def test_lnprob_calculates_multi_phase_probability_for_success(datasets_db):
"""lnprob() successfully calculates the probability for equilibrium """
dbf = Database.from_string(CU_MG_TDB, fmt='tdb')
datasets_db.insert(CU_MG_DATASET_ZPF_WORKING)
comps = ['CU', 'MG', 'VA']
phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'LAVES_C15', 'CUMG2']
param = 'VV0001'
orig_val = dbf.symbols[param].args[0].expr
initial_params = {param: orig_val}
zpf_kwargs = {
'zpf_data': get_zpf_data(dbf, comps, phases, datasets_db,
initial_params),
'data_weight': 1.0,
}
opt = EmceeOptimizer(dbf)
res = opt.predict([10],
prior_rvs=[rv_zero()],
symbols_to_fit=[param],
zpf_kwargs=zpf_kwargs)
assert np.isreal(res)
assert np.isclose(res, -31.309645520830344, rtol=1e-4)
res_2 = opt.predict([10000000],
prior_rvs=[rv_zero()],
symbols_to_fit=[param],
zpf_kwargs=zpf_kwargs)
assert not np.isclose(res_2, -31.309645520830344, rtol=1e-6)
def test_lnprob_calculates_multi_phase_probability_for_success(datasets_db):
"""lnprob() successfully calculates the probability for equilibrium """
dbf = Database.from_string(CU_MG_TDB, fmt='tdb')
datasets_db.insert(CU_MG_DATASET_ZPF_WORKING)
comps = ['CU', 'MG', 'VA']
phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'LAVES_C15', 'CUMG2']
param = 'VV0001'
orig_val = dbf.symbols[param].args[0].expr
models = instantiate_models(dbf, comps, phases, parameters={param: orig_val})
eq_callables = build_callables(dbf, comps, phases, models, parameter_symbols=[param],
output='GM', build_gradients=True, build_hessians=False,
additional_statevars={v.N, v.P, v.T})
zpf_kwargs = {
'dbf': dbf, 'phases': phases, 'zpf_data': get_zpf_data(comps, phases, datasets_db),
'phase_models': models, 'callables': eq_callables,
'data_weight': 1.0,
}
opt = EmceeOptimizer(dbf)
res = opt.predict([10], prior_rvs=[rv_zero()], symbols_to_fit=[param], zpf_kwargs=zpf_kwargs)
assert np.isreal(res)
assert np.isclose(res, -31.309645520830344, rtol=1e-6)
res_2 = opt.predict([10000000], prior_rvs=[rv_zero()], symbols_to_fit=[param], zpf_kwargs=zpf_kwargs)
assert not np.isclose(res_2, -31.309645520830344, rtol=1e-6)
def test_emcee_opitmizer_can_restart(datasets_db):
"""A restart trace can be passed to the Emcee optimizer """
dbf = Database.from_string(CU_MG_TDB, fmt='tdb')
datasets_db.insert(CU_MG_DATASET_ZPF_WORKING)
param = 'VV0001'
opt = EmceeOptimizer(dbf)
restart_tr = -4*np.ones((2, 10, 1)) # 2 chains, 10 iterations, 1 parameter
opt.fit([param], datasets_db, iterations=1, chains_per_parameter=2, restart_trace=restart_tr)
assert opt.sampler.chain.shape == (2, 1, 1)
def test_lnprob_does_not_raise_on_ValueError(datasets_db):
"""lnprob() should catch ValueError raised by equilibrium and return -np.inf"""
dbf = Database.from_string(CU_MG_TDB, fmt='tdb')
opt = EmceeOptimizer(dbf)
comps = ['CU', 'MG', 'VA']
phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'LAVES_C15', 'CUMG2']
datasets_db.insert(CU_MG_DATASET_ZPF_WORKING)
zpf_kwargs = {'dbf': dbf, 'phases': phases, 'zpf_data': get_zpf_data(comps, phases, datasets_db), 'data_weight': 1.0}
res = opt.predict([10], prior_rvs=[rv_zero()], symbols_to_fit=['VV0001'], zpf_kwargs=zpf_kwargs)
assert np.isneginf(res)
def test_parameter_initialization():
"""Determinisitically generated parameters should match."""
