notebooks/covid/evaluate_benchmark_small.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "killing-nitrogen",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "# Set paths\n",
    "sys.path.extend([\n",
    "    os.path.abspath(os.path.join(\"../..\")),\n",
    "    os.path.abspath(os.path.join(\"../../../BayesFlow_dev/BayesFlow/\"))\n",
    "])\n",
    "\n",
    "\n",
    "import time\n",
    "from functools import partial\n",
    "import pickle\n",
    "import bayesflow as bf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from simulation import setup_simulator\n",
    "from network import setup_network\n",
    "from helpers import get_metrics, calculate_setting_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c96a630",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d161b4",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d9d6759",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4096\n"
     ]
    }
   ],
   "source": [
    "RNG = np.random.default_rng(2023)\n",
    "\n",
    "SIM_BUDGET = 2**12\n",
    "print(SIM_BUDGET)\n",
    "NUM_EPOCHS = 75\n",
    "NUM_ENSEMBLE_MEMBERS = 2\n",
    "BUDGET_SETTING = f\"{SIM_BUDGET}_budget\"\n",
    "ALPHA_VALUES = [0.5, 1.0, 2.0]\n",
    "config = {\"T\": 14, \"N\": 83e6}\n",
    "CHECKPOINT_FOLDER = f\"checkpoints/benchmark/{BUDGET_SETTING}\"\n",
    "TIME_SETTINGS = [1, 3, 1000]\n",
    "TRAINING_TIMES_PATH =f\"{CHECKPOINT_FOLDER}/training_times.pkl\"\n",
    "COLOR_CODES = ['#35b779', '#31688e', '#440154'] # one for each alpha setting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cbc7241",
   "metadata": {},
   "source": [
    "# Evaluate networks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c8fa45",
   "metadata": {},
   "source": [
    "## Get posterior draws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24542616",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/powerscaled/net0\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/powerscaled/net0\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for powerscaled_net0: [1.3714542388916016, 1.3831558227539062, 1.3212015628814697]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/powerscaled/net1\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/powerscaled/net1\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for powerscaled_net1: [1.2371246814727783, 1.1950271129608154, 1.1956043243408203]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/unscaled/alpha_0.5/net0\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/unscaled/alpha_0.5/net0\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for unscaled_alpha_0.5_net0: [1.1460402011871338, 1.1610569953918457, 1.1645774841308594]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/unscaled/alpha_0.5/net1\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/unscaled/alpha_0.5/net1\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for unscaled_alpha_0.5_net1: [1.16709566116333, 1.1485354900360107, 1.1643304824829102]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/unscaled/alpha_1.0/net0\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/unscaled/alpha_1.0/net0\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for unscaled_alpha_1.0_net0: [1.1598200798034668, 1.1660878658294678, 1.162055253982544]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/unscaled/alpha_1.0/net1\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/unscaled/alpha_1.0/net1\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for unscaled_alpha_1.0_net1: [1.1650595664978027, 1.156614065170288, 1.1450419425964355]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/unscaled/alpha_2.0/net0\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/unscaled/alpha_2.0/net0\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for unscaled_alpha_2.0_net0: [1.3057966232299805, 1.2403011322021484, 1.2213633060455322]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded loss history from checkpoints/benchmark/4096_budget/unscaled/alpha_2.0/net1\\history_75.pkl.\n",
      "INFO:root:Networks loaded from checkpoints/benchmark/4096_budget/unscaled/alpha_2.0/net1\\ckpt-75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference times for unscaled_alpha_2.0_net1: [1.2060964107513428, 1.3212029933929443, 1.1441125869750977]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'powerscaled_net0': 1.358603874842326,\n",
       " 'powerscaled_net1': 1.2092520395914714,\n",
       " 'unscaled_alpha_0.5_net0': 1.1572248935699463,\n",
       " 'unscaled_alpha_0.5_net1': 1.159987211227417,\n",
       " 'unscaled_alpha_1.0_net0': 1.1626543998718262,\n",
       " 'unscaled_alpha_1.0_net1': 1.1555718580881755,\n",
       " 'unscaled_alpha_2.0_net0': 1.255820353825887,\n",
       " 'unscaled_alpha_2.0_net1': 1.223803997039795}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get draws & inference times from each network for all three alpha settings\n",
    "draws_dict = {}\n",
    "inference_times = {}\n",
    "\n",
    "def load_data(factor):\n",
    "    return pickle.load(open(f'simulations/sims_{factor}.pkl', 'rb+'))\n",
    "\n",
    "for i in range(len(ALPHA_VALUES) + 1): # Loop over 3 unscaled networks + already powerscaled network\n",
    "    \n",
    "    if i == 0: # Powerscaled network\n",
    "        network_name = \"powerscaled\"\n",
    "        checkpoint_base_path = f\"{CHECKPOINT_FOLDER}/powerscaled\"\n",
    "        is_fix_alpha = False\n",
    "        prior, simulator, model = setup_simulator(config=config)\n",
    "    else: # Unscaled networks\n",
    "        alpha_net = ALPHA_VALUES[i-1]\n",
    "        network_name = f\"unscaled_alpha_{alpha_net}\"\n",
    "        checkpoint_base_path = f\"{CHECKPOINT_FOLDER}/unscaled/alpha_{alpha_net}\"\n",
    "        is_fix_alpha = True\n",
    "        prior, simulator, model = setup_simulator(config=config, fix_alpha=ALPHA_VALUES[i-1])\n",
    "\n",
    "    draws_dict[network_name] = {}\n",
    "\n",
    "    for m in range(NUM_ENSEMBLE_MEMBERS):\n",
    "        network_num = f\"net{m}\"\n",
