sparse | Sparse matrix library in Golang | Recommender System library

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kandi X-RAY | sparse Summary

sparse is a Go library typically used in Artificial Intelligence, Recommender System applications. sparse has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

A sparse matrix library in pure Golang. Currently only addition on two DOK or two CSR matrices is supported.

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Support

sparse has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

sparse has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sparse is current.

Quality

sparse has no bugs reported.

Security

sparse has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

sparse does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

sparse releases are not available. You will need to build from source code and install.

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sparse Key Features

No Key Features are available at this moment for sparse.

sparse Examples and Code Snippets

Sparse softmax cross entropy with logits .

python

Lines of Code : 122

License : Non-SPDX (Apache License 2.0)

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def sparse_softmax_cross_entropy_with_logits(
    _sentinel=None,  # pylint: disable=invalid-name
    labels=None,
    logits=None,
    name=None):
  """Computes sparse softmax cross entropy between `logits` and `labels`.

  Measures the probability

Create a sparse placeholder .

python

Lines of Code : 108

License : Non-SPDX (Apache License 2.0)

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def sparse_placeholder(dtype, shape=None, name=None):
  """Inserts a placeholder for a sparse tensor that will be always fed.

  **Important**: This sparse tensor will produce an error if evaluated.
  Its value must be fed using the `feed_dict` optio

Stores a list of sparse tensors .

python

Lines of Code : 100

License : Non-SPDX (Apache License 2.0)

Copy

def _store_sparse_tensors(tensor_list, enqueue_many, keep_input,
                          shared_map_ops=None):
  """Store SparseTensors for feeding into batch, etc.

  If `shared_map_ops` is provided, the underlying `SparseTensorsMap` objects
  are

Community Discussions

Trending Discussions on sparse

“500 Internal Server Error” with job artifacts on minio

How to create a Table Type with columns more than 1024 columns

'MultiOutputClassifier' object is not iterable when creating a Pipeline (Python)

Force BERT transformer to use CUDA

Redis sentinel node can not sync after failover

Sparse columns in pandas: directly access the indices of non-null values

Dot product with sparse matrix and vector

Create streamplot in python, ValueError: The rows of 'x' must be equal

Using categorical encoding across multiple dataframes in python

How to get m x k Matrix from n x m and n x k Matrices

QUESTION

“500 Internal Server Error” with job artifacts on minio

Asked 2021-Jun-14 at 18:30

I'm running gitlab-ce on-prem with min.io as a local S3 service. CI/CD caching is working, and basic connectivity with the S3-compatible minio is good. (Versions: gitlab-ce:13.9.2-ce.0, gitlab-runner:v13.9.0, and minio/minio:latest currently c253244b6fb0.)

Is there additional configuration to differentiate between job-artifacts and pipeline-artifacts and storing them in on-prem S3-compatible object storage?

In my test repo, the "build" stage builds a sparse R package. When I was using local in-gitlab job artifacts, it succeeds and moves on to the "test" and "deploy" stages, no problems. (And that works with S3-stored cache, though that configuration is solely within gitlab-runner.) Now that I've configured minio as a local S3-compatible object storage for artifacts, though, it fails.

...

ANSWER

Answered 2021-Jun-14 at 18:30

The answer is to bypass the empty-string test; the underlying protocol does not support region-less configuration, nor is there a configuration option to support it.

The trick is able to work because the use of 'endpoint' causes the 'region' to be ignored. With that, setting the region to something and forcing the endpoint allows it to work:

Source https://stackoverflow.com/questions/67005428

QUESTION

How to create a Table Type with columns more than 1024 columns

Asked 2021-Jun-14 at 03:50

I want to create a table type that should have more than 1024 columns. So I tried to use sparse columns by creating a - SpecialPurposeColumns XML COLUMN_SET as shown below. That did not work. It gave me an error: Incorrect syntax near 'COLUMN_SET'

...

ANSWER

Answered 2021-May-05 at 08:53

From Restrictions for Using Sparse Columns:

Restrictions for Using Sparse Columns
Sparse columns can be of any SQL Server data type and behave like any other column with the following restrictions:

...

A sparse column cannot be part of a user-defined table type, which are used in table variables and table-valued parameters.

So you cannot use SPARSE columns in a table type object.

As for having more than 1,024 columns, again, no you can't. From Maximum capacity specifications for SQL Server:

Database Engine objects
Maximum sizes and numbers of various objects defined in SQL Server databases or referenced in Transact-SQL statements.
SQL Server Database Engine object Maximum sizes/numbers SQL Server (64-bit) Additional Information Columns per table 1,024 Tables that include sparse column sets include up to 30,000 columns. See sparse column sets.

Obviously, the "see sparse column sets" is not relevant here, as they are not supported (as outlined above).

If, however, you "need" this many columns then you more than likely really have a design flaw; probably suffer from significant denormalisation.

