Quality/extensive_tests-debrief.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanup_hive_message(mess):\n",
    "    if('TExecuteStatementResp' in mess):\n",
    "        mess1 = re.sub('^(.*)infoMessages=\\[' , '' , mess)\n",
    "        mess2 = mess1.split(':')\n",
    "        # print(\"HIVE_BEFORE\" , mess)\n",
    "        # print(\"HIVE_AFTER\" , mess2)\n",
    "        res = mess2[1] + \" \" +mess2[2] + \" \" +mess2[3]\n",
    "        # print(\"HIVE_AFTER2\" , res)\n",
    "        return res\n",
    "    return mess\n",
    "\n",
    "def normalize_model_class_name(model_class_name):\n",
    "    line1 = model_class_name\n",
    "    line1 = line1.replace(\"sklearn2sql.Helpers.Caret_Model.cCaretClassifier_\" , \"caret.classifier.caret_class_\")\n",
    "    line1 = line1.replace(\"sklearn2sql.Helpers.Caret_Model.cCaretRegressor_\" , \"caret.regressor.caret_reg_\")\n",
    "    line1 = line1.replace(\"sklearn2sql.Helpers.Caret_Model.cCaretPreprocessor_\" , \"caret.preprocessor.caret_prep_\")\n",
    "    line1 = line1.replace(\"sklearn2sql.Helpers.Keras_Model.cKerasClassifier_\" , \"keras.classifier.keras_class_\")\n",
    "    line1 = line1.replace(\"sklearn2sql.Helpers.Keras_Model.cKerasRegressor_\" , \"keras.regressor.keras_reg_\")\n",
    "    return line1\n",
    "\n",
    "def normalize_model_name(model_name):\n",
    "    line1 = model_name\n",
    "    line1 = line1.replace(\"cCaretClassifier_\" , \"caret_class_\")\n",
    "    line1 = line1.replace(\"cCaretRegressor_\" , \"caret_reg_\")\n",
    "    line1 = line1.replace(\"cCaretPreprocessor_\" , \"caret_prep_\")\n",
    "    line1 = line1.replace(\"cKerasClassifier_\" , \"keras_class_\")\n",
    "    line1 = line1.replace(\"cKerasRegressor_\" , \"keras_reg_\")\n",
    "    return line1\n",
    "\n",
    "database_name_from_dsn = {\"pgsql\" : \"PostgreSQL\",\n",
    "                          \"oracle\" : \"Oracle\", \n",
    "                          \"db2\" : \"IBM DB2\", \n",
    "                          \"sqltm\" : \"SQLite\",\n",
    "                          \"mssql\" : \"MS SQL Server\",\n",
    "                          \"mysql\" : \"MariaDB\",\n",
    "                          \"hive\" : \"Apache Hive\",\n",
    "                          \"impala\" : \"Impala\",\n",
    "                          \"firebird\" : \"Firebird\",\n",
    "                          \"monetdb\" : \"MonetDB\",\n",
    "                          \"teradata\" : \"Teradata\"}\n",
    "\n",
    "def strip_punc(x):\n",
    "    x = x .replace(\"'\" , \"\")\n",
    "    x = x .replace(\"(\" , \"\")\n",
    "    x = x .replace(\",\" , \"\")\n",
    "    return x\n",
    "\n",
    "def truncate_error_message(x):\n",
    "    lSpecialErrors = ['UnboundLocalError', \n",
    "                      'Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS', \n",
    "                      'Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL',\n",
    "                      'cTrainingError:Exception:TRAIN_FAILED', \n",
    "                      'Exception:CHECK_MATERIALIZED_DATA_ERROR', \n",
    "                      'Exception:PREDICT_FAILED', \n",
    "                      'NAN_VALUE_ENCOUNTERED_IN_MODEL', \n",
    "                      'Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED', \n",
    "                      'Exception:CONNECTION_FAILED_WITH_ERROR',\n",
    "                      'FileNotFoundError', 'IndexError', 'KeyError', \n",
    "                      'DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name:', \n",
    "                      'cTrainingError']\n",
    "    for lError in lSpecialErrors:\n",
    "        if(lError in x):\n",
    "            return lError + \" ...\"\n",
    "    lLongMessages = [\"concurrent transaction number is\", \n",
    "                     \"DOUBLE value is out of range in\",\n",
    "                     \"Unexpected end of command\" , \n",
    "                     \"Token unknown\"]\n",
    "    for mess in lLongMessages:\n",
    "        x = re.sub(mess + \".*$\", mess + \" ...\", x) \n",
    "     \n",
    "    x = re.sub(\"\\(Background on this error at:.*\\)\", \"\", x) \n",
    "    x = re.sub(\"\\[SQL.*$\", \"\\[SQL ...\", x) \n",
    "    return x[0:400]\n",
    "\n",
    "def read_bad_csv_file(name):\n",
    "    rows = [];\n",
    "    with open(name) as f:\n",
    "        content = f.readlines()\n",
    "    for line in content:\n",
    "        line1 = re.sub('^.*log:BENCH_STATUS ' , '',  line)\n",
    "        # print(line1)\n",
    "        fields = line1.split(\" \")\n",
    "        (lModel, lDatasetName, lDSN, lDialect, lDSNType) = fields[0:5]\n",
    "        lStatus = \" \".join(fields[5:-1])\n",
    "        lTime = fields[-1][:-2]\n",
    "        fields2 = lStatus.split(\" \")\n",
    "        lErrorMessage = \" \".join(fields2[5:])\n",
    "        status = fields2[4]\n",
    "\n",
    "        sql_error = lErrorMessage\n",
    "        \n",
    "        \n",
    "        row = [lModel, lDatasetName, lDialect, lDSN, status, lErrorMessage, lTime]\n",
    "        if(False):\n",
    "            print(\"[lModel]\" , row[0])\n",
    "            print(\"[lDatasetName]\" , row[1])\n",
    "            print(\"[lDialect]\" , row[2])\n",
    "            print(\"[lDSN]\" , row[3])\n",
    "            print(\"[status]\" , row[4])\n",
    "            print(\"[lErrorMessage]\" , row[5])\n",
    "            print(\"[lTime]\" , row[6])\n",
    "        rows = rows + [row]\n",
    "\n",
    "    df = pd.DataFrame(rows);\n",
    "    df.columns = ['Model' , 'dataset', 'dialect' , 'DSN' , 'status' , 'error_message' , 'elapsed_time']\n",
    "    df['dialect'] = df['dialect'].apply(lambda x : strip_punc(x))\n",
    "    df['Model'] = df['Model'].apply(lambda x : strip_punc(x))\n",
    "    df['dataset'] = df['dataset'].apply(lambda x : strip_punc(x))\n",
    "    df['status'] = df['status'].apply(lambda x : strip_punc(x))\n",
    "\n",
    "    print(list( df['dialect'].unique()))\n",
    "    df['dialect'] = df['dialect'].apply(database_name_from_dsn.get)\n",
    "    df['Model'] = df['Model'].str.replace(\"\\(\\'\" , \"\")\n",
    "    df['Model'] = df['Model'].str.replace(\"\\',\" , \"\")\n",
    "    df['Model'] = df['Model'].apply(normalize_model_name)\n",
    "    df['full_error_message'] = df['error_message']\n",
    "    df['error_message'] = df['error_message'].str.replace(\"None\\)\\)\" , \"SUCCESS\")\n",
    "    df['error_message'] = df['error_message'].str.replace(\"None\\),\" , \"SUCCESS\")\n",
    "    \n",
    "    df['error_message'] = df['error_message'].apply(lambda x : cleanup_hive_message(x))\n",
    "    df['error_message'] = df['error_message'].apply(truncate_error_message)\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('result.txt' , engine='python', sep='\\s+', index_col=False,\n",
    "#                 quotechar=\"'\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['db2', 'firebird', 'impala', 'monetdb', 'mssql', 'mysql', 'oracle', 'pgsql', 'sqltm', 'teradata']\n"
     ]
    }
   ],
   "source": [
    "df = read_bad_csv_file('result.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Model</th>\n",
       "      <th>dataset</th>\n",
       "      <th>dialect</th>\n",
       "      <th>DSN</th>\n",
       "      <th>status</th>\n",
       "      <th>error_message</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>full_error_message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_200_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model                        dataset  dialect  \\\n",
       "0       LGBMClassifier     DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "1       LGBMClassifier    DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "2  LGBMClassifier_pipe   p_DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "3       LGBMClassifier    DS_BENCH_C_200_7_2_F2617B35  IBM DB2   \n",
       "4  LGBMClassifier_pipe  p_DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "\n",
       "                                        DSN   status error_message  \\\n",
       "0  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "1  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "2  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "3  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "4  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "\n",
       "  elapsed_time full_error_message  \n",
       "0            3             None),  \n",
       "1            3             None),  \n",
       "2            3             None),  \n",
       "3            3             None),  \n",
       "4            3             None),  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelBinarizer                      180\n",
       "keras_class_LSTM                    180\n",
       "LabelEncoder                        180\n",
       "keras_class_SimpleRNN_pipe          180\n",
       "PassiveAggressiveClassifier_pipe    180\n",
       "DummyClassifier_pipe                180\n",
       "ExtraTreeClassifier                 180\n",
       "BaggingClassifier_pipe              180\n",
       "ExtraTreesClassifier_pipe           180\n",
       "DummyClassifier                     180\n",
       "DecisionTreeClassifier              180\n",
       "keras_class_LSTM_pipe               180\n",
       "RandomForestClassifier              180\n",
       "LinearSVC_pipe                      180\n",
       "RidgeClassifier_pipe                180\n",
       "ExtraTreeClassifier_pipe            180\n",
       "DecisionTreeClassifier_pipe         180\n",
       "BaggingClassifier                   180\n",
       "LinearSVC                           180\n",
       "PassiveAggressiveClassifier         180\n",
       "SGDClassifier_pipe                  180\n",
       "keras_class_GRU                     180\n",
       "ExtraTreesClassifier                180\n",
       "RandomForestClassifier_pipe         180\n",
       "keras_class_SimpleRNN               180\n",
       "RidgeClassifierCV_pipe              180\n",
       "keras_class_GRU_pipe                180\n",
       "Pipeline                            179\n",
       "RidgeClassifierCV                   179\n",
       "OneVsOneClassifier                  179\n",
       "                                   ... \n",
       "caret_reg_glmnet                     60\n",
       "OrthogonalMatchingPursuitCV          60\n",
       "BayesianRidge_pipe                   60\n",
       "BaggingRegressor_pipe                60\n",
       "caret_class_glm_pipe                 60\n",
       "keras_reg_Dense                      60\n",
       "DecisionTreeRegressor                60\n",
       "caret_reg_rpart                      60\n",
       "GradientBoostingRegressor_pipe       60\n",
       "LGBMRegressor                        60\n",
       "AdaBoostRegressor                    60\n",
       "LGBMRegressor_pipe                   60\n",
       "DummyRegressor_pipe                  60\n",
       "ExtraTreeRegressor                   60\n",
       "LassoLarsIC_pipe                     60\n",
       "RandomForestRegressor_pipe           60\n",
       "LinearSVR                            60\n",
       "LarsCV                               60\n",
       "keras_reg_SimpleRNN                  60\n",
       "LassoCV_pipe                         60\n",
       "Lasso_pipe                           60\n",
       "IsolationForest_pipe                 60\n",
       "caret_reg_ctree                      60\n",
       "KernelRidge                          60\n",
       "Lars                                 60\n",
       "caret_reg_nnet                       60\n",
       "LinearRegression_pipe                60\n",
       "caret_reg_svmPoly_pipe               59\n",
       "EllipticEnvelope_pipe                58\n",
       "EllipticEnvelope                     56\n",
       "Name: Model, Length: 251, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Model.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_classes(name):\n",
    "    lClasses = {}\n",
    "    lCategories = {}\n",
    "    rows = [];\n",
    "    with open(name) as f:\n",
    "        content = f.readlines()\n",
    "    for line in content:\n",
    "        line1 = line.replace(\"cGenerationWrapperFactory::createScikitObject() <class '\" , \"\")\n",
    "        line1 = line1.replace(\"'>\\n\" , \"\")\n",
    "        line1 = normalize_model_class_name(line1)\n",
    "        fq_class_name = line1\n",
    "        short_class_name = fq_class_name.split(\".\")[-1]\n",
    "        categeory = \".\".join(fq_class_name.split(\".\")[0:2])\n",
    "        lClasses[short_class_name] = fq_class_name\n",
    "        lCategories[short_class_name] = categeory\n",
    "    return (lClasses , lCategories)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "(Classes , Categories) = read_classes(\"classes.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LGBMClassifier': 'lightgbm.sklearn.LGBMClassifier',\n",
       " 'LGBMRegressor': 'lightgbm.sklearn.LGBMRegressor',\n",
       " 'caret_class_ctree': 'caret.classifier.caret_class_ctree',\n",
       " 'caret_class_ctree2': 'caret.classifier.caret_class_ctree2',\n",
       " 'caret_class_earth': 'caret.classifier.caret_class_earth',\n",
       " 'caret_class_glm': 'caret.classifier.caret_class_glm',\n",
       " 'caret_class_glmnet': 'caret.classifier.caret_class_glmnet',\n",
       " 'caret_class_nnet': 'caret.classifier.caret_class_nnet',\n",
       " 'caret_class_rf': 'caret.classifier.caret_class_rf',\n",
       " 'caret_class_rpart': 'caret.classifier.caret_class_rpart',\n",
       " 'caret_class_svmLinear': 'caret.classifier.caret_class_svmLinear',\n",
       " 'caret_class_svmPoly': 'caret.classifier.caret_class_svmPoly',\n",
       " 'caret_class_svmRadial': 'caret.classifier.caret_class_svmRadial',\n",
       " 'caret_class_svmRadialCost': 'caret.classifier.caret_class_svmRadialCost',\n",
       " 'caret_class_svmRadialSigma': 'caret.classifier.caret_class_svmRadialSigma',\n",
       " 'caret_class_svmRadialWeights': 'caret.classifier.caret_class_svmRadialWeights',\n",
       " 'caret_class_xgbTree': 'caret.classifier.caret_class_xgbTree',\n",
       " 'caret_prep_center_scale': 'caret.preprocessor.caret_prep_center_scale',\n",
       " 'caret_prep_ica': 'caret.preprocessor.caret_prep_ica',\n",
       " 'caret_prep_pca': 'caret.preprocessor.caret_prep_pca',\n",
       " 'caret_reg_ctree': 'caret.regressor.caret_reg_ctree',\n",
       " 'caret_reg_ctree2': 'caret.regressor.caret_reg_ctree2',\n",
       " 'caret_reg_earth': 'caret.regressor.caret_reg_earth',\n",
       " 'caret_reg_glm': 'caret.regressor.caret_reg_glm',\n",
       " 'caret_reg_glmnet': 'caret.regressor.caret_reg_glmnet',\n",
       " 'caret_reg_nnet': 'caret.regressor.caret_reg_nnet',\n",
       " 'caret_reg_rf': 'caret.regressor.caret_reg_rf',\n",
       " 'caret_reg_rpart': 'caret.regressor.caret_reg_rpart',\n",
       " 'caret_reg_svmLinear': 'caret.regressor.caret_reg_svmLinear',\n",
       " 'caret_reg_svmPoly': 'caret.regressor.caret_reg_svmPoly',\n",
       " 'caret_reg_svmRadial': 'caret.regressor.caret_reg_svmRadial',\n",
       " 'caret_reg_svmRadialCost': 'caret.regressor.caret_reg_svmRadialCost',\n",
       " 'caret_reg_svmRadialSigma': 'caret.regressor.caret_reg_svmRadialSigma',\n",
       " 'caret_reg_xgbTree': 'caret.regressor.caret_reg_xgbTree',\n",
       " 'keras_class_Dense': 'keras.classifier.keras_class_Dense',\n",
       " 'keras_class_GRU': 'keras.classifier.keras_class_GRU',\n",
       " 'keras_class_LSTM': 'keras.classifier.keras_class_LSTM',\n",
       " 'keras_class_SimpleRNN': 'keras.classifier.keras_class_SimpleRNN',\n",
       " 'keras_reg_Dense': 'keras.regressor.keras_reg_Dense',\n",
       " 'keras_reg_GRU': 'keras.regressor.keras_reg_GRU',\n",
       " 'keras_reg_LSTM': 'keras.regressor.keras_reg_LSTM',\n",
       " 'keras_reg_SimpleRNN': 'keras.regressor.keras_reg_SimpleRNN',\n",
       " 'CalibratedClassifierCV': 'sklearn.calibration.CalibratedClassifierCV',\n",
       " 'EllipticEnvelope': 'sklearn.covariance.elliptic_envelope.EllipticEnvelope',\n",
       " 'FactorAnalysis': 'sklearn.decomposition.factor_analysis.FactorAnalysis',\n",
       " 'FastICA': 'sklearn.decomposition.fastica_.FastICA',\n",
       " 'IncrementalPCA': 'sklearn.decomposition.incremental_pca.IncrementalPCA',\n",
       " 'KernelPCA': 'sklearn.decomposition.kernel_pca.KernelPCA',\n",
       " 'NMF': 'sklearn.decomposition.nmf.NMF',\n",
       " 'LatentDirichletAllocation': 'sklearn.decomposition.online_lda.LatentDirichletAllocation',\n",
       " 'PCA': 'sklearn.decomposition.pca.PCA',\n",
       " 'MiniBatchSparsePCA': 'sklearn.decomposition.sparse_pca.MiniBatchSparsePCA',\n",
       " 'SparsePCA': 'sklearn.decomposition.sparse_pca.SparsePCA',\n",
       " 'TruncatedSVD': 'sklearn.decomposition.truncated_svd.TruncatedSVD',\n",
       " 'LinearDiscriminantAnalysis': 'sklearn.discriminant_analysis.LinearDiscriminantAnalysis',\n",
       " 'DummyClassifier': 'sklearn.dummy.DummyClassifier',\n",
       " 'DummyRegressor': 'sklearn.dummy.DummyRegressor',\n",
       " 'BaggingClassifier': 'sklearn.ensemble.bagging.BaggingClassifier',\n",
       " 'BaggingRegressor': 'sklearn.ensemble.bagging.BaggingRegressor',\n",
       " 'ExtraTreesClassifier': 'sklearn.ensemble.forest.ExtraTreesClassifier',\n",
       " 'ExtraTreesRegressor': 'sklearn.ensemble.forest.ExtraTreesRegressor',\n",
       " 'RandomForestClassifier': 'sklearn.ensemble.forest.RandomForestClassifier',\n",
       " 'RandomForestRegressor': 'sklearn.ensemble.forest.RandomForestRegressor',\n",
       " 'GradientBoostingClassifier': 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier',\n",
       " 'GradientBoostingRegressor': 'sklearn.ensemble.gradient_boosting.GradientBoostingRegressor',\n",
       " 'IsolationForest': 'sklearn.ensemble.iforest.IsolationForest',\n",
       " 'AdaBoostClassifier': 'sklearn.ensemble.weight_boosting.AdaBoostClassifier',\n",
       " 'AdaBoostRegressor': 'sklearn.ensemble.weight_boosting.AdaBoostRegressor',\n",
       " 'SelectFromModel': 'sklearn.feature_selection.from_model.SelectFromModel',\n",
       " 'RFE': 'sklearn.feature_selection.rfe.RFE',\n",
       " 'RFECV': 'sklearn.feature_selection.rfe.RFECV',\n",
       " 'GenericUnivariateSelect': 'sklearn.feature_selection.univariate_selection.GenericUnivariateSelect',\n",
       " 'SelectFdr': 'sklearn.feature_selection.univariate_selection.SelectFdr',\n",
       " 'SelectFpr': 'sklearn.feature_selection.univariate_selection.SelectFpr',\n",
       " 'SelectFwe': 'sklearn.feature_selection.univariate_selection.SelectFwe',\n",
       " 'SelectKBest': 'sklearn.feature_selection.univariate_selection.SelectKBest',\n",
       " 'SelectPercentile': 'sklearn.feature_selection.univariate_selection.SelectPercentile',\n",
       " 'MissingIndicator': 'sklearn.impute.MissingIndicator',\n",
       " 'SimpleImputer': 'sklearn.impute.SimpleImputer',\n",
       " 'KernelRidge': 'sklearn.kernel_ridge.KernelRidge',\n",
       " 'LinearRegression': 'sklearn.linear_model.base.LinearRegression',\n",
       " 'ARDRegression': 'sklearn.linear_model.bayes.ARDRegression',\n",
       " 'BayesianRidge': 'sklearn.linear_model.bayes.BayesianRidge',\n",
       " 'ElasticNet': 'sklearn.linear_model.coordinate_descent.ElasticNet',\n",
       " 'ElasticNetCV': 'sklearn.linear_model.coordinate_descent.ElasticNetCV',\n",
       " 'Lasso': 'sklearn.linear_model.coordinate_descent.Lasso',\n",
       " 'LassoCV': 'sklearn.linear_model.coordinate_descent.LassoCV',\n",
       " 'MultiTaskElasticNet': 'sklearn.linear_model.coordinate_descent.MultiTaskElasticNet',\n",
       " 'MultiTaskElasticNetCV': 'sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV',\n",
       " 'MultiTaskLasso': 'sklearn.linear_model.coordinate_descent.MultiTaskLasso',\n",
       " 'MultiTaskLassoCV': 'sklearn.linear_model.coordinate_descent.MultiTaskLassoCV',\n",
       " 'Lars': 'sklearn.linear_model.least_angle.Lars',\n",
       " 'LarsCV': 'sklearn.linear_model.least_angle.LarsCV',\n",
       " 'LassoLars': 'sklearn.linear_model.least_angle.LassoLars',\n",
       " 'LassoLarsCV': 'sklearn.linear_model.least_angle.LassoLarsCV',\n",
       " 'LassoLarsIC': 'sklearn.linear_model.least_angle.LassoLarsIC',\n",
       " 'LogisticRegression': 'sklearn.linear_model.logistic.LogisticRegression',\n",
       " 'LogisticRegressionCV': 'sklearn.linear_model.logistic.LogisticRegressionCV',\n",
       " 'OrthogonalMatchingPursuit': 'sklearn.linear_model.omp.OrthogonalMatchingPursuit',\n",
       " 'OrthogonalMatchingPursuitCV': 'sklearn.linear_model.omp.OrthogonalMatchingPursuitCV',\n",
       " 'PassiveAggressiveClassifier': 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier',\n",
       " 'PassiveAggressiveRegressor': 'sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor',\n",
       " 'Perceptron': 'sklearn.linear_model.perceptron.Perceptron',\n",
       " 'RandomizedLogisticRegression': 'sklearn.linear_model.randomized_l1.RandomizedLogisticRegression',\n",
       " 'RANSACRegressor': 'sklearn.linear_model.ransac.RANSACRegressor',\n",
       " 'Ridge': 'sklearn.linear_model.ridge.Ridge',\n",
       " 'RidgeClassifier': 'sklearn.linear_model.ridge.RidgeClassifier',\n",
       " 'RidgeClassifierCV': 'sklearn.linear_model.ridge.RidgeClassifierCV',\n",
       " 'RidgeCV': 'sklearn.linear_model.ridge.RidgeCV',\n",
       " 'SGDClassifier': 'sklearn.linear_model.stochastic_gradient.SGDClassifier',\n",
       " 'SGDRegressor': 'sklearn.linear_model.stochastic_gradient.SGDRegressor',\n",
       " 'TheilSenRegressor': 'sklearn.linear_model.theil_sen.TheilSenRegressor',\n",
       " 'OneVsOneClassifier': 'sklearn.multiclass.OneVsOneClassifier',\n",
       " 'OneVsRestClassifier': 'sklearn.multiclass.OneVsRestClassifier',\n",
       " 'BernoulliNB': 'sklearn.naive_bayes.BernoulliNB',\n",
       " 'ComplementNB': 'sklearn.naive_bayes.ComplementNB',\n",
       " 'GaussianNB': 'sklearn.naive_bayes.GaussianNB',\n",
       " 'MultinomialNB': 'sklearn.naive_bayes.MultinomialNB',\n",
       " 'MLPClassifier': 'sklearn.neural_network.multilayer_perceptron.MLPClassifier',\n",
       " 'MLPRegressor': 'sklearn.neural_network.multilayer_perceptron.MLPRegressor',\n",
       " 'FeatureUnion': 'sklearn.pipeline.FeatureUnion',\n",
       " 'Pipeline': 'sklearn.pipeline.Pipeline',\n",
       " 'Binarizer': 'sklearn.preprocessing.data.Binarizer',\n",
       " 'MaxAbsScaler': 'sklearn.preprocessing.data.MaxAbsScaler',\n",
       " 'MinMaxScaler': 'sklearn.preprocessing.data.MinMaxScaler',\n",
       " 'Normalizer': 'sklearn.preprocessing.data.Normalizer',\n",
       " 'PolynomialFeatures': 'sklearn.preprocessing.data.PolynomialFeatures',\n",
       " 'PowerTransformer': 'sklearn.preprocessing.data.PowerTransformer',\n",
       " 'QuantileTransformer': 'sklearn.preprocessing.data.QuantileTransformer',\n",
       " 'RobustScaler': 'sklearn.preprocessing.data.RobustScaler',\n",
       " 'StandardScaler': 'sklearn.preprocessing.data.StandardScaler',\n",
       " 'KBinsDiscretizer': 'sklearn.preprocessing._discretization.KBinsDiscretizer',\n",
       " 'OneHotEncoder': 'sklearn.preprocessing._encoders.OneHotEncoder',\n",
       " 'OrdinalEncoder': 'sklearn.preprocessing._encoders.OrdinalEncoder',\n",
       " 'Imputer': 'sklearn.preprocessing.imputation.Imputer',\n",
       " 'LabelBinarizer': 'sklearn.preprocessing.label.LabelBinarizer',\n",
       " 'LabelEncoder': 'sklearn.preprocessing.label.LabelEncoder',\n",
       " 'GaussianRandomProjection': 'sklearn.random_projection.GaussianRandomProjection',\n",
       " 'SparseRandomProjection': 'sklearn.random_projection.SparseRandomProjection',\n",
       " 'LinearSVC': 'sklearn.svm.classes.LinearSVC',\n",
       " 'LinearSVR': 'sklearn.svm.classes.LinearSVR',\n",
       " 'NuSVC': 'sklearn.svm.classes.NuSVC',\n",
       " 'NuSVR': 'sklearn.svm.classes.NuSVR',\n",
       " 'OneClassSVM': 'sklearn.svm.classes.OneClassSVM',\n",
       " 'SVC': 'sklearn.svm.classes.SVC',\n",
       " 'SVR': 'sklearn.svm.classes.SVR',\n",
       " 'DecisionTreeClassifier': 'sklearn.tree.tree.DecisionTreeClassifier',\n",
       " 'DecisionTreeRegressor': 'sklearn.tree.tree.DecisionTreeRegressor',\n",
       " 'ExtraTreeClassifier': 'sklearn.tree.tree.ExtraTreeClassifier',\n",
       " 'ExtraTreeRegressor': 'sklearn.tree.tree.ExtraTreeRegressor',\n",
       " 'XGBClassifier': 'xgboost.sklearn.XGBClassifier',\n",
       " 'XGBRegressor': 'xgboost.sklearn.XGBRegressor'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Model</th>\n",
       "      <th>dataset</th>\n",
       "      <th>dialect</th>\n",
       "      <th>DSN</th>\n",
       "      <th>status</th>\n",
       "      <th>error_message</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>full_error_message</th>\n",
       "      <th>model_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_200_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model                        dataset  dialect  \\\n",
       "0       LGBMClassifier     DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "1       LGBMClassifier    DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "2  LGBMClassifier_pipe   p_DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "3       LGBMClassifier    DS_BENCH_C_200_7_2_F2617B35  IBM DB2   \n",
       "4  LGBMClassifier_pipe  p_DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "\n",
       "                                        DSN   status error_message  \\\n",
       "0  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "1  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "2  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "3  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "4  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "\n",
       "  elapsed_time full_error_message    model_category  \n",
       "0            3             None),  lightgbm.sklearn  \n",
       "1            3             None),  lightgbm.sklearn  \n",
       "2            3             None),  lightgbm.sklearn  \n",
       "3            3             None),  lightgbm.sklearn  \n",
       "4            3             None),  lightgbm.sklearn  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_category(model_name):\n",
    "    real_name = model_name\n",
    "    if(model_name.endswith('_pipe')):\n",
    "        real_name = model_name.replace(\"_pipe\" , \"\")\n",
    "    return Categories.get(real_name , \"bad_category\")\n",
    "\n",
    "df['model_category'] = df['Model'].apply(get_category)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_model_datasets = pd.DataFrame(df[['Model' , 'dataset']].groupby(['Model'])['dataset'].value_counts())\n",
    "datatsets_by_model = df_model_datasets.to_dict()['dataset'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datatsets_by_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "datatsets_by_model_dict = {}\n",
    "for k in datatsets_by_model:\n",
    "    (m , ds) = k\n",
    "    if(m not in datatsets_by_model_dict.keys()):\n",
    "        datatsets_by_model_dict[m] = [ds]\n",
    "    else:\n",
    "        datatsets_by_model_dict[m] = datatsets_by_model_dict[m] + [ds]\n",
    "        \n",
    "# print(datatsets_by_model_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IBM DB2' 'Firebird' 'Impala' 'MonetDB' 'MS SQL Server' 'MariaDB'\n",
      " 'Oracle' 'PostgreSQL' 'SQLite' 'Teradata']\n",
      "df.shape (27230, 9) Index(['Model', 'dataset', 'dialect', 'DSN', 'status', 'error_message',\n",
      "       'elapsed_time', 'full_error_message', 'model_category'],\n",
      "      dtype='object')\n",
      "MODEL_DATASETS_MISSING IBM DB2 caret_class_ctree2 18 9\n",
      "MODEL_DATASETS_MISSING IBM DB2 caret_class_ctree2_pipe 18 9\n",
      "MODEL_DATASETS_MISSING IBM DB2 caret_class_svmPoly_pipe 12 6\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_ctree_pipe 18 10\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_earth 18 9\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_earth_pipe 18 10\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_nnet_pipe 18 9\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_rf_pipe 18 9\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_rpart_pipe 18 10\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_svmPoly 12 6\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_svmPoly_pipe 12 8\n",
      "MODEL_DATASETS_MISSING Firebird caret_class_svmRadial_pipe 18 9\n",
      "MODEL_DATASETS_MISSING Impala BernoulliNB 18 10\n",
      "MODEL_DATASETS_MISSING Impala BernoulliNB_pipe 18 10\n",
      "MODEL_DATASETS_MISSING Impala ComplementNB 18 10\n",
      "MODEL_DATASETS_MISSING Impala ComplementNB_pipe 18 10\n",
      "MODEL_DATASETS_MISSING Impala GaussianNB 18 10\n",
      "MODEL_DATASETS_MISSING Impala GaussianNB_pipe 18 10\n",
      "MODEL_DATASETS_MISSING Impala GradientBoostingClassifier 18 10\n",
      "MODEL_DATASETS_MISSING Impala GradientBoostingClassifier_pipe 18 11\n",
      "MODEL_DATASETS_MISSING Impala LGBMClassifier 18 11\n",
      "MODEL_DATASETS_MISSING Impala LGBMClassifier_pipe 18 11\n",
      "MODEL_DATASETS_MISSING Impala LinearDiscriminantAnalysis 18 13\n",
      "MODEL_DATASETS_MISSING Impala LinearDiscriminantAnalysis_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala LogisticRegression 18 13\n",
      "MODEL_DATASETS_MISSING Impala LogisticRegressionCV 18 13\n",
      "MODEL_DATASETS_MISSING Impala LogisticRegressionCV_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala LogisticRegression_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala MLPClassifier 18 11\n",
      "MODEL_DATASETS_MISSING Impala MLPClassifier_pipe 18 12\n",
      "MODEL_DATASETS_MISSING Impala MultinomialNB 18 11\n",
      "MODEL_DATASETS_MISSING Impala MultinomialNB_pipe 18 11\n",
      "MODEL_DATASETS_MISSING Impala NuSVC 18 11\n",
      "MODEL_DATASETS_MISSING Impala NuSVC_pipe 18 11\n",
      "MODEL_DATASETS_MISSING Impala OneVsRestClassifier 18 10\n",
      "MODEL_DATASETS_MISSING Impala OneVsRestClassifier_pipe 18 10\n",
      "MODEL_DATASETS_MISSING Impala SVC 18 11\n",
      "MODEL_DATASETS_MISSING Impala SVC_pipe 18 11\n",
      "MODEL_DATASETS_MISSING Impala XGBClassifier 18 11\n",
      "MODEL_DATASETS_MISSING Impala XGBClassifier_pipe 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_earth 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_earth_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala caret_class_glmnet 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_glmnet_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala caret_class_nnet 18 10\n",
      "MODEL_DATASETS_MISSING Impala caret_class_nnet_pipe 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmLinear 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmLinear_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmPoly_pipe 12 7\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadial 17 11\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadialCost 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadialCost_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadialSigma 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadialSigma_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadialWeights 18 10\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadialWeights_pipe 18 12\n",
      "MODEL_DATASETS_MISSING Impala caret_class_svmRadial_pipe 18 13\n",
      "MODEL_DATASETS_MISSING Impala caret_class_xgbTree 18 10\n",
      "MODEL_DATASETS_MISSING Impala caret_class_xgbTree_pipe 18 11\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_ctree_pipe 18 9\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_earth_pipe 18 10\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_glmnet 18 14\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_glmnet_pipe 18 14\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_nnet 18 14\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_nnet_pipe 18 14\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_rpart_pipe 18 9\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmPoly 12 6\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmPoly_pipe 12 7\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmRadial 17 8\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialCost_pipe 18 10\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialSigma 18 9\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialSigma_pipe 18 10\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialWeights 18 9\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialWeights_pipe 18 10\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_svmRadial_pipe 18 10\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_xgbTree 18 14\n",
      "MODEL_DATASETS_MISSING MonetDB caret_class_xgbTree_pipe 18 14\n",
      "MODEL_DATASETS_MISSING MS SQL Server caret_class_ctree2 18 9\n",
      "MODEL_DATASETS_MISSING MS SQL Server caret_class_ctree2_pipe 18 18\n",
      "MODEL_DATASETS_MISSING MS SQL Server caret_class_svmPoly_pipe 12 6\n",
      "MODEL_DATASETS_MISSING Oracle NuSVC 18 10\n",
      "MODEL_DATASETS_MISSING Oracle SVC 18 10\n",
