eval_inat21_errorbars.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b16a5cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import functools\n",
    "import itertools\n",
    "import json\n",
    "import pathlib\n",
    "import pickle\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import ticker\n",
    "import ml_collections\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "import hier\n",
    "import hier_torch\n",
    "import infer\n",
    "import main\n",
    "import metrics\n",
    "import progmet\n",
    "import tree_util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2602504e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Experiment = collections.namedtuple('Experiment', ['root', 'pattern'])\n",
    "Trial = collections.namedtuple('Trial', ['root', 'config_file', 'model_file'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9bbbc9fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "EXP_ROOT = pathlib.Path('/mnt/ssd1/projects/2022-01-hierarchical/experiments')\n",
    "PLOT_DIR = 'output/plots/inat21mini-res18-ep20-repeat/'\n",
    "experiment_defs = {\n",
    "    'flat_softmax': Experiment(\n",
    "        EXP_ROOT / '2022-05-07-inat21mini-errorbar', 'flat_softmax-b64-lr0.01-wd0.0003-s*'),\n",
    "#     'multilabel_focal': Experiment(\n",
    "#         EXP_ROOT / '2022-03-31-inat21mini-dgx', 'multilabel_focal-0.9-1-lr-0.01-b-64-wd-0.0003-ep-20'),\n",
    "    'multilabel_focal-1-0.95': Experiment(\n",
    "        EXP_ROOT / '2022-10-05-inat21mini-errorbar', 'multilabel_focal-1.0-0.95-b64-lr0.01-wd0.0003-s*'),\n",
    "    'hier_softmax': Experiment(\n",
    "        EXP_ROOT / '2022-05-07-inat21mini-errorbar', 'hier_softmax-b64-lr0.01-wd0.0003-s*'),\n",
    "    'cond_multilabel': Experiment(\n",
    "        EXP_ROOT / '2022-05-07-inat21mini-errorbar', 'cond_multilabel-b64-lr0.01-wd0.0003-s*'),\n",
    "#     'share_random_cut': Experiment(\n",
    "#         EXP_ROOT / '2022-03-31-inat21mini', 'share_random_cut-0.001-lr-0.01-b-64-wd-0.0003-ep-20'),\n",
    "#     'share_random_cut': Experiment(\n",
    "#         EXP_ROOT / '2022-03-31-inat21mini', 'share_random_cut-0-lr-0.01-b-64-wd-0.0003-ep-20'),\n",
    "    'share_random_cut-0': Experiment(\n",
    "        EXP_ROOT / '2022-10-05-inat21mini-errorbar', 'share_random_cut-0-b64-lr0.01-wd0.0003-s*'),\n",
    "    'share_flat_softmax': Experiment(\n",
    "        EXP_ROOT / '2022-10-05-inat21mini-errorbar', 'share_flat_softmax-b64-lr0.01-wd0.0003-s*'),\n",
    "#     'share_levelwise': Experiment(\n",
    "#         EXP_ROOT / '2022-03-31-inat21mini', 'share_levelwise-lr-0.01-b-64-wd-0.0003-ep-20'),\n",
    "#     'descendant_softmax': Experiment(\n",
    "#         EXP_ROOT / '2022-05-07-inat21mini-errorbar', 'descendant_softmax-b64-lr0.01-wd0.0003-s*'),\n",
    "    'descendant_softmax_complement': Experiment(\n",
    "        EXP_ROOT / '2022-05-07-inat21mini-errorbar', 'descendant_softmax_complement-b64-lr0.01-wd0.0003-s*'),\n",
    "    'soft_margin-incorrect-5': Experiment(\n",
    "        EXP_ROOT / '2022-05-07-inat21mini-errorbar', 'soft_margin-incorrect-5-b64-lr0.01-wd0.0003-s*'),\n",
    "#     '': Experiment(\n",
    "#         EXP_ROOT / '', ''),\n",
    "}\n",
    "\n",
    "method_titles = {\n",
    "    'flat_softmax': 'Flat softmax',\n",
    "    'multilabel_focal-1-0.95': 'Multilabel focal',\n",
    "    'hier_softmax': 'Cond softmax',\n",
    "    'cond_multilabel': 'Cond sigmoid',\n",
    "    'share_random_cut-0': 'Deep RTC',\n",
    "    'share_flat_softmax': 'PS softmax',\n",
    "    # 'descendant_softmax': 'Soft-max-descendant (all)',\n",
    "    'descendant_softmax_complement': 'Soft-max-descendant',\n",
    "    'soft_margin-incorrect-5': 'Soft-max-margin',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "192ddfce",
   "metadata": {},
   "outputs": [],
   "source": [
    "colormap = matplotlib.cm.get_cmap('Set1')\n",
    "\n",
    "# colors = dict(zip(experiments, map(colormap, itertools.count())))\n",
    "\n",
    "color_index = {\n",
    "    'flat_softmax': 0,\n",
    "    'multilabel_focal-1-0.95': 1,\n",
    "    'hier_softmax': 2,\n",
    "    'cond_multilabel': 3,\n",
    "    'share_random_cut-0': 4,\n",
    "    'share_flat_softmax': 5,\n",
    "    'descendant_softmax': 6,\n",
    "    'descendant_softmax_complement': 8,\n",
    "    'soft_margin-incorrect-5': 7,\n",
    "}\n",
    "colors = {k: colormap(v) for k, v in color_index.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "8ac8d909",
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_names = {\n",
    "    exp_name: sorted([\n",
    "        path.name for path in exp_def.root.glob(exp_def.pattern)\n",
    "        if (path / 'checkpoints/epoch-0020.pth').exists()\n",
    "    ]) for exp_name, exp_def in experiment_defs.items()\n",
    "}\n",
    "\n",
    "trials = {\n",
    "    exp_name: {\n",
    "        trial_name: Trial(\n",
    "            root=experiment_defs[exp_name].root / trial_name,\n",
    "            config_file=experiment_defs[exp_name].root / trial_name / 'config.json',\n",
    "            model_file=experiment_defs[exp_name].root / trial_name / 'checkpoints/epoch-0020.pth')\n",
    "        for trial_name in trial_names[exp_name]\n",
    "    } for exp_name in trial_names\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "abbf1105",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "06047af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import configs.inaturalist2021mini\n",
    "\n",
    "base_config = configs.inaturalist2021mini.get_config()\n",
    "base_config.dataset_root = '/home/jack/data/manual/inaturalist2021/'\n",
    "\n",
    "_, eval_dataset, tree, _, _, eval_label_map = main.make_datasets(base_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "668d8497",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_loader = torch.utils.data.DataLoader(\n",
    "    dataset=eval_dataset,\n",
    "    batch_size=64,\n",
    "    shuffle=False,\n",
    "    num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "562d30aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_root = (np.arange(tree.num_nodes()) == 0)\n",
    "is_leaf = tree.leaf_mask()\n",
    "specificity = np.log2(tree.num_leaf_nodes()) - np.log2(tree.num_leaf_descendants())\n",
    "not_trivial = (tree.num_children() != 1)\n",
    "subtract_children_cpu = hier_torch.SubtractChildren(tree)\n",
    "subtract_children_cuda = hier_torch.SubtractChildren(tree).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "fb7f20d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "@functools.lru_cache(maxsize=None)\n",
    "def get_matrix_correct_recall():\n",
    "    gt_nodes = np.arange(tree.num_nodes())[:, None]\n",
    "    pr_nodes = np.arange(tree.num_nodes())[None, :]\n",
    "\n",
    "    matrix = (\n",
    "        metrics.correct(tree, gt_nodes, pr_nodes).astype(np.float32) *\n",
    "        metrics.LCAMetric(tree, specificity.astype(np.float32)).recall(gt_nodes, pr_nodes)\n",
    "    )\n",
    "    matrix = torch.from_numpy(matrix).cuda()\n",
    "    return matrix\n",
    "\n",
    "# matrix_info_f1 = (\n",
    "#     metrics.LCAMetric(tree, specificity.astype(np.float32)).f1(gt_nodes, pr_nodes))\n",
    "# matrix_correct_info = (\n",
    "#     metrics.correct(tree, gt_nodes, pr_nodes) * specificity[pr_nodes]\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "40150867",
   "metadata": {},
   "outputs": [],
   "source": [
    "infer_fns = {\n",
    "    'leaf': lambda p: infer.argmax_where(p, is_leaf),\n",
    "    # 'exclusive': lambda p: np.argmax(subtract_children_cpu(torch.from_numpy(p)).numpy(), axis=-1),\n",
    "    'majority': lambda p: infer.argmax_with_confidence(specificity, p, 0.5, not_trivial),\n",
    "    'plurality': lambda p: infer.argmax_where(\n",
    "        specificity,\n",
    "        not_trivial & ((p > infer.plurality_threshold(tree, p, keepdims=True)) | is_root)),\n",
    "    'expected': lambda p: infer.argmax_where(p * specificity, not_trivial),\n",
    "#     'crm_correct_recall': lambda p: (\n",
    "#         infer.argmax_where(\n",
    "#             torch.tensordot(\n",
    "#                 subtract_children_cuda(torch.from_numpy(p).cuda()),\n",
    "#                 get_matrix_correct_recall(),\n",
    "#                 1,\n",
    "#             ).cpu().numpy(),\n",
    "#             not_trivial)),\n",
    "    # 'crm_info_f1': lambda p: infer.argmax_where(np.dot(p, matrix_info_f1), not_trivial),\n",
    "    # 'crm_correct_info': lambda p: infer.argmax_where(np.dot(p, matrix_correct_info), not_trivial),\n",
    "}\n",
    "\n",
    "markers = {\n",
    "    'leaf': 'o',\n",
    "    # 'exclusive': 'x',\n",
    "    'majority': '^',\n",
    "    'plurality': '>',\n",
    "    'expected': 'd',\n",
    "    'crm_correct_recall': '+',\n",
    "}\n",
    "\n",
    "inference_titles = {\n",
    "    'leaf': 'Leaf',\n",
    "    # 'exclusive': 'Exclusive',\n",
    "    'majority': 'Majority',\n",
    "    'plurality': 'Plurality',\n",
    "    'expected': 'Expected info',\n",
    "    'crm_correct_recall': 'Cond risk (correct info)',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8f9f0657",
   "metadata": {},
   "outputs": [],
   "source": [
    "info_metric = metrics.UniformLeafInfoMetric(tree)\n",
    "depth_metric = metrics.DepthMetric(tree)\n",
    "\n",
    "metric_fns = {\n",
    "    'exact': lambda gt, pr: pr == gt,\n",
    "    'correct': metrics.IsCorrect(tree),\n",
    "    'info_excess': info_metric.excess,\n",
    "    'info_deficient': info_metric.deficient,\n",
    "    'info_dist': info_metric.dist,\n",
    "    'info_recall': info_metric.recall,\n",
    "    'info_precision': info_metric.precision,\n",
    "    'info_f1': info_metric.f1,\n",
    "    'depth_excess': depth_metric.excess,\n",
    "    'depth_deficient': depth_metric.deficient,\n",
    "    'depth_dist': depth_metric.dist,\n",
    "    'depth_recall': depth_metric.recall,\n",
    "    'depth_precision': depth_metric.precision,\n",
    "    'depth_f1': depth_metric.f1,\n",
    "}\n",
    "\n",
    "metric_titles = {\n",
    "    'exact': 'Exact',\n",
    "    'correct': 'Correct',\n",
    "    'info_recall': 'Information Recall',\n",
    "    'info_precision': 'Information Precision',\n",
    "    'depth_recall': 'Depth Recall',\n",
    "    'depth_precision': 'Depth Precision',\n",
    "    'info_lca': 'LCA Information',\n",
    "    'depth_lca': 'LCA Depth',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "321bfc2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_model(net, pred_fn, min_threshold, limit=None, temperature=None):\n",
    "    # Per-example predictions.\n",
