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playground.py
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from calendar import c
from datagen.algorithms import info, gen_multi_algo_data
from datagen.graphgen import gen_erdos_renyi, gen_barabasi_albert, gen_twod_grid
import torch
import pdb
import logging
import sys
from loss import LossAssembler, create_loss_class
import model as arch
import initialisation as init
from train import train, train_metadata
import evaluate as ev
from torch.utils.data import DataLoader
from tqdm import tqdm
from baselines import run_multi, run_seq_reptile
from logger import _logger
from get_streams import multi_stream, seq_reptile_stream
from itertools import product
import numpy as np
torch.manual_seed(10)
def gen_erdos_renyi_algo_results(rand_generator, num_graph, num_node, task_list, datasavefp='Data/', directed=False):
gen_erdos_renyi(rand_generator, int(num_graph), int(num_node), datasavefp+"_", directed)
src_nodes = torch.argmax(rand_generator.sample((int(num_graph), int(num_node))), dim=1)
pdb.set_trace()
gen_multi_algo_data(
datasavefp+"_"+"erdosrenyi" + num_graph + "_"+ num_node + ".pt",
src_nodes,
task_list,
True
)
task = 'bfs bf'
algo_names = ['bfs', 'bf']
task_list = ['bfs', 'bf'] # or noalgo_?
ngraph_train_list = ['1000'] # ['100','1000']
ngraph_val = '100'
ngraph_test = ['200', '200']
nnode = '20'
nnode_test = ['20', '50']
# rand_gen = torch.distributions.Uniform(0.0, 1.0)
# gen_erdos_renyi_algo_results(rand_gen, '5000', nnode, algo_names, 'Data/train')
# gen_erdos_renyi_algo_results(rand_gen, ngraph_val, nnode, algo_names, 'Data/val')
# for i in range(len(nnode_test)):
# gen_erdos_renyi_algo_results(rand_gen, ngraph_test[i], nnode_test[i], algo_names, 'Data/test')
device = 'cuda'
latentdim = 32
encdim = 32
noisedim = 0
lr_vals = [1e-2]
weightdecay_vals = [0.00001]
for ngraph_train in ngraph_train_list:
mean_results = []
steps = 7
param_product = list(product(lr_vals, weightdecay_vals))
print(f"Hyperparameter combinations: {param_product}")
for (lr, weightdecay) in param_product:
train_params = {}
train_params['optimizer'] = 'adam'
train_params['epochs'] = 100
train_params['lr'] = lr
train_params['warmup'] = 0
train_params['earlystop'] = False
train_params['patience'] = 2 # na
train_params['weightdecay'] = weightdecay
train_params['schedpatience'] = 0 # na
train_params['tempinit'] = 1.0
train_params['temprate'] = 1.0
train_params['tempmin'] = 1.0
train_params['earlytol'] = 1e-4
train_params['ksamples'] = 1
train_params['task'] = task
train_params['batchsize'] = 50
# for adaptive scheduling
train_params['exponent'] = 1.0
# for seq reptile
train_params['K'] = 1
train_params['alpha'] = 1e-4
for i in range(steps):
logger = _logger(logfile='Data/multi.log')
metadata = info(logger, algo_names)
model = arch.NeuralExecutor3(device,
metadata['nodedim'],
metadata['edgedim'],
latentdim,
encdim,
pred=metadata['pred'],
noise=noisedim
)
train_stream, val_stream, test_stream = multi_stream(ngraph_train, ngraph_val,
nnode, logger, algo_names,
ngraph_test, nnode_test,
batchsize=train_params['batchsize'])
res = run_multi(model, logger, task_list, train_stream, val_stream,
train_params, test_stream, device='cuda')
mean_results.append(res)
#results_arr = np.array(mean_results, dtype=object)
results_arr = np.array(mean_results)
average_arr = np.average(results_arr, axis=0)
test_size = len(nnode_test)
metrics = [m for t in task_list for m in ev.get_metrics(t)]
metrics_len = len(metrics)
for i in range(test_size):
# print("averaging on testset " + i)
for ith, metric in enumerate(metrics):
logger.info("average " + metric +
": {}".format(average_arr[i][ith]))
print("average " + metric + ": {}".format(average_arr[i][ith]))
# logger = _logger(logfile='Data/seq_reptile.log')
# metadata = info(logger, algo_names)
# model = arch.NeuralExecutor3_(device,
# metadata['nodedim'],
# metadata['edgedim'],
# latentdim,
# encdim,
# algo_names,
# pred=metadata['pred'],
# noise=noisedim
# )
# train_stream, val_stream, test_stream = seq_reptile_stream(ngraph_train, ngraph_val, nnode, logger, algo_names,
# ngraph_test, nnode_test, batchsize=train_params['batchsize'])
# run_seq_reptile(model, logger, task_list, train_stream, val_stream, train_params, test_stream)