def get_regression_array_pattern(num_obs, x_obs_dim):
"""Get a paragami pattern for an array of approximate regression posteriors.
Parameters
-------------
num_obs : `int`
The number of distinct regressions.
x_obs_dim : `int`
The dimensionality of the regressors.
Returns
----------
reg_params_pattern : A paragami pattern.
The field ``beta_mean`` is the ordinary regression
coefficient, and ``beta_info`` is the estimated inverse covariance
matrix. The field ``y_info`` contains a point estimate for the
inverse variance of each regressoin's residual.
"""
reg_params_pattern = paragami.PatternDict()
reg_params_pattern['beta_mean'] = \
paragami.NumericArrayPattern(shape=(num_obs, x_obs_dim))
reg_params_pattern['beta_info'] = \
paragami.pattern_containers.PatternArray(
array_shape=(num_obs, ),
base_pattern=\
paragami.PSDSymmetricMatrixPattern(size=x_obs_dim))
reg_params_pattern['y_info'] = \
paragami.NumericVectorPattern(length=num_obs, lb=0.0)
return reg_params_pattern
def test_pattern_array(self):
array_pattern = paragami.NumericArrayPattern(
shape=(4, ), lb=-1, ub=10.0)
pattern_array = paragami.PatternArray((2, 3), array_pattern)
valid_value = pattern_array.random()
_test_pattern(self, pattern_array, valid_value)
matrix_pattern = paragami.PSDSymmetricMatrixPattern(size=2)
pattern_array = paragami.PatternArray((2, 3), matrix_pattern)
valid_value = pattern_array.random()
_test_pattern(self, pattern_array, valid_value)
base_pattern_array = paragami.PatternArray((2, 1), matrix_pattern)
pattern_array_array = paragami.PatternArray((1, 3), base_pattern_array)
valid_value = pattern_array_array.random()
_test_pattern(self, pattern_array_array, valid_value)
# Test flat indices.
matrix_pattern = paragami.PSDSymmetricMatrixPattern(size=2)
pattern_array = paragami.PatternArray((2, 3), matrix_pattern)
_test_array_flat_indices(self, pattern_array)
self.assertTrue(
paragami.PatternArray((3, 3), matrix_pattern) !=
paragami.PatternArray((2, 3), matrix_pattern))
self.assertTrue(
paragami.PatternArray((2, 3), array_pattern) !=
paragami.PatternArray((2, 3), matrix_pattern))
pattern_array = paragami.PatternArray((2, 3), array_pattern)
self.assertEqual((2, 3), pattern_array.array_shape())
self.assertEqual((2, 3, 4), pattern_array.shape())
self.assertTrue(array_pattern == pattern_array.base_pattern())
# Test bad arguments.
with self.assertRaisesRegex(NotImplementedError,
'not numpy.ndarray types'):
paragami.PatternArray((2, 3), paragami.PatternDict())
pattern_array = paragami.PatternArray((2, 3), array_pattern)
with self.assertRaisesRegex(ValueError, 'Wrong number of dimensions'):
pattern_array.flatten(np.full((2, 3), 0), free=False)
with self.assertRaisesRegex(ValueError, 'Wrong number of dimensions'):
pattern_array.flatten(np.full((2, 3, 4, 5), 0), free=False)
with self.assertRaisesRegex(ValueError, 'Wrong shape'):
pattern_array.flatten(np.full((2, 3, 5), 0), free=False)
with self.assertRaisesRegex(ValueError, 'Bad value'):
pattern_array.flatten(np.full((2, 3, 4), -10), free=False)
with self.assertRaisesRegex(ValueError, 'must be a 1d vector'):
pattern_array.fold(np.full((24, 1), -10), free=False)
with self.assertRaisesRegex(ValueError, 'Wrong size'):
pattern_array.fold(np.full((25, ), -10), free=False)
def get_prior_params_pattern(obs_dim, num_components):
prior_params_pattern = paragami.PatternDict()
prior_params_pattern['probs_alpha'] = \
paragami.NumericVectorPattern(length=num_components, lb=0.0)
prior_params_pattern['centroid_prior_mean'] = \
paragami.NumericArrayPattern(shape=(num_components, obs_dim))
prior_params_pattern['centroid_prior_info'] = \
paragami.PSDSymmetricMatrixPattern(size=obs_dim)
return prior_params_pattern
def get_combined_parameters_pattern(reg_params_pattern, gmm_params_pattern):
"""A paragami pattern for the combined regression parameters (`reg`)
and an optimal clustering (`mix`).
