modelling_logTransformed.ipynb

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    "colab": {
      "name": "modelling_logTransformed.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishthomaschempolil/Santander-Customer-Satisfaction/blob/new_1/modelling_logTransformed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVQr_7USbczr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(palette='muted',style='whitegrid')\n",
        "import sklearn\n",
        "from sklearn import metrics,ensemble,model_selection,linear_model,tree,calibration\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "import scipy\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "np.random.seed(13154)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRB4PxAYa5xu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "0444e1d8-e691-4857-89e4-95b2294ed19b"
      },
      "source": [
        "file = [k for k in os.listdir('/content/drive/My Drive/Santander_Customer_Satisfaction') if (('train' in k)|('test' in k))&('X' not in k)] #getting all the necessary files for modelling\n",
        "file"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['train_Normal.pkl',\n",
              " 'test_Normal.pkl',\n",
              " 'train_Normal with Response encoding.pkl',\n",
              " 'test_Normal with Response encoding.pkl',\n",
              " 'train_Normal with One Hot Encoding.pkl',\n",
              " 'test_Normal with One Hot Encoding.pkl',\n",
              " 'train_Log Transformed.pkl',\n",
              " 'test_Log Transformed.pkl',\n",
              " 'train_Log Transformed with Response encoding.pkl',\n",
              " 'test_Log Transformed with Response encoding.pkl',\n",
              " 'train_Log Transformed with One Hot Encoding.pkl',\n",
              " 'test_Log Transformed with One Hot Encoding.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mV0hmG66S7ZC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e5df086c-e139-49fc-82d6-5b3d970fbabd"
      },
      "source": [
        "file_path = '/content/drive/My Drive/Santander_Customer_Satisfaction/'\n",
        "train_file = file_path+'train_Log Transformed.pkl' #log transformed\n",
        "test_file = file_path+'test_Log Transformed.pkl'\n",
        "dataset= \"Log Transformed\"\n",
        "train = pd.read_pickle(train_file)\n",
        "test = pd.read_pickle(test_file)\n",
        "X_train = train.drop(['ID','TARGET'],axis=1)\n",
        "y_train = train['TARGET'].values\n",
        "X_test = test.drop('ID',axis=1)\n",
        "del train,test\n",
        "X_train,X_val,y_train,y_val = model_selection.train_test_split(X_train,y_train,stratify=y_train,test_size=0.15)\n",
        "X_train.shape,X_val.shape,X_test.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((64617, 359), (11403, 359), (75818, 359))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4yLi9O_CtFR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "global i\n",
        "i=0\n",
        "def plot_auc(y_true,y_pred,label,dataset=dataset):\n",
        "  \"\"\"\n",
        "  Plots auc curve when y_true and y_pred are given\n",
        "  dataset is string which tells which dataset were used.\n",
        "  label: tells which model was used\n",
        "  If label is a list plots all the auc curve for all labels (here y_pred will be a list of lists )\n",
        "  \"\"\"\n",
        "  \n",
        "  \n",
        "  if (type(label)!=list) & (type(label)!=np.array):\n",
        "    print(\"\\t\\t %s on %s dataset \\t\\t \\n\"%(label,dataset))\n",
        "    fpr,tpr,threshold = metrics.roc_curve(y_true,y_pred) #getting true positive rate, false positive rate\n",
        "    auc = metrics.roc_auc_score(y_true,y_pred) #getting auc score\n",
        "    log_loss = metrics.log_loss(y_true,y_pred) #getting logloss\n",
        "    label_1 = label+' AUC=%.3f'%(auc)\n",
        "\n",
        "    #plotting tpr vs fpr\n",
        "    sns.lineplot(fpr,tpr,label=label_1)\n",
        "    x=np.arange(0,1.1,0.1) #auc=0.5 line \n",
        "    sns.lineplot(x,x,label=\"AUC=0.5\")\n",
        "    plt.title(\"ROC on %s dataset\"%(dataset))\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #legend box outside graph\n",
        "    plt.show()\n",
        "    print(\"The log loss and auc was found to be %.3f and %.3f for %s on %s dataset.\"%(log_loss,auc,label,dataset))\n",
        "    #creating a dataframe of the result\n",
        "    result_dict = {\"Model\":label,'Dataset':dataset,'log_loss':log_loss,'auc':auc}\n",
        "    \n",
        "    return pd.DataFrame(result_dict,index=[i])\n",
        "\n",
        "  else:\n",
        "    plt.figure(figsize=(12,8))\n",
        "    for k,y in enumerate(y_pred):\n",
        "      fpr,tpr,threshold = metrics.roc_curve(y_true,y)\n",
        "      auc = metrics.roc_auc_score(y_true,y)\n",
        "      label_ = label[k]+' AUC=%.3f'%(auc)\n",
        "      #plotting tpr vs fpr\n",
        "      sns.lineplot(fpr,tpr,label=label_)\n",
        "\n",
        "    x=np.arange(0,1.1,0.1)\n",
        "    sns.lineplot(x,x,label=\"AUC=0.5\")\n",
        "    plt.title(\"Combined ROC\")\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #legend box outside graph\n",
        "    plt.show()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spCK7X7beIiy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def find_best_params(model,params,cv=10,n_jobs=-1,X_train=X_train):\n",
        "  \"\"\"\n",
        "  Funcion which implements Random Seacrh CV and returns best model\n",
        "  \"\"\"\n",
        "  random_cv = model_selection.RandomizedSearchCV(model,param_distributions=params,scoring='roc_auc',n_jobs=n_jobs,cv=cv,verbose=2)\n",
        "  random_cv.fit(X_train,y_train)\n",
        "  print(\"The best auc score was %.3f\"%(random_cv.best_score_))\n",
        "  print(\"The best params were: %s\"%(random_cv.best_params_))\n",
        "  return random_cv.best_estimator_"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Mho8KPmabxm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_feature_importance(df,feat_imp,top=20,size=(6,6)):\n",
        "    \"\"\"\n",
        "    getting most important top features if df and feature_importance array is passed\n",
        "    \"\"\"\n",
        "    top_indices = np.argsort(feat_imp)[::-1][:top] #getting the indices with top feature importace\n",
        "    most_important_feat = df.columns[top_indices] #getiing the columns names\n",
        "    plt.figure(figsize=size)\n",
        "    sns.barplot(x=feat_imp[top_indices],y=most_important_feat) #plotting feature importance \n",
        "    plt.title('Feature Importance')\n",
        "    plt.xlabel('Importance')\n",
        "    plt.ylabel(\"Feature names\")\n",
        "    plt.show()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaQtOvkvfNeO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_top_features(df,feat_imp,top=50):\n",
        "  \"\"\"\n",
        "  gets the top features.\n",
        "  if top is of integer dtype it will return top features,\n",
        "  if top is between 0 and 1, it will return features that have at least feature importance value= top\n",
        "  \"\"\"\n",
        "  if (top>0.)&(top<=1.): #getting the features that have feature importance value greater than top\n",
        "    feature_to_consider = [1 if i>=top else 0 for i in feat_imp]\n",
        "    most_important_feat = [i  for i,j in zip(df.columns,feature_to_consider) if (j==1)]#getiing the columns names\n",
        "  else:\n",
        "    top_indices = np.argsort(feat_imp)[::-1][:top] #getting the indices with top feature importace\n",
        "    most_important_feat = df.columns[top_indices] #getiing the columns names\n",
        "  return most_important_feat    "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvLQsSsVAsUA",
        "colab_type": "text"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLh2j2XvAxeO",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nsbNtvxAYB2O",
        "colab": {}
      },
      "source": [
        "model = linear_model.LogisticRegression(class_weight='balanced')\n",
        "params = {'penalty':['l2','l1'], 'C':[10.**i for i in np.arange(-3,3,1)], \n",
        "          'fit_intercept':[True,False] ,\n",
        "          }\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mXTZa5oPYB2R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "b25e11e0-a83a-4f0c-904f-44d47faba708"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  1.6min\n",
            "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  4.9min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.798\n",
            "The best params were: {'penalty': 'l2', 'fit_intercept': True, 'C': 0.001}\n",
            "CPU times: user 9.71 s, sys: 1.4 s, total: 11.1 s\n",
            "Wall time: 4min 59s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eYp1oCU5YB2U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "9ac4666b-708f-460c-8ca8-b08258c27a27"
      },
      "source": [
        "model = linear_model.LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
        "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
        "                   max_iter=100, multi_class='auto', n_jobs=10, penalty='l2',\n",
