def get_labels(self, labelname='Stress'):
r = []
series = []
for tsd in self:
g = tsd.df_meta.transpose().groupby(by='SeriesID')
for k,df in g:
series.append(k)
r.append(df[labelname].tolist()[0])
return pd.DataFrame(r, columns=[labelname], index=series)
def modelmatrix(table, axis=0, factor_list=None):
if (factor_list is None):
factor_list = factor(table)
# do factor encoding
r = []
if (type(table) == pd.DataFrame or type(table) == pd.Series):
idx = table.index
table = table.values.flatten()
else:
raise Exception('Only DataFrame/Series format is supported')
for x in table.tolist():
r.append(factor_list == x)
return pd.DataFrame(np.array(r)*1, columns=factor_list, index=idx)
def get_labels_per_time(self, labelname='Stress'):
r = []
for tsd in self:
r.append( tsd.df_meta.loc[labelname] )
return pd.concat(r, axis=1)