    "\n",
    "    outputs = {\n",
    "        'gt': [],  # Node in hierarchy.\n",
    "        'pred': {method: [] for method in infer_fns},\n",
    "    }\n",
    "    # Sequence-per-example predictions. Cannot be concatenated due to ragged shape.\n",
    "    seq_outputs = {\n",
    "        'pred': [],\n",
    "        'prob': [],\n",
    "    }\n",
    "\n",
    "    net.eval()\n",
    "    with torch.inference_mode():\n",
    "        meter = progmet.ProgressMeter('apply', interval_time=5)\n",
    "        for minibatch in itertools.islice(meter(eval_loader), limit):\n",
    "            inputs, gt_labels = minibatch\n",
    "            theta = net(inputs.to(device))\n",
    "            if temperature:\n",
    "                theta = theta / temperature\n",
    "            prob = pred_fn(theta).cpu().numpy()\n",
    "            pred = {}\n",
    "            for name, infer_fn in infer_fns.items():\n",
    "                pred[name] = infer_fn(prob)\n",
    "            gt_node = eval_label_map.to_node[gt_labels]\n",
    "            pred_seqs = [\n",
    "                infer.pareto_optimal_predictions(specificity, p, min_threshold, not_trivial)\n",
    "                for p in prob\n",
    "            ]\n",
    "            prob_seqs = [prob[i, pred_i] for i, pred_i in enumerate(pred_seqs)]\n",
    "            # Caution: Predictions are *not* truncated.\n",
    "\n",
    "            outputs['gt'].append(gt_node)\n",
    "            for method in infer_fns:\n",
    "                outputs['pred'][method].append(pred[method])\n",
    "            seq_outputs['pred'].extend(pred_seqs)\n",
    "            seq_outputs['prob'].extend(prob_seqs)\n",
    "\n",
    "    # Concatenate results from minibatches.\n",
    "    leaf_predicate = lambda x: not isinstance(x, dict)  # Treat lists as values, not containers.\n",
    "    outputs = tree_util.tree_map(np.concatenate, outputs, is_leaf=leaf_predicate)\n",
    "\n",
    "    return outputs, seq_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c6303cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def assess_predictions(outputs, seq_outputs):\n",
    "    gt = outputs['gt']\n",
    "    pred = outputs['pred']\n",
    "    pred_seq = seq_outputs['pred']\n",
    "    prob_seq = seq_outputs['prob']\n",
    "\n",
    "    # Evaluate predictions for each method.\n",
    "    pred = {\n",
    "        method: hier.truncate_at_lca(tree, gt, pr)\n",
    "        for method, pr in pred.items()\n",
    "    }\n",
    "    pred_metrics = {\n",
    "        method: {field: np.mean(metric_fn(gt, pr))\n",
    "                 for field, metric_fn in metric_fns.items()}\n",
    "        for method, pr in pred.items()\n",
    "    }\n",
    "\n",
    "    # Evaluate predictions in Pareto sequence.\n",
    "    find_lca = hier.FindLCA(tree)\n",
    "    specificity_seq = [specificity[pr_i] for pr_i in pred_seq]\n",
    "    pred_seq = [hier.truncate_given_lca(gt_i, pr_i, find_lca(gt_i, pr_i)) for gt_i, pr_i in zip(gt, pred_seq)]\n",
    "    metric_values_seq = {\n",
    "        field: [metric_fn(gt_i, pr_i) for gt_i, pr_i in zip(gt, pred_seq)]\n",
    "        for field, metric_fn in metric_fns.items()\n",
    "    }\n",
    "    pareto_scores, pareto_totals = metrics.operating_curve(prob_seq, metric_values_seq)\n",
    "    pareto_means = {k: v / len(gt) for k, v in pareto_totals.items()}\n",
    "\n",
    "    return pred_metrics, pareto_scores, pareto_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "d6bf2432",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_config(config):  # For backwards compatibility.\n",
    "    if not hasattr(config.train, 'loss_weighting'):\n",
    "        setattr(config.train, 'loss_weighting', 'none')\n",
    "    if not hasattr(config.train, 'margin_tau'):\n",
    "        setattr(config.train, 'margin_tau', 1.0)\n",
    "    return config  # For convenience.\n",
    "\n",
    "def resample_threshold(scores, metric, x):\n",
    "    return metric[np.searchsorted(-scores, -x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "182e9afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# del results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "36931a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize results if not defined.\n",
    "try:\n",
    "    results\n",
    "except NameError:\n",
    "    results = {}  # Not defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "5951431b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cached: ('flat_softmax', 'flat_softmax-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('flat_softmax', 'flat_softmax-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('flat_softmax', 'flat_softmax-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('flat_softmax', 'flat_softmax-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('flat_softmax', 'flat_softmax-b64-lr0.01-wd0.0003-s4')\n",
      "cached: ('multilabel_focal-1-0.95', 'multilabel_focal-1.0-0.95-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('multilabel_focal-1-0.95', 'multilabel_focal-1.0-0.95-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('multilabel_focal-1-0.95', 'multilabel_focal-1.0-0.95-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('multilabel_focal-1-0.95', 'multilabel_focal-1.0-0.95-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('multilabel_focal-1-0.95', 'multilabel_focal-1.0-0.95-b64-lr0.01-wd0.0003-s4')\n",
      "cached: ('hier_softmax', 'hier_softmax-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('hier_softmax', 'hier_softmax-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('hier_softmax', 'hier_softmax-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('hier_softmax', 'hier_softmax-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('hier_softmax', 'hier_softmax-b64-lr0.01-wd0.0003-s4')\n",
      "cached: ('cond_multilabel', 'cond_multilabel-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('cond_multilabel', 'cond_multilabel-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('cond_multilabel', 'cond_multilabel-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('cond_multilabel', 'cond_multilabel-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('cond_multilabel', 'cond_multilabel-b64-lr0.01-wd0.0003-s4')\n",
      "cached: ('share_random_cut-0', 'share_random_cut-0-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('share_random_cut-0', 'share_random_cut-0-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('share_random_cut-0', 'share_random_cut-0-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('share_random_cut-0', 'share_random_cut-0-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('share_random_cut-0', 'share_random_cut-0-b64-lr0.01-wd0.0003-s4')\n",
      "cached: ('share_flat_softmax', 'share_flat_softmax-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('share_flat_softmax', 'share_flat_softmax-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('share_flat_softmax', 'share_flat_softmax-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('share_flat_softmax', 'share_flat_softmax-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('share_flat_softmax', 'share_flat_softmax-b64-lr0.01-wd0.0003-s4')\n",
      "cached: ('descendant_softmax_complement', 'descendant_softmax_complement-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('descendant_softmax_complement', 'descendant_softmax_complement-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('descendant_softmax_complement', 'descendant_softmax_complement-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('descendant_softmax_complement', 'descendant_softmax_complement-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('soft_margin-incorrect-5', 'soft_margin-incorrect-5-b64-lr0.01-wd0.0003-s0')\n",
      "cached: ('soft_margin-incorrect-5', 'soft_margin-incorrect-5-b64-lr0.01-wd0.0003-s1')\n",
      "cached: ('soft_margin-incorrect-5', 'soft_margin-incorrect-5-b64-lr0.01-wd0.0003-s2')\n",
      "cached: ('soft_margin-incorrect-5', 'soft_margin-incorrect-5-b64-lr0.01-wd0.0003-s3')\n",
      "cached: ('soft_margin-incorrect-5', 'soft_margin-incorrect-5-b64-lr0.01-wd0.0003-s4')\n"
     ]
    }
   ],
   "source": [
    "MIN_THRESHOLD = None  # Values less than 0.5 may increase runtime significantly.\n",
    "LIMIT = None\n",
    "IGNORE_CACHE_FILE = False\n",
    "\n",
    "score_grid = np.flip(np.linspace(0, 1, 1001))\n",
    "\n",
    "for experiment_name in trials:\n",
    "    for trial_name, trial in trials[experiment_name].items():\n",
    "        key = (experiment_name, trial_name)\n",
    "        if key in results:\n",
    "            print('cached:', key)\n",
    "            continue\n",
    "\n",
    "        results_file = pathlib.Path(trial.root) / f'results-th-{MIN_THRESHOLD}-lim-{LIMIT}.pkl'\n",
    "        if results_file.exists() and not IGNORE_CACHE_FILE:\n",
    "            print('load from file:', str(results_file))\n",
    "            # results[key] = dict(np.load(results_file, allow_pickle=True))\n",
    "            with open(results_file, 'rb') as f: \n",
    "                trial_results = pickle.load(f)\n",
    "        else:\n",
    "            print('evaluate:', key)\n",
    "            # Load model.\n",
    "            with open(trial.config_file, 'r') as f:\n",
    "                config = update_config(ml_collections.ConfigDict(json.load(f)))\n",
    "            num_outputs = main.get_num_outputs(config.predict, tree)\n",
    "            net = main.make_model(config.model, num_outputs)\n",
    "            missing_keys, unexpected_keys = net.load_state_dict(torch.load(trial.model_file), strict=True)\n",
    "            assert not missing_keys\n",
    "            assert not unexpected_keys\n",
    "\n",
    "            net.to(device)\n",
    "            _, pred_fn = main.make_loss(config, tree, device)\n",
    "            outputs, seq_outputs = apply_model(\n",
    "                net, pred_fn, min_threshold=MIN_THRESHOLD, limit=LIMIT,\n",
    "                temperature=10.0 if 'share_random_cut' in trial_name else None)\n",
    "            pred_metrics, pareto_scores, pareto_metrics = assess_predictions(outputs, seq_outputs)\n",
    "            trial_results = {\n",
    "                'pred_metrics': pred_metrics,\n",
    "                'pareto_scores': pareto_scores,\n",
    "                'pareto_metrics': pareto_metrics,\n",
    "            }\n",
    "            # np.savez(results_file, **results[key])\n",
    "            with open(results_file, 'wb') as f:\n",
    "                pickle.dump(trial_results, f)\n",
    "\n",
