fsfp_breakdown.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import argparse\n",
    "from itertools import chain\n",
    "from collections import defaultdict\n",
    "import torch\n",
    "\n",
    "from fsfp import config\n",
    "from fsfp.utils.data import trunc_sequence\n",
    "from fsfp.retrieval import Retriever"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1. Preprocess data <br>\n",
    "Learning objective: \n",
    "1. Structure of raw data\n",
    "2. Structure of data after \"preprocess.py\"\n",
    "3. Recreate the preprocess steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data preprocessing <br>\n",
    "The datasets of ProteinGym should be put under `data/substitutions/`. Run `python preprocess.py -s` to preprocess the raw datasets and pack them to `data/merged.pkl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## In preprocess.py\n",
    "\n",
    "# data_path = config.data_path.replace('.pkl', f'{args.save_postfix}.pkl') <- replace file suffix(.pkl) with user suffix with user-defined suffix in filename \n",
    "# datasets = merge_files(config.raw_data_dir, shuffle=args.shuffle, max_len=args.max_length, save_path=data_path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def merge_files(data_dir, shuffle=True, max_len=1022, save_path=None):\n",
    "#     file_names = os.listdir(data_dir)\n",
    "#     proteins = defaultdict(list) # Declare that the values in this dict are lists\n",
    "#     for file_name in file_names:\n",
    "#         if 'indels' in file_name:\n",
    "#             continue # If \"indel\" in file_name, skip the file\n",
    "#         protein = process_dms(f'{data_dir}/{file_name}', shuffle, max_len) # Carry out \"process_dms\" function on the csv file \n",
    "#         name = '_'.join(file_name.split('_')[:2]) # Extract the first 2 parts of the file name that is separated by underscore\n",
    "#         proteins[name].append(protein) # Add a key-value pair: key - name, value - output of \"process_dms\" function \n",
    "                                         # Final data structure of proteins: dict: key-name, value: list of dictionary \n",
    "                                         # Value dict: keys(['wild_type', 'df', 'n_sites', 'name', 'offset']) \n",
    "                                         # wile_type aa seqeuence\n",
    "                                         # df containing ['wt_aas', 'mt_aas', 'positions', 'DMS_score', 'DMS_score_bin'], index: 'mutant'\n",
    "                                         # n_sites: number of mutations (e.g. for ssvl, it would be 1)\n",
    "                                         # name: csv dataset name\n",
    "                                         # offset: for truncation  \n",
    "    \n",
    "#     if save_path is not None:\n",
    "#         make_dir(save_path) \n",
    "#         torch.save(proteins, save_path) # Same the dictionary in \"merged.pkl\"\n",
    "#     return proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SYUA_HUMAN_Newberry_2020.csv', 'YAP1_HUMAN_Araya_2012.csv']\n",
      "Index(['mutated_sequence', 'DMS_score', 'DMS_score_bin',\n",
      "       'Tranception_L_no_retrieval', 'Tranception_S_retrieval',\n",
      "       'Tranception_M_retrieval', 'Tranception_L_retrieval', 'EVE_single',\n",
      "       'EVE_ensemble', 'MSA_Transformer_single', 'MSA_Transformer_ensemble',\n",
      "       'ESM1v_single', 'ESM1v_ensemble', 'Wavenet', 'DeepSequence_single',\n",
      "       'DeepSequence_ensemble', 'Site_Independent', 'EVmutation', 'RITA_s',\n",
      "       'RITA_m', 'RITA_l', 'RITA_xl', 'RITA_ensemble', 'Progen2_small',\n",
      "       'Progen2_medium', 'Progen2_base', 'Progen2_large', 'Progen2_xlarge',\n",
      "       'Progen2_ensemble', 'Unirep', 'Unirep_evotune', 'GEMME', 'VESPA',\n",
      "       'VESPAl', 'ESM1b', 'ProtGPT2', 'TranceptEVE_L'],\n",
      "      dtype='object') 10075\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mutated_sequence</th>\n",
       "      <th>DMS_score</th>\n",
       "      <th>DMS_score_bin</th>\n",
       "      <th>...</th>\n",
       "      <th>ESM1b</th>\n",
       "      <th>ProtGPT2</th>\n",
       "      <th>TranceptEVE_L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E178A:M179I</th>\n",
       "      <td>MDPGQQPPPQPAPQGQ...</td>\n",
       "      <td>0.857</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-19.548</td>\n",
       "      <td>-6.515</td>\n",
       "      <td>-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E178A:L190R</th>\n",
       "      <td>MDPGQQPPPQPAPQGQ...</td>\n",
       "      <td>0.659</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-25.905</td>\n",
       "      <td>-6.483</td>\n",
       "      <td>-0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D170K:Q200R</th>\n",
       "      <td>MDPGQQPPPQPAPQGQ...</td>\n",
       "      <td>4.720</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-27.366</td>\n",
       "      <td>-6.508</td>\n",
       "      <td>-0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D170G:T198R</th>\n",
       "      <td>MDPGQQPPPQPAPQGQ...</td>\n",
