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from Orange.data import ContinuousVariable, Domain
from Orange.statistics import distribution
from Orange.util import Reprable
from Orange.preprocess.transformation import \
Identity, Indicator, Indicator1, Normalizer
from Orange.preprocess.preprocess import Continuize
__all__ = ["DomainContinuizer"]
class DomainContinuizer(Reprable):
def __init__(self, zero_based=True,
multinomial_treatment=Continuize.Indicators,
transform_class=False):
self.zero_based = zero_based
self.multinomial_treatment = multinomial_treatment
self.transform_class = transform_class
def __call__(self, data):
def transform_discrete(var):
if (len(var.values) < 2 or
treat == Continuize.Remove or
treat == Continuize.RemoveMultinomial and
len(var.values) > 2):
return []
if treat == Continuize.AsOrdinal:
new_var = ContinuousVariable(
var.name, compute_value=Identity(var), sparse=var.sparse)
return [new_var]
if treat == Continuize.AsNormalizedOrdinal:
n_values = max(1, len(var.values))
if self.zero_based:
return [ContinuousVariable(
var.name,
compute_value=Normalizer(var, 0, 1 / (n_values - 1)),
sparse=var.sparse)]
else:
return [ContinuousVariable(
var.name,
compute_value=Normalizer(var, (n_values - 1) / 2,
2 / (n_values - 1)),
sparse=var.sparse)]
new_vars = []
if treat == Continuize.Indicators:
base = -1
elif treat in (Continuize.FirstAsBase,
Continuize.RemoveMultinomial):
base = 0
else:
base = dists[var_ptr].modus()
ind_class = [Indicator1, Indicator][self.zero_based]
for i, val in enumerate(var.values):
if i == base:
continue
new_var = ContinuousVariable(
"{}={}".format(var.name, val),
compute_value=ind_class(var, i),
sparse=var.sparse)
new_vars.append(new_var)
return new_vars
def transform_list(s):
nonlocal var_ptr
new_vars = []
for var in s:
if var.is_discrete:
new_vars += transform_discrete(var)
if needs_discrete:
var_ptr += 1
else:
new_var = var
if new_var is not None:
new_vars.append(new_var)
if needs_continuous:
var_ptr += 1
return new_vars
treat = self.multinomial_treatment
transform_class = self.transform_class
domain = data if isinstance(data, Domain) else data.domain
if (treat == Continuize.ReportError and
any(var.is_discrete and len(var.values) > 2 for var in domain.variables)):
raise ValueError("data has multinomial attributes")
needs_discrete = (treat == Continuize.FrequentAsBase and
domain.has_discrete_attributes(transform_class))
needs_continuous = False
if needs_discrete:
if isinstance(data, Domain):
raise TypeError("continuizer requires data")
dists = distribution.get_distributions(
data, not needs_discrete, not needs_continuous)
var_ptr = 0
new_attrs = transform_list(domain.attributes)
if transform_class:
new_classes = transform_list(domain.class_vars)
else:
new_classes = domain.class_vars
return Domain(new_attrs, new_classes, domain.metas)
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