#!/usr/bin/env python
import os, h5py, csv, sys, numpy, theano, ast
from fuel.datasets.hdf5 import H5PYDataset
test_size = 320 # `wc -l test.csv` - 1 # Minus 1 to ignore the header
train_size = 1710670 # `wc -l train.csv` - 1
stands_size = 63 # `wc -l metaData_taxistandsID_name_GPSlocation.csv` - 1
taxi_id_size = 448 # `cut -d, -f 5 train.csv test.csv | sort -u | wc -l` - 1
origin_call_size = 57124 # `cut -d, -f 3 train.csv test.csv | sort -u | wc -l` - 3 # Minus 3 to ignore "NA", "" and the header
Call_type = h5py.special_dtype(enum=(numpy.int8, {'CENTRAL': 0, 'STAND': 1, 'STREET': 2}))
Day_type = h5py.special_dtype(enum=(numpy.int8, {'NORMAL': 0, 'HOLYDAY': 1, 'HOLYDAY_EVE': 2}))
Polyline = h5py.special_dtype(vlen=theano.config.floatX)
taxi_id_dict = {}
origin_call_dict = {0: 0}
def get_unique_taxi_id(val):
if val in taxi_id_dict:
return taxi_id_dict[val]
else:
taxi_id_dict[val] = len(taxi_id_dict)
return len(taxi_id_dict) - 1
def get_unique_origin_call(val):
if val in origin_call_dict:
return origin_call_dict[val]
else:
origin_call_dict[val] = len(origin_call_dict)
return len(origin_call_dict) - 1
def read_stands(input_directory, h5file):
stands_name = h5file.create_dataset('stands_name', shape=(stands_size+1,), dtype=('a', 24))
stands_latitude = h5file.create_dataset('stands_latitude', shape=(stands_size+1,), dtype=theano.config.floatX)
stands_longitude = h5file.create_dataset('stands_longitude', shape=(stands_size+1,), dtype=theano.config.floatX)
stands_name[0] = 'None'
stands_latitude[0] = stands_longitude[0] = 0
with open(os.path.join(input_directory, 'metaData_taxistandsID_name_GPSlocation.csv'), 'r') as f:
reader = csv.reader(f)
reader.next() # header
for line in reader:
id = int(line[0])
stands_name[id] = line[1]
stands_latitude[id] = float(line[2])
stands_longitude[id] = float(line[3])
return {'stands': {array: (0, stands_size+1) for array in ['stands_name', 'stands_latitude', 'stands_longitude' ]}}
def read_taxis(input_directory, h5file, dataset, prefix):
print >> sys.stderr, 'read %s: begin' % dataset
size=globals()['%s_size'%dataset]
trip_id = numpy.empty(shape=(size,), dtype='S19')
call_type = numpy.empty(shape=(size,), dtype=Call_type)
origin_call = numpy.empty(shape=(size,), dtype=numpy.int32)
origin_stand = numpy.empty(shape=(size,), dtype=numpy.int8)
taxi_id = numpy.empty(shape=(size,), dtype=numpy.int16)
timestamp = numpy.empty(shape=(size,), dtype=numpy.int32)
day_type = numpy.empty(shape=(size,), dtype=Day_type)
missing_data = numpy.empty(shape=(size,), dtype=numpy.bool)
latitude = numpy.empty(shape=(size,), dtype=Polyline)
longitude = numpy.empty(shape=(size,), dtype=Polyline)
with open(os.path.join(input_directory, '%s.csv'%dataset), 'r') as f:
reader = csv.reader(f)
reader.next() # header
id=0
for line in reader:
if id%10000==0 and id!=0:
print >> sys.stderr, 'read %s: %d done' % (dataset, id)
trip_id[id] = line[0]
call_type[id] = ord(line[1][0]) - ord('A')
origin_call[id] = 0 if line[2]=='NA' or line[2]=='' else get_unique_origin_call(int(line[2]))
origin_stand[id] = 0 if line[3]=='NA' or line[3]=='' else int(line[3])
taxi_id[id] = get_unique_taxi_id(int(line[4]))
timestamp[id] = int(line[5])
day_type[id] = ord(line[6][0]) - ord('A')
missing_data[id] = line[7][0] == 'T'
polyline = ast.literal_eval(line[8])
latitude[id] = numpy.array([point[1] for point in polyline], dtype=theano.config.floatX)
longitude[id] = numpy.array([point[0] for point in polyline], dtype=theano.config.floatX)
id+=1
splits = {}
print >> sys.stderr, 'read %s: writing' % dataset
for array in ['trip_id', 'call_type', 'origin_call', 'origin_stand', 'taxi_id', 'timestamp', 'day_type', 'missing_data', 'latitude', 'longitude']:
name = '%s%s' % (prefix, array)
h5file.create_dataset(name, data=locals()[array])
splits[name] = (0, size)
print >> sys.stderr, 'read %s: end' % dataset
return {dataset: splits}
def unique(h5file):
unique_taxi_id = h5file.create_dataset('unique_taxi_id', shape=(taxi_id_size,), dtype=numpy.int32)
assert len(taxi_id_dict) == taxi_id_size
for k, v in taxi_id_dict.items():
unique_taxi_id[v] = k
unique_origin_call = h5file.create_dataset('unique_origin_call', shape=(origin_call_size+1,), dtype=numpy.int32)
assert len(origin_call_dict) == origin_call_size+1
for k, v in origin_call_dict.items():
unique_origin_call[v] = k
return {'unique': {'unique_taxi_id': (0, taxi_id_size), 'unique_origin_call': (0, origin_call_size+1)}}
def convert(input_directory, save_path):
h5file = h5py.File(save_path, 'w')
split = {}
split.update(read_stands(input_directory, h5file))
split.update(read_taxis(input_directory, h5file, 'train', ''))
print 'First origin_call not present in training set: ', len(origin_call_dict)
split.update(read_taxis(input_directory, h5file, 'test', 'test_'))
split.update(unique(h5file))
h5file.attrs['split'] = H5PYDataset.create_split_array(split)
h5file.flush()
h5file.close()
if __name__ == '__main__':
if len(sys.argv) != 3:
print >> sys.stderr, 'Usage: %s download_dir output_file' % sys.argv[0]
sys.exit(1)
convert(sys.argv[1], sys.argv[2])
