2_Model_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "263450be",
   "metadata": {},
   "source": [
    "# Content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c08eb9",
   "metadata": {},
   "source": [
    "**Supervised:** The labels are included in the training data and the goal is to train a model to learn to predict the labels from the features  \n",
    "**Classification:** The label is a binary variable, 0 (will repay loan on time), 1 (will have difficulty repaying loan)\n",
    "\n",
    "- Evaluation des performances des modèles d’apprentissage supervisé selon différents critères (scores, temps d'entraînement, etc.) en adaptant les paramètres afin de choisir le modèle le plus performant pour la problématique métier.\n",
    "- Calcul un score avec fp + 10 * fn\n",
    "\n",
    "precision, recall, accuracy, roc_auc, f1\n",
    "        fbeta_macro\n",
    "        fbeta_micro\n",
    "        fbeta_weighted\n",
    "        \n",
    "hyperparameter search for the following methods: XGBoost, LightGBM, Random Forest. Save each model, best parameters and evaluation metrics with MLflow. Save the evaluation metrics for all models to a dataframe.\n",
    "\n",
    "- [ 0 - Library definition](#0) \n",
    "- [ 1 - Data definition](#1) \n",
    "- [ 1.1 - Imbalanced Data Definition](#1.1) \n",
    "- [ 2 - Metrics definition](#2) \n",
    "- [ 3 - Model definition](#3) \n",
    "- [ 4 - Run 1 EXPERIMENT with 6 RUNs](#4)\n",
    "- [ 5 - Model Evaluation](#(5)\n",
    "- [ 6 - Feature Importance](#(6)\n",
    "- [ 7 - Feature Selection after SHAP feature Importance](#(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3342a76b",
   "metadata": {},
   "source": [
    "<a name='0'></a>\n",
    "# 0 Library definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c8b0045",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import fbeta_score\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "from mlflow import MlflowClient\n",
    "from mlflow.models.signature import infer_signature\n",
    "\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "from numpy import where\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "063e4ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Python documentation found for '1.7.0'.\n",
      "Use help() to get the interactive help utility.\n",
      "Use help(str) for help on the str class.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(xgb.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f5c0495",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip uninstall xgboost\n",
    "#!pip install xgboost==1.7.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bc9296",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "# 1 Data definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bee5e0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (246008, 239)\n",
      "Y_train (246008, 1)\n",
      "Y_test (61503, 1)\n",
      "ids_test (246008, 1)\n",
      "feature names (239, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = pd.read_csv('../2_INPUT_DATA/3_SPLIT/X_train.csv')\n",
    "Y_train = pd.read_csv('../2_INPUT_DATA/3_SPLIT/Y_train.csv')\n",
    "X_test = pd.read_csv('../2_INPUT_DATA/3_SPLIT/X_test.csv')\n",
    "Y_test = pd.read_csv('../2_INPUT_DATA/3_SPLIT/Y_test.csv')\n",
    "ids_test = pd.read_csv('../2_INPUT_DATA/3_SPLIT/ids_test.csv')\n",
    "feature_names = pd.read_csv('../2_INPUT_DATA/2_FEATURE_PROCESSED/feature_names.csv')\n",
    "\n",
    "print(\"X_train\", X_train.shape)\n",
    "print(\"Y_train\", Y_train.shape)\n",
    "print(\"Y_test\", Y_test.shape)\n",
    "print(\"ids_test\",ids_test.shape)\n",
    "print(\"feature names\", feature_names.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44fb33fe",
   "metadata": {},
   "outputs": [
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       "                                   0\n",
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       "2                    FLAG_OWN_REALTY\n",
       "3                       CNT_CHILDREN\n",
       "4                   AMT_INCOME_TOTAL\n",
       "..                               ...\n",
       "234  WALLSMATERIAL_MODE_Stone, brick\n",
       "235        WALLSMATERIAL_MODE_Wooden\n",
       "236           EMERGENCYSTATE_MODE_No\n",
       "237          EMERGENCYSTATE_MODE_Yes\n",
       "238               DAYS_EMPLOYED_ANOM\n",
       "\n",
       "[239 rows x 1 columns]"
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    "feature_names"
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       "        0  1  2  3         4          5        6          7         8      9  \\\n",
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       "4       0  0  1  0  121500.0   513000.0  21865.5   513000.0  0.028663  19932   \n",
       "...    .. .. .. ..       ...        ...      ...        ...       ...    ...   \n",
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       "246004  0  0  1  0   81000.0   311877.0  17046.0   252000.0  0.020246  20782   \n",
       "246005  0  1  1  2  270000.0   254700.0  25321.5   225000.0  0.072508  11387   \n",
       "246006  0  1  0  2  225000.0   177903.0  16447.5   148500.0  0.035792  17446   \n",
       "246007  0  1  1  1  234000.0   599778.0  30753.0   477000.0  0.018801  15592   \n",
       "\n",
       "        ...    229    230    231    232    233    234    235    236    237  \\\n",
       "0       ...  False  False  False  False  False   True  False   True  False   \n",
       "1       ...   True  False  False  False  False  False  False   True  False   \n",
       "2       ...  False  False  False  False  False  False  False  False  False   \n",
       "3       ...  False  False  False  False  False  False  False  False  False   \n",
       "4       ...  False  False  False  False  False  False  False  False  False   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "246003  ...  False  False  False  False  False   True  False   True  False   \n",
       "246004  ...  False  False  False  False  False  False  False  False  False   \n",
       "246005  ...  False  False  False  False  False   True  False   True  False   \n",
       "246006  ...  False  False  False  False  False  False  False  False  False   \n",
       "246007  ...  False  False  False  False  False   True  False   True  False   \n",
       "\n",
       "          238  \n",
       "0       False  \n",
       "1       False  \n",
       "2       False  \n",
       "3       False  \n",
       "4       False  \n",
       "...       ...  \n",
       "246003  False  \n",
       "246004   True  \n",
       "246005  False  \n",
       "246006  False  \n",
       "246007  False  \n",
       "\n",
       "[246008 rows x 239 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2e4a4c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_data(df_train, df_test):\n",
    "    \"\"\"\n",
    "    Scale the features in the training and testing datasets using Min-Max scaling.\n",
    "\n",
    "    Args:\n",
    "    df_train (DataFrame): The training dataset to be scaled.\n",
    "    df_test (DataFrame): The testing dataset to be scaled.\n",
    "\n",
    "    Returns:\n",
    "    df_train_scaled (DataFrame): The scaled training dataset.\n",
    "    df_test_scaled (DataFrame): The scaled testing dataset.\n",
    "    \"\"\"\n",
    "    # Initialize MinMaxScaler with feature range between 0 and 1\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "    # Fit and transform the training dataset\n",
    "    df_train_scaled = scaler.fit_transform(df_train)\n",
    "\n",
    "    # Transform the testing dataset using the same scaler fitted on the training data\n",
    "    df_test_scaled = scaler.transform(df_test)\n",
    "\n",
    "    return df_train_scaled, df_test_scaled\n",
    "\n",
    "X_train_scale, X_test_scale = scale_data(X_train, X_test )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bede7c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test = X_train.copy()\n",
    "#test[\"ID\"] = ids_test\n",
    "#test.set_index(\"ID\", inplace=True)\n",
    "#ids_test.iloc[5]\n",
    "#test.loc[100008].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058db3e2",
   "metadata": {},
   "source": [
    "<a name='1.1'></a>\n",
    "# 1.1 Imbalanced Data treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae50308a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 226067, 1: 19941})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 45212, 1: 22606})\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def SMOTE_transformation(X, y):\n",
    "    # summarize class distribution\n",
    "    #plt.style.use('seaborn-v0_8-whitegrid')\n",
    "    plt.style.use('seaborn-v0_8-pastel')\n",
    "    counter = Counter(y)\n",
    "    print(counter)\n",
    "    plt.bar(counter.keys(), counter.values(), color = ['darkseagreen', 'wheat'])\n",
    "    plt.xlabel('Class Label')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title('Distribution of Class Labels')\n",
    "    class_labels = [0, 1]\n",
    "    # Set xticks and yticks to integer values\n",
    "    plt.xticks(range(len(class_labels)), class_labels)\n",
    "    #plt.yticks(range(max(counter.values()) + 1))  # Range up to the maximum count\n",
    "    plt.show()\n",
    "    # define pipeline\n",
    "    over = SMOTE(sampling_strategy=0.1)\n",
    "    under = RandomUnderSampler(sampling_strategy=0.5)\n",
    "    steps = [('o', over), ('u', under)]\n",
    "    pipeline = Pipeline(steps=steps)\n",
    "    # transform the dataset\n",
    "    X, y = pipeline.fit_resample(X, y)\n",
    "    # summarize the new class distribution\n",
    "    counter = Counter(y)\n",
    "    print(counter)\n",
    "    # scatter plot of examples by class label\n",
    "    #for label, _ in counter.items():\n",
    "    #    print(label)\n",
    "    #    row_ix = where(y == label)[0]\n",
    "    #    print(row_ix)\n",
    "    #    pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(label))\n",
    "\n",
    "    #pyplot.legend()\n",
    "    #pyplot.show()\n",
    "    \n",
    "    return X, y\n",
    "\n",
    "x_train_smote, y_train_smote = SMOTE_transformation(X_train, Y_train['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "672c7e6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "011ece95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67818, 239)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_smote.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f1cdbe1",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "# 2 Metrics definition\n",
