{
 "metadata": {
  "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
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python_defaultSpec_1597256294260",
   "display_name": "Python 3.8.4 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Done with the parameters reset.\nPrevious Parameters are: {'hidden_layer_sizes': [10, 50, 100], 'activation': ['identity', 'relu', 'tanh', 'logistic'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'solver': ['lbfgs', 'sgd', 'adam']}\nCurrent Parameters are updated as: {'hidden_layer_sizes': [10], 'activation': ['relu'], 'learning_rate': ['constant'], 'solver': ['sgd']}\nDone with the parameters update.\nPrevious Parameters are: {'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'C': [0.1, 1, 10]}\nCurrent Parameters are updated as: {'C': [0.1], 'kernel': ['linear']}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [50, 100, 150], 'learning_rate': [0.1, 1, 10, 100]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'learning_rate': [1]}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [5, 50, 250], 'max_depth': [2, 4, 8, 16, 32]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'max_depth': [2]}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [50, 100, 150, 200, 250, 300], 'max_depth': [1, 3, 5, 7, 9], 'learning_rate': [0.01, 0.1, 0.2, 0.3, 0.4]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'max_depth': [2], 'learning_rate': [1]}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [50, 100, 150, 200, 250, 300], 'max_depth': [3, 5, 7, 9], 'learning_rate': [0.01, 0.1, 0.2, 0.3, 0.4], 'verbosity': [0]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'max_depth': [2], 'learning_rate': [1]}\nDone with the parameters update.\n"
    }
   ],
   "source": [
    "\n",
    "from optimalflow.utilis_func import pipeline_splitting_rule, update_parameters,reset_parameters\n",
    "reset_parameters()\n",
    "\n",
    "update_parameters(mode = \"cls\", estimator_name = \"mlp\", hidden_layer_sizes = [10],activation=[\"relu\"],learning_rate = [\"constant\"],solver = [\"sgd\"])\n",
    "update_parameters(mode = \"cls\", estimator_name = \"svm\", C=[0.1],kernel=[\"linear\"])\n",
    "update_parameters(mode = \"cls\", estimator_name = \"ada\", n_estimators =[50],learning_rate=[1])\n",
    "update_parameters(mode = \"cls\", estimator_name = \"rf\", n_estimators =[50],max_depth=[2])\n",
    "update_parameters(mode = \"cls\", estimator_name = \"gb\", n_estimators =[50],max_depth=[2],learning_rate=[1])\n",
    "update_parameters(mode = \"cls\", estimator_name = \"xgb\", n_estimators =[50],max_depth=[2],learning_rate=[1])\n",
    "\n",
    "from optimalflow.autoPipe import autoPipe\n",
    "import pandas as pd\n",
    "from optimalflow.funcPP import PPtools\n",
    "from optimalflow.autoPP import dynaPreprocessing\n",
    "\n",
    "from optimalflow.autoFS import dynaFS_clf\n",
    "from optimalflow.autoCV import evaluate_model,dynaClassifier,dynaRegressor\n",
    "df = pd.read_csv('./data/preprocessing/breast_cancer.csv')\n",
    "custom_parameters = {\n",
    "    \"scaler\" : [\"None\", \"standard\"],\n",
    "    # threshold number of category dimension\n",
    "    \"encode_band\" : [4],\n",
    "    # low dimension encoding\n",
    "    \"low_encode\" : [\"onehot\",\"label\"], \n",
    "    # high dimension encoding\n",
    "    \"high_encode\" : [\"frequency\", \"mean\"],\n",
    "    \"winsorizer\" : [(0.05,0.05),(0.1,0.1)],\n",
    "    \"sparsity\" : [0.4],\n",
    "    \"cols\" : [1000]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = autoPipe(\n",
    "[(\"autoPP\",dynaPreprocessing(custom_parameters = custom_parameters, label_col = 'diagnosis', model_type = \"cls\")),\n",
    "(\"datasets_splitting\",pipeline_splitting_rule(val_size = 0.2, test_size = 0.2, random_state = 13)),\n",
    "(\"autoFS\",dynaFS_clf(fs_num = 8, random_state=13, cv = 5, in_pipeline = True, input_from_file = False)),\n",
    "(\"autoCV\",dynaClassifier(random_state = 13,cv_num = 5,in_pipeline = True, input_from_file = False)),\n",
    "(\"model_evaluate\",evaluate_model(model_type = \"cls\"))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Now in Progress - autoFS & autoCV Iteration: Estimate about 0.0 minutes left  [####################] 100.0%\nThe top 5 Models with Best Performance Metrics:\n          Dataset Model_Name  \\\n3943  Dataset_563  mlp         \n4312  Dataset_616  lgr         \n3481  Dataset_497  mlp         \n2546  Dataset_363  gb          \n1139  Dataset_162  gb          \n\n                                                                                                                      Best_Parameters  \\\n3943  [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]   \n4312  [('C', 100), ('random_state', 13)]                                                                                                \n3481  [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]   \n2546  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n1139  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n\n      Accuracy  Precision  Recall  Latency  \n3943  0.947     0.958      0.92    3.5      \n4312  0.947     0.923      0.96    3.0      \n3481  0.930     1.000      0.84    3.0      \n2546  0.930     0.957      0.88    1.0      \n1139  0.930     0.957      0.88    2.0      \n"
    }