    "        checkpoint_path = f\"{checkpoint_base_path}/{network_num}\"\n",
    "\n",
    "        #prior_means, prior_stds = load_standardization_params(STANDARDIZATION_PATH, network_name) # use alpha of net, not setting for configuration\n",
    "        amortizer, trainer = setup_network(\n",
    "            generative_model=model, \n",
    "            checkpoint_path=checkpoint_path,\n",
    "            is_fix_alpha=is_fix_alpha\n",
    "        )\n",
    "        \n",
    "        draws_dict[network_name][network_num] = {}\n",
    "        inference_times_per_setting = []\n",
    "\n",
    "        for alpha_setting in ALPHA_VALUES: # Loop over all alpha settings\n",
    "            alpha_str = str(alpha_setting)[0] + str(alpha_setting)[-1] # use some funny stuff to reuse ALPHA_VALUES (remove dot to get from 0.5 to 05 etc)\n",
    "            conf_data = trainer.configurator(load_data(alpha_str))\n",
    "            _ = amortizer.sample(\n",
    "                conf_data, \n",
    "                n_samples=250\n",
    "            ) # \"warmup\" - first draw is slower, so times would be biased by network position in loop otherwise \n",
    "            start_time = time.time()\n",
    "            draws_dict[network_name][network_num][f\"conf_{alpha_str}\"] = amortizer.sample(\n",
    "                conf_data, \n",
    "                n_samples=2500 # use many samples to get stable estimates, especially of posterior contraction\n",
    "            )\n",
    "            end_time = time.time() \n",
    "            inference_times_per_setting.append(end_time - start_time)\n",
    "\n",
    "        print(f\"Inference times for {network_name}_{network_num}: {inference_times_per_setting}\")\n",
    "        \n",
    "        # Get average time over alpha settings per network\n",
    "        inference_times[f\"{network_name}_{network_num}\"] = np.mean(inference_times_per_setting) \n",
    "inference_times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7216e7a2",
   "metadata": {},
   "source": [
    "## Calculate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c81424a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Data Scaling</th>\n",
       "      <th>Network Scaling</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>ECE</th>\n",
       "      <th>Posterior Contraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>27.471651</td>\n",
       "      <td>9.089793</td>\n",
       "      <td>0.341086</td>\n",
       "      <td>0.587292</td>\n",
       "      <td>0.021711</td>\n",
       "      <td>0.437834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>23.003726</td>\n",
       "      <td>8.91132</td>\n",
       "      <td>0.34195</td>\n",
       "      <td>0.580035</td>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.450737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26.047438</td>\n",
       "      <td>10.066422</td>\n",
       "      <td>0.307464</td>\n",
       "      <td>0.471359</td>\n",
       "      <td>0.006526</td>\n",
       "      <td>0.169265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.095522</td>\n",
       "      <td>9.259736</td>\n",
       "      <td>0.37276</td>\n",
       "      <td>0.617461</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.302479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.001875</td>\n",
       "      <td>8.387075</td>\n",
       "      <td>0.252113</td>\n",
       "      <td>0.591866</td>\n",
       "      <td>0.108763</td>\n",
       "      <td>0.683458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.664551</td>\n",
       "      <td>8.361533</td>\n",
       "      <td>0.25829</td>\n",
       "      <td>0.590586</td>\n",
       "      <td>0.101316</td>\n",
       "      <td>0.650595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.995358</td>\n",
       "      <td>8.740279</td>\n",
       "      <td>0.098609</td>\n",
       "      <td>0.527421</td>\n",
       "      <td>0.209395</td>\n",
       "      <td>0.860784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.917444</td>\n",
       "      <td>8.765802</td>\n",
       "      <td>0.097522</td>\n",
       "      <td>0.496076</td>\n",
       "      <td>0.206895</td>\n",
       "      <td>0.857852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.589965</td>\n",
       "      <td>5.737136</td>\n",
       "      <td>0.382388</td>\n",
       "      <td>0.567486</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>0.293866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.189591</td>\n",
       "      <td>5.649701</td>\n",
       "      <td>0.388416</td>\n",
       "      <td>0.5826</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>0.275213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.639518</td>\n",
       "      <td>8.841858</td>\n",
       "      <td>0.25013</td>\n",
       "      <td>0.500051</td>\n",
       "      <td>0.027368</td>\n",
       "      <td>-0.830149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>20.702742</td>\n",
       "      <td>7.397682</td>\n",
       "      <td>0.356957</td>\n",
       "      <td>0.589786</td>\n",
       "      <td>0.050737</td>\n",
       "      <td>-0.464326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.035</td>\n",
       "      <td>5.578922</td>\n",
       "      <td>0.381945</td>\n",
       "      <td>0.579025</td>\n",
       "      <td>0.009105</td>\n",
       "      <td>0.304278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.206071</td>\n",
       "      <td>5.680228</td>\n",
       "      <td>0.373502</td>\n",
       "      <td>0.573261</td>\n",
       "      <td>0.009237</td>\n",
       "      <td>0.229461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.694639</td>\n",
       "      <td>5.073295</td>\n",
       "      <td>0.286993</td>\n",
       "      <td>0.536761</td>\n",
       "      <td>0.100263</td>\n",
       "      <td>0.680715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.74495</td>\n",
       "      <td>5.123222</td>\n",
       "      <td>0.285538</td>\n",
