import datetime import gc import lightgbm as lgb import matplotlib.pyplot as plt import numpy as np import os import pandas as pd import seaborn as sns import time import warnings from contextlib import contextmanager from pandas.core.common import SettingWithCopyWarning from sklearn.metrics import mean_squared_error from sklearn.model_selection import KFold, StratifiedKFold warnings.simplefilter(action='ignore', category=SettingWithCopyWarning) warnings.simplefilter(action='ignore', category=FutureWarning) FEATS_EXCLUDED = ['first_active_month', 'target', 'card_id', 'outliers', 'hist_purchase_date_max', 'hist_purchase_date_min', 'hist_card_id_size', 'new_purchase_date_max', 'new_purchase_date_min', 'new_card_id_size', 'OOF_PRED', 'month_0'] @contextmanager def timer(title): t0 = time.time() yield print("{} - done in {:.0f}s".format(title, time.time() - t0)) # rmse def rmse(y_true, y_pred): return np.sqrt(mean_squared_error(y_true, y_pred)) # One-hot encoding for categorical columns with get_dummies def one_hot_encoder(df, nan_as_category = True): original_columns = list(df.columns) categorical_columns = [col for col in df.columns if df[col].dtype == 'object'] df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category) new_columns = [c for c in df.columns if c not in original_columns] return df, new_columns # Display/plot feature importance def display_importances(feature_importance_df_): cols = feature_importance_df_[["feature", "importance"]].groupby("feature").mean().sort_values(by="importance", ascending=False)[:40].index best_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') # reduce memory def reduce_mem_usage(df, verbose=True): numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] start_mem = df.memory_usage().sum() / 1024**2 for col in df.columns: col_type = df[col].dtypes if col_type in numerics: c_min = df[col].min() c_max = df[col].max() if str(col_type)[:3] == 'int': if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: df[col] = df[col].astype(np.int8) elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: df[col] = df[col].astype(np.int16) elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: df[col] = df[col].astype(np.int32) elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: df[col] = df[col].astype(np.int64) else: if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max: df[col] = df[col].astype(np.float16) elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max: df[col] = df[col].astype(np.float32) else: df[col] = df[col].astype(np.float64) end_mem = df.memory_usage().sum() / 1024**2 print('Memory usage after optimization is: {:.2f} MB'.format(end_mem)) print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) return df # preprocessing train & test def train_test(num_rows=None): # load csv train_df = pd.read_csv('../input/train.csv', index_col=['card_id'], nrows=num_rows) test_df = pd.read_csv('../input/test.csv', index_col=['card_id'], nrows=num_rows) print("Train samples: {}, test samples: {}".format(len(train_df), len(test_df))) # outlier train_df['outliers'] = 0 train_df.loc[train_df['target'] < -30, 'outliers'] = 1 # set target as nan test_df['target'] = np.nan # merge df = train_df.append(test_df) del train_df, test_df gc.collect() # to datetime df['first_active_month'] = pd.to_datetime(df['first_active_month']) # datetime features df['quarter'] = df['first_active_month'].dt.quarter df['elapsed_time'] = (datetime.datetime.today() - df['first_active_month']).dt.days