   ],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "pd.set_option('display.max_columns',None,'display.max_rows',None)\n",
    "pd.set_option('max_colwidth', -1)\n",
    "\n",
    "DICT_PREPROCESSING,DICT_FEATURE_SELECTION,DICT_MODELS_EVALUATION,DICT_DATA,dyna_report= pipe.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  Model_Name  Accuracy  Precision  Recall  Latency  \\\n0  lgr        0.895     0.880      0.88    3.0       \n0  svm        0.912     0.885      0.92    3.0       \n0  mlp        0.439     0.439      1.00    3.0       \n0  rf         0.877     0.821      0.92    12.0      \n0  ada        0.912     0.955      0.84    17.0      \n0  gb         0.877     0.846      0.88    3.0       \n0  xgb        0.912     0.955      0.84    2.0       \n\n                                                                                                                   Best_Parameters  \\\n0  [('C', 1000), ('random_state', 13)]                                                                                               \n0  [('C', 0.1), ('kernel', 'linear')]                                                                                                \n0  [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]   \n0  [('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                                                    \n0  [('learning_rate', 1), ('n_estimators', 50), ('random_state', 13)]                                                                \n0  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n0  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n\n     Dataset  \n0  Dataset_0  \n0  Dataset_0  \n0  Dataset_0  \n0  Dataset_0  \n0  Dataset_0  \n0  Dataset_0  \n0  Dataset_0  ",
      "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_Name</th>\n      <th>Accuracy</th>\n      <th>Precision</th>\n      <th>Recall</th>\n      <th>Latency</th>\n      <th>Best_Parameters</th>\n      <th>Dataset</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>lgr</td>\n      <td>0.895</td>\n      <td>0.880</td>\n      <td>0.88</td>\n      <td>3.0</td>\n      <td>[('C', 1000), ('random_state', 13)]</td>\n      <td>Dataset_0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>svm</td>\n      <td>0.912</td>\n      <td>0.885</td>\n      <td>0.92</td>\n      <td>3.0</td>\n      <td>[('C', 0.1), ('kernel', 'linear')]</td>\n      <td>Dataset_0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>mlp</td>\n      <td>0.439</td>\n      <td>0.439</td>\n      <td>1.00</td>\n      <td>3.0</td>\n      <td>[('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]</td>\n      <td>Dataset_0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>rf</td>\n      <td>0.877</td>\n      <td>0.821</td>\n      <td>0.92</td>\n      <td>12.0</td>\n      <td>[('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>Dataset_0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>ada</td>\n      <td>0.912</td>\n      <td>0.955</td>\n      <td>0.84</td>\n      <td>17.0</td>\n      <td>[('learning_rate', 1), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>Dataset_0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>gb</td>\n      <td>0.877</td>\n      <td>0.846</td>\n      <td>0.88</td>\n      <td>3.0</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>Dataset_0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>xgb</td>\n      <td>0.912</td>\n      <td>0.955</td>\n      <td>0.84</td>\n      <td>2.0</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>Dataset_0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "\n",
    "DICT_MODELS_EVALUATION['Dataset_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          Dataset Model_Name  \\\n3943  Dataset_563  mlp         \n4312  Dataset_616  lgr         \n3481  Dataset_497  mlp         \n2546  Dataset_363  gb          \n1139  Dataset_162  gb          \n3901  Dataset_557  mlp         \n1314  Dataset_187  gb          \n1412  Dataset_201  gb          \n1678  Dataset_239  gb          \n1916  Dataset_273  gb          \n2644  Dataset_377  gb          \n2840  Dataset_405  gb          \n2851  Dataset_407  mlp         \n2994  Dataset_427  gb          \n3176  Dataset_453  gb          \n\n                                                                                                                      Best_Parameters  \\\n3943  [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]   \n4312  [('C', 100), ('random_state', 13)]                                                                                                \n3481  [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]   \n2546  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n1139  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n3901  [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]   \n1314  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n1412  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n1678  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n1916  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n2644  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n2840  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n2851  [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]   \n2994  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n3176  [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]                                              \n\n      Accuracy  Precision  Recall  Latency  \n3943  0.947     0.958      0.92    3.5      \n4312  0.947     0.923      0.96    3.0      \n3481  0.930     1.000      0.84    3.0      \n2546  0.930     0.957      0.88    1.0      \n1139  0.930     0.957      0.88    2.0      \n3901  0.930     0.957      0.88    2.0      \n1314  0.930     0.957      0.88    3.0      \n1412  0.930     0.957      0.88    3.0      \n1678  0.930     0.957      0.88    3.0      \n1916  0.930     0.957      0.88    3.0      \n2644  0.930     0.957      0.88    3.0      \n2840  0.930     0.957      0.88    3.0      \n2851  0.930     0.957      0.88    3.0      \n2994  0.930     0.957      0.88    3.0      \n3176  0.930     0.957      0.88    3.0      ",