    "        # Re-sample the Pareto front; also keeps the memory footprint down.\n",
    "        trial_results.update({\n",
    "            'pareto_scores': score_grid[1:],\n",
    "            'pareto_metrics': {\n",
    "                field: resample_threshold(\n",
    "                    trial_results['pareto_scores'],\n",
    "                    trial_results['pareto_metrics'][field],\n",
    "                    score_grid)\n",
    "                for field in trial_results['pareto_metrics']\n",
    "            },\n",
    "        })\n",
    "        results[key] = trial_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c76361fc",
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">correct</th>\n",
       "      <th colspan=\"4\" halign=\"left\">depth_deficient</th>\n",
       "      <th colspan=\"2\" halign=\"left\">depth_dist</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">info_f1</th>\n",
       "      <th colspan=\"4\" halign=\"left\">info_precision</th>\n",
       "      <th colspan=\"4\" halign=\"left\">info_recall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>expected</th>\n",
       "      <th>leaf</th>\n",
       "      <th>majority</th>\n",
       "      <th>plurality</th>\n",
       "      <th>expected</th>\n",
       "      <th>leaf</th>\n",
       "      <th>majority</th>\n",
       "      <th>plurality</th>\n",
       "      <th>expected</th>\n",
       "      <th>leaf</th>\n",
       "      <th>...</th>\n",
       "      <th>majority</th>\n",
       "      <th>plurality</th>\n",
       "      <th>expected</th>\n",
       "      <th>leaf</th>\n",
       "      <th>majority</th>\n",
       "      <th>plurality</th>\n",
       "      <th>expected</th>\n",
       "      <th>leaf</th>\n",
       "      <th>majority</th>\n",
       "      <th>plurality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cond_multilabel</th>\n",
       "      <td>0.755414</td>\n",
       "      <td>0.444680</td>\n",
       "      <td>0.808274</td>\n",
       "      <td>0.516450</td>\n",
       "      <td>2.494090</td>\n",
       "      <td>1.774510</td>\n",
       "      <td>2.639364</td>\n",
       "      <td>2.155992</td>\n",
       "      <td>3.031672</td>\n",
       "      <td>3.549020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.567258</td>\n",
       "      <td>0.621819</td>\n",
       "      <td>0.826897</td>\n",
       "      <td>0.655240</td>\n",
       "      <td>0.891669</td>\n",
       "      <td>0.731682</td>\n",
       "      <td>0.523709</td>\n",
       "      <td>0.655240</td>\n",
       "      <td>0.476265</td>\n",
       "      <td>0.576050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descendant_softmax</th>\n",
       "      <td>0.787812</td>\n",
       "      <td>0.517472</td>\n",
       "      <td>0.870208</td>\n",
       "      <td>0.630494</td>\n",
       "      <td>2.398578</td>\n",
       "      <td>1.574582</td>\n",
       "      <td>2.584468</td>\n",
       "      <td>1.839104</td>\n",
       "      <td>2.918526</td>\n",
       "      <td>3.149164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.597179</td>\n",
       "      <td>0.687303</td>\n",
       "      <td>0.840993</td>\n",
       "      <td>0.697171</td>\n",
       "      <td>0.923905</td>\n",
       "      <td>0.793246</td>\n",
       "      <td>0.569631</td>\n",
       "      <td>0.697171</td>\n",
       "      <td>0.502089</td>\n",
       "      <td>0.646816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descendant_softmax_complement</th>\n",
       "      <td>0.782170</td>\n",
       "      <td>0.522875</td>\n",
       "      <td>0.867138</td>\n",
       "      <td>0.631748</td>\n",
       "      <td>2.391145</td>\n",
       "      <td>1.562345</td>\n",
       "      <td>2.522988</td>\n",
       "      <td>1.785093</td>\n",
       "      <td>2.927480</td>\n",
       "      <td>3.124690</td>\n",
       "      <td>...</td>\n",
       "      <td>0.609859</td>\n",
       "      <td>0.694481</td>\n",
       "      <td>0.836446</td>\n",
       "      <td>0.700079</td>\n",
       "      <td>0.919521</td>\n",
       "      <td>0.791920</td>\n",
       "      <td>0.576144</td>\n",
       "      <td>0.700079</td>\n",
       "      <td>0.517387</td>\n",
       "      <td>0.657213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flat_softmax</th>\n",
       "      <td>0.762656</td>\n",
       "      <td>0.521716</td>\n",
       "      <td>0.849040</td>\n",
       "      <td>0.601988</td>\n",
       "      <td>1.939350</td>\n",
       "      <td>1.572574</td>\n",
       "      <td>2.215638</td>\n",
       "      <td>1.724020</td>\n",
       "      <td>2.660774</td>\n",
       "      <td>3.145148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636752</td>\n",
       "      <td>0.694626</td>\n",
       "      <td>0.824203</td>\n",
       "      <td>0.698627</td>\n",
       "      <td>0.916476</td>\n",
       "      <td>0.779029</td>\n",
       "      <td>0.635018</td>\n",
       "      <td>0.698627</td>\n",
       "      <td>0.564754</td>\n",
       "      <td>0.665411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hier_softmax</th>\n",
       "      <td>0.723966</td>\n",
       "      <td>0.443082</td>\n",
       "      <td>0.786326</td>\n",
       "      <td>0.495768</td>\n",
       "      <td>2.386896</td>\n",
       "      <td>1.789674</td>\n",
       "      <td>2.533506</td>\n",
       "      <td>2.072384</td>\n",
       "      <td>3.017860</td>\n",
       "      <td>3.579348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.585439</td>\n",
       "      <td>0.629382</td>\n",
       "      <td>0.801465</td>\n",
       "      <td>0.652651</td>\n",
       "      <td>0.878799</td>\n",
       "      <td>0.715632</td>\n",
       "      <td>0.547193</td>\n",
       "      <td>0.652651</td>\n",
       "      <td>0.499291</td>\n",
       "      <td>0.594335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multilabel_focal</th>\n",
       "      <td>0.509360</td>\n",
       "      <td>0.443872</td>\n",
       "      <td>0.453870</td>\n",
       "      <td>0.521812</td>\n",
       "      <td>1.915318</td>\n",
       "      <td>1.781590</td>\n",
       "      <td>1.853284</td>\n",
       "      <td>2.290966</td>\n",
       "      <td>3.263640</td>\n",
       "      <td>3.563180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.650514</td>\n",
       "      <td>0.607296</td>\n",
       "      <td>0.685334</td>\n",
       "      <td>0.653806</td>\n",
       "      <td>0.664531</td>\n",
       "      <td>0.723363</td>\n",
       "      <td>0.636675</td>\n",
       "      <td>0.653806</td>\n",
       "      <td>0.641263</td>\n",
       "      <td>0.553297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multilabel_focal-1-0.95</th>\n",
       "      <td>0.509360</td>\n",
       "      <td>0.443872</td>\n",
       "      <td>0.453870</td>\n",
       "      <td>0.521812</td>\n",
       "      <td>1.915318</td>\n",
       "      <td>1.781590</td>\n",
       "      <td>1.853284</td>\n",
       "      <td>2.290966</td>\n",
       "      <td>3.263640</td>\n",
       "      <td>3.563180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.650514</td>\n",
       "      <td>0.607296</td>\n",
       "      <td>0.685334</td>\n",
       "      <td>0.653806</td>\n",
       "      <td>0.664531</td>\n",
       "      <td>0.723363</td>\n",
       "      <td>0.636675</td>\n",
       "      <td>0.653806</td>\n",
       "      <td>0.641263</td>\n",
       "      <td>0.553297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_flat_softmax</th>\n",
       "      <td>0.765894</td>\n",
       "      <td>0.531818</td>\n",
       "      <td>0.816034</td>\n",
       "      <td>0.589878</td>\n",
       "      <td>1.872770</td>\n",
       "      <td>1.474454</td>\n",
       "      <td>1.956970</td>\n",
       "      <td>1.591544</td>\n",
       "      <td>2.545694</td>\n",
       "      <td>2.948908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679675</td>\n",
       "      <td>0.714085</td>\n",
       "      <td>0.828793</td>\n",
       "      <td>0.716508</td>\n",
       "      <td>0.898076</td>\n",
       "      <td>0.775670</td>\n",
       "      <td>0.646561</td>\n",
       "      <td>0.716508</td>\n",
       "      <td>0.614185</td>\n",
       "      <td>0.690101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_random_cut</th>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.322092</td>\n",
       "      <td>1.474450</td>\n",
       "      <td>1.474450</td>\n",
       "      <td>1.474450</td>\n",
       "      <td>2.231910</td>\n",
       "      <td>2.948900</td>\n",
       "      <td>2.948900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.568689</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.568935</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.568689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_random_cut-0</th>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.322092</td>\n",
       "      <td>1.474450</td>\n",
       "      <td>1.474450</td>\n",
       "      <td>1.474450</td>\n",
       "      <td>2.231910</td>\n",
       "      <td>2.948900</td>\n",
       "      <td>2.948900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.568689</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.568935</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.568689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soft_margin-incorrect-5</th>\n",
       "      <td>0.709586</td>\n",
       "      <td>0.530998</td>\n",
       "      <td>0.793542</td>\n",
       "      <td>0.603626</td>\n",
       "      <td>1.771286</td>\n",
       "      <td>1.584328</td>\n",
       "      <td>1.919120</td>\n",
       "      <td>1.630448</td>\n",
       "      <td>2.712972</td>\n",
       "      <td>3.168656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679159</td>\n",
       "      <td>0.706659</td>\n",
       "      <td>0.792337</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.882891</td>\n",
       "      <td>0.770584</td>\n",
       "      <td>0.667524</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.629042</td>\n",
       "      <td>0.685901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                correct                                \\\n",
       "                               expected      leaf  majority plurality   \n",
       "cond_multilabel                0.755414  0.444680  0.808274  0.516450   \n",
       "descendant_softmax             0.787812  0.517472  0.870208  0.630494   \n",
       "descendant_softmax_complement  0.782170  0.522875  0.867138  0.631748   \n",
       "flat_softmax                   0.762656  0.521716  0.849040  0.601988   \n",