"""
comb_params_pattern = paragami.PatternDict()
comb_params_pattern['reg'] = reg_params_pattern
comb_params_pattern['mix'] = gmm_params_pattern
comb_params_pattern.lock()
return comb_params_pattern
def get_test_pattern():
# autograd will pass invalid values, so turn off value checking.
pattern = paragami.PatternDict()
pattern['array'] = paragami.NumericArrayPattern((2, 3, 4),
lb=-1,
ub=20,
default_validate=False)
pattern['mat'] = paragami.PSDSymmetricMatrixPattern(3,
default_validate=False)
pattern['simplex'] = paragami.SimplexArrayPattern(2, (3, ),
default_validate=False)
subdict = paragami.PatternDict()
subdict['array2'] = paragami.NumericArrayPattern((2, ),
lb=-3,
ub=10,
default_validate=False)
pattern['dict'] = subdict
return pattern
def get_small_test_pattern():
# autograd will pass invalid values, so turn off value checking.
pattern = paragami.PatternDict()
pattern['array'] = paragami.NumericArrayPattern((2, 3, 4),
lb=-1,
ub=10,
default_validate=False)
pattern['mat'] = paragami.PSDSymmetricMatrixPattern(3,
default_validate=False)
return pattern
def get_default_prior_params(dim):
"""
Returns a paragami patterned dictionary
that stores the prior parameters.
Default prior parameters are those set for the experiments in
"Evaluating Sensitivity to the Stick Breaking Prior in
Bayesian Nonparametrics"
https://arxiv.org/abs/1810.06587
Parameters
----------
dim : int
Dimension of the datapoints.
Returns
-------
prior_params_dict : dictionary
A dictionary that contains the prior parameters.
prior_params_paragami : paragami Patterned Dictionary
A paragami patterned dictionary that contains the prior parameters.
"""
prior_params_dict = dict()
prior_params_paragami = paragami.PatternDict()
# DP prior parameter
prior_params_dict['alpha'] = np.array([3.0])
prior_params_paragami['alpha'] = \
paragami.NumericArrayPattern(shape=(1, ), lb = 0.0)
# prior on the centroids
prior_params_dict['prior_centroid_mean'] = np.array([0.0])
prior_params_paragami['prior_centroid_mean'] = \
paragami.NumericArrayPattern(shape=(1, ))
prior_params_dict['prior_centroid_info'] = np.array([0.1])
prior_params_paragami['prior_centroid_info'] = \
paragami.NumericArrayPattern(shape=(1, ), lb = 0.0)
# prior on the variance
prior_params_dict['prior_gamma_df'] = np.array([8.0])
prior_params_paragami['prior_gamma_df'] = \
paragami.NumericArrayPattern(shape=(1, ), lb = 0.0)
prior_params_dict['prior_gamma_inv_scale'] = 0.62 * np.eye(dim)
prior_params_paragami['prior_gamma_inv_scale'] = \
paragami.PSDSymmetricMatrixPattern(size=dim)
return prior_params_dict, prior_params_paragami
def get_gmm_params_pattern(obs_dim, num_components):
"""A ``paragami`` pattern for a mixture model.
``centroids`` are the locations of the clusters.
``probs`` are the a priori probabilities of each cluster.
"""
gmm_params_pattern = paragami.PatternDict()
gmm_params_pattern['centroids'] = \
paragami.NumericArrayPattern((num_components, obs_dim))
gmm_params_pattern['probs'] = \
paragami.SimplexArrayPattern(
simplex_size=num_components, array_shape=(1,))
return gmm_params_pattern
def get_vb_params_paragami_object(dim, k_approx):
"""
Returns a paragami patterned dictionary
that stores the variational parameters.
Parameters
----------
dim : int
Dimension of the datapoints.
k_approx : int
Number of components in the model.
Returns
-------
vb_params_dict : dictionary
A dictionary that contains the variational parameters.
vb_params_paragami : paragami patterned dictionary
A paragami patterned dictionary that contains the variational parameters.
