{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Count the number of algorithm evaluations each model had"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classifier\n",
       "AdaBoostClassifier                5775\n",
       "BernoulliNB                      23100\n",
       "DecisionTreeClassifier           25410\n",
       "ExtraTreesClassifier            126605\n",
       "GaussianNB                         165\n",
       "GradientBoostingClassifier      769900\n",
       "KNeighborsClassifier              8831\n",
       "LogisticRegression               39145\n",
       "MultinomialNB                     3300\n",
       "PassiveAggressiveClassifier       7260\n",
       "RandomForestClassifier          126602\n",
       "SGDClassifier                  4258362\n",
       "SVC                             143179\n",
       "Name: parameters, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['classifier', 'dataset', 'parameters'])['accuracy'].mean().reset_index()\n",
    "data.groupby('classifier')['parameters'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Rank the parameters for each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['classifier', 'dataset', 'parameters'])['accuracy'].mean().reset_index()\n",
    "data['accuracy'] = data['accuracy'].apply(lambda x: round(x, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "number of data sets in svm_data: 165\n",
      "number of data sets: 165\n",
      "svm missing  []\n"
     ]
    }
   ],
   "source": [
    "# find data set SVM did not finish on\n",
    "svm_data = data[data['classifier']=='SVC']\n",
    "print([problem for problem,d in data.groupby('dataset') if problem not in svm_data['dataset'].unique()])\n",
    "\n",
    "print('number of data sets in svm_data:',len(svm_data['dataset'].unique()))\n",
    "print('number of data sets:',len(data['dataset'].unique()))\n",
    "print('svm missing ',[p for p in data['dataset'].unique() if p not in svm_data['dataset'].unique()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 165/165 [05:54<00:00,  2.22s/it]\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "model_param_ranks = defaultdict(list)\n",
    "\n",
    "for dataset, group_dataset in tqdm(data.groupby('dataset')):\n",
    "    num_scores = float(len(group_dataset['accuracy'].unique()))\n",
    "    accuracy_ranks = {}\n",
    "    \n",
    "    for rank, accuracy in enumerate(sorted(group_dataset['accuracy'].unique(), reverse=True)):\n",
    "        accuracy_ranks[accuracy] = (rank + 1) / num_scores\n",
    "    \n",
    "    for index, row in group_dataset.iterrows():\n",
    "        model_param_ranks['{}-{}'.format(row['classifier'],\n",
    "                                         row['parameters'])].append(accuracy_ranks[row['accuracy']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_average_param_ranks = defaultdict(float)\n",
    "for model_param in model_param_ranks:\n",
    "    model_average_param_ranks[model_param] = np.mean(model_param_ranks[model_param])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best params for each model from rankings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.093\tRandomForestClassifier-n_estimators=500,min_weight_fraction_leaf=0.0,max_features=None,criterion=entropy\n",
      "0.096\tGradientBoostingClassifier-loss=deviance,learning_rate=0.1,n_estimators=100,max_depth=5,max_features=0.5\n",
      "0.098\tExtraTreesClassifier-n_estimators=500,min_weight_fraction_leaf=0.0,max_features=0.75,criterion=entropy\n",
      "0.111\tSVC-C=100.0,gamma=0.01,kernel=rbf,degree=2,coef0=0.0,\n",
      "0.165\tKNeighborsClassifier-n_neighbors=14,weights=distance\n",
      "0.168\tDecisionTreeClassifier-min_weight_fraction_leaf=0.0,max_features=None,criterion=entropy\n",
      "0.186\tLogisticRegression-C=1.0,penalty=l1,fit_intercept=True,dual=False,\n",
      "0.203\tSGDClassifier-loss=log,penalty=elasticnet,alpha=1e-05,learning_rate=invscaling,fit_intercept=True,l1_ratio=1.0,eta0=1.0,power_t=0.5\n",
      "0.216\tPassiveAggressiveClassifier-C=0.001,loss=squared_hinge,fit_intercept=True\n",
      "0.232\tAdaBoostClassifier-learning_rate=0.5,n_estimators=500\n",
      "0.313\tBernoulliNB-alpha=0.1,fit_prior=True,binarize=0.5\n",
      "0.336\tGaussianNB-\n",
      "0.343\tMultinomialNB-alpha=0.1,fit_prior=True\n"
     ]
    }
   ],
   "source": [
    "models_seen = set()\n",
    "\n",
    "for model_param in sorted(model_average_param_ranks, key=model_average_param_ranks.get, reverse=False):\n",
    "    model = model_param.split('-')[0]\n",
    "    if model not in models_seen:\n",
    "        models_seen.add(model)\n",
    "    else:\n",
    "        continue\n",
    "\n",
    "    print('{}\\t{}'.format(round(model_average_param_ranks[model_param], 3), model_param))\n",
    "    \n",
    "    if len(models_seen) >= 15:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average each model parameter's rankings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "\n",
    "model_param_breakdown_rankings = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))\n",
    "\n",
    "for model_param in model_average_param_ranks:\n",
    "    model = model_param.split('-')[0]\n",
    "    if model == 'GaussianNB':\n",
    "        continue\n",
    "    params = '-'.join(model_param.split('-')[1:])\n",
    "    params = params.split(',')\n",
    "    rank = model_average_param_ranks[model_param]\n",
    "    for param in params:\n",
    "        model_param_breakdown_rankings[model][param.split('=')[0]][param.split('=')[-1]].append(rank)\n",
    "\n",
    "model_param_breakdown_average_rankings = defaultdict(lambda: defaultdict(lambda: defaultdict(float)))\n",
    "\n",
    "for model in sorted(model_param_breakdown_rankings):\n",
    "    for param in model_param_breakdown_rankings[model]:\n",
    "        for param_val in model_param_breakdown_rankings[model][param]:\n",
    "            model_param_breakdown_average_rankings[model][param][param_val] = round(np.mean(\n",
    "                model_param_breakdown_rankings[model][param][param_val]), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier\n",
      "--------------------\n",
      "learning_rate\n",
      "\t=0.5                      0.238\n",
      "\t=0.1                      0.248\n",
      "\t=1.0                      0.269\n",
      "\t=0.01                     0.278\n",
      "\t=50.0                     0.686\n",
      "\t=100.0                    0.715\n",
      "\t=10.0                     0.803\n",
      "n_estimators\n",
      "\t=1000                     0.453\n",
      "\t=500                      0.453\n",
      "\t=100                      0.458\n",
      "\t=50                       0.464\n",
      "\t=10                       0.484\n",
      "\n",
      "BernoulliNB\n",
      "--------------------\n",
      "alpha\n",
      "\t=10.0                     0.423\n",
      "\t=5.0                      0.425\n",
      "\t=25.0                     0.428\n",
      "\t=1.0                      0.431\n",
      "\t=0.75                     0.432\n",
      "\t=0.5                      0.433\n",
      "\t=0.25                     0.434\n",
      "\t=50.0                     0.434\n",
      "\t=0.1                      0.436\n",
      "\t=0.0                      0.714\n",
      "fit_prior\n",
      "\t=True                     0.434\n",
      "\t=False                    0.484\n",
      "binarize\n",
      "\t=0.5                      0.382\n",
      "\t=0.25                     0.396\n",
      "\t=0.75                     0.427\n",
      "\t=0.1                      0.433\n",
      "\t=0.9                      0.472\n",
      "\t=0.0                      0.491\n",
      "\t=1.0                      0.613\n",
      "\n",
      "DecisionTreeClassifier\n",
      "--------------------\n",
      "min_weight_fraction_leaf\n",
      "\t=0.0                      0.204\n",
      "\t=0.05                     0.288\n",
      "\t=0.1                      0.335\n",
      "\t=0.15000000000000002      0.374\n",
      "\t=0.2                      0.405\n",
      "\t=0.25                     0.434\n",
      "\t=0.30000000000000004      0.451\n",
      "\t=0.35000000000000003      0.476\n",
      "\t=0.4                      0.481\n",
      "\t=0.45                     0.5\n",
      "\t=0.5                      0.567\n",
      "max_features\n",
      "\t=None                     0.365\n",
      "\t=0.75                     0.375\n",
      "\t=0.5                      0.39\n",
      "\t=0.25                     0.428\n",
      "\t=sqrt                     0.429\n",
      "\t=log2                     0.432\n",
      "\t=0.1                      0.455\n",
      "criterion\n",
      "\t=gini                     0.41\n",
      "\t=entropy                  0.411\n",
      "\n",
      "ExtraTreesClassifier\n",
      "--------------------\n",
      "n_estimators\n",
      "\t=1000                     0.358\n",
      "\t=500                      0.359\n",
      "\t=100                      0.364\n",
      "\t=50                       0.368\n",
      "\t=10                       0.391\n",
      "min_weight_fraction_leaf\n",
      "\t=0.0                      0.113\n",
      "\t=0.05                     0.226\n",
      "\t=0.1                      0.276\n",
      "\t=0.15000000000000002      0.317\n",
      "\t=0.2                      0.346\n",
      "\t=0.25                     0.379\n",
      "\t=0.30000000000000004      0.405\n",
      "\t=0.35000000000000003      0.439\n",
      "\t=0.4                      0.463\n",
      "\t=0.45                     0.496\n",
      "\t=0.5                      0.588\n",
      "max_features\n",
      "\t=None                     0.343\n",
      "\t=0.75                     0.345\n",
      "\t=0.5                      0.352\n",
      "\t=0.25                     0.373\n",
      "\t=sqrt                     0.375\n",
      "\t=log2                     0.377\n",
      "\t=0.1                      0.41\n",
      "criterion\n",
      "\t=entropy                  0.368\n",
      "\t=gini                     0.368\n",
      "\n",
      "GradientBoostingClassifier\n",
      "--------------------\n",
      "loss\n",
      "\t=exponential              0.242\n",
      "\t=deviance                 0.311\n",
      "learning_rate\n",
      "\t=0.1                      0.137\n",
      "\t=0.5                      0.138\n",
      "\t=1.0                      0.166\n",
      "\t=0.01                     0.197\n",
      "\t=10.0                     0.404\n",
      "\t=50.0                     0.436\n",
      "\t=100.0                    0.458\n",
      "n_estimators\n",
      "\t=1000                     0.258\n",
      "\t=500                      0.26\n",
      "\t=100                      0.268\n",
      "\t=50                       0.277\n",
      "\t=10                       0.319\n",
      "max_depth\n",
      "\t=50                       0.189\n",
      "\t=None                     0.189\n",
      "\t=20                       0.191\n",
      "\t=10                       0.215\n",
      "\t=5                        0.277\n",
      "\t=4                        0.303\n",
      "\t=3                        0.33\n",
      "\t=2                        0.367\n",
      "\t=1                        0.428\n",
      "max_features\n",
      "\t=0.5                      0.272\n",
      "\t=0.6                      0.272\n",
      "\t=0.4                      0.274\n",
      "\t=0.7                      0.275\n",
      "\t=0.3                      0.276\n",
      "\t=0.8                      0.277\n",
      "\t=sqrt                     0.279\n",
      "\t=0.9                      0.28\n",
      "\t=0.2                      0.281\n",
      "\t=log2                     0.281\n",
      "\n",
      "KNeighborsClassifier\n",
      "--------------------\n",
      "n_neighbors\n",
      "\t=13                       0.175\n",
      "\t=12                       0.176\n",
      "\t=14                       0.176\n",
      "\t=15                       0.177\n",
      "\t=10                       0.178\n",
      "\t=11                       0.178\n",
      "\t=16                       0.179\n",
      "\t=7                        0.181\n",
      "\t=8                        0.181\n",
      "\t=17                       0.182\n",
      "\t=9                        0.182\n",
      "\t=6                        0.183\n",
      "\t=18                       0.184\n",
      "\t=5                        0.186\n",
      "\t=19                       0.187\n",
      "\t=20                       0.187\n",
      "\t=21                       0.189\n",
      "\t=23                       0.191\n",
      "\t=22                       0.192\n",
      "\t=24                       0.193\n",
      "\t=4                        0.193\n",
      "\t=25                       0.196\n",
