def test_parameters_with_nan_inf():
# create the necessary results
result_1 = create_optimization_result_nan_inf()
result_2 = create_optimization_result_nan_inf()
# test a standard call
visualize.parameters(result_1)
# test plotting of lists
visualize.parameters([result_1, result_2])
def test_parameters(scale_to_interval):
# create the necessary results
result_1 = create_optimization_result()
result_2 = create_optimization_result()
# test a standard call
visualize.parameters(result_1, scale_to_interval=scale_to_interval)
# test plotting of lists
visualize.parameters(
[result_1, result_2], scale_to_interval=scale_to_interval
)
def test_parameters_with_options():
# create the necessary results
result_1 = create_optimization_result()
result_2 = create_optimization_result()
# alternative figure size and plotting options
(_, _, _, _, ref_point) = create_plotting_options()
alt_fig_size = (9.0, 8.0)
# test calls with specific options
visualize.parameters(result_1,
parameter_indices='all',
reference=ref_point,
size=alt_fig_size,
colors=[1., .3, .3, 0.5])
visualize.parameters([result_1, result_2],
parameter_indices='all',
reference=ref_point,
balance_alpha=False,
start_indices=(0, 1, 4))
visualize.parameters([result_1, result_2],
parameter_indices='free_only',
start_indices=3)
def store_and_plot_pretraining(result: Result, pretraindir: str, prefix: str):
"""
Store optimziation results in HDF5 as well as csv for later reuse. Also
saves some visualization for debugging purposes.
:param result:
result from pretraining
:param pretraindir:
directory in which results and plots will be stored
:param prefix:
prefix for file names that can be used to differentiate between
different pretraining stages as well as models/datasets.
"""
# store full results as hdf5
rfile = os.path.join(pretraindir, prefix + '.hdf5')
if os.path.exists(rfile):
# temp bugfix for https://github.com/ICB-DCM/pyPESTO/issues/529
os.remove(rfile)
writer = OptimizationResultHDF5Writer(rfile)
writer.write(result, overwrite=True)
# store parameter values, this will be used in subsequent steps
parameter_df = pd.DataFrame(
[r for r in result.optimize_result.get_for_key('x') if r is not None],
columns=result.problem.x_names)
parameter_df.to_csv(os.path.join(pretraindir, prefix + '.csv'))
# do plotting
waterfall(result, scale_y='log10', offset_y=0.0)
plt.tight_layout()
plt.savefig(os.path.join(pretraindir, prefix + '_waterfall.pdf'))
if result.problem.dim_full < 2e3:
parameters(result)
plt.tight_layout()
plt.savefig(os.path.join(pretraindir, prefix + '_parameters.pdf'))
range(len(problem.x_names))]
opt_result['x'] = x_sorted
result.optimize_result.append(opt_result)
result.optimize_result.sort()
prefix = '__'.join([MODEL, DATA, str(N_HIDDEN), OPTIMIZER])
waterfall(result, scale_y='log10', offset_y=0.0)
plot_and_save_fig(prefix + '__waterfall.pdf')
optimizer_history(result, scale_y='log10')
plot_and_save_fig(prefix + '__optimizer_trace.pdf')
parameters(result)
plot_and_save_fig(prefix + '__parameters.pdf')
optimizer_convergence(result)
plot_and_save_fig(prefix + '__optimizer_convergence.pdf')
fig_embedding, axes_embedding = plt.subplots(1, N_STARTS,
figsize=(18.5, 10.5))
embedding_fun = theano.function(
[mae.encoder_pars],
mae.encode(mae.encoder_pars)
)
inflate_fun = theano.function(
[mae.encoder_pars],
mae.encode_params(mae.encoder_pars)
'color': [0.2, 0.4, 1., 1.],
'legend': 'second optimum'
}
ref = visualize.create_references(ref)
# new waterfall plot with reference point for second optimum
visualize.waterfall(result1_dogleg,
size=(15, 6),
scale_y='lin',
y_limits=[-1, 101],
reference=ref,
colors=[0., 0., 0., 1.])
###### Visulaize Parameters
visualize.parameters([result1_bfgs, result1_tnc],
legends=['L-BFGS-B', 'TNC'],
balance_alpha=False)
visualize.parameters(result1_dogleg,
legends='dogleg',
reference=ref,
size=(15, 10),
start_indices=[0, 1, 2, 3, 4, 5],
balance_alpha=False)
df = result1_tnc.optimize_result.as_dataframe(
['fval', 'n_fval', 'n_grad', 'n_hess', 'n_res', 'n_sres', 'time'])
df.head()
###### Optimizer History
# plot one list of waterfalls
visualize.optimizer_history([result1_bfgs, result1_tnc],