Notebooks/10_PreAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-analysis of data\n",
    "Here we join the files to be used in the analysis phase. We experiment with the parameters of the classifiers. We use those to classify galaxies in another notebook.\n",
    "First, we import all the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:34:57.197046Z",
     "start_time": "2021-07-26T10:34:56.763938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:34:58.518846Z",
     "start_time": "2021-07-26T10:34:57.197993Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from astropy.table import Table, join\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.calibration import calibration_curve\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, we include some shortcuts to simplify some code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:34:58.522365Z",
     "start_time": "2021-07-26T10:34:58.519889Z"
    }
   },
   "outputs": [],
   "source": [
    "def selt1_or_selt2(tab_phot):\n",
    "    \"\"\"Select Seyferts 1 and 2 in both VCV and CDS\"\"\"\n",
    "    selec1 = np.logical_and(tab_phot['otype_txt'] == 'Sy1', tab_phot['Sp'] == 'S1')\n",
    "    selec2 = np.logical_and(tab_phot['otype_txt'] == 'Sy2', tab_phot['Sp'] == 'S2')\n",
    "    select_sey = np.logical_or(selec1, selec2)\n",
    "    return tab_phot[select_sey]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join all clean results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read the results from the models we are going to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:34:59.081454Z",
     "start_time": "2021-07-26T10:34:58.523347Z"
    }
   },
   "outputs": [],
   "source": [
    "ModelS = Table.read('../Data/Final/CIGALEOutputs/Cleanresults_SK.fits', format='fits')\n",
    "ModelF = Table.read('../Data/Final/CIGALEOutputs/Cleanresults_Fr.fits', format='fits')\n",
    "\n",
    "ModelS37 = Table.read('../Data/Final/CIGALEOutputs/Cleanresults_SK37.fits', format='fits')\n",
    "ModelF37 = Table.read('../Data/Final/CIGALEOutputs/Cleanresults_Fr37.fits', format='fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also read the original database of galaxies, where we take the information for the classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:34:59.250924Z",
     "start_time": "2021-07-26T10:34:59.082457Z"
    }
   },
   "outputs": [],
   "source": [
    "Sample = Table.read('../Data/Final/VCV_SMB_otype.txt', format='ascii')\n",
    "Sample.rename_column('main_id', 'id')  # Renamed to use the join with the same id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We join these clean tables with the catalog. We send all the tables to pandas format. Then, we concatenate the tables to have an unique table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:34:59.689009Z",
     "start_time": "2021-07-26T10:34:59.251767Z"
    }
   },
   "outputs": [],
   "source": [
    "SPandas = join(Sample, ModelS, keys='id').to_pandas()\n",
    "FPandas = join(Sample, ModelF, keys='id').to_pandas()\n",
    "SPandas37 = join(Sample, ModelS37, keys='id').to_pandas()\n",
    "FPandas37 = join(Sample, ModelF37, keys='id').to_pandas()\n",
    "SFPandas = pd.concat([SPandas, FPandas, SPandas37, FPandas37],\n",
    "                     keys=['SKIRTOR', 'Fritz', 'S37', 'F37'],\n",
    "                     names=['Model', 'idCIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:34:59.829476Z",
     "start_time": "2021-07-26T10:34:59.689975Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of estimates we use for the classifications tasks is 29737\n"
     ]
    }
   ],
   "source": [
    "print('The number of estimates we use for the classifications tasks is %d' %\n",
    "      len(selt1_or_selt2(SFPandas)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiments with the parameters of the classifiers\n",
    "We select the physical parameters in all the models and remove the ones that are not useful for our propose (e.g. constant values, young and old contributions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:35:00.210362Z",
     "start_time": "2021-07-26T10:34:59.830883Z"
    }
   },
   "outputs": [],
   "source": [
    "GoodColumns = [col for col in selt1_or_selt2(SFPandas).loc['SKIRTOR'].columns if 'bayes' in col]\n",
    "newdf = selt1_or_selt2(SFPandas).loc['SKIRTOR'][GoodColumns]\n",
    "Descr = newdf.describe()\n",
    "BadCol = [cl for cl in newdf.columns if Descr[cl]['std'] < 1e-2]  # Removing constant columns\n",
    "newdf = newdf.drop(BadCol, axis=1)\n",
    "# Removing calculated errors\n",
    "BadCol2 = [cl for cl in newdf.columns if '_err' in cl]\n",
    "newdf = newdf.drop(BadCol2, axis=1)\n",
    "# Removing young estimations\n",
    "BadCol3 = [cl for cl in newdf.columns if '_young' in cl]\n",
    "# Removing old estimations\n",
    "BadCol4 = [cl for cl in newdf.columns if '_old' in cl]\n",
