# np.random.seed(3452)
mdp = env.SyntheticToyFS()
state_dim, action_dim, reward_dim = get_space_info(mdp)
nextstate_idx = state_dim + action_dim + reward_dim
reward_idx = action_dim + state_dim
# dataset: s, a, r, s'
dataset = evaluation.collect_episodes(mdp, n_episodes=50)
check_dataset(dataset, state_dim, action_dim, reward_dim)
selector = IFS(estimator=ExtraTreesRegressor(n_estimators=50),
scale=True, verbose=1)
fs = RFS(feature_selector=selector,
features_names=np.array(['S0', 'S1', 'S2', 'S3', 'A0', 'A1']),
verbose=1)
state, actions, reward, next_states, absorbing = \
split_dataset(dataset, state_dim, action_dim, reward_dim)
# print(dataset[:10, :])
fs.fit(state, actions, next_states, reward)
selected_features = fs.features_names[fs.get_support()]
print('selected features: {}'.format(selected_features)) # this are the selected features, it should be [s0, s2, a0]
assert np.all(selected_features == ['S0', 'S2', 'A0'])
print(fs.nodes)
g = fs.export_graphviz()
g.view()
'S' + s for s in selected_states
] + ['Absorbing', 'Finished']
else:
support = selected_states + selected_actions + ['R'] + ['S' + s for s in selected_states] + \
['Absorbing', 'Finished']
# Reduce dataset
reduced_dataset = dataframe[
support] # Keep only selected states and actions
if args.onehot:
reduced_dataset = reduced_dataset[reduced_dataset['A0'].isin(
selected_actions_values)] # Remove useless actions
reduced_dataset = reduced_dataset.as_matrix()
# Save RFS tree
tree = fs.export_graphviz(filename=logger.path + 'rfs_tree.gv')
tree.save() # Save GV source
tree.format = 'pdf'
tree.render() # Save PDF
# Save RFS dataset
if args.save_rfs:
np.savetxt(logger.path + 'rfs_dataset.csv',
np.append([support], reduced_dataset, axis=0),
fmt='%s',
delimiter=',')
rfs_time = time.time() - rfs_time
print('Done RFS. Elapsed time: %s' % rfs_time)
else:
# The dataset is already reduced
reverse_onehot_actions = [np.where(dataset[i][state_dim:state_dim + action_dim] == 1)[0][0]
for i in xrange(len(dataset))]
dataframe['A0'] = pd.Series(reverse_onehot_actions) # A0 will be the only action
support = selected_states + ['A0', 'R'] + ['S' + s for s in selected_states] + ['Absorbing', 'Finished']
else:
support = selected_states + selected_actions + ['R'] + ['S' + s for s in selected_states] + \
['Absorbing', 'Finished']
# Reduce dataset
reduced_dataset = dataframe[support] # Keep only selected states and actions
if args.onehot:
reduced_dataset = reduced_dataset[reduced_dataset['A0'].isin(selected_actions_values)] # Remove useless actions
reduced_dataset = reduced_dataset.as_matrix()
# Save RFS tree
tree = fs.export_graphviz(filename=logger.path + 'rfs_tree.gv')
tree.save() # Save GV source
tree.format = 'pdf'
tree.render() # Save PDF
# Save RFS dataset
if args.save_rfs:
np.savetxt(logger.path + 'rfs_dataset.csv', np.append([support], reduced_dataset, axis=0),
fmt='%s', delimiter=',')
rfs_time = time.time() - rfs_time
print('Done RFS. Elapsed time: %s' % rfs_time)
else:
# The dataset is already reduced
header = list(dataset[0])
reduced_dataset = dataset[1:] # Remove header