def __init__(self, **kwargs):
"""
Even Length Grid Partitioner
:param seasonality: Time granularity, from pyFTS.models.seasonal.common.DateTime
:param data: Training data of which the universe of discourse will be extracted. The universe of discourse is the open interval between the minimum and maximum values of the training data.
:param npart: The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created
:param func: Fuzzy membership function (pyFTS.common.Membership)
"""
super(TimeGridPartitioner, self).__init__(name="TimeGrid",
preprocess=False,
**kwargs)
self.season = kwargs.get('seasonality', DateTime.day_of_year)
data = kwargs.get('data', None)
if self.season == DateTime.year:
ndata = [strip_datepart(k, self.season) for k in data]
self.min = min(ndata)
self.max = max(ndata)
else:
tmp = (self.season.value / self.partitions) / 2
self.min = tmp
self.max = self.season.value + tmp
self.type = kwargs.get('type', 'seasonal')
self.sets = self.build(None)
if self.ordered_sets is None and self.setnames is not None:
self.ordered_sets = self.setnames
else:
self.ordered_sets = FS.set_ordered(self.sets)
if self.type == 'seasonal':
self.extractor = lambda x: strip_datepart(x, self.season)
def get_season_of_data(self, data):
ret = []
if isinstance(data, pd.DataFrame):
for ix in data.index:
date = data[self.date_field][ix]
season = []
for c, f in enumerate(self.fields, start=0):
tmp = common.strip_datepart(date, f)
if self.seasons[c] is not None:
tmp = tmp // self.seasons[c]
season.append(tmp)
ret.append(season)
elif isinstance(data, pd.Series):
date = data[self.date_field]
season = []
for c, f in enumerate(self.fields, start=0):
season.append(common.strip_datepart(date, f, self.seasons[c]))
ret.append(season)
return ret
def extractor(self, x):
if self.type == 'seasonal':
return strip_datepart(x, self.season, self.mask)
else:
return x