initial_parameters = np.array([1, 10, 100, 1000])
opt = EmceeOptimizer(Database())
deterministic_params = opt.initialize_new_chains(initial_parameters, 1, 0.10, deterministic=True)
expected_parameters = np.array([
[9.81708401e-01, 9.39027722e+00, 1.08016748e+02, 9.13512881e+02],
[1.03116874, 9.01412995, 112.79594345, 916.44725799],
[1.00664662e+00, 1.07178898e+01, 9.63696718e+01, 1.36872292e+03],
[1.07642366e+00, 1.16413520e+01, 8.71742457e+01, 9.61836382e+02]])
assert np.all(np.isclose(deterministic_params, expected_parameters))
def test_equilibrium_thermochemical_correct_probability(datasets_db):
"""Integration test for equilibrium thermochemical error."""
dbf = Database(CU_MG_TDB)
opt = EmceeOptimizer(dbf)
datasets_db.insert(CU_MG_EQ_HMR_LIQUID)
ctx = setup_context(dbf, datasets_db, ['VV0017'])
ctx.update(opt.get_priors(None, ['VV0017'], [0]))
prob = opt.predict(np.array([-31626.6]), **ctx)
expected_prob = norm(loc=0, scale=500).logpdf([-31626.6 * 0.5 * 0.5]).sum()
assert np.isclose(prob, expected_prob)
# change to -40000
prob = opt.predict(np.array([-40000], dtype=np.float_), **ctx)
expected_prob = norm(loc=0, scale=500).logpdf([-40000 * 0.5 * 0.5]).sum()
assert np.isclose(prob, expected_prob)
def test_emcee_optimizer_can_restart(datasets_db):
"""A restart trace can be passed to the Emcee optimizer """
dbf = Database.from_string(CU_MG_TDB, fmt='tdb')
datasets_db.insert(CU_MG_DATASET_ZPF_WORKING)
param = 'VV0001'
opt = EmceeOptimizer(dbf)
restart_tr = np.array([[[-4], [-3], [-2], [-1], [0], [1], [2], [3], [4],
[5]],
[[-6], [-4], [-2], [0], [2], [4], [6], [8], [10],
[12]]]) # 2 chains, 10 iterations, 1 parameter
opt.fit([param],
datasets_db,
iterations=1,
chains_per_parameter=2,
restart_trace=restart_tr)
assert opt.sampler.chain.shape == (2, 1, 1)
def test_equilibrium_thermochemical_context_is_pickleable(datasets_db):
"""Test that the context for equilibrium thermochemical data is pickleable"""
datasets_db.insert(CU_MG_EQ_HMR_LIQUID)
dbf = Database(CU_MG_TDB)
symbols_to_fit = database_symbols_to_fit(dbf)
initial_guess = np.array([unpack_piecewise(dbf.symbols[s]) for s in symbols_to_fit])
prior_dict = EmceeOptimizer.get_priors(None, symbols_to_fit, initial_guess)
ctx = setup_context(dbf, datasets_db)
ctx.update(prior_dict)
ctx_pickle = pickle.dumps(ctx)
ctx_unpickled = pickle.loads(ctx_pickle)
regular_predict = EmceeOptimizer.predict(initial_guess, **ctx)
unpickle_predict = EmceeOptimizer.predict(initial_guess, **ctx_unpickled)
assert np.isclose(regular_predict, unpickle_predict)
def test_zpf_context_is_pickleable(datasets_db):
"""Test that the context for ZPF data is pickleable"""
datasets_db.insert(CU_MG_DATASET_ZPF_ZERO_ERROR)
dbf = Database(CU_MG_TDB)
symbols_to_fit = database_symbols_to_fit(dbf)
initial_guess = np.array([unpack_piecewise(dbf.symbols[s]) for s in symbols_to_fit])