       "      <td>0.533918</td>\n",
       "      <td>0.106763</td>\n",
       "      <td>0.666954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>9.797719</td>\n",
       "      <td>3.713643</td>\n",
       "      <td>0.333149</td>\n",
       "      <td>0.475907</td>\n",
       "      <td>0.017053</td>\n",
       "      <td>0.354234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>9.806578</td>\n",
       "      <td>3.756246</td>\n",
       "      <td>0.314938</td>\n",
       "      <td>0.491856</td>\n",
       "      <td>0.010684</td>\n",
       "      <td>0.22516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>27.168818</td>\n",
       "      <td>9.247602</td>\n",
       "      <td>-0.479927</td>\n",
       "      <td>0.40556</td>\n",
       "      <td>0.088053</td>\n",
       "      <td>-3.088244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>20.431791</td>\n",
       "      <td>6.888973</td>\n",
       "      <td>0.030354</td>\n",
       "      <td>0.485188</td>\n",
       "      <td>0.058237</td>\n",
       "      <td>-2.290035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.946807</td>\n",
       "      <td>4.70875</td>\n",
       "      <td>0.346736</td>\n",
       "      <td>0.496741</td>\n",
       "      <td>0.052289</td>\n",
       "      <td>-0.559642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.275083</td>\n",
       "      <td>4.80399</td>\n",
       "      <td>0.337169</td>\n",
       "      <td>0.47878</td>\n",
       "      <td>0.059237</td>\n",
       "      <td>-0.658479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.562035</td>\n",
       "      <td>3.694832</td>\n",
       "      <td>0.369688</td>\n",
       "      <td>0.494499</td>\n",
       "      <td>0.005053</td>\n",
       "      <td>0.193267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.581952</td>\n",
       "      <td>3.707903</td>\n",
       "      <td>0.372409</td>\n",
       "      <td>0.500749</td>\n",
       "      <td>0.007632</td>\n",
       "      <td>0.179734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data Scaling Network Scaling       RMSE        MAE        R2 Correlation   \n",
       "0           0.5     powerscaled  27.471651   9.089793  0.341086    0.587292  \\\n",
       "1           0.5     powerscaled  23.003726    8.91132   0.34195    0.580035   \n",
       "2           0.5             0.5  26.047438  10.066422  0.307464    0.471359   \n",
       "3           0.5             0.5  24.095522   9.259736   0.37276    0.617461   \n",
       "4           0.5             1.0  23.001875   8.387075  0.252113    0.591866   \n",
       "5           0.5             1.0  21.664551   8.361533   0.25829    0.590586   \n",
       "6           0.5             2.0  22.995358   8.740279  0.098609    0.527421   \n",
       "7           0.5             2.0  22.917444   8.765802  0.097522    0.496076   \n",
       "8           1.0     powerscaled  15.589965   5.737136  0.382388    0.567486   \n",
       "9           1.0     powerscaled  15.189591   5.649701  0.388416      0.5826   \n",
       "10          1.0             0.5  24.639518   8.841858   0.25013    0.500051   \n",
       "11          1.0             0.5  20.702742   7.397682  0.356957    0.589786   \n",
       "12          1.0             1.0     15.035   5.578922  0.381945    0.579025   \n",
       "13          1.0             1.0  15.206071   5.680228  0.373502    0.573261   \n",
       "14          1.0             2.0  13.694639   5.073295  0.286993    0.536761   \n",
       "15          1.0             2.0   13.74495   5.123222  0.285538    0.533918   \n",
       "16          2.0     powerscaled   9.797719   3.713643  0.333149    0.475907   \n",
       "17          2.0     powerscaled   9.806578   3.756246  0.314938    0.491856   \n",
       "18          2.0             0.5  27.168818   9.247602 -0.479927     0.40556   \n",
       "19          2.0             0.5  20.431791   6.888973  0.030354    0.485188   \n",
       "20          2.0             1.0  12.946807    4.70875  0.346736    0.496741   \n",
       "21          2.0             1.0  13.275083    4.80399  0.337169     0.47878   \n",
       "22          2.0             2.0   9.562035   3.694832  0.369688    0.494499   \n",
       "23          2.0             2.0   9.581952   3.707903  0.372409    0.500749   \n",
       "\n",
       "         ECE Posterior Contraction  \n",
       "0   0.021711              0.437834  \n",
       "1   0.018789              0.450737  \n",
       "2   0.006526              0.169265  \n",
       "3   0.005368              0.302479  \n",
       "4   0.108763              0.683458  \n",
       "5   0.101316              0.650595  \n",
       "6   0.209395              0.860784  \n",
       "7   0.206895              0.857852  \n",
       "8   0.004395              0.293866  \n",
       "9   0.006211              0.275213  \n",
       "10  0.027368             -0.830149  \n",
       "11  0.050737             -0.464326  \n",
       "12  0.009105              0.304278  \n",
       "13  0.009237              0.229461  \n",
       "14  0.100263              0.680715  \n",
       "15  0.106763              0.666954  \n",
       "16  0.017053              0.354234  \n",
       "17  0.010684               0.22516  \n",
       "18  0.088053             -3.088244  \n",
       "19  0.058237             -2.290035  \n",
       "20  0.052289             -0.559642  \n",
       "21  0.059237             -0.658479  \n",
       "22  0.005053              0.193267  \n",
       "23  0.007632              0.179734  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute metrics\n",
    "conf_names = [\"conf_05\", \"conf_10\", \"conf_20\"]\n",
    "net_list = [\"powerscaled\", \"unscaled_alpha_0.5\", \"unscaled_alpha_1.0\", \"unscaled_alpha_2.0\"]\n",
    "# Get better & consistent names\n",
    "conf_mapping = {\"conf_05\": \"0.5\", \"conf_10\": \"1.0\", \"conf_20\": \"2.0\"} \n",
    "net_mapping = {\"powerscaled\": \"powerscaled\", \"unscaled_alpha_0.5\": \"0.5\", \"unscaled_alpha_1.0\": \"1.0\", \"unscaled_alpha_2.0\": \"2.0\"}\n",
