def setUpClass(cls):
cls.do_subclass_setup()
print("Setting up tests for %s" % cls.test_name)
cls.generating_hmm = FirstOrderHMM(
state_priors=ArrayFactor(cls.arrays["state_priors"]),
trans_probs=MatrixFactor(cls.arrays["transition_probs"]),
emission_probs=cls.get_emission_probs())
cls.naive_hmm = FirstOrderHMM(
state_priors=ArrayFactor(cls.arrays["naive_state_priors"]),
trans_probs=MatrixFactor(cls.arrays["naive_transition_probs"]),
emission_probs=cls.get_naive_emission_probs())
# generate 100 training examples
cls.training_examples = [
cls.generating_hmm.generate(cls.example_len)[1]
for _ in range(cls.num_examples)
]
# retrain
cls.training_results = train_baum_welch(
cls.naive_hmm,
cls.training_examples,
state_prior_estimator=cls.state_prior_estimator,
transition_estimator=cls.transition_estimator,
emission_estimator=cls.emission_estimator,
miniter=80,
maxiter=1000,
processes=1,
logfunc=DefaultLoggerFactory(ScreenWriter(),
cls.naive_hmm,
maxcols=5),
)
def test_from_dict_with_hmm(self):
for (starting_order, num_states), model in sorted(self.models.items()):
if starting_order >= 5:
continue
state_priors = numpy.random.random(model.low_order_states)
state_priors /= state_priors.sum()
trans_probs = numpy.random.random(
(model.low_order_states, model.low_order_states))
trans_probs = (trans_probs.T / trans_probs.sum(1)).T
my_hmm = FirstOrderHMM(state_priors=ArrayFactor(state_priors),
trans_probs=MatrixFactor(trans_probs),
emission_probs=[None] *
model.low_order_states)
dtmp = {
"starting_order": starting_order,
"num_states": num_states,
"hmm": my_hmm,
}
found = ModelReducer._from_dict(dtmp)
expected = ModelReducer(starting_order, num_states)
expected.hmm = my_hmm
for k in ("starting_order", "high_order_states"):
yield check_equal, getattr(found, k), getattr(expected, k)
for k in ("trans_probs", "state_priors"):
yield check_array_equal, getattr(found.hmm,
k).data, getattr(my_hmm,
k).data
def test_to_dict_with_hmm(self):
for (starting_order, num_states), model in sorted(self.models.items()):
if starting_order >= 5:
continue
state_priors = numpy.random.random(model.low_order_states)
state_priors /= state_priors.sum()
trans_probs = numpy.random.random(
(model.low_order_states, model.low_order_states))
trans_probs = (trans_probs.T / trans_probs.sum(1)).T
my_hmm = FirstOrderHMM(state_priors=ArrayFactor(state_priors),
trans_probs=MatrixFactor(trans_probs),
emission_probs=[None] *
model.low_order_states)
model.hmm = my_hmm
with warnings.catch_warnings():
warnings.simplefilter("ignore")
found = model._to_dict()
expected = {
"starting_order": starting_order,
"num_states": num_states,
"hmm": my_hmm,
}
model.hmm = None
for k in expected:
if k == "first_order_hmm":
yield check_dict_equal, found[k], expected[k]
else:
yield check_equal, found[k], expected[k]
def construct_factors(self,
model,
reduced_data,
noise_weight=0,
pseudocount_weight=1e-10):
"""Construct discrete transition factor for an HMM using reduced data
from observation sequences
model : :class:`~minihmm.hmm.FirstOrderHMM` or subclass
reduced_data : numpy.ndarray
sufficient statistics for observations, from
:meth:`DiscreteTransitionEstimator.reduce_data`
noise_weight : float, optional
weight of noise to add, relative to number of of observations (e.g.
transition counts, state prior counts, emission counts, et c) in
data set. (Default: 0)
pseudocount_weight : float, optional
weight of pseudocounts to add, relative to number of of
observations (transition counts, state prior counts, emission
counts, et c) in data set (Default: 1e-8)
Returns
-------
:class:`~minihmm.factors.MatrixFactor`
Transition probability factor
"""
A = sum(reduced_data)
A_sum = A.sum()
A += get_model_noise(A, noise_weight)
A += (pseudocount_weight * A_sum / len(A.ravel()))
A_normed = (A.T / A.sum(1)).T
return MatrixFactor(A_normed)
def do_subclass_setup(cls):
cls.name = "Two gaussian example"
cls.min_frac_equal = 0.7
transitions = numpy.matrix([[0.9, 0.1], [0.25, 0.75]])
cls.generating_hmm = FirstOrderHMM(
**{
"trans_probs":
MatrixFactor(transitions),
"state_priors":
ArrayFactor([0.8, 0.2]),
"emission_probs": [
ScipyDistributionFactor(scipy.stats.norm, loc=0,
scale=0.5),
ScipyDistributionFactor(scipy.stats.norm, loc=5, scale=10)
],
})
cls.hmm_dict = {
"emission_probs": [],
"state_priors": {
"shape": (1, 2),
"row": [0, 0],
"col": [0, 1],
"data": [0.8, 0.2],
},
"trans_probs": {
"shape": (2, 2),
"row": [0, 0, 1, 1],
"col": [0, 1, 0, 1],
"data": [0.9, 0.1, 0.25, 0.75],
}
}
def do_subclass_setup(cls):
cls.name = "Coin example"
cls.min_frac_equal = 0.69
cls.generating_hmm = FirstOrderHMM(
**{
"state_priors":
ArrayFactor([0.005, 0.995]),
"trans_probs":
MatrixFactor(numpy.array([[0.8, 0.2], [0.3, 0.7]])),
"emission_probs":
[ArrayFactor([0.6, 0.4]),
ArrayFactor([0.15, 0.85])],
})
cls.hmm_dict = {
"emission_probs": [],
"state_priors": {
"shape": (1, 2),
"row": [0, 0],
"col": [0, 1],
"data": [0.005, 0.995],
},
"trans_probs": {
"shape": (2, 2),
"row": [0, 0, 1, 1],
"col": [0, 1, 0, 1],
"data": [0.8, 0.2, 0.3, 0.7],
}
}
def do_subclass_setup(cls):
for my_len in range(10, 100, 200):
ary = numpy.random.random((my_len, my_len))
ary = (ary.T / ary.sum(1)).T
cls.factors.append(MatrixFactor(ary, row_conditional=True))
cls.examples.append([
(X, Y)
for (X,
Y) in numpy.random.randint(0, high=my_len, size=(50, 2))
])
cls.factors.append(MatrixFactor(ary, row_conditional=False))
cls.examples.append([
(X, Y)
for (X,
Y) in numpy.random.randint(0, high=my_len, size=(50, 2))
])
def construct_factors(self,
model,
reduced_data,
noise_weight=0,
pseudocount_weight=1e-10):
"""Construct transition factor for an HMM using reduced data from
observation sequences
model : :class:`~minihmm.hmm.FirstOrderHMM` or subclass
reduced_data : numpy.ndarray
sufficient statistics for observations, from
:meth:`TiedTransitionEstimator.reduce_data`
noise_weight : float, optional
weight of noise to add, relative to number of of observations (e.g.