prior_dict = EmceeOptimizer.get_priors(None, symbols_to_fit, initial_guess)
ctx = setup_context(dbf, datasets_db)
ctx.update(prior_dict)
ctx_pickle = pickle.dumps(ctx)
ctx_unpickled = pickle.loads(ctx_pickle)
regular_predict = EmceeOptimizer.predict(initial_guess, **ctx)
unpickle_predict = EmceeOptimizer.predict(initial_guess, **ctx_unpickled)
assert np.isclose(regular_predict, unpickle_predict)
def test_lnprob_calculates_single_phase_probability_for_success(datasets_db):
"""lnprob() succesfully calculates the probability from single phase data"""
dbf = Database.from_string(CU_MG_TDB_FCC_ONLY, fmt='tdb')
datasets_db.insert(CU_MG_HM_MIX_SINGLE_FCC_A1)
comps = ['CU', 'MG', 'VA']
phases = ['FCC_A1']
param = 'VV0003'
orig_val = -14.0865
opt = EmceeOptimizer(dbf)
thermochemical_data = get_thermochemical_data(dbf,
comps,
phases,
datasets_db,
symbols_to_fit=[param])
thermochemical_kwargs = {
'dbf': dbf,
'comps': comps,
'thermochemical_data': thermochemical_data
}
res_orig = opt.predict([orig_val],
prior_rvs=[rv_zero()],
symbols_to_fit=[param],
thermochemical_kwargs=thermochemical_kwargs)
assert np.isreal(res_orig)
assert np.isclose(res_orig, -9.119484935312146, rtol=1e-6)
res_10 = opt.predict([10],
prior_rvs=[rv_zero()],
symbols_to_fit=[param],
thermochemical_kwargs=thermochemical_kwargs)
assert np.isreal(res_10)
assert np.isclose(res_10, -9.143559131626864, rtol=1e-6)
res_1e5 = opt.predict([1e5],
prior_rvs=[rv_zero()],
symbols_to_fit=[param],
thermochemical_kwargs=thermochemical_kwargs)
assert np.isreal(res_1e5)
assert np.isclose(res_1e5, -1359.1335466316268, rtol=1e-6)
def test_lnprob_calculates_associate_tdb(datasets_db):
"""lnprob() successfully calculates the probability for equilibrium """
dbf = Database.from_string(CU_MG_TDB_ASSOC, fmt='tdb')
datasets_db.insert(CU_MG_DATASET_ZPF_WORKING)
comps = ['CU', 'MG', 'VA']
phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'LAVES_C15', 'CUMG2']
param = 'VV0001'
orig_val = dbf.symbols[param].args[0]
initial_params = {param: orig_val}
zpf_kwargs = {
'zpf_data': get_zpf_data(dbf, comps, phases, datasets_db,
initial_params),
'data_weight': 1.0,
}
opt = EmceeOptimizer(dbf)
res = opt.predict([10],
prior_rvs=[rv_zero()],
symbols_to_fit=[param],
zpf_kwargs=zpf_kwargs)
assert np.isreal(res)
assert not np.isinf(res)
assert np.isclose(res, -31.309645520830344, rtol=1e-6)
# The purpose of this part is to test that the driving forces (and probability)
# are different than the case of VV0001 = 10.
res_2 = opt.predict([-10000000],
prior_rvs=[rv_zero()],
symbols_to_fit=[param],
zpf_kwargs=zpf_kwargs)
assert np.isreal(res_2)
assert not np.isinf(res_2)
# Accept a large rtol becuase the results should be _very_ different
assert not np.isclose(res_2, -31.309645520830344, rtol=1e-2)
def run_espei(run_settings):
"""Wrapper around the ESPEI fitting procedure, taking only a settings dictionary.