    "\n",
    "metrics_names = ['RMSE', 'MAE', 'R2', 'Correlation', 'ECE', 'Posterior Contraction']\n",
    "\n",
    "num_configs = len(conf_names)\n",
    "num_networks = len(net_list) * NUM_ENSEMBLE_MEMBERS\n",
    "num_metrics = len(metrics_names)\n",
    "\n",
    "data = np.empty((num_configs * num_networks, num_metrics + 2), dtype=object)\n",
    "# Beware: dtype object leads to some weird errors when doing further computations based on the table, use .astype(float) to fix\n",
    "\n",
    "for i, conf in enumerate(conf_names):\n",
    "    parameters = load_data(conf[-2:])['prior_draws'] # Get ground-truth parameters of setting\n",
    "    for j, net in enumerate(net_list):\n",
    "        for m in range(NUM_ENSEMBLE_MEMBERS):\n",
    "            network_num = f\"net{m}\"\n",
    "            draws = draws_dict[net][network_num][conf]\n",
    "\n",
    "            rmse, mae, r2, corr, ece, cont = get_metrics(parameters, draws)\n",
    "            \n",
    "            new_conf_name = conf_mapping[conf]\n",
    "            new_net_name = net_mapping[net]\n",
    "            \n",
    "            row_index = i * num_networks + j * NUM_ENSEMBLE_MEMBERS + m\n",
    "            data[row_index] = [new_conf_name, new_net_name, rmse, mae, r2, corr, ece, cont]\n",
    "columns = ['Data Scaling', 'Network Scaling'] + metrics_names\n",
    "benchmark_results = pd.DataFrame(data, columns=columns)\n",
    "benchmark_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2626ce1b",
   "metadata": {},
   "source": [
    "## Visualize/aggregate results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "206845f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize results\n",
    "fig, axes = plt.subplots(num_configs, num_metrics, figsize=(16, 10))\n",
    "data_scalings = list(conf_mapping.values())\n",
    "\n",
    "for i, ds in enumerate(data_scalings):\n",
    "    for j, metric in enumerate(metrics_names):\n",
    "        ax = axes[i, j]\n",
    "        subset_df = benchmark_results[benchmark_results['Data Scaling'] == ds]\n",
    "\n",
    "        colors = ['grey'] * len(subset_df)\n",
    "        # Highlight powerscaled network + unscaled network trained for the respective data scaling\n",
    "        for index, index_name in enumerate(subset_df.loc[:, 'Network Scaling']):\n",
    "            if index_name == 'powerscaled':\n",
    "                colors[index] = \"#800000\"\n",
    "            if index_name == ds:\n",
    "                colors[index] = \"#000080\"\n",
    "\n",
    "        subset_df.plot(kind='bar', x='Network Scaling', y=metric, ax=ax, alpha=0.7, color=colors, legend=False)\n",
    "\n",
    "        ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "        ax.set_axisbelow(True)\n",
    "        ax.grid(True, alpha=0.3)\n",
    "\n",
    "    # Add data scaling name to the very left of each row\n",
    "    axes[i, 0].set_ylabel(f'Data Scaling {ds}', rotation=90, size=20)\n",
    "\n",
    "# Add metric names at the very top of each column\n",
    "for j, metric in enumerate(metrics_names):\n",
    "    axes[0, j].set_title(metric, size=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4358c7b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">RMSE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MAE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">R2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Correlation</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ECE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Posterior Contraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>powerscaled</th>\n",
       "      <td>15.389778</td>\n",
       "      <td>0.283107</td>\n",
       "      <td>5.693418</td>\n",
       "      <td>0.061826</td>\n",
       "      <td>0.385402</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.575043</td>\n",
       "      <td>0.010687</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.001284</td>\n",
       "      <td>0.284540</td>\n",
       "      <td>0.013190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unscaled</th>\n",
       "      <td>15.120535</td>\n",
       "      <td>0.120966</td>\n",
       "      <td>5.629575</td>\n",
       "      <td>0.071634</td>\n",
       "      <td>0.377723</td>\n",
       "      <td>0.005970</td>\n",
       "      <td>0.576143</td>\n",
       "      <td>0.004076</td>\n",
       "      <td>0.009171</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.266869</td>\n",
       "      <td>0.052903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RMSE                 MAE                  R2             \n",
       "                   mean       std      mean       std      mean       std   \n",
       "Net category                                                                \n",
       "powerscaled   15.389778  0.283107  5.693418  0.061826  0.385402  0.004263  \\\n",
       "unscaled      15.120535  0.120966  5.629575  0.071634  0.377723  0.005970   \n",
       "\n",
       "             Correlation                 ECE           Posterior Contraction   \n",
       "                    mean       std      mean       std                  mean   \n",
       "Net category                                                                   \n",
       "powerscaled     0.575043  0.010687  0.005303  0.001284              0.284540  \\\n",
       "unscaled        0.576143  0.004076  0.009171  0.000093              0.266869   \n",
       "\n",
       "                        \n",
       "                   std  \n",
       "Net category            \n",
       "powerscaled   0.013190  \n",
       "unscaled      0.052903  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate results for benchmark table in paper\n",
    "\n",
    "# Select only powerscaled or matching unscaled networks\n",
    "benchmark_results_matching = benchmark_results[\n",
    "    (benchmark_results['Network Scaling'] == 'powerscaled') | \n",
