{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Will remove later..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n",
    "from sklearn.metrics import roc_auc_score, f1_score, matthews_corrcoef, balanced_accuracy_score, auc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from my_util import *\n",
    "from lime.lime.lime_tabular import LimeTabularExplainer\n",
    "\n",
    "\n",
    "import sys, os,  pickle, time\n",
    "\n",
    "sys.path.append(os.path.abspath('../'))\n",
    "from pyexplainer.pyexplainer_pyexplainer import *\n",
    "\n",
    "# from datetime import datetime\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "from multiprocessing import Pool\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "data_path = './dataset/'\n",
    "result_dir = './eval_result/'\n",
    "dump_dataframe_dir = './dump_df/'\n",
    "pyExp_dir = './explainer_obj_20_4_2021/'\n",
    "other_object_dir = './other_object/'\n",
    "proj_name = 'qt' # ['openstack','qt']\n",
    "\n",
    "\n",
    "\n",
    "if not os.path.exists(result_dir):\n",
    "    os.makedirs(result_dir)\n",
    "    \n",
    "if not os.path.exists(dump_dataframe_dir):\n",
    "    os.makedirs(dump_dataframe_dir)\n",
    "    \n",
    "if not os.path.exists(pyExp_dir):\n",
    "    os.makedirs(pyExp_dir)\n",
    "    \n",
    "if not os.path.exists(other_object_dir):\n",
    "    os.makedirs(other_object_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = prepare_data(proj_name, mode = 'all')\n",
    "\n",
    "if proj_name == 'openstack':\n",
    "    x_train_original, x_test_original = prepare_data_all_metrics(proj_name, mode='all')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                                           la   ld  nd  ns       ent  nrev  \\\n",
       "commit_id                                                                    \n",
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       "...                                       ...  ...  ..  ..       ...   ...   \n",
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       "\n",
       "                                              rtime  hcmt  self  ndev  \\\n",
       "commit_id                                                               \n",
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       "...                                             ...   ...   ...   ...   \n",
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       "\n",
       "                                                 age  app   asexp   rsexp  \\\n",
       "commit_id                                                                   \n",
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       "...                                              ...  ...     ...     ...   \n",
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       "\n",
       "                                             asawr     rsawr  \n",
       "commit_id                                                     \n",
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       "...                                            ...       ...  \n",
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       "\n",
       "[8277 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(x_test)\n",
    "# display(x_test_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # print(y_train)\n",
    "# for col in x_test.columns:\n",
    "#     print(col,len(x_test[col].unique()))\n",
    "\n",
    "col = list(x_test.columns)\n",
    "# print(col)\n",
    "# print(col.index('self'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# find the best k_neighbor of SMOTE for max AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def find_best_k_neighbor_of_SMOTE(x_train,y_train, global_model_name = 'RF'):\n",
    "#     global_model_name = global_model_name.upper()\n",
    "#     if global_model_name not in ['RF','LR']:\n",
    "#         print('wrong global model name. the global model name must be RF or LR')\n",
    "#         return\n",
    "    \n",
    "#     if global_model_name == 'RF':\n",
    "#         global_model = RandomForestClassifier(n_estimators=100, random_state=0, n_jobs=24)\n",
    "#     elif global_model_name == 'LR':\n",
    "#         global_model = LogisticRegression(random_state=0, n_jobs=24)\n",
    "        \n",
    "#     for k in range(1,51):    \n",
    "#         smt = SMOTE(k_neighbors=k, random_state=42, n_jobs=24)\n",
    "#         new_x_train, new_y_train = smt.fit_resample(x_train, y_train)\n",
    "#         global_model.fit(new_x_train, new_y_train)\n",
    "# #         pred = global_model.predict(x_test)\n",
    "#         prob = global_model.predict_proba(x_test)[:,1]\n",
    "#         AUC = roc_auc_score(y_test, prob)\n",
    "        \n",
    "#         print('k_neighbor: {}, AUC: {}'.format(k, AUC))\n",
    "# #     pickle.dump(global_model, open(proj_name+'_'+global_model_name+'_global_model.pkl','wb'))\n",
    "# #     print('train {} finished'.format(global_model_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find_best_k_neighbor_of_SMOTE(x_train,y_train, global_model_name = 'RF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find_best_k_neighbor_of_SMOTE(x_train,y_train, global_model_name = 'LR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train global model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_global_model(x_train,y_train, global_model_name = 'RF'):\n",
    "    global_model_name = global_model_name.upper()\n",
    "    if global_model_name not in ['RF','LR']:\n",
    "        print('wrong global model name. the global model name must be RF or LR')\n",
    "        return\n",
    "    \n",
    "    smt = SMOTE(k_neighbors=5, random_state=42, n_jobs=24)\n",
    "    new_x_train, new_y_train = smt.fit_resample(x_train, y_train)\n",
    "    \n",
    "    if global_model_name == 'RF':\n",
    "        global_model = RandomForestClassifier(n_estimators=100, random_state=0, n_jobs=24)\n",
    "    elif global_model_name == 'LR':\n",
    "        global_model = LogisticRegression(random_state=0, n_jobs=24)\n",
    "        \n",
    "    global_model.fit(new_x_train, new_y_train)\n",
    "    pickle.dump(global_model, open(proj_name+'_'+global_model_name+'_global_model.pkl','wb'))\n",
    "    print('train {} finished'.format(global_model_name))\n",
    "    \n",
    "train_black_box = False\n",
    "\n",
    "if train_black_box:\n",
    "    train_global_model(x_train, y_train,'RF')\n",
    "    train_global_model(x_train, y_train,'LR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain correctly predicted defective commits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add this function to util file (done later...)\n",
    "\n",
    "def get_prediction_result_df(proj_name, global_model_name):\n",
    "    global_model_name = global_model_name.upper()\n",
    "    if global_model_name not in ['RF','LR']:\n",
    "        print('wrong global model name. the global model name must be RF or LR')\n",
    "        return\n",
    "    \n",
    "    prediction_df_dir = dump_dataframe_dir+proj_name+'_'+global_model_name+'_prediction_result.csv'\n",
    "    correctly_predict_df_dir = dump_dataframe_dir+proj_name+'_'+global_model_name+'_correctly_predict_as_defective.csv'\n",
    "    \n",
    "    if not os.path.exists(prediction_df_dir) or not os.path.exists(correctly_predict_df_dir):\n",
    "        global_model = pickle.load(open(proj_name+'_'+global_model_name+'_global_model.pkl','rb'))\n",
    "\n",
    "        pred = global_model.predict(x_test)\n",
    "        defective_prob = global_model.predict_proba(x_test)[:,1]\n",
    "\n",
    "        prediction_df = x_test.copy()\n",
    "        prediction_df['pred'] = pred\n",
    "        prediction_df['defective_prob'] = defective_prob\n",
    "        prediction_df['defect'] = y_test\n",
    "\n",
    "    #     print('AUC is',roc_auc_score(y_test, defective_prob))\n",
    "        correctly_predict_df = prediction_df[(prediction_df['pred']==1) & (prediction_df['defect']==1)]\n",
    "\n",
    "        print('total correct prediction: {}'.format(str(len(correctly_predict_df))))\n",
    "\n",
    "        prediction_df.to_csv(prediction_df_dir)\n",
    "        correctly_predict_df.to_csv(correctly_predict_df_dir)\n",
    "    \n",
    "    else:\n",
    "        prediction_df = pd.read_csv(prediction_df_dir)\n",
    "        correctly_predict_df = pd.read_csv(correctly_predict_df_dir)\n",
    "        \n",
    "        prediction_df = prediction_df.set_index('commit_id')\n",
    "        correctly_predict_df = correctly_predict_df.set_index('commit_id')\n",
    "        print('total correct prediction: {}'.format(str(len(correctly_predict_df))))\n",
    "        \n",
    "    return prediction_df, correctly_predict_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total correct prediction: 79\n",
      "total correct prediction: 255\n"
     ]
    }
   ],
   "source": [
    "rf_prediction_df, rf_correctly_predict_df = get_prediction_result_df(proj_name, 'rf')\n",
    "lr_prediction_df, lr_correctly_predict_df = get_prediction_result_df(proj_name, 'lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>la</th>\n",
       "      <th>nd</th>\n",
       "      <th>ns</th>\n",
       "      <th>ent</th>\n",
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       "      <th>age</th>\n",
       "      <th>app</th>\n",
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      "text/plain": [
       "                                           la  nd  ns       ent  nrev  \\\n",
       "commit_id                                                               \n",
       "5ce74eb5469b7f88f4448ccbc1afaa802a7cfdef   17   2   1  0.936667   7.0   \n",
       "878ac164a391e761c72c5fdcd12f0caf48c7d359   49   9   3  0.718116   5.0   \n",
       "bbab55c4da531e4695a6e3e577aaa4975f0fce79   16   2   1  0.988699   6.0   \n",
       "a2ba455e2d2bc41f4a80a08d5434b741ed715ef4  519  13   1  0.861772  24.0   \n",
       "9382ee659212285a203550cf60476dd146d27a29   89   2   1  0.996276   3.0   \n",
       "...                                       ...  ..  ..       ...   ...   \n",
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       "aaf5762be5d37cac022dc321b6400b9743a25303    4   2   1  0.413817   2.0   \n",
       "75c7f6a17a5bb78074518877bf73f0071b7758eb    0   1   1  0.000000   1.0   \n",
       "3824051b1e5618388a17c88867a3037397bc96b7   31   2   1  0.858231   7.0   \n",
       "\n",
       "                                               rtime  self   ndev        age  \\\n",
       "commit_id                                                                      \n",
       "5ce74eb5469b7f88f4448ccbc1afaa802a7cfdef   17.869722     0   38.0   0.045706   \n",
       "878ac164a391e761c72c5fdcd12f0caf48c7d359    9.979109     0  116.0  13.035437   \n",
       "bbab55c4da531e4695a6e3e577aaa4975f0fce79   81.107118     0  123.0   0.302407   \n",
       "a2ba455e2d2bc41f4a80a08d5434b741ed715ef4   16.003877     0  194.0   2.348328   \n",
       "9382ee659212285a203550cf60476dd146d27a29  105.237789     0    9.0  21.841916   \n",
       "...                                              ...   ...    ...        ...   \n",
       "209c09a76f5e90aaa2899804686e6a513703d887   20.872523     1    0.0   0.000000   \n",
       "1fdc4afa157887d45e2f326d7373a5b1e8ee7aeb   92.130301     0  411.0   0.501826   \n",
       "aaf5762be5d37cac022dc321b6400b9743a25303   18.489861     0   49.0  26.872975   \n",
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       "3824051b1e5618388a17c88867a3037397bc96b7   75.987384     0  205.0   0.044525   \n",
       "\n",
       "                                          app   rrexp     asawr     rsawr  \\\n",
       "commit_id                                                                   \n",
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       "9382ee659212285a203550cf60476dd146d27a29  3.0   632.0  0.235955  0.820225   \n",
       "...                                       ...     ...       ...       ...   \n",
       "209c09a76f5e90aaa2899804686e6a513703d887  0.0     0.0  0.000000  0.000000   \n",
       "1fdc4afa157887d45e2f326d7373a5b1e8ee7aeb  2.0   720.0  0.000268  0.094218   \n",
       "aaf5762be5d37cac022dc321b6400b9743a25303  4.0  2299.0  0.004550  0.352067   \n",
       "75c7f6a17a5bb78074518877bf73f0071b7758eb  8.0  1361.0  0.001139  0.840547   \n",
       "3824051b1e5618388a17c88867a3037397bc96b7  2.0   713.0  0.011105  0.094461   \n",
       "\n",
       "                                           pred  defective_prob  defect  \n",
       "commit_id                                                                \n",
       "5ce74eb5469b7f88f4448ccbc1afaa802a7cfdef  False            0.14   False  \n",
       "878ac164a391e761c72c5fdcd12f0caf48c7d359  False            0.16   False  \n",
