{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scripts import tabular_baselines\n",
    "\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "from datasets import load_openml_list, valid_dids_classification, test_dids_classification, open_cc_dids\n",
    "from scripts.tabular_baselines import *\n",
    "from scripts.tabular_evaluation import evaluate\n",
    "from scripts.tabular_metrics import calculate_score, make_ranks_and_wins_table, make_metric_matrix\n",
    "from scripts import tabular_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from notebook_utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cc_test_datasets_multiclass, cc_test_datasets_multiclass_df = load_openml_list(open_cc_dids, multiclass=True, shuffled=True, filter_for_nan=False, max_samples = 10000, num_feats=100, return_capped=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_datasets(selector, task_type, suite='openml'):\n",
    "    if task_type == 'binary':\n",
    "        ds = valid_datasets_binary if selector == 'valid' else test_datasets_binary\n",
    "    else:\n",
    "        if suite == 'openml':\n",
    "            ds = valid_datasets_multiclass if selector == 'valid' else test_datasets_multiclass\n",
    "        elif suite == 'cc':\n",
    "            ds = valid_datasets_multiclass if selector == 'valid' else cc_test_datasets_multiclass\n",
    "        else:\n",
    "            raise Exception(\"Unknown suite\")\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Setting params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_positions = [1000]\n",
    "max_features = 100\n",
    "bptt = 2000\n",
    "selector = 'test'\n",
    "base_path = os.path.join('.')\n",
    "overwrite=False\n",
    "max_times = [0.5, 1, 15, 30, 60, 60*5, 60*15, 60*60]\n",
    "metric_used = tabular_metrics.auc_metric\n",
    "baseline_methods = ['logistic', 'gp', 'knn', 'catboost', 'xgb', 'autosklearn2', 'autogluon']\n",
    "task_type = 'multiclass'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "suite = 'cc'\n",
    "test_datasets = get_datasets('test',task_type, suite=suite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_dict= {'gp': gp_metric\n",
    "                , 'knn': knn_metric\n",
    "                , 'catboost': catboost_metric\n",
    "                , 'xgb': xgb_metric\n",
    "                , 'logistic': logistic_metric\n",
    "           , 'autosklearn': autosklearn_metric\n",
    "             , 'autosklearn2': autosklearn2_metric\n",
    "            , 'autogluon': autogluon_metric}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cpu'\n",
    "\n",
    "def eval_method(task_type, method, dids, selector, eval_positions, max_time, metric_used, split_number, append_metric=True, fetch_only=False, verbose=False):\n",
    "    \n",
    "    dids = dids if type(dids) is list else [dids]\n",
    "    \n",
    "    for did in dids:\n",
    "\n",
    "        ds = get_datasets(selector, task_type, suite=suite)\n",
    "\n",
    "        ds = ds if did is None else ds[did:did+1]\n",
    "\n",
    "        clf = clf_dict[method]\n",
    "\n",
    "        time_string = '_time_'+str(max_time) if max_time else ''\n",
    "        metric_used_string = '_'+tabular_baselines.get_scoring_string(metric_used, usage='') if append_metric else ''\n",
    "\n",
    "        result = evaluate(datasets=ds\n",
    "                          , model=clf\n",
    "                          , method=method+time_string+metric_used_string\n",
    "                          , bptt=bptt, base_path=base_path\n",
    "                          , eval_positions=eval_positions\n",
    "                          , device=device, max_splits=1\n",
    "                          , overwrite=overwrite\n",
    "                          , save=True\n",
    "                          , metric_used=metric_used\n",
    "                          , path_interfix=task_type\n",
    "                          , fetch_only=fetch_only\n",
    "                          , split_number=split_number\n",
    "                          , verbose=verbose\n",
    "                          , max_time=max_time)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Baseline Evaluation\n",
    "This section runs baselines and saves results locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir {base_path}/results\n",
    "!mkdir {base_path}/results/tabular/\n",
    "!mkdir {base_path}/results/tabular/multiclass/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# RUN ONE METHOD ON ONE DATASET AND SPLIT\n",
    "overwrite=True\n",
    "dataset_id = 1\n",
    "split_number = 1\n",
    "maximum_runtime = 30\n",
    "r = eval_method(task_type, 'logistic', dataset_id, 'test', eval_positions, maximum_runtime, metric_used, split_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
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    },
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    },
    {
     "name": "stderr",
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     "text": [
      "Calculating splits<function knn_metric at 0x7f3636e0aaf0> 312:   0%|          | 0/1 [00:01<?, ?it/s]\n",
      "/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/numpy/lib/npyio.py:528: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  arr = np.asanyarray(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "saved results to /work/dlclarge1/hollmann-PFN_Tabular/results/tabular/multiclass/results_knn_time_1_roc_auc_balance-scale_312_624_1.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
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     "text": [
      "Calculating splits<function knn_metric at 0x7f3636e0aaf0> 312:   0%|          | 0/1 [00:01<?, ?it/s]\n",
      "/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/numpy/lib/npyio.py:528: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  arr = np.asanyarray(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "saved results to /work/dlclarge1/hollmann-PFN_Tabular/results/tabular/multiclass/results_knn_time_1_roc_auc_balance-scale_312_624_2.npy\n"
     ]
    },
    {
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    {
     "name": "stderr",
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     "text": [
      "Calculating splits<function knn_metric at 0x7f3636e0aaf0> 312:   0%|          | 0/1 [00:01<?, ?it/s]\n",
      "/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/numpy/lib/npyio.py:528: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  arr = np.asanyarray(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "saved results to /work/dlclarge1/hollmann-PFN_Tabular/results/tabular/multiclass/results_knn_time_1_roc_auc_balance-scale_312_624_3.npy\n"
     ]
    },
    {
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating splits<function knn_metric at 0x7f3636e0aaf0> 312:   0%|          | 0/1 [00:01<?, ?it/s]\n",
      "/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/numpy/lib/npyio.py:528: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  arr = np.asanyarray(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "saved results to /work/dlclarge1/hollmann-PFN_Tabular/results/tabular/multiclass/results_knn_time_1_roc_auc_balance-scale_312_624_4.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
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    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3441, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"/tmp/ipykernel_3318773/475731429.py\", line 4, in <module>\n",
      "    jobs = [\n",
      "  File \"/tmp/ipykernel_3318773/475731429.py\", line 5, in <listcomp>\n",
      "    eval_method(task, m, did, selector, eval_positions, max_time, metric_used, split_number)\n",
      "  File \"/tmp/ipykernel_3318773/551782496.py\", line 18, in eval_method\n",
      "    result = evaluate(datasets=ds\n",
      "  File \"/home/hollmann/prior-fitting-release/scripts/tabular_evaluation.py\", line 118, in evaluate\n",
      "    r = evaluate_position(X, y\n",
      "  File \"/home/hollmann/prior-fitting-release/scripts/tabular_evaluation.py\", line 266, in evaluate_position\n",
      "    _, outputs, best_configs = baseline_predict(model, eval_xs, eval_ys, categorical_feats\n",
      "  File \"/home/hollmann/prior-fitting-release/scripts/baseline_prediction_interface.py\", line 24, in baseline_predict\n",
      "    metric, output, best_config = metric_function(eval_x[:eval_pos],\n",
      "  File \"/home/hollmann/prior-fitting-release/scripts/tabular_baselines.py\", line 237, in knn_metric\n",
      "    best = fmin(\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\", line 553, in fmin\n",
      "    rval.exhaust()\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\", line 356, in exhaust\n",
      "    self.run(self.max_evals - n_done, block_until_done=self.asynchronous)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\", line 292, in run\n",
      "    self.serial_evaluate()\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\", line 170, in serial_evaluate\n",
      "    result = self.domain.evaluate(spec, ctrl)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/base.py\", line 907, in evaluate\n",
      "    rval = self.fn(pyll_rval)\n",
      "  File \"/home/hollmann/prior-fitting-release/scripts/tabular_baselines.py\", line 238, in <lambda>\n",
      "    fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),\n",
      "  File \"/home/hollmann/prior-fitting-release/scripts/tabular_baselines.py\", line 80, in eval_f\n",
      "    scores = cross_val_score(clf_(**params), x, y, cv=CV, scoring=get_scoring_string(metric_used))\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/utils/validation.py\", line 63, in inner_f\n",
      "    return f(*args, **kwargs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 445, in cross_val_score\n",
      "    cv_results = cross_validate(estimator=estimator, X=X, y=y, groups=groups,\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/utils/validation.py\", line 63, in inner_f\n",
      "    return f(*args, **kwargs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 250, in cross_validate\n",
      "    results = parallel(\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\", line 1044, in __call__\n",
      "    while self.dispatch_one_batch(iterator):\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 625, in _fit_and_score\n",
      "    test_scores = _score(estimator, X_test, y_test, scorer, error_score)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 687, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in __call__\n",
      "    score = scorer._score(cached_call, estimator,\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 277, in _score\n",
      "    y_pred = method_caller(clf, \"predict_proba\", X)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 53, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 241, in predict_proba\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 722, in kneighbors\n",
      "    chunked_results = Parallel(n_jobs, **parallel_kwargs)(\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\", line 966, in __call__\n",
      "    n_jobs = self._initialize_backend()\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\", line 733, in _initialize_backend\n",
      "    n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 489, in configure\n",
      "    n_jobs = self.effective_n_jobs(n_jobs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 509, in effective_n_jobs\n",
      "    elif mp.current_process().daemon:\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/multiprocessing/process.py\", line 41, in current_process\n",
      "    return _current_process\n",
      "KeyboardInterrupt\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 2061, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
      "    return f(*args, **kwargs)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/inspect.py\", line 1541, in getinnerframes\n",
      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/inspect.py\", line 1503, in getframeinfo\n",
      "    lines, lnum = findsource(frame)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\", line 182, in findsource\n",
      "    lines = linecache.getlines(file, globals_dict)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/linecache.py\", line 46, in getlines\n",
      "    return updatecache(filename, module_globals)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/linecache.py\", line 136, in updatecache\n",
      "    with tokenize.open(fullname) as fp:\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/tokenize.py\", line 394, in open\n",
      "    encoding, lines = detect_encoding(buffer.readline)\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/tokenize.py\", line 363, in detect_encoding\n",
      "    first = read_or_stop()\n",
      "  File \"/home/hollmann/anaconda3/envs/prior-fitting/lib/python3.9/tokenize.py\", line 321, in read_or_stop\n",
      "    return readline()\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'NoneType' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "    \u001B[0;31m[... skipping hidden 1 frame]\u001B[0m\n",
      "\u001B[0;32m/tmp/ipykernel_3318773/475731429.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0moverwrite\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 4\u001B[0;31m jobs = [\n\u001B[0m\u001B[1;32m      5\u001B[0m     \u001B[0meval_method\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtask\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mm\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdid\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mselector\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0meval_positions\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmax_time\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmetric_used\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msplit_number\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/tmp/ipykernel_3318773/475731429.py\u001B[0m in \u001B[0;36m<listcomp>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m      4\u001B[0m jobs = [\n\u001B[0;32m----> 5\u001B[0;31m     \u001B[0meval_method\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtask\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mm\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdid\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mselector\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0meval_positions\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmax_time\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmetric_used\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msplit_number\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      6\u001B[0m     \u001B[0;32mfor\u001B[0m \u001B[0mdid\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtest_datasets\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/tmp/ipykernel_3318773/551782496.py\u001B[0m in \u001B[0;36meval_method\u001B[0;34m(task_type, method, dids, selector, eval_positions, max_time, metric_used, split_number, append_metric, fetch_only, verbose)\u001B[0m\n\u001B[1;32m     17\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 18\u001B[0;31m         result = evaluate(datasets=ds\n\u001B[0m\u001B[1;32m     19\u001B[0m                           \u001B[0;34m,\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mclf\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/prior-fitting-release/scripts/tabular_evaluation.py\u001B[0m in \u001B[0;36mevaluate\u001B[0;34m(datasets, bptt, eval_positions, verbose, metric_used, return_tensor, **kwargs)\u001B[0m\n\u001B[1;32m    117\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 118\u001B[0;31m             r = evaluate_position(X, y\n\u001B[0m\u001B[1;32m    119\u001B[0m                         \u001B[0;34m,\u001B[0m \u001B[0mnum_classes\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0munique\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0my\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/prior-fitting-release/scripts/tabular_evaluation.py\u001B[0m in \u001B[0;36mevaluate_position\u001B[0;34m(X, y, categorical_feats, model, bptt, eval_position, overwrite, save, base_path, path_interfix, method, ds_name, fetch_only, max_time, split_number, per_step_normalization, **kwargs)\u001B[0m\n\u001B[1;32m    265\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 266\u001B[0;31m         _, outputs, best_configs = baseline_predict(model, eval_xs, eval_ys, categorical_feats\n\u001B[0m\u001B[1;32m    267\u001B[0m                                                     \u001B[0;34m,\u001B[0m \u001B[0meval_pos\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0meval_position\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/prior-fitting-release/scripts/baseline_prediction_interface.py\u001B[0m in \u001B[0;36mbaseline_predict\u001B[0;34m(metric_function, eval_xs, eval_ys, categorical_feats, metric_used, eval_pos, max_time, **kwargs)\u001B[0m\n\u001B[1;32m     23\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 24\u001B[0;31m             metric, output, best_config = metric_function(eval_x[:eval_pos],\n\u001B[0m\u001B[1;32m     25\u001B[0m                                                           \u001B[0meval_y\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0meval_pos\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/prior-fitting-release/scripts/tabular_baselines.py\u001B[0m in \u001B[0;36mknn_metric\u001B[0;34m(x, y, test_x, test_y, cat_features, metric_used, max_time)\u001B[0m\n\u001B[1;32m    236\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 237\u001B[0;31m     best = fmin(\n\u001B[0m\u001B[1;32m    238\u001B[0m         \u001B[0mfn\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mparams\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0meval_f\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mclf_\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmetric_used\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mstart_time\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmax_time\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\u001B[0m in \u001B[0;36mfmin\u001B[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001B[0m\n\u001B[1;32m    552\u001B[0m     \u001B[0;31m# next line is where the fmin is actually executed\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 553\u001B[0;31m     \u001B[0mrval\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mexhaust\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    554\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\u001B[0m in \u001B[0;36mexhaust\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    355\u001B[0m         \u001B[0mn_done\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrials\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 356\u001B[0;31m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrun\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmax_evals\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mn_done\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mblock_until_done\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0masynchronous\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    357\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrials\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrefresh\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\u001B[0m in \u001B[0;36mrun\u001B[0;34m(self, N, block_until_done)\u001B[0m\n\u001B[1;32m    291\u001B[0m                     \u001B[0;31m# -- loop over trials and do the jobs directly\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 292\u001B[0;31m                     \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mserial_evaluate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    293\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/fmin.py\u001B[0m in \u001B[0;36mserial_evaluate\u001B[0;34m(self, N)\u001B[0m\n\u001B[1;32m    169\u001B[0m                 \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 170\u001B[0;31m                     \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdomain\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mevaluate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mspec\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mctrl\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    171\u001B[0m                 \u001B[0;32mexcept\u001B[0m \u001B[0mException\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/hyperopt/base.py\u001B[0m in \u001B[0;36mevaluate\u001B[0;34m(self, config, ctrl, attach_attachments)\u001B[0m\n\u001B[1;32m    906\u001B[0m             )\n\u001B[0;32m--> 907\u001B[0;31m             \u001B[0mrval\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpyll_rval\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    908\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/prior-fitting-release/scripts/tabular_baselines.py\u001B[0m in \u001B[0;36m<lambda>\u001B[0;34m(params)\u001B[0m\n\u001B[1;32m    237\u001B[0m     best = fmin(\n\u001B[0;32m--> 238\u001B[0;31m         \u001B[0mfn\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mparams\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0meval_f\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mclf_\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmetric_used\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mstart_time\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmax_time\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    239\u001B[0m         \u001B[0mspace\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mparam_grid_hyperopt\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'knn'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/prior-fitting-release/scripts/tabular_baselines.py\u001B[0m in \u001B[0;36meval_f\u001B[0;34m(params, clf_, x, y, metric_used, start_time, max_time)\u001B[0m\n\u001B[1;32m     79\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnan\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 80\u001B[0;31m     \u001B[0mscores\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mcross_val_score\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mclf_\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m**\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcv\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mCV\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mscoring\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mget_scoring_string\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmetric_used\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     81\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/utils/validation.py\u001B[0m in \u001B[0;36minner_f\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m     62\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0mextra_args\u001B[0m \u001B[0;34m<=\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 63\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     64\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\u001B[0m in \u001B[0;36mcross_val_score\u001B[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001B[0m\n\u001B[1;32m    444\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 445\u001B[0;31m     cv_results = cross_validate(estimator=estimator, X=X, y=y, groups=groups,\n\u001B[0m\u001B[1;32m    446\u001B[0m                                 \u001B[0mscoring\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m{\u001B[0m\u001B[0;34m'score'\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mscorer\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcv\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcv\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/utils/validation.py\u001B[0m in \u001B[0;36minner_f\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m     