df['days_feature1'] = df['elapsed_time'] * df['feature_1'] df['days_feature2'] = df['elapsed_time'] * df['feature_2'] df['days_feature3'] = df['elapsed_time'] * df['feature_3'] df['days_feature1_ratio'] = df['feature_1'] / df['elapsed_time'] df['days_feature2_ratio'] = df['feature_2'] / df['elapsed_time'] df['days_feature3_ratio'] = df['feature_3'] / df['elapsed_time'] # one hot encoding df, cols = one_hot_encoder(df, nan_as_category=False) for f in ['feature_1','feature_2','feature_3']: order_label = df.groupby([f])['outliers'].mean() df[f] = df[f].map(order_label) df['feature_sum'] = df['feature_1'] + df['feature_2'] + df['feature_3'] df['feature_mean'] = df['feature_sum']/3 df['feature_max'] = df[['feature_1', 'feature_2', 'feature_3']].max(axis=1) df['feature_min'] = df[['feature_1', 'feature_2', 'feature_3']].min(axis=1) df['feature_var'] = df[['feature_1', 'feature_2', 'feature_3']].std(axis=1) return df # preprocessing historical transactions def historical_transactions(num_rows=None): # load csv hist_df = pd.read_csv('../input/historical_transactions.csv', nrows=num_rows) # fillna hist_df['category_2'].fillna(1.0,inplace=True) hist_df['category_3'].fillna('A',inplace=True) hist_df['merchant_id'].fillna('M_ID_00a6ca8a8a',inplace=True) hist_df['installments'].replace(-1, np.nan,inplace=True) hist_df['installments'].replace(999, np.nan,inplace=True) # trim hist_df['purchase_amount'] = hist_df['purchase_amount'].apply(lambda x: min(x, 0.8)) # Y/N to 1/0 hist_df['authorized_flag'] = hist_df['authorized_flag'].map({'Y': 1, 'N': 0}).astype(int) hist_df['category_1'] = hist_df['category_1'].map({'Y': 1, 'N': 0}).astype(int) hist_df['category_3'] = hist_df['category_3'].map({'A':0, 'B':1, 'C':2}) # datetime features hist_df['purchase_date'] = pd.to_datetime(hist_df['purchase_date']) hist_df['month'] = hist_df['purchase_date'].dt.month hist_df['day'] = hist_df['purchase_date'].dt.day hist_df['hour'] = hist_df['purchase_date'].dt.hour hist_df['weekofyear'] = hist_df['purchase_date'].dt.weekofyear hist_df['weekday'] = hist_df['purchase_date'].dt.weekday hist_df['weekend'] = (hist_df['purchase_date'].dt.weekday >=5).astype(int) # additional features hist_df['price'] = hist_df['purchase_amount'] / hist_df['installments'] #Christmas : December 25 2017 hist_df['Christmas_Day_2017']=(pd.to_datetime('2017-12-25')-hist_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #Mothers Day: May 14 2017 hist_df['Mothers_Day_2017']=(pd.to_datetime('2017-06-04')-hist_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #fathers day: August 13 2017 hist_df['fathers_day_2017']=(pd.to_datetime('2017-08-13')-hist_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #Childrens day: October 12 2017 hist_df['Children_day_2017']=(pd.to_datetime('2017-10-12')-hist_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #Valentine's Day : 12th June, 2017 hist_df['Valentine_Day_2017']=(pd.to_datetime('2017-06-12')-hist_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #Black Friday : 24th November 2017 hist_df['Black_Friday_2017']=(pd.to_datetime('2017-11-24') - hist_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #2018 #Mothers Day: May 13 2018 hist_df['Mothers_Day_2018']=(pd.to_datetime('2018-05-13')-hist_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) hist_df['month_diff'] = ((datetime.datetime.today() - hist_df['purchase_date']).dt.days)//30 hist_df['month_diff'] += hist_df['month_lag'] # additional features hist_df['duration'] = hist_df['purchase_amount']*hist_df['month_diff'] hist_df['amount_month_ratio'] = hist_df['purchase_amount']/hist_df['month_diff'] # reduce