RF Ays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# !{sys.executable} -m pip install matplotlib --user\n",
    "# !{sys.executable} -m pip install hyperopt --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "from hyperopt import tpe, fmin, hp, STATUS_OK, Trials\n",
    "from sklearn.metrics import accuracy_score, log_loss, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from copy import deepcopy\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import numpy as np\n",
    "\n",
    "# Data science imports\n",
    "import pandas as pd\n",
    "from enum import Enum\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.preprocessing import StandardScaler, Imputer, LabelBinarizer\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn_pandas import DataFrameMapper\n",
    "\n",
    "# Visualisation imports\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# File IO: Read the data file\n",
    "file = open('train_data.txt', mode = 'r')\n",
    "lines = file.readlines()\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78945"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for completeness\n",
    "len(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-processing: Clean unwanted characters \n",
    "data = []\n",
    "i = 0\n",
    "while i < len(lines)-1:\n",
    "    row = {}\n",
    "    row['id'] = lines[i+1].split(\" \")[1].strip()\n",
    "    row['durationOfStay'] = lines[i+2].split(\" \")[1].strip()\n",
    "    row['gender'] = lines[i+3].split(\" \")[1].strip()\n",
    "    row['Age'] = lines[i+4].split(\" \")[1].strip()\n",
    "    row['kids'] = lines[i+5].split(\" \")[1].strip()\n",
    "    row['destinationCode'] = lines[i+6].split(\" \")[1].strip()\n",
    "    row['AcomType'] = lines[i+7].split(\" \")[1].strip()\n",
    "    data.append(row)\n",
    "    i = i + 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Structure the data\n",
    "def structure_data_to_df(data, test_data=False):\n",
    "    if test_data:\n",
    "        df = pd.DataFrame(data, columns=['id','durationOfStay','gender','Age','kids','destinationCode'])\n",
    "        df['id'] = df['id'].astype(str)\n",
    "    else:\n",
    "        df = pd.DataFrame(data, columns=['durationOfStay','gender','Age','kids','destinationCode','AcomType'])\n",
    "        df['AcomType'] = df['AcomType'].astype(str)\n",
    "        \n",
    "    df['gender'] = df['gender'].astype(str)\n",
    "    df['kids'] = df['kids'].astype(str)\n",
    "    df['destinationCode'] = df['destinationCode'].astype(str)\n",
    "    df = df.replace('<NA>', np.nan)\n",
    "    df = df.replace('NA', np.nan)\n",
    "    df = df.replace('nan', np.nan)\n",
    "    df[['durationOfStay', 'Age']] = df[['durationOfStay', 'Age']].apply(pd.to_numeric)\n",
    "    \n",
    "    return df\n",
    "    \n",
    "df = structure_data_to_df(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# utility variables\n",
    "categorical_cols = ['gender']\n",
    "missing_categorical_cols = ['kids', 'destinationCode']\n",
    "numerical_cols = ['durationOfStay']\n",
    "missing_numerical_cols = ['Age']\n",
    "\n",
    "# transform utility function\n",
    "def build_imputer_and_transformer(missing_categorical_cols, categorical_cols, \n",
    "                                  missing_numerical_cols, numerical_cols, constant=True):\n",
    "    transformations = []\n",
    "    \n",
    "    for col in missing_categorical_cols:\n",
    "        if constant:\n",
    "            transformations.append(([col], [SimpleImputer(strategy='constant', fill_value='N/A'), LabelBinarizer()]))\n",
    "        else:\n",
    "            transformations.append(([col], [SimpleImputer(strategy='most_frequent'), LabelBinarizer()]))\n",
    "        \n",
    "    for col in categorical_cols:\n",
    "        transformations.append((col, LabelBinarizer()))\n",
    "        \n",
    "    transformations.append((missing_numerical_cols, [SimpleImputer(strategy='mean'), StandardScaler()]))\n",
    "    transformations.append((numerical_cols, StandardScaler()))\n",
    "   \n",
    "    transformer = DataFrameMapper(transformations)\n",
    "    return transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['AcomType'].copy()\n",
    "X = df.drop(['AcomType'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=0)\n",
    "    \n",
    "transformer = build_imputer_and_transformer(missing_categorical_cols, categorical_cols,\n",
    "                                             missing_numerical_cols, numerical_cols, constant=True)\n",
    "    \n",
    "X_train = transformer.fit_transform(X_train)\n",
    "X_test = transformer.transform(X_test)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion_list = ['gini', 'entropy']\n",
    "n_estimators_list = np.arange(10, 1000, 10, dtype=int)\n",
    "max_features_list = np.arange(1, X.shape[1], 1, dtype=int) \n",
    "max_depth_list = np.arange(1, 50, 1, dtype=int) \n",
    "min_samples_split_list = np.arange(10, 100, 10, dtype=int)\n",
    "min_samples_leaf_list = np.arange(1, 30, 1, dtype=int)\n",
    "max_leaf_nodes_list = np.arange(10, 30, 2, dtype=int)\n",
    "class_weights = [None, 'balanced']\n",
    "bootstrap_list =[True, False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(evals, trials, optimizer=tpe.suggest):\n",
    "    space = {\n",
    "        'criterion': hp.choice('criterion', criterion_list),\n",
    "        'n_estimators': hp.choice('n_estimators', n_estimators_list),\n",
    "        'max_features': hp.choice('max_features', max_features_list),\n",
    "        'max_depth': hp.choice('max_depth', max_depth_list),\n",
    "        'min_samples_split': hp.choice('min_samples_split', min_samples_split_list),\n",
    "        'min_samples_leaf': hp.choice('min_samples_leaf', min_samples_leaf_list),\n",
    "        'max_leaf_nodes': hp.choice('max_leaf_nodes', max_leaf_nodes_list),\n",
    "        'class_weight': hp.choice('class_weight', class_weights),\n",
    "        'bootstrap': hp.choice('bootstrap', bootstrap_list)\n",
    "    }\n",
    "    best = fmin(score, space, algo=optimizer, max_evals=evals, trials=trials)\n",
    "    pbar.close()\n",
    "    return best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc_list = []\n",
    "train_loss_list = []\n",
    "test_acc_list = []\n",
    "test_loss_list = []\n",
    "\n",
    "def score(params):\n",
    "    criterion = params['criterion']\n",
    "    n_estimators = params['n_estimators']\n",
    "    max_features = params['max_features']\n",
    "    max_depth = params['max_depth']\n",
    "    min_samples_split = params['min_samples_split']\n",
    "    min_samples_leaf = params['min_samples_leaf']\n",
    "    max_leaf_nodes = params['max_leaf_nodes']\n",
    "    class_weight = params['class_weight']\n",
    "    bootstrap = params['bootstrap']\n",
    "    \n",
    "    model = RandomForestClassifier(criterion=criterion, n_estimators=n_estimators, max_features=max_features,\n",
    "                                  max_depth=max_depth, min_samples_split=min_samples_split, \n",
    "                                   min_samples_leaf=min_samples_leaf,\n",
