def create_domain(at, cl, metas):
if OR3:
return Orange.data.Domain(at, cl, metas=metas)
else:
domain = Orange.data.Domain(at, cl)
if metas:
if isinstance(metas, dict):
metas = sorted(metas.items())
else:
metas = zip([ StringVariable.new_meta_id() for _ in metas ], metas)
domain.add_metas(dict((StringVariable.new_meta_id(), ma) for mi, ma in metas))
return domain
def create_domain(at, cl, metas):
if OR3:
return Orange.data.Domain(at, cl, metas=metas)
else:
domain = Orange.data.Domain(at, cl)
if metas:
domain.add_metas(dict((StringVariable.new_meta_id(), ma) for ma in metas))
return domain
def create_domain(at, cl, metas):
if OR3:
return Orange.data.Domain(at, cl, metas=metas)
else:
domain = Orange.data.Domain(at, cl)
if metas:
domain.add_metas(
dict((StringVariable.new_meta_id(), ma) for ma in metas))
return domain
def create_domain(at, cl, metas):
if OR3:
return Orange.data.Domain(at, cl, metas=metas)
else:
domain = Orange.data.Domain(at, cl)
if metas:
# add metas in the reverse order (because meta ids are always decreasing)
# this allows us to pass metas in the same order to create_table
metas = zip([ StringVariable.new_meta_id() for _ in metas ], reversed(metas))
domain.add_metas(dict(metas))
return domain
def create_domain(at, cl, metas):
if OR3:
return Orange.data.Domain(at, cl, metas=metas)
else:
domain = Orange.data.Domain(at, cl)
if metas:
# add metas in the reverse order (because meta ids are always decreasing)
# this allows us to pass metas in the same order to create_table
metas = zip([StringVariable.new_meta_id() for _ in metas],
reversed(metas))
domain.add_metas(dict(metas))
return domain
def transpose_labels_to_class(data, class_label=None, gene_label="gene"):
"""Converts data with genes in rows to data with genes as attributes."""
# if no class_label (attribute type) given, guess it from the data
if not class_label:
l = []
for a in data.domain.attributes:
l.extend(list(a.attributes.keys()))
l = list(set(l))
class_label = l[0]
if len(set(l)) > 1:
import warnings
warnings.warn(
"More than single attribute label types (%s), took %s" %
(", ".join(l), class_label))
if gene_label in [v.name for v in data.domain.getmetas().values()]:
atts = [ContinuousVariable(str(d[gene_label])) for d in data]
else:
atts = [ContinuousVariable("A%d" % i) for i in range(len(data))]
classvalues = list(
set([a.attributes[class_label] for a in data.domain.attributes]))
if all([isinstance(x, (int, float, complex)) for x in classvalues]):
classvar = ContinuousVariable(class_label)
else:
classvar = DiscreteVariable(class_label, values=classvalues)
domain = Orange.data.Domain(atts, classvar)
newdata = []
for a in data.domain.attributes:
newdata.append([_float_or_na(d[a])
for d in data] + [a.attributes[class_label]])
sample = StringVariable("sample")
id = StringVariable.new_meta_id()
new = Orange.data.Table(domain, newdata)
new.domain.addmeta(id, sample)
for i, d in enumerate(new):
d[sample] = data.domain.attributes[i].name
return new
def transpose_labels_to_class(data, class_label=None, gene_label="gene"):
"""Converts data with genes in rows to data with genes as attributes."""
# if no class_label (attribute type) given, guess it from the data
if not class_label:
l = []
for a in data.domain.attributes:
l.extend(list(a.attributes.keys()))
l = list(set(l))
class_label = l[0]
if len(set(l)) > 1:
import warnings
warnings.warn("More than single attribute label types (%s), took %s"
% (", ".join(l), class_label))
if gene_label in [v.name for v in data.domain.getmetas().values()]:
atts = [ContinuousVariable(str(d[gene_label])) for d in data]
else:
atts = [ContinuousVariable("A%d" % i) for i in range(len(data))]
classvalues = list(set([a.attributes[class_label] for a in data.domain.attributes]))
if all([isinstance(x, (int, float, complex)) for x in classvalues]):
classvar = ContinuousVariable(class_label)
else:
classvar = DiscreteVariable(class_label, values=classvalues)
domain = Orange.data.Domain(atts, classvar)
newdata = []
for a in data.domain.attributes:
newdata.append([_float_or_na(d[a]) for d in data] + [a.attributes[class_label]])
sample = StringVariable("sample")
id = StringVariable.new_meta_id()
new = Orange.data.Table(domain, newdata)
new.domain.addmeta(id, sample)
for i, d in enumerate(new):
d[sample] = data.domain.attributes[i].name
return new
def transpose_class_to_labels(data, attcol="sample"):
"""Converts data with genes as attributes to data with genes in rows."""
if attcol in [v.name for v in data.domain.getmetas().values()]:
atts = [ContinuousVariable(str(d[attcol])) for d in data]
else:
atts = [ContinuousVariable("S%d" % i) for i in range(len(data))]
for i, d in enumerate(data):
atts[i].setattr("class", str(d.getclass()))
domain = Orange.data.Domain(atts, None)
newdata = []
for a in data.domain.attributes:
newdata.append([_float_or_na(d[a]) for d in data])
gene = StringVariable("gene")
id = StringVariable.new_meta_id()
new = Orange.data.Table(domain, newdata)
new.domain.addmeta(id, gene)
for i, d in enumerate(new):
d[gene] = data.domain.attributes[i].name
return new
def transpose_class_to_labels(data, attcol="sample"):
"""Converts data with genes as attributes to data with genes in rows."""
if attcol in [v.name for v in data.domain.getmetas().values()]:
atts = [ContinuousVariable(str(d[attcol])) for d in data]
else:
atts = [ContinuousVariable("S%d" % i) for i in range(len(data))]
for i, d in enumerate(data):
atts[i].setattr("class", str(d.getclass()))
domain = Orange.data.Domain(atts, None)
newdata = []
for a in data.domain.attributes:
newdata.append([_float_or_na(d[a]) for d in data])
gene = StringVariable("gene")
id = StringVariable.new_meta_id()
new = Orange.data.Table(domain, newdata)
new.domain.addmeta(id, gene)
for i, d in enumerate(new):
d[gene] = data.domain.attributes[i].name
return new