      "MODEL_DATASETS_MISSING Oracle caret_class_svmPoly_pipe 12 6\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_nnet_pipe 18 9\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmLinear_pipe 18 9\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmPoly 12 6\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmPoly_pipe 12 6\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmRadialCost_pipe 18 9\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmRadialSigma 18 9\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmRadialSigma_pipe 18 9\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmRadialWeights_pipe 18 9\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_svmRadial_pipe 18 10\n",
      "MODEL_DATASETS_MISSING SQLite caret_class_xgbTree_pipe 18 9\n",
      "MODEL_DATASETS_MISSING Teradata caret_class_glmnet 18 9\n",
      "MODEL_DATASETS_MISSING Teradata caret_class_glmnet_pipe 18 10\n"
     ]
    }
   ],
   "source": [
    "missing_rows = []\n",
    "dialects = df.dialect.unique()\n",
    "print(dialects)\n",
    "\n",
    "print(\"df.shape\", df.shape , df.columns)\n",
    "for dialect in dialects:\n",
    "    df1 = df[df['dialect'] == dialect]\n",
    "    # print(\"df1.shape\", dialect, df1.shape , df1.columns)\n",
    "    for (m , datasets) in datatsets_by_model_dict.items():\n",
    "        lMissing = 0\n",
    "        cond2 = (df1['Model'] == m)\n",
    "        df2 = df1[cond2]\n",
    "        # print(\"df2.shape\", dialect, m , df2.shape, df2.columns)\n",
    "        for ds in datasets:\n",
    "            cond3 = (df2['dataset'] == ds)\n",
    "            df3 = df2[cond3]\n",
    "            # print(\"df3.shape\", dialect, m, ds, df3.shape, df3.columns)\n",
    "            if(df3.shape[0] == 0):\n",
    "                # print(\"MISSING_DATA\" , [dialect , m , ds])\n",
    "                missing_rows = missing_rows + [[m , ds, dialect , 'no_dsn' , 'failure' , 'TIMEOUT' , None, 'TIMEOUT', get_category(m)]]\n",
    "                lMissing = lMissing + 1\n",
    "        if(lMissing >= (len(datasets) // 2)):\n",
    "            print(\"MODEL_DATASETS_MISSING\" , dialect, m, len(datasets) , lMissing)\n",
    "\n",
    "missing_data = pd.DataFrame(missing_rows, columns=df.columns)\n",
    "# missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Model</th>\n",
       "      <th>dataset</th>\n",
       "      <th>dialect</th>\n",
       "      <th>DSN</th>\n",
       "      <th>status</th>\n",
       "      <th>error_message</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>full_error_message</th>\n",
       "      <th>model_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>DS_BENCH_C_200_82_10_EA8E6ACF</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>no_dsn</td>\n",
       "      <td>failure</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>None</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>sklearn.svm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>caret_class_ctree2</td>\n",
       "      <td>DS_BENCH_C_200_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>no_dsn</td>\n",
       "      <td>failure</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>None</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>caret.classifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>caret_class_ctree2</td>\n",
       "      <td>DS_BENCH_C_200_22_4_9802FBBD</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>no_dsn</td>\n",
       "      <td>failure</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>None</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>caret.classifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>caret_class_ctree2</td>\n",
       "      <td>DS_BENCH_C_200_22_10_D28EF166</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>no_dsn</td>\n",
       "      <td>failure</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>None</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>caret.classifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>caret_class_ctree2</td>\n",
       "      <td>DS_BENCH_C_200_82_10_EA8E6ACF</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>no_dsn</td>\n",
       "      <td>failure</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>None</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>caret.classifier</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model                        dataset  dialect     DSN  \\\n",
       "0                 SVC  DS_BENCH_C_200_82_10_EA8E6ACF  IBM DB2  no_dsn   \n",
       "1  caret_class_ctree2   DS_BENCH_C_200_22_2_2E64DEB6  IBM DB2  no_dsn   \n",
       "2  caret_class_ctree2   DS_BENCH_C_200_22_4_9802FBBD  IBM DB2  no_dsn   \n",
       "3  caret_class_ctree2  DS_BENCH_C_200_22_10_D28EF166  IBM DB2  no_dsn   \n",
       "4  caret_class_ctree2  DS_BENCH_C_200_82_10_EA8E6ACF  IBM DB2  no_dsn   \n",
       "\n",
       "    status error_message elapsed_time full_error_message    model_category  \n",
       "0  failure       TIMEOUT         None            TIMEOUT       sklearn.svm  \n",
       "1  failure       TIMEOUT         None            TIMEOUT  caret.classifier  \n",
       "2  failure       TIMEOUT         None            TIMEOUT  caret.classifier  \n",
       "3  failure       TIMEOUT         None            TIMEOUT  caret.classifier  \n",
       "4  failure       TIMEOUT         None            TIMEOUT  caret.classifier  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.append(missing_data , ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Model</th>\n",
       "      <th>dataset</th>\n",
       "      <th>dialect</th>\n",
       "      <th>DSN</th>\n",
       "      <th>status</th>\n",
       "      <th>error_message</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>full_error_message</th>\n",
       "      <th>model_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Model, dataset, dialect, DSN, status, error_message, elapsed_time, full_error_message, model_category]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.error_message == \"'\"].head(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28910, 9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MonetDB          2891\n",
       "Impala           2891\n",
       "IBM DB2          2891\n",
       "Firebird         2891\n",
       "MS SQL Server    2891\n",
       "Teradata         2891\n",
       "SQLite           2891\n",
       "PostgreSQL       2891\n",
       "MariaDB          2891\n",
       "Oracle           2891\n",
       "Name: dialect, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dialect.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df0[df0.ds == numpy.na]\n",
    "df_errors = df[(df.error_message != 'SUCCESS')]\n",
    "indices = df_errors.error_message.apply(lambda x : 'dialect' not in x)\n",
    "df_errors = df_errors[indices]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_by_estim_and_dsn = pd.DataFrame(df_errors.groupby(['dialect'])['error_message'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antoine/.local/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'error_message' is both an index level and a column label.\n",
      "Defaulting to column, but this will raise an ambiguity error in a future version\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firebird</th>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teradata</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firebird</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teradata</th>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...</th>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firebird</th>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Impala</th>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teradata</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Firebird</th>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Firebird</th>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while executing SQL statement:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\\', -802, 335544321) \\[SQL ...</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SQLite</th>\n",
       "      <th>Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">IBM DB2</th>\n",
       "      <th>'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N  Arithmetic overflow or other arithmetic exception occurred.  SQLSTATE=22003 SQLCODE=-802 ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:PREDICT_FAILED ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N  The name of the object to be created is identical to the existing name \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" of type \"CREATE TEMPORARY TABLE\".  SQLSTATE=42710 SQLCODE=-601 \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N  \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" is an undefined name.  SQLSTATE=42704 SQLCODE=-204 \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firebird</th>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Teradata</th>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (5639, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:PREDICT_FAILED ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Impala</th>\n",
       "      <th>'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \\'createTable\\' RPC to Hive Metastore: \\nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\\n \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\\n \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>IndexError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206010532_OEPNFRI_OVR_Score_1\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KeyError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (20004, b'DB-Lib error message 20004, severity 9:\\\\nRead from the server failed (db:1433)\\\\nNet-Lib error during Connection reset by peer (104)\\\\nDB-Lib error message 20002, severity 9:\\\\nAdaptive Server connection failed (db:1433)\\\\n') ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teradata</th>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Impala        TIMEOUT                                                       587\n",
       "MonetDB       TIMEOUT                                                       391\n",
       "PostgreSQL    Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               254\n",
       "IBM DB2       Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               254\n",
       "MS SQL Server Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               244\n",
       "Firebird      \"DatabaseError:('Error while commiting transact...            244\n",
       "MariaDB       Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               240\n",
       "Oracle        Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               239\n",
       "Teradata      Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               232\n",
       "Firebird      TIMEOUT                                                       217\n",
       "SQLite        Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               168\n",
       "              TIMEOUT                                                       162\n",
       "Teradata      'DatabaseError:(teradata.api.DatabaseError) (37...            160\n",
       "MonetDB       Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               146\n",
       "Impala        Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTE...            142\n",
       "MonetDB       Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTE...            141\n",
       "Firebird      'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...            131\n",
       "Impala        DBAPIError:(impala.error.HiveServer2Error) Anal...            118\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               118\n",
       "Teradata      TIMEOUT                                                       112\n",
       "Firebird      'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...            103\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                92\n",
       "Oracle        TIMEOUT                                                        92\n",
       "Firebird      'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             88\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...             69\n",
       "MariaDB       TIMEOUT                                                        42\n",
       "MS SQL Server TIMEOUT                                                        41\n",
       "Impala        Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_M...             36\n",
       "MariaDB       Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_M...             36\n",
       "SQLite        Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_M...             36\n",
       "...                                                                         ...\n",
       "              FileNotFoundError ...                                           1\n",
       "              cTrainingError ...                                              1\n",
       "IBM DB2       'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Erro...              1\n",
       "              cTrainingError ...                                              1\n",
       "              Exception:PREDICT_FAILED ...                                    1\n",
       "              'ProgrammingError:(ibm_db_dbi.ProgrammingError)...              1\n",
       "              'ProgrammingError:(ibm_db_dbi.ProgrammingError)...              1\n",
       "Firebird      cTrainingError ...                                              1\n",
       "Teradata      'DatabaseError:(teradata.api.DatabaseError) (56...              1\n",
       "              Exception:PREDICT_FAILED ...                                    1\n",
       "SQLite        'ValueError:Length mismatch: Expected axis has ...              1\n",
       "Impala        'DBAPIError:(impala.error.HiveServer2Error) Imp...              1\n",
       "              \"OperationalError:(impala.error.OperationalErro...              1\n",
       "              'DBAPIError:(impala.error.HiveServer2Error) Exe...              1\n",
       "Oracle        IndexError ...                                                  1\n",
       "              cTrainingError ...                                              1\n",
       "MonetDB       cTrainingError ...                                              1\n",
       "MariaDB       cTrainingError ...                                              1\n",
       "              FileNotFoundError ...                                           1\n",
       "MS SQL Server cTrainingError ...                                              1\n",
       "              'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "              'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "              'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "PostgreSQL    FileNotFoundError ...                                           1\n",
       "              KeyError ...                                                    1\n",
       "              cTrainingError ...                                              1\n",
       "MS SQL Server \"OperationalError:(pymssql.OperationalError) (2...              1\n",
       "Impala        cTrainingError ...                                              1\n",
       "Oracle        FileNotFoundError ...                                           1\n",
       "Teradata      cTrainingError ...                                              1\n",
       "\n",
       "[127 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = msg_by_estim_and_dsn.sort_values(by='error_message' , ascending=False)\n",
    "df4.head(df4.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\"DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Exceeded the maximum depth of an expression tree (1000).\\\\n \\\\[SQL ...',\n",
       " '\"DatabaseError:(\\'Error while commiting transaction:\\\\\\\\n- SQLCODE: -913\\\\\\\\n- deadlock\\\\\\\\n- update conflicts with concurrent update\\\\\\\\n- concurrent transaction number is ...',\n",
       " '\"DatabaseError:(fdb.fbcore.DatabaseError) (\\'Cursor.fetchone:\\\\\\\\n- SQLCODE: -802\\\\\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\\\\\n- numeric value is out of range\\', -802, 335544321) ,',\n",
       " '\"DatabaseError:(fdb.fbcore.DatabaseError) (\\'Cursor.fetchone:\\\\\\\\n- SQLCODE: -901\\\\\\\\n- Integer overflow.  The result of an integer operation caused the most significant bit of the result to carry.\\', -901, 335544779) ,',\n",
       " '\"DatabaseError:(teradata.api.DatabaseError) (10670, \\'[07009] [Teradata][ODBC] (10670) Invalid descriptor index, descriptor record does not exist, or descriptor record was not properly initialized.\\') ,',\n",
       " '\"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\\\\n \\\\[SQL ...',\n",
       " '\"OperationalError:(pymssql.OperationalError) (125, b\\'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\n\\') \\\\[SQL ...',\n",
       " '\"OperationalError:(pymssql.OperationalError) (125, b\\'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\nDB-Lib error message 20018, severity 15:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\nDB-Lib error message 20018, severity 15:\\\\\\\\nGeneral SQL Server error: Check messages fro',\n",
       " '\"OperationalError:(pymssql.OperationalError) (20004, b\\'DB-Lib error message 20004, severity 9:\\\\\\\\nRead from the server failed (db:1433)\\\\\\\\nNet-Lib error during Connection reset by peer (104)\\\\\\\\nDB-Lib error message 20002, severity 9:\\\\\\\\nAdaptive Server connection failed (db:1433)\\\\\\\\n\\') ,',\n",
       " '\"OperationalError:(pymssql.OperationalError) (8115, b\\'Arithmetic overflow error converting expression to data type float.DB-Lib error message 20018, severity 16:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\n\\') \\\\[SQL ...',\n",
       " '\"OperationalError:(pymssql.OperationalError) (8631, b\\'Internal error: Server stack limit has been reached. Please look for potentially deep nesting in your query, and try to simplify it.DB-Lib error message 20018, severity 17:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\n\\') \\\\[SQL ...',\n",
       " \"'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,\",\n",
       " \"'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N  Arithmetic overflow or other arithmetic exception occurred.  SQLSTATE=22003 SQLCODE=-802 ,\",\n",
       " \"'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid \",\n",
       " \"'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \\\\'createTable\\\\' RPC to Hive Metastore: \\\\nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\\\\n \\\\[SQL ...\",\n",
       " \"'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0840N  Too many items were returned in a SELECT list.  SQLSTATE=54004 SQLCODE=-840 \\\\[SQL ...\",\n",
       " \"'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL1585N  A temporary table could not be created because there is no available system temporary table space that has a compatible page size.  SQLSTATE=54048 SQLCODE=-1585 \\\\[SQL ...\",\n",
       " \"'DataError:(psycopg2.DataError) value out of range: overflow\\\\n \\\\[SQL ...\",\n",
       " \"'DataError:(psycopg2.DataError) value out of range: underflow\\\\n \\\\[SQL ...\",\n",
       " \"'DatabaseError:(fdb.fbcore.DatabaseError) (\\\\'Error while executing SQL statement:\\\\\\\\n- SQLCODE: -802\\\\\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\\\\\n- numeric value is out of range\\\\', -802, 335544321) \\\\[SQL ...\",\n",
       " \"'DatabaseError:(fdb.fbcore.DatabaseError) (\\\\'Error while preparing SQL statement:\\\\\\\\n- SQLCODE: -104\\\\\\\\n- Dynamic SQL Error\\\\\\\\n- SQL error code = -104\\\\\\\\n- Token unknown ...\",\n",
       " \"'DatabaseError:(fdb.fbcore.DatabaseError) (\\\\'Error while preparing SQL statement:\\\\\\\\n- SQLCODE: -104\\\\\\\\n- Dynamic SQL Error\\\\\\\\n- SQL error code = -104\\\\\\\\n- Unexpected end of command ...\",\n",
       " \"'DatabaseError:(fdb.fbcore.DatabaseError) (\\\\'Error while preparing SQL statement:\\\\\\\\n- SQLCODE: -901\\\\\\\\n- request size limit exceeded\\\\', -901, 335544382) \\\\[SQL ...\",\n",
       " \"'DatabaseError:(fdb.fbcore.DatabaseError) (\\\\'Error while preparing SQL statement:\\\\\\\\n- SQLCODE: -902\\\\\\\\n- Dynamic SQL Error\\\\\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\\\', -902, 335544569) \\\\[SQL ...\",\n",
       " \"'DatabaseError:(teradata.api.DatabaseError) (2616, \\\\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\\\') \\\\[SQL ...\",\n",
       " \"'DatabaseError:(teradata.api.DatabaseError) (3710, \\\\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\\\\') \\\\[SQL ...\",\n",
       " \"'DatabaseError:(teradata.api.DatabaseError) (3710, \\\\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\\\\') \\\\[SQL ...\",\n",
       " \"'DatabaseError:(teradata.api.DatabaseError) (3754, \\\\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\\\') \\\\[SQL ...\",\n",
       " \"'DatabaseError:(teradata.api.DatabaseError) (5639, \\\\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\\\\') \\\\[SQL ...\",\n",
       " '\\'InternalError:(ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0973N  Not enough storage is available in the \"AGENT_STACK_SZ\" heap or stack to process the statement.  SQLSTATE=57011 SQLCODE=-973 \\\\[SQL ...',\n",
       " '\\'OperationalError:(_mysql_exceptions.OperationalError) (1436, \"Thread stack overrun:  1238176 bytes used of a 1269760 byte stack, and 32000 bytes needed.  Use \\\\\\'mysqld --thread_stack=#\\\\\\' to specify a bigger stack\") \\\\[SQL ...',\n",
       " '\\'OperationalError:(_mysql_exceptions.OperationalError) (1690, \"DOUBLE value is out of range in ...',\n",
       " '\\'OperationalError:(psycopg2.OperationalError) stack depth limit exceeded\\\\nHINT:  Increase the configuration parameter \"max_stack_depth\" (currently 2048kB), after ensuring the platform\\\\\\'s stack depth limit is adequate.\\\\n \\\\[SQL ...',\n",
       " \"'OperationalError:(psycopg2.OperationalError) target lists can have at most 1664 entries\\\\n \\\\[SQL ...\",\n",
       " \"'OperationalError:(pymonetdb.exceptions.OperationalError) Math exception: Numerical argument out of domain\\\\n \\\\[SQL ...\",\n",
       " '\\'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\\\\\'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\\\\\\' in the database.DB-Lib error message 20018, severity 16:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\n\") \\\\[SQL ...',\n",
       " '\\'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\\\\\'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\\\\\\' in the database.DB-Lib error message 20018, severity 16:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\n\") \\\\[SQL ...',\n",
       " '\\'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\\\\\'##TMP_20181206010532_OEPNFRI_OVR_Score_1\\\\\\' in the database.DB-Lib error message 20018, severity 16:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\n\") \\\\[SQL ...',\n",
       " '\\'OperationalError:(pymssql.OperationalError) (701, b\"There is insufficient system memory in resource pool \\\\\\'default\\\\\\' to run this query.DB-Lib error message 20018, severity 17:\\\\\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\\\\\n\") \\\\[SQL ...',\n",
       " \"'OperationalError:(sqlite3.OperationalError) Expression tree is too large (maximum depth 1000) \\\\[SQL ...\",\n",
       " \"'OperationalError:(sqlite3.OperationalError) parser stack overflow \\\\[SQL ...\",\n",
       " \"'OperationalError:(sqlite3.OperationalError) too many columns in result set \\\\[SQL ...\",\n",
       " \"'OperationalError:(sqlite3.OperationalError) too many terms in compound SELECT \\\\[SQL ...\",\n",
       " \"'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0419N  A decimal divide operation is not valid because the result would have a negative scale.  SQLSTATE=42911 SQLCODE=-419 \\\\[SQL ...\",\n",
       " '\\'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N  \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" is an undefined name.  SQLSTATE=42704 SQLCODE=-204 \\\\[SQL ...',\n",
       " '\\'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N  The name of the object to be created is identical to the existing name \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" of type \"CREATE TEMPORARY TABLE\".  SQLSTATE=42710 SQLCODE=-601 \\\\[SQL ...',\n",
       " \"'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),\",\n",
       " \"'ValueError:Length mismatch: Expected axis has 5 elements, new values have 20 elements'),\",\n",
       " 'DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...',\n",
       " 'Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...',\n",
       " 'Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...',\n",
       " 'Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...',\n",
       " 'Exception:CONNECTION_FAILED_WITH_ERROR ...',\n",
       " 'Exception:PREDICT_FAILED ...',\n",
       " 'FileNotFoundError ...',\n",
       " 'IndexError ...',\n",
       " 'KeyError ...',\n",
       " 'NAN_VALUE_ENCOUNTERED_IN_MODEL ...',\n",
       " 'SUCCESS',\n",
       " 'TIMEOUT',\n",
       " 'cTrainingError ...',\n",
       " 'cTrainingError:Exception:TRAIN_FAILED ...']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df.error_message.unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "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>error_message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>23280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...</th>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...</th>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while executing SQL statement:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\\', -802, 335544321) \\[SQL ...</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:CONNECTION_FAILED_WITH_ERROR ...</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages fro</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (2616, \\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\') \\[SQL ...</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(_mysql_exceptions.OperationalError) (1690, \"DOUBLE value is out of range in ...</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (8115, b'Arithmetic overflow error converting expression to data type float.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n') \\[SQL ...</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(psycopg2.DataError) value out of range: overflow\\n \\[SQL ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymonetdb.exceptions.OperationalError) Math exception: Numerical argument out of domain\\n \\[SQL ...</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(psycopg2.DataError) value out of range: underflow\\n \\[SQL ...</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Token unknown ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) parser stack overflow \\[SQL ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n') \\[SQL ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -901\\\\n- request size limit exceeded\\', -901, 335544382) \\[SQL ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\\') \\[SQL ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(teradata.api.DatabaseError) (10670, '[07009] [Teradata][ODBC] (10670) Invalid descriptor index, descriptor record does not exist, or descriptor record was not properly initialized.') ,</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(psycopg2.OperationalError) target lists can have at most 1664 entries\\n \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) too many terms in compound SELECT \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0840N  Too many items were returned in a SELECT list.  SQLSTATE=54004 SQLCODE=-840 \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) too many columns in result set \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\\') \\[SQL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndexError ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -901\\\\n- Integer overflow.  The result of an integer operation caused the most significant bit of the result to carry.', -901, 335544779) ,</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (8631, b'Internal error: Server stack limit has been reached. Please look for potentially deep nesting in your query, and try to simplify it.DB-Lib error message 20018, severity 17:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n') \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(_mysql_exceptions.OperationalError) (1436, \"Thread stack overrun:  1238176 bytes used of a 1269760 byte stack, and 32000 bytes needed.  Use \\'mysqld --thread_stack=#\\' to specify a bigger stack\") \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) Expression tree is too large (maximum depth 1000) \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'InternalError:(ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0973N  Not enough storage is available in the \"AGENT_STACK_SZ\" heap or stack to process the statement.  SQLSTATE=57011 SQLCODE=-973 \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(psycopg2.OperationalError) stack depth limit exceeded\\nHINT:  Increase the configuration parameter \"max_stack_depth\" (currently 2048kB), after ensuring the platform\\'s stack depth limit is adequate.\\n \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL1585N  A temporary table could not be created because there is no available system temporary table space that has a compatible page size.  SQLSTATE=54048 SQLCODE=-1585 \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Exceeded the maximum depth of an expression tree (1000).\\n \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (701, b\"There is insufficient system memory in resource pool \\'default\\' to run this query.DB-Lib error message 20018, severity 17:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206010532_OEPNFRI_OVR_Score_1\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (5639, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N  Arithmetic overflow or other arithmetic exception occurred.  SQLSTATE=22003 SQLCODE=-802 ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N  The name of the object to be created is identical to the existing name \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" of type \"CREATE TEMPORARY TABLE\".  SQLSTATE=42710 SQLCODE=-601 \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (20004, b'DB-Lib error message 20004, severity 9:\\\\nRead from the server failed (db:1433)\\\\nNet-Lib error during Connection reset by peer (104)\\\\nDB-Lib error message 20002, severity 9:\\\\nAdaptive Server connection failed (db:1433)\\\\n') ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N  \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" is an undefined name.  SQLSTATE=42704 SQLCODE=-204 \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ValueError:Length mismatch: Expected axis has 5 elements, new values have 20 elements'),</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\\n \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KeyError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \\'createTable\\' RPC to Hive Metastore: \\nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\\n \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    error_message\n",
       "SUCCESS                                                     23280\n",
       "Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...              1987\n",
       "TIMEOUT                                                      1680\n",
       "Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_M...            358\n",
       "Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTE...            283\n",
       "\"DatabaseError:('Error while commiting transact...            244\n",
       "'DatabaseError:(teradata.api.DatabaseError) (37...            160\n",
       "'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...            131\n",
       "DBAPIError:(impala.error.HiveServer2Error) Anal...            118\n",
       "'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...            103\n",
       "'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             88\n",
       "cTrainingError:Exception:TRAIN_FAILED ...                      78\n",
       "\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...             69\n",
       "NAN_VALUE_ENCOUNTERED_IN_MODEL ...                             35\n",
       "'DBAPIError:(builtins.BrokenPipeError) [Errno 3...             31\n",
       "Exception:CONNECTION_FAILED_WITH_ERROR ...                     30\n",
       "\"OperationalError:(pymssql.OperationalError) (1...             23\n",
       "'DatabaseError:(teradata.api.DatabaseError) (26...             21\n",
       "'OperationalError:(_mysql_exceptions.Operationa...             20\n",
       "\"OperationalError:(pymssql.OperationalError) (8...             18\n",
       "'DataError:(psycopg2.DataError) value out of ra...             13\n",
       "'OperationalError:(pymonetdb.exceptions.Operati...             12\n",
       "cTrainingError ...                                             10\n",
       "'DataError:(psycopg2.DataError) value out of ra...              8\n",
       "FileNotFoundError ...                                           8\n",
       "'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              6\n",
       "'OperationalError:(sqlite3.OperationalError) pa...              6\n",