    "                                  max_leaf_nodes=max_leaf_nodes, class_weight=class_weight, bootstrap=bootstrap,\n",
    "                                  random_state=42, n_jobs=48)\n",
    "    \n",
    "    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='balanced_accuracy', n_jobs=40)  \n",
    "    \n",
    "    scores_mean = 1.0 - scores.mean()\n",
    "\n",
    "    \n",
    "    print('Parameters with this training loss {} :'.format(scores_mean))\n",
    "    print(params)\n",
    "    pbar.update()\n",
    "#     return {'loss': test_loss, 'status': STATUS_OK}\n",
    "    return {'loss': scores_mean, 'status': STATUS_OK}\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "\n",
      "  0%|          | 0/1000 [00:00<?, ?it/s, best loss: ?]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7671526944389784 :\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 41, 'max_features': 4, 'max_leaf_nodes': 24, 'min_samples_leaf': 3, 'min_samples_split': 90, 'n_estimators': 540}\n",
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      "  0%|          | 1/1000 [00:03<1:04:35,  3.88s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7492524575542107 :\n",
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      "  0%|          | 2/1000 [00:05<55:29,  3.34s/it, best loss: 0.23284730556102162]  \u001b[A\n",
      "\u001b[AParameters with this training score 0.7324496658859462 :\n",
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      "Hyperopt:   0%|          | 3/1000 [00:08<50:30,  3.04s/it]"
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      "  0%|          | 3/1000 [00:08<49:29,  2.98s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.4999910807489044 :\n",
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      "\u001b[AParameters with this training score 0.5853298887912659 :\n",
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      "\u001b[AParameters with this training score 0.7611032345017658 :\n",
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      "\u001b[AParameters with this training score 0.7014940891992362 :\n",
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      "\u001b[AParameters with this training score 0.7470952079023873 :\n",
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      "\u001b[AParameters with this training score 0.5 :\n",
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    },
    {
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      "\u001b[AParameters with this training score 0.49992248518387694 :\n",
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      "\u001b[AParameters with this training score 0.5778917642972272 :\n",
      "\n",
      "  1%|          | 11/1000 [00:28<39:55,  2.42s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 2, 'max_features': 4, 'max_leaf_nodes': 24, 'min_samples_leaf': 22, 'min_samples_split': 40, 'n_estimators': 330}\n",
      "\n",
      "  1%|          | 11/1000 [00:28<39:55,  2.42s/it, best loss: 0.23284730556102162]\u001b[A"
     ]
    },
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     "output_type": "stream",
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      "Hyperopt:   1%|          | 12/1000 [00:28<34:07,  2.07s/it]"
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      "  1%|          | 12/1000 [00:28<34:04,  2.07s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7507651144718819 :\n",
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      "  1%|          | 12/1000 [00:29<34:04,  2.07s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 18, 'max_features': 4, 'max_leaf_nodes': 16, 'min_samples_leaf': 18, 'min_samples_split': 70, 'n_estimators': 160}\n",
      "\n",
      "  1%|          | 12/1000 [00:29<34:04,  2.07s/it, best loss: 0.23284730556102162]\u001b[A"
     ]
    },
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      "Hyperopt:   1%|▏         | 13/1000 [00:29<28:33,  1.74s/it]"
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      "\n",
      "  1%|▏         | 13/1000 [00:29<28:31,  1.73s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7637825184483844 :\n",
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      "  1%|▏         | 13/1000 [00:32<28:31,  1.73s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 10, 'min_samples_split': 50, 'n_estimators': 820}\n",
      "\n",
      "  1%|▏         | 13/1000 [00:32<28:31,  1.73s/it, best loss: 0.23284730556102162]\u001b[A"
     ]
    },
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     "output_type": "stream",
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      "Hyperopt:   1%|▏         | 14/1000 [00:32<33:09,  2.02s/it]"
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    },
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      "\n",
      "  1%|▏         | 14/1000 [00:32<33:07,  2.02s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5343472269377688 :\n",
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      "  1%|▏         | 14/1000 [00:34<33:07,  2.02s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': None, 'criterion': 'entropy', 'max_depth': 45, 'max_features': 2, 'max_leaf_nodes': 28, 'min_samples_leaf': 23, 'min_samples_split': 90, 'n_estimators': 700}\n",
      "\n",
      "  1%|▏         | 14/1000 [00:34<33:07,  2.02s/it, best loss: 0.23284730556102162]\u001b[A"
     ]
    },
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      "Hyperopt:   2%|▏         | 15/1000 [00:34<35:19,  2.15s/it]"
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      "  2%|▏         | 15/1000 [00:34<35:18,  2.15s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7599506586774604 :\n",
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      "  2%|▏         | 15/1000 [00:37<35:18,  2.15s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 23, 'max_features': 2, 'max_leaf_nodes': 22, 'min_samples_leaf': 28, 'min_samples_split': 70, 'n_estimators': 680}\n",
      "\n",
      "  2%|▏         | 15/1000 [00:37<35:18,  2.15s/it, best loss: 0.23284730556102162]\u001b[A"
     ]
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      "Hyperopt:   2%|▏         | 16/1000 [00:37<37:17,  2.27s/it]"
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      "  2%|▏         | 16/1000 [00:37<37:16,  2.27s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5019608129468435 :\n",
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      "  2%|▏         | 16/1000 [00:37<37:16,  2.27s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'entropy', 'max_depth': 29, 'max_features': 2, 'max_leaf_nodes': 18, 'min_samples_leaf': 8, 'min_samples_split': 30, 'n_estimators': 140}\n",
      "\n",
      "  2%|▏         | 16/1000 [00:37<37:16,  2.27s/it, best loss: 0.23284730556102162]\u001b[A"
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      "Hyperopt:   2%|▏         | 17/1000 [00:37<29:54,  1.83s/it]"
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      "  2%|▏         | 17/1000 [00:37<29:54,  1.83s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7267999162151133 :\n",
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      "  2%|▏         | 17/1000 [00:38<29:54,  1.83s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 20, 'max_features': 3, 'max_leaf_nodes': 14, 'min_samples_leaf': 25, 'min_samples_split': 10, 'n_estimators': 230}\n",
