choosing-classifier-and-hyper-parameter-tuning.ipynb

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     "text": [
      "numpy version : numpy 1.19.5\n",
      "pandas version : pandas 1.2.0\n",
      "matplotlib version : matplotlib 3.3.3\n",
      "seaborn version : seaborn 0.11.1\n",
      "sklearn version : sklearn 0.24.1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, StratifiedShuffleSplit, cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, ShuffleSplit, learning_curve, cross_val_predict\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, precision_recall_curve\n",
    "from sklearn.utils import class_weight\n",
    "\n",
    "from imblearn.over_sampling import SMOTE, ADASYN, RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from collections import Counter\n",
    "#classifiers\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# Print versions of libraries\n",
    "print(f\"numpy version : numpy {np.__version__}\")\n",
    "print(f\"pandas version : pandas {pd.__version__}\")\n",
    "print(f\"matplotlib version : matplotlib {matplotlib.__version__}\")\n",
    "print(f\"seaborn version : seaborn {sns.__version__}\")\n",
    "print(f\"sklearn version : sklearn {sklearn.__version__}\")"
   ]
  },
  {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>...</th>\n",
       "      <th>V21</th>\n",
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       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
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       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../input/creditcardfraud/creditcard.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.918649e-15</td>\n",
       "      <td>5.682686e-16</td>\n",
       "      <td>-8.761736e-15</td>\n",
       "      <td>2.811118e-15</td>\n",
       "      <td>-1.552103e-15</td>\n",
       "      <td>2.040130e-15</td>\n",
       "      <td>-1.698953e-15</td>\n",
       "      <td>-1.893285e-16</td>\n",
       "      <td>-3.147640e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.473120e-16</td>\n",
       "      <td>8.042109e-16</td>\n",
       "      <td>5.282512e-16</td>\n",
       "      <td>4.456271e-15</td>\n",
       "      <td>1.426896e-15</td>\n",
       "      <td>1.701640e-15</td>\n",
       "      <td>-3.662252e-16</td>\n",
       "      <td>-1.217809e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.918649e-15  5.682686e-16 -8.761736e-15  2.811118e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552103e-15  2.040130e-15 -1.698953e-15 -1.893285e-16 -3.147640e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.473120e-16  8.042109e-16  5.282512e-16  4.456271e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.426896e-15  1.701640e-15 -3.662252e-16 -1.217809e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:01.596087Z",
     "iopub.status.busy": "2021-03-29T10:26:01.595390Z",
     "iopub.status.idle": "2021-03-29T10:26:01.620003Z",
     "shell.execute_reply": "2021-03-29T10:26:01.619409Z"
    },
    "papermill": {
     "duration": 0.06381,
     "end_time": "2021-03-29T10:26:01.620167",
     "exception": false,
     "start_time": "2021-03-29T10:26:01.556357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checks if nulls are present\n",
    "df.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:01.696080Z",
     "iopub.status.busy": "2021-03-29T10:26:01.695448Z",
     "iopub.status.idle": "2021-03-29T10:26:01.700075Z",
     "shell.execute_reply": "2021-03-29T10:26:01.699469Z"
    },
    "papermill": {
     "duration": 0.044923,
     "end_time": "2021-03-29T10:26:01.700289",
     "exception": false,
     "start_time": "2021-03-29T10:26:01.655366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:01.777170Z",
     "iopub.status.busy": "2021-03-29T10:26:01.776479Z",
     "iopub.status.idle": "2021-03-29T10:26:01.781631Z",
     "shell.execute_reply": "2021-03-29T10:26:01.780706Z"
    },
    "papermill": {
     "duration": 0.046181,
     "end_time": "2021-03-29T10:26:01.781840",
     "exception": false,
     "start_time": "2021-03-29T10:26:01.735659",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records = 284807\n",
      "Total fraudulent transactions: 492, 0.1727% of total\n"
     ]
    }
   ],
   "source": [
    "print('Total records = {}'.format(len(df)))\n",
    "print('Total fraudulent transactions: {}, {:.4f}% of total'.format(df['Class'].sum(), df['Class'].sum()/len(df)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:01.861245Z",
     "iopub.status.busy": "2021-03-29T10:26:01.860517Z",
     "iopub.status.idle": "2021-03-29T10:26:02.036173Z",
     "shell.execute_reply": "2021-03-29T10:26:02.035639Z"
    },
    "papermill": {
     "duration": 0.217373,
     "end_time": "2021-03-29T10:26:02.036350",
     "exception": false,
     "start_time": "2021-03-29T10:26:01.818977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distribution: 0=No Fraud | 1=Fraud')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Class', data=df)\n",
    "plt.title('Class Distribution: 0=No Fraud | 1=Fraud')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:02.127363Z",
     "iopub.status.busy": "2021-03-29T10:26:02.115193Z",
     "iopub.status.idle": "2021-03-29T10:26:02.956937Z",
     "shell.execute_reply": "2021-03-29T10:26:02.956266Z"
    },
    "papermill": {
     "duration": 0.883705,
     "end_time": "2021-03-29T10:26:02.957111",
     "exception": false,
     "start_time": "2021-03-29T10:26:02.073406",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['hour'] = df['Time'].apply(lambda x: np.ceil(float(x)/3600)%24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.036808,
     "end_time": "2021-03-29T10:26:03.031778",
     "exception": false,
     "start_time": "2021-03-29T10:26:02.994970",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:03.146556Z",
     "iopub.status.busy": "2021-03-29T10:26:03.128359Z",
     "iopub.status.idle": "2021-03-29T10:26:03.748359Z",
     "shell.execute_reply": "2021-03-29T10:26:03.747084Z"
    },
    "papermill": {
     "duration": 0.679731,
     "end_time": "2021-03-29T10:26:03.748525",
     "exception": false,
     "start_time": "2021-03-29T10:26:03.068794",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,4))\n",
    "\n",
    "bins=30\n",
    "ax1.hist(df.Amount[df.Class==1], bins=bins)\n",
    "ax1.set_title('Fraud')\n",
    "ax1.grid(True)\n",
    "\n",
    "ax2.hist(df.Amount[df.Class==0], bins=bins)\n",
    "ax2.set_title('Normal')\n",
    "ax2.grid(True)\n",
    "\n",
    "plt.xlabel('Amount ($)')\n",
    "plt.ylabel('Number of Transactions')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:03.830193Z",
     "iopub.status.busy": "2021-03-29T10:26:03.829543Z",
     "iopub.status.idle": "2021-03-29T10:26:03.934563Z",
     "shell.execute_reply": "2021-03-29T10:26:03.933803Z"
    },
    "papermill": {
     "duration": 0.14831,
     "end_time": "2021-03-29T10:26:03.934711",
     "exception": false,
     "start_time": "2021-03-29T10:26:03.786401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Class</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>10919</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>7687</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>4212</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>3269</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>3476</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>2185</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>2979</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>4093</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>7219</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>10266</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>15824</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>16593</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>16804</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>15400</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>15350</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>16545</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>16434</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>16435</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>16135</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>17003</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>15632</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>16739</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>17692</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>15424</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Class      0   1\n",
       "hour            \n",
       "0.0    10919  21\n",
       "1.0     7687   6\n",
       "2.0     4212  10\n",
       "3.0     3269  57\n",
       "4.0     3476  17\n",
       "5.0     2185  23\n",
       "6.0     2979  11\n",
       "7.0     4093   9\n",
       "8.0     7219  23\n",
       "9.0    10266   9\n",
       "10.0   15824  16\n",
       "11.0   16593   8\n",
       "12.0   16804  53\n",
       "13.0   15400  17\n",
       "14.0   15350  17\n",
       "15.0   16545  23\n",
       "16.0   16434  26\n",
       "17.0   16435  22\n",
       "18.0   16135  29\n",
       "19.0   17003  33\n",
       "20.0   15632  19\n",
       "21.0   16739  18\n",
       "22.0   17692  16\n",
       "23.0   15424   9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='Amount', index='hour', columns='Class', aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:04.021323Z",
     "iopub.status.busy": "2021-03-29T10:26:04.020454Z",
     "iopub.status.idle": "2021-03-29T10:26:04.023807Z",
     "shell.execute_reply": "2021-03-29T10:26:04.023157Z"
    },
    "papermill": {
     "duration": 0.050399,
     "end_time": "2021-03-29T10:26:04.023960",
     "exception": false,
     "start_time": "2021-03-29T10:26:03.973561",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plotHistogram(df, norm):\n",
    "    bins = np.arange(df['hour'].min(), df['hour'].max()+2)\n",
    "    plt.figure(figsize=(15,4))\n",
    "    sns.distplot(df[df['Class']==0.0]['hour'],\n",
    "                 norm_hist=norm,\n",
    "                 bins=bins,\n",
    "                 kde=False,\n",
    "                 color='b',\n",
    "                 hist_kws={'alpha':.5},\n",
    "                 label='legit'\n",
    "                )\n",
    "    sns.distplot(df[df['Class']==1.0]['hour'],\n",
    "                 norm_hist=norm,\n",
    "                 bins=bins,\n",
    "                 kde=False,\n",
    "                 color='r',\n",
    "                 hist_kws={'alpha':.5},\n",
    "                 label='fraud'\n",
    "                )\n",
    "    plt.xticks(range(0,24))\n",
    "    plt.legend()\n",
    "    plt.show()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:04.107427Z",
     "iopub.status.busy": "2021-03-29T10:26:04.106274Z",
     "iopub.status.idle": "2021-03-29T10:26:04.525575Z",
     "shell.execute_reply": "2021-03-29T10:26:04.524865Z"
    },
    "papermill": {
     "duration": 0.46204,
     "end_time": "2021-03-29T10:26:04.525729",
     "exception": false,
     "start_time": "2021-03-29T10:26:04.063689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized histogram of Legit/Fraud over hour of day:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Normalized histogram of Legit/Fraud over hour of day:')\n",
    "plotHistogram(df, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:04.613295Z",
     "iopub.status.busy": "2021-03-29T10:26:04.612547Z",
     "iopub.status.idle": "2021-03-29T10:26:05.033035Z",
     "shell.execute_reply": "2021-03-29T10:26:05.032349Z"
    },
    "papermill": {
     "duration": 0.467053,
     "end_time": "2021-03-29T10:26:05.033187",
     "exception": false,
     "start_time": "2021-03-29T10:26:04.566134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non Normalized histogram of Legit/Fraud over hour of day. Fraud is barely visible due to less occurences:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Non Normalized histogram of Legit/Fraud over hour of day. Fraud is barely visible due to less occurences:')\n",
    "plotHistogram(df, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:05.299599Z",
     "iopub.status.busy": "2021-03-29T10:26:05.293149Z",
     "iopub.status.idle": "2021-03-29T10:26:50.837157Z",
     "shell.execute_reply": "2021-03-29T10:26:50.837696Z"
    },
    "papermill": {
     "duration": 45.762008,
     "end_time": "2021-03-29T10:26:50.837886",
     "exception": false,
     "start_time": "2021-03-29T10:26:05.075878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x8064 with 28 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_features = df.iloc[:, 1:29].columns\n",
    "\n",
    "plt.figure(figsize=(12, 28*4))\n",
    "gs = gridspec.GridSpec(28, 1)\n",
    "for i, cn in enumerate(df[v_features]):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(df[cn][df.Class==1], bins=50, label='fraud', color='r')\n",
    "    sns.distplot(df[cn][df.Class==0], bins=50, label='normal', color='g')\n",
    "    ax.set_xlabel('')\n",
    "    ax.legend()\n",
    "    ax.set_title('Histogram of feature: ' + str(cn))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:50.956946Z",
     "iopub.status.busy": "2021-03-29T10:26:50.956150Z",
     "iopub.status.idle": "2021-03-29T10:26:50.971908Z",
     "shell.execute_reply": "2021-03-29T10:26:50.972457Z"
    },
    "papermill": {
     "duration": 0.0792,
     "end_time": "2021-03-29T10:26:50.972653",
     "exception": false,
     "start_time": "2021-03-29T10:26:50.893453",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraud Transaction distribution : \n",
      " 1.00      113\n",
      "0.00       27\n",
      "99.99      27\n",
      "0.76       17\n",
      "0.77       10\n",
      "         ... \n",
      "648.00      1\n",
      "720.38      1\n",
      "30.39       1\n",
      "22.47       1\n",
      "0.69        1\n",
      "Name: Amount, Length: 259, dtype: int64\n",
      "\n",
      "\n",
      "Maximum amount of fraud transaction -  2125.87\n",
      "Minimum amount of fraud transaction -  0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Fraud Transaction distribution : \\n\",df[(df['Class'] == 1)]['Amount'].value_counts())\n",
    "print(\"\\n\")\n",