       "\"OperationalError:(pymssql.OperationalError) (1...              6\n",
       "'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              6\n",
       "'DatabaseError:(teradata.api.DatabaseError) (37...              6\n",
       "...                                                           ...\n",
       "\"DatabaseError:(teradata.api.DatabaseError) (10...              4\n",
       "'OperationalError:(psycopg2.OperationalError) t...              4\n",
       "'OperationalError:(sqlite3.OperationalError) to...              4\n",
       "'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::D...              4\n",
       "'OperationalError:(sqlite3.OperationalError) to...              4\n",
       "'DatabaseError:(teradata.api.DatabaseError) (37...              3\n",
       "IndexError ...                                                  3\n",
       "\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...              3\n",
       "'ValueError:Length mismatch: Expected axis has ...              3\n",
       "\"OperationalError:(pymssql.OperationalError) (8...              2\n",
       "'OperationalError:(_mysql_exceptions.Operationa...              2\n",
       "'OperationalError:(sqlite3.OperationalError) Ex...              2\n",
       "'InternalError:(ibm_db_dbi.InternalError) ibm_d...              2\n",
       "'OperationalError:(psycopg2.OperationalError) s...              2\n",
       "'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::D...              2\n",
       "\"DBAPIError:(impala.error.HiveServer2Error) Ana...              2\n",
       "'OperationalError:(pymssql.OperationalError) (7...              2\n",
       "'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "'DatabaseError:(teradata.api.DatabaseError) (56...              1\n",
       "'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Erro...              1\n",
       "'ProgrammingError:(ibm_db_dbi.ProgrammingError)...              1\n",
       "\"OperationalError:(pymssql.OperationalError) (2...              1\n",
       "'DBAPIError:(impala.error.HiveServer2Error) Exe...              1\n",
       "'ProgrammingError:(ibm_db_dbi.ProgrammingError)...              1\n",
       "'ValueError:Length mismatch: Expected axis has ...              1\n",
       "\"OperationalError:(impala.error.OperationalErro...              1\n",
       "KeyError ...                                                    1\n",
       "'DBAPIError:(impala.error.HiveServer2Error) Imp...              1\n",
       "\n",
       "[62 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.error_message.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lGroupBy = df[df.status == 'failure'].groupby(['error_message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lGroupBy['rows'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Model, dtype: int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lComparisonErrorMessage = '\"Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS'\n",
    "real_errors = df[df.error_message.str.contains(lComparisonErrorMessage)]\n",
    "real_errors.Model.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MiniBatchSparsePCA           120\n",
       "SparsePCA                    118\n",
       "LatentDirichletAllocation     60\n",
       "NMF                           60\n",
       "Name: Model, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lNotImplementedMessage = 'CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL'\n",
    "\n",
    "not_impl_errors = df[df.error_message.str.contains(lNotImplementedMessage)]\n",
    "not_impl_errors.Model.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caret_class_svmRadialSigma           180\n",
       "caret_class_svmRadialWeights_pipe    180\n",
       "caret_class_svmRadialWeights         180\n",
       "caret_class_svmLinear_pipe           180\n",
       "caret_class_svmRadialCost            180\n",
       "caret_class_svmLinear                180\n",
       "caret_class_svmRadialSigma_pipe      180\n",
       "caret_class_svmRadialCost_pipe       180\n",
       "caret_class_svmRadial_pipe           159\n",
       "caret_class_svmRadial                128\n",
       "caret_class_svmPoly_pipe             120\n",
       "caret_class_svmPoly                  120\n",
       "MiniBatchSparsePCA                   120\n",
       "SparsePCA                            120\n",
       "caret_class_nnet_pipe                 98\n",
       "caret_class_xgbTree_pipe              97\n",
       "caret_class_nnet                      96\n",
       "keras_class_SimpleRNN_pipe            95\n",
       "caret_class_xgbTree                   92\n",
       "keras_class_SimpleRNN                 85\n",
       "caret_class_glmnet_pipe               78\n",
       "caret_class_rf_pipe                   75\n",
       "keras_class_LSTM_pipe                 75\n",
       "keras_class_LSTM                      73\n",
       "caret_class_earth_pipe                71\n",
       "caret_class_earth                     67\n",
       "DummyClassifier_pipe                  65\n",
       "DummyClassifier                       61\n",
       "NMF                                   60\n",
       "LatentDirichletAllocation             60\n",
       "                                    ... \n",
       "EllipticEnvelope_pipe                  2\n",
       "RandomForestRegressor_pipe             2\n",
       "Perceptron_pipe                        2\n",
       "caret_reg_earth_pipe                   2\n",
       "caret_prep_ica                         2\n",
       "caret_reg_xgbTree                      2\n",
       "DecisionTreeClassifier_pipe            2\n",
       "RandomForestRegressor                  2\n",
       "LinearRegression_pipe                  2\n",
       "caret_reg_nnet_pipe                    2\n",
       "PassiveAggressiveClassifier_pipe       2\n",
       "Lasso_pipe                             1\n",
       "OneClassSVM_pipe                       1\n",
       "SGDClassifier                          1\n",
       "MLPRegressor                           1\n",
       "RidgeClassifier_pipe                   1\n",
       "caret_reg_ctree                        1\n",
       "LassoLars_pipe                         1\n",
       "MLPRegressor_pipe                      1\n",
       "RANSACRegressor_pipe                   1\n",
       "RidgeClassifierCV_pipe                 1\n",
       "LassoLarsIC_pipe                       1\n",
       "RidgeClassifierCV                      1\n",
       "LinearSVC_pipe                         1\n",
       "Pipeline                               1\n",
       "GradientBoostingRegressor              1\n",
       "PassiveAggressiveRegressor_pipe        1\n",
       "caret_reg_rpart                        1\n",
       "keras_reg_Dense                        1\n",
       "ExtraTreeRegressor_pipe                1\n",
       "Name: Model, Length: 179, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# database related errors\n",
    "def is_other_error(x):\n",
    "    lKnownError = 'SUCCESS' in [x] or lComparisonErrorMessage in [x] or lNotImplementedMessage in [x]\n",
    "    return not lKnownError\n",
    "\n",
    "# other_errors = df.error_message.apply(lambda x : 1 if is_other_error(x)  else 0)\n",
    "other_errors = df[df.error_message.apply(is_other_error)]\n",
    "other_errors.Model.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.pivot(index = 'Model', values='status' , columns='dialect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Model', 'dataset', 'dialect', 'DSN', 'status', 'error_message',\n",
       "       'elapsed_time', 'full_error_message', 'model_category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Model</th>\n",
       "      <th>dataset</th>\n",
       "      <th>dialect</th>\n",
       "      <th>DSN</th>\n",
       "      <th>status</th>\n",
       "      <th>error_message</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>full_error_message</th>\n",
       "      <th>model_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_200_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>success</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model                        dataset  dialect  \\\n",
       "0       LGBMClassifier     DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "1       LGBMClassifier    DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "2  LGBMClassifier_pipe   p_DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "3       LGBMClassifier    DS_BENCH_C_200_7_2_F2617B35  IBM DB2   \n",
       "4  LGBMClassifier_pipe  p_DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "\n",
       "                                        DSN   status error_message  \\\n",
       "0  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "1  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "2  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "3  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "4  'db2+ibm_db://db:db@localhost:50000/db',  success       SUCCESS   \n",
       "\n",
       "  elapsed_time full_error_message    model_category  \n",
       "0            3             None),  lightgbm.sklearn  \n",
       "1            3             None),  lightgbm.sklearn  \n",
       "2            3             None),  lightgbm.sklearn  \n",
       "3            3             None),  lightgbm.sklearn  \n",
       "4            3             None),  lightgbm.sklearn  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['status'] = df['status'].apply(lambda x : 1 if('failure' in x) else 0)\n",
    "df['status_2'] = df['status'].apply(lambda x : 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = df[df['model_category'] != 'sklearn.dummy']\n",
    "df = df[~df['error_message'].str.contains(lNotImplementedMessage)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Model</th>\n",
       "      <th>dataset</th>\n",
       "      <th>dialect</th>\n",
       "      <th>DSN</th>\n",
       "      <th>status</th>\n",
       "      <th>error_message</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>full_error_message</th>\n",
       "      <th>model_category</th>\n",
       "      <th>status_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>0</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>0</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>0</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>DS_BENCH_C_200_7_2_F2617B35</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>0</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LGBMClassifier_pipe</td>\n",
       "      <td>p_DS_BENCH_C_50_22_2_2E64DEB6</td>\n",
       "      <td>IBM DB2</td>\n",
       "      <td>'db2+ibm_db://db:db@localhost:50000/db',</td>\n",
       "      <td>0</td>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3</td>\n",
       "      <td>None),</td>\n",
       "      <td>lightgbm.sklearn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model                        dataset  dialect  \\\n",
       "0       LGBMClassifier     DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "1       LGBMClassifier    DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "2  LGBMClassifier_pipe   p_DS_BENCH_C_50_7_2_F2617B35  IBM DB2   \n",
       "3       LGBMClassifier    DS_BENCH_C_200_7_2_F2617B35  IBM DB2   \n",
       "4  LGBMClassifier_pipe  p_DS_BENCH_C_50_22_2_2E64DEB6  IBM DB2   \n",
       "\n",
       "                                        DSN  status error_message  \\\n",
       "0  'db2+ibm_db://db:db@localhost:50000/db',       0       SUCCESS   \n",
       "1  'db2+ibm_db://db:db@localhost:50000/db',       0       SUCCESS   \n",
       "2  'db2+ibm_db://db:db@localhost:50000/db',       0       SUCCESS   \n",
       "3  'db2+ibm_db://db:db@localhost:50000/db',       0       SUCCESS   \n",
       "4  'db2+ibm_db://db:db@localhost:50000/db',       0       SUCCESS   \n",
       "\n",
       "  elapsed_time full_error_message    model_category  status_2  \n",
       "0            3             None),  lightgbm.sklearn         1  \n",
       "1            3             None),  lightgbm.sklearn         1  \n",
       "2            3             None),  lightgbm.sklearn         1  \n",
       "3            3             None),  lightgbm.sklearn         1  \n",
       "4            3             None),  lightgbm.sklearn         1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caret.classifier</th>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>5030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret.preprocessor</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret.regressor</th>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras.classifier</th>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras.regressor</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm.sklearn</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.calibration</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.covariance</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.decomposition</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.discriminant_analysis</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.ensemble</th>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>2520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.feature_selection</th>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.impute</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.kernel_ridge</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.linear_model</th>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>4920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.multiclass</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.naive_bayes</th>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.neural_network</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.pipeline</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.preprocessing</th>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.random_projection</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.svm</th>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>1560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.tree</th>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost.sklearn</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2807</td>\n",
       "      <td>2809</td>\n",
       "      <td>28072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   sum                                       \\\n",
       "dialect                       Firebird IBM DB2 Impala MS SQL Server MariaDB   \n",
       "model_category                                                                \n",
       "caret.classifier                   503     503    503           503     503   \n",
       "caret.preprocessor                  36      36     36            36      36   \n",
       "caret.regressor                    168     168    168           168     168   \n",
       "keras.classifier                   144     144    144           144     144   \n",
       "keras.regressor                     48      48     48            48      48   \n",
       "lightgbm.sklearn                    48      48     48            48      48   \n",
       "sklearn.calibration                 36      36     36            36      36   \n",
       "sklearn.covariance                  12      12     12            12      12   \n",
       "sklearn.decomposition               72      72     72            72      72   \n",
       "sklearn.discriminant_analysis       36      36     36            36      36   \n",
       "sklearn.ensemble                   252     252    252           252     252   \n",
       "sklearn.feature_selection          108     108    108           108     108   \n",
       "sklearn.impute                      24      24     24            24      24   \n",
       "sklearn.kernel_ridge                12      12     12            12      12   \n",
       "sklearn.linear_model               492     492    492           492     492   \n",
       "sklearn.multiclass                  72      72     72            72      72   \n",
       "sklearn.naive_bayes                144     144    144           144     144   \n",
       "sklearn.neural_network              48      48     48            48      48   \n",
       "sklearn.pipeline                    48      48     48            48      48   \n",
       "sklearn.preprocessing              180     180    180           180     180   \n",
       "sklearn.random_projection           24      24     24            24      24   \n",
       "sklearn.svm                        156     156    156           156     156   \n",
       "sklearn.tree                        96      96     96            96      96   \n",
       "xgboost.sklearn                     48      48     48            48      48   \n",
       "All                               2807    2807   2807          2807    2807   \n",
       "\n",
       "                                                                                \n",
       "dialect                       MonetDB Oracle PostgreSQL SQLite Teradata    All  \n",
       "model_category                                                                  \n",
       "caret.classifier                  503    503        503    503      503   5030  \n",
       "caret.preprocessor                 36     36         36     36       36    360  \n",
       "caret.regressor                   168    168        168    168      168   1680  \n",
       "keras.classifier                  144    144        144    144      144   1440  \n",
       "keras.regressor                    48     48         48     48       48    480  \n",
       "lightgbm.sklearn                   48     48         48     48       48    480  \n",
       "sklearn.calibration                36     36         36     36       36    360  \n",
       "sklearn.covariance                 12     12         12     12       12    120  \n",
       "sklearn.decomposition              72     72         72     72       74    722  \n",
       "sklearn.discriminant_analysis      36     36         36     36       36    360  \n",
       "sklearn.ensemble                  252    252        252    252      252   2520  \n",
       "sklearn.feature_selection         108    108        108    108      108   1080  \n",
       "sklearn.impute                     24     24         24     24       24    240  \n",
       "sklearn.kernel_ridge               12     12         12     12       12    120  \n",
       "sklearn.linear_model              492    492        492    492      492   4920  \n",
       "sklearn.multiclass                 72     72         72     72       72    720  \n",
       "sklearn.naive_bayes               144    144        144    144      144   1440  \n",
       "sklearn.neural_network             48     48         48     48       48    480  \n",
       "sklearn.pipeline                   48     48         48     48       48    480  \n",
       "sklearn.preprocessing             180    180        180    180      180   1800  \n",
       "sklearn.random_projection          24     24         24     24       24    240  \n",
       "sklearn.svm                       156    156        156    156      156   1560  \n",
       "sklearn.tree                       96     96         96     96       96    960  \n",
       "xgboost.sklearn                    48     48         48     48       48    480  \n",
       "All                              2807   2807       2807   2807     2809  28072  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many tests were used for each model category\n",
    "pvt_count = pd.pivot_table(df, index='model_category', values='status_2' , columns=['dialect'],  aggfunc=[np.sum], margins=True)\n",
    "pvt_count.head(pvt_count.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pvt = pd.pivot_table(df, index='model_category', values='status' , columns=['dialect'],  aggfunc=[np.mean], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caret.classifier</th>\n",
       "      <td>0.759443</td>\n",
       "      <td>0.495030</td>\n",
       "      <td>0.691849</td>\n",
       "      <td>0.560636</td>\n",
       "      <td>0.481113</td>\n",
       "      <td>0.755467</td>\n",
       "      <td>0.485089</td>\n",
       "      <td>0.461233</td>\n",
       "      <td>0.586481</td>\n",
       "      <td>0.606362</td>\n",
       "      <td>0.588270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret.preprocessor</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret.regressor</th>\n",
       "      <td>0.369048</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.029762</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.267857</td>\n",
       "      <td>0.079762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras.classifier</th>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.236111</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>0.243056</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>0.118056</td>\n",
       "      <td>0.294444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras.regressor</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm.sklearn</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.191667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.calibration</th>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.covariance</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.decomposition</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>0.099723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.discriminant_analysis</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.ensemble</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.091270</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>0.126587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.feature_selection</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.impute</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.kernel_ridge</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.linear_model</th>\n",
       "      <td>0.032520</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.111789</td>\n",
       "      <td>0.004065</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.028455</td>\n",
       "      <td>0.018902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.multiclass</th>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.122222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.naive_bayes</th>\n",
       "      <td>0.395833</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.652778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.184028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.neural_network</th>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.pipeline</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.012500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.preprocessing</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.077778</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.062778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.random_projection</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.svm</th>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.282051</td>\n",
       "      <td>0.006410</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.217949</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.123718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sklearn.tree</th>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.135417</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.017708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost.sklearn</th>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.181250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.340577</td>\n",
       "      <td>0.109369</td>\n",
       "      <td>0.348415</td>\n",
       "      <td>0.123620</td>\n",
       "      <td>0.106876</td>\n",
       "      <td>0.255077</td>\n",
       "      <td>0.117919</td>\n",
       "      <td>0.105094</td>\n",
       "      <td>0.122551</td>\n",
       "      <td>0.203631</td>\n",
       "      <td>0.183314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   mean                                    \\\n",
       "dialect                        Firebird   IBM DB2    Impala MS SQL Server   \n",
       "model_category                                                              \n",
       "caret.classifier               0.759443  0.495030  0.691849      0.560636   \n",
       "caret.preprocessor             0.111111  0.027778  0.000000      0.000000   \n",
       "caret.regressor                0.369048  0.011905  0.023810      0.047619   \n",
       "keras.classifier               0.291667  0.097222  0.791667      0.222222   \n",
       "keras.regressor                0.166667  0.062500  0.750000      0.062500   \n",
       "lightgbm.sklearn               0.666667  0.020833  0.625000      0.000000   \n",
       "sklearn.calibration            0.138889  0.000000  0.611111      0.000000   \n",
       "sklearn.covariance             0.166667  0.166667  0.166667      0.166667   \n",
       "sklearn.decomposition          0.222222  0.041667  0.194444      0.027778   \n",
       "sklearn.discriminant_analysis  0.111111  0.055556  0.777778      0.027778   \n",
       "sklearn.ensemble               0.500000  0.011905  0.361111      0.007937   \n",
       "sklearn.feature_selection      0.000000  0.000000  0.000000      0.000000   \n",
       "sklearn.impute                 0.000000  0.000000  0.000000      0.000000   \n",
       "sklearn.kernel_ridge           0.750000  0.000000  0.000000      0.000000   \n",
       "sklearn.linear_model           0.032520  0.002033  0.111789      0.004065   \n",
       "sklearn.multiclass             0.458333  0.000000  0.500000      0.013889   \n",
       "sklearn.naive_bayes            0.395833  0.041667  0.652778      0.027778   \n",
       "sklearn.neural_network         0.354167  0.000000  0.479167      0.000000   \n",
       "sklearn.pipeline               0.000000  0.000000  0.000000      0.000000   \n",
       "sklearn.preprocessing          0.111111  0.088889  0.033333      0.033333   \n",
       "sklearn.random_projection      0.166667  0.000000  0.000000      0.000000   \n",
       "sklearn.svm                    0.538462  0.019231  0.282051      0.006410   \n",
       "sklearn.tree                   0.020833  0.000000  0.000000      0.000000   \n",
       "xgboost.sklearn                0.645833  0.020833  0.645833      0.020833   \n",
       "All                            0.340577  0.109369  0.348415      0.123620   \n",
       "\n",
       "                                                                        \\\n",
       "dialect                         MariaDB   MonetDB    Oracle PostgreSQL   \n",
       "model_category                                                           \n",
       "caret.classifier               0.481113  0.755467  0.485089   0.461233   \n",
       "caret.preprocessor             0.000000  0.000000  0.000000   0.083333   \n",
       "caret.regressor                0.011905  0.000000  0.011905   0.029762   \n",
       "keras.classifier               0.236111  0.750000  0.097222   0.243056   \n",
       "keras.regressor                0.020833  0.750000  0.062500   0.062500   \n",
       "lightgbm.sklearn               0.020833  0.416667  0.020833   0.000000   \n",
       "sklearn.calibration            0.000000  0.000000  0.000000   0.000000   \n",
       "sklearn.covariance             0.166667  0.166667  0.166667   0.166667   \n",
       "sklearn.decomposition          0.027778  0.194444  0.027778   0.027778   \n",
       "sklearn.discriminant_analysis  0.055556  0.055556  0.055556   0.027778   \n",
       "sklearn.ensemble               0.007937  0.091270  0.007937   0.007937   \n",
       "sklearn.feature_selection      0.000000  0.000000  0.000000   0.000000   \n",
       "sklearn.impute                 0.000000  0.000000  0.000000   0.000000   \n",
       "sklearn.kernel_ridge           0.000000  0.000000  0.000000   0.000000   \n",
       "sklearn.linear_model           0.002033  0.002033  0.002033   0.002033   \n",
       "sklearn.multiclass             0.000000  0.000000  0.000000   0.000000   \n",
       "sklearn.naive_bayes            0.027778  0.472222  0.027778   0.027778   \n",
       "sklearn.neural_network         0.000000  0.333333  0.000000   0.000000   \n",
       "sklearn.pipeline               0.000000  0.000000  0.000000   0.000000   \n",
       "sklearn.preprocessing          0.033333  0.094444  0.033333   0.022222   \n",
       "sklearn.random_projection      0.000000  0.000000  0.000000   0.000000   \n",
       "sklearn.svm                    0.000000  0.076923  0.217949   0.000000   \n",
       "sklearn.tree                   0.000000  0.000000  0.135417   0.000000   \n",
       "xgboost.sklearn                0.020833  0.354167  0.020833   0.020833   \n",
       "All                            0.106876  0.255077  0.117919   0.105094   \n",
       "\n",
       "                                                             \n",
       "dialect                          SQLite  Teradata       All  \n",
       "model_category                                               \n",
       "caret.classifier               0.586481  0.606362  0.588270  \n",
       "caret.preprocessor             0.027778  0.027778  0.027778  \n",
       "caret.regressor                0.023810  0.267857  0.079762  \n",
       "keras.classifier               0.097222  0.118056  0.294444  \n",
       "keras.regressor                0.062500  0.083333  0.208333  \n",
       "lightgbm.sklearn               0.000000  0.145833  0.191667  \n",
       "sklearn.calibration            0.000000  0.083333  0.083333  \n",
       "sklearn.covariance             0.166667  0.333333  0.183333  \n",
       "sklearn.decomposition          0.027778  0.202703  0.099723  \n",
       "sklearn.discriminant_analysis  0.027778  0.138889  0.133333  \n",
       "sklearn.ensemble               0.007937  0.261905  0.126587  \n",
       "sklearn.feature_selection      0.000000  0.000000  0.000000  \n",
       "sklearn.impute                 0.000000  0.000000  0.000000  \n",
       "sklearn.kernel_ridge           0.000000  0.083333  0.083333  \n",
       "sklearn.linear_model           0.002033  0.028455  0.018902  \n",
       "sklearn.multiclass             0.000000  0.250000  0.122222  \n",
       "sklearn.naive_bayes            0.027778  0.138889  0.184028  \n",
       "sklearn.neural_network         0.000000  0.083333  0.125000  \n",
       "sklearn.pipeline               0.000000  0.125000  0.012500  \n",
       "sklearn.preprocessing          0.077778  0.100000  0.062778  \n",
       "sklearn.random_projection      0.000000  0.000000  0.016667  \n",
       "sklearn.svm                    0.000000  0.096154  0.123718  \n",
       "sklearn.tree                   0.000000  0.020833  0.017708  \n",
       "xgboost.sklearn                0.020833  0.041667  0.181250  \n",
       "All                            0.122551  0.203631  0.183314  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvt.head(pvt.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('report_extensive_tests.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.pcolor(1-pvt, cmap='RdYlGn' , vmin=0 , vmax=1)\n",
    "plt.yticks(np.arange(0.5, len(pvt.index), 1), pvt.index)\n",
    "plt.xticks(np.arange(0.5, len(pvt.columns), 1), [col[1] for col in pvt.columns])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(16, 8)\n",
    "\n",
    "plt.show()       \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Category_Labels = df.model_category.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['lightgbm.sklearn', 'caret.classifier', 'caret.preprocessor',\n",
       "       'caret.regressor', 'keras.classifier', 'keras.regressor',\n",
       "       'sklearn.calibration', 'sklearn.covariance',\n",
       "       'sklearn.decomposition', 'sklearn.discriminant_analysis',\n",
       "       'sklearn.ensemble', 'sklearn.feature_selection', 'sklearn.impute',\n",
       "       'sklearn.kernel_ridge', 'sklearn.linear_model',\n",
       "       'sklearn.multiclass', 'sklearn.naive_bayes',\n",
       "       'sklearn.neural_network', 'sklearn.pipeline',\n",
       "       'sklearn.preprocessing', 'sklearn.random_projection',\n",
       "       'sklearn.svm', 'sklearn.tree', 'xgboost.sklearn'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Category_Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY lightgbm.sklearn\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Firebird</th>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while executing SQL statement:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\\', -802, 335544321) \\[SQL ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Impala</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (2616, \\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\') \\[SQL ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      \"DatabaseError:('Error while commiting transact...             27\n",
       "              SUCCESS                                                        16\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              5\n",
       "IBM DB2       SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "Impala        TIMEOUT                                                        22\n",
       "              SUCCESS                                                        18\n",
