200, window=35, min_count=3, workers=12, iter=10, sg=1, bucket=400_000)"
 781 |    ]
 782 |   },
 783 |   {
 784 |    "cell_type": "markdown",
 785 |    "id": "minus-cleaners",
 786 |    "metadata": {},
 787 |    "source": [
 788 |     "Сохраним модель"
 789 |    ]
 790 |   },
 791 |   {
 792 |    "cell_type": "code",
 793 |    "execution_count": 18,
 794 |    "id": "illegal-advocate",
 795 |    "metadata": {},
 796 |    "outputs": [
 797 |     {
 798 |      "name": "stderr",
 799 |      "output_type": "stream",
 800 |      "text": [
 801 |       "2021-03-22 14:05:18,005 : INFO : saving FastText object under ft.model, separately None\n",
 802 |       "2021-03-22 14:05:18,006 : INFO : storing np array 'vectors' to ft.model.wv.vectors.npy\n",
 803 |       "2021-03-22 14:05:18,826 : INFO : storing np array 'vectors_vocab' to ft.model.wv.vectors_vocab.npy\n",
 804 |       "2021-03-22 14:05:19,498 : INFO : storing np array 'vectors_ngrams' to ft.model.wv.vectors_ngrams.npy\n",
 805 |       "2021-03-22 14:05:21,723 : INFO : not storing attribute vectors_norm\n",
 806 |       "2021-03-22 14:05:21,724 : INFO : not storing attribute vectors_vocab_norm\n",
 807 |       "2021-03-22 14:05:21,724 : INFO : not storing attribute vectors_ngrams_norm\n",
 808 |       "2021-03-22 14:05:21,725 : INFO : not storing attribute buckets_word\n",
 809 |       "2021-03-22 14:05:21,726 : INFO : storing np array 'syn1neg' to ft.model.trainables.syn1neg.npy\n",
 810 |       "2021-03-22 14:05:22,500 : INFO : storing np array 'vectors_vocab_lockf' to ft.model.trainables.vectors_vocab_lockf.npy\n",
 811 |       "2021-03-22 14:05:23,187 : INFO : storing np array 'vectors_ngrams_lockf' to ft.model.trainables.vectors_ngrams_lockf.npy\n",
 812 |       "2021-03-22 14:05:25,274 : INFO : saved ft.model\n"
 813 |      ]
 814 |     }
 815 |    ],
 816 |    "source": [
 817 |     "model.save('ft.model')"
 818 |    ]
 819 |   },
 820 |   {
 821 |    "cell_type": "markdown",
 822 |    "id": "searching-encoding",
 823 |    "metadata": {},
 824 |    "source": [
 825 |     "### Model optimization"
 826 |    ]
 827 |   },
 828 |   {
 829 |    "cell_type": "markdown",
 830 |    "id": "brilliant-librarian",
 831 |    "metadata": {},
 832 |    "source": [
 833 |     "Больше всего в fasttext отнимается памяти из-за ngram, на данный момент модель занимает 1Гб (!) на жёстком диске. С этим надо было как-то бороться и первое что пришло в голову: заменить float32, в котором хранятся вектора на float16. Это сожмёт нашу модель как минимум в 2 раза. Можете убедиться: это не повлияет на качество классификатора"
 834 |    ]
 835 |   },
 836 |   {
 837 |    "cell_type": "markdown",
 838 |    "id": "figured-davis",
 839 |    "metadata": {},
 840 |    "source": [
 841 |     "Посмотрим на файлы, относящиеся к FastText"
 842 |    ]
 843 |   },
 844 |   {
 845 |    "cell_type": "code",
 846 |    "execution_count": 19,
 847 |    "id": "advanced-holiday",
 848 |    "metadata": {},
 849 |    "outputs": [
 850 |     {
 851 |      "data": {
 852 |       "text/plain": [
 853 |        "['ft.model.trainables.syn1neg.npy',\n",
 854 |        " 'ft.model.trainables.vectors_ngrams_lockf.npy',\n",
 855 |        " 'ft.model.trainables.vectors_vocab_lockf.npy',\n",
 856 |        " 'ft.model.wv.vectors.npy',\n",
 857 |        " 'ft.model.wv.vectors_ngrams.npy',\n",
 858 |        " 'ft.model.wv.vectors_vocab.npy']"
 859 |       ]
 860 |      },
 861 |      "execution_count": 19,
 862 |      "metadata": {},