"""
vb_params_paragami = paragami.PatternDict()
# cluster centroids
vb_params_paragami['centroids'] = \
paragami.NumericArrayPattern(shape=(dim, k_approx))
# BNP sticks
# variational distribution for each stick is logitnormal
vb_params_paragami['stick_propn_mean'] = \
paragami.NumericArrayPattern(shape = (k_approx - 1,))
vb_params_paragami['stick_propn_info'] = \
paragami.NumericArrayPattern(shape = (k_approx - 1,), lb = 1e-4)
# cluster covariances
vb_params_paragami['gamma'] = \
paragami.pattern_containers.PatternArray(array_shape = (k_approx, ), \
base_pattern = paragami.PSDSymmetricMatrixPattern(size=dim))
vb_params_dict = vb_params_paragami.random()
return vb_params_dict, vb_params_paragami
def test_json_files(self):
pattern = paragami.PatternDict()
pattern['num'] = paragami.NumericArrayPattern((1, 2))
pattern['mat'] = paragami.PSDSymmetricMatrixPattern(5)
val_folded = pattern.random()
extra = np.random.random(5)
outfile_name = '/tmp/paragami_test_' + str(np.random.randint(1e6))
paragami.save_folded(outfile_name, val_folded, pattern, extra=extra)
val_folded_loaded, pattern_loaded, data = \
paragami.load_folded(outfile_name + '.npz')
self.assertTrue(pattern_loaded == pattern)
self.assertTrue(val_folded.keys() == val_folded_loaded.keys())
for keyname in val_folded.keys():
assert_array_almost_equal(
val_folded[keyname], val_folded_loaded[keyname])
assert_array_almost_equal(extra, data['extra'])
def _get_data_pattern(x_shape, y_shape):
data_pattern = paragami.PatternDict()
data_pattern['y'] = paragami.NumericArrayPattern(y_shape)
data_pattern['x'] = paragami.NumericArrayPattern(x_shape)
return data_pattern
def test_dictionary_patterns(self):
def test_pattern(dict_pattern, dict_val):
# autograd can't differentiate the folding of a dictionary
# because it involves assignment to elements of a dictionary.
_test_pattern(self, dict_pattern, dict_val,
check_equal=check_dict_equal,
jacobian_ad_test=False)
def check_dict_equal(dict1, dict2):
self.assertEqual(dict1.keys(), dict2.keys())
for key in dict1:
if type(dict1[key]) is collections.OrderedDict:
check_dict_equal(dict1[key], dict2[key])
else:
assert_array_almost_equal(dict1[key], dict2[key])
print('dictionary pattern test: one element')
dict_pattern = paragami.PatternDict()
dict_pattern['a'] = \
paragami.NumericArrayPattern((2, 3, 4), lb=-1, ub=2)
test_pattern(dict_pattern, dict_pattern.random())
print('dictionary pattern test: two elements')
dict_pattern['b'] = \
paragami.NumericArrayPattern((5, ), lb=-1, ub=10)
test_pattern(dict_pattern, dict_pattern.random())
print('dictionary pattern test: third matrix element')
dict_pattern['c'] = \
paragami.PSDSymmetricMatrixPattern(size=3)
test_pattern(dict_pattern, dict_pattern.random())
print('dictionary pattern test: sub-dictionary')
subdict = paragami.PatternDict()
subdict['suba'] = paragami.NumericArrayPattern((2, ))
dict_pattern['d'] = subdict
test_pattern(dict_pattern, dict_pattern.random())
# Test flat indices.
_test_array_flat_indices(self, dict_pattern)
# Test keys.
self.assertEqual(list(dict_pattern.keys()), ['a', 'b', 'c', 'd'])
# Check that it works with ordinary dictionaries, not only OrderedDict.
print('dictionary pattern test: non-ordered dictionary')
test_pattern(dict_pattern, dict(dict_pattern.random()))
# Check deletion and non-equality.
print('dictionary pattern test: deletion')
old_dict_pattern = copy.deepcopy(dict_pattern)
del dict_pattern['b']
self.assertTrue(dict_pattern != old_dict_pattern)
test_pattern(dict_pattern, dict_pattern.random())
# Check modifying an existing array element.
print('dictionary pattern test: modifying array')
dict_pattern['a'] = paragami.NumericArrayPattern((2, ), lb=-1, ub=2)
test_pattern(dict_pattern, dict_pattern.random())
# Check modifying an existing dictionary element.
print('dictionary pattern test: modifying sub-dictionary')
dict_pattern['d'] = \
paragami.NumericArrayPattern((4, ), lb=-1, ub=10)
test_pattern(dict_pattern, dict_pattern.random())
# Check locking
dict_pattern.lock()
with self.assertRaises(ValueError):
del dict_pattern['b']
with self.assertRaises(ValueError):
dict_pattern['new'] = \
paragami.NumericArrayPattern((4, ))
with self.assertRaises(ValueError):
dict_pattern['a'] = \
paragami.NumericArrayPattern((4, ))
# Check invalid values.
bad_dict = dict_pattern.random()
del bad_dict['a']
with self.assertRaisesRegex(ValueError, 'not in folded_val dictionary'):
dict_pattern.flatten(bad_dict, free=True)
bad_dict = dict_pattern.random()
bad_dict['a'] = np.array(-10)
with self.assertRaisesRegex(ValueError, 'is not valid'):
dict_pattern.flatten(bad_dict, free=True)
free_val = np.random.random(dict_pattern.flat_length(True))
with self.assertRaisesRegex(ValueError,
'argument to fold must be a 1d vector'):
dict_pattern.fold(np.atleast_2d(free_val), free=True)
with self.assertRaisesRegex(ValueError,
'Wrong size for pattern dictionary'):
dict_pattern.fold(free_val[-1], free=True)