       "              DBAPIError:(impala.error.HiveServer2Error) Anal...              8\n",
       "MS SQL Server SUCCESS                                                        48\n",
       "MariaDB       SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "MonetDB       SUCCESS                                                        28\n",
       "              TIMEOUT                                                        16\n",
       "              'DBAPIError:(builtins.BrokenPipeError) [Errno 3...              4\n",
       "Oracle        SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "PostgreSQL    SUCCESS                                                        48\n",
       "SQLite        SUCCESS                                                        48\n",
       "Teradata      SUCCESS                                                        41\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (26...              5\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LGBMClassifier</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.238889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBMClassifier_pipe</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBMRegressor</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBMRegressor_pipe</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.191667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean                                              \\\n",
       "dialect              Firebird   IBM DB2    Impala MS SQL Server   MariaDB   \n",
       "Model                                                                       \n",
       "LGBMClassifier       0.833333  0.000000  0.833333           0.0  0.000000   \n",
       "LGBMClassifier_pipe  0.611111  0.055556  0.833333           0.0  0.055556   \n",
       "LGBMRegressor        0.500000  0.000000  0.000000           0.0  0.000000   \n",
       "LGBMRegressor_pipe   0.500000  0.000000  0.000000           0.0  0.000000   \n",
       "All                  0.666667  0.020833  0.625000           0.0  0.020833   \n",
       "\n",
       "                                                                               \n",
       "dialect               MonetDB    Oracle PostgreSQL SQLite  Teradata       All  \n",
       "Model                                                                          \n",
       "LGBMClassifier       0.555556  0.000000        0.0    0.0  0.166667  0.238889  \n",
       "LGBMClassifier_pipe  0.555556  0.055556        0.0    0.0  0.166667  0.233333  \n",
       "LGBMRegressor        0.000000  0.000000        0.0    0.0  0.000000  0.050000  \n",
       "LGBMRegressor_pipe   0.000000  0.000000        0.0    0.0  0.166667  0.066667  \n",
       "All                  0.416667  0.020833        0.0    0.0  0.145833  0.191667  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY caret.classifier\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">Firebird</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while executing SQL statement:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\\', -802, 335544321) \\[SQL ...</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndexError ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Impala</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages fro</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (701, b\"There is insufficient system memory in resource pool \\'default\\' to run this query.DB-Lib error message 20018, severity 17:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (20004, b'DB-Lib error message 20004, severity 9:\\\\nRead from the server failed (db:1433)\\\\nNet-Lib error during Connection reset by peer (104)\\\\nDB-Lib error message 20002, severity 9:\\\\nAdaptive Server connection failed (db:1433)\\\\n') ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">MonetDB</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ValueError:Length mismatch: Expected axis has 5 elements, new values have 20 elements'),</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndexError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileNotFoundError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (2616, \\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\') \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\\') \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      TIMEOUT                                                       216\n",
       "              SUCCESS                                                       121\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                59\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             35\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             32\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...             20\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             13\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       4\n",
       "              \"DatabaseError:('Error while commiting transact...              3\n",
       "IBM DB2       SUCCESS                                                       254\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               207\n",
       "              TIMEOUT                                                        32\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       6\n",
       "              FileNotFoundError ...                                           2\n",
       "              IndexError ...                                                  2\n",
       "Impala        TIMEOUT                                                       241\n",
       "              SUCCESS                                                       155\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                78\n",
       "              DBAPIError:(impala.error.HiveServer2Error) Anal...             16\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                      13\n",
       "MS SQL Server SUCCESS                                                       221\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               210\n",
       "              TIMEOUT                                                        40\n",
       "              \"OperationalError:(pymssql.OperationalError) (1...             15\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                      12\n",
       "              'OperationalError:(pymssql.OperationalError) (7...              2\n",
       "              FileNotFoundError ...                                           2\n",
       "              \"OperationalError:(pymssql.OperationalError) (2...              1\n",
       "MariaDB       SUCCESS                                                       261\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               197\n",
       "...                                                                         ...\n",
       "              FileNotFoundError ...                                           1\n",
       "MonetDB       TIMEOUT                                                       266\n",
       "              SUCCESS                                                       123\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               111\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       2\n",
       "              'DBAPIError:(builtins.BrokenPipeError) [Errno 3...              1\n",
       "Oracle        SUCCESS                                                       259\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               183\n",
       "              TIMEOUT                                                        56\n",
       "              'ValueError:Length mismatch: Expected axis has ...              2\n",
       "              'ValueError:Length mismatch: Expected axis has ...              1\n",
       "              FileNotFoundError ...                                           1\n",
       "              IndexError ...                                                  1\n",
       "PostgreSQL    SUCCESS                                                       271\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               219\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       9\n",
       "              TIMEOUT                                                         3\n",
       "              FileNotFoundError ...                                           1\n",
       "SQLite        SUCCESS                                                       208\n",
       "              TIMEOUT                                                       161\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               133\n",
       "              FileNotFoundError ...                                           1\n",
       "Teradata      SUCCESS                                                       198\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...               129\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...            101\n",
       "              TIMEOUT                                                        60\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                      10\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (26...              2\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              2\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1\n",
       "\n",
       "[62 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caret_class_ctree</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.138889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_ctree2</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_ctree2_pipe</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_ctree_pipe</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.172222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_earth</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.372222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_earth_pipe</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.394444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_glm</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_glm_pipe</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_glmnet</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_glmnet_pipe</th>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_nnet</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_nnet_pipe</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.544444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_rf</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_rf_pipe</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_rpart</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.094444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_rpart_pipe</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.155556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmLinear</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmLinear_pipe</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmPoly</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmPoly_pipe</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadial</th>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.752941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadialCost</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadialCost_pipe</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadialSigma</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadialSigma_pipe</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadialWeights</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadialWeights_pipe</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_svmRadial_pipe</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_xgbTree</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_class_xgbTree_pipe</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.538889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.759443</td>\n",
       "      <td>0.495030</td>\n",
       "      <td>0.691849</td>\n",
       "      <td>0.560636</td>\n",
       "      <td>0.481113</td>\n",
       "      <td>0.755467</td>\n",
       "      <td>0.485089</td>\n",
       "      <td>0.461233</td>\n",
       "      <td>0.586481</td>\n",
       "      <td>0.606362</td>\n",
       "      <td>0.588270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       mean                                    \\\n",
       "dialect                            Firebird   IBM DB2    Impala MS SQL Server   \n",
       "Model                                                                           \n",
       "caret_class_ctree                  0.444444  0.000000  0.000000      0.000000   \n",
       "caret_class_ctree2                 0.388889  0.500000  0.000000      0.500000   \n",
       "caret_class_ctree2_pipe            0.444444  0.500000  0.333333      1.000000   \n",
       "caret_class_ctree_pipe             0.555556  0.000000  0.055556      0.000000   \n",
       "caret_class_earth                  0.611111  0.166667  0.777778      0.333333   \n",
       "caret_class_earth_pipe             0.611111  0.166667  0.777778      0.277778   \n",
       "caret_class_glm                    0.000000  0.000000  0.500000      0.166667   \n",
       "caret_class_glm_pipe               0.500000  0.333333  0.500000      0.333333   \n",
       "caret_class_glmnet                 0.555556  0.000000  0.722222      0.166667   \n",
       "caret_class_glmnet_pipe            0.722222  0.166667  0.833333      0.166667   \n",
       "caret_class_nnet                   0.833333  0.333333  0.777778      0.333333   \n",
       "caret_class_nnet_pipe              0.833333  0.333333  0.833333      0.333333   \n",
       "caret_class_rf                     0.555556  0.000000  0.555556      0.444444   \n",
       "caret_class_rf_pipe                0.611111  0.111111  0.611111      0.500000   \n",
       "caret_class_rpart                  0.444444  0.055556  0.000000      0.000000   \n",
       "caret_class_rpart_pipe             0.555556  0.000000  0.000000      0.000000   \n",
       "caret_class_svmLinear              1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmLinear_pipe         1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmPoly                1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmPoly_pipe           1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmRadial              0.941176  0.647059  0.823529      0.705882   \n",
       "caret_class_svmRadialCost          1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmRadialCost_pipe     1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmRadialSigma         1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmRadialSigma_pipe    1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmRadialWeights       1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmRadialWeights_pipe  1.000000  1.000000  1.000000      1.000000   \n",
       "caret_class_svmRadial_pipe         1.000000  0.777778  0.944444      0.777778   \n",
       "caret_class_xgbTree                0.833333  0.333333  0.833333      0.333333   \n",
       "caret_class_xgbTree_pipe           0.833333  0.333333  0.833333      0.333333   \n",
       "All                                0.759443  0.495030  0.691849      0.560636   \n",
       "\n",
       "                                                                            \\\n",
       "dialect                             MariaDB   MonetDB    Oracle PostgreSQL   \n",
       "Model                                                                        \n",
       "caret_class_ctree                  0.000000  0.388889  0.166667   0.111111   \n",
       "caret_class_ctree2                 0.000000  0.388889  0.166667   0.000000   \n",
       "caret_class_ctree2_pipe            0.000000  0.444444  0.111111   0.000000   \n",
       "caret_class_ctree_pipe             0.055556  0.500000  0.111111   0.000000   \n",
       "caret_class_earth                  0.222222  0.555556  0.166667   0.166667   \n",
       "caret_class_earth_pipe             0.277778  0.611111  0.166667   0.166667   \n",
       "caret_class_glm                    0.000000  0.000000  0.000000   0.000000   \n",
       "caret_class_glm_pipe               0.333333  0.333333  0.333333   0.333333   \n",
       "caret_class_glmnet                 0.055556  0.777778  0.000000   0.000000   \n",
       "caret_class_glmnet_pipe            0.166667  0.888889  0.111111   0.111111   \n",
       "caret_class_nnet                   0.444444  0.888889  0.333333   0.333333   \n",
       "caret_class_nnet_pipe              0.388889  0.888889  0.333333   0.333333   \n",
       "caret_class_rf                     0.000000  0.388889  0.000000   0.000000   \n",
       "caret_class_rf_pipe                0.166667  0.555556  0.111111   0.111111   \n",
       "caret_class_rpart                  0.000000  0.388889  0.055556   0.000000   \n",
       "caret_class_rpart_pipe             0.055556  0.500000  0.111111   0.000000   \n",
       "caret_class_svmLinear              1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmLinear_pipe         1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmPoly                1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmPoly_pipe           1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmRadial              0.705882  0.823529  0.705882   0.705882   \n",
       "caret_class_svmRadialCost          1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmRadialCost_pipe     1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmRadialSigma         1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmRadialSigma_pipe    1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmRadialWeights       1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmRadialWeights_pipe  1.000000  1.000000  1.000000   1.000000   \n",
       "caret_class_svmRadial_pipe         0.833333  0.944444  0.833333   0.777778   \n",
       "caret_class_xgbTree                0.333333  0.888889  0.333333   0.333333   \n",
       "caret_class_xgbTree_pipe           0.333333  0.888889  0.333333   0.333333   \n",
       "All                                0.481113  0.755467  0.485089   0.461233   \n",
       "\n",
       "                                                                 \n",
       "dialect                              SQLite  Teradata       All  \n",
       "Model                                                            \n",
       "caret_class_ctree                  0.222222  0.055556  0.138889  \n",
       "caret_class_ctree2                 0.000000  0.000000  0.194444  \n",
       "caret_class_ctree2_pipe            0.000000  0.000000  0.283333  \n",
       "caret_class_ctree_pipe             0.388889  0.055556  0.172222  \n",
       "caret_class_earth                  0.388889  0.333333  0.372222  \n",
       "caret_class_earth_pipe             0.555556  0.333333  0.394444  \n",
       "caret_class_glm                    0.000000  0.000000  0.066667  \n",
       "caret_class_glm_pipe               0.333333  0.333333  0.366667  \n",
       "caret_class_glmnet                 0.111111  0.611111  0.300000  \n",
       "caret_class_glmnet_pipe            0.444444  0.722222  0.433333  \n",
       "caret_class_nnet                   0.555556  0.500000  0.533333  \n",
       "caret_class_nnet_pipe              0.666667  0.500000  0.544444  \n",
       "caret_class_rf                     0.055556  0.833333  0.283333  \n",
       "caret_class_rf_pipe                0.444444  0.944444  0.416667  \n",
       "caret_class_rpart                  0.000000  0.000000  0.094444  \n",
       "caret_class_rpart_pipe             0.277778  0.055556  0.155556  \n",
       "caret_class_svmLinear              1.000000  1.000000  1.000000  \n",
       "caret_class_svmLinear_pipe         1.000000  1.000000  1.000000  \n",
       "caret_class_svmPoly                1.000000  1.000000  1.000000  \n",
       "caret_class_svmPoly_pipe           1.000000  1.000000  1.000000  \n",
       "caret_class_svmRadial              0.764706  0.705882  0.752941  \n",
       "caret_class_svmRadialCost          1.000000  1.000000  1.000000  \n",
       "caret_class_svmRadialCost_pipe     1.000000  1.000000  1.000000  \n",
       "caret_class_svmRadialSigma         1.000000  1.000000  1.000000  \n",
       "caret_class_svmRadialSigma_pipe    1.000000  1.000000  1.000000  \n",
       "caret_class_svmRadialWeights       1.000000  1.000000  1.000000  \n",
       "caret_class_svmRadialWeights_pipe  1.000000  1.000000  1.000000  \n",
       "caret_class_svmRadial_pipe         0.944444  1.000000  0.883333  \n",
       "caret_class_xgbTree                0.500000  0.388889  0.511111  \n",
       "caret_class_xgbTree_pipe           0.666667  0.500000  0.538889  \n",
       "All                                0.586481  0.606362  0.588270  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY caret.preprocessor\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:PREDICT_FAILED ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:PREDICT_FAILED ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:PREDICT_FAILED ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:PREDICT_FAILED ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        32\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              4\n",
       "IBM DB2       SUCCESS                                                        35\n",
       "              Exception:PREDICT_FAILED ...                                    1\n",
       "Impala        SUCCESS                                                        36\n",
       "MS SQL Server SUCCESS                                                        36\n",
       "MariaDB       SUCCESS                                                        36\n",
       "MonetDB       SUCCESS                                                        36\n",
       "Oracle        SUCCESS                                                        36\n",
       "PostgreSQL    SUCCESS                                                        33\n",
       "              Exception:PREDICT_FAILED ...                                    3\n",
       "SQLite        SUCCESS                                                        35\n",
       "              Exception:PREDICT_FAILED ...                                    1\n",
       "Teradata      SUCCESS                                                        35\n",
       "              Exception:PREDICT_FAILED ...                                    1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caret_prep_center_scale</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_prep_ica</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_prep_pca</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mean                                         \\\n",
       "dialect                  Firebird   IBM DB2 Impala MS SQL Server MariaDB   \n",
       "Model                                                                      \n",
       "caret_prep_center_scale  0.000000  0.000000    0.0           0.0     0.0   \n",
       "caret_prep_ica           0.000000  0.083333    0.0           0.0     0.0   \n",
       "caret_prep_pca           0.333333  0.000000    0.0           0.0     0.0   \n",
       "All                      0.111111  0.027778    0.0           0.0     0.0   \n",
       "\n",
       "                                                                       \\\n",
       "dialect                 MonetDB Oracle PostgreSQL    SQLite  Teradata   \n",
       "Model                                                                   \n",
       "caret_prep_center_scale     0.0    0.0   0.250000  0.000000  0.000000   \n",
       "caret_prep_ica              0.0    0.0   0.000000  0.083333  0.000000   \n",
       "caret_prep_pca              0.0    0.0   0.000000  0.000000  0.083333   \n",
       "All                         0.0    0.0   0.083333  0.027778  0.027778   \n",
       "\n",
       "                                   \n",
       "dialect                       All  \n",
       "Model                              \n",
       "caret_prep_center_scale  0.025000  \n",
       "caret_prep_ica           0.016667  \n",
       "caret_prep_pca           0.041667  \n",
       "All                      0.027778  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY caret.regressor\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages fro</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KeyError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError:Exception:TRAIN_FAILED ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (2616, \\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\') \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\\') \\[SQL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                       106\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             46\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...             15\n",
       "              \"DatabaseError:('Error while commiting transact...              1\n",
       "IBM DB2       SUCCESS                                                       166\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "Impala        SUCCESS                                                       164\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       4\n",
       "MS SQL Server SUCCESS                                                       160\n",
       "              \"OperationalError:(pymssql.OperationalError) (1...              8\n",
       "MariaDB       SUCCESS                                                       166\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       2\n",
       "MonetDB       SUCCESS                                                       168\n",
       "Oracle        SUCCESS                                                       166\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       2\n",
       "PostgreSQL    SUCCESS                                                       163\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       4\n",
       "              KeyError ...                                                    1\n",
       "SQLite        SUCCESS                                                       164\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       2\n",
       "              'ValueError:Length mismatch: Expected axis has ...              1\n",
       "              TIMEOUT                                                         1\n",
       "Teradata      SUCCESS                                                       123\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...             30\n",
       "              cTrainingError:Exception:TRAIN_FAILED ...                       6\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (26...              4\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              3\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caret_reg_ctree</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_ctree2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_ctree2_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_ctree_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_earth</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_earth_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_glm</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_glm_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_glmnet</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_glmnet_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_nnet</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_nnet_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_rf</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_rf_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_rpart</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_rpart_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmLinear</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmLinear_pipe</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmPoly</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmPoly_pipe</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmRadial</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmRadialCost</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmRadialCost_pipe</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmRadialSigma</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmRadialSigma_pipe</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_svmRadial_pipe</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_xgbTree</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caret_reg_xgbTree_pipe</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.369048</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.029762</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.267857</td>\n",
       "      <td>0.079762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   mean                                    \\\n",
       "dialect                        Firebird   IBM DB2    Impala MS SQL Server   \n",
       "Model                                                                       \n",
       "caret_reg_ctree                0.000000  0.000000  0.166667      0.000000   \n",
       "caret_reg_ctree2               0.000000  0.000000  0.166667      0.000000   \n",
       "caret_reg_ctree2_pipe          0.333333  0.000000  0.166667      0.000000   \n",
       "caret_reg_ctree_pipe           0.333333  0.000000  0.166667      0.000000   \n",
       "caret_reg_earth                0.000000  0.000000  0.000000      0.000000   \n",
       "caret_reg_earth_pipe           0.333333  0.000000  0.000000      0.000000   \n",
       "caret_reg_glm                  0.000000  0.000000  0.000000      0.000000   \n",
       "caret_reg_glm_pipe             0.333333  0.000000  0.000000      0.000000   \n",
       "caret_reg_glmnet               0.000000  0.000000  0.000000      0.000000   \n",
       "caret_reg_glmnet_pipe          0.333333  0.000000  0.000000      0.000000   \n",
       "caret_reg_nnet                 0.000000  0.000000  0.000000      0.000000   \n",
       "caret_reg_nnet_pipe            0.333333  0.000000  0.000000      0.000000   \n",
       "caret_reg_rf                   0.000000  0.000000  0.000000      0.500000   \n",
       "caret_reg_rf_pipe              0.333333  0.000000  0.000000      0.833333   \n",
       "caret_reg_rpart                0.000000  0.166667  0.000000      0.000000   \n",
       "caret_reg_rpart_pipe           0.333333  0.166667  0.000000      0.000000   \n",
       "caret_reg_svmLinear            0.500000  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmLinear_pipe       0.666667  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmPoly              0.500000  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmPoly_pipe         0.833333  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmRadial            0.833333  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmRadialCost        0.666667  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmRadialCost_pipe   0.666667  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmRadialSigma       0.833333  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmRadialSigma_pipe  0.833333  0.000000  0.000000      0.000000   \n",
       "caret_reg_svmRadial_pipe       0.833333  0.000000  0.000000      0.000000   \n",
       "caret_reg_xgbTree              0.000000  0.000000  0.000000      0.000000   \n",
       "caret_reg_xgbTree_pipe         0.500000  0.000000  0.000000      0.000000   \n",
       "All                            0.369048  0.011905  0.023810      0.047619   \n",
       "\n",
       "                                                                      \\\n",
       "dialect                         MariaDB MonetDB    Oracle PostgreSQL   \n",
       "Model                                                                  \n",
       "caret_reg_ctree                0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_ctree2               0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_ctree2_pipe          0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_ctree_pipe           0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_earth                0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_earth_pipe           0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_glm                  0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_glm_pipe             0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_glmnet               0.166667     0.0  0.166667   0.000000   \n",
       "caret_reg_glmnet_pipe          0.166667     0.0  0.166667   0.000000   \n",
       "caret_reg_nnet                 0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_nnet_pipe            0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_rf                   0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_rf_pipe              0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_rpart                0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_rpart_pipe           0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmLinear            0.000000     0.0  0.000000   0.166667   \n",