        "                  solver='lbfgs', tol=0.0001, verbose=0,\n",
        "                   warm_start=False)\n",
        "\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=10, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DlDUHTI-YB2X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "9309bf5a-6b5f-498d-ec87-4742f245f121"
      },
      "source": [
        "labels=[] #storing all labels\n",
        "y_preds = [] #storing all y_pred\n",
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"Logisic Regression\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Logisic Regression on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.542 and 0.812 for Logisic Regression on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hh6_qnWsBOVa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "2db5013f-3390-4d1e-ea1d-4cf8af33671d"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Model          Dataset  log_loss       auc\n",
              "0  Logisic Regression  Log Transformed  0.542156  0.812097"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y9PvYUNX-t2",
        "colab_type": "text"
      },
      "source": [
        "# Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kVyDscneGv3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tree.DecisionTreeClassifier(class_weight='balanced')\n",
        "params = { \n",
        "          'max_depth':[10,100,500,1000,-1], \n",
        "         'min_samples_split':[2,5,10,100,500], \n",
        "          'min_samples_leaf':[1,3,5,7,10],\n",
        "          'max_leaf_nodes':[100,500,1000,5000,-1] \n",
        "          }\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWmU9ZdnjHEk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "13fd8148-7e39-4b1e-ffd8-8cf686a3ae4a"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  1.8min\n",
            "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  5.7min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.787\n",
            "The best params were: {'min_samples_split': 5, 'min_samples_leaf': 5, 'max_leaf_nodes': 100, 'max_depth': 10}\n",
            "CPU times: user 4.76 s, sys: 364 ms, total: 5.12 s\n",
            "Wall time: 5min 47s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
              "                       max_depth=10, max_features=None, max_leaf_nodes=100,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=5,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11n9ZcTNirnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "80c5c4b7-1c59-4b76-8fd7-b2022e0f5b52"
      },
      "source": [
        "model = tree.DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
        "                       max_depth=10, max_features=None, max_leaf_nodes=5000,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=7, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
        "                        splitter='best')\n",
        "\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
              "                       max_depth=10, max_features=None, max_leaf_nodes=5000,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=7, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqO7PiHUhFIR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "51ea0f08-bbb5-4224-d295-c7624b5ef5c9"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=10,\n",
              "                                                             max_features=None,\n",
              "                                                             max_leaf_nodes=5000,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=7,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             presort='deprecated',\n",
              "                                                             random_state=None,\n",
              "                                                             splitter='best'),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIxYiOfoih44",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "57273a00-a477-4887-ad92-8ad72fc34f42"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val)[:,1]\n",
        "label=\"Decision Tree Classifier\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Decision Tree Classifier on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.141 and 0.810 for Decision Tree Classifier on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qusVRO8HdpHW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "01bfbd3c-50d7-4f8d-a8d2-450d0798d64f"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.140804</td>\n",
              "      <td>0.810465</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed  0.542156  0.812097\n",
              "1  Decision Tree Classifier  Log Transformed  0.140804  0.810465"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "msPtDUHT0sQN"
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OwHc1zRG0sQP",
        "colab": {}
      },
      "source": [
        "model = ensemble.RandomForestClassifier(class_weight='balanced')\n",
        "params = { 'n_estimators':[1000,2000],\n",
        "          'max_depth':[500,1000], \n",
        "         'min_samples_split':[100,500],\n",
        "          'min_samples_leaf':[3,5],\n",
        "          'max_leaf_nodes':[100,250] \n",
        "          }\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zDOPEuJy0sQS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "4a91804a-7629-4118-d9f2-28193b540773"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params,cv=3)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 72.8min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.817\n",
            "The best params were: {'n_estimators': 1000, 'min_samples_split': 500, 'min_samples_leaf': 3, 'max_leaf_nodes': 250, 'max_depth': 500}\n",
            "CPU times: user 3min 7s, sys: 616 ms, total: 3min 8s\n",
            "Wall time: 1h 15min 57s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RBeRKL7R0sQW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "2346f4f2-21d4-4897-fe1c-f8b9c0a73d4d"
      },
      "source": [
        "model = ensemble.RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
        "                       criterion='gini', max_depth=500, max_features='auto',\n",
        "                       max_leaf_nodes=250, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=3, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
        "                       n_jobs=-1, oob_score=False,\n",
        "                       verbose=0, warm_start=False)\n",
        "\n",
        "%time model.fit(X_train,y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 10min 3s, sys: 2.51 s, total: 10min 6s\n",
            "Wall time: 5min 8s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oJLF56Ts0sQY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "1e76544a-a431-49b0-9f83-a5aa55dfb734"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train,y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                             ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=500,\n",
              "                                                             max_features='auto',\n",
              "                                                             max_leaf_nodes=250,\n",
              "                                                             max_samples=None,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=3,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             n_estimators=2000,\n",
              "                                                             n_jobs=-1,\n",
              "                                                             oob_score=False,\n",
              "                                                             random_state=None,\n",
              "                                                             verbose=0,\n",
              "                                                             warm_start=False),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4Jct69qq0sQa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "ba8a115c-db77-4ba1-bb1a-a7088a4d450c"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val)[:,1]\n",
        "label=\"Random Forest Classifier\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Random Forest Classifier on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.138 and 0.832 for Random Forest Classifier on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0igsmQs_0sQd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "e6e8cf57-5181-430a-8e79-6312c4e3b47a"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.140804</td>\n",
              "      <td>0.810465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137558</td>\n",
              "      <td>0.832106</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed  0.542156  0.812097\n",
              "1  Decision Tree Classifier  Log Transformed  0.140804  0.810465\n",
              "2  Random Forest Classifier  Log Transformed  0.137558  0.832106"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZqVnGrga8Tu",
        "colab_type": "text"
      },
      "source": [
        "# Selecting top features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFB132azbBQS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "d15ffff8-9c6b-42d5-a025-ca275882bf9f"
      },