    "# Removing estimations with lines\n",
    "BadCol5 = [cl for cl in newdf.columns if '_lines' in cl]\n",
    "# Removing universe values, these are dependent of redshift\n",
    "BadCol6 = [cl for cl in newdf.columns if 'universe' in cl]\n",
    "# Removing filter in the attenuation module\n",
    "BadCol7 = [cl for cl in newdf.columns if 'FUV' in cl]\n",
    "BadCols = np.concatenate([BadCol3, BadCol4, BadCol5, BadCol6, BadCol7])\n",
    "newdf = newdf.drop(BadCols, axis=1)\n",
    "newdf = newdf.dropna(axis='columns')\n",
    "newdf = newdf[newdf.columns[:-31]]  # Remove the mock photometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the sample of galaxies into train and test sets. Then, we encode and scale the physical parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:35:00.289834Z",
     "start_time": "2021-07-26T10:35:00.212038Z"
    }
   },
   "outputs": [],
   "source": [
    "X = newdf\n",
    "y = selt1_or_selt2(SFPandas).loc['SKIRTOR']['otype_txt']\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.20,\n",
    "                                                    random_state=42)\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(y)\n",
    "x_train_encode = preprocessing.robust_scale(x_train)\n",
    "x_test_encode = preprocessing.robust_scale(x_test)\n",
    "y_train_encode = le.transform(y_train).reshape(-1, 1).ravel()\n",
    "y_test_encode = le.transform(y_test).reshape(-1, 1).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T10:38:02.380030Z",
     "start_time": "2021-01-22T10:38:02.375121Z"
    }
   },
   "source": [
    "## Using GridSearchCV to find the best parameters for the classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We selected two different classifiers ([Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) and [XGBoost](https://xgboost.readthedocs.io/en/latest/python/python_api.html)). We will experiment with Grid Search. We test a model with default values and we compare with the [Grid Search](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) accuracy scoring (`GridSearchCV`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:35:00.293255Z",
     "start_time": "2021-07-26T10:35:00.290802Z"
    }
   },
   "outputs": [],
   "source": [
    "clf_rf = RandomForestClassifier(random_state=42)\n",
    "clf_xg = xgb.XGBClassifier(random_state=42, use_label_encoder=False,\n",
    "                           eval_metric='logloss')\n",
    "param_grid = {\"max_depth\": [10, 15, 20, 25, 30, 35, 40],\n",
    "#               \"n_estimators\": [200, 400, 600, 800, 1000]}\n",
    "              \"n_estimators\": [100, 200, 300, 400, 500]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT COMMENT:**  If you want to see the all the details of the runs (verbose) do not set the n_jobs parameter. The evaluation on the points are shown in the terminal when the cell finish running. Do not set any n_jobs for the XGBoost classifier! It will take a longer time depending on the computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:37:41.593568Z",
     "start_time": "2021-07-26T10:35:00.294253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 35 candidates, totalling 175 fits\n",
      "[CV] max_depth=10, n_estimators=100 ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... max_depth=10, n_estimators=100, score=0.911, total=   0.5s\n",
      "[CV] max_depth=10, n_estimators=100 ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... max_depth=10, n_estimators=100, score=0.908, total=   0.4s\n",
      "[CV] max_depth=10, n_estimators=100 ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... max_depth=10, n_estimators=100, score=0.907, total=   0.5s\n",
      "[CV] max_depth=10, n_estimators=100 ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... max_depth=10, n_estimators=100, score=0.912, total=   0.5s\n",
      "[CV] max_depth=10, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=100, score=0.901, total=   0.5s\n",
      "[CV] max_depth=10, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=200, score=0.910, total=   0.7s\n",
      "[CV] max_depth=10, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=200, score=0.901, total=   0.7s\n",
      "[CV] max_depth=10, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=200, score=0.906, total=   0.8s\n",
      "[CV] max_depth=10, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=200, score=0.910, total=   0.7s\n",
      "[CV] max_depth=10, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=200, score=0.899, total=   0.7s\n",
      "[CV] max_depth=10, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=300, score=0.912, total=   1.0s\n",
      "[CV] max_depth=10, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=300, score=0.900, total=   0.9s\n",
      "[CV] max_depth=10, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=300, score=0.904, total=   1.0s\n",