      "\t=3                        0.196\n",
      "\t=50                       0.209\n",
      "\t=1                        0.224\n",
      "\t=2                        0.228\n",
      "\t=100                      0.233\n",
      "weights\n",
      "\t=distance                 0.179\n",
      "\t=uniform                  0.202\n",
      "\n",
      "LogisticRegression\n",
      "--------------------\n",
      "C\n",
      "\t=1.0                      0.236\n",
      "\t=1.5                      0.237\n",
      "\t=2.0                      0.237\n",
      "\t=2.5                      0.237\n",
      "\t=3.0                      0.237\n",
      "\t=3.5                      0.237\n",
      "\t=4.0                      0.237\n",
      "\t=4.5                      0.237\n",
      "\t=5.0                      0.237\n",
      "\t=5.5                      0.237\n",
      "\t=6.0                      0.237\n",
      "\t=6.5                      0.237\n",
      "\t=7.0                      0.237\n",
      "\t=7.5                      0.237\n",
      "\t=0.5                      0.238\n",
      "\t=10.0                     0.238\n",
      "\t=10.5                     0.238\n",
      "\t=11.0                     0.238\n",
      "\t=11.5                     0.238\n",
      "\t=12.0                     0.238\n",
      "\t=12.5                     0.238\n",
      "\t=13.0                     0.238\n",
      "\t=13.5                     0.238\n",
      "\t=14.0                     0.238\n",
      "\t=8.0                      0.238\n",
      "\t=8.5                      0.238\n",
      "\t=9.0                      0.238\n",
      "\t=9.5                      0.238\n",
      "\t=14.5                     0.239\n",
      "\t=15.0                     0.239\n",
      "\t=15.5                     0.239\n",
      "\t=16.0                     0.239\n",
      "\t=16.5                     0.239\n",
      "\t=17.0                     0.239\n",
      "\t=17.5                     0.239\n",
      "\t=18.0                     0.239\n",
      "\t=18.5                     0.239\n",
      "\t=19.0                     0.24\n",
      "\t=19.5                     0.24\n",
      "\t=20.0                     0.24\n",
      "penalty\n",
      "\t=l1                       0.234\n",
      "\t=l2                       0.24\n",
      "fit_intercept\n",
      "\t=True                     0.191\n",
      "\t=False                    0.285\n",
      "dual\n",
      "\t=False                    0.236\n",
      "\t=True                     0.242\n",
      "\n",
      "\t=                         0.238\n",
      "\n",
      "MultinomialNB\n",
      "--------------------\n",
      "alpha\n",
      "\t=0.75                     0.368\n",
      "\t=0.1                      0.369\n",
      "\t=0.25                     0.369\n",
      "\t=0.5                      0.369\n",
      "\t=1.0                      0.369\n",
      "\t=5.0                      0.369\n",
      "\t=10.0                     0.373\n",
      "\t=25.0                     0.382\n",
      "\t=50.0                     0.393\n",
      "\t=0.0                      0.505\n",
      "fit_prior\n",
      "\t=True                     0.369\n",
      "\t=False                    0.405\n",
      "\n",
      "PassiveAggressiveClassifier\n",
      "--------------------\n",
      "C\n",
      "\t=0.01                     0.268\n",
      "\t=0.001                    0.272\n",
      "\t=0.0001                   0.299\n",
      "\t=0.1                      0.313\n",
      "\t=1e-05                    0.321\n",
      "\t=1e-06                    0.333\n",
      "\t=0.5                      0.348\n",
      "\t=1.0                      0.355\n",
      "\t=10.0                     0.359\n",
      "\t=100.0                    0.359\n",
      "\t=50.0                     0.359\n",
      "loss\n",
      "\t=squared_hinge            0.324\n",
      "\t=hinge                    0.328\n",
      "fit_intercept\n",
      "\t=True                     0.28\n",
      "\t=False                    0.372\n",
      "\n",
      "RandomForestClassifier\n",
      "--------------------\n",
      "n_estimators\n",
      "\t=1000                     0.303\n",
      "\t=500                      0.305\n",
      "\t=100                      0.309\n",
      "\t=50                       0.31\n",
      "\t=10                       0.325\n",
      "min_weight_fraction_leaf\n",
      "\t=0.0                      0.106\n",
      "\t=0.05                     0.169\n",
      "\t=0.1                      0.211\n",
      "\t=0.15000000000000002      0.248\n",
      "\t=0.2                      0.278\n",
      "\t=0.25                     0.316\n",
      "\t=0.30000000000000004      0.348\n",
      "\t=0.35000000000000003      0.383\n",
      "\t=0.4                      0.401\n",
      "\t=0.45                     0.425\n",
      "\t=0.5                      0.528\n",
      "max_features\n",
      "\t=0.5                      0.303\n",
      "\t=0.75                     0.305\n",
      "\t=sqrt                     0.306\n",
      "\t=log2                     0.307\n",
      "\t=0.25                     0.31\n",
      "\t=None                     0.314\n",
      "\t=0.1                      0.328\n",
      "criterion\n",
      "\t=gini                     0.31\n",
      "\t=entropy                  0.311\n",
      "\n",
      "SGDClassifier\n",
      "--------------------\n",
      "loss\n",
      "\t=log                      0.457\n",
      "\t=modified_huber           0.465\n",
      "\t=hinge                    0.468\n",
      "\t=squared_hinge            0.477\n",
      "\t=perceptron               0.479\n",
      "penalty\n",
      "\t=l1                       0.462\n",
      "\t=elasticnet               0.469\n",
      "\t=l2                       0.478\n",
      "alpha\n",
      "\t=1e-05                    0.461\n",
      "\t=1e-06                    0.461\n",
      "\t=0.0001                   0.465\n",
      "\t=0.001                    0.473\n",
      "\t=0.01                     0.486\n",
      "learning_rate\n",
      "\t=constant                 0.374\n",
      "\t=invscaling               0.483\n",
      "fit_intercept\n",
      "\t=True                     0.433\n",
      "\t=False                    0.506\n",
      "l1_ratio\n",
      "\t=1.0                      0.46\n",
      "\t=0.9                      0.465\n",
      "\t=0.75                     0.467\n",
      "\t=0.5                      0.469\n",
      "\t=0.15                     0.471\n",
      "\t=0.25                     0.471\n",
      "\t=0.1                      0.473\n",
      "\t=0.0                      0.476\n",
      "eta0\n",
      "\t=0.01                     0.443\n",
      "\t=0.1                      0.453\n",
      "\t=0.5                      0.461\n",
      "\t=1.0                      0.465\n",
      "\t=10.0                     0.479\n",
      "\t=50.0                     0.489\n",
      "\t=100.0                    0.496\n",
      "power_t\n",
      "\t=0.5                      0.346\n",
      "\t=1.0                      0.356\n",
      "\t=0.1                      0.36\n",
      "\t=0.0                      0.374\n",
      "\t=10.0                     0.645\n",
      "\t=50.0                     0.661\n",
      "\t=100.0                    0.667\n",
      "\n",
      "SVC\n",
      "--------------------\n",
      "C\n",
      "\t=0.5                      0.266\n",
      "\t=1.0                      0.269\n",
      "\t=10.0                     0.269\n",
      "\t=100.0                    0.269\n",
      "\t=50.0                     0.272\n",
      "\t=0.1                      0.284\n",
      "\t=0.01                     0.315\n",
      "gamma\n",
      "\t=auto                     0.253\n",
      "\t=0.1                      0.267\n",
      "\t=0.5                      0.277\n",
      "\t=1.0                      0.282\n",
      "\t=100.0                    0.285\n",
      "\t=50.0                     0.285\n",
      "\t=10.0                     0.286\n",
      "\t=0.01                     0.287\n",
      "kernel\n",
      "\t=linear                   0.164\n",
      "\t=poly                     0.175\n",
      "\t=rbf                      0.363\n",
      "\t=sigmoid                  0.472\n",
      "degree\n",
      "\t=3                        0.167\n",
      "\t=2                        0.329\n",
      "coef0\n",
      "\t=0.5                      0.254\n",
      "\t=1.0                      0.254\n",
      "\t=0.1                      0.262\n",
      "\t=10.0                     0.276\n",
      "\t=50.0                     0.289\n",
      "\t=100.0                    0.291\n",
      "\t=0.0                      0.306\n",
      "\n",
      "\t=                         0.278\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for model in sorted(model_param_breakdown_average_rankings):\n",
    "    print(model)\n",
    "    print('--------------------')\n",
    "    for param in model_param_breakdown_average_rankings[model]:\n",
    "        print(param)\n",
    "        for param_val in sorted(model_param_breakdown_average_rankings[model][param],\n",
    "                                key=model_param_breakdown_average_rankings[model][param].get):\n",
    "            print('\\t={}{}{}'.format(param_val,\n",
    "                                   (' ' * 25)[:25 - len(param_val)],\n",
    "                                   model_param_breakdown_average_rankings[model][param][param_val]))\n",
    "            \n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rank each model on a per-data set basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['classifier', 'dataset', 'parameters'])['accuracy'].mean().reset_index()\n",
    "data['accuracy'] = data['accuracy'].apply(lambda x: round(x, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 165/165 [00:00<00:00, 410.63it/s]\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "model_ranks = defaultdict(list)\n",
    "\n",
    "for dataset, group_dataset in tqdm(data.groupby('dataset')):\n",
    "    if len(group_dataset['classifier'].unique()) != 14:\n",
    "        continue\n",
    "    \n",
    "    num_scores = float(len(group_dataset['accuracy'].unique()))\n",
    "    accuracy_ranks = {}\n",
    "    \n",
    "    for rank, accuracy in enumerate(sorted(group_dataset['accuracy'].unique(), reverse=True)):\n",
    "        accuracy_ranks[accuracy] = (rank + 1) / num_scores\n",
    "    \n",
    "    for index, row in group_dataset.iterrows():\n",
    "        model_ranks[row['classifier']].append(accuracy_ranks[row['accuracy']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_average_ranks = defaultdict(float)\n",
    "for model in model_ranks:\n",
    "    model_average_ranks[model] = np.mean(model_ranks[model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for model in sorted(model_average_ranks, key=model_average_ranks.get, reverse=False):\n",
    "    print('{}\\t{}'.format(round(model_average_ranks[model], 3), model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How often is model X better than model Y?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>classifier</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             dataset              classifier  \\\n",
       "0  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...      AdaBoostClassifier   \n",
       "1  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...             BernoulliNB   \n",
       "2  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  DecisionTreeClassifier   \n",
       "3  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...    ExtraTreesClassifier   \n",
       "4  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...              GaussianNB   \n",
       "\n",
       "   accuracy  \n",
       "0     0.502  \n",
       "1     0.504  \n",
       "2     0.532  \n",
       "3     0.552  \n",
       "4     0.499  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pdb\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['dataset','classifier'])['accuracy'].max().reset_index()\n",
    "data['accuracy'] = data['accuracy'].apply(lambda x: round(x, 3))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/166 [00:00<?, ?it/s]/Users/randal_olson/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "100%|██████████| 166/166 [00:09<00:00, 18.18it/s]\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "from itertools import combinations\n",
    "import pdb\n",
    "\n",
    "model_tourneys = defaultdict(int)\n",
    "all_models = sorted(data['classifier'].unique())\n",
    "\n",
    "for dataset, group_dataset in tqdm(data.groupby('dataset')):\n",
    "    \n",
    "#     pdb.set_trace()\n",
    "    group_dataset.loc[:, 'accuracy']= group_dataset['accuracy'].values / group_dataset['accuracy'].max()\n",