    "\n",
    "**Fbeta Score**\n",
    "\n",
    "![alt text for screen readers](IMAGES/fbetascore.png \"Fbeta Score\")\n",
    "\n",
    "A smaller beta value, such as 0.5, gives more weight to precision and less to recall, whereas a larger beta value, such as 2.0, gives less weight to precision and more weight to recall in the calculation of the score.\n",
    "\n",
    "#### FN et FP  \n",
    "On simule la supposition, que le coût d’un FN est dix fois supérieur au coût d’un FP.  \n",
    "On crée un score “métier” en minimisant le coût d’erreur de prédiction des FN et FP pour comparer les modèles, afin de choisir le meilleur modèle et ses meilleurs hyperparamètres.\n",
    "\n",
    "**Precision**  \n",
    "(tp / (tp + fp))\n",
    "It describes how good a model is at predicting the positive class. Precision is referred to as the positive predictive value.  \n",
    "\n",
    "**Recall** is the same as sensitivity.\n",
    "(tp / (tp + fn)    \n",
    "Recall describes how good the model is at predicting the positive class when the actual outcome is positive.\n",
    "\n",
    "\n",
    "We want to optimize recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d982f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fbeta_score_calculation(y_true, y_pred):\n",
    "    fbeta_macro = fbeta_score(y_true, y_pred, average='macro', beta=2)\n",
    "    fbeta_micro = fbeta_score(y_true, y_pred, average='micro', beta=2)\n",
    "    fbeta_weighted = fbeta_score(y_true, y_pred, average='weighted', beta=2)\n",
    "    return round(fbeta_macro,2), round(fbeta_micro,2), round(fbeta_weighted,2)\n",
    "\n",
    "def plot_roc_curve(true_y, y_prob):\n",
    "    \"\"\"\n",
    "    plots the roc curve based of the probabilities\n",
    "    \"\"\"\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(true_y, y_prob)\n",
    "    plt.plot(fpr, tpr)\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    \n",
    "def reminder_TP(TN=\"\", FP=\"\", FN=\"\", TP=\"\"):\n",
    "    #tn, fp, fn, tp\n",
    "    # Create a PrettyTable instance\n",
    "    table = PrettyTable()\n",
    "\n",
    "    # Define columns and headers\n",
    "    table.field_names = ['Confusion Matrix', 'Positive prediction', 'Negative prediction']\n",
    "\n",
    "    # Add rows\n",
    "    table.add_row(['Positive class', 'True positive (TP)', 'False negative (FN)'])\n",
    "    table.add_row(['Negative class', 'False positive (FP)', 'True negative (TN)'])\n",
    "\n",
    "    # Print the table\n",
    "    print(table)\n",
    "    \n",
    "    if TN :\n",
    "        # Create a PrettyTable instance\n",
    "        table2 = PrettyTable()\n",
    "\n",
    "        # Define columns and headers\n",
    "        table2.field_names = ['Confusion Matrix', 'Positive prediction', 'Negative prediction']\n",
    "\n",
    "        # Add rows\n",
    "        table2.add_row(['Positive class', TP, FN])\n",
    "        table2.add_row(['Negative class', FP, TN])\n",
    "\n",
    "        # Print the table\n",
    "        print(table2)\n",
    "    \n",
    "\n",
    "def generate_recall_precision_curve(model, X_test, Y_test):\n",
    "    # predict probabilities\n",
    "    lr_probs = model.predict_proba(X_test)\n",
    "    # keep probabilities for the positive outcome only\n",
    "    lr_probs = lr_probs[:, 1]\n",
    "    # predict class values\n",
    "    yhat = model.predict(X_test)\n",
    "    lr_precision, lr_recall, _ = precision_recall_curve(Y_test, lr_probs)\n",
    "    lr_f1, lr_auc = f1_score(Y_test, yhat), auc(lr_recall, lr_precision)\n",
    "    # summarize scores\n",
    "    print('Logistic: f1=%.3f auc=%.3f' % (lr_f1, lr_auc))\n",
    "    # plot the precision-recall curves\n",
    "    no_skill = len(Y_test[Y_test==1]) / len(Y_test)\n",
    "    plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')\n",
    "    plt.plot(lr_recall, lr_precision, marker='.', label='Logistic')\n",
    "    # axis labels\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    # show the legend\n",
    "    plt.legend()\n",
    "    # show the plot\n",
    "    plt.show()\n",
    "    pass\n",
    "\n",
    "def generate_auc_roc_curve(clf, X_test):\n",
    "    y_pred_proba = clf.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, thresholds = roc_curve(Y_test,  y_pred_proba)\n",
    "    auc = roc_auc_score(Y_test, y_pred_proba)\n",
    "    plt.plot(fpr,tpr,label=\"AUC ROC Curve with Area Under the curve =\"+str(auc))\n",
    "    plt.legend(loc=4)\n",
    "    plt.show()\n",
    "    pass\n",
    "\n",
    "def generate_model_report(model, model_name, X_test, Y_test, trainT):\n",
    "    print(\"\\n---------------------------------\")\n",
    "    print(\"start generate_model_report\")\n",
    "    \n",
    "    Y_Test_Pred_best_param = model.predict(X_test)\n",
    "    \n",
    "    generate_auc_roc_curve(model, X_test)\n",
    "    generate_recall_precision_curve(model, X_test, Y_test)\n",
    "    \n",
    "    cm = confusion_matrix(np.array(Y_test), Y_Test_Pred_best_param  )\n",
    "    tn, fp, fn, tp = cm.ravel()\n",
    "    reminder_TP(tn, fp, fn, tp)\n",
    "    \n",
    "    roc_auc = roc_auc_score(np.array(Y_test), Y_Test_Pred_best_param)\n",
    "\n",
    "    bu_Sc = fp + (10*fn) #business score FN cost 10 times more than fp\n",
    "    ac_Sc = accuracy_score(np.array(Y_test), Y_Test_Pred_best_param)\n",
    "    pr_Sc = precision_score(np.array(Y_test), Y_Test_Pred_best_param)\n",
    "    re_Sc = recall_score(np.array(Y_test), Y_Test_Pred_best_param)\n",
    "    F1_Sc = f1_score(np.array(Y_test), Y_Test_Pred_best_param)\n",
    "    fbeta_macro, fbeta_micro, fbeta_weighted = fbeta_score_calculation(np.array(Y_test), Y_Test_Pred_best_param)\n",
    "    print('ROC AUC: ', roc_auc)\n",
    "    print(\"Accuracy = \" , accuracy_score(np.array(Y_test), Y_Test_Pred_best_param))\n",
    "    print(\"Precision = \" ,precision_score(np.array(Y_test), Y_Test_Pred_best_param))\n",
    "    print(\"Recall = \" ,recall_score(np.array(Y_test), Y_Test_Pred_best_param))\n",
    "    print(\"F1 Score = \" ,f1_score(np.array(Y_test), Y_Test_Pred_best_param))\n",
    "    print(\"Fbeta Score = \" ,fbeta_score_calculation(np.array(Y_test), Y_Test_Pred_best_param))\n",
    "    \n",
    "    metrics = pd.DataFrame({'model': model_name,'tn': [tn], 'fp': [fp], 'fn': [fn], 'tp': [tp],'FP+10*FN': bu_Sc,\n",
    "                            'accuracy': [ac_Sc], \n",
    "                            'ROC_AUC': [roc_auc],\n",
    "                            'precision': [pr_Sc],\n",
    "                            'recall': [re_Sc],\n",
    "                            'F1_Score': [F1_Sc],\n",
    "                            'Fbeta_macro':[fbeta_macro], \n",
    "                            'Fbeta_micro':[fbeta_micro],\n",
    "                            'Fbeta_weighted':[fbeta_weighted]\n",
    "                            })\n",
    "    print(metrics)\n",
    "    \n",
    "    full_threshold_df, metrics_XGB_smote = find_optimal_business_score(model, model_name, X_test, Y_test, trainT)\n",
    "    \n",
    "    return metrics_XGB_smote, full_threshold_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ccf49b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_optimal_business_score(model, model_name, X_test, Y_true, trainT):\n",
    "    print(\"---------------------------------\")\n",
    "    print(\"start find_optimal_business_score\")\n",
    "    predictions_proba = model.predict_proba(X_test)\n",
    "    \n",
    "    print(\"prediction proba\", len(predictions_proba))\n",
    "    print(\"Y_true\", len(Y_true))\n",
    "\n",
    "    # Threshold values from 0 to 0.5\n",
    "    threshold_values = [i / 10 for i in range(6)]\n",
    "    best_B_score = 100000\n",
    "    \n",
    "    # Create an empty DataFrame to store results\n",
    "    results_df = pd.DataFrame(columns=['threshold', 'tn', 'fp', 'fn', 'tp', 'FP+10*FN', 'accuracy', 'ROC_AUC', 'precision', 'recall', 'F1_Score', 'Fbeta_macro', 'Fbeta_micro', 'Fbeta_weighted','best'])\n",
    "\n",
    "    # Loop through threshold values\n",
    "    for threshold in threshold_values:\n",
    "        best = 0\n",
    "        # Convert probabilities to binary predictions based on threshold\n",
    "        predicted_labels = [1 if x[1] >= threshold else 0 for x in predictions_proba]\n",
    "\n",
    "        # Calculate confusion matrix and other metrics\n",
    "        cm = confusion_matrix(Y_true, predicted_labels)\n",
    "        tn, fp, fn, tp = cm.ravel()\n",
    "        \n",
    "        FP_10_FN = fp + 10 * fn\n",
    "        precision = precision_score(Y_true, predicted_labels)\n",
    "        recall = recall_score(Y_true, predicted_labels)\n",
    "        accuracy = accuracy_score(Y_true, predicted_labels)\n",
    "        roc_auc = roc_auc_score(Y_true, predicted_labels)\n",
    "        f1 = f1_score(Y_true, predicted_labels)\n",
    "        fbeta_macro = fbeta_score(Y_true, predicted_labels, beta=2, average='macro')\n",
    "        fbeta_micro = fbeta_score(Y_true, predicted_labels, beta=2, average='micro')\n",
    "        fbeta_weighted = fbeta_score(Y_true, predicted_labels, beta=2, average='weighted')\n",
    "\n",
    "        # best param\n",
    "        if (best_B_score > FP_10_FN ):\n",
    "            print(results_df[results_df['best']==1]['best'])\n",
    "            results_df.loc[(results_df['best']==1),'best']  = 0\n",
    "            best_B_score = FP_10_FN\n",
    "            best = 1\n",
    "        \n",
    "        \n",
    "        # Create a DataFrame for the current threshold iteration\n",
    "        data = {'threshold': [threshold],\n",
    "                'tn': [tn],\n",
    "                'fp': [fp],\n",
    "                'fn': [fn],\n",
    "                'tp': [tp],\n",
    "                'FP+10*FN': [FP_10_FN],\n",
    "                'accuracy': [accuracy],\n",
    "                'ROC_AUC': [roc_auc],\n",
    "                'precision': [precision],\n",
    "                'recall': [recall],\n",
    "                'F1_Score': [f1],\n",