62\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0mextra_args\u001B[0m \u001B[0;34m<=\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 63\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     64\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\u001B[0m in \u001B[0;36mcross_validate\u001B[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001B[0m\n\u001B[1;32m    249\u001B[0m                         pre_dispatch=pre_dispatch)\n\u001B[0;32m--> 250\u001B[0;31m     results = parallel(\n\u001B[0m\u001B[1;32m    251\u001B[0m         delayed(_fit_and_score)(\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, iterable)\u001B[0m\n\u001B[1;32m   1043\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1044\u001B[0;31m             \u001B[0;32mwhile\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdispatch_one_batch\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0miterator\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1045\u001B[0m                 \u001B[0;32mpass\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\u001B[0m in \u001B[0;36mdispatch_one_batch\u001B[0;34m(self, iterator)\u001B[0m\n\u001B[1;32m    858\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 859\u001B[0;31m                 \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_dispatch\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtasks\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    860\u001B[0m                 \u001B[0;32mreturn\u001B[0m \u001B[0;32mTrue\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\u001B[0m in \u001B[0;36m_dispatch\u001B[0;34m(self, batch)\u001B[0m\n\u001B[1;32m    776\u001B[0m             \u001B[0mjob_idx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_jobs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 777\u001B[0;31m             \u001B[0mjob\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_backend\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply_async\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mbatch\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcallback\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcb\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    778\u001B[0m             \u001B[0;31m# A job can complete so quickly than its callback is\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001B[0m in \u001B[0;36mapply_async\u001B[0;34m(self, func, callback)\u001B[0m\n\u001B[1;32m    207\u001B[0m         \u001B[0;34m\"\"\"Schedule a func to be run\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 208\u001B[0;31m         \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mImmediateResult\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mfunc\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    209\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mcallback\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, batch)\u001B[0m\n\u001B[1;32m    571\u001B[0m         \u001B[0;31m# arguments in memory\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 572\u001B[0;31m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mresults\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mbatch\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    573\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    261\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mparallel_backend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_backend\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mn_jobs\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_n_jobs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 262\u001B[0;31m             return [func(*args, **kwargs)\n\u001B[0m\u001B[1;32m    263\u001B[0m                     for func, args, kwargs in self.items]\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\u001B[0m in \u001B[0;36m<listcomp>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m    261\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mparallel_backend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_backend\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mn_jobs\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_n_jobs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 262\u001B[0;31m             return [func(*args, **kwargs)\n\u001B[0m\u001B[1;32m    263\u001B[0m                     for func, args, kwargs in self.items]\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/utils/fixes.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m    221\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mconfig_context\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m**\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconfig\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 222\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfunction\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\u001B[0m in \u001B[0;36m_fit_and_score\u001B[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001B[0m\n\u001B[1;32m    624\u001B[0m         \u001B[0mfit_time\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtime\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtime\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mstart_time\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 625\u001B[0;31m         \u001B[0mtest_scores\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_score\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mestimator\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mX_test\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_test\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mscorer\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0merror_score\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    626\u001B[0m         \u001B[0mscore_time\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtime\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtime\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mstart_time\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mfit_time\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\u001B[0m in \u001B[0;36m_score\u001B[0;34m(estimator, X_test, y_test, scorer, error_score)\u001B[0m\n\u001B[1;32m    686\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 687\u001B[0;31m             \u001B[0mscores\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mscorer\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mestimator\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mX_test\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_test\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    688\u001B[0m     \u001B[0;32mexcept\u001B[0m \u001B[0mException\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, estimator, *args, **kwargs)\u001B[0m\n\u001B[1;32m     86\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0misinstance\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mscorer\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0m_BaseScorer\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 87\u001B[0;31m                 score = scorer._score(cached_call, estimator,\n\u001B[0m\u001B[1;32m     88\u001B[0m                                       *args, **kwargs)\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\u001B[0m in \u001B[0;36m_score\u001B[0;34m(self, method_caller, clf, X, y, sample_weight)\u001B[0m\n\u001B[1;32m    276\u001B[0m         \u001B[0my_type\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtype_of_target\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0my\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 277\u001B[0;31m         \u001B[0my_pred\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmethod_caller\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mclf\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"predict_proba\"\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mX\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    278\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0my_type\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0;34m\"binary\"\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0my_pred\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m<=\u001B[0m \u001B[0;36m2\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\u001B[0m in \u001B[0;36m_cached_call\u001B[0;34m(cache, estimator, method, *args, **kwargs)\u001B[0m\n\u001B[1;32m     52\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mcache\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 53\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mgetattr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mestimator\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmethod\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     54\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\u001B[0m in \u001B[0;36mpredict_proba\u001B[0;34m(self, X)\u001B[0m\n\u001B[1;32m    240\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 241\u001B[0;31m         \u001B[0mneigh_dist\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mneigh_ind\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mkneighbors\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    242\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/sklearn/neighbors/_base.py\u001B[0m in \u001B[0;36mkneighbors\u001B[0;34m(self, X, n_neighbors, return_distance)\u001B[0m\n\u001B[1;32m    721\u001B[0m                 \u001B[0mparallel_kwargs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0;34m\"prefer\"\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0;34m\"threads\"\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 722\u001B[0;31m             chunked_results = Parallel(n_jobs, **parallel_kwargs)(\n\u001B[0m\u001B[1;32m    723\u001B[0m                 delayed(_tree_query_parallel_helper)(\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, iterable)\u001B[0m\n\u001B[1;32m    965\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_managed_backend\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 966\u001B[0;31m             \u001B[0mn_jobs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_initialize_backend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    967\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/parallel.py\u001B[0m in \u001B[0;36m_initialize_backend\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    732\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 733\u001B[0;31m             n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n\u001B[0m\u001B[1;32m    734\u001B[0m                                              **self._backend_args)\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001B[0m in \u001B[0;36mconfigure\u001B[0;34m(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)\u001B[0m\n\u001B[1;32m    488\u001B[0m         \u001B[0;34m\"\"\"Build a process executor and return the number of workers\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 489\u001B[0;31m         \u001B[0mn_jobs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0meffective_n_jobs\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn_jobs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    490\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mn_jobs\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001B[0m in \u001B[0;36meffective_n_jobs\u001B[0;34m(self, n_jobs)\u001B[0m\n\u001B[1;32m    508\u001B[0m             \u001B[0;32mreturn\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 509\u001B[0;31m         \u001B[0;32melif\u001B[0m \u001B[0mmp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcurrent_process\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdaemon\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    510\u001B[0m             \u001B[0;31m# Daemonic processes cannot have children\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/multiprocessing/process.py\u001B[0m in \u001B[0;36mcurrent_process\u001B[0;34m()\u001B[0m\n\u001B[1;32m     40\u001B[0m     '''\n\u001B[0;32m---> 41\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0m_current_process\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     42\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/interactiveshell.py\u001B[0m in \u001B[0;36mshowtraceback\u001B[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001B[0m\n\u001B[1;32m   2060\u001B[0m                         \u001B[0;31m# in the engines. This should return a list of strings.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 2061\u001B[0;31m                         \u001B[0mstb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mvalue\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_render_traceback_\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   2062\u001B[0m                     \u001B[0;32mexcept\u001B[0m \u001B[0mException\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mAttributeError\u001B[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "    \u001B[0;31m[... skipping hidden 1 frame]\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/interactiveshell.py\u001B[0m in \u001B[0;36mshowtraceback\u001B[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001B[0m\n\u001B[1;32m   2061\u001B[0m                         \u001B[0mstb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mvalue\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_render_traceback_\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2062\u001B[0m                     \u001B[0;32mexcept\u001B[0m \u001B[0mException\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 2063\u001B[0;31m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001B[0m\u001B[1;32m   2064\u001B[0m                                             value, tb, tb_offset=tb_offset)\n\u001B[1;32m   2065\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\u001B[0m in \u001B[0;36mstructured_traceback\u001B[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001B[0m\n\u001B[1;32m   1365\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1366\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtb\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1367\u001B[0;31m         return FormattedTB.structured_traceback(\n\u001B[0m\u001B[1;32m   1368\u001B[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001B[1;32m   1369\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\u001B[0m in \u001B[0;36mstructured_traceback\u001B[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001B[0m\n\u001B[1;32m   1265\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mmode\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mverbose_modes\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1266\u001B[0m             \u001B[0;31m# Verbose modes need a full traceback\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1267\u001B[0;31m             return VerboseTB.structured_traceback(\n\u001B[0m\u001B[1;32m   1268\u001B[0m                 \u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0metype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mvalue\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtb\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtb_offset\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnumber_of_lines_of_context\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1269\u001B[0m             )\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\u001B[0m in \u001B[0;36mstructured_traceback\u001B[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001B[0m\n\u001B[1;32m   1122\u001B[0m         \u001B[0;34m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1123\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1124\u001B[0;31m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001B[0m\u001B[1;32m   1125\u001B[0m                                                                tb_offset)\n\u001B[1;32m   1126\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\u001B[0m in \u001B[0;36mformat_exception_as_a_whole\u001B[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001B[0m\n\u001B[1;32m   1080\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1081\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1082\u001B[0;31m         \u001B[0mlast_unique\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mrecursion_repeat\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mfind_recursion\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0morig_etype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mevalue\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mrecords\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1083\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1084\u001B[0m         \u001B[0mframes\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mformat_records\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrecords\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlast_unique\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mrecursion_repeat\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/anaconda3/envs/prior-fitting/lib/python3.9/site-packages/IPython/core/ultratb.py\u001B[0m in \u001B[0;36mfind_recursion\u001B[0;34m(etype, value, records)\u001B[0m\n\u001B[1;32m    380\u001B[0m     \u001B[0;31m# first frame (from in to out) that looks different.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    381\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mis_recursion_error\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0metype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mvalue\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mrecords\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 382\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrecords\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    383\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    384\u001B[0m     \u001B[0;31m# Select filename, lineno, func_name to track frames with\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mTypeError\u001B[0m: object of type 'NoneType' has no len()"
     ]
    }
   ],
   "source": [
    "# RUN ALL METHODS, SPLITS AND DATASETS\n",
    "test_datasets = get_datasets('test',task_type, suite=suite)\n",
    "\n",
    "overwrite=True\n",
    "jobs = [\n",
    "    eval_method(task_type, m, did, selector, eval_positions, max_time, metric_used, split_number)\n",
    "    for did in range(0, len(test_datasets))\n",
    "    for selector in ['test']\n",
    "    for m in baseline_methods\n",
    "    for max_time in max_times\n",
    "    for split_number in [1, 2, 3, 4, 5]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "methods = baseline_methods + ['transformer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_balance-scale_312_624_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-fourier_1000_2000_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_breast-w_349_698_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-karhunen_1000_2000_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-morphological_1000_2000_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-zernike_1000_2000_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_cmc_736_1472_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_credit-approval_345_690_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_credit-g_500_1000_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_diabetes_384_768_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_tic-tac-toe_479_958_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_vehicle_423_846_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_eucalyptus_368_736_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_analcatdata_authorship_420_840_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_analcatdata_dmft_398_796_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_pc4_729_1458_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_pc3_781_1562_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_kc2_261_522_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_pc1_554_1108_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_banknote-authentication_686_1372_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_blood-transfusion-service-center_374_748_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_ilpd_291_582_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_qsar-biodeg_527_1054_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_wdbc_284_568_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_cylinder-bands_270_540_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_dresses-sales_250_500_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_MiceProtein_540_1080_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_car_864_1728_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_steel-plates-fault_970_1940_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_climate-model-simulation-crashes_270_540_1.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_balance-scale_312_624_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-fourier_1000_2000_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_breast-w_349_698_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-karhunen_1000_2000_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-morphological_1000_2000_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-zernike_1000_2000_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_cmc_736_1472_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_credit-approval_345_690_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_credit-g_500_1000_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_diabetes_384_768_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_tic-tac-toe_479_958_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_vehicle_423_846_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_eucalyptus_368_736_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_analcatdata_authorship_420_840_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_analcatdata_dmft_398_796_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_pc4_729_1458_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_pc3_781_1562_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_kc2_261_522_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_pc1_554_1108_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_banknote-authentication_686_1372_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_blood-transfusion-service-center_374_748_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_ilpd_291_582_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_qsar-biodeg_527_1054_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_wdbc_284_568_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_cylinder-bands_270_540_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_dresses-sales_250_500_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_MiceProtein_540_1080_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_car_864_1728_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_steel-plates-fault_970_1940_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_climate-model-simulation-crashes_270_540_2.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_balance-scale_312_624_3.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-fourier_1000_2000_3.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_breast-w_349_698_3.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_logistic_time_0.5_roc_auc_mfeat-karhunen_1000_2000_3.npy\n",
