Cipla_Data_Scientist_Hiring_Challenge_Hyperparameter_Tuning_HyperOpt.ipynb

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    "colab": {
      "name": "Cipla Data Scientist Hiring Challenge - Hyperparameter Tuning HyperOpt.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1RiluD10AD1RlaFwrFZWTbsa5FUhO-ZSP",
      "authorship_tag": "ABX9TyPBN0+dAZ316X4t17wfYph3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/Machine_Learning_Hackathons/blob/master/Cipla_Data_Scientist_Hiring_Challenge_Hyperparameter_Tuning_HyperOpt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQppOEY58pOY",
        "outputId": "e8b706f7-89e2-43d6-d0a9-3ff3cc8d3b86"
      },
      "source": [
        "!pip install hyperopt\n",
        "!pip install catboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: hyperopt in /usr/local/lib/python3.7/dist-packages (0.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hyperopt) (4.41.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.4.1)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt) (0.16.0)\n",
            "Requirement already satisfied: pymongo in /usr/local/lib/python3.7/dist-packages (from hyperopt) (3.11.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.15.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from hyperopt) (2.5.1)\n",
            "Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx->hyperopt) (4.4.2)\n",
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5a/41/24e14322b9986cf72a8763e0a0a69cc256cf963cf9502c8f0044a62c1ae8/catboost-0.26-cp37-none-manylinux1_x86_64.whl (69.2MB)\n",
            "\u001b[K     |████████████████████████████████| 69.2MB 41kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "foJMOgwn6p0R"
      },
      "source": [
        "import warnings                                 \n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "trainx = pd.read_csv(\"/content/drive/MyDrive/Projects/CiplaDS_Hiring/fe_train.csv\")\n",
        "testx = pd.read_csv(\"/content/drive/MyDrive/Projects/CiplaDS_Hiring/fe_test.csv\")\n",
        "pd.set_option('display.max_columns', 500)\n",
        "\n",
        "X = trainx.drop([\"Customer ID\",\"Loan Sanction Amount (USD)\"], axis=1)\n",
        "y = trainx[\"Loan Sanction Amount (USD)\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jxy_2Ep66pca"
      },
      "source": [
        "#import required packages\n",
        "import lightgbm as lgb\n",
        "import xgboost as xgb\n",
        "import catboost as cb\n",
        "import gc\n",
        "from hyperopt import hp, tpe, Trials, STATUS_OK\n",
        "from hyperopt.fmin import fmin\n",
        "from hyperopt.pyll.stochastic import sample\n",
        "#optional but advised\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "#GLOBAL HYPEROPT PARAMETERS\n",
        "NUM_EVALS = 1000 #number of hyperopt evaluation rounds\n",
        "N_FOLDS = 5 #number of cross-validation folds on data in each evaluation round\n",
        "\n",
        "#LIGHTGBM PARAMETERS\n",
        "LGBM_MAX_LEAVES = 2**11 #maximum number of leaves per tree for LightGBM\n",
        "LGBM_MAX_DEPTH = 25 #maximum tree depth for LightGBM\n",
        "EVAL_METRIC_LGBM_REG = 'rmse' #LightGBM regression metric. Note that 'rmse' is more commonly used \n",
        "EVAL_METRIC_LGBM_CLASS = 'auc'#LightGBM classification metric\n",
        "\n",
        "#XGBOOST PARAMETERS\n",
        "XGB_MAX_LEAVES = 2**12 #maximum number of leaves when using histogram splitting\n",
        "XGB_MAX_DEPTH = 25 #maximum tree depth for XGBoost\n",
        "EVAL_METRIC_XGB_REG = 'rmse' #XGBoost regression metric\n",
        "EVAL_METRIC_XGB_CLASS = 'auc' #XGBoost classification metric\n",
        "\n",
        "#CATBOOST PARAMETERS\n",
        "CB_MAX_DEPTH = 8 #maximum tree depth in CatBoost\n",
        "OBJECTIVE_CB_REG = 'RMSE' #CatBoost regression metric\n",
        "OBJECTIVE_CB_CLASS = 'Logloss' #CatBoost classification metric\n",
        "\n",
        "#OPTIONAL OUTPUT\n",
        "BEST_SCORE = 0\n",
        "\n",
        "def quick_hyperopt(data, labels, package='lgbm', num_evals=NUM_EVALS, diagnostic=False):\n",
        "    \n",
        "    #==========\n",
        "    #LightGBM\n",
        "    #==========\n",
        "    \n",
        "    if package=='lgbm':\n",
        "        \n",
