mldata 1.0 #2
mldata 1.0 #2
Conversation
The storage requirement will instead be controlled by the driver (hdf5).
…unsupervised learning.
… datasets folders.
__iter__() and __getitem__ are false methods. They actually are class methods, which makes their definition on the fly quite tricky. The old solution would reassing the correct definition in the __init__ method. However, since it is a class method, it also affect other object (that might have a need of the other iterator or getter). Thus, the best way to make it work is the naive way that checks each time is dataset.target is None.
.travis.yml
Outdated
There was a problem hiding this comment.
Support for Python3.4 is not release yet for Travis. See travis-ci/travis-ci#1989
There was a problem hiding this comment.
By the time you'll get around to fix CI, support should be available.
|
Folder I know we discussed it, but are we planning on supporting both Python2.7.+ and Python3.+ ? |
tests/utils/test_config.py
Outdated
There was a problem hiding this comment.
This line failed when $HOME/.mldataConfig does not already exist.
There was a problem hiding this comment.
Good catch, will be fixed.
|
Why is there a capitalized D in |
|
There is no folder |
|
Oups, you're right about Regarding the uppercase D, I think it should be put in lowercase because it is refering to the file you are testing: that is |
mldata/utils/config.py
Outdated
mldata/dataset_store.py
Outdated
There was a problem hiding this comment.
If the dataset is not found in the config file, _load_from_file will fail on the os.join with None. Maybe we should display a better error message.
We can now either gave the ``splits`` be given in the form (nb_train, ..., nb_test) or (nb_train, ..., nb_train + ... + nb_valid)
Creates a tuple, each containing a generator over a part of the dataset, following the given splits.
Promote the use of itertools.cycle(iter) instead.
There was a problem hiding this comment.
If lazy==False do we want data to be a ndarray? Right now it is a HDF5 dataset but still supports iteration and indexing as numpy.
2 participants
Adds :