    "                'Fbeta_macro': [fbeta_macro],\n",
    "                'Fbeta_micro': [fbeta_micro],\n",
    "                'Fbeta_weighted': [fbeta_weighted],\n",
    "                'best': best\n",
    "               }\n",
    "\n",
    "        threshold_df = pd.DataFrame(data)\n",
    "\n",
    "        # Concatenate the current threshold results to the overall results DataFrame\n",
    "        results_df = pd.concat([results_df, threshold_df], ignore_index=True)\n",
    "        \n",
    "    \n",
    "    best_result = results_df[results_df['best']==1]\n",
    "    \n",
    "    best_metrics = {\"TN\":best_result[\"tn\"].iloc[0],\n",
    "                  \"FP\":best_result[\"fp\"].iloc[0],\n",
    "                  \"FN\":best_result[\"fn\"].iloc[0],\n",
    "                  \"TP\":best_result[\"tp\"].iloc[0],\n",
    "                  \"FP_10_FN\":best_result[\"FP+10*FN\"].iloc[0],\n",
    "                  \"Accuracy\": best_result[\"accuracy\"].iloc[0],\n",
    "                 \"F1\":best_result[\"F1_Score\"].iloc[0],\n",
    "                 \"Precision\":best_result[\"precision\"].iloc[0],\n",
    "                 \"Recall\":best_result[\"recall\"].iloc[0],\n",
    "                 \"ROC_AUC\":best_result[\"ROC_AUC\"].iloc[0],\n",
    "                \"threshold\":best_result[\"threshold\"].iloc[0],\n",
    "                    \"time_in_s\":trainT\n",
    "                 }\n",
    "    \n",
    "    print(\"best b score\", best_B_score, results_df[results_df['best']==1]['threshold'])\n",
    "    print(results_df)\n",
    "    return results_df, best_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cad3596",
   "metadata": {},
   "source": [
    "<a name='3'></a>\n",
    "# 3 Model definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42ab1899",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RFC_model(X_train, Y_train) :\n",
    "    \"\"\"Train RandomForestClassifier model using GridSearchCV.\n",
    "\n",
    "    Args:\n",
    "        X_train (pd.DataFrame): The input features for training.\n",
    "        Y_train (pd.Series): The target labels for training.\n",
    "\n",
    "    Returns:\n",
    "        tuple: A tuple containing the best RandomForestClassifier model, best hyperparameters, and training duration.\n",
    "\n",
    "    \"\"\"\n",
    "    # Start timing the function\n",
    "    start = time.time()\n",
    "    print(\"START time\", time.ctime(start))\n",
    "    \n",
    "    # Define the RandomForestClassifier\n",
    "    rf_classifier = RandomForestClassifier()\n",
    "\n",
    "    # Define the hyperparameter grid for GridSearchCV\n",
    "    param_grid = {\n",
    "        'n_estimators': [50, 100, 200],          # Number of trees in the forest\n",
    "        'max_depth': [None, 10, 20],              # Maximum depth of the tree\n",
    "        'min_samples_split': [2, 5, 10],          # Minimum number of samples required to split an internal node\n",
    "        'min_samples_leaf': [1, 2, 4]             # Minimum number of samples required to be at a leaf node\n",
    "    }\n",
    "\n",
    "    # Create the GridSearchCV object\n",
    "    grid_search = GridSearchCV(rf_classifier, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "    # Perform the grid search on the training data\n",
    "    grid_search.fit(X_train, Y_train)\n",
    "\n",
    "    # Get the best hyperparameters from the grid search\n",
    "    best_params = grid_search.best_params_\n",
    "\n",
    "    # Train the RandomForestClassifier with the best hyperparameters on the entire training set\n",
    "    best_rf_classifier = RandomForestClassifier(**best_params)\n",
    "    best_rf_classifier.fit(X_train, Y_train)\n",
    "\n",
    "    # Print the best hyperparameters\n",
    "    print(f'Best Hyperparameters: {best_params}')\n",
    "\n",
    "    # End timing the function\n",
    "    end = time.time()\n",
    "    endT = end - start\n",
    "    print(\"START time\", time.ctime(start))\n",
    "    print(\"END time\", time.ctime(end), ' duration', endT/60 , 'min')\n",
    "    \n",
    "    # Return the best RandomForestClassifier model, best hyperparameters, and the duration of training\n",
    "    return best_rf_classifier, best_params, endT\n",
    "\n",
    "def train_XGBoost_model(X_train: pd.DataFrame, Y_train: pd.Series):\n",
    "    \"\"\"Train XGBoost model using RandomizedSearchCV.\n",
    "\n",
    "    Args:\n",
    "        X_train (pd.DataFrame): The input features for training.\n",
    "        Y_train (pd.Series): The target labels for training.\n",
    "\n",
    "    Returns:\n",
    "        tuple: A tuple containing the best XGBoost model, best hyperparameters, and training duration.\n",
    "\n",
    "    \"\"\"\n",
    "    # Start timing the function\n",
    "    start = time.time()\n",
    "    print(\"START time\", time.ctime(start))\n",
    "    \n",
    "    # Define hyperparameters for XGBoost model\n",
    "    xgb_params = {\n",
    "        'n_estimators': [*range(170, 200, 5)],  # Range of values for the number of trees\n",
    "        'max_depth': [6],  # Maximum depth of a tree\n",
    "        'learning_rate': [0.1],  # Learning rate\n",
    "        'subsample': [0.3],  # Subsample ratio of the training instances\n",
    "        'colsample_bytree': [0.3]  # Subsample ratio of columns when constructing each tree\n",
    "    }\n",
    "\n",
    "    # Initialize XGBoost classifier\n",
    "    xgb_model = XGBClassifier()\n",
    "\n",
    "    # Perform RandomizedSearchCV to find the best hyperparameters\n",
    "    print(\"start RandomizedSearchCV \")\n",
    "    xgb_random_search = RandomizedSearchCV(xgb_model, param_distributions=xgb_params, n_iter=100, cv=5, n_jobs=-1, verbose=6, error_score='raise')\n",
    "    xgb_random_search.fit(X_train, Y_train)\n",
    "    \n",
    "    # Access the best hyperparameters and the best models\n",
    "    best_xgb_params = xgb_random_search.best_params_\n",
    "    best_xgb_model = xgb_random_search.best_estimator_\n",
    "    \n",
    "    param = {'booster': 'dart',\n",
    "         'max_depth': 5, 'learning_rate': 0.1,\n",
    "         'objective': 'binary:logistic',\n",
    "         'sample_type': 'uniform',\n",
    "         'normalize_type': 'tree',\n",
    "         'rate_drop': 0.1,\n",
    "         'skip_drop': 0.5}\n",
    "    num_round = 50\n",
    "    #best_xgb_model = xgb_model.fit(param, X_train, Y_train, num_round)\n",
    "    #best_xgb_params = param\n",
    "    \n",
    "    \n",
    "    \n",
    "    # End timing the function\n",
    "    end = time.time()\n",
    "    endT = end - start\n",
    "    print(\"START time\", time.ctime(start))\n",
    "    print(\"END time\", time.ctime(end), ' duration', endT/60 , 'min')\n",
    "    \n",
    "    # Return the best XGBoost model, best hyperparameters, and the duration of training\n",
    "    return best_xgb_model, best_xgb_params, endT\n",
    "\n",
    "def train_LightGBM_model(X_train: pd.DataFrame, Y_train: pd.Series) -> tuple:\n",
    "    \"\"\"Train LightGBM model using RandomizedSearchCV.\n",
    "\n",
    "    Args:\n",
    "        X_train (pd.DataFrame): The input features for training.\n",
    "        Y_train (pd.Series): The target labels for training.\n",
    "\n",
    "    Returns:\n",
    "        tuple: A tuple containing the best LightGBM model, best hyperparameters, and training duration.\n",
    "\n",
    "    \"\"\"\n",
    "    # Start timing the function\n",
    "    start = time.time()\n",
    "    print(\"START time\", time.ctime(time.time()))\n",
    "    \n",
    "    \n",
    "    # Define hyperparameters for LightGBM model\n",
    "    lgbm_params = {\n",
    "        'boosting_type': ['gbdt'],           # Gradient boosting type\n",
    "        'n_estimators': [10000],             # Number of boosting iterations\n",
    "        'objective': ['binary'],             # Objective function (binary classification)\n",
    "        'metric': ['binary_logloss'],        # Evaluation metric\n",
    "        'num_leaves': [31],                  # Maximum number of leaves in one tree\n",
    "        'learning_rate': [0.05],             # Learning rate\n",
    "        'class_weight' : ['balanced'],       # Weights associated with classes\n",
    "        'reg_alpha' : [0.1],                 # L1 regularization term\n",
    "        'reg_lambda' : [0.1],                # L2 regularization term\n",
    "        'subsample' : [ 0.8 ]                # Subsample ratio of the training instances\n",
    "    }\n",
    "    # Best hyperparameters are commented out for reference\n",
    "\n",
    "    # Initialize LightGBM classifier\n",
    "    lgbm_model = LGBMClassifier()\n",
    "\n",
    "    # Perform RandomizedSearchCV to find the best hyperparameters\n",
    "    lgbm_random_search = RandomizedSearchCV(lgbm_model, param_distributions=lgbm_params, n_iter=100, cv=5, n_jobs=-1, verbose=5)\n",
    "    lgbm_random_search.fit(X_train, Y_train)\n",
    "\n",
    "    # Access the best hyperparameters and the best models\n",
    "    best_lgbm_params = lgbm_random_search.best_params_\n",
    "    best_lgbm_model = lgbm_random_search.best_estimator_\n",
    "\n",
    "    # End timing the function\n",
    "    end = time.time()\n",
    "    endT = end - start\n",
    "    print(\"START time\", time.ctime(start))\n",
    "    print(\"END time\", time.ctime(end), ' duration', endT/60 , 'min')\n",
    "    \n",
    "    # Return the best LightGBM model, best hyperparameters, and the duration of training\n",
    "    return best_lgbm_model, best_lgbm_params, endT\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6ed98b",
   "metadata": {},
   "source": [
    "**Train models and save model, params to MLflow and the evaluation metrics to a dataframe**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b224bd54",
   "metadata": {},
   "source": [
    "The following command in Terminal to start the MLflow server  \n",
    "```!mlflow server --host 127.0.0.1 --port 8080```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ca7ad59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_MLflow(experiment_name, run_name, metrics, params, model_obj, X_train):\n",
    "    mlflow.set_tracking_uri(\"http://127.0.0.1:8080\")\n",