    "    ((benchmark_results['Data Scaling'] == benchmark_results['Network Scaling']) & \n",
    "    (benchmark_results['Network Scaling'] != 'powerscaled'))\n",
    "].copy()\n",
    "\n",
    "# Categorize as powerscaled or unscaled\n",
    "benchmark_results_matching['Net category'] = (\n",
    "    benchmark_results_matching['Network Scaling']\n",
    "    .apply(lambda x: 'powerscaled' if x == 'powerscaled' else 'unscaled')\n",
    ")\n",
    "\n",
    "agg_dict = {metric: ['mean', 'std'] for metric in metrics_names}\n",
    "benchmark_results_matching_baseline = benchmark_results_matching[benchmark_results_matching['Data Scaling'] == '1.0']\n",
    "benchmark_results_agg = benchmark_results_matching_baseline.groupby('Net category').agg(agg_dict).astype(float)\n",
    "benchmark_results_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2948e988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Data Scaling</th>\n",
       "      <th>Network Scaling</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>ECE</th>\n",
       "      <th>Posterior Contraction</th>\n",
       "      <th>Net category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.589965</td>\n",
       "      <td>5.737136</td>\n",
       "      <td>0.382388</td>\n",
       "      <td>0.567486</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>0.293866</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.189591</td>\n",
       "      <td>5.649701</td>\n",
       "      <td>0.388416</td>\n",
       "      <td>0.5826</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>0.275213</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.035</td>\n",
       "      <td>5.578922</td>\n",
       "      <td>0.381945</td>\n",
       "      <td>0.579025</td>\n",
       "      <td>0.009105</td>\n",
       "      <td>0.304278</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.206071</td>\n",
       "      <td>5.680228</td>\n",
       "      <td>0.373502</td>\n",
       "      <td>0.573261</td>\n",
       "      <td>0.009237</td>\n",
       "      <td>0.229461</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data Scaling Network Scaling       RMSE       MAE        R2 Correlation   \n",
       "8           1.0     powerscaled  15.589965  5.737136  0.382388    0.567486  \\\n",
       "9           1.0     powerscaled  15.189591  5.649701  0.388416      0.5826   \n",
       "12          1.0             1.0     15.035  5.578922  0.381945    0.579025   \n",
       "13          1.0             1.0  15.206071  5.680228  0.373502    0.573261   \n",
       "\n",
       "         ECE Posterior Contraction Net category  \n",
       "8   0.004395              0.293866  powerscaled  \n",
       "9   0.006211              0.275213  powerscaled  \n",
       "12  0.009105              0.304278     unscaled  \n",
       "13  0.009237              0.229461     unscaled  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_results_matching_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8bfeccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Scaling</th>\n",
       "      <th>Network Scaling</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>ECE</th>\n",
       "      <th>Posterior Contraction</th>\n",
       "      <th>Net category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>27.471651</td>\n",
       "      <td>9.089793</td>\n",
       "      <td>0.341086</td>\n",
       "      <td>0.587292</td>\n",
       "      <td>0.021711</td>\n",
       "      <td>0.437834</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>23.003726</td>\n",
       "      <td>8.91132</td>\n",
       "      <td>0.34195</td>\n",
       "      <td>0.580035</td>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.450737</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26.047438</td>\n",
       "      <td>10.066422</td>\n",
       "      <td>0.307464</td>\n",
       "      <td>0.471359</td>\n",
       "      <td>0.006526</td>\n",
       "      <td>0.169265</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.095522</td>\n",
       "      <td>9.259736</td>\n",
       "      <td>0.37276</td>\n",
       "      <td>0.617461</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.302479</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.589965</td>\n",
       "      <td>5.737136</td>\n",
       "      <td>0.382388</td>\n",
       "      <td>0.567486</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>0.293866</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.189591</td>\n",
       "      <td>5.649701</td>\n",
       "      <td>0.388416</td>\n",
       "      <td>0.5826</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>0.275213</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.035</td>\n",
       "      <td>5.578922</td>\n",
       "      <td>0.381945</td>\n",
       "      <td>0.579025</td>\n",
       "      <td>0.009105</td>\n",
       "      <td>0.304278</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.206071</td>\n",
       "      <td>5.680228</td>\n",
       "      <td>0.373502</td>\n",
       "      <td>0.573261</td>\n",
       "      <td>0.009237</td>\n",
       "      <td>0.229461</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>9.797719</td>\n",
       "      <td>3.713643</td>\n",
       "      <td>0.333149</td>\n",
       "      <td>0.475907</td>\n",
       "      <td>0.017053</td>\n",
       "      <td>0.354234</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>9.806578</td>\n",
       "      <td>3.756246</td>\n",
       "      <td>0.314938</td>\n",
       "      <td>0.491856</td>\n",
       "      <td>0.010684</td>\n",
       "      <td>0.22516</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.562035</td>\n",
       "      <td>3.694832</td>\n",
       "      <td>0.369688</td>\n",
       "      <td>0.494499</td>\n",
       "      <td>0.005053</td>\n",
       "      <td>0.193267</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.581952</td>\n",
       "      <td>3.707903</td>\n",
       "      <td>0.372409</td>\n",
       "      <td>0.500749</td>\n",
