taxi

commit f9a31bd246e3c4736d3f532b566b7437eba6b4de
parent 929eaf8dd0233f8423b24b93b78c99fc9df65343
Author: Étienne Simon <esimon@esimon.eu>
Date:   Mon,  4 May 2015 15:33:34 -0400

Fix hdf5 converter

Diffstat:
M
convert_data.py
 | 
52
+++++++++++++++++++++++++++++++---------------------

1 file changed, 31 insertions(+), 21 deletions(-)
diff --git a/convert_data.py b/convert_data.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 import os, h5py, csv, sys, numpy, theano, ast
-from fuel.datasets.hdf5 import H5PYDataset
+from fuel.converters.base import fill_hdf5_file
 
 test_size = 320 # `wc -l test.csv` - 1 # Minus 1 to ignore the header
 train_size = 1710670 # `wc -l train.csv` - 1
@@ -31,9 +31,9 @@ def get_unique_origin_call(val):
         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 = numpy.empty(shape=(stands_size+1,), dtype=('a', 24))
+    stands_latitude = numpy.empty(shape=(stands_size+1,), dtype=theano.config.floatX)
+    stands_longitude = numpy.empty(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:
@@ -44,9 +44,11 @@ def read_stands(input_directory, h5file):
             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' ]}}
+    return (('stands', 'stands_name', stands_name),
+            ('stands', 'stands_latitude', stands_latitude),
+            ('stands', 'stands_longitude', stands_longitude))
 
-def read_taxis(input_directory, h5file, dataset, prefix):
+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')
@@ -78,37 +80,45 @@ def read_taxis(input_directory, h5file, dataset, prefix):
             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 = {}
+    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)
+    for name in ['trip_id', 'call_type', 'origin_call', 'origin_stand', 'taxi_id', 'timestamp', 'day_type', 'missing_data', 'latitude', 'longitude']:
+        splits += ((dataset, name, locals()[name]),)
     print >> sys.stderr, 'read %s: end' % dataset
-    return {dataset: splits}
+    return splits
 
 def unique(h5file):
-    unique_taxi_id = h5file.create_dataset('unique_taxi_id', shape=(taxi_id_size,), dtype=numpy.int32)
+    unique_taxi_id = numpy.empty(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)
+    unique_origin_call = numpy.empty(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)}}
+    return (('unique_taxi_id', 'unique_taxi_id', unique_taxi_id),
+            ('unique_origin_call', 'unique_origin_call', unique_origin_call))
 
 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', ''))
+    split = ()
+    split += read_stands(input_directory, h5file)
+    split += 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)
+    split += read_taxis(input_directory, h5file, 'test')
+    split += unique(h5file)
+
+    fill_hdf5_file(h5file, split)
+
+    for name in ['stands_name', 'stands_latitude', 'stands_longitude']:
+        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()