       "      <td>0.877</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-21.221</td>\n",
       "      <td>-6.499</td>\n",
       "      <td>-0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D170G:P172L</th>\n",
       "      <td>MDPGQQPPPQPAPQGQ...</td>\n",
       "      <td>0.923</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-19.350</td>\n",
       "      <td>-6.541</td>\n",
       "      <td>-0.052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mutated_sequence  DMS_score  DMS_score_bin  ...   ESM1b  \\\n",
       "mutant                                                      ...           \n",
       "E178A:M179I  MDPGQQPPPQPAPQGQ...      0.857              1  ... -19.548   \n",
       "E178A:L190R  MDPGQQPPPQPAPQGQ...      0.659              1  ... -25.905   \n",
       "D170K:Q200R  MDPGQQPPPQPAPQGQ...      4.720              1  ... -27.366   \n",
       "D170G:T198R  MDPGQQPPPQPAPQGQ...      0.877              1  ... -21.221   \n",
       "D170G:P172L  MDPGQQPPPQPAPQGQ...      0.923              1  ... -19.350   \n",
       "\n",
       "             ProtGPT2  TranceptEVE_L  \n",
       "mutant                                \n",
       "E178A:M179I    -6.515         -0.046  \n",
       "E178A:L190R    -6.483         -0.058  \n",
       "D170K:Q200R    -6.508         -0.042  \n",
       "D170G:T198R    -6.499         -0.054  \n",
       "D170G:P172L    -6.541         -0.052  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_path = \"/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/Pro-FSFP/\"\n",
    "data_dir = os.path.join(folder_path, config.raw_data_dir)\n",
    "file_names = os.listdir(data_dir)\n",
    "print(file_names) # Currently there are 2 datasets in \"data/substitutions\"\n",
    "csv = file_names[1]\n",
    "df = pd.read_csv(data_dir + \"/\" + csv, index_col='mutant')\n",
    "print(df.columns, len(df))\n",
    "\n",
    "df = df.sample(frac=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def process_dms(file_path, shuffle=True, max_len=1022, wild_type=None): # There is option to provide the full wild_type aa sequence in str\n",
    "#     df = pd.read_csv(file_path, index_col='mutant')\n",
    "#     if shuffle:\n",
    "#         df = df.sample(frac=1)\n",
    "   \n",
    "#     new_df, n_sites = defaultdict(list), set()\n",
    "#     for mutant, row in df.iterrows(): # row is the entire row... \n",
    "#         wt_aas, mt_aas, positions = '', '', []\n",
    "#         for site in mutant.split(':'): # handle multi-site mutants\n",
    "#             wt_aa, position, mt_aa = site[0], int(site[1:-1]) - 1, site[-1]\n",
    "#             if wild_type is None:\n",
    "#                 assert row['mutated_sequence'][position] == mt_aa # If condition returns False, raise AssertionError # No error handlelation?\n",
    "#             else:\n",
    "#                 assert wild_type[position] == wt_aa\n",
    "#             wt_aas += wt_aa\n",
    "#             mt_aas += mt_aa\n",
    "#             positions.append(position)\n",
    "        \n",
    "#         new_df['wt_aas'].append(wt_aas)\n",
    "#         new_df['mt_aas'].append(mt_aas)\n",
    "#         new_df['positions'].append(tuple(positions))\n",
    "#         n_sites.add(len(positions)) # Number of mutation sites for each variant \n",
    "    \n",
    "#     new_df = pd.concat([pd.DataFrame(new_df, index=df.index),\n",
    "#                         df[['DMS_score', 'DMS_score_bin']]], axis=1)\n",
    "#     if wild_type is None:\n",
    "#         wild_type = list(row['mutated_sequence'])\n",
    "#         for wt_aa, position in zip(wt_aas, positions): # recover wild type sequence using the last row\n",
    "#             wild_type[position] = wt_aa\n",
    "#         wild_type = ''.join(wild_type)\n",
    "#     protein = dict(wild_type=wild_type, df=new_df)\n",
    "#     trunc_sequence(protein, max_len) # Why truncate sequence? \n",
    "#     protein['n_sites'] = sorted(n_sites)\n",
    "#     protein['name'] = os.path.basename(file_path).split('.')[0]\n",
    "#     return protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wt_aas</th>\n",
       "      <th>mt_aas</th>\n",
       "      <th>positions</th>\n",
       "      <th>DMS_score</th>\n",
       "      <th>DMS_score_bin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E178A:M179I</th>\n",
       "      <td>EM</td>\n",
       "      <td>AI</td>\n",
       "      <td>(177, 178)</td>\n",
       "      <td>0.857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E178A:L190R</th>\n",
       "      <td>EL</td>\n",
       "      <td>AR</td>\n",
       "      <td>(177, 189)</td>\n",
       "      <td>0.659</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D170K:Q200R</th>\n",
       "      <td>DQ</td>\n",
       "      <td>KR</td>\n",
       "      <td>(169, 199)</td>\n",
       "      <td>4.720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D170G:T198R</th>\n",
       "      <td>DT</td>\n",
       "      <td>GR</td>\n",
       "      <td>(169, 197)</td>\n",
       "      <td>0.877</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D170G:P172L</th>\n",
       "      <td>DP</td>\n",
       "      <td>GL</td>\n",
       "      <td>(169, 171)</td>\n",