       "bbab55c4da531e4695a6e3e577aaa4975f0fce79  False            0.18   False  \n",
       "a2ba455e2d2bc41f4a80a08d5434b741ed715ef4   True            0.61   False  \n",
       "9382ee659212285a203550cf60476dd146d27a29  False            0.20   False  \n",
       "...                                         ...             ...     ...  \n",
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       "1fdc4afa157887d45e2f326d7373a5b1e8ee7aeb  False            0.39   False  \n",
       "aaf5762be5d37cac022dc321b6400b9743a25303  False            0.08   False  \n",
       "75c7f6a17a5bb78074518877bf73f0071b7758eb  False            0.01   False  \n",
       "3824051b1e5618388a17c88867a3037397bc96b7  False            0.20   False  \n",
       "\n",
       "[3963 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(rf_prediction_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                           la  nd  ns       ent  nrev  \\\n",
       "commit_id                                                               \n",
       "1e6973aee7137653c62dcef970b1e2527b50517d  258   2   1  0.826278   2.0   \n",
       "5d0ccceb20780fdd3adf519d3f8e6b80b1844407  190   7   1  0.839470  25.0   \n",
       "de31210c05f464c4a79255de68b1a515d9b84ed3  121   2   1  0.758523   2.0   \n",
       "957533f685caf9ffc0d9cad569598455d59ade34  121   5   1  0.863629   7.0   \n",
       "96677735f6d8f1f5b6380127956921f719aab799  525   2   1  0.707022  29.0   \n",
       "...                                       ...  ..  ..       ...   ...   \n",
       "9bc29208bda6071a34bcc0da36a396eb8bab4f30   22   2   1  0.886541   7.0   \n",
       "1a40831f1711bf46c878dd247679bb05d19ffb5a   99   2   1  0.861487  13.0   \n",
       "605749ca12af969ac122008b4fa14904df68caf7  627   9   1  0.663883   5.0   \n",
       "85239cc81440d9e5a4aee3c0961c96a4197ad939  166   5   2  0.781693   6.0   \n",
       "f0f52a0085a1f5bbd23e6cfa8f0ea935e8fd56c8   46   2   1  0.852405   4.0   \n",
       "\n",
       "                                              rtime  self   ndev        age  \\\n",
       "commit_id                                                                     \n",
       "1e6973aee7137653c62dcef970b1e2527b50517d  18.049560     0    1.0   5.242755   \n",
       "5d0ccceb20780fdd3adf519d3f8e6b80b1844407  15.181620     0  164.0   0.833137   \n",
       "de31210c05f464c4a79255de68b1a515d9b84ed3   0.357431     0   11.0   4.983553   \n",
       "957533f685caf9ffc0d9cad569598455d59ade34  19.054421     0    4.0   7.935347   \n",
       "96677735f6d8f1f5b6380127956921f719aab799  20.117373     0    7.0   4.144878   \n",
       "...                                             ...   ...    ...        ...   \n",
       "9bc29208bda6071a34bcc0da36a396eb8bab4f30  74.999502     0   20.0   5.003831   \n",
       "1a40831f1711bf46c878dd247679bb05d19ffb5a  36.234155     0   12.0  19.055550   \n",
       "605749ca12af969ac122008b4fa14904df68caf7   8.960463     0  181.0   0.379626   \n",
       "85239cc81440d9e5a4aee3c0961c96a4197ad939   1.367859     0    2.0   1.527556   \n",
       "f0f52a0085a1f5bbd23e6cfa8f0ea935e8fd56c8  73.000278     0   23.0   5.182778   \n",
       "\n",
       "                                          app   rrexp     asawr     rsawr  \\\n",
       "commit_id                                                                   \n",
       "1e6973aee7137653c62dcef970b1e2527b50517d  4.0   465.0  0.004262  0.082046   \n",
       "5d0ccceb20780fdd3adf519d3f8e6b80b1844407  2.0  1931.0  0.122340  0.361879   \n",
       "de31210c05f464c4a79255de68b1a515d9b84ed3  2.0   812.0  0.057416  0.873206   \n",
       "957533f685caf9ffc0d9cad569598455d59ade34  5.0  1364.0  0.197917  0.911458   \n",
       "96677735f6d8f1f5b6380127956921f719aab799  1.0    98.0  0.002381  0.104762   \n",
       "...                                       ...     ...       ...       ...   \n",
       "9bc29208bda6071a34bcc0da36a396eb8bab4f30  8.0  1214.0  0.033998  0.864009   \n",
       "1a40831f1711bf46c878dd247679bb05d19ffb5a  2.0   562.0  0.166096  0.512842   \n",
       "605749ca12af969ac122008b4fa14904df68caf7  4.0  1467.0  0.005115  0.216853   \n",
       "85239cc81440d9e5a4aee3c0961c96a4197ad939  3.0  1315.0  0.136022  0.947368   \n",
       "f0f52a0085a1f5bbd23e6cfa8f0ea935e8fd56c8  5.0  1174.0  0.006821  0.847885   \n",
       "\n",
       "                                          pred  defective_prob  defect  \n",
       "commit_id                                                               \n",
       "1e6973aee7137653c62dcef970b1e2527b50517d  True            0.57    True  \n",
       "5d0ccceb20780fdd3adf519d3f8e6b80b1844407  True            0.58    True  \n",
       "de31210c05f464c4a79255de68b1a515d9b84ed3  True            0.61    True  \n",
       "957533f685caf9ffc0d9cad569598455d59ade34  True            0.60    True  \n",
       "96677735f6d8f1f5b6380127956921f719aab799  True            0.56    True  \n",
       "...                                        ...             ...     ...  \n",
       "9bc29208bda6071a34bcc0da36a396eb8bab4f30  True            0.70    True  \n",
       "1a40831f1711bf46c878dd247679bb05d19ffb5a  True            0.60    True  \n",
       "605749ca12af969ac122008b4fa14904df68caf7  True            0.51    True  \n",
       "85239cc81440d9e5a4aee3c0961c96a4197ad939  True            0.54    True  \n",
       "f0f52a0085a1f5bbd23e6cfa8f0ea935e8fd56c8  True            0.51    True  \n",
       "\n",
       "[198 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(rf_correctly_predict_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf_prediction_df_exclude_correct_prediction = rf_prediction_df.drop(index=rf_correctly_predict_df.index)\n",
    "# lr_prediction_df_exclude_correct_prediction = lr_prediction_df.drop(index=lr_correctly_predict_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_indices = set(x_test.index)\n",
    "# correct_indices = set(rf_correctly_predict_df.index)\n",
    "# remain_indices = all_indices-correct_indices\n",
    "\n",
    "# print(len(all_indices))\n",
    "# print(len(correct_indices), len(remain_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(x_test.loc[rf_correctly_predict_df.index])\n",
    "# display(y_test.loc[rf_correctly_predict_df.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training local model\n",
    "\n",
    "Note: this step includes instance generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def create_every_explainer(global_model_name, df_indices):\n",
    "    \n",
    "    global_model_name = global_model_name.upper()\n",
    "    if global_model_name not in ['RF','LR']:\n",
    "        print('wrong global model name. the global model name must be RF or LR')\n",
    "        return\n",
    "    \n",
    "    global_model = pickle.load(open(proj_name+'_'+global_model_name+'_global_model.pkl','rb'))\n",
    "\n",
    "    indep = x_test.columns\n",
    "    dep = 'defect'\n",
    "    class_label = ['clean', 'defect']\n",
    "    \n",
    "    # for our apporach\n",
    "    pyExp = PyExplainer(x_train, y_train, indep, dep, global_model, class_label)\n",
    "\n",
    "    # for baseline\n",
    "    # note: 6 is index of 'self' feature\n",
    "    lime_explainer = LimeTabularExplainer(x_train.values, categorical_features=[6],\n",
    "                                      feature_names=indep, class_names=class_label, \n",
    "                                      random_state=0)\n",
    "\n",
    "    \n",
    "\n",
    "    feature_df = x_test.loc[df_indices]\n",
    "    test_label = y_test.loc[df_indices]\n",
    "\n",
    "    save_dir = os.path.join(pyExp_dir,proj_name,global_model_name)\n",
    "        \n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "            \n",
    "    for i in range(0,len(feature_df)):\n",
    "        X_explain = feature_df.iloc[[i]]\n",
    "        y_explain = test_label.iloc[[i]]\n",
    "\n",
    "        row_index = str(X_explain.index[0])\n",
    "\n",
    "        pyExp_obj = pyExp.explain(X_explain,\n",
    "                                   y_explain,\n",
    "                                   search_function = 'CrossoverInterpolation')\n",
    "#             synt_pred = pyExp_obj['synthetic_predictions']\n",
    "        pyExp_obj['commit_id'] = row_index\n",
    "\n",
    "        # because I don't want to change key name in another evaluation file\n",
    "        pyExp_obj['local_model'] = pyExp_obj['local_rulefit_model']\n",
    "        del pyExp_obj['local_rulefit_model']\n",
    "#             print('{}: found {} defect from total {}'.format(row_index, str(np.sum(synt_pred)), \n",
    "#                                                          str(len(synt_pred))))\n",
    "#         pickle.dump(pyExp_obj, open(pyExp_dir+proj_name+'_'+explainer+'_'+search_function.lower()+'_'+row_index+'_20_rules.pkl','wb'))\n",
    "        \n",
    "        X_explain = feature_df.iloc[i] # to prevent error in LIME\n",
    "        exp, synt_inst, synt_inst_for_local_model, selected_feature_indices, local_model = lime_explainer.explain_instance(X_explain, \n",
    "                                                                                                                           global_model.predict_proba, \n",
    "                                                                                                                           num_samples=5000)\n",
    "\n",
    "        lime_obj = {}\n",
    "        lime_obj['rule'] = exp\n",
    "        lime_obj['synthetic_instance_for_global_model'] = synt_inst\n",
    "        lime_obj['synthetic_instance_for_lobal_model'] = synt_inst_for_local_model\n",
    "        lime_obj['local_model'] = local_model\n",
    "        lime_obj['selected_feature_indeces'] = selected_feature_indices\n",
    "        lime_obj['commit_id'] = row_index\n",
    "#         pickle.dump(lime_obj, open(pyExp_dir+proj_name+'_lime_'+row_index+'.pkl','wb'))\n",
    "\n",
    "        all_explainer = {'pyExplainer':pyExp_obj, 'LIME': lime_obj}\n",
    "    \n",
    "\n",
    "            \n",
    "        pickle.dump(all_explainer, open(save_dir+'/all_explainer_'+row_index+'.pkl','wb'))\n",
    "        \n",
    "        print('finished',row_index)\n",
    "#         break\n",
    "\n",
    "\n",
    "#         end = time.time()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_indices = set(x_test.index)\n",
    "rf_correct_indices = set(rf_correctly_predict_df.index)\n",
    "rf_remain_indices = all_indices-rf_correct_indices\n",
    "\n",
    "lr_correct_indices = set(lr_correctly_predict_df.index)\n",
    "lr_remain_indices = all_indices-lr_correct_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "create_every_explainer('RF',rf_correct_indices)\n",
    "create_every_explainer('LR',lr_correct_indices)\n",
    "# create_every_explainer('RF',rf_remain_indices)\n",
    "# create_every_explainer('LR',lr_remain_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(len(correct_indices))\n",
    "# print(len(remain_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dir1/dir2file.f'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join('dir1','dir2')+'/file.f'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['pyExplainer', 'LIME'])\n",
      "dict_keys(['synthetic_data', 'synthetic_predictions', 'X_explain', 'y_explain', 'indep', 'dep', 'top_k_positive_rules', 'top_k_negative_rules', 'commit_id', 'local_model'])\n"
     ]
    }
   ],
   "source": [
    "obj = pickle.load(open(pyExp_dir+proj_name+'_'+'LR'+'_all_explainer_'+'99379d6ec00b7bdfe5a625877d8e680f9240efcc'+'.pkl','rb'))\n",
    "print(obj.keys())\n",
    "print(obj['pyExplainer'].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['la', 'nd', 'ns', 'ent', 'nrev', 'rtime', 'self', 'ndev', 'age', 'app',\n",
      "       'rrexp', 'asawr', 'rsawr'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(correctly_predict_df.columns[:-3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global_model = pickle.load(open(proj_name+'_global_model.pkl','rb'))\n",
    "\n",
    "# load_prediction_from_file = True\n",
    "# class_label = ['clean', 'defect']\n",
    "\n",
    "# if load_prediction_from_file:\n",
    "#     correctly_predict_df = pd.read_csv(dump_dataframe_dir+proj_name+'_correctly_predict_as_defective.csv')\n",
    "#     correctly_predict_df = correctly_predict_df.set_index('commit_id')\n",
    "    \n",
    "# dep = 'defect'\n",
    "# indep = correctly_predict_df.columns[:-3] # exclude the last 3 columns\n",
    "\n",
    "# # print(correctly_predict_df.columns)\n",
    "# # print(len(correctly_predict_df.columns))\n",
    "# # print(indep)\n",
    "# # display(correctly_predict_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_df = correctly_predict_df.loc[:, indep]\n",
    "test_label = correctly_predict_df.loc[:, dep]\n",