      "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>Dataset</th>\n      <th>Model_Name</th>\n      <th>Best_Parameters</th>\n      <th>Accuracy</th>\n      <th>Precision</th>\n      <th>Recall</th>\n      <th>Latency</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3943</th>\n      <td>Dataset_563</td>\n      <td>mlp</td>\n      <td>[('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]</td>\n      <td>0.947</td>\n      <td>0.958</td>\n      <td>0.92</td>\n      <td>3.5</td>\n    </tr>\n    <tr>\n      <th>4312</th>\n      <td>Dataset_616</td>\n      <td>lgr</td>\n      <td>[('C', 100), ('random_state', 13)]</td>\n      <td>0.947</td>\n      <td>0.923</td>\n      <td>0.96</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>3481</th>\n      <td>Dataset_497</td>\n      <td>mlp</td>\n      <td>[('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]</td>\n      <td>0.930</td>\n      <td>1.000</td>\n      <td>0.84</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2546</th>\n      <td>Dataset_363</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1139</th>\n      <td>Dataset_162</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>3901</th>\n      <td>Dataset_557</td>\n      <td>mlp</td>\n      <td>[('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1314</th>\n      <td>Dataset_187</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1412</th>\n      <td>Dataset_201</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1678</th>\n      <td>Dataset_239</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1916</th>\n      <td>Dataset_273</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2644</th>\n      <td>Dataset_377</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2840</th>\n      <td>Dataset_405</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2851</th>\n      <td>Dataset_407</td>\n      <td>mlp</td>\n      <td>[('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2994</th>\n      <td>Dataset_427</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>3176</th>\n      <td>Dataset_453</td>\n      <td>gb</td>\n      <td>[('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)]</td>\n      <td>0.930</td>\n      <td>0.957</td>\n      <td>0.88</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "dyna_report.head(15)\n",
    "dyna_report.to_csv(\"dyna_report.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     concavity_mean  concave points_mean  perimeter_mean  radius_mean  \\\n264  0.09061         0.065270             111.60          17.19         \n231  0.01633         0.006588             71.76           11.32         \n197  0.11030         0.057780             117.40          18.08         \n172  0.20320         0.109700             102.50          15.46         \n54   0.05253         0.033340             97.26           15.10         \n33   0.16570         0.075930             127.90          19.27         \n68   0.25080         0.043750             60.73           9.72          \n237  0.09042         0.060220             132.50          20.48         \n51   0.01857         0.017230             87.21           13.64         \n196  0.13850         0.065260             90.63           13.77         \n\n     texture_mean  \n264  22.07         \n231  26.60         \n197  21.84         \n172  13.04         \n54   22.02         \n33   26.47         \n68   17.33         \n237  21.46         \n51   16.34         \n196  22.29         ",
      "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>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>perimeter_mean</th>\n      <th>radius_mean</th>\n      <th>texture_mean</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>264</th>\n      <td>0.09061</td>\n      <td>0.065270</td>\n      <td>111.60</td>\n      <td>17.19</td>\n      <td>22.07</td>\n    </tr>\n    <tr>\n      <th>231</th>\n      <td>0.01633</td>\n      <td>0.006588</td>\n      <td>71.76</td>\n      <td>11.32</td>\n      <td>26.60</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>0.11030</td>\n      <td>0.057780</td>\n      <td>117.40</td>\n      <td>18.08</td>\n      <td>21.84</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>0.20320</td>\n      <td>0.109700</td>\n      <td>102.50</td>\n      <td>15.46</td>\n      <td>13.04</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>0.05253</td>\n      <td>0.033340</td>\n      <td>97.26</td>\n      <td>15.10</td>\n      <td>22.02</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>0.16570</td>\n      <td>0.075930</td>\n      <td>127.90</td>\n      <td>19.27</td>\n      <td>26.47</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>0.25080</td>\n      <td>0.043750</td>\n      <td>60.73</td>\n      <td>9.72</td>\n      <td>17.33</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>0.09042</td>\n      <td>0.060220</td>\n      <td>132.50</td>\n      <td>20.48</td>\n      <td>21.46</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>0.01857</td>\n      <td>0.017230</td>\n      <td>87.21</td>\n      <td>13.64</td>\n      <td>16.34</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>0.13850</td>\n      <td>0.065260</td>\n      <td>90.63</td>\n      <td>13.77</td>\n      <td>22.29</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "DICT_DATA['Dataset_0']['DICT_TEST'][\"X\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "\"winsor_0-Scaler_None-- Encoded Features:['diagnosis', 'Size_3', 'area_mean', 'compactness_mean', 'concave points_mean', 'concavity_mean', 'fractal_dimension_mean', 'perimeter_mean', 'radius_mean', 'smoothness_mean', 'symmetry_mean', 'texture_mean', 'Frequency_Age', 'onehot_Position_1_left', 'onehot_Position_1_right', 'Frequency_Position_2', 'Frequency_Size_1', 'Frequency_Size_2', 'onehot_Treatment_no-recurrence-events', 'onehot_Treatment_recurrence-events', 'onehot_Type_1_ge40', 'onehot_Type_1_lt40', 'onehot_Type_1_premeno', 'onehot_Type_2_NaN', 'onehot_Type_2_no', 'onehot_Type_2_yes', 'onehot_Type_3_no', 'onehot_Type_3_yes']\""
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "test = DICT_PREPROCESSING['Dataset_0']\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "def save_obj(obj, name ):\n",
    "    with open(name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "def load_obj(name ):\n",
    "    with open(name + '.pkl', 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_obj(DICT_PREPROCESSING,\"dict_preprocess\")\n",
    "save_obj(DICT_DATA,\"dict_data\")\n",
    "save_obj(DICT_MODELS_EVALUATION,\"dict_models_evaluate\")\n",
    "save_obj(dyna_report,\"dyna_report\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "\"winsor_0-Scaler_None-- Encoded Features:['diagnosis', 'Size_3', 'area_mean', 'compactness_mean', 'concave points_mean', 'concavity_mean', 'fractal_dimension_mean', 'perimeter_mean', 'radius_mean', 'smoothness_mean', 'symmetry_mean', 'texture_mean', 'Frequency_Age', 'onehot_Position_1_left', 'onehot_Position_1_right', 'Frequency_Position_2', 'Frequency_Size_1', 'Frequency_Size_2', 'onehot_Treatment_no-recurrence-events', 'onehot_Treatment_recurrence-events', 'onehot_Type_1_ge40', 'onehot_Type_1_lt40', 'onehot_Type_1_premeno', 'onehot_Type_2_NaN', 'onehot_Type_2_no', 'onehot_Type_2_yes', 'onehot_Type_3_no', 'onehot_Type_3_yes']\""
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "DICT_PREP = load_obj(\"dict_preprocess\")\n",
    "dyna_report = load_obj(\"dyna_report\")\n",
    "DICT_DATA = load_obj(\"dict_data\")\n",
    "DICT_PREP['Dataset_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     concave points_mean  concavity_mean  radius_mean  area_mean  \\\n204  0.03821              0.08005         12.470       481.9       \n198  0.06772              0.11140         19.180       1148.0      \n93   0.02780              0.03974         13.450       555.1       \n78   0.12790              0.25080         20.180       1245.0      \n128  0.08534              0.11380         15.100       674.5       \n268  0.01615              0.03900         12.870       512.2       \n273  0.01407              0.01103         9.742        289.9       \n10   0.03323              0.03299         16.020       797.8       \n282  0.09464              0.16260         19.400       1145.0      \n38   0.02899              0.02398         14.990       698.8       \n\n     perimeter_mean  texture_mean  compactness_mean  smoothness_mean  \n204  81.09           18.60         0.10580           0.09965          \n198  127.50          22.49         0.14280           0.08523          \n93   86.60           18.30         0.08165           0.10220          \n78   135.10          23.97         0.22330           0.12150          \n128  99.58           16.39         0.18070           0.11500          \n268  82.38           16.21         0.06219           0.09425          \n273  61.50           15.67         0.04689           0.09037          \n10   102.70          23.24         0.06669           0.08206          \n282  127.20          18.18         0.14420           0.10370          \n38   95.54           25.20         0.05131           0.09387          ",
      "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>concave points_mean</th>\n      <th>concavity_mean</th>\n      <th>radius_mean</th>\n      <th>area_mean</th>\n      <th>perimeter_mean</th>\n      <th>texture_mean</th>\n      <th>compactness_mean</th>\n      <th>smoothness_mean</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>204</th>\n      <td>0.03821</td>\n      <td>0.08005</td>\n      <td>12.470</td>\n      <td>481.9</td>\n      <td>81.09</td>\n      <td>18.60</td>\n      <td>0.10580</td>\n      <td>0.09965</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>0.06772</td>\n      <td>0.11140</td>\n      <td>19.180</td>\n      <td>1148.0</td>\n      <td>127.50</td>\n      <td>22.49</td>\n      <td>0.14280</td>\n      <td>0.08523</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>0.02780</td>\n      <td>0.03974</td>\n      <td>13.450</td>\n      <td>555.1</td>\n      <td>86.60</td>\n      <td>18.30</td>\n      <td>0.08165</td>\n      <td>0.10220</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>0.12790</td>\n      <td>0.25080</td>\n      <td>20.180</td>\n      <td>1245.0</td>\n      <td>135.10</td>\n      <td>23.97</td>\n      <td>0.22330</td>\n      <td>0.12150</td>\n    </tr>\n    <tr>\n      <th>128</th>\n      <td>0.08534</td>\n      <td>0.11380</td>\n      <td>15.100</td>\n      <td>674.5</td>\n      <td>99.58</td>\n      <td>16.39</td>\n      <td>0.18070</td>\n      <td>0.11500</td>\n    </tr>\n    <tr>\n      <th>268</th>\n      <td>0.01615</td>\n      <td>0.03900</td>\n      <td>12.870</td>\n      <td>512.2</td>\n      <td>82.38</td>\n      <td>16.21</td>\n      <td>0.06219</td>\n      <td>0.09425</td>\n    </tr>\n    <tr>\n      <th>273</th>\n      <td>0.01407</td>\n      <td>0.01103</td>\n      <td>9.742</td>\n      <td>289.9</td>\n      <td>61.50</td>\n      <td>15.67</td>\n      <td>0.04689</td>\n      <td>0.09037</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0.03323</td>\n      <td>0.03299</td>\n      <td>16.020</td>\n      <td>797.8</td>\n      <td>102.70</td>\n      <td>23.24</td>\n      <td>0.06669</td>\n      <td>0.08206</td>\n    </tr>\n    <tr>\n      <th>282</th>\n      <td>0.09464</td>\n      <td>0.16260</td>\n      <td>19.400</td>\n      <td>1145.0</td>\n      <td>127.20</td>\n      <td>18.18</td>\n      <td>0.14420</td>\n      <td>0.10370</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>0.02899</td>\n      <td>0.02398</td>\n      <td>14.990</td>\n      <td>698.8</td>\n      <td>95.54</td>\n      <td>25.20</td>\n      <td>0.05131</td>\n      <td>0.09387</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "204    0\n198    1\n93     0\n78     1\n128    0\n268    0\n273    0\n10     1\n282    1\n38     1\nName: diagnosis, dtype: int32"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns',None,'display.max_rows',None)\n",
    "pd.set_option('max_colwidth', -1)\n",
    "DICT_DATA['Dataset_0']['DICT_Train'][\"X\"].head(10)\n",
    "DICT_DATA['Dataset_0']['DICT_Train'][\"y\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "SVC()"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "import joblib\n",
    "from sklearn.svm import SVC\n",
    "# mdl = joblib.load('./pkl/_clf_model.pkl')\n",
    "X_train = DICT_DATA['Dataset_0']['DICT_Train'][\"X\"]\n",
    "y_train = DICT_DATA['Dataset_0']['DICT_Train'][\"y\"]\n",
    "X_test = DICT_DATA['Dataset_0']['DICT_TEST'][\"X\"]\n",
    "y_test = DICT_DATA['Dataset_0']['DICT_TEST'][\"y\"]\n",
    "mdl = SVC()\n",
    "mdl.fit(X_train,y_train)\n",
    "y_true = y_test\n",
    "y_pred = mdl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "precision    recall  f1-score   support\n\n           0       0.78      0.93      0.85        27\n           1       0.92      0.77      0.84        31\n\n    accuracy                           0.84        58\n   macro avg       0.85      0.85      0.84        58\nweighted avg       0.86      0.84      0.84        58\n\n"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "SVC()"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Receiver Operating Characteristic')"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x21066c23dc0>]"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x21066c00a30>"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x21066c43af0>]"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(0.0, 1.0)"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(0.0, 1.0)"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0, 0.5, 'True Positive Rate')"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0.5, 0, 'False Positive Rate')"
     },
     "metadata": {},
     "execution_count": 36
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"277.314375pt\" version=\"1.1\" viewBox=\"0 0 393.732813 277.314375\" width=\"393.732813pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n  <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n  </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 277.314375 \r\nL 393.732813 277.314375 \r\nL 393.732813 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n  </g>\r\n  <g id=\"axes_1\">\r\n   <g id=\"patch_2\">\r\n    <path d=\"M 43.78125 239.758125 \r\nL 378.58125 239.758125 \r\nL 378.58125 22.318125 \r\nL 43.78125 22.318125 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_1\">\r\n 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "import sklearn.metrics as metrics\n",
    "# mdl = joblib.load('./pkl/_clf_model.pkl')\n",
    "X_train = DICT_DATA['Dataset_0']['DICT_Train'][\"X\"]\n",
    "y_train = DICT_DATA['Dataset_0']['DICT_Train'][\"y\"]\n",
    "X_test = DICT_DATA['Dataset_0']['DICT_TEST'][\"X\"]\n",
    "y_test = DICT_DATA['Dataset_0']['DICT_TEST'][\"y\"]\n",
    "mdl = SVC()\n",
    "mdl.fit(X_train,y_train)\n",
    "preds = mdl.predict(X_test)\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "plt.title('Receiver Operating Characteristic(ROC Plot)')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "SVC()"
     },
     "metadata": {},
     "execution_count": 38
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Confusion matrix, without normalization')"
     },
     "metadata": {},
     "execution_count": 38
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Confusion matrix, without normalization\n[[25  2]\n [ 7 24]]\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Normalized confusion matrix')"
     },
     "metadata": {},
     "execution_count": 38