    "    # Sets the current active experiment to the \"LightGBM_Models\" experiment and returns the Experiment metadata\n",
    "    mlflow_experiment = mlflow.set_experiment(experiment_name)\n",
    "\n",
    "    # Define an artifact path that the model will be saved to.\n",
    "    artifact_path = run_name+\"_artifactPATH\"\n",
    "    print(\"Artifact PATH\" , artifact_path)\n",
    "    \n",
    "    # Assemble the metrics we're going to write into a collection\n",
    "    print(metrics)\n",
    "    print(params)\n",
    "    \n",
    "    signature = infer_signature(X_train, model_obj.predict(X_train))\n",
    "\n",
    "    # Initiate the MLflow run context\n",
    "    with mlflow.start_run(run_name=run_name) as run:\n",
    "        #run = mlflow.active_run()\n",
    "        print(f\"Active run_id: {run.info.run_id}\")\n",
    "        # Log the parameters used for the model fit\n",
    "        mlflow.log_params(params)\n",
    "         # Log parameters and metrics with MLflow\n",
    "        #for key, value in params.items():\n",
    "        #    mlflow.log_param(key, value)\n",
    "        # Log the error metrics that were calculated during validation\n",
    "        mlflow.log_metrics(metrics)\n",
    "        # Log an instance of the trained model for later use\n",
    "        mlflow.sklearn.log_model(sk_model=model_obj, artifact_path=artifact_path, signature=signature)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6cab8a",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "# 4 Run 1 EXPERIMENT with 6 RUNs\n",
    "\n",
    "**Unbalanced Data**\n",
    "* Random Forest Classifier RFC\n",
    "* XGBoost XGB\n",
    "* LightGBM LGBM\n",
    "\n",
    "**SMOTE transformed Data**\n",
    "* Random Forest Classifier RFC\n",
    "* XGBoost XGB\n",
    "* LightGBM LGBM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8df72b",
   "metadata": {},
   "source": [
    "**Initiate MLFlow**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "63f2cb9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MlflowClient(tracking_uri=\"http://127.0.0.1:8080/\")\n",
    "# Provide an Experiment description that will appear in the UI\n",
    "experiment_description = (\n",
    "    \"This is the credit score project. \"\n",
    "    \"This experiment contains the LightGBM, XGboost and RFC model.\"\n",
    ")\n",
    "\n",
    "# Provide searchable tags that define characteristics of the Runs that will be in this Experiment\n",
    "experiment_tags = {\n",
    "    \"project_name\": \"credit-score-classification\",\n",
    "    \"store_dept\": \"Prêt à dépenser\",\n",
    "    \"team\": \"cred-ml\",\n",
    "    \"project_quarter\": \"Q1-2024\",\n",
    "    \"mlflow.note.content\": experiment_description,\n",
    "}\n",
    "\n",
    "experiment_name = \"Credit_scoring\"\n",
    "# Create the Experiment, providing a unique name\n",
    "#experiment = client.create_experiment(name=experiment_name, tags=experiment_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6712832",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ad474b89",
   "metadata": {},
   "source": [
    "**1/6 RUN XGB with unbalanced data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ccefa55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "45af51da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start train_XGBoost_model\n",
      "START time Sun Mar 17 23:27:06 2024\n",
      "start RandomizedSearchCV \n",
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The total space of parameters 6 is smaller than n_iter=100. Running 6 iterations. For exhaustive searches, use GridSearchCV.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 5/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=175, subsample=0.3;, score=0.919 total time= 6.5min\n",
      "[CV 3/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=185, subsample=0.3;, score=0.919 total time= 6.8min\n",
      "[CV 5/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=195, subsample=0.3;, score=0.919 total time= 3.9min\n",
      "START time Sun Mar 17 23:27:06 2024\n",
      "END time Sun Mar 17 23:45:05 2024  duration 17.98096135457357 min\n",
      "\n",
      "---------------------------------\n",
      "start generate_model_report\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic: f1=0.039 auc=0.233\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+---------------------+---------------------+\n",
      "| Confusion Matrix | Positive prediction | Negative prediction |\n",
      "+------------------+---------------------+---------------------+\n",
      "|  Positive class  |  True positive (TP) | False negative (FN) |\n",
      "|  Negative class  | False positive (FP) |  True negative (TN) |\n",
      "+------------------+---------------------+---------------------+\n",
      "+------------------+---------------------+---------------------+\n",
      "| Confusion Matrix | Positive prediction | Negative prediction |\n",
      "+------------------+---------------------+---------------------+\n",
      "|  Positive class  |          98         |         4786        |\n",
      "|  Negative class  |          84         |        56535        |\n",
      "+------------------+---------------------+---------------------+\n",
      "ROC AUC:  0.5092909595770826\n",
      "Accuracy =  0.9208168707217534\n",
      "Precision =  0.5384615384615384\n",
      "Recall =  0.020065520065520065\n",
      "F1 Score =  0.038689301223845236\n",
      "Fbeta Score =  (0.5, 0.92, 0.91)\n",
      "  model     tn  fp    fn  tp  FP+10*FN  accuracy   ROC_AUC  precision  \\\n",
      "0   XGB  56535  84  4786  98     47944  0.920817  0.509291   0.538462   \n",
      "\n",
      "     recall  F1_Score  Fbeta_macro  Fbeta_micro  Fbeta_weighted  \n",
      "0  0.020066  0.038689          0.5         0.92            0.91  \n",
      "---------------------------------\n",
      "start find_optimal_business_score\n",
      "prediction proba 61503\n",
      "Y_true 61503\n",
      "Series([], Name: best, dtype: object)\n",
      "0    1\n",
      "Name: best, dtype: object\n",
      "best b score 32675 1    0.1\n",
      "Name: threshold, dtype: float64\n",
      "   threshold     tn     fp    fn    tp FP+10*FN  accuracy   ROC_AUC  \\\n",
      "0        0.0      0  56619     0  4884    56619  0.079411  0.500000   \n",
      "1        0.1  43984  12635  2004  2880    32675  0.761979  0.683261   \n",
      "2        0.2  53217   3402  3564  1320    39042  0.886737  0.605092   \n",
      "3        0.3  55606   1013  4261   623    43623  0.914248  0.554834   \n",
      "4        0.4  56337    282  4620   264    46482  0.920297  0.524537   \n",
      "5        0.5  56535     84  4786    98    47944  0.920817  0.509291   \n",
      "\n",
      "   precision    recall  F1_Score  Fbeta_macro  Fbeta_micro  Fbeta_weighted  \\\n",
      "0   0.079411  1.000000  0.147137     0.150668     0.079411        0.023929   \n",
      "1   0.185627  0.589681  0.282367     0.608991     0.761979        0.775680   \n",
      "2   0.279543  0.270270  0.274828     0.605726     0.886737        0.886386   \n",
      "3   0.380807  0.127559  0.191104     0.559048     0.914248        0.905547   \n",
      "4   0.483516  0.054054  0.097238     0.522866     0.920297        0.907399   \n",
      "5   0.538462  0.020066  0.038689     0.503527     0.920817        0.906179   \n",
      "\n",
      "  best  \n",
      "0    0  \n",
      "1    1  \n",
      "2    0  \n",
      "3    0  \n",
      "4    0  \n",
      "5    0  \n",
      "[CV 4/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=175, subsample=0.3;, score=0.919 total time= 6.5min\n",
      "[CV 4/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=185, subsample=0.3;, score=0.919 total time= 6.9min\n",
      "[CV 2/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=175, subsample=0.3;, score=0.919 total time= 6.5min\n",
      "[CV 5/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=185, subsample=0.3;, score=0.919 total time= 6.9min\n",
      "[CV 1/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=175, subsample=0.3;, score=0.919 total time= 6.6min\n",
      "[CV 1/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=190, subsample=0.3;, score=0.919 total time= 7.0min\n",
      "[CV 3/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=175, subsample=0.3;, score=0.919 total time= 6.6min\n",
      "[CV 2/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=190, subsample=0.3;, score=0.919 total time= 7.0min\n",
      "[CV 1/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=180, subsample=0.3;, score=0.919 total time= 6.7min\n",
      "[CV 3/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=190, subsample=0.3;, score=0.919 total time= 6.9min\n",
      "[CV 2/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=180, subsample=0.3;, score=0.919 total time= 6.7min\n",
      "[CV 4/5] END colsample_bytree=0.3, learning_rate=0.1, max_depth=6, n_estimators=190, subsample=0.3;, score=0.919 total time= 6.9min\n"
     ]
    },
    {
     "ename": "MlflowException",
     "evalue": "API request to http://127.0.0.1:8080/api/2.0/mlflow/experiments/get-by-name failed with exception HTTPConnectionPool(host='127.0.0.1', port=8080): Max retries exceeded with url: /api/2.0/mlflow/experiments/get-by-name?experiment_name=Credit_scoring (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fcfdebc5640>: Failed to establish a new connection: [Errno 61] Connection refused'))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mConnectionRefusedError\u001b[0m                    Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connection.py:203\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 203\u001b[0m     sock \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    205\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[43m        \u001b[49m\u001b[43msource_address\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    207\u001b[0m \u001b[43m        \u001b[49m\u001b[43msocket_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msocket_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    208\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 85\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/util/connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 73\u001b[0m \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
