def learn_supports_adaptive(dataset, seed, bg_knowledge=None, timeout=None, initial=None, mult=None,
hops=None, max_mult=None, negative_bootstrap=None):
if timeout is None:
timeout = DEF_TIMEOUT
if initial is None:
initial = DEF_INITIAL
if mult is None:
mult = DEF_MULT
if hops is None:
hops = DEF_HOPS
if max_mult is None:
max_mult = DEF_MAX_MULT
results = []
discovered = set()
t_mults = set()
last = initial
i = 0
msg = "Adaptive support learning. timeout = {}, init = {}, mult = {}, hops = {}"
logger.info(msg.format(timeout, initial, mult, hops))
while i < hops and last < max_mult:
logger.debug("i: {} last: {}".format(i, last))
t_mults.add(last)
res = learn_support(dataset, seed, last, timeout=timeout, bg_knowledge=bg_knowledge,
symmetry_breaking="mvn",
negative_bootstrap=negative_bootstrap)
if res is not None:
chi, k, h, thresholds = res
chistr = serialize(chi)
smaller = {t for t in t_mults if t < last}
if chistr not in discovered:
discovered.add(chistr)
results.append(res + (last,))
if len(smaller) > 0:
last = (last + max(smaller)) / 2
i += 1
else:
last = last / mult
else: # last t_mult timed out
larger = {t for t in t_mults if t > last}
if len(larger) > 0:
last = (last + min(larger)) / 2
i += 1
else:
last = last * mult
return results
def learn_wrap(data, labels, learn_inc, queue):
res = learn_bottom_up(data, labels, learn_inc, 1, 1, 1, 1, None, None)
(new_data, new_labels, formula), k, h = res
msg = "Learned CNF(k={}, h={})"
logger.debug(msg.format(k, h))
msg = "Data-set grew from {} to {} entries"
logger.debug(msg.format(len(labels), len(new_labels)))
queue.put((formula, k, h))
def dump(self, problem_path):
if problem_path is None and self.original_path is None:
raise IOError("Unspecified path")
elif problem_path is not None and exists(problem_path):
raise IOError("File exists: {}".format(problem_path))
elif problem_path is None and self.original_path is not None:
msg = "Dumping the problem with no specified path, using {}"
logger.debug(msg.format(self.original_path))
problem_path = self.original_path
problem_name = basename(problem_path)
folder = abspath(dirname(problem_path))
model_filename = Problem.MODEL_TEMPL.format(problem_name)
model_path = join(folder, model_filename)
if self.original_path is None:
self.model.dump(model_path)
index = {
'model_path': relpath(model_path, folder),
'dataset_paths': {}
}
for dataset_name, dataset in self.datasets.items():
dataset_filename = Problem.DATASET_TEMPL.format(
problem_name, dataset_name)
dataset_path = join(folder, dataset_filename)
if self.original_path is None:
dataset.dump(dataset_path)
index['dataset_paths'][dataset_name] = relpath(
dataset_path, folder)
if len(self.learned_supports) > 0:
index['support_paths'] = []
for i, chi in enumerate(self.learned_supports):
support_filename = Problem.SUPPORT_TEMPL.format(
problem_name, i)
support_path = join(folder, support_filename)
logger.debug("Writing support file: {}".format(support_path))
write_smtlib(chi, support_path)
index['support_paths'].append(relpath(support_path, folder))
if self.bounds is not None:
index['bounds'] = self.bounds
if self.metadata is not None:
index['metadata'] = self.metadata
with open(problem_path, 'wb') as f:
pickle.dump(index, f)
def renorm_wrap(inst, support, support_path, weight_path):
try:
inst.renormalize(support)
support = inst.tree_to_WMI_support()
weight = inst.tree_to_WMI_weightfun()
msg = "Writing result to files:\n{}\n{}"
logger.debug(msg.format(support_path, weight_path))
write_smtlib(support, support_path)
write_smtlib(weight, weight_path)
logger.debug("Done.")