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      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_ilpd_291_582_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_qsar-biodeg_527_1054_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_wdbc_284_568_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_cylinder-bands_270_540_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_dresses-sales_250_500_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_MiceProtein_540_1080_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_car_864_1728_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_steel-plates-fault_970_1940_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_climate-model-simulation-crashes_270_540_4.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_balance-scale_312_624_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_mfeat-fourier_1000_2000_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_breast-w_349_698_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_mfeat-karhunen_1000_2000_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_mfeat-morphological_1000_2000_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_mfeat-zernike_1000_2000_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_cmc_736_1472_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_credit-approval_345_690_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_credit-g_500_1000_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_diabetes_384_768_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_tic-tac-toe_479_958_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_vehicle_423_846_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_eucalyptus_368_736_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_analcatdata_authorship_420_840_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_analcatdata_dmft_398_796_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_pc4_729_1458_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_pc3_781_1562_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_kc2_261_522_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_pc1_554_1108_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_banknote-authentication_686_1372_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_blood-transfusion-service-center_374_748_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_ilpd_291_582_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_qsar-biodeg_527_1054_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_wdbc_284_568_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_cylinder-bands_270_540_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_dresses-sales_250_500_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_MiceProtein_540_1080_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_car_864_1728_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_steel-plates-fault_970_1940_5.npy\n",
      "Could not load saved result for ./results/tabular/multiclass/results_autogluon_time_3600_roc_auc_climate-model-simulation-crashes_270_540_5.npy\n"
     ]
    }
   ],
   "source": [
    "pos = str(eval_positions[0])\n",
    "\n",
    "global_results = {}\n",
    "overwrite=False\n",
    "\n",
    "for method in baseline_methods:\n",
    "    for max_time in max_times:\n",
    "        for split_number in range(1,5+1):\n",
    "            global_results[method+'_time_'+str(max_time)+tabular_baselines.get_scoring_string(metric_used, usage='')+'_split_'+str(split_number)] = eval_method(task_type, method,  None, selector, \n",
    "                                                                                                                                    eval_positions, fetch_only=True, \n",
    "                                                                                                                                    verbose=False, max_time=max_time,\n",
    "                                                                                                                                    metric_used=metric_used, split_number=split_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_ = 'prior_tuning_result.pkl'\n",
    "\n",
    "try:\n",
    "    output = open(path_, 'rb')\n",
    "    _, metrics, _, _, _, _ = CustomUnpickler(output).load()\n",
    "except:\n",
    "    output = open(path_, 'rb')\n",
    "    _, metrics, _, _, _ = CustomUnpickler(output).load()\n",
    "if isinstance(metrics, list):\n",
    "    for i in range(1, len(metrics[1])+1):\n",
    "        global_results['transformer_split_'+str(i)] = metrics[2][i-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verify integrity of results\n",
    "for bl in set(global_results.keys()):\n",
    "    if 'split_1' in bl:\n",
    "        for ds in test_datasets:\n",
    "            if f'{ds[0]}_ys_at_1000' not in global_results[bl]:\n",
    "                continue\n",
    "            match = (global_results[bl][f'{ds[0]}_ys_at_1000'] == global_results['transformer_split_1'][f'{ds[0]}_ys_at_1000']).float().mean()\n",
    "            if not match:\n",
    "                raise Exception(\"Not the same labels used\")\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "limit_to = ''\n",
    "calculate_score(tabular_metrics.auc_metric, 'roc', global_results, test_datasets, eval_positions + [-1], limit_to=limit_to)\n",
    "calculate_score(tabular_metrics.cross_entropy, 'cross_entropy', global_results, test_datasets, eval_positions + [-1], limit_to=limit_to)\n",
    "calculate_score(tabular_metrics.accuracy_metric, 'acc', global_results, test_datasets, eval_positions + [-1])\n",
    "calculate_score(tabular_metrics.time_metric, 'time', global_results, test_datasets, eval_positions + [-1], aggregator='sum', limit_to=limit_to)\n",
    "calculate_score(tabular_metrics.time_metric, 'time', global_results, test_datasets, eval_positions + [-1], aggregator='mean', limit_to=limit_to)\n",
    "calculate_score(tabular_metrics.count_metric, 'count', global_results, test_datasets, eval_positions + [-1], aggregator='sum', limit_to=limit_to)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### ROC and AUC plots from TabPFN Paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_ranks_and_wins_table(global_results_filtered, metric_key, max_time, split_number, time_matrix):\n",
    "    global_results_filtered_split = {**global_results_filtered}\n",
    "    global_results_filtered_split = {k: global_results_filtered_split[k] for k in global_results_filtered_split.keys() if '_time_'+str(max_time)+tabular_baselines.get_scoring_string(metric_used, usage='')+'_split_'+str(split_number) in k or 'transformer_split_'+str(split_number) in k}\n",
    "\n",
    "    matrix, matrix_stds = make_metric_matrix(global_results_filtered_split, methods, pos, metric_key, test_datasets)\n",
    "    for method in methods:\n",
    "        if time_matrix[method] > max_time * 2:\n",
    "            matrix[method] = np.nan\n",
    "        # = np.nan\n",
    "\n",
    "    if metric_key == 'cross_entropy':\n",
    "        matrix = -(matrix.fillna(-100))\n",
    "    else:\n",
    "        matrix = matrix.fillna(-1)\n",
    "    return make_ranks_and_wins_table(matrix.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "df_ = []\n",
    "metric_keys = ['roc', 'cross_entropy', 'time']\n",
    "\n",
    "for max_time in max_times:\n",
    "    global_results_filtered = {**global_results}\n",
    "    global_results_filtered = {k: global_results_filtered[k] for k in global_results_filtered.keys() if '_time_'+str(max_time)+tabular_baselines.get_scoring_string(metric_used, usage='')+'_' in k or 'transformer' in k}\n",
    "    \n",
    "    time_matrix, _ = make_metric_matrix(global_results_filtered, methods, pos, 'time', test_datasets)\n",
    "    time_matrix = time_matrix.mean()\n",
    "    \n",
    "    if len(global_results_filtered) == 0:\n",
    "        continue\n",
    "        \n",
    "    # Calculate ranks and wins per split\n",
    "    for metric_key in metric_keys:\n",
    "        for split_number in range(1,6):\n",
    "            ranks, wins = generate_ranks_and_wins_table(global_results_filtered, metric_key, max_time, split_number, time_matrix)\n",
    "\n",
    "            for method in methods:\n",
    "                method_ = method+'_time_'+str(max_time)+tabular_baselines.get_scoring_string(metric_used, usage='') if method != 'transformer' else method\n",
    "                global_results[method_+'_split_'+str(split_number)]['mean_rank_'+metric_key+f'_at_{pos}'] = ranks[method]\n",
    "                global_results[method_+'_split_'+str(split_number)]['mean_wins_'+metric_key+f'_at_{pos}'] = wins[method]\n",
    "    \n",
    "    #for method in global_results.keys():\n",
    "    #    global_results[method]['mean_rank_'+metric_key+f'_at_{pos}'] = ranks[]\n",
    "    \n",
    "    avg_times = {}\n",
    "    for method_ in methods:\n",
    "        avg_times[method_] = []\n",
    "        for split_number in range(1,6):\n",
    "            if method_ != 'transformer':\n",
    "                method = method_+'_time_'+str(max_time)+tabular_baselines.get_scoring_string(metric_used, usage='')+'_split_'+str(split_number)\n",
    "            else:\n",
    "                method = method_+'_split_'+str(split_number)\n",
    "            avg_times[method_] += [global_results[method][f'mean_time_at_{pos}']]\n",
    "    avg_times = pd.DataFrame(avg_times).mean()\n",
    "    \n",
    "    for metric_key in metric_keys:\n",
    "        for ranking in ['', 'rank_', 'wins_']:\n",
    "            for method_ in methods:\n",
    "                for split_number in range(1,6):\n",
    "                    method = method_\n",
    "                    if method_ != 'transformer':\n",
    "                        method = method_+'_time_'+str(max_time)+tabular_baselines.get_scoring_string(metric_used, usage='')+'_split_'+str(split_number)\n",
    "                    else:\n",
    "                        method = method_+'_split_'+str(split_number)\n",
    "\n",
    "                    if global_results[method][f'sum_count_at_{pos}'] <= 29:\n",
    "                        print('Warning not all datasets generated for '+method+' '+ str(global_results[method][f'sum_count_at_{pos}']))\n",
    "                        \n",
    "                    time = global_results[method]['mean_time'] if ranking == '' else max_time\n",
    "                    time = max_time # Todo: This is not the real time\n",
    "                    df_ += [{'metric'+ranking+metric_key: global_results[method]['mean_'+ranking+metric_key+f'_at_{pos}'], 'real_time': avg_times[method_], 'time': time, 'method': method_, 'split_number': split_number}]\n",
    "                    # For Roc AUC Plots\n",
    "                    #if 'transformer' in method:\n",
    "                    #    df_ += [{'metric'+ranking+metric_key: global_results[method]['mean_'+ranking+metric_key+f'_at_{pos}'], 'real_time': avg_times[method_], 'time': time, 'method': method_, 'split_number': split_number}]\n",
    "                    #    df_ += [{'metric'+ranking+metric_key: global_results[method]['mean_'+ranking+metric_key+f'_at_{pos}'], 'real_time': max(avg_times), 'time': max(max_times), 'method': method_, 'split_number': split_number}]\n",
    "                            \n",
    "            \n",
    "df_ = pd.DataFrame(df_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric_renamer = {'roc': 'ROC AUC', 'cross_entropy': 'Cross entropy'\n",
    "                  , 'rank_roc': 'Mean ROC AUC Rank', 'rank_cross_entropy': 'Mean Cross entropy Rank'\n",
    "                  , 'wins_roc': 'Mean ROC AUC Wins', 'wins_cross_entropy': 'Mean Cross entropy Wins'\n",
    "                  , 'time': 'actual time taken'}\n",
    "max_times_renamer = {0.5: \"0.5s\", 1: \"1s\", 5: \"5s\", 15: \"15s\", 30: \"30s\", 60: \"1min\", 300: \"5min\", 900: \"15min\", 3600: \"1h\", 14400: \"4h\"}\n",
    "\n",
    "def make_tabular_results_plot(metric_key, exclude, max_times, df_, grouping=True):\n",
    "    f, ax = plt.subplots(figsize=(7, 7))\n",
    "    #ax.set(xscale=\"log\")\n",
    "    \n",
    "    df_.loc[:, 'time_log'] = np.log10(df_.time)\n",
    "    df_.loc[:, 'real_time_log'] = np.log10(df_.real_time)\n",
    "    time_column = 'time_log' if grouping else 'real_time_log'\n",
    "\n",
    "    sns.set_palette(\"tab10\")\n",
    "    for method in methods:\n",
    "        if method in exclude or method=='transformer':\n",
    "            continue\n",
    "        df_method = df_[df_.method==method].copy()\n",
    "        ax = sns.lineplot(time_column, 'metric'+metric_key, data=df_method, marker='o', label=method, ax=ax)\n",
    "    #sns.scatterplot(data=df_, x='time', y='metric', hue='method', ax=ax, style='method') #\n",
    "    df_trans = df_[df_.method=='transformer']\n",
    "    if time_column == 'real_time_log':\n",
    "        # Removing dots for line for transformers\n",
    "        df_trans = df_trans[np.logical_or(df_trans.real_time == df_trans.real_time.min(), df_trans.real_time == df_trans.real_time.max())]\n",
    "        df_trans.loc[:, 'metric'+metric_key] = df_trans['metric'+metric_key].mean()\n",
    "        df_trans.loc[:, time_column] = np.log(1) # Hacky code to get the right time from our measurements\n",
    "    ax = sns.lineplot(time_column, 'metric'+metric_key, data=df_trans, linestyle='--', marker='o', ci=\"sd\", ax=ax)\n",
    "    \n",
    "    #ax = sns.scatterplot(data = df_trans, x=time_column, y='metric'+metric_key, s=800, marker='*', color='grey') #\n",
    "    #ax = plt.scatter(df_trans[time_column], df_trans['metric'+metric_key], s=600, marker=['*']) #\n",
    "    \n",
    "    if grouping:\n",
    "        ax.set_xlabel(\"Time (s, requested, not actual)\")\n",
    "    else:\n",
    "        ax.set_xlabel(\"Time taken\")\n",
    "    ax.set_ylabel(metric_renamer[metric_key])\n",
    "\n",
    "    #ax.legend()\n",
    "    \n",
    "    times = np.log10(max_times)\n",
    "    ax.set_xticks(times)\n",
    "    ax.set_xticklabels([max_times_renamer[t] for t in max_times])\n",
    "    \n",
    "    #ax.legend([],[], frameon=False)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_absolute = df_.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_absolute = df_.copy()\n",
    "df_absolute = df_absolute[np.logical_or(df_.method != 'autogluon', df_.time >= 30)] # Autogluon did not yield any useful results before 30s\n",
    "\n",
    "knn_extend = df_absolute[np.logical_and(df_absolute.method=='knn', df_absolute.time == 3600)].copy()\n",
    "knn_extend['real_time'] = 14400\n",
    "knn_extend['time'] = 14400\n",
    "df_absolute = df_absolute.append(knn_extend, ignore_index=True).reindex()\n",
    "\n",
    "knn_extend = df_absolute[np.logical_and(df_absolute.method=='logistic', df_absolute.time == 3600)].copy()\n",
    "knn_extend['real_time'] = 14400\n",
    "knn_extend['time'] = 14400\n",
    "\n",
    "df_absolute = df_absolute.append(knn_extend, ignore_index=True).reindex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8bb4d11510>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exclude=['']\n",
    "#ax = make_tabular_results_plot('time', exclude=exclude)\n",
    "ax = make_tabular_results_plot('roc', df_=df_absolute, exclude=exclude, grouping=False, max_times=[1, 5, 30, 60*5, 60*60])\n",
    "ax.set_ylim([0.84, 0.9])\n",
    "ax.set_xlim([np.log10(0.7), np.log10(3600)])\n",
    "ax.legend([],[], frameon=False)\n",
    "\n",
    "#tikzplotlib.save(f'roc_over_time.tex', axis_height='5cm', axis_width='6cm', strict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = make_tabular_results_plot('rank_roc', df_=df_[df_.time >= 1].copy(), exclude=['tabnet'], max_times=[1, 5, 30, 60*5, 60*60])\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlim([np.log10(1.0), np.log10(3600)])\n",
    "ax.legend([],[], frameon=False)\n",
    "tikzplotlib.save(f'roc_raks_tabular.tex', axis_height='5cm', axis_width='6cm', strict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = make_tabular_results_plot('wins_roc', df_=df_[df_.time >= 1].copy(), exclude=exclude, max_times=[1, 5, 30, 60*5, 60*60])\n",
    "ax.set_xlim([np.log10(1.0), np.log10(3600)])\n",
    "ax.legend([],[], frameon=False)\n",
    "tikzplotlib.save(f'roc_wins_tabular.tex', axis_height='5cm', axis_width='6cm', strict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Big Table metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_time = '3600'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_results_filtered = {**global_results}\n",
    "global_results_filtered = {k: global_results_filtered[k] for k in global_results_filtered.keys() if '_time_'+str(max_time)+tabular_baselines.get_scoring_string(metric_used, usage='')+'_' in k or 'transformer' in k}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_matrix, roc_matrix_stds = make_metric_matrix(global_results_filtered, methods, pos, 'roc', test_datasets_multiclass_filtered)\n",
    "acc_matrix, acc_matrix_stds = make_metric_matrix(global_results_filtered, methods, pos, 'acc', test_datasets_multiclass_filtered)\n",
    "cross_entropy_matrix, cross_entropy_matrix_stds = make_metric_matrix(global_results_filtered, methods, pos, 'cross_entropy', test_datasets_multiclass_filtered)\n",
    "time_matrix, time_matrix_stds = make_metric_matrix(global_results_filtered, methods, pos, 'time', test_datasets_multiclass_filtered)\n",
    "\n",
    "roc_rank, rocs_wins = make_ranks_and_wins_table(roc_matrix.copy())\n",
    "acc_rank, acc_wins = make_ranks_and_wins_table(acc_matrix.copy())\n",
    "cross_entropy_rank, cross_entropy_wins = make_ranks_and_wins_table(-cross_entropy_matrix.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wins_vs_idx(matrix, idx):\n",
    "    wins_auc = np.array([[(matrix.values[:, j] < matrix.values[:, i]).sum() if i != j else 0 for i,method in enumerate(methods)] for j in [idx]])\n",
    "    ties_auc = np.array([[(matrix.values[:, j] == matrix.values[:, i]).sum() if i != j else 0 for i,method in enumerate(methods)] for j in [idx]])\n",
    "    losses_auc = np.array([[(matrix.values[:, j] > matrix.values[:, i]).sum() if i != j else 0 for i,method in enumerate(methods)] for j in [idx]])\n",
    "    \n",
    "    return wins_auc, ties_auc, losses_auc\n",
    "\n",
    "transformer_idx = np.where(roc_matrix.columns == 'transformer')[0][0]\n",
    "\n",
    "wins_roc_vs_us, ties_roc_vs_us, losses_roc_vs_us = wins_vs_idx(roc_matrix, transformer_idx)\n",
    "wins_acc_vs_us, ties_acc_vs_us, losses_acc_vs_us = wins_vs_idx(acc_matrix, transformer_idx)\n",
    "wins_ce_vs_us, ties_ce_vs_us, losses_ce_vs_us = wins_vs_idx(-cross_entropy_matrix, transformer_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename(table):\n",
    "    return table.rename(columns=relabeler).T.rename(columns={'blood-transfusion-service-center': 'blood-transfus..'\n",
    "                                                                , 'jungle_chess_2pcs_raw_endgame_complete': 'jungle\\_chess..', 'bank-marketing': 'bank-market..'}).T\n",
    "\n",
    "def get_suffix(i, k):\n",
    "    suffix = ''\n",
    "    suffix = suffix+'s' if test_datasets[i][5]['samples_capped'] == True else suffix\n",
    "    suffix = suffix+'f' if test_datasets[i][5]['feats_capped'] == True else suffix\n",
    "    suffix = suffix+'c' if test_datasets[i][5]['classes_capped'] == True else suffix\n",
    "    suffix = '' if len(suffix) == 0 else f' [{suffix}]'\n",
    "    \n",
    "    return k + suffix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "relabeler = {'transformer': 'Tabular PFN'\n",
    "             , 'autogluon': 'Autogluon'\n",
    "             , 'autosklearn2': 'Autosklearn2'\n",
    "             , 'gp': 'GP (RBF)'\n",
    "             , 'logistic': 'Log. Regr.'\n",
    "             , 'knn': 'KNN'\n",
    "             , 'catboost': 'Catboost'\n",
    "            , 'xgb': 'XGB'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b5b46_row0_col5, #T_b5b46_row1_col6, #T_b5b46_row2_col5, #T_b5b46_row3_col6, #T_b5b46_row4_col6, #T_b5b46_row5_col7, #T_b5b46_row6_col5, #T_b5b46_row7_col4, #T_b5b46_row8_col6, #T_b5b46_row9_col2, #T_b5b46_row10_col6, #T_b5b46_row11_col7, #T_b5b46_row12_col7, #T_b5b46_row13_col7, #T_b5b46_row14_col7, #T_b5b46_row15_col6, #T_b5b46_row16_col7, #T_b5b46_row17_col7, #T_b5b46_row18_col7, #T_b5b46_row19_col2, #T_b5b46_row19_col5, #T_b5b46_row19_col6, #T_b5b46_row19_col7, #T_b5b46_row20_col7, #T_b5b46_row21_col7, #T_b5b46_row22_col7, #T_b5b46_row23_col7, #T_b5b46_row24_col6, #T_b5b46_row25_col2, #T_b5b46_row26_col7, #T_b5b46_row27_col5, #T_b5b46_row28_col5, #T_b5b46_row29_col2, #T_b5b46_row30_col7, #T_b5b46_row31_col7, #T_b5b46_row32_col7, #T_b5b46_row33_col3, #T_b5b46_row34_col3, #T_b5b46_row35_col5, #T_b5b46_row36_col7, #T_b5b46_row37_col6, #T_b5b46_row38_col7, #T_b5b46_row39_col2, #T_b5b46_row40_col0, #T_b5b46_row41_col0, #T_b5b46_row42_col0, #T_b5b46_row43_col0, #T_b5b46_row44_col0, #T_b5b46_row45_col3 {\n",
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       "</style>\n",
       "<table id=\"T_b5b46_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >KNN</th>\n",
       "      <th class=\"col_heading level0 col1\" >Log. Regr.</th>\n",
       "      <th class=\"col_heading level0 col2\" >GP (RBF)</th>\n",
       "      <th class=\"col_heading level0 col3\" >Catboost</th>\n",
       "      <th class=\"col_heading level0 col4\" >XGB</th>\n",
       "      <th class=\"col_heading level0 col5\" >Autosklearn2</th>\n",
       "      <th class=\"col_heading level0 col6\" >Autogluon</th>\n",
       "      <th class=\"col_heading level0 col7\" >Tabular PFN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row0\" class=\"row_heading level0 row0\" >balance-scale</th>\n",
       "      <td id=\"T_b5b46_row0_col0\" class=\"data row0 col0\" >0.880</td>\n",
       "      <td id=\"T_b5b46_row0_col1\" class=\"data row0 col1\" >0.962</td>\n",
       "      <td id=\"T_b5b46_row0_col2\" class=\"data row0 col2\" >0.983</td>\n",
       "      <td id=\"T_b5b46_row0_col3\" class=\"data row0 col3\" >0.924</td>\n",
       "      <td id=\"T_b5b46_row0_col4\" class=\"data row0 col4\" >0.994</td>\n",
       "      <td id=\"T_b5b46_row0_col5\" class=\"data row0 col5\" >0.997</td>\n",
       "      <td id=\"T_b5b46_row0_col6\" class=\"data row0 col6\" >0.992</td>\n",
       "      <td id=\"T_b5b46_row0_col7\" class=\"data row0 col7\" >0.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row1\" class=\"row_heading level0 row1\" >mfeat-fourier</th>\n",
       "      <td id=\"T_b5b46_row1_col0\" class=\"data row1 col0\" >0.970</td>\n",
       "      <td id=\"T_b5b46_row1_col1\" class=\"data row1 col1\" >0.976</td>\n",
       "      <td id=\"T_b5b46_row1_col2\" class=\"data row1 col2\" >0.923</td>\n",
       "      <td id=\"T_b5b46_row1_col3\" class=\"data row1 col3\" >0.982</td>\n",
       "      <td id=\"T_b5b46_row1_col4\" class=\"data row1 col4\" >0.980</td>\n",
       "      <td id=\"T_b5b46_row1_col5\" class=\"data row1 col5\" >0.983</td>\n",
       "      <td id=\"T_b5b46_row1_col6\" class=\"data row1 col6\" >0.984</td>\n",
       "      <td id=\"T_b5b46_row1_col7\" class=\"data row1 col7\" >0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row2\" class=\"row_heading level0 row2\" >breast-w</th>\n",
       "      <td id=\"T_b5b46_row2_col0\" class=\"data row2 col0\" >0.984</td>\n",
       "      <td id=\"T_b5b46_row2_col1\" class=\"data row2 col1\" >0.994</td>\n",
       "      <td id=\"T_b5b46_row2_col2\" class=\"data row2 col2\" >0.992</td>\n",
       "      <td id=\"T_b5b46_row2_col3\" class=\"data row2 col3\" >0.993</td>\n",
       "      <td id=\"T_b5b46_row2_col4\" class=\"data row2 col4\" >0.990</td>\n",
       "      <td id=\"T_b5b46_row2_col5\" class=\"data row2 col5\" >0.994</td>\n",
       "      <td id=\"T_b5b46_row2_col6\" class=\"data row2 col6\" >0.993</td>\n",
       "      <td id=\"T_b5b46_row2_col7\" class=\"data row2 col7\" >0.993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row3\" class=\"row_heading level0 row3\" >mfeat-karhunen</th>\n",
       "      <td id=\"T_b5b46_row3_col0\" class=\"data row3 col0\" >0.994</td>\n",
       "      <td id=\"T_b5b46_row3_col1\" class=\"data row3 col1\" >0.995</td>\n",