        "        print('Running {} rounds of LightGBM parameter optimisation:'.format(num_evals))\n",
        "        #clear space\n",
        "        gc.collect()\n",
        "        \n",
        "        integer_params = ['max_depth',\n",
        "                         'num_leaves',\n",
        "                          'max_bin',\n",
        "                         'min_data_in_leaf',\n",
        "                         'min_data_in_bin']\n",
        "        \n",
        "        def objective(space_params):\n",
        "            \n",
        "            #cast integer params from float to int\n",
        "            for param in integer_params:\n",
        "                space_params[param] = int(space_params[param])\n",
        "            \n",
        "            #extract nested conditional parameters\n",
        "            if space_params['boosting']['boosting'] == 'goss':\n",
        "                top_rate = space_params['boosting'].get('top_rate')\n",
        "                other_rate = space_params['boosting'].get('other_rate')\n",
        "                #0 <= top_rate + other_rate <= 1\n",
        "                top_rate = max(top_rate, 0)\n",
        "                top_rate = min(top_rate, 0.5)\n",
        "                other_rate = max(other_rate, 0)\n",
        "                other_rate = min(other_rate, 0.5)\n",
        "                space_params['top_rate'] = top_rate\n",
        "                space_params['other_rate'] = other_rate\n",
        "            \n",
        "            subsample = space_params['boosting'].get('subsample', 1.0)\n",
        "            space_params['boosting'] = space_params['boosting']['boosting']\n",
        "            space_params['subsample'] = subsample\n",
        "            \n",
        "            #for classification, set stratified=True and metrics=EVAL_METRIC_LGBM_CLASS\n",
        "            cv_results = lgb.cv(space_params, train, nfold = N_FOLDS, stratified=False,\n",
        "                                early_stopping_rounds=100, metrics=EVAL_METRIC_LGBM_REG, seed=42)\n",
        "            \n",
        "            best_loss = cv_results['rmse-mean'][-1] #'l2-mean' for rmse\n",
        "            #for classification, comment out the line above and uncomment the line below:\n",
        "            #best_loss = 1 - cv_results['auc-mean'][-1]\n",
        "            #if necessary, replace 'auc-mean' with '[your-preferred-metric]-mean'\n",
        "            return{'loss':best_loss, 'status': STATUS_OK }\n",
        "        \n",
        "        train = lgb.Dataset(data, labels)\n",
        "                \n",
        "        #integer and string parameters, used with hp.choice()\n",
        "        boosting_list = [{'boosting': 'gbdt',\n",
        "                          'subsample': hp.uniform('subsample', 0.5, 1)},\n",
        "                         {'boosting': 'goss',\n",
        "                          'subsample': 1.0,\n",
        "                         'top_rate': hp.uniform('top_rate', 0, 0.5),\n",
        "                         'other_rate': hp.uniform('other_rate', 0, 0.5)}] #if including 'dart', make sure to set 'n_estimators'\n",
        "        metric_list = ['MAE', 'RMSE'] \n",
        "        #for classification comment out the line above and uncomment the line below\n",
        "        #metric_list = ['auc'] #modify as required for other classification metrics\n",
        "        objective_list_reg = ['huber', 'gamma', 'fair', 'tweedie']\n",
        "        objective_list_class = ['binary', 'cross_entropy']\n",
        "        #for classification set objective_list = objective_list_class\n",
        "        objective_list = objective_list_reg\n",
        "\n",
        "        space ={'boosting' : hp.choice('boosting', boosting_list),\n",
        "                'num_leaves' : hp.quniform('num_leaves', 2, LGBM_MAX_LEAVES, 1),\n",
        "                'max_depth': hp.quniform('max_depth', 2, LGBM_MAX_DEPTH, 1),\n",
        "                'max_bin': hp.quniform('max_bin', 32, 255, 1),\n",
        "                'min_data_in_leaf': hp.quniform('min_data_in_leaf', 1, 256, 1),\n",
        "                'min_data_in_bin': hp.quniform('min_data_in_bin', 1, 256, 1),\n",