      "[CV] max_depth=10, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=300, score=0.910, total=   0.9s\n",
      "[CV] max_depth=10, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=300, score=0.901, total=   1.0s\n",
      "[CV] max_depth=10, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=400, score=0.908, total=   1.2s\n",
      "[CV] max_depth=10, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=400, score=0.901, total=   1.1s\n",
      "[CV] max_depth=10, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=400, score=0.905, total=   1.1s\n",
      "[CV] max_depth=10, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=400, score=0.907, total=   1.1s\n",
      "[CV] max_depth=10, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=400, score=0.902, total=   1.1s\n",
      "[CV] max_depth=10, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=500, score=0.908, total=   1.3s\n",
      "[CV] max_depth=10, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=500, score=0.902, total=   1.3s\n",
      "[CV] max_depth=10, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=500, score=0.904, total=   1.3s\n",
      "[CV] max_depth=10, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=500, score=0.907, total=   1.3s\n",
      "[CV] max_depth=10, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=10, n_estimators=500, score=0.901, total=   1.3s\n",
      "[CV] max_depth=15, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=100, score=0.915, total=   0.5s\n",
      "[CV] max_depth=15, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=100, score=0.906, total=   0.5s\n",
      "[CV] max_depth=15, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=100, score=0.904, total=   0.5s\n",
      "[CV] max_depth=15, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=100, score=0.905, total=   0.5s\n",
      "[CV] max_depth=15, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=100, score=0.897, total=   0.5s\n",
      "[CV] max_depth=15, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=200, score=0.913, total=   0.7s\n",
      "[CV] max_depth=15, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=200, score=0.905, total=   0.7s\n",
      "[CV] max_depth=15, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=200, score=0.900, total=   0.7s\n",
      "[CV] max_depth=15, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=200, score=0.908, total=   0.7s\n",
      "[CV] max_depth=15, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=200, score=0.901, total=   0.7s\n",
      "[CV] max_depth=15, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=300, score=0.913, total=   0.9s\n",
      "[CV] max_depth=15, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=300, score=0.908, total=   0.9s\n",
      "[CV] max_depth=15, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=300, score=0.901, total=   0.9s\n",
      "[CV] max_depth=15, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=300, score=0.909, total=   0.9s\n",
      "[CV] max_depth=15, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=300, score=0.901, total=   0.9s\n",
      "[CV] max_depth=15, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=400, score=0.914, total=   1.1s\n",
      "[CV] max_depth=15, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=400, score=0.908, total=   1.1s\n",
      "[CV] max_depth=15, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=400, score=0.904, total=   1.1s\n",
      "[CV] max_depth=15, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=400, score=0.908, total=   1.1s\n",
      "[CV] max_depth=15, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=400, score=0.900, total=   1.1s\n",
      "[CV] max_depth=15, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=500, score=0.915, total=   1.3s\n",
      "[CV] max_depth=15, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=500, score=0.907, total=   1.3s\n",
      "[CV] max_depth=15, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=500, score=0.903, total=   1.3s\n",
      "[CV] max_depth=15, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=500, score=0.907, total=   1.3s\n",
      "[CV] max_depth=15, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=15, n_estimators=500, score=0.899, total=   1.3s\n",
      "[CV] max_depth=20, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=100, score=0.915, total=   0.5s\n",
      "[CV] max_depth=20, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=100, score=0.902, total=   0.5s\n",
      "[CV] max_depth=20, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=100, score=0.906, total=   0.5s\n",
      "[CV] max_depth=20, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=100, score=0.908, total=   0.5s\n",
      "[CV] max_depth=20, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=100, score=0.898, total=   0.5s\n",