       "      <td id=\"T_b5b46_row3_col2\" class=\"data row3 col2\" >0.915</td>\n",
       "      <td id=\"T_b5b46_row3_col3\" class=\"data row3 col3\" >0.999</td>\n",
       "      <td id=\"T_b5b46_row3_col4\" class=\"data row3 col4\" >0.998</td>\n",
       "      <td id=\"T_b5b46_row3_col5\" class=\"data row3 col5\" >0.998</td>\n",
       "      <td id=\"T_b5b46_row3_col6\" class=\"data row3 col6\" >0.999</td>\n",
       "      <td id=\"T_b5b46_row3_col7\" class=\"data row3 col7\" >0.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row4\" class=\"row_heading level0 row4\" >mfeat-morphological</th>\n",
       "      <td id=\"T_b5b46_row4_col0\" class=\"data row4 col0\" >0.951</td>\n",
       "      <td id=\"T_b5b46_row4_col1\" class=\"data row4 col1\" >0.964</td>\n",
       "      <td id=\"T_b5b46_row4_col2\" class=\"data row4 col2\" >0.959</td>\n",
       "      <td id=\"T_b5b46_row4_col3\" class=\"data row4 col3\" >0.963</td>\n",
       "      <td id=\"T_b5b46_row4_col4\" class=\"data row4 col4\" >0.961</td>\n",
       "      <td id=\"T_b5b46_row4_col5\" class=\"data row4 col5\" >0.967</td>\n",
       "      <td id=\"T_b5b46_row4_col6\" class=\"data row4 col6\" >0.970</td>\n",
       "      <td id=\"T_b5b46_row4_col7\" class=\"data row4 col7\" >0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row5\" class=\"row_heading level0 row5\" >mfeat-zernike</th>\n",
       "      <td id=\"T_b5b46_row5_col0\" class=\"data row5 col0\" >0.976</td>\n",
       "      <td id=\"T_b5b46_row5_col1\" class=\"data row5 col1\" >0.979</td>\n",
       "      <td id=\"T_b5b46_row5_col2\" class=\"data row5 col2\" >0.981</td>\n",
       "      <td id=\"T_b5b46_row5_col3\" class=\"data row5 col3\" >0.976</td>\n",
       "      <td id=\"T_b5b46_row5_col4\" class=\"data row5 col4\" >0.973</td>\n",
       "      <td id=\"T_b5b46_row5_col5\" class=\"data row5 col5\" >0.981</td>\n",
       "      <td id=\"T_b5b46_row5_col6\" class=\"data row5 col6\" >0.794</td>\n",
       "      <td id=\"T_b5b46_row5_col7\" class=\"data row5 col7\" >0.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row6\" class=\"row_heading level0 row6\" >cmc</th>\n",
       "      <td id=\"T_b5b46_row6_col0\" class=\"data row6 col0\" >0.634</td>\n",
       "      <td id=\"T_b5b46_row6_col1\" class=\"data row6 col1\" >0.675</td>\n",
       "      <td id=\"T_b5b46_row6_col2\" class=\"data row6 col2\" >0.673</td>\n",
       "      <td id=\"T_b5b46_row6_col3\" class=\"data row6 col3\" >0.726</td>\n",
       "      <td id=\"T_b5b46_row6_col4\" class=\"data row6 col4\" >0.730</td>\n",
       "      <td id=\"T_b5b46_row6_col5\" class=\"data row6 col5\" >0.738</td>\n",
       "      <td id=\"T_b5b46_row6_col6\" class=\"data row6 col6\" >0.733</td>\n",
       "      <td id=\"T_b5b46_row6_col7\" class=\"data row6 col7\" >0.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row7\" class=\"row_heading level0 row7\" >credit-approval</th>\n",
       "      <td id=\"T_b5b46_row7_col0\" class=\"data row7 col0\" >0.914</td>\n",
       "      <td id=\"T_b5b46_row7_col1\" class=\"data row7 col1\" >0.912</td>\n",
       "      <td id=\"T_b5b46_row7_col2\" class=\"data row7 col2\" >0.921</td>\n",
       "      <td id=\"T_b5b46_row7_col3\" class=\"data row7 col3\" >0.939</td>\n",
       "      <td id=\"T_b5b46_row7_col4\" class=\"data row7 col4\" >0.942</td>\n",
       "      <td id=\"T_b5b46_row7_col5\" class=\"data row7 col5\" >0.941</td>\n",
       "      <td id=\"T_b5b46_row7_col6\" class=\"data row7 col6\" >0.941</td>\n",
       "      <td id=\"T_b5b46_row7_col7\" class=\"data row7 col7\" >0.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row8\" class=\"row_heading level0 row8\" >credit-g</th>\n",
       "      <td id=\"T_b5b46_row8_col0\" class=\"data row8 col0\" >0.726</td>\n",
       "      <td id=\"T_b5b46_row8_col1\" class=\"data row8 col1\" >0.760</td>\n",
       "      <td id=\"T_b5b46_row8_col2\" class=\"data row8 col2\" >0.778</td>\n",
       "      <td id=\"T_b5b46_row8_col3\" class=\"data row8 col3\" >0.782</td>\n",
       "      <td id=\"T_b5b46_row8_col4\" class=\"data row8 col4\" >0.785</td>\n",
       "      <td id=\"T_b5b46_row8_col5\" class=\"data row8 col5\" >0.793</td>\n",
       "      <td id=\"T_b5b46_row8_col6\" class=\"data row8 col6\" >0.794</td>\n",
       "      <td id=\"T_b5b46_row8_col7\" class=\"data row8 col7\" >0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row9\" class=\"row_heading level0 row9\" >diabetes</th>\n",
       "      <td id=\"T_b5b46_row9_col0\" class=\"data row9 col0\" >0.806</td>\n",
       "      <td id=\"T_b5b46_row9_col1\" class=\"data row9 col1\" >0.837</td>\n",
       "      <td id=\"T_b5b46_row9_col2\" class=\"data row9 col2\" >0.847</td>\n",
       "      <td id=\"T_b5b46_row9_col3\" class=\"data row9 col3\" >0.838</td>\n",
       "      <td id=\"T_b5b46_row9_col4\" class=\"data row9 col4\" >0.838</td>\n",
       "      <td id=\"T_b5b46_row9_col5\" class=\"data row9 col5\" >0.834</td>\n",
       "      <td id=\"T_b5b46_row9_col6\" class=\"data row9 col6\" >0.839</td>\n",
       "      <td id=\"T_b5b46_row9_col7\" class=\"data row9 col7\" >0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row10\" class=\"row_heading level0 row10\" >tic-tac-toe</th>\n",
       "      <td id=\"T_b5b46_row10_col0\" class=\"data row10 col0\" >0.986</td>\n",
       "      <td id=\"T_b5b46_row10_col1\" class=\"data row10 col1\" >0.994</td>\n",
       "      <td id=\"T_b5b46_row10_col2\" class=\"data row10 col2\" >0.998</td>\n",
       "      <td id=\"T_b5b46_row10_col3\" class=\"data row10 col3\" >0.999</td>\n",
       "      <td id=\"T_b5b46_row10_col4\" class=\"data row10 col4\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row10_col5\" class=\"data row10 col5\" >0.994</td>\n",
       "      <td id=\"T_b5b46_row10_col6\" class=\"data row10 col6\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row10_col7\" class=\"data row10 col7\" >0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row11\" class=\"row_heading level0 row11\" >vehicle</th>\n",
       "      <td id=\"T_b5b46_row11_col0\" class=\"data row11 col0\" >0.881</td>\n",
       "      <td id=\"T_b5b46_row11_col1\" class=\"data row11 col1\" >0.934</td>\n",
       "      <td id=\"T_b5b46_row11_col2\" class=\"data row11 col2\" >0.898</td>\n",
       "      <td id=\"T_b5b46_row11_col3\" class=\"data row11 col3\" >0.930</td>\n",
       "      <td id=\"T_b5b46_row11_col4\" class=\"data row11 col4\" >0.928</td>\n",
       "      <td id=\"T_b5b46_row11_col5\" class=\"data row11 col5\" >0.950</td>\n",
       "      <td id=\"T_b5b46_row11_col6\" class=\"data row11 col6\" >0.942</td>\n",
       "      <td id=\"T_b5b46_row11_col7\" class=\"data row11 col7\" >0.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row12\" class=\"row_heading level0 row12\" >eucalyptus</th>\n",
       "      <td id=\"T_b5b46_row12_col0\" class=\"data row12 col0\" >0.798</td>\n",
       "      <td id=\"T_b5b46_row12_col1\" class=\"data row12 col1\" >0.885</td>\n",
       "      <td id=\"T_b5b46_row12_col2\" class=\"data row12 col2\" >0.850</td>\n",
       "      <td id=\"T_b5b46_row12_col3\" class=\"data row12 col3\" >0.898</td>\n",
       "      <td id=\"T_b5b46_row12_col4\" class=\"data row12 col4\" >0.900</td>\n",
       "      <td id=\"T_b5b46_row12_col5\" class=\"data row12 col5\" >0.913</td>\n",
       "      <td id=\"T_b5b46_row12_col6\" class=\"data row12 col6\" >0.920</td>\n",
       "      <td id=\"T_b5b46_row12_col7\" class=\"data row12 col7\" >0.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row13\" class=\"row_heading level0 row13\" >analcatdata_authorship</th>\n",
       "      <td id=\"T_b5b46_row13_col0\" class=\"data row13 col0\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row13_col1\" class=\"data row13 col1\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row13_col2\" class=\"data row13 col2\" >0.983</td>\n",
       "      <td id=\"T_b5b46_row13_col3\" class=\"data row13 col3\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row13_col4\" class=\"data row13 col4\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row13_col5\" class=\"data row13 col5\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row13_col6\" class=\"data row13 col6\" >0.999</td>\n",
       "      <td id=\"T_b5b46_row13_col7\" class=\"data row13 col7\" >1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row14\" class=\"row_heading level0 row14\" >analcatdata_dmft</th>\n",
       "      <td id=\"T_b5b46_row14_col0\" class=\"data row14 col0\" >0.543</td>\n",
       "      <td id=\"T_b5b46_row14_col1\" class=\"data row14 col1\" >0.566</td>\n",
       "      <td id=\"T_b5b46_row14_col2\" class=\"data row14 col2\" >0.572</td>\n",
       "      <td id=\"T_b5b46_row14_col3\" class=\"data row14 col3\" >0.559</td>\n",
       "      <td id=\"T_b5b46_row14_col4\" class=\"data row14 col4\" >0.574</td>\n",
       "      <td id=\"T_b5b46_row14_col5\" class=\"data row14 col5\" >0.575</td>\n",
       "      <td id=\"T_b5b46_row14_col6\" class=\"data row14 col6\" >0.566</td>\n",
       "      <td id=\"T_b5b46_row14_col7\" class=\"data row14 col7\" >0.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row15\" class=\"row_heading level0 row15\" >pc4</th>\n",
       "      <td id=\"T_b5b46_row15_col0\" class=\"data row15 col0\" >0.822</td>\n",
       "      <td id=\"T_b5b46_row15_col1\" class=\"data row15 col1\" >0.891</td>\n",
       "      <td id=\"T_b5b46_row15_col2\" class=\"data row15 col2\" >0.899</td>\n",
       "      <td id=\"T_b5b46_row15_col3\" class=\"data row15 col3\" >0.941</td>\n",
       "      <td id=\"T_b5b46_row15_col4\" class=\"data row15 col4\" >0.929</td>\n",
       "      <td id=\"T_b5b46_row15_col5\" class=\"data row15 col5\" >0.933</td>\n",
       "      <td id=\"T_b5b46_row15_col6\" class=\"data row15 col6\" >0.943</td>\n",
       "      <td id=\"T_b5b46_row15_col7\" class=\"data row15 col7\" >0.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row16\" class=\"row_heading level0 row16\" >pc3</th>\n",
       "      <td id=\"T_b5b46_row16_col0\" class=\"data row16 col0\" >0.753</td>\n",
       "      <td id=\"T_b5b46_row16_col1\" class=\"data row16 col1\" >0.772</td>\n",
       "      <td id=\"T_b5b46_row16_col2\" class=\"data row16 col2\" >0.795</td>\n",
       "      <td id=\"T_b5b46_row16_col3\" class=\"data row16 col3\" >0.825</td>\n",
       "      <td id=\"T_b5b46_row16_col4\" class=\"data row16 col4\" >0.829</td>\n",
       "      <td id=\"T_b5b46_row16_col5\" class=\"data row16 col5\" >0.827</td>\n",
       "      <td id=\"T_b5b46_row16_col6\" class=\"data row16 col6\" >0.828</td>\n",
       "      <td id=\"T_b5b46_row16_col7\" class=\"data row16 col7\" >0.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row17\" class=\"row_heading level0 row17\" >kc2</th>\n",
       "      <td id=\"T_b5b46_row17_col0\" class=\"data row17 col0\" >0.790</td>\n",
       "      <td id=\"T_b5b46_row17_col1\" class=\"data row17 col1\" >0.826</td>\n",
       "      <td id=\"T_b5b46_row17_col2\" class=\"data row17 col2\" >0.828</td>\n",
       "      <td id=\"T_b5b46_row17_col3\" class=\"data row17 col3\" >0.832</td>\n",
       "      <td id=\"T_b5b46_row17_col4\" class=\"data row17 col4\" >0.823</td>\n",
       "      <td id=\"T_b5b46_row17_col5\" class=\"data row17 col5\" >0.831</td>\n",
       "      <td id=\"T_b5b46_row17_col6\" class=\"data row17 col6\" >0.824</td>\n",
       "      <td id=\"T_b5b46_row17_col7\" class=\"data row17 col7\" >0.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row18\" class=\"row_heading level0 row18\" >pc1</th>\n",
       "      <td id=\"T_b5b46_row18_col0\" class=\"data row18 col0\" >0.785</td>\n",
       "      <td id=\"T_b5b46_row18_col1\" class=\"data row18 col1\" >0.821</td>\n",
       "      <td id=\"T_b5b46_row18_col2\" class=\"data row18 col2\" >0.824</td>\n",
       "      <td id=\"T_b5b46_row18_col3\" class=\"data row18 col3\" >0.860</td>\n",
       "      <td id=\"T_b5b46_row18_col4\" class=\"data row18 col4\" >0.849</td>\n",
       "      <td id=\"T_b5b46_row18_col5\" class=\"data row18 col5\" >0.853</td>\n",
       "      <td id=\"T_b5b46_row18_col6\" class=\"data row18 col6\" >0.858</td>\n",
       "      <td id=\"T_b5b46_row18_col7\" class=\"data row18 col7\" >0.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row19\" class=\"row_heading level0 row19\" >banknote-authentication</th>\n",
       "      <td id=\"T_b5b46_row19_col0\" class=\"data row19 col0\" >0.999</td>\n",
       "      <td id=\"T_b5b46_row19_col1\" class=\"data row19 col1\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row19_col2\" class=\"data row19 col2\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row19_col3\" class=\"data row19 col3\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row19_col4\" class=\"data row19 col4\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row19_col5\" class=\"data row19 col5\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row19_col6\" class=\"data row19 col6\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row19_col7\" class=\"data row19 col7\" >1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row20\" class=\"row_heading level0 row20\" >blood-transfus..</th>\n",
       "      <td id=\"T_b5b46_row20_col0\" class=\"data row20 col0\" >0.708</td>\n",
       "      <td id=\"T_b5b46_row20_col1\" class=\"data row20 col1\" >0.742</td>\n",
       "      <td id=\"T_b5b46_row20_col2\" class=\"data row20 col2\" >0.757</td>\n",
       "      <td id=\"T_b5b46_row20_col3\" class=\"data row20 col3\" >0.743</td>\n",
       "      <td id=\"T_b5b46_row20_col4\" class=\"data row20 col4\" >0.731</td>\n",
       "      <td id=\"T_b5b46_row20_col5\" class=\"data row20 col5\" >0.750</td>\n",
       "      <td id=\"T_b5b46_row20_col6\" class=\"data row20 col6\" >0.737</td>\n",
       "      <td id=\"T_b5b46_row20_col7\" class=\"data row20 col7\" >0.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row21\" class=\"row_heading level0 row21\" >ilpd</th>\n",
       "      <td id=\"T_b5b46_row21_col0\" class=\"data row21 col0\" >0.646</td>\n",
       "      <td id=\"T_b5b46_row21_col1\" class=\"data row21 col1\" >0.709</td>\n",
       "      <td id=\"T_b5b46_row21_col2\" class=\"data row21 col2\" >0.663</td>\n",
       "      <td id=\"T_b5b46_row21_col3\" class=\"data row21 col3\" >0.728</td>\n",
       "      <td id=\"T_b5b46_row21_col4\" class=\"data row21 col4\" >0.717</td>\n",
       "      <td id=\"T_b5b46_row21_col5\" class=\"data row21 col5\" >0.721</td>\n",
       "      <td id=\"T_b5b46_row21_col6\" class=\"data row21 col6\" >0.723</td>\n",
       "      <td id=\"T_b5b46_row21_col7\" class=\"data row21 col7\" >0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row22\" class=\"row_heading level0 row22\" >qsar-biodeg</th>\n",
       "      <td id=\"T_b5b46_row22_col0\" class=\"data row22 col0\" >0.892</td>\n",
       "      <td id=\"T_b5b46_row22_col1\" class=\"data row22 col1\" >0.920</td>\n",
       "      <td id=\"T_b5b46_row22_col2\" class=\"data row22 col2\" >0.922</td>\n",
       "      <td id=\"T_b5b46_row22_col3\" class=\"data row22 col3\" >0.922</td>\n",
       "      <td id=\"T_b5b46_row22_col4\" class=\"data row22 col4\" >0.919</td>\n",
       "      <td id=\"T_b5b46_row22_col5\" class=\"data row22 col5\" >0.925</td>\n",
       "      <td id=\"T_b5b46_row22_col6\" class=\"data row22 col6\" >0.928</td>\n",
       "      <td id=\"T_b5b46_row22_col7\" class=\"data row22 col7\" >0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row23\" class=\"row_heading level0 row23\" >wdbc</th>\n",
       "      <td id=\"T_b5b46_row23_col0\" class=\"data row23 col0\" >0.991</td>\n",
       "      <td id=\"T_b5b46_row23_col1\" class=\"data row23 col1\" >0.995</td>\n",
       "      <td id=\"T_b5b46_row23_col2\" class=\"data row23 col2\" >0.995</td>\n",
       "      <td id=\"T_b5b46_row23_col3\" class=\"data row23 col3\" >0.993</td>\n",
       "      <td id=\"T_b5b46_row23_col4\" class=\"data row23 col4\" >0.990</td>\n",
       "      <td id=\"T_b5b46_row23_col5\" class=\"data row23 col5\" >0.995</td>\n",
       "      <td id=\"T_b5b46_row23_col6\" class=\"data row23 col6\" >0.996</td>\n",
       "      <td id=\"T_b5b46_row23_col7\" class=\"data row23 col7\" >0.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row24\" class=\"row_heading level0 row24\" >cylinder-bands</th>\n",
       "      <td id=\"T_b5b46_row24_col0\" class=\"data row24 col0\" >0.781</td>\n",
       "      <td id=\"T_b5b46_row24_col1\" class=\"data row24 col1\" >0.823</td>\n",
       "      <td id=\"T_b5b46_row24_col2\" class=\"data row24 col2\" >0.839</td>\n",
       "      <td id=\"T_b5b46_row24_col3\" class=\"data row24 col3\" >0.876</td>\n",
       "      <td id=\"T_b5b46_row24_col4\" class=\"data row24 col4\" >0.878</td>\n",
       "      <td id=\"T_b5b46_row24_col5\" class=\"data row24 col5\" >0.872</td>\n",
       "      <td id=\"T_b5b46_row24_col6\" class=\"data row24 col6\" >0.888</td>\n",
       "      <td id=\"T_b5b46_row24_col7\" class=\"data row24 col7\" >0.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row25\" class=\"row_heading level0 row25\" >dresses-sales</th>\n",
       "      <td id=\"T_b5b46_row25_col0\" class=\"data row25 col0\" >0.557</td>\n",
       "      <td id=\"T_b5b46_row25_col1\" class=\"data row25 col1\" >0.609</td>\n",
       "      <td id=\"T_b5b46_row25_col2\" class=\"data row25 col2\" >0.624</td>\n",
       "      <td id=\"T_b5b46_row25_col3\" class=\"data row25 col3\" >0.570</td>\n",
       "      <td id=\"T_b5b46_row25_col4\" class=\"data row25 col4\" >0.582</td>\n",
       "      <td id=\"T_b5b46_row25_col5\" class=\"data row25 col5\" >0.571</td>\n",
       "      <td id=\"T_b5b46_row25_col6\" class=\"data row25 col6\" >0.551</td>\n",
       "      <td id=\"T_b5b46_row25_col7\" class=\"data row25 col7\" >0.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row26\" class=\"row_heading level0 row26\" >MiceProtein</th>\n",
       "      <td id=\"T_b5b46_row26_col0\" class=\"data row26 col0\" >0.997</td>\n",
       "      <td id=\"T_b5b46_row26_col1\" class=\"data row26 col1\" >0.998</td>\n",
       "      <td id=\"T_b5b46_row26_col2\" class=\"data row26 col2\" >0.988</td>\n",
       "      <td id=\"T_b5b46_row26_col3\" class=\"data row26 col3\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row26_col4\" class=\"data row26 col4\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row26_col5\" class=\"data row26 col5\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row26_col6\" class=\"data row26 col6\" >0.801</td>\n",
       "      <td id=\"T_b5b46_row26_col7\" class=\"data row26 col7\" >1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row27\" class=\"row_heading level0 row27\" >car</th>\n",
       "      <td id=\"T_b5b46_row27_col0\" class=\"data row27 col0\" >0.924</td>\n",
       "      <td id=\"T_b5b46_row27_col1\" class=\"data row27 col1\" >0.978</td>\n",
       "      <td id=\"T_b5b46_row27_col2\" class=\"data row27 col2\" >0.895</td>\n",
       "      <td id=\"T_b5b46_row27_col3\" class=\"data row27 col3\" >0.996</td>\n",
       "      <td id=\"T_b5b46_row27_col4\" class=\"data row27 col4\" >0.995</td>\n",
       "      <td id=\"T_b5b46_row27_col5\" class=\"data row27 col5\" >0.998</td>\n",
       "      <td id=\"T_b5b46_row27_col6\" class=\"data row27 col6\" >0.997</td>\n",
       "      <td id=\"T_b5b46_row27_col7\" class=\"data row27 col7\" >0.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row28\" class=\"row_heading level0 row28\" >steel-plates-fault</th>\n",
       "      <td id=\"T_b5b46_row28_col0\" class=\"data row28 col0\" >0.920</td>\n",
       "      <td id=\"T_b5b46_row28_col1\" class=\"data row28 col1\" >0.935</td>\n",
       "      <td id=\"T_b5b46_row28_col2\" class=\"data row28 col2\" >0.939</td>\n",
       "      <td id=\"T_b5b46_row28_col3\" class=\"data row28 col3\" >0.966</td>\n",
       "      <td id=\"T_b5b46_row28_col4\" class=\"data row28 col4\" >0.966</td>\n",
       "      <td id=\"T_b5b46_row28_col5\" class=\"data row28 col5\" >0.969</td>\n",
       "      <td id=\"T_b5b46_row28_col6\" class=\"data row28 col6\" >0.967</td>\n",
       "      <td id=\"T_b5b46_row28_col7\" class=\"data row28 col7\" >0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row29\" class=\"row_heading level0 row29\" >climate-model-simulation-crashes</th>\n",
       "      <td id=\"T_b5b46_row29_col0\" class=\"data row29 col0\" >0.849</td>\n",
       "      <td id=\"T_b5b46_row29_col1\" class=\"data row29 col1\" >0.932</td>\n",
       "      <td id=\"T_b5b46_row29_col2\" class=\"data row29 col2\" >0.941</td>\n",
       "      <td id=\"T_b5b46_row29_col3\" class=\"data row29 col3\" >0.934</td>\n",
       "      <td id=\"T_b5b46_row29_col4\" class=\"data row29 col4\" >0.925</td>\n",
       "      <td id=\"T_b5b46_row29_col5\" class=\"data row29 col5\" >0.929</td>\n",
       "      <td id=\"T_b5b46_row29_col6\" class=\"data row29 col6\" >0.939</td>\n",
       "      <td id=\"T_b5b46_row29_col7\" class=\"data row29 col7\" >0.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row30\" class=\"row_heading level0 row30\" >Wins AUC OVO</th>\n",
       "      <td id=\"T_b5b46_row30_col0\" class=\"data row30 col0\" >0.000</td>\n",
       "      <td id=\"T_b5b46_row30_col1\" class=\"data row30 col1\" >0.000</td>\n",
       "      <td id=\"T_b5b46_row30_col2\" class=\"data row30 col2\" >3.000</td>\n",
       "      <td id=\"T_b5b46_row30_col3\" class=\"data row30 col3\" >0.000</td>\n",
       "      <td id=\"T_b5b46_row30_col4\" class=\"data row30 col4\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row30_col5\" class=\"data row30 col5\" >4.000</td>\n",
       "      <td id=\"T_b5b46_row30_col6\" class=\"data row30 col6\" >5.000</td>\n",
       "      <td id=\"T_b5b46_row30_col7\" class=\"data row30 col7\" >10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row31\" class=\"row_heading level0 row31\" >Wins Acc.</th>\n",
       "      <td id=\"T_b5b46_row31_col0\" class=\"data row31 col0\" >0.000</td>\n",
       "      <td id=\"T_b5b46_row31_col1\" class=\"data row31 col1\" >2.000</td>\n",
       "      <td id=\"T_b5b46_row31_col2\" class=\"data row31 col2\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row31_col3\" class=\"data row31 col3\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row31_col4\" class=\"data row31 col4\" >3.000</td>\n",
       "      <td id=\"T_b5b46_row31_col5\" class=\"data row31 col5\" >4.000</td>\n",
       "      <td id=\"T_b5b46_row31_col6\" class=\"data row31 col6\" >5.000</td>\n",
       "      <td id=\"T_b5b46_row31_col7\" class=\"data row31 col7\" >11.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row32\" class=\"row_heading level0 row32\" >Wins CE</th>\n",
       "      <td id=\"T_b5b46_row32_col0\" class=\"data row32 col0\" >0.000</td>\n",
       "      <td id=\"T_b5b46_row32_col1\" class=\"data row32 col1\" >0.000</td>\n",
       "      <td id=\"T_b5b46_row32_col2\" class=\"data row32 col2\" >2.000</td>\n",
       "      <td id=\"T_b5b46_row32_col3\" class=\"data row32 col3\" >1.000</td>\n",
       "      <td id=\"T_b5b46_row32_col4\" class=\"data row32 col4\" >2.000</td>\n",
       "      <td id=\"T_b5b46_row32_col5\" class=\"data row32 col5\" >3.000</td>\n",
       "      <td id=\"T_b5b46_row32_col6\" class=\"data row32 col6\" >9.000</td>\n",
       "      <td id=\"T_b5b46_row32_col7\" class=\"data row32 col7\" >12.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row33\" class=\"row_heading level0 row33\" >Win/T/L AUC vs Us</th>\n",
       "      <td id=\"T_b5b46_row33_col0\" class=\"data row33 col0\" >2/0/28</td>\n",
       "      <td id=\"T_b5b46_row33_col1\" class=\"data row33 col1\" >3/0/27</td>\n",
       "      <td id=\"T_b5b46_row33_col2\" class=\"data row33 col2\" >4/1/25</td>\n",
       "      <td id=\"T_b5b46_row33_col3\" class=\"data row33 col3\" >7/0/23</td>\n",