Parameters
----------
run_settings : dict
Dictionary of input settings
Returns
-------
Either a Database (for generate parameters only) or a tuple of (Database, sampler)
"""
run_settings = get_run_settings(run_settings)
system_settings = run_settings['system']
output_settings = run_settings['output']
generate_parameters_settings = run_settings.get('generate_parameters')
mcmc_settings = run_settings.get('mcmc')
# handle verbosity
verbosity = {
0: logging.WARNING,
1: logging.INFO,
2: TRACE,
3: logging.DEBUG
}
logging.basicConfig(level=verbosity[output_settings['verbosity']],
filename=output_settings['logfile'])
log_version_info()
# load datasets and handle i/o
logging.log(TRACE, 'Loading and checking datasets.')
dataset_path = system_settings['datasets']
datasets = load_datasets(sorted(recursive_glob(dataset_path, '*.json')))
if len(datasets.all()) == 0:
logging.warning(
'No datasets were found in the path {}. This should be a directory containing dataset files ending in `.json`.'
.format(dataset_path))
apply_tags(datasets, system_settings.get('tags', dict()))
add_ideal_exclusions(datasets)
logging.log(TRACE, 'Finished checking datasets')
with open(system_settings['phase_models']) as fp:
phase_models = json.load(fp)
if generate_parameters_settings is not None:
refdata = generate_parameters_settings['ref_state']
excess_model = generate_parameters_settings['excess_model']
ridge_alpha = generate_parameters_settings['ridge_alpha']
aicc_penalty = generate_parameters_settings['aicc_penalty_factor']
input_dbf = generate_parameters_settings.get('input_db', None)
if input_dbf is not None:
input_dbf = Database(input_dbf)
dbf = generate_parameters(
phase_models,
datasets,
refdata,
excess_model,
ridge_alpha=ridge_alpha,
dbf=input_dbf,
aicc_penalty_factor=aicc_penalty,
)
dbf.to_file(output_settings['output_db'], if_exists='overwrite')
if mcmc_settings is not None:
tracefile = output_settings['tracefile']
probfile = output_settings['probfile']
# check that the MCMC output files do not already exist
# only matters if we are actually running MCMC
if os.path.exists(tracefile):
raise OSError(
'Tracefile "{}" exists and would be overwritten by a new run. Use the ``output.tracefile`` setting to set a different name.'
.format(tracefile))
if os.path.exists(probfile):
raise OSError(
'Probfile "{}" exists and would be overwritten by a new run. Use the ``output.probfile`` setting to set a different name.'
.format(probfile))
# scheduler setup
if mcmc_settings['scheduler'] == 'dask':
_raise_dask_work_stealing() # check for work-stealing
from distributed import LocalCluster
cores = mcmc_settings.get('cores', multiprocessing.cpu_count())
if (cores > multiprocessing.cpu_count()):
cores = multiprocessing.cpu_count()
logging.warning(
"The number of cores chosen is larger than available. "
"Defaulting to run on the {} available cores.".format(
cores))
# TODO: make dask-scheduler-verbosity a YAML input so that users can debug. Should have the same log levels as verbosity
scheduler = LocalCluster(n_workers=cores,
threads_per_worker=1,
processes=True,
memory_limit=0)
client = ImmediateClient(scheduler)
client.run(logging.basicConfig,
level=verbosity[output_settings['verbosity']],
filename=output_settings['logfile'])
logging.info("Running with dask scheduler: %s [%s cores]" %
(scheduler, sum(client.ncores().values())))
try:
bokeh_server_info = client.scheduler_info(
)['services']['bokeh']
logging.info(
"bokeh server for dask scheduler at localhost:{}".format(
bokeh_server_info))
except KeyError:
logging.info("Install bokeh to use the dask bokeh server.")
elif mcmc_settings['scheduler'] == 'None':
client = None
logging.info(
"Not using a parallel scheduler. ESPEI is running MCMC on a single core."