        "                'min_gain_to_split' : hp.quniform('min_gain_to_split', 0.1, 5, 0.01),\n",
        "                'lambda_l1' : hp.uniform('lambda_l1', 0, 5),\n",
        "                'lambda_l2' : hp.uniform('lambda_l2', 0, 5),\n",
        "                'learning_rate' : hp.loguniform('learning_rate', np.log(0.005), np.log(0.2)),\n",
        "                'metric' : hp.choice('metric', metric_list),\n",
        "                'objective' : hp.choice('objective', objective_list),\n",
        "                'feature_fraction' : hp.quniform('feature_fraction', 0.5, 1, 0.01),\n",
        "                'bagging_fraction' : hp.quniform('bagging_fraction', 0.5, 1, 0.01)\n",
        "            }\n",
        "        \n",
        "        #optional: activate GPU for LightGBM\n",
        "        #follow compilation steps here:\n",
        "        #https://www.kaggle.com/vinhnguyen/gpu-acceleration-for-lightgbm/\n",
        "        #then uncomment lines below:\n",
        "        #space['device'] = 'gpu'\n",
        "        #space['gpu_platform_id'] = 0,\n",
        "        #space['gpu_device_id'] =  0\n",
        "\n",
        "        trials = Trials()\n",
        "        best = fmin(fn=objective,\n",
        "                    space=space,\n",
        "                    algo=tpe.suggest,\n",
        "                    max_evals=num_evals, \n",
        "                    trials=trials)\n",
        "                \n",
        "        #fmin() will return the index of values chosen from the lists/arrays in 'space'\n",
        "        #to obtain actual values, index values are used to subset the original lists/arrays\n",
        "        best['boosting'] = boosting_list[best['boosting']]['boosting']#nested dict, index twice\n",
        "        best['metric'] = metric_list[best['metric']]\n",
        "        best['objective'] = objective_list[best['objective']]\n",
        "                \n",
        "        #cast floats of integer params to int\n",
        "        for param in integer_params:\n",
        "            best[param] = int(best[param])\n",
        "        \n",
        "        print('{' + '\\n'.join('{}: {}'.format(k, v) for k, v in best.items()) + '}')\n",
        "        if diagnostic:\n",
        "            return(best, trials)\n",
        "        else:\n",
        "            return(best)\n",
        "    \n",
        "    #==========\n",
        "    #XGBoost\n",
        "    #==========\n",
        "    \n",
        "    if package=='xgb':\n",
        "        \n",
        "        print('Running {} rounds of XGBoost parameter optimisation:'.format(num_evals))\n",
        "        #clear space\n",
        "        gc.collect()\n",
        "        \n",
        "        integer_params = ['max_depth']\n",
        "        \n",
        "        def objective(space_params):\n",
        "            \n",
        "            for param in integer_params:\n",
        "                space_params[param] = int(space_params[param])\n",
        "                \n",
        "            #extract multiple nested tree_method conditional parameters\n",
        "            #libera te tutemet ex inferis\n",
        "            if space_params['tree_method']['tree_method'] == 'hist':\n",
        "                max_bin = space_params['tree_method'].get('max_bin')\n",
        "                space_params['max_bin'] = int(max_bin)\n",
        "                if space_params['tree_method']['grow_policy']['grow_policy']['grow_policy'] == 'depthwise':\n",
        "                    grow_policy = space_params['tree_method'].get('grow_policy').get('grow_policy').get('grow_policy')\n",
        "                    space_params['grow_policy'] = grow_policy\n",
        "                    space_params['tree_method'] = 'hist'\n",
        "                else:\n",
        "                    max_leaves = space_params['tree_method']['grow_policy']['grow_policy'].get('max_leaves')\n",
        "                    space_params['grow_policy'] = 'lossguide'\n",
        "                    space_params['max_leaves'] = int(max_leaves)\n",
        "                    space_params['tree_method'] = 'hist'\n",
        "            else:\n",
        "                space_params['tree_method'] = space_params['tree_method'].get('tree_method')\n",
        "                \n",