       "      <td id=\"T_b5b46_row33_col4\" class=\"data row33 col4\" >5/0/25</td>\n",
       "      <td id=\"T_b5b46_row33_col5\" class=\"data row33 col5\" >11/1/18</td>\n",
       "      <td id=\"T_b5b46_row33_col6\" class=\"data row33 col6\" >14/1/15</td>\n",
       "      <td id=\"T_b5b46_row33_col7\" class=\"data row33 col7\" >0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row34\" class=\"row_heading level0 row34\" >Win/T/L Acc vs Us</th>\n",
       "      <td id=\"T_b5b46_row34_col0\" class=\"data row34 col0\" >2/0/28</td>\n",
       "      <td id=\"T_b5b46_row34_col1\" class=\"data row34 col1\" >5/0/25</td>\n",
       "      <td id=\"T_b5b46_row34_col2\" class=\"data row34 col2\" >6/1/23</td>\n",
       "      <td id=\"T_b5b46_row34_col3\" class=\"data row34 col3\" >8/0/22</td>\n",
       "      <td id=\"T_b5b46_row34_col4\" class=\"data row34 col4\" >10/0/20</td>\n",
       "      <td id=\"T_b5b46_row34_col5\" class=\"data row34 col5\" >11/1/18</td>\n",
       "      <td id=\"T_b5b46_row34_col6\" class=\"data row34 col6\" >11/0/19</td>\n",
       "      <td id=\"T_b5b46_row34_col7\" class=\"data row34 col7\" >0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row35\" class=\"row_heading level0 row35\" >Win/T/L CE vs Us</th>\n",
       "      <td id=\"T_b5b46_row35_col0\" class=\"data row35 col0\" >0/0/30</td>\n",
       "      <td id=\"T_b5b46_row35_col1\" class=\"data row35 col1\" >1/0/29</td>\n",
       "      <td id=\"T_b5b46_row35_col2\" class=\"data row35 col2\" >4/0/26</td>\n",
       "      <td id=\"T_b5b46_row35_col3\" class=\"data row35 col3\" >5/0/25</td>\n",
       "      <td id=\"T_b5b46_row35_col4\" class=\"data row35 col4\" >7/0/23</td>\n",
       "      <td id=\"T_b5b46_row35_col5\" class=\"data row35 col5\" >8/0/22</td>\n",
       "      <td id=\"T_b5b46_row35_col6\" class=\"data row35 col6\" >14/0/16</td>\n",
       "      <td id=\"T_b5b46_row35_col7\" class=\"data row35 col7\" >0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row36\" class=\"row_heading level0 row36\" >Mean AUC OVO</th>\n",
       "      <td id=\"T_b5b46_row36_col0\" class=\"data row36 col0\" >0.849</td>\n",
       "      <td id=\"T_b5b46_row36_col1\" class=\"data row36 col1\" >0.879</td>\n",
       "      <td id=\"T_b5b46_row36_col2\" class=\"data row36 col2\" >0.873</td>\n",
       "      <td id=\"T_b5b46_row36_col3\" class=\"data row36 col3\" >0.890</td>\n",
       "      <td id=\"T_b5b46_row36_col4\" class=\"data row36 col4\" >0.891</td>\n",
       "      <td id=\"T_b5b46_row36_col5\" class=\"data row36 col5\" >0.894</td>\n",
       "      <td id=\"T_b5b46_row36_col6\" class=\"data row36 col6\" >0.882</td>\n",
       "      <td id=\"T_b5b46_row36_col7\" class=\"data row36 col7\" >0.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row37\" class=\"row_heading level0 row37\" >Mean AUC OVO Stds</th>\n",
       "      <td id=\"T_b5b46_row37_col0\" class=\"data row37 col0\" >0.015</td>\n",
       "      <td id=\"T_b5b46_row37_col1\" class=\"data row37 col1\" >0.011</td>\n",
       "      <td id=\"T_b5b46_row37_col2\" class=\"data row37 col2\" >0.027</td>\n",
       "      <td id=\"T_b5b46_row37_col3\" class=\"data row37 col3\" >0.011</td>\n",
       "      <td id=\"T_b5b46_row37_col4\" class=\"data row37 col4\" >0.011</td>\n",
       "      <td id=\"T_b5b46_row37_col5\" class=\"data row37 col5\" >0.010</td>\n",
       "      <td id=\"T_b5b46_row37_col6\" class=\"data row37 col6\" >0.039</td>\n",
       "      <td id=\"T_b5b46_row37_col7\" class=\"data row37 col7\" >0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row38\" class=\"row_heading level0 row38\" >Mean Acc.</th>\n",
       "      <td id=\"T_b5b46_row38_col0\" class=\"data row38 col0\" >0.785</td>\n",
       "      <td id=\"T_b5b46_row38_col1\" class=\"data row38 col1\" >0.808</td>\n",
       "      <td id=\"T_b5b46_row38_col2\" class=\"data row38 col2\" >0.786</td>\n",
       "      <td id=\"T_b5b46_row38_col3\" class=\"data row38 col3\" >0.818</td>\n",
       "      <td id=\"T_b5b46_row38_col4\" class=\"data row38 col4\" >0.821</td>\n",
       "      <td id=\"T_b5b46_row38_col5\" class=\"data row38 col5\" >0.821</td>\n",
       "      <td id=\"T_b5b46_row38_col6\" class=\"data row38 col6\" >0.818</td>\n",
       "      <td id=\"T_b5b46_row38_col7\" class=\"data row38 col7\" >0.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row39\" class=\"row_heading level0 row39\" >Mean Acc. Stds</th>\n",
       "      <td id=\"T_b5b46_row39_col0\" class=\"data row39 col0\" >0.016</td>\n",
       "      <td id=\"T_b5b46_row39_col1\" class=\"data row39 col1\" >0.013</td>\n",
       "      <td id=\"T_b5b46_row39_col2\" class=\"data row39 col2\" >0.058</td>\n",
       "      <td id=\"T_b5b46_row39_col3\" class=\"data row39 col3\" >0.011</td>\n",
       "      <td id=\"T_b5b46_row39_col4\" class=\"data row39 col4\" >0.013</td>\n",
       "      <td id=\"T_b5b46_row39_col5\" class=\"data row39 col5\" >0.016</td>\n",
       "      <td id=\"T_b5b46_row39_col6\" class=\"data row39 col6\" >0.040</td>\n",
       "      <td id=\"T_b5b46_row39_col7\" class=\"data row39 col7\" >0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row40\" class=\"row_heading level0 row40\" >Mean CE</th>\n",
       "      <td id=\"T_b5b46_row40_col0\" class=\"data row40 col0\" >1.791</td>\n",
       "      <td id=\"T_b5b46_row40_col1\" class=\"data row40 col1\" >0.770</td>\n",
       "      <td id=\"T_b5b46_row40_col2\" class=\"data row40 col2\" >0.849</td>\n",
       "      <td id=\"T_b5b46_row40_col3\" class=\"data row40 col3\" >0.767</td>\n",
       "      <td id=\"T_b5b46_row40_col4\" class=\"data row40 col4\" >0.758</td>\n",
       "      <td id=\"T_b5b46_row40_col5\" class=\"data row40 col5\" >0.815</td>\n",
       "      <td id=\"T_b5b46_row40_col6\" class=\"data row40 col6\" >0.730</td>\n",
       "      <td id=\"T_b5b46_row40_col7\" class=\"data row40 col7\" >0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row41\" class=\"row_heading level0 row41\" >Mean CE Stds</th>\n",
       "      <td id=\"T_b5b46_row41_col0\" class=\"data row41 col0\" >1.143</td>\n",
       "      <td id=\"T_b5b46_row41_col1\" class=\"data row41 col1\" >0.024</td>\n",
       "      <td id=\"T_b5b46_row41_col2\" class=\"data row41 col2\" >0.052</td>\n",
       "      <td id=\"T_b5b46_row41_col3\" class=\"data row41 col3\" >0.062</td>\n",
       "      <td id=\"T_b5b46_row41_col4\" class=\"data row41 col4\" >0.047</td>\n",
       "      <td id=\"T_b5b46_row41_col5\" class=\"data row41 col5\" >0.060</td>\n",
       "      <td id=\"T_b5b46_row41_col6\" class=\"data row41 col6\" >0.037</td>\n",
       "      <td id=\"T_b5b46_row41_col7\" class=\"data row41 col7\" >0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row42\" class=\"row_heading level0 row42\" >M. rank AUC OVO</th>\n",
       "      <td id=\"T_b5b46_row42_col0\" class=\"data row42 col0\" >7.433</td>\n",
       "      <td id=\"T_b5b46_row42_col1\" class=\"data row42 col1\" >5.400</td>\n",
       "      <td id=\"T_b5b46_row42_col2\" class=\"data row42 col2\" >5.300</td>\n",
       "      <td id=\"T_b5b46_row42_col3\" class=\"data row42 col3\" >4.067</td>\n",
       "      <td id=\"T_b5b46_row42_col4\" class=\"data row42 col4\" >4.533</td>\n",
       "      <td id=\"T_b5b46_row42_col5\" class=\"data row42 col5\" >3.133</td>\n",
       "      <td id=\"T_b5b46_row42_col6\" class=\"data row42 col6\" >3.317</td>\n",
       "      <td id=\"T_b5b46_row42_col7\" class=\"data row42 col7\" >2.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row43\" class=\"row_heading level0 row43\" >Mean rank Acc.</th>\n",
       "      <td id=\"T_b5b46_row43_col0\" class=\"data row43 col0\" >6.867</td>\n",
       "      <td id=\"T_b5b46_row43_col1\" class=\"data row43 col1\" >5.050</td>\n",
       "      <td id=\"T_b5b46_row43_col2\" class=\"data row43 col2\" >5.300</td>\n",
       "      <td id=\"T_b5b46_row43_col3\" class=\"data row43 col3\" >4.100</td>\n",
       "      <td id=\"T_b5b46_row43_col4\" class=\"data row43 col4\" >4.433</td>\n",
       "      <td id=\"T_b5b46_row43_col5\" class=\"data row43 col5\" >3.983</td>\n",
       "      <td id=\"T_b5b46_row43_col6\" class=\"data row43 col6\" >3.450</td>\n",
       "      <td id=\"T_b5b46_row43_col7\" class=\"data row43 col7\" >2.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row44\" class=\"row_heading level0 row44\" >Mean rank CE</th>\n",
       "      <td id=\"T_b5b46_row44_col0\" class=\"data row44 col0\" >7.150</td>\n",
       "      <td id=\"T_b5b46_row44_col1\" class=\"data row44 col1\" >5.133</td>\n",
       "      <td id=\"T_b5b46_row44_col2\" class=\"data row44 col2\" >5.617</td>\n",
       "      <td id=\"T_b5b46_row44_col3\" class=\"data row44 col3\" >4.467</td>\n",
       "      <td id=\"T_b5b46_row44_col4\" class=\"data row44 col4\" >4.250</td>\n",
       "      <td id=\"T_b5b46_row44_col5\" class=\"data row44 col5\" >4.617</td>\n",
       "      <td id=\"T_b5b46_row44_col6\" class=\"data row44 col6\" >2.433</td>\n",
       "      <td id=\"T_b5b46_row44_col7\" class=\"data row44 col7\" >2.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5b46_level0_row45\" class=\"row_heading level0 row45\" >Mean time (s)</th>\n",
       "      <td id=\"T_b5b46_row45_col0\" class=\"data row45 col0\" >0.500</td>\n",
       "      <td id=\"T_b5b46_row45_col1\" class=\"data row45 col1\" >60</td>\n",
       "      <td id=\"T_b5b46_row45_col2\" class=\"data row45 col2\" >2836.718</td>\n",
       "      <td id=\"T_b5b46_row45_col3\" class=\"data row45 col3\" >3717.076</td>\n",
       "      <td id=\"T_b5b46_row45_col4\" class=\"data row45 col4\" >3289.989</td>\n",
       "      <td id=\"T_b5b46_row45_col5\" class=\"data row45 col5\" >3601.100</td>\n",
       "      <td id=\"T_b5b46_row45_col6\" class=\"data row45 col6\" >3130.262</td>\n",
       "      <td id=\"T_b5b46_row45_col7\" class=\"data row45 col7\" >0.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f35fb7a5be0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = roc_matrix.copy()\n",
    "#table = roc_ovr_matrix.copy()\n",
    "#table = acc_matrix.copy()\n",
    "#table = cross_entropy_matrix.copy()\n",
    "\n",
    "#table = table_acc\n",
    "table.index = [get_suffix(i, k) for i, k in enumerate(table.index[0:table.shape[0]])]\n",
    "\n",
    "table.loc['Wins AUC OVO'] = rocs_wins.values\n",
    "#table.loc['Mean AUC OVR'] = roc_ovr_matrix.mean(skipna=True)\n",
    "table.loc['Wins Acc.'] = acc_wins.values\n",
    "#table.loc['Mean Bal. Acc.'] = balanced_acc_matrix.mean()\n",
    "table.loc['Wins CE'] = cross_entropy_wins.values\n",
    "\n",
    "table.loc['Win/T/L AUC vs Us'] = [\"{:d}/{:d}/{:d}\".format(w, t, l) for w,t,l in zip(wins_roc_vs_us[-1, :], ties_roc_vs_us[-1, :], losses_roc_vs_us[-1, :])]\n",
    "table.loc['Win/T/L Acc vs Us'] = [\"{:d}/{:d}/{:d}\".format(w, t, l) for w,t,l in zip(wins_acc_vs_us[-1, :], ties_acc_vs_us[-1, :], losses_acc_vs_us[-1, :])]\n",
    "table.loc['Win/T/L CE vs Us'] = [\"{:d}/{:d}/{:d}\".format(w, t, l) for w,t,l in zip(wins_ce_vs_us[-1, :], ties_ce_vs_us[-1, :], losses_ce_vs_us[-1, :])]\n",
    "\n",
    "table.loc['Mean AUC OVO'] = roc_matrix.mean(skipna=True)\n",
    "table.loc['Mean AUC OVO Stds'] = roc_matrix_stds.mean(skipna=True)\n",
    "\n",
    "#table.loc['Mean AUC OVR'] = roc_ovr_matrix.mean(skipna=True)\n",
    "table.loc['Mean Acc.'] = acc_matrix.mean()\n",
    "table.loc['Mean Acc. Stds'] = acc_matrix_stds.mean(skipna=True)\n",
    "\n",
    "#table.loc['Mean Bal. Acc.'] = balanced_acc_matrix.mean()\n",
    "table.loc['Mean CE'] = cross_entropy_matrix.mean()\n",
    "table.loc['Mean CE Stds'] = cross_entropy_matrix_stds.mean()\n",
    "\n",
    "table.loc['M. rank AUC OVO'] = roc_rank.values\n",
    "#table.loc['Mean rank AUC OVR'] = roc_ovr_rank.values\n",
    "table.loc['Mean rank Acc.'] = acc_rank.values\n",
    "#table.loc['Mean rank Bal. Acc.'] = balanced_acc_rank.values\n",
    "table.loc['Mean rank CE'] = cross_entropy_rank.values\n",
    "\n",
    "table.loc['Mean time (s)'] = time_matrix.mean()\n",
    "table.loc['Mean time (s)', 'knn'] = 0.5\n",
    "table.loc['Mean time (s)', 'logistic'] = 60\n",
    "\n",
    "table = table[['knn', 'logistic', 'gp', 'catboost', 'xgb', 'autosklearn2', 'autogluon', 'transformer']]\n",
    "rename(table).round(decimals=3).style.highlight_max(axis = 1, props= 'font-weight: bold;').format(precision=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bold_extreme_values(data, format_string=\"%.3g\", max_=True):\n",
    "    data = data.astype(float).round(3)\n",
    "    if max_:\n",
    "        extrema = data != data.max()\n",
    "    else:\n",
    "        extrema = data != data.min()\n",
    "    bolded = data.apply(lambda x : \"\\\\textbf{%s}\" % format_string % x)\n",
    "    formatted = data.apply(lambda x : format_string % x)\n",
    "    return formatted.where(extrema, bolded) \n",
    "\n",
    "def to_str(data, format_string=\"%.3g\"):\n",
    "    formatted = data.apply(lambda x : format_string % x)\n",
    "    return formatted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KNN</th>\n",
       "      <th>Log. Regr.</th>\n",
       "      <th>GP (RBF)</th>\n",
       "      <th>Catboost</th>\n",
       "      <th>XGB</th>\n",
       "      <th>Autosklearn2</th>\n",
       "      <th>Autogluon</th>\n",
       "      <th>Tabular PFN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>balance-scale</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.994</td>\n",
       "      <td>\\textbf{0.997}</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-fourier</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.983</td>\n",
       "      <td>\\textbf{0.984}</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breast-w</th>\n",
       "      <td>0.984</td>\n",
       "      <td>\\textbf{0.994}</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.99</td>\n",
       "      <td>\\textbf{0.994}</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-karhunen</th>\n",
       "      <td>0.994</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.915</td>\n",
       "      <td>\\textbf{0.999}</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.998</td>\n",
       "      <td>\\textbf{0.999}</td>\n",
       "      <td>0.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-morphological</th>\n",
       "      <td>0.951</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.967</td>\n",
       "      <td>\\textbf{0.97}</td>\n",
       "      <td>0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-zernike</th>\n",
       "      <td>0.976</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.794</td>\n",
       "      <td>\\textbf{0.983}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmc</th>\n",
       "      <td>0.634</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.73</td>\n",
       "      <td>\\textbf{0.738}</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit-approval</th>\n",
       "      <td>0.914</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.939</td>\n",
       "      <td>\\textbf{0.942}</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit-g</th>\n",
       "      <td>0.726</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.793</td>\n",
       "      <td>\\textbf{0.794}</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>0.806</td>\n",
       "      <td>0.837</td>\n",
       "      <td>\\textbf{0.847}</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic-tac-toe</th>\n",
       "      <td>0.986</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.999</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>0.994</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle</th>\n",
       "      <td>0.881</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.942</td>\n",
       "      <td>\\textbf{0.958}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eucalyptus</th>\n",
       "      <td>0.798</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.92</td>\n",
       "      <td>\\textbf{0.927}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analcatdata_authorship</th>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>0.983</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>0.999</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analcatdata_dmft</th>\n",
       "      <td>0.543</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.566</td>\n",
       "      <td>\\textbf{0.58}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc4</th>\n",
       "      <td>0.822</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.933</td>\n",
       "      <td>\\textbf{0.943}</td>\n",
       "      <td>0.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc3</th>\n",
       "      <td>0.753</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.828</td>\n",
       "      <td>\\textbf{0.834}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kc2</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.824</td>\n",
       "      <td>\\textbf{0.836}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc1</th>\n",
       "      <td>0.785</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.858</td>\n",
       "      <td>\\textbf{0.877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banknote-authentication</th>\n",
       "      <td>0.999</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood-transfus..</th>\n",
       "      <td>0.708</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.737</td>\n",
       "      <td>\\textbf{0.759}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilpd</th>\n",
       "      <td>0.646</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.723</td>\n",
       "      <td>\\textbf{0.736}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qsar-biodeg</th>\n",
       "      <td>0.892</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.928</td>\n",
       "      <td>\\textbf{0.933}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wdbc</th>\n",
       "      <td>0.991</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>\\textbf{0.996}</td>\n",
       "      <td>\\textbf{0.996}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinder-bands</th>\n",
       "      <td>0.781</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.872</td>\n",
       "      <td>\\textbf{0.888}</td>\n",
       "      <td>0.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dresses-sales</th>\n",
       "      <td>0.557</td>\n",
       "      <td>0.609</td>\n",
       "      <td>\\textbf{0.624}</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiceProtein</th>\n",
       "      <td>0.997</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.988</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "      <td>0.801</td>\n",
       "      <td>\\textbf{1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car</th>\n",
       "      <td>0.924</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.995</td>\n",
       "      <td>\\textbf{0.998}</td>\n",
       "      <td>0.997</td>\n",
       "      <td>0.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>steel-plates-fault</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.966</td>\n",
       "      <td>\\textbf{0.969}</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>climate-model-simulation-crashes</th>\n",
       "      <td>0.849</td>\n",
       "      <td>0.932</td>\n",
       "      <td>\\textbf{0.941}</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wins AUC OVO</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>\\textbf{10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wins Acc.</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>\\textbf{11}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wins CE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>\\textbf{12}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win/T/L AUC vs Us</th>\n",
       "      <td>2/0/28</td>\n",
       "      <td>3/0/27</td>\n",
       "      <td>4/1/25</td>\n",
       "      <td>7/0/23</td>\n",
       "      <td>5/0/25</td>\n",
       "      <td>11/1/18</td>\n",
       "      <td>14/1/15</td>\n",
       "      <td>0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win/T/L Acc vs Us</th>\n",
       "      <td>2/0/28</td>\n",
       "      <td>5/0/25</td>\n",
       "      <td>6/1/23</td>\n",
       "      <td>8/0/22</td>\n",
       "      <td>10/0/20</td>\n",
       "      <td>11/1/18</td>\n",
       "      <td>11/0/19</td>\n",
       "      <td>0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win/T/L CE vs Us</th>\n",
       "      <td>0/0/30</td>\n",
       "      <td>1/0/29</td>\n",
       "      <td>4/0/26</td>\n",
       "      <td>5/0/25</td>\n",
       "      <td>7/0/23</td>\n",
       "      <td>8/0/22</td>\n",
       "      <td>14/0/16</td>\n",
       "      <td>0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean AUC OVO</th>\n",
       "      <td>0.849$\\pm$0.015</td>\n",
       "      <td>0.879$\\pm$0.011</td>\n",
       "      <td>0.873$\\pm$0.027</td>\n",
       "      <td>0.89$\\pm$0.011</td>\n",
       "      <td>0.891$\\pm$0.011</td>\n",
       "      <td>\\textbf{0.894}$\\pm$0.01</td>\n",
       "      <td>0.882$\\pm$0.039</td>\n",
       "      <td>\\textbf{0.894}$\\pm$0.0097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Acc.</th>\n",
       "      <td>0.785$\\pm$0.016</td>\n",
       "      <td>0.808$\\pm$0.013</td>\n",
       "      <td>0.786$\\pm$0.058</td>\n",
       "      <td>0.818$\\pm$0.011</td>\n",
       "      <td>0.821$\\pm$0.013</td>\n",
       "      <td>0.821$\\pm$0.016</td>\n",
       "      <td>0.818$\\pm$0.04</td>\n",
       "      <td>\\textbf{0.826}$\\pm$0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean CE</th>\n",
       "      <td>1.79$\\pm$1.1</td>\n",
       "      <td>0.77$\\pm$0.024</td>\n",
       "      <td>0.849$\\pm$0.052</td>\n",
       "      <td>0.767$\\pm$0.062</td>\n",
       "      <td>0.758$\\pm$0.047</td>\n",
       "      <td>0.815$\\pm$0.06</td>\n",
       "      <td>\\textbf{0.73}$\\pm$0.037</td>\n",
       "      <td>0.731$\\pm$0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. rank AUC OVO</th>\n",
       "      <td>7.43</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.53</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.32</td>\n",
       "      <td>\\textbf{2.82}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean rank Acc.</th>\n",
       "      <td>6.87</td>\n",
       "      <td>5.05</td>\n",
       "      <td>5.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.45</td>\n",
       "      <td>\\textbf{2.82}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean rank CE</th>\n",
       "      <td>7.15</td>\n",
       "      <td>5.13</td>\n",
       "      <td>5.62</td>\n",
       "      <td>4.47</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.62</td>\n",
       "      <td>2.43</td>\n",
       "      <td>\\textbf{2.33}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean time (s)</th>\n",
       "      <td>\\textbf{0.5}</td>\n",
       "      <td>60</td>\n",
       "      <td>2837</td>\n",
       "      <td>3717</td>\n",
       "      <td>3290</td>\n",
       "      <td>3601</td>\n",
       "      <td>3130</td>\n",
       "      <td>0.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              KNN       Log. Regr.  \\\n",
       "balance-scale                                0.88            0.962   \n",
       "mfeat-fourier                                0.97            0.976   \n",
       "breast-w                                    0.984   \\textbf{0.994}   \n",
       "mfeat-karhunen                              0.994            0.995   \n",
       "mfeat-morphological                         0.951            0.964   \n",
       "mfeat-zernike                               0.976            0.979   \n",
       "cmc                                         0.634            0.675   \n",