       "      <td>0.923</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q195K:Q200H</th>\n",
       "      <td>QQ</td>\n",
       "      <td>KH</td>\n",
       "      <td>(194, 199)</td>\n",
       "      <td>2.976</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E178N:I193S</th>\n",
       "      <td>EI</td>\n",
       "      <td>NS</td>\n",
       "      <td>(177, 192)</td>\n",
       "      <td>0.784</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R187P:Q200E</th>\n",
       "      <td>RQ</td>\n",
       "      <td>PE</td>\n",
       "      <td>(186, 199)</td>\n",
       "      <td>0.626</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T182Y</th>\n",
       "      <td>T</td>\n",
       "      <td>Y</td>\n",
       "      <td>(181,)</td>\n",
       "      <td>0.867</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R187S:Q195H</th>\n",
       "      <td>RQ</td>\n",
       "      <td>SH</td>\n",
       "      <td>(186, 194)</td>\n",
       "      <td>0.921</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10075 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            wt_aas mt_aas   positions  DMS_score  DMS_score_bin\n",
       "mutant                                                         \n",
       "E178A:M179I     EM     AI  (177, 178)      0.857              1\n",
       "E178A:L190R     EL     AR  (177, 189)      0.659              1\n",
       "D170K:Q200R     DQ     KR  (169, 199)      4.720              1\n",
       "D170G:T198R     DT     GR  (169, 197)      0.877              1\n",
       "D170G:P172L     DP     GL  (169, 171)      0.923              1\n",
       "...            ...    ...         ...        ...            ...\n",
       "Q195K:Q200H     QQ     KH  (194, 199)      2.976              1\n",
       "E178N:I193S     EI     NS  (177, 192)      0.784              1\n",
       "R187P:Q200E     RQ     PE  (186, 199)      0.626              1\n",
       "T182Y            T      Y      (181,)      0.867              1\n",
       "R187S:Q195H     RQ     SH  (186, 194)      0.921              1\n",
       "\n",
       "[10075 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 0\n",
    "wild_type = None\n",
    "\n",
    "new_df, n_sites = defaultdict(list), set()\n",
    "for mutant, row in df.iterrows():\n",
    "    wt_aas, mt_aas, positions = '', '', []\n",
    "    for site in mutant.split(':'): # handle multi-site mutants\n",
    "        wt_aa, position, mt_aa = site[0], int(site[1:-1]) - 1, site[-1]\n",
    "        # print(wt_aa, position, mt_aa)\n",
    "        wt_aas += wt_aa\n",
    "        mt_aas += mt_aa\n",
    "        positions.append(position)\n",
    "\n",
    "    new_df['wt_aas'].append(wt_aas)\n",
    "    new_df['mt_aas'].append(mt_aas)\n",
    "    new_df['positions'].append(tuple(positions))\n",
    "    n_sites.add(len(positions)) # Number of mutation sites for each variant \n",
    "\n",
    "    # i += 1\n",
    "    # if i == 4:\n",
    "    #     break\n",
    "    \n",
    "new_df = pd.concat([pd.DataFrame(new_df, index=df.index),\n",
    "                    df[['DMS_score', 'DMS_score_bin']]], axis=1)\n",
    "    \n",
    "if wild_type is None:\n",
    "    wild_type = list(row['mutated_sequence'])\n",
    "    for wt_aa, position in zip(wt_aas, positions): # recover wild type sequence using the last row\n",
    "        wild_type[position] = wt_aa\n",
    "    wild_type = ''.join(wild_type)\n",
    "protein = dict(wild_type=wild_type, df=new_df)\n",
    "# trunc_sequence(protein, max_len) # Only carries out if protein wilt_type len is over max_len # How is max_len determined? \n",
    "protein['n_sites'] = sorted(n_sites)\n",
    "protein['name'] = os.path.basename(csv).split('.')[0]\n",
    "    \n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['wild_type', 'df', 'n_sites', 'name'])\n",
      "MDPGQQPPPQPAPQGQGQPPSQPPQGQGPPSGPGQPAPAATQAAPQAPPAGHQIVHVRGDSETDLEALFNAVMNPKTANVPQTVPMRLRKLPDSFFKPPEPKSHSRQASTDAGTAGALTPQHVRAHSSPASLQLGAVSPGTLTPTGVVSGPAATPTAQHLRQSSFEIPDDVPLPAGWEMAKTSSGQRYFLNHIDQTTTWQDPRKAMLSQMNVTAPTSPPVQQNMMNSASGPLPDGWEQAMTQDGEIYYINHKNKTTSWLDPRLDPRFAMNQRISQSAPVKQPPPLAPQSPQGGVMGGSNSNQQQQMRLQQLQMEKERLRLKQQELLRQAMRNINPSTANSPKCQELALRSQLPTLEQDGGTQNPVSSPGMSQELRTMTTNSSDPFLNSGTYHSRDESTDSGLSMSSYSVPRTPDDFLNSVDEMDTGDTINQSTLPSQQNRFPDYLEAIPGTNVDLGTLEGDGMNIEGEELMPSLQEALSSDILNDMESVLAATKLDKESFLTWL\n",
      "[1, 2]\n",
      "YAP1_HUMAN_Araya_2012\n"
     ]
    }
   ],
   "source": [
    "print(protein.keys())\n",
    "print(protein['wild_type'])\n",
    "print(protein['n_sites'])\n",
    "print(protein['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['wild_type', 'df', 'n_sites', 'name', 'offset'])\n",
      "Index(['wt_aas', 'mt_aas', 'positions', 'DMS_score', 'DMS_score_bin'], dtype='object')\n",
      "            wt_aas mt_aas   positions  DMS_score  DMS_score_bin\n",
      "mutant                                                         \n",
      "E178A:M179I     EM     AI  (177, 178)      0.857              1\n",
      "E178A:L190R     EL     AR  (177, 189)      0.659              1\n",
      "D170K:Q200R     DQ     KR  (169, 199)      4.720              1\n",
