baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import biovec\n",
    "import numpy as np\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pv = biovec.models.load_protvec(\"swissprot-reviewed.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    }
   ],
   "source": [
    "subdatasets = [\"1000\",\"2000\", \"3000\", \"4000\", \"full\"]\n",
    "for subdataset in subdatasets:\n",
    "    with open(f\"./data/{subdataset}/full_vocab.txt\", \"r\")    as vocab_file:\n",
    "        vocab = [\n",
    "            w[:-1]\n",
    "            for w in vocab_file\n",
    "            if 1 == len(w[:-1].split())\n",
    "        ]\n",
    "    embeddings = []\n",
    "    oov = 0\n",
    "    for w in tqdm(vocab):\n",
    "        try:\n",
    "            e = np.sum(pv.to_vecs(w), axis=0)\n",
    "        except :\n",
    "            e = np.zeros(100)\n",
    "            oov+=1\n",
    "            print(oov)\n",
    "        embeddings.append(e)\n",
    "    X = np.array(embeddings)\n",
    "    np.save(f\"protvec_{subdataset}\",X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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