transition counts, state prior counts, emission counts, et c) in
data set. (Default: 0)
pseudocount_weight : float, optional
weight of pseudocounts to add, relative to number of of
observations (transition counts, state prior counts, emission
counts, et c) in data set (Default: 1e-8)
Returns
-------
:class:`~minihmm.factors.MatrixFactor`
Tied state transition probability table
"""
A_raw = sum(reduced_data)
A_sum = A_raw.sum()
reduced_vector = numpy.zeros(1 + self.index_map.max())
for i in range(A_raw.shape[0]):
for j in range(A_raw.shape[1]):
reduced_vector[self.index_map[i, j]] += A_raw[i, j]
# add noise
reduced_vector += get_model_noise(
reduced_vector,
noise_weight,
assymetric_weights=self.index_weights
) # FIXME: THIS WILL ADD NOISE TO FORBIDDEN CELLS
# divide each starting cell by number of destination cells
reduced_vector /= self.index_weights
# populate destination vector
A_proc = numpy.zeros_like(A_raw, dtype=float)
for i in range(A_proc.shape[0]):
for j in range(A_proc.shape[1]):
A_proc[i, j] = reduced_vector[self.index_map[i, j]]
# add pseudocounts
A_proc += (pseudocount_weight * A_sum *
self.pseudocount_array) / self.pseudocount_array.sum()
A_proc *= self.pseudocount_mask # re-zero forbidden cells that became zero via noise addition
# normalize
A_proc = (A_proc.T / A_proc.sum(1)).T
return MatrixFactor(A_proc)
def remap_from_first_order(self, native_hmm):
"""Remap parameters from a native first order HMM onto a first-order
translation of a high-order HMM, in order to, for example, provide a
reasonable non-random starting point for refinement training of the
high-order HMM.
Parameters
----------
native_hmm : :class:`minihmm.hmm.FirstOrderHMM`
Native, first-order HMM, preferably with trained parameters
Returns
-------
:class:`~minihmm.hmm.FirstOrderHMM`
First-order representation of the high-order HMM structure
described by `self`, with parameters from `native_hmm` remapped
into corresponding positions.
"""
htl = self.high_states_to_low
# check that number of states is compatible
if self.high_order_states != native_hmm.num_states:
raise ValueError(
"Native HMM (%d states), has different number of states than `self` (%d states)" %
(native_hmm.num_states, self.high_order_states)
)
# For transitions
# Each high-order state transitiono `(n-i, ... , n-1) -> (n-i+1 , ... , n)`
# should be mapped to appropiate transformations of the parameters (n - 1 , n)
# For state priors and emission probabilities
# each high-order state (n-i, ..., n) should be given the parameters matching
# native state `n`
# will need to make appropriate state-tying matrices for emissions, as well
sp_source = native_hmm.state_priors.data
sp_dest = numpy.zeros(self.low_order_states, dtype=float)
trans_source = native_hmm.trans_probs.data
trans_dest = numpy.zeros((self.low_order_states, self.low_order_states), dtype=float)
em_source = native_hmm.emission_probs
em_dest = [None] * self.low_order_states
for my_tuple, trans_state in htl.items():
native_state = my_tuple[-1]
sp_dest[trans_state] = sp_source[native_state]
em_dest[trans_state] = copy.deepcopy(em_source[native_state])
for next_native_state in range(self.high_order_states):
next_tuple = tuple(list(my_tuple)[1:] + [next_native_state])
next_trans_state = htl[next_tuple]
trans_dest[trans_state, next_trans_state] = trans_source[native_state,
next_native_state]
# renormalize
sp_dest /= sp_dest.sum()
sp_dest = ArrayFactor(sp_dest)
# shoudln't have to renormalize; check this
trans_dest = (trans_dest.T / trans_dest.sum(1)).T
trans_dest = MatrixFactor(trans_dest)
return FirstOrderHMM(state_priors=sp_dest, emission_probs=em_dest, trans_probs=trans_dest)