def test_3(self):
""" This test just locks in the evaluation of the criterion function
for the original Keane & Wolpin data.
"""
# Sample one task
resources = ['kw_data_one.ini', 'kw_data_two.ini', 'kw_data_three.ini']
fname = np.random.choice(resources)
# Select expected result
rslt = None
if 'one' in fname:
rslt = 0.261487735867433
elif 'two' in fname:
rslt = 1.126138097174159
elif 'three' in fname:
rslt = 1.895699121131644
# Evaluate criterion function at true values.
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/' + fname)
respy_obj.unlock()
respy_obj.set_attr('maxfun', 0)
respy_obj.lock()
simulate(respy_obj)
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, rslt)
def test_1(self):
""" Compare results from the RESTUD program and the RESPY package.
"""
# Impose some constraints on the initialization file which ensures that
# the problem can be solved by the RESTUD code. The code is adjusted to
# run with zero draws.
constraints = dict()
constraints['edu'] = (10, 20)
constraints['is_deterministic'] = True
# Generate random initialization file. The RESTUD code uses the same
# random draws for the solution and simulation of the model. Thus,
# the number of draws is required to be less or equal to the number
# of agents.
init_dict = generate_random_dict(constraints)
num_agents_sim = init_dict['SIMULATION']['agents']
num_draws_emax = init_dict['SOLUTION']['draws']
if num_draws_emax < num_agents_sim:
init_dict['SOLUTION']['draws'] = num_agents_sim
print_init_dict(init_dict)
# Indicate RESTUD code the special case of zero disturbance.
open('.restud.testing.scratch', 'a').close()
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
# This flag aligns the random components between the RESTUD program and
# RESPY package. The existence of the file leads to the RESTUD program
# to write out the random components.
model_paras, edu_start, edu_max, num_agents_sim, num_periods, \
num_draws_emax, delta = \
dist_class_attributes(respy_obj,
'model_paras', 'edu_start', 'edu_max', 'num_agents_sim',
'num_periods', 'num_draws_emax', 'delta')
transform_respy_to_restud(model_paras, edu_start, edu_max,
num_agents_sim, num_periods, num_draws_emax,
delta)
# Solve model using RESTUD code.
cmd = TEST_RESOURCES_DIR + '/kw_dp3asim'
subprocess.check_call(cmd, shell=True)
# Solve model using RESPY package.
simulate(respy_obj)
# Compare the simulated datasets generated by the programs.
py = pd.DataFrame(
np.array(np.genfromtxt('data.respy.dat', missing_values='.'),
ndmin=2)[:, -4:])
fort = pd.DataFrame(
np.array(np.genfromtxt('ftest.txt', missing_values='.'),
ndmin=2)[:, -4:])
assert_frame_equal(py, fort)
def test_5(self):
""" Test the scripts.
"""
# Constraints that ensure that two alternative initialization files
# can be used for the same simulated data.
for _ in range(10):
constr = dict()
constr['periods'] = np.random.randint(1, 4)
constr['agents'] = np.random.randint(5, 100)
constr['is_estimation'] = True
constr['edu'] = (7, 15)
# Simulate a dataset
generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
# Create output to process a baseline.
respy_obj.unlock()
respy_obj.set_attr('maxfun', 0)
respy_obj.lock()
estimate(respy_obj)
# Potentially evaluate at different points.
generate_init(constr)
init_file = 'test.respy.ini'
file_sim = 'sim.respy.dat'
gradient = np.random.choice([True, False])
single = np.random.choice([True, False])
resume = np.random.choice([True, False])
update = np.random.choice([True, False])
action = np.random.choice(['fix', 'free', 'value'])
num_draws = np.random.randint(1, 20)
# The set of identifiers is a little complicated as we only allow
# sampling of the diagonal terms of the covariance matrix. Otherwise,
# we sometimes run into the problem of very ill conditioned matrices
# resulting in a failed Cholesky decomposition.
set_ = list(range(16)) + [16, 18, 21, 25]
identifiers = np.random.choice(set_, num_draws, replace=False)
values = np.random.uniform(size=num_draws)
scripts_estimate(resume, single, init_file, gradient)
scripts_update(init_file)
# The error can occur as the RESPY package is actually running an
# estimation step that can result in very ill-conditioned covariance
# matrices.
try:
scripts_simulate(update, init_file, file_sim, None)
scripts_modify(identifiers, values, action, init_file)
except np.linalg.linalg.LinAlgError:
pass
def test_5(self):
""" Test the scripts.
"""
# Constraints that ensure that two alternative initialization files
# can be used for the same simulated data.
for _ in range(10):
constr = dict()
constr['periods'] = np.random.randint(1, 4)
constr['agents'] = np.random.randint(5, 100)
constr['is_estimation'] = True
constr['edu'] = (7, 15)
# Simulate a dataset
generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
# Create output to process a baseline.
respy_obj.unlock()
respy_obj.set_attr('maxfun', 0)
respy_obj.lock()
estimate(respy_obj)
# Potentially evaluate at different points.
generate_init(constr)
init_file = 'test.respy.ini'
file_sim = 'sim.respy.dat'
gradient = np.random.choice([True, False])
single = np.random.choice([True, False])
resume = np.random.choice([True, False])
update = np.random.choice([True, False])
action = np.random.choice(['fix', 'free', 'value'])
num_draws = np.random.randint(1, 20)
# The set of identifiers is a little complicated as we only allow
# sampling of the diagonal terms of the covariance matrix. Otherwise,
# we sometimes run into the problem of very ill conditioned matrices
# resulting in a failed Cholesky decomposition.
set_ = list(range(16)) + [16, 18, 21, 25]
identifiers = np.random.choice(set_, num_draws, replace=False)
values = np.random.uniform(size=num_draws)
scripts_estimate(resume, single, init_file, gradient)
scripts_update(init_file)
# The error can occur as the RESPY package is actually running an
# estimation step that can result in very ill-conditioned covariance
# matrices.
try:
scripts_simulate(update, init_file, file_sim, None)
scripts_modify(identifiers, values, action, init_file)
except np.linalg.linalg.LinAlgError:
pass
def run(spec_dict, fname, grid_slaves):
""" Run an estimation task that allows to get a sense of the scalability
of the code.