       "      <td>0.007632</td>\n",
       "      <td>0.179734</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data Scaling Network Scaling       RMSE        MAE        R2 Correlation   \n",
       "0           0.5     powerscaled  27.471651   9.089793  0.341086    0.587292  \\\n",
       "1           0.5     powerscaled  23.003726    8.91132   0.34195    0.580035   \n",
       "2           0.5             0.5  26.047438  10.066422  0.307464    0.471359   \n",
       "3           0.5             0.5  24.095522   9.259736   0.37276    0.617461   \n",
       "8           1.0     powerscaled  15.589965   5.737136  0.382388    0.567486   \n",
       "9           1.0     powerscaled  15.189591   5.649701  0.388416      0.5826   \n",
       "12          1.0             1.0     15.035   5.578922  0.381945    0.579025   \n",
       "13          1.0             1.0  15.206071   5.680228  0.373502    0.573261   \n",
       "16          2.0     powerscaled   9.797719   3.713643  0.333149    0.475907   \n",
       "17          2.0     powerscaled   9.806578   3.756246  0.314938    0.491856   \n",
       "22          2.0             2.0   9.562035   3.694832  0.369688    0.494499   \n",
       "23          2.0             2.0   9.581952   3.707903  0.372409    0.500749   \n",
       "\n",
       "         ECE Posterior Contraction Net category  \n",
       "0   0.021711              0.437834  powerscaled  \n",
       "1   0.018789              0.450737  powerscaled  \n",
       "2   0.006526              0.169265     unscaled  \n",
       "3   0.005368              0.302479     unscaled  \n",
       "8   0.004395              0.293866  powerscaled  \n",
       "9   0.006211              0.275213  powerscaled  \n",
       "12  0.009105              0.304278     unscaled  \n",
       "13  0.009237              0.229461     unscaled  \n",
       "16  0.017053              0.354234  powerscaled  \n",
       "17  0.010684               0.22516  powerscaled  \n",
       "22  0.005053              0.193267     unscaled  \n",
       "23  0.007632              0.179734     unscaled  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_results_matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1270a18a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"2\" halign=\"left\">RMSE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MAE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">R2</th>\n",
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       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
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       "    <tr>\n",
       "      <th>Net category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>powerscaled</th>\n",
       "      <td>15.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>5.69</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unscaled</th>\n",
       "      <td>15.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>5.63</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RMSE         MAE          R2       Correlation         ECE   \n",
       "               mean   std  mean   std  mean   std        mean   std  mean   \n",
       "Net category                                                                \n",
       "powerscaled   15.39  0.28  5.69  0.06  0.39  0.00        0.58  0.01  0.01  \\\n",
       "unscaled      15.12  0.12  5.63  0.07  0.38  0.01        0.58  0.00  0.01   \n",
       "\n",
       "                  Posterior Contraction        \n",
       "              std                  mean   std  \n",
       "Net category                                   \n",
       "powerscaled   0.0                  0.28  0.01  \n",
       "unscaled      0.0                  0.27  0.05  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_results_agg.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1033fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">RMSE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MAE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">R2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Correlation</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ECE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Posterior Contraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>powerscaled</th>\n",
       "      <td>15.390</td>\n",
       "      <td>0.283</td>\n",
       "      <td>5.693</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unscaled</th>\n",
       "      <td>15.121</td>\n",
       "      <td>0.121</td>\n",
       "      <td>5.630</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                RMSE           MAE            R2        Correlation          \n",
       "                mean    std   mean    std   mean    std        mean    std   \n",
       "Net category                                                                 \n",
       "powerscaled   15.390  0.283  5.693  0.062  0.385  0.004       0.575  0.011  \\\n",
       "unscaled      15.121  0.121  5.630  0.072  0.378  0.006       0.576  0.004   \n",
       "\n",
       "                ECE        Posterior Contraction         \n",
       "               mean    std                  mean    std  \n",
       "Net category                                             \n",
       "powerscaled   0.005  0.001                 0.285  0.013  \n",
       "unscaled      0.009  0.000                 0.267  0.053  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_results_agg.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9ca574f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Data Scaling</th>\n",
       "      <th>Network Scaling</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>ECE</th>\n",
       "      <th>Posterior Contraction</th>\n",
       "      <th>Net category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>27.256002</td>\n",
       "      <td>9.075805</td>\n",
       "      <td>0.34108</td>\n",
       "      <td>0.588203</td>\n",
       "      <td>0.021447</td>\n",
