25.node2vec-embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create STRING embeddings using [node2vec](https://snap.stanford.edu/node2vec/)\n",
    "\n",
    "node2vec method described in\n",
    "\n",
    "> **node2vec: Scalable Feature Learning for Networks**  \n",
    "Aditya Grover, Jure Leskovec  \n",
    "*Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16* (2016) <https://doi.org/gftdzj>  \n",
    "DOI: [10.1145/2939672.2939754](https://doi.org/10.1145/2939672.2939754) · PMID: [27853626](https://www.ncbi.nlm.nih.gov/pubmed/27853626) · PMCID: [PMC5108654](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5108654)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import csv\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.sparse\n",
    "import networkx as nx\n",
    "\n",
    "from utils import get_string_dataset_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# edgelist export parameters\n",
    "score_threshold = 0.0\n",
    "edgelist_path = \"data/score-edgelists/combined-score.edgelist.tsv\"\n",
    "\n",
    "# node2vec parameters\n",
    "n_dimensions = 128\n",
    "raw_embeddings_path = f\"data/embeddings/node2vec-{n_dimensions}d.txt\"\n",
    "embeddings_path = f\"data/embeddings/node2vec-{n_dimensions}d.tsv.xz\"\n",
    "\n",
    "# if True, only run on a susbset of the network\n",
    "development = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read network and convert to edgelist format\n",
    "\n",
    "The edgelist format is not well defined. The following resources provide some documentation:\n",
    "\n",
    "- https://github.com/aditya-grover/node2vec/blob/master/node2vec_spark/README.md\n",
    "- https://github.com/snap-stanford/snap/issues/161#issuecomment-546146427\n",
    "- https://networkx.github.io/documentation/stable/reference/readwrite/edgelist.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 436 ms, sys: 7.91 ms, total: 444 ms\n",
      "Wall time: 444 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "matrix = scipy.sparse.load_npz(\"data/score-matrices/combined_score.sparse.npz\")\n",
    "if development:\n",
    "    n_nodes = 2000\n",
    "    matrix = matrix[:n_nodes, :n_nodes] # subset matrix for development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_nonzero_values(matrix: scipy.sparse.spmatrix, score_threshold=0.0, one_index=False):\n",
    "    \"\"\"\n",
    "    Return an iterator of (row_index: int, col_index: int, score: int) tuples\n",
    "    one_index adds 1 to row and col indices for compatability with node2vec,\n",
    "    which possibility requires node ids to start at 1 for directed graphs.\n",
    "    See https://github.com/snap-stanford/snap/issues/161#issuecomment-546146427\n",
    "    \"\"\"\n",
    "    coo = matrix.tocoo()\n",
    "    # take upper triangle since matrix is symmetric with a zero diagonal\n",
    "    coo = scipy.sparse.triu(coo)\n",
    "    print(f\"{sum(coo.data >= score_threshold):,} edges above threshold\")\n",
    "    for row, col, score in zip(coo.row, coo.col, coo.data):\n",
    "        if score < score_threshold:\n",
    "            continue\n",
    "        if one_index:\n",
    "            row += 1\n",
    "            col += 1\n",
    "        yield row, col, score\n",
    "\n",
    "\n",
    "def write_edgelist(edge_tuples, path):\n",
    "    \"\"\"\n",
    "    Write edge_tuples (from generate_nonzero_values) to a .edgelist.tsv file.\n",
    "    This file format is not well documented or standardized. This function\n",
    "    does not write a header.\n",
    "    \"\"\"\n",
    "    with pathlib.Path(path).open(\"w\") as write_file:\n",
    "        writer = csv.writer(write_file, delimiter=\"\\t\")\n",
    "        writer.writerows(edge_tuples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5,879,727 edges above threshold\n",
      "CPU times: user 31.4 s, sys: 284 ms, total: 31.7 s\n",
      "Wall time: 31.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "edge_tuples = generate_nonzero_values(matrix, score_threshold=score_threshold, one_index=False)\n",
    "write_edgelist(edge_tuples, edgelist_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run node2vec command\n",
    "\n",
    "node2vec CLI documentation:\n",
    "\n",
    "```\n",
    "An algorithmic framework for representational learning on graphs. [Aug 29 2019]\n",
    "================================================================================\n",
    "Input graph path (-i:)=graph/karate.edgelist\n",
    "Output graph path (-o:)=emb/karate.emb\n",
    "Number of dimensions. Default is 128 (-d:)=128\n",
    "Length of walk per source. Default is 80 (-l:)=80\n",
    "Number of walks per source. Default is 10 (-r:)=10\n",
    "Context size for optimization. Default is 10 (-k:)=10\n",
    "Number of epochs in SGD. Default is 1 (-e:)=1\n",
    "Return hyperparameter. Default is 1 (-p:)=1\n",
    "Inout hyperparameter. Default is 1 (-q:)=1\n",
    "Verbose output. (-v)=NO\n",
