source/official-code/micro_hf/process_get_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "15e7bc6a-c8fd-4803-9907-6b744a5819be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2536cab8-1d4d-4d69-8d34-ba1547c892c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import json\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams.update({\n",
    "    \"figure.figsize\": (6, 4),\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.xmargin\": 0,\n",
    "    \"axes.grid\": True,\n",
    "    \"grid.linestyle\": \"--\",\n",
    "    \"grid.linewidth\": 0.5,\n",
    "    \"axes.titlesize\": 14,\n",
    "    \"axes.labelsize\": 12,\n",
    "    \"xtick.labelsize\": 10,\n",
    "    \"ytick.labelsize\": 10,\n",
    "    \"font.size\": 11,\n",
    "    \"lines.linewidth\": 2,\n",
    "    \"lines.markersize\": 6,\n",
    "    \"legend.frameon\": True,\n",
    "})\n",
    "\n",
    "# mpl.rcParams.update({\n",
    "#     \"figure.figsize\": (6, 4),\n",
    "#     \"axes.spines.top\": False,\n",
    "#     \"axes.spines.right\": False,\n",
    "#     \"axes.facecolor\": \"#fafafa\",\n",
    "#     \"axes.grid\": True,\n",
    "#     \"grid.color\": \"#dddddd\",\n",
    "#     \"grid.linestyle\": \"--\",\n",
    "#     \"axes.prop_cycle\": mpl.cycler(color=[\"#4C72B0\", \"#55A868\", \"#C44E52\",\n",
    "#                                          \"#8172B2\", \"#CCB974\", \"#64B5CD\"]),\n",
    "#     \"font.size\": 11,\n",
    "#     \"axes.labelsize\": 12,\n",
    "#     \"axes.titlesize\": 14,\n",
    "#     \"legend.frameon\": True,\n",
    "#     \"legend.facecolor\": \"white\",\n",
    "#     \"legend.edgecolor\": \"#dddddd\",\n",
    "#     \"lines.linewidth\": 2,\n",
    "# })\n",
    "\n",
    "from plt_utils import plot, savefig\n",
    "\n",
    "import os\n",
    "\n",
    "# task_name, data_name, default_window, default_token_dim = \"assoc-recall-mk\", \"data_100_0_8\", 20, 8\n",
    "# task_name, data_name, default_window, default_token_dim = \"assoc-recall-mk\", \"data_100_0_8\", 20, 16\n",
    "task_name, data_name, default_window, default_token_dim = \"assoc-recall-mk\", \"data_100_0_8\", 100, 16\n",
    "# task_name, data_name, default_window, default_token_dim = \"decode-recall\", \"data_100_2_32\", 100, 24\n",
    "# task_name, data_name, default_window, default_token_dim = \"decode-recall-last\", \"data_100_2_32\", 100, 24\n",
    "# task_name, data_name, default_window, default_token_dim = \"var-copy\", \"data_100_5_26\", 20, 8 # 20, 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d84cc6a2-95b6-4091-8434-d157a6605ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dashed_task_name = '-'.join(task_name.split('_'))\n",
    "\n",
    "all_losses = {}\n",
    "all_accs = {}\n",
    "all_train_accs = {}\n",
    "all_params = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "db00c2f6-aad7-4330-b4c0-35edc28bc8ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[0.5605889558792114, 0.5148648619651794, 0.5369927883148193, 0.5855569243431091]\n",
      "[0.4715840220451355, 0.44455578923225403, 0.4635152816772461, 0.4393274784088135]\n",
      "[0.5467730164527893, 0.494987815618515, 0.505121648311615, 0.5238168239593506]\n",
      "[0.1880994737148285, 0.1884227991104126, 0.23520445823669434, 0.19657085835933685]\n",
      "[0.18475568294525146, 0.18333245813846588, 0.16986429691314697, 0.13499131798744202]\n",
      "[0.197109192609787, 0.16555406153202057, 0.1804492175579071, 0.1629798412322998]\n",
      "[0.18367207050323486, 0.13623426854610443, 0.16557630896568298, 0.16539795696735382]\n",
      "[0.18415206670761108, 0.24716238677501678, 0.1998032033443451, 0.21723952889442444]\n",
      "[0.12298571318387985, 0.16041778028011322, 0.1728234589099884, 0.15679702162742615]\n",
      "[0.16060298681259155, 0.15445983409881592, 0.18857404589653015, 0.15877345204353333]\n",