      "D170G:T198R     DT     GR  (169, 197)      0.877              1\n",
      "D170G:P172L     DP     GL  (169, 171)      0.923              1\n",
      "...            ...    ...         ...        ...            ...\n",
      "Q195K:Q200H     QQ     KH  (194, 199)      2.976              1\n",
      "E178N:I193S     EI     NS  (177, 192)      0.784              1\n",
      "R187P:Q200E     RQ     PE  (186, 199)      0.626              1\n",
      "T182Y            T      Y      (181,)      0.867              1\n",
      "R187S:Q195H     RQ     SH  (186, 194)      0.921              1\n",
      "\n",
      "[10075 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "trunc_sequence(protein, 1022) \n",
    "print(protein.keys())\n",
    "print(protein['df'].columns)\n",
    "print(protein['df'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "34\n"
     ]
    }
   ],
   "source": [
    "positions = list(chain(*new_df['positions']))\n",
    "print(positions)\n",
    "max_pos, min_pos = max(positions), min(positions)\n",
    "gap = max_pos - min_pos + 1\n",
    "print(gap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skipping further breakdown of this function for now. 2024.09.17\n",
    "\n",
    "# def trunc_sequence(protein, max_len):\n",
    "#     L = len(protein['wild_type'])\n",
    "#     if L <= max_len:\n",
    "#         protein['offset'] = 0\n",
    "#         return\n",
    "    \n",
    "#     df = protein['df']\n",
    "#     positions = list(chain(*df['positions']))\n",
    "#     max_pos, min_pos = max(positions), min(positions)\n",
    "#     gap = max_pos - min_pos + 1\n",
    "    \n",
    "#     if max_pos < max_len:\n",
    "#         protein['wild_type'] = protein['wild_type'][:max_len] # If max_pos is smaller than max_len, remove the sequences exceeding max_len?\n",
    "#         protein['offset'] = 0\n",
    "#         return\n",
    "    \n",
    "#     if gap <= max_len:\n",
    "#         window_l = max(min_pos - (max_len - gap) // 2, 0)\n",
    "#         window_r = min(max_pos + (max_len - gap) // 2, L - 1)\n",
    "#         seq_lr = protein['wild_type'][window_l: window_l + max_len]\n",
    "#         seq_rl = protein['wild_type'][window_r - max_len + 1: window_r + 1]\n",
    "        \n",
    "#         if len(seq_lr) > len(seq_rl):\n",
    "#             protein['wild_type'] = seq_lr\n",
    "#             left, right = window_l, window_l + max_len\n",
    "#         else:\n",
    "#             protein['wild_type'] = seq_rl\n",
    "#             left, right = window_r - max_len + 1, window_r + 1\n",
    "#     else:\n",
    "#         n = 0\n",
    "#         left, right = min_pos, max_len\n",
    "#         window_l, window_r = min_pos, max_len\n",
    "#         while window_r < L:\n",
    "#             window_n = df['positions'].apply(\n",
    "#                 lambda positions: all(window_l <= pos < window_r for pos in positions)).sum()\n",
    "#             if window_n > n:\n",
    "#                 left, right = window_l, window_r\n",
    "#                 n = window_n\n",
    "#             window_l += 1\n",
    "#             window_r += 1\n",
    "        \n",
    "#         if right - left + 1 < max_len:\n",
    "#             left = right - max_len\n",
    "#         protein['wild_type'] = protein['wild_type'][left:right]\n",
    "    \n",
    "#     df_bool = df.apply(lambda row: all(left <= pos < right for pos in row['positions']), axis=1)\n",
    "#     df = df.loc[df_bool].copy()\n",
    "#     df.loc[:, 'positions'] = df['positions'].apply(lambda positions: tuple(pos - left for pos in positions))\n",
    "#     protein['df'] = df\n",
    "#     protein['offset'] = left\n",
    "#     return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/Pro-FSFP/data/merged.pkl\n",
      "{'SYUA_HUMAN': 'MDVYMKGLSKAKEGVVAAAEKTKQGVAEAAGKTKEGVLFVGSKTKEGVVHGVATVAEKTKEQVTNVGGAVVTGVTAVAQKTVEGAGSIAAATGYVKKDQLGKNEEGAPQEGILEDMPVDPDNEAFEMPSEEGFQDFEPEA', 'YAP1_HUMAN': 'MDPGQQPPPQPAPQGQGQPPSQPPQGQGPPSGPGQPAPAATQAAPQAPPAGHQIVHVRGDSETDLEALFNAVMNPKTANVPQTVPMRLRKLPDSFFKPPEPKSHSRQASTDAGTAGALTPQHVRAHSSPASLQLGAVSPGTLTPTGVVSGPAATPTAQHLRQSSFEIPDDVPLPAGWEMAKTSSGQRYFLNHIDQTTTWQDPRKAMLSQMNVTAPTSPPVQQNMMNSASGPLPDGWEQAMTQDGEIYYINHKNKTTSWLDPRLDPRFAMNQRISQSAPVKQPPPLAPQSPQGGVMGGSNSNQQQQMRLQQLQMEKERLRLKQQELLRQAMRNINPSTANSPKCQELALRSQLPTLEQDGGTQNPVSSPGMSQELRTMTTNSSDPFLNSGTYHSRDESTDSGLSMSSYSVPRTPDDFLNSVDEMDTGDTINQSTLPSQQNRFPDYLEAIPGTNVDLGTLEGDGMNIEGEELMPSLQEALSSDILNDMESVLAATKLDKESFLTWL'}\n"
     ]
    }
   ],
   "source": [
    "data_path = os.path.join(folder_path, config.data_path)\n",
    "print(data_path)\n",
    "proteins = torch.load(data_path)\n",
    "\n",
    "sequences = {name: datasets[0]['wild_type'] for name, datasets in proteins.items()}\n",
    "print(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode: vectorize\n",
      "Model: esm2\n",
      "Batch Size: 64\n"
     ]
    }
   ],
   "source": [
    "# Simulate argparse behavior in Jupyter\n",
    "\n",
    "# Create a class to mimic argparse.Namespace\n",
    "class Args:\n",
    "    def __init__(self, **kwargs):\n",
    "        self.__dict__.update(kwargs)\n",
    "\n",
    "# Define default arguments in a dictionary\n",
