After doing step 1 (load data set) it shows error in line 15 and onwards of factorise.py(i am new to python) is there something i am doing wrong 1)i installed python first 2)then pip and dependies 3)then placed movie lens data set in respective places 4)install python-recys-master

Now when i run first step this error even though i have installed divis2 ::

Traceback (most recent call last): File "load.py", line 1, in from recsys.algorithm.factorize import SVD File "/home/abhimanyu/Desktop/python-recsys-master/recsys/algorithm/factorize.py", line 15, in from csc import divisi2 ImportError: No module named csc

According to the python-recsys v1.0 documentation Algorithms，i put movielens-1M ratiing.dat in /usr/local/python-recsys-master/recsys/data/movielens/, ,then i load data,!!! but when i comput,ValueError: No data set. Matrix is empty!I want to konw why ?

from recsys.algorithm.factorize import SVD
filename = '/usr/local/python-recsys-master/recsys/data/movielens/ratings.dat'
svd = SVD()
svd.load_data(filename=filename, sep='::', format={'col':0, 'row':1, 'value':2, 'ids':int})

from recsys.datamodel.data import Data
from recsys.algorithm.factorize import SVD
filename = '/usr/local/python-recsys-master/recsys/data/movielens/ratings.dat'
data = Data()
format = {'col':0, 'row':1, 'value':2, 'ids': int}
data.load(filename, sep='::', format=format)
train, test = data.split_train_test(percent=80)
svd = SVD()
svd.set_data(train)

from recsys.utils.svdlibc import SVDLIBC
svdlibc = SVDLIBC('/usr/local/python-recsys-master/recsys/data/movielens/ratings.dat')
svdlibc.to_sparse_matrix(sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
svdlibc.compute(k=100)
svd = svdlibc.export()

!!! but when i comput,ValueError: No data set. Matrix is empty!I want to konw why ?

K=100
svd.compute(k=K, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True, 
savefile=None)
Traceback (most recent call last):
File "", line 3, in 
File "recsys/algorithm/factorize.py", line 244, in compute
super(SVD, self).compute(min_values)
File "recsys/algorithm/baseclass.py", line 126, in compute
raise ValueError('No data set. Matrix is empty!')
ValueError: No data set. Matrix is empty!

I have a dat-file with 3705912 lines, matrix 602x6156. When I call svd.recommend for identifiers have little values I get KeyError.

svd.get_matrix().get_col(50652)

SparseVector (4 of 602 entries): [6840789=14, 6843925=100, 6843926=100, 6843927=16]


svd.recommend(50652, is_row=False)

Traceback (most recent call last):
  File "svd.py", line 251, in <module>
    print svd.recommend(50652, is_row=False)
  File "/home/igor/sandbox/svd/local/lib/python2.7/site-packages/recsys/algorithm/factorize.py", line 352, in recommend
    item = self._get_col_reconstructed(i, zeros)
  File "/home/igor/sandbox/svd/local/lib/python2.7/site-packages/recsys/algorithm/factorize.py", line 300, in _get_col_reconstructed
    return self._matrix_reconstructed.col_named(j)
  File "/home/igor/sandbox/svd/local/lib/python2.7/site-packages/divisi2/labels.py", line 65, in col_named
    return self[:,self.col_index(label)]
  File "/home/igor/sandbox/svd/local/lib/python2.7/site-packages/divisi2/labels.py", line 57, in col_index
    return self.col_labels.index(label)
KeyError: 50652

But for identifiers have many values

svd.get_matrix().get_col(10536)

SparseVector (22 of 602 entries): [6840778=96, 6840779=100, 6840780=100, 6840781=100, 6840782=100, 6840783=100, 6840784=100, 6840785=100, 6840786=65, 6840818=83, 6840819=100, 6840820=100, 6840821=100, 6840822=100, 6840823=100, 6840824=100, 6840825=100, 6840826=100, 6840827=100, 6840828=21, ...]


svd.recommend(10536, is_row=False)

[(6900161, 100.00000000232269), (6840819, 100.00000000214945), (6840822, 100.00000000186564), (6840821, 100.00000000178625), (6840820, 100.0000000016603), (6840783, 100.00000000144556), (6840827, 100.00000000137024), (6840826, 100.00000000134551), (6840784, 100.00000000123218), (6840825, 100.00000000112296)]

hi, please I need to do recommender with id_user="X3escB3aJ_rP1u5DaTN9cw" but It does not allow it because the character hyphen. in the case id_user="X3escB3aJ_rP1u5DaTN9cw"

"X3escB3aJ_rP1u5DaTN9cw Creating matrix (426351 tuples) Matrix density is: 0.0237% Updating matrix: squish to at least 10 values Computing svd k=10, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True [WARNING] mean_center is True. svd.similar(...) might return nan's. If so, then do svd.compute(..., mean_center=False) /root/anaconda2/lib/python2.7/site-packages/divisi2/dense.py:269: RuntimeWarning: invalid value encountered in divide return self / norms Traceback (most recent call last): File "ejemplo.py", line 47, in recomendaciones= svd.recommend(id_user, n=10,is_row=False) File "build/bdist.linux-x86_64/egg/recsys/algorithm/factorize.py", line 352, in recommend File "build/bdist.linux-x86_64/egg/recsys/algorithm/factorize.py", line 300, in _get_col_reconstructed File "/root/anaconda2/lib/python2.7/site-packages/divisi2/labels.py", line 65, in col_named return self[:,self.col_index(label)] File "/root/anaconda2/lib/python2.7/site-packages/divisi2/labels.py", line 57, in col_index return self.col_labels.index(label) KeyError: 'X3escB3aJ_rP1u5DaTN9cw'"

Thanks

Used the Movielens SVD example in my class tonight to show them a rec system.

