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"}}},{"cell_type":"markdown","source":"#### If this helped in your learning, then please UPVOTE – as they are the source of motivation!**\n\n#### Happy Learning**","metadata":{}},{"cell_type":"markdown","source":"1. [Introduction](#1) <a id=18></a>\n2. [Preparation](#2)\n    - 2.1 [Packages](#3)\n    - 2.2 [Initial Setup](#4)\n3. [Background](#5)\n4. [Conclusions from EDA](#6)\n5. [Data Preprocessing](#7)\n    - 5.1 [Numerical feature selection based on correlation](#8)\n    - 5.2 [Target encoding categorical variables](#9)\n    - 5.3 [Rounding numerical feature values to 2 decimal places](#10)\n    - 5.4 [Handling missing values](#11)\n    - 5.5 [Categorical feature importance (based on chi2 statistics)](#12)\n    - 5.6 [Numerical feature importance (based on Anova test)](#13)\n    - 5.7 [Creating polynomial features](#14)\n    - 5.8 [Polynomial feature selection (using chi2 and anova statistics)](#15)\n6. [Model Training & Hyperparameter Tuning](#16)\n    - 6.1 [XGBoost model with HyperOpt](#17)","metadata":{}},{"cell_type":"markdown","source":"## 1. Introduction <a id=1></a>\n[Table of Contents](#18)\n\nIn a world shaped by the emergence of new uses and lifestyles, everything is going faster and faster. When facing unexpected events, customers expect their insurer to support them as soon as possible. However, claims management may require different levels of check before a claim can be approved and a payment can be made. With the new practices and behaviors generated by the digital economy, this process needs adaptation thanks to data science to meet the new needs and expectations of customers.\n\n![image.png](attachment:0d84e211-2a3a-4c7b-9261-6272273e2f71.png)\n\nIn this challenge, BNP Paribas Cardif is providing an anonymized database with two categories of claims:\n1. Suitable for approval.\n2. Not suitable for approval.\n\nWe need to predict the category of a claim based on features available early in the process, helping BNP Paribas Cardif accelerate its claims process and therefore provide a better service to its 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"}}},{"cell_type":"markdown","source":"## 2 Preparation <a id=2></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"markdown","source":"### 2.1 Packages <a id=3></a>","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport gc\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Train test split \nfrom sklearn.model_selection import train_test_split\n\n# Data Cleaning\nfrom sklearn.feature_selection import VarianceThreshold\n## outlier removal\nfrom sklearn.neighbors import LocalOutlierFactor\n## Imputer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.impute import KNNImputer\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\n\n# Feature Selection\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import mutual_info_classif\nfrom sklearn.feature_selection import chi2\nfrom sklearn.feature_selection import f_classif\nfrom sklearn.feature_selection import RFE\nfrom sklearn.feature_selection import SelectFromModel\n\n# Data Transformation\n## Feature scaling\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import RobustScaler\n## Encoder\nfrom sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.preprocessing import LabelEncoder\nfrom category_encoders import TargetEncoder\n## Distribution transformer\nfrom sklearn.preprocessing import PowerTransformer\nfrom sklearn.preprocessing import QuantileTransformer\n## Numerical to Categorical data transformation\nfrom sklearn.preprocessing import KBinsDiscretizer\n## Polynomial features\nfrom sklearn.preprocessing import PolynomialFeatures\n\n# Pipeline\nfrom sklearn.pipeline import Pipeline\n\n# Model\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.compose import TransformedTargetRegressor\n\n# Model evaluation\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import RepeatedKFold\n\n# Hyperparameter tuning\nfrom sklearn.model_selection import RandomizedSearchCV, GridSearchCV\nfrom sklearn.model_selection import StratifiedKFold\n\n# Metrics\nfrom sklearn.metrics import log_loss\nfrom sklearn.metrics import accuracy_score","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:11.105744Z","iopub.execute_input":"2022-07-28T09:32:11.106458Z","iopub.status.idle":"2022-07-28T09:32:12.282823Z","shell.execute_reply.started":"2022-07-28T09:32:11.106414Z","shell.execute_reply":"2022-07-28T09:32:12.281564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 2.2 Initial Setup <a id=4></a>","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv('../input/bnp-paribas-cardif-claims-management/train.csv.zip')\n\n# Dropping ID column, it is an index values.