    "problem_index = [] # store index that cannot build pyExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prepare data done...\n",
      "--------------------------------------------------------------------------------\n",
      "finished bdba16035dd03310259203f7baf576fc6fcb530b\n",
      "--------------------------------------------------------------------------------\n",
      "finished b63c6f9f839188ade489d77747164b0fec8ab116\n",
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      "finished 9d98070f7b2e06ac8cc30d12523798df4418eed0\n",
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      "finished 55bfb412f1732e97da76f1b34e581d2429df38e6\n",
      "--------------------------------------------------------------------------------\n",
      "finished da42fe01d99eaeb0cda5387efefeb9e284c6e20b\n",
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      "finished 69191a1858cd3df651240a5ad1c384a470baa356\n",
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      "finished 69a4e6cde8769992d06ec3126257cf2adea0e525\n",
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      "--------------------------------------------------------------------------------\n",
      "finished 96a8a1643b90219b15aa3aa83f075044e7b85c40\n",
      "finished 36be3145c76d185ecabac20c4df145b7f46f67b1\n",
      "--------------------------------------------------------------------------------\n",
      "finished 4227b128a42a1c5785ac13245de511fbcd358e37\n",
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      "finished 8db51da1e25f1c148fbe8d8f27d144a39d1850f3\n",
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      "finished 85b8ef62da3ae13923ebf449da3dcb3da224adb1\n",
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      "finished 983d815a80039d1f4e0d735c3041882833ecc488\n",
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      "finished dec1e329c2ebba9fd0f9e96d1ae85aa084ccba6f\n",
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      "finished e43c5c1ea6441ad7d36e25bd283a5a580bf5cb09\n",
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      "finished e19cf5c0475fcc69cda1a883f29181247c4c8f54\n",
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      "finished 878ac164a391e761c72c5fdcd12f0caf48c7d359\n",
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      "finished e120c7301942e3c4f352ae250a1b4f6bf89aabde\n",
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      "finished 844a228c3c42dac802a82510eb17fcfaccb0e1f8\n",
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      "finished f0b77aa053a2d5456d72d178d4fac8e2684cc111\n",
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      "finished 15ef6089e5dbc54f8d6cdbfa9281ff523d60548a\n",
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      "finished 72f85b5e616e3aabcab862b944c1a3a2c2844431\n",
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      "finished ab55af8ed5afd0765a23a85d608d4b6d35bdd166\n",
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      "finished f2dfdd4ab37965a6559f3c033cdf877e0fe172be\n",
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      "finished 928a119ac0f8d750f616f3eff4fadc82b23fc7c6\n",
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      "finished ba4ed39616c3c6fabf24ca390c7037877c672fba\n",
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      "finished 12c8ea8569a64e0688544db18da1946e4a3ffd82\n",
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      "finished 51fc2bd41e236404a8db8e4beefd0ec265fb1a5a\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "finished 01a44568cc60bb5a6dd7b55d69b20bba57d1b94b\n",
      "finished 0a87ff988cd21586daeb6ed886e44aedaa49c320\n",
      "--------------------------------------------------------------------------------\n",
      "finished de31210c05f464c4a79255de68b1a515d9b84ed3\n",
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      "finished dc5c2cb7f2ddfb8b7311373be423a51cdf700f9f\n",
      "--------------------------------------------------------------------------------\n",
      "finished d91bc54749fa4ffdda203db5ad88dc4f20c72eb8\n",
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      "finished d7796858f1e23e5284b12f48d79ddfc63929e7fb\n",
      "--------------------------------------------------------------------------------\n",
      "finished bdaf3a26bb924a0debc7e753cdf2135dcc20d833\n",
      "--------------------------------------------------------------------------------\n",
      "finished 7aea4d8b21013d5ef6b14111b42c4bd101789ed1\n",
      "--------------------------------------------------------------------------------\n",
      "finished 2dbea4a9f524337576ffe5610708f90e08be8853\n",
      "--------------------------------------------------------------------------------\n",
      "finished f9cf84273bd4d89cdbe267b2a5ed9c9ed20492e9\n"
     ]
    }
   ],
   "source": [
    "create_every_explainer(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "    search function: lime or crossoverinterpolation\n",
    "'''\n",
    "def create_pyExplainer_obj(search_function, feature_df, test_label, explainer='rulefit'):\n",
    "    \n",
    "    if search_function not in ['lime','CrossoverInterpolation']:\n",
    "        print('the search function must be \"lime\" or \"CrossoverInterpolation\"')\n",
    "        return\n",
    "    \n",
    "    problem_index = []\n",
    "    time_spent = []\n",
    "    \n",
    "    for i in range(0,len(feature_df)):\n",
    "        X_explain = feature_df.iloc[[i]]\n",
    "        y_explain = test_label.iloc[[i]]\n",
    "\n",
    "        row_index = str(X_explain.index[0])\n",
    "\n",
    "        start = time.time()\n",
    "        try:\n",
    "            if search_function=='CrossoverInterpolation':\n",
    "                # the returned object is dictionary\n",
    "                pyExp_obj = pyExp.explain(X_explain,\n",
    "                                           y_explain,\n",
    "                                           search_function = 'CrossoverInterpolation', \n",
    "                                           top_k = 15,\n",
    "                                           max_rules=2000, \n",
    "                                           max_iter = None, \n",
    "                                           cv=5,\n",
    "                                           debug = False)\n",
    "    #             synt_pred = pyExp_obj['synthetic_predictions']\n",
    "                pyExp_obj['commit_id'] = row_index\n",
    "        \n",
    "                # because I don't want to change key name in another evaluation file\n",
    "                pyExp_obj['local_model'] = pyExp_obj['local_rulefit_model']\n",
    "                del pyExp_obj['local_rulefit_model']\n",
    "    #             print('{}: found {} defect from total {}'.format(row_index, str(np.sum(synt_pred)), \n",
    "    #                                                          str(len(synt_pred))))\n",
    "                pickle.dump(pyExp_obj, open(pyExp_dir+proj_name+'_'+explainer+'_'+search_function.lower()+'_'+row_index+'_20_rules.pkl','wb'))\n",
    "        \n",
    "            else:\n",
    "                X_explain = feature_df.iloc[i] # to prevent error in LIME\n",
    "                exp, synt_inst, synt_inst_for_local_model, selected_feature_indices, local_model = lime_explainer.explain_instance(X_explain, \n",
    "                                                                                                                                   global_model.predict_proba, \n",
    "                                                                                                                                   num_samples=5000)\n",
    "\n",
    "                lime_obj = {}\n",
    "                lime_obj['rule'] = exp\n",
    "                lime_obj['synthetic_instance_for_global_model'] = synt_inst\n",
    "                lime_obj['synthetic_instance_for_lobal_model'] = synt_inst_for_local_model\n",
    "                lime_obj['local_model'] = local_model\n",
    "                lime_obj['selected_feature_indeces'] = selected_feature_indices\n",
    "                lime_obj['commit_id'] = row_index\n",
    "                pickle.dump(lime_obj, open(pyExp_dir+proj_name+'_lime_'+row_index+'.pkl','wb'))\n",
    "                \n",
    "            print('finished',row_index)\n",
    "#             print(row_index)\n",
    "#             print('just one rulefit is enough')\n",
    "#             break\n",
    "            \n",
    "        except Exception as e:\n",
    "            problem_index.append(row_index)\n",
    "            print('-'*100)\n",
    "            print(e)\n",
    "#             print('found total {} problematic commit'.format(str(len(problem_index))))\n",
    "            print('-'*100)\n",
    "            \n",
    "#         break\n",
    "\n",
    "        end = time.time()\n",
    "\n",
    "        time_spent.append(str(end-start))\n",
    "#     print(row_index)\n",
    "#     break\n",
    "    \n",
    "    print('from total {} commits, there are {} problematic commits'.format(len(feature_df),len(problem_index)))\n",
    "    return time_spent, problem_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "finished f5dbc876378ae58a7bdfe1e9664fc81caca18dfb\n",
      "--------------------------------------------------------------------------------\n",
      "finished 602bd9873786ccadcb67da3036329f3122555cf8\n",
      "--------------------------------------------------------------------------------\n",
      "finished 9d75626b1073113d77988bcb52e99215d5af4787\n",
      "--------------------------------------------------------------------------------\n",
      "finished 3168c94577cc604fd033ce4e741da5c411f74666\n",
      "--------------------------------------------------------------------------------\n",
      "finished 23bdca417bde716c79168ab372083fd885607123\n",
      "--------------------------------------------------------------------------------\n",
      "finished 438211ec627073817fcaf6d3a07b76f2aa5d90e0\n",
      "--------------------------------------------------------------------------------\n",
      "finished d8090022f66cc6cff6af5ed2ae702212fd172ff7\n",
      "--------------------------------------------------------------------------------\n",
      "finished 06eaeb0ac8d861cf3e60b11a8fbbabdeef1d6759\n",
      "--------------------------------------------------------------------------------\n",
      "finished 4dbf574b7acb7ae8f852219700afa95f8d568f0e\n",
      "--------------------------------------------------------------------------------\n",
      "finished 2eb28f51ce4150fa03b2ddde8c39b502ae57d18a\n",
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      "finished 61b56a89a1cf8a388ff925492700e5eef019c3aa\n",
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      "finished 07860794da5863610f38295c9d517fc457c5de95\n",
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      "finished b508dc98a5085df6ea44b154177fbb6d8b0e8434\n",
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      "finished b449791c3565940e701c68b3f705014804af0c2b\n",
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      "finished 3abecf2ee9fc724af571f8c7da4302f7bee9eadb\n",
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      "finished d8d903826daa8db49e437b76fb80693ab8feb01e\n",
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      "finished 5b456c8da4462f9e11fa4da78a9e6ea86423a1e8\n",
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      "finished 37ca2224eca671200a2710f57f970d2993e62aa5\n",
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      "finished fd619946be51784dc709363324897be6af144c52\n",
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      "finished 0ed9f7496656fa0ea52d703c7fddff26c2192857\n",
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      "finished a25b1fdf7d642d9f252fbfa1789efaa32e03b994\n",
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      "finished 2a11286469bf28b348821f015d72f8cb06f54b95\n",
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      "finished 42cfb5fe4daa586f382bde6936b0ee33b5298f4d\n",
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      "finished 9c82c105a1886473ca144b802ce9f5bec01e35e8\n",
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      "finished aed9a8d49b7470de6809c3bf747b14c7150d7ae6\n",
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      "finished 19b7afca523f221494bd165680f1aa9ddf3a0e31\n",
      "--------------------------------------------------------------------------------\n",
      "finished 52fc0a95a109d2e9fa279eeb0284a8178563080b\n",
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      "finished d6d119ac6e606a30993bfea1ac3309f74d15bedc\n",
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      "finished f89f099c55576992b39a8021aace64ff32747624\n",