       "hier_softmax                   0.723966  0.443082  0.786326  0.495768   \n",
       "multilabel_focal               0.509360  0.443872  0.453870  0.521812   \n",
       "multilabel_focal-1-0.95        0.509360  0.443872  0.453870  0.521812   \n",
       "share_flat_softmax             0.765894  0.531818  0.816034  0.589878   \n",
       "share_random_cut               0.531832  0.531832  0.531832  0.322092   \n",
       "share_random_cut-0             0.531832  0.531832  0.531832  0.322092   \n",
       "soft_margin-incorrect-5        0.709586  0.530998  0.793542  0.603626   \n",
       "\n",
       "                              depth_deficient                                \\\n",
       "                                     expected      leaf  majority plurality   \n",
       "cond_multilabel                      2.494090  1.774510  2.639364  2.155992   \n",
       "descendant_softmax                   2.398578  1.574582  2.584468  1.839104   \n",
       "descendant_softmax_complement        2.391145  1.562345  2.522988  1.785093   \n",
       "flat_softmax                         1.939350  1.572574  2.215638  1.724020   \n",
       "hier_softmax                         2.386896  1.789674  2.533506  2.072384   \n",
       "multilabel_focal                     1.915318  1.781590  1.853284  2.290966   \n",
       "multilabel_focal-1-0.95              1.915318  1.781590  1.853284  2.290966   \n",
       "share_flat_softmax                   1.872770  1.474454  1.956970  1.591544   \n",
       "share_random_cut                     1.474450  1.474450  1.474450  2.231910   \n",
       "share_random_cut-0                   1.474450  1.474450  1.474450  2.231910   \n",
       "soft_margin-incorrect-5              1.771286  1.584328  1.919120  1.630448   \n",
       "\n",
       "                              depth_dist            ...   info_f1            \\\n",
       "                                expected      leaf  ...  majority plurality   \n",
       "cond_multilabel                 3.031672  3.549020  ...  0.567258  0.621819   \n",
       "descendant_softmax              2.918526  3.149164  ...  0.597179  0.687303   \n",
       "descendant_softmax_complement   2.927480  3.124690  ...  0.609859  0.694481   \n",
       "flat_softmax                    2.660774  3.145148  ...  0.636752  0.694626   \n",
       "hier_softmax                    3.017860  3.579348  ...  0.585439  0.629382   \n",
       "multilabel_focal                3.263640  3.563180  ...  0.650514  0.607296   \n",
       "multilabel_focal-1-0.95         3.263640  3.563180  ...  0.650514  0.607296   \n",
       "share_flat_softmax              2.545694  2.948908  ...  0.679675  0.714085   \n",
       "share_random_cut                2.948900  2.948900  ...  0.716511  0.568689   \n",
       "share_random_cut-0              2.948900  2.948900  ...  0.716511  0.568689   \n",
       "soft_margin-incorrect-5         2.712972  3.168656  ...  0.679159  0.706659   \n",
       "\n",
       "                              info_precision                                \\\n",
       "                                    expected      leaf  majority plurality   \n",
       "cond_multilabel                     0.826897  0.655240  0.891669  0.731682   \n",
       "descendant_softmax                  0.840993  0.697171  0.923905  0.793246   \n",
       "descendant_softmax_complement       0.836446  0.700079  0.919521  0.791920   \n",
       "flat_softmax                        0.824203  0.698627  0.916476  0.779029   \n",
       "hier_softmax                        0.801465  0.652651  0.878799  0.715632   \n",
       "multilabel_focal                    0.685334  0.653806  0.664531  0.723363   \n",
       "multilabel_focal-1-0.95             0.685334  0.653806  0.664531  0.723363   \n",
       "share_flat_softmax                  0.828793  0.716508  0.898076  0.775670   \n",
       "share_random_cut                    0.716511  0.716511  0.716511  0.568935   \n",
       "share_random_cut-0                  0.716511  0.716511  0.716511  0.568935   \n",
       "soft_margin-incorrect-5             0.792337  0.698872  0.882891  0.770584   \n",
       "\n",
       "                              info_recall                                \n",
       "                                 expected      leaf  majority plurality  \n",
       "cond_multilabel                  0.523709  0.655240  0.476265  0.576050  \n",
       "descendant_softmax               0.569631  0.697171  0.502089  0.646816  \n",
       "descendant_softmax_complement    0.576144  0.700079  0.517387  0.657213  \n",
       "flat_softmax                     0.635018  0.698627  0.564754  0.665411  \n",
       "hier_softmax                     0.547193  0.652651  0.499291  0.594335  \n",
       "multilabel_focal                 0.636675  0.653806  0.641263  0.553297  \n",
       "multilabel_focal-1-0.95          0.636675  0.653806  0.641263  0.553297  \n",
       "share_flat_softmax               0.646561  0.716508  0.614185  0.690101  \n",
       "share_random_cut                 0.716511  0.716511  0.716511  0.568689  \n",
       "share_random_cut-0               0.716511  0.716511  0.716511  0.568689  \n",
       "soft_margin-incorrect-5          0.667524  0.698872  0.629042  0.685901  \n",
       "\n",
       "[11 rows x 56 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute statistics over trials.\n",
    "\n",
    "pred_metrics = {\n",
    "    trial_key + (inference_name,): results[trial_key]['pred_metrics'][inference_name]\n",
    "    for trial_key in results\n",
    "    for inference_name in infer_fns\n",
    "}\n",
    "pred_metrics = pd.DataFrame.from_dict(pred_metrics, orient='index').groupby(level=(0, 2)).describe()\n",
    "pred_metrics_mean = pred_metrics.stack(0)['mean'].unstack().unstack()\n",
    "pred_metrics_std = pred_metrics.stack(0)['std'].unstack().unstack()\n",
    "pred_metrics_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ecf94a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>AC(info)</th>\n",
       "      <th>AP(info)</th>\n",
       "      <th>AC(exact)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cond_multilabel</th>\n",
       "      <td>0.571285</td>\n",
       "      <td>0.606399</td>\n",
       "      <td>0.317070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descendant_softmax</th>\n",
       "      <td>0.634787</td>\n",
       "      <td>0.659978</td>\n",
       "      <td>0.399962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descendant_softmax_complement</th>\n",
       "      <td>0.641935</td>\n",
       "      <td>0.664552</td>\n",
       "      <td>0.409051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flat_softmax</th>\n",
       "      <td>0.647121</td>\n",
       "      <td>0.669003</td>\n",
       "      <td>0.446086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hier_softmax</th>\n",
       "      <td>0.574724</td>\n",
       "      <td>0.607170</td>\n",
       "      <td>0.321750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multilabel_focal</th>\n",
       "      <td>0.540794</td>\n",
       "      <td>0.587543</td>\n",
       "      <td>0.276899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multilabel_focal-1-0.95</th>\n",
       "      <td>0.540794</td>\n",
       "      <td>0.587543</td>\n",
       "      <td>0.276899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_flat_softmax</th>\n",
       "      <td>0.668565</td>\n",
       "      <td>0.689018</td>\n",
       "      <td>0.451494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_random_cut</th>\n",
       "      <td>0.606744</td>\n",
       "      <td>0.660540</td>\n",
       "      <td>0.458922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_random_cut-0</th>\n",
       "      <td>0.606744</td>\n",
       "      <td>0.660540</td>\n",
       "      <td>0.458922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soft_margin-incorrect-5</th>\n",
       "      <td>0.659703</td>\n",
       "      <td>0.676264</td>\n",
       "      <td>0.480606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               AC(info)  AP(info)  AC(exact)\n",
       "cond_multilabel                0.571285  0.606399   0.317070\n",
       "descendant_softmax             0.634787  0.659978   0.399962\n",
       "descendant_softmax_complement  0.641935  0.664552   0.409051\n",
       "flat_softmax                   0.647121  0.669003   0.446086\n",
       "hier_softmax                   0.574724  0.607170   0.321750\n",
       "multilabel_focal               0.540794  0.587543   0.276899\n",
       "multilabel_focal-1-0.95        0.540794  0.587543   0.276899\n",
       "share_flat_softmax             0.668565  0.689018   0.451494\n",
       "share_random_cut               0.606744  0.660540   0.458922\n",
       "share_random_cut-0             0.606744  0.660540   0.458922\n",
       "soft_margin-incorrect-5        0.659703  0.676264   0.480606"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INTEGRALS = {\n",
    "    # 'AC(depth)': ('depth_recall', 'correct'),\n",
    "    'AC(info)': ('info_recall', 'correct'),\n",
    "    # 'AP(depth)': ('depth_recall', 'depth_precision'),\n",
    "    'AP(info)': ('info_recall', 'info_precision'),\n",
    "    'AC(exact)': ('exact', 'correct'),\n",
    "}\n",
    "\n",
    "auc = {\n",
    "    integral_name: {\n",
    "        trial_key: metrics.pareto_integrate(\n",
    "            results[trial_key]['pareto_metrics'][x],\n",
    "            results[trial_key]['pareto_metrics'][y])\n",
    "        for trial_key in results\n",
    "    } for integral_name, (x, y) in INTEGRALS.items()\n",
    "}\n",
    "\n",
    "# pd.DataFrame(auc).groupby(level=0).describe()\n",
    "auc_mean = pd.DataFrame(auc).groupby(level=0).mean()\n",
    "auc_std = pd.DataFrame(auc).groupby(level=0).std()\n",
    "auc_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "20042c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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=\"4\" halign=\"left\">depth_recall</th>\n",
       "      <th colspan=\"4\" halign=\"left\">info_recall</th>\n",
       "      <th colspan=\"4\" halign=\"left\">exact</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>50</th>\n",
       "      <th>80</th>\n",
       "      <th>90</th>\n",
       "      <th>95</th>\n",
       "      <th>50</th>\n",
       "      <th>80</th>\n",
       "      <th>90</th>\n",
       "      <th>95</th>\n",
       "      <th>50</th>\n",
       "      <th>80</th>\n",
       "      <th>90</th>\n",
       "      <th>95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cond_multilabel</th>\n",
       "      <td>0.722087</td>\n",