\ndf.drop('ID', axis=1, inplace=True)\ntarget_col = ['target']\ncat_cols = ['v3', 'v22', 'v24', 'v30', 'v31', 'v38', 'v47', 'v52', 'v56', 'v62', 'v66', 'v71', 'v72', 'v74', 'v75', 'v79', 'v91', 'v107', 'v110', 'v112', 'v113', 'v125', 'v129'] \ncon_cols = list(df.columns.drop(target_col + cat_cols))\n\nprint(\"There are {} Categorical cols : {}\".format(len(cat_cols), cat_cols))\nprint(\"There are {} Continuous cols : {}\".format(len(con_cols), con_cols))\nprint(\"There are {} Target cols : {}\".format(len(target_col), target_col))","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:12.284673Z","iopub.execute_input":"2022-07-28T09:32:12.285088Z","iopub.status.idle":"2022-07-28T09:32:16.441220Z","shell.execute_reply.started":"2022-07-28T09:32:12.285056Z","shell.execute_reply":"2022-07-28T09:32:16.439815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 3. Background <a id=5></a>\n[Table of Contents](#18)\n\nIn this notebook we'll be covering data preprocessing and hyperparameter tuning for model. \n\n**To understand EDA of this project please refer this [CMS EDA notebook](https://www.kaggle.com/code/crashoverdrive/cms-complete-eda-guide).**","metadata":{}},{"cell_type":"markdown","source":"## 4. Conclusions from EDA <a id=6></a>\n[Table of Contents](#18)\n1. There are both categorical and continuous features present in data and target feature has binary value.\n2. Feature values are mixed of int, float64 & object data types.\n3. There are multiple columns with a high degree of missing values.\n4. There is a class imbalance in target variable ('label 0':23.1%, 'label 1':76.9%).\n5. Based on univariate count plots of categorical variables, features <b> v3, v30, v31, v38, v62, v74 & v129 </b> seemed single valued feature i.e. zero-vector predictor. These columns have same value in more than <b>95%</b> of the samples and not adding useful information for model building.\n6. <b>Outliers</b> are present in a `majority of continuous feature variables` that we need to address during data pre-processing.\n7. There are continuous variables with skewed distributions.\n8. PCA shows that we can keep <b>99% data variance</b> with 5 columns only. Hence, there are dependent variables in data.\n9. There are highly correlated dependent features to other dependent features based on the heatmap and scatter plot heatmap shown in EDA notebook.","metadata":{}},{"cell_type":"markdown","source":"## 5. Data Preprocessing <a id=7></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"markdown","source":"### 5.1 Numerical feature selection based on correlation <a id=8></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"code","source":"corr_matrix = df[con_cols].corr()\n\ndropped_cols = []\ncorr_cols = corr_matrix.columns\n\nfor i in range(len(corr_cols)-1):\n    if corr_cols[i] in dropped_cols:\n#         print(\"disjoint col={} for {}, \".format(corr_cols[i], i))\n        continue\n\n    for j in range(i+1, len(corr_cols)):\n        if abs(corr_matrix.iloc[i,j]) > 0.8 and corr_cols[j] not in dropped_cols:\n#             print(\"removing col:{} for [i,j]=[{},{}]\".format(corr_cols[j], i, j))\n            dropped_cols.append(corr_cols[j])\n            \nprint(\"Number of numerical feature columns dropped:{}\".format(len(dropped_cols)))\n\n# Updating con_cols & dataset based on correlation \n[con_cols.remove(x) for x in dropped_cols]\ndf.drop(dropped_cols, axis=1, inplace=True)\n\ndel corr_matrix, corr_cols, dropped_cols\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:16.442768Z","iopub.execute_input":"2022-07-28T09:32:16.443201Z","iopub.status.idle":"2022-07-28T09:32:21.476524Z","shell.execute_reply.started":"2022-07-28T09:32:16.443156Z","shell.execute_reply":"2022-07-28T09:32:21.475763Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- \n#### Train