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Normalized confusion matrix\n[[0.93 0.07]\n [0.23 0.77]]\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "X_train = DICT_DATA['Dataset_0']['DICT_Train'][\"X\"]\n",
    "y_train = DICT_DATA['Dataset_0']['DICT_Train'][\"y\"]\n",
    "X_test = DICT_DATA['Dataset_0']['DICT_TEST'][\"X\"]\n",
    "y_test = DICT_DATA['Dataset_0']['DICT_TEST'][\"y\"]\n",
    "mdl = SVC()\n",
    "mdl.fit(X_train,y_train)\n",
    "\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
    "                  (\"Normalized confusion matrix\", 'true')]\n",
    "for title, normalize in titles_options:\n",
    "    disp = plot_confusion_matrix(mdl, X_test, y_test,\n",
    "                                 cmap=plt.cm.Blues,\n",
    "                                 normalize=normalize)\n",
    "    disp.ax_.set_title(title)\n",
    "\n",
    "    print(title)\n",
    "    print(disp.confusion_matrix)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optimalflow.autoViz import autoViz\n",
    "viz = autoViz(preprocess_dict=DICT_PREP,report=dyna_report)\n",
    "viz.clf_model_retrieval(metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     Dataset Encode_low_dimension Encode_high_dimension    Winsorize  \\\n0  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n1  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n2  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n\n             Scale  Accuracy         Level  cnt  \n0  Scale_No Scaler  0.912     Top Accuracy  1    \n1  Scale_No Scaler  0.912     Top Accuracy  1    \n2  Scale_No Scaler  0.912     Top Accuracy  1    ",
      "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>Dataset</th>\n      <th>Encode_low_dimension</th>\n      <th>Encode_high_dimension</th>\n      <th>Winsorize</th>\n      <th>Scale</th>\n      <th>Accuracy</th>\n      <th>Level</th>\n      <th>cnt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_No Scaler</td>\n      <td>0.912</td>\n      <td>Top Accuracy</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_No Scaler</td>\n      <td>0.912</td>\n      <td>Top Accuracy</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_No Scaler</td>\n      <td>0.912</td>\n      <td>Top Accuracy</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     Dataset Encode_low_dimension Encode_high_dimension    Winsorize  \\\n0  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n1  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n2  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n\n                   Scale  Precision           Level  cnt  \n0  Scale_Scale_No Scaler  0.955      Top Precision   1    \n1  Scale_Scale_No Scaler  0.955      Top Precision   1    \n2  Scale_Scale_No Scaler  0.885      High Precision  1    ",
      "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>Dataset</th>\n      <th>Encode_low_dimension</th>\n      <th>Encode_high_dimension</th>\n      <th>Winsorize</th>\n      <th>Scale</th>\n      <th>Precision</th>\n      <th>Level</th>\n      <th>cnt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_Scale_No Scaler</td>\n      <td>0.955</td>\n      <td>Top Precision</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_Scale_No Scaler</td>\n      <td>0.955</td>\n      <td>Top Precision</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_Scale_No Scaler</td>\n      <td>0.885</td>\n      <td>High Precision</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     Dataset Encode_low_dimension Encode_high_dimension    Winsorize  \\\n0  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n1  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n2  Dataset_0  Low Dim_Onehot       High Dim_Frequency    Winsorize_0   \n\n                   Scale  Recall        Level  cnt  \n0  Scale_Scale_No Scaler  0.84    High Recall  1    \n1  Scale_Scale_No Scaler  0.84    High Recall  1    \n2  Scale_Scale_No Scaler  0.92    Top Recall   1    ",
      "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>Dataset</th>\n      <th>Encode_low_dimension</th>\n      <th>Encode_high_dimension</th>\n      <th>Winsorize</th>\n      <th>Scale</th>\n      <th>Recall</th>\n      <th>Level</th>\n      <th>cnt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_Scale_No Scaler</td>\n      <td>0.84</td>\n      <td>High Recall</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_Scale_No Scaler</td>\n      <td>0.84</td>\n      <td>High Recall</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>High Dim_Frequency</td>\n      <td>Winsorize_0</td>\n      <td>Scale_Scale_No Scaler</td>\n      <td>0.92</td>\n      <td>Top Recall</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "columns = [\"Dataset\",\"Encode_low_dimension\",\"Encode_high_dimension\",\"Winsorize\",\"Scale\"]\n",
    "df_pp = pd.DataFrame(columns=columns)\n",
    "\n",
    "for i in list(DICT_PREPROCESSING.keys()):\n",
    "    row_pp = [i]\n",
    "    s = DICT_PREPROCESSING[i]\n",
    "    ext = re.search(\"Encoded Features:(.*)']\", s).group(1)\n",
    "    \n",
    "    if (\"onehot_\" in ext) and (\"Frequency_\" in ext):\n",
    "        row_pp.append('Low Dim_Onehot')\n",
    "        row_pp.append('High Dim_Frequency')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp\n",
    "    elif (\"onehot_\" in ext) and (\"Mean_\" in ext):\n",
    "        row_pp.append('Low Dim_Onehot')\n",