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      "finished 0eade30f37980c38b14d5cfa475837d15b69a8c5\n",
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      "finished 87f475cbdb89fcbfbce68c3b676a240bb255a6d9\n",
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      "finished e07e1931ac3d2e270d7696f631aba39ea54ba3c4\n",
      "--------------------------------------------------------------------------------\n",
      "finished 8d441936d2e8ab8c39a66831a658c192b80ca597\n",
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      "finished 9329f786da8e167130fa36b91ff288bfdb046ce1\n",
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      "finished a0f8be4021caa9bb5055923f0eea3bee0e345235\n",
      "--------------------------------------------------------------------------------\n",
      "finished 78b49cf8361b1462cc94a061916a15f0b98e27e3\n",
      "--------------------------------------------------------------------------------\n",
      "finished 81dea57593b5b28990bb7f012aae1387c8d2de33\n",
      "--------------------------------------------------------------------------------\n",
      "finished f2233c725078d49f6b185e642325dacb47b33240\n",
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      "finished fc0f784e54d5dce72cc6a7e4b1fad243dadfcd76\n",
      "--------------------------------------------------------------------------------\n",
      "finished 661e624121004dd73467300e467aacae6d8d2f66\n",
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      "finished a967a9bdcc2c75a0270c2be48d845ded5332e4f0\n",
      "--------------------------------------------------------------------------------\n",
      "finished 954b92e1207bfe5ab5a117e8393c191cdf0044d2\n",
      "--------------------------------------------------------------------------------\n",
      "finished b7a585ebd57f85c89ee20eda5b1a06819b3e1af0\n",
      "--------------------------------------------------------------------------------\n",
      "finished 906d5c5c40183468f9521277c6244a6c46730de6\n",
      "--------------------------------------------------------------------------------\n",
      "finished 8480dcbc7180cf1bcafcd03a6ad7087c83582a97\n",
      "--------------------------------------------------------------------------------\n",
      "finished ec61d7b9e82e6d653c02df14f34acdba8fc060db\n",
      "--------------------------------------------------------------------------------\n",
      "finished 71338b4b2f01268759f7ac6b3eff5abb17420a7c\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "finished 72420889aa54342da4472683e67e2c70b76a170f\n",
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      "finished 1e8de50674f5b33a50c45224b7e07b3f974f6ab0\n",
      "--------------------------------------------------------------------------------\n",
      "finished 979a0406f0013560efbdcc486b32ba93ce8c946f\n",
      "from total 79 commits, there are 0 problematic commits\n"
     ]
    }
   ],
   "source": [
    "time_spent_rand, problem_index_rand = create_pyExplainer_obj('lime', feature_df, test_label)\n",
    "pickle.dump(time_spent_rand, open(other_object_dir+proj_name+'_train_time_lime_randompertubation.pkl','wb'))\n",
    "pickle.dump(problem_index_rand, open(other_object_dir+proj_name+'_problem_index_lime_randompertubation.pkl','wb'))\n",
    "\n",
    "# time_spent_rand, problem_index_rand = create_pyExplainer_obj('lime', feature_df, test_label,'rulefit')\n",
    "# pickle.dump(time_spent_rand, open(other_object_dir+proj_name+'_train_time_lime.pkl','wb'))\n",
    "# pickle.dump(problem_index_rand, open(other_object_dir+proj_name+'_problem_index_lime.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished fc27c5e3df4b047f2da31e6217e36936b331093f\n",
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      "finished f0f52a0085a1f5bbd23e6cfa8f0ea935e8fd56c8\n",
      "from total 198 commits, there are 0 problematic commits\n"
     ]
    }
   ],
   "source": [
    "# time_spent_ci, problem_index_ci = create_pyExplainer_obj('crossoverinterpolation', feature_df, test_label)\n",
    "# pickle.dump(time_spent_ci, open(other_object_dir+proj_name+'_train_time_LRR_crossoverinterpolation.pkl','wb'))\n",
    "# pickle.dump(problem_index_ci, open(other_object_dir+proj_name+'_problem_index_LRR_crossoverinterpolation.pkl','wb'))\n",
    "\n",
    "time_spent_ci, problem_index_ci = create_pyExplainer_obj('CrossoverInterpolation', feature_df, test_label,'rulefit')\n",
    "pickle.dump(time_spent_ci, open(other_object_dir+proj_name+'_train_time_rulefit_crossoverinterpolation.pkl','wb'))\n",
    "pickle.dump(problem_index_ci, open(other_object_dir+proj_name+'_problem_index_rulefit_crossoverinterpolation.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Just for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = 'rulefit'\n",
    "pyExp_random = pickle.load(open(pyExp_dir+proj_name+'_'+explainer+'_randompertubation_3126.pkl','rb'))\n",
    "pyExp_crossover = pickle.load(open(pyExp_dir+proj_name+'_'+explainer+'_crossoverinterpolation_3126.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['synthetic_data', 'synthetic_predictions', 'X_explain', 'y_explain', 'indep', 'dep', 'local_model', 'top_k_positive_rules', 'top_k_negative_rules'])\n"
     ]
    }
   ],
   "source": [
    "print(pyExp_random.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# euclid_dist = euclidean_distances(sample_instance.values.reshape(1,-1), synthetic_instances.values)[0]\n",
    "\n",
    "dist_rand = euclidean_distances(pyExp_random['X_explain'].values.reshape(1,-1), pyExp_random['synthetic_data'].values)[0]\n",
    "# plt.boxplot(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_cross = euclidean_distances(pyExp_crossover['X_explain'].values.reshape(1,-1), pyExp_crossover['synthetic_data'].values)[0]\n",
    "# plt.boxplot(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7f991f275760>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991f275ac0>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991f2caa00>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991f24c1f0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7f991f26bc70>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991f26bd60>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991eedcaf0>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991eedcbb0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7f991f275d30>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991f265400>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7f991f265a30>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991eedc100>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7f991f265c10>,\n",
       "  <matplotlib.lines.Line2D at 0x7f991eedcf40>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVIElEQVR4nO3dfWxd9X3H8c8ncew8AQuJGyV2SFiVVgmBUdViFe2qZtUWClOSCnUyQi1aIlIq4lFtEhTyB2xSJOgaKmFtREFYBal1xlRIkSBraBVRRaJNnYLSPMDmlECeBCYJYGxsx853f/g4uw4mD76+9zj3935JV/fe7z3nnq8l6+Pj3/mdcxwRAgCkYULeDQAAyofQB4CEEPoAkBBCHwASQugDQEKq8m7gfGbNmhULFizIuw0AuKTs2rXrvYioPbs+7kN/wYIFamtry7sNALik2H5rpDrDOwCQEEIfABJC6ANAQgh9AEgIoQ8ACSH0AeSqtbVVS5Ys0cSJE7VkyRK1trbm3VJFG/dTNgFUrtbWVq1bt05PPvmkvvKVr2jHjh1avXq1JOm2227LubvK5PF+aeWGhoZgnj5QmZYsWaLm5mYtXbr0TG379u1qamrSnj17cuzs0md7V0Q0fKJO6APIy8SJE9XT06NJkyadqZ06dUqTJ0/WwMBAjp1d+j4t9BnTB5CbRYsWaceOHcNqO3bs0KJFi3LqqPIR+gBys27dOq1evVrbt2/XqVOntH37dq1evVrr1q3Lu7WKxYFcALkZOljb1NSk/fv3a9GiRVq/fj0HcUuIMX0AqECM6QMACH0ASAmhDyBXnJFbXhzIBZAbzsgtPw7kAsjNkiVLtHLlSm3ZsuXM7J2h95yRW5xPO5DLnj6A3Ozbt09dXV1qaWk5s6e/atUqvfXWiHf6wxhgTB9Abqqrq9XU1KSlS5dq0qRJWrp0qZqamlRdXZ13axWL4R0AuZkwYYJmzZqladOm6a233tL8+fPV1dWl9957T6dPn867vUsa8/QBjDt1dXXq6+uTJNmWJPX19amuri7PtioaoQ8gV1OnTlVLS4t6enrU0tKiqVOn5t1SRSP0AeTm6NGjeuSRR9TU1KTJkyerqalJjzzyiI4ePZp3axWL0AeQm0WLFunZZ59Ve3u7Tp8+rfb2dj377LNcWrmEzhv6tltsv2t7T0HtIdtHbL+WPW4u+Ox+2+2237C9rKD+Rdt/zD57zEMDeACSVVdXpy1btmjVqlV6//33tWrVKm3ZsoUx/RI67+wd21+V9JGkpyNiSVZ7SNJHEfGjs5ZdLKlV0g2S5kr6laTPRcSA7Z2S7pH0W0kvSnosIraer0Fm7wCVa/LkyWpoaFBbW5t6e3tVU1Nz5n1PT0/e7V3SRj17JyJ+I+nEBW5nhaTNEdEbEW9Kapd0g+05ki6PiFdi8K/M05JWXnD3ACpSb2+vXn/9dc2ZM0cTJkzQnDlz9Prrr6u3tzfv1ipWMWP6a23vzoZ/ZmS1OkmHCpY5nNXqstdn10dke43tNtttHR0dRbQIYLzr6+sbNntnaAonSmO0of+4pM9Kul7SMUkbsvpI4/RxjvqIImJTRDRERENtbe0oWwRwKeju7tarr76qU6dO6dVXX1V3d3feLVW0UYV+RLwTEQMRcVrSExocw5cG9+DnFSxaL+loVq8foQ4gcbfccoseeOABTZs2TQ888IBuueWWvFuqaKMK/WyMfsg3JQ3N7HleUqPtGttXS1ooaWdEHJPUaftL2ayd70j6RRF9A6gA9fX12rlzp7Zu3aq+vj5t3bpVO3fuVH19/flXxqic9yqbtlslfU3SLNuHJT0o6Wu2r9fgEM1BSd+VpIjYa/sZSfsk9Uu6OyIGsq/6nqSfSJoiaWv2AJCwH/7wh7rnnnvOXFlz/vz5GhgY0KOPPpp3axWLC64ByFVTU5OeeOKJM1M277zzTjU3N+fd1iWPC64BGHdaW1v1wgsvDBveeeGFF7hlYgmxpw8gN9w5q3S4cxaAcWffvn3q7u7+xD1yDx48mHdrFYvQB5Cb6upq3XjjjWpqajqzp3/jjTdylc0SYkwfQG76+vq0efNmrVq1Sp2dnVq1apU2b97MWbklROgDyE11dbUaGxvV0tKiyy67TC0tLWpsbOQeuSVE6APITV9fn7Zt26auri5FhLq6urRt2zb29EuIMX0Auamrq9Px48f1/vvvKyJ05MgRVVVVcT39EmJPH0Buuru71dfXp4cfflhdXV16+OGH1dfXx0XXSojQB5CbEydO6N577x02pn/vvffqxIkLvYUHLhahDyBXS5cu1Z49ezQwMKA9e/Zo6dKlebdU0RjTB5Cb+vp6rVixQv39/Tp16pQmTZqkqqoqrrJZQuzpA8jN4sWL9fHHH2v69OmSpOnTp+vjjz/W4sWLc+6schH6AHLz8ssv6/bbb9fcuXM1YcIEzZ07V7fffrtefvnlvFurWFxwDUBubKurq0tTp049U+vu7ta0adM03rNpvOPSygDGnZqaGm3cuHFYbePGjaqpqcmpo8rHgVwAubnzzjt13333SZLuuusubdy4Uffdd5/uuuuunDurXAzvAMjVVVddpUOHDp15P2/ePL399ts5dlQZGN4BMO4sW7ZsWOBL0qFDh7Rs2bKcOqp8hD6A3Gzbtk2StHz5cnV0dGj58uXD6hh7hD6AXF133XU6cOCAZs+erQMHDui6667Lu6WKdt7Qt91i+13bewpq/2b7ddu7bT9n+8+y+gLbH9t+LXtsLFjni7b/aLvd9mO2XZKfCMAl5cCBA2publZPT4+am5t14MCBvFuqaBeyp/8TSTedVXtJ0pKIuE7S/0i6v+CzAxFxffYoPAT/uKQ1khZmj7O/E0CCurq6dOutt6q6ulq33nqrurq68m6pop039CPiN5JOnFXbFhH92dvfSjrnhTJsz5F0eUS8EoPThZ6WtHJUHQOoOCdPnhz2jNIZizH9VZK2Fry/2vartl+2/VdZrU7S4YJlDme1EdleY7vNdltHR8cYtAhgPKqpqdEVV1wxrHbFFVdwclYJFRX6ttdJ6pf006x0TNJVEfEFSf8k6We2L5c00vj9p54gEBGbIqIhIhpqa2uLaRHAONbb26vOzk7Nnj1bkjR79mx1dnaqt7c3584q16jPyLV9h6S/k/T1bMhGEdErqTd7vcv2AUmf0+CefeEQUL2ko6PdNoDKUFVVpZqaGk2ZMkUTJkzQlClTNGXKFEK/hEa1p2/7Jkn3SVoeEd0F9VrbE7PXf67BA7Z/iohjkjptfymbtfMdSb8ounsAl7T+/n5VVQ3uew5dHaCqqkr9/f3nWg1FOO+evu1WSV+TNMv2YUkPanC2To2kl7KZl7/NZup8VdK/2u6XNCDprogYOgj8PQ3OBJqiwWMAhccBACSqu7tbH3zwgSTp4MGDmjRpUs4dVTauvQMgNxMmTFBEaMaMGTp58uSZZ9s6ffp03u1d0rj2DoBxZ2ins7Ozc9jzeN8ZvZQR+gByNXXqVNXX18u26uvrh91QBWOP6+kDyFVfX5+OHDmiiDjzjNIh9AHkqnCmzqlTp3LsJA0M7wBAQgh9AEgIoQ8gdxMnThz2jNIh9AHkbujgLQdxS4/QB5C7oROxOCGr9Ah9AEgIoQ8gd9OnTx/2jNIh9AHk7qOPPhr2jNIh9AEgIYQ+ACSE0AeAhBD6AHI1c+ZMZTdjkm3NnDkz544qG6EPIFfHjx8fFvrHjx/PuaPKRugDQEIIfQC544zc8iH0ASAhhD4AJOS8oW+7xfa7tvcU1K60/ZLt/82eZxR8dr/tdttv2F5WUP+i7T9mnz3moSM3AICyuZA9/Z9Iuums2g8k/ToiFkr6dfZethdLapR0TbbOf9geukD245LWSFqYPc7+TgBAiZ039CPiN5JOnFVeIemp7PVTklYW1DdHRG9EvCmpXdINtudIujwiXonBC2Y/XbAOAKBMRjumPzsijklS9vyZrF4n6VDBcoezWl32+uz6iGyvsd1mu62jo2OULQIAzjbWB3JHGqePc9RHFBGbIqIhIhpqa2vHrDkASN1oQ/+dbMhG2fO7Wf2wpHkFy9VLOprV60eoAwDKaLSh/7ykO7LXd0j6RUG90XaN7as1eMB2ZzYE1Gn7S9msne8UrAMAKJOq8y1gu1XS1yTNsn1Y0oOSHpb0jO3Vkt6W9C1Jioi9tp+RtE9Sv6S7I2Ig+6rvaXAm0BRJW7MHAKCMPN7vPt/Q0BBtbW15twGgBM51us54z6bxzvauiGg4u84ZuQCQEEIfABJC6ANAQgh9AEjIeWfvAMBYutBrLRYux0HdsUPoAyirwgBn9k75MbwDIDfXXnvtRdVRPEIfQG527979iYC/9tprtXv37pw6qnwM7wDI1VDA22ZIpwzY0weAhBD6AJAQQh8AEkLoA0BCCH0ASAihDwAJIfQBICGEPgAkhNAHgIQQ+gCQEEIfABJC6ANAQkYd+rY/b/u1gseHtr9v+yHbRwrqNxesc7/tdttv2F42Nj8CAOBCjfoqmxHxhqTrJcn2RElHJD0n6R8k/TgiflS4vO3FkholXSNprqRf2f5cRAyMtgcAwMUZq+Gdr0s6EBFvnWOZFZI2R0RvRLwpqV3SDWO0fQDABRir0G+U1Frwfq3t3bZbbM/IanWSDhUsczirfYLtNbbbbLd1dHSMUYsAgKJD33a1pOWS/isrPS7psxoc+jkmacPQoiOsPuIdEyJiU0Q0RERDbW1tsS0CADJjsaf/DUl/iIh3JCki3omIgYg4LekJ/f8QzmFJ8wrWq5d0dAy2DwC4QGMR+repYGjH9pyCz74paU/2+nlJjbZrbF8taaGknWOwfQDABSrqHrm2p0r6G0nfLSj/0Pb1Ghy6OTj0WUTstf2MpH2S+iXdzcwdACivokI/IrolzTyr9u1zLL9e0vpitgkAGD3OyAWAhBD6AJAQQh8AEkLoA0BCCH0ASAihDwAJIfQBICGEPgAkhNAHgIQQ+gCQEEIfABJC6ANAQgh9AEgIoQ8ACSH0ASAhhD4AJITQT8DMmTNl+8xj5syZ518JQEUi9CvczJkzdeLEiWG1EydOEPxAogj9CjcU+Bs2bFBXV5c2bNgwrA4gLY6IvHs4p4aGhmhra8u7jUuK7VGtN95/F1DZbPM7OIZs74qIhrPrRd0YHTl76IoRy/Hg5WP6fXrog9F9H4Bxp6jQt31QUqekAUn9EdFg+0pJ/ylpgaSDkv4+Ik5my98vaXW2/D9GxC+L2X7yLiCMh/b6r7nmGr344ou6+eabtXfvXkns2aN0rrzySp08efKi17uY/1JnzJjBMOUojMWY/tKIuL7g34gfSPp1RCyU9OvsvWwvltQo6RpJN0n6D9sTx2D7OIe1a9dKkvbu3av58+efCfyhOlAKJ0+eVESU9DGaPyoozYHcFZKeyl4/JWllQX1zRPRGxJuS2iXdUILto0Bzc7PWrl2rmpoaSVJNTY3Wrl2r5ubmnDsDkIdiQz8kbbO9y/aarDY7Io5JUvb8maxeJ+lQwbqHs9on2F5ju812W0dHR5Etorm5WT09PYoI9fT0EPhAwoo9kPvliDhq+zOSXrL9+jmWHWmwbsRB5YjYJGmTNDh7p8geAQCZovb0I+Jo9vyupOc0OFzzju05kpQ9v5stfljSvILV6yUdLWb7AICLM+rQtz3N9mVDryX9raQ9kp6XdEe22B2SfpG9fl5So+0a21dLWihp52i3DwC4eMUM78yW9Fw2xapK0s8i4r9t/17SM7ZXS3pb0rckKSL22n5G0j5J/ZLujoiBoroHAFyUUYd+RPxJ0l+MUD8u6eufss56SetHu00AQHG49g4AJITQB4CEEPoAkBBCHwASQugDQEIIfQBICKEPAAkh9AEgIYQ+ACSE0AeAhBD6AJAQbowOYMzFg5dLD11R+m3gohH6AMac/+VDRZT2/ke2FQ+VdBMVieEdAEgIoQ8ACSH0ASAhhD4AJITQB4CEEPoAkBBCHwASQugDQEJGHfq259nebnu/7b2278nqD9k+Yvu17HFzwTr32263/YbtZWPxAwAALlwxZ+T2S/rniPiD7csk7bL9UvbZjyPiR4UL214sqVHSNZLmSvqV7c9FxEARPQAYp2yX9PtnzJhR0u+vVKMO/Yg4JulY9rrT9n5JdedYZYWkzRHRK+lN2+2SbpD0ymh7ADA+jeYSDLZLfukGjNGYvu0Fkr4g6XdZaa3t3bZbbA/9Oa6TdKhgtcP6lD8SttfYbrPd1tHRMRYtAgA0BqFve7qkn0v6fkR8KOlxSZ+VdL0G/xPYMLToCKuP+Gc9IjZFRENENNTW1hbbIgAgU1To256kwcD/aUQ8K0kR8U5EDETEaUlPaHAIRxrcs59XsHq9pKPFbB8AcHGKmb1jSU9K2h8RjxbU5xQs9k1Je7LXz0tqtF1j+2pJCyXtHO32AQAXr5jZO1+W9G1Jf7T9WlZ7QNJttq/X4NDNQUnflaSI2Gv7GUn7NDjz525m7gBAeRUze2eHRh6nf/Ec66yXtH602wQAFIczcgEgIYQ+ACSE0AeAhBD6AJAQQh8AEkLoA0BCCH0ASAihDwAJIfQBICGEPgAkhNAHgIQQ+gCQEEIfABJC6ANAQgh9AEgIoQ8ACSH0ASAhhD4AJITQB4CEEPoAkJCyh77tm2y/Ybvd9g/KvX0ASFlZQ9/2REn/LukbkhZLus324nL2AAApK/ee/g2S2iPiTxHRJ2mzpBVl7gFAjmyP+DjfZxgbVWXeXp2kQwXvD0v6yzL3ACBHEZF3C0kr957+SH+yP/EbYHuN7TbbbR0dHWVoCwDSUO7QPyxpXsH7eklHz14oIjZFRENENNTW1patOQCodOUO/d9LWmj7atvVkholPV/mHgAgWWUd04+IfttrJf1S0kRJLRGxt5w9AEDKyn0gVxHxoqQXy71dAABn5AJAUgh9AEgIoQ8ACfF4P1HCdoekt/Luo0LMkvRe3k0An4Lfz7E1PyI+Med93Ic+xo7ttohoyLsPYCT8fpYHwzsAkBBCHwASQuinZVPeDQDnwO9nGTCmDwAJYU8fABJC6ANAQgj9BNhusf2u7T159wIUsj3P9nbb+23vtX1P3j1VOsb0E2D7q5I+kvR0RCzJux9giO05kuZExB9sXyZpl6SVEbEv59YqFnv6CYiI30g6kXcfwNki4lhE/CF73SlpvwZvq4oSIfQBjAu2F0j6gqTf5dxKRSP0AeTO9nRJP5f0/Yj4MO9+KhmhDyBXtidpMPB/GhHP5t1PpSP0AeTGtiU9KWl/RDyadz8pIPQTYLtV0iuSPm/7sO3VefcEZL4s6duS/tr2a9nj5rybqmRM2QSAhLCnDwAJIfQBICGEPgAkhNAHgIQQ+gCQEEIfABJC6ANAQv4PXoQXXpe2e9UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [dist_rand, dist_cross]\n",