"""
dirname = fname.replace('.ini', '')
os.mkdir(dirname)
os.chdir(dirname)
respy_obj = respy.RespyCls(SPEC_DIR + fname)
respy_obj.unlock()
respy_obj.set_attr('is_debug', False)
respy_obj.set_attr('file_est', '../data.respy.dat')
for key_ in spec_dict.keys():
respy_obj.set_attr(key_, spec_dict[key_])
respy_obj.lock()
maxfun = respy_obj.get_attr('maxfun')
min_slave = min(grid_slaves)
# Simulate the baseline dataset, which is used regardless of the number
# of slaves.
respy.simulate(respy_obj)
respy_obj.write_out()
# Iterate over the grid of requested slaves.
for num_slaves in grid_slaves:
dirname = '{:}'.format(num_slaves)
os.mkdir(dirname)
os.chdir(dirname)
respy_obj.unlock()
respy_obj.set_attr('num_procs', num_slaves + 1)
if num_slaves > 1:
respy_obj.set_attr('is_parallel', True)
else:
respy_obj.set_attr('is_parallel', False)
respy_obj.lock()
respy_obj.write_out()
start_time = datetime.now()
respy.estimate(respy_obj)
finish_time = datetime.now()
if num_slaves == min_slave:
duration_baseline = finish_time - start_time
num_evals = get_actual_evaluations()
os.chdir('../')
record_information(start_time, finish_time, num_slaves, maxfun,
duration_baseline, num_evals, min_slave)
os.chdir('../')
def test_5(self):
""" This test ensures that the logging looks exactly the same for the
different versions.
"""
max_draws = np.random.randint(10, 300)
# Generate random initialization file
constr = dict()
constr['flag_parallelism'] = False
constr['max_draws'] = max_draws
constr['flag_interpolation'] = False
constr['maxfun'] = 0
# Generate random initialization file
init_dict = generate_init(constr)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
# Iterate over alternative implementations
base_sol_log, base_est_info_log, base_est_log = None, None, None
base_sim_log = None
num_periods = init_dict['BASICS']['periods']
write_draws(num_periods, max_draws)
for version in ['FORTRAN', 'PYTHON']:
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
simulate(respy_obj)
estimate(respy_obj)
# Check for identical logging
if base_sol_log is None:
base_sol_log = open('sol.respy.log', 'r').read()
assert open('sol.respy.log', 'r').read() == base_sol_log
# Check for identical logging
if base_sim_log is None:
base_sim_log = open('sim.respy.log', 'r').read()
assert open('sim.respy.log', 'r').read() == base_sim_log
if base_est_info_log is None:
base_est_info_log = open('est.respy.info', 'r').read()
assert open('est.respy.info', 'r').read() == base_est_info_log
if base_est_log is None:
base_est_log = open('est.respy.log', 'r').readlines()
compare_est_log(base_est_log)
def test_6(self):
""" This test ensures that the logging looks exactly the same for the
different versions.
"""
max_draws = np.random.randint(10, 300)
# Generate random initialization file
constr = dict()
constr['flag_parallelism'] = False
constr['max_draws'] = max_draws
constr['flag_interpolation'] = False
constr['maxfun'] = 0
# Generate random initialization file
init_dict = generate_init(constr)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
# Iterate over alternative implementations
base_sol_log, base_est_info_log, base_est_log = None, None, None
base_sim_log = None
num_periods = init_dict['BASICS']['periods']
write_draws(num_periods, max_draws)
for version in ['FORTRAN', 'PYTHON']:
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
simulate(respy_obj)
estimate(respy_obj)
# Check for identical logging
if base_sol_log is None:
base_sol_log = open('sol.respy.log', 'r').read()
assert open('sol.respy.log', 'r').read() == base_sol_log
# Check for identical logging
if base_sim_log is None:
base_sim_log = open('sim.respy.log', 'r').read()
assert open('sim.respy.log', 'r').read() == base_sim_log
if base_est_info_log is None:
base_est_info_log = open('est.respy.info', 'r').read()
assert open('est.respy.info', 'r').read() == base_est_info_log
if base_est_log is None:
base_est_log = open('est.respy.log', 'r').readlines()
compare_est_log(base_est_log)
def test_1(self):
""" Compare results from the RESTUD program and the RESPY package.
"""
# Impose some constraints on the initialization file which ensures that
# the problem can be solved by the RESTUD code. The code is adjusted to
# run with zero draws.
constraints = dict()
constraints['edu'] = (10, 20)
constraints['is_deterministic'] = True
# Generate random initialization file. The RESTUD code uses the same
# random draws for the solution and simulation of the model. Thus,
# the number of draws is required to be less or equal to the number
# of agents.
init_dict = generate_random_dict(constraints)
num_agents_sim = init_dict['SIMULATION']['agents']
num_draws_emax = init_dict['SOLUTION']['draws']
if num_draws_emax < num_agents_sim:
init_dict['SOLUTION']['draws'] = num_agents_sim
print_init_dict(init_dict)
# Indicate RESTUD code the special case of zero disturbance.
open('.restud.testing.scratch', 'a').close()
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
# This flag aligns the random components between the RESTUD program and
# RESPY package. The existence of the file leads to the RESTUD program
# to write out the random components.
model_paras, edu_start, edu_max, num_agents_sim, num_periods, \
num_draws_emax, delta = \
dist_class_attributes(respy_obj,
'model_paras', 'edu_start', 'edu_max', 'num_agents_sim',
'num_periods', 'num_draws_emax', 'delta')
transform_respy_to_restud(model_paras, edu_start, edu_max,
num_agents_sim, num_periods, num_draws_emax, delta)
# Solve model using RESTUD code.
cmd = TEST_RESOURCES_DIR + '/kw_dp3asim'
subprocess.check_call(cmd, shell=True)
# Solve model using RESPY package.
simulate(respy_obj)
# Compare the simulated datasets generated by the programs.
py = pd.DataFrame(np.array(np.genfromtxt('data.respy.dat',
missing_values='.'), ndmin=2)[:, -4:])
fort = pd.DataFrame(np.array(np.genfromtxt('ftest.txt',
missing_values='.'), ndmin=2)[:, -4:])
assert_frame_equal(py, fort)
def test_1(self):
""" Testing whether random model specifications can be simulated
and processed.
"""
# Generate random initialization file
generate_init()
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
process(respy_obj)
def test_1(self):
""" Testing whether random model specifications can be simulated
and processed.
"""
# Generate random initialization file
generate_init()
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
process(respy_obj)
def simulate_specification(respy_obj, subdir, update, paras=None):
""" Simulate results to assess the estimation performance. Note that we do
not update the object that is passed in.
"""
os.mkdir(subdir)
os.chdir(subdir)
respy_copy = deepcopy(respy_obj)
if update:
assert (paras is not None)
respy_copy.update_model_paras(paras)
respy_copy.write_out()
respy.simulate(respy_copy)
os.chdir('../')
def test_6(self):
""" Test short estimation tasks.
"""
# Constraints that ensures that the maximum number of iterations and
# the number of function evaluations is set to the minimum values of
# one.
constr = dict()
constr['is_estimation'] = True
generate_init(constr)
# Run estimation task.
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
estimate(respy_obj)
def simulate_specification(respy_obj, subdir, update, paras=None):
""" Simulate results to assess the estimation performance. Note that we do
not update the object that is passed in.
"""
os.mkdir(subdir)
os.chdir(subdir)
respy_copy = deepcopy(respy_obj)
if update:
assert (paras is not None)
respy_copy.update_model_paras(paras)
respy_copy.write_out()
respy.simulate(respy_copy)
os.chdir('../')
def test_6(self):
""" Test short estimation tasks.