    "args_dict = {\n",
    "    'mode': 'vectorize',\n",
    "    'model': 'esm2',\n",
    "    'pooling': 'average',\n",
    "    'batch_size': 64,\n",
    "    'top_k': 10,\n",
    "    'metric': 'cosine',\n",
    "    'force_cpu': False,\n",
    "    'save_postfix': ''\n",
    "}\n",
    "\n",
    "# Simulate the parser behavior and return an Args object\n",
    "def parse_args_notebook():\n",
    "    # Return an Args object with the simulated arguments\n",
    "    return Args(**args_dict)\n",
    "\n",
    "# Now you can access arguments just like you would with argparse\n",
    "args = parse_args_notebook()\n",
    "\n",
    "# Example usage\n",
    "print(\"Mode:\", args.mode)\n",
    "print(\"Model:\", args.model)\n",
    "print(\"Batch Size:\", args.batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "unable to mmap 2609506392 bytes from file </home/lyi/.cache/huggingface/hub/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/model.safetensors>: Cannot allocate memory (12)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[50], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m retriever \u001b[38;5;241m=\u001b[39m Retriever(args)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mretriever\u001b[49m\u001b[43m(\u001b[49m\u001b[43msequences\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/Pro-FSFP/fsfp/retrieval.py:90\u001b[0m, in \u001b[0;36mRetriever.__call__\u001b[0;34m(self, proteins)\u001b[0m\n\u001b[1;32m     88\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m args\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvectorize\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 90\u001b[0m     vectors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_protein_vectors\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mproteins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     91\u001b[0m     vectors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(names\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(proteins\u001b[38;5;241m.\u001b[39mkeys()), vectors\u001b[38;5;241m=\u001b[39mvectors)\n\u001b[1;32m     92\u001b[0m     save_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_save_path(args\u001b[38;5;241m.\u001b[39mmode)\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/Pro-FSFP/fsfp/retrieval.py:60\u001b[0m, in \u001b[0;36mRetriever.get_protein_vectors\u001b[0;34m(self, proteins)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_protein_vectors\u001b[39m(\u001b[38;5;28mself\u001b[39m, proteins):\n\u001b[1;32m     59\u001b[0m     args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\n\u001b[0;32m---> 60\u001b[0m     model, tokenizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_base_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_constructor(proteins, tokenizer, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m     62\u001b[0m     data_iter \u001b[38;5;241m=\u001b[39m DataLoader(data, batch_size\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mbatch_size, collate_fn\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mcollate)\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/Pro-FSFP/fsfp/retrieval.py:47\u001b[0m, in \u001b[0;36mRetriever.get_base_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_base_model\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     46\u001b[0m     model_name \u001b[38;5;241m=\u001b[39m model_names[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mmodel]\n\u001b[0;32m---> 47\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[43mEsmForMaskedLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     48\u001b[0m     tokenizer \u001b[38;5;241m=\u001b[39m EsmTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(model_name)\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m model, tokenizer\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/transformers/modeling_utils.py:3613\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3593\u001b[0m     resolved_archive_file, sharded_metadata \u001b[38;5;241m=\u001b[39m get_checkpoint_shard_files(\n\u001b[1;32m   3594\u001b[0m         pretrained_model_name_or_path,\n\u001b[1;32m   3595\u001b[0m         resolved_archive_file,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3605\u001b[0m         _commit_hash\u001b[38;5;241m=\u001b[39mcommit_hash,\n\u001b[1;32m   3606\u001b[0m     )\n\u001b[1;32m   3608\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   3609\u001b[0m     is_safetensors_available()\n\u001b[1;32m   3610\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resolved_archive_file, \u001b[38;5;28mstr\u001b[39m)\n\u001b[1;32m   3611\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m resolved_archive_file\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.safetensors\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   3612\u001b[0m ):\n\u001b[0;32m-> 3613\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43msafe_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframework\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m   3614\u001b[0m         metadata \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mmetadata()\n\u001b[1;32m   3616\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m metadata\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[0;31mRuntimeError\u001b[0m: unable to mmap 2609506392 bytes from file </home/lyi/.cache/huggingface/hub/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/model.safetensors>: Cannot allocate memory (12)"