      "[CV] max_depth=20, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=200, score=0.917, total=   0.7s\n",
      "[CV] max_depth=20, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=200, score=0.904, total=   0.7s\n",
      "[CV] max_depth=20, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=200, score=0.907, total=   0.7s\n",
      "[CV] max_depth=20, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=200, score=0.909, total=   0.7s\n",
      "[CV] max_depth=20, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=200, score=0.899, total=   0.7s\n",
      "[CV] max_depth=20, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=300, score=0.916, total=   1.0s\n",
      "[CV] max_depth=20, n_estimators=300 ..................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... max_depth=20, n_estimators=300, score=0.907, total=   0.9s\n",
      "[CV] max_depth=20, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=300, score=0.906, total=   0.9s\n",
      "[CV] max_depth=20, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=300, score=0.908, total=   0.9s\n",
      "[CV] max_depth=20, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=300, score=0.901, total=   0.9s\n",
      "[CV] max_depth=20, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=400, score=0.917, total=   1.1s\n",
      "[CV] max_depth=20, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=400, score=0.904, total=   1.1s\n",
      "[CV] max_depth=20, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=400, score=0.906, total=   1.1s\n",
      "[CV] max_depth=20, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=400, score=0.907, total=   1.1s\n",
      "[CV] max_depth=20, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=400, score=0.902, total=   1.1s\n",
      "[CV] max_depth=20, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=500, score=0.917, total=   1.3s\n",
      "[CV] max_depth=20, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=500, score=0.902, total=   1.3s\n",
      "[CV] max_depth=20, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=500, score=0.907, total=   1.3s\n",
      "[CV] max_depth=20, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=500, score=0.909, total=   1.3s\n",
      "[CV] max_depth=20, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=20, n_estimators=500, score=0.902, total=   1.3s\n",
      "[CV] max_depth=25, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=100, score=0.915, total=   0.5s\n",
      "[CV] max_depth=25, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=100, score=0.902, total=   0.5s\n",
      "[CV] max_depth=25, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=100, score=0.913, total=   0.5s\n",
      "[CV] max_depth=25, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=100, score=0.908, total=   0.5s\n",
      "[CV] max_depth=25, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=100, score=0.896, total=   0.5s\n",
      "[CV] max_depth=25, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=200, score=0.915, total=   0.7s\n",
      "[CV] max_depth=25, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=200, score=0.903, total=   0.7s\n",
      "[CV] max_depth=25, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=200, score=0.917, total=   0.7s\n",
      "[CV] max_depth=25, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=200, score=0.908, total=   0.7s\n",
      "[CV] max_depth=25, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=200, score=0.900, total=   0.7s\n",
      "[CV] max_depth=25, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=300, score=0.914, total=   0.9s\n",
      "[CV] max_depth=25, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=300, score=0.905, total=   0.9s\n",
      "[CV] max_depth=25, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=300, score=0.917, total=   0.9s\n",
      "[CV] max_depth=25, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=300, score=0.908, total=   0.9s\n",
      "[CV] max_depth=25, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=300, score=0.900, total=   0.9s\n",
      "[CV] max_depth=25, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=400, score=0.914, total=   1.1s\n",
      "[CV] max_depth=25, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=400, score=0.903, total=   1.1s\n",
      "[CV] max_depth=25, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=400, score=0.915, total=   1.1s\n",
      "[CV] max_depth=25, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=400, score=0.908, total=   1.1s\n",
      "[CV] max_depth=25, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=400, score=0.902, total=   1.1s\n",
      "[CV] max_depth=25, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=500, score=0.914, total=   1.3s\n",
      "[CV] max_depth=25, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=500, score=0.903, total=   1.3s\n",
