def remove_nodes_incrementally(dataset, esn, removed_nodes_file):
try:
removed_nodes = pickle.load(open(removed_nodes_file, 'rb'))
except FileNotFoundError:
removed_nodes = []
for node in removed_nodes:
esn.remove_hidden_node(node)
while esn.hidden_nodes > 1:
default_nrmse = evaluate_esn(dataset, esn)
nrmse_diffs = []
for i in tqdm.tqdm(range(esn.hidden_nodes)):
_esn = copy.deepcopy(esn)
_esn.remove_hidden_node(i)
nrmse = evaluate_esn(dataset, _esn)
nrmse_diffs.append(nrmse - default_nrmse)
best_node = np.argmin(nrmse_diffs)
esn.remove_hidden_node(best_node)
removed_nodes.append(best_node)
print()
best_nrmse = default_nrmse + nrmse_diffs[best_node]
print(f'it: nrmse-{best_nrmse}')
pickle.dump(removed_nodes, open(removed_nodes_file, 'wb'))
def evaluate_esn_1d(dataset, params, runs_per_iteration=1, test_snrs=None):
if len(params.keys()) != 1:
class InvalidParameterDictException(Exception):
pass
raise InvalidParameterDictException(
'1d grid search requires exactly 1 parameter lists')
output = []
param_grid = ParameterGrid(params)
p1 = sorted(params.keys())[0]
# We need the SNR of u/v.
u_train, _, u_test, _ = dataset
for params in param_grid:
print(params)
nrmses = []
test_snrs_t = []
for i in range(runs_per_iteration):
esn = ESN(**params)
nrmses.append(evaluate_esn(dataset, esn))
if test_snrs is not None:
test_snrs_t.append(snr(u_test.var(), esn.v.var()))
v = esn.v
output.append(np.mean(nrmses))
if test_snrs is not None:
test_snrs.append(np.mean(test_snrs_t))
return output
def find_esn(dataset, required_nrmse, **kwargs):
attempts = 1000
for i in range(attempts):
esn = ESN(**kwargs)
nrmse = metric.evaluate_esn(dataset, esn)
if nrmse < required_nrmse:
return esn
raise ESNException(
f'Could not find suitable ESN within {attempts} attempts')
def make_undirected_incrementally(dataset, esn, changed_edges_file):
try:
changed_edges = pickle.load(open(changed_edges_file, 'rb'))
except FileNotFoundError:
changed_edges = OrderedDict()
original_edges = [edge for edge in esn.G.edges]
for edge in changed_edges:
esn.make_edge_undirected(edge)
while len(changed_edges) < len(original_edges) * 2:
default_nrmse = evaluate_esn(dataset, esn)
nrmse_diffs = []
for i, edge in enumerate(tqdm.tqdm(original_edges)):
if edge in changed_edges:
nrmse_diffs.append(np.inf)
continue
_esn = copy.deepcopy(esn)
_esn.make_edge_undirected(edge)
nrmse = evaluate_esn(dataset, _esn)
nrmse_diffs.append(nrmse - default_nrmse)
best_edge = np.argmin(nrmse_diffs)
edge_to_change = list(original_edges)[best_edge]
esn.make_edge_undirected(edge_to_change)
changed_edges[edge_to_change] = None
changed_edges[(edge_to_change[1], edge_to_change[0])] = None
print()
best_nrmse = default_nrmse + nrmse_diffs[best_edge]
print(
f'it: removed-{best_edge}, nrmse-{best_nrmse}, total-{len(changed_edges)//2}'
)
pickle.dump(changed_edges, open(changed_edges_file, 'wb'))
def find_best_node_to_remove(dataset, esn):
best_nrmse = np.inf
best_node = None
for i in tqdm.tqdm(range(esn.hidden_nodes)):
_esn = copy.deepcopy(esn)
_esn.remove_hidden_node(i)
nrmse = evaluate_esn(dataset, _esn)
if nrmse < best_nrmse:
best_nrmse = nrmse
best_node = i
return i
def evaluate_incremental_node_removal(dataset,
esn_file,
removed_nodes_file,
return_esns=False):
esn = pickle.load(open(esn_file, 'rb'))
removed_nodes = pickle.load(open(removed_nodes_file, 'rb'))
nrmses = []
esns = []
for node in tqdm.tqdm(removed_nodes):
esn.remove_hidden_node(node)
nrmses.append(evaluate_esn(dataset, esn))
esns.append(copy.deepcopy(esn))
if return_esns:
return nrmses, esns
else:
return nrmses
def evaluate_incremental_undirection(dataset,
esn,
changed_edges_file,
esns=False):
changed_edges = pickle.load(open(changed_edges_file, 'rb'))
changed_edges = [e for i, e in enumerate(changed_edges) if i % 2 == 0]
nrmses = []
esns = []
for edge in tqdm.tqdm(changed_edges):
esn.make_edge_undirected(edge)
nrmses.append(evaluate_esn(dataset, esn))
esns.append(copy.deepcopy(esn))
if esns:
return nrmses, esns
else:
return nrmses
def find_best_node_to_add(dataset, esn):
frontier = find_tetragonal_frontier(esn.G)
possible_edges = {}
for node in frontier:
possible_edges[node] = find_ways_to_add_node(esn.G, node)
best_nrmse = np.inf
best_node_and_edges = None
for node in tqdm.tqdm(possible_edges.keys()):
for edges in possible_edges[node]:
_esn = copy.deepcopy(esn)
_esn.add_hidden_node(node, edges)
nrmse = evaluate_esn(dataset, _esn)
if nrmse < best_nrmse:
best_nrmse = nrmse
best_node_and_edges = (node, edges)
return best_node_and_edges