      "[0.12338285148143768, 0.18647995591163635, 0.1803666651248932, 0.12597522139549255]\n",
      "[0.15500935912132263, 0.16162487864494324, 0.12478838115930557, 0.1442965865135193]\n",
      "[0.16292235255241394, 0.17213350534439087, 0.1576158106327057, 0.19896124303340912]\n",
      "[0.15974877774715424, 0.12086423486471176, 0.1438516527414322, 0.13088253140449524]\n",
      "[0.16023463010787964, 0.13415780663490295, 0.09367085993289948, 0.1931203156709671]\n",
      "[0.1558106392621994, 0.12730298936367035, 0.14712001383304596, 0.1555417776107788]\n",
      "[0.15544714033603668, 0.1491488516330719, 0.15549254417419434, 0.17903749644756317]\n",
      "[0.12290766090154648, 0.13187402486801147, 0.13219523429870605, 0.15463677048683167]\n",
      "[0.11848141998052597, 0.1640305519104004, 0.17488789558410645, 0.15792441368103027]\n",
      "[0.5898016095161438, 0.5868765711784363, 0.5797806978225708, 0.5788813829421997]\n",
      "[0.675767183303833, 0.6713101267814636, 0.6753434538841248, 0.6320018768310547]\n",
      "[0.4526846706867218, 0.4717397093772888, 0.4599359631538391, 0.40751567482948303]\n",
      "[0.5243709087371826, 0.4907861649990082, 0.5426511168479919, 0.5656881928443909]\n",
      "[0.5919266939163208, 0.5892937183380127, 0.5397747159004211, 0.6421780586242676]\n",
      "[0.16049334406852722, 0.16709402203559875, 0.23376663029193878, 0.14856134355068207]\n",
      "[0.6028116941452026, 0.6901167631149292, 0.6448677778244019, 0.6287549138069153]\n",
      "[0.5752226710319519, 0.5658721327781677, 0.6109029054641724, 0.5715929269790649]\n",
      "[0.1790245622396469, 0.1827176958322525, 0.2031310349702835, 0.1917020082473755]\n",
      "[0.5573130249977112, 0.5873966217041016, 0.5363818407058716, 0.5391883850097656]\n",
      "[0.5842569470405579, 0.6115173697471619, 0.5833387970924377, 0.5878012776374817]\n",
      "[0.10558441281318665, 0.1380949169397354, 0.13452847301959991, 0.10069644451141357]\n",
      "[0.12991780042648315, 0.10354926437139511, 0.10208772867918015, 0.1433241069316864]\n",
      "[0.20021282136440277, 0.19651444256305695, 0.15189611911773682, 0.15004867315292358]\n",
      "[0.2350904941558838, 0.20631171762943268, 0.19812892377376556, 0.21785873174667358]\n",
      "[0.12431441992521286, 0.13613495230674744, 0.12858955562114716, 0.14108920097351074]\n",
      "[0.22242452204227448, 0.1532794088125229, 0.21131545305252075, 0.21336860954761505]\n",
      "[0.42857375741004944, 0.4160177707672119, 0.401437371969223, 0.40117841958999634]\n",
      "[0.33393657207489014, 0.33596768975257874, 0.2762833535671234, 0.3140271306037903]\n",
      "[0.43724238872528076, 0.45333415269851685, 0.4789501428604126, 0.47634798288345337]\n",
      "[0.45043399930000305, 0.4401681125164032, 0.43914541602134705, 0.47841912508010864]\n",
      "[0.4141985774040222, 0.4122104346752167, 0.4511125683784485, 0.46736443042755127]\n",
      "[0.4151131510734558, 0.4181056618690491, 0.3687587380409241, 0.4364733099937439]\n",
      "[0.459891676902771, 0.4484608769416809, 0.40513384342193604, 0.45787346363067627]\n",
      "[0.2537800967693329, 0.21551011502742767, 0.2541660666465759, 0.19222640991210938]\n",
      "[0.284164696931839, 0.27560263872146606, 0.29643598198890686, 0.2825396955013275]\n",
      "[0.352859765291214, 0.3812389671802521, 0.3789927065372467, 0.31317615509033203]\n",
      "[0.5169747471809387, 0.5205395221710205, 0.5149601697921753, 0.5641290545463562]\n",
      "[0.5061262845993042, 0.5184645056724548, 0.49947887659072876, 0.5502372980117798]\n",
      "[0.47042229771614075, 0.4910651445388794, 0.5145890712738037, 0.5398126840591431]\n",
      "[0.43765008449554443, 0.4738388657569885, 0.5012149214744568, 0.4789796769618988]\n",
      "[0.4670739769935608, 0.47851765155792236, 0.5372003316879272, 0.5368655920028687]\n",
      "[0.46262258291244507, 0.4878719747066498, 0.42988646030426025, 0.48223569989204407]\n",
