polylearn
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A library for factorization machines and polynomial networks for classification and regression in Python.
installed as directed but still get error File "E:\Factirization_Machines\my_code\S_poly_learn_try_Aug7.py", line 3, in import polylearn as pl File "e:\Python environemnts\MYENVS\may9_sklearn0242\Lib\site-packages\polylearn-0.1.dev0-py3.8-win-amd64.egg\polylearn\__init__.py", line 1, in from .factorization_machine import FactorizationMachineRegressor File "e:\Python environemnts\MYENVS\may9_sklearn0242\Lib\site-packages\polylearn-0.1.dev0-py3.8-win-amd64.egg\polylearn\factorization_machine.py", line...
Any plans on packaging it up for pip so that the sudo isn't needed? I'm building it on a machine where I don't have sudo access so I can't finish...
I installed successfully on Windows computer by using conda but still have error polylearn_master_Jan21_2018\polylearn\tests\test_factorization_machine.py", line 14, in from polylearn.kernels import _poly_predict ImportError: No module named polylearn.kernels prove of correct installation...
pls see error mesage Microsoft Windows [Version 10.0.14393] (c) 2016 Microsoft Corporation. All rights reserved. e:\factirizatoin machine including FFM\code\polylearn_master_June6>python setup.py build Appending polylearn.tests configuration to polylearn Ignoring attempt to set...
First, thanks for the hard work on this package. It looks like a great way to get higher-order interactions to potentially improve on the standard FM models/packages. It looks like...
I'd like to apply the PolynomialNetworkRegressor to a domain in which the predicted values are constrained to be non negative. Does polylearn support non negativity constraints?
`In [1]: import polylearn --------------------------------------------------------------------------- ImportError Traceback (most recent call last) in () ----> 1 import polylearn /Users/manas/projects/polylearn/polylearn/__init__.py in () ----> 1 from .factorization_machine import FactorizationMachineRegressor 2 from .factorization_machine import...
Implements the SG algorithm from [Higher-order Factorization Machines.](http://arxiv.org/abs/1607.07195) Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata. In Proceedings of Neural Information Processing Systems (NIPS), December 2016. Todos - [x] Explicit...
I read all issues but i stil have problem python setup.py build python setup.py install pip install numpy scipy scikit-learn nose pip install sklearn-contrib-lightning SyntaxError: invalid syntax import polylearn as...