       "      <td>0.439529</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>22.991388</td>\n",
       "      <td>8.910167</td>\n",
       "      <td>0.34265</td>\n",
       "      <td>0.580686</td>\n",
       "      <td>0.018947</td>\n",
       "      <td>0.450928</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>25.976725</td>\n",
       "      <td>10.056245</td>\n",
       "      <td>0.308036</td>\n",
       "      <td>0.473048</td>\n",
       "      <td>0.006316</td>\n",
       "      <td>0.169492</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.08768</td>\n",
       "      <td>9.257009</td>\n",
       "      <td>0.374988</td>\n",
       "      <td>0.618105</td>\n",
       "      <td>0.008132</td>\n",
       "      <td>0.307345</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.586246</td>\n",
       "      <td>5.736469</td>\n",
       "      <td>0.382172</td>\n",
       "      <td>0.568111</td>\n",
       "      <td>0.006289</td>\n",
       "      <td>0.28809</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>15.190357</td>\n",
       "      <td>5.647482</td>\n",
       "      <td>0.389506</td>\n",
       "      <td>0.582853</td>\n",
       "      <td>0.006026</td>\n",
       "      <td>0.270064</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.037126</td>\n",
       "      <td>5.57392</td>\n",
       "      <td>0.381661</td>\n",
       "      <td>0.57913</td>\n",
       "      <td>0.009684</td>\n",
       "      <td>0.30821</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.21406</td>\n",
       "      <td>5.68253</td>\n",
       "      <td>0.372534</td>\n",
       "      <td>0.571396</td>\n",
       "      <td>0.008921</td>\n",
       "      <td>0.227188</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>9.811642</td>\n",
       "      <td>3.71721</td>\n",
       "      <td>0.331486</td>\n",
       "      <td>0.475411</td>\n",
       "      <td>0.015368</td>\n",
       "      <td>0.353494</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0</td>\n",
       "      <td>powerscaled</td>\n",
       "      <td>9.807992</td>\n",
       "      <td>3.757082</td>\n",
       "      <td>0.31468</td>\n",
       "      <td>0.489239</td>\n",
       "      <td>0.010184</td>\n",
       "      <td>0.23065</td>\n",
       "      <td>powerscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.565733</td>\n",
       "      <td>3.69551</td>\n",
       "      <td>0.368076</td>\n",
       "      <td>0.495392</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.195078</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.584138</td>\n",
       "      <td>3.709183</td>\n",
       "      <td>0.371665</td>\n",
       "      <td>0.501251</td>\n",
       "      <td>0.005789</td>\n",
       "      <td>0.176564</td>\n",
       "      <td>unscaled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data Scaling Network Scaling       RMSE        MAE        R2 Correlation   \n",
       "0           0.5     powerscaled  27.256002   9.075805   0.34108    0.588203  \\\n",
       "1           0.5     powerscaled  22.991388   8.910167   0.34265    0.580686   \n",
       "2           0.5             0.5  25.976725  10.056245  0.308036    0.473048   \n",
       "3           0.5             0.5   24.08768   9.257009  0.374988    0.618105   \n",
       "8           1.0     powerscaled  15.586246   5.736469  0.382172    0.568111   \n",
       "9           1.0     powerscaled  15.190357   5.647482  0.389506    0.582853   \n",
       "12          1.0             1.0  15.037126    5.57392  0.381661     0.57913   \n",
       "13          1.0             1.0   15.21406    5.68253  0.372534    0.571396   \n",
       "16          2.0     powerscaled   9.811642    3.71721  0.331486    0.475411   \n",
       "17          2.0     powerscaled   9.807992   3.757082   0.31468    0.489239   \n",
       "22          2.0             2.0   9.565733    3.69551  0.368076    0.495392   \n",
       "23          2.0             2.0   9.584138   3.709183  0.371665    0.501251   \n",
       "\n",
       "         ECE Posterior Contraction Net category  \n",
       "0   0.021447              0.439529  powerscaled  \n",
       "1   0.018947              0.450928  powerscaled  \n",
       "2   0.006316              0.169492     unscaled  \n",
       "3   0.008132              0.307345     unscaled  \n",
       "8   0.006289               0.28809  powerscaled  \n",
       "9   0.006026              0.270064  powerscaled  \n",
       "12  0.009684               0.30821     unscaled  \n",
       "13  0.008921              0.227188     unscaled  \n",
       "16  0.015368              0.353494  powerscaled  \n",
       "17  0.010184               0.23065  powerscaled  \n",
       "22  0.006711              0.195078     unscaled  \n",
       "23  0.005789              0.176564     unscaled  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_results_matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "35d4150d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Time in minutes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>powerscaled</th>\n",
       "      <td>1.861680</td>\n",
       "      <td>1.909172</td>\n",
       "      <td>25.583778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unscaled</th>\n",
       "      <td>1.875968</td>\n",
       "      <td>5.627904</td>\n",
       "      <td>1875.968093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time in minutes                       \n",
       "                       1         3            1000\n",
       "powerscaled        1.861680  1.909172    25.583778\n",
       "unscaled           1.875968  5.627904  1875.968093"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get training times\n",
    "\n",
    "with open(TRAINING_TIMES_PATH, \"rb\") as f:\n",
    "    training_times = pickle.load(f)\n",
    "\n",
    "# Filter out training times for unscaled networks in the 0.5 and 2.0 setting\n",
    "training_times = {key: value for key, value in training_times.items() if '0.5' not in key and '2.0' not in key}\n",
    "\n",
