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narma30_rbn.py
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narma30_rbn.py
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import Oger
import mdp
import numpy as np
from utils import user_confirms, user_denies, default_input
from utils import dump, log_git_info, glob_load, get_working_dir
from rbn import rbn_node
from tasks import temporal
from rbn_reservoir_problem import RBNReservoirProblem
from ea.solve import solve
import log
import logging
def create_dataset():
dataset_type = default_input(
'Dataset [temporal_parity, temporal_density]', 'temporal_parity')
n_datasets = default_input('Datasets', 10)
task_size = default_input('Dataset length', 200)
window_size = default_input('Window size', 3)
datasets = temporal.create_datasets(
n_datasets,
task_size=task_size,
window_size=window_size,
dataset_type=dataset_type)
dataset_description = '[{}-{}-{}-{}]'.format(
dataset_type, n_datasets, task_size, window_size)
return datasets, dataset_description
def create_reservoir():
connectivity = default_input('connectivity', 2)
n_nodes = default_input('n_nodes', 100)
input_connectivity = default_input('input_connectivity', 50)
rbn_reservoir = rbn_node.RBNNode(connectivity=connectivity,
output_dim=n_nodes,
input_connectivity=input_connectivity)
return rbn_reservoir
if __name__ == '__main__':
# Set pickle working dir
working_dir = get_working_dir()
log.setup(logging.DEBUG, path=working_dir)
log_git_info()
# Create datasets
use_existing_dataset = user_confirms('Use existing dataset in folder?')
if use_existing_dataset:
test_dataset, _ = glob_load(working_dir + '*-dataset')[0]
dataset_description = '[dataset_from_folder]'
else:
datasets, dataset_description = create_dataset()
training_dataset, test_dataset = datasets[:-1], datasets[-1]
if not use_existing_dataset and not user_denies('Pickle test dataset?'):
dump(test_dataset, dataset_description + '-dataset',
folder=working_dir)
# Create or load reservoir and readout layer
if user_confirms('Use readout layer from folder?'):
readout, _ = glob_load(working_dir + '*readout')[0]
else:
rbn_reservoir = create_reservoir()
readout = Oger.nodes.RidgeRegressionNode(
input_dim=rbn_reservoir.output_dim,
output_dim=1,
verbose=True)
# Train and execute newly created flow
flow = mdp.Flow([rbn_reservoir, readout], verbose=1)
flow.train([None, training_dataset])
reservoir_input, expected_output = test_dataset
actual_output = flow.execute(reservoir_input)
for output in actual_output:
output[0] = 1 if output[0] > 0.5 else 0
errors = sum(actual_output != expected_output)
accuracy = 1 - float(errors) / len(actual_output)
logging.info("Accuracy: {} on {} items."
.format(accuracy, len(reservoir_input)))
# Optionally dump newly created flow
if not user_denies('Pickle reservoir and readout layer?'):
flow_description = '{}-{}-[ACC:{}]'.format(
dataset_description,
rbn_reservoir.describe(),
accuracy)
dump(rbn_reservoir, flow_description + '-reservoir',
folder=working_dir)
dump(readout, flow_description + '-readout',
folder=working_dir)
# Evolve other reservoirs with similar dynamics
if not user_denies('Use readout layer to evolve similar rbn_reservoirs?'):
n_nodes = readout.input_dim
connectivity = default_input('Connectivity', 2)
n_runs = default_input('How many GA runs?', 1)
for i in range(n_runs):
reservoir_problem = RBNReservoirProblem(
n_nodes, connectivity, readout, test_dataset)
generation, adults = solve(reservoir_problem, path=working_dir)
fitnesses = [x.fitness for x in adults]
top3 = fitnesses[-3:]
top3.reverse()
mean = np.mean(fitnesses)
std = np.std(fitnesses)
description = (
'{}-[N:{}-K:{}]-[TOP:{}-MEAN:{}-STD:{}-GEN:{}]-[{}of{}]-evolved'
.format(dataset_description,
n_nodes,
connectivity,
top3,
mean,
std,
generation,
i,
n_runs))
dump(adults, description, folder=working_dir)
logging.info(
'GA run {} of {} completed, adults pickled'.format(i, n_runs))