 863 |      "output_type": "execute_result"
 864 |     }
 865 |    ],
 866 |    "source": [
 867 |     "list(map(str,Path('.').glob('*.npy')))"
 868 |    ]
 869 |   },
 870 |   {
 871 |    "cell_type": "code",
 872 |    "execution_count": 20,
 873 |    "id": "annual-hearing",
 874 |    "metadata": {},
 875 |    "outputs": [
 876 |     {
 877 |      "name": "stderr",
 878 |      "output_type": "stream",
 879 |      "text": [
 880 |       "2021-03-22 14:05:26,489 : INFO : ft.model.trainables.syn1neg.npy\n",
 881 |       "2021-03-22 14:05:26,565 : INFO : min/max values before: (-2.090169, 2.0418446)\n",
 882 |       "2021-03-22 14:05:27,033 : INFO : min/max values after: (-2.09, 2.041)\n",
 883 |       "2021-03-22 14:05:27,322 : INFO : __________________________________\n",
 884 |       "2021-03-22 14:05:27,323 : INFO : ft.model.trainables.vectors_ngrams_lockf.npy\n",
 885 |       "2021-03-22 14:05:27,500 : INFO : min/max values before: (1.0, 1.0)\n",
 886 |       "2021-03-22 14:05:28,249 : INFO : min/max values after: (1.0, 1.0)\n",
 887 |       "2021-03-22 14:05:29,133 : INFO : __________________________________\n",
 888 |       "2021-03-22 14:05:29,134 : INFO : ft.model.trainables.vectors_vocab_lockf.npy\n",
 889 |       "2021-03-22 14:05:29,220 : INFO : min/max values before: (1.0, 1.0)\n",
 890 |       "2021-03-22 14:05:29,518 : INFO : min/max values after: (1.0, 1.0)\n",
 891 |       "2021-03-22 14:05:29,820 : INFO : __________________________________\n",
 892 |       "2021-03-22 14:05:29,820 : INFO : ft.model.wv.vectors.npy\n",
 893 |       "2021-03-22 14:05:29,899 : INFO : min/max values before: (-2.720382, 2.59036)\n",
 894 |       "2021-03-22 14:05:30,374 : INFO : min/max values after: (-2.72, 2.59)\n",
 895 |       "2021-03-22 14:05:30,679 : INFO : __________________________________\n",
 896 |       "2021-03-22 14:05:30,680 : INFO : ft.model.wv.vectors_ngrams.npy\n",
 897 |       "2021-03-22 14:05:30,860 : INFO : min/max values before: (-9.1180725, 10.212996)\n",
 898 |       "2021-03-22 14:05:32,053 : INFO : min/max values after: (-9.12, 10.21)\n",
 899 |       "2021-03-22 14:05:33,198 : INFO : __________________________________\n",
 900 |       "2021-03-22 14:05:33,199 : INFO : ft.model.wv.vectors_vocab.npy\n",
 901 |       "2021-03-22 14:05:33,285 : INFO : min/max values before: (-11.192483, 12.355845)\n",
 902 |       "2021-03-22 14:05:33,753 : INFO : min/max values after: (-11.195, 12.36)\n",
 903 |       "2021-03-22 14:05:34,062 : INFO : __________________________________\n"
 904 |      ]
 905 |     }
 906 |    ],
 907 |    "source": [
 908 |     "for file in map(str,Path('.').glob('*.npy')):\n",
 909 |     "    logging.info(file)\n",
 910 |     "    spam = np.load(file)\n",
 911 |     "    logging.info(f'min/max values before: {spam.min(), spam.max()}')\n",
 912 |     "    spam = spam.astype('float16')\n",
 913 |     "    logging.info(f'min/max values after: {spam.min(), spam.max()}')\n",
 914 |     "    with open(file, 'wb') as f:\n",
 915 |     "        np.save(f, spam)\n",
 916 |     "    logging.info('__________________________________')"
 917 |    ]
 918 |   },
 919 |   {
 920 |    "cell_type": "markdown",
 921 |    "id": "conscious-bristol",
 922 |    "metadata": {},
 923 |    "source": [
 924 |     "После этих манипуляций файлы занимают 500Мб. Но этого всё-равно недостаточно: надо будет как-то уместить классификатор\n",
 925 |     "\n",