      "source": [
        "plot_feature_importance(X_train,model.feature_importances_,top=50,size=(7,12))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxwkRX5gnXWi",
        "colab_type": "text"
      },
      "source": [
        "We will create new datasets with less features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfArYVshrZNh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7b7b6619-5104-428f-dc52-aacfbe5cd2b9"
      },
      "source": [
        "#getting top k features\n",
        "top=250\n",
        "features = get_top_features(X_train,model.feature_importances_,top=top)\n",
        "print(\"Taking top %i features\"%(top))\n",
        "X_train_200 = X_train[features].copy()\n",
        "X_val_200 = X_val[features].copy()\n",
        "X_test_200 = X_test[features].copy()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Taking top 250 features\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xA9bSzUARRZ_"
      },
      "source": [
        "# XGB Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3UWwclfyRRaC",
        "colab": {}
      },
      "source": [
        "model = xgb.XGBClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.5,\n",
        "                      subsample = 0.9,\n",
        "                      objective='binary:logistic', \n",
        "                      n_estimators=750,\n",
        "                      reg_alpha = 0.2,\n",
        "                      max_depth=6, \n",
        "                      gamma=0, random_state=42\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0CEyW37Dai9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d214fd67-d0dc-4dc6-c4f7-636441203253"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train,y_train,eval_set=[(X_val,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-auc:0.805762\tvalidation_0-error:0.039726\n",
            "[1]\tvalidation_0-auc:0.814789\tvalidation_0-error:0.039551\n",
            "[2]\tvalidation_0-auc:0.824039\tvalidation_0-error:0.039551\n",
            "[3]\tvalidation_0-auc:0.823033\tvalidation_0-error:0.039551\n",
            "[4]\tvalidation_0-auc:0.823704\tvalidation_0-error:0.039551\n",
            "[5]\tvalidation_0-auc:0.824998\tvalidation_0-error:0.039551\n",
            "[6]\tvalidation_0-auc:0.826246\tvalidation_0-error:0.039551\n",
            "[7]\tvalidation_0-auc:0.827871\tvalidation_0-error:0.039551\n",
            "[8]\tvalidation_0-auc:0.827678\tvalidation_0-error:0.039551\n",
            "[9]\tvalidation_0-auc:0.827459\tvalidation_0-error:0.039551\n",
            "[10]\tvalidation_0-auc:0.826225\tvalidation_0-error:0.039551\n",
            "[11]\tvalidation_0-auc:0.825665\tvalidation_0-error:0.039551\n",
            "[12]\tvalidation_0-auc:0.82375\tvalidation_0-error:0.039551\n",
            "[13]\tvalidation_0-auc:0.824506\tvalidation_0-error:0.039551\n",
            "[14]\tvalidation_0-auc:0.824725\tvalidation_0-error:0.039551\n",
            "[15]\tvalidation_0-auc:0.82555\tvalidation_0-error:0.039551\n",
            "[16]\tvalidation_0-auc:0.826104\tvalidation_0-error:0.039551\n",
            "[17]\tvalidation_0-auc:0.824918\tvalidation_0-error:0.039551\n",
            "[18]\tvalidation_0-auc:0.826056\tvalidation_0-error:0.039551\n",
            "[19]\tvalidation_0-auc:0.826898\tvalidation_0-error:0.039551\n",
            "[20]\tvalidation_0-auc:0.827055\tvalidation_0-error:0.039551\n",
            "[21]\tvalidation_0-auc:0.82727\tvalidation_0-error:0.039551\n",
            "[22]\tvalidation_0-auc:0.827105\tvalidation_0-error:0.039551\n",
            "[23]\tvalidation_0-auc:0.826857\tvalidation_0-error:0.039551\n",
            "[24]\tvalidation_0-auc:0.827379\tvalidation_0-error:0.039551\n",
            "[25]\tvalidation_0-auc:0.827653\tvalidation_0-error:0.039551\n",
            "[26]\tvalidation_0-auc:0.827409\tvalidation_0-error:0.039551\n",
            "[27]\tvalidation_0-auc:0.827724\tvalidation_0-error:0.039551\n",
            "[28]\tvalidation_0-auc:0.827902\tvalidation_0-error:0.039551\n",
            "[29]\tvalidation_0-auc:0.827513\tvalidation_0-error:0.039551\n",
            "[30]\tvalidation_0-auc:0.827558\tvalidation_0-error:0.039551\n",
            "[31]\tvalidation_0-auc:0.828339\tvalidation_0-error:0.039551\n",
            "[32]\tvalidation_0-auc:0.82845\tvalidation_0-error:0.039551\n",
            "[33]\tvalidation_0-auc:0.829522\tvalidation_0-error:0.039551\n",
            "[34]\tvalidation_0-auc:0.830406\tvalidation_0-error:0.039551\n",
            "[35]\tvalidation_0-auc:0.830435\tvalidation_0-error:0.039551\n",
            "[36]\tvalidation_0-auc:0.830216\tvalidation_0-error:0.039551\n",
            "[37]\tvalidation_0-auc:0.829947\tvalidation_0-error:0.039551\n",
            "[38]\tvalidation_0-auc:0.82953\tvalidation_0-error:0.039551\n",
            "[39]\tvalidation_0-auc:0.82917\tvalidation_0-error:0.039551\n",
            "[40]\tvalidation_0-auc:0.83004\tvalidation_0-error:0.039551\n",
            "[41]\tvalidation_0-auc:0.829258\tvalidation_0-error:0.039551\n",
            "[42]\tvalidation_0-auc:0.829412\tvalidation_0-error:0.039551\n",
            "[43]\tvalidation_0-auc:0.829235\tvalidation_0-error:0.039551\n",
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            "[730]\tvalidation_0-auc:0.846038\tvalidation_0-error:0.039551\n",
            "[731]\tvalidation_0-auc:0.846063\tvalidation_0-error:0.039551\n",
            "[732]\tvalidation_0-auc:0.846066\tvalidation_0-error:0.039551\n",
            "[733]\tvalidation_0-auc:0.846039\tvalidation_0-error:0.039551\n",
            "[734]\tvalidation_0-auc:0.846063\tvalidation_0-error:0.039551\n",
            "[735]\tvalidation_0-auc:0.846116\tvalidation_0-error:0.039551\n",
            "[736]\tvalidation_0-auc:0.846118\tvalidation_0-error:0.039551\n",
            "[737]\tvalidation_0-auc:0.846138\tvalidation_0-error:0.039551\n",
            "[738]\tvalidation_0-auc:0.846115\tvalidation_0-error:0.039551\n",
            "[739]\tvalidation_0-auc:0.84612\tvalidation_0-error:0.039551\n",
            "[740]\tvalidation_0-auc:0.846178\tvalidation_0-error:0.039551\n",
            "[741]\tvalidation_0-auc:0.846137\tvalidation_0-error:0.039551\n",
            "[742]\tvalidation_0-auc:0.84611\tvalidation_0-error:0.039551\n",
            "[743]\tvalidation_0-auc:0.846131\tvalidation_0-error:0.039551\n",
            "[744]\tvalidation_0-auc:0.846131\tvalidation_0-error:0.039551\n",
            "[745]\tvalidation_0-auc:0.846156\tvalidation_0-error:0.039551\n",
            "[746]\tvalidation_0-auc:0.84618\tvalidation_0-error:0.039551\n",
            "[747]\tvalidation_0-auc:0.846188\tvalidation_0-error:0.039551\n",
            "[748]\tvalidation_0-auc:0.846185\tvalidation_0-error:0.039551\n",
            "[749]\tvalidation_0-auc:0.84617\tvalidation_0-error:0.039551\n",
            "CPU times: user 11min 12s, sys: 1.4 s, total: 11min 13s\n",
            "Wall time: 5min 47s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0.5, gamma=0,\n",
              "              learning_rate=0.01, max_delta_step=0, max_depth=6,\n",
              "              min_child_weight=1, missing=None, n_estimators=750, n_jobs=-1,\n",
              "              nthread=-1, objective='binary:logistic', random_state=42,\n",
              "              reg_alpha=0.2, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=0.9, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "q3LRv6T8RRaN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "b871c51e-1d6b-4d5e-d2ff-d4f4458865f3"
      },
      "source": [
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"XGBoost Classifer\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t XGBoost Classifer on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.846 for XGBoost Classifer on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qUwaU5nuRRaR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "779b26df-cccb-40b1-f38d-f6b5be302b8f"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.140804</td>\n",
              "      <td>0.810465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137558</td>\n",
              "      <td>0.832106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133120</td>\n",
              "      <td>0.846170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed  0.542156  0.812097\n",
              "1  Decision Tree Classifier  Log Transformed  0.140804  0.810465\n",
              "2  Random Forest Classifier  Log Transformed  0.137558  0.832106\n",
              "3         XGBoost Classifer  Log Transformed  0.133120  0.846170"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LeV2q75ULzvm"
      },
      "source": [
        "# LightGBM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "32dzyVRQLzvn",
        "colab": {}
      },
      "source": [
        "model = lgb.LGBMClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = .5,\n",
        "                      subsample = 0.7,\n",
        "                      objective='binary', \n",
        "                      n_estimators=500,\n",
        "                      reg_alpha = 0.3,\n",
        "                      max_depth=8, \n",