Source https://stackoverflow.com/questions/67397555

QUESTION

'MultiOutputClassifier' object is not iterable when creating a Pipeline (Python)

Asked 2021-Jun-13 at 13:58

I want to create a pipeline that continues encoding, scaling then the xgboost classifier for multilabel problem. The code block;

...

ANSWER

Answered 2021-Jun-13 at 13:57

Two things: first, you need to pass the transformers or the estimators themselves to the pipeline, not the result of fitting/transforming them (that would give the resultant arrays to the pipeline not the transformers, and it'd fail). Pipeline itself will be fitting/transforming. Second, since you have specific transformations to the specific columns, ColumnTransformer is needed.

Putting these together:

Source https://stackoverflow.com/questions/67958609

QUESTION

Force BERT transformer to use CUDA

Asked 2021-Jun-13 at 09:57

I want to force the Huggingface transformer (BERT) to make use of CUDA. nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0 or .to(cuda:0).

The code below is basically a customized part from german sentiment BERT working example

...

ANSWER

Answered 2021-Jun-12 at 16:19

You can make the entire class inherit torch.nn.Module like so:

Source https://stackoverflow.com/questions/67948945

QUESTION

Redis sentinel node can not sync after failover

Asked 2021-Jun-13 at 07:24

We have setup Redis with sentinel high availability using 3 nodes. Suppose fist node is master, when we reboot first node, failover happens and second node becomes master, until this point every thing is OK. But when fist node comes back it cannot sync with master and we saw that in its config no "masterauth" is set.
Here is the error log and Generated by CONFIG REWRITE config:

...

ANSWER

Answered 2021-Jun-13 at 07:24

For those who may run into same problem, problem was REDIS misconfiguration, after third deployment we carefully set parameters and no problem was found.

Source https://stackoverflow.com/questions/67749867

QUESTION

Sparse columns in pandas: directly access the indices of non-null values

Asked 2021-Jun-12 at 12:53

I have a large dataframe (approx. 10^8 rows) with some sparse columns. I would like to be able to quickly access the non-null values in a given column, i.e. the values that are actually saved in the array. I figured that this could be achieved by df.[]. However, I can't see how to access directly, i.e. without any computation. When I try df..index it tells me that it's a RangeIndex, which doesn't help. I can even see when I run df..values, but looking through dir(df..values) I still cant't see a way to access them.

To make clear what I mean, here is a toy example:

In this example is [0,1,3].

EDIT: The answer below by @Piotr Żak is a viable solution, but it requires computation. Is there a way to access directly via an attribute of the column or array?

...

ANSWER

Answered 2021-Jun-12 at 12:36

import pandas as pd
import numpy as np

df = pd.DataFrame(np.array([[1], [np.nan], [4], [np.nan], [9]]),
                   columns=['a'])

Source https://stackoverflow.com/questions/67948849

QUESTION

Dot product with sparse matrix and vector

Asked 2021-Jun-11 at 19:01

Im having a very hard time trying to program a dot product with a matrix in sparse format and a vector.

My matrix have the shape 3 x 3 in the folowing format:

...

ANSWER

Answered 2021-Jun-11 at 19:01

You can take advantage of the fact that if A is a matrix of shape (M, N), and b is a vector of shape (N, 1), then A.b equals a vector c of shape (M, 1).

A row x_c in c = sum((x_A, a row in A) * b).

Source https://stackoverflow.com/questions/67939205

QUESTION

Create streamplot in python, ValueError: The rows of 'x' must be equal

Asked 2021-Jun-11 at 19:01

I have a vector field:

...but when I want to plot the associated streamplot, I get an error:

ValueError: The rows of 'x' must be equal

Here is my code:

...

ANSWER

Answered 2021-Jun-11 at 19:01

Thanks to the comment from TrentonMcKinney I realized what the issue was:

In my case:

The values in each of my rows are the same, but each row is increasing.

But what I need for streamplot to work is:

Each row is the same, but the values in each row are increasing.

So I changed indexing = 'ij' to = 'xy':

Source https://stackoverflow.com/questions/67941121

QUESTION

Using categorical encoding across multiple dataframes in python

Asked 2021-Jun-11 at 12:48

I have a DataFrame X_Train with two categorical columns and a numerical column, for example:

A B N 'a1' 'b1' 0.5 'a1' 'b2' -0.8 'a2' 'b2' 0.1 'a2' 'b3' -0.2 'a3' 'b4' 0.4

Before sending this into a sklearn's linear regression, I change it into a sparse matrix. To do that, I need to change the categorical data into numerical indexes like so:

...

ANSWER

Answered 2021-Jun-11 at 12:48

You have to apply the categorical encoding in advance of splitting:

Sample:

Source https://stackoverflow.com/questions/67936796

QUESTION

How to get m x k Matrix from n x m and n x k Matrices

Asked 2021-Jun-10 at 19:23

No sure how to specify the question, but say I have sparse matrix:

...

ANSWER

Answered 2021-Jun-10 at 19:23

Maybe you can try crossprod like below

Source https://stackoverflow.com/questions/67923262

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Vulnerabilities

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Install sparse

You can download it from GitHub.

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