test_compat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "torch.manual_seed(0)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.benchmark = False\n",
    "\n",
    "from src.service import CompatModel\n",
    "\n",
    "\n",
    "from types import SimpleNamespace\n",
    "import os\n",
    "path = \"/home/eshikov/work/avatar/compatibility/LASER\"\n",
    "\n",
    "os.environ[\"LASER\"] = path\n",
    "from LASER.source.embed import EncodeLoad\n",
    "from LASER.source.lib.text_processing import TokenLine\n",
    "import fastBPE\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model_dir=path + \"/models\"\n",
    "encoder=model_dir+\"/bilstm.93langs.2018-12-26.pt\"\n",
    "bpe_codes=model_dir+\"/93langs.fcodes\"\n",
    "args = SimpleNamespace(encoder=encoder,buffer_size=10000,\n",
    "                       max_sentences=None,max_tokens=12000,cpu=True,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - loading encoder /home/eshikov/work/avatar/compatibility/LASER/models/bilstm.93langs.2018-12-26.pt\n"
     ]
    }
   ],
   "source": [
    "enc = EncodeLoad(args)\n",
    "bpe = fastBPE.fastBPE(model_dir + \"/93langs.fcodes\", model_dir + \"/93langs.fvocab\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = [\"One of the main sources of wind information is \\\n",
    "reanalysis data on wind speed at the height of 10 m above the surface which can be used without previous correction for wave calculation not in all sea areas\"]\n",
    "s2 = [\"Early detection of disease risks and accurate appointment of diagnostic procedures makes a significant contribution to value-based medicine\"]\n",
    "\n",
    "\n",
    "s1_ru = [\"Одним из основных источников информации о ветре являются данные повторного анализа скорости ветра на \\\n",
    " высоте 10 м над поверхностью, которые можно использовать без предварительной корректировки для расчета волн не во всех морских районах\"]\n",
    "s2_ru = [\"Раннее выявление риска заболеваний и точное назначение диагностических процедур вносит значительный вклад в ценностную медицину\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1 = enc.encode_sentences(bpe.apply(s1))\n",
    "v2 = enc.encode_sentences(bpe.apply(s2))\n",
    "v1_ru = enc.encode_sentences(bpe.apply(s1_ru))\n",
    "v2_ru = enc.encode_sentences(bpe.apply(s2_ru))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3632982"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(np.ravel(v1),np.ravel(v2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57399994"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(np.ravel(v1),np.ravel(v1_ru))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41270727"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(np.ravel(v2),np.ravel(v2_ru))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30931157"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(np.ravel(v1_ru),np.ravel(v2_ru))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def EncodeFilep(encoder, inp_file, out_file, buffer_size=10000, verbose=False):\n",
    "    n = 0\n",
    "    t = time.time()\n",
    "    for sentences in buffered_read(inp_file, buffer_size):\n",
    "        encoder.encode_sentences(sentences).tofile(out_file)\n",
    "\n",
    "\n",
    "\n",
    "def EncodeFile(encoder, inp_fname, out_fname,\n",
    "               buffer_size=10000, verbose=False, over_write=False,\n",
    "               inp_encoding='utf-8'):\n",
    "    # TODO :handle over write\n",
    "    if not os.path.isfile(out_fname):\n",
    "        if verbose:\n",
    "            print(' - Encoder: {} to {}'.\n",
    "                  format(os.path.basename(inp_fname) if len(inp_fname) > 0 else 'stdin',\n",
    "                         os.path.basename(out_fname)))\n",
    "        fin = open(inp_fname, 'r', encoding=inp_encoding, errors='surrogateescape') if len(inp_fname) > 0 else sys.stdin\n",
    "        fout = open(out_fname, mode='wb')\n",
    "        EncodeFilep(encoder, fin, fout, buffer_size=buffer_size, verbose=verbose)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parser = argparse.ArgumentParser(description='LASER: Embed sentences')\n",
    "    parser.add_argument('--encoder', type=str, required=True,\n",
    "                        help='encoder to be used')\n",
    "    parser.add_argument('--token-lang', type=str, default='--',\n",
    "                        help=\"Perform tokenization with given language ('--' for no tokenization)\")\n",
    "    parser.add_argument('--bpe-codes', type=str, default=None,\n",
    "                        help='Apply BPE using specified codes')\n",
    "    parser.add_argument('-v', '--verbose', action='store_true',\n",
    "                        help='Detailed output')\n",
    "\n",
    "    parser.add_argument('-o', '--output', required=True,\n",
    "                        help='Output sentence embeddings')\n",
    "    parser.add_argument('--buffer-size', type=int, default=10000,\n",
    "                        help='Buffer size (sentences)')\n",
    "    parser.add_argument('--max-tokens', type=int, default=12000,\n",
    "                        help='Maximum number of tokens to process in a batch')\n",
    "    parser.add_argument('--max-sentences', type=int, default=None,\n",