)
else: # we were passed a scheduler file name
_raise_dask_work_stealing() # check for work-stealing
client = ImmediateClient(scheduler_file=mcmc_settings['scheduler'])
client.run(logging.basicConfig,
level=verbosity[output_settings['verbosity']],
filename=output_settings['logfile'])
logging.info("Running with dask scheduler: %s [%s cores]" %
(client.scheduler, sum(client.ncores().values())))
# get a Database
if mcmc_settings.get('input_db'):
dbf = Database(mcmc_settings.get('input_db'))
# load the restart trace if needed
if mcmc_settings.get('restart_trace'):
restart_trace = np.load(mcmc_settings.get('restart_trace'))
else:
restart_trace = None
# load the remaining mcmc fitting parameters
iterations = mcmc_settings.get('iterations')
save_interval = mcmc_settings.get('save_interval')
chains_per_parameter = mcmc_settings.get('chains_per_parameter')
chain_std_deviation = mcmc_settings.get('chain_std_deviation')
deterministic = mcmc_settings.get('deterministic')
prior = mcmc_settings.get('prior')
data_weights = mcmc_settings.get('data_weights')
syms = mcmc_settings.get('symbols')
# set up and run the EmceeOptimizer
optimizer = EmceeOptimizer(dbf, scheduler=client)
optimizer.save_interval = save_interval
all_symbols = syms if syms is not None else database_symbols_to_fit(
dbf)
optimizer.fit(all_symbols,
datasets,
prior=prior,
iterations=iterations,
chains_per_parameter=chains_per_parameter,
chain_std_deviation=chain_std_deviation,
deterministic=deterministic,
restart_trace=restart_trace,
tracefile=tracefile,
probfile=probfile,
mcmc_data_weights=data_weights)
optimizer.commit()
optimizer.dbf.to_file(output_settings['output_db'],
if_exists='overwrite')
# close the scheduler, if possible
if hasattr(client, 'close'):
client.close()
return optimizer.dbf, optimizer.sampler
return dbf
def mcmc_fit(dbf,
datasets,
iterations=1000,
save_interval=1,
chains_per_parameter=2,
chain_std_deviation=0.1,
scheduler=None,
tracefile=None,
probfile=None,
restart_trace=None,
deterministic=True,
prior=None,
mcmc_data_weights=None):
"""
Run MCMC via the EmceeOptimizer class
Parameters
----------
dbf : Database
A pycalphad Database to fit with symbols to fit prefixed with `VV`
followed by a number, e.g. `VV0001`
datasets : PickleableTinyDB
A database of single- and multi-phase data to fit
iterations : int
Number of trace iterations to calculate in MCMC. Default is 1000 iterations.
save_interval :int
interval of iterations to save the tracefile and probfile
chains_per_parameter : int
number of chains for each parameter. Must be an even integer greater or
equal to 2. Defaults to 2.
chain_std_deviation : float
standard deviation of normal for parameter initialization as a fraction
of each parameter. Must be greater than 0. Default is 0.1, which is 10%.
scheduler : callable
Scheduler to use with emcee. Must implement a map method.
tracefile : str
filename to store the trace with NumPy.save. Array has shape
(chains, iterations, parameters)
probfile : str
filename to store the log probability with NumPy.save. Has shape (chains, iterations)
restart_trace : np.ndarray
ndarray of the previous trace. Should have shape (chains, iterations, parameters)
deterministic : bool
If True, the emcee sampler will be seeded to give deterministic sampling
draws. This will ensure that the runs with the exact same database,
chains_per_parameter, and chain_std_deviation (or restart_trace) will
produce exactly the same results.
prior : str
Prior to use to generate priors. Defaults to 'zero', which keeps
backwards compatibility. Can currently choose 'normal', 'uniform',
'triangular', or 'zero'.
mcmc_data_weights : dict
Dictionary of weights for each data type, e.g. {'ZPF': 20, 'HM': 2}
"""
warnings.warn("The mcmc convenience function will be removed in ESPEI 0.8")
all_symbols = database_symbols_to_fit(dbf)
optimizer = EmceeOptimizer(dbf, scheduler=scheduler)
optimizer.save_interval = save_interval
optimizer.fit(all_symbols,
datasets,
prior=prior,
iterations=iterations,
chains_per_parameter=chains_per_parameter,
chain_std_deviation=chain_std_deviation,
deterministic=deterministic,
restart_trace=restart_trace,
tracefile=tracefile,
probfile=probfile,
mcmc_data_weights=mcmc_data_weights)
optimizer.commit()
return optimizer.dbf, optimizer.sampler
