SuperposPrompt_Thesis/11_wandb_api/softmax.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54a7edcf-605f-40f1-9e89-d62067f55dd3",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from latex_table import generate_table, generate_rows\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "class WandBWrapper:\n",
    "    def __init__(self, prefix=''):\n",
    "        import wandb\n",
    "        self.api = wandb.Api()\n",
    "        self.prefix = prefix\n",
    "    \n",
    "    def get_runs(self, name):\n",
    "        return self.api.runs(f\"{self.prefix}{name}\")\n",
    "    \n",
    "    def _preprocess_config(self, run):\n",
    "        return {\n",
    "            k: v for k,v in run.config.items()\n",
    "            if not k.startswith('_')\n",
    "        }\n",
    "    \n",
    "    def _best_in_history(self, run, key):\n",
    "        out = run.history()[key].astype('float').fillna(0).max()\n",
    "        return max(out, 0)\n",
    "    \n",
    "    def get_full_history(self, runs, tasks, model_size=''):\n",
    "        task_names = [model_size + '_' + task_name for task_name in tasks]\n",
    "        return {\n",
    "            task_name: pd.DataFrame({\n",
    "                run.name: run.history()['valid_mean']\n",
    "                for run in self.get_runs(task_name)\n",
    "                if run.name in runs\n",
    "            })[runs]\n",
    "            for task_name in task_names\n",
    "        }\n",
    "    \n",
    "    def get_runs_best(self, name, run_name_filter=None):\n",
    "        runs = self.get_runs(name)\n",
    "        return {\n",
    "            run.name: self._best_in_history(run, 'valid_mean')\n",
    "            for run in runs\n",
    "            if run_name_filter is None or run.name in run_name_filter\n",
    "        }\n",
    "    \n",
    "    def get_runs_tasks_df(self, runs, tasks, model_size=''):\n",
    "        task_names = [model_size + '_' + task_name for task_name in tasks]\n",
    "        results = {task_name: self.get_runs_best(task_name, runs) for task_name in task_names}\n",
    "        return pd.DataFrame(results).T[runs].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d044235-2d14-4e4b-ad87-2077c9cd89a4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tasks = [\n",
    "    # 'glue-wnli',\n",
    "    # 'glue-rte',\n",
    "    'glue-qqp',  # new datasets\n",
    "    'glue-qnli', # new datasets\n",
    "    'glue-mnli', # new datasets\n",
    "    'glue-sst2', # new datasets\n",
    "    'glue-stsb', # new datasets\n",
    "    'glue-mrpc',\n",
    "    'glue-cola',\n",
    "    'superglue-multirc', # new datasets\n",
    "    'superglue-rte',\n",
    "    'superglue-cb',\n",
    "    'superglue-copa',\n",
    "    'superglue-wic',\n",
    "    'superglue-boolq',\n",
    "]\n",
    "\n",
    "runs = [\n",
    "    '10_combine_128',\n",
    "]    \n",
    "\n",
    "# small_df_softmax =  WandBWrapper(\"mohalisad/iclr_softmax_effect_t5_\").get_runs_tasks_df(\n",
    "#     runs=runs,\n",
    "#     tasks=tasks,\n",
    "#     model_size='small'\n",
    "# )\n",
    "small_df_no_softmax =  WandBWrapper(\"mohalisad/hzi_cluster_t5_\").get_runs_tasks_df(\n",
    "    runs=runs,\n",
    "    tasks=tasks,\n",
    "    model_size='small'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7300ed8f-4477-4e4c-b818-c265c3f02aae",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "small_df = pd.concat([small_df_no_softmax, small_df_no_softmax], ignore_index=True)\n",
    "small_df['name'] = ['softmax', 'no_softmax']\n",
    "small_df.set_index('name', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe96e491-24ce-4cb8-a25e-0db9cb98435d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def _tblr_args():\n",
    "    return r\"\"\"column{2-16} = {c},\n",
    "    cell{1}{3} = {r=3}{b},\n",
    "    cell{1}{4} = {c=7}{c},\n",
    "    cell{1}{11} = {c=6}{},\n",
    "    vline{3, 4,11,17} = {1-3}{},\n",
    "    hline{2} = {3-15}{},\n",
    "    row{4, 7} = {c},\n",
    "    cell{4, 7}{1} = {c=16}{},\n",
    "    hline{6, 9} = {-}{},\n",
    "    hline{4, 7, 10} = {-}{2px},,\"\"\"\n",
    "\n",
    "def _head_rows():\n",
    "    return [\n",
    "        r\" & \\rot{\\eztb{Softmax}} & \\rot{\\eztb{Dropout}} & GLUE &&&&&&& SuperGLUE &&&&&&\",\n",
    "        r\"Task→ &&& QQP & QNLI & MNLI & SST-2 & STS-B & MRPC & CoLA & MultiRC & RTE & CB & COPA & WiC & BoolQ & Avg.\",\n",
    "        r\"Method↓ &&& F1/Acc. & Acc. & Acc. & Acc. & PCC/$\\rho$ & F1/Acc. & MCC & F1a/EM & Acc. & F1/Acc. & Acc. & Acc. & Acc. & -\"\n",
    "    ]\n",
    "\n",