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      "  2%|▏         | 17/1000 [00:38<29:54,  1.83s/it, best loss: 0.23284730556102162]\u001b[A"
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      "Hyperopt:   2%|▏         | 18/1000 [00:39<26:11,  1.60s/it]"
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      "  2%|▏         | 18/1000 [00:38<26:10,  1.60s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5416825131679964 :\n",
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      "  2%|▏         | 18/1000 [00:41<26:10,  1.60s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_features': 4, 'max_leaf_nodes': 18, 'min_samples_leaf': 29, 'min_samples_split': 50, 'n_estimators': 720}\n",
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      "  2%|▏         | 18/1000 [00:41<26:10,  1.60s/it, best loss: 0.23284730556102162]\u001b[A"
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      "Hyperopt:   2%|▏         | 19/1000 [00:41<30:25,  1.86s/it]"
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      "  2%|▏         | 19/1000 [00:41<30:25,  1.86s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7486826329478024 :\n",
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      "  2%|▏         | 19/1000 [00:44<30:25,  1.86s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 46, 'max_features': 2, 'max_leaf_nodes': 20, 'min_samples_leaf': 5, 'min_samples_split': 40, 'n_estimators': 850}\n",
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      "  2%|▏         | 19/1000 [00:44<30:25,  1.86s/it, best loss: 0.23284730556102162]\u001b[A"
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      "Hyperopt:   2%|▏         | 20/1000 [00:44<35:01,  2.14s/it]"
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      "  2%|▏         | 20/1000 [00:44<35:01,  2.14s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7428529362890043 :\n",
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      "  2%|▏         | 20/1000 [00:45<35:01,  2.14s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 41, 'max_features': 1, 'max_leaf_nodes': 28, 'min_samples_leaf': 3, 'min_samples_split': 20, 'n_estimators': 180}\n",
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      "  2%|▏         | 20/1000 [00:45<35:01,  2.14s/it, best loss: 0.23284730556102162]\u001b[A"
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      "Hyperopt:   2%|▏         | 21/1000 [00:45<28:52,  1.77s/it]"
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      "  2%|▏         | 21/1000 [00:45<28:52,  1.77s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7672934181698483 :\n",
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      "  2%|▏         | 21/1000 [00:48<28:52,  1.77s/it, best loss: 0.23284730556102162]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_features': 3, 'max_leaf_nodes': 24, 'min_samples_leaf': 19, 'min_samples_split': 90, 'n_estimators': 820}\n",
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      "  2%|▏         | 21/1000 [00:48<28:52,  1.77s/it, best loss: 0.23284730556102162]\u001b[A"
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      "Hyperopt:   2%|▏         | 22/1000 [00:48<35:31,  2.18s/it]"
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      "  2%|▏         | 22/1000 [00:48<35:31,  2.18s/it, best loss: 0.2327065818301517] \u001b[A\n",
      "\u001b[AParameters with this training score 0.7570854618391767 :\n",
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      "  2%|▏         | 22/1000 [00:50<35:31,  2.18s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 30, 'max_features': 1, 'max_leaf_nodes': 24, 'min_samples_leaf': 19, 'min_samples_split': 90, 'n_estimators': 670}\n",
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      "  2%|▏         | 22/1000 [00:50<35:31,  2.18s/it, best loss: 0.2327065818301517]\u001b[A"
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      "Hyperopt:   2%|▏         | 23/1000 [00:50<35:59,  2.21s/it]"
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      "  2%|▏         | 23/1000 [00:50<35:59,  2.21s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[AParameters with this training score 0.741823343441292 :\n",
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      "  2%|▏         | 23/1000 [00:53<35:59,  2.21s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_features': 3, 'max_leaf_nodes': 24, 'min_samples_leaf': 2, 'min_samples_split': 90, 'n_estimators': 540}\n",
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      "  2%|▏         | 23/1000 [00:53<35:59,  2.21s/it, best loss: 0.2327065818301517]\u001b[A"
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      "  2%|▏         | 24/1000 [00:53<38:10,  2.35s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7606684579574107 :\n",
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      "  2%|▏         | 24/1000 [00:53<38:10,  2.35s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 37, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 13, 'min_samples_split': 90, 'n_estimators': 30}\n",
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      "  2%|▏         | 24/1000 [00:53<38:10,  2.35s/it, best loss: 0.2327065818301517]\u001b[A"
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      "Hyperopt:   2%|▎         | 25/1000 [00:53<28:49,  1.77s/it]"
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      "  2%|▎         | 25/1000 [00:53<28:49,  1.77s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7524179637686836 :\n",
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      "  2%|▎         | 25/1000 [00:56<28:49,  1.77s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 43, 'max_features': 1, 'max_leaf_nodes': 24, 'min_samples_leaf': 3, 'min_samples_split': 90, 'n_estimators': 530}\n",
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      "  2%|▎         | 25/1000 [00:56<28:49,  1.77s/it, best loss: 0.2327065818301517]\u001b[A"
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      "  3%|▎         | 26/1000 [00:56<34:40,  2.14s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7673794099907735 :\n",
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      "  3%|▎         | 26/1000 [00:59<34:40,  2.14s/it, best loss: 0.2327065818301517]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_features': 3, 'max_leaf_nodes': 24, 'min_samples_leaf': 19, 'min_samples_split': 80, 'n_estimators': 860}\n",
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      "  3%|▎         | 26/1000 [00:59<34:40,  2.14s/it, best loss: 0.2327065818301517]\u001b[A"
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      "  3%|▎         | 27/1000 [00:59<38:31,  2.38s/it, best loss: 0.23262059000922652]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7257103696542513 :\n",
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      "  3%|▎         | 27/1000 [01:02<38:31,  2.38s/it, best loss: 0.23262059000922652]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_features': 3, 'max_leaf_nodes': 10, 'min_samples_leaf': 19, 'min_samples_split': 80, 'n_estimators': 860}\n",