        "            #for classification replace EVAL_METRIC_XGB_REG with EVAL_METRIC_XGB_CLASS\n",
        "            cv_results = xgb.cv(space_params, train, nfold=N_FOLDS, metrics=[EVAL_METRIC_XGB_REG],\n",
        "                             early_stopping_rounds=100, stratified=False, seed=42)\n",
        "            \n",
        "            best_loss = cv_results['test-rmse-mean'].iloc[-1] #or 'test-rmse-mean' if using RMSE  test-mae-mean\n",
        "            #for classification, comment out the line above and uncomment the line below:\n",
        "            #best_loss = 1 - cv_results['test-auc-mean'].iloc[-1]\n",
        "            #if necessary, replace 'test-auc-mean' with 'test-[your-preferred-metric]-mean'\n",
        "            return{'loss':best_loss, 'status': STATUS_OK }\n",
        "        \n",
        "        train = xgb.DMatrix(data, labels)\n",
        "        \n",
        "        #integer and string parameters, used with hp.choice()\n",
        "        boosting_list = ['gbtree', 'gblinear'] #if including 'dart', make sure to set 'n_estimators'\n",
        "        metric_list = ['MAE', 'RMSE'] \n",
        "        #for classification comment out the line above and uncomment the line below\n",
        "        #metric_list = ['auc']\n",
        "        #modify as required for other classification metrics classification\n",
        "        \n",
        "        tree_method = [{'tree_method' : 'exact'},\n",
        "               {'tree_method' : 'approx'},\n",
        "               {'tree_method' : 'hist',\n",
        "                'max_bin': hp.quniform('max_bin', 2**3, 2**7, 1),\n",
        "                'grow_policy' : {'grow_policy': {'grow_policy':'depthwise'},\n",
        "                                'grow_policy' : {'grow_policy':'lossguide',\n",
        "                                                  'max_leaves': hp.quniform('max_leaves', 32, XGB_MAX_LEAVES, 1)}}}]\n",
        "        \n",
        "        #if using GPU, replace 'exact' with 'gpu_exact' and 'hist' with\n",
        "        #'gpu_hist' in the nested dictionary above\n",
        "        \n",
        "        objective_list_reg = ['reg:squarederror', 'reg:gamma', 'reg:tweedie']\n",
        "        objective_list_class = ['reg:logistic', 'binary:logistic']\n",
        "        #for classification change line below to 'objective_list = objective_list_class'\n",
        "        objective_list = objective_list_reg\n",
        "        \n",
        "        space ={'boosting' : hp.choice('boosting', boosting_list),\n",
        "                'tree_method' : hp.choice('tree_method', tree_method),\n",
        "                'max_depth': hp.quniform('max_depth', 2, XGB_MAX_DEPTH, 1),\n",
        "                'reg_alpha' : hp.uniform('reg_alpha', 0, 5),\n",
        "                'reg_lambda' : hp.uniform('reg_lambda', 0, 5),\n",
        "                'min_child_weight' : hp.uniform('min_child_weight', 0, 5),\n",
        "                'gamma' : hp.uniform('gamma', 0, 5),\n",
        "                'learning_rate' : hp.loguniform('learning_rate', np.log(0.005), np.log(0.2)),\n",
        "                'eval_metric' : hp.choice('eval_metric', metric_list),\n",
        "                'objective' : hp.choice('objective', objective_list),\n",
        "                'colsample_bytree' : hp.quniform('colsample_bytree', 0.1, 1, 0.01),\n",
        "                'colsample_bynode' : hp.quniform('colsample_bynode', 0.1, 1, 0.01),\n",
        "                'colsample_bylevel' : hp.quniform('colsample_bylevel', 0.1, 1, 0.01),\n",
        "                'subsample' : hp.quniform('subsample', 0.5, 1, 0.05),\n",
        "                'nthread' : -1\n",
        "            }\n",
        "        \n",
        "        trials = Trials()\n",
        "        best = fmin(fn=objective,\n",
        "                    space=space,\n",
        "                    algo=tpe.suggest,\n",
        "                    max_evals=num_evals, \n",
        "                    trials=trials)\n",
        "        \n",
        "        best['tree_method'] = tree_method[best['tree_method']]['tree_method']\n",
        "        best['boosting'] = boosting_list[best['boosting']]\n",