     ]
    }
   ],
   "source": [
    "retriever = Retriever(args)\n",
    "retriever(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] [--mode {vectorize,retrieve}]\n",
      "                             [--model {esm1b,esm1v-1,esm1v-2,esm1v-3,esm1v-4,esm1v-5,esm2,saprot}]\n",
      "                             [--pooling {average,max,last}]\n",
      "                             [--batch_size BATCH_SIZE] [--top_k TOP_K]\n",
      "                             [--metric {cosine,l2}] [--force_cpu]\n",
      "                             [--save_postfix SAVE_POSTFIX]\n",
      "ipykernel_launcher.py: error: argument --force_cpu/-cpu: ignored explicit argument '/home/lyi/.local/share/jupyter/runtime/kernel-v39fb4683d00544df002713de5697d64fc1a41bca1.json'\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'tb_frame'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/argparse.py:1866\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1865\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1866\u001b[0m     namespace, args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1867\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ArgumentError:\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/argparse.py:2079\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args\u001b[0;34m(self, arg_strings, namespace)\u001b[0m\n\u001b[1;32m   2078\u001b[0m     \u001b[38;5;66;03m# consume the next optional and any arguments for it\u001b[39;00m\n\u001b[0;32m-> 2079\u001b[0m     start_index \u001b[38;5;241m=\u001b[39m \u001b[43mconsume_optional\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2081\u001b[0m \u001b[38;5;66;03m# consume any positionals following the last Optional\u001b[39;00m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/argparse.py:2001\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args.<locals>.consume_optional\u001b[0;34m(start_index)\u001b[0m\n\u001b[1;32m   2000\u001b[0m         msg \u001b[38;5;241m=\u001b[39m _(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignored explicit argument \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 2001\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ArgumentError(action, msg \u001b[38;5;241m%\u001b[39m explicit_arg)\n\u001b[1;32m   2003\u001b[0m \u001b[38;5;66;03m# if there is no explicit argument, try to match the\u001b[39;00m\n\u001b[1;32m   2004\u001b[0m \u001b[38;5;66;03m# optional's string arguments with the following strings\u001b[39;00m\n\u001b[1;32m   2005\u001b[0m \u001b[38;5;66;03m# if successful, exit the loop\u001b[39;00m\n\u001b[1;32m   2006\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mArgumentError\u001b[0m: argument --force_cpu/-cpu: ignored explicit argument '/home/lyi/.local/share/jupyter/runtime/kernel-v39fb4683d00544df002713de5697d64fc1a41bca1.json'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mSystemExit\u001b[0m                                Traceback (most recent call last)",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "Cell \u001b[0;32mIn[46], line 21\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\u001b[38;5;241m.\u001b[39mparse_args()\n\u001b[0;32m---> 21\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[43mparse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[46], line 19\u001b[0m, in \u001b[0;36mparse_args\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m parser\u001b[38;5;241m.\u001b[39madd_argument(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--save_postfix\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-sp\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m, default\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     18\u001b[0m                     help\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma custom string to append to the data path\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/argparse.py:1833\u001b[0m, in \u001b[0;36mArgumentParser.parse_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1832\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse_args\u001b[39m(\u001b[38;5;28mself\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, namespace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 1833\u001b[0m     args, argv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1834\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m argv:\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/argparse.py:1869\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1868\u001b[0m         err \u001b[38;5;241m=\u001b[39m _sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m-> 1869\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merr\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1870\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/argparse.py:2594\u001b[0m, in \u001b[0;36mArgumentParser.error\u001b[0;34m(self, message)\u001b[0m\n\u001b[1;32m   2593\u001b[0m