        "                      gamma=0, random_state=42\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yJS6sV0qLzvr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "883ae7e6-060b-4338-e21b-1aa03dd44a7a"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train,y_train,eval_set=[(X_val,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's auc: 0.819973\tvalid_0's binary_logloss: 0.165719\n",
            "[2]\tvalid_0's auc: 0.821431\tvalid_0's binary_logloss: 0.165076\n",
            "[3]\tvalid_0's auc: 0.82962\tvalid_0's binary_logloss: 0.164341\n",
            "[4]\tvalid_0's auc: 0.830261\tvalid_0's binary_logloss: 0.163747\n",
            "[5]\tvalid_0's auc: 0.829651\tvalid_0's binary_logloss: 0.16314\n",
            "[6]\tvalid_0's auc: 0.83059\tvalid_0's binary_logloss: 0.162556\n",
            "[7]\tvalid_0's auc: 0.831028\tvalid_0's binary_logloss: 0.162029\n",
            "[8]\tvalid_0's auc: 0.832365\tvalid_0's binary_logloss: 0.161447\n",
            "[9]\tvalid_0's auc: 0.832561\tvalid_0's binary_logloss: 0.160951\n",
            "[10]\tvalid_0's auc: 0.833345\tvalid_0's binary_logloss: 0.160383\n",
            "[11]\tvalid_0's auc: 0.833285\tvalid_0's binary_logloss: 0.159919\n",
            "[12]\tvalid_0's auc: 0.832625\tvalid_0's binary_logloss: 0.159469\n",
            "[13]\tvalid_0's auc: 0.831923\tvalid_0's binary_logloss: 0.159044\n",
            "[14]\tvalid_0's auc: 0.83105\tvalid_0's binary_logloss: 0.158627\n",
            "[15]\tvalid_0's auc: 0.830849\tvalid_0's binary_logloss: 0.15822\n",
            "[16]\tvalid_0's auc: 0.830143\tvalid_0's binary_logloss: 0.157862\n",
            "[17]\tvalid_0's auc: 0.830958\tvalid_0's binary_logloss: 0.15743\n",
            "[18]\tvalid_0's auc: 0.831424\tvalid_0's binary_logloss: 0.156961\n",
            "[19]\tvalid_0's auc: 0.83297\tvalid_0's binary_logloss: 0.156519\n",
            "[20]\tvalid_0's auc: 0.834126\tvalid_0's binary_logloss: 0.156105\n",
            "[21]\tvalid_0's auc: 0.835315\tvalid_0's binary_logloss: 0.155708\n",
            "[22]\tvalid_0's auc: 0.835339\tvalid_0's binary_logloss: 0.155329\n",
            "[23]\tvalid_0's auc: 0.834635\tvalid_0's binary_logloss: 0.155053\n",
            "[24]\tvalid_0's auc: 0.834394\tvalid_0's binary_logloss: 0.154696\n",
            "[25]\tvalid_0's auc: 0.834797\tvalid_0's binary_logloss: 0.154312\n",
            "[26]\tvalid_0's auc: 0.834987\tvalid_0's binary_logloss: 0.153944\n",
            "[27]\tvalid_0's auc: 0.836075\tvalid_0's binary_logloss: 0.153555\n",
            "[28]\tvalid_0's auc: 0.836112\tvalid_0's binary_logloss: 0.153236\n",
            "[29]\tvalid_0's auc: 0.836202\tvalid_0's binary_logloss: 0.152932\n",
            "[30]\tvalid_0's auc: 0.835669\tvalid_0's binary_logloss: 0.152681\n",
            "[31]\tvalid_0's auc: 0.835939\tvalid_0's binary_logloss: 0.152346\n",
            "[32]\tvalid_0's auc: 0.83622\tvalid_0's binary_logloss: 0.152053\n",
            "[33]\tvalid_0's auc: 0.835818\tvalid_0's binary_logloss: 0.151813\n",
            "[34]\tvalid_0's auc: 0.835888\tvalid_0's binary_logloss: 0.151525\n",
            "[35]\tvalid_0's auc: 0.835724\tvalid_0's binary_logloss: 0.15128\n",
            "[36]\tvalid_0's auc: 0.835949\tvalid_0's binary_logloss: 0.150987\n",
            "[37]\tvalid_0's auc: 0.835598\tvalid_0's binary_logloss: 0.150759\n",
            "[38]\tvalid_0's auc: 0.835628\tvalid_0's binary_logloss: 0.150536\n",
            "[39]\tvalid_0's auc: 0.835765\tvalid_0's binary_logloss: 0.150306\n",
            "[40]\tvalid_0's auc: 0.835652\tvalid_0's binary_logloss: 0.150087\n",
            "[41]\tvalid_0's auc: 0.835621\tvalid_0's binary_logloss: 0.149875\n",
            "[42]\tvalid_0's auc: 0.836076\tvalid_0's binary_logloss: 0.149597\n",
            "[43]\tvalid_0's auc: 0.835801\tvalid_0's binary_logloss: 0.149388\n",
            "[44]\tvalid_0's auc: 0.835752\tvalid_0's binary_logloss: 0.149175\n",
            "[45]\tvalid_0's auc: 0.836154\tvalid_0's binary_logloss: 0.14891\n",
            "[46]\tvalid_0's auc: 0.836481\tvalid_0's binary_logloss: 0.148653\n",
            "[47]\tvalid_0's auc: 0.83677\tvalid_0's binary_logloss: 0.148443\n",
            "[48]\tvalid_0's auc: 0.836736\tvalid_0's binary_logloss: 0.148261\n",
            "[49]\tvalid_0's auc: 0.836959\tvalid_0's binary_logloss: 0.148049\n",
            "[50]\tvalid_0's auc: 0.836531\tvalid_0's binary_logloss: 0.147869\n",
            "[51]\tvalid_0's auc: 0.836472\tvalid_0's binary_logloss: 0.147683\n",
            "[52]\tvalid_0's auc: 0.836469\tvalid_0's binary_logloss: 0.147517\n",
            "[53]\tvalid_0's auc: 0.83615\tvalid_0's binary_logloss: 0.147353\n",
            "[54]\tvalid_0's auc: 0.836412\tvalid_0's binary_logloss: 0.147175\n",
            "[55]\tvalid_0's auc: 0.836343\tvalid_0's binary_logloss: 0.147017\n",
            "[56]\tvalid_0's auc: 0.836424\tvalid_0's binary_logloss: 0.146844\n",
            "[57]\tvalid_0's auc: 0.836292\tvalid_0's binary_logloss: 0.146697\n",
            "[58]\tvalid_0's auc: 0.836564\tvalid_0's binary_logloss: 0.146508\n",
            "[59]\tvalid_0's auc: 0.836575\tvalid_0's binary_logloss: 0.146338\n",
            "[60]\tvalid_0's auc: 0.836662\tvalid_0's binary_logloss: 0.146182\n",
            "[61]\tvalid_0's auc: 0.836766\tvalid_0's binary_logloss: 0.145991\n",
            "[62]\tvalid_0's auc: 0.836628\tvalid_0's binary_logloss: 0.145848\n",
            "[63]\tvalid_0's auc: 0.836938\tvalid_0's binary_logloss: 0.145654\n",
            "[64]\tvalid_0's auc: 0.837047\tvalid_0's binary_logloss: 0.145507\n",
            "[65]\tvalid_0's auc: 0.837391\tvalid_0's binary_logloss: 0.14533\n",
            "[66]\tvalid_0's auc: 0.837151\tvalid_0's binary_logloss: 0.145185\n",
            "[67]\tvalid_0's auc: 0.837008\tvalid_0's binary_logloss: 0.145062\n",
            "[68]\tvalid_0's auc: 0.83675\tvalid_0's binary_logloss: 0.144963\n",
            "[69]\tvalid_0's auc: 0.837124\tvalid_0's binary_logloss: 0.144793\n",
            "[70]\tvalid_0's auc: 0.837355\tvalid_0's binary_logloss: 0.144623\n",
            "[71]\tvalid_0's auc: 0.837516\tvalid_0's binary_logloss: 0.144471\n",
            "[72]\tvalid_0's auc: 0.83793\tvalid_0's binary_logloss: 0.144317\n",
            "[73]\tvalid_0's auc: 0.838151\tvalid_0's binary_logloss: 0.144197\n",
            "[74]\tvalid_0's auc: 0.838201\tvalid_0's binary_logloss: 0.144083\n",
            "[75]\tvalid_0's auc: 0.837975\tvalid_0's binary_logloss: 0.143961\n",
            "[76]\tvalid_0's auc: 0.838333\tvalid_0's binary_logloss: 0.143837\n",
            "[77]\tvalid_0's auc: 0.83807\tvalid_0's binary_logloss: 0.143725\n",
            "[78]\tvalid_0's auc: 0.838358\tvalid_0's binary_logloss: 0.143583\n",
            "[79]\tvalid_0's auc: 0.837908\tvalid_0's binary_logloss: 0.143468\n",
            "[80]\tvalid_0's auc: 0.838315\tvalid_0's binary_logloss: 0.143318\n",
            "[81]\tvalid_0's auc: 0.838193\tvalid_0's binary_logloss: 0.143219\n",
            "[82]\tvalid_0's auc: 0.838406\tvalid_0's binary_logloss: 0.143069\n",
            "[83]\tvalid_0's auc: 0.838399\tvalid_0's binary_logloss: 0.142991\n",
            "[84]\tvalid_0's auc: 0.838372\tvalid_0's binary_logloss: 0.142893\n",
            "[85]\tvalid_0's auc: 0.838553\tvalid_0's binary_logloss: 0.14276\n",
            "[86]\tvalid_0's auc: 0.838752\tvalid_0's binary_logloss: 0.142627\n",
            "[87]\tvalid_0's auc: 0.838826\tvalid_0's binary_logloss: 0.142521\n",
            "[88]\tvalid_0's auc: 0.839014\tvalid_0's binary_logloss: 0.142428\n",
            "[89]\tvalid_0's auc: 0.838847\tvalid_0's binary_logloss: 0.142345\n",
            "[90]\tvalid_0's auc: 0.839098\tvalid_0's binary_logloss: 0.142227\n",
            "[91]\tvalid_0's auc: 0.839173\tvalid_0's binary_logloss: 0.142103\n",
            "[92]\tvalid_0's auc: 0.839217\tvalid_0's binary_logloss: 0.141983\n",
            "[93]\tvalid_0's auc: 0.839408\tvalid_0's binary_logloss: 0.141853\n",
            "[94]\tvalid_0's auc: 0.839681\tvalid_0's binary_logloss: 0.141757\n",
            "[95]\tvalid_0's auc: 0.839546\tvalid_0's binary_logloss: 0.141679\n",
            "[96]\tvalid_0's auc: 0.83976\tvalid_0's binary_logloss: 0.141559\n",
            "[97]\tvalid_0's auc: 0.840108\tvalid_0's binary_logloss: 0.141493\n",
            "[98]\tvalid_0's auc: 0.840103\tvalid_0's binary_logloss: 0.141394\n",
            "[99]\tvalid_0's auc: 0.840019\tvalid_0's binary_logloss: 0.141317\n",
            "[100]\tvalid_0's auc: 0.839957\tvalid_0's binary_logloss: 0.141227\n",
            "[101]\tvalid_0's auc: 0.840371\tvalid_0's binary_logloss: 0.141125\n",
            "[102]\tvalid_0's auc: 0.840562\tvalid_0's binary_logloss: 0.140997\n",
            "[103]\tvalid_0's auc: 0.840518\tvalid_0's binary_logloss: 0.140939\n",
            "[104]\tvalid_0's auc: 0.840548\tvalid_0's binary_logloss: 0.140837\n",
            "[105]\tvalid_0's auc: 0.840513\tvalid_0's binary_logloss: 0.140755\n",
            "[106]\tvalid_0's auc: 0.840539\tvalid_0's binary_logloss: 0.140681\n",
            "[107]\tvalid_0's auc: 0.840611\tvalid_0's binary_logloss: 0.140587\n",
            "[108]\tvalid_0's auc: 0.840668\tvalid_0's binary_logloss: 0.140487\n",
            "[109]\tvalid_0's auc: 0.840743\tvalid_0's binary_logloss: 0.140387\n",
            "[110]\tvalid_0's auc: 0.8408\tvalid_0's binary_logloss: 0.140299\n",
            "[111]\tvalid_0's auc: 0.840926\tvalid_0's binary_logloss: 0.140195\n",