    "plt.boxplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(feature_df)\n",
    "# for c in feature_df:\n",
    "#     print(c)\n",
    "\n",
    "# for k in range(0,1):\n",
    "#     print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# test feature binarizer\n",
    "# from pyexplainer.features import *\n",
    "# fb = FeatureBinarizer(negations=True)\n",
    "# fb.fit(x_train)\n",
    "# display(fb.transform(feature_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# search_function='randompertubation'\n",
    "# i = 3\n",
    "\n",
    "# X_explain = feature_df.iloc[[i]]\n",
    "# y_explain = test_label.iloc[[i]]\n",
    "\n",
    "# row_index = str(X_explain.index[0])\n",
    "\n",
    "# start = time.time()\n",
    "\n",
    "# pyExp_obj = pyExp.explain(X_explain,\n",
    "#                            y_explain,\n",
    "#                            search_function = search_function, \n",
    "#                            top_k = 1000,\n",
    "#                            max_rules=2000, \n",
    "#                            max_iter =None, \n",
    "#                            cv=5,\n",
    "#                            explainer='rulefit',\n",
    "#                            debug = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['synthetic_data', 'synthetic_predictions', 'X_explain', 'y_explain', 'indep', 'dep', 'local_model', 'top_k_positive_rules', 'top_k_negative_rules'])\n"
     ]
    }
   ],
   "source": [
    "print(pyExp_obj.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(pyExp_obj['synthetic_data_fb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\n"
     ]
    }
   ],
   "source": [
    "# local_model = pyExp_obj['local_model']\n",
    "# print(local_model.predict(X_explain.values))\n",
    "# print('------------------Explanation from local model-------------------------')\n",
    "# print(local_model.explain())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(pyExp_obj['X_explain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(local_model.predict(pyExp_obj['X_explain_fb']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sample code for RQ1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# synthetic_instances = pyExp_obj['synthetic_data']\n",
    "# sample_instance = pyExp_obj['X_explain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def agg_list(val):\n",
    "#     return np.mean(val), np.median(val), np.max(val)\n",
    "\n",
    "# cos_sim = cosine_similarity(sample_instance.values.reshape(1,-1), synthetic_instances.values)[0]\n",
    "# euclid_dist = euclidean_distances(sample_instance.values.reshape(1,-1), synthetic_instances.values)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sample code for RQ2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False] [0.06562197]\n"
     ]
    }
   ],
   "source": [
    "# '''get prediction from global model then compare with result obtained from model (but how to compare??)'''\n",
    "\n",
    "# local_model = pyExp_obj['local_model']\n",
    "# '''In case pyExp uses logistic rule regression'''\n",
    "# fb_sample_instance = pyExp.feature_binarizer.transform(sample_instance)\n",
    "# # print(pyExp.feature_binarizer.transform(sample_instance))\n",
    "# local_pred = local_model.predict(fb_sample_instance)\n",
    "# local_pred_prob = local_model.predict_proba(fb_sample_instance)\n",
    "\n",
    "# '''In case pyExp uses RuleFit'''\n",
    "# # local_pred = local_model.predict(sample_instance)\n",
    "# # local_pred_prob = local_model.predict_proba(sample_instance)\n",
    "\n",
    "# print(local_pred, local_pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Prediction from local model-------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           rule coefficient\n",
      "0   (intercept)    0.926494\n",
      "1  nrev <= 1.00  -11.265911\n",
      "2    nd <= 1.00   -3.340535\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            rule coefficient\n",
      "0    (intercept)   -4.822969\n",
      "1   nrev <= 1.00  -21.790036\n",
      "2    ent <= 0.90    11.04842\n",
      "3  asawr <= 0.16    2.480303\n",
      "4    nuc <= 3.00    2.480303\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           rule coefficient\n",
      "0   (intercept)   -9.127939\n",
      "1  nrev <= 2.00   12.185013\n",
      "2  nrev <= 1.00   -7.495421\n",
      "3   ent <= 0.63   -7.495421\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "there is only 1 class in the generated instances\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            rule coefficient\n",
      "0    (intercept)    0.721183\n",
      "1   nrev <= 1.00   -8.482928\n",
      "2  asawr <= 0.05   -7.399822\n",
      "----------------------------------------------------------------------------------------------------\n",
      "          rule coefficient\n",
      "0  (intercept)   -3.748032\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           rule coefficient\n",
      "0   (intercept)   -9.149672\n",
      "1  nrev <= 2.00   14.773528\n",
      "2  nrev <= 1.00    -9.91744\n",
      "3   ent <= 0.00   -8.247625\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            rule coefficient\n",
      "0    (intercept)  -10.434791\n",
      "1   nrev <= 1.00  -15.862358\n",
      "2  asawr <= 0.09   15.725896\n",
      "----------------------------------------------------------------------------------------------------\n",
      "          rule coefficient\n",
      "0  (intercept)   -2.933625\n",
      "----------------------------------------------------------------------------------------------------\n",
      "            rule coefficient\n",
      "0    (intercept)    1.111113\n",
      "1     ld <= 0.00  -11.000013\n",
      "2   nrev <= 1.00   -2.505401\n",
      "3  asawr <= 0.00   -0.249322\n",
      "4  asawr <= 0.00   -0.249322\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
      "/home/oathaha/.conda/envs/env_oat/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    }
   ],
   "source": [
    "# test rulefit\n",
    "search_function='randompertubation'\n",
    "print('------------------Prediction from local model-------------------------')\n",
    "for i in [3,5,7,20,50,100,83,25,163,127]:\n",
    "    X_explain = feature_df.iloc[[i]]\n",
    "    y_explain = test_label.iloc[[i]]\n",
    "\n",
    "    row_index = str(X_explain.index[0])\n",
    "\n",
    "    start = time.time()\n",
    "\n",
    "    try:\n",
    "        pyExp_obj = pyExp.explain(X_explain,\n",
    "                                   y_explain,\n",
    "                                   search_function = search_function, \n",
    "                                   top_k = 1000,\n",
    "                                   max_rules=2000, \n",
    "                                   max_iter =None, \n",
    "                                   cv=5,\n",
    "                                   explainer='LRR',\n",
    "                                   debug = False)\n",
    "        end = time.time()\n",
    "#         print('time spent to train LRR:',str(end-start),'secs')\n",
    "        \n",
    "        local_model = pyExp_obj['local_model']\n",
    "        print(local_model.explain())\n",
    "        \n",
    "#         print(global_model.predict_proba(X_explain)[:,1], local_model.predict_proba(pyExp.scaler.transform(X_explain.values))[:,1])\n",
    "#         print(local_model.explain())\n",
    "        print('-'*100)\n",
    "    except:\n",
    "        print('-'*100)\n",
    "        print('there is only 1 class in the generated instances')\n",
    "        print('-'*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Prediction from local model-------------------------\n",
      "[0.84] [0.96695341]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.73] [0.49372837]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.83] [0.99548978]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.67] [0.93530316]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.6] [0.94639136]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.66] [0.94685406]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.62] [0.77372941]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.75] [0.91593334]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.7] [0.66951567]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[0.58] [0.82852018]\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# test rulefit\n",
    "search_function='crossoverinterpolation'\n",
    "print('------------------Prediction from local model-------------------------')\n",
    "for i in [3,5,7,20,50,100,83,25,163,127]:\n",
    "    X_explain = feature_df.iloc[[i]]\n",
    "    y_explain = test_label.iloc[[i]]\n",
    "\n",
    "    row_index = str(X_explain.index[0])\n",
    "\n",
    "    start = time.time()\n",
    "\n",
    "    start = time.time()\n",
    "    try:\n",
    "        pyExp_obj = pyExp.explain(X_explain,\n",
    "                                   y_explain,\n",
    "                                   search_function = search_function, \n",
    "                                   top_k = 1000,\n",
    "                                   max_rules=2000, \n",
    "                                   max_iter =None, \n",
    "                                   cv=5,\n",
    "                                   explainer='rulefit',\n",
    "                                   debug = False)\n",
    "        end = time.time()\n",
    "#         print('time spent to train LRR:',str(end-start),'secs')\n",
    "        \n",
    "        local_model = pyExp_obj['local_model']\n",
    "        \n",
    "        print(global_model.predict_proba(X_explain)[:,1], local_model.predict_proba(X_explain.values)[:,1])\n",
    "#         print(local_model.explain())\n",
    "        print('-'*100)\n",
    "    except:\n",
    "        print('-'*100)\n",
    "        print('there is only 1 class in the generated instances')\n",
    "        print('-'*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time spent to train LRR: 1.4275202751159668 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 1.738269329071045 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 1.0873517990112305 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 0.9002327919006348 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 1.3496229648590088 secs\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "positional indexers are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_list_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1474\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take_with_is_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1475\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_take_with_is_copy\u001b[0;34m(self, indices, axis)\u001b[0m\n\u001b[1;32m   3599\u001b[0m         \"\"\"\n\u001b[0;32m-> 3600\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3601\u001b[0m         \u001b[0;31m# Maybe set copy if we didn't actually change the index.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indices, axis, is_copy, **kwargs)\u001b[0m\n\u001b[1;32m   3585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3586\u001b[0;31m         new_data = self._mgr.take(\n\u001b[0m\u001b[1;32m   3587\u001b[0m             \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[1;32m   1466\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1467\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_convert_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexers.py\u001b[0m in \u001b[0;36mmaybe_convert_indices\u001b[0;34m(indices, n)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"indices are out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: indices are out-of-bounds",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-73e92f53bfa7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m83\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m163\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m127\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mX_explain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0my_explain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_label\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1490\u001b[0m         \u001b[0;31m# a list of integers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1491\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1492\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_list_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1494\u001b[0m         \u001b[0;31m# a single integer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_list_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1475\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1476\u001b[0m             \u001b[0;31m# re-raise with different error message\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1477\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"positional indexers are out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1479\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