except ModelException as e:
logger.error(
"Couldn't renormalize the DET: {}".format(e))
def normalize(model, seed, sample_count, engine='pa'):
if engine == 'pa':
solver = PredicateAbstractionEngine(model.domain, model.support, model.weightfun)
elif engine == 'rej':
solver = RejectionEngine(model.domain, model.support, model.weightfun,
sample_count=sample_count, seed=seed)
else:
raise NotImplementedError()
Z = solver.compute_volume()
assert(Z >= 0), "Z is negative"
if not np.isclose(Z, 1.0):
logger.debug("Normalizing w with Z: {}".format(Z))
model.weightfun = Times(Real(1.0/Z), model.weightfun)
return Z
def check_Z_normalize(model, seed, sample_count):
"""Tests whether the model is normalized. If not, updates the weight
function accordingly."""
logger.debug("Approximating Z")
solver = RejectionEngine(model.domain, model.support, model.weightfun,
sample_count=sample_count, seed=seed)
all_ohes = dict()
for var in model.domain.bool_vars:
print("VAR:", var)
if "_OHE_" in var:
prefix = var.partition("_OHE_")[0]
if prefix not in all_ohes:
all_ohes[prefix] = []
all_ohes[prefix].append(var)
ohe_variables = list(all_ohes.values()) if len(all_ohes) > 0 else None
Z_approx = solver.compute_volume(ohe_variables=ohe_variables)
logger.debug("Z_approx: {}".format(Z_approx))
if Z_approx <= 0:
raise ModelException("Partition function is <= 0")
if not abs(Z_approx - 1.0) <= DEF_CLOSE_ENOUGH:
model.weightfun = Times(Real(float(1.0/Z_approx)), model.weightfun)
def renormalize(self, support):
assert (support is not None), "Can't renormalize with support = None"
self.support = support
# mark the tree
queue = []
for leaf in self.root.get_leaves():
domA = [
var.symbol_name() for var in leaf.bounds
if var.symbol_type() == BOOL
]
domX = []
bs = []
for var, b in leaf.bounds.items():
if var.symbol_type() == REAL:
domX.append(var.symbol_name())
bs.append(tuple(b))
domain = Domain.make(domA, domX, bs)
intersection = And(support, leaf.bounds_to_SMT())
engine = PredicateAbstractionEngine(domain, intersection, Real(1))
intervol = engine.compute_volume()
leaf.marked = intervol <= 0
if leaf.marked:
logger.debug("Marked a leaf")
queue.append(leaf)
while len(queue) > 0:
n = queue.pop(0)
if not n.parent.marked:
if n.parent.pos.marked and n.parent.neg.marked:
n.parent.marked = True
queue.append(n.parent)
self.root.merge_marked()
self.root.renormalize_node(support)
def approx_IAE(model1, model2, seed, sample_count):
assert(set(model1.get_vars()) == set(model2.get_vars())),\
"M1 vars: {}\n M2 vars: {}".format(model1.get_vars(),model2.get_vars())
domain, bounds = merged_domain(model1, model2)
samples, pos_ratio = positive(sample_count, domain,
Or(model1.support, model2.support),
weight=None)
samples_m1 = samples[evaluate(domain,
And(model1.support, Not(model2.support)),
samples)]
samples_m2 = samples[evaluate(domain,
And(Not(model1.support), model2.support),
samples)]
samples_inter = samples[evaluate(domain, And(model1.support, model2.support),
samples)]
weights_m1 = sum(evaluate(domain, model1.weightfun, samples_m1))
weights_m2 = sum(evaluate(domain, model2.weightfun, samples_m2))
weights_inter = sum(abs(evaluate(domain, model1.weightfun, samples_inter) -
evaluate(domain, model2.weightfun, samples_inter)))
n_m1 = len(samples_m1)
n_m2 = len(samples_m2)
n_inter = len(samples_inter)
norm_m1 = weights_m1 / sample_count
norm_m2 = weights_m2 / sample_count
norm_inter = weights_inter / sample_count
logger.debug(f"[ S1 ~S2] len: {n_m1}, sum: {weights_m1}, norm: {norm_m1}")
logger.debug(f"[ S1 ~S2] len: {n_m2}, sum: {weights_m2}, norm: {norm_m2}")
logger.debug(f"[ S1 ~S2] len: {n_inter}, sum: {weights_inter}, norm: {norm_inter}")
approx_vol = pos_ratio * 2**len(domain.bool_vars)
for lb, ub in bounds.values():
approx_vol *= (ub - lb)
return approx_vol*(weights_m1 + weights_m2 + weights_inter) / sample_count
def run_problem(problem,
learner,
seed,
n_samples,
timeout,
global_norm,
use_lariat=True):
ground_truth = problem.model
evaluation = dict()
train = problem.datasets['train']
valid = problem.datasets['valid']
train_valid = Dataset(train.features, train.data + valid.data,
train.constraints)
if problem.learned_supports is not None:
prior_supports = {
problem.metadata['supports_metadata'][i]['support_threshold_mult']:
chi
for i, chi in enumerate(problem.learned_supports)
}
else:
logger.warning("Couldn't find any learned support.")