      "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 61] Connection refused",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py:790\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    789\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 790\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    805\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py:496\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    495\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 496\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43menforce_content_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43menforce_content_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    505\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[38;5;66;03m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[1;32m    508\u001b[0m \u001b[38;5;66;03m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[1;32m    509\u001b[0m \u001b[38;5;66;03m# With this behaviour, the received response is still readable.\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connection.py:395\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, chunked, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    394\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mputheader(header, value)\n\u001b[0;32m--> 395\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    397\u001b[0m \u001b[38;5;66;03m# If we're given a body we start sending that in chunks.\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/http/client.py:1250\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1250\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/http/client.py:1010\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1009\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1010\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1012\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1013\u001b[0m \n\u001b[1;32m   1014\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/http/client.py:950\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[0;32m--> 950\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    951\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connection.py:243\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 243\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    244\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n\u001b[1;32m    245\u001b[0m         \u001b[38;5;66;03m# If we're tunneling it means we're connected to our proxy.\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connection.py:218\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 218\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NewConnectionError(\n\u001b[1;32m    219\u001b[0m         \u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to establish a new connection: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    220\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;66;03m# Audit hooks are only available in Python 3.8+\u001b[39;00m\n",
      "\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0x7fcfdebc5640>: Failed to establish a new connection: [Errno 61] Connection refused",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py:874\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    871\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    873\u001b[0m     )\n\u001b[0;32m--> 874\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    888\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    892\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py:874\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    871\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    873\u001b[0m     )\n\u001b[0;32m--> 874\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    888\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    892\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
      "    \u001b[0;31m[... skipping similar frames: HTTPConnectionPool.urlopen at line 874 (2 times)]\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py:874\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    871\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    873\u001b[0m     )\n\u001b[0;32m--> 874\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    888\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    892\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py:844\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    842\u001b[0m     new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[0;32m--> 844\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    845\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_e\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m    846\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    847\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/util/retry.py:515\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m    514\u001b[0m     reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[0;32m--> 515\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m    517\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
      "\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=8080): Max retries exceeded with url: /api/2.0/mlflow/experiments/get-by-name?experiment_name=Credit_scoring (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fcfdebc5640>: Failed to establish a new connection: [Errno 61] Connection refused'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/utils/rest_utils.py:107\u001b[0m, in \u001b[0;36mhttp_request\u001b[0;34m(host_creds, endpoint, method, max_retries, backoff_factor, backoff_jitter, extra_headers, retry_codes, timeout, raise_on_status, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 107\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_http_response_with_retries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    108\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    109\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    110\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    111\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbackoff_factor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    112\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbackoff_jitter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretry_codes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    114\u001b[0m \u001b[43m        \u001b[49m\u001b[43mraise_on_status\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    115\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    116\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhost_creds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    117\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    119\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;28;01mas\u001b[39;00m to:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/utils/request_utils.py:197\u001b[0m, in \u001b[0;36m_get_http_response_with_retries\u001b[0;34m(method, url, max_retries, backoff_factor, backoff_jitter, retry_codes, raise_on_status, allow_redirects, **kwargs)\u001b[0m\n\u001b[1;32m    195\u001b[0m allow_redirects \u001b[38;5;241m=\u001b[39m env_value \u001b[38;5;28;01mif\u001b[39;00m allow_redirects \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m allow_redirects\n\u001b[0;32m--> 197\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_redirects\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/adapters.py:519\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    517\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m SSLError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m--> 519\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m    521\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ClosedPoolError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[0;31mConnectionError\u001b[0m: HTTPConnectionPool(host='127.0.0.1', port=8080): Max retries exceeded with url: /api/2.0/mlflow/experiments/get-by-name?experiment_name=Credit_scoring (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fcfdebc5640>: Failed to establish a new connection: [Errno 61] Connection refused'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mMlflowException\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[24], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m best_xgb_model, best_xgb_params, timeXGB \u001b[38;5;241m=\u001b[39m train_XGBoost_model(X_train, Y_train)    \n\u001b[1;32m      5\u001b[0m XGB_metrics, best_XGB  \u001b[38;5;241m=\u001b[39m generate_model_report(best_xgb_model, run_name, X_test, Y_test, timeXGB)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mrun_MLflow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexperiment_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mXGB_metrics\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbest_xgb_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbest_xgb_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[16], line 4\u001b[0m, in \u001b[0;36mrun_MLflow\u001b[0;34m(experiment_name, run_name, metrics, params, model_obj, X_train)\u001b[0m\n\u001b[1;32m      2\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mset_tracking_uri(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttp://127.0.0.1:8080\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Sets the current active experiment to the \"LightGBM_Models\" experiment and returns the Experiment metadata\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m mlflow_experiment \u001b[38;5;241m=\u001b[39m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_experiment\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexperiment_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Define an artifact path that the model will be saved to.\u001b[39;00m\n\u001b[1;32m      7\u001b[0m artifact_path \u001b[38;5;241m=\u001b[39m run_name\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_artifactPATH\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/tracking/fluent.py:135\u001b[0m, in \u001b[0;36mset_experiment\u001b[0;34m(experiment_name, experiment_id)\u001b[0m\n\u001b[1;32m    133\u001b[0m client \u001b[38;5;241m=\u001b[39m MlflowClient()\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m experiment_id \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m     experiment \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_experiment_by_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexperiment_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m experiment:\n\u001b[1;32m    137\u001b[0m         _logger\u001b[38;5;241m.