People (including me) who were using a MAC went though the tutorial without issues.

All people on Windows (7 and 8.1): the kernel fails on the "svd.compute" step.

I believe everyone is using the 2.7+ Anaconda version of Python.

thoughts?

I have tried some data set, but if I call the functions svd.similarity() or svd.recommend(), the ouput of the console is :

Traceback (most recent call last): File "recsys_data.py", line 20, in svd.compute(k=k, min_values=5, pre_normalize=None, mean_center=True, post_normalize=True,savefile=None) File "/usr/local/lib/python2.7/dist-packages/python_recsys-0.2-py2.7.egg/recsys/algorithm/factorize.py", line 244, in compute super(SVD, self).compute(min_values) File "/usr/local/lib/python2.7/dist-packages/python_recsys-0.2-py2.7.egg/recsys/algorithm/baseclass.py", line 126, in compute raise ValueError('No data set. Matrix is empty!') ValueError: No data set. Matrix is empty!

I want to konw why ?

If you do this:

rmse = RMSE() rmse.load(x, y)

with x and y being numpy uni-dimensional arrays, you get this:

in compute(self) 98 Computes the evaluation using the loaded ground truth and test lists 99 """ --> 100 if not self._ground_truth: 101 raise ValueError('Ground Truth dataset is empty!') 102 if not self._test:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I fixed it this way:

    if self._ground_truth is None:
        raise ValueError('Ground Truth dataset is empty!')
    if self._test is None:
        raise ValueError('Test dataset is empty!')

hi

I have this user=1 stars=nn.get("rating") movie_id=nn.get("movieId") id_user=nn.get("userId") tupla=(stars,movie_id,id_user) #if tupla=(4.0, 1193, 2) data.add_tuple(tupla)

and I have this problem when: recomendaciones= svd.recommend(user, n=10, only_unknowns=True, is_row=False)

ERROR

File "/usr/local/lib/python2.7/dist-packages/pysparse/sparse/pysparseMatrix.py", line 149, in getitem m = self.matrix[index] IndexError: Error creating second index list

I want to try out the python-recsys but it is giving me the error while loading the movie ratings mail.

**>>> svd.load_data(filename='./movierates.dat', sep='::', format={'col':0,'row':1,'value':2,'ids': int})

Error (ID is not int) while reading: [u'userId', u'movieId', u'rating', u'timestamp'] Error while reading: [u'']**

Hi, now I am testing recsys algorithm for only similar users finding. Each output result contains 10 users with similarity scores (actually it has 9 similar users and 1 search/target user). I want to increase the number of results from 10 to 50. I couldn't find about it in documentation of parameter setting . Could you please give me a direction?

When trying to get SVD.similarity(i, j), how do I specify if I'm looking users or items?

Thanks!

http://ocelma.net/software/python-recsys/build/html/api.html#recsys.algorithm.baseclass.Algorithm.similarity

Hi @ocelma ,

I currently "own" (but do not use) the recsys package name on PyPI. recsys was the name I wanted to use when I was developing Surprise, but I later realized that it would conflict with your own python-recsys package, whose import-time name is also recsys. (so I had to come up with a more far-fetched acronym! 😅).

@jiwidi recently reached out to me, asking if I'd be open to transferring the PyPI recsys name to him. I don't use the namespace myself so I'm happy to transfer; that being said, @jiwidi and I both agree that it's best to reach out to you first.

Is it fine with you for @jiwidi to own and use the recsys name on PyPI? (that means users will install and use the project with pip install recsys / import recsys). We also reached out to you via email a few weeks ago, so feel free to follow up there if you prefer.

Thank you!

There is a small typo in recsys/datamodel/data.py.

Should read relationships rather than relationshops.

Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

• Now supports incrementally adding new users or items instead of building the model from scratch for these new users or items via the folding-in technique which was mentioned in Sarwar et al.'s paper_ (Titled: Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems), this latest commit is simply an implementation to it for python-recsys.

.. _paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.7894&rep=rep1&type=pdf

• A Demonstration video is available_ for this latest commit in form of a demo site built using the MEAN stack which uses the updated python-recsys as backend for the recommender which folds-in the website's user in to the SVD model and gets recommendations instantaneously instead of building the model from scratch.

.. _Demonstration video is available: https://youtu.be/tIvQxBfa2d4

• There is also an accompanying bachelor thesis paper_ (For those interested) which outlines the background, architecture and discusses the "Folding-in" approach.

.. _bachelor thesis paper: https://drive.google.com/file/d/0BylQe2cRVWE_RmZoUTJYSGZNaXM/view

Hi Oscar, The calculation of AveragePrecision in recsys.evaluation.ranking is not correct. The returned value should be sum(p_at_k)/number of relevant items, rather than sum(p_at_k)/hits.

In your document --> evaluation, the corresponding part also needs to be changed.

from recsys.evaluation.ranking import AveragePrecision

ap = AveragePrecision()

GT = [1,2,3,4,5] q = [1,3,5] ap.load(GT, q) ap.compute() # returns 1.0, should return 0.6

GT = [1,2,3,4,5] q = [99,3,5] ap.load(GT, q) ap.compute() # returns 0.5833335, should return 0.23333

Kind Regards, Siqi

while I was trying to install csc-pyparse I got this error

pysparse/sparse/src/spmatrixmodule.c:1:20: fatal error: Python.h: No such file or directory

so it seems that csc-pyparse couldn't be installed for newer versions of python (python 3) check here

so could you port python-recsys to SciPy as an alternative or solve this problem please :/ .