      "[0.4971538186073303, 0.5073611736297607, 0.5746281147003174, 0.46579840779304504]\n",
      "[0.5025578141212463, 0.48128458857536316, 0.47743475437164307, 0.5300654172897339]\n",
      "[0.49632781744003296, 0.5148781538009644, 0.48186182975769043, 0.4815836548805237]\n",
      "[0.1410142481327057, 0.161418154835701, 0.20160600543022156, 0.15764644742012024]\n",
      "[0.5024785995483398, 0.45140978693962097, 0.49167338013648987, 0.42681342363357544]\n"
     ]
    }
   ],
   "source": [
    "for run_name in os.listdir(\"results/\" + task_name + \"/\" + data_name):\n",
    "    if len(run_name.split(\"_\")) != 7: continue\n",
    "\n",
    "    # if int(run_name.split(\"_\")[4][1:]) > 50: # Filter for large dimension runs\n",
    "        # continue\n",
    "\n",
    "    for run in os.listdir(\"results/\" + task_name + \"/\" + data_name + \"/\" + run_name):\n",
    "        if run.startswith(\".\"):\n",
    "            continue\n",
    "\n",
    "        # print(\"results/\" + task_name + \"/\" + data_name + \"/\" + run_name + \"/\" + run)\n",
    "        # data = torch.load(\"results/\" + task_name + \"/\" + data_name + \"/\" + run_name + \"/\" + run, weights_only=False)\n",
    "        with open(\"results/\" + task_name + \"/\" + data_name + \"/\" + run_name + \"/\" + run) as infile:\n",
    "            data = json.load(infile)\n",
    "        loss = data[\"final_loss\"]\n",
    "        accs = data[\"train_accs\"]\n",
    "        print(accs)\n",
    "        acc = data[\"final_acc\"]\n",
    "        # args = data[\"args\"]\n",
    "        params = data[\"params\"]\n",
    "\n",
    "        if run_name not in all_losses.keys():\n",
    "            all_losses[run_name] = [loss]\n",
    "            all_accs[run_name] = [acc[-1]]\n",
    "            all_params[run_name] = params\n",
    "            all_train_accs[run_name] = [accs]\n",
    "        else:\n",
    "            all_losses[run_name].append(loss)\n",
    "            all_accs[run_name].append(acc[-1])\n",
    "            all_train_accs[run_name].append(accs)\n",
    "\n",
    "for run_name in all_losses.keys():\n",
    "    all_losses[run_name] = np.array(all_losses[run_name])\n",
    "    all_accs[run_name] = np.array(all_accs[run_name])\n",
    "    all_train_accs[run_name] = np.array(all_train_accs[run_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e02ccf18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run_assoc-recall-mk_TF-SSM_w100_d24_nh1_sd1 11496 0.5239278945055875\n",
      "run_assoc-recall-mk_SSM-SSM_w100_d24_nh1_sd1 12936 0.5170181285251271\n",
      "run_assoc-recall-mk_SSM-TF_w100_d24_nh1_sd1 11496 0.9891958724368702\n",
      "run_assoc-recall-mk_TF-TF_w100_d24_nh1_sd1 10056 0.6680852215398442\n"
     ]
    }
   ],
   "source": [
    "state_dim = 1\n",
    "num_heads = 1\n",
    "dim = 24 # default_token_dim\n",
    "window = 100 # Default window\n",
    "for k, v in all_accs.items():\n",
    "    split = k.split(\"_\")\n",
    "    if split[2] not in [\"SSM-SSM\", \"SSM-TF\", \"TF-SSM\", \"TF-TF\"]:\n",
    "        continue    \n",
    "    if split[-4] != \"w%d\" % default_window:\n",
    "        continue\n",
    "    if split[-3] != \"d%d\" % dim:\n",
    "        continue\n",
    "    if split[-2] != \"nh%d\" % num_heads:\n",
    "        continue\n",
    "    if split[-1] != \"sd%d\" % state_dim:\n",
    "        continue\n",
    "    print(k, all_params[k], np.mean(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c6fa37b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['d12', 'd16', 'd20', 'd24', 'd4', 'd8'], dtype='<U3')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(np.array([k.split(\"_\")[-3] for k in all_accs.keys()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f9de9b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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