            "[112]\tvalid_0's auc: 0.840804\tvalid_0's binary_logloss: 0.140131\n",
            "[113]\tvalid_0's auc: 0.840919\tvalid_0's binary_logloss: 0.140038\n",
            "[114]\tvalid_0's auc: 0.841008\tvalid_0's binary_logloss: 0.139975\n",
            "[115]\tvalid_0's auc: 0.840969\tvalid_0's binary_logloss: 0.139914\n",
            "[116]\tvalid_0's auc: 0.841209\tvalid_0's binary_logloss: 0.139843\n",
            "[117]\tvalid_0's auc: 0.841212\tvalid_0's binary_logloss: 0.139768\n",
            "[118]\tvalid_0's auc: 0.841312\tvalid_0's binary_logloss: 0.139709\n",
            "[119]\tvalid_0's auc: 0.841297\tvalid_0's binary_logloss: 0.139652\n",
            "[120]\tvalid_0's auc: 0.841398\tvalid_0's binary_logloss: 0.139569\n",
            "[121]\tvalid_0's auc: 0.841432\tvalid_0's binary_logloss: 0.13949\n",
            "[122]\tvalid_0's auc: 0.841317\tvalid_0's binary_logloss: 0.139455\n",
            "[123]\tvalid_0's auc: 0.841368\tvalid_0's binary_logloss: 0.139366\n",
            "[124]\tvalid_0's auc: 0.841384\tvalid_0's binary_logloss: 0.139306\n",
            "[125]\tvalid_0's auc: 0.841368\tvalid_0's binary_logloss: 0.139252\n",
            "[126]\tvalid_0's auc: 0.841302\tvalid_0's binary_logloss: 0.139179\n",
            "[127]\tvalid_0's auc: 0.841277\tvalid_0's binary_logloss: 0.13912\n",
            "[128]\tvalid_0's auc: 0.841365\tvalid_0's binary_logloss: 0.139065\n",
            "[129]\tvalid_0's auc: 0.841333\tvalid_0's binary_logloss: 0.138997\n",
            "[130]\tvalid_0's auc: 0.841349\tvalid_0's binary_logloss: 0.138944\n",
            "[131]\tvalid_0's auc: 0.841369\tvalid_0's binary_logloss: 0.138891\n",
            "[132]\tvalid_0's auc: 0.841503\tvalid_0's binary_logloss: 0.138816\n",
            "[133]\tvalid_0's auc: 0.841552\tvalid_0's binary_logloss: 0.138748\n",
            "[134]\tvalid_0's auc: 0.841572\tvalid_0's binary_logloss: 0.138682\n",
            "[135]\tvalid_0's auc: 0.841772\tvalid_0's binary_logloss: 0.138605\n",
            "[136]\tvalid_0's auc: 0.841764\tvalid_0's binary_logloss: 0.138535\n",
            "[137]\tvalid_0's auc: 0.841953\tvalid_0's binary_logloss: 0.138458\n",
            "[138]\tvalid_0's auc: 0.841927\tvalid_0's binary_logloss: 0.138402\n",
            "[139]\tvalid_0's auc: 0.841912\tvalid_0's binary_logloss: 0.138345\n",
            "[140]\tvalid_0's auc: 0.841848\tvalid_0's binary_logloss: 0.138302\n",
            "[141]\tvalid_0's auc: 0.841849\tvalid_0's binary_logloss: 0.13826\n",
            "[142]\tvalid_0's auc: 0.841777\tvalid_0's binary_logloss: 0.138218\n",
            "[143]\tvalid_0's auc: 0.841684\tvalid_0's binary_logloss: 0.138165\n",
            "[144]\tvalid_0's auc: 0.841813\tvalid_0's binary_logloss: 0.138122\n",
            "[145]\tvalid_0's auc: 0.841952\tvalid_0's binary_logloss: 0.138062\n",
            "[146]\tvalid_0's auc: 0.841993\tvalid_0's binary_logloss: 0.138004\n",
            "[147]\tvalid_0's auc: 0.842226\tvalid_0's binary_logloss: 0.137935\n",
            "[148]\tvalid_0's auc: 0.842257\tvalid_0's binary_logloss: 0.137893\n",
            "[149]\tvalid_0's auc: 0.842355\tvalid_0's binary_logloss: 0.137834\n",
            "[150]\tvalid_0's auc: 0.842386\tvalid_0's binary_logloss: 0.137778\n",
            "[151]\tvalid_0's auc: 0.842477\tvalid_0's binary_logloss: 0.137723\n",
            "[152]\tvalid_0's auc: 0.842479\tvalid_0's binary_logloss: 0.137676\n",
            "[153]\tvalid_0's auc: 0.842429\tvalid_0's binary_logloss: 0.137625\n",
            "[154]\tvalid_0's auc: 0.842425\tvalid_0's binary_logloss: 0.137569\n",
            "[155]\tvalid_0's auc: 0.842493\tvalid_0's binary_logloss: 0.137524\n",
            "[156]\tvalid_0's auc: 0.842554\tvalid_0's binary_logloss: 0.13749\n",
            "[157]\tvalid_0's auc: 0.842453\tvalid_0's binary_logloss: 0.137459\n",
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            "[159]\tvalid_0's auc: 0.842531\tvalid_0's binary_logloss: 0.137364\n",
            "[160]\tvalid_0's auc: 0.842525\tvalid_0's binary_logloss: 0.137332\n",
            "[161]\tvalid_0's auc: 0.842591\tvalid_0's binary_logloss: 0.137267\n",
            "[162]\tvalid_0's auc: 0.842647\tvalid_0's binary_logloss: 0.137226\n",
            "[163]\tvalid_0's auc: 0.842722\tvalid_0's binary_logloss: 0.137167\n",
            "[164]\tvalid_0's auc: 0.842718\tvalid_0's binary_logloss: 0.137122\n",
            "[165]\tvalid_0's auc: 0.842705\tvalid_0's binary_logloss: 0.137093\n",
            "[166]\tvalid_0's auc: 0.842668\tvalid_0's binary_logloss: 0.137054\n",
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            "[493]\tvalid_0's auc: 0.846013\tvalid_0's binary_logloss: 0.133151\n",
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            "[498]\tvalid_0's auc: 0.845939\tvalid_0's binary_logloss: 0.133144\n",
            "[499]\tvalid_0's auc: 0.845981\tvalid_0's binary_logloss: 0.133141\n",
            "[500]\tvalid_0's auc: 0.845953\tvalid_0's binary_logloss: 0.133139\n",
            "CPU times: user 1min 6s, sys: 398 ms, total: 1min 6s\n",
            "Wall time: 35.4 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.5,\n",
              "               gamma=0, importance_type='split', learning_rate=0.01,\n",
              "               max_depth=8, min_child_samples=20, min_child_weight=0.001,\n",
              "               min_split_gain=0.0, n_estimators=500, n_jobs=-1, nthread=-1,\n",
              "               num_leaves=31, objective='binary', random_state=42,\n",
              "               reg_alpha=0.3, reg_lambda=0.0, scale_pos_weight=1, silent=True,\n",
              "               subsample=0.7, subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "k_CEadVqLzvy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7720aafd-7f4c-4de0-c8d4-9759a837818b"
      },
      "source": [
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"LightGBM Classifer\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t LightGBM Classifer on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.846 for LightGBM Classifer on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BF22prvpLzv1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d392662-53eb-441f-cc41-3e55560d214e"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.140804</td>\n",
              "      <td>0.810465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137558</td>\n",
              "      <td>0.832106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133120</td>\n",
              "      <td>0.846170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133139</td>\n",
              "      <td>0.845953</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Log Transformed  0.542156  0.812097\n",
              "1  Decision Tree Classifier  Log Transformed  0.140804  0.810465\n",
              "2  Random Forest Classifier  Log Transformed  0.137558  0.832106\n",
              "3         XGBoost Classifer  Log Transformed  0.133120  0.846170\n",
              "4        LightGBM Classifer  Log Transformed  0.133139  0.845953"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LECmbx42-V2m"
      },
      "source": [
        "# Random Forest (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CUgv74lV-V2n",
        "colab": {}
      },
      "source": [
        "model = ensemble.RandomForestClassifier(class_weight='balanced')\n",
        "params = { 'n_estimators':[1000,2000],\n",
        "          'max_depth':[500,1000], \n",
        "         'min_samples_split':[100,500],\n",
        "          'min_samples_leaf':[3,5],\n",
        "          'max_leaf_nodes':[100,250] \n",
        "          }\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eCt8L0AV-V2q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f39d1c65-b161-4ab5-88d2-8839253c9bdf"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params,cv=3,X_train=X_train_200)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 48.1min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.819\n",
            "The best params were: {'n_estimators': 2000, 'min_samples_split': 500, 'min_samples_leaf': 3, 'max_leaf_nodes': 250, 'max_depth': 500}\n",
            "CPU times: user 5min 24s, sys: 595 ms, total: 5min 24s\n",
            "Wall time: 53min 30s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DVdFANOy-V2u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "9f917d26-40cd-4dd7-bd5a-7458ce8e0d67"
      },
      "source": [
        "model = ensemble.RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
        "                       criterion='gini', max_depth=500, max_features='auto',\n",
        "                       max_leaf_nodes=250, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=3, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
        "                       n_jobs=-1, oob_score=False,\n",
        "                       verbose=0, warm_start=False)\n",
        "model.fit(X_train_200,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "okg4Xksh-V2y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "b26652ab-fc66-4453-ce2f-8b2516a88957"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train_200,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                             ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=500,\n",
