significance_analysis_example/analysis_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from significance_analysis import dataframe_validator,model,seed_dependency_check, benchmark_information_check, fidelity_check, metafeature_analysis, cd_diagram\n",
    "\n",
    "# Load example dataset\n",
    "data = pd.read_parquet(\"datasets/priorband_data.parquet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we analyze the dataset for compatibility with our methods and if necessary convert columns to another data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted column seed to object.\n"
     ]
    }
   ],
   "source": [
    "data = dataframe_validator(data)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We demonstrate our sanity checks with LMEM-based hypothesis testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value~algorithm (-269924.2443) >> value~(0+algorithm|seed) (-269958.1791)\n",
      "Chi-Square: 67.86948838911485, P-Value: 1.7763568394002505e-15\n",
      "=> Seed is not a significant effect\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We check the dependency of the performances on the random seed\n",
    "\n",
    "seed_dependency_check(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Benchmark: LC-167190\n",
      "value~1 (12884.4064) << value~algorithm (13113.7332)\n",
      "Chi-Square: 458.65341885616, P-Value: 0.0\n",
      "=> Benchmark LC-167190    is informative.\n",
      "\n",
      "Benchmark: JAHS-C10\n",
      "value~1 (-25493.0466) << value~algorithm (-25478.2102)\n",
      "Chi-Square: 29.672838636499364, P-Value: 3.602673946945245e-07\n",
      "=> Benchmark JAHS-C10     is informative.\n",
      "\n",
      "Benchmark: LC-126026\n",
      "value~1 (12683.8569) << value~algorithm (12721.7273)\n",
      "Chi-Square: 75.74078563572402, P-Value: 0.0\n",
      "=> Benchmark LC-126026    is informative.\n",
      "\n",
      "Benchmark: PD1-Cifar100\n",
      "value~1 (3718.3627) << value~algorithm (3738.4751)\n",
      "Chi-Square: 40.224876708227384, P-Value: 1.8419549219217402e-09\n",
      "=> Benchmark PD1-Cifar100 is informative.\n",
      "\n",
      "Benchmark: PD1-ImageNet\n",
      "value~1 (5462.3515) << value~algorithm (5583.9874)\n",
      "Chi-Square: 243.2717470523712, P-Value: 0.0\n",
      "=> Benchmark PD1-ImageNet is informative.\n",
      "\n",
      "Benchmark: LC-168330\n",
      "value~1 (10267.6418) << value~algorithm (10362.9098)\n",
      "Chi-Square: 190.53600217378698, P-Value: 0.0\n",
      "=> Benchmark LC-168330    is informative.\n",
      "\n",
      "Benchmark: JAHS-CH\n",
      "value~1 (-25545.48) << value~algorithm (-25514.2256)\n",
      "Chi-Square: 62.50876848853659, P-Value: 2.6645352591003757e-14\n",
      "=> Benchmark JAHS-CH      is informative.\n",
      "\n",
      "Benchmark: LC-168910\n",
      "value~1 (7389.9171) << value~algorithm (7590.2161)\n",
      "Chi-Square: 400.5980704009635, P-Value: 0.0\n",
      "=> Benchmark LC-168910    is informative.\n",
      "\n",
      "Benchmark: PD1-LM1B\n",
      "value~1 (13272.0208) << value~algorithm (13475.6896)\n",
      "Chi-Square: 407.3375468719605, P-Value: 0.0\n",
      "=> Benchmark PD1-LM1B     is informative.\n",
      "\n",
      "Benchmark: PD1-WMT\n",
      "value~1 (7729.2786) << value~algorithm (7885.9814)\n",
      "Chi-Square: 313.40556506397843, P-Value: 0.0\n",
      "=> Benchmark PD1-WMT      is informative.\n",
      "\n",
      "Benchmark: JAHS-FM\n",
      "value~1 (-24979.2196) << value~algorithm (-24940.1688)\n",
      "Chi-Square: 78.1015741356241, P-Value: 0.0\n",
      "=> Benchmark JAHS-FM      is informative.\n",
      "\n",
      "Benchmark: LC-189906\n",
      "value~1 (6724.8666) << value~algorithm (6886.8469)\n",
      "Chi-Square: 323.96061828182064, P-Value: 0.0\n",
      "=> Benchmark LC-189906    is informative.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LC-167190': True,\n",
       " 'JAHS-C10': True,\n",
       " 'LC-126026': True,\n",
       " 'PD1-Cifar100': True,\n",
       " 'PD1-ImageNet': True,\n",
       " 'LC-168330': True,\n",
       " 'JAHS-CH': True,\n",
       " 'LC-168910': True,\n",
       " 'PD1-LM1B': True,\n",
       " 'PD1-WMT': True,\n",
       " 'JAHS-FM': True,\n",
       " 'LC-189906': True}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each benchmark, we check if the performances are significantly different\n",
    "\n",
    "benchmark_information_check(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value~algorithm+(1|benchmark) (-244290.1072) << value~algorithm+(1|benchmark)+used_fidelity (-243312.5612)\n",
      "Chi-Square: 1955.0920213969657, P-Value: 0.0\n",
      "\n",
      "value~algorithm+(1|benchmark) (-244290.1072) << value~algorithm+(1|benchmark)+algorithm:used_fidelity (-243001.2452)\n",
      "Chi-Square: 2577.7240843258332, P-Value: 0.0\n",
      "\n",
      "value~algorithm+(1|benchmark)+used_fidelity (-243312.5612) << value~algorithm+(1|benchmark)+algorithm:used_fidelity (-243001.2452)\n",
      "Chi-Square: 622.6320629288675, P-Value: 0.0\n",
      "\n",
      "=> Fidelity used_fidelity is both as simple and interaction effect significant, but interaction effect performs better.\n"
     ]
    }
   ],
   "source": [
    "# For model building, we check wether a fidelity increases the model's ability to predict the performance\n",
    "\n",
    "fidelity_check(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the built model, we can now conduct performance analysis with a few simple calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-values adjusted by tukey method for family of 3 estimates\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x140 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We use the data of all benchmarks combined, at 10x budget\n",
    "df=data.loc[data[\"used_fidelity\"]==10]\n",
    "\n",
    "# Creating the model and running post-hoc analysis using the tukey HSD test\n",
    "mod=model(\"value ~ algorithm + (1|benchmark) + prior\",df)\n",
    "post_hoc_results=mod.post_hoc(\"algorithm\")\n",
    "\n",
    "# Plotting the results\n",
    "cd_diagram(post_hoc_results)\n",
    "\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     benchmark_variant  wins  fidelity     benchmark metafeature\n",
      "0      LC-167190 x bad  -1.0       1.0     LC-167190         bad\n",
      "1      JAHS-C10 x at25   1.0       1.0      JAHS-C10        at25\n",
      "2      LC-126026 x bad  -1.0       1.0     LC-126026         bad\n",
      "3  PD1-Cifar100 x at25   1.0       1.0  PD1-Cifar100        at25\n",
      "4  PD1-ImageNet x at25   1.0       1.0  PD1-ImageNet        at25\n",
      "P-values adjusted by tukey method for family of 276 estimates\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = metafeature_analysis(data,(\"HB\",\"PB\"),\"prior\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv_3_10_0",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}