prior_supports = dict()
prior_supports['None'] = None
prior_supports['gt-renorm'] = ground_truth.support
t_0 = time()
learner.estimate_density(train, validation_data=valid)
t_f = time() - t_0
logger.info("training time: {}".format(t_f))
evaluation['training_time'] = t_f
learned_models = []
cached_models = dict()
max_ll = None
best = None
logger.info("Evaluating:\n {}".format("\n".join(
map(str, prior_supports.keys()))))
for t_mult, prior_support in prior_supports.items():
if t_mult != 'None' and not use_lariat:
continue
evaluation[t_mult] = dict()
ps_str = serialize(prior_support) if not isinstance(t_mult,
str) else t_mult
if ps_str in cached_models:
learned_model, evaluation[t_mult] = cached_models[ps_str]
else:
try:
logger.info(
"--------------------------------------------------")
logger.info("Support: {}".format(t_mult))
mode = RENORM_FULL if prior_support is not None else RENORM_OFF
t_0 = time()
learned_model, renormd = learner.renormalize(
train,
seed,
mode=mode,
support=prior_support,
timeout=timeout,
global_norm=global_norm)
t_f = time() - t_0
if not renormd and prior_support is not None:
continue
evaluation[t_mult]['renorm_time'] = t_f
except CalledProcessError as e:
logger.warning("XADD error: {}".format(e))
continue
except ModelException as e:
logger.warning("Model error: {}".format(e))
continue
logger.debug("Computing approx-IAE")
iae = approx_IAE(learned_model, ground_truth, seed, n_samples)
evaluation[t_mult]['approx-iae'] = iae
logger.debug("Computing train-LL")
train_ll, train_out = learned_model.log_likelihood(train)
evaluation[t_mult]['train-ll'] = train_ll
evaluation[t_mult]['train-out'] = train_out
logger.debug("Computing valid-LL")
valid_ll, valid_out = learned_model.log_likelihood(valid)
evaluation[t_mult]['valid-ll'] = valid_ll
evaluation[t_mult]['valid-out'] = valid_out
train_valid_ll, train_valid_out = learned_model.log_likelihood(
train_valid)
evaluation[t_mult]['train-valid-ll'] = train_valid_ll
evaluation[t_mult]['train-valid-out'] = train_valid_out
if t_mult not in ['None','gt-renorm'] \
and (max_ll is None or valid_ll > max_ll):
max_ll = valid_ll
best = t_mult
logger.debug("Computing volume difference")
poly1 = Model(learned_model.support, None, ground_truth.get_vars(),
ground_truth.bounds)
poly2 = Model(ground_truth.support, None, ground_truth.get_vars(),
ground_truth.bounds)
vol_diff = ISE(poly1, poly2, seed, n_samples, engine='rej')
evaluation[t_mult]['vol-diff'] = vol_diff
cached_models[ps_str] = (learned_model, evaluation[t_mult])
domain = Domain.make(
map(lambda v: v.symbol_name(), ground_truth.boolean_vars),
learned_model.bounds)
eval_falses = evaluate(domain, learned_model.support,
np.asarray(train.data))
learned_models.append((t_mult, learned_model))
evaluation['best'] = best
tmuls = sorted([
key for key in evaluation
if key not in ['None', 'gt-renorm', 'training_time', 'best']
])
eval_msg = """RESULTS:
Training time: {}
No renorm: {}
GT renorm: {}
Best chi : {}
All chis:
{}
""".format(evaluation['training_time'], evaluation['None'],
evaluation['gt-renorm'], (best, evaluation.get(best)),
"\n".join([str((tmul, evaluation[tmul])) for tmul in tmuls]))
logger.info(eval_msg)
return learned_models, evaluation
def generate_experiment(seed, n_problems, n_train, n_valid, n_reals, n_bools,
depth, bias, k, literals, h, ratio, errors):
logger.info("Generating experiment:\n" +
"seed: {}\n".format(seed) +
"n_problems: {}\n".format(n_problems) +
"n_train: {}\n".format(n_train) +
"n_valid: {}\n".format(n_valid) +
"n_reals: {}\n".format(n_reals) +
"n_bools: {}\n".format(n_bools) +
"bias: {}\n".format(bias) +