       "credit-approval                             0.914            0.912   \n",
       "credit-g                                    0.726             0.76   \n",
       "diabetes                                    0.806            0.837   \n",
       "tic-tac-toe                                 0.986            0.994   \n",
       "vehicle                                     0.881            0.934   \n",
       "eucalyptus                                  0.798            0.885   \n",
       "analcatdata_authorship                 \\textbf{1}       \\textbf{1}   \n",
       "analcatdata_dmft                            0.543            0.566   \n",
       "pc4                                         0.822            0.891   \n",
       "pc3                                         0.753            0.772   \n",
       "kc2                                          0.79            0.826   \n",
       "pc1                                         0.785            0.821   \n",
       "banknote-authentication                     0.999       \\textbf{1}   \n",
       "blood-transfus..                            0.708            0.742   \n",
       "ilpd                                        0.646            0.709   \n",
       "qsar-biodeg                                 0.892             0.92   \n",
       "wdbc                                        0.991            0.995   \n",
       "cylinder-bands                              0.781            0.823   \n",
       "dresses-sales                               0.557            0.609   \n",
       "MiceProtein                                 0.997            0.998   \n",
       "car                                         0.924            0.978   \n",
       "steel-plates-fault                           0.92            0.935   \n",
       "climate-model-simulation-crashes            0.849            0.932   \n",
       "Wins AUC OVO                                    0                0   \n",
       "Wins Acc.                                       0                2   \n",
       "Wins CE                                         0                0   \n",
       "Win/T/L AUC vs Us                          2/0/28           3/0/27   \n",
       "Win/T/L Acc vs Us                          2/0/28           5/0/25   \n",
       "Win/T/L CE vs Us                           0/0/30           1/0/29   \n",
       "Mean AUC OVO                      0.849$\\pm$0.015  0.879$\\pm$0.011   \n",
       "Mean Acc.                         0.785$\\pm$0.016  0.808$\\pm$0.013   \n",
       "Mean CE                              1.79$\\pm$1.1   0.77$\\pm$0.024   \n",
       "M. rank AUC OVO                              7.43              5.4   \n",
       "Mean rank Acc.                               6.87             5.05   \n",
       "Mean rank CE                                 7.15             5.13   \n",
       "Mean time (s)                        \\textbf{0.5}               60   \n",
       "\n",
       "                                         GP (RBF)         Catboost  \\\n",
       "balance-scale                               0.983            0.924   \n",
       "mfeat-fourier                               0.923            0.982   \n",
       "breast-w                                    0.992            0.993   \n",
       "mfeat-karhunen                              0.915   \\textbf{0.999}   \n",
       "mfeat-morphological                         0.959            0.963   \n",
       "mfeat-zernike                               0.981            0.976   \n",
       "cmc                                         0.673            0.726   \n",
       "credit-approval                             0.921            0.939   \n",
       "credit-g                                    0.778            0.782   \n",
       "diabetes                           \\textbf{0.847}            0.838   \n",
       "tic-tac-toe                                 0.998            0.999   \n",
       "vehicle                                     0.898             0.93   \n",
       "eucalyptus                                   0.85            0.898   \n",
       "analcatdata_authorship                      0.983       \\textbf{1}   \n",
       "analcatdata_dmft                            0.572            0.559   \n",
       "pc4                                         0.899            0.941   \n",
       "pc3                                         0.795            0.825   \n",
       "kc2                                         0.828            0.832   \n",
       "pc1                                         0.824             0.86   \n",
       "banknote-authentication                \\textbf{1}       \\textbf{1}   \n",
       "blood-transfus..                            0.757            0.743   \n",
       "ilpd                                        0.663            0.728   \n",
       "qsar-biodeg                                 0.922            0.922   \n",
       "wdbc                                        0.995            0.993   \n",
       "cylinder-bands                              0.839            0.876   \n",
       "dresses-sales                      \\textbf{0.624}             0.57   \n",
       "MiceProtein                                 0.988       \\textbf{1}   \n",
       "car                                         0.895            0.996   \n",
       "steel-plates-fault                          0.939            0.966   \n",
       "climate-model-simulation-crashes   \\textbf{0.941}            0.934   \n",
       "Wins AUC OVO                                    3                0   \n",
       "Wins Acc.                                       1                1   \n",
       "Wins CE                                         2                1   \n",
       "Win/T/L AUC vs Us                          4/1/25           7/0/23   \n",
       "Win/T/L Acc vs Us                          6/1/23           8/0/22   \n",
       "Win/T/L CE vs Us                           4/0/26           5/0/25   \n",
       "Mean AUC OVO                      0.873$\\pm$0.027   0.89$\\pm$0.011   \n",
       "Mean Acc.                         0.786$\\pm$0.058  0.818$\\pm$0.011   \n",
       "Mean CE                           0.849$\\pm$0.052  0.767$\\pm$0.062   \n",
       "M. rank AUC OVO                               5.3             4.07   \n",
       "Mean rank Acc.                                5.3              4.1   \n",
       "Mean rank CE                                 5.62             4.47   \n",
       "Mean time (s)                                2837             3717   \n",
       "\n",
       "                                              XGB             Autosklearn2  \\\n",
       "balance-scale                               0.994           \\textbf{0.997}   \n",
       "mfeat-fourier                                0.98                    0.983   \n",
       "breast-w                                     0.99           \\textbf{0.994}   \n",
       "mfeat-karhunen                              0.998                    0.998   \n",
       "mfeat-morphological                         0.961                    0.967   \n",
       "mfeat-zernike                               0.973                    0.981   \n",
       "cmc                                          0.73           \\textbf{0.738}   \n",
       "credit-approval                    \\textbf{0.942}                    0.941   \n",
       "credit-g                                    0.785                    0.793   \n",
       "diabetes                                    0.838                    0.834   \n",
       "tic-tac-toe                            \\textbf{1}                    0.994   \n",
       "vehicle                                     0.928                     0.95   \n",
       "eucalyptus                                    0.9                    0.913   \n",
       "analcatdata_authorship                 \\textbf{1}               \\textbf{1}   \n",
       "analcatdata_dmft                            0.574                    0.575   \n",
       "pc4                                         0.929                    0.933   \n",
       "pc3                                         0.829                    0.827   \n",
       "kc2                                         0.823                    0.831   \n",
       "pc1                                         0.849                    0.853   \n",
       "banknote-authentication                \\textbf{1}               \\textbf{1}   \n",
       "blood-transfus..                            0.731                     0.75   \n",
       "ilpd                                        0.717                    0.721   \n",
       "qsar-biodeg                                 0.919                    0.925   \n",
       "wdbc                                         0.99                    0.995   \n",
       "cylinder-bands                              0.878                    0.872   \n",
       "dresses-sales                               0.582                    0.571   \n",
       "MiceProtein                            \\textbf{1}               \\textbf{1}   \n",
       "car                                         0.995           \\textbf{0.998}   \n",
       "steel-plates-fault                          0.966           \\textbf{0.969}   \n",
       "climate-model-simulation-crashes            0.925                    0.929   \n",
       "Wins AUC OVO                                    1                        4   \n",
       "Wins Acc.                                       3                        4   \n",
       "Wins CE                                         2                        3   \n",
       "Win/T/L AUC vs Us                          5/0/25                  11/1/18   \n",
       "Win/T/L Acc vs Us                         10/0/20                  11/1/18   \n",
       "Win/T/L CE vs Us                           7/0/23                   8/0/22   \n",
       "Mean AUC OVO                      0.891$\\pm$0.011  \\textbf{0.894}$\\pm$0.01   \n",
       "Mean Acc.                         0.821$\\pm$0.013          0.821$\\pm$0.016   \n",
       "Mean CE                           0.758$\\pm$0.047           0.815$\\pm$0.06   \n",
       "M. rank AUC OVO                              4.53                     3.13   \n",
       "Mean rank Acc.                               4.43                     3.98   \n",
       "Mean rank CE                                 4.25                     4.62   \n",
       "Mean time (s)                                3290                     3601   \n",
       "\n",
       "                                                Autogluon  \\\n",
       "balance-scale                                       0.992   \n",
       "mfeat-fourier                              \\textbf{0.984}   \n",
       "breast-w                                            0.993   \n",
       "mfeat-karhunen                             \\textbf{0.999}   \n",
       "mfeat-morphological                         \\textbf{0.97}   \n",
       "mfeat-zernike                                       0.794   \n",
       "cmc                                                 0.733   \n",
       "credit-approval                                     0.941   \n",
       "credit-g                                   \\textbf{0.794}   \n",
       "diabetes                                            0.839   \n",
       "tic-tac-toe                                    \\textbf{1}   \n",
       "vehicle                                             0.942   \n",
       "eucalyptus                                           0.92   \n",
       "analcatdata_authorship                              0.999   \n",
       "analcatdata_dmft                                    0.566   \n",
       "pc4                                        \\textbf{0.943}   \n",
       "pc3                                                 0.828   \n",
       "kc2                                                 0.824   \n",
       "pc1                                                 0.858   \n",
       "banknote-authentication                        \\textbf{1}   \n",
       "blood-transfus..                                    0.737   \n",
       "ilpd                                                0.723   \n",
       "qsar-biodeg                                         0.928   \n",
       "wdbc                                       \\textbf{0.996}   \n",
       "cylinder-bands                             \\textbf{0.888}   \n",
       "dresses-sales                                       0.551   \n",
       "MiceProtein                                         0.801   \n",
       "car                                                 0.997   \n",
       "steel-plates-fault                                  0.967   \n",
       "climate-model-simulation-crashes                    0.939   \n",
       "Wins AUC OVO                                            5   \n",
       "Wins Acc.                                               5   \n",
       "Wins CE                                                 9   \n",
       "Win/T/L AUC vs Us                                 14/1/15   \n",
       "Win/T/L Acc vs Us                                 11/0/19   \n",
       "Win/T/L CE vs Us                                  14/0/16   \n",
       "Mean AUC OVO                              0.882$\\pm$0.039   \n",
       "Mean Acc.                                  0.818$\\pm$0.04   \n",
       "Mean CE                           \\textbf{0.73}$\\pm$0.037   \n",
       "M. rank AUC OVO                                      3.32   \n",
       "Mean rank Acc.                                       3.45   \n",
       "Mean rank CE                                         2.43   \n",
       "Mean time (s)                                        3130   \n",
       "\n",
       "                                                Tabular PFN  \n",
       "balance-scale                                         0.996  \n",
       "mfeat-fourier                                         0.982  \n",
       "breast-w                                              0.993  \n",
       "mfeat-karhunen                                        0.998  \n",
       "mfeat-morphological                                   0.968  \n",
       "mfeat-zernike                                \\textbf{0.983}  \n",
       "cmc                                                   0.726  \n",
       "credit-approval                                       0.932  \n",
       "credit-g                                               0.79  \n",
       "diabetes                                              0.842  \n",
       "tic-tac-toe                                           0.966  \n",
       "vehicle                                      \\textbf{0.958}  \n",
       "eucalyptus                                   \\textbf{0.927}  \n",
       "analcatdata_authorship                           \\textbf{1}  \n",
       "analcatdata_dmft                              \\textbf{0.58}  \n",
       "pc4                                                   0.939  \n",
       "pc3                                          \\textbf{0.834}  \n",
       "kc2                                          \\textbf{0.836}  \n",
       "pc1                                          \\textbf{0.877}  \n",
       "banknote-authentication                          \\textbf{1}  \n",
       "blood-transfus..                             \\textbf{0.759}  \n",
       "ilpd                                         \\textbf{0.736}  \n",
       "qsar-biodeg                                  \\textbf{0.933}  \n",
       "wdbc                                         \\textbf{0.996}  \n",
       "cylinder-bands                                        0.846  \n",
       "dresses-sales                                         0.534  \n",
       "MiceProtein                                      \\textbf{1}  \n",
       "car                                                   0.997  \n",
       "steel-plates-fault                                    0.966  \n",
       "climate-model-simulation-crashes                      0.939  \n",
       "Wins AUC OVO                                    \\textbf{10}  \n",
       "Wins Acc.                                       \\textbf{11}  \n",
       "Wins CE                                         \\textbf{12}  \n",
       "Win/T/L AUC vs Us                                     0/0/0  \n",
       "Win/T/L Acc vs Us                                     0/0/0  \n",
       "Win/T/L CE vs Us                                      0/0/0  \n",
       "Mean AUC OVO                      \\textbf{0.894}$\\pm$0.0097  \n",
       "Mean Acc.                          \\textbf{0.826}$\\pm$0.011  \n",
       "Mean CE                                     0.731$\\pm$0.015  \n",
       "M. rank AUC OVO                               \\textbf{2.82}  \n",
       "Mean rank Acc.                                \\textbf{2.82}  \n",
       "Mean rank CE                                  \\textbf{2.33}  \n",
       "Mean time (s)                                         0.688  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys_max = [\"Mean rank CE\", \"Mean rank Acc.\", \"Mean rank AUC OVO\", \"Mean rank AUC OVR\", \"Mean rank Bal. Acc.\", \"Mean AUC OVO\", \"Mean Acc.\"]\n",
    "keys_max = [\"Mean AUC OVO\", \"Mean Acc.\", \"Wins AUC OVO\", \"Wins Acc.\", \"Wins CE\"]\n",
    "\n",
    "keys_min = [\"Mean rank CE\", \"Mean rank Acc.\", \"M. rank AUC OVO\", \"Mean CE\"]\n",
    "\n",
    "table_latex = rename(table).copy()\n",
    "\n",
    "table_latex.iloc[0:30] = table_latex.iloc[0:30].apply(lambda data : bold_extreme_values(data),axis=1)\n",
    "table_latex.loc[[\"Mean time (s)\"]] = table_latex.loc[[\"Mean time (s)\"]].apply(lambda data : bold_extreme_values(data, format_string=\"%.4g\", max_=False), axis=1)\n",
    "table_latex.loc[keys_max] = table_latex.loc[keys_max].apply(lambda data : bold_extreme_values(data),axis=1)\n",
    "table_latex.loc[keys_min] = table_latex.loc[keys_min].apply(lambda data : bold_extreme_values(data, max_=False),axis=1)\n",
    "\n",
    "table_latex.loc[['Mean CE Stds']] = table_latex.loc[['Mean CE Stds']].apply(lambda data : to_str(data, format_string=\"%.2g\"),axis=1)\n",
    "table_latex.loc['Mean CE'] = table_latex.loc['Mean CE'] + '$\\pm$' + table_latex.loc['Mean CE Stds']\n",
    "table_latex = table_latex.drop(['Mean CE Stds'])\n",
    "\n",
    "table_latex.loc[['Mean Acc. Stds']] = table_latex.loc[['Mean Acc. Stds']].apply(lambda data : to_str(data, format_string=\"%.2g\"),axis=1)\n",
    "table_latex.loc['Mean Acc.'] = table_latex.loc['Mean Acc.'] + '$\\pm$' + table_latex.loc['Mean Acc. Stds']\n",
    "table_latex = table_latex.drop(['Mean Acc. Stds'])\n",
    "\n",
    "table_latex.loc[['Mean AUC OVO Stds']] = table_latex.loc[['Mean AUC OVO Stds']].apply(lambda data : to_str(data, format_string=\"%.2g\"),axis=1)\n",
    "table_latex.loc['Mean AUC OVO'] = table_latex.loc['Mean AUC OVO'] + '$\\pm$' + table_latex.loc['Mean AUC OVO Stds']\n",
    "table_latex = table_latex.drop(['Mean AUC OVO Stds'])\n",
    "\n",
    "table_latex\n",
    "#print(table_latex.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllll}\n",
      "\\toprule\n",
      "{} &              KNN &       Log. Regr. &         GP (RBF) &         Catboost &              XGB &             Autosklearn2 &                Autogluon &                Tabular PFN \\\\\n",
      "\\midrule\n",
      "balance-scale                    &             0.88 &            0.962 &            0.983 &            0.924 &            0.994 &           \\textbf{0.997} &                    0.992 &                      0.996 \\\\\n",
      "mfeat-fourier                    &             0.97 &            0.976 &            0.923 &            0.982 &             0.98 &                    0.983 &           \\textbf{0.984} &                      0.982 \\\\\n",
      "breast-w                         &            0.984 &   \\textbf{0.994} &            0.992 &            0.993 &             0.99 &           \\textbf{0.994} &                    0.993 &                      0.993 \\\\\n",
      "mfeat-karhunen                   &            0.994 &            0.995 &            0.915 &   \\textbf{0.999} &            0.998 &                    0.998 &           \\textbf{0.999} &                      0.998 \\\\\n",
      "mfeat-morphological              &            0.951 &            0.964 &            0.959 &            0.963 &            0.961 &                    0.967 &            \\textbf{0.97} &                      0.968 \\\\\n",
      "mfeat-zernike                    &            0.976 &            0.979 &            0.981 &            0.976 &            0.973 &                    0.981 &                    0.794 &             \\textbf{0.983} \\\\\n",
      "cmc                              &            0.634 &            0.675 &            0.673 &            0.726 &             0.73 &           \\textbf{0.738} &                    0.733 &                      0.726 \\\\\n",
      "credit-approval                  &            0.914 &            0.912 &            0.921 &            0.939 &   \\textbf{0.942} &                    0.941 &                    0.941 &                      0.932 \\\\\n",
      "credit-g                         &            0.726 &             0.76 &            0.778 &            0.782 &            0.785 &                    0.793 &           \\textbf{0.794} &                       0.79 \\\\\n",
      "diabetes                         &            0.806 &            0.837 &   \\textbf{0.847} &            0.838 &            0.838 &                    0.834 &                    0.839 &                      0.842 \\\\\n",
      "tic-tac-toe                      &            0.986 &            0.994 &            0.998 &            0.999 &       \\textbf{1} &                    0.994 &               \\textbf{1} &                      0.966 \\\\\n",
      "vehicle                          &            0.881 &            0.934 &            0.898 &             0.93 &            0.928 &                     0.95 &                    0.942 &             \\textbf{0.958} \\\\\n",
      "eucalyptus                       &            0.798 &            0.885 &             0.85 &            0.898 &              0.9 &                    0.913 &                     0.92 &             \\textbf{0.927} \\\\\n",
      "analcatdata_authorship           &       \\textbf{1} &       \\textbf{1} &            0.983 &       \\textbf{1} &       \\textbf{1} &               \\textbf{1} &                    0.999 &                 \\textbf{1} \\\\\n",
      "analcatdata_dmft                 &            0.543 &            0.566 &            0.572 &            0.559 &            0.574 &                    0.575 &                    0.566 &              \\textbf{0.58} \\\\\n",
      "pc4                              &            0.822 &            0.891 &            0.899 &            0.941 &            0.929 &                    0.933 &           \\textbf{0.943} &                      0.939 \\\\\n",
      "pc3                              &            0.753 &            0.772 &            0.795 &            0.825 &            0.829 &                    0.827 &                    0.828 &             \\textbf{0.834} \\\\\n",
      "kc2                              &             0.79 &            0.826 &            0.828 &            0.832 &            0.823 &                    0.831 &                    0.824 &             \\textbf{0.836} \\\\\n",
      "pc1                              &            0.785 &            0.821 &            0.824 &             0.86 &            0.849 &                    0.853 &                    0.858 &             \\textbf{0.877} \\\\\n",