memory usage hist_df = reduce_mem_usage(hist_df) col_unique =['subsector_id', 'merchant_id', 'merchant_category_id'] col_seas = ['month', 'hour', 'weekofyear', 'weekday', 'day'] aggs = {} for col in col_unique: aggs[col] = ['nunique'] for col in col_seas: aggs[col] = ['nunique', 'mean', 'min', 'max'] aggs['purchase_amount'] = ['sum','max','min','mean','var','skew'] aggs['installments'] = ['sum','max','mean','var','skew'] aggs['purchase_date'] = ['max','min'] aggs['month_lag'] = ['max','min','mean','var','skew'] aggs['month_diff'] = ['max','min','mean','var','skew'] aggs['authorized_flag'] = ['mean'] aggs['weekend'] = ['mean'] # overwrite aggs['weekday'] = ['mean'] # overwrite aggs['day'] = ['nunique', 'mean', 'min'] # overwrite aggs['category_1'] = ['mean'] aggs['category_2'] = ['mean'] aggs['category_3'] = ['mean'] aggs['card_id'] = ['size','count'] aggs['price'] = ['sum','mean','max','min','var'] aggs['Christmas_Day_2017'] = ['mean'] aggs['Mothers_Day_2017'] = ['mean'] aggs['fathers_day_2017'] = ['mean'] aggs['Children_day_2017'] = ['mean'] aggs['Valentine_Day_2017'] = ['mean'] aggs['Black_Friday_2017'] = ['mean'] aggs['Mothers_Day_2018'] = ['mean'] aggs['duration']=['mean','min','max','var','skew'] aggs['amount_month_ratio']=['mean','min','max','var','skew'] for col in ['category_2','category_3']: hist_df[col+'_mean'] = hist_df.groupby([col])['purchase_amount'].transform('mean') hist_df[col+'_min'] = hist_df.groupby([col])['purchase_amount'].transform('min') hist_df[col+'_max'] = hist_df.groupby([col])['purchase_amount'].transform('max') hist_df[col+'_sum'] = hist_df.groupby([col])['purchase_amount'].transform('sum') aggs[col+'_mean'] = ['mean'] hist_df = hist_df.reset_index().groupby('card_id').agg(aggs) # change column name hist_df.columns = pd.Index([e[0] + "_" + e[1] for e in hist_df.columns.tolist()]) hist_df.columns = ['hist_'+ c for c in hist_df.columns] hist_df['hist_purchase_date_diff'] = (hist_df['hist_purchase_date_max']-hist_df['hist_purchase_date_min']).dt.days hist_df['hist_purchase_date_average'] = hist_df['hist_purchase_date_diff']/hist_df['hist_card_id_size'] hist_df['hist_purchase_date_uptonow'] = (datetime.datetime.today()-hist_df['hist_purchase_date_max']).dt.days hist_df['hist_purchase_date_uptomin'] = (datetime.datetime.today()-hist_df['hist_purchase_date_min']).dt.days # reduce memory usage hist_df = reduce_mem_usage(hist_df) return hist_df # preprocessing new_merchant_transactions def new_merchant_transactions(num_rows=None): # load csv new_merchant_df = pd.read_csv('../input/new_merchant_transactions.csv', nrows=num_rows) # fillna new_merchant_df['category_2'].fillna(1.0,inplace=True) new_merchant_df['category_3'].fillna('A',inplace=True) new_merchant_df['merchant_id'].fillna('M_ID_00a6ca8a8a',inplace=True) new_merchant_df['installments'].replace(-1, np.nan,inplace=True) new_merchant_df['installments'].replace(999, np.nan,inplace=True) # trim new_merchant_df['purchase_amount'] = new_merchant_df['purchase_amount'].apply(lambda x: min(x, 0.8)) # Y/N to 1/0 new_merchant_df['authorized_flag'] = new_merchant_df['authorized_flag'].map({'Y': 1, 'N': 0}).astype(int) new_merchant_df['category_1'] = new_merchant_df['category_1'].map({'Y': 1, 'N': 0}).astype(int) new_merchant_df['category_3'] = new_merchant_df['category_3'].map({'A':0, 'B':1, 'C':2}).astype(int) # datetime features new_merchant_df['purchase_date'] = pd.to_datetime(new_merchant_df['purchase_date']) new_merchant_df['month'] = new_merchant_df['purchase_date'].dt.month