ian09 / cf-nade Goto Github PK

Hi sir, please give me equation to calculate MAE in Testing. i need it so much. Please tell me as soon as possible. Thanks a lot

When I use the code to generate hdf5 dataset, an IOError occurred.
This is the Traceback :

Traceback (most recent call last):
  File "movielens_1m_shuffle_itermbased.py", line 192, in <module>
    2341)
  File "movielens_1m_shuffle_itermbased.py", line 183, in main
    write_movie_data(ratings, data_path, output, seed)
  File "movielens_1m_shuffle_itermbased.py", line 111, in write_movie_data
    f = h5py.File(os.path.join(output, 'movielens-1m.hdf5'), 'w')
  File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/files.py", line 272, in __init__
    fid = make_fid(name, mode, userblock_size, fapl, swmr=swmr)
  File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/files.py", line 98, in make_fid
    fid = h5f.create(name, h5f.ACC_TRUNC, fapl=fapl, fcpl=fcpl)
  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2684)
  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2642)
  File "h5py/h5f.pyx", line 96, in h5py.h5f.create (/tmp/pip-4rPeHA-build/h5py/h5f.c:2097)
IOError: Unable to create file (Unable to open file: name = '/home/path/nade/movielens1m-shuffle-itembased-1/movielens-1m.hdf5', errno = 2, error message = 'no such file or directory', flags = 13, o_flags = 242)

But when I use the same path and code in python, it can work.

>>> import h5py
>>> h5py.File('/home/path/nade/movielens1m-shuffle-itembased-1/movielens-1m.hdf5', 'w')
<HDF5 file "movielens-1m.hdf5" (mode r+)>

How can I fix it?

hi, can you provide your splits of movielens1m and movielens10m? thanks very much!!!@Ian09

These are error message:
MovieLens1M=/home/xlf/ml_datasets/MovieLens1M-shuffle-itembased-1 python learner_masked_ordinalcost_directly_itembased_newsoftmax_timing.py 512 10 60 0.001 0.1 0.001 1e-8 500 tanh 0 0.02 Adam 0 1 0.995 /home/xlf/CF-NADE

Epoch 0
Traceback (most recent call last):
File "learner_masked_ordinalcost_directly_itembased_newsoftmax_timing.py", line 729, in
cost_value = f_get_grad(inp_r, inp_m, out_r, out_m)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 871, in call
storage_map=getattr(self.fn, 'storage_map', None))
File "/usr/local/lib/python2.7/dist-packages/theano/gof/link.py", line 314, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 859, in call
outputs = self.fn()
MemoryError:
Apply node that caused the error: CumsumOp{2}(rating_cost_sf_apply_output)
Toposort index: 112
Inputs types: [TensorType(float64, 3D)]
Inputs shapes: [(512, 6040, 5)]
Inputs strides: [(241600, 40, 8)]
Inputs values: ['not shown']
Outputs clients: [[Subtensor{::, ::, ::int64}(CumsumOp{2}.0, Constant{-1}), Elemwise{Composite{((i0 - log(i1)) * i2)}}[(0, 0)](Elemwise{log,no_inplace}.0, CumsumOp{2}.0, Subtensor{::, ::, ::int64}.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "learner_masked_ordinalcost_directly_itembased_newsoftmax_timing.py", line 651, in
cost, nll, nll_item_ratings, cost_ordinal_1N, cost_ordinal_N1, prob_item_ratings = rating_cost(y, output_ratings, input_masks, output_masks, D, d, alpha=alpha, std=std)
File "learner_masked_ordinalcost_directly_itembased_newsoftmax_timing.py", line 53, in rating_cost
accu_prob_1N = T.extra_ops.cumsum(prob_item_ratings, axis=2)

HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

Could you give me some help?Thanks a lot.

Hi,
Sorry to interupt you.
I got really impressed on your work of 'cafe-nade' and after reading the paper I found that the best rmse result on Movielen1M can reach down to 0.830,
however I can't make the result that good, and I wonder if there's something wrong with parameters or this is not the Cordinal input version?
Could you please tell me the parameters' set of the lowest RMSE and could you please tell me the sparse code version?
I really need your help, Thanks a ton!

from blocks.utils import shared_floatx_nans
from blocks.roles import add_role

pack is change. it is over.

can it run onwindows??

I was looking through the NADE code and found a strange weight matrix Q inside the TensorLinear class.

output_ = T.tensordot(input_, W, axes=[[1, 2], [0, 1]]) + b
input_mask = T.sum(input_, axis = 2)
output_masked = T.dot(input_mask, Q)
output = output_ + output_masked

Can anyone explain what this does cause I don't remember reading anything like this in the paper.

Hello,
I really interest of your model 'CF-NADE'.
However, when i test using my own dataset with h5py(like MovieLens1M), i get a memory error.
Could please release the code about sparse matrix version of code?
I think you maybe use sciply sparse matrix when experimenting with Netflix of MovieLens 10M data.

I need your help, thanks