    "    group_dataset = group_dataset.set_index('classifier')['accuracy'].to_dict()\n",
    "\n",
    "    for (model1, model2) in combinations(group_dataset.keys(), 2):\n",
    "        if group_dataset[model1] >= group_dataset[model2] + 0.01:\n",
    "            model_tourneys[(model1, model2)] += 1\n",
    "        elif group_dataset[model2] >= group_dataset[model1] + 0.01:\n",
    "            model_tourneys[(model2, model1)] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier                    W / L\n",
      "--------------------\n",
      "\tBernoulliNB                   98 / 25\n",
      "\tDecisionTreeClassifier        53 / 71\n",
      "\tExtraTreesClassifier          24 / 105\n",
      "\tGaussianNB                    126 / 19\n",
      "\tGradientBoostingClassifier    2 / 126\n",
      "\tKNeighborsClassifier          57 / 74\n",
      "\tLogisticRegression            65 / 58\n",
      "\tMultinomialNB                 127 / 23\n",
      "\tPassiveAggressiveClassifier   77 / 47\n",
      "\tRandomForestClassifier        12 / 114\n",
      "\tSGDClassifier                 49 / 73\n",
      "\tSVC                           17 / 102\n",
      "\n",
      "BernoulliNB                           W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            25 / 98\n",
      "\tDecisionTreeClassifier        29 / 114\n",
      "\tExtraTreesClassifier          7 / 133\n",
      "\tGaussianNB                    103 / 37\n",
      "\tGradientBoostingClassifier    0 / 148\n",
      "\tKNeighborsClassifier          21 / 116\n",
      "\tLogisticRegression            37 / 89\n",
      "\tMultinomialNB                 113 / 22\n",
      "\tPassiveAggressiveClassifier   42 / 82\n",
      "\tRandomForestClassifier        3 / 140\n",
      "\tSGDClassifier                 16 / 109\n",
      "\tSVC                           4 / 131\n",
      "\n",
      "DecisionTreeClassifier                W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            71 / 53\n",
      "\tBernoulliNB                   114 / 29\n",
      "\tExtraTreesClassifier          14 / 100\n",
      "\tGaussianNB                    132 / 18\n",
      "\tGradientBoostingClassifier    3 / 128\n",
      "\tKNeighborsClassifier          63 / 70\n",
      "\tLogisticRegression            79 / 54\n",
      "\tMultinomialNB                 135 / 16\n",
      "\tPassiveAggressiveClassifier   94 / 46\n",
      "\tRandomForestClassifier        4 / 117\n",
      "\tSGDClassifier                 69 / 67\n",
      "\tSVC                           33 / 93\n",
      "\n",
      "ExtraTreesClassifier                  W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            105 / 24\n",
      "\tBernoulliNB                   133 / 7\n",
      "\tDecisionTreeClassifier        100 / 14\n",
      "\tGaussianNB                    154 / 3\n",
      "\tGradientBoostingClassifier    14 / 63\n",
      "\tKNeighborsClassifier          104 / 14\n",
      "\tLogisticRegression            110 / 14\n",
      "\tMultinomialNB                 150 / 4\n",
      "\tPassiveAggressiveClassifier   115 / 9\n",
      "\tRandomForestClassifier        23 / 38\n",
      "\tSGDClassifier                 95 / 25\n",
      "\tSVC                           50 / 41\n",
      "\n",
      "GaussianNB                            W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            19 / 126\n",
      "\tBernoulliNB                   37 / 103\n",
      "\tDecisionTreeClassifier        18 / 132\n",
      "\tExtraTreesClassifier          3 / 154\n",
      "\tGradientBoostingClassifier    0 / 157\n",
      "\tKNeighborsClassifier          10 / 146\n",
      "\tLogisticRegression            15 / 130\n",
      "\tMultinomialNB                 74 / 64\n",
      "\tPassiveAggressiveClassifier   17 / 134\n",
      "\tRandomForestClassifier        2 / 156\n",
      "\tSGDClassifier                 10 / 147\n",
      "\tSVC                           5 / 156\n",
      "\n",
      "GradientBoostingClassifier            W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            126 / 2\n",
      "\tBernoulliNB                   148 / 0\n",
      "\tDecisionTreeClassifier        128 / 3\n",
      "\tExtraTreesClassifier          63 / 14\n",
      "\tGaussianNB                    157 / 0\n",
      "\tKNeighborsClassifier          118 / 7\n",
      "\tLogisticRegression            128 / 8\n",
      "\tMultinomialNB                 156 / 2\n",
      "\tPassiveAggressiveClassifier   135 / 4\n",
      "\tRandomForestClassifier        52 / 15\n",
      "\tSGDClassifier                 110 / 13\n",
      "\tSVC                           75 / 20\n",
      "\n",
      "KNeighborsClassifier                  W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            74 / 57\n",
      "\tBernoulliNB                   116 / 21\n",
      "\tDecisionTreeClassifier        70 / 63\n",
      "\tExtraTreesClassifier          14 / 104\n",
      "\tGaussianNB                    146 / 10\n",
      "\tGradientBoostingClassifier    7 / 118\n",
      "\tLogisticRegression            86 / 51\n",
      "\tMultinomialNB                 141 / 8\n",
      "\tPassiveAggressiveClassifier   88 / 30\n",
      "\tRandomForestClassifier        13 / 108\n",
      "\tSGDClassifier                 58 / 62\n",
      "\tSVC                           11 / 107\n",
      "\n",
      "LogisticRegression                    W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            58 / 65\n",
      "\tBernoulliNB                   89 / 37\n",
      "\tDecisionTreeClassifier        54 / 79\n",
      "\tExtraTreesClassifier          14 / 110\n",
      "\tGaussianNB                    130 / 15\n",
      "\tGradientBoostingClassifier    8 / 128\n",
      "\tKNeighborsClassifier          51 / 86\n",
      "\tMultinomialNB                 134 / 6\n",
      "\tPassiveAggressiveClassifier   61 / 22\n",
      "\tRandomForestClassifier        16 / 117\n",
      "\tSGDClassifier                 18 / 68\n",
      "\tSVC                           5 / 111\n",
      "\n",
      "MultinomialNB                         W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            23 / 127\n",
      "\tBernoulliNB                   22 / 113\n",
      "\tDecisionTreeClassifier        16 / 135\n",
      "\tExtraTreesClassifier          4 / 150\n",
      "\tGaussianNB                    64 / 74\n",
      "\tGradientBoostingClassifier    2 / 156\n",
      "\tKNeighborsClassifier          8 / 141\n",
      "\tLogisticRegression            6 / 134\n",
      "\tPassiveAggressiveClassifier   8 / 131\n",
      "\tRandomForestClassifier        2 / 149\n",
      "\tSGDClassifier                 4 / 144\n",
      "\tSVC                           3 / 154\n",
      "\n",
      "PassiveAggressiveClassifier           W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            47 / 77\n",
      "\tBernoulliNB                   82 / 42\n",
      "\tDecisionTreeClassifier        46 / 94\n",
      "\tExtraTreesClassifier          9 / 115\n",
      "\tGaussianNB                    134 / 17\n",
      "\tGradientBoostingClassifier    4 / 135\n",
      "\tKNeighborsClassifier          30 / 88\n",
      "\tLogisticRegression            22 / 61\n",
      "\tMultinomialNB                 131 / 8\n",
      "\tRandomForestClassifier        10 / 126\n",
      "\tSGDClassifier                 0 / 100\n",
      "\tSVC                           2 / 122\n",
      "\n",
      "RandomForestClassifier                W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            114 / 12\n",
      "\tBernoulliNB                   140 / 3\n",
      "\tDecisionTreeClassifier        117 / 4\n",
      "\tExtraTreesClassifier          38 / 23\n",
      "\tGaussianNB                    156 / 2\n",
      "\tGradientBoostingClassifier    15 / 52\n",
      "\tKNeighborsClassifier          108 / 13\n",
      "\tLogisticRegression            117 / 16\n",
      "\tMultinomialNB                 149 / 2\n",
      "\tPassiveAggressiveClassifier   126 / 10\n",
      "\tSGDClassifier                 102 / 27\n",
      "\tSVC                           54 / 35\n",
      "\n",
      "SGDClassifier                         W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            73 / 49\n",
      "\tBernoulliNB                   109 / 16\n",
      "\tDecisionTreeClassifier        67 / 69\n",
      "\tExtraTreesClassifier          25 / 95\n",
      "\tGaussianNB                    147 / 10\n",
      "\tGradientBoostingClassifier    13 / 110\n",
      "\tKNeighborsClassifier          62 / 58\n",
      "\tLogisticRegression            68 / 18\n",
      "\tMultinomialNB                 144 / 4\n",
      "\tPassiveAggressiveClassifier   100 / 0\n",
      "\tRandomForestClassifier        27 / 102\n",
      "\tSVC                           15 / 91\n",
      "\n",
      "SVC                                   W / L\n",
      "--------------------\n",
      "\tAdaBoostClassifier            102 / 17\n",
      "\tBernoulliNB                   131 / 4\n",
      "\tDecisionTreeClassifier        93 / 33\n",
      "\tExtraTreesClassifier          41 / 50\n",
      "\tGaussianNB                    156 / 5\n",
      "\tGradientBoostingClassifier    20 / 75\n",
      "\tKNeighborsClassifier          107 / 11\n",
      "\tLogisticRegression            111 / 5\n",
      "\tMultinomialNB                 154 / 3\n",
      "\tPassiveAggressiveClassifier   122 / 2\n",
      "\tRandomForestClassifier        35 / 54\n",
      "\tSGDClassifier                 91 / 15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from itertools import product\n",
    "\n",
    "for model1 in all_models:\n",
    "    print('{}{}W / L'.format(model1,\n",
    "                               ' ' * (38 - len(model1))))\n",
    "    print('--------------------')\n",
    "    for model2 in all_models:\n",
    "        if model1 == model2:\n",
    "            continue\n",
    "        print('\\t{}{}{} / {}'.format(model2,\n",
    "                                     ' ' * (30 - len(model2)),\n",
    "                                     model_tourneys[(model1, model2)],\n",
    "                                     model_tourneys[(model2, model1)]))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "import numpy as np\n",
    "\n",
    "model_tourney_matrix = []\n",
    "\n",
    "for pair in list(product(all_models, all_models)):\n",
    "    model_tourney_matrix.append(model_tourneys[pair])\n",
    "    \n",
    "model_tourney_matrix = np.array(model_tourney_matrix).reshape((13, 13))\n",
    "all_models = list(np.array(all_models)[np.argsort(model_tourney_matrix.sum(axis=1))[::-1]])\n",
    "model_tourney_matrix = model_tourney_matrix[:, np.argsort(model_tourney_matrix.sum(axis=1))[::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "import numpy as np\n",
    "\n",
    "model_tourney_matrix = []\n",
    "\n",
    "for pair in list(product(all_models, all_models)):\n",
    "    model_tourney_matrix.append(model_tourneys[pair])\n",
    "    \n",
    "model_tourney_matrix = np.array(model_tourney_matrix).reshape((13, 13))\n",
    "all_models = list(np.array(all_models)[np.argsort(model_tourney_matrix.sum(axis=1))[::-1]])\n",
    "model_tourney_matrix = model_tourney_matrix[:, np.argsort(model_tourney_matrix.sum(axis=1))[::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ad0/GuIjkxszIty46PAvMFJGIXCjH8DcQEfIEBQPgcDqwOx0IUK9iFfz9dHev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8CS1WuJjo1lcO/771m2sLPneGPcJ+mvz4eHM+qhwVyJjGLzzl2EVKjAe688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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11613ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "model_nice_dict = {\n",
    "    'AdaBoostClassifier': 'AB',\n",
    "    'BernoulliNB': 'BNB',\n",
    "    'LogisticRegression': 'LR',\n",
    "    'MultinomialNB': 'MNB',\n",
    "    'PassiveAggressiveClassifier': 'PA',\n",
    "    'SGDClassifier': 'SGD',\n",
    "    'GaussianNB': 'GNB',\n",
    "    'DecisionTreeClassifier': 'DT',\n",
    "    'ExtraTreesClassifier': 'ERF',\n",
    "    'RandomForestClassifier': 'RF',\n",
    "    'GradientBoostingClassifier':'GTB',\n",
    "    'KNeighborsClassifier': 'KNN',\n",
    "    'SVC': 'SVM'\n",
    "}\n",
    "model_nice_dict_y = {\n",
    "    'AdaBoostClassifier': 'AdaBoost',\n",
    "    'BernoulliNB': 'Bernoulli Naive Bayes',\n",
    "    'LogisticRegression': 'Logistic Regression',\n",
    "    'MultinomialNB': 'Multinomial Naive Bayes',\n",
    "    'PassiveAggressiveClassifier': 'Passive Aggressive',\n",
    "    'SGDClassifier': 'Linear Model trained via\\nStochastic Gradient Descent',\n",
    "    'GaussianNB': 'Gaussian Naive Bayes',\n",
    "    'DecisionTreeClassifier': 'Decision Tree',\n",
    "    'ExtraTreesClassifier': 'Extra Random Forest',\n",