    "        row_pp.append('High Dim_Mean')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp\n",
    "    elif (\"onehot_\" in ext) and (\"Mean_\" not in ext) and (\"Frequency_\" not in ext):\n",
    "        row_pp.append('Low Dim_Onehot')\n",
    "        row_pp.append('High Dim_No Encoder')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp\n",
    "    elif (\"Label_\" in ext) and (\"Frequency_\" in ext):\n",
    "        row_pp.append('Low Dim_Label')\n",
    "        row_pp.append('High Dim_Frequency')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp\n",
    "    elif (\"Label_\" in ext) and (\"Mean_\" in ext):\n",
    "        row_pp.append('Low Dim_Label')\n",
    "        row_pp.append('High Dim_Mean')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp\n",
    "    elif (\"Label_\" in ext) and (\"Mean_\" not in ext) and (\"Frequency_\" not in ext):\n",
    "        row_pp.append('Low Dim_Label')\n",
    "        row_pp.append('High Dim_No Encoder')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp\n",
    "    elif (\"Frequency_\" in ext) and (\"onehot_\" not in ext) and (\"Label_\" not in ext):\n",
    "        row_pp.append('Low Dim_No Encoder')\n",
    "        row_pp.append('High Dim_Frequency')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp    \n",
    "    elif (\"Mean_\" in ext) and (\"onehot_\" not in ext) and (\"Label_\" not in ext):\n",
    "        row_pp.append('Low Dim_No Encoder')\n",
    "        row_pp.append('High Dim_Mean')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp    \n",
    "    elif (\"Frequency_\" not in ext) and (\"Mean_\" not in ext) and (\"onehot_\" not in ext) and (\"Label_\" not in ext):\n",
    "        row_pp.append('Low Dim_No Encoder')\n",
    "        row_pp.append('High Dim_No Encoder')\n",
    "        row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
    "        row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
    "        df_pp.loc[len(df_pp)] = row_pp    \n",
    "\n",
    "\n",
    "df_report_Accuracy = df_pp.merge(dyna_report[['Dataset','Accuracy']], how = 'left', on = 'Dataset')\n",
    "bins = [0, 0.70, 0.90, 1]\n",
    "labels = [\"Low Accuracy\",\"High Accuracy\",\"Top Accuracy\"]\n",
    "df_report_Accuracy['Level'] = pd.cut(df_report_Accuracy['Accuracy'], bins=bins, labels=labels)\n",
    "df_report_Accuracy['cnt'] = 1\n",
    "df_report_Accuracy.loc[df_report_Accuracy['Scale'] == 'None','Scale'] = \"No Scaler\"\n",
    "df_report_Accuracy['Scale'] = 'Scale_'+df_report_Accuracy['Scale']\n",
    "df_report_Accuracy['Winsorize'] = 'Winsorize_' + df_report_Accuracy['Winsorize']\n",
    "df_report_Accuracy.head(3)\n",
    "\n",
    "df_report_Precision = df_pp.merge(dyna_report[['Dataset','Precision']], how = 'left', on = 'Dataset')\n",
    "bins = [0, 0.70, 0.90, 1]\n",
    "labels = [\"Low Precision\",\"High Precision\",\"Top Precision\"]\n",
    "df_report_Precision['Level'] = pd.cut(df_report_Precision['Precision'], bins=bins, labels=labels)\n",
    "df_report_Precision['cnt'] = 1\n",
    "df_report_Precision.loc[df_report_Precision['Scale'] == 'None','Scale'] = \"No Scaler\"\n",
    "df_report_Precision['Scale'] = 'Scale_'+df_report_Accuracy['Scale']\n",
    "df_report_Precision['Winsorize'] = 'Winsorize_' + df_report_Precision['Winsorize']\n",
    "df_report_Precision.head(3)\n",
    "\n",
    "df_report_Recall = df_pp.merge(dyna_report[['Dataset','Recall']], how = 'left', on = 'Dataset')\n",
    "bins = [0, 0.70, 0.90, 1]\n",
    "labels = [\"Low Recall\",\"High Recall\",\"Top Recall\"]\n",
    "df_report_Recall['Level'] = pd.cut(df_report_Recall['Recall'], bins=bins, labels=labels)\n",
    "df_report_Recall['cnt'] = 1\n",
    "df_report_Recall.loc[df_report_Recall['Scale'] == 'None','Scale'] = \"No Scaler\"\n",
    "df_report_Recall['Scale'] = 'Scale_'+df_report_Accuracy['Scale']\n",
    "df_report_Recall['Winsorize'] = 'Winsorize_' + df_report_Recall['Winsorize']\n",
    "df_report_Recall.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  antecedentIndex consequentIndex  Total\n0  Dataset_0       Low Dim_Onehot  7.0  \n1  Dataset_1       Low Dim_Onehot  7.0  \n2  Dataset_10      Low Dim_Onehot  7.0  \n3  Dataset_100     Low Dim_Onehot  7.0  \n4  Dataset_101     Low Dim_Onehot  7.0  \n5  Dataset_102     Low Dim_Onehot  7.0  \n6  Dataset_103     Low Dim_Onehot  7.0  \n7  Dataset_104     Low Dim_Onehot  7.0  \n8  Dataset_105     Low Dim_Onehot  7.0  \n9  Dataset_106     Low Dim_Onehot  7.0  ",
      "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>antecedentIndex</th>\n      <th>consequentIndex</th>\n      <th>Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dataset_0</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dataset_1</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Dataset_10</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Dataset_100</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Dataset_101</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Dataset_102</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Dataset_103</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Dataset_104</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Dataset_105</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Dataset_106</td>\n      <td>Low Dim_Onehot</td>\n      <td>7.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "step1_df = df_report_Accuracy.groupby(['Encode_low_dimension','Dataset'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Dataset\":\"antecedentIndex\",\"Encode_low_dimension\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
