def _train(self, train_datas):
train_data = concat(train_datas, axis=1)
y, ff = build_target_safe(self.target, self.data)
y = reindex_safe(y, train_data.index)
arg = self.threshold_arg
if arg is None:
arg = self.n_keep
cols = self.selector.select(train_data, y, arg)
return cols
def _train(self, train_datas):
train_data = concat(train_datas, axis=1)
y, ff = build_target_safe(self.target, self.data)
y = reindex_safe(y, train_data.index)
arg = self.threshold_arg
if arg is None:
arg = self.n_keep
cols = self.selector.select(train_data, y, arg)
return cols
def build(self, data, prep_index=None, train_index=None):
if prep_index is None:
prep_index = data.index
if train_index is None:
train_index = data.index
datas = []
fitted_features = []
for feature in self.features:
feature_data, ff = feature.build(data, prep_index, train_index)
datas.append(feature_data)
fitted_features.append(ff)
ff = FittedFeature(self,
prep_index=prep_index,
train_index=train_index,
inner_fitted_features=fitted_features)
ff.prepped_data = self.prepare([reindex_safe(d, prep_index) for d in datas])
ff.trained_data = self.train([reindex_safe(d, train_index) for d in datas])
feature_data = self._combine_apply(datas, ff)
feature_data = self._prepend_feature_name_to_all_columns(feature_data)
return feature_data, ff
def build(self, data, prep_index=None, train_index=None):
if prep_index is None:
prep_index = data.index
if train_index is None:
train_index = data.index
datas = []
fitted_features = []
for feature in self.features:
feature_data, ff = feature.build(data, prep_index, train_index)
datas.append(feature_data)
fitted_features.append(ff)
ff = FittedFeature(self,
prep_index=prep_index,
train_index=train_index,
inner_fitted_features=fitted_features)
ff.prepped_data = self.prepare(
[reindex_safe(d, prep_index) for d in datas])
ff.trained_data = self.train(
[reindex_safe(d, train_index) for d in datas])
feature_data = self._combine_apply(datas, ff)
feature_data = self._prepend_feature_name_to_all_columns(feature_data)
return feature_data, ff
