Finding similar API functions between Pytorch and Tensorflow with Doc2Vec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab301b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from urllib.parse import urljoin\n",
    "from nltk.tokenize import word_tokenize\n",
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f99e9c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_text_and_links(html):\n",
    "    soup = BeautifulSoup(html, 'html.parser')\n",
    "    text = ' '.join(soup.find('article').text.split())\n",
    "    links = [link['href'] for link in soup.find_all('a', href=True)]\n",
    "    return text, links\n",
    "\n",
    "def link_prefix(link):\n",
    "    if '#' in link:\n",
    "        link = link.split('#')[0]\n",
    "    if '?' in link:\n",
    "        link = link.split('?')[0]\n",
    "    return link\n",
    "\n",
    "def resolve_links(base_url, links):\n",
    "    return set([link_prefix(urljoin(base_url, link)) for link in links])\n",
    "\n",
    "def filter_links(base_domain, links):\n",
    "    return [link for link in links if link.startswith('https://') and base_domain in link and '.pdf' not in link]\n",
    "\n",
    "base_url = 'tensorflow.org/api_docs/python/tf'\n",
    "num_pages = 10\n",
    "\n",
    "def crawl_and_save(base_url, num_pages):\n",
    "    print(f'Crawling {base_url}...')\n",
    "    base_domain = base_url.split('/')[0]\n",
    "    urls_queued, urls_crawled, saved_pages = ['https://' + base_url], set(), {}\n",
    "    while len(urls_queued) > 0 and len(urls_crawled) < num_pages:\n",
    "        url = urls_queued.pop(0)\n",
    "        urls_crawled.add(url)\n",
    "        print(f'Fetching {url}')\n",
    "        try:\n",
    "            html = requests.get(url).text\n",
    "            text, links = extract_text_and_links(html)\n",
    "            if base_url in url:\n",
    "                saved_pages[url] = text\n",
    "            links = resolve_links(url, links)\n",
    "            links = filter_links(base_url, links)\n",
    "            links = [link for link in links if (link not in set(urls_queued) and link not in urls_crawled)]\n",
    "            urls_queued.extend(links)\n",
    "        except:\n",
    "            pass\n",
    "    print(f'Done!')\n",
    "    print(f'Crawled {len(urls_crawled)} total pages, saved {len(urls_crawled)} target pages')\n",
    "    print(f'Total content extracted: {int(sum([len(v)/1000 for v in saved_pages.values()]))} kbytes')\n",
    "    return urls_crawled, saved_pages"
   ]
  },
  {
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     ]
    }
   ],
   "source": [
    "_, tf_saved_pages = crawl_and_save(\n",
    "    base_url='tensorflow.org/api_docs/python/tf',\n",
    "    num_pages=5000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab4bcc63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "C:\\Users\\mtrencseni\\anaconda3\\lib\\site-packages\\bs4\\builder\\__init__.py:545: XMLParsedAsHTMLWarning: It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features=\"xml\"` into the BeautifulSoup constructor.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching https://pytorch.org/docs/stable/_modules/torch/distributed/elastic/metrics/www.lfprojects.org/policies/\n",
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      "Fetching https://pytorch.org/docs/stable/_modules/torch/nn/intrinsic/quantized/dynamic/modules/www.linuxfoundation.org/policies/\n",
      "Fetching https://pytorch.org/docs/stable/_modules/torch/nn/intrinsic/quantized/dynamic/modules/www.lfprojects.org/policies/\n",
      "Fetching https://pytorch.org/docs/stable/_modules/torch/ao/nn/qat/dynamic/modules/www.lfprojects.org/policies/\n",
      "Fetching https://pytorch.org/docs/stable/_modules/torch/ao/nn/qat/dynamic/modules/www.linuxfoundation.org/policies/\n",
      "Done!\n",
      "Crawled 3912 total pages, saved 3912 target pages\n",
      "Total content extracted: 7921 kbytes\n"
     ]
    }
   ],
   "source": [
    "_, pt_saved_pages = crawl_and_save(\n",
    "    base_url='pytorch.org/docs/stable',\n",
    "    num_pages=5000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b3be756",
   "metadata": {},
   "outputs": [],
   "source": [
    "pages = {**tf_saved_pages, **pt_saved_pages}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4af4f0e5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "119e2113",
   "metadata": {},