"""
# Constraints that ensures that the maximum number of iterations and
# the number of function evaluations is set to the minimum values of
# one.
constr = dict()
constr['is_estimation'] = True
generate_init(constr)
# Run estimation task.
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
estimate(respy_obj)
def test_2(self):
""" This test ensures that the evaluation of the criterion function
at the starting value is identical between the different versions.
"""
max_draws = np.random.randint(10, 100)
# Generate random initialization file
constr = dict()
constr['flag_parallelism'] = False
constr['max_draws'] = max_draws
constr['flag_interpolation'] = False
constr['maxfun'] = 0
# Generate random initialization file
init_dict = generate_init(constr)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
# Simulate a dataset
simulate(respy_obj)
# Iterate over alternative implementations
base_x, base_val = None, None
num_periods = init_dict['BASICS']['periods']
write_draws(num_periods, max_draws)
for version in ['FORTRAN', 'PYTHON']:
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
x, val = estimate(respy_obj)
# Check for the returned parameters.
if base_x is None:
base_x = x
np.testing.assert_allclose(base_x, x)
# Check for the value of the criterion function.
if base_val is None:
base_val = val
np.testing.assert_allclose(base_val, val)
def test_2(self):
""" This test ensures that the evaluation of the criterion function
at the starting value is identical between the different versions.
"""
max_draws = np.random.randint(10, 100)
# Generate random initialization file
constr = dict()
constr['flag_parallelism'] = False
constr['max_draws'] = max_draws
constr['flag_interpolation'] = False
constr['maxfun'] = 0
# Generate random initialization file
init_dict = generate_init(constr)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
# Simulate a dataset
simulate(respy_obj)
# Iterate over alternative implementations
base_x, base_val = None, None
num_periods = init_dict['BASICS']['periods']
write_draws(num_periods, max_draws)
for version in ['FORTRAN', 'PYTHON']:
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
x, val = estimate(respy_obj)
# Check for the returned parameters.
if base_x is None:
base_x = x
np.testing.assert_allclose(base_x, x)
# Check for the value of the criterion function.
if base_val is None:
base_val = val
np.testing.assert_allclose(base_val, val)
def test_4(self):
""" Test the solution of deterministic model with ambiguity and
interpolation. This test has the same result as in the absence of
random variation in payoffs, it does not matter whether the
environment is ambiguous or not.
"""
# Solve specified economy
for version in ['FORTRAN', 'PYTHON']:
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_fifth.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
respy_obj = simulate(respy_obj)
# Assess expected future value
val = respy_obj.get_attr('periods_emax')[0, :1]
np.testing.assert_allclose(val, 88750)
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, -1.0)
def test_3(self):
""" Test the solution of deterministic model with ambiguity and
interpolation. This test has the same result as in the absence of
random variation in payoffs, it does not matter whether the
environment is ambiguous or not.
"""
# Solve specified economy
for version in ['FORTRAN', 'PYTHON']:
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_fifth.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
respy_obj = simulate(respy_obj)
# Assess expected future value
val = respy_obj.get_attr('periods_emax')[0, :1]
np.testing.assert_allclose(val, 88750)
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, -1.0)
def test_3(self):
""" Testing whether the a simulated dataset and the evaluation of the
criterion function are the same for a tiny delta and a myopic agent.
"""
# Generate random initialization dictionary
constr = dict()
constr['maxfun'] = 0
generate_init(constr)
# Iterate over alternative discount rates.
base_data, base_val = None, None
for delta in [0.00, 0.000001]:
respy_obj = RespyCls('test.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('delta', delta)
respy_obj.lock()
simulate(respy_obj)
# This parts checks the equality of simulated dataset for the
# different versions of the code.
data_frame = pd.read_csv('data.respy.dat', delim_whitespace=True)
if base_data is None:
base_data = data_frame.copy()
assert_frame_equal(base_data, data_frame)
# This part checks the equality of an evaluation of the
# criterion function.
_, crit_val = estimate(respy_obj)
if base_val is None:
base_val = crit_val
np.testing.assert_allclose(base_val,
crit_val,
rtol=1e-03,
atol=1e-03)
def test_3(self):
""" Testing whether the a simulated dataset and the evaluation of the
criterion function are the same for a tiny delta and a myopic agent.
"""
# Generate random initialization dictionary
constr = dict()
constr['maxfun'] = 0
generate_init(constr)
# Iterate over alternative discount rates.
base_data, base_val = None, None
for delta in [0.00, 0.000001]:
respy_obj = RespyCls('test.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('delta', delta)
respy_obj.lock()
simulate(respy_obj)
# This parts checks the equality of simulated dataset for the
# different versions of the code.
data_frame = pd.read_csv('data.respy.dat', delim_whitespace=True)
if base_data is None:
base_data = data_frame.copy()
assert_frame_equal(base_data, data_frame)
# This part checks the equality of an evaluation of the
# criterion function.
_, crit_val = estimate(respy_obj)
if base_val is None:
base_val = crit_val
np.testing.assert_allclose(base_val, crit_val, rtol=1e-03, atol=1e-03)
def test_1(self):
""" Testing ten admissible realizations of state space for the first
three periods.
"""
# Generate constraint periods
constraints = dict()
constraints['periods'] = np.random.randint(3, 5)
# Generate random initialization file
generate_init(constraints)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Distribute class attributes
states_number_period = respy_obj.get_attr('states_number_period')
states_all = respy_obj.get_attr('states_all')
# The next hard-coded results assume that at least two more
# years of education are admissible.
edu_max = respy_obj.get_attr('edu_max')
edu_start = respy_obj.get_attr('edu_start')
if edu_max - edu_start < 2:
return
# The number of admissible states in the first three periods
for j, number_period in enumerate([1, 4, 13]):
assert (states_number_period[j] == number_period)
# The actual realizations of admissible states in period one
assert ((states_all[0, 0, :] == [0, 0, 0, 1]).all())
# The actual realizations of admissible states in period two
states = [[0, 0, 0, 0], [0, 0, 1, 1], [0, 1, 0, 0]]
states += [[1, 0, 0, 0]]
for j, state in enumerate(states):
assert ((states_all[1, j, :] == state).all())
# The actual realizations of admissible states in period three
states = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 1]]
states += [[0, 0, 2, 1], [0, 1, 0, 0], [0, 1, 1, 0]]
states += [[0, 1, 1, 1], [0, 2, 0, 0], [1, 0, 0, 0]]
states += [[1, 0, 1, 0], [1, 0, 1, 1], [1, 1, 0, 0]]
states += [[2, 0, 0, 0]]
for j, state in enumerate(states):
assert ((states_all[2, j, :] == state).all())
def scripts_simulate(update, init_file, file_sim, solved):
""" Wrapper for the estimation.