     ]
    }
   ],
   "source": [
    "# sample LRR when used with random perturbation\n",
    "\n",
    "search_function='randompertubation'\n",
    "\n",
    "for i in [3,5,7,20,50,100,83,25,163,127]:\n",
    "    X_explain = feature_df.iloc[[i]]\n",
    "    y_explain = test_label.iloc[[i]]\n",
    "\n",
    "    row_index = str(X_explain.index[0])\n",
    "\n",
    "    start = time.time()\n",
    "\n",
    "    start = time.time()\n",
    "    try:\n",
    "        pyExp_obj = pyExp.explain(X_explain,\n",
    "                                   y_explain,\n",
    "                                   search_function = search_function, \n",
    "                                   top_k = 1000,\n",
    "                                   max_rules=2000, \n",
    "                                   max_iter =None, \n",
    "                                   cv=5,\n",
    "                                   explainer='LRR',\n",
    "                                   debug = False)\n",
    "        end = time.time()\n",
    "        print('time spent to train LRR:',str(end-start),'secs')\n",
    "\n",
    "#         local_model = pyExp_obj['local_model']\n",
    "#         print('------------------Explanation from local model-------------------------')\n",
    "#         print(local_model.explain())\n",
    "        print('-'*100)\n",
    "    except:\n",
    "        print('-'*100)\n",
    "        print('there is only 1 class in the generated instances')\n",
    "        print('-'*100)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time spent to train LRR: 28.87514853477478 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 26.213839769363403 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 25.021041870117188 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 29.421844244003296 secs\n",
      "----------------------------------------------------------------------------------------------------\n",
      "time spent to train LRR: 26.97343945503235 secs\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "positional indexers are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_list_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1474\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take_with_is_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1475\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_take_with_is_copy\u001b[0;34m(self, indices, axis)\u001b[0m\n\u001b[1;32m   3599\u001b[0m         \"\"\"\n\u001b[0;32m-> 3600\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3601\u001b[0m         \u001b[0;31m# Maybe set copy if we didn't actually change the index.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indices, axis, is_copy, **kwargs)\u001b[0m\n\u001b[1;32m   3585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3586\u001b[0;31m         new_data = self._mgr.take(\n\u001b[0m\u001b[1;32m   3587\u001b[0m             \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[1;32m   1466\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1467\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_convert_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexers.py\u001b[0m in \u001b[0;36mmaybe_convert_indices\u001b[0;34m(indices, n)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"indices are out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: indices are out-of-bounds",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-b3ee45065751>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m83\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m163\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m127\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mX_explain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0my_explain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_label\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1490\u001b[0m         \u001b[0;31m# a list of integers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1491\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1492\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_list_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1494\u001b[0m         \u001b[0;31m# a single integer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/env_oat/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_list_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1475\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1476\u001b[0m             \u001b[0;31m# re-raise with different error message\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1477\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"positional indexers are out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1479\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
     ]
    }
   ],
   "source": [
    "# sample LRR when used with crossover interpolation\n",
    "\n",
    "search_function='crossoverinterpolation'\n",
    "\n",
    "for i in [3,5,7,20,50,100,83,25,163,127]:\n",
    "    X_explain = feature_df.iloc[[i]]\n",
    "    y_explain = test_label.iloc[[i]]\n",
    "\n",
    "    row_index = str(X_explain.index[0])\n",
    "\n",
    "    start = time.time()\n",
    "\n",
    "    start = time.time()\n",
    "    pyExp_obj = pyExp.explain(X_explain,\n",
    "                               y_explain,\n",
    "                               search_function = search_function, \n",
    "                               top_k = 1000,\n",
    "                               max_rules=2000, \n",
    "                               max_iter =None, \n",
    "                               cv=5,\n",
    "                               explainer='LRR',\n",
    "                               debug = False)\n",
    "    end = time.time()\n",
    "    print('time spent to train LRR:',str(end-start),'secs')\n",
    "    \n",
    "#     local_model = pyExp_obj['local_model']\n",
    "#     print('------------------Explanation from local model-------------------------')\n",
    "#     print(local_model.explain())\n",
    "    print('-'*100)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['synthetic_data', 'synthetic_predictions', 'X_explain', 'y_explain', 'X_explain_fb', 'indep', 'dep', 'local_model'])\n"
     ]
    }
   ],
   "source": [
    "print(pyExp_obj.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(pyExp_obj['synthetic_data'].columns)\n",
    "# print(feature_df.index)\n",
    "local_model = pyExp_obj['local_model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          rule coefficient\n",
      "0  (intercept)     1.21719\n",
      "1  la <= 44.00    -2.10262\n",
      "2  la <= 76.00   -0.561556\n"
     ]
    }
   ],
   "source": [
    "print(local_model.explain( maxCoeffs=None))\n",
    "# print(local_model.z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([(   'la', '<=',                 1.0),\n",
      "            (   'la', '<=',                 2.0),\n",
      "            (   'la', '<=',                 5.0),\n",
      "            (   'la', '<=',                 9.0),\n",
      "            (   'la', '<=',                17.0),\n",
      "            (   'la', '<=',                27.0),\n",
      "            (   'la', '<=',                44.0),\n",
      "            (   'la', '<=',                76.0),\n",
      "            (   'la', '<=',               163.0),\n",
      "            (   'ld', '<=',                 0.0),\n",
      "            ...\n",
      "            ('asawr', '<=',   0.433364602876798),\n",
      "            ('rsawr', '<=', 0.18055330452007923),\n",
      "            ('rsawr', '<=',  0.2564102564102564),\n",
      "            ('rsawr', '<=',  0.3196254791765793),\n",
      "            ('rsawr', '<=',  0.3754889178617992),\n",
      "            ('rsawr', '<=',  0.4287529047714299),\n",
      "            ('rsawr', '<=',  0.4816326530612245),\n",
      "            ('rsawr', '<=',  0.5758975125536251),\n",
      "            ('rsawr', '<=',  0.7078384798099763),\n",
      "            ('rsawr', '<=',  0.8487118531623176)],\n",
      "           names=['feature', 'operation', 'value'], length=165)\n"
     ]
    },
    {
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2118</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2120</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2121 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "feature           la                                                         \\\n",
       "operation         <=                                                          \n",
       "value     1.000000   2.000000   5.000000   9.000000   17.000000  27.000000    \n",
       "0                  0          0          0          0          0          0   \n",
       "1                  0          0          0          0          0          0   \n",
       "2                  0          0          0          0          0          0   \n",
       "3                  0          0          0          0          0          0   \n",
       "4                  0          0          0          0          0          1   \n",
       "...              ...        ...        ...        ...        ...        ...   \n",
       "2116               1          1          1          1          1          1   \n",
       "2117               0          0          0          0          0          0   \n",
       "2118               0          0          0          0          0          0   \n",
       "2119               1          1          1          1          1          1   \n",
       "2120               0          0          0          0          0          0   \n",
       "\n",
       "feature                                            ld  ...      asawr  \\\n",
       "operation                                          <=  ...         <=   \n",
       "value     44.000000  76.000000  163.000000 0.000000    ... 0.433365     \n",
       "0                  0          0          0          0  ...          1   \n",
       "1                  0          0          0          1  ...          1   \n",
       "2                  0          0          0          0  ...          1   \n",
       "3                  0          0          0          0  ...          1   \n",
       "4                  1          1          1          0  ...          1   \n",
       "...              ...        ...        ...        ...  ...        ...   \n",
       "2116               1          1          1          0  ...          1   \n",
       "2117               0          0          1          0  ...          1   \n",
       "2118               1          1          1          1  ...          1   \n",
       "2119               1          1          1          0  ...          1   \n",
       "2120               0          0          0          0  ...          1   \n",
       "\n",
       "feature        rsawr                                                         \\\n",
       "operation         <=                                                          \n",
       "value     0.180553   0.256410   0.319625   0.375489   0.428753   0.481633     \n",
       "0                  0          0          0          0          0          0   \n",
       "1                  0          0          0          0          0          0   \n",
       "2                  0          0          0          0          0          0   \n",
       "3                  0          0          0          0          0          0   \n",
       "4                  0          0          0          0          0          0   \n",