        "        best['eval_metric'] = metric_list[best['eval_metric']]\n",
        "        best['objective'] = objective_list[best['objective']]\n",
        "        \n",
        "        #cast floats of integer params to int\n",
        "        for param in integer_params:\n",
        "            best[param] = int(best[param])\n",
        "        if 'max_leaves' in best:\n",
        "            best['max_leaves'] = int(best['max_leaves'])\n",
        "        if 'max_bin' in best:\n",
        "            best['max_bin'] = int(best['max_bin'])\n",
        "        \n",
        "        print('{' + '\\n'.join('{}: {}'.format(k, v) for k, v in best.items()) + '}')\n",
        "        \n",
        "        if diagnostic:\n",
        "            return(best, trials)\n",
        "        else:\n",
        "            return(best)\n",
        "    \n",
        "    #==========\n",
        "    #CatBoost\n",
        "    #==========\n",
        "    \n",
        "    if package=='cb':\n",
        "        \n",
        "        print('Running {} rounds of CatBoost parameter optimisation:'.format(num_evals))\n",
        "        \n",
        "        #clear memory \n",
        "        gc.collect()\n",
        "            \n",
        "        integer_params = ['depth',\n",
        "                          #'one_hot_max_size', #for categorical data\n",
        "                          'min_data_in_leaf',\n",
        "                          'max_bin']\n",
        "        \n",
        "        def objective(space_params):\n",
        "                        \n",
        "            #cast integer params from float to int\n",
        "            for param in integer_params:\n",
        "                space_params[param] = int(space_params[param])\n",
        "                \n",
        "            #extract nested conditional parameters\n",
        "            if space_params['bootstrap_type']['bootstrap_type'] == 'Bayesian':\n",
        "                bagging_temp = space_params['bootstrap_type'].get('bagging_temperature')\n",
        "                space_params['bagging_temperature'] = bagging_temp\n",
        "                \n",
        "            if space_params['grow_policy']['grow_policy'] == 'LossGuide':\n",
        "                max_leaves = space_params['grow_policy'].get('max_leaves')\n",
        "                space_params['max_leaves'] = int(max_leaves)\n",
        "                \n",
        "            space_params['bootstrap_type'] = space_params['bootstrap_type']['bootstrap_type']\n",
        "            space_params['grow_policy'] = space_params['grow_policy']['grow_policy']\n",
        "                           \n",
        "            #random_strength cannot be < 0\n",
        "            space_params['random_strength'] = max(space_params['random_strength'], 0)\n",
        "            #fold_len_multiplier cannot be < 1\n",
        "            space_params['fold_len_multiplier'] = max(space_params['fold_len_multiplier'], 1)\n",
        "                       \n",
        "            #for classification set stratified=True\n",
        "            cv_results = cb.cv(train, space_params, fold_count=N_FOLDS, \n",
        "                             early_stopping_rounds=25, stratified=False, partition_random_seed=42)\n",
        "           \n",
        "            best_loss = cv_results['test-RMSE-mean'].iloc[-1] #'test-RMSE-mean' for RMSE  test-MAE-mean\n",
        "            #for classification, comment out the line above and uncomment the line below:\n",
        "            #best_loss = cv_results['test-Logloss-mean'].iloc[-1]\n",
        "            #if necessary, replace 'test-Logloss-mean' with 'test-[your-preferred-metric]-mean'\n",
        "            \n",
        "            return{'loss':best_loss, 'status': STATUS_OK}\n",
        "        \n",
        "        train = cb.Pool(data, labels.astype('float32'))\n",
        "        \n",
        "        #integer and string parameters, used with hp.choice()\n",
        "        bootstrap_type = [{'bootstrap_type':'Bernoulli'}, {'bootstrap_type':'Bayesian','bagging_temperature' : hp.loguniform('bagging_temperature', np.log(1), np.log(50))}] \n",
        "        LEB = ['No', 'AnyImprovement'] #remove 'Armijo' if using CPU -- 'Armijo'\n",