test split of training data","metadata":{}},{"cell_type":"code","source":"X = df[cat_cols + con_cols]\ny = df[target_col]\n\nX_train, X_test, y_train, y_test = train_test_split(X[cat_cols + con_cols], y, test_size=0.25, random_state=1)\n\nprint(\"Shape of X={}\\ny={}\\nX_train={}\\nX_test={}\\ny_train={}\\ny_test={}\".format(X.shape, y.shape, X_train.shape, \n                                                                                 X_test.shape,y_train.shape, y_test.shape))\n# Reseting indexes as index got shuffled after train-test split\nX_train.reset_index(inplace=True, drop=True)\ny_train.reset_index(inplace=True, drop=True)\nX_test.reset_index(inplace=True, drop=True)\ny_test.reset_index(inplace=True, drop=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:21.477880Z","iopub.execute_input":"2022-07-28T09:32:21.478514Z","iopub.status.idle":"2022-07-28T09:32:21.737667Z","shell.execute_reply.started":"2022-07-28T09:32:21.478465Z","shell.execute_reply":"2022-07-28T09:32:21.736774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5.2 Target encoding categorical variables <a id=9></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"code","source":"# Target encoding categorical variable\ntarget_encoder = TargetEncoder(verbose=1, cols=cat_cols, handle_missing='return_nan',\n                              handle_unknown='return_nan')\nX_train = target_encoder.fit_transform(X_train, y_train)\nX_test = target_encoder.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:21.740521Z","iopub.execute_input":"2022-07-28T09:32:21.741186Z","iopub.status.idle":"2022-07-28T09:32:25.817049Z","shell.execute_reply.started":"2022-07-28T09:32:21.741137Z","shell.execute_reply":"2022-07-28T09:32:25.815832Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5.3 Rounding numerical feature values to 2 decimal places <a id=10></a>\n[Table of Contents](#18)\n\nInterestingly, it improves the model performance effectively by 0.3 value in terms of log_loss.","metadata":{}},{"cell_type":"code","source":"# Rounding numerical values to 2nd decimal\nX_train = X_train.round(decimals=2)\nX_test = X_test.round(decimals=2)","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:25.818908Z","iopub.execute_input":"2022-07-28T09:32:25.819297Z","iopub.status.idle":"2022-07-28T09:32:25.911654Z","shell.execute_reply.started":"2022-07-28T09:32:25.819265Z","shell.execute_reply":"2022-07-28T09:32:25.910456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5.4 Handling missing values <a id=11></a>\n[Table of Contents](#18)\n\nWe are using constant value (0 for category columns and -1 for numerical columns) to impute missing values.","metadata":{}},{"cell_type":"code","source":"imputer_strategies = ['mean', 'median', 'most_frequent', 'constant']\ntransformer = [('cat', SimpleImputer(strategy=imputer_strategies[3], fill_value=0), cat_cols), \n     ('num', SimpleImputer(strategy=imputer_strategies[3], fill_value=-1), con_cols)]\nimp_transformer = ColumnTransformer(transformers=transformer, remainder='passthrough', verbose_feature_names_out=False)\n\nX_train = pd.DataFrame(imp_transformer.fit_transform(X_train), columns=cat_cols + con_cols)\nX_test = pd.DataFrame(imp_transformer.transform(X_test), columns=cat_cols + con_cols)","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:25.913127Z","iopub.execute_input":"2022-07-28T09:32:25.913809Z","iopub.status.idle":"2022-07-28T09:32:26.209928Z","shell.execute_reply.started":"2022-07-28T09:32:25.913770Z","shell.execute_reply":"2022-07-28T09:32:26.208787Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5.5 Categorical feature importance (based on chi2 statistics) <a id=12></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"code","source":"# categorical_input-categorical_output scoring function\nscore_func = [chi2, mutual_info_classif]\nfs = SelectKBest(score_func=score_func[0], k='all')\n\nfs.fit(X_train[cat_cols], y_train)\nup_cat_cols = list(fs.get_feature_names_out())\n\nfig = plt.figure(figsize=(15,6))\ngrid = sns.barplot(y=fs.scores_, x=fs.get_feature_names_out())\ngrid.axes.set_title(\"Chi2 relation: Cat_cols vs target\")","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:26.211415Z","iopub.execute_input":"2022-07-28T09:32:26.212084Z","iopub.status.idle":"2022-07-28T09:32:26.641886Z","shell.execute_reply.started":"2022-07-28T09:32:26.212048Z","shell.execute_reply":"2022-07-28T09:32:26.640781Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5.6 Numerical feature importance (based on Anova test) <a id=13></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"code","source":"# numerical_input-categorical_output scoring function\nscore_func = [chi2, mutual_info_classif, f_classif]\nfs = SelectKBest(score_func=score_func[2], k='all')\n\nfs.fit(X_train[con_cols], y_train)\nup_con_cols = list(fs.get_feature_names_out())\nfig = plt.figure(figsize=(28,10))\nsns.barplot(x=fs.get_feature_names_out(), y=fs.scores_)","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:26.643509Z","iopub.execute_input":"2022-07-28T09:32:26.644444Z","iopub.status.idle":"2022-07-28T09:32:27.495334Z","shell.execute_reply.started":"2022-07-28T09:32:26.644400Z","shell.execute_reply":"2022-07-28T09:32:27.493967Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5.7 Creating polynomial features <a id=14></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"markdown","source":"#### 5.7.1 Polynomial features using categorical features","metadata":{}},{"cell_type":"code","source":"# # Create the polynomial object with specified degree\n\npoly_transformer_cat = ColumnTransformer([('poly_trans', PolynomialFeatures(degree = 2), up_cat_cols)], \n                                      remainder='drop', verbose_feature_names_out=False)\n# # Train the polynomial features\n# # Transform the features\npoly_features_cat = pd.DataFrame(poly_transformer_cat.fit_transform(X_train[up_cat_cols]), \n                             columns=poly_transformer_cat.get_feature_names_out())\npoly_features_cat_test = pd.DataFrame(poly_transformer_cat.transform(X_test[up_cat_cols]),\n                                  columns=poly_transformer_cat.get_feature_names_out())\n\nprint('Polynomial Features shape: ', poly_features_cat.shape)\nprint(\"Polynomial Categorical features:{}\".format(poly_transformer_cat.get_feature_names_out()))","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:27.497205Z","iopub.execute_input":"2022-07-28T09:32:27.497598Z","iopub.status.idle":"2022-07-28T09:32:28.036557Z","shell.execute_reply.started":"2022-07-28T09:32:27.497563Z","shell.execute_reply":"2022-07-28T09:32:28.035350Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### 5.7.2 Polynomial features using numerical features","metadata":{}},{"cell_type":"code","source":"# # Create the polynomial object with specified degree\n\npoly_transformer_con = ColumnTransformer([('poly_trans', PolynomialFeatures(degree = 2), up_con_cols)], \n                                      remainder='drop', verbose_feature_names_out=False)\n# # Train the polynomial features\n\n# # Transform the features\npoly_features_con = pd.DataFrame(poly_transformer_con.fit_transform(X_train[up_con_cols]), \n                             columns=poly_transformer_con.get_feature_names_out())\npoly_features_con_test = pd.DataFrame(poly_transformer_con.transform(X_test[up_con_cols]),\n                                  columns=poly_transformer_con.get_feature_names_out())\n\nprint('Polynomial Features shape: ', poly_features_con.shape)\nprint(\"Polynomial Continuous features:{}\".format(poly_transformer_con.get_feature_names_out()))","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:28.038061Z","iopub.execute_input":"2022-07-28T09:32:28.038438Z","iopub.status.idle":"2022-07-28T09:32:30.499099Z","shell.execute_reply.started":"2022-07-28T09:32:28.038406Z","shell.execute_reply":"2022-07-28T09:32:30.497502Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5.8 Polynomial feature selection (using chi2 and anova statistics) <a id=15></a>\n[Table of Contents](#18)\n\nWe are plotting **barplot** for each polynomial feature representing its importance to predict target variable. Based on barplot we can pick k best features among all features.