"""
# Read in baseline model specification.
if solved is not None:
respy_obj = pkl.load(open(solved, 'rb'))
else:
respy_obj = RespyCls(init_file)
# Update parametrization of the model if resuming from a previous
# estimation run.
if update:
respy_obj.update_model_paras(get_est_info()['paras_step'])
# Update file for output.
if file_sim is not None:
respy_obj.unlock()
respy_obj.set_attr('file_sim', file_sim)
respy_obj.lock()
# Optimize the criterion function.
simulate(respy_obj)
def scripts_simulate(update, init_file, file_sim, solved):
""" Wrapper for the estimation.
"""
# Read in baseline model specification.
if solved is not None:
respy_obj = pkl.load(open(solved, 'rb'))
else:
respy_obj = RespyCls(init_file)
# Update parametrization of the model if resuming from a previous
# estimation run.
if update:
respy_obj.update_model_paras(get_est_info()['paras_step'])
# Update file for output.
if file_sim is not None:
respy_obj.unlock()
respy_obj.set_attr('file_sim', file_sim)
respy_obj.lock()
# Optimize the criterion function.
simulate(respy_obj)
def test_2(self):
""" This test ensures that the record files are identical.
"""
# Generate random initialization file. The number of periods is
# higher than usual as only FORTRAN implementations are used to
# solve the random request. This ensures that also some cases of
# interpolation are explored.
constr = dict()
constr['version'] = 'FORTRAN'
constr['periods'] = np.random.randint(3, 10)
constr['maxfun'] = 0
init_dict = generate_random_dict(constr)
base_sol_log, base_est_info_log, base_est_log = None, None, None
for is_parallel in [False, True]:
init_dict['PARALLELISM']['flag'] = is_parallel
print_init_dict(init_dict)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
estimate(respy_obj)
# Check for identical records
if base_sol_log is None:
base_sol_log = open('sol.respy.log', 'r').read()
assert open('sol.respy.log', 'r').read() == base_sol_log
if base_est_info_log is None:
base_est_info_log = open('est.respy.info', 'r').read()
assert open('est.respy.info', 'r').read() == base_est_info_log
if base_est_log is None:
base_est_log = open('est.respy.log', 'r').readlines()
compare_est_log(base_est_log)
def test_5(self):
""" This test reproduces the results from evaluations of the
criterion function for previously analyzed scenarios.
"""
# Prepare setup
version = str(sys.version_info[0])
fname = 'test_vault_' + version + '.respy.pkl'
tests = pkl.load(open(TEST_RESOURCES_DIR + '/' + fname, 'rb'))
# We want this test to run even when not FORTRAN version is available.
while True:
idx = np.random.randint(0, len(tests))
init_dict, crit_val = tests[idx]
version = init_dict['PROGRAM']['version']
if not IS_FORTRAN and version == 'FORTRAN':
pass
else:
break
# In the case where no parallelism is available, we need to ensure
# that the request remains valid. This is fine as the disturbances
# are aligned across parallel and scalar implementation.
if not IS_PARALLEL:
init_dict['PARALLELISM']['flag'] = False
if not IS_FORTRAN:
init_dict['PROGRAM']['version'] = 'PYTHON'
print_init_dict(init_dict)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
_, val = estimate(respy_obj)
np.testing.assert_almost_equal(val, crit_val)
def test_4(self):
""" Test the evaluation of the criterion function for random
requests, not just at the true values.
"""
# Constraints that ensure that two alternative initialization files
# can be used for the same simulated data.
constr = dict()
constr['periods'] = np.random.randint(1, 4)
constr['agents'] = np.random.randint(1, 100)
constr['edu'] = (7, 15)
constr['maxfun'] = 0
# Simulate a dataset
generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
# Evaluate at different points, ensuring that the simulated dataset
# still fits.
generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
estimate(respy_obj)
def test_4(self):
""" Test the solution of model with ambiguity.
"""
# Solve specified economy
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_fourth.respy.ini')
respy_obj = simulate(respy_obj)
# Assess expected future value
val = respy_obj.get_attr('periods_emax')[0, :1]
np.testing.assert_allclose(val, 75.719528)
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, 2.802285449312437)
def test_4(self):
""" Test the evaluation of the criterion function for random
requests, not just at the true values.
"""
# Constraints that ensure that two alternative initialization files
# can be used for the same simulated data.
constr = dict()
constr['periods'] = np.random.randint(1, 4)
constr['agents'] = np.random.randint(1, 100)
constr['edu'] = (7, 15)
constr['maxfun'] = 0
# Simulate a dataset
generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
# Evaluate at different points, ensuring that the simulated dataset
# still fits.
generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
estimate(respy_obj)
def test_3(self):
""" Test the solution of model with ambiguity.
"""
# Solve specified economy
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_third.respy.ini')
respy_obj = simulate(respy_obj)
# Assess expected future value
val = respy_obj.get_attr('periods_emax')[0, :1]
np.testing.assert_allclose(val, 86121.335057)
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, 1.9162587639887239)
def test_1(self):
""" Compare the evaluation of the criterion function for the ambiguity
optimization and the simulated expected future value between the FORTRAN
and PYTHON implementations. These tests are set up a separate test case
due to the large setup cost to construct the ingredients for the interface.
"""
# Generate constraint periods
constraints = dict()
constraints['version'] = 'PYTHON'
# Generate random initialization file
generate_init(constraints)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
periods_payoffs_systematic, states_number_period, mapping_state_idx, \
periods_emax, num_periods, states_all, num_draws_emax, edu_start, \
edu_max, delta = \
dist_class_attributes(respy_obj,
'periods_payoffs_systematic', 'states_number_period',
'mapping_state_idx', 'periods_emax', 'num_periods',
'states_all', 'num_draws_emax', 'edu_start', 'edu_max',
'delta')
# Sample draws
draws_standard = np.random.multivariate_normal(np.zeros(4),
np.identity(4),
(num_draws_emax, ))
# Sampling of random period and admissible state index
period = np.random.choice(range(num_periods))
k = np.random.choice(range(states_number_period[period]))
# Select systematic payoffs
payoffs_systematic = periods_payoffs_systematic[period, k, :]
# Evaluation of simulated expected future values
args = (num_periods, num_draws_emax, period, k, draws_standard,
payoffs_systematic, edu_max, edu_start, periods_emax,
states_all, mapping_state_idx, delta)
py = get_future_value(*args)
f90 = fort_debug.wrapper_get_future_value(*args)
np.testing.assert_allclose(py, f90, rtol=1e-05, atol=1e-06)
def test_1(self):
""" Test solution of simple model against hard-coded results.
"""
# Solve specified economy
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_first.respy.ini')
respy_obj = simulate(respy_obj)
# Assess expected future value
val = respy_obj.get_attr('periods_emax')[0, :1]
np.testing.assert_allclose(val, 103320.40501)
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, 1.9775860444869962)
def test_1(self):
""" Compare the evaluation of the criterion function for the ambiguity
optimization and the simulated expected future value between the FORTRAN
and PYTHON implementations. These tests are set up a separate test case
due to the large setup cost to construct the ingredients for the interface.