        "        #score_function = ['Correlation', 'L2', 'NewtonCorrelation', 'NewtonL2']\n",
        "        grow_policy = [{'grow_policy':'SymmetricTree'},\n",
        "                       {'grow_policy':'Depthwise'},\n",
        "                       {'grow_policy':'Lossguide',\n",
        "                        'max_leaves': hp.quniform('max_leaves', 2, 32, 1)}]\n",
        "        eval_metric_list_reg = ['MAE', 'RMSE', 'Poisson']\n",
        "        eval_metric_list_class = ['Logloss', 'AUC', 'F1']\n",
        "        #for classification change line below to 'eval_metric_list = eval_metric_list_class'\n",
        "        eval_metric_list = eval_metric_list_reg\n",
        "                \n",
        "        space ={'depth': hp.quniform('depth', 2, CB_MAX_DEPTH, 1),\n",
        "                'max_bin' : hp.quniform('max_bin', 1, 32, 1), #if using CPU just set this to 254\n",
        "                'l2_leaf_reg' : hp.uniform('l2_leaf_reg', 0, 5),\n",
        "                'min_data_in_leaf' : hp.quniform('min_data_in_leaf', 1, 50, 1),\n",
        "                'random_strength' : hp.loguniform('random_strength', np.log(0.005), np.log(5)),\n",
        "                #'one_hot_max_size' : hp.quniform('one_hot_max_size', 2, 16, 1), #uncomment if using categorical features\n",
        "                'bootstrap_type' : hp.choice('bootstrap_type', bootstrap_type),\n",
        "                'learning_rate' : hp.uniform('learning_rate', 0.05, 0.25),\n",
        "                'eval_metric' : hp.choice('eval_metric', eval_metric_list),\n",
        "                'objective' : OBJECTIVE_CB_REG,\n",
        "                #'score_function' : hp.choice('score_function', score_function), #crashes kernel - reason unknown\n",
        "                'leaf_estimation_backtracking' : hp.choice('leaf_estimation_backtracking', LEB),\n",
        "                'grow_policy': hp.choice('grow_policy', grow_policy),\n",
        "                #'colsample_bylevel' : hp.quniform('colsample_bylevel', 0.1, 1, 0.01),# CPU only\n",
        "                'fold_len_multiplier' : hp.loguniform('fold_len_multiplier', np.log(1.01), np.log(2.5)),\n",
        "                'od_type' : 'Iter',\n",
        "                'od_wait' : 25,\n",
        "                'task_type' : 'GPU',\n",
        "                'verbose' : 0\n",
        "            }\n",
        "        \n",
        "        #optional: run CatBoost without GPU\n",
        "        #uncomment line below\n",
        "        space['task_type'] = 'CPU'\n",
        "            \n",
        "        trials = Trials()\n",
        "        best = fmin(fn=objective,\n",
        "                    space=space,\n",
        "                    algo=tpe.suggest,\n",
        "                    max_evals=num_evals, \n",
        "                    trials=trials)\n",
        "        \n",
        "        #unpack nested dicts first\n",
        "        best['bootstrap_type'] = bootstrap_type[best['bootstrap_type']]['bootstrap_type']\n",
        "        best['grow_policy'] = grow_policy[best['grow_policy']]['grow_policy']\n",
        "        best['eval_metric'] = eval_metric_list[best['eval_metric']]\n",
        "        \n",
        "        #best['score_function'] = score_function[best['score_function']] \n",
        "        #best['leaf_estimation_method'] = LEM[best['leaf_estimation_method']] #CPU only\n",
        "        best['leaf_estimation_backtracking'] = LEB[best['leaf_estimation_backtracking']]        \n",
        "        \n",
        "        #cast floats of integer params to int\n",
        "        for param in integer_params:\n",
        "            best[param] = int(best[param])\n",
        "        if 'max_leaves' in best:\n",
        "            best['max_leaves'] = int(best['max_leaves'])\n",
        "        \n",
        "        print('{' + '\\n'.join('{}: {}'.format(k, v) for k, v in best.items()) + '}')\n",
        "        \n",
        "        if diagnostic:\n",
        "            return(best, trials)\n",
        "        else:\n",
        "            return(best)\n",
        "    \n",
        "    else:\n",
        "        print('Package not recognised. Please use \"lgbm\" for LightGBM, \"xgb\" for XGBoost or \"cb\" for CatBoost.')  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cc-1zKR-AkWC"
