THIS-ODE/run_Neural.py at main · shib0li/THIS-ODE · GitHub

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import numpy as np
import torch
import torch.distributions as distributions
from torch.optim import Adam
from torch.optim import LBFGS
from torchdiffeq import odeint
import torch.autograd.functional as F
import random
import matplotlib.pyplot as plt

import torch.nn as nn
import torch.optim as optim
import os
import pickle
import time
import fire

from infrastructure.misc import *
from tqdm.auto import tqdm, trange
from torch.utils.data import Dataset, DataLoader

from data.real_events import EventData, EventDataBT
from baselines.Neural_linear import *
from baselines.Neural_rnn import *
from baselines.Neural_time import *

from infrastructure.configs import *

np.random.seed(0)
torch.manual_seed(0)
random.seed(0)


def evaluation(**kwargs):

    config = NeuralExpConfig()
    config.parse(kwargs)

    device = torch.device(config.device)

    domain = config.domain
    fold = config.fold

    if config.trans=='linear':
        method = 'Neural_linear'
        dataset_train = EventDataBT(domain, mode='train', fold=fold)
        dataset_test = EventDataBT(domain, mode='test', fold=fold)
    elif config.trans=='rnn':
        method = 'Neural_rnn'
        dataset_train = EventDataBT(domain, mode='train', fold=fold)
        dataset_test = EventDataBT(domain, mode='test', fold=fold)
    elif config.trans=='time':
        method = 'Neural_time'
        dataset_train = EventData(domain, mode='train', fold=fold)
        dataset_test = EventData(domain, mode='test', fold=fold)
    else:
        raise Exception('Error in run_Neural.py')

    ndims = dataset_train.nvec
    nmod = dataset_train.nmod

    #cprint('g', ndims)
    #cprint('g', nmod)

    res_path = os.path.join(
        '__res__',
        dataset_train.domain,
        method,
        'rank'+str(config.R),
        'fold{}'.format(dataset_train.fold)
    )

    log_path = os.path.join(
        '__log__',
        dataset_train.domain,
        method,
        'rank'+str(config.R),
        'fold{}'.format(dataset_train.fold)
    )
    create_path(res_path)
    create_path(log_path)

    logger = get_logger(logpath=os.path.join(log_path, 'exp.log'), displaying=config.verbose)
    logger.info(config)

    batch_size = config.batch_size
    R = config.R

    if config.trans=='linear':
        model = Neural_linear(
            nmod = dataset_train.nmod,
            nvec = dataset_train.nvec,
            R = R,
            nFF=config.nFF,
            batch_size=batch_size
        )
    elif config.trans=='rnn':
        model = Neural_rnn(
            nmod = dataset_train.nmod,
            nvec = dataset_train.nvec,
            R = R,
            nFF=config.nFF,
            batch_size=batch_size
        )
    elif config.trans=='time':
        model = Neural_time(
            nmod = dataset_train.nmod,
            nvec = dataset_train.nvec,
            R = R,
            nFF=config.nFF,
            batch_size=batch_size
        )
    else:
        raise Exception('Error in model run_Neural.py')


    model.todev(device)

    max_epochs = config.max_epochs
    learning_rate = config.learning_rate

    perform_meters = PerformMeters(save_path=res_path, logger=logger, test_interval=config.test_interval)

    model.train(dataset_train, dataset_test, max_epochs, learning_rate, perform_meters)


if __name__=='__main__':
    fire.Fire(evaluation)