    "    'RandomForestClassifier': 'Random Forest',\n",
    "    'GradientBoostingClassifier':'Gradient Tree Boosting',\n",
    "    'KNeighborsClassifier': 'K-Nearest Neighbors',\n",
    "    'SVC': 'Support Vector Machine'\n",
    "}\n",
    "\n",
    "model_nice = []\n",
    "model_nice_y = []\n",
    "for m in all_models:\n",
    "    model_nice.append(model_nice_dict[m])\n",
    "    model_nice_y.append(model_nice_dict_y[m])\n",
    "\n",
    "mask_matrix = []\n",
    "for x in range(len(model_nice_dict)):\n",
    "    for y in range(len(model_nice_dict)):\n",
    "        mask_matrix.append(x == y)\n",
    "mask_matrix = np.array(mask_matrix).reshape(13, 13)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "sb.heatmap(np.round(model_tourney_matrix / 165., 2), fmt='0.0%',\n",
    "           mask=mask_matrix,\n",
    "           cmap=sb.cubehelix_palette(500, light=0.95, dark=0.15),\n",
    "           square=True, annot=True, vmin=0., vmax=1.0,\n",
    "           xticklabels=model_nice, yticklabels=model_nice_y, cbar=False)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xlabel('Losses', fontsize=14)\n",
    "plt.ylabel('Wins', fontsize=14)\n",
    "plt.title('% out of 165 datasets where model A outperformed model B', fontsize=18)\n",
    "h = plt.gcf()\n",
    "plt.tight_layout()\n",
    "h.savefig('figs/model_outperformance.pdf', bbox_inches='tight')\n",
    "#plt.savefig('figures/sklearn-model-x-outperform-model-y.pdf', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many models do we need to cover all data sets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>classifier</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             dataset              classifier  \\\n",
       "0  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...      AdaBoostClassifier   \n",
       "1  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...             BernoulliNB   \n",
       "2  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  DecisionTreeClassifier   \n",
       "3  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...    ExtraTreesClassifier   \n",
       "4  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...              GaussianNB   \n",
       "\n",
       "   accuracy  \n",
       "0     0.502  \n",
       "1     0.504  \n",
       "2     0.532  \n",
       "3     0.552  \n",
       "4     0.499  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pdb\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['dataset','classifier'])['accuracy'].max().reset_index()\n",
    "data['accuracy'] = data['accuracy'].apply(lambda x: round(x, 3))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/randal_olson/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model & Data Set Coverage \\\\ \\hline\n",
      "GradientBoostingClassifier & 126 \\\\\n",
      "RandomForestClassifier & 90 \\\\\n",
      "ExtraTreesClassifier & 84 \\\\\n",
      "SVC & 79 \\\\\n",
      "SGDClassifier & 43 \\\\\n",
      "AdaBoostClassifier & 30 \\\\\n",
      "DecisionTreeClassifier & 30 \\\\\n",
      "LogisticRegression & 26 \\\\\n",
      "KNeighborsClassifier & 24 \\\\\n",
      "PassiveAggressiveClassifier & 21 \\\\\n",
      "BernoulliNB & 14 \\\\\n",
      "MultinomialNB & 6 \\\\\n",
      "GaussianNB & 5 \\\\\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "dataset_best_models = defaultdict(list)\n",
    "model_counts = defaultdict(int)\n",
    "\n",
    "for dataset, group_dataset in data.groupby('dataset'):\n",
    "    group_dataset['accuracy'] /= group_dataset['accuracy'].max()\n",
    "    dataset_best_models[dataset] = group_dataset.loc[\n",
    "        group_dataset['accuracy'] >= 0.99, 'classifier'].values\n",
    "\n",
    "for dataset in dataset_best_models:\n",
    "    for model in dataset_best_models[dataset]:\n",
    "        model_counts[model] += 1\n",
    "print('Model','&','Data Set Coverage','\\\\\\\\ \\\\hline')\n",
    "for model in sorted(model_counts, key=model_counts.get, reverse=True):\n",
    "    print(model,'&',model_counts[model],'\\\\\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/randal_olson/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/Users/randal_olson/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "dataset_best_models = defaultdict(list)\n",
    "model_counts = defaultdict(int)\n",
    "\n",
    "for dataset, group_dataset in data.groupby('dataset'):\n",
    "    group_dataset['accuracy'] /= group_dataset['accuracy'].max()\n",
    "    dataset_best_models[dataset] = group_dataset.loc[\n",
    "        group_dataset['accuracy'] >= 0.99, 'classifier'].values\n",
    "\n",
    "for dataset in dataset_best_models:\n",
    "    for model in dataset_best_models[dataset]:\n",
    "        model_counts[model] += 1\n",
    "\n",
    "dataset_exclude_set = set()\n",
    "top_models = []\n",
    "\n",
    "while len(dataset_exclude_set) != len(data['dataset'].unique()):\n",
    "    next_top_model = sorted(model_counts, key=model_counts.get, reverse=True)[0]\n",
    "    top_models.append((model_counts[next_top_model], next_top_model))\n",
    "    \n",
    "    for dataset in dataset_best_models:\n",
    "        if next_top_model in dataset_best_models[dataset]:\n",
    "            dataset_exclude_set.add(dataset)\n",
    "\n",
    "    dataset_best_models = defaultdict(list)\n",
    "    model_counts = defaultdict(int)\n",
    "    \n",
    "    for dataset, group_dataset in data.groupby('dataset'):\n",
    "        if dataset in dataset_exclude_set:\n",
    "            continue\n",
    "        group_dataset['accuracy'] /= group_dataset['accuracy'].max()\n",
    "        dataset_best_models[dataset] = group_dataset.loc[\n",
    "            group_dataset['accuracy'] >= 0.99, 'classifier'].values\n",
    "        \n",
    "    for dataset in dataset_best_models:\n",
    "        for model in dataset_best_models[dataset]:\n",
    "            model_counts[model] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(126, 'GradientBoostingClassifier'),\n",
       " (22, 'SVC'),\n",
       " (8, 'RandomForestClassifier'),\n",
       " (4, 'ExtraTreesClassifier'),\n",
       " (2, 'SGDClassifier'),\n",
       " (1, 'LogisticRegression'),\n",
       " (1, 'MultinomialNB'),\n",
       " (1, 'DecisionTreeClassifier'),\n",
       " (1, 'AdaBoostClassifier')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Same analysis but excluding SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/randal_olson/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/Users/randal_olson/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "dataset_best_models = defaultdict(list)\n",
    "model_counts = defaultdict(int)\n",
    "\n",
    "for dataset, group_dataset in data.loc[data['classifier'] != 'SGDClassifier'].groupby('dataset'):\n",
    "    group_dataset['accuracy'] /= group_dataset['accuracy'].max()\n",
    "    dataset_best_models[dataset] = group_dataset.loc[\n",
    "        group_dataset['accuracy'] >= 0.99, 'classifier'].values\n",
    "\n",
    "for dataset in dataset_best_models:\n",
    "    for model in dataset_best_models[dataset]:\n",
    "        model_counts[model] += 1\n",
    "\n",
    "dataset_exclude_set = set()\n",
    "top_models = []\n",
    "\n",
    "while len(dataset_exclude_set) != len(data['dataset'].unique()):\n",
    "    next_top_model = sorted(model_counts, key=model_counts.get, reverse=True)[0]\n",
    "    top_models.append((model_counts[next_top_model], next_top_model))\n",
    "    \n",
    "    for dataset in dataset_best_models:\n",
    "        if next_top_model in dataset_best_models[dataset]:\n",
    "            dataset_exclude_set.add(dataset)\n",
    "\n",
    "    dataset_best_models = defaultdict(list)\n",
    "    model_counts = defaultdict(int)\n",
    "    \n",
    "    for dataset, group_dataset in data.loc[data['classifier'] != 'SGDClassifier'].groupby('dataset'):\n",
    "        if dataset in dataset_exclude_set:\n",
    "            continue\n",
    "        group_dataset['accuracy'] /= group_dataset['accuracy'].max()\n",
    "        dataset_best_models[dataset] = group_dataset.loc[\n",
    "            group_dataset['accuracy'] >= 0.99, 'classifier'].values\n",
    "        \n",
    "    for dataset in dataset_best_models:\n",
    "        for model in dataset_best_models[dataset]:\n",
    "            model_counts[model] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(127, 'GradientBoostingClassifier'),\n",
       " (22, 'SVC'),\n",
       " (8, 'ExtraTreesClassifier'),\n",
       " (5, 'RandomForestClassifier'),\n",
       " (1, 'LogisticRegression'),\n",
       " (1, 'MultinomialNB'),\n",
       " (1, 'DecisionTreeClassifier'),\n",
       " (1, 'AdaBoostClassifier')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many model-parameter combinations do we need to cover all data sets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>classifier</th>\n",
       "      <th>parameters</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>classifier-params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>learning_rate=0.01,n_estimators=10</td>\n",
       "      <td>0.479</td>\n",
       "      <td>AdaBoostClassifier-learning_rate=0.01,n_estima...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>learning_rate=0.01,n_estimators=100</td>\n",
       "      <td>0.477</td>\n",
       "      <td>AdaBoostClassifier-learning_rate=0.01,n_estima...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>learning_rate=0.01,n_estimators=1000</td>\n",
       "      <td>0.488</td>\n",
       "      <td>AdaBoostClassifier-learning_rate=0.01,n_estima...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>learning_rate=0.01,n_estimators=50</td>\n",
       "      <td>0.484</td>\n",
       "      <td>AdaBoostClassifier-learning_rate=0.01,n_estima...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>learning_rate=0.01,n_estimators=500</td>\n",
       "      <td>0.496</td>\n",
       "      <td>AdaBoostClassifier-learning_rate=0.01,n_estima...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             dataset          classifier  \\\n",
       "0  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  AdaBoostClassifier   \n",
       "1  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  AdaBoostClassifier   \n",
       "2  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  AdaBoostClassifier   \n",
       "3  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  AdaBoostClassifier   \n",
       "4  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  AdaBoostClassifier   \n",
       "\n",
       "                             parameters  accuracy  \\\n",
       "0    learning_rate=0.01,n_estimators=10     0.479   \n",
       "1   learning_rate=0.01,n_estimators=100     0.477   \n",
       "2  learning_rate=0.01,n_estimators=1000     0.488   \n",
       "3    learning_rate=0.01,n_estimators=50     0.484   \n",
       "4   learning_rate=0.01,n_estimators=500     0.496   \n",
       "\n",
       "                                   classifier-params  \n",
       "0  AdaBoostClassifier-learning_rate=0.01,n_estima...  \n",
       "1  AdaBoostClassifier-learning_rate=0.01,n_estima...  \n",
       "2  AdaBoostClassifier-learning_rate=0.01,n_estima...  \n",
       "3  AdaBoostClassifier-learning_rate=0.01,n_estima...  \n",
       "4  AdaBoostClassifier-learning_rate=0.01,n_estima...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pdb\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['dataset','classifier','parameters'])['accuracy'].max().reset_index()\n",
    "data = data[data['classifier']!='LinearSVC']\n",
    "data['classifier-params'] = data['classifier'].values + '-' + data['parameters'].values\n",
    "data['accuracy'] = data['accuracy'].apply(lambda x: round(x, 3))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict \n",
    "\n",
    "dataset_best_models = defaultdict(list)\n",
    "model_counts = defaultdict(int)\n",
    "\n",
    "for dataset, group_dataset in data.groupby('dataset'):\n",
    "    group_dataset.loc[:, 'accuracy'] = group_dataset['accuracy'].values / group_dataset['accuracy'].max()\n",
    "    dataset_best_models[dataset] = group_dataset.loc[\n",
    "        group_dataset['accuracy'] >= 0.99, 'classifier-params'].values\n",
    "\n",
    "for dataset in dataset_best_models:\n",
    "    for model in dataset_best_models[dataset]:\n",
    "        model_counts[model] += 1\n",
    "\n",
    "dataset_exclude_set = set()\n",
    "top_models = []\n",
    "\n",
    "while len(dataset_exclude_set) != len(data['dataset'].unique()):\n",
    "    next_top_model = sorted(model_counts, key=model_counts.get, reverse=True)[0]\n",
    "    top_models.append((model_counts[next_top_model], next_top_model))\n",
    "    \n",
    "    if len(top_models) == 5:\n",