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      "  3%|▎         | 28/1000 [01:02<39:21,  2.43s/it, best loss: 0.23262059000922652]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7711439385257279 :\n",
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      "  3%|▎         | 28/1000 [01:03<39:21,  2.43s/it, best loss: 0.23262059000922652]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_features': 3, 'max_leaf_nodes': 24, 'min_samples_leaf': 24, 'min_samples_split': 80, 'n_estimators': 510}\n",
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      "  3%|▎         | 29/1000 [01:03<36:12,  2.24s/it, best loss: 0.22885606147427207]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7768120155823635 :\n",
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      "  3%|▎         | 29/1000 [01:05<36:12,  2.24s/it, best loss: 0.22885606147427207]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 31, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 24, 'min_samples_split': 80, 'n_estimators': 510}\n",
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      "  3%|▎         | 29/1000 [01:05<36:12,  2.24s/it, best loss: 0.22885606147427207]\u001b[A"
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      "  3%|▎         | 30/1000 [01:05<33:51,  2.09s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7763735959896528 :\n",
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      "  3%|▎         | 30/1000 [01:07<33:51,  2.09s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 31, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 24, 'min_samples_split': 80, 'n_estimators': 450}\n",
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      "  3%|▎         | 31/1000 [01:07<31:32,  1.95s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  3%|▎         | 32/1000 [01:07<24:47,  1.54s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7763735959896528 :\n",
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      "  3%|▎         | 32/1000 [01:09<24:47,  1.54s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 31, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 24, 'min_samples_split': 80, 'n_estimators': 450}\n",
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      "  3%|▎         | 33/1000 [01:09<25:11,  1.56s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 21, 'min_samples_split': 20, 'n_estimators': 790}\n",
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      "  3%|▎         | 34/1000 [01:11<29:13,  1.81s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 21, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 6, 'min_samples_split': 80, 'n_estimators': 350}\n",
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      "  4%|▎         | 35/1000 [01:13<26:56,  1.68s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 24, 'max_features': 1, 'max_leaf_nodes': 26, 'min_samples_leaf': 26, 'min_samples_split': 80, 'n_estimators': 310}\n",
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      "  4%|▎         | 37/1000 [01:15<24:29,  1.53s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  4%|▍         | 41/1000 [01:22<24:35,  1.54s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  5%|▍         | 46/1000 [01:31<25:29,  1.60s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  5%|▍         | 47/1000 [01:34<30:55,  1.95s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  5%|▍         | 48/1000 [01:36<31:23,  1.98s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  5%|▍         | 49/1000 [01:38<34:18,  2.16s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  5%|▌         | 50/1000 [01:39<26:36,  1.68s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 27, 'max_features': 3, 'max_leaf_nodes': 16, 'min_samples_leaf': 23, 'min_samples_split': 70, 'n_estimators': 870}\n",
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      "  5%|▌         | 51/1000 [01:41<31:22,  1.98s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "\u001b[AParameters with this training score 0.7634257898083877 :\n",
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      "  5%|▌         | 52/1000 [01:45<26:04,  1.65s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 38, 'max_features': 2, 'max_leaf_nodes': 26, 'min_samples_leaf': 25, 'min_samples_split': 80, 'n_estimators': 770}\n",
      "\n",
      "  5%|▌         | 52/1000 [01:45<26:04,  1.65s/it, best loss: 0.22318798441763654]\u001b[A"
     ]
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      "  5%|▌         | 53/1000 [01:45<31:03,  1.97s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5991581265518422 :\n",
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      "  5%|▌         | 53/1000 [01:47<31:03,  1.97s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 15, 'max_features': 4, 'max_leaf_nodes': 18, 'min_samples_leaf': 22, 'min_samples_split': 50, 'n_estimators': 450}\n",
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      "  5%|▌         | 53/1000 [01:47<31:03,  1.97s/it, best loss: 0.22318798441763654]\u001b[A"
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      "Hyperopt:   5%|▌         | 54/1000 [01:47<28:53,  1.83s/it]"
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      "  5%|▌         | 54/1000 [01:47<28:53,  1.83s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.6894039490880722 :\n",
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      "  5%|▌         | 54/1000 [01:47<28:53,  1.83s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 13, 'max_features': 3, 'max_leaf_nodes': 10, 'min_samples_leaf': 10, 'min_samples_split': 80, 'n_estimators': 90}\n",
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      "  5%|▌         | 54/1000 [01:47<28:53,  1.83s/it, best loss: 0.22318798441763654]\u001b[A"
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      "Hyperopt:   6%|▌         | 55/1000 [01:47<22:55,  1.46s/it]"
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      "  6%|▌         | 55/1000 [01:47<22:55,  1.46s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7587340926272871 :\n",
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      "  6%|▌         | 55/1000 [01:49<22:55,  1.46s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 40, 'max_features': 1, 'max_leaf_nodes': 26, 'min_samples_leaf': 29, 'min_samples_split': 20, 'n_estimators': 440}\n",
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      "  6%|▌         | 55/1000 [01:49<22:55,  1.46s/it, best loss: 0.22318798441763654]\u001b[A"
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      "  6%|▌         | 56/1000 [01:49<24:38,  1.57s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.72094042492778 :\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 42, 'max_features': 3, 'max_leaf_nodes': 12, 'min_samples_leaf': 5, 'min_samples_split': 30, 'n_estimators': 500}\n",
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      "  6%|▌         | 56/1000 [01:51<24:38,  1.57s/it, best loss: 0.22318798441763654]\u001b[A"
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      "  6%|▌         | 57/1000 [01:51<25:28,  1.62s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7755308886313529 :\n",