              "                                                             max_features='auto',\n",
              "                                                             max_leaf_nodes=250,\n",
              "                                                             max_samples=None,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=3,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             n_estimators=2000,\n",
              "                                                             n_jobs=-1,\n",
              "                                                             oob_score=False,\n",
              "                                                             random_state=None,\n",
              "                                                             verbose=0,\n",
              "                                                             warm_start=False),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5eSsmef9-V21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "49c5aff6-e565-4656-83fb-8656f73ab5f3"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val_200)[:,1]\n",
        "label=\"Random Forest Classifier (top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Random Forest Classifier (top 250) on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.137 and 0.834 for Random Forest Classifier (top 250) on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Mo702Rt-V23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "c38ec03c-ac38-496b-d6e6-b36b0012ac90"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.140804</td>\n",
              "      <td>0.810465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137558</td>\n",
              "      <td>0.832106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133120</td>\n",
              "      <td>0.846170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133139</td>\n",
              "      <td>0.845953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137108</td>\n",
              "      <td>0.833931</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model          Dataset  log_loss       auc\n",
              "0                  Logisic Regression  Log Transformed  0.542156  0.812097\n",
              "1            Decision Tree Classifier  Log Transformed  0.140804  0.810465\n",
              "2            Random Forest Classifier  Log Transformed  0.137558  0.832106\n",
              "3                   XGBoost Classifer  Log Transformed  0.133120  0.846170\n",
              "4                  LightGBM Classifer  Log Transformed  0.133139  0.845953\n",
              "5  Random Forest Classifier (top 250)  Log Transformed  0.137108  0.833931"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "i-y4IONn-V26"
      },
      "source": [
        "# XGB Classifier (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Gjg5rhGa-V27",
        "colab": {}
      },
      "source": [
        "model = xgb.XGBClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.8, \n",
        "                      subsample = 0.7,\n",
        "                      objective='binary:logistic', \n",
        "                      n_estimators=845,\n",
        "                      reg_alpha = 0.3,\n",
        "                      max_depth=7,\n",
        "                      gamma=0, random_state=42\n",
        "                     )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Q0dR9Kt-V2-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4c9aa329-ac32-4a97-80b4-c2c51ff3b9c8"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train_200,y_train,eval_set=[(X_val_200,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-auc:0.787881\tvalidation_0-error:0.040691\n",
            "[1]\tvalidation_0-auc:0.790846\tvalidation_0-error:0.039551\n",
            "[2]\tvalidation_0-auc:0.798136\tvalidation_0-error:0.039814\n",
            "[3]\tvalidation_0-auc:0.80953\tvalidation_0-error:0.039639\n",
            "[4]\tvalidation_0-auc:0.815435\tvalidation_0-error:0.039551\n",
            "[5]\tvalidation_0-auc:0.82069\tvalidation_0-error:0.039639\n",
            "[6]\tvalidation_0-auc:0.822988\tvalidation_0-error:0.039551\n",
            "[7]\tvalidation_0-auc:0.825401\tvalidation_0-error:0.039551\n",
            "[8]\tvalidation_0-auc:0.826905\tvalidation_0-error:0.039551\n",
            "[9]\tvalidation_0-auc:0.827275\tvalidation_0-error:0.039551\n",
            "[10]\tvalidation_0-auc:0.827274\tvalidation_0-error:0.039551\n",
            "[11]\tvalidation_0-auc:0.825432\tvalidation_0-error:0.039551\n",
            "[12]\tvalidation_0-auc:0.826985\tvalidation_0-error:0.039551\n",
            "[13]\tvalidation_0-auc:0.825223\tvalidation_0-error:0.039551\n",
            "[14]\tvalidation_0-auc:0.825304\tvalidation_0-error:0.039551\n",
            "[15]\tvalidation_0-auc:0.827608\tvalidation_0-error:0.039551\n",
            "[16]\tvalidation_0-auc:0.825988\tvalidation_0-error:0.039551\n",
            "[17]\tvalidation_0-auc:0.826023\tvalidation_0-error:0.039551\n",
            "[18]\tvalidation_0-auc:0.826254\tvalidation_0-error:0.039551\n",
            "[19]\tvalidation_0-auc:0.826415\tvalidation_0-error:0.039551\n",
            "[20]\tvalidation_0-auc:0.826803\tvalidation_0-error:0.039551\n",
            "[21]\tvalidation_0-auc:0.828095\tvalidation_0-error:0.039551\n",
            "[22]\tvalidation_0-auc:0.827076\tvalidation_0-error:0.039551\n",
            "[23]\tvalidation_0-auc:0.827439\tvalidation_0-error:0.039551\n",
            "[24]\tvalidation_0-auc:0.828472\tvalidation_0-error:0.039551\n",
            "[25]\tvalidation_0-auc:0.828818\tvalidation_0-error:0.039551\n",
            "[26]\tvalidation_0-auc:0.829125\tvalidation_0-error:0.039551\n",
            "[27]\tvalidation_0-auc:0.828926\tvalidation_0-error:0.039551\n",
            "[28]\tvalidation_0-auc:0.829091\tvalidation_0-error:0.039551\n",
            "[29]\tvalidation_0-auc:0.82898\tvalidation_0-error:0.039551\n",
            "[30]\tvalidation_0-auc:0.828907\tvalidation_0-error:0.039551\n",
            "[31]\tvalidation_0-auc:0.829314\tvalidation_0-error:0.039551\n",
            "[32]\tvalidation_0-auc:0.829331\tvalidation_0-error:0.039551\n",
            "[33]\tvalidation_0-auc:0.828885\tvalidation_0-error:0.039551\n",
            "[34]\tvalidation_0-auc:0.82885\tvalidation_0-error:0.039551\n",
            "[35]\tvalidation_0-auc:0.829098\tvalidation_0-error:0.039551\n",
            "[36]\tvalidation_0-auc:0.829384\tvalidation_0-error:0.039551\n",
            "[37]\tvalidation_0-auc:0.829907\tvalidation_0-error:0.039551\n",
            "[38]\tvalidation_0-auc:0.829731\tvalidation_0-error:0.039551\n",
            "[39]\tvalidation_0-auc:0.829619\tvalidation_0-error:0.039551\n",
            "[40]\tvalidation_0-auc:0.829893\tvalidation_0-error:0.039551\n",
            "[41]\tvalidation_0-auc:0.829932\tvalidation_0-error:0.039551\n",
            "[42]\tvalidation_0-auc:0.830456\tvalidation_0-error:0.039551\n",
            "[43]\tvalidation_0-auc:0.83068\tvalidation_0-error:0.039551\n",
            "[44]\tvalidation_0-auc:0.831423\tvalidation_0-error:0.039551\n",
            "[45]\tvalidation_0-auc:0.830797\tvalidation_0-error:0.039551\n",
            "[46]\tvalidation_0-auc:0.830671\tvalidation_0-error:0.039551\n",
            "[47]\tvalidation_0-auc:0.830521\tvalidation_0-error:0.039551\n",
            "[48]\tvalidation_0-auc:0.8308\tvalidation_0-error:0.039551\n",
            "[49]\tvalidation_0-auc:0.830925\tvalidation_0-error:0.039551\n",
            "[50]\tvalidation_0-auc:0.831232\tvalidation_0-error:0.039551\n",
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            "[58]\tvalidation_0-auc:0.832486\tvalidation_0-error:0.039551\n",
            "[59]\tvalidation_0-auc:0.832771\tvalidation_0-error:0.039551\n",
            "[60]\tvalidation_0-auc:0.832566\tvalidation_0-error:0.039551\n",
            "[61]\tvalidation_0-auc:0.832515\tvalidation_0-error:0.039551\n",
            "[62]\tvalidation_0-auc:0.832478\tvalidation_0-error:0.039551\n",
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            "[67]\tvalidation_0-auc:0.832464\tvalidation_0-error:0.039551\n",
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            "[88]\tvalidation_0-auc:0.834104\tvalidation_0-error:0.039551\n",
            "[89]\tvalidation_0-auc:0.83448\tvalidation_0-error:0.039551\n",
            "[90]\tvalidation_0-auc:0.834606\tvalidation_0-error:0.039551\n",
            "[91]\tvalidation_0-auc:0.834626\tvalidation_0-error:0.039551\n",
            "[92]\tvalidation_0-auc:0.834517\tvalidation_0-error:0.039551\n",
            "[93]\tvalidation_0-auc:0.834578\tvalidation_0-error:0.039551\n",
            "[94]\tvalidation_0-auc:0.834684\tvalidation_0-error:0.039551\n",
            "[95]\tvalidation_0-auc:0.834478\tvalidation_0-error:0.039551\n",
            "[96]\tvalidation_0-auc:0.834559\tvalidation_0-error:0.039551\n",
            "[97]\tvalidation_0-auc:0.835057\tvalidation_0-error:0.039551\n",
            "[98]\tvalidation_0-auc:0.835402\tvalidation_0-error:0.039551\n",
            "[99]\tvalidation_0-auc:0.835386\tvalidation_0-error:0.039551\n",
            "[100]\tvalidation_0-auc:0.83538\tvalidation_0-error:0.039551\n",