args \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprog\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprog, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m'\u001b[39m: message}\n\u001b[0;32m-> 2594\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%(prog)s\u001b[39;49;00m\u001b[38;5;124;43m: error: \u001b[39;49m\u001b[38;5;132;43;01m%(message)s\u001b[39;49;00m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/argparse.py:2581\u001b[0m, in \u001b[0;36mArgumentParser.exit\u001b[0;34m(self, status, message)\u001b[0m\n\u001b[1;32m   2580\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_print_message(message, _sys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[0;32m-> 2581\u001b[0m \u001b[43m_sys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mSystemExit\u001b[0m: 2",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2095\u001b[0m, in \u001b[0;36mInteractiveShell.showtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2092\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exception_only:\n\u001b[1;32m   2093\u001b[0m     stb \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAn exception has occurred, use \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mtb to see \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2094\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe full traceback.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m-> 2095\u001b[0m     stb\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mInteractiveTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_exception_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2096\u001b[0m \u001b[43m                                                     \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   2097\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2098\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2099\u001b[0m         \u001b[38;5;66;03m# Exception classes can customise their traceback - we\u001b[39;00m\n\u001b[1;32m   2100\u001b[0m         \u001b[38;5;66;03m# use this in IPython.parallel for exceptions occurring\u001b[39;00m\n\u001b[1;32m   2101\u001b[0m         \u001b[38;5;66;03m# in the engines. This should return a list of strings.\u001b[39;00m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/ultratb.py:710\u001b[0m, in \u001b[0;36mListTB.get_exception_only\u001b[0;34m(self, etype, value)\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_exception_only\u001b[39m(\u001b[38;5;28mself\u001b[39m, etype, value):\n\u001b[1;32m    703\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Only print the exception type and message, without a traceback.\u001b[39;00m\n\u001b[1;32m    704\u001b[0m \n\u001b[1;32m    705\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[38;5;124;03m    value : exception value\u001b[39;00m\n\u001b[1;32m    709\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 710\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mListTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/ultratb.py:568\u001b[0m, in \u001b[0;36mListTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, context)\u001b[0m\n\u001b[1;32m    565\u001b[0m     chained_exc_ids\u001b[38;5;241m.\u001b[39madd(\u001b[38;5;28mid\u001b[39m(exception[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m    566\u001b[0m     chained_exceptions_tb_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    567\u001b[0m     out_list \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 568\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[43m            \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    570\u001b[0m \u001b[43m            \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    571\u001b[0m \u001b[43m            \u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchained_exc_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    572\u001b[0m \u001b[43m            \u001b[49m\u001b[43mchained_exceptions_tb_offset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    573\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    574\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    575\u001b[0m         \u001b[38;5;241m+\u001b[39m chained_exception_message\n\u001b[1;32m    576\u001b[0m         \u001b[38;5;241m+\u001b[39m out_list)\n\u001b[1;32m    578\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out_list\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/ultratb.py:1428\u001b[0m, in \u001b[0;36mAutoFormattedTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1426\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1427\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtb \u001b[38;5;241m=\u001b[39m etb\n\u001b[0;32m-> 1428\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mFormattedTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1429\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\n\u001b[1;32m   1430\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/ultratb.py:1319\u001b[0m, in \u001b[0;36mFormattedTB.structured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1316\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode\n\u001b[1;32m   1317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose_modes:\n\u001b[1;32m   1318\u001b[0m     \u001b[38;5;66;03m# Verbose modes need a full traceback\u001b[39;00m\n\u001b[0;32m-> 1319\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVerboseTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1320\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\n\u001b[1;32m   1321\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1322\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMinimal\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m   1323\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ListTB\u001b[38;5;241m.