    "print(\"Maximum amount of fraud transaction - \",df[(df['Class'] == 1)]['Amount'].max())\n",
    "print(\"Minimum amount of fraud transaction - \",df[(df['Class'] == 1)]['Amount'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.055042,
     "end_time": "2021-03-29T10:26:51.083158",
     "exception": false,
     "start_time": "2021-03-29T10:26:51.028116",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Feature Engineering\n",
    "\n",
    "scale amount and time features because they were not scaled in original dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.056305,
     "end_time": "2021-03-29T10:26:51.195176",
     "exception": false,
     "start_time": "2021-03-29T10:26:51.138871",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### 1. Amount\n",
    "\n",
    "Try 4 ways of scaling amount:\n",
    "1. Log\n",
    "2. Standardization\n",
    "3. Normalization (Min Max Scaler)\n",
    "4. Robust Scaler (takes care of outliers better than others)\n",
    "\n",
    "Choose best among the top 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:51.314422Z",
     "iopub.status.busy": "2021-03-29T10:26:51.313679Z",
     "iopub.status.idle": "2021-03-29T10:26:51.349702Z",
     "shell.execute_reply": "2021-03-29T10:26:51.349076Z"
    },
    "papermill": {
     "duration": 0.0991,
     "end_time": "2021-03-29T10:26:51.349850",
     "exception": false,
     "start_time": "2021-03-29T10:26:51.250750",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# RobustScaler is less prone to outliers\n",
    "std_scaler = StandardScaler()\n",
    "rob_scaler = RobustScaler()\n",
    "mm_scaler = MinMaxScaler()\n",
    "#added standard scaled amount for learning purpose - wanted to compare vs robust scaler\n",
    "df['amount_log'] = np.log(df['Amount'] + 0.01) #some amounts are 0\n",
    "df['amount_robust'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['amount_scaled'] = std_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['amount_minmax'] = mm_scaler.fit_transform(df['Amount'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:51.490898Z",
     "iopub.status.busy": "2021-03-29T10:26:51.486742Z",
     "iopub.status.idle": "2021-03-29T10:26:53.062000Z",
     "shell.execute_reply": "2021-03-29T10:26:53.061086Z"
    },
    "papermill": {
     "duration": 1.656296,
     "end_time": "2021-03-29T10:26:53.062345",
     "exception": false,
     "start_time": "2021-03-29T10:26:51.406049",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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H3M697n4IoWB14PI6xHxXjPUHafM+BrYxs7Zp87oSWgCrXq4O287KzL5D6C/xE8JlMe0J1+ln3Ylm+AHh8qSbzOwTM/uE8KMY2MBwPiZ8XqnYtiRcFjKfcAkQhDMLKdvXY9sN/ozyVCn8HtLlyo0FhMs2U89Z+nQWgwjv+42Ysy/H+Y2St4Tf58fx/y9Jy1kzq0/OJq3ocywelAwDhscGvY+BncwsfX+bWd7ulPrHzNoQLuv6mFrKKHf/xN1PdfcdCWc/brIGjPDt7ivc/UbClTp7EQ6Gu9ZwcHop4bP4uru3I3R3qalsn0sYN6F92mNzd0/9xtLf9xaE319NBhEOcubE39j/Es58nJBjnZp8RjgDl/kbS30n1X5jbLxfWMqGbkKZsu0XCjGPPyP0ZUxdZpjtfdUnFzJV+/4Jn3+6ewlnZXdy960Il7Fmbjs9ptq2VyN3f5dw1mmfOGsuobtXNfGz/SdwJuFsWXvgrSxxpbbxXEbut3H30wlXV6yta7xm9m1gN+D8tOOibwIn1LUCmSFz32cxllT+f0XTHxcV/b6A0CB0LuFKnaYyKsdrnEtoiP1m/H0eGucbhEYFwlUJo4DTa9l3DAR2Scu/qwmND0c3IOba6iC1lb+51Pm4vKQrwO6+lHAZ1o1mdlxsLWlpZkeb2RVZVmlLuGyqkvDlXJJ6wsItJX5uZlu5+xrCpVrr4nPHWLgNkaXNX7fR1kNMrxMKx9uBJ919SdzGHmZ2hIURkVcSWlOybiNje2sJrVa/T5s3l9Cn8FIz29zMvk4YTOKe2rZXR20JhfsioNzM/kyo1NbFIMKlUfsSLtfoCRwM9DSzfRsQy73AL8ysZ/zsLgFedvfZ7r6I8IM7MbYu/pIsO70cPiX0Oy4KRf57KIu5nnpsRo7cIAwgtW/8HMoJl4pmLYTNbHNCY89gNuRsT8IlZz9v4AHKfcAfzayjmW1L+F7ujs9NBfaOcW9O+H3XR2J5W+Q5lr7NCYSd/GBCY8iXwO/ie+1NOBC7P22V/hZuy7IZ4Yqdl919bm1llJn92MxSDTOfE3b+qRhzfs8WbpnR28xam1m5mQ0ifN6vE/pXLQAus3Drlc0tXOVAXOYLYImZdSYMvFOTW4CL4wElMZ8HxOceBI5Je99/pYZjkvg6RxIGk+kZH/sRDkIzz4LVKp6VGB1jaxvjO4cNv7E3gEPNrKuZbUXo0pBuHeGyyhvN7DjC2WQzs6MJjR/rCZcTpxREHpvZ5Wa2T8yHtoRxL2bEM0OL4vtKz6n65EKm0cDJZraXhcaPv2Q835ZwlmilhVteZV4Flm17Qy3cymlrwmBVWZnZnhauUusSp3ciNKS8FBe5HfiNmfWyoEfMkS0Jv7FFcb1fsKHSnOkxYHczOyn+7lua2TfM7Gsx/8YAF8YycC9y5/EgwmBae7Eh//ch5FJDKiCjge+Z2ZEWzuCdS8jPF+LzbxDO7pWZWT/gsHpsu077lxLZF1xH6Gc9qQ7LNtQDhMv9R2d5ri0h1iVmtg0b/8b+EP/+ktDYNcqqnx0GwMLZ9F0J44r0ZEP+3UvDyt/a6iBvEPaJ21ho4D+rHpv/FOgQy+2cSroCDODuVxN2fH8kJP5cQuvew1kWH0U4TT+f0AfopYznTwJmW7jU4DRCayiElrunCDuKF4Gb3H1ijrDuI7Ry3Zs2rxWhb85nbOi0/oeNV61xewsy5p1AaEH7GHiIMDjChDpurzZPEka8e5/wea0k+yXV1aQd5Fwbz2ykHlOAcTTsh/Y0ob/Lvwmfwa6EAT5STiXstCsJAxG9kLmNHIYTWgs/N7Pr6htbPiri38N5hB1B6vFMrtyIZz9+TOhbUkk48JhM9ttkHBe3OSo9bwl9jMsIg33U19/j671J6J/zWpyHu79PqCw8Reh3/Hw9t30hMNLCZXnNfr/iIs6xTFey4RLl7xMOVD8jDPwxMJ51SrmXcHCyGOhF9VtM5CqjvgG8bGZfEM6WDXP3WfG5C8n9Pa8gjJfwSYzrDOCH7j4zHqAfS+j7Nodw+f9P43oXEQb0WUpoKBqT4zMYHuMab2bLCd/fNwHcfXp8zXsJv7/PqbmbwUmE/o/jM35j1wFfN7OaKiG5DCE0TMwk/IbuJTS+phowHiD8/qYQKjOZJrAhj68nfBdnEir2FxMOKn9iZgdROHm8BeF4YAnhc9mZkLupLkAXA/+NOXUQ9cuFatz9CcIlx88QLuN8JmORXwN/jXnzZ7If4Kf7J+HYYyqhvMwVy3JCHr5sZl8Svo+3CBVB3P1/Ce/13rjsw4TRy98m/GZeJBxo70voQ5/t/S0nVEyOJxxnfUJosGkVFzmTcEXDJ4Szz3dl245taGC9PuO4aBZhsL2GHBe9R8ix6wl5cSxhNODVcZFhcd4SQln0cD02fwewV8yRnOsV+77A3Re7+9Pu3mRXC8ard57y7JdYX0voe/wZ4fMal3rCzHoRPvuBsby/nNC4k63haBBhvIVpGeXvcEIj5jYNCD1XHeRfhN/xbMII+w/UdaNxv3ofMDPmYI2XqlsTfi8iIkXBwuWr8wiDjDybdDwiIiIi0jAlfwZYRCQbM/uumbWPlz39gXAmL7PVWUREREQKiCrAIiLZfYswEmjqErHjarjMSEREREQKhC6BFhERERERkZKgM8AiIiIiIiJSEhpye46Ctu2223q3bt2SDkOK3JQpUz5z945JvLZyXJqDclyKmfJbip1yXIpdrhwvuQpwt27dmDx5ctJhSJEzs4+Sem3luDQH5bgUM+W3FDvluBS7XDmuS6BFRERERESkJKgCLCIiIiIiIiVBFWAREREREREpCaoAi4iIiIiISElQBThPVFZWMnToUCorK5MORaTRKb+l2CnHpdgpx6WYKb9LiyrAeWLkyJFMmzaNUaNGJR2KSKNTfkuxU45LsVOOlx4zu9PMFprZWzU8b2Z2nZnNMLM3zeyA5o6xsSi/S4sqwHmgsrKScePG4e6MGzdOrU9SVJTfUuyU41LslOMlawTQL8fzRwO7xcdg4OZmiKnRKb9LjyrAeWDkyJGsX78egHXr1qn1qQRla2U1s23MbIKZfRD/bp1kjA2l/JZipxyXYqccL03uPglYnGORAcAoD14C2pvZDs0TXeNRfpceVYDzwFNPPcXatWsBWLt2LRMmTEg4IknACDZuZT0PeNrddwOejtMFR/ktxU45LsVOOS416AzMTZueF+dtxMwGm9lkM5u8aNGiZgmurpTfpUcV4DzQp08fysvLASgvL6dv374JRyTNrYZW1gHAyPj/SOC45oypsfTp06fatPJbio3KcCl2ynGpgWWZ59kWdPfb3L3C3Ss6duzYxGHVj/K79DRZBdjMdjKzZ83sHTObbmbD4vwLzWy+mb0RH/3T1jk/dqR/z8y+mza/l5lNi89dZ2YW57cyswfi/JfNrFtTvZ+mNGjQIFq0CF9FWVkZAwcOTDgiyROd3H0BQPy7XU0L5nPL6qGHHppzWoqHmW1uZq+Y2dRY7l8U59e73C8kKsNLR327qxRDfoNyXGo0D9gpbboL8HFCsTSY8rv0NOUZ4LXAue7+NeAg4Awz2ys+d42794yPxwHic8cDexMuBb3JzMri8jcTOtenOtqnLhU9Bfjc3XsA1wCXN+H7aTIdOnSgX79+mBn9+vWjQ4cOSYckBSafW1ZvuOGGatPXX399QpFIM1gFHOHu+wE9gX5mdlB8rr7lfsFQGV5SRlDH7irFkt+gHJcaPQoMjKNBHwQsTTXcFxLld+lpsgqwuy9w99fi/8uBd6ihX0A0ALjf3Ve5+yxgBnBg7Ezfzt1fdHcHRrHhUtD0S0QfBI5MnR0uNIMGDWLfffdVq5Ok+zQ1mET8uzDheBpk9uzZOaeleMSBUL6Iky3jI+vlcFHWcr+Jw2wSKsNLQz27qxRNfoNyvBSZ2X3Ai8AeZjbPzE4xs9PM7LS4yOPATEJu/xP4dUKhbjLld2kpb44XiZcm7w+8DBwMnGlmA4HJhLPEnxMqxy+lrZbqSL8m/p85H9I637v7WjNbCnQAPmuyN9NEOnTowHXXXZd0GJJfHgUGAZfFv48kG07DdOvWrVqlt1u3bonFIk0vnuGaAvQAbnT3l83saOpX7mfb7mDClUB07dq1Cd9Bw6gML2nVuquYWaq7StHkNyjHS5G7n1DL8w6c0UzhNCnld2lp8kGwzKwN8G/gLHdfRriceVfC5XELgH+kFs2yuueYn2udzBjytn+kCGRvZSVUfPua2QdA3zhdcP74xz/mnJbi4u7r3L0noS/YgWa2D/Uv97NtN28v8xepgfJbRCQPNekZYDNrSaj83uPuYwDc/dO05/8JPBYna+pIPy/+nzk/fZ15ZlYObEWW+5W5+23AbQAVFRW5LscTSUSOVtYjmzWQJrD11lvnnJbi5O5LzGwi0M/dr0rNr2O5L1JIPjWzHeLZ3/TuKspvEZE81JSjQBtwB/COu1+dNj/9Btk/AFIjKT4KHB9Hdu5OGOzqlXhZ0XIzOyhucyAbLgVNXSIK8CPgmXg5hojkiZEjR1ab1g3mi5eZdTSz9vH/1kAf4N36lvvNGLJIY0g/FknvrqL8FhHJQ015Bvhg4CRgmpm9Eef9ATjBzHoSLgOaDfwKwN2nm9lo4G3CCNJnuPu6uN7phJEXWwNPxAeECva/zGwG4czv8U34fkSkAcaPH19t+sknn+Tss89OKBppYjsAI2M/4BbAaHd/zMz+1YByXyTvxO4qvYFtzWwe8BdC95TRsevKHODHoPwWEclXTVYBdvfnyd7/5fEc61wMXJxl/mRgnyzzVxJ3NCKSn1I3l69pWoqHu79JGPAwc/5JOdbJWu6L5KP6dldRfouI5J8mHwRLRErbF198kXNaRERERKS5qAIsIk1KZ4BFREREJF+oAiwiTWrt2rU5p0VEREREmosqwCIiIiIiIlISVAEWERERERGRkqAKsIiIiIiIiJQEVYBFRERERESkJKgCLCIiIiIiIiVBFWAREREREREpCaoAi4iIiIiISElQBVhERERERERKgirAIiIiIiIiUhJUARYREREREZGSoAqwiIiIiIiIlITypAMQkcJw/fXXM2PGjEbZ1rBhw+q9To8ePRgyZEijvL6IiIiIlCadARbJc2Z2tplNN7O3zOw+M9s86ZhERERERAqRzgCL5DEz6wwMBfZy9xVmNho4HhjR3LE09Oxr7969N5o3fPjwTYxGRERERKT+dAZYJP+VA63NrBzYAvg44Xjq5aqrrqo2/Y9//COhSERERESk1KkCLJLH3H0+cBUwB1gALHX38ZnLmdlgM5tsZpMXLVrU3GHmVFFRUW26V69eCUUiTc3MNjezV8xsarxs/6I4fxszm2BmH8S/W6etc76ZzTCz98zsu8lFLyIiIqVAFWCRPBYrCgOA7sCOwJZmdmLmcu5+m7tXuHtFx44dmzvMWnXv3h3Q2d8SsAo4wt33A3oC/czsIOA84Gl33w14Ok5jZnsRLunfG+gH3GRmZUkELiIiIqVBFWCR/NYHmOXui9x9DTAG+HbCMdVbu3bt2G+//XT2t8h58EWcbBkfTmjEGRnnjwSOi/8PAO5391XuPguYARzYfBGLiIhIqVEFWCS/zQEOMrMtzMyAI4F3Eo5JpEZmVmZmbwALgQnu/jLQyd0XAMS/28XFOwNz01afF+eJiIiINAmNAi2Sx9z9ZTN7EHgNWAu8DtyWbFRSV5ty7+T58+cD0Llzw+uDSdw72d3XAT3NrD3wkJntk2Nxy7aJrAuaDQYGA3Tt2nVTwxQREZESpTPAInnO3f/i7nu6+z7ufpK7r0o6Jml6K1asYMWKFUmH0WDuvgSYSOjb+6mZ7QAQ/y6Mi80DdkpbrQs1jHKe7/3cRUREpDDoDLCISBPZlLOvw4YNAwrrnslm1hFY4+5LzKw1oQ/75cCjwCDgsvj3kbjKo8C9ZnY1YZC33YBXmj1wERERKRmqAIuISGPZARgZR3JuAYx298fM7EVgtJmdQujX/mMAd59uZqOBtwmX+J8RL6EWERERaRKqAIuISKNw9zeB/bPMryQM4JZtnYuBi5s4NBERaQAz6wcMB8qA2939sozntwLuBroS6hVXuftdzR6oSD2oD7CIiIiIiFQTr+a5ETga2As4Id6/Pd0ZwNvx/u+9gX+Y2WbNGqhIPakCLCIiIiIimQ4EZrj7THdfDdxPuH97Ogfaxls1tgEWE7q0iOQtVYBFRERENpGZnW1m083sLTO7z8w2N7NtzGyCmX0Q/26ddJwi9VCXe7XfAHyNMIL/NGCYu6/P3JCZDTazyWY2edGiRU0Vr0idNFkF2Mx2MrNnzeyduEMYFufXuDMws/PNbIaZvWdm302b38vMpsXnroutTJhZKzN7IM5/2cy6NdX7EREREcnGzDoDQ4EKd9+H0F/yeOA84Gl33w14Ok6LFIq63Kv9u8AbhJH8ewI3mFm7jVbSrewkjzTlGeC1wLnu/jXgIOCM2G8g684gPnc8sDfhvpE3xb4HADcDgwm3yNgtPg9wCvC5u/cAriHcbkNERESkuZUDrc2sHNiCcEZsADAyPj8SOC6Z0EQapC73av8FMMaDGcAsYM9mik+kQZqsAuzuC9z9tfj/cuAdwmUTNe0MBgD3u/sqd58FzAAONLMdgHbu/qK7OzAqY53Uth4EjkydHS40lZWVDB06lMrKyqRDERGRelIZXtrcfT5wFeE2XwuApe4+Hujk7gviMguA7bKtXwiXhyrHS9KrwG5m1j0ObHU84f7t6eYQR/k3s07AHsDMZo2yESi/S0uz9AGOlybvD7xMzTuDmvoZdI7/Z86vto67rwWWAh2yvH7e71hGjhzJtGnTGDVqVNKhiIhIPakML22xO9cAoDvhUtAtzezEuq5fCJeHKsdLTzy2PhN4knAia3S8f/tpZnZaXOxvwLfNbBrhys7fu/tnyUTccMrv0tLkFWAzawP8GzjL3ZflWjTLPM8xP9c61Wfk+Y6lsrKScePG4e6MGzdOrU8iIgVEZbgAfYBZ7r7I3dcAY4BvA5/GK9mIfxcmGGODKcdLl7s/7u67u/uu8b7tuPst7n5L/P9jdz/K3fd1933c/e5kI64/5XfpadIKsJm1JFR+73H3MXF2TTuDmvoZzIv/Z86vtk7sc7MVYfj1gjJy5EjWrw8D5q1bt06tTyIiBURluBAuAz3IzLaIXbGOJJwxexQYFJcZBDySUHybRDkuxUz5XXqachRoA+4A3nH3q9Oeqmln8ChwfBzZuTthsKtX4mXSy83soLjNgRnrpLb1I+CZ2E+4oDz11FOsXRtumbZ27VomTJiQcEQiIlJXKsPF3V8mjEXyGuFWMC2A24DLgL5m9gHQN04XHOW4FDPld+lpyjPABwMnAUeY2Rvx0Z8adgbuPh0YDbwNjAPOcPd1cVunA7cTBsb6EHgizr8D6GBmM4BzKNDbC/Tp04eysjDgdVlZGX379k04IhERqSuV4QLg7n9x9z3jZaAnxUE9K939SHffLf4tuKvUQDkuxU35XXqachTo593d3P3r7t4zPh7PtTNw94tjH4M93P2JtPmT4w5lV3c/M3WW191XuvuP3b2Hux/o7gU36hzAoEGDSJ24dncGDhyYcEQiIlJXKsOl2CnHpZgpv0tPs4wCLSIiIiIiIpI0VYDzwMiRI2nRInwVLVq0UOd7EZECojJcip1yXIqZ8rv0qAKcB9T5XkSkcKkMl2KnHJdipvwuPaoA54E+ffpQXl4OQHl5uTrfSzVm1t7MHjSzd83sHTP7VtIxicgGKsOl2CnHpZgpv0uPKsB5YNCgQVWXXpSVlanzvWQaDoxz9z2B/Qj3lhSRPKEyXIqdclyKmfK79KgCnAc6dOhAv379MDP69etHhw4dkg5J8oSZtQMOJdzyC3df7e5LEg1KRKpRGS7FTjkuxUz5XXrKkw5AgkGDBjF79my1OkmmXYBFwF1mth8wBRjm7l+mL2Rmg4HBAF27dm32IEVKncpwKXbKcSlmyu/SojPAeaJDhw5cd911anWSTOXAAcDN7r4/8CVwXuZC7n6bu1e4e0XHjh2bO0YRAMxsJzN7NvZVn25mw+L8C81svpm9ER/909Y538xmmNl7Zvbd5KLfNCrDpdgpx6WYKb9Li84Ai+S3ecA8d385Tj9IlgqwSJ5YC5zr7q+ZWVtgipmlhtO8xt2vSl/YzPYCjgf2BnYEnjKz3d19XbNGLSIiIiVDZ4BF8pi7fwLMNbM94qwjgbcTDEmkRu6+wN1fi/8vJwzY1jnHKgOA+919lbvPAmYABzZ9pCIiIlKqVAEWyX9DgHvM7E2gJ3BJsuGI1M7MugH7A6mrF840szfN7E4z2zrO6wzMTVttHjVUmM1ssJlNNrPJixYtaqqwRUSK0ttvb9x2bma9mz0QkTygCrBInnP3N2L/3q+7+3Hu/nnSMYnkYmZtgH8DZ7n7MuBmYFdCA84C4B+pRbOs7tm2qX7uIiIN95Of/ITLL78cd2fFihUAOwGXJhyWSCJUARYRkUZjZi0Jld973H0MgLt/6u7r3H098E82XOY8j3AQltIF+Lg54xURKQUvv/wyc+fO5dvf/jbf+MY3ANYABycclkgiVAEWEZFGYWZGuGf1O+5+ddr8HdIW+wHwVvz/UeB4M2tlZt2B3YBXmiteEZFS0bJlS1q3bs2KFStYuXIlwKrYKClSclQBFhGRjZx00kl1mpfhYOAk4IiMWx5dYWbTYj/2w4GzAdx9OjCaMLDbOOAMjQAtItL4vvGNb9C6dWteffVVnn/+eYBtzOzBpOMSSYJugyQiIhuZPn16tel169YxZcqUnOu4+/Nk79f7eI51LgYubkCIIg1y7LHHEi5WyO7RRx9txmhEmscdd9xBRUUFANtvvz3Ah8AjScYkkhRVgEVEpMqll17KJZdcwooVK2jXrh0A7s5mm23G4MGDE45OZNP95je/AWDMmDF88sknnHjiiQDcd999dOvWLcHIRJpOqvK7cOHC1CXQmwHPJRmTSFJUARYRkSrnn39+1ePSSzVAqBSfww47DIA//elPTJo0qWr+sccey6GHHppUWCJNauzYsZxzzjl8/PHHbLfddgD7Ak8AeycbmUjzUx9gERHZyDHHHMOXX34JwN13380555zDRx99lHBUIo1n0aJFzJw5s2p61qxZ6B7TUqz++Mc/8tJLL7H77rsza9YsgPeB/yYclkgiVAEWEZGNnH766WyxxRZMnTqVK664gp133pmBAwcmHZZIo7nmmmvo3bt31ePwww/n2muvTToskSbRsmVLOnTowPr161m/fj3AcsK92UVKji6BFhGRjZSXl2NmPPLIIwwbNoxTTjmFkSNHJh2WSKPp168fH3zwAe+++y4Ae+65J61atUo4KpGm0b59e7744gsOPfRQfv7zn0O4B/uHCYclkgidARYRkY20bduWSy+9lLvvvpvvfe97rFu3jjVr1iQdlkij+eqrr7jyyiu54YYb2G+//ZgzZw6PPfZY0mGJNIlHHnmE1q1bc80119CvXz+AVcCxCYclkghVgEVEZCMPPPAArVq14o477mD77bdn/vz5/Pa3v006LJFG84tf/ILNNtuMF198EYAuXbrwxz/+MeGoRJrGlltuSVlZGV999RXHHnssQCXgCYclkghdAi3SSDLuLdnDzB4FlgKTgVvdfWViwUmDXH/99cyYMSOR10697rBhwxJ5/R49enDOOedUTXft2lV9gKWofPjhhzzwwAPcd999ALRu3Rp31QekON166638+c9/pnXr1rRo0QJgL8LxyS7JRibS/FQBFmkku+yyC4sWLeKEE05g7Nixi4BlwKfA7sA/gZMSDVDqbcaMGXww/XW6tlnX7K+92Zpwgc6qjyY3+2vP+aKMm2++mQsuuACA1atXs2bNGtq0acPSpUubPR6RprDZZpuxYsWKqobLDz/8UH2ApWhdddVVTJ8+nW233RYAM5vm7hUJhyWSCFWARRrJ66+/nn5PyaXufqKZTXL3Q81sepKxScN1bbOOPxywLOkwmtUlr7Xj9NNPZ/jw4VXzHn74YV555ZUEoxJpXBdddBH9+vVj7ty5/PznP+e///0vI0aMSDoskSax6667ssUWWyQdhkheUAVYpJEsWrSIOXPm0LVrVwDMrCuwbXx6dWKBiTSC4447jssuuyzpMEQaTd++fTnggAN46aWXcHeGDx9edXZMpNhceumlfPvb3+ab3/xm6kqHnczsOncfmms9M+sHDAfKgNvdfaMdgZn1Bq4FWgKfufthjRy+SKNSBVikkfzjH//gkEMOYddddwXYA/gP8Gsz2xJI/P4xpd6fdciQIYm8dqGaMWMGY8aMAWD9+vVMnjw5vY+7SMF67bXXqk3vsMMOAMyZM4c5c+ZwwAEHJBGWSJP61a9+xRFHHMG+++6b6gP8FTAl1zpmVgbcCPQF5gGvmtmj7v522jLtgZuAfu4+x8y2a6r3INJYVAEWaST9+/evuqdkz5495wC90ga+ujbB0IBQoXnjrXdYt8U2zf7aLVaHgWWmzPy02V+77KvFzf6axWDWrFmMHTsWCPcE7tatG4888kjCUYlsunPPPbfG58yMZ555phmjEWke5eXlXH311VXTJ598cqW719Y4fyAww91nApjZ/cAA4O20ZX4GjHH3OQDuvrBRAxdpAk1WATazO4FjgIXuvk+cdyFwKrAoLvYHd388Pnc+cAqwDhjq7k/G+b2AEUBr4HFgmLu7mbUCRgG9CEO5/9TdZzfV+xGpzZo1a7j11ltT/YB3AP7HzG5197y5eeq6LbZhxZ79kw6jWbV+9/GkQyhIffv2rdYHWKRYPPvss02y3Xgm7HZgH8LtZX4JvAc8AHQDZgM/cffPmyQAkRwOP/xwbrvtNo499tjUJdBlZraNu+dqJe4MzE2bngd8M2OZ3YGWZjYRaAsMd/dRmRsys8HAYKCqq5hIUpryPsAjgH5Z5l/j7j3jI1X53Qs4Htg7rnNTvOwC4GbCD2a3+Eht8xTgc3fvAVwDXN5Ub0SkLk4//XSmTJnCr3/9awiNPL0I+bvJzKzMzF43s8caY3sitVm6dCnHHnssHTt2ZLvttmPAgAHMnDkz6bBEGtVbb73F6NGjGTVqVNVjEwwHxrn7nsB+wDvAecDT7r4b8HScFml29957b1U/4F69esGG2yDlkq3fS+a9wsoJxzvfA74L/MnMdt9oJffb3L3C3Ss6duxY/zcg0oia7Aywu08ys251XHwAcL+7rwJmmdkM4EAzmw20c/cXAcxsFHAc8ERc58K4/oPADWZmrpv4SUJeffVVpk6dmppc7u6/MLOpudaph2GEg6l2jbQ9kZzGjRvHlVdeyUMPPQTA/fffzwknnMDLL7+ccGQijeOiiy5i4sSJvP322/Tv358nnniCQw45pEH3uzazdsChwMkA7r4aWG1mA4DecbGRwETg940Qvki9zJo1q9p0HW+DNA/YKW26C/BxlmU+c/cvgS/NbBKhAej9TYtYpOnUegbYzJ6uy7x6ONPM3jSzO81s6zgv2yUWneNjXpb51dZx97XAUqDDJsQlsknKysr48MMPq6bNbBfCJf2bxMy6EFpWb9/UbYnUx0knnUR5eTnl5eWceOKJGgRLisqDDz7I008/zfbbb89dd93F1KlTWbVqVUM3twvhyp+74tU6t8cBEDu5+wKA+DfrAEFmNtjMJpvZ5EWLFmVbRGSTvfDCC9x7772pKx06mFltrT2vAruZWXcz24xwteajGcs8AnzHzMrNbAvCJdLvNHbsIo2pxjPAZrY5sAWwbayopo582gE7NvD1bgb+Rrh84m/APwh9ZGq6xCLXpRd1uSwjLKh+B9IMrrzySg4//HB22WUXCKNAPwP8ohE2fS3wO0LfmqyU49JYlny1lpWr19ClSxcuu+wyjj/+eMyMBx54gO9973tJhyfSaFq3bk2LFi0oLy9n2bJlbLfddptymX85cAAwxN1fNrPh1ONyZ3e/DbgNoKKiQleySaM76aST+PDDD+nZsydlZWUQjvErCOPpZOXua83sTOBJwm2Q7nT36WZ2Wnz+Fnd/x8zGAW8C6wm3Snqrqd+PyKbIdQn0r4CzCJXdKWyocC4jDIleb+5eNQSsmf0TSPVnrOkSi3nx/8z56evMM7NyYCsga0d+7VikORx55JF88MEHvPfee+y3335zgAPiZf0NZmapgeSmxPvsZaUcl8byvVs/ZOnqFlh5K2699VZuvfXWqufMjD/96U81rmtmOxEOprYnHAjd5u7DzWwbahgIqKYBEEWaWkVFBUuWLOHUU0+lV69etGnThgMPPLChm5sHzHP3VB+BBwkV4E/NbAd3X2BmOwAaIVcSMXnyZN5+++2qK3luuOGGubXdAxggjtfzeMa8WzKmrwSubMRwRZpUjRVgdx8ODDezIe5+fWO8WGonECd/AKRaiB4F7jWzqwkV7t2AV9x9nZktN7ODgJeBgcD1aesMAl4EfgQ8o/6/koTUvVIztAK+Z2a4e9YF6uhg4Ptm1h/YHGhnZne7+4mbsE2RGv337D245LV2tNq5oiGjQK8FznX318ysLTDFzCYQ+kU+7e6Xmdl5hIrB7zMGQNwReMrMdnf3Te46IFKbm266CYDTTjuNfv36sWzZMr7+9a83aFvu/omZzTWzPdz9PeBIwq1i3iYcq1wW/+peYpKIffbZh08++aTqvtcipazWQbDc/Xoz+zah5b48bX7OoRLN7D7CwA/bmtk84C9AbzPrSbhUeTbhLDPxcorRhB3FWuCMtAOg09lwG6Qn4gPgDuBfccCsxYSDKJFml7pXaob2wLGEXG9wBdjdzwfOB4hngH+jyq80h3Xr1nHdddelbutF7969+dWvfkXLli1rXCc2cKb6Oy43s3cI4zXUNBBQ1gEQCQ2bIk3qoYce4ogjjmCrrbaiW7duLFmyhIcffpjjjjuuoZscAtwT+0rOJHSBaQGMNrNTgDnAjxsjdpH6+uyzz9hrr7048MADU7dB6mFmj7r795OOTaS51VoBNrN/AbsCb7BhQB8nR58BAHc/IcvsO3IsfzFwcZb5kwn31MucvxLtSCQP3HXXXRvNGzFixGx3r9b/18wG1eGm8yJ54dlnn2X58uWp23rxr3/9i9NPP53bb6/bWGzxLgD7E67eqTYQkJmlBgLqDLyUtlr6QIciTeqiiy7iBz/4QdV0+/btueiiixpcAXb3Nwh9KjMd2aANijSiCy+8sNr02LFjPyGMxSNScupyG6QKYC9dXiyyyYYRzn41iLtPJJw5E2lyCxcuZOTIDel6xBFHsN9++9VpXTNrA/wbOMvdl+UYPVqDGUpi1q9fv9G8tWvXJhCJSNM77LDDMmd94e7PJRGLSNJqvQ0SoZ/u9k0diEgJ0D1kpGCYWbXbes2cOTM1cmht67UkVH7vSev//mkcAIiMgYDqco9JIAz05u4V7l7RsWPH+r4dkY1UVFRwzjnn8OGHHzJz5kzOPvtsevXqlXRYIo3qkEMOAaBt27a0a9eu6gHsb2bLEg1OJCF1OQO8LfC2mb0CVI1oqz4DIvWmqyikYBxyyCFVt/Vydz766KOsl/uns3Cq9w7gHXe/Ou2p1KCFmQMBZR0AsZHfikhW119/PX/729/46U9/irtz1FFHceONDbrJhUjeev755wFYvnx5tflm9rq7Z7tkX6To1aUCfGFTByFSInQGWArCenc+++yzqtt6uTt77rlnauCUXA4GTgKmmdkbcd4fCBXfjQYCqmUARJEmteWWW3LZZZcBYdC3L7/8ki233DLhqESazueff87cuXNTl/pvYWYHuPtrSccl0tzqMgq0+geI1MGsWbPo3r17tXlm1t3dZ8XJ/zZ/VCL118KMmTNn0qpVq3rdFsbdn6fmhp6sAwHVNACiSFP72c9+xi233EJZWRm9evVi6dKlnHPOOfz2t79NOjSRRvenP/2JESNGsMsuu9CiRQsIXU6uAo5INjKR5ldrH+B4H95l8bHSzNapz4DIxn74wx9mm/1g6h93P7P5ohHZNDvssANnnnkm//nPf3jttdeqHiLF4u2336Zdu3Y8/PDD9O/fnzlz5vCvf/0r6bBEmsTo0aP58MMPee6553j22WcB3nd3VX6lJNXlDHDb9GkzO45wn0YRAd59912mT5/O0qVLGTOm6pa/7c3sZGDz5CKrbv78+ZR9tZTW7z6edCjNquyrSubP18iu9bVgwQKmT5/On//856p5ZsYzzzyTYFQijWfNmjWsWbOGhx9+mDPPPJOWLVuSY8RykYK2zz77sGTJErbbbrvaFxYpcnUZBboad38YXS7R6CorKxk6dCiVlZVJhyL19N577/HYY4+xZMkSxo4dy9ixYwHaAwcApyYanEgD/fCHP+TZZ5+t9sio/HZIKrZ8pDK88PzqV7+iW7dufPnllxx66KF89NFHqdFxJQvleGE7//zz2X///fnud7/L97//fYAeZvZo0nHlC+V3aan1DLCZ/b+0yRaE+wJrNNtGNnLkSKZNm8aoUaM4++yzkw5H6mHAgAEMGDCAF198kW9961sAjBgxYra7D004tGo6d+7MJ6vKWbFn/6RDaVat332czp07JR1GMdJphDQqwwvP0KFDGTp0QzHdtWvX1KWhAJjZIHdv8L3bi41yvLANGjSI3//+9+y77760aNGCsWPHfgL8I+m48oXyu7TU5QzwsWmP7wLLgQFNGVSpqaysZNy4cbg748aNU+tTgerRoweXXHIJgwcPBuhmZnea2Z1JxyXSRHStaKQyvDiYGeXl1c4LDEsqlnyjHC982267LUOHDuXwww/nsMMOA/hCA90Gyu/SU2sF2N1/kfY41d0vdveFzRFcqRg5ciTr168Hwq0YRo0alXBE0hADBgxg6dKl9OnTB2AJ8H/xIVKMdCVQpDK8aKmRJ1KOF75evXpx/vnn8+KLL6YGNNzCzA5IOq58oPwuPXUZBbqLmT1kZgvN7FMz+7eZdWmO4ErFU089lbonG2vXrmXChAkJRyQN8dVXX3H55Zfzk5/8BGCJu//b3f+ddFwiTUSVg0hleNFSI0+kHC98r7/+Oi+99BJ/+MMfOPfcc2HDbZBKnvK79NTlEui7gEeBHYHOwNg4TxpJnz59qi67Ki8vp2/fvglHJA1xzDHH8PjjpTXCshSvpUuXbjRv1qxZ6ZNfNFsweU5leNFSI0+kHC98mYMaknEbJDMblFx0yVJ+l566VIA7uvtd7r42PkYAHZs4rpIyaNCg1E3JKSsrY+DAgQlHJA0xfPhwjjnmGFq3bg2wf+oe2knHJdIQ2RpzfvSjH6VPzmm2YPKcyvDClNGgk23ef5stmDynHC8JJdvnXfldeupSAf7MzE40s7L4OBFQ7/BG1KFDB3r37g1A79696dBBdxcpRMuXL2f9+vWsWLEC4HV3b+vuuqeGFJQZi1Yx8+NKVq1axZgxY6oeI0aMYOXKlUmHl5dUhhemH/7whxvNS2/kcfczmzOefKYcLwkle8WD8rv01HobJOCXwA3ANYT+MC/EedKIVq9eDcCqVasSjkQaatKkSemTbczsUAB3n5R9DZH8M7NyFR99soLVq1en7mkNQNu2bfnnP/+ZYGT5TWV44Xj33XeZPn06S5cuZcyYMVXzly1bpkaeHJTjRa+k+7wrv0tLrRVgd58DfL8ZYilZlZWVVZWnSZMmUVlZqdanAnTllVemT+5A6C8/BTgi6woieeioPdsx+asuLN6sM3fdpeEe6kJleGF57733eOyxx1iyZIkaeepIOV4SSvYMsPK79NRaATaz7sAQoFv68u6uSnEjufXWW6uGX1+/fj233XYb559/fsJRSX2lH0iZ2QfAD4ArEgtINtn8+fP5cnkZl7xWWleyf7S8jPItVnDJJZcwe/bsqtExAe68U7e2zqQyvLAMGDCAAQMG8OKLL/Ktb30r6XAKgnK88M2aNYvu3btXm2dm3d091fG9ZPu8K79LT136AD8MzAauB/6R9pBG8vTTT1ebfuqppxKKRBrZPGCfTdmAme1kZs+a2TtmNt3MSnaQCmleL7zwQtV9rb/3ve9VPWRjKsMLU48ePbjkkksYPHgwv/zlL6sesjHleOHL1ucdeDD1Tyn3eVd+l5669AFe6e7XNXkkJczMck5LYRgyZEj6d9cV+A8wdRM3uxY4191fM7O2wBQzm+Dub2/idqUOOnfuzKq1C/jDAaU1mPclr7VjcosWXH755UmHUhBUhhemAQMG8J3vfIc+ffpQVlaWdDh5TTleuGrq8w50AL5KKKy8ovwuPXWpAA83s78A44GqnuHu/lqTRVVijjzySJ588slq01J4Kioq0ie/AH7v7pt0SZG7LwAWxP+Xm9k7hPtxqwIsTap79+48/vjj9O/fP+lQ8p7K8ML01VdfqZGnjpTjhaumPu/AFsDPEworryi/S09dKsD7AicRBvJZH+c5Gtin0QwePJgJEyawfv16WrRoweDBg5MOSRpg0KBBrF69mvfffx9Cq+orjbl9M+sG7A+8nOW5wcBggK5duzbmy0qJeuONNzjmmGNo1aoVLVu2xN0xM5YtK62z4XWhMrwwHXPMMWrkqSPleOGqqc/7iBEj5rr7CwmGljeU36WnLn2AfwDs4u6Hufvh8aHKbyPq0KEDffv2BaBv374aea5ATZw4kd12240zzjgDYGfg/dStkDaVmbUB/g2c5e4b1UDc/TZ3r3D3io4dOzbGS0qJO/3006vua71s2TKWL1+uym8NVIYXpuHDh3PMMcfQunVr2rVrR9u2bWnXrrQGvKsr5Xjhy+zzDnQzM41qiPK7FNXlDPBUoD2wsGlDKW2DBw9mwYIFanUqYOeeey7jx49njz32wMzeA34G3Af02pTtmllLQuX3HncfU9vyIo1h/vz5mfe2BuDQQxulTafoqAwvPMuXL086hIKiHC9smX3e77rrriXA/yUdV75QfpeWulSAOwHvmtmrbOgD7O4+oOnCKj0dOnTguus01lghW7NmDXvssUfVtLu/HyuvDWZhJIY7gHfc/epNDJGyrxbT+t3HN3Uz9dZiZThzuH7z5j+7UvbVYkIxJvUxZcqUqntbr1y5kldeeYVevXrxzDPP5FwvnlE4Bljo7vvEeRcCpwKL4mJ/cPfH43PnA6cA64Ch7v7kRhstACrDC0+2Bh5QI09NlOOFLUuf9yXu/u+k4sk3yu/SUpcK8F/S/jfgEOCEpglHpHBVVFRwyimncNJJJwG0NbN/AlM2cbMHE/rgTzOzN+K8qspDffTo0WMTQ2m4GTPCmZYeuyRREe2U6HsvVN///vcZPnx41fTcuXP53e9+V5dVRwA3AKMy5l/j7lelzzCzvYDjgb2BHYGnzGx3d1+3CaGL1EmqgQfq18gjUojU511kg1orwO7+nJn1JFzO+RNgFnBLE8clUnBuvvlmbrzxxlQL4naEkZpv2pRtuvvzhIanTTZkyJDG2EyDDBsWbl+cXqGSwtKlSxfeeuutWpdz90lxwLa6GADc7+6rgFlmNgM4EHixwYGK1FHGiLj1aeQRKTjDhw/nkksuqRrYENjfzJa5uzq+S8mpcRAsM9vdzP4cb7tyAzAXsDgI1vXNFmGJqKysZOjQoVRWViYdijRQq1atOOecc1L32fvQ3a+JB/YiBWfixIkMHTqUoUOHcuaZZ/Kd73yH/fbbb1M2eaaZvWlmd5rZ1nFeZ8K+JWVenLcRMxtsZpPNbPKiRYuyLZIoleGFr66NPKVKOV7Yli9fXm1gQ+B1VX43UH6XllyjQL8LHAkc6+6HxEqvLktrIiNHjmTatGmMGpV51aAUiscee4z999+fbbbZBkLL6nIz07C5UpA6depEr1696NWrF9/61re4/PLLufvuuxu6uZuBXYGehPta/yPOz3Z1g2fbQL6PdK4yvPAMGTKksRt5ippyvLBNmjSp2gNoU5c7VZhZPzN7z8xmmNl5OZb7hpmtM7MfNWbczUX5XVpyXQL9Q0LfrGfNbBxwP410KaZUV1lZybhx43B3nnjiCQYOHKgh2AvQWWedxZgxY9h3331p0aLF6+5ekXRMIg31ta99jRNOOCF1X+tqA7zVl7t/mvo/9o1/LE7OA3ZKW7QL8HGDXyghKsMLU0XFhiK6vLycE044gYMPPniTtmlmZcBkYL67H2Nm2wAPAN2A2cBP3P3zTXqRBCjHC19mn3egB3AhUOOtTWM+3wj0JZTXr5rZo+7+dpblLgcKchBD5XfpqfEMsLs/5O4/BfYEJgJnA53M7GYzO6q2DcfL3Baa2Vtp87Yxswlm9kH8u3Xac+fH1qX3zOy7afN7mdm0+Nx1cVRczKyVmT0Q579cjz5neWfkyJGsWbMGCCMJq/WpMO20007ss88+xBQVKWjz5s2ruq/1r3/9a3bfffcaR82tjZntkDb5AyC1X3gUOD6W592B3YBXNinwBKgML0yDBg3ihBNOoFevXuy3334ceOCBjbHZYcA7adPnAU+7+27A03G64CjHC9/YsWOrHhMmTACYDnxay2oHAjPcfaa7ryacDMt2F5ghhNs1FuQtU5XfpSfXJdAAuPuX7n6Pux9DaJ1/g7oV4COAfhnzsu4IMkYC7QfcFFuTIFw6N5hwYLRb2jZPAT539x7ANYSWp4I0YcIE3MNVf+7O+PHjE45IGuKKK66gf//+XHrppRAai84xs3OSjkukIf7zn/8wfvx4nnvuOSZNmsSTTz7J2WefXet6ZnYfYRCrPcxsnpmdAlwRGzLfBA4nNKji7tOB0YQB48YBZxTiCNAqwwvTxIkTG62RB8DMugDfA25Pmz0AGBn/Hwkc1+AXSJByvCitAfapZZlax2kws86Ehs2cA+Tm8zgOyu/SU5fbIFVx98XArfFR27LZRgIdAPSO/48knFn+PTWMBGpms4F27v4igJmNIuw8nojrXBi39SBwg5mZpzK4gHTq1InZs2dXm5bCc8EFF9CmTZvUpUUtgLYJhyTSYOvXr6922fPuu+9e1UKei7tnu03eHTmWvxi4uCEx5guV4YXp3HPPZfz48VV5/v7773PCCScwZUqD7153LfA7qpf9ndx9AYC7LzCz7bKtaGaDCY39dO3ataGv32SU44VvyJAhVVeorV+/HmAP4JFaVqvLOA3XAr9393W5roBz99uA2wAqKiry6lhd+V166lUBbgQ17Qg6Ay+lLZdqYVoT/8+cn1pnbtzWWjNbCnQAPst80XzfsXz66ac5p6UwLF68uKrV8K9//esCd78o4ZBEGqxTp07p97XmnnvuoVevXglHlZ9UhhemNWvWNKiRJxszOwZY6O5TzKx3fdfP58oBKMeLQWaf9xtvvHGeu59Yy2p1GaehArg/Vn63Bfqb2Vp3f3iTg24myu/SU+sl0M2kphamXC1PRTN6aN++fata5cyMo46qtYu15KE+ffroshkpGr1792bvvffmuuuuY/jw4ey1117ccotuAZ+NyvDCVFFRwSmnnMLEiROZOHEip5566qY08hwMfD9euXY/cISZ3Q18muoDH/8WZB9J5Xjhy+zzDnxVh9VeBXYzs+5mthmhu+Kj6Qu4e3d37+bu3QhXZP66kCq/oPwuRc1dAa5pR1BTC9O8+H/m/GrrmFk5sBWwuMkib0KDBg2ivDycjG/ZsiUDBw5MOCJpiBtvvJF+/frRunVr0G2QpMCVl5dX3df6oYce4uyzz6ZVq1ZJh5WXVIYXpptvvrnRGnnc/Xx37xIrAccDz8Sza48Cg+Jig6j9ktO8pBwvfJl93oF9arsNkruvBc4kjO78DjDa3aeb2WlmdlrTR908lN+lp7krwDXtCLKOBBovl15uZgfF0Z8HZqyT2taPCDubvLtsqC46dOjA0UcfjZlx9NFHa+j1ArV8+XI+++wzJk6cCPABcAxwbKJBiTTQrFmzqu5r3a5dO9q2bUu7du2SDisvqQwvTK1atWqORp7LgL5m9gHhVjKXNfYLNAfleOFL9XlPDWwIvE8YRDYnd3/c3Xd3913jmA24+y3uvlFrkbuf7O4PNnrwTUz5XXqarA9wHAm0N7Ctmc0D/kIo+EfHUUHnAD+GMBKomaVGAl1L9ZFATyeMKN2aMPjVE3H+HcC/4oBZiwktrgVr0KBBzJ49W61OBez2229n+PDhzJs3D2BHwqi2LwBHJhqYSANMmjSJSZMmse++++rWXnWgMrzwPPbYY/zpT3/io48+Yu3atbg7ZsayZZt24Y67TyQM8om7V1Ik+wDleGHL7PMOrAI2SyicvKP8Li1NVgGuYSRQqGFHUNNIoO4+mSzDtLv7SmIFuhh8/vnnfPjhh3z++edqeSpQw4cP59VXX+Wggw5iyZIl7wMnAhoISwpSmzZtdF/relAZXnjOOussxowZo0aeOlKOF7ZUn/fUwIbAzsBjCYaUV5TfpSVfBsEqeX//+9/58ssv+fvf/550KNJAm2++OZtvvnlq0tz9XcJtBkQKziGHHFJ1X+urr7666iHZqQwvPDvttJMaeepBOV7YMvu8AyuAounHu6mU36WluW+DJFnMmDGj6v5js2fPZsaMGfTo0SPZoKTeunTpwpIlSzjuuOOYOnXq7mb2CBvfLkCkILzwwgtUVFSwcuVKVq9enXQ4eU1leGG64oor6N+/P4cddli1vr/nnHNOglHlJ+V44Uv1eU/lt5ktdPdVCYeVF5TfpUdngPNAZmuTWp8K00MPPUT79u258MILAeYT+qkfl2RMIg21atUqxowZw0UXXcRf/vKXqodsTGV4YbrgggvYYostWLlyJcuXL696yMaU44XvscceqzawIeFuFbpTBcrvUqQzwHkg1epU07QUpC/c/dHaFxPJTzvttBPjx4/X/RDrQGV4YVq8eLHu3V5HyvHCl9nn3cxed/eKpOPKB8rv0qMzwHmgW7duOadFRJrbm2++WXVfa90GKTeV4YWpT58+qgDXkXK88KnPe82U36VHFeA8cOaZZ1abHjJkSEKRiIgEp59+etV9rceOHctjjz3G2LFjkw4rL6kML0w33nijGnnqSDle+FJ93lMDGwKdzEwd3lF+lyJdAp0HMlugx48fT69evRKKRvKNmfUDhgNlwO3uflnCIUkJeOuttzjssMOYN28ePXv25KWXXuLb3/42Tz/9dNKh5R2V4YVp+fLlLF68mA8++ICVK1cmHU5eU44XvgsuuIA2bdqkD2zYAmibcFh5QfldenQGOA9kHlA+9dRTCUUi+cbMyoAbgaOBvYATzGyvZKOSUjB16lReffVVdt55Z5599llef/11tt1226TDyksqwwvT7bffzmGHHUa/fv248MIL6devH3/961+TDisvKccL3+LFi6sNbAgscPeLko4rHyi/S4/OAOcBd885LSXtQGCGu88EMLP7gQHA24lGVULmfFHGJa81/2WRn34V2ic7bbG+2V97zhdllJWVVd3XetWqVey555689957zR5LIVAZXpiGDx/Oq6++ykEHHcSzzz7Lu+++q5HOa6AcL3ypPu8a2HBjyu/SowpwHth888356quvqk2LRJ2BuWnT84BvZi5kZoOBwQBdu3ZtnshKQJL3AVw9YwYArXZu/hh2A+YvWlp1X+u+ffuy9dZbs+OOOzZ7LIVAZXhh2nzzzdXIU0fK8cJ34403csUVV9CqVStatmwJ8TZI7l7yHd+V36VHFeA8kP6jyzYtJS3bcI0bNU26+23AbQAVFRVqumwkSQ6EMWzYMCCcpUrShRdeyOGHH87SpUvp169forHkK5XhhalLly5q5Kkj5Xjhy+zz3rt37w+AXyUdVz5QfpceVYBF8ts8YKe06S7AxwnFIiXqsMMOSzoEkUb30EMPAWrkkdJw++23M3z48KqBDYHdgT8DRyYamEgCVAEWyW+vAruZWXdgPnA88LNkQxIRKS5q5JFil9nn3czeBj5LOi6RJGgUaJE85u5rgTOBJ4F3gNHuPj3ZqERqZmZ3mtlCM3srbd42ZjbBzD6If7dOe+58M5thZu+Z2XeTiVpEpLhl9nkHVgJ7JBmTSFJUARbJc+7+uLvv7u67uvvFSccjUosRQOZ1pOcBT7v7bsDTcZp4S6/jgb3jOjfFW3+JiEgjyuzzDuyKulRJiVIFOA9ss8021aY7dOiQUCQiIpvG3ScBizNmDwBGxv9HAselzb/f3Ve5+yxgBuHWXwVFZbgUO+V44XvooYdo3749F154IX/7298gXP58XLJR5Qfld+lRBTgPZI429+WXXyYUiYhIk+jk7gsA4t/t4vxst/nqnG0DZjbYzCab2eRFixY1abD1pTJcip1yvLjEPu9L3X110rHkA+V36VEFOA+sXLky57SISJGq022+INzqy90r3L2iY8eOTRxW/agMl2KnHJdipvwuPaoAi4hIU/vUzHYAiH8Xxvm6zZeIiIg0K1WA80CLFi1yTouIFLhHgUHx/0HAI2nzjzezVvFWX7sBryQQ3yZRGS7FTjkuxUz5XXp0H+A8sH79+pzTIiKFwszuA3oD25rZPOAvwGXAaDM7BZgD/BjA3aeb2WjgbWAtcIa7r0sk8E2gMlyKnXJcipnyu/SoAiwiIo3G3U+o4akja1j+YkC39xIREZFmoXP8eSB1Y/KU1q1bJxSJiIjUl8pwKXbKcSlmyu/SowpwHsgcbW7FihUJRSIiIvWlMlyKnXJcipnyu/SoAiwiIiIiIiIlQRVgERERERERKQmqAIuIiIiIiEhJUAVYRERERERESoIqwCIiIiIishEz62dm75nZDDM7L8vzPzezN+PjBTPbL4k4ReojkQqwmc02s2lm9oaZTY7ztjGzCWb2Qfy7ddry58cf3ntm9t20+b3idmaY2XVmZkm8HxERESldZraTmT1rZu+Y2XQzGxbn13hsI5LvzKwMuBE4GtgLOMHM9spYbBZwmLt/HfgbcFvzRilSf0meAT7c3Xu6e0WcPg942t13A56O08Qf2vHA3kA/4Kb4gwS4GRgM7BYf/ZoxfhERERGAtcC57v414CDgjHj8kvXYRqRAHAjMcPeZ7r4auB8YkL6Au7/g7p/HyZeALs0co0i95dMl0AOAkfH/kcBxafPvd/dV7j4LmAEcaGY7AO3c/UV3d2BU2joiIiIizcLdF7j7a/H/5cA7QGdqPrYRKQSdgblp0/PivJqcAjyR7QkzG2xmk81s8qJFixoxRJH6S6oC7MB4M5tiZoPjvE7uvgDCjgTYLs6v6cfXOf6fOX8j+tGJiIhIczCzbsD+wMvUfGyTuY6OUyQfZeta6FkXNDucUAH+fbbn3f02d69w94qOHTs2Yogi9ZdUBfhgdz+A0KfgDDM7NMeyNf346vyj1I9OCpGZXWlm78aBJR4ys/ZJxyQiIjUzszbAv4Gz3H1ZXdfTcYrkqXnATmnTXYCPMxcys68DtwMD3L2ymWITabBEKsDu/nH8uxB4iNDH4NN4WTPx78K4eE0/vnlU72eQ9UcpUsAmAPvEgSXeB85POB4REamBmbUkVH7vcfcxcXZNxzYiheBVYDcz625mmxHG5Hk0fQEz6wqMAU5y9/cTiFGk3pq9AmxmW5pZ29T/wFHAW4Qf1KC42CDgkfj/o8DxZtbKzLoTBrt6JV5KtNzMDoqjPw9MW0ek4Ln7eHdfGycLemCJqVOnMnXqVA4//PCkQxERaXTxOOQO4B13vzrtqZqObUTyXjwGORN4ktCvfbS7Tzez08zstLjYn4EOhEFqq+7uIpLPyhN4zU7AQ/GOReXAve4+zsxeBUab2SnAHODHAPGHNhp4mzDK4hnuvi5u63RgBNCa0Ok+a8d7kSLwS+CBmp6MfekHA3Tt2rW5Yqq3MF6diEjRORg4CZhmZm/EeX8ALiPLsY1IoXD3x4HHM+bdkvb//wD/09xxiWyKZq8Au/tMYKObZMc+A0fWsM7FwMVZ5k8G9mnsGEWai5k9BWyf5akL3P2RuMwFhMafe2rajrvfRrz3XkVFRZPUMq+//npmzJhR7/WmTp1abbp3797st99GRUCtevTowZAhQ+q9nohIU3P358k+NgnUcGwjIiLJSOIMsIhE7t4n1/NmNgg4BjjSdfpURERERGSTqAIskqfMrB/hdgKHuftXScfT0LOvvXv33mje8OHDNzEaEREREZH6UwVYJH/dALQCJsQ+8y+5+2m5VxHJX2Y2G1gOrAPWunuFmW1D6N/eDZgN/MTdP08qRhERESluqgCL5Cl375F0DCJN4HB3/yxt+jzgaXe/zMzOi9O/TyY0ERERKXaJ3AdYREQkGgCMjP+PBI5LLhQREREpdqoAi4hIc3FgvJlNibfuAugU7+tO/LtdthXNbLCZTTazyYsWLWqmcEVERKTY6BJoERFpLge7+8dmth2hb/u7dV2xOW71JSIiIsVPZ4BFRKRZuPvH8e9C4CHgQOBTM9sBIP5dmFyEIiIiUuxUARYRkSZnZluaWdvU/8BRwFvAo8CguNgg4JFkIhQREZFSoEugRUSkOXQCHoq39CoH7nX3cWb2KjDazE4B5gA/TjBGERERKXKqAIuISJNz95nAflnmVwJHNn9EIiIiUop0CbSIiIiIiIiUBFWARUREREREpCSoAiwFq7KykqFDh1JZWZl0KJJD7PNZ47SIlCaV4VLslONSzAo5v1UBloI1cuRIpk2bxqhRo5IORXJw95zTIlKaVIZLsVOOSzEr5PxWBVgKUmVlJePGjcPdeeKJJwqy9alUtGnTJue0iJQeleFS7JTjUswKPb9VAZaCNHLkSNasWQPAmjVrCrL1qVR88cUXOadFpPSoDJdipxyXYlbo+a0KsBSkCRMmVF1K6+6MHz8+4YhERKSuVIZLsVOOSzEr9PxWBVgKUqdOnXJOi4hI/lIZLsVOOS7FrNDzWxVgKUiffPJJzmkREclfKsOl2CnHpZgVen6rAiwFqV27dtWmt9pqq4QiERGR+lIZLsVOOS7FrNDzWxVgKUgLFy6sNv3pp58mFInUZuLEiTmnRaT0qAyXYqccl2JW6PmtCrBInjOz35iZm9m2ScfSEL179845LSIiIiLSXFQBFsljZrYT0BeYk3QsIiIiIiKFThVgkfx2DfA7wJMORERERESk0KkCLJKnzOz7wHx3n1qHZQeb2WQzm7xo0aJmiE5EREREpPCUJx2ASCkzs6eA7bM8dQHwB+CoumzH3W8DbgOoqKjQ2WIRERERkSxUARZJkLv3yTbfzPYFugNTzQygC/CamR3o7gV1s7WJEydWG/hKo0CLiIiISFJUARbJQ+4+DdguNW1ms4EKd/8ssaBERERERAqc+gCLiIiINBEz62dm75nZDDM7L+l4RERKXcGfATazfsBwoAy43d0vSyKO66+/nhkzZjTa9oYNG1av5Xv06MGQIUMa7fWbgz6zunP3bknHINJUirEcL+byKJ0+s9zMrAy4kXA7u3nAq2b2qLu/3dyxaJ9bf/rMai+fLfTTGg70B74CTnb315o9UJItjyA/vq/6KtXPrKArwI29Y9mUJJg/fz4rVqxo0LrZ1DeO+fPnNzj2TUk+fWZSm/T+v6lp9QOWlMYsxzd1R96YZVJzlkfQ8DJJn1mTl+MHAjPcfSaAmd0PDAB0nFIPOk6pv8bI7zqWz0cDu8XHN4Gb498GyZfvqyExlHqOF9JnVtAVYBp5x/Lcc8/x2Wf50cXyyy+/rPfyDY19/vz5DU4gfWYiNduUnVJqvYa0qKYUSENNo5XjpVoeQcPLJH1mTf776AzMTZueR5bKgZkNBgYDdO3atcaNler3peOU+muk/K5L+TwAGOXuDrxkZu3NbAd3X9CQF8yX76u+31VqnVLO8UL6zAq9AtyoO5b27ds3uBVk1apVrF+/vkHrZluvRYv6dc9u0aIFrVq1atDrt2/fvkHrpdbVZybS+Fq3bp10CM2l0crxTSmPoOFlUtLlETS8TNJn1uQsy7yNblVX11vZaZ/bsHX1mTVYXcrnbMt0BqpVgPP5WLwxvqvUOqWS44X+mRV6BbhRdyy3335740VWT4V6mxh9ZiI1K4Czr/mg0cpxlUf1p8+syc0Ddkqb7gJ83NCN6fuqP31mm6Qu5XNRHIsX8nelz6z+Cn0U6EbdsUhhOfroowE49thjE45ERDaByvESVSJl+KvAbmbW3cw2A44HHk04JmkmRZDjdSmfVYaXqELObwuX7BcmMysH3geOBOYTdjQ/c/fpNa1TUVHhkydPbqYIpVSZ2RR3r0jitfMxxwu5lVCya6wcVzku+agxy3Az6w9cSxhF9053vzjX8spvaQ51yfG6lM9m9j3gTMIo0N8ErnP3A3NtVzkuzSFXjhf0JdDuvtbMzgSeZMOOpcaDJhERyS8qx6XYufvjwONJxyFSXzWVz2Z2Wnz+FkJu9wdmEG6D9Iuk4hWpq4KuAIN2LCKFQGd9JReV4yIi+Slb+Rwrvqn/HTijueMS2RSF3gdYREREREREpE5UARYREREREZGSoAqwiIiIiIiIlARVgEVERERERKQkFPRtkBrCzBYBHyUdRw22BT5LOogCk6+f2c7u3jGJF87jHM/X7yqf5fNnphzfWD5/X/kqXz8z5Xd2+fp95bN8/cyU4xvL1+8qn+XzZ1ZjjpdcBTifmdnkpO4dW6j0mRUOfVf1p8+ssOj7qj99ZoVF31f96TMrHPqu6q9QPzNdAi0iIiIiIiIlQRVgERERERERKQmqAOeX25IOoADpMysc+q7qT59ZYdH3VX/6zAqLvq/602dWOPRd1V9BfmbqAywiIiIiIiIlQWeARUREREREpCSoAiwiIiIiIiIlQRXgPGBm/czsPTObYWbnJR1PvjOzO81soZm9lXQsUjfK8fpRjhcW5Xf9KccLi3K8/pTjhUU5Xj+Fnt+qACfMzMqAG4Gjgb2AE8xsr2SjynsjgH5JByF1oxxvkBEoxwuC8rvBRqAcLwjK8QYbgXK8ICjHG2QEBZzfqgAn70BghrvPdPfVwP3AgIRjymvuPglYnHQcUmfK8XpSjhcU5XcDKMcLinK8AZTjBUU5Xk+Fnt+qACevMzA3bXpenCdSLJTjUsyU31LslONS7JTjJUYV4ORZlnm6N5UUE+W4FDPltxQ75bgUO+V4iVEFOHnzgJ3SprsAHycUi0hTUI5LMVN+S7FTjkuxU46XGFWAk/cqsJuZdTezzYDjgUcTjkmkMSnHpZgpv6XYKcel2CnHS4wqwAlz97XAmcCTwDvAaHefnmxU+c3M7gNeBPYws3lmdkrSMUnNlOP1pxwvHMrvhlGOFw7leMMoxwuHcrz+Cj2/zV2XuIuIiIiIiEjx0xlgERERERERKQmqAIuIiIiIiEhJUAVYRERERERESoIqwCIiIiIiIlISVAEWERERERGRkqAKcAkys+3N7H4z+9DM3jazx81sdzN7K+nYRBqDclyKnXJcip1yXIqZ8jtZ5UkHIM3LzAx4CBjp7sfHeT2BTknGJdJYlONS7JTjUuyU41LMlN/J0xng0nM4sMbdb0nNcPc3gLmpaTPrZmb/MbPX4uPbcf4OZjbJzN4ws7fM7DtmVmZmI+L0NDM7u9nfkUh1ynEpdspxKXbKcSlmyu+E6Qxw6dkHmFLLMguBvu6+0sx2A+4DKoCfAU+6+8VmVgZsAfQEOrv7PgBm1r6pAhepI+W4FDvluBQ75bgUM+V3wlQBlmxaAjfEyzHWAbvH+a8Cd5pZS+Bhd3/DzGYCu5jZ9cD/AeOTCFiknpTjUuyU41LslONSzJTfTUiXQJee6UCvWpY5G/gU2I/Q2rQZgLtPAg4F5gP/MrOB7v55XG4icAZwe9OELVJnynEpdspxKXbKcSlmyu+EqQJcep4BWpnZqakZZvYNYOe0ZbYCFrj7euAkoCwutzOw0N3/CdwBHGBm2wIt3P3fwJ+AA5rnbYjUSDkuxU45LsVOOS7FTPmdMF0CXWLc3c3sB8C1ZnYesBKYDZyVtthNwL/N7MfAs8CXcX5v4Ldmtgb4AhgIdAbuMrNUY8r5Tf0eRHJRjkuxU45LsVOOSzFTfifP3D3pGERERERERESanC6BFhERERERkZKgCrCIiIiIiIiUBFWARUREREREpCSoAiwiIiIiIiIlQRVgERERERERKQmqAIuIiIiIiEhJUAVYRERERERESsL/B4tP1T/9I6DpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See