new_merchant_df['day'] = new_merchant_df['purchase_date'].dt.day new_merchant_df['hour'] = new_merchant_df['purchase_date'].dt.hour new_merchant_df['weekofyear'] = new_merchant_df['purchase_date'].dt.weekofyear new_merchant_df['weekday'] = new_merchant_df['purchase_date'].dt.weekday new_merchant_df['weekend'] = (new_merchant_df['purchase_date'].dt.weekday >=5).astype(int) # additional features new_merchant_df['price'] = new_merchant_df['purchase_amount'] / new_merchant_df['installments'] #Christmas : December 25 2017 new_merchant_df['Christmas_Day_2017']=(pd.to_datetime('2017-12-25')-new_merchant_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #Childrens day: October 12 2017 new_merchant_df['Children_day_2017']=(pd.to_datetime('2017-10-12')-new_merchant_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #Black Friday : 24th November 2017 new_merchant_df['Black_Friday_2017']=(pd.to_datetime('2017-11-24') - new_merchant_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) #Mothers Day: May 13 2018 new_merchant_df['Mothers_Day_2018']=(pd.to_datetime('2018-05-13')-new_merchant_df['purchase_date']).dt.days.apply(lambda x: x if x > 0 and x < 100 else 0) new_merchant_df['month_diff'] = ((datetime.datetime.today() - new_merchant_df['purchase_date']).dt.days)//30 new_merchant_df['month_diff'] += new_merchant_df['month_lag'] # additional features new_merchant_df['duration'] = new_merchant_df['purchase_amount']*new_merchant_df['month_diff'] new_merchant_df['amount_month_ratio'] = new_merchant_df['purchase_amount']/new_merchant_df['month_diff'] # reduce memory usage new_merchant_df = reduce_mem_usage(new_merchant_df) col_unique =['subsector_id', 'merchant_id', 'merchant_category_id'] col_seas = ['month', 'hour', 'weekofyear', 'weekday', 'day'] aggs = {} for col in col_unique: aggs[col] = ['nunique'] for col in col_seas: aggs[col] = ['nunique', 'mean', 'min', 'max'] aggs['purchase_amount'] = ['sum','max','min','mean','var','skew'] aggs['installments'] = ['sum','max','mean','var','skew'] aggs['purchase_date'] = ['max','min'] aggs['month_lag'] = ['max','min','mean','var','skew'] aggs['month_diff'] = ['mean','var','skew'] aggs['weekend'] = ['mean'] aggs['month'] = ['mean', 'min', 'max'] aggs['weekday'] = ['mean', 'min', 'max'] aggs['category_1'] = ['mean'] aggs['category_2'] = ['mean'] aggs['category_3'] = ['mean'] aggs['card_id'] = ['size','count'] aggs['price'] = ['mean','max','min','var'] aggs['Christmas_Day_2017'] = ['mean'] aggs['Children_day_2017'] = ['mean'] aggs['Black_Friday_2017'] = ['mean'] aggs['Mothers_Day_2018'] = ['mean'] aggs['duration']=['mean','min','max','var','skew'] aggs['amount_month_ratio']=['mean','min','max','var','skew'] for col in ['category_2','category_3']: new_merchant_df[col+'_mean'] = new_merchant_df.groupby([col])['purchase_amount'].transform('mean') new_merchant_df[col+'_min'] = new_merchant_df.groupby([col])['purchase_amount'].transform('min') new_merchant_df[col+'_max'] = new_merchant_df.groupby([col])['purchase_amount'].transform('max') new_merchant_df[col+'_sum'] = new_merchant_df.groupby([col])['purchase_amount'].transform('sum') aggs[col+'_mean'] = ['mean'] new_merchant_df = new_merchant_df.reset_index().groupby('card_id').agg(aggs) # change column name new_merchant_df.columns = pd.Index([e[0] + "_" + e[1] for e in new_merchant_df.columns.tolist()]) new_merchant_df.columns = ['new_'+ c for c in