import numpy as np

np.random.seed(1003)

import pandas as pd

import os

import time

import datetime

import pickle

import math

import sys

import pdb

import json

import re

#Suppress messages from tensorflow

stderr = sys.stderr

sys.stderr = open(os.devnull, 'w')

import keras

from keras import callbacks

from keras import layers

from keras import optimizers

from keras.models import Model

from keras import backend as K

from keras.models import load_model

import tensorflow as tf

sys.stderr = stderr

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' #Stop TensorFlow Debugging Info

import sklearn.linear_model

from sklearn.multioutput import MultiOutputRegressor

from sklearn.metrics import mean_squared_error as mse

from sklearn.model_selection import ParameterGrid

from sklearn import preprocessing

## Mine IMPORTS:

import customModels

import customCallbacks

import customUtils

from customMetrics import atan_mse,modulo_2pi_error,log_likelihood_normal_cost,nll,mean_squared_error_mu,acc_dist,cv_test,custom_loss,phase_mse,smape,custom_loss_TRP,custom_metroc_TRP

import prep

from run_eval import run_eval_base

import run_eval

import eval_ensemble

import email_logger

RANDOM_SEED = 1003

EPSILON = np.finfo(np.float32).eps

def runSimpleModelWithParams(model_params,data):

)

def runSimpleEncoderDecoderModelWithParams(model_params,data):

)

def runDistEncoderDecoderModelWithParams(model_params,data):

)

def runDistModelWithParams(model_params,data):

)

def runCustomAutpEncoderWithParams(model_params,data):

)

def runBaseLineRegression(model_params,data,estimator):

return (mse_totoal_train.tolist(),mse_totoal_dev.tolist(),mse_totoal_train.sum(),mse_totoal_dev.sum())

def main():

print(message.replace("<br>","\n"))

if __name__ == "__main__":

main()