def _prepare(self, data):
dct = super(SelectNgramCounts, self)._prepare(data)
if self.train_only:
y = get_single_column(self.target.create(self.context)).reindex(self.context.train_index)
x = data.reindex(self.context.train_index)
else:
y = get_single_column(self.target.create(self.context))
x = data
cols = self.select(x, y)
return cols, dct
def _prepare(self, data):
dct = super(SelectNgramCounts, self)._prepare(data)
if self.train_only:
y = get_single_column(self.target.create(self.context)).reindex(
self.context.train_index)
x = data.reindex(self.context.train_index)
else:
y = get_single_column(self.target.create(self.context))
x = data
cols = self.select(x, y)
return cols, dct
def _create(self, data):
data = get_single_column(data)
vecs = None #self.load('topic_vecs')
if vecs is None or self.force:
vecs = self.make_vectors(data)
vecs.columns = ['%s_%s'%(c, data.name) for c in vecs.columns]
return vecs
def _create(self, data):
data = get_single_column(data)
vecs = None #self.load('topic_vecs')
if vecs is None or self.force:
vecs = self.make_vectors(data)
vecs.columns = ['%s_%s' % (c, data.name) for c in vecs.columns]
return vecs
def _prepare(self, data):
data = get_single_column(data)
docs = list(data)
if self.verbose:
print docs[:10]
dct = self.dictionary.get_dict(self.context, docs)
if self.verbose:
print dct
return dct
def _prepare(self, data):
data = get_single_column(data)
docs = list(data)
if self.verbose:
print docs[:10]
dct = self.dictionary.get_dict(self.context, docs)
if self.verbose:
print dct
return dct
def _create(self, data):
dct = self.get_prep_data(data)
data = get_single_column(data)
docs = [dct.doc2bow(d) for d in data]
ids = dct.keys()
df = DataFrame([dict(row) for row in docs], columns=ids, index=data.index)
df.columns = ["%s_%s" % (dct[i], data.name) for i in ids]
df = df.fillna(0)
if self.bool_:
df = df.astype(bool).astype(int)
return df
def _create(self, data):
dct = self.get_prep_data(data)
data = get_single_column(data)
docs = [dct.doc2bow(d) for d in data]
ids = sorted(dct.keys())
df = DataFrame([dict(row) for row in docs],
columns=ids,
index=data.index)
df.columns = ['%s_%s' % (dct[i], data.name) for i in ids]
df = df.fillna(0)
if self.bool_:
df = df.astype(bool).astype(int)
return df
def combine(self, datas):
datas = [get_single_column(d) for d in datas]
d = []
for x in zip(*datas):
d.append(self.sep.join(x))
return DataFrame(d, columns=['_'.join([c.name for c in datas])])
def combine(self, datas):
datas = [get_single_column(d) for d in datas]
d = []
for x in zip(*datas):
d.append(self.sep.join(x))
return DataFrame(d, columns=['_'.join([c.name for c in datas])])
