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import os |
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import pickle |
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import logging |
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from fuel.datasets import H5PYDataset |
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logger = logging.getLogger(__name__) |
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class CASASFuel(object): |
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"""CASASFuel Class to retrieve CASAS smart home data as a fuel dataset object |
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Args: |
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dir_name (:obj:`string`): |
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Directory path that contains HDF5 dataset file and complementary dataset information pkl file |
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Attributes: |
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data_filename (:obj:`str`): Path to `data.hdf5` dataset file |
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info (:obj:`dict`): complementary dataset information stored in dict format |
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keys of info includes: |
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""" |
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def __init__(self, dir_name): |
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logger.debug('Load Casas H5PYDataset from ' + dir_name) |
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self.data_filename = dir_name + '/data.hdf5' |
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if os.path.isfile(dir_name + '/info.pkl'): |
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f = open(dir_name + '/info.pkl', 'rb') |
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self.info = pickle.load(f) |
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f.close() |
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else: |
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logger.error('Cannot find info.pkl from current H5PYDataset directory %s' % dir_name) |
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def get_dataset(self, which_sets, load_in_memory=False, **kwargs): |
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"""Return fuel dataset object specified by which_sets tuple and load it in memory |
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Args: |
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which_sets (:obj:`tuple` of :obj:`str`): containing the name of splits to load. |
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Valid value are determined by the ``info.pkl`` loaded. |
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You can get the list of split set names by :meth:`get_set_list()`. |
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Usually, if the dataset is split by weeks, the split name is in the form of ``week <num>``. |
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If the dataset is split by days, the split name is in the form of ``day <num>``. |
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load_in_memory (:obj:`bool`, Optional): Default to False. |
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Whether to load the data in main memory. |
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Returns: |
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:class:`fuel.datasets.base.Dataset`: A Fuel dataset object created by |
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:class:`fuel.datasets.h5py.H5PYDataset` |
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""" |
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# Check if sets exist as split name in metadata |
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for set_name in which_sets: |
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if set_name not in self.info['split_sets']: |
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logger.error('set %s not found in splits' % set_name) |
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# Load specified splits and return |
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return H5PYDataset(file_or_path=self.data_filename, |
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which_sets=which_sets, |
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load_in_memory=load_in_memory, **kwargs) |
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def get_set_list(self): |
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"""Get the split set list |
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Returns: |
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:obj:`tuple` of :obj:`str`: A list of split set names |
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""" |
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return self.info['split_sets'] |
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def get_input_dims(self): |
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"""Get the dimension of features |
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Returns: |
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:obj:`int` : the input feature length |
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""" |
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dims = len(self.info['index_to_feature']) |
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return dims |
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def get_output_dims(self): |
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"""Get the dimension of target indices |
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Returns: |
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:obj:`int` : the target indices |
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""" |
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dims = len(self.info['index_to_activity']) |
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return dims |
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def get_activity_by_index(self, index): |
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"""Get activity name by index |
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Args: |
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index (:obj:`int`): Activity index |
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Returns: |
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:obj:`str`: Activity label |
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""" |
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activity_len = len(self.info['index_to_activity']) |
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if index < activity_len: |
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return self.info['index_to_activity'][index] |
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else: |
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logger.error('Activity index %d out of bound. Dataset has %d activities' % (index, activity_len)) |
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return '' |
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def get_feature_by_index(self, index): |
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"""Get feature string by index |
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Args: |
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index (:obj:`int`): Feature index |
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Returns: |
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:obj:`str`: Feature string |
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""" |
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feature_len = len(self.info['index_to_feature']) |
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if index < feature_len: |
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return self.info['index_to_feature'][index] |
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else: |
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logger.error('Feature index %d out of bound. Dataset has %d features' % (index, feature_len)) |
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return '' |
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TinghuiWang /
pyActLearn