new_merchant_df.columns] new_merchant_df['new_purchase_date_diff'] = (new_merchant_df['new_purchase_date_max']-new_merchant_df['new_purchase_date_min']).dt.days new_merchant_df['new_purchase_date_average'] = new_merchant_df['new_purchase_date_diff']/new_merchant_df['new_card_id_size'] new_merchant_df['new_purchase_date_uptonow'] = (datetime.datetime.today()-new_merchant_df['new_purchase_date_max']).dt.days new_merchant_df['new_purchase_date_uptomin'] = (datetime.datetime.today()-new_merchant_df['new_purchase_date_min']).dt.days # reduce memory usage new_merchant_df = reduce_mem_usage(new_merchant_df) return new_merchant_df # additional features def additional_features(df): df['hist_first_buy'] = (df['hist_purchase_date_min'] - df['first_active_month']).dt.days df['hist_last_buy'] = (df['hist_purchase_date_max'] - df['first_active_month']).dt.days df['new_first_buy'] = (df['new_purchase_date_min'] - df['first_active_month']).dt.days df['new_last_buy'] = (df['new_purchase_date_max'] - df['first_active_month']).dt.days date_features=['hist_purchase_date_max','hist_purchase_date_min', 'new_purchase_date_max', 'new_purchase_date_min'] for f in date_features: df[f] = df[f].astype(np.int64) * 1e-9 df['card_id_total'] = df['new_card_id_size']+df['hist_card_id_size'] df['card_id_cnt_total'] = df['new_card_id_count']+df['hist_card_id_count'] df['card_id_cnt_ratio'] = df['new_card_id_count']/df['hist_card_id_count'] df['purchase_amount_total'] = df['new_purchase_amount_sum']+df['hist_purchase_amount_sum'] df['purchase_amount_mean'] = df['new_purchase_amount_mean']+df['hist_purchase_amount_mean'] df['purchase_amount_max'] = df['new_purchase_amount_max']+df['hist_purchase_amount_max'] df['purchase_amount_min'] = df['new_purchase_amount_min']+df['hist_purchase_amount_min'] df['purchase_amount_ratio'] = df['new_purchase_amount_sum']/df['hist_purchase_amount_sum'] df['month_diff_mean'] = df['new_month_diff_mean']+df['hist_month_diff_mean'] df['month_diff_ratio'] = df['new_month_diff_mean']/df['hist_month_diff_mean'] df['month_lag_mean'] = df['new_month_lag_mean']+df['hist_month_lag_mean'] df['month_lag_max'] = df['new_month_lag_max']+df['hist_month_lag_max'] df['month_lag_min'] = df['new_month_lag_min']+df['hist_month_lag_min'] df['category_1_mean'] = df['new_category_1_mean']+df['hist_category_1_mean'] df['installments_total'] = df['new_installments_sum']+df['hist_installments_sum'] df['installments_mean'] = df['new_installments_mean']+df['hist_installments_mean'] df['installments_max'] = df['new_installments_max']+df['hist_installments_max'] df['installments_ratio'] = df['new_installments_sum']/df['hist_installments_sum'] df['price_total'] = df['purchase_amount_total'] / df['installments_total'] df['price_mean'] = df['purchase_amount_mean'] / df['installments_mean'] df['price_max'] = df['purchase_amount_max'] / df['installments_max'] df['duration_mean'] = df['new_duration_mean']+df['hist_duration_mean'] df['duration_min'] = df['new_duration_min']+df['hist_duration_min'] df['duration_max'] = df['new_duration_max']+df['hist_duration_max'] df['amount_month_ratio_mean']=df['new_amount_month_ratio_mean']+df['hist_amount_month_ratio_mean'] df['amount_month_ratio_min']=df['new_amount_month_ratio_min']+df['hist_amount_month_ratio_min'] df['amount_month_ratio_max']=df['new_amount_month_ratio_max']+df['hist_amount_month_ratio_max'] df['new_CLV'] = df['new_card_id_count'] * df['new_purchase_amount_sum'] / df['new_month_diff_mean'] df['hist_CLV'] = df['hist_card_id_count'] * df['hist_purchase_amount_sum'] / df['hist_month_diff_mean'] df['CLV_ratio'] = df['new_CLV'] / df['hist_CLV'] return