When applying ttv (tensor times vector) between a sparse tensor and a set of vectors, if the result is a sparse tensor, I get the following error:

"TypeError: arange: scalar arguments expected instead of a tuple."

The error can be reproduced with this test case:

    def test_ttv():
        subs = (
            array([0, 1, 0, 5, 7, 8]),
            array([2, 0, 4, 5, 3, 9]),
            array([0, 1, 2, 2, 1, 0])
        )
        vals = array([1, 1, 1, 1, 1, 1])
        S = sptensor(subs, vals, shape=[10, 10, 3])

        sttv = S.ttv((zeros(10), zeros(10)), modes=[0, 1])
        assert_equal(type(sttv), sptensor)
        assert_true((allclose(zeros(3), sttv.vals)))
        assert_true((allclose(np.arange(3), sttv.subs)))

This is a first effort to document this package. The docs can be created using

python setup.py build_sphinx

The result ends up in build/sphinx/html/index.html.

All inline LaTeX will be rendered using pngmath (custom macros like \tens, \kr and \unfold are defined in docs/conf.py).

setup.py defined some redundant build_sphinx command that was trying to build scipy first. I removed this as we do not need that and can directly build docs.

I recommend creating the package on https://readthedocs.org/ so you will have automatically generated online docs.

This PR enables scikit-tensor to install without user interaction. Instead of failing during install

pip install scikit-tensor

will now automatically install numpy, scipy and nose, if not already installed.

Reproduction script is the example in the README

Python 3.4.1 |Anaconda 2.0.0 (64-bit)| (default, May 19 2014, 13:02:41) [GCC 4.1.2 20080704 (Red Hat 4.1.2-54)] on linux

git commit ef063d0e2f7f3160caf05b2b988ef3479ea2e1c9

Works fine in 2.7 (I switched to a 2.7 environment using Anaconda)

Traceback (most recent call last):
  File "tmp.py", line 13, in <module>
    P, fit, itr, exectimes = cp_als(T, 3, init='random')
  File "/home/kkastner/src/scikit-tensor/sktensor/cp.py", line 143, in als
    Unew = X.uttkrp(U, n)
  File "/home/kkastner/src/scikit-tensor/sktensor/dtensor.py", line 162, in uttkrp
    order = range(n) + range(n + 1, self.ndim)
TypeError: unsupported operand type(s) for +: 'range' and 'range'

I will take a look and see if I can submit a PR

Some fixes in sptensor:

• accum correction for cases with more than one dimension: accum function was not behaving as expected when the number of dimensions in subs was more than one.
• Corrections in sptensor and test case to avoid test_unfold failure: with random initialization of test data, test_unfold was also failing randomly (only when cases with duplicated indexes are created). I added a seed initialization for a case where the test was failing and corrected the problems.
• Optimized sptensor creation in ttv_compute when result is vector-like. Also, in this case, the sptensor should be initialized only with nonzero values (also corrected).

This error occurs when calling uttkrp and when ttv (inside uttkrp) returns a sptensor. It can be reproduced with this test case:

def test_uttkrp():
    subs, vals, shape = mysetup()
    S = sptensor(subs, vals, shape)
    U = []
    for shp in (25, 11, 18, 7, 2):
        U.append(np.zeros((shp, 5)))
    SU = S.uttkrp(U, mode=0)
    assert_equal(SU.shape, (25, 5))

The error is "TypeError: float() argument must be a string or a number", and it happens in sptensor.py, line 191, in uttkrp:

    V[:, r] = self.ttv(Z, mode, without=True)

It seems that, when the result of ttv is a sptensor, it cannot be assigned to V using this operation.

This PR replaces Nosetests with Pytest. Nosetests has been dead for a long time and Nose2 is nowhere near Pytest in terms of development activity, number of users and maturity.

What this PR does:

• Replace Nose with Pytest
• Remove "fixtures" that are messing with sys.modules. This was a huge antipattern actually, you never want tests to mess with global variables whose state you cannot track. Pytest fixtures do a similar thing to what you wanted them to do, but in a nice way of explicitly injecting them into tests.
• Adds a .travis.yml file so integration testing can be used (you only need to enable it).

What this PR doesn't do:

• Fix problems in sktensor/tests/test_tucker_hooi.py: You are importing sktensor.rotation.orthomax which doesn't exist and, judging from Git logs, never existed before. I don't think these tests ever passed in the past.

mnick/scikit-tensor now has a Chat Room on Gitter

@mnick has just created a chat room. You can visit it here: https://gitter.im/mnick/scikit-tensor.

This pull-request adds this badge to your README.md:

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This pull request should fix the tensor shape inference when creating a sparse tensor. When inferring the shape of the tensor from the maximum values of the subs arrays, the shape values must be incremented by 1 in order to fit a 0-based index array.

https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/tucker.py#L103

This calculation of U_tilde does not correspond to the formula in the paper!!

I need to run the tucker with a sparse tensor. I got this error : TypeError: 'numpy.int32' object is not iterable When I run this code: from sktensor import tucker_hooi from sktensor import sptensor

S = sptensor(([0,1,2], [3,2,0], [2,2,2]), [1,1,1], shape=(10, 20, 5), dtype=np.float)

tucker_hooi(X=S, rank=[5, 5, 4], init='nvecs')

Can you help me, please? Thanks.

