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import datetime
import numpy
import theano
import fuel
from fuel.schemes import ConstantScheme
from fuel.transformers import Batch, Mapping, SortMapping, Transformer, Unpack
import data
fuel.config.default_seed = 123
def at_least_k(k, v, pad_at_begin, is_longitude):
if len(v) == 0:
v = numpy.array([data.train_gps_mean[1 if is_longitude else 0]], dtype=theano.config.floatX)
if len(v) < k:
if pad_at_begin:
v = numpy.concatenate((numpy.full((k - len(v),), v[0]), v))
else:
v = numpy.concatenate((v, numpy.full((k - len(v),), v[-1])))
return v
class Select(Transformer):
produces_examples = True
def __init__(self, data_stream, sources):
super(Select, self).__init__(data_stream)
self.ids = [data_stream.sources.index(source) for source in sources]
self.sources=sources
def get_data(self, request=None):
if request is not None:
raise ValueError
data=next(self.child_epoch_iterator)
return [data[id] for id in self.ids]
class TaxiExcludeTrips(Transformer):
produces_examples = True
def __init__(self, stream, exclude_list):
super(TaxiExcludeTrips, self).__init__(stream)
self.id_trip_id = stream.sources.index('trip_id')
self.exclude = {v: True for v in exclude_list}
def get_data(self, request=None):
if request is not None: raise ValueError
while True:
data = next(self.child_epoch_iterator)
if not data[self.id_trip_id] in self.exclude: break
return data
class TaxiExcludeEmptyTrips(Transformer):
produces_examples = True
def __init__(self, stream):
super(TaxiExcludeEmptyTrips, self).__init__(stream)
self.latitude = stream.sources.index('latitude')
def get_data(self, request=None):
if request is not None: raise ValueError
while True:
data = next(self.child_epoch_iterator)
if len(data[self.latitude])>0: break
return data
class TaxiGenerateSplits(Transformer):
produces_examples = True
def __init__(self, data_stream, max_splits=-1):
super(TaxiGenerateSplits, self).__init__(data_stream)
self.sources = data_stream.sources
if not data.tvt:
self.sources += ('destination_latitude', 'destination_longitude', 'travel_time')
self.max_splits = max_splits
self.data = None
self.splits = []
self.isplit = 0
self.id_latitude = data_stream.sources.index('latitude')
self.id_longitude = data_stream.sources.index('longitude')
self.rng = numpy.random.RandomState(fuel.config.default_seed)
def get_data(self, request=None):
if request is not None:
raise ValueError
while self.isplit >= len(self.splits):
self.data = next(self.child_epoch_iterator)
self.splits = range(len(self.data[self.id_longitude]))
self.rng.shuffle(self.splits)
if self.max_splits != -1 and len(self.splits) > self.max_splits:
self.splits = self.splits[:self.max_splits]
self.isplit = 0
i = self.isplit
self.isplit += 1
n = self.splits[i]+1
r = list(self.data)
r[self.id_latitude] = numpy.array(r[self.id_latitude][:n], dtype=theano.config.floatX)
r[self.id_longitude] = numpy.array(r[self.id_longitude][:n], dtype=theano.config.floatX)
r = tuple(r)
if data.tvt:
return r
else:
dlat = numpy.float32(self.data[self.id_latitude][-1])
dlon = numpy.float32(self.data[self.id_longitude][-1])
ttime = numpy.int32(15 * (len(self.data[self.id_longitude]) - 1))
return r + (dlat, dlon, ttime)
class _taxi_add_first_last_len_helper(object):
def __init__(self, k, id_latitude, id_longitude):
self.k = k
self.id_latitude = id_latitude
self.id_longitude = id_longitude
def __call__(self, data):
first_k = (numpy.array(at_least_k(self.k, data[self.id_latitude], False, False)[:self.k],
dtype=theano.config.floatX),
numpy.array(at_least_k(self.k, data[self.id_longitude], False, True)[:self.k],
dtype=theano.config.floatX))
last_k = (numpy.array(at_least_k(self.k, data[self.id_latitude], True, False)[-self.k:],
dtype=theano.config.floatX),
numpy.array(at_least_k(self.k, data[self.id_longitude], True, True)[-self.k:],
dtype=theano.config.floatX))
input_time = (numpy.int32(15 * (len(data[self.id_latitude]) - 1)),)
return first_k + last_k + input_time
def taxi_add_first_last_len(stream, k):
fun = _taxi_add_first_last_len_helper(k, stream.sources.index('latitude'), stream.sources.index('longitude'))
return Mapping(stream, fun, add_sources=('first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude', 'input_time'))
class _taxi_add_datetime_helper(object):
def __init__(self, key):
self.key = key
def __call__(self, data):
ts = data[self.key]
date = datetime.datetime.utcfromtimestamp(ts)
yearweek = date.isocalendar()[1] - 1
info = (numpy.int8(51 if yearweek == 52 else yearweek),
numpy.int8(date.weekday()),
numpy.int8(date.hour * 4 + date.minute / 15))
return info
def taxi_add_datetime(stream):
fun = _taxi_add_datetime_helper(stream.sources.index('timestamp'))
return Mapping(stream, fun, add_sources=('week_of_year', 'day_of_week', 'qhour_of_day'))
class _balanced_batch_helper(object):
def __init__(self, key):
self.key = key
def __call__(self, data):
return len(data[self.key])
def balanced_batch(stream, key, batch_size, batch_sort_size):
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size * batch_sort_size))
comparison = _balanced_batch_helper(stream.sources.index(key))
stream = Mapping(stream, SortMapping(comparison))
stream = Unpack(stream)
return Batch(stream, iteration_scheme=ConstantScheme(batch_size))
class _taxi_remove_test_only_clients_helper(object):
def __init__(self, key):
self.key = key
def __call__(self, x):
x = list(x)
if x[self.key] >= data.origin_call_train_size:
x[self.key] = numpy.int32(0)
return tuple(x)
def taxi_remove_test_only_clients(stream):
fun = _taxi_remove_test_only_clients_helper(stream.sources.index('origin_call'))
return Mapping(stream, fun)
class _add_destination_helper(object):
def __init__(self, latitude, longitude):
self.latitude = latitude
self.longitude = longitude
def __call__(self, data):
return (data[self.latitude][-1], data[self.longitude][-1])
def add_destination(stream):
fun = _add_destination_helper(stream.sources.index('latitude'), stream.sources.index('longitude'))
return Mapping(stream, fun, add_sources=('destination_latitude', 'destination_longitude'))
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