    "        break\n",
    "    \n",
    "    # Don't allow repeat models\n",
    "    data = data.loc[data['classifier'] != next_top_model.split('-')[0].strip()]\n",
    "    \n",
    "    for dataset in dataset_best_models:\n",
    "        if next_top_model in dataset_best_models[dataset]:\n",
    "            dataset_exclude_set.add(dataset)\n",
    "\n",
    "    dataset_best_models = defaultdict(list)\n",
    "    model_counts = defaultdict(int)\n",
    "    \n",
    "    for dataset, group_dataset in data.groupby('dataset'):\n",
    "        if dataset in dataset_exclude_set:\n",
    "            continue\n",
    "        group_dataset.loc[:, 'accuracy'] = group_dataset.loc[:, 'accuracy'].values / group_dataset['accuracy'].max()\n",
    "        dataset_best_models[dataset] = group_dataset.loc[\n",
    "            group_dataset['accuracy'] >= 0.99, 'classifier-params'].values\n",
    "        \n",
    "    for dataset in dataset_best_models:\n",
    "        for model in dataset_best_models[dataset]:\n",
    "            model_counts[model] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(51,\n",
       "  'GradientBoostingClassifier-loss=deviance,learning_rate=0.1,n_estimators=500,max_depth=3,max_features=log2'),\n",
       " (19,\n",
       "  'RandomForestClassifier-n_estimators=500,min_weight_fraction_leaf=0.0,max_features=0.25,criterion=entropy'),\n",
       " (16, 'SVC-C=0.01,gamma=0.1,kernel=poly,degree=3,coef0=10.0,'),\n",
       " (12,\n",
       "  'ExtraTreesClassifier-n_estimators=1000,min_weight_fraction_leaf=0.0,max_features=log2,criterion=entropy'),\n",
       " (8, 'LogisticRegression-C=16.5,penalty=l1,fit_intercept=True,dual=False,')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('LogisticRegression-C=16.5,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=17.5,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=18.0,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=18.5,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=19.0,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=19.5,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=20.0,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=1.5,penalty=l1,fit_intercept=True,dual=False,', 8),\n",
       " ('AdaBoostClassifier-learning_rate=0.01,n_estimators=1000', 8),\n",
       " ('LogisticRegression-C=10.0,penalty=l2,fit_intercept=True,dual=False,', 8),\n",
       " ('LogisticRegression-C=12.0,penalty=l2,fit_intercept=True,dual=True,', 7),\n",
       " ('LogisticRegression-C=15.0,penalty=l2,fit_intercept=True,dual=True,', 7),\n",
       " ('LogisticRegression-C=15.5,penalty=l2,fit_intercept=True,dual=True,', 7),\n",
       " ('LogisticRegression-C=1.5,penalty=l2,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=1.5,penalty=l2,fit_intercept=True,dual=True,', 7),\n",
       " ('LogisticRegression-C=10.5,penalty=l2,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=11.0,penalty=l1,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=11.0,penalty=l2,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=11.5,penalty=l1,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=11.5,penalty=l2,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=12.0,penalty=l1,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=12.0,penalty=l2,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=12.5,penalty=l1,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=12.5,penalty=l2,fit_intercept=True,dual=False,', 7),\n",
       " ('LogisticRegression-C=13.0,penalty=l1,fit_intercept=True,dual=False,', 7)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(x, model_counts[x]) for x in sorted(model_counts, key=model_counts.get, reverse=True)[:25]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model/data biclustering analysis\n",
    "\n",
    "Create matrix of data sets vs. best model accuracy on those data sets.\n",
    "\n",
    "Cluster the matrix.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded  165 datasets and  13 classifiers\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['classifier', 'dataset'])['bal_accuracy'].max().reset_index()\n",
    "# print(\"classifiers before drop:\",data['classifier'].unique())\n",
    "# data = data[data['classifier']!='LinearSVC']\n",
    "# data = data[data['classifier']!='SVC']\n",
    "print('loaded ',data['dataset'].unique().shape[0],'datasets and ', data['classifier'].unique().shape[0],'classifiers')\n",
    "# data['classifier-params'] = data['classifier'].values + '-' + data['parameters'].values\n",
    "data['bal_accuracy'] = data['bal_accuracy'].apply(lambda x: round(x, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/165 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clf: GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1\n",
      "clf: GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1\n",
      "clf: GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1\n",
      "clf: GAMETES_Epistasis_3-Way_20atts_0.2H_EDM-1_1\n",
      "clf: GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_50_EDM-2_001\n",
      "clf: GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_75_EDM-2_001\n",
      "clf: Hill_Valley_with_noise\n",
      "clf: Hill_Valley_without_noise\n",
      "clf: adult\n",
      "clf: agaricus-lepiota\n",
      "clf: allbp\n",
      "clf: allhyper\n",
      "clf: allhypo\n",
      "clf: allrep\n",
      "clf: analcatdata_aids\n",
      "clf: analcatdata_asbestos\n",
      "clf: analcatdata_authorship\n",
      "clf: analcatdata_bankruptcy\n",
      "clf: analcatdata_boxing1\n",
      "clf: analcatdata_boxing2\n",
      "clf: analcatdata_creditscore\n",
      "clf: analcatdata_cyyoung8092\n",
      "clf: analcatdata_cyyoung9302\n",
      "clf: analcatdata_dmft\n",
      "clf: analcatdata_fraud\n",
      "clf: analcatdata_germangss\n",
      "clf: analcatdata_happiness\n",
      "clf: analcatdata_japansolvent\n",
      "clf: analcatdata_lawsuit\n",
      "clf: ann-thyroid\n",
      "clf: appendicitis\n",
      "clf: australian\n",
      "clf: auto\n",
      "clf: backache\n",
      "clf: balance-scale\n",
      "clf: banana\n",
      "clf: biomed\n",
      "clf: breast\n",
      "clf: breast-cancer\n",
      "clf: breast-cancer-wisconsin\n",
      "clf: breast-w\n",
      "clf: buggyCrx\n",
      "clf: bupa\n",
      "clf: calendarDOW\n",
      "clf: car\n",
      "clf: car-evaluation\n",
      "clf: cars\n",
      "clf: cars1\n",
      "clf: chess\n",
      "clf: churn\n",
      "clf: clean1\n",
      "clf: clean2\n",
      "clf: cleve\n",
      "clf: cleveland\n",
      "clf: cleveland-nominal\n",
      "clf: cloud\n",
      "clf: cmc\n",
      "clf: coil2000\n",
      "clf: colic\n",
      "clf: collins\n",
      "clf: confidence\n",
      "clf: connect-4\n",
      "clf: contraceptive\n",
      "clf: corral\n",
      "clf: credit-a\n",
      "clf: credit-g\n",
      "clf: crx\n",
      "clf: dermatology\n",
      "clf: diabetes\n",
      "clf: dis\n",
      "clf: dna\n",
      "clf: ecoli\n",
      "clf: fars\n",
      "clf: flags\n",
      "clf: flare\n",
      "clf: german\n",
      "clf: glass\n",
      "clf: glass2\n",
      "clf: haberman\n",
      "clf: hayes-roth\n",
      "clf: heart-c\n",
      "clf: heart-h\n",
      "clf: heart-statlog\n",
      "clf: hepatitis\n",
      "clf: horse-colic\n",
      "clf: house-votes-84\n",
      "clf: hungarian\n",
      "clf: hypothyroid\n",
      "clf: ionosphere\n",
      "clf: iris\n",
      "clf: irish\n",
      "clf: kddcup\n",
      "clf: kr-vs-kp\n",
      "clf: krkopt\n",
      "clf: labor\n",
      "clf: led24\n",
      "clf: led7\n",
      "clf: letter\n",
      "clf: liver-disorder\n",
      "clf: lupus\n",
      "clf: lymphography\n",
      "clf: magic\n",
      "clf: mfeat-factors\n",
      "clf: mfeat-fourier\n",
      "clf: mfeat-karhunen\n",
      "clf: mfeat-morphological\n",
      "clf: mfeat-pixel\n",
      "clf: mfeat-zernike\n",
      "clf: mnist\n",
      "clf: mofn-3-7-10\n",
      "clf: molecular-biology_promoters\n",
      "clf: monk1\n",
      "clf: monk2\n",
      "clf: monk3\n",
      "clf: movement_libras\n",
      "clf: mushroom\n",
      "clf: mux6\n",
      "clf: new-thyroid\n",
      "clf: nursery\n",
      "clf: optdigits\n",
      "clf: page-blocks\n",
      "clf: parity5\n",
      "clf: parity5+5\n",
      "clf: pendigits\n",
      "clf: phoneme\n",
      "clf: pima\n",
      "clf: postoperative-patient-data\n",
      "clf: prnn_crabs\n",
      "clf: prnn_fglass\n",
      "clf: prnn_synth\n",
      "clf: profb\n",
      "clf: promoters\n",
      "clf: ring\n",
      "clf: saheart\n",
      "clf: satimage\n",
      "clf: schizo\n",
      "clf: segmentation\n",
      "clf: shuttle\n",
      "clf: sleep\n",
      "clf: solar-flare_1\n",
      "clf: solar-flare_2\n",
      "clf: sonar\n",
      "clf: soybean\n",
      "clf: spambase\n",
      "clf: spect\n",
      "clf: spectf\n",
      "clf: splice\n",
      "clf: tae\n",
      "clf: texture\n",
      "clf: threeOf9\n",
      "clf: tic-tac-toe\n",
      "clf: titanic\n",
      "clf: tokyo1\n",
      "clf: twonorm\n",
      "clf: vehicle\n",
      "clf: vote\n",
      "clf: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 165/165 [00:00<00:00, 1920.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vowel\n",
      "clf: waveform-21\n",
      "clf: waveform-40\n",
      "clf: wdbc\n",
      "clf: wine-quality-red\n",
      "clf: wine-quality-white\n",
      "clf: wine-recognition\n",
      "clf: xd6\n",
      "clf: yeast\n",
      "clustering...\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from sklearn.cluster.bicluster import SpectralBiclustering\n",
    "from sklearn.metrics import consensus_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pdb\n",
    "# get model results with best parameters \n",
    "\n",
    "all_models = np.asarray((data['classifier'].unique()))\n",
    "all_datasets = np.asarray((data['dataset'].unique()))\n",
    "model_data_acc = np.zeros([len(all_models),len(all_datasets)])\n",
    "model_data_acc_norm = np.zeros([len(all_models),len(all_datasets)])\n",
    "\n",
    "ranks = np.zeros([len(all_models),len(all_datasets)])\n",
    "#print(\"model_data_acc.shape:\",model_data_acc.shape)\n",
    "# all_models = []\n",
    "for i,(clf, group_clf) in enumerate(tqdm(data.groupby('dataset'))):\n",
    "    print('clf:',clf)\n",
    "    data_acc  = group_clf['bal_accuracy'].transpose()\n",
    "    model_data_acc[:,i] = data_acc\n",
    "#     model_data_acc_norm[:,i] = (data_acc-data_acc.min())/(data_acc.max() - data_acc.min())\n",
    "    model_data_acc_norm[:,i] = (data_acc-data_acc.mean())\n",
    "\n",
    "#     all_models.append(clf)\n",
    "    \n",
    "all_models = np.asarray(all_models)\n",
    "# get ranks\n",
    "for i,mda in enumerate(model_data_acc.transpose()):\n",
    "    #print(\"mda shape:\",mda.shape)\n",
    "    temp = mda.argsort()\n",
    "    ranks[temp,i] = np.arange(len(mda))\n",
    "np.savetxt('figs/ranks',ranks)\n",
    "np.savetxt('figs/model_data_accuracy',model_data_acc)\n",
    "\n",
    "\n",
    "print(\"clustering...\")\n",
    "np.random.seed(42)\n",
    "model = SpectralBiclustering(n_clusters=(4,10), random_state=0)\n",
    "model.fit(model_data_acc)\n",
    "#score = consensus_score(model.biclusters_, (rows[:, row_idx], columns[:, col_idx]))\n",
    "\n",
    "#print(\"consensus score: {:.3f}\".format(score))\n",
    "r = model.row_labels_[:]\n",
    "c = model.column_labels_[:]\n",
    "#rearrange data into biclusters\n",
    "fit_data_norm = model_data_acc_norm[np.argsort(r)]\n",
    "fit_data_norm = fit_data_norm[:, np.argsort(c)]\n",
    "\n",
    "fit_data = model_data_acc[np.argsort(r)]\n",
    "fit_data = fit_data[:, np.argsort(c)]\n",
    "\n",
    "all_models = all_models[np.argsort(r)]\n",
    "all_datasets = all_datasets[np.argsort(c)]\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "biclusters_: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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SGXEM0Dabz05Q1aOANsDlIlIxm3x7yvVASVVdEiadgUAgEAgE9pbU1JRs//7u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0AAAg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\nWUX8H9XyE7ZM9/S2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bc08438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster 1 :\n",
      "\"phoneme\",\"analcatdata_boxing2\",\"GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1\",\"analcatdata_boxing1\",\"GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_50_EDM-2_001\",\"GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_75_EDM-2_001\",\"collins\",\"spectf\",\"connect-4\",\"cars\",\"monk3\",\"corral\",\"glass\",\"prnn_fglass\",\"movement_libras\",\"auto\", \n",
      "cluster 2 :\n",
