203 lines (202 with data), 5.0 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "import numpy\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, auc, confusion_matrix, f1_score, precision_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "t = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infile = \"/repos/tables/glom_xml_split.tab\"\n",
    "df = pd.read_table(infile, usecols=[\"file_id\", \"split\"])\n",
    "# df['png'] = df.file_id.map(lambda x: x+\".png\")\n",
    "df = df.set_index(\"file_id\")[\"split\"]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.set_index('id', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# indir = \"/repos/data/glom/data_1024/glom_split/all\"\n",
    "# indir = \"/repos/data/glom/data_512_subsample_2x/glom_split/all\"\n",
    "indir = \"/repos/data/glom/data_256_subsample_4x/glom_split/all\"\n",
    "indir = \"/repos/data/glom/data_128_subsample_8x/glom_split/all\"\n",
    "outdir = os.path.dirname(indir.rstrip('/'))\n",
    "def datagen(indir):\n",
    "    for dd in os.scandir(indir):\n",
    "        for ff in os.scandir(dd.path):\n",
    "#             if os.path.isdir(ff.path) or not (ff.name.endswith(\"png\") or ff.name.endswith(\"json\")):\n",
    "            if os.path.isdir(ff.path) or not (ff.name.endswith(\"json\")):\n",
    "                continue\n",
    "#             print(ff.name.split('-')[0], ff.path)\n",
    "            yield (ff.name.split('-')[0], ff)\n",
    "    \n",
    "def gen_set(indir, outdir, df):\n",
    "    for slideid, ff in datagen(indir):\n",
    "        posnegset = os.path.basename(os.path.dirname(ff.path))\n",
    "        set_ = df.loc[slideid]\n",
    "        yield ff.path, os.path.join(outdir, set_, posnegset, ff.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11710"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen = gen_set(indir, outdir, df)\n",
    "sum((1 for _ in gen))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/repos/data/glom/data_128_subsample_8x/glom_split/train/normal\n",
      "/repos/data/glom/data_128_subsample_8x/glom_split/train/glom\n",
      "/repos/data/glom/data_128_subsample_8x/glom_split/test/normal\n",
      "/repos/data/glom/data_128_subsample_8x/glom_split/test/glom\n",
      "/repos/data/glom/data_128_subsample_8x/glom_split/val/normal\n",
      "/repos/data/glom/data_128_subsample_8x/glom_split/val/glom\n"
     ]
    }
   ],
   "source": [
    "posnegset = os.listdir(indir)\n",
    "for _, set_ in df.drop_duplicates().items():\n",
    "    for pn in posnegset:\n",
    "        setdir = os.path.join(outdir, set_, pn)\n",
    "        print(setdir)\n",
    "        os.makedirs(setdir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n",
      "7000\n",
      "8000\n",
      "9000\n",
      "10000\n",
      "11000\n"
     ]
    }
   ],
   "source": [
    "gen = gen_set(indir, outdir, df)\n",
    "for nn, (ifn, ofn) in enumerate(gen):\n",
    "    try:\n",
    "        os.symlink(ifn, ofn)\n",
    "    except FileExistsError as ee:\n",
    "        print(ee)\n",
    "        continue\n",
    "    if nn % 1000 == 0:\n",
    "        print(nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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