"k: {}\n".format(k) +
"literals: {}\n".format(literals) +
"h: {}\n".format(h) +
"ratio: {}\n".format(ratio) +
"errors: {}\n".format(errors))
model_generator = ModelGenerator(n_reals, n_bools, seed,
templ_bools="b{}",
templ_reals="r{}",
initial_bounds=[0, 1])
problems = []
while len(problems) < n_problems:
try:
# generating the ground truth model
# not complex enough
#chi = model_generator.generate_support_tree(depth)
sample_count = 1000
chi = support_generator(1, n_bools, n_reals, bias, k, literals, h,
sample_count, ratio, errors, seed)[0]
w = model_generator.generate_weights_tree(depth, nonnegative=True,
splits_only=True)
boolean_vars = list(set(v for v in chi.get_free_variables()
if v.symbol_type() == BOOL).union(
set(model_generator.bools)))
real_vars = list(set(v for v in chi.get_free_variables()
if v.symbol_type() == REAL).union(
set(model_generator.reals)))
bounds = {v.symbol_name() : model_generator.initial_bounds
for v in real_vars}
fbounds = And([And(LE(Real(bounds[var.symbol_name()][0]), var),
LE(var, Real(bounds[var.symbol_name()][1])))
for var in real_vars])
model = Model(And(fbounds, chi), w, boolean_vars + real_vars, bounds)
# use exact inference to normalize the ground truth
sample_count = None
normalize(model, seed, sample_count, engine='pa')
logger.debug("model generator reals: {}".format(model_generator.reals))
logger.debug("model generator IDs: {}".format(list(map(id, model_generator.reals))))
logger.debug("model reals: {}".format(model.continuous_vars))
logger.debug("model IDs: {}".format(list(map(id, model.continuous_vars))))
# sampling the dataset from the ground truth model
datasets = {}
datasets['train'] = sample_dataset(model, n_train)
datasets['valid'] = sample_dataset(model, n_valid)
except ModelException as e:
logger.debug(e.msg)
continue
logger.debug("Model {}\n".format(len(problems)+1) +
"chi: {}\n".format(serialize(model.support)) +
"w: {}\n".format(serialize(model.weightfun)))
problem = Problem(model,
datasets,
bounds=bounds)
problems.append(problem)
# better safe than sorry?
metadata = {'n_reals' : n_reals, 'n_bools' : n_bools, 'depth' : depth,
'n_train' : n_train, 'n_valid' : n_valid, 'seed' : seed}
return Experiment(problems, metadata=metadata)
def renormalize(self,
training_data,
seed,
mode=RENORM_OFF,
support=None,
timeout=None,
global_norm=False):
if timeout is None:
timeout = DEF_RENORM_TIMEOUT
detcopy = self.det.copy()
model_support = detcopy.tree_to_WMI_support()
model_weight = detcopy.tree_to_WMI_weightfun()
bounds = {
v.symbol_name(): b
for v, b in detcopy.root.bounds.items() if v.symbol_type() == REAL
}
renorm_support = None
if mode == RENORM_BG_ONLY and training_data.constraints is not None:
renorm_support = training_data.constraints
elif mode == RENORM_FULL:
if training_data.constraints is not None and support is not None:
renorm_support = training_data.constraints & support
elif training_data.constraints is not None:
renorm_support = training_data.constraints
elif support is not None:
renorm_support = support
renormalized = False
if renorm_support is not None:
if global_norm:
logger.debug("Global renormalization")
model_support = model_support & renorm_support
renormalized = True
else:
logger.debug("Local renormalization")
def renorm_wrap(inst, support, support_path, weight_path):
try:
inst.renormalize(support)
support = inst.tree_to_WMI_support()
weight = inst.tree_to_WMI_weightfun()
msg = "Writing result to files:\n{}\n{}"
logger.debug(msg.format(support_path, weight_path))
write_smtlib(support, support_path)
write_smtlib(weight, weight_path)
logger.debug("Done.")