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      "  6%|▌         | 57/1000 [01:53<25:28,  1.62s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 34, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 18, 'min_samples_split': 40, 'n_estimators': 800}\n",
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      "  6%|▌         | 57/1000 [01:53<25:28,  1.62s/it, best loss: 0.22318798441763654]\u001b[A"
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      "  6%|▌         | 58/1000 [01:53<29:50,  1.90s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5 :\n",
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      "  6%|▌         | 58/1000 [01:56<29:50,  1.90s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'entropy', 'max_depth': 1, 'max_features': 2, 'max_leaf_nodes': 14, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 730}\n",
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      "  6%|▌         | 58/1000 [01:56<29:50,  1.90s/it, best loss: 0.22318798441763654]\u001b[A"
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      "  6%|▌         | 59/1000 [01:56<32:08,  2.05s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7741110148820306 :\n",
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      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 47, 'max_features': 4, 'max_leaf_nodes': 22, 'min_samples_leaf': 24, 'min_samples_split': 70, 'n_estimators': 560}\n",
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      "Hyperopt:   6%|▌         | 60/1000 [01:58<32:35,  2.08s/it]"
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      "  6%|▌         | 60/1000 [01:58<32:35,  2.08s/it, best loss: 0.22318798441763654]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7491924722080274 :\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 26, 'max_features': 1, 'max_leaf_nodes': 20, 'min_samples_leaf': 28, 'min_samples_split': 80, 'n_estimators': 960}\n",
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      "  6%|▌         | 61/1000 [02:01<36:33,  2.34s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 19, 'max_features': 3, 'max_leaf_nodes': 16, 'min_samples_leaf': 21, 'min_samples_split': 50, 'n_estimators': 80}\n",
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      "  6%|▌         | 62/1000 [02:01<28:19,  1.81s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 6, 'min_samples_split': 60, 'n_estimators': 400}\n",
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      "  6%|▋         | 63/1000 [02:03<26:24,  1.69s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  6%|▋         | 64/1000 [02:04<24:37,  1.58s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  6%|▋         | 65/1000 [02:06<27:33,  1.77s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  7%|▋         | 66/1000 [02:08<27:11,  1.75s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  7%|▋         | 67/1000 [02:08<20:59,  1.35s/it, best loss: 0.22318798441763654]\u001b[A\n",
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      "  7%|▋         | 68/1000 [02:10<20:13,  1.30s/it, best loss: 0.2218986540679584] \u001b[A\n",
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      "  7%|▋         | 69/1000 [02:13<27:58,  1.80s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "  7%|▋         | 70/1000 [02:14<24:15,  1.56s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "  7%|▋         | 71/1000 [02:15<22:33,  1.46s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 23, 'max_features': 3, 'max_leaf_nodes': 18, 'min_samples_leaf': 26, 'min_samples_split': 90, 'n_estimators': 940}\n",
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      "  7%|▋         | 72/1000 [02:18<30:21,  1.96s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 22, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 14, 'min_samples_split': 80, 'n_estimators': 690}\n",
      "\n",
      "  7%|▋         | 72/1000 [02:20<30:21,  1.96s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
    },
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     "output_type": "stream",
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      "Hyperopt:   7%|▋         | 73/1000 [02:20<31:37,  2.05s/it]"
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    },
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      "  7%|▋         | 73/1000 [02:20<31:37,  2.05s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7754193515794047 :\n",
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      "  7%|▋         | 73/1000 [02:22<31:37,  2.05s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 26, 'min_samples_split': 30, 'n_estimators': 600}\n",
      "\n",
      "  7%|▋         | 73/1000 [02:22<31:37,  2.05s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
    },
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     "output_type": "stream",
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      "Hyperopt:   7%|▋         | 74/1000 [02:22<31:50,  2.06s/it]"
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      "  7%|▋         | 74/1000 [02:22<31:50,  2.06s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7570521729887865 :\n",
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      "  7%|▋         | 74/1000 [02:24<31:50,  2.06s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 29, 'max_features': 3, 'max_leaf_nodes': 14, 'min_samples_leaf': 27, 'min_samples_split': 80, 'n_estimators': 570}\n",
      "\n",
      "  7%|▋         | 74/1000 [02:24<31:50,  2.06s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 75/1000 [02:24<30:36,  1.99s/it]"
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    },
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      "\n",
      "  8%|▊         | 75/1000 [02:24<30:36,  1.99s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7034364630243681 :\n",
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      "  8%|▊         | 75/1000 [02:27<30:36,  1.99s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 17, 'max_features': 1, 'max_leaf_nodes': 12, 'min_samples_leaf': 20, 'min_samples_split': 70, 'n_estimators': 810}\n",
      "\n",
      "  8%|▊         | 75/1000 [02:27<30:36,  1.99s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
    },
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      "Hyperopt:   8%|▊         | 76/1000 [02:27<33:15,  2.16s/it]"
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      "  8%|▊         | 76/1000 [02:27<33:15,  2.16s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7649370330263451 :\n",
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      "  8%|▊         | 76/1000 [02:30<33:15,  2.16s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 41, 'max_features': 4, 'max_leaf_nodes': 22, 'min_samples_leaf': 9, 'min_samples_split': 40, 'n_estimators': 920}\n",
      "\n",
      "  8%|▊         | 76/1000 [02:30<33:15,  2.16s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
    },
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      "Hyperopt:   8%|▊         | 77/1000 [02:30<36:50,  2.40s/it]"
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      "  8%|▊         | 77/1000 [02:30<36:50,  2.40s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7650745682066515 :\n",