       "...              ...        ...        ...        ...        ...        ...   \n",
       "2116               0          0          0          0          0          1   \n",
       "2117               1          1          1          1          1          1   \n",
       "2118               0          0          0          0          0          0   \n",
       "2119               0          0          0          0          0          0   \n",
       "2120               1          1          1          1          1          1   \n",
       "\n",
       "feature                                     \n",
       "operation                                   \n",
       "value     0.575898   0.707838   0.848712    \n",
       "0                  0          1          1  \n",
       "1                  0          1          1  \n",
       "2                  1          1          1  \n",
       "3                  0          1          1  \n",
       "4                  0          1          1  \n",
       "...              ...        ...        ...  \n",
       "2116               1          1          1  \n",
       "2117               1          1          1  \n",
       "2118               0          1          1  \n",
       "2119               0          0          1  \n",
       "2120               1          1          1  \n",
       "\n",
       "[2121 rows x 165 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generated_instance = pyExp_obj['synthetic_data']\n",
    "print(generated_instance.columns)\n",
    "display(generated_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature     la\n",
      "operation   <=\n",
      "value     44.0\n",
      "0            0\n",
      "1            0\n",
      "2            0\n",
      "3            0\n",
      "4            1\n",
      "...        ...\n",
      "2116         1\n",
      "2117         0\n",
      "2118         1\n",
      "2119         1\n",
      "2120         0\n",
      "\n",
      "[2121 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(generated_instance.loc[:, (generated_instance.columns.get_level_values(0)=='la') & \n",
    "                            (generated_instance.columns.get_level_values(1)=='<=') &\n",
    "                            (generated_instance.columns.get_level_values(2)==44.0)  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # search_function = 'crossoverinterpolation' # 'randompertubation' or 'crossoverinterpolation'\n",
    "# search_functions = ['randompertubation', 'crossoverinterpolation']\n",
    "\n",
    "# for i in range(0,len(feature_df)):\n",
    "#     X_explain = feature_df.iloc[[i]]\n",
    "#     y_explain = test_label.iloc[[i]]\n",
    "    \n",
    "#     row_index = str(X_explain.index[0])\n",
    "    \n",
    "#     try:\n",
    "#         pyExp_obj = pyExp.explain(X_explain,\n",
    "#                                    y_explain,\n",
    "#                                    search_function = search_function, \n",
    "#                                    top_k = 1000, \n",
    "#                                    max_rules=2000, \n",
    "#                                    max_iter =None, \n",
    "#                                    cv=5,\n",
    "#                                    debug = False)\n",
    "#         pickle.dump(pyExp_obj, open(pyExp_dir+search_function+'_'+row_index+'.pkl','wb'))\n",
    "        \n",
    "#         synt_pred = pyExp_obj['synthetic_predictions']\n",
    "#         print('{}: found {} defect from total {}'.format(row_index, str(np.sum(synt_pred)), \n",
    "#                                                          str(len(synt_pred))))\n",
    "# #         print('finished', row_index)\n",
    "#     except:\n",
    "#         problem_index.append(row_index)\n",
    "# #     print(row_index)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# explain_index = 13\n",
    "# X_explain = feature_df.iloc[[explain_index]]\n",
    "# X_explain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# y_explain = test_label.iloc[[explain_index]]\n",
    "# y_explain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# search_function = 'crossoverinterpolation' # 'randompertubation' or 'crossoverinterpolation''\n",
    "# start = time.time()\n",
    "# create_pyExp_rule_obj = pyExp.explain(X_explain,\n",
    "#                                y_explain,\n",
    "#                                search_function = search_function, \n",
    "#                                top_k = 1000, \n",
    "#                                max_rules=2000, \n",
    "#                                max_iter =None, \n",
    "#                                cv=5,\n",
    "#                                debug = False)\n",
    "\n",
    "# end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('time spent {}'.format(str(end-start)))\n",
    "# pickle.dump(create_pyExp_rule_obj, open(pyExp_dir+search_function+'_'+str(explain_index)+'.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(create_pyExp_rule_obj['synthetic_data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # print(create_pyExp_rule_obj['synthetic_predictions'])\n",
    "# # print(np.sum(create_pyExp_rule_obj['synthetic_predictions']))\n",
    "# display(create_pyExp_rule_obj.keys())\n",
    "# print(create_pyExp_rule_obj['synthetic_predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyexp_obj = pickle.load(open(pyExp_dir+'openstack_rulefit_crossoverinterpolation_2a4e50caeaa271937a23ab7c052c7e9c47c1933f_200_rules.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['synthetic_data', 'synthetic_predictions', 'X_explain', 'y_explain', 'indep', 'dep', 'top_k_positive_rules', 'top_k_negative_rules', 'commit_id', 'local_model'])\n"
     ]
    }
   ],
   "source": [
    "print(pyexp_obj.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_model = pyexp_obj['local_model']\n",
    "rule = local_model.get_rules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(rule)\n",
    "rule = rule[rule['type']=='rule']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  rule  type      coef  \\\n",
      "13   ndev > -0.9049999713897705 & rsawr <= 0.075000...  rule -0.035452   \n",
      "14                              la <= 73.2599983215332  rule -0.005483   \n",
      "15   la > 104.8949966430664 & rrexp <= 425.71501159...  rule -0.034989   \n",
      "16   la > 104.8949966430664 & ns > 1.32500004768371...  rule -0.028039   \n",
      "17    ns > 1.4300000071525574 & la > 87.27999877929688  rule -0.008070   \n",
      "..                                                 ...   ...       ...   \n",
      "206  la > 49.97999954223633 & asawr <= 0.0049999998...  rule -0.044510   \n",
      "207  la > 67.8499984741211 & rrexp > 386.1300048828...  rule -0.076416   \n",
      "208  la > 55.260000228881836 & rsawr > 0.0899999998...  rule  0.103231   \n",
      "209                            la <= 55.14999961853027  rule -0.006513   \n",
      "210  age <= -0.10999999940395355 & ns <= 1.48000001...  rule -0.044921   \n",
      "\n",
      "      support  importance  \n",
      "13   0.029333    0.005982  \n",
      "14   0.157333    0.001996  \n",
      "15   0.120000    0.011370  \n",
      "16   0.029333    0.004731  \n",
      "17   0.074667    0.002121  \n",
      "..        ...         ...  \n",
      "206  0.226667    0.018635  \n",
      "207  0.168000    0.028570  \n",
      "208  0.754667    0.044419  \n",
      "209  0.136000    0.002233  \n",
      "210  0.090667    0.012898  \n",
      "\n",
      "[198 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(rule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rule</th>\n",
       "      <th>type</th>\n",
       "      <th>coef</th>\n",
       "      <th>support</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ns &lt;= 1.350000023841858 &amp; ent &lt;= 0.90500000119...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.150145</td>\n",
       "      <td>0.634667</td>\n",
       "      <td>0.072299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>la &gt; 23.53499984741211 &amp; asawr &gt; 0.00499999988...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.116750</td>\n",
       "      <td>0.501333</td>\n",
       "      <td>0.058375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>asawr &lt;= 0.13499999791383743 &amp; la &gt; 51.75 &amp; ns...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.121400</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.057775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>ent &lt;= 0.925000011920929 &amp; asawr &gt; 0.004999999...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.112495</td>\n",
       "      <td>0.608000</td>\n",
       "      <td>0.054920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>app &lt;= 3.9550000429153442 &amp; ns &lt;= 1.4550000429...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.110274</td>\n",
       "      <td>0.594667</td>\n",
       "      <td>0.054140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>la &gt; 46.94999885559082 &amp; ns &gt; 1.5849999785423279</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.003994</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>la &lt;= 66.48500061035156</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.002735</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.000914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>la &lt;= 56.47999954223633</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.002193</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.000804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>ns &gt; 1.6449999809265137 &amp; la &gt; 73.2599983215332</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.003284</td>\n",
       "      <td>0.045333</td>\n",
       "      <td>0.000683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>la &lt;= 67.8499984741211</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.165333</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  rule  type      coef  \\\n",
       "27   ns <= 1.350000023841858 & ent <= 0.90500000119...  rule  0.150145   \n",
       "125  la > 23.53499984741211 & asawr > 0.00499999988...  rule  0.116750   \n",
       "24   asawr <= 0.13499999791383743 & la > 51.75 & ns...  rule  0.121400   \n",
       "182  ent <= 0.925000011920929 & asawr > 0.004999999...  rule  0.112495   \n",
       "126  app <= 3.9550000429153442 & ns <= 1.4550000429...  rule  0.110274   \n",
       "..                                                 ...   ...       ...   \n",
       "134   la > 46.94999885559082 & ns > 1.5849999785423279  rule -0.003994   \n",
       "204                            la <= 66.48500061035156  rule  0.002735   \n",
       "191                            la <= 56.47999954223633  rule -0.002193   \n",
       "132    ns > 1.6449999809265137 & la > 73.2599983215332  rule  0.003284   \n",
       "77                              la <= 67.8499984741211  rule  0.000101   \n",
       "\n",
       "      support  importance  \n",
       "27   0.634667    0.072299  \n",
       "125  0.501333    0.058375  \n",
       "24   0.653333    0.057775  \n",
       "182  0.608000    0.054920  \n",
       "126  0.594667    0.054140  \n",
       "..        ...         ...  \n",
       "134  0.066667    0.000996  \n",
       "204  0.128000    0.000914  \n",
       "191  0.160000    0.000804  \n",
       "132  0.045333    0.000683  \n",
       "77   0.165333    0.000038  \n",
       "\n",
       "[198 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(rule.sort_values(by='importance',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rule</th>\n",
       "      <th>type</th>\n",
       "      <th>coef</th>\n",
       "      <th>support</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>rtime &gt; 40.69499969482422 &amp; app &gt; 2.0299999713...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.977063</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.440281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>asawr &gt; 0.044999999925494194 &amp; la &lt;= 2245.4250...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.948651</td>\n",
       "      <td>0.386667</td>\n",
       "      <td>0.461980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>app &lt;= 3.975000023841858 &amp; age &lt;= 0.8499999940...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.941952</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.076653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>nd &gt; 2.3850001096725464 &amp; rrexp &lt;= 2276.844970...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.866176</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.396932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>nrev &lt;= 15.140000343322754 &amp; nd &gt; 4.2650001049...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.853926</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.109319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>rsawr &lt;= 0.3050000071525574 &amp; rtime &gt; 7.904999...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.889239</td>\n",