\u001b[39mget_exception_only(\u001b[38;5;28mself\u001b[39m, etype, value)\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/ultratb.py:1172\u001b[0m, in \u001b[0;36mVerboseTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstructured_traceback\u001b[39m(\n\u001b[1;32m   1164\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1165\u001b[0m     etype: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1169\u001b[0m     number_of_lines_of_context: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m   1170\u001b[0m ):\n\u001b[1;32m   1171\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1172\u001b[0m     formatted_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat_exception_as_a_whole\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1173\u001b[0m \u001b[43m                                                           \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1175\u001b[0m     colors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mColors  \u001b[38;5;66;03m# just a shorthand + quicker name lookup\u001b[39;00m\n\u001b[1;32m   1176\u001b[0m     colorsnormal \u001b[38;5;241m=\u001b[39m colors\u001b[38;5;241m.\u001b[39mNormal  \u001b[38;5;66;03m# used a lot\u001b[39;00m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/ultratb.py:1062\u001b[0m, in \u001b[0;36mVerboseTB.format_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1059\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tb_offset, \u001b[38;5;28mint\u001b[39m)\n\u001b[1;32m   1060\u001b[0m head \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_header(\u001b[38;5;28mstr\u001b[39m(etype), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlong_header)\n\u001b[1;32m   1061\u001b[0m records \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m-> 1062\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_records\u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m etb \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[1;32m   1063\u001b[0m )\n\u001b[1;32m   1065\u001b[0m frames \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m   1066\u001b[0m skipped \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m/illumina/scratch/SG_PROTEIN_ENG/iPEG/Liuyi/anaconda3/envs/cpeg-mlde/lib/python3.10/site-packages/IPython/core/ultratb.py:1130\u001b[0m, in \u001b[0;36mVerboseTB.get_records\u001b[0;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m cf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1129\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1130\u001b[0m         mod \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39mgetmodule(\u001b[43mcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtb_frame\u001b[49m)\n\u001b[1;32m   1131\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1132\u001b[0m             mod_name \u001b[38;5;241m=\u001b[39m mod\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'tb_frame'"
     ]
    }
   ],
   "source": [
    "def parse_args():\n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument('--mode', '-m', type=str, choices=['vectorize', 'retrieve'], default='retrieve',\n",
    "                        help='compute embedding vectors, or retrieve top-k dms datasets using the saved vectors')\n",
    "    parser.add_argument('--model', '-md', type=str, choices=config.model_dir.keys(), default='esm1v-1',\n",
    "                        help='name of the foundation model for vectorizing')\n",
    "    parser.add_argument('--pooling', '-p', type=str, choices=['average', 'max', 'last'], default='average',\n",
    "                        help='the method to reduce the model hidden states when vectorizing')\n",
    "    parser.add_argument('--batch_size', '-b', type=int, default=64,\n",
    "                        help='batch size for vectorizing and retrieving')\n",
    "    parser.add_argument('--top_k', '-k', type=int, default=10,\n",
    "                        help='number of similar datasets to retrieve')\n",
    "    parser.add_argument('--metric', '-mt', type=str, choices=['cosine', 'l2'], default='cosine',\n",
    "                        help='similarity metric')\n",
    "    parser.add_argument('--force_cpu', '-cpu', action='store_true',\n",
    "                        help='use cpu for vectorizing and retrieving even if gpu is available')\n",
    "    parser.add_argument('--save_postfix', '-sp', type=str, default='',\n",
    "                        help='a custom string to append to the data path')\n",
    "    return parser.parse_args()\n",
    "\n",
    "args = parse_args()"
   ]
  }
 ],
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