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

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

kandi X-RAY | sparse Summary

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
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            kandi-support 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 4 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

            kandi-Quality Quality

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

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse 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 .
            pythondot img1Lines of Code : 122dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            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 .
            pythondot img2Lines of Code : 108dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            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 .
            pythondot img3Lines of Code : 100dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            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

            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 .
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            Install
          • PyPI

            pip install sparse

          • CLONE
          • HTTPS

            https://github.com/pydata/sparse.git

          • CLI

            gh repo clone pydata/sparse

          • sshUrl

            git@github.com:pydata/sparse.git

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