    "time_data = {\n",
    "    'powerscaled': calculate_setting_times(TIME_SETTINGS, training_times, inference_times, 'powerscaled'),\n",
    "    'unscaled': calculate_setting_times(TIME_SETTINGS, training_times, inference_times, 'unscaled')\n",
    "}\n",
    "\n",
    "time_results = pd.DataFrame(time_data).T\n",
    "time_results.columns = pd.MultiIndex.from_product([['Time in minutes'], TIME_SETTINGS])\n",
    "time_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "852c0ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'powerscaled_net0': 112.1929817199707,\n",
       " 'powerscaled_net1': 108.35912942886353,\n",
       " 'unscaled_alpha_1.0_net0': 111.93056464195251,\n",
       " 'unscaled_alpha_1.0_net1': 110.76564526557922}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7699eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Time in minutes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>powerscaled</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unscaled</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1876.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time in minutes             \n",
       "                       1    3       1000\n",
       "powerscaled             2.0  2.0    26.0\n",
       "unscaled                2.0  6.0  1876.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_results.round(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b30038",
   "metadata": {},
   "source": [
    "## Additional exploration of results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51cf216c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>ECE</th>\n",
       "      <th>Posterior Contraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986386</td>\n",
       "      <td>-0.461571</td>\n",
       "      <td>0.145163</td>\n",
       "      <td>0.306567</td>\n",
       "      <td>-0.212538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.986386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.399614</td>\n",
       "      <td>0.184922</td>\n",
       "      <td>0.326724</td>\n",
       "      <td>-0.107084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>-0.461571</td>\n",
       "      <td>-0.399614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.574577</td>\n",
       "      <td>-0.491243</td>\n",
       "      <td>0.679845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Correlation</th>\n",
       "      <td>0.145163</td>\n",
       "      <td>0.184922</td>\n",
       "      <td>0.574577</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.118707</td>\n",
       "      <td>0.570964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECE</th>\n",
       "      <td>0.306567</td>\n",
       "      <td>0.326724</td>\n",
       "      <td>-0.491243</td>\n",
       "      <td>-0.118707</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Posterior Contraction</th>\n",
       "      <td>-0.212538</td>\n",
       "      <td>-0.107084</td>\n",
       "      <td>0.679845</td>\n",
       "      <td>0.570964</td>\n",
       "      <td>0.122677</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           RMSE       MAE        R2  Correlation       ECE   \n",
       "RMSE                   1.000000  0.986386 -0.461571     0.145163  0.306567  \\\n",
       "MAE                    0.986386  1.000000 -0.399614     0.184922  0.326724   \n",
       "R2                    -0.461571 -0.399614  1.000000     0.574577 -0.491243   \n",
       "Correlation            0.145163  0.184922  0.574577     1.000000 -0.118707   \n",
       "ECE                    0.306567  0.326724 -0.491243    -0.118707  1.000000   \n",
       "Posterior Contraction -0.212538 -0.107084  0.679845     0.570964  0.122677   \n",
       "\n",
       "                       Posterior Contraction  \n",
       "RMSE                               -0.212538  \n",
       "MAE                                -0.107084  \n",
       "R2                                  0.679845  \n",
       "Correlation                         0.570964  \n",
       "ECE                                 0.122677  \n",
       "Posterior Contraction               1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation matrix: Agreement between metrics\n",
    "metric_corr_matrix = np.corrcoef(benchmark_results.loc[:, metrics_names].astype(float).transpose())\n",
    "pd.DataFrame(metric_corr_matrix, columns=metrics_names, index=metrics_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47f1a6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recovery for all combinations\n",
    "for i, conf in enumerate(conf_names):\n",
    "    parameters = load_data(conf[-2:])['prior_draws']\n",
    "    for net in net_list:\n",
    "        for m in range(NUM_ENSEMBLE_MEMBERS):\n",
    "            network_num = f\"net{m}\"\n",
    "            f = bf.diagnostics.plot_recovery(\n",
    "                draws_dict[net][network_num][conf_names[i]],\n",
    "                parameters,\n",
    "                param_names=[\"Recovery\", \"for\", f\"data scaling {data_scalings[i]}\", \"and\", f\"{net} net\"], # Funny title hack\n",
    "                color=COLOR_CODES[i]\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7eb559b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calibration for all combinations\n",
    "for i, conf in enumerate(conf_names):\n",
    "    parameters = load_data(conf[-2:])['prior_draws']\n",
    "    for net in net_list:\n",
    "        for m in range(NUM_ENSEMBLE_MEMBERS):\n",
    "            network_num = f\"net{m}\"\n",
    "            f = bf.diagnostics.plot_sbc_ecdf(\n",
    "                draws_dict[net][network_num][conf_names[i]],\n",
    "                parameters,\n",
    "                param_names=[\"Calibration\", \"for\", f\"data scaling {data_scalings[i]}\", \"and\", f\"{net} net\"], # Funny title hack\n",
    "                rank_ecdf_color=COLOR_CODES[i],\n",
    "                difference = True\n",
    "            )"
   ]
  }
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