distribution of 'scaled' amounts wrt class\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=5, figsize=(16,4))\n",
    "\n",
    "sns.boxplot(x='Class', y='Amount', data=df, ax=ax[0])\n",
    "ax[0].set_title('Class vs Normal Amount')\n",
    "\n",
    "sns.boxplot(x='Class', y='amount_log', data=df, ax=ax[1])\n",
    "ax[1].set_title('Class vs Log Amount')\n",
    "\n",
    "sns.boxplot(x='Class', y='amount_robust', data=df, ax=ax[2])\n",
    "ax[2].set_title('Class vs Robust Scaled Amount')\n",
    "\n",
    "sns.boxplot(x='Class', y='amount_scaled', data=df, ax=ax[3])\n",
    "ax[3].set_title('Class vs Standard Scaled Amount')\n",
    "\n",
    "sns.boxplot(x='Class', y='amount_minmax', data=df, ax=ax[4])\n",
    "ax[4].set_title('Class vs Min Max Amount')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.066123,
     "end_time": "2021-03-29T10:26:53.205549",
     "exception": false,
     "start_time": "2021-03-29T10:26:53.139426",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Hightlights\n",
    "\n",
    "* We can see a slight difference in the log amount of our two Classes.\n",
    "* The IQR of fraudulent transactions are higher than normal transactions, but normal transactions have the highest values.\n",
    "* By seeing the above three graphs, I think scaling the amount by log will best suit for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:53.331123Z",
     "iopub.status.busy": "2021-03-29T10:26:53.330401Z",
     "iopub.status.idle": "2021-03-29T10:26:53.434149Z",
     "shell.execute_reply": "2021-03-29T10:26:53.433607Z"
    },
    "papermill": {
     "duration": 0.168534,
     "end_time": "2021-03-29T10:26:53.434343",
     "exception": false,
     "start_time": "2021-03-29T10:26:53.265809",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(['Time','hour','Amount','amount_robust','amount_scaled','amount_minmax'], inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:53.576723Z",
     "iopub.status.busy": "2021-03-29T10:26:53.573059Z",
     "iopub.status.idle": "2021-03-29T10:26:53.580888Z",
     "shell.execute_reply": "2021-03-29T10:26:53.581410Z"
    },
    "papermill": {
     "duration": 0.090222,
     "end_time": "2021-03-29T10:26:53.581616",
     "exception": false,
     "start_time": "2021-03-29T10:26:53.491394",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>amount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
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       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>5.008166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>0.993252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>5.936665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>4.816322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>4.248495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Class  amount_log  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053      0    5.008166  \n",
       "1  0.167170  0.125895 -0.008983  0.014724      0    0.993252  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752      0    5.936665  \n",
       "3  0.647376 -0.221929  0.062723  0.061458      0    4.816322  \n",
       "4 -0.206010  0.502292  0.219422  0.215153      0    4.248495  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.057581,
     "end_time": "2021-03-29T10:26:53.697651",
     "exception": false,
     "start_time": "2021-03-29T10:26:53.640070",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Splitting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:53.823983Z",
     "iopub.status.busy": "2021-03-29T10:26:53.822872Z",
     "iopub.status.idle": "2021-03-29T10:26:53.830033Z",
     "shell.execute_reply": "2021-03-29T10:26:53.830544Z"
    },
    "papermill": {
     "duration": 0.074649,
     "end_time": "2021-03-29T10:26:53.830741",
     "exception": false,
     "start_time": "2021-03-29T10:26:53.756092",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df length = 284807\n",
      "df fraud size = 492\n"
     ]
    }
   ],
   "source": [
    "print('df length =', len(df))\n",
    "print('df fraud size =', len(df[df['Class']==1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.058026,
     "end_time": "2021-03-29T10:26:53.947538",
     "exception": false,
     "start_time": "2021-03-29T10:26:53.889512",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Since the given dataset is a unified list of records, split them into train-test. \n",
    "\n",
    "Model building and validation will be done on train, final evaluation on tet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:54.074872Z",
     "iopub.status.busy": "2021-03-29T10:26:54.074159Z",
     "iopub.status.idle": "2021-03-29T10:26:54.606792Z",
     "shell.execute_reply": "2021-03-29T10:26:54.606146Z"
    },
    "papermill": {
     "duration": 0.600246,
     "end_time": "2021-03-29T10:26:54.606949",
     "exception": false,
     "start_time": "2021-03-29T10:26:54.006703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n",
      "\n",
      "Train: [ 11710  11841  11880 ... 284804 284805 284806] Test: [    0     1     2 ... 28522 28523 28524]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [11710 11841 11880 ... 57018 57019 57020]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [30442 30473 30496 ... 85497 85498 85499]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 48094  50211  50537 ... 113966 113967 113968]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 80760  81186  81609 ... 142427 142428 142429]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [119714 119781 120505 ... 170948 170949 170950]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [150647 150654 150660 ... 199403 199404 199405]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [154697 154718 154719 ... 227867 227868 227869]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [212516 212644 213092 ... 256351 256352 256353]\n",
      "Train: [     0      1      2 ... 256351 256352 256353] Test: [243749 243848 244004 ... 284804 284805 284806]\n",
      "--------------------------------------------------\n",
      "Label distribution:\n",
      "\n",
      "443\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df)*100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df)*100,2), '% of the dataset\\n')\n",
    "\n",
    "x = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "sss = StratifiedKFold(n_splits=10, random_state=None, shuffle=False)\n",
    "for train_index, test_index in sss.split(x,y):\n",
    "    print('Train:', train_index, 'Test:', test_index)\n",
    "    train = df.loc[train_index]\n",
    "    test = df.loc[test_index]\n",
    "    \n",
    "#check distribution of labels\n",
    "train_fraud_labels = train['Class'].sum()\n",
    "test_fraud_labels = test['Class'].sum()\n",
    "\n",
    "print('-'*50)\n",
    "print('Label distribution:\\n')\n",
    "print(train_fraud_labels)\n",
    "print(test_fraud_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:54.734247Z",
     "iopub.status.busy": "2021-03-29T10:26:54.733412Z",
     "iopub.status.idle": "2021-03-29T10:26:54.737430Z",
     "shell.execute_reply": "2021-03-29T10:26:54.736827Z"
    },
    "papermill": {
     "duration": 0.070611,
     "end_time": "2021-03-29T10:26:54.737584",
     "exception": false,
     "start_time": "2021-03-29T10:26:54.666973",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of fraud samples in train = 443, percent = 0.1728%\n",
      "Size of fraud samples in test = 49, percent = 0.1721%\n"
     ]
    }
   ],
   "source": [
    "#at this point, we have created train and test datasets, training and validation will be done\n",
    "#completely on train dataset, and test dataset will be used to judge the final performance\n",
    "print('Size of fraud samples in train = {}, percent = {:.4f}%'.format(\n",
    "    train_fraud_labels, train_fraud_labels/len(train)*100))\n",
    "print('Size of fraud samples in test = {}, percent = {:.4f}%'.format(\n",
    "    test_fraud_labels, test_fraud_labels/len(test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:54.889298Z",
     "iopub.status.busy": "2021-03-29T10:26:54.888572Z",
     "iopub.status.idle": "2021-03-29T10:26:54.892186Z",
     "shell.execute_reply": "2021-03-29T10:26:54.891651Z"
    },
    "papermill": {
     "duration": 0.094422,
     "end_time": "2021-03-29T10:26:54.892376",
     "exception": false,
     "start_time": "2021-03-29T10:26:54.797954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = train['Class'].values\n",
    "x = train.drop(['Class'], axis=1).values\n",
    "\n",
    "y_test = test['Class'].values\n",
    "x_test = test.drop(['Class'], axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:55.022350Z",
     "iopub.status.busy": "2021-03-29T10:26:55.021638Z",
     "iopub.status.idle": "2021-03-29T10:26:55.024025Z",
     "shell.execute_reply": "2021-03-29T10:26:55.024639Z"
    },
    "papermill": {
     "duration": 0.07057,
     "end_time": "2021-03-29T10:26:55.024825",
     "exception": false,
     "start_time": "2021-03-29T10:26:54.954255",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#create a function to print error metrics\n",
    "def print_results(y_test, y_pred):\n",
    "    print('Accuracy   : {:.5f}'.format(accuracy_score(y_pred , y_test))) \n",
    "    print('AUC        : {:.5f}'.format(roc_auc_score(y_test , y_pred)))\n",
    "    print('Precision  : {:.5f}'.format(precision_score(y_test , y_pred)))\n",
    "    print('Recall     : {:.5f}'.format(recall_score(y_test , y_pred)))\n",
    "    print('F1         : {:.5f}'.format(f1_score(y_test , y_pred)))\n",
    "    print('Confusion Matrix : \\n', confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.060716,
     "end_time": "2021-03-29T10:26:55.145366",
     "exception": false,
     "start_time": "2021-03-29T10:26:55.084650",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Baseline for models\n",
    "We will train four types of classifiers and decide which classifier will be more effective in detecting frauds:\n",
    "\n",
    "1. **Classification models:**\n",
    "    * Logistic Regression \n",
    "    * Decision Trees\n",
    "    * Random Forest\n",
    "    * Naive Bayes Classifier\n",
    "\n",
    "\n",
    "2. **Class imbalance solutions:**\n",
    "    * Undersampling\n",
    "    * Oversampling\n",
    "    * SMOTE\n",
    "    * ADASYN\n",
    "\n",
    "\n",
    "3. **Metrics:**\n",
    "    * Confusion matrix\n",
    "    * Precision\n",
    "    * Recall\n",
    "    * F1\n",
    "    * AUC ROC "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.059403,
     "end_time": "2021-03-29T10:26:55.265494",
     "exception": false,
     "start_time": "2021-03-29T10:26:55.206091",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1. Creating different balanced datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:55.390646Z",
     "iopub.status.busy": "2021-03-29T10:26:55.389843Z",
     "iopub.status.idle": "2021-03-29T10:26:59.678304Z",
     "shell.execute_reply": "2021-03-29T10:26:59.679410Z"
    },
    "papermill": {
     "duration": 4.354338,
     "end_time": "2021-03-29T10:26:59.679772",
     "exception": false,
     "start_time": "2021-03-29T10:26:55.325434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of original train: train=(179428, 29), test=(179428,)\n",
      "Label counts in original: Counter({0: 179115, 1: 313})\n",
      "\n",
      "Shape after random undersampling: train=(626, 29), test=(626,)\n",
      "Label counts after random undersampling: Counter({0: 313, 1: 313})\n",
      "\n",
      "Shape after random oversampling: train=(358230, 29), test=(358230,)\n",
      "Label counts after random oversampling: Counter({0: 179115, 1: 179115})\n",
      "\n",
      "Shape after SMOTE: train=(358230, 29), test=(358230,)\n",
      "Label counts after SMOTE: Counter({0: 179115, 1: 179115})\n",
      "\n",
      "Shape after ADASYN: train=(358230, 29), test=(358230,)\n",
      "Label counts after ADASYN: Counter({0: 179115, 1: 179115})\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.3, shuffle=True, random_state=0) \n",
    "print('Shape of original train: train={}, test={}'.format(x_train.shape, y_train.shape))\n",
