Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "from  pystempel import Stemmer\n",
    "import pandas as pd\n",
    "import os\n",
    "import time\n",
    "import morfeusz2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_rules(fpath):\n",
    "    rules = {}\n",
    "    with open(fpath, 'r', encoding='utf-8') as lines:\n",
    "        for line in lines:\n",
    "            fields = line.strip().split(' ')\n",
    "            lemma = fields[0]\n",
    "            forms = fields[1:]\n",
    "            rules[lemma] = set(forms)\n",
    "    return rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dict: 7381663it [00:14, 501070.60it/s]\n",
      "Stats for rules: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 323681/323681 [00:14<00:00, 22653.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All cases: 323681\n",
      "All forms: 4917282\n",
      "Ambiguous forms: 151539 (3%)\n"
     ]
    }
   ],
   "source": [
    "rules = morfeusz2.load_dict('dicts/polimorf-20190818.tab.gz')\n",
    "all_forms = defaultdict(set)\n",
    "for lemma, forms in tqdm(rules.items(), desc='Stats for rules'):\n",
    "    for form in forms:\n",
    "        all_forms[form].add(lemma)\n",
    "ambiguous_forms = [form for form in all_forms.keys() if len(all_forms[form])>1]\n",
    "print('All cases: {}'.format(len(rules.keys())))\n",
    "print('All forms: {}'.format(len(all_forms)))\n",
    "print('Ambiguous forms: {} ({:.0%})'.format(len(ambiguous_forms), len(ambiguous_forms)/len(all_forms)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval(stem, rules):\n",
    "    cases = rules.keys()\n",
    "    total_cases = len(cases)\n",
    "    total_words = 0\n",
    "    no_stem_found = 0\n",
    "    valid_lemma = 0\n",
    "    unique_stem = 0\n",
    "    start = time.time()\n",
    "    for lemma, forms in tqdm(rules.items(), desc='Stemming'):\n",
    "        stemmed_forms = set()\n",
    "        for form in forms:\n",
    "            stemmed = stem(form)\n",
    "            stemmed_forms.add(stemmed)\n",
    "            total_words += 1\n",
    "            if stemmed is None:\n",
    "                no_stem_found += 1\n",
    "        if len(stemmed_forms) == 1:\n",
    "            unique_stem += 1\n",
    "            stemmed = next(iter(stemmed_forms))\n",
    "            if stemmed == lemma:\n",
    "                valid_lemma += 1\n",
    "    stemming_time_sec = time.time() - start\n",
    "\n",
    "    result = {\n",
    "        'unique_stem':unique_stem,\n",
    "        'unique_stem_frac': unique_stem / total_cases,\n",
    "        'valid_lemma':valid_lemma,\n",
    "        'valid_lemma_frac':valid_lemma / unique_stem,\n",
    "        'no_stem_found': no_stem_found,\n",
    "        'no_stem_found_frac': no_stem_found /  total_words,\n",
    "        'stemming_time_sec': stemming_time_sec,\n",
    "        'stemming_time_per_word' : stemming_time_sec / total_words,\n",
    "        'words_per_second' : total_words / stemming_time_sec\n",
    "    }\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2225192/2225192 [00:01<00:00, 1496815.12bytes/s]\n",
      "Stemming: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 323681/323681 [02:06<00:00, 2558.84it/s]\n",
      "Loading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11368252/11368252 [00:12<00:00, 944607.59bytes/s]\n",
      "Stemming: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 323681/323681 [02:45<00:00, 1951.84it/s]\n"
     ]
    }
   ],
   "source": [
    "stemming_tables = ['src/pystempel/data/original/stemmer_20000.tbl.gz',\n",
    "                   'src/pystempel/data/polimorf/stemmer_polimorf.tbl.gz'\n",
    "                  ]\n",
    "\n",
    "results = []\n",
    "for stemming_table in stemming_tables:\n",
    "    start = time.time()\n",
    "    stemmer = Stemmer.from_file(stemming_table)\n",
    "    loading_time = time.time() - start\n",
    "    result = eval(stemmer, rules)\n",
    "    result['loading_time_sec'] = loading_time\n",
    "    result['stemming_table'] = stemming_table\n",
    "    result['stemming_table_size_mb'] = os.stat(stemming_table).st_size / (1024*1024)\n",
    "    results.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>unique_stem</th>\n",
       "      <th>unique_stem_frac</th>\n",
       "      <th>valid_lemma</th>\n",
       "      <th>valid_lemma_frac</th>\n",
       "      <th>no_stem_found</th>\n",
       "      <th>no_stem_found_frac</th>\n",
       "      <th>stemming_time_sec</th>\n",
       "      <th>stemming_time_per_word</th>\n",
       "      <th>words_per_second</th>\n",
       "      <th>loading_time_sec</th>\n",
       "      <th>stemming_table_size_mb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stemming_table</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>src/pystempel/data/original/stemmer_20000.tbl.gz</th>\n",
       "      <td>120649</td>\n",
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       "      <td>0.688178</td>\n",
       "      <td>312</td>\n",
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       "      <td>126.498529</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>40124.466599</td>\n",
       "      <td>1.490314</td>\n",
       "      <td>0.367842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>src/pystempel/data/polimorf/stemmer_polimorf.tbl.gz</th>\n",
       "      <td>283775</td>\n",
       "      <td>0.876712</td>\n",
       "      <td>247315</td>\n",
       "      <td>0.871518</td>\n",
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       "      <td>0.000033</td>\n",
       "      <td>30605.950910</td>\n",
       "      <td>12.061594</td>\n",
       "      <td>2.206561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    unique_stem  \\\n",
       "stemming_table                                                    \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz         120649   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...       283775   \n",
       "\n",
       "                                                    unique_stem_frac  \\\n",
       "stemming_table                                                         \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz            0.372740   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...          0.876712   \n",
       "\n",
       "                                                    valid_lemma  \\\n",
       "stemming_table                                                    \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz          83028   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...       247315   \n",
       "\n",
       "                                                    valid_lemma_frac  \\\n",
       "stemming_table                                                         \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz            0.688178   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...          0.871518   \n",
       "\n",
       "                                                    no_stem_found  \\\n",
       "stemming_table                                                      \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz              312   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...             22   \n",
       "\n",
       "                                                    no_stem_found_frac  \\\n",
       "stemming_table                                                           \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz              0.000061   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...            0.000004   \n",
       "\n",
       "                                                    stemming_time_sec  \\\n",
       "stemming_table                                                          \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz           126.498529   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...         165.839840   \n",
       "\n",
       "                                                    stemming_time_per_word  \\\n",
       "stemming_table                                                               \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz                  0.000025   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...                0.000033   \n",
       "\n",
       "                                                    words_per_second  \\\n",
       "stemming_table                                                         \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz        40124.466599   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...      30605.950910   \n",
       "\n",
       "                                                    loading_time_sec  \\\n",
       "stemming_table                                                         \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz            1.490314   \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...         12.061594   \n",
       "\n",
       "                                                    stemming_table_size_mb  \n",
       "stemming_table                                                              \n",
       "src/pystempel/data/original/stemmer_20000.tbl.gz                  0.367842  \n",
       "src/pystempel/data/polimorf/stemmer_polimorf.tb...                2.206561  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame.from_dict(results)\n",
    "results.set_index('stemming_table', inplace=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot.barh(y='unique_stem_frac', figsize =(15,5) );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot.barh(y='valid_lemma_frac', figsize =(15,5) );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot.barh(y='words_per_second', figsize =(15,5) );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot.barh(y='loading_time_sec', figsize =(15,5) );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
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