\u001b[39minfo(\n\u001b[1;32m    138\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExperiment with name \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m does not exist. Creating a new experiment.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    139\u001b[0m             experiment_name,\n\u001b[1;32m    140\u001b[0m         )\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/tracking/client.py:520\u001b[0m, in \u001b[0;36mMlflowClient.get_experiment_by_name\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    489\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_experiment_by_name\u001b[39m(\u001b[38;5;28mself\u001b[39m, name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Experiment]:\n\u001b[1;32m    490\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;124;03m    Retrieve an experiment by experiment name from the backend store\u001b[39;00m\n\u001b[1;32m    492\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[38;5;124;03m        Lifecycle_stage: active\u001b[39;00m\n\u001b[1;32m    519\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tracking_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_experiment_by_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/tracking/_tracking_service/client.py:221\u001b[0m, in \u001b[0;36mTrackingServiceClient.get_experiment_by_name\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_experiment_by_name\u001b[39m(\u001b[38;5;28mself\u001b[39m, name):\n\u001b[1;32m    217\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;124;03m    :param name: The experiment name.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;124;03m    :return: :py:class:`mlflow.entities.Experiment`\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 221\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_experiment_by_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/store/tracking/rest_store.py:306\u001b[0m, in \u001b[0;36mRestStore.get_experiment_by_name\u001b[0;34m(self, experiment_name)\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    305\u001b[0m     req_body \u001b[38;5;241m=\u001b[39m message_to_json(GetExperimentByName(experiment_name\u001b[38;5;241m=\u001b[39mexperiment_name))\n\u001b[0;32m--> 306\u001b[0m     response_proto \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_endpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mGetExperimentByName\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq_body\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    307\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Experiment\u001b[38;5;241m.\u001b[39mfrom_proto(response_proto\u001b[38;5;241m.\u001b[39mexperiment)\n\u001b[1;32m    308\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m MlflowException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/store/tracking/rest_store.py:59\u001b[0m, in \u001b[0;36mRestStore._call_endpoint\u001b[0;34m(self, api, json_body)\u001b[0m\n\u001b[1;32m     57\u001b[0m endpoint, method \u001b[38;5;241m=\u001b[39m _METHOD_TO_INFO[api]\n\u001b[1;32m     58\u001b[0m response_proto \u001b[38;5;241m=\u001b[39m api\u001b[38;5;241m.\u001b[39mResponse()\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall_endpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_host_creds\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mendpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_proto\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/utils/rest_utils.py:215\u001b[0m, in \u001b[0;36mcall_endpoint\u001b[0;34m(host_creds, endpoint, method, json_body, response_proto, extra_headers)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGET\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    214\u001b[0m     call_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m json_body\n\u001b[0;32m--> 215\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mhttp_request\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcall_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    217\u001b[0m     call_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjson\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m json_body\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mlflow/utils/rest_utils.py:129\u001b[0m, in \u001b[0;36mhttp_request\u001b[0;34m(host_creds, endpoint, method, max_retries, backoff_factor, backoff_jitter, extra_headers, retry_codes, timeout, raise_on_status, **kwargs)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m InvalidUrlException(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid url: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01miu\u001b[39;00m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MlflowException(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAPI request to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m failed with exception \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mMlflowException\u001b[0m: API request to http://127.0.0.1:8080/api/2.0/mlflow/experiments/get-by-name failed with exception HTTPConnectionPool(host='127.0.0.1', port=8080): Max retries exceeded with url: /api/2.0/mlflow/experiments/get-by-name?experiment_name=Credit_scoring (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fcfdebc5640>: Failed to establish a new connection: [Errno 61] Connection refused'))"
     ]
    }
   ],
   "source": [
    "# XGB no SMOTE data   \n",
    "run_name = \"XGB\"\n",
    "print(\"start train_XGBoost_model\")\n",
    "best_xgb_model, best_xgb_params, timeXGB = train_XGBoost_model(X_train, Y_train)    \n",
    "XGB_metrics, best_XGB  = generate_model_report(best_xgb_model, run_name, X_test, Y_test, timeXGB)\n",
    "run_MLflow(experiment_name, run_name, XGB_metrics, best_xgb_params, best_xgb_model, X_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c93a91",
   "metadata": {},
   "source": [
    "**2/6 RUN XGB with SMOTE data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434d4c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGB with SMOTE data   \n",
    "run_name = \"XGB_smote\"\n",
    "XGB_model_smote, best_xfb_params_smote, timeXGBsmote =  train_XGBoost_model(x_train_smote, y_train_smote)\n",
    "XGB_smote_metrics, best_XGB_smote     = generate_model_report(XGB_model_smote, run_name, X_test, Y_test, timeXGBsmote)\n",
    "run_MLflow(experiment_name, run_name, XGB_smote_metrics, best_xfb_params_smote, XGB_model_smote, x_train_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a909db02",
   "metadata": {},
   "source": [
    "**3/6 LightGBM on unbalanced data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d68417fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"LightGBM\"\n",
    "LGBM_model, best_LGBM_params, time_LGBM =  train_LightGBM_model(X_train, Y_train)\n",
    "metrics_LGBM, LGBM_metrics     = generate_model_report(LGBM_model, run_name, X_test, Y_test, time_LGBM)\n",
    "run_MLflow(experiment_name, run_name, metrics_LGBM, best_LGBM_params, LGBM_model, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af8163c",
   "metadata": {},
   "source": [
    "**4/6 LightGBM on smote data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "292f3789",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"LightGBM_smote\"\n",
    "LGBM_smote_model, best_LGBM_smote_params, time_LGBM_smote =  train_LightGBM_model(x_train_smote, y_train_smote)\n",
    "metrics_LGBM_smote, LGBM_smote_metrics     = generate_model_report(LGBM_smote_model, run_name, X_test, Y_test, time_LGBM_smote)\n",
    "run_MLflow(experiment_name, run_name, metrics_LGBM_smote, best_LGBM_smote_params,\n",
    "           LGBM_smote_model, x_train_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67893829",
   "metadata": {},
   "source": [
    "**5/6 RFC on unbalanced data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3c3b1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"RFC\"\n",
    "RFC_model, best_RFC_params, time_RFC = RFC_model(X_train, Y_train)\n",
    "RFC_metrics, best_metrics_RFC        = generate_model_report(RFC_model, run_name, X_test, Y_test, time_RFC)\n",
    "run_MLflow(experiment_name, run_name, RFC_metrics, \n",
    "           best_RFC_params, RFC_model, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e44e3f40",
   "metadata": {},
   "source": [
    "**6/6 RFC on Smote data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8449b44c",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"RFC_smote\"\n",
    "RFC_model_smote, best_RFC_params_smote, time_RFC_smote = RFC_model(x_train_smote, y_train_smote)\n",
    "RFC_smote_metrics, best_metrics_RFC_smote              = generate_model_report(RFC_model_smote, run_name, X_test, Y_test, time_RFC_smote)\n",
    "run_MLflow(experiment_name, run_name, RFC_smote_metrics, \n",
    "           best_RFC_params_smote, RFC_model_smote, x_train_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e08b8cf",
   "metadata": {},
   "source": [
    "<a name='5'></a>\n",
    "# 5 Evalutation and selection of best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73845c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the experiment by name\n",
    "experiment = mlflow.get_experiment_by_name(experiment_name)\n",
    "print(experiment)\n",
    "# Get all runs within the experiment\n",
    "runs = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\n",
    "\n",
    "# Create an empty list to store metrics data\n",
    "metrics_data = []\n",
    "\n",
    "# Iterate over each run and append its metrics to the list\n",
    "for index, run in runs.iterrows():\n",
    "    run_id = run[\"run_id\"]\n",
    "    #print(run_id)\n",
    "    run_info = mlflow.get_run(run_id)\n",
    "    #print(run_info)\n",
    "    run_name = run_info.data.tags.get(\"mlflow.runName\")\n",
    "    metrics = run_info.data.metrics\n",
    "    metrics_data.append({**metrics, \"Run Name\": run_name})\n",
    "\n",
    "# Create a DataFrame from the metrics data\n",
    "metrics_df = pd.DataFrame(metrics_data)\n",
    "metrics_df.set_index(\"Run Name\", inplace=True)  # Set the run name as the index\n",
    "\n",
    "#metrics_df['t_in_s'] = round(metrics_df['time_in_s'],0)\n",
    "metrics_df['t_in_min'] = round(metrics_df['time_in_s']/60,1)\n",
    "#metrics_df['FP_10_FN'] = round(metrics_df['FP_10_FN'],0)\n",