       "      <td>0.626571</td>\n",
       "      <td>0.567942</td>\n",
       "      <td>0.516585</td>\n",
       "      <td>0.621616</td>\n",
       "      <td>0.481175</td>\n",
       "      <td>0.405228</td>\n",
       "      <td>0.342864</td>\n",
       "      <td>0.374768</td>\n",
       "      <td>0.161610</td>\n",
       "      <td>0.081642</td>\n",
       "      <td>0.036892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descendant_softmax</th>\n",
       "      <td>0.775089</td>\n",
       "      <td>0.681418</td>\n",
       "      <td>0.612367</td>\n",
       "      <td>0.564646</td>\n",
       "      <td>0.697219</td>\n",
       "      <td>0.573031</td>\n",
       "      <td>0.478049</td>\n",
       "      <td>0.418035</td>\n",
       "      <td>0.517472</td>\n",
       "      <td>0.248640</td>\n",
       "      <td>0.115474</td>\n",
       "      <td>0.069962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descendant_softmax_complement</th>\n",
       "      <td>0.776817</td>\n",
       "      <td>0.689901</td>\n",
       "      <td>0.619387</td>\n",
       "      <td>0.571601</td>\n",
       "      <td>0.700090</td>\n",
       "      <td>0.586421</td>\n",
       "      <td>0.491479</td>\n",
       "      <td>0.432883</td>\n",
       "      <td>0.522875</td>\n",
       "      <td>0.264820</td>\n",
       "      <td>0.120210</td>\n",
       "      <td>0.075513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flat_softmax</th>\n",
       "      <td>0.775389</td>\n",
       "      <td>0.707218</td>\n",
       "      <td>0.652168</td>\n",
       "      <td>0.599911</td>\n",
       "      <td>0.698679</td>\n",
       "      <td>0.597209</td>\n",
       "      <td>0.524407</td>\n",
       "      <td>0.458772</td>\n",
       "      <td>0.521716</td>\n",
       "      <td>0.356894</td>\n",
       "      <td>0.273488</td>\n",
       "      <td>0.206410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hier_softmax</th>\n",
       "      <td>0.724775</td>\n",
       "      <td>0.632377</td>\n",
       "      <td>0.576912</td>\n",
       "      <td>0.525331</td>\n",
       "      <td>0.626265</td>\n",
       "      <td>0.491812</td>\n",
       "      <td>0.420345</td>\n",
       "      <td>0.357119</td>\n",
       "      <td>0.382210</td>\n",
       "      <td>0.175084</td>\n",
       "      <td>0.098928</td>\n",
       "      <td>0.049620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multilabel_focal</th>\n",
       "      <td>0.708947</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.511661</td>\n",
       "      <td>0.457885</td>\n",
       "      <td>0.604708</td>\n",
       "      <td>0.409338</td>\n",
       "      <td>0.326431</td>\n",
       "      <td>0.268001</td>\n",
       "      <td>0.324328</td>\n",
       "      <td>0.068690</td>\n",
       "      <td>0.023430</td>\n",
       "      <td>0.007812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multilabel_focal-1-0.95</th>\n",
       "      <td>0.708947</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.511661</td>\n",
       "      <td>0.457885</td>\n",
       "      <td>0.604708</td>\n",
       "      <td>0.409338</td>\n",
       "      <td>0.326431</td>\n",
       "      <td>0.268001</td>\n",
       "      <td>0.324328</td>\n",
       "      <td>0.068690</td>\n",
       "      <td>0.023430</td>\n",
       "      <td>0.007812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_flat_softmax</th>\n",
       "      <td>0.789432</td>\n",
       "      <td>0.726750</td>\n",
       "      <td>0.675755</td>\n",
       "      <td>0.625447</td>\n",
       "      <td>0.716577</td>\n",
       "      <td>0.622873</td>\n",
       "      <td>0.555525</td>\n",
       "      <td>0.491349</td>\n",
       "      <td>0.531818</td>\n",
       "      <td>0.355362</td>\n",
       "      <td>0.263804</td>\n",
       "      <td>0.188844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_random_cut</th>\n",
       "      <td>0.789364</td>\n",
       "      <td>0.524534</td>\n",
       "      <td>0.319659</td>\n",
       "      <td>0.182748</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.492951</td>\n",
       "      <td>0.305618</td>\n",
       "      <td>0.176379</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.393658</td>\n",
       "      <td>0.251264</td>\n",
       "      <td>0.147122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>share_random_cut-0</th>\n",
       "      <td>0.789364</td>\n",
       "      <td>0.524534</td>\n",
       "      <td>0.319659</td>\n",
       "      <td>0.182748</td>\n",
       "      <td>0.716511</td>\n",
       "      <td>0.492951</td>\n",
       "      <td>0.305618</td>\n",
       "      <td>0.176379</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.393658</td>\n",
       "      <td>0.251264</td>\n",
       "      <td>0.147122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soft_margin-incorrect-5</th>\n",
       "      <td>0.773798</td>\n",
       "      <td>0.723443</td>\n",
       "      <td>0.673297</td>\n",
       "      <td>0.621845</td>\n",
       "      <td>0.699036</td>\n",
       "      <td>0.626081</td>\n",
       "      <td>0.565325</td>\n",
       "      <td>0.506455</td>\n",
       "      <td>0.530998</td>\n",
       "      <td>0.426290</td>\n",
       "      <td>0.364252</td>\n",
       "      <td>0.309544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              depth_recall                                \\\n",
       "                                        50        80        90        95   \n",
       "cond_multilabel                   0.722087  0.626571  0.567942  0.516585   \n",
       "descendant_softmax                0.775089  0.681418  0.612367  0.564646   \n",
       "descendant_softmax_complement     0.776817  0.689901  0.619387  0.571601   \n",
       "flat_softmax                      0.775389  0.707218  0.652168  0.599911   \n",
       "hier_softmax                      0.724775  0.632377  0.576912  0.525331   \n",
       "multilabel_focal                  0.708947  0.576882  0.511661  0.457885   \n",
       "multilabel_focal-1-0.95           0.708947  0.576882  0.511661  0.457885   \n",
       "share_flat_softmax                0.789432  0.726750  0.675755  0.625447   \n",
       "share_random_cut                  0.789364  0.524534  0.319659  0.182748   \n",
       "share_random_cut-0                0.789364  0.524534  0.319659  0.182748   \n",
       "soft_margin-incorrect-5           0.773798  0.723443  0.673297  0.621845   \n",
       "\n",
       "                              info_recall                                \\\n",
       "                                       50        80        90        95   \n",
       "cond_multilabel                  0.621616  0.481175  0.405228  0.342864   \n",
       "descendant_softmax               0.697219  0.573031  0.478049  0.418035   \n",
       "descendant_softmax_complement    0.700090  0.586421  0.491479  0.432883   \n",
       "flat_softmax                     0.698679  0.597209  0.524407  0.458772   \n",
       "hier_softmax                     0.626265  0.491812  0.420345  0.357119   \n",
       "multilabel_focal                 0.604708  0.409338  0.326431  0.268001   \n",
       "multilabel_focal-1-0.95          0.604708  0.409338  0.326431  0.268001   \n",
       "share_flat_softmax               0.716577  0.622873  0.555525  0.491349   \n",
       "share_random_cut                 0.716511  0.492951  0.305618  0.176379   \n",
       "share_random_cut-0               0.716511  0.492951  0.305618  0.176379   \n",
       "soft_margin-incorrect-5          0.699036  0.626081  0.565325  0.506455   \n",
       "\n",
       "                                  exact                                \n",
       "                                     50        80        90        95  \n",
       "cond_multilabel                0.374768  0.161610  0.081642  0.036892  \n",
       "descendant_softmax             0.517472  0.248640  0.115474  0.069962  \n",
       "descendant_softmax_complement  0.522875  0.264820  0.120210  0.075513  \n",
       "flat_softmax                   0.521716  0.356894  0.273488  0.206410  \n",
       "hier_softmax                   0.382210  0.175084  0.098928  0.049620  \n",
       "multilabel_focal               0.324328  0.068690  0.023430  0.007812  \n",
       "multilabel_focal-1-0.95        0.324328  0.068690  0.023430  0.007812  \n",
       "share_flat_softmax             0.531818  0.355362  0.263804  0.188844  \n",
       "share_random_cut               0.531832  0.393658  0.251264  0.147122  \n",
       "share_random_cut-0             0.531832  0.393658  0.251264  0.147122  \n",
       "soft_margin-incorrect-5        0.530998  0.426290  0.364252  0.309544  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INTERCEPTS = [\n",
    "    'depth_recall',\n",
    "    'info_recall',\n",
    "    'exact',\n",
    "]\n",
    "\n",
    "CORRECT = [50, 80, 90, 95]\n",
    "\n",
    "recalls = {\n",
    "    (y, c): {\n",
    "        trial_key: metrics.pareto_intercept(\n",
    "            results[trial_key]['pareto_metrics']['correct'],\n",
    "            results[trial_key]['pareto_metrics'][y],\n",
    "            c / 100)\n",
    "        for trial_key in results\n",
    "    } for y in INTERCEPTS for c in CORRECT\n",
    "}\n",
    "\n",
    "# pd.DataFrame(recalls).groupby(level=0).describe().style.format('{:.3f}'.format)\n",
    "intercept_mean = pd.DataFrame(recalls).groupby(level=0).mean()\n",
    "intercept_std = pd.DataFrame(recalls).groupby(level=0).std()\n",
    "# intercept_mean.columns = intercept_mean.columns.to_flat_index().map(lambda xs: '_'.join(map(str, xs)))\n",
    "# intercept_std.columns = intercept_std.columns.to_flat_index().map(lambda xs: '_'.join(map(str, xs)))\n",
    "intercept_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "8e746c7f",
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">auc</th>\n",
       "      <th colspan=\"4\" halign=\"left\">intercept</th>\n",
       "      <th colspan=\"4\" halign=\"left\">pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">AP(info)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AC(info)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">(info_recall, 90)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">(info_recall, 95)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">(info_f1, majority)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">(info_f1, leaf)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>err</th>\n",
       "      <th>mean</th>\n",
       "      <th>err</th>\n",
       "      <th>mean</th>\n",
       "      <th>err</th>\n",