df # LightGBM GBDT with KFold or Stratified KFold def kfold_lightgbm(train_df, test_df, num_folds, stratified = False, debug= False): print("Starting LightGBM. Train shape: {}, test shape: {}".format(train_df.shape, test_df.shape)) # Cross validation model if stratified: folds = StratifiedKFold(n_splits= num_folds, shuffle=True, random_state=326) else: folds = KFold(n_splits= num_folds, shuffle=True, random_state=326) # Create arrays and dataframes to store results oof_preds = np.zeros(train_df.shape[0]) sub_preds = np.zeros(test_df.shape[0]) feature_importance_df = pd.DataFrame() feats = [f for f in train_df.columns if f not in FEATS_EXCLUDED] # k-fold for n_fold, (train_idx, valid_idx) in enumerate(folds.split(train_df[feats], train_df['outliers'])): train_x, train_y = train_df[feats].iloc[train_idx], train_df['target'].iloc[train_idx] valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df['target'].iloc[valid_idx] # set data structure lgb_train = lgb.Dataset(train_x, label=train_y, free_raw_data=False) lgb_test = lgb.Dataset(valid_x, label=valid_y, free_raw_data=False) # params optimized by optuna params ={ 'task': 'train', 'boosting': 'goss', 'objective': 'regression', 'metric': 'rmse', 'learning_rate': 0.01, 'subsample': 0.9855232997390695, 'max_depth': 7, 'top_rate': 0.9064148448434349, 'num_leaves': 63, 'min_child_weight': 41.9612869171337, 'other_rate': 0.0721768246018207, 'reg_alpha': 9.677537745007898, 'colsample_bytree': 0.5665320670155495, 'min_split_gain': 9.820197773625843, 'reg_lambda': 8.2532317400459, 'min_data_in_leaf': 21, 'verbose': -1, 'seed':int(2**n_fold), 'bagging_seed':int(2**n_fold), 'drop_seed':int(2**n_fold) } reg = lgb.train( params, lgb_train, valid_sets=[lgb_train, lgb_test], valid_names=['train', 'test'], num_boost_round=10000, early_stopping_rounds= 200, verbose_eval=100 ) oof_preds[valid_idx] = reg.predict(valid_x, num_iteration=reg.best_iteration) sub_preds += reg.predict(test_df[feats], num_iteration=reg.best_iteration) / folds.n_splits fold_importance_df = pd.DataFrame() fold_importance_df["feature"] = feats fold_importance_df["importance"] = np.log1p(reg.feature_importance(importance_type='gain', iteration=reg.best_iteration)) fold_importance_df["fold"] = n_fold + 1 feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0) print('Fold %2d RMSE : %.6f' % (n_fold + 1, rmse(valid_y, oof_preds[valid_idx]))) del reg, train_x, train_y, valid_x, valid_y gc.collect() # display importances display_importances(feature_importance_df) if not debug: # save submission file test_df.loc[:,'target'] = sub_preds test_df = test_df.reset_index() test_df[['card_id', 'target']].to_csv(submission_file_name, index=False) def main(debug=False): num_rows = 10000 if debug else None with timer("train & test"): df = train_test(num_rows) with timer("historical transactions"): df = pd.merge(df, historical_transactions(num_rows), on='card_id', how='outer') with timer("new merchants"): df = pd.merge(df, new_merchant_transactions(num_rows), on='card_id', how='outer') with timer("additional features"): df = additional_features(df) with timer("split train & test"): train_df = df[df['target'].notnull()] test_df = df[df['target'].isnull()] del df gc.collect() with timer("Run LightGBM with kfold"): kfold_lightgbm(train_df, test_df, num_folds=11, stratified=False, debug=debug) if __name__ == "__main__": submission_file_name = "submission.csv" with timer("Full model run"): main(debug=False)