       "caret_reg_svmLinear_pipe       0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmPoly              0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmPoly_pipe         0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmRadial            0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmRadialCost        0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmRadialCost_pipe   0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmRadialSigma       0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmRadialSigma_pipe  0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_svmRadial_pipe       0.000000     0.0  0.000000   0.000000   \n",
       "caret_reg_xgbTree              0.000000     0.0  0.000000   0.333333   \n",
       "caret_reg_xgbTree_pipe         0.000000     0.0  0.000000   0.333333   \n",
       "All                            0.011905     0.0  0.011905   0.029762   \n",
       "\n",
       "                                                             \n",
       "dialect                          SQLite  Teradata       All  \n",
       "Model                                                        \n",
       "caret_reg_ctree                0.000000  0.000000  0.016667  \n",
       "caret_reg_ctree2               0.166667  0.000000  0.033333  \n",
       "caret_reg_ctree2_pipe          0.000000  0.000000  0.050000  \n",
       "caret_reg_ctree_pipe           0.000000  0.000000  0.050000  \n",
       "caret_reg_earth                0.000000  0.000000  0.000000  \n",
       "caret_reg_earth_pipe           0.000000  0.000000  0.033333  \n",
       "caret_reg_glm                  0.000000  0.000000  0.000000  \n",
       "caret_reg_glm_pipe             0.000000  0.000000  0.033333  \n",
       "caret_reg_glmnet               0.000000  0.000000  0.033333  \n",
       "caret_reg_glmnet_pipe          0.000000  0.000000  0.066667  \n",
       "caret_reg_nnet                 0.000000  0.000000  0.000000  \n",
       "caret_reg_nnet_pipe            0.000000  0.000000  0.033333  \n",
       "caret_reg_rf                   0.000000  0.666667  0.116667  \n",
       "caret_reg_rf_pipe              0.000000  0.666667  0.183333  \n",
       "caret_reg_rpart                0.000000  0.000000  0.016667  \n",
       "caret_reg_rpart_pipe           0.000000  0.000000  0.050000  \n",
       "caret_reg_svmLinear            0.000000  0.333333  0.100000  \n",
       "caret_reg_svmLinear_pipe       0.000000  1.000000  0.166667  \n",
       "caret_reg_svmPoly              0.000000  0.166667  0.066667  \n",
       "caret_reg_svmPoly_pipe         0.166667  1.000000  0.200000  \n",
       "caret_reg_svmRadial            0.000000  0.166667  0.100000  \n",
       "caret_reg_svmRadialCost        0.166667  0.333333  0.116667  \n",
       "caret_reg_svmRadialCost_pipe   0.166667  1.000000  0.183333  \n",
       "caret_reg_svmRadialSigma       0.000000  0.166667  0.100000  \n",
       "caret_reg_svmRadialSigma_pipe  0.000000  1.000000  0.183333  \n",
       "caret_reg_svmRadial_pipe       0.000000  1.000000  0.183333  \n",
       "caret_reg_xgbTree              0.000000  0.000000  0.033333  \n",
       "caret_reg_xgbTree_pipe         0.000000  0.000000  0.083333  \n",
       "All                            0.023810  0.267857  0.079762  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY keras.classifier\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Impala</th>\n",
       "      <th>Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (8115, b'Arithmetic overflow error converting expression to data type float.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n') \\[SQL ...</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(_mysql_exceptions.OperationalError) (1690, \"DOUBLE value is out of range in ...</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MonetDB</th>\n",
       "      <th>Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(psycopg2.DataError) value out of range: overflow\\n \\[SQL ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(psycopg2.DataError) value out of range: underflow\\n \\[SQL ...</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                       102\n",
       "              \"DatabaseError:('Error while commiting transact...             30\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                11\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "IBM DB2       SUCCESS                                                       130\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                13\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "Impala        Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTE...            107\n",
       "              SUCCESS                                                        30\n",
       "              TIMEOUT                                                         6\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "MS SQL Server SUCCESS                                                       112\n",
       "              \"OperationalError:(pymssql.OperationalError) (8...             18\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                13\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "MariaDB       SUCCESS                                                       110\n",
       "              'OperationalError:(_mysql_exceptions.Operationa...             20\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                13\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "MonetDB       Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTE...            107\n",
       "              SUCCESS                                                        36\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "Oracle        SUCCESS                                                       130\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                13\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "PostgreSQL    SUCCESS                                                       109\n",
       "              'DataError:(psycopg2.DataError) value out of ra...             13\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                13\n",
       "              'DataError:(psycopg2.DataError) value out of ra...              8\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "SQLite        SUCCESS                                                       130\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                13\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "Teradata      SUCCESS                                                       127\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                12\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              4\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>keras_class_Dense</th>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_class_Dense_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.061111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_class_GRU</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.211111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_class_GRU_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_class_LSTM</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.405556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_class_LSTM_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_class_SimpleRNN</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_class_SimpleRNN_pipe</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.527778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.236111</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>0.243056</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>0.118056</td>\n",
       "      <td>0.294444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mean                                    \\\n",
       "dialect                     Firebird   IBM DB2    Impala MS SQL Server   \n",
       "Model                                                                    \n",
       "keras_class_Dense           0.277778  0.000000  0.111111      0.000000   \n",
       "keras_class_Dense_pipe      0.333333  0.000000  0.222222      0.000000   \n",
       "keras_class_GRU             0.111111  0.000000  1.000000      0.000000   \n",
       "keras_class_GRU_pipe        0.166667  0.000000  1.000000      0.000000   \n",
       "keras_class_LSTM            0.166667  0.055556  1.000000      0.555556   \n",
       "keras_class_LSTM_pipe       0.333333  0.000000  1.000000      0.500000   \n",
       "keras_class_SimpleRNN       0.388889  0.333333  1.000000      0.333333   \n",
       "keras_class_SimpleRNN_pipe  0.555556  0.388889  1.000000      0.388889   \n",
       "All                         0.291667  0.097222  0.791667      0.222222   \n",
       "\n",
       "                                                                             \\\n",
       "dialect                      MariaDB MonetDB    Oracle PostgreSQL    SQLite   \n",
       "Model                                                                         \n",
       "keras_class_Dense           0.000000    0.00  0.000000   0.000000  0.000000   \n",
       "keras_class_Dense_pipe      0.000000    0.00  0.000000   0.000000  0.000000   \n",
       "keras_class_GRU             0.000000    1.00  0.000000   0.000000  0.000000   \n",
       "keras_class_GRU_pipe        0.000000    1.00  0.000000   0.000000  0.000000   \n",
       "keras_class_LSTM            0.555556    1.00  0.055556   0.555556  0.055556   \n",
       "keras_class_LSTM_pipe       0.611111    1.00  0.000000   0.666667  0.000000   \n",
       "keras_class_SimpleRNN       0.333333    1.00  0.333333   0.333333  0.333333   \n",
       "keras_class_SimpleRNN_pipe  0.388889    1.00  0.388889   0.388889  0.388889   \n",
       "All                         0.236111    0.75  0.097222   0.243056  0.097222   \n",
       "\n",
       "                                                \n",
       "dialect                     Teradata       All  \n",
       "Model                                           \n",
       "keras_class_Dense           0.000000  0.038889  \n",
       "keras_class_Dense_pipe      0.055556  0.061111  \n",
       "keras_class_GRU             0.000000  0.211111  \n",
       "keras_class_GRU_pipe        0.055556  0.222222  \n",
       "keras_class_LSTM            0.055556  0.405556  \n",
       "keras_class_LSTM_pipe       0.055556  0.416667  \n",
       "keras_class_SimpleRNN       0.333333  0.472222  \n",
       "keras_class_SimpleRNN_pipe  0.388889  0.527778  \n",
       "All                         0.118056  0.294444  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY keras.regressor\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Impala</th>\n",
       "      <th>Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MonetDB</th>\n",
       "      <th>Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAN_VALUE_ENCOUNTERED_IN_MODEL ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        40\n",
       "              \"DatabaseError:('Error while commiting transact...              5\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              3\n",
       "IBM DB2       SUCCESS                                                        45\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              3\n",
       "Impala        Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTE...             35\n",
       "              SUCCESS                                                        12\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "MS SQL Server SUCCESS                                                        45\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              3\n",
       "MariaDB       SUCCESS                                                        47\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              1\n",
       "MonetDB       Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTE...             34\n",
       "              SUCCESS                                                        12\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              2\n",
       "Oracle        SUCCESS                                                        45\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              3\n",
       "PostgreSQL    SUCCESS                                                        45\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              3\n",
       "SQLite        SUCCESS                                                        45\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              3\n",
       "Teradata      SUCCESS                                                        44\n",
       "              NAN_VALUE_ENCOUNTERED_IN_MODEL ...                              3\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>keras_reg_Dense</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_reg_Dense_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_reg_GRU</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_reg_GRU_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_reg_LSTM</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_reg_LSTM_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_reg_SimpleRNN</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras_reg_SimpleRNN_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean                                           \\\n",
       "dialect                   Firebird   IBM DB2 Impala MS SQL Server   MariaDB   \n",
       "Model                                                                         \n",
       "keras_reg_Dense           0.166667  0.000000   0.00      0.000000  0.000000   \n",
       "keras_reg_Dense_pipe      0.333333  0.000000   0.00      0.000000  0.000000   \n",
       "keras_reg_GRU             0.000000  0.000000   1.00      0.000000  0.000000   \n",
       "keras_reg_GRU_pipe        0.000000  0.000000   1.00      0.000000  0.000000   \n",
       "keras_reg_LSTM            0.166667  0.000000   1.00      0.000000  0.000000   \n",
       "keras_reg_LSTM_pipe       0.000000  0.000000   1.00      0.000000  0.000000   \n",
       "keras_reg_SimpleRNN       0.500000  0.333333   1.00      0.166667  0.000000   \n",
       "keras_reg_SimpleRNN_pipe  0.166667  0.166667   1.00      0.333333  0.166667   \n",
       "All                       0.166667  0.062500   0.75      0.062500  0.020833   \n",
       "\n",
       "                                                                           \\\n",
       "dialect                  MonetDB    Oracle PostgreSQL    SQLite  Teradata   \n",
       "Model                                                                       \n",
       "keras_reg_Dense             0.00  0.000000   0.000000  0.000000  0.000000   \n",
       "keras_reg_Dense_pipe        0.00  0.000000   0.000000  0.000000  0.000000   \n",
       "keras_reg_GRU               1.00  0.000000   0.000000  0.000000  0.000000   \n",
       "keras_reg_GRU_pipe          1.00  0.000000   0.000000  0.000000  0.000000   \n",
       "keras_reg_LSTM              1.00  0.000000   0.000000  0.000000  0.166667   \n",
       "keras_reg_LSTM_pipe         1.00  0.000000   0.000000  0.000000  0.000000   \n",
       "keras_reg_SimpleRNN         1.00  0.333333   0.333333  0.333333  0.333333   \n",
       "keras_reg_SimpleRNN_pipe    1.00  0.166667   0.166667  0.166667  0.166667   \n",
       "All                         0.75  0.062500   0.062500  0.062500  0.083333   \n",
       "\n",
       "                                    \n",
       "dialect                        All  \n",
       "Model                               \n",
       "keras_reg_Dense           0.016667  \n",
       "keras_reg_Dense_pipe      0.033333  \n",
       "keras_reg_GRU             0.200000  \n",
       "keras_reg_GRU_pipe        0.200000  \n",
       "keras_reg_LSTM            0.233333  \n",
       "keras_reg_LSTM_pipe       0.200000  \n",
       "keras_reg_SimpleRNN       0.433333  \n",
       "keras_reg_SimpleRNN_pipe  0.350000  \n",
       "All                       0.208333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.calibration\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        31\n",
       "              \"DatabaseError:('Error while commiting transact...              5\n",
       "IBM DB2       SUCCESS                                                        36\n",
       "Impala        SUCCESS                                                        14\n",
       "              DBAPIError:(impala.error.HiveServer2Error) Anal...             11\n",
       "              TIMEOUT                                                        11\n",
       "MS SQL Server SUCCESS                                                        36\n",
       "MariaDB       SUCCESS                                                        36\n",
       "MonetDB       SUCCESS                                                        36\n",
       "Oracle        SUCCESS                                                        36\n",
       "PostgreSQL    SUCCESS                                                        36\n",
       "SQLite        SUCCESS                                                        36\n",
       "Teradata      SUCCESS                                                        33\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CalibratedClassifierCV</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.072222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CalibratedClassifierCV_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.094444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean                                          \\\n",
       "dialect                      Firebird IBM DB2    Impala MS SQL Server MariaDB   \n",
       "Model                                                                           \n",
       "CalibratedClassifierCV       0.111111     0.0  0.555556           0.0     0.0   \n",
       "CalibratedClassifierCV_pipe  0.166667     0.0  0.666667           0.0     0.0   \n",
       "All                          0.138889     0.0  0.611111           0.0     0.0   \n",
       "\n",
       "                                                                        \\\n",
       "dialect                     MonetDB Oracle PostgreSQL SQLite  Teradata   \n",
       "Model                                                                    \n",
       "CalibratedClassifierCV          0.0    0.0        0.0    0.0  0.055556   \n",
       "CalibratedClassifierCV_pipe     0.0    0.0        0.0    0.0  0.111111   \n",
       "All                             0.0    0.0        0.0    0.0  0.083333   \n",
       "\n",
       "                                       \n",
       "dialect                           All  \n",
       "Model                                  \n",
       "CalibratedClassifierCV       0.072222  \n",
       "CalibratedClassifierCV_pipe  0.094444  \n",
       "All                          0.083333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.covariance\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'InternalError:(ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0973N  Not enough storage is available in the \"AGENT_STACK_SZ\" heap or stack to process the statement.  SQLSTATE=57011 SQLCODE=-973 \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Exceeded the maximum depth of an expression tree (1000).\\n \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (8631, b'Internal error: Server stack limit has been reached. Please look for potentially deep nesting in your query, and try to simplify it.DB-Lib error message 20018, severity 17:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n') \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(_mysql_exceptions.OperationalError) (1436, \"Thread stack overrun:  1238176 bytes used of a 1269760 byte stack, and 32000 bytes needed.  Use \\'mysqld --thread_stack=#\\' to specify a bigger stack\") \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(psycopg2.OperationalError) stack depth limit exceeded\\nHINT:  Increase the configuration parameter \"max_stack_depth\" (currently 2048kB), after ensuring the platform\\'s stack depth limit is adequate.\\n \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) Expression tree is too large (maximum depth 1000) \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        10\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              2\n",
       "IBM DB2       SUCCESS                                                        10\n",
       "              'InternalError:(ibm_db_dbi.InternalError) ibm_d...              2\n",
       "Impala        SUCCESS                                                        10\n",
       "              \"DBAPIError:(impala.error.HiveServer2Error) Ana...              2\n",
       "MS SQL Server SUCCESS                                                        10\n",
       "              \"OperationalError:(pymssql.OperationalError) (8...              2\n",
       "MariaDB       SUCCESS                                                        10\n",
       "              'OperationalError:(_mysql_exceptions.Operationa...              2\n",
       "MonetDB       SUCCESS                                                        10\n",
       "              'DBAPIError:(builtins.BrokenPipeError) [Errno 3...              2\n",
       "Oracle        SUCCESS                                                        10\n",
       "              TIMEOUT                                                         2\n",
       "PostgreSQL    SUCCESS                                                        10\n",
       "              'OperationalError:(psycopg2.OperationalError) s...              2\n",
       "SQLite        SUCCESS                                                        10\n",
       "              'OperationalError:(sqlite3.OperationalError) Ex...              2\n",
       "Teradata      SUCCESS                                                         8\n",
       "              TIMEOUT                                                         4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EllipticEnvelope</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EllipticEnvelope_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean                                              \\\n",
       "dialect                Firebird   IBM DB2    Impala MS SQL Server   MariaDB   \n",
       "Model                                                                         \n",
       "EllipticEnvelope       0.333333  0.333333  0.333333      0.333333  0.333333   \n",
       "EllipticEnvelope_pipe  0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "All                    0.166667  0.166667  0.166667      0.166667  0.166667   \n",
       "\n",
       "                                                                          \\\n",
       "dialect                 MonetDB    Oracle PostgreSQL    SQLite  Teradata   \n",
       "Model                                                                      \n",
       "EllipticEnvelope       0.333333  0.333333   0.333333  0.333333  0.333333   \n",
       "EllipticEnvelope_pipe  0.000000  0.000000   0.000000  0.000000  0.333333   \n",
       "All                    0.166667  0.166667   0.166667  0.166667  0.333333   \n",
       "\n",
       "                                 \n",
       "dialect                     All  \n",
       "Model                            \n",
       "EllipticEnvelope       0.333333  \n",
       "EllipticEnvelope_pipe  0.033333  \n",
       "All                    0.183333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.decomposition\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -901\\\\n- Integer overflow.  The result of an integer operation caused the most significant bit of the result to carry.', -901, 335544779) ,</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Token unknown ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N  Arithmetic overflow or other arithmetic exception occurred.  SQLSTATE=22003 SQLCODE=-802 ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:CONNECTION_FAILED_WITH_ERROR ...</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (2616, \\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\') \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        56\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             11\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...              2\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...              2\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              1\n",
       "IBM DB2       SUCCESS                                                        69\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "              'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Erro...              1\n",
       "Impala        SUCCESS                                                        58\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                14\n",
       "MS SQL Server SUCCESS                                                        70\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "MariaDB       SUCCESS                                                        70\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "MonetDB       SUCCESS                                                        58\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                14\n",
       "Oracle        SUCCESS                                                        70\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "PostgreSQL    SUCCESS                                                        70\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "SQLite        SUCCESS                                                        70\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "Teradata      SUCCESS                                                        59\n",
       "              Exception:CONNECTION_FAILED_WITH_ERROR ...                      7\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (26...              4\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "              TIMEOUT                                                         2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FactorAnalysis</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FastICA</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IncrementalPCA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KernelPCA</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.258333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparsePCA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TruncatedSVD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>0.099723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean                                              \\\n",
       "dialect         Firebird   IBM DB2    Impala MS SQL Server   MariaDB   \n",
       "Model                                                                  \n",
       "FactorAnalysis  0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "FastICA         0.333333  0.166667  0.166667      0.166667  0.166667   \n",
       "IncrementalPCA  0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "KernelPCA       1.000000  0.083333  1.000000      0.000000  0.000000   \n",
       "PCA             0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "SparsePCA            NaN       NaN       NaN           NaN       NaN   \n",
       "TruncatedSVD    0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "All             0.222222  0.041667  0.194444      0.027778  0.027778   \n",
       "\n",
       "                                                                             \n",
       "dialect          MonetDB    Oracle PostgreSQL    SQLite  Teradata       All  \n",
       "Model                                                                        \n",
       "FactorAnalysis  1.000000  0.000000   0.000000  0.000000  0.000000  0.100000  \n",
       "FastICA         0.166667  0.166667   0.166667  0.166667  0.166667  0.183333  \n",
       "IncrementalPCA  0.000000  0.000000   0.000000  0.000000  0.416667  0.041667  \n",
       "KernelPCA       0.000000  0.000000   0.000000  0.000000  0.500000  0.258333  \n",
       "PCA             0.000000  0.000000   0.000000  0.000000  0.000000  0.000000  \n",
       "SparsePCA            NaN       NaN        NaN       NaN  1.000000  1.000000  \n",
       "TruncatedSVD    0.000000  0.000000   0.000000  0.000000  0.000000  0.000000  \n",
       "All             0.194444  0.027778   0.027778  0.027778  0.202703  0.099723  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.discriminant_analysis\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -901\\\\n- request size limit exceeded\\', -901, 335544382) \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Impala</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        32\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              2\n",
       "              \"DatabaseError:('Error while commiting transact...              1\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "IBM DB2       SUCCESS                                                        34\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "Impala        TIMEOUT                                                        26\n",
       "              SUCCESS                                                         8\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "MS SQL Server SUCCESS                                                        35\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "MariaDB       SUCCESS                                                        34\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "MonetDB       SUCCESS                                                        34\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "Oracle        SUCCESS                                                        34\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "PostgreSQL    SUCCESS                                                        35\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "SQLite        SUCCESS                                                        35\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "Teradata      SUCCESS                                                        31\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "              TIMEOUT                                                         2\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LinearDiscriminantAnalysis</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.094444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearDiscriminantAnalysis_pipe</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.172222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     mean                                    \\\n",
       "dialect                          Firebird   IBM DB2    Impala MS SQL Server   \n",
       "Model                                                                         \n",
       "LinearDiscriminantAnalysis       0.111111  0.000000  0.722222      0.000000   \n",
       "LinearDiscriminantAnalysis_pipe  0.111111  0.111111  0.833333      0.055556   \n",
       "All                              0.111111  0.055556  0.777778      0.027778   \n",
       "\n",
       "                                                                          \\\n",
       "dialect                           MariaDB   MonetDB    Oracle PostgreSQL   \n",
       "Model                                                                      \n",
       "LinearDiscriminantAnalysis       0.000000  0.000000  0.000000   0.000000   \n",
       "LinearDiscriminantAnalysis_pipe  0.111111  0.111111  0.111111   0.055556   \n",
       "All                              0.055556  0.055556  0.055556   0.027778   \n",
       "\n",
       "                                                               \n",
       "dialect                            SQLite  Teradata       All  \n",
       "Model                                                          \n",
       "LinearDiscriminantAnalysis       0.000000  0.111111  0.094444  \n",
       "LinearDiscriminantAnalysis_pipe  0.055556  0.166667  0.172222  \n",
       "All                              0.027778  0.138889  0.133333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.ensemble\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while executing SQL statement:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\\', -802, 335544321) \\[SQL ...</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Token unknown ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\\n \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \\'createTable\\' RPC to Hive Metastore: \\nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\\n \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (2616, \\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\') \\[SQL ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                       126\n",
       "              \"DatabaseError:('Error while commiting transact...             90\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             22\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              9\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              3\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              1\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "IBM DB2       SUCCESS                                                       249\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 3\n",
