S6_NN_main.py

import warnings
warnings.simplefilter('ignore')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gc,os,random
import time,datetime
from tqdm import tqdm

from utils import *
from model import *
root = args.root
seed = args.seed

df =  pd.read_feather('./input/nn_series.feather')
y = pd.read_csv('./input/train_labels.csv')

f = pd.read_feather('./input/nn_all_feature.feather')
df['idx'] = df.index
series_idx = df.groupby('customer_ID',sort=False).idx.agg(['min','max'])
series_idx['feature_idx'] = np.arange(len(series_idx))
df = df.drop(['idx'],axis=1)
print(f.head())
nn_config = {
    'id_name':id_name,
    'feature_name':[],
    'label_name':label_name,
    'obj_max': 1,
    'epochs': 10,
    'smoothing': 0.001,
    'clipnorm': 1,
    'patience': 100,
    'lr': 3e-4,
    'batch_size': 256,
    'folds': 5,
    'seed': seed,
    'remark': args.remark
}

NN_train_and_predict([df,f,y,series_idx.values[:y.shape[0]]],[df,f,series_idx.values[y.shape[0]:]],Amodel,nn_config,use_series_oof=False,run_id='NN_with_series_feature')

NN_train_and_predict([df,f,y,series_idx.values[:y.shape[0]]],[df,f,series_idx.values[y.shape[0]:]],Amodel,nn_config,use_series_oof=True,run_id='NN_with_series_and_all_feature')