\n\n**Note:** *Importance of features are computed using ch2 for categorical features and Anova test for numerical features.*","metadata":{}},{"cell_type":"markdown","source":"#### 5.8.1 Select K best categorical features","metadata":{}},{"cell_type":"code","source":"# categorical_input-categorical_output scoring function\nscore_func = [chi2, mutual_info_classif]\n# using small value of k=6 for sake of this demonstration\nfs = SelectKBest(score_func=score_func[0], k=6)\n\nfs.fit(poly_features_cat, y_train)\nup_cat_cols_2 = list(fs.get_feature_names_out())\n\nfig = plt.figure(figsize=(15,6))\nsns.barplot(x=fs.get_feature_names_out(), y=fs.scores_[fs.get_support()])\n\nprint(\"K best categorical features:\\n {}\".format(up_cat_cols_2))","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:30.500438Z","iopub.execute_input":"2022-07-28T09:32:30.500839Z","iopub.status.idle":"2022-07-28T09:32:30.896944Z","shell.execute_reply.started":"2022-07-28T09:32:30.500796Z","shell.execute_reply":"2022-07-28T09:32:30.895791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### 5.8.2 Select K best continuous features","metadata":{}},{"cell_type":"code","source":"# categorical_input-categorical_output scoring function\nscore_func = [chi2, mutual_info_classif, f_classif]\n# using small value of k=6 for sake of this demonstration\nfs = SelectKBest(score_func=score_func[2], k=6)\n\nfs.fit(poly_features_con, y_train)\nup_con_cols_2 = list(fs.get_feature_names_out())\n\nfig = plt.figure(figsize=(15,6))\nsns.barplot(x=fs.get_feature_names_out(), y=fs.scores_[fs.get_support()])\n\nprint(\"K best continuous features:\\n {}\".format(up_con_cols_2))","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:30.898568Z","iopub.execute_input":"2022-07-28T09:32:30.899136Z","iopub.status.idle":"2022-07-28T09:32:33.388776Z","shell.execute_reply.started":"2022-07-28T09:32:30.899082Z","shell.execute_reply":"2022-07-28T09:32:33.387515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Setup processed train and test dataset","metadata":{}},{"cell_type":"code","source":"train = poly_features_con[up_con_cols_2].join(poly_features_cat[up_cat_cols_2])\ntest = poly_features_con_test[up_con_cols_2].join(poly_features_cat_test[up_cat_cols_2])","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:33.393322Z","iopub.execute_input":"2022-07-28T09:32:33.394459Z","iopub.status.idle":"2022-07-28T09:32:33.438005Z","shell.execute_reply.started":"2022-07-28T09:32:33.394406Z","shell.execute_reply":"2022-07-28T09:32:33.436772Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 6. Model Creation and Hyperparameter tuning <a id=16></a>\n[Table of Contents](#18)\n\nWe will be using XGBoost model and HyperOpt library for hyperparameter tuning.\n\nTo know more about HyperOpt and XGBoost hyperparameter tuning, you can refer this [notebook](https://www.kaggle.com/code/prashant111/a-guide-on-xgboost-hyperparameters-tuning) ","metadata":{}},{"cell_type":"markdown","source":"### 6.1 XGBoost model with HyperOpt <a id=17></a>\n[Table of Contents](#18)","metadata":{}},{"cell_type":"markdown","source":"#### 6.1.1 Require Packages","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBClassifier\nimport xgboost as xgb\n\n# import packages for hyperparameters tuning\nfrom hyperopt import STATUS_OK, Trials, fmin, hp, tpe","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:33.439632Z","iopub.execute_input":"2022-07-28T09:32:33.440319Z","iopub.status.idle":"2022-07-28T09:32:33.836666Z","shell.execute_reply.started":"2022-07-28T09:32:33.440275Z","shell.execute_reply":"2022-07-28T09:32:33.835482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### 6.1.2 Creating parameter space for hyperparameter tuning","metadata":{}},{"cell_type":"code","source":"space={'max_depth': hp.quniform(\"max_depth\", 2, 10, 1),\n        'gamma': hp.uniform ('gamma', 1,9),\n        'reg_alpha' : hp.quniform('reg_alpha', 40,180,1),\n        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n        'colsample_bytree' : hp.uniform('colsample_bytree', 0.3,1),\n        'min_child_weight' : hp.quniform('min_child_weight', 0, 10, 1),\n        'n_estimators': 300,\n        'seed': 0\n    }","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:33.838018Z","iopub.execute_input":"2022-07-28T09:32:33.838373Z","iopub.status.idle":"2022-07-28T09:32:33.846404Z","shell.execute_reply.started":"2022-07-28T09:32:33.838342Z","shell.execute_reply":"2022-07-28T09:32:33.845242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### 6.1.3 Objective function to compare different tuning parameters","metadata":{}},{"cell_type":"code","source":"def objective(space):\n    clf=xgb.XGBClassifier(eta=eta,\n                    n_estimators =space['n_estimators'], max_depth = int(space['max_depth']), gamma = space['gamma'],\n                    reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n                    colsample_bytree=int(space['colsample_bytree']), objective='binary:logistic')\n    \n    evaluation = [( train, y_train), ( test, y_test)]\n    \n    clf.fit(train, y_train,\n            eval_set=evaluation, \n            early_stopping_rounds=10,verbose=False)\n    \n    y_pred_prob = clf.predict_proba(test)[:, 1]\n    y_pred = clf.predict(test)\n    accuracy = accuracy_score(y_test, y_pred)\n    log_loss_score = log_loss(y_test, y_pred_prob)\n    print(\"accuracy:{}, log_loss:{}\".format(accuracy, log_loss_score))\n    \n    return {'loss': log_loss_score, 'status': STATUS_OK }","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:33.847825Z","iopub.execute_input":"2022-07-28T09:32:33.848640Z","iopub.status.idle":"2022-07-28T09:32:33.860927Z","shell.execute_reply.started":"2022-07-28T09:32:33.848604Z","shell.execute_reply":"2022-07-28T09:32:33.859910Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set max evaluation count based on your requirement, generally >50 is a good estimate.\nbest_params = dict()\ntrials_map = dict()\nfor eta in [0.01, 0.03, 0.1, 0.3]:\n    print(\"\\nTuning with eta={}\\n\".format(eta))\n    trials = Trials()\n    best_hyperparams = fmin(fn = objective,\n                            space = space,\n                            algo = tpe.suggest,\n                            max_evals = 2,\n                            trials = trials)\n    best_params[eta] = best_hyperparams\n    trials_map[eta] = trials\n    print(\"best_params for {} are:{}\".format(eta, best_params[eta]))","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:32:33.862271Z","iopub.execute_input":"2022-07-28T09:32:33.862654Z","iopub.status.idle":"2022-07-28T09:33:06.050941Z","shell.execute_reply.started":"2022-07-28T09:32:33.862618Z","shell.execute_reply":"2022-07-28T09:33:06.049614Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Choose best_params that gives minimum loss and then use those params for final model creation.","metadata":{}},{"cell_type":"markdown","source":"#### 6.1.4 Final tuned model (with best_params)","metadata":{}},{"cell_type":"code","source":"# Training model with best_params identified with HyperOpt parameter tuning.\nmodel = XGBClassifier(subsample= 1.0,\n                        min_child_weight= 5,\n                        max_depth= 4,\n                        gamma=2,\n                        eta=0.1,\n                        colsample_bytree=0.8,\n                        n_estimators=300, \n                        objective='binary:logistic', \n                        nthread=1, \n                        verbosity=1)\n\nmodel.fit(train, y_train, verbose=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-28T09:33:06.053007Z","iopub.execute_input":"2022-07-28T09:33:06.053358Z","iopub.status.idle":"2022-07-28T09:33:27.678395Z","shell.execute_reply.started":"2022-07-28T09:33:06.053327Z","shell.execute_reply":"2022-07-28T09:33:27.676752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### If you like the notebook, consider giving an upvote.\n[Table of Contents](#18)\n\n**Check my other notebooks**\n\n1. https://www.kaggle.com/code/crashoverdrive/data-science-salary-complete-eda\n2. https://www.kaggle.com/code/crashoverdrive/studentsperformance-data-visualization-beginners\n3. https://www.kaggle.com/code/crashoverdrive/heart-attack-analysis-prediction-90-accuracy\n4. https://www.kaggle.com/code/crashoverdrive/song-popularity-prediction-visualizations","metadata":{}}]}