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      "  8%|▊         | 77/1000 [02:32<36:50,  2.40s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 20, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 1, 'min_samples_split': 80, 'n_estimators': 750}\n",
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      "  8%|▊         | 77/1000 [02:32<36:50,  2.40s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
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      "Hyperopt:   8%|▊         | 78/1000 [02:32<36:30,  2.38s/it]"
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      "  8%|▊         | 78/1000 [02:32<36:30,  2.38s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7619181299577428 :\n",
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      "  8%|▊         | 78/1000 [02:32<36:30,  2.38s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 18, 'max_features': 3, 'max_leaf_nodes': 24, 'min_samples_leaf': 7, 'min_samples_split': 60, 'n_estimators': 110}\n",
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      "  8%|▊         | 78/1000 [02:32<36:30,  2.38s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
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      "Hyperopt:   8%|▊         | 79/1000 [02:33<28:13,  1.84s/it]"
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      "  8%|▊         | 79/1000 [02:32<28:13,  1.84s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5843287884482378 :\n",
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      "  8%|▊         | 79/1000 [02:34<28:13,  1.84s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 3, 'min_samples_split': 90, 'n_estimators': 610}\n",
      "\n",
      "  8%|▊         | 79/1000 [02:34<28:13,  1.84s/it, best loss: 0.2218986540679584]\u001b[A"
     ]
    },
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     "output_type": "stream",
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      "Hyperopt:   8%|▊         | 80/1000 [02:35<29:07,  1.90s/it]"
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      "  8%|▊         | 80/1000 [02:34<29:07,  1.90s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7394040984187169 :\n",
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      "  8%|▊         | 80/1000 [02:36<29:07,  1.90s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 46, 'max_features': 2, 'max_leaf_nodes': 20, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 480}\n",
      "\n",
      "  8%|▊         | 80/1000 [02:36<29:07,  1.90s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   8%|▊         | 81/1000 [02:36<27:37,  1.80s/it]"
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      "  8%|▊         | 81/1000 [02:36<27:37,  1.80s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.6612803585475755 :\n",
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      "  8%|▊         | 81/1000 [02:39<27:37,  1.80s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_features': 1, 'max_leaf_nodes': 16, 'min_samples_leaf': 11, 'min_samples_split': 50, 'n_estimators': 940}\n",
      "\n",
      "  8%|▊         | 81/1000 [02:39<27:37,  1.80s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   8%|▊         | 82/1000 [02:40<34:32,  2.26s/it]"
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      "  8%|▊         | 82/1000 [02:39<34:32,  2.26s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7190175733896821 :\n",
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      "  8%|▊         | 82/1000 [02:41<34:32,  2.26s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 22, 'max_features': 3, 'max_leaf_nodes': 10, 'min_samples_leaf': 16, 'min_samples_split': 20, 'n_estimators': 300}\n",
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      "  8%|▊         | 82/1000 [02:41<34:32,  2.26s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   8%|▊         | 83/1000 [02:41<29:45,  1.95s/it]"
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      "  8%|▊         | 83/1000 [02:41<29:45,  1.95s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.6663877724620677 :\n",
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      "  8%|▊         | 83/1000 [02:42<29:45,  1.95s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 43, 'max_features': 4, 'max_leaf_nodes': 26, 'min_samples_leaf': 17, 'min_samples_split': 80, 'n_estimators': 220}\n",
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      "  8%|▊         | 83/1000 [02:42<29:45,  1.95s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   8%|▊         | 84/1000 [02:42<25:25,  1.67s/it]"
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      "  8%|▊         | 84/1000 [02:42<25:25,  1.67s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.6713076206768981 :\n",
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      "  8%|▊         | 84/1000 [02:43<25:25,  1.67s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_features': 3, 'max_leaf_nodes': 18, 'min_samples_leaf': 23, 'min_samples_split': 80, 'n_estimators': 430}\n",
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      "  8%|▊         | 84/1000 [02:43<25:25,  1.67s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   8%|▊         | 85/1000 [02:43<25:17,  1.66s/it]"
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      "  8%|▊         | 85/1000 [02:43<25:17,  1.66s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7514182610407766 :\n",
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      "  8%|▊         | 85/1000 [02:45<25:17,  1.66s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 22, 'min_samples_split': 80, 'n_estimators': 380}\n",
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      "  8%|▊         | 85/1000 [02:45<25:17,  1.66s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   9%|▊         | 86/1000 [02:45<24:55,  1.64s/it]"
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      "  9%|▊         | 86/1000 [02:45<24:55,  1.64s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7715487821220471 :\n",
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      "  9%|▊         | 86/1000 [02:48<24:55,  1.64s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 45, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 8, 'min_samples_split': 40, 'n_estimators': 970}\n",
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      "  9%|▊         | 86/1000 [02:48<24:55,  1.64s/it, best loss: 0.2218986540679584]\u001b[A"
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     "output_type": "stream",
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      "Hyperopt:   9%|▊         | 87/1000 [02:48<31:29,  2.07s/it]"
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      "  9%|▊         | 87/1000 [02:48<31:29,  2.07s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7416377136784809 :\n",
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      "  9%|▊         | 87/1000 [02:51<31:29,  2.07s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 28, 'max_features': 2, 'max_leaf_nodes': 14, 'min_samples_leaf': 25, 'min_samples_split': 30, 'n_estimators': 900}\n",