    "print('Label counts in original: {}\\n'.format(Counter(y_train)))\n",
    "\n",
    "# Random Undersampling\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "x_rus, y_rus = rus.fit_resample(x_train, y_train)\n",
    "print('Shape after random undersampling: train={}, test={}'.format(x_rus.shape, y_rus.shape))\n",
    "print('Label counts after random undersampling: {}\\n'.format(Counter(y_rus)))\n",
    "\n",
    "# Random Oversampling\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "x_ros, y_ros = ros.fit_resample(x_train, y_train)\n",
    "print('Shape after random oversampling: train={}, test={}'.format(x_ros.shape, y_ros.shape))\n",
    "print('Label counts after random oversampling: {}\\n'.format(Counter(y_ros)))\n",
    "\n",
    "#SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "x_smote, y_smote = smote.fit_resample(x_train, y_train)\n",
    "print('Shape after SMOTE: train={}, test={}'.format(x_smote.shape, y_smote.shape))\n",
    "print('Label counts after SMOTE: {}\\n'.format(Counter(y_smote)))\n",
    "\n",
    "#ADASYN\n",
    "adasyn = ADASYN(random_state=42)\n",
    "x_adasyn, y_adasyn = adasyn.fit_resample(x_train, y_train)\n",
    "print('Shape after ADASYN: train={}, test={}'.format(x_adasyn.shape, y_adasyn.shape))\n",
    "print('Label counts after ADASYN: {}\\n'.format(Counter(y_adasyn)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:59.810697Z",
     "iopub.status.busy": "2021-03-29T10:26:59.809805Z",
     "iopub.status.idle": "2021-03-29T10:26:59.814014Z",
     "shell.execute_reply": "2021-03-29T10:26:59.813434Z"
    },
    "papermill": {
     "duration": 0.072316,
     "end_time": "2021-03-29T10:26:59.814167",
     "exception": false,
     "start_time": "2021-03-29T10:26:59.741851",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "names_list = []\n",
    "\n",
    "#empty lists to store performance matrix for train sets\n",
    "aucs_train_list = []\n",
    "precision_train_list = []\n",
    "recall_train_list = []\n",
    "f1_train_list = []\n",
    "\n",
    "#empty lists to store performance matrix for val sets\n",
    "aucs_val_list = []\n",
    "precision_val_list = []\n",
    "recall_val_list = []\n",
    "f1_val_list = []\n",
    "\n",
    "#empty lists to store performance matrix for test sets\n",
    "aucs_test_list = []\n",
    "precision_test_list = []\n",
    "recall_test_list = []\n",
    "f1_test_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:26:59.947905Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def build_measure_models(models):\n",
    "    for name, model, x_train, y_train, x_val, y_val in models:\n",
    "        print('--'*50)\n",
    "        print('model name = ', name)\n",
    "        names_list.append(name)\n",
    "        \n",
    "        # build the model\n",
    "        model.fit(x_train, y_train)\n",
    "        \n",
    "        # predict\n",
    "        y_train_pred = model.predict(x_train)\n",
    "        y_val_pred = model.predict(x_val)\n",
    "        \n",
    "        # calculate auc roc\n",
    "        aucs_train = roc_auc_score(y_train, y_train_pred)\n",
    "        aucs_val = roc_auc_score(y_val, y_val_pred)\n",
    "        \n",
    "        aucs_train_list.append(aucs_train)\n",
    "        aucs_val_list.append(aucs_val)\n",
    "        \n",
    "        # calculate precision\n",
    "        precision_train = precision_score(y_train, y_train_pred)\n",
    "        precision_val = precision_score(y_val, y_val_pred)\n",
    "        \n",
    "        precision_train_list.append(precision_train)\n",
    "        precision_val_list.append(precision_val)\n",
    "    \n",
    "        # calculate recall\n",
    "        recall_train = recall_score(y_train, y_train_pred)\n",
    "        recall_val = recall_score(y_val, y_val_pred)\n",
    "        \n",
    "        recall_train_list.append(recall_train)\n",
    "        recall_val_list.append(recall_val)\n",
    "        \n",
    "        # calculate f1 score\n",
    "        f1_train = f1_score(y_train, y_train_pred)\n",
    "        f1_val = f1_score(y_val, y_val_pred)\n",
    "        \n",
    "        f1_train_list.append(f1_train)\n",
    "        f1_val_list.append(f1_val)\n",
    "        \n",
    "        print('\\nval results =')\n",
    "        print_results(y_val, y_val_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:27:00.086563Z",
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    },
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     "end_time": "2021-03-29T10:27:20.016173",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  LR Imbalanced Class Weight Vanilla\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.97521\n",
      "AUC        : 0.94151\n",
      "Precision  : 0.05865\n",
      "Recall     : 0.90769\n",
      "F1         : 0.11018\n",
      "Confusion Matrix : \n",
      " [[74875  1894]\n",
      " [   12   118]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  LR Imbalanced\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99919\n",
      "AUC        : 0.81529\n",
      "Precision  : 0.85417\n",
      "Recall     : 0.63077\n",
      "F1         : 0.72566\n",
      "Confusion Matrix : \n",
      " [[76755    14]\n",
      " [   48    82]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  LR Undersampling\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.96287\n",
      "AUC        : 0.93533\n",
      "Precision  : 0.03985\n",
      "Recall     : 0.90769\n",
      "F1         : 0.07635\n",
      "Confusion Matrix : \n",
      " [[73926  2843]\n",
      " [   12   118]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  LR Oversampling\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.97532\n",
      "AUC        : 0.94156\n",
      "Precision  : 0.05888\n",
      "Recall     : 0.90769\n",
      "F1         : 0.11059\n",
      "Confusion Matrix : \n",
      " [[74883  1886]\n",
      " [   12   118]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  LR SMOTE\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.97420\n",
      "AUC        : 0.94484\n",
      "Precision  : 0.05688\n",
      "Recall     : 0.91538\n",
      "F1         : 0.10711\n",
      "Confusion Matrix : \n",
      " [[74796  1973]\n",
      " [   11   119]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  LR ADASYN\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.91837\n",
      "AUC        : 0.93224\n",
      "Precision  : 0.01924\n",
      "Recall     : 0.94615\n",
      "F1         : 0.03771\n",
      "Confusion Matrix : \n",
      " [[70499  6270]\n",
      " [    7   123]]\n"
     ]
    }
   ],
   "source": [
    "LRmodels = []\n",
    "\n",
    "# try logistic regression using 'class_weight' hyperparameter and imbalanced dataset\n",
    "LRmodels.append(('LR Imbalanced Class Weight Vanilla', \n",
    "                 LogisticRegression(solver='liblinear', multi_class='ovr', class_weight='balanced'),\n",
    "                 x_train, y_train, x_val, y_val))\n",
    "\n",
    "LRmodels.append(('LR Imbalanced', LogisticRegression(solver='liblinear', multi_class='ovr'),\n",
    "               x_train, y_train, x_val, y_val))\n",
    "LRmodels.append(('LR Undersampling', LogisticRegression(solver='liblinear', multi_class='ovr'),\n",
    "               x_rus, y_rus, x_val, y_val))\n",
    "LRmodels.append(('LR Oversampling', LogisticRegression(solver='liblinear', multi_class='ovr'),\n",
    "               x_ros, y_ros, x_val, y_val))\n",
    "LRmodels.append(('LR SMOTE', LogisticRegression(solver='liblinear', multi_class='ovr'),\n",
    "               x_smote, y_smote, x_val, y_val))\n",
    "LRmodels.append(('LR ADASYN', LogisticRegression(solver='liblinear', multi_class='ovr'),\n",
    "               x_adasyn, y_adasyn, x_val, y_val))\n",
    "\n",
    "build_measure_models(LRmodels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:27:20.150304Z",
     "iopub.status.busy": "2021-03-29T10:27:20.149602Z",
     "iopub.status.idle": "2021-03-29T10:27:20.215044Z",
     "shell.execute_reply": "2021-03-29T10:27:20.214497Z"
    },
    "papermill": {
     "duration": 0.13428,
     "end_time": "2021-03-29T10:27:20.215198",
     "exception": false,
     "start_time": "2021-03-29T10:27:20.080918",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.50087374, 286.62619808])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hyper parameter tune class_weight for logistic regression\n",
    "class_weight.compute_class_weight('balanced', np.unique(y_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:27:20.350869Z",
     "iopub.status.busy": "2021-03-29T10:27:20.350002Z",
     "iopub.status.idle": "2021-03-29T10:30:03.752591Z",
     "shell.execute_reply": "2021-03-29T10:30:03.753115Z"
    },
    "papermill": {
     "duration": 163.473751,
     "end_time": "2021-03-29T10:30:03.753351",
     "exception": false,
     "start_time": "2021-03-29T10:27:20.279600",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Best parameters: {'class_weight': {0: 0.09736842105263158, 1: 0.9026315789473685}}\n",
      "CPU times: user 2min 42s, sys: 270 ms, total: 2min 43s\n",
      "Wall time: 2min 43s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "weights = np.linspace(0.05, 0.95, 20)\n",
    "gsc = GridSearchCV(\n",
    "    LogisticRegression(), \n",
    "    param_grid={'class_weight':[{0: x, 1: 1.0-x} for x in weights]},\n",
    "    scoring='f1',\n",
    "    cv=5,\n",
    "    verbose=1,\n",
    ")\n",
    "grid_result = gsc.fit(x_train, y_train)\n",
    "print('Best parameters: %s' % grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:30:03.893902Z",
     "iopub.status.busy": "2021-03-29T10:30:03.893103Z",
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     "shell.execute_reply": "2021-03-29T10:30:04.091914Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       score    weight\n",
      "0   0.752065  0.050000\n",
      "1   0.802862  0.097368\n",
      "2   0.801001  0.144737\n",
      "3   0.795369  0.192105\n",
      "4   0.793412  0.239474\n",
      "5   0.786240  0.286842\n",
      "6   0.763040  0.334211\n",
      "7   0.754971  0.381579\n",
      "8   0.720403  0.428947\n",
      "9   0.716563  0.476316\n",
      "10  0.718018  0.523684\n",
      "11  0.699554  0.571053\n",
      "12  0.665320  0.618421\n",
      "13  0.638122  0.665789\n",
      "14  0.613279  0.713158\n",
      "15  0.588862  0.760526\n",
      "16  0.568934  0.807895\n",
      "17  0.554934  0.855263\n",
      "18  0.518646  0.902632\n",
      "19  0.455288  0.950000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_out = pd.DataFrame({'score':grid_result.cv_results_['mean_test_score'],'weight':weights})\n",
    "print(data_out)\n",
    "data_out.plot(x='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:30:04.234608Z",
     "iopub.status.busy": "2021-03-29T10:30:04.233891Z",
     "iopub.status.idle": "2021-03-29T10:30:06.322730Z",
     "shell.execute_reply": "2021-03-29T10:30:06.323268Z"
    },
    "papermill": {
     "duration": 2.163476,
     "end_time": "2021-03-29T10:30:06.323443",
     "exception": false,
     "start_time": "2021-03-29T10:30:04.159967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.09736842105263158, 1: 0.9026315789473685}\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  LR Imbalanced Class Weight Tuned\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99940\n",
      "AUC        : 0.91139\n",
      "Precision  : 0.82308\n",
      "Recall     : 0.82308\n",
      "F1         : 0.82308\n",
      "Confusion Matrix : \n",
      " [[76746    23]\n",
      " [   23   107]]\n"
     ]
    }
   ],
   "source": [
    "best_class_weight = grid_result.best_params_['class_weight']\n",
    "print(best_class_weight)\n",
    "\n",
    "LRmodels_tuned = []\n",
    "\n",
    "LRmodels_tuned.append(('LR Imbalanced Class Weight Tuned', \n",
    "                 LogisticRegression(class_weight=best_class_weight),\n",
    "                 x_train, y_train, x_val, y_val))\n",
    "build_measure_models(LRmodels_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.068194,
     "end_time": "2021-03-29T10:30:06.459140",
     "exception": false,
     "start_time": "2021-03-29T10:30:06.390946",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:30:06.602166Z",
     "iopub.status.busy": "2021-03-29T10:30:06.601436Z",
     "iopub.status.idle": "2021-03-29T10:32:02.258551Z",
     "shell.execute_reply": "2021-03-29T10:32:02.259174Z"
    },
    "papermill": {
     "duration": 115.732708,
     "end_time": "2021-03-29T10:32:02.259571",
     "exception": false,
     "start_time": "2021-03-29T10:30:06.526863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  DT Imbalanced Class Weight Vanilla\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99925\n",
      "AUC        : 0.87291\n",
      "Precision  : 0.79508\n",
      "Recall     : 0.74615\n",
      "F1         : 0.76984\n",
      "Confusion Matrix : \n",
      " [[76744    25]\n",
      " [   33    97]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  DT Imbalanced\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99923\n",
      "AUC        : 0.86139\n",
      "Precision  : 0.80342\n",
      "Recall     : 0.72308\n",
      "F1         : 0.76113\n",
      "Confusion Matrix : \n",
      " [[76746    23]\n",
      " [   36    94]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  DT Undersampling\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.90203\n",
      "AUC        : 0.91253\n",
      "Precision  : 0.01570\n",
      "Recall     : 0.92308\n",
      "F1         : 0.03087\n",
      "Confusion Matrix : \n",
      " [[69245  7524]\n",
      " [   10   120]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  DT Oversampling\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99923\n",
      "AUC        : 0.87291\n",
      "Precision  : 0.78862\n",
      "Recall     : 0.74615\n",
      "F1         : 0.76680\n",
      "Confusion Matrix : \n",
      " [[76743    26]\n",
      " [   33    97]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  DT SMOTE\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99727\n",
      "AUC        : 0.89112\n",
      "Precision  : 0.35915\n",
      "Recall     : 0.78462\n",