    "                        help='Maximum number of sentences to process in a batch')\n",
    "    parser.add_argument('--cpu', action='store_true',\n",
    "                        help='Use CPU instead of GPU')\n",
    "    parser.add_argument('--stable', action='store_true',\n",
    "                        help='Use stable merge sort instead of quick sort')\n",
    "    args = parser.parse_args()\n",
    "\n",
    "\n",
    "    if args.verbose:\n",
    "        print(' - Encoder: loading {}'.format(args.encoder))\n",
    "    encoder = SentenceEncoder(args.encoder,\n",
    "                              max_sentences=args.max_sentences,\n",
    "                              max_tokens=args.max_tokens,\n",
    "                              sort_kind='mergesort' if args.stable else 'quicksort',\n",
    "                              cpu=args.cpu)\n",
    "\n",
    "    with tempfile.TemporaryDirectory() as tmpdir:\n",
    "        ifname = ''  # stdin will be used\n",
    "        if args.token_lang != '--':\n",
    "            tok_fname = os.path.join(tmpdir, 'tok')\n",
    "            Token(ifname,\n",
    "                  tok_fname,\n",
    "                  lang=args.token_lang,\n",
    "                  romanize=True if args.token_lang == 'el' else False,\n",
    "                  lower_case=True, gzip=False,\n",
    "                  verbose=args.verbose, over_write=False)\n",
    "            ifname = tok_fname\n",
    "\n",
    "        if args.bpe_codes:\n",
    "            bpe_fname = os.path.join(tmpdir, 'bpe')\n",
    "            BPEfastApply(ifname,\n",
    "                         bpe_fname,\n",
    "                         args.bpe_codes,\n",
    "                         verbose=args.verbose, over_write=False)\n",
    "            ifname = bpe_fname\n",
    "\n",
    "        EncodeFile(encoder,\n",
    "                   ifname,\n",
    "                   args.output,\n",
    "                   verbose=args.verbose, over_write=False,\n",
    "                   buffer_size=args.buffer_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'one of the main sources of wind information is reanalysis data on wind speed at the height of 10 m above the surface which can be used without previous correction for wave calculation not in all sea areas'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TokenLine(\"One of the main sources of wind information is \\\n",
    "reanalysis data on wind speed at the height of 10 m above the surface which can be used without previous correction for wave calculation not in all sea areas\",lang='ru')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R@@ o@@ ast@@ ed bar@@ ram@@ un@@ di fish']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import fastBPE\n",
    "bpe = fastBPE.fastBPE(model_dir + \"/93langs.fcodes\", model_dir + \"/93langs.fvocab\")\n",
    "bpe.apply([\"Roasted barramundi fish\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1024)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmodel = CompatModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid_loss =  0.7109176944463681\n",
      "ROC =  0.613192138671875\n",
      "valid_loss =  0.7109054235311655\n",
      "ROC =  0.637225341796875\n",
      "valid_loss =  0.7103322285872239\n",
      "ROC =  0.633341064453125\n",
      "valid_loss =  0.7013366757295071\n",
      "ROC =  0.624844970703125\n",
      "valid_loss =  0.6911636759073306\n",
      "ROC =  0.647908935546875\n",
      "valid_loss =  0.6822013258934021\n",
      "ROC =  0.6451550292968751\n",
      "valid_loss =  0.6814975814941602\n",
      "ROC =  0.6448291015625001\n",
      "valid_loss =  0.6640346386493781\n",
      "ROC =  0.675462646484375\n",
      "valid_loss =  0.6520214967238598\n",
      "ROC =  0.697537841796875\n",
      "valid_loss =  0.6437425399437929\n",
      "ROC =  0.7206384277343749\n",
      "valid_loss =  0.6363430176025782\n",
      "ROC =  0.720714111328125\n",
      "valid_loss =  0.6254607072243323\n",
      "ROC =  0.73448486328125\n",
      "valid_loss =  0.6269760926564535\n",
      "ROC =  0.7404699707031249\n",
      "valid_loss =  0.6154594008739178\n",
      "ROC =  0.7442346191406249\n",
      "valid_loss =  0.6037875383328168\n",
      "ROC =  0.758372802734375\n",
      "valid_loss =  0.6050020204140589\n",
      "ROC =  0.764007568359375\n",
      "valid_loss =  0.5980190367270739\n",
      "ROC =  0.772974853515625\n",
      "valid_loss =  0.5933654507001241\n",
      "ROC =  0.779417724609375\n",
      "valid_loss =  0.58469696304737\n",
      "ROC =  0.791614990234375\n"
     ]
    }
   ],
   "source": [
    "cmodel.train(max_epochs=20, reset_params = True, save_params = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'args' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-22480886ed51>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'args' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'7102202067': 0.998536817719759}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = cmodel.single_score('10138903600', '7102202067')\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "cmodel.all_scores('10138903600')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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