       "Impala        SUCCESS                                                       161\n",
       "              DBAPIError:(impala.error.HiveServer2Error) Anal...             55\n",
       "              TIMEOUT                                                        32\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "              \"OperationalError:(impala.error.OperationalErro...              1\n",
       "              'DBAPIError:(impala.error.HiveServer2Error) Imp...              1\n",
       "MS SQL Server SUCCESS                                                       250\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "MariaDB       SUCCESS                                                       250\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "MonetDB       SUCCESS                                                       229\n",
       "              TIMEOUT                                                        16\n",
       "              'DBAPIError:(builtins.BrokenPipeError) [Errno 3...              4\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 3\n",
       "Oracle        SUCCESS                                                       250\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "PostgreSQL    SUCCESS                                                       250\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "SQLite        SUCCESS                                                       250\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 2\n",
       "Teradata      SUCCESS                                                       186\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                55\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (26...              6\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AdaBoostClassifier</th>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoostClassifier_pipe</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoostRegressor</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoostRegressor_pipe</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaggingClassifier</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.105556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaggingClassifier_pipe</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaggingRegressor</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaggingRegressor_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreesClassifier</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.127778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreesClassifier_pipe</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreesRegressor</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreesRegressor_pipe</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingClassifier</th>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingClassifier_pipe</th>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.205556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingRegressor</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingRegressor_pipe</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsolationForest</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsolationForest_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.094444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestRegressor_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.091270</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>0.126587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     mean                                    \\\n",
       "dialect                          Firebird   IBM DB2    Impala MS SQL Server   \n",
       "Model                                                                         \n",
       "AdaBoostClassifier               0.944444  0.000000  0.555556      0.000000   \n",
       "AdaBoostClassifier_pipe          0.888889  0.055556  0.666667      0.000000   \n",
       "AdaBoostRegressor                0.000000  0.000000  0.000000      0.000000   \n",
       "AdaBoostRegressor_pipe           0.666667  0.000000  0.000000      0.000000   \n",
       "BaggingClassifier                0.444444  0.000000  0.333333      0.000000   \n",
       "BaggingClassifier_pipe           0.222222  0.000000  0.333333      0.000000   \n",
       "BaggingRegressor                 0.666667  0.000000  0.000000      0.000000   \n",
       "BaggingRegressor_pipe            0.333333  0.000000  0.000000      0.000000   \n",
       "ExtraTreesClassifier             0.388889  0.000000  0.333333      0.000000   \n",
       "ExtraTreesClassifier_pipe        0.444444  0.111111  0.444444      0.111111   \n",
       "ExtraTreesRegressor              0.666667  0.000000  0.000000      0.000000   \n",
       "ExtraTreesRegressor_pipe         0.500000  0.000000  0.000000      0.000000   \n",
       "GradientBoostingClassifier       0.722222  0.000000  0.777778      0.000000   \n",
       "GradientBoostingClassifier_pipe  0.722222  0.000000  0.833333      0.000000   \n",
       "GradientBoostingRegressor        0.166667  0.000000  0.000000      0.000000   \n",
       "GradientBoostingRegressor_pipe   0.500000  0.000000  0.000000      0.000000   \n",
       "IsolationForest                  0.000000  0.000000  0.000000      0.000000   \n",
       "IsolationForest_pipe             0.333333  0.000000  0.000000      0.000000   \n",
       "RandomForestClassifier           0.388889  0.000000  0.333333      0.000000   \n",
       "RandomForestClassifier_pipe      0.333333  0.000000  0.444444      0.000000   \n",
       "RandomForestRegressor            0.333333  0.000000  0.000000      0.000000   \n",
       "RandomForestRegressor_pipe       0.333333  0.000000  0.000000      0.000000   \n",
       "All                              0.500000  0.011905  0.361111      0.007937   \n",
       "\n",
       "                                                                          \\\n",
       "dialect                           MariaDB   MonetDB    Oracle PostgreSQL   \n",
       "Model                                                                      \n",
       "AdaBoostClassifier               0.000000  0.111111  0.000000   0.000000   \n",
       "AdaBoostClassifier_pipe          0.000000  0.166667  0.000000   0.000000   \n",
       "AdaBoostRegressor                0.000000  0.000000  0.000000   0.000000   \n",
       "AdaBoostRegressor_pipe           0.000000  0.000000  0.000000   0.000000   \n",
       "BaggingClassifier                0.000000  0.000000  0.000000   0.000000   \n",
       "BaggingClassifier_pipe           0.000000  0.000000  0.000000   0.000000   \n",
       "BaggingRegressor                 0.000000  0.000000  0.000000   0.000000   \n",
       "BaggingRegressor_pipe            0.000000  0.000000  0.000000   0.000000   \n",
       "ExtraTreesClassifier             0.000000  0.000000  0.000000   0.000000   \n",
       "ExtraTreesClassifier_pipe        0.111111  0.111111  0.111111   0.111111   \n",
       "ExtraTreesRegressor              0.000000  0.000000  0.000000   0.000000   \n",
       "ExtraTreesRegressor_pipe         0.000000  0.000000  0.000000   0.000000   \n",
       "GradientBoostingClassifier       0.000000  0.444444  0.000000   0.000000   \n",
       "GradientBoostingClassifier_pipe  0.000000  0.444444  0.000000   0.000000   \n",
       "GradientBoostingRegressor        0.000000  0.000000  0.000000   0.000000   \n",
       "GradientBoostingRegressor_pipe   0.000000  0.000000  0.000000   0.000000   \n",
       "IsolationForest                  0.000000  0.000000  0.000000   0.000000   \n",
       "IsolationForest_pipe             0.000000  0.000000  0.000000   0.000000   \n",
       "RandomForestClassifier           0.000000  0.000000  0.000000   0.000000   \n",
       "RandomForestClassifier_pipe      0.000000  0.000000  0.000000   0.000000   \n",
       "RandomForestRegressor            0.000000  0.000000  0.000000   0.000000   \n",
       "RandomForestRegressor_pipe       0.000000  0.000000  0.000000   0.000000   \n",
       "All                              0.007937  0.091270  0.007937   0.007937   \n",
       "\n",
       "                                                               \n",
       "dialect                            SQLite  Teradata       All  \n",
       "Model                                                          \n",
       "AdaBoostClassifier               0.000000  0.333333  0.194444  \n",
       "AdaBoostClassifier_pipe          0.000000  0.444444  0.222222  \n",
       "AdaBoostRegressor                0.000000  0.000000  0.000000  \n",
       "AdaBoostRegressor_pipe           0.000000  0.000000  0.066667  \n",
       "BaggingClassifier                0.000000  0.277778  0.105556  \n",
       "BaggingClassifier_pipe           0.000000  0.277778  0.083333  \n",
       "BaggingRegressor                 0.000000  0.000000  0.066667  \n",
       "BaggingRegressor_pipe            0.000000  0.000000  0.033333  \n",
       "ExtraTreesClassifier             0.000000  0.555556  0.127778  \n",
       "ExtraTreesClassifier_pipe        0.111111  0.777778  0.244444  \n",
       "ExtraTreesRegressor              0.000000  0.000000  0.066667  \n",
       "ExtraTreesRegressor_pipe         0.000000  0.166667  0.066667  \n",
       "GradientBoostingClassifier       0.000000  0.000000  0.194444  \n",
       "GradientBoostingClassifier_pipe  0.000000  0.055556  0.205556  \n",
       "GradientBoostingRegressor        0.000000  0.000000  0.016667  \n",
       "GradientBoostingRegressor_pipe   0.000000  0.000000  0.050000  \n",
       "IsolationForest                  0.000000  0.666667  0.066667  \n",
       "IsolationForest_pipe             0.000000  0.666667  0.100000  \n",
       "RandomForestClassifier           0.000000  0.222222  0.094444  \n",
       "RandomForestClassifier_pipe      0.000000  0.222222  0.100000  \n",
       "RandomForestRegressor            0.000000  0.000000  0.033333  \n",
       "RandomForestRegressor_pipe       0.000000  0.000000  0.033333  \n",
       "All                              0.007937  0.261905  0.126587  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.feature_selection\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             error_message\n",
       "dialect       error_message               \n",
       "Firebird      SUCCESS                  108\n",
       "IBM DB2       SUCCESS                  108\n",
       "Impala        SUCCESS                  108\n",
       "MS SQL Server SUCCESS                  108\n",
       "MariaDB       SUCCESS                  108\n",
       "MonetDB       SUCCESS                  108\n",
       "Oracle        SUCCESS                  108\n",
       "PostgreSQL    SUCCESS                  108\n",
       "SQLite        SUCCESS                  108\n",
       "Teradata      SUCCESS                  108"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GenericUnivariateSelect</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFE</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFECV</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SelectFdr</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SelectFpr</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SelectFromModel</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SelectFwe</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SelectKBest</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SelectPercentile</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            mean                                               \\\n",
       "dialect                 Firebird IBM DB2 Impala MS SQL Server MariaDB MonetDB   \n",
       "Model                                                                           \n",
       "GenericUnivariateSelect        0       0      0             0       0       0   \n",
       "RFE                            0       0      0             0       0       0   \n",
       "RFECV                          0       0      0             0       0       0   \n",
       "SelectFdr                      0       0      0             0       0       0   \n",
       "SelectFpr                      0       0      0             0       0       0   \n",
       "SelectFromModel                0       0      0             0       0       0   \n",
       "SelectFwe                      0       0      0             0       0       0   \n",
       "SelectKBest                    0       0      0             0       0       0   \n",
       "SelectPercentile               0       0      0             0       0       0   \n",
       "All                            0       0      0             0       0       0   \n",
       "\n",
       "                                                               \n",
       "dialect                 Oracle PostgreSQL SQLite Teradata All  \n",
       "Model                                                          \n",
       "GenericUnivariateSelect      0          0      0        0   0  \n",
       "RFE                          0          0      0        0   0  \n",
       "RFECV                        0          0      0        0   0  \n",
       "SelectFdr                    0          0      0        0   0  \n",
       "SelectFpr                    0          0      0        0   0  \n",
       "SelectFromModel              0          0      0        0   0  \n",
       "SelectFwe                    0          0      0        0   0  \n",
       "SelectKBest                  0          0      0        0   0  \n",
       "SelectPercentile             0          0      0        0   0  \n",
       "All                          0          0      0        0   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.impute\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             error_message\n",
       "dialect       error_message               \n",
       "Firebird      SUCCESS                   24\n",
       "IBM DB2       SUCCESS                   24\n",
       "Impala        SUCCESS                   24\n",
       "MS SQL Server SUCCESS                   24\n",
       "MariaDB       SUCCESS                   24\n",
       "MonetDB       SUCCESS                   24\n",
       "Oracle        SUCCESS                   24\n",
       "PostgreSQL    SUCCESS                   24\n",
       "SQLite        SUCCESS                   24\n",
       "Teradata      SUCCESS                   24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MissingIndicator</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SimpleImputer</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean                                                      \\\n",
       "dialect          Firebird IBM DB2 Impala MS SQL Server MariaDB MonetDB Oracle   \n",
       "Model                                                                           \n",
       "MissingIndicator        0       0      0             0       0       0      0   \n",
       "SimpleImputer           0       0      0             0       0       0      0   \n",
       "All                     0       0      0             0       0       0      0   \n",
       "\n",
       "                                                 \n",
       "dialect          PostgreSQL SQLite Teradata All  \n",
       "Model                                            \n",
       "MissingIndicator          0      0        0   0  \n",
       "SimpleImputer             0      0        0   0  \n",
       "All                       0      0        0   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.kernel_ridge\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Firebird</th>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Token unknown ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...              6\n",
       "              SUCCESS                                                         3\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              2\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              1\n",
       "IBM DB2       SUCCESS                                                        12\n",
       "Impala        SUCCESS                                                        12\n",
       "MS SQL Server SUCCESS                                                        12\n",
       "MariaDB       SUCCESS                                                        12\n",
       "MonetDB       SUCCESS                                                        12\n",
       "Oracle        SUCCESS                                                        12\n",
       "PostgreSQL    SUCCESS                                                        12\n",
       "SQLite        SUCCESS                                                        12\n",
       "Teradata      SUCCESS                                                        11\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KernelRidge</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KernelRidge_pipe</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean                                                      \\\n",
       "dialect          Firebird IBM DB2 Impala MS SQL Server MariaDB MonetDB Oracle   \n",
       "Model                                                                           \n",
       "KernelRidge          1.00     0.0    0.0           0.0     0.0     0.0    0.0   \n",
       "KernelRidge_pipe     0.50     0.0    0.0           0.0     0.0     0.0    0.0   \n",
       "All                  0.75     0.0    0.0           0.0     0.0     0.0    0.0   \n",
       "\n",
       "                                                        \n",
       "dialect          PostgreSQL SQLite  Teradata       All  \n",
       "Model                                                   \n",
       "KernelRidge             0.0    0.0  0.166667  0.116667  \n",
       "KernelRidge_pipe        0.0    0.0  0.000000  0.050000  \n",
       "All                     0.0    0.0  0.083333  0.083333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.linear_model\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -901\\\\n- request size limit exceeded\\', -901, 335544382) \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cTrainingError ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                       476\n",
       "              \"DatabaseError:('Error while commiting transact...             10\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              4\n",
       "              TIMEOUT                                                         1\n",
       "              cTrainingError ...                                              1\n",
       "IBM DB2       SUCCESS                                                       491\n",
       "              cTrainingError ...                                              1\n",
       "Impala        SUCCESS                                                       437\n",
       "              TIMEOUT                                                        54\n",
       "              cTrainingError ...                                              1\n",
       "MS SQL Server SUCCESS                                                       490\n",
       "              'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "              cTrainingError ...                                              1\n",
       "MariaDB       SUCCESS                                                       491\n",
       "              cTrainingError ...                                              1\n",
       "MonetDB       SUCCESS                                                       491\n",
       "              cTrainingError ...                                              1\n",
       "Oracle        SUCCESS                                                       491\n",
       "              cTrainingError ...                                              1\n",
       "PostgreSQL    SUCCESS                                                       491\n",
       "              cTrainingError ...                                              1\n",
       "SQLite        SUCCESS                                                       491\n",
       "              cTrainingError ...                                              1\n",
       "Teradata      SUCCESS                                                       478\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              7\n",
       "              TIMEOUT                                                         6\n",
       "              cTrainingError ...                                              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARDRegression</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARDRegression_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BayesianRidge</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BayesianRidge_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNet</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNetCV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNetCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElasticNet_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lars</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LarsCV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LarsCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lars_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoCV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoLars</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoLarsCV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoLarsCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoLarsIC</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoLarsIC_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LassoLars_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearRegression</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearRegression_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegressionCV</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegressionCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.077778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression_pipe</th>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrthogonalMatchingPursuit</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrthogonalMatchingPursuitCV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrthogonalMatchingPursuitCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrthogonalMatchingPursuit_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassiveAggressiveClassifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassiveAggressiveClassifier_pipe</th>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.011111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassiveAggressiveRegressor</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassiveAggressiveRegressor_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptron</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptron_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.011111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANSACRegressor</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANSACRegressor_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeCV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeClassifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.011111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeClassifierCV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeClassifierCV_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeClassifier_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDClassifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDClassifier_pipe</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDRegressor</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDRegressor_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TheilSenRegressor</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TheilSenRegressor_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.032520</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.111789</td>\n",
       "      <td>0.004065</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.028455</td>\n",
       "      <td>0.018902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      mean                                    \\\n",
       "dialect                           Firebird   IBM DB2    Impala MS SQL Server   \n",
       "Model                                                                          \n",
       "ARDRegression                     0.000000  0.000000  0.000000      0.000000   \n",
       "ARDRegression_pipe                0.000000  0.000000  0.000000      0.000000   \n",
       "BayesianRidge                     0.000000  0.000000  0.000000      0.000000   \n",
       "BayesianRidge_pipe                0.000000  0.000000  0.000000      0.000000   \n",
       "ElasticNet                        0.000000  0.000000  0.000000      0.000000   \n",
       "ElasticNetCV                      0.000000  0.000000  0.000000      0.000000   \n",
       "ElasticNetCV_pipe                 0.000000  0.000000  0.000000      0.000000   \n",
       "ElasticNet_pipe                   0.000000  0.000000  0.000000      0.000000   \n",
       "Lars                              0.000000  0.000000  0.000000      0.000000   \n",
       "LarsCV                            0.000000  0.000000  0.000000      0.000000   \n",
       "LarsCV_pipe                       0.000000  0.000000  0.000000      0.000000   \n",
       "Lars_pipe                         0.000000  0.000000  0.000000      0.000000   \n",
       "Lasso                             0.000000  0.000000  0.000000      0.000000   \n",
       "LassoCV                           0.000000  0.000000  0.000000      0.000000   \n",
       "LassoCV_pipe                      0.000000  0.000000  0.000000      0.000000   \n",
       "LassoLars                         0.000000  0.000000  0.000000      0.000000   \n",
       "LassoLarsCV                       0.000000  0.000000  0.000000      0.000000   \n",
       "LassoLarsCV_pipe                  0.000000  0.000000  0.000000      0.000000   \n",
       "LassoLarsIC                       0.000000  0.000000  0.000000      0.000000   \n",
       "LassoLarsIC_pipe                  0.166667  0.000000  0.000000      0.000000   \n",
       "LassoLars_pipe                    0.166667  0.000000  0.000000      0.000000   \n",
       "Lasso_pipe                        0.166667  0.000000  0.000000      0.000000   \n",
       "LinearRegression                  0.000000  0.000000  0.000000      0.000000   \n",
       "LinearRegression_pipe             0.166667  0.000000  0.000000      0.166667   \n",
       "LogisticRegression                0.111111  0.000000  0.722222      0.000000   \n",
       "LogisticRegressionCV              0.166667  0.000000  0.722222      0.000000   \n",
       "LogisticRegressionCV_pipe         0.000000  0.000000  0.722222      0.000000   \n",
       "LogisticRegression_pipe           0.055556  0.000000  0.722222      0.000000   \n",
       "OrthogonalMatchingPursuit         0.000000  0.000000  0.000000      0.000000   \n",
       "OrthogonalMatchingPursuitCV       0.000000  0.000000  0.000000      0.000000   \n",
       "OrthogonalMatchingPursuitCV_pipe  0.000000  0.000000  0.000000      0.000000   \n",
       "OrthogonalMatchingPursuit_pipe    0.000000  0.000000  0.000000      0.000000   \n",
       "PassiveAggressiveClassifier       0.000000  0.000000  0.000000      0.000000   \n",
       "PassiveAggressiveClassifier_pipe  0.055556  0.000000  0.000000      0.000000   \n",
       "PassiveAggressiveRegressor        0.000000  0.000000  0.000000      0.000000   \n",
       "PassiveAggressiveRegressor_pipe   0.166667  0.000000  0.000000      0.000000   \n",
       "Perceptron                        0.000000  0.000000  0.055556      0.000000   \n",
       "Perceptron_pipe                   0.000000  0.000000  0.055556      0.000000   \n",
       "RANSACRegressor                   0.166667  0.166667  0.166667      0.166667   \n",
       "RANSACRegressor_pipe              0.166667  0.000000  0.000000      0.000000   \n",
       "Ridge                             0.000000  0.000000  0.000000      0.000000   \n",
       "RidgeCV                           0.000000  0.000000  0.000000      0.000000   \n",
       "RidgeCV_pipe                      0.000000  0.000000  0.000000      0.000000   \n",
       "RidgeClassifier                   0.000000  0.000000  0.000000      0.000000   \n",
       "RidgeClassifierCV                 0.000000  0.000000  0.000000      0.000000   \n",
       "RidgeClassifierCV_pipe            0.000000  0.000000  0.000000      0.000000   \n",
       "RidgeClassifier_pipe              0.000000  0.000000  0.000000      0.000000   \n",
       "Ridge_pipe                        0.000000  0.000000  0.000000      0.000000   \n",
       "SGDClassifier                     0.000000  0.000000  0.000000      0.000000   \n",
       "SGDClassifier_pipe                0.111111  0.000000  0.000000      0.000000   \n",
       "SGDRegressor                      0.000000  0.000000  0.000000      0.000000   \n",
       "SGDRegressor_pipe                 0.000000  0.000000  0.000000      0.000000   \n",
       "TheilSenRegressor                 0.000000  0.000000  0.000000      0.000000   \n",
       "TheilSenRegressor_pipe            0.000000  0.000000  0.000000      0.000000   \n",
       "All                               0.032520  0.002033  0.111789      0.004065   \n",
       "\n",
       "                                                                           \\\n",
       "dialect                            MariaDB   MonetDB    Oracle PostgreSQL   \n",
       "Model                                                                       \n",
       "ARDRegression                     0.000000  0.000000  0.000000   0.000000   \n",
       "ARDRegression_pipe                0.000000  0.000000  0.000000   0.000000   \n",
       "BayesianRidge                     0.000000  0.000000  0.000000   0.000000   \n",
       "BayesianRidge_pipe                0.000000  0.000000  0.000000   0.000000   \n",
       "ElasticNet                        0.000000  0.000000  0.000000   0.000000   \n",
       "ElasticNetCV                      0.000000  0.000000  0.000000   0.000000   \n",
       "ElasticNetCV_pipe                 0.000000  0.000000  0.000000   0.000000   \n",
       "ElasticNet_pipe                   0.000000  0.000000  0.000000   0.000000   \n",
       "Lars                              0.000000  0.000000  0.000000   0.000000   \n",
       "LarsCV                            0.000000  0.000000  0.000000   0.000000   \n",
       "LarsCV_pipe                       0.000000  0.000000  0.000000   0.000000   \n",
       "Lars_pipe                         0.000000  0.000000  0.000000   0.000000   \n",
       "Lasso                             0.000000  0.000000  0.000000   0.000000   \n",
       "LassoCV                           0.000000  0.000000  0.000000   0.000000   \n",
       "LassoCV_pipe                      0.000000  0.000000  0.000000   0.000000   \n",
       "LassoLars                         0.000000  0.000000  0.000000   0.000000   \n",
       "LassoLarsCV                       0.000000  0.000000  0.000000   0.000000   \n",
       "LassoLarsCV_pipe                  0.000000  0.000000  0.000000   0.000000   \n",
       "LassoLarsIC                       0.000000  0.000000  0.000000   0.000000   \n",
       "LassoLarsIC_pipe                  0.000000  0.000000  0.000000   0.000000   \n",
       "LassoLars_pipe                    0.000000  0.000000  0.000000   0.000000   \n",
       "Lasso_pipe                        0.000000  0.000000  0.000000   0.000000   \n",
       "LinearRegression                  0.000000  0.000000  0.000000   0.000000   \n",
       "LinearRegression_pipe             0.000000  0.000000  0.000000   0.000000   \n",
       "LogisticRegression                0.000000  0.000000  0.000000   0.000000   \n",
       "LogisticRegressionCV              0.000000  0.000000  0.000000   0.000000   \n",
       "LogisticRegressionCV_pipe         0.000000  0.000000  0.000000   0.000000   \n",
       "LogisticRegression_pipe           0.000000  0.000000  0.000000   0.000000   \n",
       "OrthogonalMatchingPursuit         0.000000  0.000000  0.000000   0.000000   \n",
       "OrthogonalMatchingPursuitCV       0.000000  0.000000  0.000000   0.000000   \n",
       "OrthogonalMatchingPursuitCV_pipe  0.000000  0.000000  0.000000   0.000000   \n",
       "OrthogonalMatchingPursuit_pipe    0.000000  0.000000  0.000000   0.000000   \n",
       "PassiveAggressiveClassifier       0.000000  0.000000  0.000000   0.000000   \n",
       "PassiveAggressiveClassifier_pipe  0.000000  0.000000  0.000000   0.000000   \n",
       "PassiveAggressiveRegressor        0.000000  0.000000  0.000000   0.000000   \n",
       "PassiveAggressiveRegressor_pipe   0.000000  0.000000  0.000000   0.000000   \n",
       "Perceptron                        0.000000  0.000000  0.000000   0.000000   \n",
       "Perceptron_pipe                   0.000000  0.000000  0.000000   0.000000   \n",
       "RANSACRegressor                   0.166667  0.166667  0.166667   0.166667   \n",
       "RANSACRegressor_pipe              0.000000  0.000000  0.000000   0.000000   \n",