      "[CV] max_depth=25, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=500, score=0.913, total=   1.3s\n",
      "[CV] max_depth=25, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=500, score=0.909, total=   1.3s\n",
      "[CV] max_depth=25, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=25, n_estimators=500, score=0.902, total=   1.3s\n",
      "[CV] max_depth=30, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=100, score=0.915, total=   0.5s\n",
      "[CV] max_depth=30, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=100, score=0.902, total=   0.5s\n",
      "[CV] max_depth=30, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=100, score=0.913, total=   0.5s\n",
      "[CV] max_depth=30, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=100, score=0.908, total=   0.5s\n",
      "[CV] max_depth=30, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=100, score=0.896, total=   0.5s\n",
      "[CV] max_depth=30, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=200, score=0.915, total=   0.7s\n",
      "[CV] max_depth=30, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=200, score=0.903, total=   0.7s\n",
      "[CV] max_depth=30, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=200, score=0.917, total=   0.7s\n",
      "[CV] max_depth=30, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=200, score=0.908, total=   0.7s\n",
      "[CV] max_depth=30, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=200, score=0.900, total=   0.7s\n",
      "[CV] max_depth=30, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=300, score=0.914, total=   1.0s\n",
      "[CV] max_depth=30, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=300, score=0.905, total=   0.9s\n",
      "[CV] max_depth=30, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=300, score=0.917, total=   0.9s\n",
      "[CV] max_depth=30, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=300, score=0.908, total=   0.9s\n",
      "[CV] max_depth=30, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=300, score=0.900, total=   0.9s\n",
      "[CV] max_depth=30, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=400, score=0.914, total=   1.1s\n",
      "[CV] max_depth=30, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=400, score=0.903, total=   1.1s\n",
      "[CV] max_depth=30, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=400, score=0.915, total=   1.1s\n",
      "[CV] max_depth=30, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=400, score=0.908, total=   1.1s\n",
      "[CV] max_depth=30, n_estimators=400 ..................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... max_depth=30, n_estimators=400, score=0.902, total=   1.1s\n",
      "[CV] max_depth=30, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=500, score=0.914, total=   1.3s\n",
      "[CV] max_depth=30, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=500, score=0.903, total=   1.3s\n",
      "[CV] max_depth=30, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=500, score=0.913, total=   1.3s\n",
      "[CV] max_depth=30, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=500, score=0.909, total=   1.3s\n",
      "[CV] max_depth=30, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=30, n_estimators=500, score=0.902, total=   1.3s\n",
      "[CV] max_depth=35, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=100, score=0.915, total=   0.5s\n",
      "[CV] max_depth=35, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=100, score=0.902, total=   0.5s\n",
      "[CV] max_depth=35, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=100, score=0.913, total=   0.5s\n",
      "[CV] max_depth=35, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=100, score=0.908, total=   0.5s\n",
      "[CV] max_depth=35, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=100, score=0.896, total=   0.5s\n",
      "[CV] max_depth=35, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=200, score=0.915, total=   0.7s\n",
      "[CV] max_depth=35, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=200, score=0.903, total=   0.7s\n",
      "[CV] max_depth=35, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=200, score=0.917, total=   0.7s\n",
      "[CV] max_depth=35, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=200, score=0.908, total=   0.7s\n",
      "[CV] max_depth=35, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=200, score=0.900, total=   0.7s\n",
      "[CV] max_depth=35, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=300, score=0.914, total=   1.0s\n",
      "[CV] max_depth=35, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=300, score=0.905, total=   0.9s\n",
      "[CV] max_depth=35, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=300, score=0.917, total=   0.9s\n",
      "[CV] max_depth=35, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=300, score=0.908, total=   0.9s\n",