       "      <td>0.096667</td>\n",
       "      <td>0.262773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>app &lt;= 2.0049999952316284 &amp; rrexp &gt; 849.420013...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.909646</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.284619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>la &gt; 204.33499908447266 &amp; la &gt; 33.825000762939...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-1.049840</td>\n",
       "      <td>0.263333</td>\n",
       "      <td>0.462393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>ndev &lt;= 67.1349983215332 &amp; age &gt; 2.54999995231...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-1.339971</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.650404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>nd &gt; 4.265000104904175 &amp; nrev &gt; 15.14000034332...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-1.504175</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>0.616202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1863 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   rule  type      coef  \\\n",
       "540   rtime > 40.69499969482422 & app > 2.0299999713...  rule  0.977063   \n",
       "588   asawr > 0.044999999925494194 & la <= 2245.4250...  rule  0.948651   \n",
       "690   app <= 3.975000023841858 & age <= 0.8499999940...  rule  0.941952   \n",
       "882   nd > 2.3850001096725464 & rrexp <= 2276.844970...  rule  0.866176   \n",
       "527   nrev <= 15.140000343322754 & nd > 4.2650001049...  rule  0.853926   \n",
       "...                                                 ...   ...       ...   \n",
       "1054  rsawr <= 0.3050000071525574 & rtime > 7.904999...  rule -0.889239   \n",
       "980   app <= 2.0049999952316284 & rrexp > 849.420013...  rule -0.909646   \n",
       "1114  la > 204.33499908447266 & la > 33.825000762939...  rule -1.049840   \n",
       "331   ndev <= 67.1349983215332 & age > 2.54999995231...  rule -1.339971   \n",
       "347   nd > 4.265000104904175 & nrev > 15.14000034332...  rule -1.504175   \n",
       "\n",
       "       support  importance  \n",
       "540   0.283333    0.440281  \n",
       "588   0.386667    0.461980  \n",
       "690   0.006667    0.076653  \n",
       "882   0.700000    0.396932  \n",
       "527   0.016667    0.109319  \n",
       "...        ...         ...  \n",
       "1054  0.096667    0.262773  \n",
       "980   0.110000    0.284619  \n",
       "1114  0.263333    0.462393  \n",
       "331   0.380000    0.650404  \n",
       "347   0.213333    0.616202  \n",
       "\n",
       "[1863 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(rule.sort_values(by='coef',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rule</th>\n",
       "      <th>type</th>\n",
       "      <th>coef</th>\n",
       "      <th>support</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ns &lt;= 1.350000023841858 &amp; ent &lt;= 0.90500000119...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.150145</td>\n",
       "      <td>0.634667</td>\n",
       "      <td>0.072299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>asawr &lt;= 0.13499999791383743 &amp; la &gt; 51.75 &amp; ns...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.121400</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.057775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>la &gt; 23.53499984741211 &amp; asawr &gt; 0.00499999988...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.116750</td>\n",
       "      <td>0.501333</td>\n",
       "      <td>0.058375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>ent &lt;= 0.925000011920929 &amp; asawr &gt; 0.004999999...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.112495</td>\n",
       "      <td>0.608000</td>\n",
       "      <td>0.054920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>app &lt;= 3.9550000429153442 &amp; ns &lt;= 1.4550000429...</td>\n",
       "      <td>rule</td>\n",
       "      <td>0.110274</td>\n",
       "      <td>0.594667</td>\n",
       "      <td>0.054140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>asawr &lt;= 0.004999999888241291</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.082666</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.035953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>la &gt; 46.64999961853027 &amp; app &gt; 3.9550000429153442</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.082695</td>\n",
       "      <td>0.149333</td>\n",
       "      <td>0.029474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ns &lt;= 1.4550000429153442 &amp; app &gt; 3.95500004291...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.088113</td>\n",
       "      <td>0.157333</td>\n",
       "      <td>0.032083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>asawr &gt; 0.13499999791383743 &amp; la &gt; 51.75 &amp; ns ...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.100196</td>\n",
       "      <td>0.141333</td>\n",
       "      <td>0.034905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>rtime &gt; 96.99500274658203 &amp; app &gt; 2.9750000238...</td>\n",
       "      <td>rule</td>\n",
       "      <td>-0.127815</td>\n",
       "      <td>0.064000</td>\n",
       "      <td>0.031283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  rule  type      coef  \\\n",
       "27   ns <= 1.350000023841858 & ent <= 0.90500000119...  rule  0.150145   \n",
       "24   asawr <= 0.13499999791383743 & la > 51.75 & ns...  rule  0.121400   \n",
       "125  la > 23.53499984741211 & asawr > 0.00499999988...  rule  0.116750   \n",
       "182  ent <= 0.925000011920929 & asawr > 0.004999999...  rule  0.112495   \n",
       "126  app <= 3.9550000429153442 & ns <= 1.4550000429...  rule  0.110274   \n",
       "..                                                 ...   ...       ...   \n",
       "150                      asawr <= 0.004999999888241291  rule -0.082666   \n",
       "45   la > 46.64999961853027 & app > 3.9550000429153442  rule -0.082695   \n",
       "22   ns <= 1.4550000429153442 & app > 3.95500004291...  rule -0.088113   \n",
       "177  asawr > 0.13499999791383743 & la > 51.75 & ns ...  rule -0.100196   \n",
       "136  rtime > 96.99500274658203 & app > 2.9750000238...  rule -0.127815   \n",
       "\n",
       "      support  importance  \n",
       "27   0.634667    0.072299  \n",
       "24   0.653333    0.057775  \n",
       "125  0.501333    0.058375  \n",
       "182  0.608000    0.054920  \n",
       "126  0.594667    0.054140  \n",
       "..        ...         ...  \n",
       "150  0.253333    0.035953  \n",
       "45   0.149333    0.029474  \n",
       "22   0.157333    0.032083  \n",
       "177  0.141333    0.034905  \n",
       "136  0.064000    0.031283  \n",
       "\n",
       "[198 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(rule.sort_values(by=['coef'],ascending=[False]))\n",
    "# sort by importance then get coef > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ns <= 1.350000023841858 & ent <= 0.9050000011920929 & asawr > 0.004999999888241291',\n",
       " 'asawr <= 0.13499999791383743 & la > 51.75 & ns <= 1.4300000071525574',\n",
       " 'la > 23.53499984741211 & asawr > 0.004999999888241291 & app <= 3.9000000953674316',\n",
       " 'ent <= 0.925000011920929 & asawr > 0.004999999888241291 & la > 26.139999389648438',\n",
       " 'app <= 3.9550000429153442 & ns <= 1.4550000429153442 & la > 82.54999923706055',\n",
       " 'ns <= 1.4749999642372131 & ndev > 3.1200000047683716 & la > 44.34499931335449',\n",
       " 'la > 87.41499710083008 & ent <= 0.9749999940395355 & la <= 1110.3800048828125',\n",
       " 'app <= 3.9550000429153442 & la > 46.64999961853027 & ns <= 1.4399999976158142',\n",
       " 'la > 55.260000228881836 & rsawr > 0.08999999985098839 & rtime <= 148.375',\n",
       " 'rsawr > 0.08500000089406967 & la > 88.8650016784668 & rrexp > -407.61500549316406',\n",
       " 'la > 85.48500061035156 & ent <= 0.9600000083446503 & la <= 975.3250122070312',\n",
       " 'la > 67.8499984741211 & rtime <= 72.5099983215332 & rrexp > 386.1300048828125',\n",
       " 'asawr > 0.004999999888241291 & la <= 917.9400024414062 & la > 55.760000228881836',\n",
       " 'ndev > -0.9049999713897705 & rsawr > 0.07500000111758709 & la > 56.35499954223633',\n",
       " 'la > 45.53499984741211 & app <= 3.975000023841858 & ns <= 1.4850000143051147',\n",
       " 'la > 13.130000591278076 & age > -2.5549999475479126 & ns <= 1.425000011920929',\n",
       " 'asawr > -0.014999999664723873 & la <= 1089.7150268554688 & la > 43.68000030517578',\n",
       " 'la > 104.8949966430664 & rrexp > 425.7150115966797 & ns <= 1.3250000476837158',\n",
       " 'la <= 1146.614990234375 & la > 46.77499961853027 & ent <= 0.9549999833106995',\n",
       " 'ns <= 1.3650000095367432 & asawr > 0.004999999888241291 & la > 15.87999963760376',\n",
       " 'la > 37.34499931335449 & nrev <= 46.760000228881836 & ns <= 1.4649999737739563',\n",
       " 'la > 12.25499963760376 & ns <= 1.4550000429153442 & ndev > -10.069999933242798',\n",
       " 'la > 84.95000076293945 & ent <= 0.9350000023841858 & asawr > -0.024999999441206455',\n",
       " 'la > 46.78999900817871 & ns <= 1.5 & ndev > -0.9549999833106995',\n",
       " 'ns <= 1.4449999928474426 & ent <= 0.9350000023841858 & la > -11.50499963760376',\n",
       " 'la > 44.26499938964844 & ns <= 1.4950000047683716 & rrexp > 423.87998962402344',\n",
       " 'la > 46.0049991607666 & rrexp > 426.10499572753906 & ns <= 1.4699999690055847',\n",
       " 'ent <= 0.925000011920929 & la > 83.50500106811523 & ns <= 1.4900000095367432',\n",
       " 'la > 87.27999877929688 & app <= 4.924999952316284 & ns <= 1.4300000071525574',\n",
       " 'ns <= 1.4800000190734863 & age > -0.10999999940395355 & la > 87.01499938964844',\n",
       " 'rtime <= 96.99500274658203 & la > 48.40999984741211',\n",
       " 'ent <= 0.9549999833106995 & ns <= 1.4800000190734863 & la > 83.02000045776367',\n",
       " 'ns <= 1.465000033378601 & age > -0.014999999664723873 & la > 55.06999969482422',\n",
       " 'asawr > 0.004999999888241291 & la > 49.97999954223633 & ns <= 1.465000033378601',\n",
       " 'la > 83.20499801635742 & ns <= 1.5049999952316284 & ent <= 0.9600000083446503',\n",
       " 'ns <= 1.4449999928474426 & asawr > 0.004999999888241291 & la > 51.55999946594238',\n",
       " 'ndev > -1.8149999976158142 & ns <= 1.5049999952316284 & la > 61.70000076293945',\n",
       " 'la > 46.709999084472656 & ndev > -7.425000190734863 & rtime <= 131.2300033569336',\n",
       " 'app <= 2.975000023841858 & rtime > 96.99500274658203 & la > 48.40999984741211',\n",
       " 'la > 55.13999938964844 & rtime <= 172.20000457763672 & ns <= 1.48499995470047',\n",
       " 'ns <= 1.39000004529953 & ent <= 0.9600000083446503 & la > 85.54500198364258',\n",
       " 'ent <= 0.925000011920929 & la > 51.55999946594238 & ns <= 1.4600000381469727',\n",
       " 'la > 46.54999923706055 & ent <= 0.8550000190734863 & ns <= 1.4399999976158142',\n",
       " 'la > 46.94999885559082 & app <= 4.974999904632568 & ns <= 1.5849999785423279',\n",
       " 'ns <= 1.6449999809265137 & asawr > 0.004999999888241291 & la > 73.2599983215332',\n",
       " 'rrexp > 430.3350067138672 & ns <= 1.5449999570846558 & la > 56.47999954223633',\n",
       " 'la > 70.68000030517578 & ns <= 1.3949999809265137 & ent <= 0.8149999976158142',\n",
       " 'ent <= 0.9350000023841858 & la > 46.64999961853027 & ns <= 1.3450000286102295',\n",
       " 'la > 55.14999961853027 & ns <= 1.465000033378601 & nrev > 6.015000104904175',\n",
       " 'age > -7.080000162124634 & la > 98.27499771118164 & ns <= 1.175000011920929',\n",
       " 'asawr > -0.014999999664723873 & ns <= 1.5 & la > 66.48500061035156',\n",
       " 'ns > 1.175000011920929 & la > 98.27499771118164',\n",
       " 'la <= 61.70000076293945',\n",
       " 'ns > 1.6449999809265137 & la > 73.2599983215332',\n",
       " 'la <= 66.48500061035156',\n",
       " 'la <= 67.8499984741211']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(list(rule[rule['coef']>0].sort_values(by=['coef','importance'],ascending=[False,False])['rule']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rule[rule['coef']<0].sort_values(by=['importance'],ascending=False))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python_Oat",
   "language": "python",
   "name": "env_oat"
  },
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    "python": {
     "delete_cmd_postfix": "",
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     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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}