 926 |     "Видим, что у нас есть файлы целиком состоящие из единичек (минимальное и максимальное значения - это единицы). Взглянем на них разок"
 927 |    ]
 928 |   },
 929 |   {
 930 |    "cell_type": "code",
 931 |    "execution_count": 21,
 932 |    "id": "chief-healthcare",
 933 |    "metadata": {},
 934 |    "outputs": [],
 935 |    "source": [
 936 |     "spam = np.load('ft.model.trainables.vectors_ngrams_lockf.npy')"
 937 |    ]
 938 |   },
 939 |   {
 940 |    "cell_type": "code",
 941 |    "execution_count": 22,
 942 |    "id": "unlimited-prisoner",
 943 |    "metadata": {},
 944 |    "outputs": [
 945 |     {
 946 |      "data": {
 947 |       "text/plain": [
 948 |        "array([[1., 1., 1., ..., 1., 1., 1.],\n",
 949 |        "       [1., 1., 1., ..., 1., 1., 1.],\n",
 950 |        "       [1., 1., 1., ..., 1., 1., 1.],\n",
 951 |        "       ...,\n",
 952 |        "       [1., 1., 1., ..., 1., 1., 1.],\n",
 953 |        "       [1., 1., 1., ..., 1., 1., 1.],\n",
 954 |        "       [1., 1., 1., ..., 1., 1., 1.]], dtype=float16)"
 955 |       ]
 956 |      },
 957 |      "execution_count": 22,
 958 |      "metadata": {},
 959 |      "output_type": "execute_result"
 960 |     }
 961 |    ],
 962 |    "source": [
 963 |     "spam"
 964 |    ]
 965 |   },
 966 |   {
 967 |    "cell_type": "code",
 968 |    "execution_count": 23,
 969 |    "id": "revised-taylor",
 970 |    "metadata": {},
 971 |    "outputs": [
 972 |     {
 973 |      "data": {
 974 |       "text/plain": [
 975 |        "(400000, 200)"
 976 |       ]
 977 |      },
 978 |      "execution_count": 23,
 979 |      "metadata": {},
 980 |      "output_type": "execute_result"
 981 |     }
 982 |    ],
 983 |    "source": [
 984 |     "spam.shape"
 985 |    ]
 986 |   },
 987 |   {
 988 |    "cell_type": "markdown",
 989 |    "id": "postal-contamination",
 990 |    "metadata": {},
 991 |    "source": [
 992 |     "Ого, вы видели, сколько эта вещь занимает в памяти?! К счастью, архиваторы умеют грамотно сжимать такие файлы из повторяющихся элементов, так что можем быть уверены, что в итоге у нас останется память для классификатора"
 993 |    ]
 994 |   },
 995 |   {
 996 |    "cell_type": "markdown",
 997 |    "id": "wired-objective",
 998 |    "metadata": {},
 999 |    "source": [
1000 |     "### Vector averaging"
1001 |    ]
1002 |   },
1003 |   {
1004 |    "cell_type": "markdown",
1005 |    "id": "residential-kitty",
1006 |    "metadata": {},
1007 |    "source": [
1008 |     "Извлечём фичи. Для этого получим вектора каждого слова из предложения, сложим их и отнормируем его, чтобы его размер был равен 1. "
1009 |    ]
1010 |   },
1011 |   {
1012 |    "cell_type": "code",
1013 |    "execution_count": 24,
1014 |    "id": "encouraging-football",
1015 |    "metadata": {},
1016 |    "outputs": [
1017 |     {
1018 |      "name": "stderr",
1019 |      "output_type": "stream",
1020 |      "text": [
1021 |       "2021-03-22 14:05:41,479 : INFO : loading FastText object from ft.model\n",
1022 |       "2021-03-22 14:05:41,690 : INFO : loading wv recursively from ft.model.wv.* with mmap=None\n",
1023 |       "2021-03-22 14:05:41,691 : INFO : loading vectors from ft.model.wv.vectors.npy with mmap=None\n",
1024 |       "2021-03-22 14:05:41,724 : INFO : loading vectors_vocab from ft.model.wv.vectors_vocab.npy with mmap=None\n",
1025 |       "2021-03-22 14:05:41,760 : INFO : loading vectors_ngrams from ft.model.wv.vectors_ngrams.npy with mmap=None\n",
1026 |       "2021-03-22 14:05:41,857 : INFO : setting ignored attribute vectors_norm to None\n",
