From 1f2ff96e6480a62089fcac35154a956c218ed678 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=89tienne=20Simon?= <esimon@esimon.eu>
Date: Tue, 5 May 2015 21:55:13 -0400
Subject: Clean data module and generalize use of hdf5.

---
 data/hdf5.py | 61 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 data/hdf5.py

(limited to 'data/hdf5.py')

diff --git a/data/hdf5.py b/data/hdf5.py
new file mode 100644
index 0000000..d848023
--- /dev/null
+++ b/data/hdf5.py
@@ -0,0 +1,61 @@
+import os
+
+import h5py
+from fuel.datasets import H5PYDataset
+from fuel.iterator import DataIterator
+from fuel.schemes import SequentialExampleScheme
+from fuel.streams import DataStream
+
+import data
+
+
+class TaxiDataset(H5PYDataset):
+    def __init__(self, which_set, filename='data.hdf5', **kwargs):
+        self.filename = filename
+        kwargs.setdefault('load_in_memory', True)
+        super(TaxiDataset, self).__init__(self.data_path, which_set, **kwargs)
+
+    @property
+    def data_path(self):
+        return os.path.join(data.path, self.filename)
+
+class TaxiStream(DataStream):
+    def __init__(self, which_set, filename='data.hdf5', iteration_scheme=None, **kwargs):
+        dataset = TaxiDataset(which_set, filename, **kwargs)
+        if iteration_scheme is None:
+            iteration_scheme = SequentialExampleScheme(dataset.num_examples)
+        super(TaxiStream, self).__init__(dataset, iteration_scheme=iteration_scheme)
+
+_origin_calls = None
+_reverse_origin_calls = None
+
+def origin_call_unnormalize(x):
+    if _origin_calls is None:
+        _origin_calls = h5py.File(os.path.join(data.path, 'data.hdf5'), 'r')['unique_origin_call']
+    return _origin_calls[x]
+
+def origin_call_normalize(x):
+    if _reverse_origin_calls is None:
+        origin_call_unnormalize(0)
+        _reverse_origin_calls = { _origin_calls[i]: i for i in range(_origin_calls.shape[0]) }
+    return _reverse_origin_calls[x]
+
+_taxi_ids = None
+_reverse_taxi_ids = None
+
+def taxi_id_unnormalize(x):
+    if _taxi_ids is None:
+        _taxi_ids = h5py.File(os.path.join(data.path, 'data.hdf5'), 'r')['unique_taxi_id']
+    return _taxi_ids[x]
+
+def taxi_id_normalize(x):
+    if _reverse_taxi_ids is None:
+        taxi_id_unnormalize(0)
+        _reverse_taxi_ids = { _taxi_ids[i]: i for i in range(_taxi_ids.shape[0]) }
+    return _reverse_taxi_ids[x]
+
+def taxi_it(which_set, filename='data.hdf5', sub=None, as_dict=True):
+    dataset = TaxiDataset(which_set, filename)
+    if sub is None:
+        sub = xrange(dataset.num_examples)
+    return DataIterator(DataStream(dataset), iter(sub), as_dict)
-- 
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