       "Ridge                             0.000000  0.000000  0.000000   0.000000   \n",
       "RidgeCV                           0.000000  0.000000  0.000000   0.000000   \n",
       "RidgeCV_pipe                      0.000000  0.000000  0.000000   0.000000   \n",
       "RidgeClassifier                   0.000000  0.000000  0.000000   0.000000   \n",
       "RidgeClassifierCV                 0.000000  0.000000  0.000000   0.000000   \n",
       "RidgeClassifierCV_pipe            0.000000  0.000000  0.000000   0.000000   \n",
       "RidgeClassifier_pipe              0.000000  0.000000  0.000000   0.000000   \n",
       "Ridge_pipe                        0.000000  0.000000  0.000000   0.000000   \n",
       "SGDClassifier                     0.000000  0.000000  0.000000   0.000000   \n",
       "SGDClassifier_pipe                0.000000  0.000000  0.000000   0.000000   \n",
       "SGDRegressor                      0.000000  0.000000  0.000000   0.000000   \n",
       "SGDRegressor_pipe                 0.000000  0.000000  0.000000   0.000000   \n",
       "TheilSenRegressor                 0.000000  0.000000  0.000000   0.000000   \n",
       "TheilSenRegressor_pipe            0.000000  0.000000  0.000000   0.000000   \n",
       "All                               0.002033  0.002033  0.002033   0.002033   \n",
       "\n",
       "                                                                \n",
       "dialect                             SQLite  Teradata       All  \n",
       "Model                                                           \n",
       "ARDRegression                     0.000000  0.000000  0.000000  \n",
       "ARDRegression_pipe                0.000000  0.000000  0.000000  \n",
       "BayesianRidge                     0.000000  0.000000  0.000000  \n",
       "BayesianRidge_pipe                0.000000  0.000000  0.000000  \n",
       "ElasticNet                        0.000000  0.000000  0.000000  \n",
       "ElasticNetCV                      0.000000  0.000000  0.000000  \n",
       "ElasticNetCV_pipe                 0.000000  0.000000  0.000000  \n",
       "ElasticNet_pipe                   0.000000  0.000000  0.000000  \n",
       "Lars                              0.000000  0.000000  0.000000  \n",
       "LarsCV                            0.000000  0.000000  0.000000  \n",
       "LarsCV_pipe                       0.000000  0.000000  0.000000  \n",
       "Lars_pipe                         0.000000  0.000000  0.000000  \n",
       "Lasso                             0.000000  0.000000  0.000000  \n",
       "LassoCV                           0.000000  0.000000  0.000000  \n",
       "LassoCV_pipe                      0.000000  0.000000  0.000000  \n",
       "LassoLars                         0.000000  0.000000  0.000000  \n",
       "LassoLarsCV                       0.000000  0.000000  0.000000  \n",
       "LassoLarsCV_pipe                  0.000000  0.000000  0.000000  \n",
       "LassoLarsIC                       0.000000  0.000000  0.000000  \n",
       "LassoLarsIC_pipe                  0.000000  0.000000  0.016667  \n",
       "LassoLars_pipe                    0.000000  0.000000  0.016667  \n",
       "Lasso_pipe                        0.000000  0.000000  0.016667  \n",
       "LinearRegression                  0.000000  0.000000  0.000000  \n",
       "LinearRegression_pipe             0.000000  0.000000  0.033333  \n",
       "LogisticRegression                0.000000  0.000000  0.083333  \n",
       "LogisticRegressionCV              0.000000  0.000000  0.088889  \n",
       "LogisticRegressionCV_pipe         0.000000  0.055556  0.077778  \n",
       "LogisticRegression_pipe           0.000000  0.055556  0.083333  \n",
       "OrthogonalMatchingPursuit         0.000000  0.000000  0.000000  \n",
       "OrthogonalMatchingPursuitCV       0.000000  0.000000  0.000000  \n",
       "OrthogonalMatchingPursuitCV_pipe  0.000000  0.000000  0.000000  \n",
       "OrthogonalMatchingPursuit_pipe    0.000000  0.000000  0.000000  \n",
       "PassiveAggressiveClassifier       0.000000  0.000000  0.000000  \n",
       "PassiveAggressiveClassifier_pipe  0.000000  0.055556  0.011111  \n",
       "PassiveAggressiveRegressor        0.000000  0.000000  0.000000  \n",
       "PassiveAggressiveRegressor_pipe   0.000000  0.000000  0.016667  \n",
       "Perceptron                        0.000000  0.111111  0.016667  \n",
       "Perceptron_pipe                   0.000000  0.055556  0.011111  \n",
       "RANSACRegressor                   0.166667  0.166667  0.166667  \n",
       "RANSACRegressor_pipe              0.000000  0.000000  0.016667  \n",
       "Ridge                             0.000000  0.000000  0.000000  \n",
       "RidgeCV                           0.000000  0.000000  0.000000  \n",
       "RidgeCV_pipe                      0.000000  0.000000  0.000000  \n",
       "RidgeClassifier                   0.000000  0.111111  0.011111  \n",
       "RidgeClassifierCV                 0.000000  0.055556  0.005556  \n",
       "RidgeClassifierCV_pipe            0.000000  0.055556  0.005556  \n",
       "RidgeClassifier_pipe              0.000000  0.055556  0.005556  \n",
       "Ridge_pipe                        0.000000  0.000000  0.000000  \n",
       "SGDClassifier                     0.000000  0.055556  0.005556  \n",
       "SGDClassifier_pipe                0.000000  0.055556  0.016667  \n",
       "SGDRegressor                      0.000000  0.000000  0.000000  \n",
       "SGDRegressor_pipe                 0.000000  0.000000  0.000000  \n",
       "TheilSenRegressor                 0.000000  0.000000  0.000000  \n",
       "TheilSenRegressor_pipe            0.000000  0.000000  0.000000  \n",
       "All                               0.002033  0.028455  0.018902  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.multiclass\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206010532_OEPNFRI_OVR_Score_1\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:CONNECTION_FAILED_WITH_ERROR ...</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        39\n",
       "              \"DatabaseError:('Error while commiting transact...             19\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             14\n",
       "IBM DB2       SUCCESS                                                        72\n",
       "Impala        SUCCESS                                                        36\n",
       "              TIMEOUT                                                        23\n",
       "              DBAPIError:(impala.error.HiveServer2Error) Anal...             12\n",
       "              'DBAPIError:(impala.error.HiveServer2Error) Exe...              1\n",
       "MS SQL Server SUCCESS                                                        71\n",
       "              'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "MariaDB       SUCCESS                                                        72\n",
       "MonetDB       SUCCESS                                                        72\n",
       "Oracle        SUCCESS                                                        72\n",
       "PostgreSQL    SUCCESS                                                        72\n",
       "SQLite        SUCCESS                                                        72\n",
       "Teradata      SUCCESS                                                        54\n",
       "              Exception:CONNECTION_FAILED_WITH_ERROR ...                     16\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OneVsOneClassifier</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.127778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OneVsOneClassifier_pipe</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OneVsRestClassifier</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OneVsRestClassifier_pipe</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.122222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean                                          \\\n",
       "dialect                   Firebird IBM DB2    Impala MS SQL Server MariaDB   \n",
       "Model                                                                        \n",
       "OneVsOneClassifier        0.666667     0.0  0.388889      0.000000     0.0   \n",
       "OneVsOneClassifier_pipe   0.444444     0.0  0.444444      0.000000     0.0   \n",
       "OneVsRestClassifier       0.333333     0.0  0.555556      0.000000     0.0   \n",
       "OneVsRestClassifier_pipe  0.388889     0.0  0.611111      0.055556     0.0   \n",
       "All                       0.458333     0.0  0.500000      0.013889     0.0   \n",
       "\n",
       "                                                                               \n",
       "dialect                  MonetDB Oracle PostgreSQL SQLite  Teradata       All  \n",
       "Model                                                                          \n",
       "OneVsOneClassifier           0.0    0.0        0.0    0.0  0.222222  0.127778  \n",
       "OneVsOneClassifier_pipe      0.0    0.0        0.0    0.0  0.277778  0.116667  \n",
       "OneVsRestClassifier          0.0    0.0        0.0    0.0  0.222222  0.111111  \n",
       "OneVsRestClassifier_pipe     0.0    0.0        0.0    0.0  0.277778  0.133333  \n",
       "All                          0.0    0.0        0.0    0.0  0.250000  0.122222  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.naive_bayes\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Token unknown ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL1585N  A temporary table could not be created because there is no available system temporary table space that has a compatible page size.  SQLSTATE=54048 SQLCODE=-1585 \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Impala</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymssql.OperationalError) (2714, b\"There is already an object named \\'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\") \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        87\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             40\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              7\n",
       "              \"DatabaseError:('Error while commiting transact...              5\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              1\n",
       "IBM DB2       SUCCESS                                                       138\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "              'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::D...              2\n",
       "Impala        TIMEOUT                                                        82\n",
       "              SUCCESS                                                        50\n",
       "              DBAPIError:(impala.error.HiveServer2Error) Anal...              8\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "MS SQL Server SUCCESS                                                       140\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 3\n",
       "              'OperationalError:(pymssql.OperationalError) (2...              1\n",
       "MariaDB       SUCCESS                                                       140\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "MonetDB       SUCCESS                                                        76\n",
       "              TIMEOUT                                                        52\n",
       "              'DBAPIError:(builtins.BrokenPipeError) [Errno 3...             12\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "Oracle        SUCCESS                                                       140\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "PostgreSQL    SUCCESS                                                       140\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "SQLite        SUCCESS                                                       140\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "Teradata      SUCCESS                                                       124\n",
       "              TIMEOUT                                                        14\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 4\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BernoulliNB</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.155556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BernoulliNB_pipe</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.161111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ComplementNB</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.255556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ComplementNB_pipe</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.261111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.227778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB_pipe</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.177778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultinomialNB</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultinomialNB_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.395833</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.652778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.184028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean                                              \\\n",
       "dialect             Firebird   IBM DB2    Impala MS SQL Server   MariaDB   \n",
       "Model                                                                      \n",
       "BernoulliNB         0.333333  0.000000  0.555556      0.000000  0.000000   \n",
       "BernoulliNB_pipe    0.388889  0.000000  0.555556      0.000000  0.000000   \n",
       "ComplementNB        0.444444  0.111111  0.666667      0.111111  0.111111   \n",
       "ComplementNB_pipe   0.444444  0.111111  0.666667      0.111111  0.111111   \n",
       "GaussianNB          0.500000  0.111111  0.777778      0.000000  0.000000   \n",
       "GaussianNB_pipe     0.388889  0.000000  0.777778      0.000000  0.000000   \n",
       "MultinomialNB       0.333333  0.000000  0.611111      0.000000  0.000000   \n",
       "MultinomialNB_pipe  0.333333  0.000000  0.611111      0.000000  0.000000   \n",
       "All                 0.395833  0.041667  0.652778      0.027778  0.027778   \n",
       "\n",
       "                                                                       \\\n",
       "dialect              MonetDB    Oracle PostgreSQL    SQLite  Teradata   \n",
       "Model                                                                   \n",
       "BernoulliNB         0.444444  0.000000   0.000000  0.000000  0.222222   \n",
       "BernoulliNB_pipe    0.444444  0.000000   0.000000  0.000000  0.222222   \n",
       "ComplementNB        0.666667  0.111111   0.111111  0.111111  0.111111   \n",
       "ComplementNB_pipe   0.666667  0.111111   0.111111  0.111111  0.166667   \n",
       "GaussianNB          0.555556  0.000000   0.000000  0.000000  0.333333   \n",
       "GaussianNB_pipe     0.555556  0.000000   0.000000  0.000000  0.055556   \n",
       "MultinomialNB       0.222222  0.000000   0.000000  0.000000  0.000000   \n",
       "MultinomialNB_pipe  0.222222  0.000000   0.000000  0.000000  0.000000   \n",
       "All                 0.472222  0.027778   0.027778  0.027778  0.138889   \n",
       "\n",
       "                              \n",
       "dialect                  All  \n",
       "Model                         \n",
       "BernoulliNB         0.155556  \n",
       "BernoulliNB_pipe    0.161111  \n",
       "ComplementNB        0.255556  \n",
       "ComplementNB_pipe   0.261111  \n",
       "GaussianNB          0.227778  \n",
       "GaussianNB_pipe     0.177778  \n",
       "MultinomialNB       0.116667  \n",
       "MultinomialNB_pipe  0.116667  \n",
       "All                 0.184028  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.neural_network\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        31\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             12\n",
       "              \"DatabaseError:('Error while commiting transact...              5\n",
       "IBM DB2       SUCCESS                                                        48\n",
       "Impala        SUCCESS                                                        25\n",
       "              TIMEOUT                                                        23\n",
       "MS SQL Server SUCCESS                                                        48\n",
       "MariaDB       SUCCESS                                                        48\n",
       "MonetDB       SUCCESS                                                        32\n",
       "              TIMEOUT                                                        16\n",
       "Oracle        SUCCESS                                                        48\n",
       "PostgreSQL    SUCCESS                                                        48\n",
       "SQLite        SUCCESS                                                        48\n",
       "Teradata      SUCCESS                                                        44\n",
       "              TIMEOUT                                                         3\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.155556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier_pipe</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPRegressor</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPRegressor_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean                                          \\\n",
       "dialect             Firebird IBM DB2    Impala MS SQL Server MariaDB   \n",
       "Model                                                                  \n",
       "MLPClassifier       0.388889     0.0  0.611111           0.0     0.0   \n",
       "MLPClassifier_pipe  0.444444     0.0  0.666667           0.0     0.0   \n",
       "MLPRegressor        0.166667     0.0  0.000000           0.0     0.0   \n",
       "MLPRegressor_pipe   0.166667     0.0  0.000000           0.0     0.0   \n",
       "All                 0.354167     0.0  0.479167           0.0     0.0   \n",
       "\n",
       "                                                                           \n",
       "dialect              MonetDB Oracle PostgreSQL SQLite  Teradata       All  \n",
       "Model                                                                      \n",
       "MLPClassifier       0.444444    0.0        0.0    0.0  0.111111  0.155556  \n",
       "MLPClassifier_pipe  0.444444    0.0        0.0    0.0  0.111111  0.166667  \n",
       "MLPRegressor        0.000000    0.0        0.0    0.0  0.000000  0.016667  \n",
       "MLPRegressor_pipe   0.000000    0.0        0.0    0.0  0.000000  0.016667  \n",
       "All                 0.333333    0.0        0.0    0.0  0.083333  0.125000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.pipeline\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:CONNECTION_FAILED_WITH_ERROR ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          error_message\n",
       "dialect       error_message                                            \n",
       "Firebird      SUCCESS                                                48\n",
       "IBM DB2       SUCCESS                                                48\n",
       "Impala        SUCCESS                                                48\n",
       "MS SQL Server SUCCESS                                                48\n",
       "MariaDB       SUCCESS                                                48\n",
       "MonetDB       SUCCESS                                                48\n",
       "Oracle        SUCCESS                                                48\n",
       "PostgreSQL    SUCCESS                                                48\n",
       "SQLite        SUCCESS                                                48\n",
       "Teradata      SUCCESS                                                42\n",
       "              TIMEOUT                                                 5\n",
       "              Exception:CONNECTION_FAILED_WITH_ERROR ...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FeatureUnion</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pipeline</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pipeline_pipe</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.012500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean                                                      \\\n",
       "dialect       Firebird IBM DB2 Impala MS SQL Server MariaDB MonetDB Oracle   \n",
       "Model                                                                        \n",
       "FeatureUnion       0.0     0.0    0.0           0.0     0.0     0.0    0.0   \n",
       "Pipeline           0.0     0.0    0.0           0.0     0.0     0.0    0.0   \n",
       "Pipeline_pipe      0.0     0.0    0.0           0.0     0.0     0.0    0.0   \n",
       "All                0.0     0.0    0.0           0.0     0.0     0.0    0.0   \n",
       "\n",
       "                                                     \n",
       "dialect       PostgreSQL SQLite  Teradata       All  \n",
       "Model                                                \n",
       "FeatureUnion         0.0    0.0  0.000000  0.000000  \n",
       "Pipeline             0.0    0.0  0.055556  0.005556  \n",
       "Pipeline_pipe        0.0    0.0  0.277778  0.027778  \n",
       "All                  0.0    0.0  0.125000  0.012500  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.preprocessing\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0419N  A decimal divide operation is not valid because the result would have a negative scale.  SQLSTATE=42911 SQLCODE=-419 \\[SQL ...</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0840N  Too many items were returned in a SELECT list.  SQLSTATE=54004 SQLCODE=-840 \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n') \\[SQL ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(pymonetdb.exceptions.OperationalError) Math exception: Numerical argument out of domain\\n \\[SQL ...</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(psycopg2.OperationalError) target lists can have at most 1664 entries\\n \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) parser stack overflow \\[SQL ...</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) too many columns in result set \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'OperationalError:(sqlite3.OperationalError) too many terms in compound SELECT \\[SQL ...</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(teradata.api.DatabaseError) (10670, '[07009] [Teradata][ODBC] (10670) Invalid descriptor index, descriptor record does not exist, or descriptor record was not properly initialized.') ,</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (5639, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                       160\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             15\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 3\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...              2\n",
       "IBM DB2       SUCCESS                                                       164\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 7\n",
       "              'ProgrammingError:(ibm_db_dbi.ProgrammingError)...              5\n",
       "              'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::D...              4\n",
       "Impala        SUCCESS                                                       174\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 6\n",
       "MS SQL Server SUCCESS                                                       174\n",
       "              \"OperationalError:(pymssql.OperationalError) (1...              6\n",
       "MariaDB       SUCCESS                                                       174\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 6\n",
       "MonetDB       SUCCESS                                                       163\n",
       "              'OperationalError:(pymonetdb.exceptions.Operati...             12\n",
       "              'DBAPIError:(builtins.BrokenPipeError) [Errno 3...              4\n",
       "              TIMEOUT                                                         1\n",
       "Oracle        SUCCESS                                                       174\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 6\n",
       "PostgreSQL    SUCCESS                                                       176\n",
       "              'OperationalError:(psycopg2.OperationalError) t...              4\n",
       "SQLite        SUCCESS                                                       166\n",
       "              'OperationalError:(sqlite3.OperationalError) pa...              6\n",
       "              'OperationalError:(sqlite3.OperationalError) to...              4\n",
       "              'OperationalError:(sqlite3.OperationalError) to...              4\n",
       "Teradata      SUCCESS                                                       162\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 9\n",
       "              \"DatabaseError:(teradata.api.DatabaseError) (10...              4\n",
       "              TIMEOUT                                                         4\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (56...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Binarizer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBinsDiscretizer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LabelBinarizer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LabelEncoder</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxAbsScaler</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinMaxScaler</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normalizer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OneHotEncoder</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrdinalEncoder</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.408333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PolynomialFeatures</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PowerTransformer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QuantileTransformer</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RobustScaler</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StandardScaler</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.077778</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.062778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean                                              \\\n",
       "dialect              Firebird   IBM DB2    Impala MS SQL Server   MariaDB   \n",
       "Model                                                                       \n",
       "Binarizer            0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "KBinsDiscretizer     0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "LabelBinarizer       0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "LabelEncoder         0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "MaxAbsScaler         0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "MinMaxScaler         0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "Normalizer           0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "OneHotEncoder        0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "OrdinalEncoder       0.500000  0.500000  0.500000      0.500000  0.500000   \n",
       "PolynomialFeatures   0.333333  0.333333  0.000000      0.000000  0.000000   \n",
       "PowerTransformer     0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "QuantileTransformer  0.833333  0.500000  0.000000      0.000000  0.000000   \n",
       "RobustScaler         0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "StandardScaler       0.000000  0.000000  0.000000      0.000000  0.000000   \n",
       "All                  0.111111  0.088889  0.033333      0.033333  0.033333   \n",
       "\n",
       "                                                                        \\\n",
       "dialect               MonetDB    Oracle PostgreSQL    SQLite  Teradata   \n",
       "Model                                                                    \n",
       "Binarizer            0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "KBinsDiscretizer     0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "LabelBinarizer       0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "LabelEncoder         0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "MaxAbsScaler         0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "MinMaxScaler         0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "Normalizer           0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "OneHotEncoder        0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "OrdinalEncoder       0.083333  0.500000   0.000000  0.500000  0.500000   \n",
       "PolynomialFeatures   0.000000  0.000000   0.333333  0.333333  0.333333   \n",
       "PowerTransformer     1.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "QuantileTransformer  0.333333  0.000000   0.000000  0.333333  0.666667   \n",
       "RobustScaler         0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "StandardScaler       0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "All                  0.094444  0.033333   0.022222  0.077778  0.100000   \n",
       "\n",
       "                               \n",
       "dialect                   All  \n",
       "Model                          \n",
       "Binarizer            0.000000  \n",
       "KBinsDiscretizer     0.000000  \n",
       "LabelBinarizer       0.000000  \n",
       "LabelEncoder         0.000000  \n",
       "MaxAbsScaler         0.000000  \n",
       "MinMaxScaler         0.000000  \n",
       "Normalizer           0.000000  \n",
       "OneHotEncoder        0.000000  \n",
       "OrdinalEncoder       0.408333  \n",
       "PolynomialFeatures   0.166667  \n",
       "PowerTransformer     0.100000  \n",
       "QuantileTransformer  0.266667  \n",
       "RobustScaler         0.000000  \n",
       "StandardScaler       0.000000  \n",
       "All                  0.062778  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.random_projection\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -901\\\\n- Integer overflow.  The result of an integer operation caused the most significant bit of the result to carry.', -901, 335544779) ,</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        20\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...              3\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...              1\n",
       "IBM DB2       SUCCESS                                                        24\n",
       "Impala        SUCCESS                                                        24\n",
       "MS SQL Server SUCCESS                                                        24\n",
       "MariaDB       SUCCESS                                                        24\n",
       "MonetDB       SUCCESS                                                        24\n",
       "Oracle        SUCCESS                                                        24\n",
       "PostgreSQL    SUCCESS                                                        24\n",
       "SQLite        SUCCESS                                                        24\n",
       "Teradata      SUCCESS                                                        24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GaussianRandomProjection</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseRandomProjection</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean                                       \\\n",
       "dialect                   Firebird IBM DB2 Impala MS SQL Server MariaDB   \n",
       "Model                                                                     \n",
       "GaussianRandomProjection  0.333333     0.0    0.0           0.0     0.0   \n",
       "SparseRandomProjection    0.000000     0.0    0.0           0.0     0.0   \n",
       "All                       0.166667     0.0    0.0           0.0     0.0   \n",
       "\n",
       "                                                                              \n",
       "dialect                  MonetDB Oracle PostgreSQL SQLite Teradata       All  \n",
       "Model                                                                         \n",
       "GaussianRandomProjection     0.0    0.0        0.0    0.0      0.0  0.033333  \n",
       "SparseRandomProjection       0.0    0.0        0.0    0.0      0.0  0.000000  \n",
       "All                          0.0    0.0        0.0    0.0      0.0  0.016667  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.svm\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while executing SQL statement:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\\', -802, 335544321) \\[SQL ...</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range', -802, 335544321) ,</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N  \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" is an undefined name.  SQLSTATE=42704 SQLCODE=-204 \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N  The name of the object to be created is identical to the existing name \"DB.TMP_20181205235842_F5UC68X_KERNAGG_B9\" of type \"CREATE TEMPORARY TABLE\".  SQLSTATE=42710 SQLCODE=-601 \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:CONNECTION_FAILED_WITH_ERROR ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        72\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             29\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...             22\n",