       "      <th>mean</th>\n",
       "      <th>err</th>\n",
       "      <th>mean</th>\n",
       "      <th>err</th>\n",
       "      <th>mean</th>\n",
       "      <th>err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Flat softmax</th>\n",
       "      <td>66.90</td>\n",
       "      <td>$\\pm$0.16</td>\n",
       "      <td>64.71</td>\n",
       "      <td>$\\pm$0.18</td>\n",
       "      <td>52.44</td>\n",
       "      <td>$\\pm$0.42</td>\n",
       "      <td>45.88</td>\n",
       "      <td>$\\pm$0.30</td>\n",
       "      <td>63.68</td>\n",
       "      <td>$\\pm$0.19</td>\n",
       "      <td>69.86</td>\n",
       "      <td>$\\pm$0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multilabel focal</th>\n",
       "      <td>58.75</td>\n",
       "      <td>$\\pm$0.24</td>\n",
       "      <td>54.08</td>\n",
       "      <td>$\\pm$0.20</td>\n",
       "      <td>32.64</td>\n",
       "      <td>$\\pm$0.16</td>\n",
       "      <td>26.80</td>\n",
       "      <td>$\\pm$0.28</td>\n",
       "      <td>65.05</td>\n",
       "      <td>$\\pm$0.24</td>\n",
       "      <td>65.38</td>\n",
       "      <td>$\\pm$0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cond softmax</th>\n",
       "      <td>60.72</td>\n",
       "      <td>$\\pm$0.11</td>\n",
       "      <td>57.47</td>\n",
       "      <td>$\\pm$0.13</td>\n",
       "      <td>42.03</td>\n",
       "      <td>$\\pm$0.12</td>\n",
       "      <td>35.71</td>\n",
       "      <td>$\\pm$0.20</td>\n",
       "      <td>58.54</td>\n",
       "      <td>$\\pm$0.20</td>\n",
       "      <td>65.27</td>\n",
       "      <td>$\\pm$0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cond sigmoid</th>\n",
       "      <td>60.64</td>\n",
       "      <td>$\\pm$0.17</td>\n",
       "      <td>57.13</td>\n",
       "      <td>$\\pm$0.16</td>\n",
       "      <td>40.52</td>\n",
       "      <td>$\\pm$0.13</td>\n",
       "      <td>34.29</td>\n",
       "      <td>$\\pm$0.18</td>\n",
       "      <td>56.73</td>\n",
       "      <td>$\\pm$0.07</td>\n",
       "      <td>65.52</td>\n",
       "      <td>$\\pm$0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deep RTC</th>\n",
       "      <td>66.05</td>\n",
       "      <td>$\\pm$0.16</td>\n",
       "      <td>60.67</td>\n",
       "      <td>$\\pm$0.15</td>\n",
       "      <td>30.56</td>\n",
       "      <td>$\\pm$0.79</td>\n",
       "      <td>17.64</td>\n",
       "      <td>$\\pm$1.01</td>\n",
       "      <td>{\\bf 71.65}</td>\n",
       "      <td>$\\pm$0.12</td>\n",
       "      <td>{\\bf 71.65}</td>\n",
       "      <td>$\\pm$0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS softmax</th>\n",
       "      <td>{\\bf 68.90}</td>\n",
       "      <td>$\\pm$0.13</td>\n",
       "      <td>{\\bf 66.86}</td>\n",
       "      <td>$\\pm$0.12</td>\n",
       "      <td>{\\it 55.55}</td>\n",
       "      <td>$\\pm$0.22</td>\n",
       "      <td>{\\it 49.13}</td>\n",
       "      <td>$\\pm$0.15</td>\n",
       "      <td>{\\it 67.97}</td>\n",
       "      <td>$\\pm$0.19</td>\n",
       "      <td>{\\it 71.65}</td>\n",
       "      <td>$\\pm$0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soft-max-descendant</th>\n",
       "      <td>66.46</td>\n",
       "      <td>$\\pm$0.37</td>\n",
       "      <td>64.19</td>\n",
       "      <td>$\\pm$0.39</td>\n",
       "      <td>49.15</td>\n",
       "      <td>$\\pm$0.36</td>\n",
       "      <td>43.29</td>\n",
       "      <td>$\\pm$0.56</td>\n",
       "      <td>60.99</td>\n",
       "      <td>$\\pm$0.33</td>\n",
       "      <td>70.01</td>\n",
       "      <td>$\\pm$0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soft-max-margin</th>\n",
       "      <td>{\\it 67.63}</td>\n",
       "      <td>$\\pm$0.25</td>\n",
       "      <td>{\\it 65.97}</td>\n",
       "      <td>$\\pm$0.26</td>\n",
       "      <td>{\\bf 56.53}</td>\n",
       "      <td>$\\pm$0.28</td>\n",
       "      <td>{\\bf 50.65}</td>\n",
       "      <td>$\\pm$0.41</td>\n",
       "      <td>67.92</td>\n",
       "      <td>$\\pm$0.27</td>\n",
       "      <td>69.89</td>\n",
       "      <td>$\\pm$0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             auc                                     \\\n",
       "                        AP(info)                AC(info)              \n",
       "                            mean        err         mean        err   \n",
       "Flat softmax               66.90  $\\pm$0.16        64.71  $\\pm$0.18   \n",
       "Multilabel focal           58.75  $\\pm$0.24        54.08  $\\pm$0.20   \n",
       "Cond softmax               60.72  $\\pm$0.11        57.47  $\\pm$0.13   \n",
       "Cond sigmoid               60.64  $\\pm$0.17        57.13  $\\pm$0.16   \n",
       "Deep RTC                   66.05  $\\pm$0.16        60.67  $\\pm$0.15   \n",
       "PS softmax           {\\bf 68.90}  $\\pm$0.13  {\\bf 66.86}  $\\pm$0.12   \n",
       "Soft-max-descendant        66.46  $\\pm$0.37        64.19  $\\pm$0.39   \n",
       "Soft-max-margin      {\\it 67.63}  $\\pm$0.25  {\\it 65.97}  $\\pm$0.26   \n",
       "\n",
       "                            intercept                                          \\\n",
       "                    (info_recall, 90)            (info_recall, 95)              \n",
       "                                 mean        err              mean        err   \n",
       "Flat softmax                    52.44  $\\pm$0.42             45.88  $\\pm$0.30   \n",
       "Multilabel focal                32.64  $\\pm$0.16             26.80  $\\pm$0.28   \n",
       "Cond softmax                    42.03  $\\pm$0.12             35.71  $\\pm$0.20   \n",
       "Cond sigmoid                    40.52  $\\pm$0.13             34.29  $\\pm$0.18   \n",
       "Deep RTC                        30.56  $\\pm$0.79             17.64  $\\pm$1.01   \n",
       "PS softmax                {\\it 55.55}  $\\pm$0.22       {\\it 49.13}  $\\pm$0.15   \n",
       "Soft-max-descendant             49.15  $\\pm$0.36             43.29  $\\pm$0.56   \n",
       "Soft-max-margin           {\\bf 56.53}  $\\pm$0.28       {\\bf 50.65}  $\\pm$0.41   \n",
       "\n",
       "                                   pred                                        \n",
       "                    (info_f1, majority)            (info_f1, leaf)             \n",
       "                                   mean        err            mean        err  \n",
       "Flat softmax                      63.68  $\\pm$0.19           69.86  $\\pm$0.13  \n",
       "Multilabel focal                  65.05  $\\pm$0.24           65.38  $\\pm$0.29  \n",
       "Cond softmax                      58.54  $\\pm$0.20           65.27  $\\pm$0.11  \n",
       "Cond sigmoid                      56.73  $\\pm$0.07           65.52  $\\pm$0.19  \n",
       "Deep RTC                    {\\bf 71.65}  $\\pm$0.12     {\\bf 71.65}  $\\pm$0.12  \n",
       "PS softmax                  {\\it 67.97}  $\\pm$0.19     {\\it 71.65}  $\\pm$0.12  \n",
       "Soft-max-descendant               60.99  $\\pm$0.33           70.01  $\\pm$0.33  \n",
       "Soft-max-margin                   67.92  $\\pm$0.27           69.89  $\\pm$0.24  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Main table for paper!\n",
    "\n",
    "tables = {\n",
    "    'pred_mean': pred_metrics_mean,\n",
    "    'pred_std': pred_metrics_std,\n",
    "    'auc_mean': auc_mean,\n",
    "    'auc_std': auc_std,\n",
    "    'intercept_mean': intercept_mean,\n",
    "    'intercept_std': intercept_std,\n",
    "}\n",
    "\n",
    "cols = [\n",
    "    ('auc', 'AP(info)'),\n",
    "    ('auc', 'AC(info)'),\n",
    "    # ('intercept', ('info_recall', 50)),\n",
    "    # ('intercept', ('info_recall', 80)),\n",
    "    ('intercept', ('info_recall', 90)),\n",
    "    ('intercept', ('info_recall', 95)),\n",
    "    # ('intercept', ('exact', 90)),\n",
    "    # ('intercept', ('exact', 95)),\n",
    "    # ('pred', ('correct', 'leaf')),\n",
    "    # ('pred', ('info_recall', 'leaf')),\n",
    "    # ('pred', ('correct', 'majority')),\n",
    "    # ('pred', ('info_recall', 'majority')),\n",
    "    ('pred', ('info_f1', 'majority')),\n",
    "    ('pred', ('info_f1', 'leaf')),\n",
    "]\n",
    "\n",
    "table = pd.DataFrame(dict(itertools.chain.from_iterable(\n",
    "    [((table, col, 'mean'), tables[table + '_mean'][col]),\n",
    "     ((table, col, 'err'), 1.96 * tables[table + '_std'][col])]\n",
    "    for table, col in cols\n",
    ")))\n",
    "table = table.loc[list(experiment_defs)]\n",
    "table = table.set_index(table.index.map(method_titles))\n",
    "\n",
    "def format_column(data, format_fn, highlight=None):\n",
    "    formatted = data.apply(format_fn)\n",
    "    if highlight:\n",
    "        rank = data.rank(ascending=False)\n",
    "        is_max = (rank < 2)\n",
    "        is_second = (2 <= rank) & (rank < 3)\n",
    "        bolded = formatted.apply(lambda x: r'{\\bf %s}' % x)\n",
    "        italicized = formatted.apply(lambda x: r'{\\it %s}' % x)\n",
    "        formatted = italicized.where(is_second, formatted)\n",
    "        formatted = bolded.where(is_max, formatted)\n",
    "    return formatted\n",
    "\n",
    "table = pd.DataFrame({\n",
    "    c: format_column(\n",
    "        table[c],\n",
    "        lambda x: ((r'$\\pm$' if 'err' in c else '') + f'{x * 100:.2f}') if x == x else '',\n",
    "        highlight='max' if 'mean' in c else None)\n",
    "    for c in table.columns\n",
    "})\n",
    "table\n",
    "\n",
    "# bold_extreme_values(table, )\n",
    "# table.style.format(lambda x: f'{x * 100:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "bb58d3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllllllll}\n",
      "\\toprule\n",
      "{} & \\multicolumn{4}{l}{auc} & \\multicolumn{4}{l}{intercept} & \\multicolumn{4}{l}{pred} \\\\\n",
      "{} & \\multicolumn{2}{l}{AP(info)} & \\multicolumn{2}{l}{AC(info)} & \\multicolumn{2}{l}{(info_recall, 90)} & \\multicolumn{2}{l}{(info_recall, 95)} & \\multicolumn{2}{l}{(info_f1, majority)} & \\multicolumn{2}{l}{(info_f1, leaf)} \\\\\n",
      "{} &         mean &        err &         mean &        err &              mean &        err &              mean &        err &                mean &        err &            mean &        err \\\\\n",
      "\\midrule\n",
      "Flat softmax        &        66.90 &  $\\pm$0.16 &        64.71 &  $\\pm$0.18 &             52.44 &  $\\pm$0.42 &             45.88 &  $\\pm$0.30 &               63.68 &  $\\pm$0.19 &           69.86 &  $\\pm$0.13 \\\\\n",
