Remove enums from hdf5

1 files changed, 9 insertions, 13 deletions

diff --git a/convert_data.py b/convert_data.py
index f069580..ca66786 100755
--- a/convert_data.py
+++ b/convert_data.py
@@ -9,8 +9,6 @@ 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 = {}
@@ -52,12 +50,12 @@ def read_taxis(input_directory, h5file, dataset):
     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)
+    call_type = numpy.empty(shape=(size,), dtype=numpy.uint8)
+    origin_call = numpy.empty(shape=(size,), dtype=numpy.uint32)
+    origin_stand = numpy.empty(shape=(size,), dtype=numpy.uint8)
+    taxi_id = numpy.empty(shape=(size,), dtype=numpy.uint16)
+    timestamp = numpy.empty(shape=(size,), dtype=numpy.uint32)
+    day_type = numpy.empty(shape=(size,), dtype=numpy.uint8)
     missing_data = numpy.empty(shape=(size,), dtype=numpy.bool)
     latitude = numpy.empty(shape=(size,), dtype=Polyline)
     longitude = numpy.empty(shape=(size,), dtype=Polyline)
@@ -88,12 +86,12 @@ def read_taxis(input_directory, h5file, dataset):
     return splits
 
 def unique(h5file):
-    unique_taxi_id = numpy.empty(shape=(taxi_id_size,), dtype=numpy.int32)
+    unique_taxi_id = numpy.empty(shape=(taxi_id_size,), dtype=numpy.uint32)
     assert len(taxi_id_dict) == taxi_id_size
     for k, v in taxi_id_dict.items():
         unique_taxi_id[v] = k
 
-    unique_origin_call = numpy.empty(shape=(origin_call_size+1,), dtype=numpy.int32)
+    unique_origin_call = numpy.empty(shape=(origin_call_size+1,), dtype=numpy.uint32)
     assert len(origin_call_dict) == origin_call_size+1
     for k, v in origin_call_dict.items():
         unique_origin_call[v] = k
@@ -112,12 +110,10 @@ def convert(input_directory, save_path):
 
     fill_hdf5_file(h5file, split)
 
-    for name in ['stands_name', 'stands_latitude', 'stands_longitude']:
+    for name in ['stands_name', 'stands_latitude', 'stands_longitude', 'unique_taxi_id', 'unique_origin_call']:
         h5file[name].dims[0].label = 'index'
     for name in ['trip_id', 'call_type', 'origin_call', 'origin_stand', 'taxi_id', 'timestamp', 'day_type', 'missing_data', 'latitude', 'longitude']:
         h5file[name].dims[0].label = 'batch'
-    h5file['unique_taxi_id'].dims[0].label = 'unormalized taxi_id'
-    h5file['unique_origin_call'].dims[0].label = 'unormalized origin_call'
 
     h5file.flush()
     h5file.close()