      },
      "source": [
        "###LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjXv9ZBa7dfd",
        "outputId": "b8a4f96a-ff1a-47d1-eefa-41737126a918"
      },
      "source": [
        "lgbm_params = quick_hyperopt(X, y, 'lgbm', 500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running 500 rounds of LightGBM parameter optimisation:\n",
            "100%|██████████| 500/500 [2:04:02<00:00, 14.88s/it, best loss: 21361.336218131346]\n",
            "{bagging_fraction: 0.5\n",
            "boosting: gbdt\n",
            "feature_fraction: 0.96\n",
            "lambda_l1: 4.77689014327168\n",
            "lambda_l2: 4.499321371418711\n",
            "learning_rate: 0.10786399263172929\n",
            "max_bin: 46\n",
            "max_depth: 6\n",
            "metric: RMSE\n",
            "min_data_in_bin: 228\n",
            "min_data_in_leaf: 20\n",
            "min_gain_to_split: 1.73\n",
            "num_leaves: 9\n",
            "objective: tweedie\n",
            "subsample: 0.8636282309962746}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbSyxI9v7dhe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68e52ac5-1e25-4d42-a232-caa1abf2c417"
      },
      "source": [
        "import lightgbm as lgb\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "lgb_reg = lgb.LGBMRegressor(bagging_fraction= 0.5,\n",
        "                            boosting= 'gbdt',\n",
        "                            feature_fraction= 0.96,\n",
        "                            lambda_l1= 4.77689014327168,\n",
        "                            lambda_l2= 4.499321371418711,\n",
        "                            learning_rate= 0.10786399263172929,\n",
        "                            max_bin= 46,\n",
        "                            max_depth= 6,\n",
        "                            min_data_in_bin= 228,\n",
        "                            min_data_in_leaf= 20,\n",
        "                            min_gain_to_split= 1.73,\n",
        "                            num_leaves= 9,\n",
        "                            objective= 'tweedie',\n",
        "                            subsample= 0.8636282309962746)\n",
        "\n",
        "\n",
        "scores = cross_val_score(lgb_reg, X, y, scoring='r2', cv=10) \n",
        "np.mean(scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7937341178939272"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7BOUj4x-H5d"
      },
      "source": [
        "lgb_reg.fit(X,y)\n",
        "preds = lgb_reg.predict(testx.drop(labels=['Customer ID'],axis=1))\n",
        "testx['Loan Sanction Amount (USD)'] = preds\n",
        "sub = testx[['Customer ID','Loan Sanction Amount (USD)']]\n",
        "sub.to_csv(\"sub2.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1R3tCAfAnhf"
      },
      "source": [
        "###XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9YBNmavAm3l",
        "outputId": "b0cef510-5349-46c9-a1b5-1c6a64939662"
      },
      "source": [
        "xgb_params = quick_hyperopt(X, y, 'xgb', 500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running 500 rounds of XGBoost parameter optimisation:\n",
            "100%|██████████| 500/500 [2:42:47<00:00, 19.53s/it, best loss: 23031.74375]\n",
            "{boosting: gbtree\n",
            "colsample_bylevel: 0.66\n",
            "colsample_bynode: 0.8200000000000001\n",
            "colsample_bytree: 0.97\n",
            "eval_metric: MAE\n",
            "gamma: 1.4127400227564677\n",
            "learning_rate: 0.19949042730420335\n",
            "max_depth: 6\n",
            "min_child_weight: 1.2198659007882582\n",
            "objective: reg:squarederror\n",
            "reg_alpha: 1.859401255487128\n",
            "reg_lambda: 3.2882463739391916\n",
            "subsample: 1.0\n",
            "tree_method: exact}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qh1MKhlvArvh",
        "outputId": "244e8055-43b2-4149-d839-9b915839d42c"
      },
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "xg_reg = xgb.XGBRegressor(boosting =  'gbtree' ,\n",
        "                          colsample_bylevel = 0.66,\n",
        "                          colsample_bynode = 0.82,\n",
        "                          colsample_bytree = 0.97,\n",