       "              \"DatabaseError:(fdb.fbcore.DatabaseError) ('Cur...             21\n",
       "              \"DatabaseError:('Error while commiting transact...             12\n",
       "IBM DB2       SUCCESS                                                       153\n",
       "              'ProgrammingError:(ibm_db_dbi.ProgrammingError)...              1\n",
       "              'ProgrammingError:(ibm_db_dbi.ProgrammingError)...              1\n",
       "              TIMEOUT                                                         1\n",
       "Impala        SUCCESS                                                       112\n",
       "              TIMEOUT                                                        44\n",
       "MS SQL Server SUCCESS                                                       155\n",
       "              TIMEOUT                                                         1\n",
       "MariaDB       SUCCESS                                                       156\n",
       "MonetDB       SUCCESS                                                       144\n",
       "              TIMEOUT                                                         8\n",
       "              'DBAPIError:(builtins.BrokenPipeError) [Errno 3...              3\n",
       "              Exception:CONNECTION_FAILED_WITH_ERROR ...                      1\n",
       "Oracle        SUCCESS                                                       122\n",
       "              TIMEOUT                                                        34\n",
       "PostgreSQL    SUCCESS                                                       156\n",
       "SQLite        SUCCESS                                                       156\n",
       "Teradata      SUCCESS                                                       141\n",
       "              TIMEOUT                                                        12\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              2\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVR</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVR_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NuSVC</th>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NuSVC_pipe</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NuSVR</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NuSVR_pipe</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OneClassSVM</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OneClassSVM_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.261111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC_pipe</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR_pipe</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.282051</td>\n",
       "      <td>0.006410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.217949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.123718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean                                            \\\n",
       "dialect           Firebird   IBM DB2    Impala MS SQL Server MariaDB   \n",
       "Model                                                                  \n",
       "LinearSVC         0.000000  0.000000  0.000000      0.000000     0.0   \n",
       "LinearSVC_pipe    0.000000  0.000000  0.000000      0.000000     0.0   \n",
       "LinearSVR         0.000000  0.000000  0.000000      0.000000     0.0   \n",
       "LinearSVR_pipe    0.000000  0.000000  0.000000      0.000000     0.0   \n",
       "NuSVC             0.777778  0.055556  0.611111      0.000000     0.0   \n",
       "NuSVC_pipe        0.833333  0.055556  0.611111      0.000000     0.0   \n",
       "NuSVR             1.000000  0.000000  0.000000      0.000000     0.0   \n",
       "NuSVR_pipe        0.333333  0.000000  0.000000      0.000000     0.0   \n",
       "OneClassSVM       0.833333  0.000000  0.000000      0.000000     0.0   \n",
       "OneClassSVM_pipe  0.166667  0.000000  0.000000      0.000000     0.0   \n",
       "SVC               0.888889  0.055556  0.611111      0.055556     0.0   \n",
       "SVC_pipe          0.833333  0.000000  0.611111      0.000000     0.0   \n",
       "SVR               1.000000  0.000000  0.000000      0.000000     0.0   \n",
       "SVR_pipe          0.666667  0.000000  0.000000      0.000000     0.0   \n",
       "All               0.538462  0.019231  0.282051      0.006410     0.0   \n",
       "\n",
       "                                                                            \n",
       "dialect            MonetDB    Oracle PostgreSQL SQLite  Teradata       All  \n",
       "Model                                                                       \n",
       "LinearSVC         0.000000  0.000000        0.0    0.0  0.000000  0.000000  \n",
       "LinearSVC_pipe    0.000000  0.000000        0.0    0.0  0.055556  0.005556  \n",
       "LinearSVR         0.000000  0.000000        0.0    0.0  0.000000  0.000000  \n",
       "LinearSVR_pipe    0.000000  0.000000        0.0    0.0  0.000000  0.000000  \n",
       "NuSVC             0.166667  0.555556        0.0    0.0  0.277778  0.244444  \n",
       "NuSVC_pipe        0.055556  0.388889        0.0    0.0  0.055556  0.200000  \n",
       "NuSVR             0.166667  0.000000        0.0    0.0  0.000000  0.116667  \n",
       "NuSVR_pipe        0.000000  0.000000        0.0    0.0  0.000000  0.033333  \n",
       "OneClassSVM       0.000000  0.000000        0.0    0.0  0.000000  0.083333  \n",
       "OneClassSVM_pipe  0.000000  0.000000        0.0    0.0  0.000000  0.016667  \n",
       "SVC               0.166667  0.555556        0.0    0.0  0.277778  0.261111  \n",
       "SVC_pipe          0.055556  0.388889        0.0    0.0  0.111111  0.200000  \n",
       "SVR               0.166667  0.000000        0.0    0.0  0.166667  0.133333  \n",
       "SVR_pipe          0.333333  0.000000        0.0    0.0  0.000000  0.100000  \n",
       "All               0.076923  0.217949        0.0    0.0  0.096154  0.123718  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY sklearn.tree\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Firebird</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impala</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      SUCCESS                                                        94\n",
       "              \"DatabaseError:('Error while commiting transact...              2\n",
       "IBM DB2       SUCCESS                                                        96\n",
       "Impala        SUCCESS                                                        96\n",
       "MS SQL Server SUCCESS                                                        96\n",
       "MariaDB       SUCCESS                                                        96\n",
       "MonetDB       SUCCESS                                                        96\n",
       "Oracle        SUCCESS                                                        83\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                13\n",
       "PostgreSQL    SUCCESS                                                        96\n",
       "SQLite        SUCCESS                                                        96\n",
       "Teradata      SUCCESS                                                        94\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier_pipe</th>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.011111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeRegressor</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeRegressor_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreeClassifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreeClassifier_pipe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreeRegressor</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreeRegressor_pipe</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.017708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean                                       \\\n",
       "dialect                      Firebird IBM DB2 Impala MS SQL Server MariaDB   \n",
       "Model                                                                        \n",
       "DecisionTreeClassifier       0.000000     0.0    0.0           0.0     0.0   \n",
       "DecisionTreeClassifier_pipe  0.055556     0.0    0.0           0.0     0.0   \n",
       "DecisionTreeRegressor        0.000000     0.0    0.0           0.0     0.0   \n",
       "DecisionTreeRegressor_pipe   0.000000     0.0    0.0           0.0     0.0   \n",
       "ExtraTreeClassifier          0.000000     0.0    0.0           0.0     0.0   \n",
       "ExtraTreeClassifier_pipe     0.000000     0.0    0.0           0.0     0.0   \n",
       "ExtraTreeRegressor           0.000000     0.0    0.0           0.0     0.0   \n",
       "ExtraTreeRegressor_pipe      0.166667     0.0    0.0           0.0     0.0   \n",
       "All                          0.020833     0.0    0.0           0.0     0.0   \n",
       "\n",
       "                                                                           \\\n",
       "dialect                     MonetDB    Oracle PostgreSQL SQLite  Teradata   \n",
       "Model                                                                       \n",
       "DecisionTreeClassifier          0.0  0.000000        0.0    0.0  0.000000   \n",
       "DecisionTreeClassifier_pipe     0.0  0.000000        0.0    0.0  0.055556   \n",
       "DecisionTreeRegressor           0.0  0.000000        0.0    0.0  0.000000   \n",
       "DecisionTreeRegressor_pipe      0.0  0.000000        0.0    0.0  0.000000   \n",
       "ExtraTreeClassifier             0.0  0.222222        0.0    0.0  0.000000   \n",
       "ExtraTreeClassifier_pipe        0.0  0.500000        0.0    0.0  0.055556   \n",
       "ExtraTreeRegressor              0.0  0.000000        0.0    0.0  0.000000   \n",
       "ExtraTreeRegressor_pipe         0.0  0.000000        0.0    0.0  0.000000   \n",
       "All                             0.0  0.135417        0.0    0.0  0.020833   \n",
       "\n",
       "                                       \n",
       "dialect                           All  \n",
       "Model                                  \n",
       "DecisionTreeClassifier       0.000000  \n",
       "DecisionTreeClassifier_pipe  0.011111  \n",
       "DecisionTreeRegressor        0.000000  \n",
       "DecisionTreeRegressor_pipe   0.000000  \n",
       "ExtraTreeClassifier          0.022222  \n",
       "ExtraTreeClassifier_pipe     0.055556  \n",
       "ExtraTreeRegressor           0.000000  \n",
       "ExtraTreeRegressor_pipe      0.016667  \n",
       "All                          0.017708  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR_REPORT_FOR_CATEGORY xgboost.sklearn\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>error_message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>error_message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Firebird</th>\n",
       "      <th>\"DatabaseError:('Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBM DB2</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Impala</th>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MS SQL Server</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MariaDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MonetDB</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMEOUT</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oracle</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PostgreSQL</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SQLite</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Teradata</th>\n",
       "      <th>SUCCESS</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  error_message\n",
       "dialect       error_message                                                    \n",
       "Firebird      \"DatabaseError:('Error while commiting transact...             29\n",
       "              SUCCESS                                                        17\n",
       "              'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Er...              1\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "IBM DB2       SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "Impala        TIMEOUT                                                        23\n",
       "              SUCCESS                                                        17\n",
       "              DBAPIError:(impala.error.HiveServer2Error) Anal...              8\n",
       "MS SQL Server SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "MariaDB       SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "MonetDB       SUCCESS                                                        31\n",
       "              TIMEOUT                                                        16\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "Oracle        SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "PostgreSQL    SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "SQLite        SUCCESS                                                        47\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1\n",
       "Teradata      SUCCESS                                                        46\n",
       "              'DatabaseError:(teradata.api.DatabaseError) (37...              1\n",
       "              Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...                 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <th>Firebird</th>\n",
       "      <th>IBM DB2</th>\n",
       "      <th>Impala</th>\n",
       "      <th>MS SQL Server</th>\n",
       "      <th>MariaDB</th>\n",
       "      <th>MonetDB</th>\n",
       "      <th>Oracle</th>\n",
       "      <th>PostgreSQL</th>\n",
       "      <th>SQLite</th>\n",
       "      <th>Teradata</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGBClassifier</th>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.205556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBClassifier_pipe</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBRegressor</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBRegressor_pipe</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.181250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean                                              \\\n",
       "dialect             Firebird   IBM DB2    Impala MS SQL Server   MariaDB   \n",
       "Model                                                                      \n",
       "XGBClassifier       0.777778  0.000000  0.833333      0.000000  0.000000   \n",
       "XGBClassifier_pipe  0.611111  0.000000  0.888889      0.000000  0.000000   \n",
       "XGBRegressor        0.500000  0.166667  0.000000      0.166667  0.166667   \n",
       "XGBRegressor_pipe   0.500000  0.000000  0.000000      0.000000  0.000000   \n",
       "All                 0.645833  0.020833  0.645833      0.020833  0.020833   \n",
       "\n",
       "                                                                       \\\n",
       "dialect              MonetDB    Oracle PostgreSQL    SQLite  Teradata   \n",
       "Model                                                                   \n",
       "XGBClassifier       0.444444  0.000000   0.000000  0.000000  0.000000   \n",
       "XGBClassifier_pipe  0.444444  0.000000   0.000000  0.000000  0.055556   \n",
       "XGBRegressor        0.166667  0.166667   0.166667  0.166667  0.166667   \n",
       "XGBRegressor_pipe   0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "All                 0.354167  0.020833   0.020833  0.020833  0.041667   \n",
       "\n",
       "                              \n",
       "dialect                  All  \n",
       "Model                         \n",
       "XGBClassifier       0.205556  \n",
       "XGBClassifier_pipe  0.200000  \n",
       "XGBRegressor        0.183333  \n",
       "XGBRegressor_pipe   0.050000  \n",
       "All                 0.181250  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for cat in Category_Labels:\n",
    "    print(\"ERROR_REPORT_FOR_CATEGORY\" , cat)\n",
    "    df1 = df[df.model_category == cat]\n",
    "    real_errors = df1 # df1[df1.error_message != \"SUCCESS\"]\n",
    "    if(real_errors.shape[0] > 0):\n",
    "        msg_by_estim_and_dsn = pd.DataFrame(real_errors.groupby(['dialect'])['error_message'].value_counts())\n",
    "        from IPython.core.display import display, HTML\n",
    "        display(msg_by_estim_and_dsn)\n",
    "        pvt1 = pd.pivot_table(df1, index='Model', values='status' , columns=['dialect'],  aggfunc=[np.mean], margins=True)\n",
    "        display(pvt1)\n",
    "    else:\n",
    "        print(\"NO_ERROR_FOR_CATEGORY\" , cat)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "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>Model</th>\n",
       "      <th>dataset</th>\n",
       "      <th>dialect</th>\n",
       "      <th>DSN</th>\n",
       "      <th>status</th>\n",
       "      <th>error_message</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>full_error_message</th>\n",
       "      <th>model_category</th>\n",
       "      <th>status_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Model, dataset, dialect, DSN, status, error_message, elapsed_time, full_error_message, model_category, status_2]\n",
       "Index: []"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.model_category == \"bad_category\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IBM DB2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAD8CAYAAABgmUMCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFGFJREFUeJzt3X+w5XV93/HnS1D8hbKUzZYAyaKz2pJpg5sNYUZNTaz8sgqmLYVJw5Yw2bSFqUzSaVbNBCYZZrT1R0tjMVh3BKsiRonbkRQXxtHJH/xYyIafkr0ilF0X2IgVDY4EfPeP87lwWPfePZ/dc+45l30+Zs7c73l/f5z393t27+t+f5zvSVUhSdKoXjTtBiRJy4vBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpy6HTbmASjjrqqFq9evW025CkZeX222//m6paua/pXpDBsXr1arZu3TrtNiRpWUny0CjTeahKktTF4JAkdTE4JEldDA5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1OUF+cnxA7V645f3e94H3//2MXYiSbPHPQ5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LUZWLBkeS4JF9Ncm+Se5K8u9UvTbIzybb2OGNonvckmUtyf5JTh+qntdpcko2T6lmStG+T/D6Op4Hfrao7khwO3J5kSxv3kar64PDESU4AzgF+Dvhp4MYkr2ujPwq8DdgB3JZkc1XdO8HeJUkLmFhwVNUuYFcb/n6S+4BjFpnlTOCaqvoR8K0kc8BJbdxcVT0AkOSaNq3BIUlTsCTnOJKsBt4A3NJKFyW5M8mmJCta7Rjg4aHZdrTaQnVJ0hRMPDiSvBL4AnBxVT0BXAG8FjiRwR7Jh8b0OhuSbE2ydffu3eNYpCRpLyYaHElezCA0Pl1VXwSoqker6pmq+jHwcZ47HLUTOG5o9mNbbaH681TVlVW1rqrWrVy5cvwrI0kCJntVVYBPAPdV1YeH6kcPTfYu4O42vBk4J8lhSY4H1gC3ArcBa5Icn+QlDE6gb55U35KkxU3yqqo3Ar8B3JVkW6u9Fzg3yYlAAQ8Cvw1QVfckuZbBSe+ngQur6hmAJBcBNwCHAJuq6p4J9i1JWsQkr6r6CyB7GXX9IvNcBly2l/r1i80nSVo6fnJcktTF4JAkdTE4JEldDA5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LUZWLBkeS4JF9Ncm+Se5K8u9WPTLIlyfb2c0WrJ8nlSeaS3Jlk7dCy1rfptydZP6meJUn7Nsk9jqeB362qE4CTgQuTnABsBG6qqjXATe05wOnAmvbYAFwBg6ABLgF+CTgJuGQ+bCRJS29iwVFVu6rqjjb8feA+4BjgTOCqNtlVwFlt+Ezg6hq4GTgiydHAqcCWqnq8qr4LbAFOm1TfkqTFLck5jiSrgTcAtwCrqmpXG/UIsKoNHwM8PDTbjlZbqC5JmoKJB0eSVwJfAC6uqieGx1VVATWm19mQZGuSrbt37x7HIiVJezHR4EjyYgah8emq+mIrP9oOQdF+PtbqO4HjhmY/ttUWqj9PVV1ZVeuqat3KlSvHuyKSpGdN8qqqAJ8A7quqDw+N2gzMXxm1HvjSUP28dnXVycD32iGtG4BTkqxoJ8VPaTVJ0hQcOsFlvxH4DeCuJNta7b3A+4Frk1wAPASc3cZdD5wBzAFPAucDVNXjSf4IuK1N94dV9fgE+5YkLWJiwVFVfwFkgdFv3cv0BVy4wLI2AZvG150kaX/5yXFJUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldRgqOJP9o0o1IkpaHUfc4/keSW5P8+ySvnmhHkqSZNlJwVNWbgV9n8E18tyf5TJK3TbQzSdJMGvkcR1VtB34f+D3gnwCXJ/lGkl+bVHOSpNkz6jmOf5zkI8B9wK8C76iqf9iGPzLB/iRJM2bUbwD878D/BN5bVT+cL1bVt5P8/kQ6kyTNpFGD4+3AD6vqGYAkLwJeWlVPVtWnJtadJGnmjHqO40bgZUPPX95qkqSDzKjB8dKq+sH8kzb88sm0JEmaZaMGx98mWTv/JMkvAD9cZHpJ0gvUqOc4LgY+n+TbQIC/D/yriXUlSZpZIwVHVd2W5B8Ar2+l+6vq7ybXliRpVo26xwHwi8DqNs/aJFTV1RPpSpI0s0YKjiSfAl4LbAOeaeUCDA5JOsiMusexDjihqmqSzUiSZt+oV1XdzeCEuCTpIDdqcBwF3JvkhiSb5x+LzZBkU5LHktw9VLs0yc4k29rjjKFx70kyl+T+JKcO1U9rtbkkG3tXUJI0XqMeqrp0P5b9SeCP+cnzIB+pqg8OF5KcAJwD/Bzw08CNSV7XRn8UeBuwA7gtyeaqunc/+pEkjcGol+N+LcnPAmuq6sYkLwcO2cc8X0+yesQ+zgSuqaofAd9KMgec1MbNVdUDAEmuadMaHJI0JaPeVv23gD8F/qSVjgH+bD9f86Ikd7ZDWSuGlvfw0DQ7Wm2huiRpSkY9x3Eh8EbgCXj2S51+aj9e7woGl/WeCOwCPrQfy9irJBuSbE2ydffu3eNarCRpD6MGx4+q6qn5J0kOZfA5ji5V9WhVPVNVPwY+znOHo3Yy+Fraece22kL1vS37yqpaV1XrVq5c2duaJGlEowbH15K8F3hZ+67xzwP/u/fFkhw99PRdDC7zBdgMnJPksCTHA2uAW4HbgDVJjk/yEgYn0Be9mkuSNFmjXlW1EbgAuAv4beB6Bt8IuKAknwXeAhyVZAdwCfCWJCcy2Ft5sC2LqronybUMTno/DVw49KVRFwE3MDgZv6mq7ulYP0nSmI16VdX8oaWPj7rgqjp3L+VPLDL9ZcBle6lfzyCoJEkzYNR7VX2LvZzTqKrXjL0jSdJM67lX1byXAv8SOHL87UiSZt1IJ8er6jtDj51V9V+Bt0+4N0nSDBr1UNXaoacvYrAH0vNdHpKkF4hRf/kPf1DvaQZXRJ099m4kSTNv1KuqfmXSjUiSlodRD1X9zmLjq+rD42lHkjTreq6q+kWe+9T2Oxh8snv7JJqSJM2uUYPjWGBtVX0fBl/IBHy5qv71pBqTJM2mUe9VtQp4auj5U60mSTrIjLrHcTVwa5Lr2vOzgKsm05IkaZaNelXVZUn+HHhzK51fVX85ubYkSbNq1ENVAC8Hnqiq/wbsaLc/lyQdZEb96thLgN8D3tNKLwb+16SakiTNrlH3ON4FvBP4W4Cq+jZw+KSakiTNrlGD46mqKtqt1ZO8YnItSZJm2ajBcW2SPwGOSPJbwI10fKmTJOmFY9Srqj7Yvmv8CeD1wB9U1ZaJdiZJmkn7DI4khwA3thsdGhaSdJDb56GqqnoG+HGSVy9BP5KkGTfqJ8d/ANyVZAvtyiqAqvoPE+lKkjSzRg2OL7aHJOkgt2hwJPmZqvq/VeV9qSRJwL7PcfzZ/ECSL0y4F0nSMrCv4MjQ8Gsm2YgkaXnYV3DUAsOSpIPUvk6O/3ySJxjsebysDdOeV1W9aqLdSZJmzqLBUVWHLFUjkqTloef7OCRJmlxwJNmU5LEkdw/VjkyyJcn29nNFqyfJ5UnmktyZZO3QPOvb9NuTrJ9Uv5Kk0Uxyj+OTwGl71DYCN1XVGuCm9hzgdGBNe2wAroBB0ACXAL8EnARcMh82kqTpmFhwVNXXgcf3KJ8JzH+Y8CrgrKH61TVwM4Pbtx8NnApsqarHq+q7DG6yuGcYSZKW0FKf41hVVbva8CPAqjZ8DPDw0HQ7Wm2huiRpSqZ2cnz4GwXHIcmGJFuTbN29e/e4FitJ2sNSB8ej7RAU7edjrb4TOG5oumNbbaH6T6iqK6tqXVWtW7ly5dgblyQNLHVwbAbmr4xaD3xpqH5eu7rqZOB77ZDWDcApSVa0k+KntJokaUpGva16tySfBd4CHJVkB4Oro97P4PvLLwAeAs5uk18PnAHMAU8C5wNU1eNJ/gi4rU33h1W15wl3SdISmlhwVNW5C4x6616mLeDCBZazCdg0xtYkSQfAT45LkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6TCU4kjyY5K4k25JsbbUjk2xJsr39XNHqSXJ5krkkdyZZO42eJUkD09zj+JWqOrGq1rXnG4GbqmoNcFN7DnA6sKY9NgBXLHmnkqRnzdKhqjOBq9rwVcBZQ/Wra+Bm4IgkR0+jQUnS9IKjgK8kuT3JhlZbVVW72vAjwKo2fAzw8NC8O1rteZJsSLI1ydbdu3dPqm9JOugdOqXXfVNV7UzyU8CWJN8YHllVlaR6FlhVVwJXAqxbt65rXknS6Kayx1FVO9vPx4DrgJOAR+cPQbWfj7XJdwLHDc1+bKtJkqZgyYMjySuSHD4/DJwC3A1sBta3ydYDX2rDm4Hz2tVVJwPfGzqkJUlaYtM4VLUKuC7J/Ot/pqr+T5LbgGuTXAA8BJzdpr8eOAOYA54Ezl/6liVJ85Y8OKrqAeDn91L/DvDWvdQLuHAJWpMkjWCWLseVJC0DBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkrosm+BIclqS+5PMJdk47X4k6WC1LIIjySHAR4HTgROAc5OcMN2uJOngdOi0GxjRScBcVT0AkOQa4Ezg3ql2JUkTsHrjl/d73gff//YxdrJ3y2KPAzgGeHjo+Y5WkyQtseWyx7FPSTYAG9rTHyS5/wAWdxTwN/vVxwcO4FX77XefU7Bcel0ufcLy6XW59AnLp9cF+zzA30E/O8pEyyU4dgLHDT0/ttWeVVVXAleO48WSbK2qdeNY1iQtlz5h+fS6XPqE5dPrcukTlk+v0+5zuRyqug1Yk+T4JC8BzgE2T7knSTooLYs9jqp6OslFwA3AIcCmqrpnym1J0kFpWQQHQFVdD1y/RC83lkNeS2C59AnLp9fl0icsn16XS5+wfHqdap+pqmm+viRpmVku5zgkSTPC4Bgy7duaJDkuyVeT3JvkniTvbvVLk+xMsq09zhia5z2t3/uTnLqU65LkwSR3tZ62ttqRSbYk2d5+rmj1JLm89XNnkrVDy1nfpt+eZP2Ye3z90HbbluSJJBfPyjZNsinJY0nuHqqNbRsm+YX2Hs21eTPGPv9Lkm+0Xq5LckSrr07yw6Ft+7F99bPQOo+x17G93xlcpHNLq38ugwt2xtXn54Z6fDDJtlaf6jb9CVXlY3C47hDgm8BrgJcAfwWcsMQ9HA2sbcOHA3/N4BYrlwL/cS/Tn9D6PAw4vvV/yFKtC/AgcNQetf8MbGzDG4EPtOEzgD8HApwM3NLqRwIPtJ8r2vCKCb7HjzC4Vn0mtinwy8Ba4O5JbEPg1jZt2rynj7HPU4BD2/AHhvpcPTzdHsvZaz8LrfMYex3b+w1cC5zThj8G/Ltx9bnH+A8BfzAL23TPh3scz3n2tiZV9RQwf1uTJVNVu6rqjjb8feA+Fv+E/JnANVX1o6r6FjDHYD2muS5nAle14auAs4bqV9fAzcARSY4GTgW2VNXjVfVdYAtw2oR6eyvwzap6aJFplnSbVtXXgcf30sMBb8M27lVVdXMNfntcPbSsA+6zqr5SVU+3pzcz+HzVgvbRz0LrPJZeF9H1fre/5n8V+NMD7XWxPtvrnA18drFlLNU23ZPB8ZyZuq1JktXAG4BbWumidkhg09Au50I9L9W6FPCVJLdn8Ml9gFVVtasNPwKsmpFeYfD5n+H/iLO4TWF82/CYNrxnfRJ+k8Ffu/OOT/KXSb6W5M2ttlg/C63zOI3j/f57wP8bCsxJbdM3A49W1fah2sxsU4NjBiV5JfAF4OKqegK4AngtcCKwi8Eu7Cx4U1WtZXDX4guT/PLwyPYX0ExctteOQ78T+Hwrzeo2fZ5Z2oYLSfI+4Gng0620C/iZqnoD8DvAZ5K8atTlTWidl8X7PeRcnv9HzkxtU4PjOfu8rclSSPJiBqHx6ar6IkBVPVpVz1TVj4GPM9iNhoV7XpJ1qaqd7edjwHWtr0fb7vP8bvRjs9Arg3C7o6oebT3P5DZtxrUNd/L8w0dj7znJvwH+GfDr7ZcT7bDPd9rw7QzOFbxuH/0stM5jMcb3+zsMDhEeukd9bNqyfw343FD/M7VNDY7nTP22Ju245ieA+6rqw0P1o4cmexcwfxXGZuCcJIclOR5Yw+BE2cTXJckrkhw+P8zgROnd7XXmr+pZD3xpqNfzMnAy8L22G30DcEqSFe3wwSmtNm7P+wtuFrfpkLFswzbuiSQnt39b5w0t64AlOQ34T8A7q+rJofrKDL5DhySvYbANH9hHPwut87h6Hcv73cLxq8C/mFSvwD8FvlFVzx6CmrltOq6z7C+EB4OrVv6aQZq/bwqv/yYGu5N3Atva4wzgU8Bdrb4ZOHponve1fu9n6IqZSa8Lg6tN/qo97pl/DQbHgG8CtgM3Ake2ehh8Gdc327qsG1rWbzI4KTkHnD+BXl/B4C/FVw/VZmKbMgizXcDfMTg+fcE4tyGwjsEvyW8Cf0z70O+Y+pxjcB5g/t/qx9q0/7z9m9gG3AG8Y1/9LLTOY+x1bO93+7d/a1v/zwOHjavPVv8k8G/3mHaq23TPh58clyR18VCVJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQu/x9IrOCTN1+70gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Firebird\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Impala\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAD8CAYAAABkbJM/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFm5JREFUeJzt3XuwXWd93vHvg4UBO+CrorqSHZmgQj1tsJUDdQpkCIYUm2A5LbhmaK26miidOg2UZoKATKAz6YydJji4Q50omEam3IzBsRqcFGMumc7UNvIFX3F8MDaWkG1hjA0YcAS//rFf1dvKkfSeI61ztjjfz8ye/a53vWvvn9Zs6dG6p6qQJGlfnrHQBUiSDg4GhiSpi4EhSepiYEiSuhgYkqQuBoYkqYuBIUnqYmBIkroYGJKkLksWuoD9ceyxx9bKlSsXugxJOqjceOON36yqpbNd7qAOjJUrV7Jly5aFLkOSDipJ7p/Lcu6SkiR1MTAkSV0MDElSFwNDktRl0MBI8h+T3JHk9iQfTfLsJCcmuT7JdJKPJzm0jX1Wm55u81cOWZskaXYGC4wky4HfBKaq6h8BhwDnABcCF1XVC4BHgXVtkXXAo63/ojZOkjQhht4ltQR4TpIlwGHAduBVwBVt/ibgrNZe06Zp809LkoHrkyR1Giwwqmob8AfA1xkFxWPAjcC3q2pnG7YVWN7ay4EH2rI72/hjdv/cJOuTbEmyZceOHUOVL0nazZC7pI5itNVwIvD3gcOB1+7v51bVxqqaqqqppUtnfaGiJGmOhrzS+9XA16pqB0CSTwEvA45MsqRtRawAtrXx24Djga1tF9YRwCNDFbdyw6f3a/n7LnjdAapEkg4OQx7D+DpwapLD2rGI04A7gc8Db2hj1gJXtfbmNk2b/7mqqgHrkyTNwpDHMK5ndPD6JuC29l0bgbcDb0syzegYxaVtkUuBY1r/24ANQ9UmSZq9QW8+WFXvBt69W/e9wEtnGPsD4I1D1iNJmjuv9JYkdTEwJEldDAxJUhcDQ5LUxcCQJHUxMCRJXQwMSVIXA0OS1MXAkCR1MTAkSV0MDElSFwNDktTFwJAkdTEwJEldDAxJUhcDQ5LUxcCQJHUZLDCSvDDJLWOvx5O8NcnRSa5Jck97P6qNT5KLk0wnuTXJ6qFqkyTN3pDP9L67qk6uqpOBnweeAK5k9Kzua6tqFXAtTz27+3RgVXutBy4ZqjZJ0uzN1y6p04CvVtX9wBpgU+vfBJzV2muAy2rkOuDIJMfNU32SpH2Yr8A4B/hoay+rqu2t/SCwrLWXAw+MLbO19T1NkvVJtiTZsmPHjqHqlSTtZvDASHIocCbwid3nVVUBNZvPq6qNVTVVVVNLly49QFVKkvZlPrYwTgduqqqH2vRDu3Y1tfeHW/824Pix5Va0PknSBJiPwHgTT+2OAtgMrG3ttcBVY/3ntrOlTgUeG9t1JUlaYEuG/PAkhwOvAX59rPsC4PIk64D7gbNb/9XAGcA0ozOqzhuyNknS7AwaGFX1PeCY3foeYXTW1O5jCzh/yHokSXPnld6SpC4GhiSpi4EhSepiYEiSuhgYkqQuBoYkqYuBIUnqYmBIkroYGJKkLgaGJKmLgSFJ6mJgSJK6GBiSpC4GhiSpi4EhSepiYEiSuhgYkqQugwZGkiOTXJHkK0nuSvILSY5Ock2Se9r7UW1sklycZDrJrUlWD1mbJGl2ht7CeB/wV1X1IuDFwF3ABuDaqloFXNumAU4HVrXXeuCSgWuTJM3CYIGR5AjgF4FLAarqyar6NrAG2NSGbQLOau01wGU1ch1wZJLjhqpPkjQ7Q25hnAjsAP5HkpuTfCDJ4cCyqtrexjwILGvt5cADY8tvbX2SpAkwZGAsAVYDl1TVKcD3eGr3EwBVVUDN5kOTrE+yJcmWHTt2HLBiJUl7N2RgbAW2VtX1bfoKRgHy0K5dTe394TZ/G3D82PIrWt/TVNXGqpqqqqmlS5cOVrwk6ekGC4yqehB4IMkLW9dpwJ3AZmBt61sLXNXam4Fz29lSpwKPje26kiQtsCUDf/5/AD6c5FDgXuA8RiF1eZJ1wP3A2W3s1cAZwDTwRBsrSZoQgwZGVd0CTM0w67QZxhZw/pD1SJLmziu9JUldDAxJUhcDQ5LUxcCQJHUxMCRJXQwMSVIXA0OS1MXAkCR1MTAkSV0MDElSFwNDktTFwJAkdTEwJEldDAxJUhcDQ5LUxcCQJHUxMCRJXQYNjCT3JbktyS1JtrS+o5Nck+Se9n5U60+Si5NMJ7k1yeoha5MkzU5XYCT5x/vxHb9UVSdX1a5HtW4Arq2qVcC1bRrgdGBVe60HLtmP75QkHWC9Wxj/PckNSf59kiP28zvXAJtaexNw1lj/ZTVyHXBkkuP287skSQdIV2BU1SuANwPHAzcm+UiS1/QsCnwmyY1J1re+ZVW1vbUfBJa19nLggbFlt7Y+SdIEWNI7sKruSfI7wBbgYuCUJAHeWVWf2sNiL6+qbUl+GrgmyVd2+8xKUrMpuAXPeoATTjhhNotKkvZD7zGMn0tyEXAX8Crg9VX1D1v7oj0tV1Xb2vvDwJXAS4GHdu1qau8Pt+HbGG3B7LKi9e3+mRuraqqqppYuXdpTviTpAOg9hvHfgJuAF1fV+VV1E0BVfQP4nZkWSHJ4kufuagO/DNwObAbWtmFrgataezNwbjtb6lTgsbFdV5KkBda7S+p1wPer6kcASZ4BPLuqnqiqD+1hmWXAlaO9ViwBPlJVf5XkS8DlSdYB9wNnt/FXA2cA08ATwHlz+QNJkobRGxifBV4NfLdNHwZ8Bvine1qgqu4FXjxD/yPAaTP0F3B+Zz2SpHnWu0vq2VW1Kyxo7cOGKUmSNIl6A+N741deJ/l54PvDlCRJmkS9u6TeCnwiyTeAAH8P+JeDVSVJmjhdgVFVX0ryIuCFrevuqvrb4cqSJE2a7gv3gJcAK9syq5NQVZcNUpUkaeJ0BUaSDwE/C9wC/Kh1F2BgSNIi0buFMQWc1E59lSQtQr1nSd3O6EC3JGmR6t3COBa4M8kNwA93dVbVmYNUJUmaOL2B8Z4hi5AkTb7e02q/mORngFVV9dkkhwGHDFuaJGmS9N7e/NeAK4A/aV3LgT8fqihJ0uTpPeh9PvAy4HEYPUwJ+OmhipIkTZ7ewPhhVT25ayLJEkbXYUiSFonewPhikncCz2nP8v4E8L+GK0uSNGl6A2MDsAO4Dfh1Rg87mvFJe5Kkn0y9Z0n9GPjT9pIkLUK9Z0l9Lcm9u786lz0kyc1J/qJNn5jk+iTTST6e5NDW/6w2Pd3mr5zrH0qSdOD17pKaYnS32pcArwAuBv5n57JvAe4am74QuKiqXgA8Cqxr/euAR1v/RW2cJGlCdAVGVT0y9tpWVX8EvG5fyyVZ0cZ9oE0HeBWjazoANgFntfaaNk2bf1obL0maAL23N189NvkMRlscPcv+EfDbwHPb9DHAt6tqZ5veyugiQNr7AwBVtTPJY238N3tqlCQNq/deUn841t4J3AecvbcFkvwK8HBV3ZjklXOqbubPXQ+sBzjhhBMO1MdKkvah9yypX5rDZ78MODPJGcCzgecB7wOOTLKkbWWsALa18duA44Gt7cLAI4BHZqhlI7ARYGpqyosHJWme9O6Setve5lfVe2foewfwjrb8K4Hfqqo3J/kE8AbgY8Ba4Kq2yOY2/X/b/M/5wCZJmhyzeeLeSxj9ow7weuAG4J45fOfbgY8l+T3gZuDS1n8p8KEk08C3gHPm8NmSpIH0BsYKYHVVfQcgyXuAT1fVv+pZuKq+AHyhte8FXjrDmB8Ab+ys56C2csOn57zsfRfs8+Q0SRpE73UYy4Anx6afbH2SpEWidwvjMuCGJFe26bN46poJSdIi0HuW1H9J8peMrvIGOK+qbh6uLEnSpOndJQVwGPB4Vb2P0amvJw5UkyRpAvXefPDdjM5uekfreib995KSJP0E6N3C+FXgTOB7AFX1DZ663YckaRHoDYwn20V0BZDk8OFKkiRNot7AuDzJnzC6rcevAZ/FhylJ0qLSe5bUH7RneT8OvBD43aq6ZtDKJEkTZZ+BkeQQ4LPtBoSGhCQtUvvcJVVVPwJ+nOSIeahHkjSheq/0/i5wW5JraGdKAVTVbw5SlSRp4vQGxqfaS5K0SO01MJKcUFVfryrvGyVJi9y+jmH8+a5Gkk8OXIskaYLtKzAy1n7+kIVIkibbvgKj9tCWJC0y+wqMFyd5PMl3gJ9r7ceTfCfJ43tbMMmzk9yQ5MtJ7kjyn1v/iUmuTzKd5ONJDm39z2rT023+ygPxB5QkHRh7DYyqOqSqnldVz62qJa29a/p5+/jsHwKvqqoXAycDr01yKnAhcFFVvQB4FFjXxq8DHm39F7VxkqQJMZvnYcxKjXy3TT6zvQp4FXBF69/E6Ol9AGt46il+VwCnJRk/hiJJWkC912HMSbutyI3AC4D3A18Fvl1VO9uQrcDy1l4OPABQVTuTPAYcA3xzyBrnauWGTy90CZI0rwbbwoDRbUWq6mRgBfBS4EX7+5lJ1ifZkmTLjh079rtGSVKfQQNjl6r6NvB54BcY3SJ915bNCmBba28Djgdo848AHpnhszZW1VRVTS1dunTw2iVJI4MFRpKlSY5s7ecArwHuYhQcb2jD1gJXtfbmNk2b/7n20CZJ0gQY8hjGccCmdhzjGcDlVfUXSe4EPpbk94CbgUvb+EuBDyWZBr4FnDNgbZKkWRosMKrqVuCUGfrvZXQ8Y/f+HwBvHKoeSdL+mZdjGJKkg5+BIUnqYmBIkroYGJKkLgaGJKmLgSFJ6mJgSJK6GBiSpC4GhiSpi4EhSepiYEiSuhgYkqQuBoYkqYuBIUnqYmBIkroYGJKkLgaGJKnLkM/0Pj7J55PcmeSOJG9p/UcnuSbJPe39qNafJBcnmU5ya5LVQ9UmSZq9IbcwdgL/qapOAk4Fzk9yErABuLaqVgHXtmmA04FV7bUeuGTA2iRJszTkM723A9tb+ztJ7gKWA2uAV7Zhm4AvAG9v/ZdVVQHXJTkyyXHtc9Ss3PDpOS973wWvO4CVSFps5uUYRpKVwCnA9cCysRB4EFjW2suBB8YW29r6JEkTYPDASPJTwCeBt1bV4+Pz2tZEzfLz1ifZkmTLjh07DmClkqS9GTQwkjyTUVh8uKo+1bofSnJcm38c8HDr3wYcP7b4itb3NFW1saqmqmpq6dKlwxUvSXqaIc+SCnApcFdVvXds1mZgbWuvBa4a6z+3nS11KvCYxy8kaXIMdtAbeBnwr4HbktzS+t4JXABcnmQdcD9wdpt3NXAGMA08AZw3YG2SpFka8iyp/wNkD7NPm2F8AecPVY8kaf8MuYWhCeMpuZL2h7cGkSR1MTAkSV0MDElSFwNDktTFwJAkdTEwJEldDAxJUhcDQ5LUxcCQJHUxMCRJXQwMSVIXA0OS1MWbD6rL/ty4ELx5ofSTwC0MSVIXA0OS1MXAkCR1GfKZ3h9M8nCS28f6jk5yTZJ72vtRrT9JLk4yneTWJKuHqkuSNDdDbmH8GfDa3fo2ANdW1Srg2jYNcDqwqr3WA5cMWJckaQ4GC4yq+mvgW7t1rwE2tfYm4Kyx/stq5DrgyCTHDVWbJGn25vsYxrKq2t7aDwLLWns58MDYuK2tT5I0IRbsoHdVFVCzXS7J+iRbkmzZsWPHAJVJkmYy34Hx0K5dTe394da/DTh+bNyK1vd3VNXGqpqqqqmlS5cOWqwk6SnzHRibgbWtvRa4aqz/3Ha21KnAY2O7riRJE2CwW4Mk+SjwSuDYJFuBdwMXAJcnWQfcD5zdhl8NnAFMA08A5w1VlyRpbgYLjKp60x5mnTbD2ALOH6oWSdL+80pvSVIXA0OS1MXAkCR18XkYmhf78zwNn6UhTQa3MCRJXQwMSVIXA0OS1MVjGJp4Hv+QJoNbGJKkLgaGJKmLgSFJ6uIxDP1E8/iHdOC4hSFJ6mJgSJK6GBiSpC4GhiSpi4EhSeoyUWdJJXkt8D7gEOADVXXBApekRcwzrKSnm5gtjCSHAO8HTgdOAt6U5KSFrUqStMskbWG8FJiuqnsBknwMWAPcuaBVSXOwP1sni5VbZZNvkgJjOfDA2PRW4J8sUC2S5tli2wW4v/+pWIg/8yQFRpck64H1bfK7Se6e40cdC3zzwFQ1rw7Guq15fhyMNcMBqDsXHqBK+i34up7Dn3m85p+Zy3dOUmBsA44fm17R+p6mqjYCG/f3y5Jsqaqp/f2c+XYw1m3N8+NgrBkOzroXa80Tc9Ab+BKwKsmJSQ4FzgE2L3BNkqRmYrYwqmpnkt8A/jej02o/WFV3LHBZkqRmYgIDoKquBq6ep6/b791aC+RgrNua58fBWDMcnHUvyppTVQeiEEnST7hJOoYhSZpgizIwkrw2yd1JppNsWOh6ZpLk+CSfT3JnkjuSvKX1vyfJtiS3tNcZC13ruCT3Jbmt1bal9R2d5Jok97T3oxa6znFJXji2Pm9J8niSt07auk7ywSQPJ7l9rG/GdZuRi9tv/NYkqyeo5v+a5CutriuTHNn6Vyb5/tj6/uOFqHkvde/x95DkHW1d353kn01QzR8fq/e+JLe0/rmt66paVC9GB9S/CjwfOBT4MnDSQtc1Q53HAatb+7nA3zC6Zcp7gN9a6Pr2Uvd9wLG79f0+sKG1NwAXLnSd+/h9PMjoPPWJWtfALwKrgdv3tW6BM4C/BAKcClw/QTX/MrCktS8cq3nl+LgJXNcz/h7a38svA88CTmz/vhwyCTXvNv8Pgd/dn3W9GLcw/v8tSKrqSWDXLUgmSlVtr6qbWvs7wF2MroY/GK0BNrX2JuCsBaxlX04DvlpV9y90Iburqr8GvrVb957W7Rrgshq5DjgyyXHzU+lTZqq5qj5TVTvb5HWMrrmaKHtY13uyBvhYVf2wqr4GTDP6d2Ze7a3mJAHOBj66P9+xGANjpluQTPQ/xElWAqcA17eu32ib8x+ctN07QAGfSXJjuyofYFlVbW/tB4FlC1Nal3N4+l+qSV7XsOd1e7D8zv8toy2hXU5McnOSLyZ5xUIVtRcz/R4OhnX9CuChqrpnrG/W63oxBsZBJclPAZ8E3lpVjwOXAD8LnAxsZ7SZOUleXlWrGd11+Pwkvzg+s0bbwxN5al67YPRM4BOta9LX9dNM8rqdSZJ3ATuBD7eu7cAJVXUK8DbgI0met1D1zeCg+j3s5k08/T9Cc1rXizEwum5BMgmSPJNRWHy4qj4FUFUPVdWPqurHwJ+yAJu+e1NV29r7w8CVjOp7aNfukPb+8MJVuFenAzdV1UMw+eu62dO6nejfeZJ/A/wK8OYWdLRdOo+09o2MjgX8gwUrcjd7+T1M+rpeAvxz4OO7+ua6rhdjYBwUtyBp+xwvBe6qqveO9Y/vh/5V4Pbdl10oSQ5P8txdbUYHN29ntH7XtmFrgasWpsJ9etr/wiZ5XY/Z07rdDJzbzpY6FXhsbNfVgsroQWm/DZxZVU+M9S/N6Lk4JHk+sAq4d2Gq/Lv28nvYDJyT5FlJTmRU9w3zXd9evBr4SlVt3dUx53U930fyJ+HF6AySv2GUqu9a6Hr2UOPLGe1euBW4pb3OAD4E3Nb6NwPHLXStYzU/n9HZIl8G7ti1boFjgGuBe4DPAkcvdK0z1H448AhwxFjfRK1rRmG2HfhbRvvJ1+1p3TI6O+r97Td+GzA1QTVPM9rnv+t3/cdt7L9ov5tbgJuA10/Yut7j7wF4V1vXdwOnT0rNrf/PgH+329g5rWuv9JYkdVmMu6QkSXNgYEiSuhgYkqQuBoYkqYuBIUnqYmBIkroYGJKkLgaGJKnL/wP3hzNlmvAQ7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonetDB\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MS SQL Server\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MariaDB\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oracle\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PostgreSQL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQLite\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Teradata\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_float(x):\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        # print(\"PROBLEM_CONVERTING\" , x)\n",
    "        return None\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "for dialect in dialects:\n",
    "    print(dialect)\n",
    "    df1 = df[df.dialect == dialect] # .sample(4000)\n",
    "    times = df1['elapsed_time'].apply(to_float)\n",
    "    # times= times[times > 100]\n",
    "    times.plot(kind='hist' , bins=20)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}