      "[CV] max_depth=35, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=300, score=0.900, total=   0.9s\n",
      "[CV] max_depth=35, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=400, score=0.914, total=   1.1s\n",
      "[CV] max_depth=35, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=400, score=0.903, total=   1.1s\n",
      "[CV] max_depth=35, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=400, score=0.915, total=   1.1s\n",
      "[CV] max_depth=35, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=400, score=0.908, total=   1.1s\n",
      "[CV] max_depth=35, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=400, score=0.902, total=   1.2s\n",
      "[CV] max_depth=35, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=500, score=0.914, total=   1.3s\n",
      "[CV] max_depth=35, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=500, score=0.903, total=   1.3s\n",
      "[CV] max_depth=35, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=500, score=0.913, total=   1.3s\n",
      "[CV] max_depth=35, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=500, score=0.909, total=   1.3s\n",
      "[CV] max_depth=35, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=35, n_estimators=500, score=0.902, total=   1.3s\n",
      "[CV] max_depth=40, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=100, score=0.915, total=   0.5s\n",
      "[CV] max_depth=40, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=100, score=0.902, total=   0.5s\n",
      "[CV] max_depth=40, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=100, score=0.913, total=   0.5s\n",
      "[CV] max_depth=40, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=100, score=0.908, total=   0.5s\n",
      "[CV] max_depth=40, n_estimators=100 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=100, score=0.896, total=   0.5s\n",
      "[CV] max_depth=40, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=200, score=0.915, total=   0.7s\n",
      "[CV] max_depth=40, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=200, score=0.903, total=   0.7s\n",
      "[CV] max_depth=40, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=200, score=0.917, total=   0.7s\n",
      "[CV] max_depth=40, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=200, score=0.908, total=   0.7s\n",
      "[CV] max_depth=40, n_estimators=200 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=200, score=0.900, total=   0.8s\n",
      "[CV] max_depth=40, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=300, score=0.914, total=   0.9s\n",
      "[CV] max_depth=40, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=300, score=0.905, total=   0.9s\n",
      "[CV] max_depth=40, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=300, score=0.917, total=   0.9s\n",
      "[CV] max_depth=40, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=300, score=0.908, total=   0.9s\n",
      "[CV] max_depth=40, n_estimators=300 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=300, score=0.900, total=   0.9s\n",
      "[CV] max_depth=40, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=400, score=0.914, total=   1.1s\n",
      "[CV] max_depth=40, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=400, score=0.903, total=   1.1s\n",
      "[CV] max_depth=40, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=400, score=0.915, total=   1.1s\n",
      "[CV] max_depth=40, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=400, score=0.908, total=   1.1s\n",
      "[CV] max_depth=40, n_estimators=400 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=400, score=0.902, total=   1.1s\n",
      "[CV] max_depth=40, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=500, score=0.914, total=   1.3s\n",
      "[CV] max_depth=40, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=500, score=0.903, total=   1.3s\n",
      "[CV] max_depth=40, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=500, score=0.913, total=   1.3s\n",
      "[CV] max_depth=40, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=500, score=0.909, total=   1.3s\n",
      "[CV] max_depth=40, n_estimators=500 ..................................\n",
      "[CV] ...... max_depth=40, n_estimators=500, score=0.902, total=   1.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 175 out of 175 | elapsed:  2.7min finished\n"
     ]
    }
   ],
   "source": [
    "search = GridSearchCV(clf_xg, param_grid, scoring='accuracy',\n",
    "                      verbose=4).fit(x_train_encode, y_train_encode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results from the `GridSearchCV` gives us the best parameters for the classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:37:41.597419Z",
     "start_time": "2021-07-26T10:37:41.594465Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 25, 'n_estimators': 300}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_params_ ## For the XGB classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:39:23.769732Z",