    "Graph is directed. (-dr)=NO\n",
    "Graph is weighted. (-w)=NO\n",
    "Output random walks instead of embeddings. (-ow)=NO\n",
    "```\n",
    "\n",
    "For node2vec installation via snap on Linux, [see this commit](https://github.com/related-sciences/string-protein-network/commit/4d02d6ce11c792758a1ee82419e2bd6f789617a3). Note that the `node2vec` command can be \"killed\" when system resources are insufficient ([see issue](https://github.com/snap-stanford/snap/issues/188))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node2vec \\\n",
      "  -i:data/score-edgelists/combined-score.edgelist.tsv \\\n",
      "  -o:data/embeddings/node2vec-128d.txt \\\n",
      "  -d:128 -w -v\n"
     ]
    }
   ],
   "source": [
    "# interpolate command\n",
    "command = f'''\n",
    "node2vec \\\\\n",
    "  -i:{edgelist_path} \\\\\n",
    "  -o:{raw_embeddings_path} \\\\\n",
    "  -d:{n_dimensions} -w -v\n",
    "'''.strip()\n",
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "An algorithmic framework for representational learning on graphs. [Aug 29 2019]\n",
      "================================================================================\n",
      "Input graph path (-i:)=data/score-edgelists/combined-score.edgelist.tsv\n",
      "Output graph path (-o:)=data/embeddings/node2vec-128d.txt\n",
      "Number of dimensions. Default is 128 (-d:)=128\n",
      "Length of walk per source. Default is 80 (-l:)=80\n",
      "Number of walks per source. Default is 10 (-r:)=10\n",
      "Context size for optimization. Default is 10 (-k:)=10\n",
      "Number of epochs in SGD. Default is 1 (-e:)=1\n",
      "Return hyperparameter. Default is 1 (-p:)=1\n",
      "Inout hyperparameter. Default is 1 (-q:)=1\n",
      "Verbose output. (-v)=YES\n",
      "Graph is directed. (-dr)=NO\n",
      "Graph is weighted. (-w)=YES\n",
      "Output random walks instead of embeddings. (-ow)=NO\n",
      "Read 5879727 lines from data/score-edgelists/combined-score.edgelist.tsv\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# execute the command with a system call\n",
    "! {command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidy embeddings\n",
    "\n",
    "Read embeddings and rewrite them in a cleaner format.\n",
    "See [issue on file format](https://github.com/snap-stanford/snap/issues/192)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>protein_external_id</th>\n",
       "      <th>preferred_name</th>\n",
       "      <th>protein_size</th>\n",
       "      <th>annotation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19564</th>\n",
       "      <td>19564</td>\n",
       "      <td>9606.ENSP00000485675</td>\n",
       "      <td>ENSG00000280116</td>\n",
       "      <td>84</td>\n",
       "      <td>annotation not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19565</th>\n",
       "      <td>19565</td>\n",
       "      <td>9606.ENSP00000485678</td>\n",
       "      <td>OR6Q1</td>\n",
       "      <td>317</td>\n",
       "      <td>Olfactory receptor 6Q1; Odorant receptor; Olfa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index   protein_external_id   preferred_name  protein_size  \\\n",
       "19564  19564  9606.ENSP00000485675  ENSG00000280116            84   \n",
       "19565  19565  9606.ENSP00000485678            OR6Q1           317   \n",
       "\n",
       "                                              annotation  \n",
       "19564                           annotation not available  \n",
       "19565  Olfactory receptor 6Q1; Odorant receptor; Olfa...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the head of protein info\n",
    "gene_path = get_string_dataset_path(\"protein.info\")\n",
    "gene_df = pd.read_csv(gene_path, sep='\\t')\n",
    "gene_df = gene_df.reset_index()\n",
    "index_to_name = gene_df.set_index('index')['preferred_name'].to_dict()\n",
    "gene_df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>emb_1</th>\n",
       "      <th>emb_2</th>\n",
       "      <th>emb_3</th>\n",
       "      <th>emb_4</th>\n",
       "      <th>emb_5</th>\n",
       "      <th>emb_6</th>\n",
       "      <th>emb_7</th>\n",
       "      <th>emb_8</th>\n",
       "      <th>emb_9</th>\n",
       "      <th>emb_10</th>\n",
       "      <th>...</th>\n",
       "      <th>emb_119</th>\n",
       "      <th>emb_120</th>\n",
       "      <th>emb_121</th>\n",
       "      <th>emb_122</th>\n",
       "      <th>emb_123</th>\n",
       "      <th>emb_124</th>\n",
       "      <th>emb_125</th>\n",
       "      <th>emb_126</th>\n",
       "      <th>emb_127</th>\n",
       "      <th>emb_128</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node_id</th>\n",
       "      <th>preferred_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>ARF5</th>\n",
       "      <td>0.495522</td>\n",
       "      <td>-0.264478</td>\n",
       "      <td>0.383956</td>\n",
       "      <td>-1.48516</td>\n",
       "      <td>1.13501</td>\n",
       "      <td>-0.687379</td>\n",