"""
# Generate constraint periods
constraints = dict()
constraints['version'] = 'PYTHON'
# Generate random initialization file
generate_init(constraints)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
periods_payoffs_systematic, states_number_period, mapping_state_idx, \
periods_emax, num_periods, states_all, num_draws_emax, edu_start, \
edu_max, delta = \
dist_class_attributes(respy_obj,
'periods_payoffs_systematic', 'states_number_period',
'mapping_state_idx', 'periods_emax', 'num_periods',
'states_all', 'num_draws_emax', 'edu_start', 'edu_max',
'delta')
# Sample draws
draws_standard = np.random.multivariate_normal(np.zeros(4),
np.identity(4), (num_draws_emax,))
# Sampling of random period and admissible state index
period = np.random.choice(range(num_periods))
k = np.random.choice(range(states_number_period[period]))
# Select systematic payoffs
payoffs_systematic = periods_payoffs_systematic[period, k, :]
# Evaluation of simulated expected future values
args = (num_periods, num_draws_emax, period, k, draws_standard,
payoffs_systematic, edu_max, edu_start, periods_emax, states_all,
mapping_state_idx, delta)
py = get_future_value(*args)
f90 = fort_debug.wrapper_get_future_value(*args)
np.testing.assert_allclose(py, f90, rtol=1e-05, atol=1e-06)
def test_2(self):
""" If there is no random variation in payoffs then the number of
draws to simulate the expected future value should have no effect.
"""
# Generate constraints
constr = dict()
constr['is_deterministic'] = True
# Generate random initialization file
generate_init(constr)
# Initialize auxiliary objects
base = None
for _ in range(2):
# Draw a random number of draws for
# expected future value calculations.
num_draws_emax = np.random.randint(1, 100)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('num_draws_emax', num_draws_emax)
respy_obj.lock()
respy_obj = simulate(respy_obj)
# Distribute class attributes
periods_emax = respy_obj.get_attr('periods_emax')
if base is None:
base = periods_emax.copy()
# Statistic
diff = np.max(
abs(
np.ma.masked_invalid(base) -
np.ma.masked_invalid(periods_emax)))
# Checks
assert (np.isfinite(diff))
assert (diff < 10e-10)
def test_2(self):
""" If there is no random variation in payoffs then the number of
draws to simulate the expected future value should have no effect.
"""
# Generate constraints
constr = dict()
constr['is_deterministic'] = True
# Generate random initialization file
generate_init(constr)
# Initialize auxiliary objects
base = None
for _ in range(2):
# Draw a random number of draws for
# expected future value calculations.
num_draws_emax = np.random.randint(1, 100)
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('num_draws_emax', num_draws_emax)
respy_obj.lock()
respy_obj = simulate(respy_obj)
# Distribute class attributes
periods_emax = respy_obj.get_attr('periods_emax')
if base is None:
base = periods_emax.copy()
# Statistic
diff = np.max(abs(np.ma.masked_invalid(base) - np.ma.masked_invalid(
periods_emax)))
# Checks
assert (np.isfinite(diff))
assert (diff < 10e-10)
def test_2(self):
""" This test compares the results from a solution using the
interpolation code for the special case where the number of interpolation
points is exactly the number of states in the final period. In this case
the interpolation code is run and then all predicted values replaced
with their actual values.
"""
# Set initial constraints
constraints = dict()
constraints['flag_interpolation'] = False
constraints['periods'] = np.random.randint(3, 6)
# Initialize request
init_dict = generate_random_dict(constraints)
baseline = None
# Solve with and without interpolation code
for _ in range(2):
# Write out request
print_init_dict(init_dict)
# Process and solve
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
states_number_period, periods_emax = \
dist_class_attributes(respy_obj,
'states_number_period', 'periods_emax')
# Store and check results
if baseline is None:
baseline = periods_emax
else:
np.testing.assert_array_almost_equal(baseline, periods_emax)
# Updates for second iteration
init_dict['INTERPOLATION']['points'] = max(states_number_period)
init_dict['INTERPOLATION']['flag'] = True
def test_2(self):
""" This test compares the results from a solution using the
interpolation code for the special case where the number of interpolation
points is exactly the number of states in the final period. In this case
the interpolation code is run and then all predicted values replaced
with their actual values.
"""
# Set initial constraints
constraints = dict()
constraints['flag_interpolation'] = False
constraints['periods'] = np.random.randint(3, 6)
# Initialize request
init_dict = generate_random_dict(constraints)
baseline = None
# Solve with and without interpolation code
for _ in range(2):
# Write out request
print_init_dict(init_dict)
# Process and solve
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
states_number_period, periods_emax = \
dist_class_attributes(respy_obj,
'states_number_period', 'periods_emax')
# Store and check results
if baseline is None:
baseline = periods_emax
else:
np.testing.assert_array_almost_equal(baseline, periods_emax)
# Updates for second iteration
init_dict['INTERPOLATION']['points'] = max(states_number_period)
init_dict['INTERPOLATION']['flag'] = True
def test_1(self):
""" This is the special case where the EMAX better be equal to the MAXE.
"""
# Set initial constraints
constraints = dict()
constraints['flag_interpolation'] = False
constraints['periods'] = np.random.randint(3, 6)
constraints['is_deterministic'] = True
# Initialize request
init_dict = generate_random_dict(constraints)
baseline = None
# Solve with and without interpolation code
for _ in range(2):
# Write out request
print_init_dict(init_dict)
# Process and solve
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
states_number_period, periods_emax = \
dist_class_attributes(respy_obj,
'states_number_period', 'periods_emax')
# Store and check results
if baseline is None:
baseline = periods_emax
else:
np.testing.assert_array_almost_equal(baseline, periods_emax)
# Updates for second iteration. This ensures that there is at least
# one interpolation taking place.
init_dict['INTERPOLATION']['points'] = max(
states_number_period) - 1
init_dict['INTERPOLATION']['flag'] = True
def test_1(self):
""" This is the special case where the EMAX better be equal to the MAXE.
"""
# Set initial constraints
constraints = dict()
constraints['flag_interpolation'] = False
constraints['periods'] = np.random.randint(3, 6)
constraints['is_deterministic'] = True
# Initialize request
init_dict = generate_random_dict(constraints)
baseline = None
# Solve with and without interpolation code
for _ in range(2):
# Write out request
print_init_dict(init_dict)
# Process and solve
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
states_number_period, periods_emax = \
dist_class_attributes(respy_obj,
'states_number_period', 'periods_emax')
# Store and check results
if baseline is None:
baseline = periods_emax
else:
np.testing.assert_array_almost_equal(baseline, periods_emax)
# Updates for second iteration. This ensures that there is at least
# one interpolation taking place.
init_dict['INTERPOLATION']['points'] = max(states_number_period) - 1
init_dict['INTERPOLATION']['flag'] = True
def test_1(self):
""" This test ensures that it makes no difference whether the
criterion function is evaluated in parallel or not.