except ModelException as e:
logger.error(
"Couldn't renormalize the DET: {}".format(e))
# communication with wrapper process through file
# NEVER use multiprocessing.Queue with huge pysmt formulas
rndstr = ''.join(choice(TMP_CHARS) for _ in range(TMP_LEN))
support_path = "{}.support".format(rndstr)
weight_path = "{}.weight".format(rndstr)
timed_proc = Process(target=renorm_wrap,
args=(detcopy, renorm_support,
support_path, weight_path))
logger.debug(
"Starting renormalization with timeout: {}".format(
timeout))
timed_proc.start()
logger.debug("Timed proc started")
timed_proc.join(timeout)
logger.debug("Timed proc joined")
if timed_proc.is_alive():
logger.warning("Renormalization timed out")
pid = timed_proc.pid
logger.warning(
"Killing process {} and its children".format(pid))
kill_recursive(pid)
else:
try:
model_support = read_smtlib(support_path)
remove(support_path)
except FileNotFoundError:
model_support = None
try:
model_weight = read_smtlib(weight_path)
remove(weight_path)
except FileNotFoundError:
model_weight = None
if model_support is None or model_weight is None:
raise ModelException("Couldn't renormalize the DET")
logger.debug("Renormalization done")
renormalized = True
model = Model(model_support,
model_weight,
list(map(lambda x: x[0], training_data.features)),
bounds,
metadata=self.learner_args)
# is Z = 1?
if renormalized:
check_Z_normalize(model, seed, TEST_AND_NORM_SAMPLES)
elif not global_norm:
# fallback strategy for local: to global
model, renormalized = self.renormalize(training_data,
seed,
mode=mode,
support=support,
timeout=timeout,
global_norm=True)
return model, renormalized
def renormalize(self,
training_data,
seed,
mode=RENORM_OFF,
support=None,
timeout=None,
global_norm=True):
if timeout is None:
timeout = DEF_RENORM_TIMEOUT
feature_dict = {
var.symbol_name(): var
for var, _ in training_data.features
}
model_weightfun, model_support = SPN_to_WMI(self.spn.root,
feature_dict)
bounds = {}
for i, feat in enumerate(training_data.features):
var = feat[0]
if var.symbol_type() == REAL:
xi = list(map(lambda row: row[i], training_data.data))
bounds[var.symbol_name()] = [min(xi), max(xi)]
renorm_support = None
if mode == RENORM_BG_ONLY and training_data.constraints is not None:
renorm_support = training_data.constraints
elif mode == RENORM_FULL:
if training_data.constraints is not None and support is not None:
renorm_support = training_data.constraints & support
elif training_data.constraints is not None:
renorm_support = training_data.constraints
elif support is not None:
renorm_support = support
renormalized = False
if renorm_support is not None:
if global_norm:
logger.debug("Global renormalization")
model_support = model_support & renorm_support
renormalized = True
else:
logger.debug("Local renormalization")
domain = Domain.make([
v.symbol_name() for v, _ in training_data.features
if v.symbol_type() == BOOL
], bounds)
nc_model_support = normalize_formula(model_support)
nc_model_weightfun = normalize_formula(model_weightfun)
nc_renorm_support = normalize_formula(renorm_support)
t_0 = time()
xaddsolver = XaddEngine(domain,
nc_model_support,
nc_model_weightfun,
mode="original",
timeout=timeout)
t_init = time() - t_0
logger.debug("XADDEngine t_init: {}".format(t_init))
try:
res = xaddsolver.normalize(renorm_support)
t_norm = time() - t_init
except CalledProcessError as e:
raise ModelException("CalledProcessError")
if res is None:
logger.warning("Timeout.")