       "      <td>-0.092868</td>\n",
       "      <td>-0.428224</td>\n",
       "      <td>-0.148345</td>\n",
       "      <td>-0.028783</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002594</td>\n",
       "      <td>0.308740</td>\n",
       "      <td>-0.062943</td>\n",
       "      <td>0.140117</td>\n",
       "      <td>0.177642</td>\n",
       "      <td>0.132789</td>\n",
       "      <td>0.032162</td>\n",
       "      <td>0.093262</td>\n",
       "      <td>-0.260608</td>\n",
       "      <td>0.014748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>M6PR</th>\n",
       "      <td>0.371978</td>\n",
       "      <td>-0.189449</td>\n",
       "      <td>0.336587</td>\n",
       "      <td>-1.45652</td>\n",
       "      <td>1.08911</td>\n",
       "      <td>-0.992916</td>\n",
       "      <td>-0.500337</td>\n",
       "      <td>-0.429294</td>\n",
       "      <td>0.179531</td>\n",
       "      <td>-0.128398</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.213474</td>\n",
       "      <td>-0.102929</td>\n",
       "      <td>0.170698</td>\n",
       "      <td>-0.014626</td>\n",
       "      <td>0.150232</td>\n",
       "      <td>0.078505</td>\n",
       "      <td>0.129128</td>\n",
       "      <td>0.193226</td>\n",
       "      <td>-0.458337</td>\n",
       "      <td>0.285326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>FKBP4</th>\n",
       "      <td>0.325708</td>\n",
       "      <td>-0.233374</td>\n",
       "      <td>0.253502</td>\n",
       "      <td>-1.38490</td>\n",
       "      <td>1.19367</td>\n",
       "      <td>-0.764975</td>\n",
       "      <td>-0.272214</td>\n",
       "      <td>-0.360279</td>\n",
       "      <td>-0.172938</td>\n",
       "      <td>-0.213736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070447</td>\n",
       "      <td>0.177522</td>\n",
       "      <td>-0.123333</td>\n",
       "      <td>0.123212</td>\n",
       "      <td>0.028557</td>\n",
       "      <td>0.069089</td>\n",
       "      <td>0.197630</td>\n",
       "      <td>-0.030261</td>\n",
       "      <td>-0.049011</td>\n",
       "      <td>0.036173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           emb_1     emb_2     emb_3    emb_4    emb_5  \\\n",
       "node_id preferred_name                                                   \n",
       "0       ARF5            0.495522 -0.264478  0.383956 -1.48516  1.13501   \n",
       "1       M6PR            0.371978 -0.189449  0.336587 -1.45652  1.08911   \n",
       "2       FKBP4           0.325708 -0.233374  0.253502 -1.38490  1.19367   \n",
       "\n",
       "                           emb_6     emb_7     emb_8     emb_9    emb_10  ...  \\\n",
       "node_id preferred_name                                                    ...   \n",
       "0       ARF5           -0.687379 -0.092868 -0.428224 -0.148345 -0.028783  ...   \n",
       "1       M6PR           -0.992916 -0.500337 -0.429294  0.179531 -0.128398  ...   \n",
       "2       FKBP4          -0.764975 -0.272214 -0.360279 -0.172938 -0.213736  ...   \n",
       "\n",
       "                         emb_119   emb_120   emb_121   emb_122   emb_123  \\\n",
       "node_id preferred_name                                                     \n",
       "0       ARF5           -0.002594  0.308740 -0.062943  0.140117  0.177642   \n",
       "1       M6PR           -0.213474 -0.102929  0.170698 -0.014626  0.150232   \n",
       "2       FKBP4           0.070447  0.177522 -0.123333  0.123212  0.028557   \n",
       "\n",
       "                         emb_124   emb_125   emb_126   emb_127   emb_128  \n",
       "node_id preferred_name                                                    \n",
       "0       ARF5            0.132789  0.032162  0.093262 -0.260608  0.014748  \n",
       "1       M6PR            0.078505  0.129128  0.193226 -0.458337  0.285326  \n",
       "2       FKBP4           0.069089  0.197630 -0.030261 -0.049011  0.036173  \n",
       "\n",
       "[3 rows x 128 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_df = (\n",
    "    pd.read_csv(raw_embeddings_path, sep=\" \", skiprows=1, index_col=0, header=None)\n",
    "    .rename_axis(index=\"node_id\")\n",
    "    .add_prefix(\"emb_\")\n",
    "    .sort_index()\n",
    ")\n",
    "embedding_df[\"preferred_name\"] = embedding_df.index.map(index_to_name)\n",
    "embedding_df = embedding_df.set_index(\"preferred_name\", append=True)\n",
    "embedding_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19354"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(embedding_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_df.to_csv(embeddings_path, sep=\"\\t\", float_format=\"%.6g\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot first 2 embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "%matplotlib inline\n",
    "seaborn.scatterplot(\"emb_1\", \"emb_2\", data=embedding_df, alpha=0.1);"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}