        "                          gamma = 1.4127400227564677,\n",
        "                          learning_rate = 0.19949042730420335,\n",
        "                          max_bin = 6,\n",
        "                          min_child_weight = 1.2198659007882582,\n",
        "                          objective =  'reg:squarederror' ,\n",
        "                          reg_alpha = 1.859401255487128,\n",
        "                          reg_lambda = 3.2882463739391916,\n",
        "                          subsample = 1.0,\n",
        "                          tree_method =  'hist' )\n",
        "\n",
        "scores = cross_val_score(xg_reg, X, y, scoring='r2', cv=10) \n",
        "np.mean(scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7284611037194745"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWT9KO93ArtH"
      },
      "source": [
        "xg_reg.fit(X,y)\n",
        "preds = xg_reg.predict(testx.drop(labels=['Customer ID'],axis=1))\n",
        "testx['Loan Sanction Amount (USD)'] = preds\n",
        "sub = testx[['Customer ID','Loan Sanction Amount (USD)']]\n",
        "sub.to_csv(\"sub3.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-SsFyG2AhS0"
      },
      "source": [
        "###CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIvZMKJt_TIl",
        "outputId": "c4ad5feb-76de-483e-d828-b39a2e458c2b"
      },
      "source": [
        "cb_params = quick_hyperopt(X, y, 'cb', 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running 10 rounds of CatBoost parameter optimisation:\n",
            "  0%|          | 0/10 [00:00<?, ?it/s, best loss: ?]Stopped by overfitting detector  (25 iterations wait)\n",
            " 40%|████      | 4/10 [19:41<29:32, 295.44s/it, best loss: 23735.737704165185]Stopped by overfitting detector  (25 iterations wait)\n",
            " 80%|████████  | 8/10 [31:00<07:31, 225.61s/it, best loss: 23117.7122553992]Stopped by overfitting detector  (25 iterations wait)\n",
            "100%|██████████| 10/10 [33:52<00:00, 203.25s/it, best loss: 22340.60945541969]\n",
            "{bagging_temperature: 1.0533951627285427\n",
            "bootstrap_type: Bayesian\n",
            "depth: 4\n",
            "eval_metric: RMSE\n",
            "fold_len_multiplier: 1.77261654004014\n",
            "grow_policy: Depthwise\n",
            "l2_leaf_reg: 1.848322548272635\n",
            "leaf_estimation_backtracking: No\n",
            "learning_rate: 0.22729338132226334\n",
            "max_bin: 24\n",
            "min_data_in_leaf: 12\n",
            "random_strength: 0.02072616892556881}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d39ivZ33_8ei",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9576682-692e-45df-e02b-1e02c3c97039"
      },
      "source": [
        "import catboost as cb\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cb_reg = cb.CatBoostRegressor(bootstrap_type =  'Bayesian' ,\n",
        "                          bagging_temperature = 1.0533951627285427,\n",
        "                          depth = 4,\n",
        "                          grow_policy = 'Depthwise',\n",
        "                          fold_len_multiplier = 1.77261654004014,\n",
        "                          l2_leaf_reg = 1.848322548272635,\n",
        "                          leaf_estimation_backtracking = 'No',\n",
        "                          learning_rate =  0.22729338132226334 ,\n",
        "                          max_bin = 24,\n",
        "                          min_data_in_leaf = 12,\n",
        "                          random_strength = 0.02072616892556881 )\n",
        "\n",
        "scores = cross_val_score(cb_reg, X, y, scoring='r2', cv=10) \n",
        "np.mean(scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7647668619127058"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJpwzr4ZEDsn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5549f971-8435-4053-c785-477a767ba0fe"
      },
      "source": [
        "cb_reg.fit(X,y)\n",
        "preds = cb_reg.predict(testx.drop(labels=['Customer ID'],axis=1))\n",
        "testx['Loan Sanction Amount (USD)'] = preds\n",
        "sub = testx[['Customer ID','Loan Sanction Amount (USD)']]\n",
        "sub.to_csv(\"sub4.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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}