      "banknote-authentication          &            0.999 &       \\textbf{1} &       \\textbf{1} &       \\textbf{1} &       \\textbf{1} &               \\textbf{1} &               \\textbf{1} &                 \\textbf{1} \\\\\n",
      "blood-transfus..                 &            0.708 &            0.742 &            0.757 &            0.743 &            0.731 &                     0.75 &                    0.737 &             \\textbf{0.759} \\\\\n",
      "ilpd                             &            0.646 &            0.709 &            0.663 &            0.728 &            0.717 &                    0.721 &                    0.723 &             \\textbf{0.736} \\\\\n",
      "qsar-biodeg                      &            0.892 &             0.92 &            0.922 &            0.922 &            0.919 &                    0.925 &                    0.928 &             \\textbf{0.933} \\\\\n",
      "wdbc                             &            0.991 &            0.995 &            0.995 &            0.993 &             0.99 &                    0.995 &           \\textbf{0.996} &             \\textbf{0.996} \\\\\n",
      "cylinder-bands                   &            0.781 &            0.823 &            0.839 &            0.876 &            0.878 &                    0.872 &           \\textbf{0.888} &                      0.846 \\\\\n",
      "dresses-sales                    &            0.557 &            0.609 &   \\textbf{0.624} &             0.57 &            0.582 &                    0.571 &                    0.551 &                      0.534 \\\\\n",
      "MiceProtein                      &            0.997 &            0.998 &            0.988 &       \\textbf{1} &       \\textbf{1} &               \\textbf{1} &                    0.801 &                 \\textbf{1} \\\\\n",
      "car                              &            0.924 &            0.978 &            0.895 &            0.996 &            0.995 &           \\textbf{0.998} &                    0.997 &                      0.997 \\\\\n",
      "steel-plates-fault               &             0.92 &            0.935 &            0.939 &            0.966 &            0.966 &           \\textbf{0.969} &                    0.967 &                      0.966 \\\\\n",
      "climate-model-simulation-crashes &            0.849 &            0.932 &   \\textbf{0.941} &            0.934 &            0.925 &                    0.929 &                    0.939 &                      0.939 \\\\\n",
      "Wins AUC OVO                     &                0 &                0 &                3 &                0 &                1 &                        4 &                        5 &                \\textbf{10} \\\\\n",
      "Wins Acc.                        &                0 &                2 &                1 &                1 &                3 &                        4 &                        5 &                \\textbf{11} \\\\\n",
      "Wins CE                          &                0 &                0 &                2 &                1 &                2 &                        3 &                        9 &                \\textbf{12} \\\\\n",
      "Win/T/L AUC vs Us                &           2/0/28 &           3/0/27 &           4/1/25 &           7/0/23 &           5/0/25 &                  11/1/18 &                  14/1/15 &                      0/0/0 \\\\\n",
      "Win/T/L Acc vs Us                &           2/0/28 &           5/0/25 &           6/1/23 &           8/0/22 &          10/0/20 &                  11/1/18 &                  11/0/19 &                      0/0/0 \\\\\n",
      "Win/T/L CE vs Us                 &           0/0/30 &           1/0/29 &           4/0/26 &           5/0/25 &           7/0/23 &                   8/0/22 &                  14/0/16 &                      0/0/0 \\\\\n",
      "Mean AUC OVO                     &  0.849$\\pm$0.015 &  0.879$\\pm$0.011 &  0.873$\\pm$0.027 &   0.89$\\pm$0.011 &  0.891$\\pm$0.011 &  \\textbf{0.894}$\\pm$0.01 &          0.882$\\pm$0.039 &  \\textbf{0.894}$\\pm$0.0097 \\\\\n",
      "Mean Acc.                        &  0.785$\\pm$0.016 &  0.808$\\pm$0.013 &  0.786$\\pm$0.058 &  0.818$\\pm$0.011 &  0.821$\\pm$0.013 &          0.821$\\pm$0.016 &           0.818$\\pm$0.04 &   \\textbf{0.826}$\\pm$0.011 \\\\\n",
      "Mean CE                          &     1.79$\\pm$1.1 &   0.77$\\pm$0.024 &  0.849$\\pm$0.052 &  0.767$\\pm$0.062 &  0.758$\\pm$0.047 &           0.815$\\pm$0.06 &  \\textbf{0.73}$\\pm$0.037 &            0.731$\\pm$0.015 \\\\\n",
      "M. rank AUC OVO                  &             7.43 &              5.4 &              5.3 &             4.07 &             4.53 &                     3.13 &                     3.32 &              \\textbf{2.82} \\\\\n",
      "Mean rank Acc.                   &             6.87 &             5.05 &              5.3 &              4.1 &             4.43 &                     3.98 &                     3.45 &              \\textbf{2.82} \\\\\n",
      "Mean rank CE                     &             7.15 &             5.13 &             5.62 &             4.47 &             4.25 &                     4.62 &                     2.43 &              \\textbf{2.33} \\\\\n",
      "Mean time (s)                    &     \\textbf{0.5} &               60 &             2837 &             3717 &             3290 &                     3601 &                     3130 &                      0.688 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(table_latex.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>KNN</th>\n",
       "      <th>Log. Regr.</th>\n",
       "      <th>GP (RBF)</th>\n",
       "      <th>Catboost</th>\n",
       "      <th>XGB</th>\n",
       "      <th>Autosklearn2</th>\n",
       "      <th>Autogluon</th>\n",
       "      <th>Tabular PFN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wins AUC OVO</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>\\textbf{10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wins Acc.</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>\\textbf{11}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wins CE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>\\textbf{12}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win/T/L AUC vs Us</th>\n",
       "      <td>2/0/28</td>\n",
       "      <td>3/0/27</td>\n",
       "      <td>4/1/25</td>\n",
       "      <td>7/0/23</td>\n",
       "      <td>5/0/25</td>\n",
       "      <td>11/1/18</td>\n",
       "      <td>14/1/15</td>\n",
       "      <td>0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win/T/L Acc vs Us</th>\n",
       "      <td>2/0/28</td>\n",
       "      <td>5/0/25</td>\n",
       "      <td>6/1/23</td>\n",
       "      <td>8/0/22</td>\n",
       "      <td>10/0/20</td>\n",
       "      <td>11/1/18</td>\n",
       "      <td>11/0/19</td>\n",
       "      <td>0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win/T/L CE vs Us</th>\n",
       "      <td>0/0/30</td>\n",
       "      <td>1/0/29</td>\n",
       "      <td>4/0/26</td>\n",
       "      <td>5/0/25</td>\n",
       "      <td>7/0/23</td>\n",
       "      <td>8/0/22</td>\n",
       "      <td>14/0/16</td>\n",
       "      <td>0/0/0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean AUC OVO</th>\n",
       "      <td>0.849$\\pm$0.015</td>\n",
       "      <td>0.879$\\pm$0.011</td>\n",
       "      <td>0.873$\\pm$0.027</td>\n",
       "      <td>0.89$\\pm$0.011</td>\n",
       "      <td>0.891$\\pm$0.011</td>\n",
       "      <td>\\textbf{0.894}$\\pm$0.01</td>\n",
       "      <td>0.882$\\pm$0.039</td>\n",
       "      <td>\\textbf{0.894}$\\pm$0.0097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Acc.</th>\n",
       "      <td>0.785$\\pm$0.016</td>\n",
       "      <td>0.808$\\pm$0.013</td>\n",
       "      <td>0.786$\\pm$0.058</td>\n",
       "      <td>0.818$\\pm$0.011</td>\n",
       "      <td>0.821$\\pm$0.013</td>\n",
       "      <td>0.821$\\pm$0.016</td>\n",
       "      <td>0.818$\\pm$0.04</td>\n",
       "      <td>\\textbf{0.826}$\\pm$0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean CE</th>\n",
       "      <td>1.79$\\pm$1.1</td>\n",
       "      <td>0.77$\\pm$0.024</td>\n",
       "      <td>0.849$\\pm$0.052</td>\n",
       "      <td>0.767$\\pm$0.062</td>\n",
       "      <td>0.758$\\pm$0.047</td>\n",
       "      <td>0.815$\\pm$0.06</td>\n",
       "      <td>\\textbf{0.73}$\\pm$0.037</td>\n",
       "      <td>0.731$\\pm$0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. rank AUC OVO</th>\n",
       "      <td>7.43</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.53</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.32</td>\n",
       "      <td>\\textbf{2.82}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean rank Acc.</th>\n",
       "      <td>6.87</td>\n",
       "      <td>5.05</td>\n",
       "      <td>5.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.45</td>\n",
       "      <td>\\textbf{2.82}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean rank CE</th>\n",
       "      <td>7.15</td>\n",
       "      <td>5.13</td>\n",
       "      <td>5.62</td>\n",
       "      <td>4.47</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.62</td>\n",
       "      <td>2.43</td>\n",
       "      <td>\\textbf{2.33}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean time (s)</th>\n",
       "      <td>\\textbf{0.5}</td>\n",
       "      <td>60</td>\n",
       "      <td>2837</td>\n",
       "      <td>3717</td>\n",
       "      <td>3290</td>\n",
       "      <td>3601</td>\n",
       "      <td>3130</td>\n",
       "      <td>0.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               KNN       Log. Regr.         GP (RBF)  \\\n",
       "Wins AUC OVO                     0                0                3   \n",
       "Wins Acc.                        0                2                1   \n",
       "Wins CE                          0                0                2   \n",
       "Win/T/L AUC vs Us           2/0/28           3/0/27           4/1/25   \n",
       "Win/T/L Acc vs Us           2/0/28           5/0/25           6/1/23   \n",
       "Win/T/L CE vs Us            0/0/30           1/0/29           4/0/26   \n",
       "Mean AUC OVO       0.849$\\pm$0.015  0.879$\\pm$0.011  0.873$\\pm$0.027   \n",
       "Mean Acc.          0.785$\\pm$0.016  0.808$\\pm$0.013  0.786$\\pm$0.058   \n",
       "Mean CE               1.79$\\pm$1.1   0.77$\\pm$0.024  0.849$\\pm$0.052   \n",
       "M. rank AUC OVO               7.43              5.4              5.3   \n",
       "Mean rank Acc.                6.87             5.05              5.3   \n",
       "Mean rank CE                  7.15             5.13             5.62   \n",
       "Mean time (s)         \\textbf{0.5}               60             2837   \n",
       "\n",
       "                          Catboost              XGB             Autosklearn2  \\\n",
       "Wins AUC OVO                     0                1                        4   \n",
       "Wins Acc.                        1                3                        4   \n",
       "Wins CE                          1                2                        3   \n",
       "Win/T/L AUC vs Us           7/0/23           5/0/25                  11/1/18   \n",
       "Win/T/L Acc vs Us           8/0/22          10/0/20                  11/1/18   \n",
       "Win/T/L CE vs Us            5/0/25           7/0/23                   8/0/22   \n",
       "Mean AUC OVO        0.89$\\pm$0.011  0.891$\\pm$0.011  \\textbf{0.894}$\\pm$0.01   \n",
       "Mean Acc.          0.818$\\pm$0.011  0.821$\\pm$0.013          0.821$\\pm$0.016   \n",
       "Mean CE            0.767$\\pm$0.062  0.758$\\pm$0.047           0.815$\\pm$0.06   \n",
       "M. rank AUC OVO               4.07             4.53                     3.13   \n",
       "Mean rank Acc.                 4.1             4.43                     3.98   \n",
       "Mean rank CE                  4.47             4.25                     4.62   \n",
       "Mean time (s)                 3717             3290                     3601   \n",
       "\n",
       "                                 Autogluon                Tabular PFN  \n",
       "Wins AUC OVO                             5                \\textbf{10}  \n",
       "Wins Acc.                                5                \\textbf{11}  \n",
       "Wins CE                                  9                \\textbf{12}  \n",
       "Win/T/L AUC vs Us                  14/1/15                      0/0/0  \n",
       "Win/T/L Acc vs Us                  11/0/19                      0/0/0  \n",
       "Win/T/L CE vs Us                   14/0/16                      0/0/0  \n",
       "Mean AUC OVO               0.882$\\pm$0.039  \\textbf{0.894}$\\pm$0.0097  \n",
       "Mean Acc.                   0.818$\\pm$0.04   \\textbf{0.826}$\\pm$0.011  \n",
       "Mean CE            \\textbf{0.73}$\\pm$0.037            0.731$\\pm$0.015  \n",
       "M. rank AUC OVO                       3.32              \\textbf{2.82}  \n",
       "Mean rank Acc.                        3.45              \\textbf{2.82}  \n",
       "Mean rank CE                          2.43              \\textbf{2.33}  \n",
       "Mean time (s)                         3130                      0.688  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_latex_small = table_latex.iloc[-len(keys_min+keys_max)-1-3:]\n",
    "table_latex_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllll}\n",
      "\\toprule\n",
      "{} &              KNN &       Log. Regr. &         GP (RBF) &         Catboost &              XGB &               Autosklearn2 &                Autogluon &               Tabular PFN \\\\\n",
      "\\midrule\n",
      "Wins AUC OVO      &                0 &                0 &                2 &                0 &                1 &                          5 &                        5 &                \\textbf{8} \\\\\n",
      "Wins Acc.         &                0 &                3 &                1 &                2 &                1 &                          4 &                        6 &               \\textbf{10} \\\\\n",
      "Wins CE           &                0 &                0 &                1 &                0 &                2 &                          1 &              \\textbf{13} &                        11 \\\\\n",
      "Win/T/L AUC vs Us &           2/0/28 &           3/0/27 &           3/1/26 &           7/1/22 &           5/0/25 &                    11/1/18 &                  13/1/16 &                     0/0/0 \\\\\n",
      "Win/T/L Acc vs Us &           2/0/28 &           6/0/24 &           4/1/25 &          10/0/20 &           7/0/23 &                    11/0/19 &                  11/0/19 &                     0/0/0 \\\\\n",
      "Win/T/L CE vs Us  &           0/0/30 &           1/0/29 &           3/0/27 &           4/0/26 &           4/0/26 &                     6/0/24 &                  15/0/15 &                     0/0/0 \\\\\n",
      "Mean AUC OVO      &  0.849$\\pm$0.015 &   0.88$\\pm$0.012 &  0.873$\\pm$0.024 &  0.888$\\pm$0.012 &   0.89$\\pm$0.012 &  \\textbf{0.895}$\\pm$0.0097 &          0.889$\\pm$0.022 &          0.894$\\pm$0.0097 \\\\\n",
      "Mean Acc.         &  0.785$\\pm$0.016 &  0.809$\\pm$0.014 &  0.774$\\pm$0.075 &  0.818$\\pm$0.013 &   0.82$\\pm$0.014 &            0.818$\\pm$0.025 &          0.825$\\pm$0.021 &  \\textbf{0.826}$\\pm$0.011 \\\\\n",
      "Mean CE           &     1.79$\\pm$1.1 &  0.769$\\pm$0.027 &  0.859$\\pm$0.068 &  0.774$\\pm$0.073 &  0.763$\\pm$0.059 &            0.839$\\pm$0.065 &  \\textbf{0.72}$\\pm$0.019 &           0.731$\\pm$0.015 \\\\\n",
      "M. rank AUC OVO   &             7.53 &             5.28 &             5.55 &             4.22 &             4.72 &              \\textbf{2.75} &                     3.13 &                      2.82 \\\\\n",
      "Mean rank Acc.    &             6.78 &             4.98 &             5.67 &             4.22 &             4.53 &                       4.08 &                     2.97 &             \\textbf{2.77} \\\\\n",
      "Mean rank CE      &             7.17 &             5.17 &             5.77 &             4.52 &              4.7 &                       4.77 &            \\textbf{1.78} &                      2.13 \\\\\n",
      "Mean time         &     \\textbf{0.5} &               60 &            941.1 &             1121 &            921.9 &                      901.5 &                    809.1 &                     0.688 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(table_latex_small.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>logistic</th>\n",
       "      <th>gp</th>\n",
       "      <th>knn</th>\n",
       "      <th>catboost</th>\n",
       "      <th>xgb</th>\n",
       "      <th>autosklearn2</th>\n",
       "      <th>autogluon</th>\n",
       "      <th>tabnet</th>\n",
       "      <th>transformer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>balance-scale</th>\n",
       "      <td>0.962</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.979</td>\n",
       "      <td>\\textbf{0.997}</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-fourier</th>\n",
       "      <td>0.976</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.983</td>\n",
       "      <td>\\textbf{0.985}</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breast-w</th>\n",
       "      <td>\\textbf{0.994}</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.992</td>\n",
       "      <td>\\textbf{0.994}</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-karhunen</th>\n",
       "      <td>0.995</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.994</td>\n",
       "      <td>\\textbf{0.999}</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.998</td>\n",
       "      <td>\\textbf{0.999}</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-morphological</th>\n",
       "      <td>0.964</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.967</td>\n",
       "      <td>\\textbf{0.970}</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mfeat-zernike</th>\n",
       "      <td>0.979</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.981</td>\n",
       "      <td>\\textbf{0.991}</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmc</th>\n",
       "      <td>0.675</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.724</td>\n",
       "      <td>\\textbf{0.738}</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit-approval</th>\n",
       "      <td>0.912</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.941</td>\n",
       "      <td>\\textbf{0.943}</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit-g</th>\n",
       "      <td>0.760</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.780</td>\n",
       "      <td>\\textbf{0.793}</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>0.837</td>\n",
       "      <td>\\textbf{0.847}</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic-tac-toe</th>\n",
       "      <td>0.994</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.994</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle</th>\n",
       "      <td>0.934</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.635</td>\n",
       "      <td>\\textbf{0.958}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eucalyptus</th>\n",
       "      <td>0.885</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.675</td>\n",
       "      <td>\\textbf{0.925}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analcatdata_authorship</th>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>0.983</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>0.986</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analcatdata_dmft</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.550</td>\n",
       "      <td>\\textbf{0.584}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc4</th>\n",
       "      <td>0.891</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.933</td>\n",
       "      <td>\\textbf{0.941}</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc3</th>\n",
       "      <td>0.772</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.802</td>\n",
       "      <td>\\textbf{0.834}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kc2</th>\n",
       "      <td>0.826</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.754</td>\n",
       "      <td>\\textbf{0.832}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc1</th>\n",
       "      <td>0.821</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.759</td>\n",
       "      <td>\\textbf{0.867}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banknote-authentication</th>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>0.999</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>0.963</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood-transfusion-service-center</th>\n",
       "      <td>0.742</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.669</td>\n",
       "      <td>\\textbf{0.762}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilpd</th>\n",
       "      <td>0.709</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.683</td>\n",
       "      <td>\\textbf{0.734}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qsar-biodeg</th>\n",
       "      <td>0.920</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.804</td>\n",
       "      <td>\\textbf{0.928}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wdbc</th>\n",
       "      <td>0.995</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.995</td>\n",
       "      <td>\\textbf{0.996}</td>\n",
       "      <td>0.962</td>\n",
       "      <td>\\textbf{0.996}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinder-bands</th>\n",
       "      <td>0.823</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.872</td>\n",
       "      <td>\\textbf{0.879}</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dresses-sales</th>\n",
       "      <td>0.609</td>\n",
       "      <td>\\textbf{0.624}</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiceProtein</th>\n",
       "      <td>0.998</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.997</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "      <td>0.889</td>\n",
       "      <td>\\textbf{1.000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car</th>\n",
       "      <td>0.978</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.995</td>\n",
       "      <td>\\textbf{0.998}</td>\n",
       "      <td>\\textbf{0.998}</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>steel-plates-fault</th>\n",
       "      <td>0.935</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.964</td>\n",
       "      <td>\\textbf{0.969}</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>climate-model-simulation-crashes</th>\n",
       "      <td>0.932</td>\n",
       "      <td>\\textbf{0.941}</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean AUC OVO</th>\n",
       "      <td>0.879445</td>\n",
       "      <td>0.872712</td>\n",
       "      <td>0.848596</td>\n",
       "      <td>0.888729</td>\n",
       "      <td>0.888126</td>\n",
       "      <td>0.894336</td>\n",
       "      <td>0.895064</td>\n",
       "      <td>0.806375</td>\n",
       "      <td>0.893478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean CE</th>\n",
       "      <td>0.769542</td>\n",
       "      <td>0.84852</td>\n",
       "      <td>1.790564</td>\n",
       "      <td>0.77023</td>\n",
       "      <td>0.895484</td>\n",
       "      <td>0.814998</td>\n",
       "      <td>0.71703</td>\n",
       "      <td>1.197307</td>\n",
       "      <td>0.73392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean rank AUC</th>\n",
       "      <td>5.55</td>\n",
       "      <td>5.466667</td>\n",
