sparse | Sparse multi-dimensional arrays for the PyData ecosystem | Data Manipulation library

by pydata Python Version: 0.16.0a9 License: BSD-3-Clause

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | sparse Summary

sparse is a Python library typically used in Utilities, Data Manipulation, Numpy applications. sparse has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However sparse has 22 bugs. You can install using 'pip install sparse' or download it from GitHub, PyPI.

Sparse multi-dimensional arrays for the PyData ecosystem

Support

Quality

Security

License

Reuse

Support

sparse has a low active ecosystem.

It has 498 star(s) with 111 fork(s). There are 23 watchers for this library.

There were 5 major release(s) in the last 6 months.

There are 59 open issues and 200 have been closed. On average issues are closed in 110 days. There are 9 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sparse is 0.16.0a9

Quality

sparse has 22 bugs (0 blocker, 0 critical, 22 major, 0 minor) and 81 code smells.

Security

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

sparse code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sparse is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

sparse saves you 4106 person hours of effort in developing the same functionality from scratch.

It has 8724 lines of code, 601 functions and 38 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed sparse and discovered the below as its top functions. This is intended to give you an instant insight into sparse implemented functionality, and help decide if they suit your requirements.

Return a dict of command - line arguments
Get the project root directory
Extract version information from VCS
Construct a ConfigParser from a root
Getitem from x
Return dok format
Check that index is in bounds
Normalize index
Densify the sparse matrix
Moves an axis from source to destination
Diagonalize an array
Get the equivalent fill value
Transpose the tensor
Clip an array
Sanitize an index
Convert to a sparse matrix
Compute the triples of a sparse matrix
R Trilate the sparse matrix
Calculate the mean of an array
Kronecker product of two arrays
Create the versioneer config file
Create a diagonal matrix
Create a box object
Pad an array
Calculate the standard deviation along axis
Concatenate multiple arrays

Get all kandi verified functions for this library.

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)

Copy

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)

Copy

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

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install sparse

You can install using 'pip install sparse' or download it from GitHub, PyPI.
You can use sparse like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: