def continuize_domain(data_or_domain,
multinomial_treatment=Continuize.Indicators,
continuous_treatment=Continuize.Leave,
class_treatment=Continuize.Leave,
zero_based=True):
if isinstance(data_or_domain, Orange.data.Domain):
data, domain = None, data_or_domain
else:
data, domain = data_or_domain, data_or_domain.domain
def needs_dist(var, mtreat, ctreat):
"Does the `var` need a distribution given specified flags"
if var.is_discrete:
return mtreat == Continuize.FrequentAsBase
elif var.is_continuous:
return ctreat != Continuize.Leave
else:
raise ValueError
# Compute the column indices which need a distribution.
attr_needs_dist = [
needs_dist(var, multinomial_treatment, continuous_treatment)
for var in domain.attributes
]
cls_needs_dist = [
needs_dist(var, class_treatment, Continuize.Leave)
for var in domain.class_vars
]
columns = [
i for i, needs in enumerate(attr_needs_dist + cls_needs_dist) if needs
]
if columns:
if data is None:
raise TypeError("continuizer requires data")
dist = distribution.get_distributions_for_columns(data, columns)
else:
dist = []
dist_iter = iter(dist)
newattrs = [
continuize_var(var,
next(dist_iter) if needs_dist else None,
multinomial_treatment, continuous_treatment, zero_based)
for var, needs_dist in zip(domain.attributes, attr_needs_dist)
]
newclass = [
continuize_var(var,
next(dist_iter) if needs_dist else None,
class_treatment, Continuize.Remove, zero_based)
for var, needs_dist in zip(domain.class_vars, cls_needs_dist)
]
newattrs = reduce(list.__iadd__, newattrs, [])
newclass = reduce(list.__iadd__, newclass, [])
return Orange.data.Domain(newattrs, newclass, domain.metas)
def continuize_domain(
data_or_domain,
multinomial_treatment=DomainContinuizer.NValues,
continuous_treatment=DomainContinuizer.Leave,
class_treatment=DomainContinuizer.Leave,
zero_based=True,
):
if isinstance(data_or_domain, Orange.data.Domain):
data, domain = None, data_or_domain
else:
data, domain = data_or_domain, data_or_domain.domain
def needs_dist(var, mtreat, ctreat):
"Does the `var` need a distribution given specified flags"
if isinstance(var, Orange.data.DiscreteVariable):
return mtreat == DomainContinuizer.FrequentIsBase
elif isinstance(var, Orange.data.ContinuousVariable):
return ctreat != DomainContinuizer.Leave
else:
raise ValueError
# Compute the column indices which need a distribution.
attr_needs_dist = [needs_dist(var, multinomial_treatment, continuous_treatment) for var in domain.attributes]
cls_needs_dist = [needs_dist(var, class_treatment, DomainContinuizer.Leave) for var in domain.class_vars]
columns = [i for i, needs in enumerate(attr_needs_dist + cls_needs_dist) if needs]
if columns:
if data is None:
raise TypeError("continuizer requires data")
dist = distribution.get_distributions_for_columns(data, columns)
else:
dist = []
dist_iter = iter(dist)
newattrs = [
continuize_var(
var, next(dist_iter) if needs_dist else None, multinomial_treatment, continuous_treatment, zero_based
)
for var, needs_dist in zip(domain.attributes, attr_needs_dist)
]
newclass = [
continuize_var(
var, next(dist_iter) if needs_dist else None, class_treatment, DomainContinuizer.Ignore, zero_based
)
for var, needs_dist in zip(domain.class_vars, cls_needs_dist)
]
newattrs = reduce(list.__iadd__, newattrs, [])
newclass = reduce(list.__iadd__, newclass, [])
return Orange.data.Domain(newattrs, newclass, domain.metas)