    "step2_df = df_report_Accuracy.groupby(['Encode_low_dimension','Encode_high_dimension'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Encode_low_dimension\":\"antecedentIndex\",\"Encode_high_dimension\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
    "step3_df = df_report_Accuracy.groupby(['Encode_high_dimension','Winsorize'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Encode_high_dimension\":\"antecedentIndex\",\"Winsorize\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
    "step4_df = df_report_Accuracy.groupby(['Winsorize','Scale'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Winsorize\":\"antecedentIndex\",\"Scale\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
    "step5_df = df_report_Accuracy.groupby(['Scale','Level'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Scale\":\"antecedentIndex\",\"Level\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']].dropna()\n",
    "integrated_df = pd.concat([step1_df,step2_df,step3_df,step4_df,step5_df],axis = 0)\n",
    "integrated_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_df = pd.DataFrame(integrated_df['antecedentIndex'].append(integrated_df['consequentIndex']).drop_duplicates(),columns = {\"label\"})\n",
    "label_df['Number'] = label_df.reset_index().index\n",
    "label_list = list(label_df.label)\n",
    "\n",
    "\n",
    "\n",
    "source_df = pd.DataFrame(integrated_df['antecedentIndex'])\n",
    "source_df = source_df.merge(label_df, left_on=['antecedentIndex'], right_on = ['label'],how = 'left')\n",
    "source_list = list(source_df['Number'])\n",
    "\n",
    "\n",
    "target_df = pd.DataFrame(integrated_df['consequentIndex'])\n",
    "target_df = target_df.merge(label_df, left_on=['consequentIndex'], right_on = ['label'],how = 'left')\n",
    "target_list = list(target_df['Number'])\n",
    "\n",
    "value_list = [int(i) for i in list(integrated_df.Total)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
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        },
        "title": {
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        }
       }
      }
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'temp-plot.html'"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 10,\n",
    "      line = dict(color = 'rgb(25,100,90)', width = 0.5),\n",
    "      label = label_list,\n",
    "      color = 'rgb(71,172,55)'\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = source_list, \n",
    "      target = target_list,\n",
    "      value = value_list\n",
    "  ))])\n",
    "\n",
    "fig.update_layout(title = 'Pipeline Cluster Traversal Experiments - autoViz Model Retrieval Diagram <a href=\"https://www.linkedin.com/in/lei-tony-dong/\"> ©Tony Dong</a>', font_size=8)\n",
    "from plotly.offline import plot\n",
    "plot(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "'pip' is not recognized as an internal or external command,\noperable program or batch file.\n"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[('learning_rate', 1),\n ('max_depth', 2),\n ('n_estimators', 50),\n ('random_state', 13)]"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "a = {'learning_rate': 1, 'max_depth': 2, 'n_estimators': 50, 'random_state': 13}\n",
    "lis = a.items()\n",
    "[i for i in lis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Dash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\n Warning: This is a development server. Do not use app.run_server\n Warning: This is a development server. Do not use app.run_server\n Warning: This is a development server. Do not use app.run_server\n in production, use a production WSGI server like gunicorn instead.\n\n in production, use a production WSGI server like gunicorn instead.\n\n in production, use a production WSGI server like gunicorn instead.\n\n * Serving Flask app \"__main__\" (lazy loading)\n * Environment: production\n   WARNING: This is a development server. Do not use it in a production deployment.\n   Use a production WSGI server instead.\n * Debug mode: on\n"
    },
    {
     "output_type": "error",
     "ename": "SystemExit",
     "evalue": "2",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns',None,'display.max_rows',None)\n",
    "pd.set_option('max_colwidth', -1)\n",
    "\n",
    "iris_X, iris_y = datasets.load_iris(return_X_y=True,as_frame=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n0  5.1                3.5               1.4                0.2             \n1  4.9                3.0               1.4                0.2             \n2  4.7                3.2               1.3                0.2             \n3  4.6                3.1               1.5                0.2             \n4  5.0                3.6               1.4                0.2             ",
      "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>sepal length (cm)</th>\n      <th>sepal width (cm)</th>\n      <th>petal length (cm)</th>\n      <th>petal width (cm)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0\n1    0\n2    0\n3    0\n4    0\nName: target, dtype: int32"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "iris_X.head()\n",
    "iris_y.head()"
   ]
  }
 ]
}