     "start_time": "2021-07-26T10:37:41.598250Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 35 candidates, totalling 175 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  13 tasks      | elapsed:    7.4s\n",
      "[Parallel(n_jobs=6)]: Done  86 tasks      | elapsed:   46.6s\n",
      "[Parallel(n_jobs=6)]: Done 175 out of 175 | elapsed:  1.7min finished\n"
     ]
    }
   ],
   "source": [
    "search2 = GridSearchCV(clf_rf, param_grid, scoring='accuracy', n_jobs=6,\n",
    "                       verbose=4).fit(x_train_encode, y_train_encode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:39:23.777755Z",
     "start_time": "2021-07-26T10:39:23.772519Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 25, 'n_estimators': 200}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search2.best_params_ ## For the RF classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we use those values for the n_estimator and max_depth in these two classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:39:23.786272Z",
     "start_time": "2021-07-26T10:39:23.779631Z"
    }
   },
   "outputs": [],
   "source": [
    "clf_xg2 = xgb.XGBClassifier(n_estimators=search.best_params_['n_estimators'],\n",
    "                            max_depth=search.best_params_['max_depth'],\n",
    "                            random_state=42, use_label_encoder=False, eval_metric='logloss')\n",
    "clf_rf2 = RandomForestClassifier(n_estimators=search2.best_params_['n_estimators'],\n",
    "                                 max_depth=search2.best_params_['max_depth'], random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T10:38:42.989975Z",
     "start_time": "2021-01-22T10:38:42.985050Z"
    }
   },
   "source": [
    "##### Minor check on the calibration curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add a simple calibration plot to see if the probabilistic predictions of the classifiers are calibrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:39:27.474589Z",
     "start_time": "2021-07-26T10:39:23.787118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n",
    "ax2 = plt.subplot2grid((3, 1), (2, 0))\n",
    "\n",
    "ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "for clf, name in [(clf_xg2, 'XBG'),\n",
    "                  (clf_rf2, 'Random Forest')]:\n",
    "    clf.fit(x_train_encode, y_train_encode)\n",
    "    if hasattr(clf, \"predict_proba\"):\n",
    "        prob_pos = clf.predict_proba(x_test_encode)[:, 1]\n",
    "    else:  # use decision function\n",
    "        prob_pos = clf.decision_function(x_test_encode)\n",
    "        prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n",
    "    fraction_of_positives, mean_predicted_value = calibration_curve(y_test_encode,\n",
    "                                                                    prob_pos, n_bins=20)\n",
    "    ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
    "             label=\"%s\" % (name, ))\n",
    "\n",
    "    ax2.hist(prob_pos, range=(0, 1), bins=20, label=name,\n",
    "             histtype=\"step\", lw=2)\n",
    "\n",
    "ax1.set_ylabel(\"Fraction of positives\")\n",
    "ax1.set_ylim([-0.05, 1.05])\n",
    "ax1.legend(loc=\"lower right\")\n",
    "ax1.set_title('Calibration plots (reliability curve)')\n",
    "\n",
    "ax2.set_xlabel(\"Mean predicted value\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "ax2.legend(loc=\"upper center\", ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Notebook info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-26T10:39:27.543235Z",
     "start_time": "2021-07-26T10:39:27.475497Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Andres Ramos\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.3\n",
      "IPython version      : 7.16.1\n",
      "\n",
      "Compiler    : GCC 7.3.0\n",
      "OS          : Linux\n",
      "Release     : 3.10.0-1160.25.1.el7.x86_64\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 8\n",
      "Architecture: 64bit\n",
      "\n",
      "Specific Python packages\n",
      "matplotlib: 3.2.2\n",
      "sys       : 3.8.3 (default, Jul  2 2020, 16:21:59) \n",
      "[GCC 7.3.0]\n",
      "json      : 2.0.9\n",
      "pandas    : 1.2.0\n",
      "logging   : 0.5.1.2\n",
      "numpy     : 1.19.5\n",
      "xgboost   : 1.3.3\n",
      "autopep8  : 1.5.7\n",
      "sklearn   : 0.23.1\n",
      "re        : 2.2.1\n",
      "\n",
      "Watermark: 2.1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a \"Andres Ramos\" -d -v -m\n",
    "print('Specific Python packages')\n",
    "%watermark -iv -w "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": "10",
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "198px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}