      "Multilabel focal    &        58.75 &  $\\pm$0.24 &        54.08 &  $\\pm$0.20 &             32.64 &  $\\pm$0.16 &             26.80 &  $\\pm$0.28 &               65.05 &  $\\pm$0.24 &           65.38 &  $\\pm$0.29 \\\\\n",
      "Cond softmax        &        60.72 &  $\\pm$0.11 &        57.47 &  $\\pm$0.13 &             42.03 &  $\\pm$0.12 &             35.71 &  $\\pm$0.20 &               58.54 &  $\\pm$0.20 &           65.27 &  $\\pm$0.11 \\\\\n",
      "Cond sigmoid        &        60.64 &  $\\pm$0.17 &        57.13 &  $\\pm$0.16 &             40.52 &  $\\pm$0.13 &             34.29 &  $\\pm$0.18 &               56.73 &  $\\pm$0.07 &           65.52 &  $\\pm$0.19 \\\\\n",
      "Deep RTC            &        66.05 &  $\\pm$0.16 &        60.67 &  $\\pm$0.15 &             30.56 &  $\\pm$0.79 &             17.64 &  $\\pm$1.01 &         {\\bf 71.65} &  $\\pm$0.12 &     {\\bf 71.65} &  $\\pm$0.12 \\\\\n",
      "PS softmax          &  {\\bf 68.90} &  $\\pm$0.13 &  {\\bf 66.86} &  $\\pm$0.12 &       {\\it 55.55} &  $\\pm$0.22 &       {\\it 49.13} &  $\\pm$0.15 &         {\\it 67.97} &  $\\pm$0.19 &     {\\it 71.65} &  $\\pm$0.12 \\\\\n",
      "Soft-max-descendant &        66.46 &  $\\pm$0.37 &        64.19 &  $\\pm$0.39 &             49.15 &  $\\pm$0.36 &             43.29 &  $\\pm$0.56 &               60.99 &  $\\pm$0.33 &           70.01 &  $\\pm$0.33 \\\\\n",
      "Soft-max-margin     &  {\\it 67.63} &  $\\pm$0.25 &  {\\it 65.97} &  $\\pm$0.26 &       {\\bf 56.53} &  $\\pm$0.28 &       {\\bf 50.65} &  $\\pm$0.41 &               67.92 &  $\\pm$0.27 &           69.89 &  $\\pm$0.24 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1575951/1166261648.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(table.to_latex(escape=False))\n"
     ]
    }
   ],
   "source": [
    "print(table.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "1dbef47a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = np.linspace(0, 1, 1001)\n",
    "# stacked_resampled = {\n",
    "#     field: np.stack([\n",
    "#         resample_threshold(results[k]['pareto_scores'], results[k]['pareto_metrics'][field], x)\n",
    "#         for k in results\n",
    "#     ], axis=0) for field in metric_fns\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5cb3ab5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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=\"10\" halign=\"left\">cond_multilabel</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">soft_margin-incorrect-5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>correct</th>\n",
       "      <th>depth_deficient</th>\n",
       "      <th>depth_dist</th>\n",
       "      <th>depth_excess</th>\n",
       "      <th>depth_f1</th>\n",
       "      <th>depth_precision</th>\n",
       "      <th>depth_recall</th>\n",
       "      <th>exact</th>\n",
       "      <th>info_deficient</th>\n",
       "      <th>info_dist</th>\n",
       "      <th>...</th>\n",
       "      <th>depth_f1</th>\n",
       "      <th>depth_precision</th>\n",
       "      <th>depth_recall</th>\n",
       "      <th>exact</th>\n",
       "      <th>info_deficient</th>\n",
       "      <th>info_dist</th>\n",
       "      <th>info_excess</th>\n",
       "      <th>info_f1</th>\n",
       "      <th>info_precision</th>\n",
       "      <th>info_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.955908</td>\n",
       "      <td>6.955908</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.245005</td>\n",
       "      <td>13.245005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.104100</td>\n",
       "      <td>0.999854</td>\n",
       "      <td>0.080823</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>12.615665</td>\n",
       "      <td>12.616302</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.068672</td>\n",
       "      <td>0.999848</td>\n",
       "      <td>0.050577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999540</td>\n",
       "      <td>5.522318</td>\n",
       "      <td>5.523236</td>\n",
       "      <td>0.000918</td>\n",
       "      <td>0.290994</td>\n",
       "      <td>0.999632</td>\n",
       "      <td>0.211097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.831909</td>\n",
       "      <td>11.832911</td>\n",
       "      <td>...</td>\n",
       "      <td>0.316373</td>\n",
       "      <td>0.999372</td>\n",
       "      <td>0.254036</td>\n",
       "      <td>0.078592</td>\n",
       "      <td>11.017108</td>\n",
       "      <td>11.020329</td>\n",
       "      <td>0.003220</td>\n",
       "      <td>0.216578</td>\n",
       "      <td>0.999352</td>\n",
       "      <td>0.170880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.999212</td>\n",
       "      <td>5.251990</td>\n",
       "      <td>5.253532</td>\n",
       "      <td>0.001542</td>\n",
       "      <td>0.341903</td>\n",
       "      <td>0.999408</td>\n",
       "      <td>0.249716</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>11.554455</td>\n",
       "      <td>11.556142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.365832</td>\n",
       "      <td>0.999085</td>\n",
       "      <td>0.295881</td>\n",
       "      <td>0.094508</td>\n",
       "      <td>10.586773</td>\n",
       "      <td>10.591924</td>\n",
       "      <td>0.005151</td>\n",
       "      <td>0.255274</td>\n",
       "      <td>0.999047</td>\n",
       "      <td>0.203266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.998852</td>\n",
       "      <td>5.086392</td>\n",
       "      <td>5.088572</td>\n",
       "      <td>0.002180</td>\n",
       "      <td>0.372654</td>\n",
       "      <td>0.999158</td>\n",
       "      <td>0.273373</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>11.379167</td>\n",
       "      <td>11.381584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396283</td>\n",
       "      <td>0.998822</td>\n",
       "      <td>0.322085</td>\n",
       "      <td>0.105842</td>\n",
       "      <td>10.309723</td>\n",
       "      <td>10.316658</td>\n",
       "      <td>0.006935</td>\n",
       "      <td>0.279709</td>\n",
       "      <td>0.998763</td>\n",
       "      <td>0.224116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.998538</td>\n",
       "      <td>4.967818</td>\n",
       "      <td>4.970546</td>\n",
       "      <td>0.002728</td>\n",
       "      <td>0.394431</td>\n",
       "      <td>0.998954</td>\n",
       "      <td>0.290312</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>11.249799</td>\n",
       "      <td>11.252860</td>\n",
       "      <td>...</td>\n",
       "      <td>0.418375</td>\n",
       "      <td>0.998602</td>\n",
       "      <td>0.341385</td>\n",
       "      <td>0.114854</td>\n",
       "      <td>10.100629</td>\n",
       "      <td>10.109429</td>\n",
       "      <td>0.008799</td>\n",
       "      <td>0.297866</td>\n",
       "      <td>0.998517</td>\n",
       "      <td>0.239852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.444678</td>\n",
       "      <td>1.774536</td>\n",
       "      <td>3.548962</td>\n",
       "      <td>1.774426</td>\n",
       "      <td>0.746497</td>\n",
       "      <td>0.746501</td>\n",
       "      <td>0.746495</td>\n",
       "      <td>0.444678</td>\n",
       "      <td>4.581137</td>\n",
       "      <td>9.162138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.773669</td>\n",
       "      <td>0.773670</td>\n",
       "      <td>0.773669</td>\n",
       "      <td>0.530998</td>\n",
       "      <td>4.001281</td>\n",
       "      <td>8.002539</td>\n",
       "      <td>4.001258</td>\n",
       "      <td>0.698874</td>\n",
       "      <td>0.698874</td>\n",
       "      <td>0.698874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.444680</td>\n",
       "      <td>1.774510</td>\n",
       "      <td>3.548994</td>\n",
       "      <td>1.774484</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.746500</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.444680</td>\n",
       "      <td>4.581072</td>\n",
       "      <td>9.162110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.773668</td>\n",
       "      <td>0.773668</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.530998</td>\n",
       "      <td>4.001302</td>\n",
       "      <td>8.002599</td>\n",
       "      <td>4.001297</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.444680</td>\n",
       "      <td>1.774510</td>\n",
       "      <td>3.549014</td>\n",
       "      <td>1.774504</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.444680</td>\n",
       "      <td>4.581072</td>\n",
       "      <td>9.162134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.530998</td>\n",
       "      <td>4.001302</td>\n",
       "      <td>8.002604</td>\n",
       "      <td>4.001302</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.444680</td>\n",
       "      <td>1.774510</td>\n",
       "      <td>3.549020</td>\n",
       "      <td>1.774510</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.444680</td>\n",
       "      <td>4.581072</td>\n",
       "      <td>9.162145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.530998</td>\n",
       "      <td>4.001302</td>\n",
       "      <td>8.002604</td>\n",
       "      <td>4.001302</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.444680</td>\n",
       "      <td>1.774510</td>\n",
       "      <td>3.549020</td>\n",
       "      <td>1.774510</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.746499</td>\n",
       "      <td>0.444680</td>\n",
       "      <td>4.581072</td>\n",
       "      <td>9.162145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.773667</td>\n",
       "      <td>0.530998</td>\n",
       "      <td>4.001302</td>\n",
       "      <td>8.002604</td>\n",
       "      <td>4.001302</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "      <td>0.698872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cond_multilabel                                                    \\\n",
       "             correct depth_deficient depth_dist depth_excess  depth_f1   \n",
       "0           1.000000        6.955908   6.955908     0.000000  0.009810   \n",
       "1           0.999540        5.522318   5.523236     0.000918  0.290994   \n",
       "2           0.999212        5.251990   5.253532     0.001542  0.341903   \n",
       "3           0.998852        5.086392   5.088572     0.002180  0.372654   \n",
       "4           0.998538        4.967818   4.970546     0.002728  0.394431   \n",
       "...              ...             ...        ...          ...       ...   \n",
       "996         0.444678        1.774536   3.548962     1.774426  0.746497   \n",