   "outputs": [],
   "source": [
    "tagged_posts = {url : TaggedDocument(word_tokenize(text), [idx]) for idx, (url, text) in enumerate(pages.items())}\n",
    "idx_lookup = {idx : url for idx, url in enumerate(pages.keys())}\n",
    "model = Doc2Vec(tagged_posts.values(), vector_size=100, alpha=0.025, min_count=1, workers=16, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bccf33c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def similar_pages(which, n=3):\n",
    "    if not isinstance(which, str):\n",
    "        which = idx_lookup[which]\n",
    "    # at this point which is the url\n",
    "    if n == 'all':\n",
    "        return model.dv.most_similar(positive=[model.infer_vector(tagged_posts[which][0])], topn=None)\n",
    "    results = model.dv.most_similar(positive=[model.infer_vector(tagged_posts[which][0])], topn=len(pages))\n",
    "    results = [(idx_lookup[idx], f'{score:.3f}') for idx, score in results if idx != tagged_posts[which][1][0] and 'www' not in idx_lookup[idx]]\n",
    "    return results[:n]\n",
    "\n",
    "def similar_pages_cross(which, n=3):\n",
    "    if not isinstance(which, str):\n",
    "        which = idx_lookup[which]\n",
    "    # at this point which is the url\n",
    "    results = model.dv.most_similar(positive=[model.infer_vector(tagged_posts[which][0])], topn=len(pages))\n",
    "    exclude = 'tensorflow.org' if 'tensorflow.org' in which else 'pytorch.org'\n",
    "    results = [(idx_lookup[idx], f'{score:.3f}') for idx, score in results if idx != tagged_posts[which][1][0] and exclude not in idx_lookup[idx] and 'www' not in idx_lookup[idx]]\n",
    "    return results[:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "73867dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('https://tensorflow.org/api_docs/python/tf/raw_ops/BatchMatrixSolve',\n",
       "  '0.661'),\n",
       " ('https://tensorflow.org/api_docs/python/tf/linalg/det', '0.651'),\n",
       " ('https://tensorflow.org/api_docs/python/tf/linalg/logdet', '0.640')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_pages('https://tensorflow.org/api_docs/python/tf/linalg/adjoint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5543827e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('https://tensorflow.org/api_docs/python/tf/keras/losses/BinaryFocalCrossentropy',\n",
       "  '0.885'),\n",
       " ('https://tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy',\n",
       "  '0.826'),\n",
       " ('https://tensorflow.org/api_docs/python/tf/keras/losses/CosineSimilarity',\n",
       "  '0.820')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_pages('https://tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4b5e4e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('https://pytorch.org/docs/stable/generated/torch.Tensor.tril_.html',\n",
       "  '0.581'),\n",
       " ('https://pytorch.org/docs/stable/_modules/torch/_C/_distributed_c10d.html',\n",
       "  '0.581'),\n",
       " ('https://pytorch.org/docs/stable/_modules/torch/testing/_creation.html',\n",
       "  '0.578')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_pages_cross('https://tensorflow.org/api_docs/python/tf/linalg/adjoint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd0d6c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('https://pytorch.org/docs/stable/generated/torch.nn.functional.binary_cross_entropy_with_logits.html',\n",
       "  '0.559'),\n",
       " ('https://pytorch.org/docs/stable/generated/torch.Tensor.logit_.html',\n",
       "  '0.536'),\n",
       " ('https://pytorch.org/docs/stable/generated/torch.nn.HuberLoss.html',\n",
       "  '0.526')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_pages_cross('https://tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75936673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('https://pytorch.org/docs/stable/generated/torch.matrix_exp.html', '0.537'),\n",
       " ('https://pytorch.org/docs/stable/generated/torch.Tensor.narrow.html',\n",
       "  '0.501'),\n",
       " ('https://pytorch.org/docs/stable/generated/torch.Tensor.masked_scatter.html',\n",
       "  '0.500')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_pages_cross('https://tensorflow.org/api_docs/python/tf/math/argmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dbd49c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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