"""
# Generate random initialization file
constr = dict()
constr['version'] = 'FORTRAN'
constr['maxfun'] = np.random.randint(0, 50)
init_dict = generate_random_dict(constr)
base = None
for is_parallel in [True, False]:
init_dict['PARALLELISM']['flag'] = is_parallel
print_init_dict(init_dict)
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
_, crit_val = estimate(respy_obj)
if base is None:
base = crit_val
np.testing.assert_equal(base, crit_val)
def test_4(self):
""" Test the solution of deterministic model without ambiguity,
but with interpolation. As a deterministic model is requested,
all versions should yield the same result without any additional effort.
"""
# Solve specified economy
for version in ['FORTRAN', 'PYTHON']:
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_fifth.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
respy_obj = simulate(respy_obj)
# Assess expected future value
val = respy_obj.get_attr('periods_emax')[0, :1]
np.testing.assert_allclose(val, 88750)
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, -1.0)
def test_5(self):
""" Test the solution of deterministic model without ambiguity,
but with interpolation. As a deterministic model is requested,
all versions should yield the same result without any additional effort.
"""
# Solve specified economy
for version in ['FORTRAN', 'PYTHON']:
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_fifth.respy.ini')
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
respy_obj = simulate(respy_obj)
# Assess expected future value
val = respy_obj.get_attr('periods_emax')[0, :1]
np.testing.assert_allclose(val, 88750)
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, -1.0)
from codes.random_init import generate_init
############################################################################
# RUN
############################################################################
fname = 'test_vault_' + str(PYTHON_VERSION) + '.respy.pkl'
tests = []
for idx in range(num_tests):
print('\n Creating Test ', idx, 'with version ', PYTHON_VERSION)
constr = dict()
constr['maxfun'] = int(np.random.choice([0, 1, 2, 3, 5, 6], p=[0.5, 0.1, 0.1, 0.1, 0.1, 0.1]))
constr['flag_scaling'] = np.random.choice([True, False], p=[0.1, 0.9])
constr['flag_scaling'] = np.random.choice([True, False], p=[0.1, 0.9])
constr['is_store'] = False
init_dict = generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
crit_val = estimate(respy_obj)[1]
test = (init_dict, crit_val)
tests += [test]
pkl.dump(tests, open(fname, 'wb'))
from respy.python.evaluate.evaluate_auxiliary import check_input
from respy.python.evaluate.evaluate_auxiliary import check_output
from respy.python.shared.shared_auxiliary import dist_class_attributes
from respy.python.shared.shared_auxiliary import create_draws
from respy import simulate, RespyCls, estimate
import numpy as np
import pickle as pkl
respy_obj = RespyCls('model.respy.ini')
simulate(respy_obj)
base = None
for num_procs in [1, 2]:
respy_obj.unlock()
respy_obj.set_attr('num_procs', num_procs)
respy_obj.set_attr('is_parallel', (num_procs > 1))
respy_obj.lock()
x, crit_val = estimate(respy_obj)
if base is None:
base = crit_val
np.testing.assert_equal(crit_val, base)
def run(args):
""" Test the different releases against each other.
"""
# Set up auxiliary information to construct commands.
env_dir = os.environ['HOME'] + '/.envs'
old_exec = env_dir + '/' + OLD_RELEASE + '/bin/python'
new_exec = env_dir + '/' + NEW_RELEASE + '/bin/python'
# # Create fresh virtual environments.
# for release in [OLD_RELEASE, NEW_RELEASE]:
# cmd = ['pyvenv', env_dir + '/' + release, '--clear']
# subprocess.check_call(cmd)
#
# # Set up the virtual environments with the two releases under investigation.
# for which in ['old', 'new']:
# if which == 'old':
# release, python_exec = OLD_RELEASE, old_exec
# elif which == 'new':
# release, python_exec = NEW_RELEASE, new_exec
# else:
# raise AssertionError
# cmd = [python_exec, '../_modules/auxiliary_release.py', release]
# subprocess.check_call(cmd)
# Run tests
# TODO: How about log files.
cleanup()
is_failed = False
# Evaluation loop.
start, timeout = datetime.now(), timedelta(hours=args.hours)
num_tests = 0
is_running = True
while is_running:
num_tests += 1
# Set seed.
seed = random.randrange(1, 100000)
np.random.seed(seed)
# Create a random estimation task.
constr = dict()
constr['is_estimation'] = True
generate_init(constr)
respy_obj = RespyCls('test.respy.ini')
simulate(respy_obj)
crit_val = None
for which in ['old', 'new']:
if which == 'old':
release, python_exec = OLD_RELEASE, old_exec
elif which == 'new':
release, python_exec = NEW_RELEASE, new_exec
else:
raise AssertionError
cmd = [python_exec, '../_modules/auxiliary_release.py']
subprocess.check_call(cmd)
if crit_val is None:
crit_val = np.genfromtxt('.crit_val')
try:
np.testing.assert_equal(crit_val, np.genfromtxt(
'.crit_val'))
except AssertionError:
is_failed = True
is_running = False
# Timeout.
if timeout < datetime.now() - start:
break
send_notification('release', hours=args.hours, is_failed=is_failed,
seed=seed, num_tests=num_tests)
def test_1(self):
""" Testing the equality of an evaluation of the criterion function for
a random request.
"""
# Run evaluation for multiple random requests.
is_deterministic = np.random.choice([True, False], p=[0.10, 0.9])
is_interpolated = np.random.choice([True, False], p=[0.10, 0.9])
is_myopic = np.random.choice([True, False], p=[0.10, 0.9])
max_draws = np.random.randint(10, 100)
# Generate random initialization file
constr = dict()
constr['is_deterministic'] = is_deterministic
constr['flag_parallelism'] = False
constr['is_myopic'] = is_myopic
constr['max_draws'] = max_draws
constr['maxfun'] = 0
init_dict = generate_random_dict(constr)
# The use of the interpolation routines is a another special case.
# Constructing a request that actually involves the use of the
# interpolation routine is a little involved as the number of
# interpolation points needs to be lower than the actual number of
# states. And to know the number of states each period, I need to
# construct the whole state space.
if is_interpolated:
# Extract from future initialization file the information
# required to construct the state space. The number of periods
# needs to be at least three in order to provide enough state
# points.
num_periods = np.random.randint(3, 6)
edu_start = init_dict['EDUCATION']['start']
edu_max = init_dict['EDUCATION']['max']
min_idx = min(num_periods, (edu_max - edu_start + 1))
max_states_period = pyth_create_state_space(
num_periods, edu_start, edu_max, min_idx)[3]
# Updates to initialization dictionary that trigger a use of the
# interpolation code.
init_dict['BASICS']['periods'] = num_periods
init_dict['INTERPOLATION']['flag'] = True
init_dict['INTERPOLATION']['points'] = \
np.random.randint(10, max_states_period)
# Print out the relevant initialization file.
print_init_dict(init_dict)
# Write out random components and interpolation grid to align the
# three implementations.
num_periods = init_dict['BASICS']['periods']
write_draws(num_periods, max_draws)
write_interpolation_grid('test.respy.ini')
# Clean evaluations based on interpolation grid,
base_val, base_data = None, None
for version in ['PYTHON', 'FORTRAN']:
respy_obj = RespyCls('test.respy.ini')
