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import ast, csv
import socket
import fuel
import numpy
import h5py
from enum import Enum
from fuel.datasets import Dataset
from fuel.streams import DataStream
from fuel.iterator import DataIterator
import theano
if socket.gethostname() == "adeb.laptop":
DATA_PATH = "/Users/adeb/data/taxi"
else:
DATA_PATH="/data/lisatmp3/auvolat/taxikaggle"
H5DATA_PATH = '/data/lisatmp3/simonet/taxi/data.hdf5'
porto_center = numpy.array([41.1573, -8.61612], dtype=theano.config.floatX)
data_std = numpy.sqrt(numpy.array([0.00549598, 0.00333233], dtype=theano.config.floatX))
n_clients = 57124
n_train_clients = 57105
n_stands = 63
dataset_size = 1710670
# ---- Read client IDs and create reverse dictionnary
def make_client_ids():
f = h5py.File(H5DATA_PATH, "r")
l = f['unique_origin_call']
r = {}
for i in range(l.shape[0]):
r[l[i]] = i
return r
client_ids = make_client_ids()
def get_client_id(n):
if n in client_ids and client_ids[n] <= n_train_clients:
return client_ids[n]
else:
return 0
class CallType(Enum):
CENTRAL = 0
STAND = 1
STREET = 2
@classmethod
def from_data(cls, val):
if val=='A':
return cls.CENTRAL
elif val=='B':
return cls.STAND
elif val=='C':
return cls.STREET
@classmethod
def to_data(cls, val):
if val==cls.CENTRAL:
return 'A'
elif val==cls.STAND:
return 'B'
elif val==cls.STREET:
return 'C'
class DayType(Enum):
NORMAL = 0
HOLIDAY = 1
HOLIDAY_EVE = 2
@classmethod
def from_data(cls, val):
if val=='A':
return cls.NORMAL
elif val=='B':
return cls.HOLIDAY
elif val=='C':
return cls.HOLIDAY_EVE
@classmethod
def to_data(cls, val):
if val==cls.NORMAL:
return 'A'
elif val==cls.HOLIDAY:
return 'B'
elif val==cls.HOLIDAY_EVE:
return 'C'
class TaxiData(Dataset):
example_iteration_scheme=None
class State:
__slots__ = ('file', 'index', 'reader')
def __init__(self, pathes, columns, has_header=False):
if not isinstance(pathes, list):
pathes=[pathes]
assert len(pathes)>0
self.columns=columns
self.provides_sources = tuple(map(lambda x: x[0], columns))
self.pathes=pathes
self.has_header=has_header
super(TaxiData, self).__init__()
def open(self):
state=self.State()
state.file=open(self.pathes[0])
state.index=0
state.reader=csv.reader(state.file)
if self.has_header:
state.reader.next()
return state
def close(self, state):
state.file.close()
def reset(self, state):
if state.index==0:
state.file.seek(0)
else:
state.index=0
state.file.close()
state.file=open(self.pathes[0])
state.reader=csv.reader(state.file)
return state
def get_data(self, state, request=None):
if request is not None:
raise ValueError
try:
line=state.reader.next()
except (ValueError, StopIteration):
# print state.index
state.file.close()
state.index+=1
if state.index>=len(self.pathes):
raise StopIteration
state.file=open(self.pathes[state.index])
state.reader=csv.reader(state.file)
if self.has_header:
state.reader.next()
return self.get_data(state)
values = []
for _, constructor in self.columns:
values.append(constructor(line))
return tuple(values)
taxi_columns = [
("trip_id", lambda l: l[0]),
("call_type", lambda l: CallType.from_data(l[1])),
("origin_call", lambda l: 0 if l[2] == '' or l[2] == 'NA' else get_client_id(int(l[2]))),
("origin_stand", lambda l: 0 if l[3] == '' or l[3] == 'NA' else int(l[3])),
("taxi_id", lambda l: int(l[4])),
("timestamp", lambda l: int(l[5])),
("day_type", lambda l: DayType.from_data(l[6])),
("missing_data", lambda l: l[7][0] == 'T'),
("polyline", lambda l: map(tuple, ast.literal_eval(l[8]))),
("longitude", lambda l: map(lambda p: p[0], ast.literal_eval(l[8]))),
("latitude", lambda l: map(lambda p: p[1], ast.literal_eval(l[8]))),
]
taxi_columns_valid = taxi_columns + [
("destination_longitude", lambda l: float(l[9])),
("destination_latitude", lambda l: float(l[10])),
("time", lambda l: int(l[11])),
]
train_files=["%s/split/train-%02d.csv" % (DATA_PATH, i) for i in range(100)]
valid_files=["%s/split/valid.csv" % (DATA_PATH,)]
test_file="%s/test.csv" % (DATA_PATH,)
train_data=TaxiData(train_files, taxi_columns)
valid_data = TaxiData(valid_files, taxi_columns_valid)
test_data = TaxiData(test_file, taxi_columns, has_header=True)
def train_it():
return DataIterator(DataStream(train_data))
def test_it():
return DataIterator(DataStream(valid_data))
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