      "F1         : 0.49275\n",
      "Confusion Matrix : \n",
      " [[76587   182]\n",
      " [   28   102]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  DT ADASYN\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99754\n",
      "AUC        : 0.86438\n",
      "Precision  : 0.38153\n",
      "Recall     : 0.73077\n",
      "F1         : 0.50132\n",
      "Confusion Matrix : \n",
      " [[76615   154]\n",
      " [   35    95]]\n",
      "CPU times: user 1min 55s, sys: 114 ms, total: 1min 55s\n",
      "Wall time: 1min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "DTmodels = []\n",
    "dt = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# try decision tree using 'class_weight' hyperparameter and imbalanced dataset\n",
    "DTmodels.append(('DT Imbalanced Class Weight Vanilla', \n",
    "                 DecisionTreeClassifier(class_weight='balanced', random_state=42),\n",
    "                 x_train, y_train, x_val, y_val))\n",
    "\n",
    "DTmodels.append(('DT Imbalanced', dt,\n",
    "               x_train, y_train, x_val, y_val))\n",
    "DTmodels.append(('DT Undersampling', dt,\n",
    "               x_rus, y_rus, x_val, y_val))\n",
    "DTmodels.append(('DT Oversampling', dt,\n",
    "               x_ros, y_ros, x_val, y_val))\n",
    "DTmodels.append(('DT SMOTE', dt,\n",
    "               x_smote, y_smote, x_val, y_val))\n",
    "DTmodels.append(('DT ADASYN', dt,\n",
    "               x_adasyn, y_adasyn, x_val, y_val))\n",
    "\n",
    "build_measure_models(DTmodels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:32:02.412149Z",
     "iopub.status.busy": "2021-03-29T10:32:02.411474Z",
     "iopub.status.idle": "2021-03-29T10:39:53.156745Z",
     "shell.execute_reply": "2021-03-29T10:39:53.157319Z"
    },
    "papermill": {
     "duration": 470.826219,
     "end_time": "2021-03-29T10:39:53.157595",
     "exception": false,
     "start_time": "2021-03-29T10:32:02.331376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 15 candidates, totalling 45 fits\n",
      "Best parameters: {'class_weight': {0: 0.8857142857142857, 1: 0.11428571428571432}}\n",
      "CPU times: user 7min 50s, sys: 90 ms, total: 7min 50s\n",
      "Wall time: 7min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "weights = np.linspace(0.05, 0.95, 15)\n",
    "gsc = GridSearchCV(\n",
    "    DecisionTreeClassifier(random_state=42), \n",
    "    param_grid={'class_weight':[{0: x, 1: 1.0-x} for x in weights]},\n",
    "    scoring='f1',\n",
    "    cv=3,\n",
    "    verbose=1,\n",
    ")\n",
    "grid_result = gsc.fit(x_train, y_train)\n",
    "print('Best parameters: %s' % grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:39:53.304647Z",
     "iopub.status.busy": "2021-03-29T10:39:53.303664Z",
     "iopub.status.idle": "2021-03-29T10:39:53.485868Z",
     "shell.execute_reply": "2021-03-29T10:39:53.485173Z"
    },
    "papermill": {
     "duration": 0.257028,
     "end_time": "2021-03-29T10:39:53.486026",
     "exception": false,
     "start_time": "2021-03-29T10:39:53.228998",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       score    weight\n",
      "0   0.744111  0.050000\n",
      "1   0.765591  0.114286\n",
      "2   0.763494  0.178571\n",
      "3   0.757829  0.242857\n",
      "4   0.731804  0.307143\n",
      "5   0.750005  0.371429\n",
      "6   0.749323  0.435714\n",
      "7   0.746187  0.500000\n",
      "8   0.743550  0.564286\n",
      "9   0.748135  0.628571\n",
      "10  0.761468  0.692857\n",
      "11  0.740361  0.757143\n",
      "12  0.743937  0.821429\n",
      "13  0.771830  0.885714\n",
      "14  0.745550  0.950000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_out = pd.DataFrame({'score':grid_result.cv_results_['mean_test_score'],'weight':weights})\n",
    "print(data_out)\n",
    "data_out.plot(x='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:39:53.640685Z",
     "iopub.status.busy": "2021-03-29T10:39:53.639345Z",
     "iopub.status.idle": "2021-03-29T10:40:12.786441Z",
     "shell.execute_reply": "2021-03-29T10:40:12.785744Z"
    },
    "papermill": {
     "duration": 19.227181,
     "end_time": "2021-03-29T10:40:12.786596",
     "exception": false,
     "start_time": "2021-03-29T10:39:53.559415",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.8857142857142857, 1: 0.11428571428571432}\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  DT Imbalanced Class Weight Tuned\n",
      "\n",
      "val results =\n",
      "Accuracy   : 0.99938\n",
      "AUC        : 0.90754\n",
      "Precision  : 0.81538\n",
      "Recall     : 0.81538\n",
      "F1         : 0.81538\n",
      "Confusion Matrix : \n",
      " [[76745    24]\n",
      " [   24   106]]\n"
     ]
    }
   ],
   "source": [
    "best_class_weight = grid_result.best_params_['class_weight']\n",
    "print(best_class_weight)\n",
    "\n",
    "DTmodels_tuned = []\n",
    "\n",
    "DTmodels_tuned.append(('DT Imbalanced Class Weight Tuned', \n",
    "                 DecisionTreeClassifier(class_weight=best_class_weight, random_state=42),\n",
    "                 x_train, y_train, x_val, y_val))\n",
    "build_measure_models(DTmodels_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.073446,
     "end_time": "2021-03-29T10:40:12.934243",
     "exception": false,
     "start_time": "2021-03-29T10:40:12.860797",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:40:13.093867Z",
     "iopub.status.busy": "2021-03-29T10:40:13.092503Z",
     "iopub.status.idle": "2021-03-29T10:47:13.158321Z",
     "shell.execute_reply": "2021-03-29T10:47:13.158819Z"
    },
    "papermill": {
     "duration": 420.151223,
     "end_time": "2021-03-29T10:47:13.159095",
     "exception": false,
     "start_time": "2021-03-29T10:40:13.007872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  RF Imbalanced Class Weight Vanilla\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   16.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   37.7s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val results =\n",
      "Accuracy   : 0.99957\n",
      "AUC        : 0.88460\n",
      "Precision  : 0.97087\n",
      "Recall     : 0.76923\n",
      "F1         : 0.85837\n",
      "Confusion Matrix : \n",
      " [[76766     3]\n",
      " [   30   100]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  RF Imbalanced\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val results =\n",
      "Accuracy   : 0.99961\n",
      "AUC        : 0.89997\n",
      "Precision  : 0.96296\n",
      "Recall     : 0.80000\n",
      "F1         : 0.87395\n",
      "Confusion Matrix : \n",
      " [[76765     4]\n",
      " [   26   104]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  RF Undersampling\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val results =\n",
      "Accuracy   : 0.98485\n",
      "AUC        : 0.94634\n",
      "Precision  : 0.09284\n",
      "Recall     : 0.90769\n",
      "F1         : 0.16845\n",
      "Confusion Matrix : \n",
      " [[75616  1153]\n",
      " [   12   118]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  RF Oversampling\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   27.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.0min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val results =\n",
      "Accuracy   : 0.99962\n",
      "AUC        : 0.89998\n",
      "Precision  : 0.97196\n",
      "Recall     : 0.80000\n",
      "F1         : 0.87764\n",
      "Confusion Matrix : \n",
      " [[76766     3]\n",
      " [   26   104]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  RF SMOTE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   50.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.9min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val results =\n",
      "Accuracy   : 0.99961\n",
      "AUC        : 0.91149\n",
      "Precision  : 0.93860\n",
      "Recall     : 0.82308\n",
      "F1         : 0.87705\n",
      "Confusion Matrix : \n",
      " [[76762     7]\n",
      " [   23   107]]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  RF ADASYN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   53.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  2.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val results =\n",
      "Accuracy   : 0.99957\n",
      "AUC        : 0.90379\n",
      "Precision  : 0.92920\n",
      "Recall     : 0.80769\n",
      "F1         : 0.86420\n",
      "Confusion Matrix : \n",
      " [[76761     8]\n",
      " [   25   105]]\n",
      "CPU times: user 26min 42s, sys: 2.5 s, total: 26min 45s\n",
      "Wall time: 7min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "RFmodels = []\n",
    "rf = RandomForestClassifier(verbose=1, n_jobs=-1, random_state=42)\n",
    "\n",
    "\n",
    "# try decision tree using 'class_weight' hyperparameter and imbalanced dataset\n",
    "RFmodels.append(('RF Imbalanced Class Weight Vanilla', \n",
    "                 RandomForestClassifier(class_weight='balanced', verbose=1, n_jobs=-1, random_state=42),\n",
    "                 x_train, y_train, x_val, y_val))\n",
    "\n",
    "RFmodels.append(('RF Imbalanced', rf,\n",
    "               x_train, y_train, x_val, y_val))\n",
    "RFmodels.append(('RF Undersampling', rf,\n",
    "               x_rus, y_rus, x_val, y_val))\n",
    "RFmodels.append(('RF Oversampling', rf,\n",
    "               x_ros, y_ros, x_val, y_val))\n",
    "RFmodels.append(('RF SMOTE', rf,\n",
    "               x_smote, y_smote, x_val, y_val))\n",
    "RFmodels.append(('RF ADASYN', rf,\n",
    "               x_adasyn, y_adasyn, x_val, y_val))\n",
    "\n",
    "build_measure_models(RFmodels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T10:47:13.340489Z",
     "iopub.status.busy": "2021-03-29T10:47:13.339788Z",
     "iopub.status.idle": "2021-03-29T11:18:46.153466Z",
     "shell.execute_reply": "2021-03-29T11:18:46.152843Z"
    },
    "papermill": {
     "duration": 1892.908629,
     "end_time": "2021-03-29T11:18:46.153655",
     "exception": false,
     "start_time": "2021-03-29T10:47:13.245026",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 15 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'class_weight': {0: 0.3714285714285714, 1: 0.6285714285714286}}\n",
      "CPU times: user 4min 23s, sys: 673 ms, total: 4min 24s\n",
      "Wall time: 31min 32s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "weights = np.linspace(0.05, 0.95, 15)\n",
    "gsc = GridSearchCV(\n",
    "    RandomForestClassifier(n_jobs=-1, verbose=1, random_state=42), \n",
    "    param_grid={'class_weight':[{0: x, 1: 1.0-x} for x in weights]},\n",
    "    scoring='f1',\n",
    "    cv=3,\n",
    "    verbose=1,\n",
    "    n_jobs=-1,\n",
    ")\n",
    "grid_result = gsc.fit(x_train, y_train)\n",
    "print('Best parameters: %s' % grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T11:18:46.441194Z",
     "iopub.status.busy": "2021-03-29T11:18:46.440106Z",
     "iopub.status.idle": "2021-03-29T11:18:46.629331Z",
     "shell.execute_reply": "2021-03-29T11:18:46.628637Z"
    },
    "papermill": {
     "duration": 0.329591,
     "end_time": "2021-03-29T11:18:46.629477",
     "exception": false,
     "start_time": "2021-03-29T11:18:46.299886",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       score    weight\n",
      "0   0.838668  0.050000\n",
      "1   0.842065  0.114286\n",
      "2   0.840654  0.178571\n",
      "3   0.840531  0.242857\n",
      "4   0.841187  0.307143\n",
      "5   0.850402  0.371429\n",
      "6   0.842941  0.435714\n",
      "7   0.842311  0.500000\n",
      "8   0.843801  0.564286\n",
      "9   0.839465  0.628571\n",
      "10  0.838025  0.692857\n",
      "11  0.841993  0.757143\n",
      "12  0.844060  0.821429\n",
      "13  0.837686  0.885714\n",
      "14  0.843174  0.950000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_out = pd.DataFrame({'score':grid_result.cv_results_['mean_test_score'],'weight':weights})\n",
    "print(data_out)\n",
    "data_out.plot(x='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T11:18:46.871100Z",
     "iopub.status.busy": "2021-03-29T11:18:46.869789Z",
     "iopub.status.idle": "2021-03-29T11:19:56.344946Z",
     "shell.execute_reply": "2021-03-29T11:19:56.343636Z"
    },
    "papermill": {
     "duration": 69.604238,
     "end_time": "2021-03-29T11:19:56.345255",
     "exception": false,
     "start_time": "2021-03-29T11:18:46.741017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.3714285714285714, 1: 0.6285714285714286}\n",
      "----------------------------------------------------------------------------------------------------\n",
      "model name =  RF Imbalanced Class Weight Tuned\n",
      "building tree 1 of 100\n",
      "building tree 2 of 100\n",
      "building tree 3 of 100\n",
      "building tree 4 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 5 of 100\n",
      "building tree 6 of 100\n",
      "building tree 7 of 100\n",
      "building tree 8 of 100\n",
      "building tree 9 of 100\n",
      "building tree 10 of 100\n",
      "building tree 11 of 100\n",
      "building tree 12 of 100\n",
      "building tree 13 of 100\n",
      "building tree 14 of 100\n",
      "building tree 15 of 100\n",
      "building tree 16 of 100\n",
      "building tree 17 of 100\n",
      "building tree 18 of 100\n",
      "building tree 19 of 100\n",
      "building tree 20 of 100\n",
      "building tree 21 of 100\n",
      "building tree 22 of 100\n",
      "building tree 23 of 100\n",
      "building tree 24 of 100\n",
      "building tree 25 of 100\n",
      "building tree 26 of 100\n",
      "building tree 27 of 100\n",
      "building tree 28 of 100\n",
      "building tree 29 of 100\n",
      "building tree 30 of 100\n",
      "building tree 31 of 100\n",
      "building tree 32 of 100\n",
      "building tree 33 of 100\n",
      "building tree 34 of 100\n",
      "building tree 35 of 100\n",
      "building tree 36 of 100\n",
      "building tree 37 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   23.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 38 of 100\n",
      "building tree 39 of 100\n",
      "building tree 40 of 100\n",
      "building tree 41 of 100\n",
      "building tree 42 of 100\n",