    "def _section_row(name):\n",
    "    return name + \"&&&&&&& &&&&&&&&&\"\n",
    "\n",
    "def _convert_number(n):\n",
    "    if n == 0:\n",
    "        return '0.0 $\\\\dag$'\n",
    "    return f\"{100 * n:.1f}\"\n",
    "\n",
    "def _normal_row(name, is_softmax, is_dropout, numbers, bold_mask=None):\n",
    "    numbers_str = [_convert_number(n) for n in numbers]\n",
    "    if bold_mask is not None:\n",
    "        for idx, bold_state in enumerate(bold_mask):\n",
    "            if bold_state:\n",
    "                numbers_str[idx] = \"\\\\textbf{\" + numbers_str[idx] + \"}\"\n",
    "    \n",
    "    soft_mark = \"\\\\cmark\" if is_softmax else \"\\\\xmark\"\n",
    "    drop_mark = \"\\\\cmark\" if is_dropout else \"\\\\xmark\"\n",
    "    return \" & \".join([name, soft_mark, drop_mark, *numbers_str])\n",
    "    \n",
    "def generate_rows(names, softmaxes, dropouts, numbers):\n",
    "    mean = numbers.mean(axis=1, keepdims=True)\n",
    "    numbers = np.concatenate((numbers, mean), axis=1)\n",
    "    pefts = numbers\n",
    "    pefts_best = pefts.max(axis=0)\n",
    "    \n",
    "    rows = [\n",
    "        _normal_row(name, is_softmax, drop, peft_row, peft_row == pefts_best)\n",
    "        for (name, is_softmax, drop, peft_row) in zip(names, softmaxes, dropouts, pefts)\n",
    "    ]\n",
    "    return rows\n",
    "    \n",
    "def generate_table(rows1_key, rows1, rows2_key, rows2):\n",
    "    end_line = '\\\\\\\\\\n'\n",
    "    rows = [\n",
    "        *_head_rows(),\n",
    "        _section_row(rows1_key),\n",
    "        *rows1,\n",
    "        _section_row(rows2_key),\n",
    "        *rows2,\n",
    "    ]\n",
    "    return r\"\"\"\\begin{tblr}{\n",
    "    %s\n",
    "}\n",
    "%s\n",
    "\\end{tblr}\n",
    "\"\"\" % (_tblr_args(), end_line.join(rows + [\"\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac11ea00-a9af-4454-982f-2aed9b552e5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tblr}{\n",
      "    column{2-16} = {c},\n",
      "    cell{1}{3} = {r=3}{b},\n",
      "    cell{1}{4} = {c=7}{c},\n",
      "    cell{1}{11} = {c=6}{},\n",
      "    vline{3, 4,11,17} = {1-3}{},\n",
      "    hline{2} = {3-15}{},\n",
      "    row{4, 7} = {c},\n",
      "    cell{4, 7}{1} = {c=16}{},\n",
      "    hline{6, 9} = {-}{},\n",
      "    hline{4, 7, 10} = {-}{2px},,\n",
      "}\n",
      " & \\rot{\\eztb{Softmax}} & \\rot{\\eztb{Dropout}} & GLUE &&&&&&& SuperGLUE &&&&&&\\\\\n",
      "Task→ &&& QQP & QNLI & MNLI & SST-2 & STS-B & MRPC & CoLA & MultiRC & RTE & CB & COPA & WiC & BoolQ & Avg.\\\\\n",
      "Method↓ &&& F1/Acc. & Acc. & Acc. & Acc. & PCC/$\\rho$ & F1/Acc. & MCC & F1a/EM & Acc. & F1/Acc. & Acc. & Acc. & Acc. & -\\\\\n",
      "T5v1.1 Small LM-Adapted&&&&&&& &&&&&&&&&\\\\\n",
      "SuperPos PT & \\cmark & \\xmark & \\textbf{81.2} & \\textbf{85.3} & \\textbf{71.7} & \\textbf{89.8} & \\textbf{84.0} & \\textbf{87.9} & \\textbf{38.9} & \\textbf{41.6} & \\textbf{64.6} & \\textbf{75.2} & \\textbf{58.0} & \\textbf{65.7} & \\textbf{68.9} & \\textbf{70.2}\\\\\n",
      "SuperPos PT & \\xmark & \\xmark & \\textbf{81.2} & \\textbf{85.3} & \\textbf{71.7} & \\textbf{89.8} & \\textbf{84.0} & \\textbf{87.9} & \\textbf{38.9} & \\textbf{41.6} & \\textbf{64.6} & \\textbf{75.2} & \\textbf{58.0} & \\textbf{65.7} & \\textbf{68.9} & \\textbf{70.2}\\\\\n",
      "T5v1.1 Base LM-Adapted&&&&&&& &&&&&&&&&\\\\\n",
      "SuperPos PT & \\cmark & \\xmark & \\textbf{81.2} & \\textbf{85.3} & \\textbf{71.7} & \\textbf{89.8} & \\textbf{84.0} & \\textbf{87.9} & \\textbf{38.9} & \\textbf{41.6} & \\textbf{64.6} & \\textbf{75.2} & \\textbf{58.0} & \\textbf{65.7} & \\textbf{68.9} & \\textbf{70.2}\\\\\n",
      "SuperPos PT & \\xmark & \\xmark & \\textbf{81.2} & \\textbf{85.3} & \\textbf{71.7} & \\textbf{89.8} & \\textbf{84.0} & \\textbf{87.9} & \\textbf{38.9} & \\textbf{41.6} & \\textbf{64.6} & \\textbf{75.2} & \\textbf{58.0} & \\textbf{65.7} & \\textbf{68.9} & \\textbf{70.2}\\\\\n",
      "\n",
      "\\end{tblr}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dropouts = [False, False]\n",
    "softmaxes = [True, False]\n",
    "names = ['SuperPos PT'] * 2\n",
    "# base_rows = generate_rows(names, dropouts, base_df.to_numpy())\n",
    "small_rows = generate_rows(names, softmaxes, dropouts, small_df.to_numpy())\n",
    "print(generate_table('T5v1.1 Small LM-Adapted', small_rows, 'T5v1.1 Base LM-Adapted', small_rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e138dc33-5b68-4b27-95e9-39c76f4cbc37",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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