      "\"haberman\",\"german\",\"heart-c\",\"heart-h\",\"flags\",\"dna\",\"GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1\",\"crx\",\"credit-g\",\"credit-a\",\"contraceptive\",\"colic\",\"cmc\",\"dermatology\",\"hepatitis\",\"mfeat-factors\",\"led24\",\"wine-recognition\",\"tokyo1\",\"titanic\",\"spect\",\"solar-flare_2\",\"saheart\",\"promoters\",\"profb\",\"hungarian\",\"postoperative-patient-data\",\"molecular-biology_promoters\",\"mfeat-zernike\",\"mfeat-pixel\",\"mfeat-karhunen\",\"mfeat-fourier\",\"cleveland-nominal\",\"lymphography\",\"led7\",\"optdigits\",\"cleveland\",\"heart-statlog\",\"analcatdata_asbestos\",\"analcatdata_dmft\",\"breast-w\",\"buggyCrx\",\"analcatdata_cyyoung9302\",\"breast\",\"breast-cancer\",\"cleve\",\"backache\",\"australian\",\"adult\",\"analcatdata_authorship\", \n",
      "cluster 3 :\n",
      "\"parity5\", \n",
      "cluster 4 :\n",
      "\"xd6\",\"krkopt\",\"parity5+5\",\"banana\",\"mux6\",\"monk2\",\"car\",\"tic-tac-toe\",\"monk1\", \n",
      "cluster 5 :\n",
      "\"ann-thyroid\",\"irish\",\"letter\",\"cloud\",\"allhypo\",\"shuttle\",\"ring\",\"threeOf9\",\"hayes-roth\",\"schizo\",\"glass2\",\"nursery\",\"vowel\", \n",
      "cluster 6 :\n",
      "\"mfeat-morphological\",\"texture\",\"analcatdata_happiness\",\"sleep\",\"analcatdata_cyyoung8092\",\"analcatdata_germangss\",\"yeast\",\"analcatdata_aids\",\"wdbc\",\"waveform-40\",\"confidence\",\"waveform-21\",\"diabetes\",\"pima\",\"ecoli\",\"breast-cancer-wisconsin\",\"spambase\",\"balance-scale\",\"analcatdata_bankruptcy\",\"house-votes-84\",\"iris\",\"vote\",\"horse-colic\", \n",
      "cluster 7 :\n",
      "\"sonar\",\"agaricus-lepiota\",\"prnn_synth\",\"segmentation\",\"analcatdata_lawsuit\",\"ionosphere\",\"GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1\",\"cars1\",\"calendarDOW\",\"bupa\",\"biomed\",\"appendicitis\",\"hypothyroid\",\"mushroom\",\"solar-flare_1\",\"labor\",\"liver-disorder\",\"lupus\",\"analcatdata_japansolvent\",\"magic\",\"analcatdata_fraud\",\"mnist\", \n",
      "cluster 8 :\n",
      "\"Hill_Valley_without_noise\",\"flare\",\"coil2000\",\"Hill_Valley_with_noise\", \n",
      "cluster 9 :\n",
      "\"twonorm\",\"vehicle\",\"clean2\",\"mofn-3-7-10\",\"clean1\",\"prnn_crabs\",\"chess\",\"kr-vs-kp\", \n",
      "cluster 10 :\n",
      "\"wine-quality-white\",\"wine-quality-red\",\"churn\",\"car-evaluation\",\"dis\",\"fars\",\"kddcup\",\"allbp\",\"analcatdata_creditscore\",\"new-thyroid\",\"page-blocks\",\"pendigits\",\"tae\",\"splice\",\"allrep\",\"allhyper\",\"soybean\",\"satimage\",\"GAMETES_Epistasis_3-Way_20atts_0.2H_EDM-1_1\", \n"
     ]
    }
   ],
   "source": [
    "model_nice_dict = {\n",
    "    'AdaBoostClassifier': 'AdaBoost',\n",
    "    'BernoulliNB': 'Bernoulli NB',\n",
    "    'LinearSVC': 'Linear SVC',\n",
    "    'LogisticRegression': 'Logistic Regression',\n",
    "    'MultinomialNB': 'Multinomial NB',\n",
    "    'PassiveAggressiveClassifier': 'Passive Aggressive',\n",
    "    'SGDClassifier': 'SGD',\n",
    "    'GaussianNB': 'Gaussian NB',\n",
    "    'DecisionTreeClassifier': 'Decision Tree',\n",
    "    'ExtraTreesClassifier': 'Extra Trees',\n",
    "    'RandomForestClassifier': 'Random Forest',\n",
    "    'GradientBoostingClassifier':'Gradient Boosting',\n",
    "    'KNeighborsClassifier': 'K-Nearest Neighbor',\n",
    "    'SVC': 'SVC'\n",
    "}\n",
    "model_nice = []\n",
    "for m in all_models:\n",
    "    model_nice.append(model_nice_dict[m])\n",
    "    \n",
    "    \n",
    "print(\"biclusters_:\",len(model.biclusters_))\n",
    "#plot\n",
    "# h = plt.figure(figsize=(4,3),sharey=True)\n",
    "# ax = plt.subplot(111)\n",
    "h,ax = plt.subplots(3,figsize=(10,9))\n",
    "# ax = h.add_subplot(311)\n",
    "tmp = ax[0].imshow(fit_data[:,:],cmap=plt.cm.RdBu) \n",
    "# ax[0].set_title('A')\n",
    "# ax[0].set_xlabel('A')\n",
    "cbar=plt.colorbar(tmp,ax=ax[0],orientation='vertical',shrink=0.8)\n",
    "cbar.set_label('Balanced Accuracy')\n",
    "ax[0].set_yticks(range(len(all_models))) #,rotation=90\n",
    "ax[0].set_yticklabels(model_nice) #,rotation=90\n",
    "# ax[1].set_xlabel('Data Set',size=16)\n",
    "ax[0].set_xticks(np.arange(len(all_datasets),step=10))\n",
    "ax[0].xaxis.tick_top()\n",
    "\n",
    "# h = plt.gcf()\n",
    "# ax = plt.gca( )\n",
    "ax[0].set_aspect(4)\n",
    "\n",
    "# h.tight_layout()\n",
    "# h = plt.gcf()\n",
    "\n",
    "# h.savefig(\"figs/bicluster.pdf\",dpi=100)\n",
    "\n",
    "# k = plt.figure(figsize=(10,3))\n",
    "# ax = h.add_subplot(312)\n",
    "tmp = ax[1].matshow(fit_data_norm[:,:],cmap=plt.cm.RdBu) \n",
    "\n",
    "cbar=plt.colorbar(tmp,ax=ax[1],orientation='vertical',shrink=0.8)\n",
    "cbar.set_label('Deviation from Mean')\n",
    "ax[1].set_yticks(range(len(all_models))) #,rotation=90\n",
    "ax[1].set_yticklabels(model_nice) #,rotation=90\n",
    "# ax[1].set_xlabel('Data Set',size=16)\n",
    "ax[1].set_xticks(np.arange(len(all_datasets),step=10))\n",
    "# ax[1].set_xlabel('B')\n",
    "# h = plt.gcf()\n",
    "# ax = plt.gca( )\n",
    "ax[1].set_aspect(4)\n",
    "# h.tight_layout()\n",
    "# h = plt.gcf()\n",
    "\n",
    "# k.savefig(\"figs/bicluster_zeromean.pdf\",dpi=100)\n",
    "\n",
    "\n",
    "# h2 = plt.figure(figsize=(10,3))\n",
    "# ax = h.add_subplot(313)\n",
    "cluster_labels  = np.outer(np.sort(model.row_labels_) + 1,np.sort(model.column_labels_) + 1)\n",
    "boundary = np.zeros((cluster_labels.shape[0],cluster_labels.shape[1]))\n",
    "for i,cr in enumerate(cluster_labels[1:]):\n",
    "    for j,cc in enumerate(cr[1:]):\n",
    "        if cluster_labels[i-1,j] != cluster_labels[i,j]:\n",
    "            boundary[i,j] = 1\n",
    "        if cluster_labels[i,j-1] != cluster_labels[i,j]:\n",
    "            boundary[i,j] = 1\n",
    "            \n",
    "tmp=ax[2].matshow(cluster_labels,cmap=plt.cm.Purples,alpha=1)\n",
    "# tmp = \n",
    "# ydata = [0,165,0,165,0,165]\n",
    "# tmp=ax[2].plot((0,165),(2.5,2.5))\n",
    "# plt.gca().invert_yaxis()\n",
    "cbar=plt.colorbar(tmp,ax=ax[2],orientation='vertical',shrink=0.8)\n",
    "cbar.set_label('Bicluster ID')\n",
    "plt.yticks(range(len(all_models)), model_nice) #,rotation=90\n",
    "ax[2].set_xlabel('Dataset',size=16)\n",
    "plt.xticks(np.arange(len(all_datasets),step=10))\n",
    "# ax[2].set_xlabel('C')\n",
    "# h = plt.gcf()\n",
    "# ax = plt.gca( )\n",
    "ax[2].set_aspect(4)\n",
    "h.tight_layout()\n",
    "# plt.subplots_adjust(top=0.95)\n",
    "h.savefig(\"figs/cluster_all.pdf\",dpi=100)\n",
    "h.savefig(\"figs/cluster_all.eps\",dpi=100)\n",
    "h.savefig(\"figs/cluster_all.png\",dpi=100)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "j=0\n",
    "for c in np.unique(cluster_labels[0,:]):\n",
    "    print('cluster',c,':')\n",
    "    for d in all_datasets[cluster_labels[0,:]==c]:\n",
    "#         print('',j,\":\",d)\n",
    "        print('\"'+d+'\"',end=',')\n",
    "        j+=1\n",
    "    print(' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How do the algorithms cluster?\n",
    "\n",
    "Create matrix of data sets vs. median model accuracy on those data sets.\n",
    "\n",
    "Cluster the matrix using Agglomerative Clustering. Look at the resulting dendrogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "# data = data.groupby(['classifier', 'dataset', 'parameters'])['accuracy'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:25<00:00,  4.43s/it]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from scipy.cluster import hierarchy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "all_models = np.asarray(sorted(data['classifier'].unique()))\n",
    "# remove SVC from all_models\n",
    "\n",
    "all_datasets = np.asarray(sorted(data['dataset'].unique()))\n",
    "\n",
    "model_data_acc = np.zeros([len(all_models),len(all_datasets)])\n",
    "ranks = np.zeros([len(all_models),len(all_datasets)])\n",
    "#print(\"model_data_acc.shape:\",model_data_acc.shape)\n",
    "all_models = []\n",
    "\n",
    "for i,(clf, group_clf) in enumerate(tqdm(data.groupby('classifier'))):\n",
    "#     if clf != 'SVC':\n",
    "    model_best_params_acc = np.zeros(len(all_datasets))\n",
    "    # find best parameter setings for model, based on median cv score for each parameter setting\n",
    "    for params,group_clf_params in group_clf.groupby('parameters'):\n",
    "        # across data sets\n",
    "        for j,a in enumerate(group_clf_params.groupby('dataset')['accuracy'].median()):\n",
    "            if a > model_best_params_acc[j]:\n",
    "                model_best_params_acc[j] = a\n",
    "    # model i's accuracy is the median cv accuracy of the best parameter settings for that model, across data sets\n",
    "    model_data_acc[i,:] = model_best_params_acc\n",
    "\n",
    "    all_models.append(clf)\n",
    "\n",
    "all_models = np.asarray(all_models)\n",
    "\n",
    "# get ranks\n",
    "for i,mda in enumerate(model_data_acc.transpose()):\n",
    "    #print(\"mda shape:\",mda.shape)\n",
    "    temp = mda.argsort()\n",
    "    ranks[temp,i] = np.arange(len(mda))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering...\n"
     ]
    },
    {
     "data": {
      "image/png": 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PzLlW01+dnW10dCw49w3EDCqEEFpER0c348bN7n3HkogZVAghzAcjRsDf/las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\n1MQlwB75sf8DbrJ9tKQ24FJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12abd3940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qg97y5yihiUyeOmnWJdvu1Ldvn25f7KEn1GucsVBFCJ0TM84QWtDkqZO47pm/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\nB6tQRGsAHXHiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110347630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_nice_dict = {\n",
    "    'AdaBoostClassifier': 'AdaBoost',\n",
    "    'BernoulliNB': 'Bernoulli NB',\n",
    "    'LinearSVC': 'Linear SVC',\n",
    "    'LogisticRegression': 'Logistic Regression',\n",
    "    'MultinomialNB': 'Multinomial NB',\n",
    "    'PassiveAggressiveClassifier': 'Passive Aggressive',\n",
    "    'SGDClassifier': 'SGD',\n",
    "    'GaussianNB': 'Gaussian NB',\n",
    "    'DecisionTreeClassifier': 'Decision Tree',\n",
    "    'ExtraTreesClassifier': 'Extra Trees',\n",
    "    'RandomForestClassifier': 'Random Forest',\n",
    "    'GradientBoostingClassifier':'Gradient Boosting',\n",
    "    'KNeighborsClassifier': 'K-Nearest Neighbor',\n",
    "    'SVC': 'SVC'\n",
    "}\n",
    "model_nice = []\n",
    "for m in all_models:\n",
    "    model_nice.append(model_nice_dict[m])\n",
    "    \n",
    "print(\"clustering...\")\n",
    "Z = hierarchy.linkage(ranks, 'single')\n",
    "dn = hierarchy.dendrogram(Z, labels = model_nice, orientation='right')\n",
    "plt.title('Ranking Dendrogram')\n",
    "h = plt.gcf()\n",
    "plt.figure(1,figsize=(6,4))\n",
    "plt.tight_layout()\n",
    "h.savefig('figs/HAC_models_ranking.pdf')\n",
    "\n",
    "plt.figure()\n",
    "Z = hierarchy.linkage(model_data_acc, 'single')\n",
    "dn = hierarchy.dendrogram(Z, labels = model_nice, orientation='right')\n",
    "plt.title('Accuracy Dendrogram')\n",
    "h = plt.gcf()\n",
    "plt.figure(1,figsize=(6,4))\n",
    "plt.tight_layout()\n",