# Modify the version of the program for the different requests.
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
# Solve the model
respy_obj = simulate(respy_obj)
# This parts checks the equality of simulated dataset for the
# different versions of the code.
data_frame = pd.read_csv('data.respy.dat', delim_whitespace=True)
if base_data is None:
base_data = data_frame.copy()
assert_frame_equal(base_data, data_frame)
# This part checks the equality of an evaluation of the
# criterion function.
_, crit_val = estimate(respy_obj)
if base_val is None:
base_val = crit_val
np.testing.assert_allclose(base_val,
crit_val,
rtol=1e-05,
atol=1e-06)
# We know even more for the deterministic case.
if constr['is_deterministic']:
assert (crit_val in [-1.0, 0.0])
def test_6(self):
""" Further tests for the interpolation routines.
"""
# Generate random initialization file
generate_init()
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
periods_payoffs_systematic, states_number_period, mapping_state_idx, seed_prob, periods_emax, num_periods, states_all, num_points_interp, edu_start, num_draws_emax, is_debug, edu_max, delta = dist_class_attributes(
respy_obj, 'periods_payoffs_systematic', 'states_number_period',
'mapping_state_idx', 'seed_prob', 'periods_emax', 'num_periods',
'states_all', 'num_points_interp', 'edu_start', 'num_draws_emax',
'is_debug', 'edu_max', 'delta')
# Add some additional objects required for the interfaces to the
# functions.
period = np.random.choice(range(num_periods))
periods_draws_emax = create_draws(num_periods, num_draws_emax,
seed_prob, is_debug)
draws_emax = periods_draws_emax[period, :, :]
num_states = states_number_period[period]
shifts = np.random.randn(4)
# Slight modification of request which assures that the
# interpolation code is working.
num_points_interp = min(num_points_interp, num_states)
# Get the IS_SIMULATED indicator for the subset of points which are
# used for the predication model.
args = (num_points_interp, num_states, period, is_debug)
is_simulated = get_simulated_indicator(*args)
# Construct the exogenous variables for all points of the state
# space.
args = (period, num_periods, num_states, delta,
periods_payoffs_systematic, shifts, edu_max, edu_start,
mapping_state_idx, periods_emax, states_all)
py = get_exogenous_variables(*args)
f90 = fort_debug.wrapper_get_exogenous_variables(*args)
np.testing.assert_equal(py, f90)
# Distribute validated results for further functions.
exogenous, maxe = py
# Construct endogenous variable so that the prediction model can be
# fitted.
args = (period, num_periods, num_states, delta,
periods_payoffs_systematic, edu_max, edu_start,
mapping_state_idx, periods_emax, states_all, is_simulated,
num_draws_emax, maxe, draws_emax)
py = get_endogenous_variable(*args)
f90 = fort_debug.wrapper_get_endogenous_variable(*args)
np.testing.assert_equal(py, replace_missing_values(f90))
# Distribute validated results for further functions.
endogenous = py
args = (endogenous, exogenous, maxe, is_simulated, num_points_interp,
num_states, is_debug)
py = get_predictions(*args)
f90 = fort_debug.wrapper_get_predictions(*args[:-1])
np.testing.assert_array_almost_equal(py, f90)
import respy as rp
import sys
import pandas as pd
import os
if __name__ == '__main__':
if not os.path.exists('simulated_data'):
os.mkdir('simulated_data')
model_name = sys.argv[1]
params, options, _ = rp.get_example_model(model_name)
options['solution_draws'] = 250
options['simulation_agents'] = 750
state_space, data = rp.simulate(params, options)
pd.to_pickle(data, os.path.join("simulated_data", f"{model_name}.pickle"))
def test_1(self):
""" Testing the equality of an evaluation of the criterion function for
a random request.
"""
# Run evaluation for multiple random requests.
is_deterministic = np.random.choice([True, False], p=[0.10, 0.9])
is_interpolated = np.random.choice([True, False], p=[0.10, 0.9])
is_myopic = np.random.choice([True, False], p=[0.10, 0.9])
max_draws = np.random.randint(10, 100)
# Generate random initialization file
constr = dict()
constr['is_deterministic'] = is_deterministic
constr['flag_parallelism'] = False
constr['is_myopic'] = is_myopic
constr['max_draws'] = max_draws
constr['maxfun'] = 0
init_dict = generate_random_dict(constr)
# The use of the interpolation routines is a another special case.
# Constructing a request that actually involves the use of the
# interpolation routine is a little involved as the number of
# interpolation points needs to be lower than the actual number of
# states. And to know the number of states each period, I need to
# construct the whole state space.
if is_interpolated:
# Extract from future initialization file the information
# required to construct the state space. The number of periods
# needs to be at least three in order to provide enough state
# points.
num_periods = np.random.randint(3, 6)
edu_start = init_dict['EDUCATION']['start']
edu_max = init_dict['EDUCATION']['max']
min_idx = min(num_periods, (edu_max - edu_start + 1))
max_states_period = pyth_create_state_space(num_periods, edu_start,
edu_max, min_idx)[3]
# Updates to initialization dictionary that trigger a use of the
# interpolation code.
init_dict['BASICS']['periods'] = num_periods
init_dict['INTERPOLATION']['flag'] = True
init_dict['INTERPOLATION']['points'] = \
np.random.randint(10, max_states_period)
# Print out the relevant initialization file.
print_init_dict(init_dict)
# Write out random components and interpolation grid to align the
# three implementations.
num_periods = init_dict['BASICS']['periods']
write_draws(num_periods, max_draws)
write_interpolation_grid('test.respy.ini')
# Clean evaluations based on interpolation grid,
base_val, base_data = None, None
for version in ['PYTHON', 'FORTRAN']:
respy_obj = RespyCls('test.respy.ini')
# Modify the version of the program for the different requests.
respy_obj.unlock()
respy_obj.set_attr('version', version)
respy_obj.lock()
# Solve the model
respy_obj = simulate(respy_obj)
# This parts checks the equality of simulated dataset for the
# different versions of the code.
data_frame = pd.read_csv('data.respy.dat', delim_whitespace=True)
if base_data is None:
base_data = data_frame.copy()
assert_frame_equal(base_data, data_frame)
# This part checks the equality of an evaluation of the
# criterion function.
_, crit_val = estimate(respy_obj)
if base_val is None:
base_val = crit_val
np.testing.assert_allclose(base_val, crit_val, rtol=1e-05,
atol=1e-06)
# We know even more for the deterministic case.
if constr['is_deterministic']:
assert (crit_val in [-1.0, 0.0])
def test_6(self):
""" Further tests for the interpolation routines.
