def from_hdf5(cls, path):
"""
Load model from a HDF5 file.
Requires ``h5py`` http://docs.h5py.org/
Parameters
----------
path : str
Full path to file.
Returns
-------
Model instance
"""
if not HDF5_INSTALLED:
raise ImportError(h5py_msg)
model = hdftools.load_dict(path, 'data')
model = cls._byte2string(model)
for k in model['hyper_params'].keys():
if model['hyper_params'][k] == 'None':
model['hyper_params'][k] = None
return cls._organize_model(cls, model)
def test_hdftools():
dtypes = [numpy.int, numpy.int8, numpy.int16, numpy.int32, numpy.int64,
numpy.float, numpy.float32, numpy.float64]
d = {}
for dtype in dtypes:
name = numpy.dtype(dtype).name
d[name] = (numpy.random.rand(100, 100) * 10).astype(dtype)
fname = os.path.join(tmp_dir, 'hdf_test.hdf5')
hdftools.save_dict(d, filename=fname, group='data')
d2 = hdftools.load_dict(fname, 'data')
for k in d2.keys():
numpy.testing.assert_equal(d[k], d2[k])