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            "[109]\tvalidation_0-auc:0.835689\tvalidation_0-error:0.039551\n",
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            "[116]\tvalidation_0-auc:0.836064\tvalidation_0-error:0.039551\n",
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            "[127]\tvalidation_0-auc:0.837431\tvalidation_0-error:0.039551\n",
            "[128]\tvalidation_0-auc:0.837374\tvalidation_0-error:0.039551\n",
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            "[734]\tvalidation_0-auc:0.845439\tvalidation_0-error:0.039551\n",
            "[735]\tvalidation_0-auc:0.845469\tvalidation_0-error:0.039551\n",
            "[736]\tvalidation_0-auc:0.84544\tvalidation_0-error:0.039551\n",
            "[737]\tvalidation_0-auc:0.845445\tvalidation_0-error:0.039551\n",
            "[738]\tvalidation_0-auc:0.845426\tvalidation_0-error:0.039551\n",
            "[739]\tvalidation_0-auc:0.84539\tvalidation_0-error:0.039551\n",
            "[740]\tvalidation_0-auc:0.845397\tvalidation_0-error:0.039551\n",
            "[741]\tvalidation_0-auc:0.845391\tvalidation_0-error:0.039551\n",
            "[742]\tvalidation_0-auc:0.845431\tvalidation_0-error:0.039551\n",
            "[743]\tvalidation_0-auc:0.845415\tvalidation_0-error:0.039551\n",
            "[744]\tvalidation_0-auc:0.845388\tvalidation_0-error:0.039551\n",
            "[745]\tvalidation_0-auc:0.845351\tvalidation_0-error:0.039551\n",
            "[746]\tvalidation_0-auc:0.845348\tvalidation_0-error:0.039551\n",
            "[747]\tvalidation_0-auc:0.845365\tvalidation_0-error:0.039551\n",
            "[748]\tvalidation_0-auc:0.845371\tvalidation_0-error:0.039551\n",
            "[749]\tvalidation_0-auc:0.845422\tvalidation_0-error:0.039551\n",
            "[750]\tvalidation_0-auc:0.845439\tvalidation_0-error:0.039551\n",
            "[751]\tvalidation_0-auc:0.845456\tvalidation_0-error:0.039551\n",
            "[752]\tvalidation_0-auc:0.845448\tvalidation_0-error:0.039551\n",
            "[753]\tvalidation_0-auc:0.845443\tvalidation_0-error:0.039551\n",
            "[754]\tvalidation_0-auc:0.845436\tvalidation_0-error:0.039551\n",
            "[755]\tvalidation_0-auc:0.845456\tvalidation_0-error:0.039551\n",
            "[756]\tvalidation_0-auc:0.845438\tvalidation_0-error:0.039551\n",
            "[757]\tvalidation_0-auc:0.845364\tvalidation_0-error:0.039551\n",
            "[758]\tvalidation_0-auc:0.845341\tvalidation_0-error:0.039551\n",
            "[759]\tvalidation_0-auc:0.845329\tvalidation_0-error:0.039551\n",
            "[760]\tvalidation_0-auc:0.84536\tvalidation_0-error:0.039551\n",
            "[761]\tvalidation_0-auc:0.845364\tvalidation_0-error:0.039551\n",
            "[762]\tvalidation_0-auc:0.845363\tvalidation_0-error:0.039551\n",
            "[763]\tvalidation_0-auc:0.845358\tvalidation_0-error:0.039551\n",
            "[764]\tvalidation_0-auc:0.845357\tvalidation_0-error:0.039551\n",
            "[765]\tvalidation_0-auc:0.845378\tvalidation_0-error:0.039551\n",
            "[766]\tvalidation_0-auc:0.845349\tvalidation_0-error:0.039551\n",
            "[767]\tvalidation_0-auc:0.845348\tvalidation_0-error:0.039551\n",
            "[768]\tvalidation_0-auc:0.845332\tvalidation_0-error:0.039551\n",
            "[769]\tvalidation_0-auc:0.845279\tvalidation_0-error:0.039551\n",
            "[770]\tvalidation_0-auc:0.845231\tvalidation_0-error:0.039551\n",
            "[771]\tvalidation_0-auc:0.84521\tvalidation_0-error:0.039551\n",
            "[772]\tvalidation_0-auc:0.845238\tvalidation_0-error:0.039551\n",
            "[773]\tvalidation_0-auc:0.845197\tvalidation_0-error:0.039551\n",
            "[774]\tvalidation_0-auc:0.845188\tvalidation_0-error:0.039551\n",
            "[775]\tvalidation_0-auc:0.845192\tvalidation_0-error:0.039551\n",
            "[776]\tvalidation_0-auc:0.845154\tvalidation_0-error:0.039551\n",
            "[777]\tvalidation_0-auc:0.845139\tvalidation_0-error:0.039551\n",
            "[778]\tvalidation_0-auc:0.84512\tvalidation_0-error:0.039551\n",
            "[779]\tvalidation_0-auc:0.84512\tvalidation_0-error:0.039551\n",
            "[780]\tvalidation_0-auc:0.845114\tvalidation_0-error:0.039551\n",
            "[781]\tvalidation_0-auc:0.845101\tvalidation_0-error:0.039551\n",
            "[782]\tvalidation_0-auc:0.845073\tvalidation_0-error:0.039551\n",
            "[783]\tvalidation_0-auc:0.845075\tvalidation_0-error:0.039551\n",
            "[784]\tvalidation_0-auc:0.845082\tvalidation_0-error:0.039551\n",
            "[785]\tvalidation_0-auc:0.845044\tvalidation_0-error:0.039551\n",
            "[786]\tvalidation_0-auc:0.845008\tvalidation_0-error:0.039551\n",
            "[787]\tvalidation_0-auc:0.844978\tvalidation_0-error:0.039551\n",
            "[788]\tvalidation_0-auc:0.845002\tvalidation_0-error:0.039551\n",
            "[789]\tvalidation_0-auc:0.844994\tvalidation_0-error:0.039551\n",
            "[790]\tvalidation_0-auc:0.844997\tvalidation_0-error:0.039551\n",
            "[791]\tvalidation_0-auc:0.844945\tvalidation_0-error:0.039551\n",
            "[792]\tvalidation_0-auc:0.844928\tvalidation_0-error:0.039551\n",
            "[793]\tvalidation_0-auc:0.844947\tvalidation_0-error:0.039551\n",
            "[794]\tvalidation_0-auc:0.844947\tvalidation_0-error:0.039551\n",
            "[795]\tvalidation_0-auc:0.844941\tvalidation_0-error:0.039551\n",
            "[796]\tvalidation_0-auc:0.844922\tvalidation_0-error:0.039551\n",
            "[797]\tvalidation_0-auc:0.844975\tvalidation_0-error:0.039551\n",
            "[798]\tvalidation_0-auc:0.845014\tvalidation_0-error:0.039551\n",
            "[799]\tvalidation_0-auc:0.844994\tvalidation_0-error:0.039551\n",
            "[800]\tvalidation_0-auc:0.844951\tvalidation_0-error:0.039551\n",
            "[801]\tvalidation_0-auc:0.844992\tvalidation_0-error:0.039551\n",
            "[802]\tvalidation_0-auc:0.844985\tvalidation_0-error:0.039551\n",
            "[803]\tvalidation_0-auc:0.844988\tvalidation_0-error:0.039551\n",
            "[804]\tvalidation_0-auc:0.844971\tvalidation_0-error:0.039551\n",
            "[805]\tvalidation_0-auc:0.844963\tvalidation_0-error:0.039551\n",
            "[806]\tvalidation_0-auc:0.84499\tvalidation_0-error:0.039551\n",
            "[807]\tvalidation_0-auc:0.845038\tvalidation_0-error:0.039551\n",
            "[808]\tvalidation_0-auc:0.844987\tvalidation_0-error:0.039551\n",
            "[809]\tvalidation_0-auc:0.844975\tvalidation_0-error:0.039551\n",
            "[810]\tvalidation_0-auc:0.84497\tvalidation_0-error:0.039551\n",
            "[811]\tvalidation_0-auc:0.845015\tvalidation_0-error:0.039551\n",
            "[812]\tvalidation_0-auc:0.844944\tvalidation_0-error:0.039551\n",
            "[813]\tvalidation_0-auc:0.844889\tvalidation_0-error:0.039551\n",
            "[814]\tvalidation_0-auc:0.844884\tvalidation_0-error:0.039639\n",
            "[815]\tvalidation_0-auc:0.844861\tvalidation_0-error:0.039639\n",
            "[816]\tvalidation_0-auc:0.844867\tvalidation_0-error:0.039639\n",
            "[817]\tvalidation_0-auc:0.84487\tvalidation_0-error:0.039639\n",
            "[818]\tvalidation_0-auc:0.844852\tvalidation_0-error:0.039639\n",
            "[819]\tvalidation_0-auc:0.84483\tvalidation_0-error:0.039639\n",
            "[820]\tvalidation_0-auc:0.844844\tvalidation_0-error:0.039639\n",
            "[821]\tvalidation_0-auc:0.844859\tvalidation_0-error:0.039639\n",
            "[822]\tvalidation_0-auc:0.844781\tvalidation_0-error:0.039639\n",
            "[823]\tvalidation_0-auc:0.844776\tvalidation_0-error:0.039639\n",
            "[824]\tvalidation_0-auc:0.844786\tvalidation_0-error:0.039639\n",
            "[825]\tvalidation_0-auc:0.844784\tvalidation_0-error:0.039639\n",
            "[826]\tvalidation_0-auc:0.844764\tvalidation_0-error:0.039639\n",
            "[827]\tvalidation_0-auc:0.844739\tvalidation_0-error:0.039639\n",
            "[828]\tvalidation_0-auc:0.844779\tvalidation_0-error:0.039639\n",
            "[829]\tvalidation_0-auc:0.844778\tvalidation_0-error:0.039639\n",
            "[830]\tvalidation_0-auc:0.844796\tvalidation_0-error:0.039639\n",
            "[831]\tvalidation_0-auc:0.844782\tvalidation_0-error:0.039639\n",
            "[832]\tvalidation_0-auc:0.844804\tvalidation_0-error:0.039639\n",
            "[833]\tvalidation_0-auc:0.844775\tvalidation_0-error:0.039639\n",
            "[834]\tvalidation_0-auc:0.84478\tvalidation_0-error:0.039639\n",
            "[835]\tvalidation_0-auc:0.844822\tvalidation_0-error:0.039639\n",
            "[836]\tvalidation_0-auc:0.844833\tvalidation_0-error:0.039639\n",
            "[837]\tvalidation_0-auc:0.844773\tvalidation_0-error:0.039639\n",
            "[838]\tvalidation_0-auc:0.844772\tvalidation_0-error:0.039639\n",
            "[839]\tvalidation_0-auc:0.844775\tvalidation_0-error:0.039639\n",
            "[840]\tvalidation_0-auc:0.844791\tvalidation_0-error:0.039639\n",
            "[841]\tvalidation_0-auc:0.84477\tvalidation_0-error:0.039639\n",
            "[842]\tvalidation_0-auc:0.844751\tvalidation_0-error:0.039639\n",
            "[843]\tvalidation_0-auc:0.844718\tvalidation_0-error:0.039639\n",
            "[844]\tvalidation_0-auc:0.844712\tvalidation_0-error:0.039639\n",