       "997         0.444680        1.774510   3.548994     1.774484  0.746499   \n",
       "998         0.444680        1.774510   3.549014     1.774504  0.746499   \n",
       "999         0.444680        1.774510   3.549020     1.774510  0.746499   \n",
       "1000        0.444680        1.774510   3.549020     1.774510  0.746499   \n",
       "\n",
       "                                                                       ...  \\\n",
       "     depth_precision depth_recall     exact info_deficient  info_dist  ...   \n",
       "0           1.000000     0.006299  0.000000      13.245005  13.245005  ...   \n",
       "1           0.999632     0.211097  0.000000      11.831909  11.832911  ...   \n",
       "2           0.999408     0.249716  0.000024      11.554455  11.556142  ...   \n",
       "3           0.999158     0.273373  0.000036      11.379167  11.381584  ...   \n",
       "4           0.998954     0.290312  0.000060      11.249799  11.252860  ...   \n",
       "...              ...          ...       ...            ...        ...  ...   \n",
       "996         0.746501     0.746495  0.444678       4.581137   9.162138  ...   \n",
       "997         0.746500     0.746499  0.444680       4.581072   9.162110  ...   \n",
       "998         0.746499     0.746499  0.444680       4.581072   9.162134  ...   \n",
       "999         0.746499     0.746499  0.444680       4.581072   9.162145  ...   \n",
       "1000        0.746499     0.746499  0.444680       4.581072   9.162145  ...   \n",
       "\n",
       "     soft_margin-incorrect-5                                         \\\n",
       "                    depth_f1 depth_precision depth_recall     exact   \n",
       "0                   0.104100        0.999854     0.080823  0.000754   \n",
       "1                   0.316373        0.999372     0.254036  0.078592   \n",
       "2                   0.365832        0.999085     0.295881  0.094508   \n",
       "3                   0.396283        0.998822     0.322085  0.105842   \n",
       "4                   0.418375        0.998602     0.341385  0.114854   \n",
       "...                      ...             ...          ...       ...   \n",
       "996                 0.773669        0.773670     0.773669  0.530998   \n",
       "997                 0.773668        0.773668     0.773667  0.530998   \n",
       "998                 0.773667        0.773667     0.773667  0.530998   \n",
       "999                 0.773667        0.773667     0.773667  0.530998   \n",
       "1000                0.773667        0.773667     0.773667  0.530998   \n",
       "\n",
       "                                                                     \\\n",
       "     info_deficient  info_dist info_excess   info_f1 info_precision   \n",
       "0         12.615665  12.616302    0.000637  0.068672       0.999848   \n",
       "1         11.017108  11.020329    0.003220  0.216578       0.999352   \n",
       "2         10.586773  10.591924    0.005151  0.255274       0.999047   \n",
       "3         10.309723  10.316658    0.006935  0.279709       0.998763   \n",
       "4         10.100629  10.109429    0.008799  0.297866       0.998517   \n",
       "...             ...        ...         ...       ...            ...   \n",
       "996        4.001281   8.002539    4.001258  0.698874       0.698874   \n",
       "997        4.001302   8.002599    4.001297  0.698872       0.698872   \n",
       "998        4.001302   8.002604    4.001302  0.698872       0.698872   \n",
       "999        4.001302   8.002604    4.001302  0.698872       0.698872   \n",
       "1000       4.001302   8.002604    4.001302  0.698872       0.698872   \n",
       "\n",
       "                  \n",
       "     info_recall  \n",
       "0       0.050577  \n",
       "1       0.170880  \n",
       "2       0.203266  \n",
       "3       0.224116  \n",
       "4       0.239852  \n",
       "...          ...  \n",
       "996     0.698874  \n",
       "997     0.698872  \n",
       "998     0.698872  \n",
       "999     0.698872  \n",
       "1000    0.698872  \n",
       "\n",
       "[1001 rows x 154 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked_resampled = {\n",
    "    (trial_key + (field,)): results[trial_key]['pareto_metrics'][field]\n",
    "    for trial_key in results\n",
    "    for field in metric_fns\n",
    "}\n",
    "resampled_mean = pd.DataFrame.from_dict(stacked_resampled).T.groupby(level=(0, 2)).mean().T\n",
    "resampled_std = pd.DataFrame.from_dict(stacked_resampled).T.groupby(level=(0, 2)).std().T\n",
    "resampled_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "2729a7a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pareto_with_std(x_mean, y_mean, x_std, y_std, num_std=1.96, x_grid=None, **kwargs):\n",
    "    lower_x = x_mean - num_std * x_std\n",
    "    lower_y = y_mean - num_std * y_std\n",
    "    upper_x = x_mean + num_std * x_std\n",
    "    upper_y = y_mean + num_std * y_std\n",
    "    if x_grid is None:\n",
    "        plt.fill(\n",
    "            np.concatenate((lower_x, np.flip(upper_x))),\n",
    "            np.concatenate((lower_y, np.flip(upper_y))), **kwargs)\n",
    "    else:\n",
    "        # Sample lower and upper bounds at grid.\n",
    "        lower = metrics.pareto_intercept(lower_x, lower_y, x_grid)\n",
    "        upper = metrics.pareto_intercept(upper_x, upper_y, x_grid)\n",
    "        plt.fill_between(x_grid, lower, upper, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "eb402b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example usage.\n",
    "\n",
    "plot_pareto_with_std(\n",
    "    x_mean=resampled_mean['flat_softmax']['info_recall'],\n",
    "    x_std=resampled_std['flat_softmax']['info_recall'],\n",
    "    y_mean=resampled_mean['flat_softmax']['correct'],\n",
    "    y_std=resampled_std['flat_softmax']['correct'],\n",
    "    num_std=1.96,\n",
    "    # x_grid=np.linspace(0, 1, 101),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "19078a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metrics(x, y, tickres=None, save=False, xlim=None, ylim=None, figsize=(5, 4)):\n",
    "    line_handles = []\n",
    "    point_handles = []\n",
    "    first_line = True\n",
    "    plt.figure(figsize=figsize)\n",
    "\n",
    "    for method_name in trials:\n",
    "        method_trials = [\n",
    "            trial_name for trial_name in trials[method_name]\n",
    "            if (method_name, trial_name) in results\n",
    "        ]\n",
    "        # print('num trials:', len(method_trials))\n",
    "        if not trial_names:\n",
    "            continue\n",
    "\n",
    "        # Plot errorbars.\n",
    "        plot_pareto_with_std(\n",
    "            x_mean=resampled_mean[method_name][x],\n",
    "            y_mean=resampled_mean[method_name][y],\n",
    "            x_std=resampled_std[method_name][x],\n",
    "            y_std=resampled_std[method_name][y],\n",
    "            color=colors[method_name],\n",
    "            alpha=0.333)\n",
    "\n",
    "        ge = (score_grid >= 0.5)\n",
    "        le = (score_grid <= 0.5)\n",
    "        plt.plot(\n",
    "            resampled_mean[method_name][x][le], resampled_mean[method_name][y][le],\n",
    "            color=colors[method_name], linestyle='--')\n",
    "        h = plt.plot(\n",
    "            resampled_mean[method_name][x][ge], resampled_mean[method_name][y][ge],\n",
    "            color=colors[method_name], label=method_titles.get(method_name, method_name))\n",
    "        line_handles.extend(h)\n",
    "\n",
    "        for inference_name in infer_fns:  # pred_metrics_mean[x][method_name].keys():\n",
    "            h, = plt.plot(\n",
    "                pred_metrics_mean[x][inference_name][method_name],\n",
    "                pred_metrics_mean[y][inference_name][method_name],\n",
    "                color=colors[method_name], linestyle='none',\n",
    "                marker=markers[inference_name], markerfacecolor='none',\n",
    "                label=inference_titles.get(inference_name, inference_name))\n",
    "            if first_line:\n",
    "                point_handles.append(h)\n",
    "        first_line = False\n",
    "\n",
    "    plt.grid('on')\n",
    "    plt.xlabel(metric_titles.get(x, x))\n",
    "    plt.ylabel(metric_titles.get(y, y))\n",
    "    ax = plt.gca()\n",
    "\n",
    "    if xlim:\n",
    "        plt.xlim(**xlim)\n",
    "    if ylim:\n",
    "        plt.ylim(**ylim)\n",
    "    # plt.ylim(np.clip(plt.ylim(), 0, 1))\n",
    "    # plt.xlim(np.clip(plt.xlim(), 0, 1))\n",
    "    if tickres:\n",
    "        ax.xaxis.set_major_locator(ticker.MultipleLocator(tickres))\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(tickres))\n",
    "    # plt.axis('equal')\n",
    "    # plt.gca().set_aspect(1)\n",
    "\n",
    "    if save:\n",
    "        pathlib.Path(PLOT_DIR).mkdir(parents=True, exist_ok=True)\n",
    "        plt.savefig(pathlib.Path(PLOT_DIR) / f'{x}-{y}.pdf')\n",
    "        main_fig = plt.gcf()\n",
    "        plt.figure(figsize=figsize)\n",
    "        plt.legend(handles=[*line_handles, *point_handles], loc='center left')\n",
    "        plt.axis('off')\n",
    "        plt.savefig(pathlib.Path(PLOT_DIR) / f'{x}-{y}-legend.pdf')\n",
    "        plt.close()\n",
    "        plt.figure(main_fig.number)\n",
    "\n",
    "    plt.legend(handles=[*line_handles, *point_handles], loc='lower left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "bfb700a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('exact', 'correct', tickres=0.1, xlim=dict(left=0), ylim=dict(top=1, bottom=0.4), save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "7917e2b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('info_recall', 'correct', tickres=0.05, xlim=dict(left=0.3, right=0.75), ylim=dict(bottom=0.4, top=1), save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "4cbf2628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('info_recall', 'info_precision', tickres=0.05, xlim=dict(left=0.3, right=0.75), ylim=dict(bottom=0.6, top=1), save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3de93848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('depth_recall', 'correct', tickres=0.05, xlim=dict(left=0.5, right=0.8), ylim=dict(bottom=0.4, top=1), save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c187ce11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('depth_recall', 'depth_precision', tickres=0.05, xlim=dict(left=0.5, right=0.8), ylim=dict(bottom=0.7, top=1), save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7defbd73",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}