      "building tree 43 of 100\n",
      "building tree 44 of 100\n",
      "building tree 45 of 100\n",
      "building tree 46 of 100\n",
      "building tree 47 of 100\n",
      "building tree 48 of 100\n",
      "building tree 49 of 100\n",
      "building tree 50 of 100\n",
      "building tree 51 of 100\n",
      "building tree 52 of 100\n",
      "building tree 53 of 100\n",
      "building tree 54 of 100\n",
      "building tree 55 of 100\n",
      "building tree 56 of 100building tree 57 of 100\n",
      "\n",
      "building tree 58 of 100\n",
      "building tree 59 of 100\n",
      "building tree 60 of 100\n",
      "building tree 61 of 100\n",
      "building tree 62 of 100\n",
      "building tree 63 of 100\n",
      "building tree 64 of 100\n",
      "building tree 65 of 100\n",
      "building tree 66 of 100\n",
      "building tree 67 of 100\n",
      "building tree 68 of 100\n",
      "building tree 69 of 100\n",
      "building tree 70 of 100\n",
      "building tree 71 of 100\n",
      "building tree 72 of 100\n",
      "building tree 73 of 100\n",
      "building tree 74 of 100\n",
      "building tree 75 of 100\n",
      "building tree 76 of 100\n",
      "building tree 77 of 100\n",
      "building tree 78 of 100\n",
      "building tree 79 of 100\n",
      "building tree 80 of 100\n",
      "building tree 81 of 100\n",
      "building tree 82 of 100\n",
      "building tree 83 of 100\n",
      "building tree 84 of 100\n",
      "building tree 85 of 100\n",
      "building tree 86 of 100\n",
      "building tree 87 of 100\n",
      "building tree 88 of 100\n",
      "building tree 89 of 100\n",
      "building tree 90 of 100\n",
      "building tree 91 of 100\n",
      "building tree 92 of 100\n",
      "building tree 93 of 100\n",
      "building tree 94 of 100\n",
      "building tree 95 of 100\n",
      "building tree 96 of 100\n",
      "building tree 97 of 100\n",
      "building tree 98 of 100\n",
      "building tree 99 of 100\n",
      "building tree 100 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val results =\n",
      "Accuracy   : 0.99960\n",
      "AUC        : 0.89229\n",
      "Precision  : 0.97143\n",
      "Recall     : 0.78462\n",
      "F1         : 0.86809\n",
      "Confusion Matrix : \n",
      " [[76766     3]\n",
      " [   28   102]]\n"
     ]
    }
   ],
   "source": [
    "best_class_weight = grid_result.best_params_['class_weight']\n",
    "print(best_class_weight)\n",
    "\n",
    "RFmodels_tuned = []\n",
    "\n",
    "RFmodels_tuned.append(('RF Imbalanced Class Weight Tuned', \n",
    "                 RandomForestClassifier(class_weight=best_class_weight, n_jobs=-1,verbose=2, random_state=42),\n",
    "                 x_train, y_train, x_val, y_val))\n",
    "build_measure_models(RFmodels_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T11:19:56.578985Z",
     "iopub.status.busy": "2021-03-29T11:19:56.578302Z",
     "iopub.status.idle": "2021-03-29T11:19:56.598677Z",
     "shell.execute_reply": "2021-03-29T11:19:56.598001Z"
    },
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     "duration": 0.137526,
     "end_time": "2021-03-29T11:19:56.598821",
     "exception": false,
     "start_time": "2021-03-29T11:19:56.461295",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Precision_val_score</th>\n",
       "      <th>Recall_val_score</th>\n",
       "      <th>F1_val_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RF Oversampling</td>\n",
       "      <td>0.971963</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.877637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RF SMOTE</td>\n",
       "      <td>0.938596</td>\n",
       "      <td>0.823077</td>\n",
       "      <td>0.877049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RF Imbalanced</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.873950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RF Imbalanced Class Weight Tuned</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>0.868085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RF ADASYN</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.864198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RF Imbalanced Class Weight Vanilla</td>\n",
       "      <td>0.970874</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.858369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR Imbalanced Class Weight Tuned</td>\n",
       "      <td>0.823077</td>\n",
       "      <td>0.823077</td>\n",
       "      <td>0.823077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DT Imbalanced Class Weight Tuned</td>\n",
       "      <td>0.815385</td>\n",
       "      <td>0.815385</td>\n",
       "      <td>0.815385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DT Imbalanced Class Weight Vanilla</td>\n",
       "      <td>0.795082</td>\n",
       "      <td>0.746154</td>\n",
       "      <td>0.769841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT Oversampling</td>\n",
       "      <td>0.788618</td>\n",
       "      <td>0.746154</td>\n",
       "      <td>0.766798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DT Imbalanced</td>\n",
       "      <td>0.803419</td>\n",
       "      <td>0.723077</td>\n",
       "      <td>0.761134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LR Imbalanced</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>0.725664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DT ADASYN</td>\n",
       "      <td>0.381526</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.501319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DT SMOTE</td>\n",
       "      <td>0.359155</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>0.492754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RF Undersampling</td>\n",
       "      <td>0.092840</td>\n",
       "      <td>0.907692</td>\n",
       "      <td>0.168451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LR Oversampling</td>\n",
       "      <td>0.058882</td>\n",
       "      <td>0.907692</td>\n",
       "      <td>0.110590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR Imbalanced Class Weight Vanilla</td>\n",
       "      <td>0.058648</td>\n",
       "      <td>0.907692</td>\n",
       "      <td>0.110177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR SMOTE</td>\n",
       "      <td>0.056883</td>\n",
       "      <td>0.915385</td>\n",
       "      <td>0.107111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LR Undersampling</td>\n",
       "      <td>0.039851</td>\n",
       "      <td>0.907692</td>\n",
       "      <td>0.076351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LR ADASYN</td>\n",
       "      <td>0.019240</td>\n",
       "      <td>0.946154</td>\n",
       "      <td>0.037713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DT Undersampling</td>\n",
       "      <td>0.015699</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.030872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Model  Precision_val_score  Recall_val_score  \\\n",
       "17                     RF Oversampling             0.971963          0.800000   \n",
       "18                            RF SMOTE             0.938596          0.823077   \n",
       "15                       RF Imbalanced             0.962963          0.800000   \n",
       "20    RF Imbalanced Class Weight Tuned             0.971429          0.784615   \n",
       "19                           RF ADASYN             0.929204          0.807692   \n",
       "14  RF Imbalanced Class Weight Vanilla             0.970874          0.769231   \n",
       "6     LR Imbalanced Class Weight Tuned             0.823077          0.823077   \n",
       "13    DT Imbalanced Class Weight Tuned             0.815385          0.815385   \n",
       "7   DT Imbalanced Class Weight Vanilla             0.795082          0.746154   \n",
       "10                     DT Oversampling             0.788618          0.746154   \n",
       "8                        DT Imbalanced             0.803419          0.723077   \n",
       "1                        LR Imbalanced             0.854167          0.630769   \n",
       "12                           DT ADASYN             0.381526          0.730769   \n",
       "11                            DT SMOTE             0.359155          0.784615   \n",
       "16                    RF Undersampling             0.092840          0.907692   \n",
       "3                      LR Oversampling             0.058882          0.907692   \n",
       "0   LR Imbalanced Class Weight Vanilla             0.058648          0.907692   \n",
       "4                             LR SMOTE             0.056883          0.915385   \n",
       "2                     LR Undersampling             0.039851          0.907692   \n",
       "5                            LR ADASYN             0.019240          0.946154   \n",
       "9                     DT Undersampling             0.015699          0.923077   \n",
       "\n",
       "    F1_val_score  \n",
       "17      0.877637  \n",
       "18      0.877049  \n",
       "15      0.873950  \n",
       "20      0.868085  \n",
       "19      0.864198  \n",
       "14      0.858369  \n",
       "6       0.823077  \n",
       "13      0.815385  \n",
       "7       0.769841  \n",
       "10      0.766798  \n",
       "8       0.761134  \n",
       "1       0.725664  \n",
       "12      0.501319  \n",
       "11      0.492754  \n",
       "16      0.168451  \n",
       "3       0.110590  \n",
       "0       0.110177  \n",
       "4       0.107111  \n",
       "2       0.076351  \n",
       "5       0.037713  \n",
       "9       0.030872  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert results into a dataframe for easy comparison\n",
    "data = {'Model': names_list,\n",
    "        'Precision_val_score': precision_val_list,\n",
    "        'Recall_val_score': recall_val_list,\n",
    "        'F1_val_score': f1_val_list\n",
    "       }\n",
    "\n",
    "performance_df = pd.DataFrame(data)\n",
    "performance_df.sort_values(['F1_val_score'],ascending=False, inplace=True)\n",
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.114767,
     "end_time": "2021-03-29T11:19:56.829454",
     "exception": false,
     "start_time": "2021-03-29T11:19:56.714687",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From above results, it looks clear that random forest is able to produce best performance. We will try to tune hyper parameters of random forest classifier..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
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     "start_time": "2021-03-29T11:19:56.946066",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Hyperparameter tuning - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T11:19:57.301350Z",
     "iopub.status.busy": "2021-03-29T11:19:57.300683Z",
     "iopub.status.idle": "2021-03-29T14:10:17.137360Z",
     "shell.execute_reply": "2021-03-29T14:10:17.138116Z"
    },
    "papermill": {
     "duration": 10219.956525,
     "end_time": "2021-03-29T14:10:17.138399",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=2; total time=  27.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.6s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=2; total time=  27.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=2; total time=  26.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.1s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=5; total time=  27.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=5; total time=  26.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.8s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=5; total time=  27.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=10; total time=  26.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.1s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=10; total time=  26.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=1, min_samples_split=10; total time=  26.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.1s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=2; total time=  27.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=2; total time=  26.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=2; total time=  27.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=5; total time=  27.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.6s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=5; total time=  27.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=5; total time=  27.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=10; total time=  27.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=10; total time=  27.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.7s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=2, min_samples_split=10; total time=  28.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.1s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=2; total time=  27.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   26.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=2; total time=  28.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=2; total time=  28.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=5; total time=  27.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=5; total time=  27.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   24.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=5; total time=  27.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=10; total time=  27.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.6s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=10; total time=  28.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   25.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=4, min_samples_leaf=4, min_samples_split=10; total time=  27.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.7s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=2; total time=  53.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   49.8s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=2; total time=  52.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=2; total time=  53.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.8s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=5; total time=  53.