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      "  9%|▊         | 87/1000 [02:51<31:29,  2.07s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   9%|▉         | 88/1000 [02:51<34:59,  2.30s/it]"
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      "  9%|▉         | 88/1000 [02:51<34:59,  2.30s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5 :\n",
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      "  9%|▉         | 88/1000 [02:52<34:59,  2.30s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 37, 'max_features': 1, 'max_leaf_nodes': 12, 'min_samples_leaf': 29, 'min_samples_split': 70, 'n_estimators': 150}\n",
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      "  9%|▉         | 88/1000 [02:52<34:59,  2.30s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   9%|▉         | 89/1000 [02:52<27:57,  1.84s/it]"
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      "  9%|▉         | 89/1000 [02:52<27:57,  1.84s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7733701876979374 :\n",
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      "  9%|▉         | 89/1000 [02:54<27:57,  1.84s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 16, 'max_features': 4, 'max_leaf_nodes': 24, 'min_samples_leaf': 26, 'min_samples_split': 10, 'n_estimators': 630}\n",
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      "  9%|▉         | 89/1000 [02:54<27:57,  1.84s/it, best loss: 0.2218986540679584]\u001b[A"
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     "output_type": "stream",
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      "Hyperopt:   9%|▉         | 90/1000 [02:54<29:44,  1.96s/it]"
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      "\n",
      "  9%|▉         | 90/1000 [02:54<29:44,  1.96s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7618498688082151 :\n",
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      "  9%|▉         | 90/1000 [02:55<29:44,  1.96s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 24, 'max_features': 3, 'max_leaf_nodes': 22, 'min_samples_leaf': 18, 'min_samples_split': 60, 'n_estimators': 290}\n",
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      "  9%|▉         | 90/1000 [02:55<29:44,  1.96s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   9%|▉         | 91/1000 [02:55<26:11,  1.73s/it]"
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      "  9%|▉         | 91/1000 [02:55<26:11,  1.73s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "  9%|▉         | 91/1000 [02:57<26:11,  1.73s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 32, 'max_features': 3, 'max_leaf_nodes': 20, 'min_samples_leaf': 10, 'min_samples_split': 80, 'n_estimators': 590}\n",
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      "  9%|▉         | 91/1000 [02:57<26:11,  1.73s/it, best loss: 0.2218986540679584]\u001b[A"
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      "  9%|▉         | 92/1000 [02:57<26:55,  1.78s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "  9%|▉         | 92/1000 [03:00<26:55,  1.78s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 21, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 5, 'min_samples_split': 50, 'n_estimators': 930}\n",
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      "Hyperopt:   9%|▉         | 93/1000 [03:00<32:01,  2.12s/it]"
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      "  9%|▉         | 93/1000 [03:00<32:01,  2.12s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7724609233727782 :\n",
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      "  9%|▉         | 93/1000 [03:02<32:01,  2.12s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 39, 'max_features': 2, 'max_leaf_nodes': 26, 'min_samples_leaf': 28, 'min_samples_split': 90, 'n_estimators': 650}\n",
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      "  9%|▉         | 93/1000 [03:02<32:01,  2.12s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:   9%|▉         | 94/1000 [03:02<32:20,  2.14s/it]"
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      "  9%|▉         | 94/1000 [03:02<32:20,  2.14s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7103636354043806 :\n",
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      "  9%|▉         | 94/1000 [03:03<32:20,  2.14s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 31, 'max_features': 1, 'max_leaf_nodes': 16, 'min_samples_leaf': 24, 'min_samples_split': 80, 'n_estimators': 340}\n",
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      "  9%|▉         | 94/1000 [03:03<32:20,  2.14s/it, best loss: 0.2218986540679584]\u001b[A"
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      "Hyperopt:  10%|▉         | 95/1000 [03:03<28:41,  1.90s/it]"
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      " 10%|▉         | 95/1000 [03:03<28:41,  1.90s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.6873707857919369 :\n",
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      " 10%|▉         | 95/1000 [03:04<28:41,  1.90s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 14, 'max_features': 3, 'max_leaf_nodes': 10, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 210}\n",
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      " 10%|▉         | 95/1000 [03:04<28:41,  1.90s/it, best loss: 0.2218986540679584]\u001b[A"
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      " 10%|▉         | 96/1000 [03:04<24:41,  1.64s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7751682585300596 :\n",
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      " 10%|▉         | 96/1000 [03:05<24:41,  1.64s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 12, 'max_features': 4, 'max_leaf_nodes': 26, 'min_samples_leaf': 13, 'min_samples_split': 80, 'n_estimators': 120}\n",
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      " 10%|▉         | 96/1000 [03:05<24:41,  1.64s/it, best loss: 0.2218986540679584]\u001b[A"
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      " 10%|▉         | 97/1000 [03:05<19:55,  1.32s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      " 10%|▉         | 97/1000 [03:07<19:55,  1.32s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 11, 'max_features': 3, 'max_leaf_nodes': 26, 'min_samples_leaf': 21, 'min_samples_split': 80, 'n_estimators': 580}\n",
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      " 10%|▉         | 98/1000 [03:07<22:34,  1.50s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 35, 'max_features': 3, 'max_leaf_nodes': 18, 'min_samples_leaf': 6, 'min_samples_split': 30, 'n_estimators': 830}\n",
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      " 10%|▉         | 99/1000 [03:09<27:21,  1.82s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      " 10%|▉         | 99/1000 [03:11<27:21,  1.82s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[A{'bootstrap': True, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 10, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 12, 'min_samples_split': 40, 'n_estimators': 490}\n",
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      " 10%|█         | 100/1000 [03:11<26:49,  1.79s/it, best loss: 0.2218986540679584]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7252861262218664 :\n",