1027 |       "2021-03-22 14:05:41,857 : INFO : setting ignored attribute vectors_vocab_norm to None\n",
1028 |       "2021-03-22 14:05:41,858 : INFO : setting ignored attribute vectors_ngrams_norm to None\n",
1029 |       "2021-03-22 14:05:41,859 : INFO : setting ignored attribute buckets_word to None\n",
1030 |       "2021-03-22 14:05:41,859 : INFO : loading vocabulary recursively from ft.model.vocabulary.* with mmap=None\n",
1031 |       "2021-03-22 14:05:41,860 : INFO : loading trainables recursively from ft.model.trainables.* with mmap=None\n",
1032 |       "2021-03-22 14:05:41,861 : INFO : loading syn1neg from ft.model.trainables.syn1neg.npy with mmap=None\n",
1033 |       "2021-03-22 14:05:41,895 : INFO : loading vectors_vocab_lockf from ft.model.trainables.vectors_vocab_lockf.npy with mmap=None\n",
1034 |       "2021-03-22 14:05:41,929 : INFO : loading vectors_ngrams_lockf from ft.model.trainables.vectors_ngrams_lockf.npy with mmap=None\n",
1035 |       "2021-03-22 14:05:41,997 : INFO : loaded ft.model\n"
1036 |      ]
1037 |     }
1038 |    ],
1039 |    "source": [
1040 |     "model = FastText.load('ft.model')"
1041 |    ]
1042 |   },
1043 |   {
1044 |    "cell_type": "code",
1045 |    "execution_count": 25,
1046 |    "id": "proper-married",
1047 |    "metadata": {},
1048 |    "outputs": [],
1049 |    "source": [
1050 |     "def word_averaging(wv, words):\n",
1051 |     "    mean = np.zeros((wv.vector_size,))\n",
1052 |     "    \n",
1053 |     "    for word in words:\n",
1054 |     "        mean += wv.get_vector(word)\n",
1055 |     "\n",
1056 |     "    mean = gensim.matutils.unitvec(mean)\n",
1057 |     "    return mean\n",
1058 |     "\n",
1059 |     "def word_averaging_list(wv, text_list):\n",
1060 |     "    return np.vstack([word_averaging(wv, review) for review in text_list])"
1061 |    ]
1062 |   },
1063 |   {
1064 |    "cell_type": "markdown",
1065 |    "id": "proof-disorder",
1066 |    "metadata": {},
1067 |    "source": [
1068 |     "Также для ускорения алгоритмов классификации приведём полученную матрицу к типу float16"
1069 |    ]
1070 |   },
1071 |   {
1072 |    "cell_type": "code",
1073 |    "execution_count": 31,
1074 |    "id": "close-junction",
1075 |    "metadata": {},
1076 |    "outputs": [],
1077 |    "source": [
1078 |     "df.item_name = df.item_name.apply(lambda x: [i for i in x.split() if len(i) > 1])\n",
1079 |     "X_wv = word_averaging_list(model.wv, df.item_name)\n",
1080 |     "X_wv = X_wv.astype('float16')\n",
1081 |     "\n",
1082 |     "y = df.category_id"
1083 |    ]
1084 |   },
1085 |   {
1086 |    "cell_type": "markdown",
1087 |    "id": "front-shareware",
1088 |    "metadata": {},
1089 |    "source": [
1090 |     "Огромный плюс fasttext'а в том, что он может векторизовать слова, даже которых нет в словаре, благодаря ngram'ам"
1091 |    ]
1092 |   },
1093 |   {
1094 |    "cell_type": "markdown",
1095 |    "id": "expected-think",
1096 |    "metadata": {},
1097 |    "source": [
1098 |     "## Classification"
1099 |    ]
1100 |   },
1101 |   {
1102 |    "cell_type": "markdown",
1103 |    "id": "approximate-palace",
1104 |    "metadata": {},
1105 |    "source": [
1106 |     "Наконец-то перейдём к классификации. Будем использовать модель SVM для классификации. Она показала здесь лучшее качество. Также не забудем отнормировать данные перед подачей в классификатор: сделаем пайплайн"
1107 |    ]
1108 |   },
1109 |   {