            "CPU times: user 17min 39s, sys: 1.62 s, total: 17min 41s\n",
            "Wall time: 9min 4s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0.8, gamma=0,\n",
              "              learning_rate=0.01, max_delta_step=0, max_depth=7,\n",
              "              min_child_weight=1, missing=None, n_estimators=845, n_jobs=-1,\n",
              "              nthread=-1, objective='binary:logistic', random_state=42,\n",
              "              reg_alpha=0.3, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=0.7, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9ZI4N9TM-V3D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "d4cc1ebf-8202-40d3-f976-4a1f5dc98410"
      },
      "source": [
        "y_pred = model.predict_proba(X_val_200)[:,1]\n",
        "label=\"XGBoost Classifer(top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t XGBoost Classifer(top 250) on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.845 for XGBoost Classifer(top 250) on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LN2FW-s2-V3G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "af2d3383-bd38-41f8-d4c3-0ce74843f452"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.140804</td>\n",
              "      <td>0.810465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137558</td>\n",
              "      <td>0.832106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133120</td>\n",
              "      <td>0.846170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133139</td>\n",
              "      <td>0.845953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137108</td>\n",
              "      <td>0.833931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBoost Classifer(top 250)</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133440</td>\n",
              "      <td>0.844712</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model          Dataset  log_loss       auc\n",
              "0                  Logisic Regression  Log Transformed  0.542156  0.812097\n",
              "1            Decision Tree Classifier  Log Transformed  0.140804  0.810465\n",
              "2            Random Forest Classifier  Log Transformed  0.137558  0.832106\n",
              "3                   XGBoost Classifer  Log Transformed  0.133120  0.846170\n",
              "4                  LightGBM Classifer  Log Transformed  0.133139  0.845953\n",
              "5  Random Forest Classifier (top 250)  Log Transformed  0.137108  0.833931\n",
              "6          XGBoost Classifer(top 250)  Log Transformed  0.133440  0.844712"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mYW7_x88-V3J"
      },
      "source": [
        "# LightGBM Classifier (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "EYaevQf8-V3J",
        "colab": {}
      },
      "source": [
        "model = lgb.LGBMClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.5,\n",
        "                      subsample = 0.9,\n",
        "                      objective='binary', \n",
        "                      n_estimators=447,\n",
        "                      reg_alpha = 0,\n",
        "                      max_depth=6, random_state=42\n",
        "                     )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JhKg3-Lv-V3M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "400be715-2f23-4513-b262-bd142d8d6721"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train_200,y_train,eval_set=[(X_val_200,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's auc: 0.79329\tvalid_0's binary_logloss: 0.165848\n",
            "[2]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.16517\n",
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            "[434]\tvalid_0's auc: 0.845152\tvalid_0's binary_logloss: 0.13343\n",
            "[435]\tvalid_0's auc: 0.845148\tvalid_0's binary_logloss: 0.133432\n",
            "[436]\tvalid_0's auc: 0.84514\tvalid_0's binary_logloss: 0.133432\n",
            "[437]\tvalid_0's auc: 0.845172\tvalid_0's binary_logloss: 0.133428\n",
            "[438]\tvalid_0's auc: 0.845177\tvalid_0's binary_logloss: 0.133422\n",
            "[439]\tvalid_0's auc: 0.845179\tvalid_0's binary_logloss: 0.133414\n",
            "[440]\tvalid_0's auc: 0.845209\tvalid_0's binary_logloss: 0.1334\n",
            "[441]\tvalid_0's auc: 0.845196\tvalid_0's binary_logloss: 0.133398\n",
            "[442]\tvalid_0's auc: 0.845212\tvalid_0's binary_logloss: 0.133392\n",
            "[443]\tvalid_0's auc: 0.845321\tvalid_0's binary_logloss: 0.133371\n",
            "[444]\tvalid_0's auc: 0.845318\tvalid_0's binary_logloss: 0.133371\n",
            "[445]\tvalid_0's auc: 0.84532\tvalid_0's binary_logloss: 0.133371\n",
            "[446]\tvalid_0's auc: 0.845332\tvalid_0's binary_logloss: 0.133365\n",
            "[447]\tvalid_0's auc: 0.845345\tvalid_0's binary_logloss: 0.133359\n",
            "CPU times: user 42 s, sys: 238 ms, total: 42.3 s\n",
            "Wall time: 22.3 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.5,\n",
              "               importance_type='split', learning_rate=0.01, max_depth=6,\n",
              "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "               n_estimators=447, n_jobs=-1, nthread=-1, num_leaves=31,\n",
              "               objective='binary', random_state=42, reg_alpha=0, reg_lambda=0.0,\n",
              "               scale_pos_weight=1, silent=True, subsample=0.9,\n",
              "               subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OFf2WKQC-V3R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5331147-9f69-4830-af19-8bf1b994b0b8"
      },
      "source": [
        "y_pred = model.predict_proba(X_val_200)[:,1]\n",
        "label=\"LightGBM Classifer(top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t LightGBM Classifer(top 250) on Log Transformed dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.845 for LightGBM Classifer(top 250) on Log Transformed dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8r0vIjs9i27",
        "colab_type": "text"
      },
      "source": [
        "# Combined"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfDFKhQ9DajM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "653fc3db-98f4-47c3-884c-734f5b900a50"
      },
      "source": [
        "plot_auc(y_val,y_preds,labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWJqDqYVCUJv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c95b44bc-14c3-4fdf-afca-964e55ff913d"
      },
      "source": [
        "result_df.sort_values(by='auc',ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133120</td>\n",
              "      <td>0.846170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133139</td>\n",
              "      <td>0.845953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LightGBM Classifer(top 250)</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133359</td>\n",
              "      <td>0.845345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBoost Classifer(top 250)</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.133440</td>\n",
              "      <td>0.844712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137108</td>\n",
              "      <td>0.833931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.137558</td>\n",
              "      <td>0.832106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.542156</td>\n",
              "      <td>0.812097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Log Transformed</td>\n",
              "      <td>0.140804</td>\n",
              "      <td>0.810465</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model          Dataset  log_loss       auc\n",
              "3                   XGBoost Classifer  Log Transformed  0.133120  0.846170\n",
              "4                  LightGBM Classifer  Log Transformed  0.133139  0.845953\n",
              "7         LightGBM Classifer(top 250)  Log Transformed  0.133359  0.845345\n",
              "6          XGBoost Classifer(top 250)  Log Transformed  0.133440  0.844712\n",
              "5  Random Forest Classifier (top 250)  Log Transformed  0.137108  0.833931\n",
              "2            Random Forest Classifier  Log Transformed  0.137558  0.832106\n",
              "0                  Logisic Regression  Log Transformed  0.542156  0.812097\n",
              "1            Decision Tree Classifier  Log Transformed  0.140804  0.810465"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lF-jgX2JfCuI"
      },
      "source": [
        "# Saving all the datasets created"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7X50CQtb2SL5",
        "colab": {}
      },
      "source": [
        "\n",
        "filename = 'log_train.pkl'\n",
        "X_train.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_val.pkl'\n",
        "X_val.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_test.pkl'\n",
        "X_test.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_train_250.pkl'\n",
        "X_train_200.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_val_250.pkl'\n",
        "X_val_200.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'log_test_250.pkl'\n",
        "X_test_200.to_pickle(os.path.join(file_path,'best_models',filename))\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}