else:
logger.debug("XADDEngine t_norm: {}".format(t_norm))
model_weightfun = get_env().formula_manager.normalize(res)
model_support = get_env().formula_manager.normalize(
And(model_support, renorm_support))
renormalized = True
model = Model(model_support,
model_weightfun,
list(map(lambda x: x[0], training_data.features)),
bounds,
metadata=self.learner_args)
if renormalized:
check_Z_normalize(model, seed, TEST_AND_NORM_SAMPLES)
elif not global_norm:
# fallback strategy for local: to global
model, renormalized = self.renormalize(training_data,
seed,
mode=mode,
support=support,
timeout=timeout,
global_norm=True)
return model, renormalized
def learn_support(dataset, seed, threshold_mult, timeout=None, bg_knowledge=None,
symmetry_breaking=None,
negative_bootstrap=None):
logger.info(f"Running INCAL+. Symmetry breaking = {symmetry_breaking} negative_bootstrap = {negative_bootstrap}")
# default might become symmetry_breaking = "mvn"
if symmetry_breaking is None:
symmetry_breaking = ""
if negative_bootstrap is None:
negative_bootstrap = 0
else:
try:
# absolute count is specified with an integer
negative_bootstrap = int(negative_bootstrap)
except ValueError:
pass
# relative count (wrt |D|) is specified with a float
negative_bootstrap = int(len(dataset) * float(negative_bootstrap))
# compute bounds and add positive labels to the data
bounds = {}
for row in dataset.data:
for i, feat in enumerate(dataset.features):
var = feat[0]
if var.symbol_type() == BOOL:
continue
varname = var.symbol_name()
if not varname in bounds:
bounds[varname] = [row[i], row[i]]
else:
if row[i] < bounds[varname][0]:
bounds[varname][0] = row[i]
elif row[i] > bounds[varname][1]:
bounds[varname][1] = row[i]
data = np.array(dataset.data)
labels = np.ones(data.shape[0])
# create a Domain instance
varnames = []
vartypes = {}
for v, _ in dataset.features:
varnames.append(v.symbol_name())
vartypes[v.symbol_name()] = v.symbol_type()
domain = Domain(varnames, vartypes, bounds)
distance = Distance(domain, Distance.l_inf)
max_closest = None
for i1 in range(len(data)):
min_distance = None
for i2 in range(0, len(data)):
if i1 != i2:
p1, p2 = dataset.data[i1], dataset.data[i2]
d = distance.between(p1, p2)
min_distance = d if min_distance is None else min(min_distance, d)
if min_distance < 1:
max_closest = min_distance if max_closest is None else max(max_closest, min_distance)
logger.debug("Maximum distance between closest neighbors: {}".format(max_closest))
threshold = threshold_mult * max_closest
logger.debug("Overall threshold: {}".format(threshold))
thresholds = {r: threshold * domain.domain_size(r) for r in domain.real_vars}
logger.debug("Thresholds per dimension: {}".format(thresholds))
def learn_inc(_data, _labels, _i, _k, _h):
strategy = OneClassStrategy(RandomViolationsStrategy(10), thresholds,
background_knowledge=bg_knowledge)
if negative_bootstrap > 0:
_data, _labels = OneClassStrategy.add_negatives(domain, _data, _labels, thresholds, negative_bootstrap)
learner = KCnfSmtLearner(_k, _h, strategy, symmetry_breaking)
random.seed(seed)
initial_indices = LearnOptions.initial_random(20)(list(range(len(_data))))
res = learner.learn(domain, _data, _labels, initial_indices)
return res
# wrapping INCAL+ into a timed process
def learn_wrap(data, labels, learn_inc, queue):
res = learn_bottom_up(data, labels, learn_inc, 1, 1, 1, 1, None, None)
(new_data, new_labels, formula), k, h = res
msg = "Learned CNF(k={}, h={})"
logger.debug(msg.format(k, h))
msg = "Data-set grew from {} to {} entries"
logger.debug(msg.format(len(labels), len(new_labels)))
queue.put((formula, k, h))
queue = Queue()
timed_proc = Process(target=learn_wrap, args=(data, labels, learn_inc, queue))
timed_proc.start()
timed_proc.join(timeout)
if timed_proc.is_alive():
# timed process didn't complete the job
timed_proc.terminate()
timed_proc.join()
return None
else:
# get the learned formula, (k,h)
chi, k, h = queue.get()
return chi, k, h, thresholds
def SPN_to_WMI(node, feature_dict):
w_children = []
chi_children = []
for child in node.children:
subw, subchi = SPN_to_WMI(child, feature_dict)
w_children.append(subw)
if subchi is not None:
chi_children.append(subchi)
if isinstance(node, SumNode):
wmi_weights = list(map(lambda w: Real(float(w)), node.weights))
weighted_sum = [
Times(wmi_weights[i], w_children[i])
for i in range(len(wmi_weights))
]
w_node = Plus(weighted_sum)
chi_node = Or(chi_children)
elif isinstance(node, ProductNode):
w_node = Times(w_children)
chi_node = And(chi_children)
else: # it's a leaf
wmi_var = feature_dict[node.featureName]
if isinstance(node, BernoulliNode):
assert (0 <= node.p and node.p <= 1)
w_node = boolean_leaf(wmi_var, node.p, 1 - node.p)
chi_node = None
elif isinstance(node, CategoricalNode):
# I think this is never going to be used
assert (node.values == 2), "Not a Boolean variable"
w_node = boolean_leaf(wmi_var, node.probs[0], node.probs[1])
chi_node = None
elif isinstance(node, PiecewiseLinearPDFNodeOld):
# I think this is never going to be used
logger.debug("Var: {}".format(wmi_var.symbol_name()) +
" x_range: {}".format(node.x_range) +
" y_range: {}".format(node.y_range) +
" dom: {}".format(node.domain))
if wmi_var.symbol_type() == REAL:
w_node = datapoints_to_piecewise_linear(
wmi_var, node.x_range, node.y_range)
chi_node = And(LE(Real(float(node.domain[0])), wmi_var),
LE(wmi_var, Real(float(node.domain[-1]))))
else:
w_node = boolean_leaf(wmi_var, node.y_range[2],
node.y_range[1])
chi_node = None
logger.debug("Leaf: {}".format(w_node))
elif isinstance(node, PiecewiseLinearPDFNode) or \
isinstance(node, IsotonicUnimodalPDFNode):
logger.debug("Var: {}".format(wmi_var.symbol_name()) +
" x_range: {}".format(node.x_range) +
" y_range: {}".format(node.y_range) +
" dom: {}".format(node.domain))
if wmi_var.symbol_type() == REAL:
actual_prob = datapoints_to_piecewise_linear(
wmi_var, node.x_range, node.y_range)
w_node = Plus(
Times(Real(float(1 - node.prior_weight)), actual_prob),
Times(Real(float(node.prior_weight)),
Real(float(node.prior_density))))
chi_node = And(LE(Real(float(node.domain[0])), wmi_var),
LE(wmi_var, Real(float(node.domain[-1]))))
else:
p_true = node.y_range[list(node.x_range).index(True)]
p_false = node.y_range[list(node.x_range).index(False)]
print("p_true", p_true, "p_false", p_false)
w_node = boolean_leaf(wmi_var, p_true, p_false)
"""
if isclose(p_true, 1.0):
chi_node = wmi_var
elif isclose(p_true, 1.0):
chi_node = Not(wmi_var)
else:
chi_node = None
"""
chi_node = None
logger.debug("Leaf: {}".format(w_node))
elif isinstance(node, HistNode):
actual_prob = hist_to_piecewise_constant(wmi_var, node.breaks,
node.densities)
w_node = Plus(
Times(Real(float(1 - node.prior_weight)), actual_prob),
Times(Real(float(node.prior_weight)),
Real(float(node.prior_density))))
chi_node = And(LE(Real(float(node.domain[0])), wmi_var),
LE(wmi_var, Real(float(node.domain[-1]))))
elif isinstance(node, KernelDensityEstimatorNode):
raise NotImplementedError()
else:
raise NotImplementedError("Node type {} not supported".format(
type(node)))
return w_node, chi_node