"""
# Generate random initialization file
generate_init()
# Perform toolbox actions
respy_obj = RespyCls('test.respy.ini')
respy_obj = simulate(respy_obj)
# Extract class attributes
periods_payoffs_systematic, states_number_period, mapping_state_idx, seed_prob, periods_emax, num_periods, states_all, num_points_interp, edu_start, num_draws_emax, is_debug, edu_max, delta = dist_class_attributes(
respy_obj, 'periods_payoffs_systematic', 'states_number_period',
'mapping_state_idx', 'seed_prob', 'periods_emax',
'num_periods', 'states_all', 'num_points_interp', 'edu_start',
'num_draws_emax', 'is_debug', 'edu_max', 'delta')
# Add some additional objects required for the interfaces to the
# functions.
period = np.random.choice(range(num_periods))
periods_draws_emax = create_draws(num_periods, num_draws_emax, seed_prob,
is_debug)
draws_emax = periods_draws_emax[period, :, :]
num_states = states_number_period[period]
shifts = np.random.randn(4)
# Slight modification of request which assures that the
# interpolation code is working.
num_points_interp = min(num_points_interp, num_states)
# Get the IS_SIMULATED indicator for the subset of points which are
# used for the predication model.
args = (num_points_interp, num_states, period, is_debug)
is_simulated = get_simulated_indicator(*args)
# Construct the exogenous variables for all points of the state
# space.
args = (
period, num_periods, num_states, delta, periods_payoffs_systematic, shifts,
edu_max, edu_start, mapping_state_idx, periods_emax, states_all)
py = get_exogenous_variables(*args)
f90 = fort_debug.wrapper_get_exogenous_variables(*args)
np.testing.assert_equal(py, f90)
# Distribute validated results for further functions.
exogenous, maxe = py
# Construct endogenous variable so that the prediction model can be
# fitted.
args = (period, num_periods, num_states, delta,
periods_payoffs_systematic, edu_max, edu_start,
mapping_state_idx, periods_emax, states_all, is_simulated,
num_draws_emax, maxe, draws_emax)
py = get_endogenous_variable(*args)
f90 = fort_debug.wrapper_get_endogenous_variable(*args)
np.testing.assert_equal(py, replace_missing_values(f90))
# Distribute validated results for further functions.
endogenous = py
args = (endogenous, exogenous, maxe, is_simulated, num_points_interp,
num_states, is_debug)
py = get_predictions(*args)
f90 = fort_debug.wrapper_get_predictions(*args[:-1])
np.testing.assert_array_almost_equal(py, f90)
import shutil
import glob
import os
import respy
# We can simply iterate over the different model specifications outlined in
# Table 1 of the paper.
for spec in ['kw_data_one.ini', 'kw_data_two.ini', 'kw_data_three.ini']:
# Process relevant model initialization file
respy_obj = respy.RespyCls(spec)
# Let us simulate the datasets discussed on the page 658.
respy.simulate(respy_obj)
# To start estimations for the Monte Carlo exercises. For now, we just
# evaluate the model at the starting values, i.e. maxfun set to zero in
# the initialization file.
respy_obj.unlock()
respy_obj.set_attr('maxfun', 0)
respy_obj.lock()
respy.estimate(respy_obj)
# Store results in directory for later inspection.
dirname = spec.replace('.ini', '')
os.mkdir(dirname)
for fname in glob.glob('*.respy.*'):
shutil.move(fname, dirname)
def test_2(self):
""" Compare the solution of simple model against hard-coded results.
"""
# Solve specified economy
respy_obj = RespyCls(TEST_RESOURCES_DIR + '/test_second.respy.ini')
respy_obj = simulate(respy_obj)
# Distribute class attributes
systematic = respy_obj.get_attr('periods_payoffs_systematic')
emax = respy_obj.get_attr('periods_emax')
# PERIOD 3: Check the systematic payoffs against hand calculations.
vals = [[2.7456010000000000, 07.5383250000000000, -3999.60, 1.140]]
vals += [[3.0343583944356758, 09.2073308658822519, -3999.60, 1.140]]
vals += [[3.0343583944356758, 09.2073308658822519, 0000.90, 1.140]]
vals += [[3.3534846500000000, 11.2458593100000000, 0000.40, 1.140]]
vals += [[3.5966397255692826, 12.0612761204447200, -3999.60, 1.140]]
vals += [[3.9749016274947495, 14.7316759204425760, -3999.60, 1.140]]
vals += [[3.9749016274947495, 14.7316759204425760, 0000.90, 1.140]]
vals += [[6.2338866585247175, 31.1869581683094590, -3999.60, 1.140]]
vals += [[3.4556134647626764, 11.5883467192233920, -3999.60, 1.140]]
vals += [[3.8190435053663370, 14.1540386453758080, -3999.60, 1.140]]
vals += [[3.8190435053663370, 14.1540386453758080, 0000.90, 1.140]]
vals += [[4.5267307943142532, 18.5412874597468690, -3999.60, 1.140]]
vals += [[5.5289614776240041, 27.6603505585167470, -3999.60, 1.140]]
for i, val in enumerate(vals):
(np.testing.assert_allclose(systematic[2, i, :], val))
# PERIOD 3: Check expected future values. As there are no
# random draws, this corresponds to the maximum
# value in the last period.
vals = [7.53832493366, 9.20733086588, 9.20733086588, 11.2458593149]
vals += [12.06127612040, 14.7316759204, 14.7316759204, 31.1869581683]
vals += [11.58834671922, 14.1540386453, 14.1540386453, 18.5412874597]
vals += [27.660350558516747]
for i, val in enumerate(vals):
(np.testing.assert_allclose(emax[2, i], [val]))
# PERIOD 2: Check the systematic payoffs against hand calculations.
vals = [[2.7456010150169163, 07.5383249336619222, -3999.60, 1.140]]
vals += [[3.0343583944356758, 09.2073308658822519, 0000.90, 1.140]]
vals += [[3.5966397255692826, 12.0612761204447200, -3999.60, 1.140]]
vals += [[3.4556134647626764, 11.5883467192233920, -3999.60, 1.140]]
for i, val in enumerate(vals):
(np.testing.assert_allclose(systematic[1, i, :], val))
# PERIOD 2: Check expected future values.
vals = [18.9965372481, 23.2024229903, 41.6888863803, 29.7329464954]
for i, val in enumerate(vals):
(np.testing.assert_allclose(emax[1, i], [val]))
# PERIOD 1: Check the systematic payoffs against hand calculations.
vals = [[2.7456010150169163, 7.5383249336619222, 0.90, 1.140]]
for i, val in enumerate(vals):
(np.testing.assert_allclose(systematic[0, i, :], val))
# PERIOD 1 Check expected future values.
vals = [47.142766995]
for i, val in enumerate(vals):
(np.testing.assert_allclose(emax[0, 0], [val]))
# Assess evaluation
_, val = estimate(respy_obj)
np.testing.assert_allclose(val, 0.00)