    "\n",
    "#metrics_df.drop(['t_in_s'],axis=1, inplace=True)\n",
    "\n",
    "metrics_df = metrics_df.sort_values(by='FP_10_FN', ascending=True)\n",
    "#print(round(metrics_df,2))\n",
    "#metrics_df = round(metrics_df,2)\n",
    "metrics_out = metrics_df[['FP_10_FN','ROC_AUC','t_in_min','FP','TP','FN','TN','Accuracy','Recall','threshold','Precision','F1']]\n",
    "metrics_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff175169",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1003146",
   "metadata": {},
   "outputs": [],
   "source": [
    "#smote 67818\n",
    "metrics_out['sum'] = metrics_out['FP'] + metrics_out['TP'] + metrics_out['FN'] + metrics_out['TN']\n",
    "metrics_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61e53b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(metrics_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ff01d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/03/17 23:25:02 WARNING mlflow.pyfunc: Detected one or more mismatches between the model's dependencies and the current Python environment:\n",
      " - xgboost (current: 1.7.0, required: xgboost==1.7.6)\n",
      "To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:25:02] WARNING: /Users/runner/work/xgboost/xgboost/python-package/build/temp.macosx-10.9-x86_64-cpython-38/xgboost/src/learner.cc:553: \n",
      "  If you are loading a serialized model (like pickle in Python, RDS in R) generated by\n",
      "  older XGBoost, please export the model by calling `Booster.save_model` from that version\n",
      "  first, then load it back in current version. See:\n",
      "\n",
      "    https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html\n",
      "\n",
      "  for more details about differences between saving model and serializing.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load saved model and make predictions\n",
    "logged_model = 'runs:/546a6c80762547cfb2f52e2cfb9bb735/XGB_artifactPATH'\n",
    "\n",
    "xgb_classifier_saved = mlflow.pyfunc.load_model(logged_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f09f03",
   "metadata": {},
   "source": [
    "The model **XGB** receives the best business score (FP_10_FN) with 32675, at the same time it has the second highest Accuracy and the second highest Precision."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688750c6",
   "metadata": {},
   "source": [
    "<a name='6'></a>\n",
    "# 6 Feature Importance\n",
    "\n",
    "- get feature importance\n",
    "- select the features with SHAP values equal 0 and retrain model to assess change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "829aa82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature_names.iloc[:,0].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b02f7c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names.iloc[:,0].values.tolist(),\n",
    "    'Importance': RFC_model.feature_importances_\n",
    "})\n",
    "\n",
    "# Sorting the DataFrame by importance scores\n",
    "feature_importance_df_sorted = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Displaying the sorted DataFrame\n",
    "#feature_importance_df_sorted['Feature'][:20].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a4aedda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns with data type 'int64'\n",
    "int_columns = X_train.select_dtypes(include=['int64']).columns\n",
    "\n",
    "# Convert selected columns to int\n",
    "X_train[int_columns] = X_train[int_columns].astype('float')\n",
    "# Select columns with data type 'int64'\n",
    "int_columns = X_train.select_dtypes(include=['bool']).columns\n",
    "\n",
    "# Convert selected columns to int\n",
    "X_train[int_columns] = X_train[int_columns].astype('float')\n",
    "#X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e173c5e8",
   "metadata": {},
   "source": [
    "**Conversion of type of the test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0b2a29c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 1.        , 0.        , 0.00151187,\n",
       "        0.09028652, 0.09572891, 0.08666667, 0.25381035, 0.11133695,\n",
       "        0.96369543, 0.85214008, 0.70543282, 0.        , 1.        ,\n",
       "        1.        , 0.        , 1.        , 1.        , 0.        ,\n",
       "        0.05      , 0.5       , 0.5       , 0.43478261, 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.0862549 , 0.30754233, 0.15555167, 0.0247    , 0.0369    ,\n",
       "        0.9722    , 0.6192    , 0.0143    , 0.        , 0.069     ,\n",
       "        0.0833    , 0.125     , 0.0369    , 0.0202    , 0.019     ,\n",
       "        0.        , 0.        , 0.0252    , 0.0383    , 0.9722    ,\n",
       "        0.6341    , 0.0144    , 0.        , 0.069     , 0.0833    ,\n",
       "        0.125     , 0.0377    , 0.022     , 0.0198    , 0.        ,\n",
       "        0.        , 0.025     , 0.0369    , 0.9722    , 0.6243    ,\n",
       "        0.0144    , 0.        , 0.069     , 0.0833    , 0.125     ,\n",
       "        0.0375    , 0.0205    , 0.0193    , 0.        , 0.        ,\n",
       "        0.0149    , 0.00574713, 0.05882353, 0.00581395, 0.08333333,\n",
       "        0.73578751, 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.04      , 0.        , 1.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        1.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 1.        , 0.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 1.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 1.        ,\n",
       "        0.        , 1.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 1.        ,\n",
       "        0.        , 1.        , 0.        , 0.        ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = X_train_scale.copy()\n",
    "#test[\"ID\"] = ids_test\n",
    "#test.set_index(\"ID\", inplace=True)\n",
    "#ids_test.iloc[5]\n",
    "#test.loc[100008].values.tolist()\n",
    "#data_point = test.loc[100030].values.tolist()\n",
    "data_point = test[1]\n",
    "data_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bd1e3ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 239)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_P = pd.DataFrame(data_point)\n",
    "data_P.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9334ba",
   "metadata": {},
   "source": [
    "### Local feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "867de57b",
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.Explainer(best_xgb_model, X_train_scale)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "46ac641a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SHAP Values:\n",
      "                             Feature  SHAP Value\n",
      "0                 NAME_CONTRACT_TYPE    0.002118\n",
      "1                       FLAG_OWN_CAR    0.054242\n",
      "2                    FLAG_OWN_REALTY    0.033389\n",
      "3                       CNT_CHILDREN    0.000000\n",
      "4                   AMT_INCOME_TOTAL    0.000000\n",
      "..                               ...         ...\n",
      "234  WALLSMATERIAL_MODE_Stone, brick    0.173780\n",
      "235        WALLSMATERIAL_MODE_Wooden    0.000000\n",
      "236           EMERGENCYSTATE_MODE_No    0.001487\n",
      "237          EMERGENCYSTATE_MODE_Yes    0.000000\n",
      "238               DAYS_EMPLOYED_ANOM    0.000358\n",
      "\n",
      "[239 rows x 2 columns]\n",
      "\n",
      " SHAP Values:\n",
      "                                     Feature  SHAP Value\n",
      "148       OCCUPATION_TYPE_Low-skill Laborers    0.000000\n",
      "81                           FLAG_DOCUMENT_2    0.000000\n",
      "80                    DAYS_LAST_PHONE_CHANGE    0.000000\n",
      "79                  DEF_60_CNT_SOCIAL_CIRCLE    0.000000\n",
      "78                  OBS_60_CNT_SOCIAL_CIRCLE    0.000000\n",
      "..                                       ...         ...\n",
      "234          WALLSMATERIAL_MODE_Stone, brick    0.173780\n",
      "131  NAME_FAMILY_STATUS_Single / not married    0.212290\n",
      "30                              EXT_SOURCE_1    0.414712\n",
      "31                              EXT_SOURCE_2    0.443462\n",
      "32                              EXT_SOURCE_3    0.841761\n",
      "\n",
      "[239 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "shap_values = explainer.shap_values(data_P)\n",
    "shap_df = pd.DataFrame({'Feature': feature_names['0'].tolist(), 'SHAP Value': abs(shap_values[0])})\n",
    "print(\"\\nSHAP Values:\")\n",
    "print(shap_df)\n",
    "print(\"\\n SHAP Values:\")\n",
    "# Sort shap_df by the 'SHAP Value' column in ascending order\n",
    "sorted_shap_local_df = shap_df.sort_values(by='SHAP Value')\n",
    "\n",
    "# Print the sorted DataFrame\n",
    "print(sorted_shap_local_df)\n",
    "shap.summary_plot(shap_values, shap_df['Feature'], plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aa9ebad",
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.Explainer(best_xgb_model, X_train)\n",
    "#explainer = shap.Explainer(xgb_classifier_saved, X_train)\n",
    "\n",
    "shap_values = explainer.shap_values(X_train)\n",
    "shap_df = pd.DataFrame({'Feature': feature_names['0'].tolist(), 'SHAP Value': shap_values[0]})\n",
    "print(\"\\n SHAP Values:\")\n",
    "# Sort shap_df by the 'SHAP Value' column in ascending order\n",
    "sorted_shap_df = shap_df.sort_values(by='SHAP Value')\n",
    "\n",
    "# Print the sorted DataFrame\n",
    "print(sorted_shap_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508b0553",
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_df.to_csv('3_Streamlit_dashboard/data/shap_df.csv', index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e50cfcb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.summary_plot(shap_values, shap_df['Feature'], plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dc0a2b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install --upgrade xgboost \n",
    "#pip install xgboost==1.7.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b13a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(xgb.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cf0144f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip freeze #> requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ee89e9",
   "metadata": {},
   "source": [