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      " 10%|█         | 100/1000 [03:12<26:49,  1.79s/it, best loss: 0.2218986540679584]\u001b[A\n",
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      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 33, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 19, 'min_samples_split': 80, 'n_estimators': 200}\n",
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      "\n",
      "100%|█████████▉| 996/1000 [29:38<00:08,  2.18s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[AParameters with this training score 0.674842376090899 :\n",
      "\n",
      "100%|█████████▉| 996/1000 [29:39<00:08,  2.18s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 16, 'min_samples_split': 70, 'n_estimators': 490}\n",
      "\n",
      "100%|█████████▉| 996/1000 [29:39<00:08,  2.18s/it, best loss: 0.2197485425425898]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 997/1000 [29:40<00:06,  2.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|█████████▉| 997/1000 [29:39<00:06,  2.09s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7723765749251558 :\n",
      "\n",
      "100%|█████████▉| 997/1000 [29:40<00:06,  2.09s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 43, 'max_features': 2, 'max_leaf_nodes': 28, 'min_samples_leaf': 27, 'min_samples_split': 90, 'n_estimators': 220}\n",
      "\n",
      "100%|█████████▉| 997/1000 [29:40<00:06,  2.09s/it, best loss: 0.2197485425425898]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 998/1000 [29:41<00:03,  1.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|█████████▉| 998/1000 [29:40<00:03,  1.76s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[AParameters with this training score 0.7703023137005699 :\n",
      "\n",
      "100%|█████████▉| 998/1000 [29:43<00:03,  1.76s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 26, 'max_features': 3, 'max_leaf_nodes': 20, 'min_samples_leaf': 22, 'min_samples_split': 20, 'n_estimators': 590}\n",
      "\n",
      "100%|█████████▉| 998/1000 [29:43<00:03,  1.76s/it, best loss: 0.2197485425425898]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 999/1000 [29:43<00:01,  1.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|█████████▉| 999/1000 [29:43<00:01,  1.86s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[AParameters with this training score 0.5129734681576275 :\n",
      "\n",
      "100%|█████████▉| 999/1000 [29:44<00:01,  1.86s/it, best loss: 0.2197485425425898]\u001b[A\n",
      "\u001b[A{'bootstrap': False, 'class_weight': None, 'criterion': 'gini', 'max_depth': 21, 'max_features': 3, 'max_leaf_nodes': 16, 'min_samples_leaf': 27, 'min_samples_split': 50, 'n_estimators': 360}\n",
      "\n",
      "100%|█████████▉| 999/1000 [29:44<00:01,  1.86s/it, best loss: 0.2197485425425898]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|██████████| 1000/1000 [29:44<00:00,  1.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 1000/1000 [29:44<00:00,  1.73s/it, best loss: 0.2197485425425898]\u001b[A------------------------------------\n",
      "The best hyperparameters are:  \n",
      "\n",
      "{'bootstrap': 1, 'class_weight': 1, 'criterion': 0, 'max_depth': 25, 'max_features': 2, 'max_leaf_nodes': 9, 'min_samples_leaf': 26, 'min_samples_split': 8, 'n_estimators': 34}\n",
      "Time elapsed to optimize 1000 executions: 1784.6413373947144\n",
      "\n",
      " Best score:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training score 0.7802514574574102 :\n",
      "{'bootstrap': False, 'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 26, 'max_features': 3, 'max_leaf_nodes': 28, 'min_samples_leaf': 27, 'min_samples_split': 90, 'n_estimators': 350}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss': 0.2197485425425898, 'status': 'ok'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "trials = Trials()\n",
    "cores = 48\n",
    "start = time.time()\n",
    "evaluations = 1000\n",
    "pbar = tqdm(total=evaluations, desc=\"Hyperopt\")\n",
    "best_param = optimize(evals=evaluations,\n",
    "                      optimizer=tpe.suggest,\n",
    "                      trials=trials)\n",
    "print(\"------------------------------------\")\n",
    "print(\"The best hyperparameters are: \", \"\\n\")\n",
    "print(best_param)\n",
    "end = time.time()\n",
    "print('Time elapsed to optimize {0} executions: {1}'.format(evaluations, end - start))\n",
    "best_param['criterion'] = criterion_list[best_param['criterion']]\n",
    "best_param['max_leaf_nodes'] = max_leaf_nodes_list[best_param['max_leaf_nodes']]\n",
    "best_param['min_samples_leaf'] = min_samples_leaf_list[best_param['min_samples_leaf']]\n",
    "best_param['n_estimators'] = n_estimators_list[best_param['n_estimators']]\n",
    "best_param['max_features'] = max_features_list[best_param['max_features']]\n",
    "best_param['min_samples_split'] = min_samples_split_list[best_param['min_samples_split']]\n",
    "best_param['max_depth'] = max_depth_list[best_param['max_depth']]\n",
    "best_param['class_weight'] = class_weights[best_param['class_weight']]\n",
    "best_param['bootstrap'] = bootstrap_list[best_param['bootstrap']]\n",
    "print('\\n Best score:')\n",
    "score(best_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_param="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the best params and train the model\n",
    "RF_opt = RandomForestClassifier(criterion=best_param['criterion'], n_estimators=best_param['n_estimators'], max_features=best_param['max_features'],\n",
    "                                  max_depth=best_param['max_depth'], min_samples_split=best_param['min_samples_split'], min_samples_leaf=best_param['min_samples_leaf'],\n",
    "                                  max_leaf_nodes=best_param['max_leaf_nodes'], class_weight=best_param['class_weight'], bootstrap=best_param['bootstrap'],\n",
    "                                  random_state=42, n_jobs=48)\n",
    "\n",
    "\n",
    "fitted_model = RF_opt.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.47585445, 0.52414555],\n",
       "       [0.57627697, 0.42372303],\n",
       "       [0.43789423, 0.56210577],\n",
       "       ...,\n",
       "       [0.43938594, 0.56061406],\n",
       "       [0.51392128, 0.48607872],\n",
       "       [0.50080173, 0.49919827]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_score = fitted_model.predict_proba(X_test)\n",
    "y_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7720364741641338"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_test, fitted_model.predict(X_test))\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "%matplotlib inline \n",
    "x_list =range(len(train_acc_list))\n",
    "\n",
    "plt.plot(x_list, train_acc_list, label='Training accuracy')\n",
    "plt.plot(x_list, test_acc_list, label='Testing accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x_list, train_loss_list, label='Training loss')\n",
    "plt.plot(x_list, test_loss_list, label='Testing loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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