       "      <td>7.783333</td>\n",
       "      <td>4.25</td>\n",
       "      <td>5.033333</td>\n",
       "      <td>3.033333</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>8.366667</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean rank CE</th>\n",
       "      <td>4.933333</td>\n",
       "      <td>4.266667</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>5.35</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.25</td>\n",
       "      <td>8.483334</td>\n",
       "      <td>2.283333</td>\n",
       "      <td>7.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean time</th>\n",
       "      <td>1210.62841</td>\n",
       "      <td>2836.718452</td>\n",
       "      <td>695.03541</td>\n",
       "      <td>3546.857335</td>\n",
       "      <td>1525.18558</td>\n",
       "      <td>3601.100162</td>\n",
       "      <td>3144.060955</td>\n",
       "      <td>3674.092902</td>\n",
       "      <td>0.678991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        logistic              gp  \\\n",
       "balance-scale                              0.962           0.983   \n",
       "mfeat-fourier                              0.976           0.923   \n",
       "breast-w                          \\textbf{0.994}           0.992   \n",
       "mfeat-karhunen                             0.995           0.915   \n",
       "mfeat-morphological                        0.964           0.959   \n",
       "mfeat-zernike                              0.979           0.981   \n",
       "cmc                                        0.675           0.673   \n",
       "credit-approval                            0.912           0.921   \n",
       "credit-g                                   0.760           0.778   \n",
       "diabetes                                   0.837  \\textbf{0.847}   \n",
       "tic-tac-toe                                0.994           0.998   \n",
       "vehicle                                    0.934           0.898   \n",
       "eucalyptus                                 0.885           0.850   \n",
       "analcatdata_authorship            \\textbf{1.000}           0.983   \n",
       "analcatdata_dmft                           0.566           0.572   \n",
       "pc4                                        0.891           0.899   \n",
       "pc3                                        0.772           0.795   \n",
       "kc2                                        0.826           0.828   \n",
       "pc1                                        0.821           0.824   \n",
       "banknote-authentication           \\textbf{1.000}  \\textbf{1.000}   \n",
       "blood-transfusion-service-center           0.742           0.757   \n",
       "ilpd                                       0.709           0.663   \n",
       "qsar-biodeg                                0.920           0.922   \n",
       "wdbc                                       0.995           0.995   \n",
       "cylinder-bands                             0.823           0.839   \n",
       "dresses-sales                              0.609  \\textbf{0.624}   \n",
       "MiceProtein                                0.998           0.988   \n",
       "car                                        0.978           0.895   \n",
       "steel-plates-fault                         0.935           0.939   \n",
       "climate-model-simulation-crashes           0.932  \\textbf{0.941}   \n",
       "Mean AUC OVO                            0.879445        0.872712   \n",
       "Mean CE                                 0.769542         0.84852   \n",
       "Mean rank AUC                               5.55        5.466667   \n",
       "Mean rank CE                            4.933333        4.266667   \n",
       "Mean time                             1210.62841     2836.718452   \n",
       "\n",
       "                                             knn        catboost  \\\n",
       "balance-scale                              0.880           0.926   \n",
       "mfeat-fourier                              0.970           0.981   \n",
       "breast-w                                   0.984           0.993   \n",
       "mfeat-karhunen                             0.994  \\textbf{0.999}   \n",
       "mfeat-morphological                        0.951           0.963   \n",
       "mfeat-zernike                              0.976           0.976   \n",
       "cmc                                        0.634           0.726   \n",
       "credit-approval                            0.914           0.939   \n",
       "credit-g                                   0.726           0.782   \n",
       "diabetes                                   0.806           0.838   \n",
       "tic-tac-toe                                0.986           0.999   \n",
       "vehicle                                    0.881           0.927   \n",
       "eucalyptus                                 0.798           0.896   \n",
       "analcatdata_authorship            \\textbf{1.000}  \\textbf{1.000}   \n",
       "analcatdata_dmft                           0.543           0.563   \n",
       "pc4                                        0.822           0.937   \n",
       "pc3                                        0.753           0.824   \n",
       "kc2                                        0.790           0.826   \n",
       "pc1                                        0.785           0.840   \n",
       "banknote-authentication                    0.999  \\textbf{1.000}   \n",
       "blood-transfusion-service-center           0.708           0.740   \n",
       "ilpd                                       0.646           0.721   \n",
       "qsar-biodeg                                0.892           0.921   \n",
       "wdbc                                       0.991           0.994   \n",
       "cylinder-bands                             0.781           0.868   \n",
       "dresses-sales                              0.557           0.589   \n",
       "MiceProtein                                0.997  \\textbf{1.000}   \n",
       "car                                        0.924           0.996   \n",
       "steel-plates-fault                         0.920           0.965   \n",
       "climate-model-simulation-crashes           0.849           0.933   \n",
       "Mean AUC OVO                            0.848596        0.888729   \n",
       "Mean CE                                 1.790564         0.77023   \n",
       "Mean rank AUC                           7.783333            4.25   \n",
       "Mean rank CE                            2.316667            5.35   \n",
       "Mean time                              695.03541     3546.857335   \n",
       "\n",
       "                                             xgb    autosklearn2  \\\n",
       "balance-scale                              0.979  \\textbf{0.997}   \n",
       "mfeat-fourier                              0.980           0.983   \n",
       "breast-w                                   0.992  \\textbf{0.994}   \n",
       "mfeat-karhunen                             0.998           0.998   \n",
       "mfeat-morphological                        0.961           0.967   \n",
       "mfeat-zernike                              0.974           0.981   \n",
       "cmc                                        0.724  \\textbf{0.738}   \n",
       "credit-approval                            0.940           0.941   \n",
       "credit-g                                   0.780  \\textbf{0.793}   \n",
       "diabetes                                   0.831           0.834   \n",
       "tic-tac-toe                                0.996           0.994   \n",
       "vehicle                                    0.927           0.950   \n",
       "eucalyptus                                 0.900           0.913   \n",
       "analcatdata_authorship            \\textbf{1.000}  \\textbf{1.000}   \n",
       "analcatdata_dmft                           0.576           0.575   \n",
       "pc4                                        0.931           0.933   \n",
       "pc3                                        0.823           0.827   \n",
       "kc2                                        0.828           0.831   \n",
       "pc1                                        0.848           0.853   \n",
       "banknote-authentication                    0.963  \\textbf{1.000}   \n",
       "blood-transfusion-service-center           0.733           0.750   \n",
       "ilpd                                       0.717           0.721   \n",
       "qsar-biodeg                                0.921           0.925   \n",
       "wdbc                                       0.993           0.995   \n",
       "cylinder-bands                             0.876           0.872   \n",
       "dresses-sales                              0.576           0.571   \n",
       "MiceProtein                       \\textbf{1.000}  \\textbf{1.000}   \n",
       "car                                        0.995  \\textbf{0.998}   \n",
       "steel-plates-fault                         0.964  \\textbf{0.969}   \n",
       "climate-model-simulation-crashes           0.918           0.929   \n",
       "Mean AUC OVO                            0.888126        0.894336   \n",
       "Mean CE                                 0.895484        0.814998   \n",
       "Mean rank AUC                           5.033333        3.033333   \n",
       "Mean rank CE                                 4.9            5.25   \n",
       "Mean time                             1525.18558     3601.100162   \n",
       "\n",
       "                                       autogluon          tabnet  \\\n",
       "balance-scale                              0.993           0.918   \n",
       "mfeat-fourier                     \\textbf{0.985}           0.899   \n",
       "breast-w                                   0.993           0.983   \n",
       "mfeat-karhunen                    \\textbf{0.999}           0.987   \n",
       "mfeat-morphological               \\textbf{0.970}           0.886   \n",
       "mfeat-zernike                     \\textbf{0.991}           0.952   \n",
       "cmc                                        0.734           0.659   \n",
       "credit-approval                   \\textbf{0.943}           0.852   \n",
       "credit-g                                   0.792           0.710   \n",
       "diabetes                                   0.837           0.748   \n",
       "tic-tac-toe                       \\textbf{1.000}           0.868   \n",
       "vehicle                                    0.941           0.635   \n",
       "eucalyptus                                 0.920           0.675   \n",
       "analcatdata_authorship            \\textbf{1.000}           0.986   \n",
       "analcatdata_dmft                           0.571           0.550   \n",
       "pc4                               \\textbf{0.941}           0.848   \n",
       "pc3                                        0.830           0.802   \n",
       "kc2                                        0.829           0.754   \n",
       "pc1                                        0.865           0.759   \n",
       "banknote-authentication           \\textbf{1.000}  \\textbf{1.000}   \n",
       "blood-transfusion-service-center           0.742           0.669   \n",
       "ilpd                                       0.726           0.683   \n",
       "qsar-biodeg                                0.926           0.804   \n",
       "wdbc                              \\textbf{0.996}           0.962   \n",
       "cylinder-bands                    \\textbf{0.879}           0.556   \n",
       "dresses-sales                              0.556           0.504   \n",
       "MiceProtein                       \\textbf{1.000}           0.889   \n",
       "car                               \\textbf{0.998}           0.981   \n",
       "steel-plates-fault                         0.967           0.835   \n",
       "climate-model-simulation-crashes           0.931           0.837   \n",
       "Mean AUC OVO                            0.895064        0.806375   \n",
       "Mean CE                                  0.71703        1.197307   \n",
       "Mean rank AUC                           2.566667        8.366667   \n",
       "Mean rank CE                            8.483334        2.283333   \n",
       "Mean time                            3144.060955     3674.092902   \n",
       "\n",
       "                                     transformer  \n",
       "balance-scale                              0.996  \n",
       "mfeat-fourier                              0.980  \n",
       "breast-w                                   0.992  \n",
       "mfeat-karhunen                             0.998  \n",
       "mfeat-morphological                        0.967  \n",
       "mfeat-zernike                              0.983  \n",
       "cmc                                        0.727  \n",
       "credit-approval                            0.932  \n",
       "credit-g                                   0.787  \n",
       "diabetes                                   0.843  \n",
       "tic-tac-toe                                0.958  \n",
       "vehicle                           \\textbf{0.958}  \n",
       "eucalyptus                        \\textbf{0.925}  \n",
       "analcatdata_authorship            \\textbf{1.000}  \n",
       "analcatdata_dmft                  \\textbf{0.584}  \n",
       "pc4                                        0.935  \n",
       "pc3                               \\textbf{0.834}  \n",
       "kc2                               \\textbf{0.832}  \n",
       "pc1                               \\textbf{0.867}  \n",
       "banknote-authentication           \\textbf{1.000}  \n",
       "blood-transfusion-service-center  \\textbf{0.762}  \n",
       "ilpd                              \\textbf{0.734}  \n",
       "qsar-biodeg                       \\textbf{0.928}  \n",
       "wdbc                              \\textbf{0.996}  \n",
       "cylinder-bands                             0.849  \n",
       "dresses-sales                              0.536  \n",
       "MiceProtein                       \\textbf{1.000}  \n",
       "car                                        0.996  \n",
       "steel-plates-fault                         0.965  \n",
       "climate-model-simulation-crashes           0.939  \n",
       "Mean AUC OVO                            0.893478  \n",
       "Mean CE                                  0.73392  \n",
       "Mean rank AUC                               2.95  \n",
       "Mean rank CE                            7.216667  \n",
       "Mean time                               0.678991  "
      ]
     },
     "execution_count": 1130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_latex = table.copy()\n",
    "\n",
    "table_latex.iloc[:-5] = table_latex.iloc[:-5].apply(lambda data : bold_extreme_values(data),axis=1)\n",
    "table_latex.iloc[-5:-5] = table_latex.iloc[-5:-5].apply(lambda data : bold_extreme_values(data, max_=False),axis=1)\n",
    "\n",
    "table_latex\n",
    "#print(table_latex.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9b8af_row0_col10, #T_9b8af_row1_col10, #T_9b8af_row2_col10, #T_9b8af_row3_col10, #T_9b8af_row4_col10, #T_9b8af_row5_col10, #T_9b8af_row6_col10 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9b8af_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Log. Regr.</th>\n",
       "      <th class=\"col_heading level0 col1\" >GP</th>\n",
       "      <th class=\"col_heading level0 col2\" >KNN</th>\n",
       "      <th class=\"col_heading level0 col3\" >Catboost</th>\n",
       "      <th class=\"col_heading level0 col4\" >XGB</th>\n",
       "      <th class=\"col_heading level0 col5\" >autosklearn</th>\n",
       "      <th class=\"col_heading level0 col6\" >autosklearn_fast</th>\n",
       "      <th class=\"col_heading level0 col7\" >h2o</th>\n",
       "      <th class=\"col_heading level0 col8\" >h2o_fast</th>\n",
       "      <th class=\"col_heading level0 col9\" >transformer</th>\n",
       "      <th class=\"col_heading level0 col10\" >diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9b8af_level0_row0\" class=\"row_heading level0 row0\" >Mean Bal. Acc.</th>\n",
       "      <td id=\"T_9b8af_row0_col0\" class=\"data row0 col0\" >0.461</td>\n",
       "      <td id=\"T_9b8af_row0_col1\" class=\"data row0 col1\" >0.593</td>\n",
       "      <td id=\"T_9b8af_row0_col2\" class=\"data row0 col2\" >0.628</td>\n",
       "      <td id=\"T_9b8af_row0_col3\" class=\"data row0 col3\" >0.684</td>\n",
       "      <td id=\"T_9b8af_row0_col4\" class=\"data row0 col4\" >0.645</td>\n",
       "      <td id=\"T_9b8af_row0_col5\" class=\"data row0 col5\" >0.683</td>\n",
       "      <td id=\"T_9b8af_row0_col6\" class=\"data row0 col6\" >0.671</td>\n",
       "      <td id=\"T_9b8af_row0_col7\" class=\"data row0 col7\" >0.693</td>\n",
       "      <td id=\"T_9b8af_row0_col8\" class=\"data row0 col8\" >0.701</td>\n",
       "      <td id=\"T_9b8af_row0_col9\" class=\"data row0 col9\" >0.669</td>\n",
       "      <td id=\"T_9b8af_row0_col10\" class=\"data row0 col10\" >0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b8af_level0_row1\" class=\"row_heading level0 row1\" >Mean CE</th>\n",
       "      <td id=\"T_9b8af_row1_col0\" class=\"data row1 col0\" >0.989</td>\n",
       "      <td id=\"T_9b8af_row1_col1\" class=\"data row1 col1\" >0.787</td>\n",
       "      <td id=\"T_9b8af_row1_col2\" class=\"data row1 col2\" >1.223</td>\n",
       "      <td id=\"T_9b8af_row1_col3\" class=\"data row1 col3\" >0.808</td>\n",
       "      <td id=\"T_9b8af_row1_col4\" class=\"data row1 col4\" >0.844</td>\n",
       "      <td id=\"T_9b8af_row1_col5\" class=\"data row1 col5\" >1.011</td>\n",
       "      <td id=\"T_9b8af_row1_col6\" class=\"data row1 col6\" >0.910</td>\n",
       "      <td id=\"T_9b8af_row1_col7\" class=\"data row1 col7\" >0.935</td>\n",
       "      <td id=\"T_9b8af_row1_col8\" class=\"data row1 col8\" >0.833</td>\n",
       "      <td id=\"T_9b8af_row1_col9\" class=\"data row1 col9\" >0.820</td>\n",
       "      <td id=\"T_9b8af_row1_col10\" class=\"data row1 col10\" >-0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b8af_level0_row2\" class=\"row_heading level0 row2\" >Mean rank AUC</th>\n",
       "      <td id=\"T_9b8af_row2_col0\" class=\"data row2 col0\" >7.889</td>\n",
       "      <td id=\"T_9b8af_row2_col1\" class=\"data row2 col1\" >6.708</td>\n",
       "      <td id=\"T_9b8af_row2_col2\" class=\"data row2 col2\" >8.375</td>\n",
       "      <td id=\"T_9b8af_row2_col3\" class=\"data row2 col3\" >4.208</td>\n",
       "      <td id=\"T_9b8af_row2_col4\" class=\"data row2 col4\" >4.639</td>\n",
       "      <td id=\"T_9b8af_row2_col5\" class=\"data row2 col5\" >4.139</td>\n",
       "      <td id=\"T_9b8af_row2_col6\" class=\"data row2 col6\" >4.708</td>\n",
       "      <td id=\"T_9b8af_row2_col7\" class=\"data row2 col7\" >4.278</td>\n",
       "      <td id=\"T_9b8af_row2_col8\" class=\"data row2 col8\" >5.014</td>\n",
       "      <td id=\"T_9b8af_row2_col9\" class=\"data row2 col9\" >5.042</td>\n",
       "      <td id=\"T_9b8af_row2_col10\" class=\"data row2 col10\" >-0.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b8af_level0_row3\" class=\"row_heading level0 row3\" >Mean rank AUC OVR</th>\n",
       "      <td id=\"T_9b8af_row3_col0\" class=\"data row3 col0\" >7.889</td>\n",
       "      <td id=\"T_9b8af_row3_col1\" class=\"data row3 col1\" >6.708</td>\n",
       "      <td id=\"T_9b8af_row3_col2\" class=\"data row3 col2\" >8.431</td>\n",
       "      <td id=\"T_9b8af_row3_col3\" class=\"data row3 col3\" >4.153</td>\n",
       "      <td id=\"T_9b8af_row3_col4\" class=\"data row3 col4\" >4.569</td>\n",
       "      <td id=\"T_9b8af_row3_col5\" class=\"data row3 col5\" >4.250</td>\n",
       "      <td id=\"T_9b8af_row3_col6\" class=\"data row3 col6\" >4.764</td>\n",
       "      <td id=\"T_9b8af_row3_col7\" class=\"data row3 col7\" >4.250</td>\n",
       "      <td id=\"T_9b8af_row3_col8\" class=\"data row3 col8\" >4.944</td>\n",
       "      <td id=\"T_9b8af_row3_col9\" class=\"data row3 col9\" >5.042</td>\n",
       "      <td id=\"T_9b8af_row3_col10\" class=\"data row3 col10\" >-0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b8af_level0_row4\" class=\"row_heading level0 row4\" >Mean rank Acc.</th>\n",
       "      <td id=\"T_9b8af_row4_col0\" class=\"data row4 col0\" >8.069</td>\n",
       "      <td id=\"T_9b8af_row4_col1\" class=\"data row4 col1\" >7.319</td>\n",
       "      <td id=\"T_9b8af_row4_col2\" class=\"data row4 col2\" >7.583</td>\n",
       "      <td id=\"T_9b8af_row4_col3\" class=\"data row4 col3\" >4.111</td>\n",
       "      <td id=\"T_9b8af_row4_col4\" class=\"data row4 col4\" >4.556</td>\n",
       "      <td id=\"T_9b8af_row4_col5\" class=\"data row4 col5\" >3.764</td>\n",
       "      <td id=\"T_9b8af_row4_col6\" class=\"data row4 col6\" >4.403</td>\n",
       "      <td id=\"T_9b8af_row4_col7\" class=\"data row4 col7\" >4.458</td>\n",
       "      <td id=\"T_9b8af_row4_col8\" class=\"data row4 col8\" >5.139</td>\n",
       "      <td id=\"T_9b8af_row4_col9\" class=\"data row4 col9\" >5.597</td>\n",
       "      <td id=\"T_9b8af_row4_col10\" class=\"data row4 col10\" >-1.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b8af_level0_row5\" class=\"row_heading level0 row5\" >Mean rank Bal. Acc.</th>\n",
       "      <td id=\"T_9b8af_row5_col0\" class=\"data row5 col0\" >8.306</td>\n",
       "      <td id=\"T_9b8af_row5_col1\" class=\"data row5 col1\" >7.292</td>\n",
       "      <td id=\"T_9b8af_row5_col2\" class=\"data row5 col2\" >7.319</td>\n",
       "      <td id=\"T_9b8af_row5_col3\" class=\"data row5 col3\" >3.819</td>\n",
       "      <td id=\"T_9b8af_row5_col4\" class=\"data row5 col4\" >4.917</td>\n",
       "      <td id=\"T_9b8af_row5_col5\" class=\"data row5 col5\" >3.847</td>\n",
       "      <td id=\"T_9b8af_row5_col6\" class=\"data row5 col6\" >4.556</td>\n",
       "      <td id=\"T_9b8af_row5_col7\" class=\"data row5 col7\" >4.361</td>\n",
       "      <td id=\"T_9b8af_row5_col8\" class=\"data row5 col8\" >5.069</td>\n",
       "      <td id=\"T_9b8af_row5_col9\" class=\"data row5 col9\" >5.514</td>\n",
       "      <td id=\"T_9b8af_row5_col10\" class=\"data row5 col10\" >-1.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b8af_level0_row6\" class=\"row_heading level0 row6\" >Mean rank CE</th>\n",
       "      <td id=\"T_9b8af_row6_col0\" class=\"data row6 col0\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col1\" class=\"data row6 col1\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col2\" class=\"data row6 col2\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col3\" class=\"data row6 col3\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col4\" class=\"data row6 col4\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col5\" class=\"data row6 col5\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col6\" class=\"data row6 col6\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col7\" class=\"data row6 col7\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col8\" class=\"data row6 col8\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col9\" class=\"data row6 col9\" >5.500</td>\n",
       "      <td id=\"T_9b8af_row6_col10\" class=\"data row6 col10\" >0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8505602730>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename(table[-7:]).round(decimals=3).style.highlight_min(axis = 1, props= 'font-weight: bold;').format(precision=3)"
   ]
  }
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