    "h.savefig('figs/HAC_models_accuracy.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# How do the data sets cluster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 145/145 [00:30<00:00,  2.09it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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NHLpP1R9fOsThqhYuXFLMqvn52JPG98sAyPc8XiZq7BM1bpDY3817DyeafZ2+\nDfwW+BDwNjAdc3fdsbQBke9qiUw0SimLUupu4FLgeq11ADgIPKy1DmitDwKNQGEU7yVOUnjLjREa\nJTbuqaasup3fPXuAr93/Fs3tw28rL4QQp1pUGw9qrQ8AVwFPaa07gGg2D9oAXAkQnIPaPej1+4Fk\n4LqIob6PY85VoZQqwqzCqqOJUZyc/nuhhjZKdPX00dnjZVZxBuctKqClw8PWA3WnO0QhxCQVzVp8\ntUqp/wGWAx9RSt0DlEdx3uPApUqpjZiroN+qlLoJczhvK+Y+U28CryilwFyU9jfA75RS6zEbMT4+\n2vCeePfC6/ENU0GFtu2Ylp/OFaumsmF3DXvKmrh0xZQRr1fT1EV2uuOkhgKFECJSNAnqw8A64F6t\ndadS6ihR3CMVnGf61KCnD0R8PVL1dlMUMYlTZLQ9oepbugFwZyaT7UqmKNeJLm+mz+sjaZjGlMbW\nHr754GauWj2N684feTeWPq8fgCRbVAW8EGKSimbDwnbgoYjHYzVHiAlktCG+/gRlNk8snJHNC1sq\nOFjZyoLp2UOOr2vuwucPUN04euPKz/+2i47uPr59y4p3G74Q4gwmv8JOcqMP8Q1NUAB7jzYNe63W\nTg8A7V2eEd+vq6ePfWVNHK9pp7fPd/KBCyHOeJKgJrnohvjMBDVnSiZJNgt7yhqHvVZbMEGNtjLF\nwYrW8AKPoesLIcRwokpQSqmblFLfU0qlKqU+GuugxOkzVpOEy2nHYTfnm+xJVuZMyaSyvnPYdvP+\nCmrkBKUrmsNf1zVLghJCjGzMBKWU+iFmu/j7Meesbg128okzwEh7Qvn8fhrbenBnJg94PjzMVzZ0\nmC+yggoEhl8I/0B5S/hrSVBCiNFEU0FdDtwM9ASXOLoUuCKmUYnTxmqxkOKw0tE9sIJqbuvF5w+E\nh/dCQs0RkZVQSKiC8vkDdPUOrci6eryU17aH96uqkyE+IcQooklQ/uDfoV+JHRHPiTOAMzmJuuYu\nnttcTncwsYTnnzIGJqjC3FSSbBYq6jqGXCdUQcHww3yHKlsIBODchQWA2fU3Hn1enzRWCDGJRJOg\n/gL8GchWSt0FvAE8GtOoxGl16fIpGIbBX149zL/9+i3qW7rD1c3gCspqsVDidnKioROvb+DvKa0D\nEtTQTj5dYQ7vLZqZQ4bTPu4hvl8+vocfPLxtXOcIISauMROU1vq/MVd4eAyYCnxba/39WAcmTp9L\nV0zhx3fGD8gTAAAgAElEQVSey2UrptDR3ceLWyvCq0gMnoMCmJKXjtc38H4nvz8woGoaroLS5c1Y\nLQazijLIy0qhsa1nSJIbTWV9B+W1HeEbfYUQZ7ZomiQuALqBfwBPAK1KqeVKqcxYBydOn7SUJD6w\ndiaZaXY27K6mst4cwsvLSh1y7NT8NADKa/tXPu7o7sMf0RgxuILq7vVyvKaDGYUuHHYreVkpBALQ\n0NoTdYyhTsPm9ujPEUJMXNEM8X0LeAr4POYOuE8CDwBbg7viijOEzWph7dJiunt97DrSiM1qISNt\n6LrAU/PMReoj56FC80+himtwBVVZ34E/EKC0yAVAXnDoMNphPq/PT6/HnH9qbJMV1YWYDKJJUAZw\nltb6eq31+zH3bKoHzga+EsvgxOl34eIirME9otyZyViG2WW32O0EBiao1mDFVJxrVleDE1Rtk5mI\nCnPMiixUmUXbKBHZFdjUJhWUEJNBNAmqSGsdXr1ca30CKAy2nMse4WeYjDQHK+bmAUMbJEJSHDby\nslIor20P3+/U1hFMUMHk1d49cIivNpiI8rNCCWp8FVRXjyQoISabaFYz36CUehR4BDOhfQh4Syl1\nFTC011hMeJcsn8LmfbVMyUsb8ZipeWls1fU0t/eS7UoOd/CVuM1zOoZUUMEElT0oQUV5L1RkgpIh\nPiEmh2gqqE8BG4E7MHfSXQ98BvO+qJtjF5qIl9IiF9+9fSVXnzt9xGOm5JvzUOXBYb7QHFRuZjL2\nJMuQIb6apm4cSVYyg3NazuQknMm2cVRQ/ddrkiYJISaFaLbb8AYrqCcxh/SswAVa62diHZyIn8Ic\n56ivTw1WVxW17SyZlRuuoDJS7aSn2AcM8fn9AepauijISsWImNPKy0qlvLYdvz+AxTL6aHHngCE+\nqaCEmAyiaTP/AVAGaMzq6TDwgxjHJRLc1CEVlJk0XE476alJtHf1r8fX1NaDp89PXvbAlvW8rBR8\n/kBUFVFkBdXY1jPiWn9CiDNHNEN8HwKmYK4mcRFwCWYXn5jEMtPspKUkcbzGvBeqtbOPFIcVe5KV\n9FQ7fV5/eFmiEw1mEivIHth0MZ5W81AXn81qodfjCy/JJIQ4c0WToKqDHXt7gMVa61eB/NiGJRKd\nYRioKZk0tPZQ1dBJW2cvLqcDgPRUczHY0DxUVX0n0N/BFzKeTr7QEF+oS1AaJYQ480WToFqVUjcD\n24B/UUqtArJiG5aYCJYpNwBb9tfS3t1HhtNsgBicoE4EV6XIH2aID6Lr5AsN8U0Jdgk2Squ5EGe8\naBLUJ4A8rfVrwDHgfuAbMYxJTBCLZ+Visxq88c4JAgFz/gkIb6cRWu7oRLiCGjTEF75ZN/oKqiTY\nnNEsCUqIM14090F9T2t9K4DW+ksxjkdMICkOGwumZ/POEXML+P4Kyvw7XEE1dOBMtoUTV4grNQmH\n3RrVahKh+6CmyBCfEJNGNBXUQqXUyHdsikltmcoLf+0aPMTX7cHn91PT2EneoBZzMOex8jJTqGvp\nHtKVd6Khk4eeO4An2GjR1ePFkWTFHazC5F4oIc580VRQfqBcKaUxVzUHQGv9nphFJSaMJbNzsVoM\nfP7AkAqqo6uPxrZevL7AkA6+kLysFCrqOmjt9JCZ5gg//8+3jrFpby1nzcxlyexcOnv6SE22kZnm\nwACaxrEKuhBiYoomQX31ZC6slLIA9wGLgV7gNq314YjXP4y5OroX2A3cGXxpxHNE4klLSWLutCz2\nljUNraC6+vqXOBpm2w4Y2MkXSlBen59dh81hw1Cl1NXjJdvlwGa1kJnukCE+ISaBaDYsfB0zicwD\nNgGB4HNjuQ5I1lqvBr4G3BN6QSmVAnwXuEhrfR6QAVw92jkicV2xciqlRS5mFWcAkJ4SmoPyUFbd\nBgzt4AsZ7l6ow5Wt4fueGtt68AcCdPd6SXWYv09lpzto6ejF75ebdYU4k41ZQSmlPo+ZOIoxd9W9\nXyn1G6313WOcugZ4DkBrvUkptTzitV7gXK11aHbcBvQA7x3lnGFlZaVis1nHOmwAtzt9XMcnkkSM\n/UJ3OheumBZ+HAgEsFkNDlW1sutoIykOK6sWF5M7zOroc2bkANDh8YU/25Mbj4df7+z14UxLJgBk\nZaTgdqdT6E7jyIk2bMlJ5GQMP3Q4ls7uPpIdtvDWIqMZ7Xu+dX8tv/r7Ln7w6fOGrJSRCBLx30s0\nJmrcILGfStEM8d0CrAQ2a60blVIrgLeBsRKUC2iNeOxTStm01l6ttR+oBVBKfQ5IA14EPjjSOSO9\nSXOU+wmFuN3p1Ne3j31gAppIsaelJNHS4cHltPOdO1YT6PMOG7sjmB+OVbVQX29u3/HWrhM47FY8\nfT6q6zsor2oBwGZAfX07acFKau+heuZNG/8teS0dvfz7/Zu4ctVUrjlvxqjHjvU9//srh6hr6mL9\n9grOX1w07lhiaSL9e4k0UeMGif3dvPdwouni82mtIzf36QF8UZzXBkS+qyUy0SilLEqpu4FLgeu1\n1oGxzhETx+ySTIpznXz95mXMKskc8bjMdHNeKTTEd6Kxi7qWbhbNyCYr3UFTW0+4xTw12ZzbWlCa\nDcCLWypOKrbDla309vk4cqLtpM4P6e71sv94EwBVDZ3v6lpCiKGiqaBeDyYSp1LqOsxtN16O4rwN\nwDXAX4KrT+we9Pr9mEN91wUrqmjOERPEp65dADCktXwwi2HgzkwOJ6idh8xlHpfMzqWl08PRqrbw\nyujOZPOf6/xpWcwpyWDn4QaOnGhlZlHGuGI7Xmv+llgf5V5UI9lT1oTXZ86DSYIS4tSLJkF9Bbgd\neAf4KPAM8OsoznscuFQptRFzm45blVI3YQ7nbcVcoeJN4BWlFMC9w50zrk8jEsZYiSlSflYq1Y1d\ntHV62LyvDsOAs2bmsutII4cDAU40mMO4KcEEZRgG6y4o5b8f3cHjbxzlyx9aGr7WkROtPPFmGV6v\nnySbhctXTmXB9OwB71deay691Nhqroo+nlgj7QgmU6vF4MQ4E1RVfQfPbi7npktmhyvDkLYuD5v3\n1bJ2SRFJUcyvvrajiq5eL1eumjbmsUJMJNEkqJ8AD2ut7x/PhYNV0acGPX0g4uuRhhcHnyPOcKGt\n5R96XlNZ38GqBfmkpSSR40oGoKLOrHhCFRSAmprFghnZ7C1rYv+xJuZNz6azp4/7Ht9Dc3t/C3pV\nQyffv30VDnv/D/pQBeXx+mnr9JARcf9VtEKt8NkuB4U5TvaWNdHV0zck2YzkyfVlbNX1zCrJYO2S\n4gGvPfPWcV7YUkFbp4frL5w56nV2HKrnoec1hgEXLyvBkTS+hqGT4enz4fMHSHFE8+NDiJMXzRzU\nIeBnSql9SqlvKKWmxzgmMcmE7oXafrCejDQ7N10yB4DsYIKqrDOrk8E//NedXwrALx/fw8GKFh5+\n4SDN7b2su6CUB//tIq5aPY3m9l6e3dzfFdjS0Rve/RegvuXkbvg9VNFCV6+XJbNyKQkuvzTSMF+f\n18fP/7qLrQfqAOjo7mPn4QYA9h9rHnL87qPmPWDPbS6nKrjQ7nDqWrp58J/7AQgEGHcVdzICgQA/\n/cs7fOe3W2RPLhFz0dwH9Uu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Fik37anhqQ9mQe62a23vD29W//s6JU/65xKkjCUoIERcz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e6efb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x185686f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy\n",
    "from tqdm import tqdm\n",
    "#==========\n",
    "# optimal K via elbow method with silhouette score which produces a better elbow.\n",
    "#==========\n",
    "X = model_data_acc.transpose()\n",
    "Ks = np.arange(2,147,1)\n",
    "Inertias = []\n",
    "Silhouettes = []\n",
    "np.random.seed(2)\n",
    "# loop through k values\n",
    "for K in tqdm(Ks):\n",
    "\tkm = KMeans(n_clusters=K, init='k-means++',copy_x=False).fit(X)\n",
    "\tlabels = km.labels_\n",
    "\tcenters = km.cluster_centers_\n",
    "\tinertia = km.inertia_\n",
    "\tSilhouettes.append(silhouette_score(X,labels))\n",
    "\t# Inertias[K-1] = km.inertia_\n",
    "\tInertias.append(km.inertia_)\n",
    "\n",
    "# line plot of K versus Silhouette score with best value marked with x \n",
    "plt.figure(1)\n",
    "plt.plot(Ks,Silhouettes,label='silhouette')\n",
    "plt.plot(Ks[np.argmax(Silhouettes)],Silhouettes[np.argmax(Silhouettes)],marker = 'o',color='r',markersize=7)\n",
    "plt.text(Ks[np.argmax(Silhouettes)]-2,Silhouettes[np.argmax(Silhouettes)],\"K = \"+repr(Ks[np.argmax(Silhouettes)]))\n",
    "plt.ylim(0.95*np.min(Silhouettes),1.05*np.max(Silhouettes))\n",
    "plt.ylabel(\"Average silhouette score\") #Y-axis label\n",
    "plt.xlabel(\"K\") #X-axis label\n",
    "plt.title(\"Choice of K\") #Plot title\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figs/k_silhouette.pdf\")\n",
    "\n",
    "plt.figure(2)\n",
    "plt.plot(Ks,Inertias,label='inertia')\n",
    "plt.plot(Ks[np.argmin(Inertias)],Inertias[np.argmin(Inertias)],marker = 'o',color='r',markersize=7)\n",
    "plt.text(Ks[np.argmin(Inertias)]-2,Inertias[np.argmin(Inertias)],\"K = \"+repr(Ks[np.argmin(Inertias)]))\n",
    "plt.ylim(0.95*np.min(Inertias),1.05*np.max(Inertias))\n",
    "plt.ylabel(\"Inertias\") #Y-axis label\n",
    "plt.xlabel(\"K\") #X-axis label\n",
    "plt.title(\"Choice of K\") #Plot title\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figs/k_inertia.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/randal_olson/anaconda/lib/python3.6/site-packages/matplotlib/lines.py:1206: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  if self._markerfacecolor != fc:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "col: [ 0.10588235  0.61960784  0.46666667  1.        ]\n",
      "col: [ 0.45882353  0.43921569  0.70196078  1.        ]\n",
      "col: [ 0.90196078  0.67058824  0.00784314  1.        ]\n",
      "col: [ 0.4  0.4  0.4  1. ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11435ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ClF13vyhyTID9zewRsg0hE3wAk54h0jF4n3IgdjQwPLtcmJgRbdJLVmGhKqsK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\noEREJJeUoEREJJeUoEREJJf+H6bt6ZTCza8eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131785828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# =====\n",
    "# plot cluster centers on 2 principal component axes\n",
    "# =====\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import itertools\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "marker =('+', 'x', 'o', '*','s','^','<','v','>') \n",
    "h = plt.figure()\n",
    "ss = StandardScaler()\n",
    "X = ss.fit_transform(model_data_acc.transpose())\n",
    "\n",
    "pca = PCA(n_components = 2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "nc=4\n",
    "unique_classes = np.array((0,1,2,3)) \n",
    "km = KMeans(n_clusters=nc, init='k-means++',copy_x=False,random_state=0).fit(X)\n",
    "labels = km.labels_\n",
    "centers = km.cluster_centers_\n",
    "unique_labels = np.unique(labels)\n",
    "# centers_pca = centers\n",
    "centers_pca = pca.transform(centers)\n",
    "colors = plt.cm.Dark2(np.linspace(0, 1, len(unique_labels)))\n",
    "\n",
    "for k, col in zip(unique_labels, colors):\n",
    "    label_mask = (k==labels)\n",
    "    xy = X_pca[label_mask]\n",
    "    plt.plot(xy[:,0], xy[:, 1], linestyle = '', marker=marker[k%nc], markerfacecolor=col, markersize=5, alpha=1)\n",
    "\n",
    "for k, col in zip(unique_labels, colors): \n",
    "     plt.plot(centers_pca[k,0],centers_pca[k,1], linestyle='', marker=marker[k%nc], markerfacecolor=col,markersize=20,alpha=0.3)\n",
    "\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.tight_layout()\n",
    "h.savefig('figs/k_means_PCA_data.pdf')\n",
    "\n",
    "h2 = plt.figure()\n",
    "features = model_nice\n",
    "    \n",
    "for k,col in zip(unique_labels,colors):\n",
    "    label_mask = (k==labels)\n",
    "    coverage = np.sum(label_mask)\n",
    "    xk_mean = np.mean(ss.inverse_transform(X[label_mask]),axis=0)\n",
    "    offset = k*0.1-np.mean(np.unique(labels))*0.1\n",
    "    print('col:',col)\n",
    "    plt.bar(np.arange(len(features))+offset,xk_mean,align='center',width=0.1,facecolor=col,label='cluster '+marker[k%nc]+' ('+str(coverage)+' instances)')\n",
    "\n",
    "plt.ylim(0,1.1)\n",
    "plt.gca().set_xticks(np.arange(len(features)))\n",
    "plt.gca().set_xticklabels(list(features),fontsize=8,rotation=90)\n",
    "plt.legend(loc=3,fontsize=6) #(bbox_to_anchor=(1.05, 1), \n",
    "plt.tight_layout()\n",
    "h2.savefig('figs/data_ml_bar.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of tuned to un-tuned results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "\n",
    "data = data.groupby(['classifier', 'dataset', 'parameters'])['accuracy'].mean().reset_index()\n",
    "data['accuracy'] = data['accuracy'].apply(lambda x: round(x, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_defaults_dict = {\n",
    "    'GradientBoostingClassifier': 'loss=deviance,learning_rate=0.1,n_estimators=100,max_depth=3,max_features=None',\n",
    "    'RandomForestClassifier': 'n_estimators=10,min_weight_fraction_leaf=0.0,max_features=sqrt,criterion=gini',\n",
    "    'SVC': 'C=1.0,gamma=auto,kernel=rbf,degree=2,coef0=0.0,',\n",
    "    'ExtraTreesClassifier': 'n_estimators=10,min_weight_fraction_leaf=0.0,max_features=sqrt,criterion=gini',\n",
    "    'KNeighborsClassifier': 'n_neighbors=5,weights=uniform',\n",
    "    'LogisticRegression': 'C=1.0,penalty=l2,fit_intercept=True,dual=False,',\n",
    "    'DecisionTreeClassifier': 'min_weight_fraction_leaf=0.0,max_features=None,criterion=gini',\n",
    "    'SGDClassifier': 'loss=hinge,penalty=l2,alpha=0.0001,learning_rate=optimal,fit_intercept=True,l1_ratio=0.15,eta0=0.0,power_t=0.5',\n",
    "    'PassiveAggressiveClassifier': 'C=1.0,loss=hinge,fit_intercept=False',\n",
    "    'AdaBoostClassifier': 'learning_rate=1.0,n_estimators=50',\n",
    "    'BernoulliNB': 'alpha=1.0,fit_prior=True,binarize=0.0',\n",
    "    'GaussianNB': '',\n",
    "    'MultinomialNB': 'alpha=1.0,fit_prior=True'\n",
    "}\n",
    "\n",
    "default_params_list = ['-'.join([k, v]) for k, v in clf_defaults_dict.items()]\n",
    "\n",
    "default_scores = data.loc[\n",
    "    data.apply(\n",
    "        lambda record: '-'.join([record['classifier'],\n",
    "                                 record['parameters']]) in default_params_list,\n",
    "        axis=1)].drop('parameters', axis=1)\n",
    "\n",
    "best_scores = data.groupby(['dataset', 'classifier'])['accuracy'].max().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>classifier</th>\n",
       "      <th>dataset</th>\n",
       "      <th>accuracy_default</th>\n",
       "      <th>accuracy_best</th>\n",
       "      <th>accuracy_default_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>GAMETES_Epistasis_3-Way_20atts_0.2H_EDM-1_1</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_...</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  classifier                                            dataset  \\\n",
       "0   AdaBoost  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...   \n",
       "1   AdaBoost        GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1   \n",
       "2   AdaBoost        GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1   \n",
       "3   AdaBoost        GAMETES_Epistasis_3-Way_20atts_0.2H_EDM-1_1   \n",
       "4   AdaBoost  GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_...   \n",
       "\n",
       "   accuracy_default  accuracy_best  accuracy_default_scaled  \n",
       "0             0.474          0.502                    0.028  \n",
       "1             0.480          0.506                    0.026  \n",
       "2             0.459          0.503                    0.044  \n",
       "3             0.518          0.526                    0.008  \n",
       "4             0.493          0.527                    0.034  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_scores = default_scores.merge(best_scores,\n",
    "                                     on=['classifier', 'dataset'],\n",
    "                                     suffixes=['_default', '_best'])\n",
    "\n",
    "merged_scores['accuracy_default_scaled'] = merged_scores['accuracy_best'] - merged_scores['accuracy_default']\n",
    "\n",