I have a custom layer in keras where the input is a 2D tensor, but the layer takes a batch of those and the shape it gets is (?, 61,80). The '?' part is for the batch_size . While using the ttm function of sktensor I am getting the error . please help.

The custom layer is

 def call(self, inputs):

num, n, m = inputs.shape 
print(inputs.shape)
(k1,k2) = self.output_dim
input_tensor = inputs 

print(input_tensor)
     
input_tensor = dtensor(input_tensor)
kernel1=np.array(self.W1)
kernel2=np.array(self.W2)



feed_forward_product=input_tensor.ttm([kernel1, kernel2],mode=0, transp=False, without=True)

feed_forward_product = np.array(feed_forward_product )
result = K.tanh(feed_forward_product)

return result

Variables W1 and W2 are declared beforehand. The error I am getting is-

x_train shape: (269, 61, 80) 269 train samples 70 test samples <tf.Variable 'neural_tensor_layer_2/W1:0' shape=(40, 61) dtype=float32_ref> <tf.Variable 'neural_tensor_layer_2/W2:0' shape=(40, 80) dtype=float32_ref> (?, 61, 80) Tensor("sequential_2_input:0", shape=(?, 61, 80), dtype=float32) Traceback (most recent call last):

File "", line 1, in runfile('/home/hanumant/Documents/December_experiments/python_codes/TFNN_v2.py', wdir='/home/hanumant/Documents/December_experiments/python_codes')

File "/home/hanumant/.conda/envs/myenv/lib/python3.5/site-packages/spyder_kernels/customize/spydercustomize.py", line 668, in runfile execfile(filename, namespace)

File "/home/hanumant/.conda/envs/myenv/lib/python3.5/site-packages/spyder_kernels/customize/spydercustomize.py", line 108, in execfile exec(compile(f.read(), filename, 'exec'), namespace)

File "/home/hanumant/Documents/December_experiments/python_codes/TFNN_v2.py", line 79, in validation_data=(np.array(x_test),np.array(y_test)))

File "/home/hanumant/.conda/envs/myenv/lib/python3.5/site-packages/keras/engine/training.py", line 952, in fit batch_size=batch_size)

File "/home/hanumant/.conda/envs/myenv/lib/python3.5/site-packages/keras/engine/training.py", line 677, in _standardize_user_data self._set_inputs(x)

File "/home/hanumant/.conda/envs/myenv/lib/python3.5/site-packages/keras/engine/training.py", line 589, in _set_inputs self.build(input_shape=(None,) + inputs.shape[1:])

File "/home/hanumant/.conda/envs/myenv/lib/python3.5/site-packages/keras/engine/sequential.py", line 221, in build x = layer(x)

File "/home/hanumant/.conda/envs/myenv/lib/python3.5/site-packages/keras/engine/base_layer.py", line 457, in call output = self.call(inputs, **kwargs)

File "/home/hanumant/Documents/December_experiments/python_codes/Neural_Tensor_layer.py", line 138, in call feed_forward_product=input_tensor.ttm([kernel1, kernel2],mode=0, transp=False, without=True)

File "/home/hanumant/Downloads/scikit-tensor-master/scikit-tensor/sktensor/core.py", line 99, in ttm dims, vidx = check_multiplication_dims(mode, self.ndim, len(V), vidx=True, without=without)

File "/home/hanumant/Downloads/scikit-tensor-master/scikit-tensor/sktensor/core.py", line 256, in check_multiplication_dims raise ValueError('More multiplicants than dimensions')

ValueError: More multiplicants than dimensions

please help

These three metrics are returned on the CP-ALS function. but not on the HOOI.

I believe it is a simple add, since in the hooi() function fit, itr and exectimes are already calculated.

Maybe sktensor/tests/test_tucker_hooi.py also needs to be changed.

I'm getting a fatal installation error when installing scikit-tensor on my Mac:

~$ pip3 --version pip 10.0.1 from /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pip (python 3.6) ~$ pip3 install scikit-tensor Collecting scikit-tensor Using cached https://files.pythonhosted.org/packages/e9/5e/2ce76cc8f9da0517085e17cd70210ed996aeb8f972e7080d0bc89d82bbd9/scikit-tensor-0.1.tar.gz Complete output from command python setup.py egg_info: Traceback (most recent call last): File "", line 1, in File "/private/var/folders/4y/cwpdv5dd37q0djhsgnr558m0000b4c/T/pip-install-4m41b2gw/scikit-tensor/setup.py", line 79 print mod.version ^ SyntaxError: Missing parentheses in call to 'print'. Did you mean print(mod.version)?

----------------------------------------

Command "python setup.py egg_info" failed with error code 1 in /private/var/folders/4y/cwpdv5dd37q0djhsgnr558m0000b4c/T/pip-install-4m41b2gw/scikit-tensor/ ~$

In line 71 of sptensor.py, I believe the condition to check should be len(subs) == len(vals). The reason is that we want to check the number of provided subscripts to be equal to the number of values. Currently, it is checking the dimension of the tensor to be equal to the number of values which probably isn't right. Although, it works in the provided example because the dimension, the subscripts, and the values are all three. Correct me, if I am wrong.