import matplotlib.pyplot as pltimport seaborn as snsimport gcimport reimport pandas as pdimport lightgbm as lgbimport numpy as npfrom sklearn.metrics import roc_auc_score, precision_recall_curve, roc_curve, average_precision_scorefrom sklearn.model_selection import KFoldfrom lightgbm import LGBMClassifierimport matplotlib.pyplot as pltimport seaborn as snsimport gcfrom sklearn.model_selection import StratifiedKFoldfrom dateutil.relativedelta import relativedeltatrain_data = pd.read_csv('raw_data/train_public.csv')submit_example = pd.read_csv('raw_data/submit_example.csv')test_public = pd.read_csv('raw_data/test_public.csv')train_inte = pd.read_csv('raw_data/train_internet.csv')pd.set_option('max_columns', None)pd.set_option('max_rows', 200)pd.set_option('float_format', lambda x: '%.3f' % x)def train_model(data_, test_, y_, folds_):oof_preds = np.zeros(data_.shape[0])sub_preds = np.zeros(test_.shape[0])feature_importance_df = pd.DataFrame()feats = [f for f in data_.columns if f not in ['loan_id', 'user_id', 'isDefault'] ]for n_fold, (trn_idx, val_idx) in enumerate(folds_.split(data_)):trn_x, trn_y = data_[feats].iloc[trn_idx], y_.iloc[trn_idx]val_x, val_y = data_[feats].iloc[val_idx], y_.iloc[val_idx]clf = LGBMClassifier(n_estimators=4000,learning_rate=0.08,num_leaves=2**5,colsample_bytree=.65,subsample=.9,max_depth=5,# max_bin=250,reg_alpha=.3,reg_lambda=.3,min_split_gain=.01,min_child_weight=2,silent=-1,verbose=-1,)clf.fit(trn_x, trn_y,eval_set= [(trn_x, trn_y), (val_x, val_y)],eval_metric='auc', verbose=100, early_stopping_rounds=40 #30)oof_preds[val_idx] = clf.predict_proba(val_x, num_iteration=clf.best_iteration_)[:, 1]sub_preds += clf.predict_proba(test_[feats], num_iteration=clf.best_iteration_)[:, 1] / folds_.n_splitsfold_importance_df = pd.DataFrame()fold_importance_df["feature"] = featsfold_importance_df["importance"] = clf.feature_importances_fold_importance_df["fold"] = n_fold + 1feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(val_y, oof_preds[val_idx])))del clf, trn_x, trn_y, val_x, val_ygc.collect()print('Full AUC score %.6f' % roc_auc_score(y, oof_preds))test_['isDefault'] = sub_predsreturn oof_preds, test_[['loan_id', 'isDefault']], feature_importance_dfdef display_importances(feature_importance_df_):# Plot feature importancescols = feature_importance_df_[["feature", "importance"]].groupby("feature").mean().sort_values(by="importance", ascending=False)[:50].indexbest_features = feature_importance_df_.loc[feature_importance_df_.feature.isin(cols)]plt.figure(figsize=(8,10))sns.barplot(x="importance", y="feature",data=best_features.sort_values(by="importance", ascending=False))plt.title('LightGBM Features (avg over folds)')plt.tight_layout()plt.savefig('lgbm_importances.png')def workYearDIc(x):if str(x)=='nan':return -1x = x.replace('< 1','0')return int(re.search('(\d+)', x).group())def findDig(val):fd = re.search('(\d+-)', val)if fd is None:return '1-'+valreturn val + '-01'class_dict = {'A': 1,'B': 2,'C': 3,'D': 4,'E': 5,'F': 6,'G': 7,}timeMax = pd.to_datetime('1-Dec-21')train_data['work_year'] = train_data['work_year'].map(workYearDIc)test_public['work_year'] = test_public['work_year'].map(workYearDIc)train_data['class'] = train_data['class'].map(class_dict)test_public['class'] = test_public['class'].map(class_dict)train_data['earlies_credit_mon'] = pd.to_datetime(train_data['earlies_credit_mon'].map(findDig))test_public['earlies_credit_mon'] = pd.to_datetime(test_public['earlies_credit_mon'].map(findDig))train_data.loc[ train_data['earlies_credit_mon']>timeMax,'earlies_credit_mon' ] = train_data.loc[ train_data['earlies_credit_mon']>timeMax,'earlies_credit_mon' ]+ pd.offsets.DateOffset(years=-100)test_public.loc[ test_public['earlies_credit_mon']>timeMax,'earlies_credit_mon' ] = test_public.loc[ test_public['earlies_credit_mon']>timeMax,'earlies_credit_mon' ]+ pd.offsets.DateOffset(years=-100)train_data['issue_date'] = pd.to_datetime(train_data['issue_date'])test_public['issue_date'] = pd.to_datetime(test_public['issue_date'])#Internet数据处理train_inte['work_year'] = train_inte['work_year'].map(workYearDIc)train_inte['class'] = train_inte['class'].map(class_dict)train_inte['earlies_credit_mon'] = pd.to_datetime(train_inte['earlies_credit_mon'])train_inte['issue_date'] = pd.to_datetime(train_inte['issue_date'])train_data['issue_date_month'] = train_data['issue_date'].dt.monthtest_public['issue_date_month'] = test_public['issue_date'].dt.monthtrain_data['issue_date_dayofweek'] = train_data['issue_date'].dt.dayofweektest_public['issue_date_dayofweek'] = test_public['issue_date'].dt.dayofweektrain_data['earliesCreditMon'] = train_data['earlies_credit_mon'].dt.monthtest_public['earliesCreditMon'] = test_public['earlies_credit_mon'].dt.monthtrain_data['earliesCreditYear'] = train_data['earlies_credit_mon'].dt.yeartest_public['earliesCreditYear'] = test_public['earlies_credit_mon'].dt.year###internet数据train_inte['issue_date_month'] = train_inte['issue_date'].dt.monthtrain_inte['issue_date_dayofweek'] = train_inte['issue_date'].dt.dayofweektrain_inte['earliesCreditMon'] = train_inte['earlies_credit_mon'].dt.monthtrain_inte['earliesCreditYear'] = train_inte['earlies_credit_mon'].dt.yearcat_cols = ['employer_type', 'industry']from sklearn.preprocessing import LabelEncoderfor col in cat_cols:lbl = LabelEncoder().fit(train_data[col])train_data[col] = lbl.transform(train_data[col])test_public[col] = lbl.transform(test_public[col])#Internet处理train_inte[col] = lbl.transform(train_inte[col])# 'f1','policy_code','app_type' 这三个去掉是881# ,'f1','policy_code','app_type'col_to_drop = ['issue_date', 'earlies_credit_mon']train_data = train_data.drop(col_to_drop, axis=1)test_public = test_public.drop(col_to_drop, axis=1 )##internet处理train_inte = train_inte.drop(col_to_drop, axis=1 )# 暂时不变# train_inte = train_inte.rename(columns={'is_default':'isDefault'})# data = pd.concat( [train_data,test_public] )tr_cols = set(train_data.columns)same_col = list(tr_cols.intersection(set(train_inte.columns)))train_inteSame = train_inte[same_col].copy()Inte_add_cos = list(tr_cols.difference(set(same_col)))for col in Inte_add_cos:train_inteSame[col] = np.nan#81后加# for col in cat_cols:# dum = pd.get_dummies(data[col], prefix='OneHot_'+col +'_')# data = pd.concat([data, dum], axis=1)# # del data[col]# del dumy = train_data['isDefault']folds = KFold(n_splits=5, shuffle=True, random_state=546789)oof_preds, IntePre, importances = train_model(train_data, train_inteSame, y, folds)IntePre['isDef'] = train_inte['is_default']from sklearn.metrics import roc_auc_scoreroc_auc_score(IntePre['isDef'],IntePre.isDefault)## 选择阈值0.05，从internet表中提取预测小于该概率的样本，并对不同来源的样本赋予来源值InteId = IntePre.loc[IntePre.isDefault<0.05, 'loan_id'].tolist()train_data['dataSourse'] = 1test_public['dataSourse'] = 1train_inteSame['dataSourse'] = 0train_inteSame['isDefault'] = train_inte['is_default']use_te = train_inteSame[train_inteSame.loan_id.isin( InteId )].copy()data = pd.concat([ train_data,test_public,use_te]).reset_index(drop=True)# InteId = IntePre.loc[IntePre.isDefault<0.05, 'loan_id'].tolist()# train_inte = train_inte.rename(columns={'is_default':'isDefault'})# train_data['dataSourse'] = 1# test_public['dataSourse'] = 1# train_inte['dataSourse'] = 0# use_te = train_inte[train_inte.loan_id.isin( InteId )].copy()# data = pd.concat([ train_data,test_public,use_te]).reset_index(drop=True)# IntePre.isDefaultplt.figure(figsize=(16,6))plt.title("Distribution of Default values IntePre")sns.distplot(IntePre['isDefault'],color="black", kde=True,bins=120, label='train_data')# sns.distplot(train_inte[col],color="red", kde=True,bins=120, label='train_inte')plt.legend();plt.show()train = data[data['isDefault'].notna()]test = data[data['isDefault'].isna()]# for col in ['sub_class', 'work_type']:# del train[col]# del test[col]del datadel train_data,test_publicy = train['isDefault']folds = KFold(n_splits=5, shuffle=True, random_state=546789)oof_preds, test_preds, importances = train_model(train, test, y, folds)test_preds.rename({'loan_id': 'id'}, axis=1)[['id', 'isDefault']].to_csv('nn2.csv', index=False)
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