    "model_names_dict = {\n",
    "    'AdaBoostClassifier': 'AdaBoost',\n",
    "    'BernoulliNB': 'Bernoulli Naive Bayes',\n",
    "    'LogisticRegression': 'Logistic Regression',\n",
    "    'MultinomialNB': 'Multinomial Naive Bayes',\n",
    "    'PassiveAggressiveClassifier': 'Passive Aggressive',\n",
    "    'SGDClassifier': 'Linear Model trained via\\nStochastic Gradient Descent',\n",
    "    'GaussianNB': 'Gaussian Naive Bayes',\n",
    "    'DecisionTreeClassifier': 'Decision Tree',\n",
    "    'ExtraTreesClassifier': 'Extra Random Forest',\n",
    "    'RandomForestClassifier': 'Random Forest',\n",
    "    'GradientBoostingClassifier':'Gradient Tree Boosting',\n",
    "    'KNeighborsClassifier': 'K-Nearest Neighbors',\n",
    "    'SVC': 'Support Vector Machine'\n",
    "}\n",
    "\n",
    "merged_scores['classifier'] = merged_scores['classifier'].apply(lambda x: model_names_dict[x])\n",
    "\n",
    "merged_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nnXeIiYmxtpf94N7EiROJiIhg0KBBNGrUiIkTJzJhwoSSD/QOnp6evPTSS0yd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\nEEIIIQCA/wO/Q/dHUG1IAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cbdce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sb.set_style('whitegrid')\n",
    "\n",
    "plt.figure(figsize=(9, 12))\n",
    "sb.boxplot(data=merged_scores.sort_values('classifier'),\n",
    "           y='classifier', x='accuracy_default_scaled', notch=True,\n",
    "           palette=[sb.color_palette('Purples', n_colors=2)[1]])\n",
    "plt.ylabel('')\n",
    "plt.xlabel('10-fold CV accuracy improvement by tuning models', fontsize=16)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlim(0., 0.5)\n",
    "#plt.title('Tuning machine learning model parameters almost always improves\\nmodel performance', fontsize=22)\n",
    "plt.savefig('figs/tuned_untuned_accuracy_boxplot.pdf', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# print model abbreviation table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n",
      "{'AdaBoostClassifier': 'AB', 'BernoulliNB': 'BNB', 'LinearSVC': 'LSVC', 'LogisticRegression': 'LR', 'MultinomialNB': 'MNB', 'PassiveAggressiveClassifier': 'PAC', 'SGDClassifier': 'SGD', 'GaussianNB': 'GNB', 'DecisionTreeClassifier': 'DT', 'ExtraTreesClassifier': 'ET', 'RandomForestClassifier': 'RF', 'GradientBoostingClassifier': 'GB', 'KNeighborsClassifier': 'KNN'}\n"
     ]
    }
   ],
   "source": [
    "model_nice_dict = {\n",
    "    'AdaBoostClassifier': 'AB',\n",
    "    'BernoulliNB': 'BNB',\n",
    "    'LinearSVC': 'LSVC',\n",
    "    'LogisticRegression': 'LR',\n",
    "    'MultinomialNB': 'MNB',\n",
    "    'PassiveAggressiveClassifier': 'PAC',\n",
    "    'SGDClassifier': 'SGD',\n",
    "    'GaussianNB': 'GNB',\n",
    "    'DecisionTreeClassifier': 'DT',\n",
    "    'ExtraTreesClassifier': 'ET',\n",
    "    'RandomForestClassifier': 'RF',\n",
    "    'GradientBoostingClassifier':'GB',\n",
    "    'KNeighborsClassifier': 'KNN'\n",
    "}\n",
    "model_nice = []\n",
    "for m in model_nice_dict:\n",
    "    print(model_nice_dict)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             dataset              classifier  \\\n",
      "0  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...      AdaBoostClassifier   \n",
      "1  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...             BernoulliNB   \n",
      "2  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...  DecisionTreeClassifier   \n",
      "3  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...    ExtraTreesClassifier   \n",
      "4  GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...              GaussianNB   \n",
      "\n",
      "   bal_accuracy  \n",
      "0      0.502500  \n",
      "1      0.503750  \n",
      "2      0.531875  \n",
      "3      0.551875  \n",
      "4      0.499375  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('sklearn-benchmark5-data-edited.tsv.gz', sep='\\t', names=['dataset',\n",
    "                                                                     'classifier',\n",
    "                                                                     'parameters',\n",
    "                                                                     'accuracy', \n",
    "                                                                     'macrof1',\n",
    "                                                                     'bal_accuracy']).fillna('')\n",
    "data = data.groupby(['dataset','classifier'])['bal_accuracy'].max().reset_index()\n",
    "\n",
    "print(data[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    bal_accuracy\n",
      "dataset                                                         \n",
      "postoperative-patient-data                              0.522577\n",
      "GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1             0.528319\n",
      "analcatdata_dmft                                        0.539730\n",
      "GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_7...      0.542770\n",
      "coil2000                                                0.557337\n",
      "analcatdata_germangss                                   0.589339\n",
      "cleveland-nominal                                       0.605934\n",
      "cleveland                                               0.618177\n",
      "wine-quality-red                                        0.624348\n",
      "cloud                                                   0.632068\n",
      "schizo                                                  0.634562\n",
      "krkopt                                                  0.638377\n",
      "cmc                                                     0.642090\n",
      "Hill_Valley_without_noise                               0.644895\n",
      "flags                                                   0.656955\n",
      "connect-4                                               0.661333\n",
      "analcatdata_aids                                        0.682703\n",
      "saheart                                                 0.685939\n",
      "bupa                                                    0.686393\n",
      "dis                                                     0.702945\n",
      "analcatdata_boxing1                                     0.707929\n",
      "yeast                                                   0.720693\n",
      "diabetes                                                0.726813\n",
      "calendarDOW                                             0.732421\n",
      "mux6                                                    0.743983\n",
      "allhypo                                                 0.744471\n",
      "allbp                                                   0.750713\n",
      "banana                                                  0.752352\n",
      "analcatdata_cyyoung8092                                 0.761772\n",
      "sleep                                                   0.768057\n",
      "...                                                          ...\n",
      "buggyCrx                                                0.872378\n",
      "satimage                                                0.875534\n",
      "mfeat-zernike                                           0.884871\n",
      "ann-thyroid                                             0.891510\n",
      "waveform-40                                             0.894152\n",
      "mfeat-fourier                                           0.898323\n",
      "splice                                                  0.907190\n",
      "spectf                                                  0.908353\n",
      "labor                                                   0.913469\n",
      "corral                                                  0.919708\n",
      "tokyo1                                                  0.920764\n",
      "shuttle                                                 0.934655\n",
      "segmentation                                            0.958106\n",
      "house-votes-84                                          0.959479\n",
      "dna                                                     0.966451\n",
      "pendigits                                               0.966759\n",
      "soybean                                                 0.967504\n",
      "breast                                                  0.968438\n",
      "breast-cancer-wisconsin                                 0.973054\n",
      "mfeat-karhunen                                          0.976352\n",
      "prnn_crabs                                              0.978712\n",
      "optdigits                                               0.981265\n",
      "promoters                                               0.982580\n",
      "mfeat-factors                                           0.985505\n",
      "dermatology                                             0.986739\n",
      "wine-recognition                                        0.992841\n",
      "analcatdata_authorship                                  0.998210\n",
      "agaricus-lepiota                                        1.001801\n",
      "clean2                                                  1.006553\n",
      "mofn-3-7-10                                             1.012666\n",
      "\n",
      "[83 rows x 1 columns]\n",
      "0    GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...\n",
      "1    GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...\n",
      "2    GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...\n",
      "3    GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...\n",
      "4    GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_ED...\n",
      "Name: dataset, dtype: object\n",
      "90% cutoff: 86\n",
      "80% cutoff: 121\n",
      "70% cutoff: 149\n",
      "60% cutoff: 163\n"
     ]
    },
    {
     "data": {
      "image/png": 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GKTAYWAd8PVkDv3hlHQAnDxtGZWUlAH6/n8rKSoKBYKRcMBCMkx/Q5Qdw19Zy7+t/B+Cz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\nlCCLiIiIiEQoQRYRERERifj/NSgIFEBmUBQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11033f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "datan = data.groupby(['dataset']).max().sort_values('bal_accuracy',ascending=False)\n",
    "# print(datan)\n",
    "\n",
    "datamv = data.groupby(['dataset']).median()+data.groupby(['dataset']).var()\n",
    "datamv = datamv.sort_values('bal_accuracy',ascending=True)\n",
    "\n",
    "# for i in datamv.index:\n",
    "#     print(data[i,'bal_accuracy'])\n",
    "print(datamv[::2])\n",
    "# print(datan.index)\n",
    "\n",
    "print(data['dataset'][:5])\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "s=sns.boxplot(data=data,x='dataset',y='bal_accuracy',order=datan.index,fliersize=3,linewidth=0.75)\n",
    "s.set_xticks(np.arange(len(np.unique(data['dataset'])),step=10))\n",
    "s.set_xticklabels(np.arange(len(np.unique(data['dataset'])),step=10))\n",
    "yticks = np.hstack((np.arange(0.6,step=0.1),np.arange(0.6,1.05,step=0.05)))\n",
    "s.set_yticks(yticks)\n",
    "s.set_yticklabels(['{0:.2f}'.format(x) for x in yticks],size=9)\n",
    "plt.ylim(0,1.1)\n",
    "plt.ylabel('Balanced Accuracy',size=16)\n",
    "plt.xlabel('Dataset',size=16)\n",
    "h = plt.gcf()\n",
    "h.tight_layout()\n",
    "h.savefig('figs/boxplot_all.pdf',bbox_inches='tight')\n",
    "h.savefig('figs/boxplot_all.png',bbox_inches='tight')\n",
    "\n",
    "\n",
    "print('90% cutoff:',len(datan[datan['bal_accuracy']>=0.9]))\n",
    "print('80% cutoff:',len(datan[datan['bal_accuracy']>=0.8]))\n",
    "print('70% cutoff:',len(datan[datan['bal_accuracy']>=0.7]))\n",
    "print('60% cutoff:',len(datan[datan['bal_accuracy']>=0.6]))\n",
    "\n",
    "# for i,d in enumerate(datan.index):\n",
    "\n",
    "#     print('data set ',i,':',data['dataset'][d])\n",
    "# plt.gca().set_xticks(np.arange(len(data),step=10))\n",
    "# plt.gca().set_xticklabels(str(np.arange(len(data),step=10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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li/bs2aP29nbNnz9fkyZNCsux2LFjh7788ktJUmtrq3755Rdt27ZNxcXFYTcW7e3tWr16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\n29YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ae4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoff = np.empty(100)\n",
    "for i in np.arange(100):\n",
    "    cutoff[i] = len(datan[datan['bal_accuracy']>=i/100])/len(datan)\n",
    "plt.bar(np.arange(len(cutoff)),cutoff)\n",
    "plt.xlim(50,100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9030303030303031"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "149./165"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}