1110 |    "cell_type": "code",
1111 |    "execution_count": 32,
1112 |    "id": "caring-armstrong",
1113 |    "metadata": {},
1114 |    "outputs": [],
1115 |    "source": [
1116 |     "pipe = make_pipeline(\n",
1117 |     "    StandardScaler(),\n",
1118 |     "    SVC(random_state=0),\n",
1119 |     ")"
1120 |    ]
1121 |   },
1122 |   {
1123 |    "cell_type": "code",
1124 |    "execution_count": 33,
1125 |    "id": "editorial-federation",
1126 |    "metadata": {},
1127 |    "outputs": [
1128 |     {
1129 |      "name": "stdout",
1130 |      "output_type": "stream",
1131 |      "text": [
1132 |       "Wall time: 5min 56s\n"
1133 |      ]
1134 |     },
1135 |     {
1136 |      "data": {
1137 |       "text/plain": [
1138 |        "array([0.86135208, 0.85232645, 0.85836571, 0.85216471, 0.86253755])"
1139 |       ]
1140 |      },
1141 |      "execution_count": 33,
1142 |      "metadata": {},
1143 |      "output_type": "execute_result"
1144 |     }
1145 |    ],
1146 |    "source": [
1147 |     "%%time\n",
1148 |     "cross_val_score(pipe, \n",
1149 |     "                X_wv, \n",
1150 |     "                y, \n",
1151 |     "                scoring='f1_weighted',\n",
1152 |     "                cv=StratifiedKFold(5, \n",
1153 |     "                                   shuffle=True, \n",
1154 |     "                                   random_state=0), \n",
1155 |     "                n_jobs=5\n",
1156 |     ")"
1157 |    ]
1158 |   },
1159 |   {
1160 |    "cell_type": "markdown",
1161 |    "id": "starting-mandate",
1162 |    "metadata": {},
1163 |    "source": [
1164 |     "Итак, получили неплохое качество на крос-валидации. Обучим на всех данных и сохраним модель"
1165 |    ]
1166 |   },
1167 |   {
1168 |    "cell_type": "code",
1169 |    "execution_count": null,
1170 |    "id": "fifteen-sampling",
1171 |    "metadata": {},
1172 |    "outputs": [],
1173 |    "source": [
1174 |     "pipe.fit(X_wv, y)\n",
1175 |     "pickle.dump(pipe, open(f'clf_task1', 'wb'))"
1176 |    ]
1177 |   },
1178 |   {
1179 |    "cell_type": "markdown",
1180 |    "id": "young-speaker",
1181 |    "metadata": {},
1182 |    "source": [
1183 |     "## Conclusion"
1184 |    ]
1185 |   },
1186 |   {
1187 |    "cell_type": "markdown",
1188 |    "id": "weekly-possibility",
1189 |    "metadata": {},
1190 |    "source": [
1191 |     "На тесте данное решение получило 0.865. Особую благодарность хотелось бы выразить [@dremovd](https://github.com/dremovd), с которым мы были в одной команде. Он направлял меня в нужную сторону, и без него мы бы не вышли в топ-10.\n",
1192 |     "\n",
1193 |     "Спасибо за ресёрч данного ноутбука, в нём я привёл в краткой форме решение задачи. Ждите более расширенного решения с анализом ошибок, стакингом и постпроцессингом :) \n",
1194 |     "\n",
1195 |     "До новых встреч!"
1196 |    ]
1197 |   }
1198 |  ],
1199 |  "metadata": {
1200 |   "kernelspec": {
1201 |    "display_name": "Python 3",
1202 |    "language": "python",
1203 |    "name": "python3"
1204 |   },
1205 |   "language_info": {
1206 |    "codemirror_mode": {
1207 |     "name": "ipython",
1208 |     "version": 3
1209 |    },
1210 |    "file_extension": ".py",
1211 |    "mimetype": "text/x-python",
1212 |    "name": "python",
1213 |    "nbconvert_exporter": "python",
1214 |    "pygments_lexer": "ipython3",
1215 |    "version": "3.9.1"
1216 |   }
1217 |  },
1218 |  "nbformat": 4,
1219 |  "nbformat_minor": 5
1220 | }
1221 | 


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