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=5; total time=  53.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=5; total time=  53.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=10; total time=  52.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   49.6s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=10; total time=  52.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=1, min_samples_split=10; total time=  53.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=2; total time=  53.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   49.8s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=2; total time=  52.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   51.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=2; total time=  54.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   53.3s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=5; total time=  56.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=5; total time=  53.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=5; total time=  53.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   49.7s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=10; total time=  52.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   49.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=10; total time=  52.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.6s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=2, min_samples_split=10; total time=  53.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=2; total time=  53.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   51.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=2; total time=  54.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   51.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=2; total time=  54.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=5; total time=  53.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.6s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=5; total time=  53.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.7s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=5; total time=  53.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   24.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   53.1s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=10; total time=  55.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   21.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   49.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=10; total time=  52.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   50.8s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=9, min_samples_leaf=4, min_samples_split=10; total time=  53.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=2; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=2; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=5; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   27.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=5; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=5; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=1, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=10; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=10; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=2, min_samples_split=10; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   27.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=2; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=2; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=10; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=10; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   28.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=14, min_samples_leaf=4, min_samples_split=10; total time= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=1, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=2, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=19, min_samples_leaf=4, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   32.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=1, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=2, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=24, min_samples_leaf=4, min_samples_split=10; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   32.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   32.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   32.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.3min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.0s\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.6s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5; total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   51.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18min 39s, sys: 18.7 s, total: 18min 57s\n",
      "Wall time: 2h 50min 19s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=RandomForestClassifier(n_jobs=-1, oob_score=True,\n",
       "                                                    random_state=42, verbose=1,\n",
       "                                                    warm_start=True),\n",
       "                   n_iter=100,\n",
       "                   param_distributions={'max_depth': [4, 9, 14, 19, 24, 30],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10]},\n",
       "                   random_state=42, scoring='f1', verbose=2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "max_depth = [int(x) for x in np.linspace(4, 30, num=6)]\n",
    "\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "\n",
    "random_grid = {\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split':min_samples_split,\n",
    "               'min_samples_leaf':min_samples_leaf}\n",
    "\n",
    "rf_clf = RandomForestClassifier(random_state=42, verbose=1, n_jobs=-1, oob_score=True, warm_start=True)\n",
    "\n",
    "rf_cv = RandomizedSearchCV(estimator=rf_clf, scoring='f1',param_distributions=random_grid, n_iter=100, cv=3, \n",
    "                               verbose=2, random_state=42)\n",
    "\n",
    "rf_cv.fit(x_smote, y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T14:10:17.948711Z",
     "iopub.status.busy": "2021-03-29T14:10:17.948033Z",
     "iopub.status.idle": "2021-03-29T14:10:17.955886Z",
     "shell.execute_reply": "2021-03-29T14:10:17.955315Z"
    },
    "papermill": {
     "duration": 0.41444,
     "end_time": "2021-03-29T14:10:17.956035",
     "exception": false,
     "start_time": "2021-03-29T14:10:17.541595",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 24,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': -1,\n",
       " 'oob_score': True,\n",
       " 'random_state': 42,\n",
       " 'verbose': 1,\n",
       " 'warm_start': True}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = rf_cv.best_estimator_\n",
    "best.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T14:10:18.787586Z",
     "iopub.status.busy": "2021-03-29T14:10:18.786516Z",
     "iopub.status.idle": "2021-03-29T15:33:43.290707Z",
     "shell.execute_reply": "2021-03-29T15:33:43.290141Z"
    },
    "papermill": {
     "duration": 5004.92624,
     "end_time": "2021-03-29T15:33:43.290883",
     "exception": false,
     "start_time": "2021-03-29T14:10:18.364643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] END ....................................n_estimators=50; total time=  48.7s\n",
      "[CV] END ....................................n_estimators=50; total time=  49.3s\n",
      "[CV] END ....................................n_estimators=50; total time=  48.9s\n",
      "[CV] END ....................................n_estimators=50; total time=  49.2s\n",
      "[CV] END ....................................n_estimators=50; total time=  48.3s\n",
      "[CV] END ...................................n_estimators=100; total time= 1.6min\n",
      "[CV] END ...................................n_estimators=100; total time= 1.6min\n",
      "[CV] END ...................................n_estimators=100; total time= 1.6min\n",
      "[CV] END ...................................n_estimators=100; total time= 1.6min\n",
      "[CV] END ...................................n_estimators=100; total time= 1.6min\n",
      "[CV] END ...................................n_estimators=150; total time= 2.3min\n",
      "[CV] END ...................................n_estimators=150; total time= 2.4min\n",
      "[CV] END ...................................n_estimators=150; total time= 2.3min\n",
      "[CV] END ...................................n_estimators=150; total time= 2.4min\n",
      "[CV] END ...................................n_estimators=150; total time= 2.3min\n",
      "[CV] END ...................................n_estimators=200; total time= 3.0min\n",
      "[CV] END ...................................n_estimators=200; total time= 3.1min\n",
      "[CV] END ...................................n_estimators=200; total time= 3.1min\n",
      "[CV] END ...................................n_estimators=200; total time= 3.1min\n",
      "[CV] END ...................................n_estimators=200; total time= 3.1min\n",
      "[CV] END ...................................n_estimators=250; total time= 3.9min\n",
      "[CV] END ...................................n_estimators=250; total time= 3.9min\n",
      "[CV] END ...................................n_estimators=250; total time= 3.9min\n",
      "[CV] END ...................................n_estimators=250; total time= 4.0min\n",
      "[CV] END ...................................n_estimators=250; total time= 3.9min\n",
      "[CV] END ...................................n_estimators=300; total time= 4.6min\n",
      "[CV] END ...................................n_estimators=300; total time= 4.7min\n",
      "[CV] END ...................................n_estimators=300; total time= 4.7min\n",
      "[CV] END ...................................n_estimators=300; total time= 4.7min\n",
      "[CV] END ...................................n_estimators=300; total time= 4.8min\n",
      "CPU times: user 7min 49s, sys: 4.96 s, total: 7min 54s\n",
      "Wall time: 1h 23min 24s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=RandomForestClassifier(max_depth=24, n_jobs=-1,\n",
       "                                                    oob_score=True,\n",
       "                                                    random_state=42,\n",
       "                                                    warm_start=True),\n",
       "                   n_iter=100,\n",
       "                   param_distributions={'n_estimators': [50, 100, 150, 200, 250,\n",
       "                                                         300]},\n",
       "                   random_state=42, scoring='f1', verbose=2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "#tune n-estimators (I did not include n_estimators in above randomsearch to reduce computation time)\n",
    "n_estimators = [50, 100, 150, 200, 250, 300]\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators}\n",
    "\n",
    "rf_clf = RandomForestClassifier(max_depth=24, min_samples_split=2, min_samples_leaf=1,\n",
    "                                random_state=42, n_jobs=-1, oob_score=True, warm_start=True)\n",
    "\n",
    "rf_cv = RandomizedSearchCV(estimator=rf_clf, scoring='f1',param_distributions=random_grid, n_iter=100, cv=5, \n",
    "                               verbose=2, random_state=42)\n",
    "\n",
    "rf_cv.fit(x_smote, y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T15:33:44.116164Z",
     "iopub.status.busy": "2021-03-29T15:33:44.115512Z",
     "iopub.status.idle": "2021-03-29T15:33:44.122447Z",
     "shell.execute_reply": "2021-03-29T15:33:44.122907Z"
    },
    "papermill": {
     "duration": 0.4247,
     "end_time": "2021-03-29T15:33:44.123093",
     "exception": false,
     "start_time": "2021-03-29T15:33:43.698393",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 24,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 50,\n",
       " 'n_jobs': -1,\n",
       " 'oob_score': True,\n",
       " 'random_state': 42,\n",
       " 'verbose': 0,\n",
       " 'warm_start': True}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = rf_cv.best_estimator_\n",
    "best.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T15:33:44.948455Z",
     "iopub.status.busy": "2021-03-29T15:33:44.947725Z",
     "iopub.status.idle": "2021-03-29T15:33:45.357448Z",
     "shell.execute_reply": "2021-03-29T15:33:45.356801Z"
    },
    "papermill": {
     "duration": 0.823915,
     "end_time": "2021-03-29T15:33:45.357595",
     "exception": false,
     "start_time": "2021-03-29T15:33:44.533680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy   : 0.99957\n",
      "AUC        : 0.91147\n",
      "Precision  : 0.91453\n",
      "Recall     : 0.82308\n",
      "F1         : 0.86640\n",
      "Confusion Matrix : \n",
      " [[76759    10]\n",
      " [   23   107]]\n"
     ]
    }
   ],
   "source": [
    "print_results(y_val, best.predict(x_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-29T15:33:46.193907Z",
     "iopub.status.busy": "2021-03-29T15:33:46.193216Z",
     "iopub.status.idle": "2021-03-29T15:33:46.376021Z",
     "shell.execute_reply": "2021-03-29T15:33:46.375039Z"
    },
    "papermill": {
     "duration": 0.607007,
     "end_time": "2021-03-29T15:33:46.376245",
     "exception": false,
     "start_time": "2021-03-29T15:33:45.769238",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy   : 0.99937\n",
      "AUC        : 0.86726\n",
      "Precision  : 0.87805\n",
      "Recall     : 0.73469\n",
      "F1         : 0.80000\n",
      "Confusion Matrix : \n",
      " [[28426     5]\n",
      " [   13    36]]\n"
     ]
    }
   ],
   "source": [
    "print_results(y_test, best.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.447118,
     "end_time": "2021-03-29T15:33:47.238407",
     "exception": false,
     "start_time": "2021-03-29T15:33:46.791289",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Credits - \n",
    "\n",
    "I have referred to follwowing notebook - \n",
    "* https://www.kaggle.com/drscarlat/fraud-detection-under-oversampling-smote-adasyn/notebook"
   ]
  }
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