Add data converter to hdf5

1 files changed, 118 insertions, 0 deletions

diff --git a/convert_data.py b/convert_data.py
new file mode 100755
index 0000000..c58de3f
--- /dev/null
+++ b/convert_data.py
@@ -0,0 +1,118 @@
+#!/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 = numpy.array(ast.literal_eval(line[8]), dtype=theano.config.floatX)
+            latitude[id] = polyline[:, 1] if polyline.ndim==2 else polyline
+            longitude[id] = polyline[:, 0] if polyline.ndim==2 else polyline
+            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, 'test', 'test_'))
+    split.update(read_taxis(input_directory, h5file, 'train', ''))
+    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])