    "<a name='7'></a>\n",
    "## 7 Feature Selection after SHAP feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4973c53c",
   "metadata": {},
   "source": [
    "## Filter not useful features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49198917",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Displaying the sorted DataFrame\n",
    "SHAP_feature_important_001 = shap_df[abs(shap_df['SHAP Value'])>0.001]['Feature'].tolist()\n",
    "len(SHAP_feature_important)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c288254",
   "metadata": {},
   "source": [
    "Retrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ded83c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(SHAP_feature_unimportant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6551c804",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(SHAP_feature_important)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0f96f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SHAP_feature_important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fedb572c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2282a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_columns(X_train, feature_names, shap_df, threshold):\n",
    "    SHAP_feature_important = shap_df[abs(shap_df['SHAP Value'])>threshold]['Feature'].tolist()\n",
    "    print(\"length important features \", len(SHAP_feature_important))\n",
    "    df = X_train.copy()\n",
    "    df.columns = feature_names['0'].tolist()\n",
    "    df = df[SHAP_feature_important]\n",
    "    # Remove all column names\n",
    "    #df.rename(columns={x:y for x,y in zip(df.columns,range(0,len(df.columns)))})\n",
    "    df.columns = [x for x in range(0, len(df.columns))] \n",
    "    print(df.shape)\n",
    "    #print(df.info())\n",
    "    print(df.head())\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10aaba6f",
   "metadata": {},
   "source": [
    "### First attempt to improve feature selection and model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e6ef7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X_train = select_columns(X_train, feature_names, shap_df, 0.001)\n",
    "new_X_test  = select_columns(X_test, feature_names, shap_df, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2578818",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"RFC_newFEATURE_001\"\n",
    "RFC_model_001, best_RFC_params, time_RFC = RFC_model(new_X_train, Y_train)\n",
    "RFC_metrics, best_metrics_RFC        = generate_model_report(RFC_model_001, run_name, new_X_test, Y_test, time_RFC)\n",
    "run_MLflow(experiment_name, run_name, RFC_metrics, \n",
    "           best_RFC_params, RFC_model_001, new_X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa7eb0e",
   "metadata": {},
   "source": [
    "### Second attempt to improve feature selection and model improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5dcb9e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X_train_002 = select_columns(X_train, feature_names, shap_df, 0.002)\n",
    "new_X_test_002  = select_columns(X_test, feature_names, shap_df, 0.002)\n",
    "\n",
    "X_train_002_scaled, X_test_002_scaled  = scale_data(new_X_train_002, new_X_test_002 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8ef87ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"RFC_newFEATURE_002\"\n",
    "RFC_model_002, best_RFC_params, time_RFC = RFC_model(new_X_train_002, Y_train)\n",
    "RFC_metrics, best_metrics_RFC            = generate_model_report(RFC_model_002, run_name, new_X_test_002, Y_test, time_RFC)\n",
    "run_MLflow(experiment_name, run_name, RFC_metrics, \n",
    "           best_RFC_params, RFC_model_002, new_X_train_002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bad2e8ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"XGB_Shap002\"\n",
    "XGB_model_002, XGB_002_params, time_XGB_002 = train_XGBoost_model(new_X_train_002, Y_train)\n",
    "XGB_002_metrics, best_metrics_XGB           = generate_model_report(XGB_model_002, run_name, new_X_test_002, Y_test, time_XGB_002)\n",
    "run_MLflow(experiment_name, run_name, XGB_002_metrics, \n",
    "           XGB_002_params, XGB_model_002, new_X_train_002)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aac7f034",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"LGBM_Shap002\"\n",
    "LGBM_model_002, LGBM_002_params, time_LGBM_002 = train_LightGBM_model(new_X_train_002, Y_train)\n",
    "LGBM_002_metrics, best_metrics_LGBM           = generate_model_report(LGBM_model_002, run_name, new_X_test_002, Y_test, time_LGBM_002)\n",
    "run_MLflow(experiment_name, run_name, LGBM_002_metrics, \n",
    "           LGBM_002_params, LGBM_model_002, new_X_train_002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2591fdb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X_train_002.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0dc7d2",
   "metadata": {},
   "source": [
    "## Run with Shap filtered and scaled data to assess impact on metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6d3e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_002_scale, X_test_002_scale = scale_data(new_X_train_002, new_X_test_002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eac17be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"LGBM_Shap002_scaled\"\n",
    "#LGBM_model_002_scale, LGBM_002_scale_params, time_LGBM_002 = train_LightGBM_model(X_train_002_scale, Y_train)\n",
    "#LGBM_002_scale_metrics, best_metrics_LGBM_scale           = generate_model_report(LGBM_model_002_scale, run_name, X_test_002_scale, Y_test, time_LGBM_002)\n",
    "run_MLflow(experiment_name, run_name, LGBM_002_scale_metrics, \n",
    "           LGBM_002_scale_params, LGBM_model_002_scale, X_train_002_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "850543a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb8270d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ad935c",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"XGB_Shap002_scaled\"\n",
    "XGB_model_002_scaled, XGB_002_scaled_params, time_XGB_002_scaled = train_XGBoost_model(X_train_002_scale, Y_train)\n",
    "XGB_002_scaled_metrics, best_metrics_XGB_scaled           = generate_model_report(XGB_model_002_scaled, run_name, X_test_002_scale, Y_test, time_XGB_002_scaled)\n",
    "run_MLflow(experiment_name, run_name, XGB_002_scaled_metrics, \n",
    "           XGB_002_scaled_params, XGB_model_002_scaled, X_train_002_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8233f52e",
   "metadata": {},
   "source": [
    "## Test MLflow on Fastapi model serving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38b56318",
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl http://127.0.0.1:8000/predict -H 'Content-Type: application/json' -d '{\"inputs\": [[0, 0, 1, 1, 63000.0, 310500.0, 15232.5, 310500.0, 0.026392, 16263, -214.0, -8930.0, -573, 0.0, 1, 1, 0, 1, 1, 0, 2.0, 2, 2, 11, 0, 0, 0, 0, 1, 1, 0.0, 0.0765011930557638, 0.0005272652387098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, true, false, false, false, false, false, false, false, true, false, false, false, false, false, false, true, false, false, false, false, true, false, false, false, true, false, false, true, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false]]}'\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2780b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl http://127.0.0.1:8000/predict -H 'Content-Type: application/json' -d '{\"data_point\": [[0, 0, 1, 1, 63000.0, 310500.0, 15232.5, 310500.0, 0.026392, 16263, -214.0, -8930.0, -573, 0.0, 1, 1, 0, 1, 1, 0, 2.0, 2, 2, 11, 0, 0, 0, 0, 1, 1, 0.0, 0.0765011930557638, 0.0005272652387098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, true, false, false, false, false, false, false, false, true, false, false, false, false, false, false, true, false, false, false, false, true, false, false, false, true, false, false, true, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false]]}'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d7b8851",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52314e2d",
   "metadata": {},
   "source": [
    "**Selection of a data point for testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4718bed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = X_train.copy()\n",
    "test[\"ID\"] = ids_test\n",
    "test.set_index(\"ID\", inplace=True)\n",
    "ids_test.iloc[5]\n",
    "#test.loc[100008].values.tolist()\n",
    "data_for_request = test.loc[100030].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a4a0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_for_request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d5b7cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "# initialised with: mlflow models serve -m model_LGBM02/ --port 8092\n",
    "#http://127.0.0.1:8092\n",
    "\n",
    "host = '127.0.0.1'\n",
    "port = '8000'\n",
    "\n",
    "# endpoint\n",
    "url = f'http://{host}:{port}/predict'\n",
    "print(\"URI : \", url)\n",
    "headers = {\n",
    "    'Content-Type': 'application/json',\n",
    "}\n",
    "\n",
    "headers = {'Content-Type': 'application/json'}\n",
    "\n",
    "# Send the POST request with the data\n",
    "response = requests.post(url, json={\"data_point\": data_for_request})\n",
    "\n",
    "print(f'Predictions: {response.text}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9329b5",
   "metadata": {},
   "source": [
    "**TEST with empty data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3773827d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send the POST request with the data\n",
    "response = requests.post(url, json={\"data_point\":[]})\n",
    "\n",
    "print(f'Predictions: {response.text}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c9c2b4",
   "metadata": {},
   "source": [
    "**TEST on hosting environment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e705243c",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://fastapi-cd-webapp.azurewebsites.net/predict'\n",
    "# Send the POST request with the data\n",
    "response = requests.post(url, json={\"data_point\": data_for_request})\n",
    "\n",
    "print(f'Predictions: {response.text}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40be404e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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