Shuffle | general purpose security automation platform | Automation library

by frikky JavaScript Version: 0.9.25 License: AGPL-3.0

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

kandi X-RAY | Shuffle Summary

Shuffle is a JavaScript library typically used in Automation, Docker applications. Shuffle has a Strong Copyleft License and it has low support. However Shuffle has 4 bugs and it has 1 vulnerabilities. You can download it from GitHub.

Shuffle is an automation platform for and by the community, focusing on accessibility for anyone to automate. Security operations is complex, but it doesn't have to be. Key Features — Community & Support — Documentation — Getting Started — Development. Follow us on Twitter at @shuffleio.

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Quality

Security

License

Reuse

Support

Shuffle has a low active ecosystem.

It has 693 star(s) with 155 fork(s). There are 28 watchers for this library.

It had no major release in the last 12 months.

There are 289 open issues and 318 have been closed. On average issues are closed in 39 days. There are 31 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Shuffle is 0.9.25

Quality

Shuffle has 4 bugs (0 blocker, 0 critical, 4 major, 0 minor) and 558 code smells.

Security

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

Shuffle code analysis shows 1 unresolved vulnerabilities (0 blocker, 0 critical, 0 major, 1 minor).

There are 6 security hotspots that need review.

License

Shuffle is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

Shuffle releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

Shuffle saves you 8826 person hours of effort in developing the same functionality from scratch.

It has 18080 lines of code, 351 functions and 68 files.

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

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Shuffle Key Features

No Key Features are available at this moment for Shuffle.

Shuffle Examples and Code Snippets

Shuffle a batch of tensors .

python

Lines of Code : 97

License : Non-SPDX (Apache License 2.0)

Copy

def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
                  num_threads=1, seed=None, enqueue_many=False, shapes=None,
                  allow_smaller_final_batch=False, shared_name=None, name=None):
  """Creates batches by

Shuffle a batch .

python

Lines of Code : 91

License : Non-SPDX (Apache License 2.0)

Copy

def shuffle_batch_join(tensors_list, batch_size, capacity,
                       min_after_dequeue, seed=None, enqueue_many=False,
                       shapes=None, allow_smaller_final_batch=False,
                       shared_name=None, name=Non

Shuffle a random index .

python

Lines of Code : 75

License : Non-SPDX (Apache License 2.0)

Copy

def index_shuffle(index, seed, max_index):
  """Outputs the position of `index` in a permutation of [0, ..., max_index].

  For each possible `seed` and `max_index` there is one pseudorandom permutation
  of the sequence S=[0, ..., max_index]. Instea

Community Discussions

Trending Discussions on Shuffle

Columns not properly moving in QTableView (QAbstractTableModel) using beginMoveColumns?

Youtube IFrame API Cannot Cue Specific Playlists; no Error?

Why does Spark perform an unnecessary shuffle during a joinWith on a pre-partitioned dataframe?

Move 2nd level sub-array to the top of the 1st level multidimentional array based on value of the sub-array

How to pass Spark configuration parameters to DBT?

How to calculate the f1-score?

What does Collections.shuffle function do

How to get indices of instances during cross-validation

From train test split to cross validation in sklearn using pipeline

Spark executors and shuffle in local mode

QUESTION

Columns not properly moving in QTableView (QAbstractTableModel) using beginMoveColumns?

Asked 2021-Jun-15 at 20:13

I am trying to use beginMoveColumns to move a single column over in a QTableView, but it doesn't work properly in my example below. The cell selections get shuffled and column widths don't move. Moving rows using the same logic seems to work correctly. What am I doing wrong?

Video: https://www.screencast.com/t/5UJ0iByZCEE

...

ANSWER

Answered 2021-Jun-15 at 20:13

Turns out it was a bug, I made a bug report here: https://bugreports.qt.io/browse/QTBUG-94503

As a workaround I just clear cell selection on column move, and use this snippet to move column widths

Source https://stackoverflow.com/questions/67941424

QUESTION

Youtube IFrame API Cannot Cue Specific Playlists; no Error?

Asked 2021-Jun-15 at 13:19

I've been using the YouTube IFrame API to shuffle multiple of my playlists together. I've got a very bare-bones HTML page with a 'next' and 'previous' button, and a bunch of javascript that loads up and plays videos and handles the button events.

The general order of events when the script loads is

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ANSWER

Answered 2021-Jun-15 at 13:19

This issue appears to have resolved itself. I suspect it was a bug in the iframe api or maybe the youtube backend which has been fixed by the youtube engineers. So iframe team, if you see this, thanks!

Source https://stackoverflow.com/questions/67888984

QUESTION

Why does Spark perform an unnecessary shuffle during a joinWith on a pre-partitioned dataframe?

Asked 2021-Jun-15 at 12:49

This example has been tested with Spark 2.4.x. Let's consider 2 simple dataframes:

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ANSWER

Answered 2021-Jun-15 at 12:49

This seems like a bug introduced by a bug fix in this ticket. The result was wrong for outer joins. Hence the need to add a Project node (packing of the struct) before the Join node.

However, we end up with this kind of query plan:

Source https://stackoverflow.com/questions/67400097

QUESTION

Move 2nd level sub-array to the top of the 1st level multidimentional array based on value of the sub-array

Asked 2021-Jun-14 at 21:35

I'm looping through a multidimensional array and am left with some values.

This is the complete PHP code.

...

ANSWER

Answered 2021-Jun-09 at 18:41

This will reorder $array2 (into a new variable $final). First it assembles a simple array $people of the names, then it reassembles $array2 by prioritizing based on the $people array. Was this what you wanted to accomplish?

Source https://stackoverflow.com/questions/67909656

QUESTION

How to pass Spark configuration parameters to DBT?

Asked 2021-Jun-14 at 12:34

I am using DBT to connect to AWS/EMR. I am able to run Spark/SQL queries but where do I set parameters like for example spark.sql.shuffle.partitions, that in normal code you will pass with:

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ANSWER

Answered 2021-Jun-14 at 12:34

As I do not get any answer here, I write what I think is the way at the moment (but repeat, not sure if it is the right way to go):

Source https://stackoverflow.com/questions/67933530

QUESTION

How to calculate the f1-score?

Asked 2021-Jun-14 at 07:07

I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)

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ANSWER

Answered 2021-Jun-13 at 15:17

You can use sklearn to calculate f1_score

Source https://stackoverflow.com/questions/67959327

QUESTION

What does Collections.shuffle function do

Asked 2021-Jun-14 at 07:07

code

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ANSWER

Answered 2021-Mar-29 at 09:18

Collections.shuffle() Randomly permutes the specified list using a default source of randomness. All permutations occur with approximately equal likelihood.

So if tasks ar shuffled, sometimes task2 run first and then output is different.

Source https://stackoverflow.com/questions/66851547

QUESTION

How to get indices of instances during cross-validation

Asked 2021-Jun-13 at 17:04

I am doing a binary classification. May I know how to extract the real indexes of the misclassified or classified instances of the training data frame while doing K fold cross-validation? I found no answer to this question here.

I got the values in folds as described here:

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ANSWER

Answered 2021-Jun-13 at 17:04

From cross_val_predict you already have the predictions. It's a matter of subsetting your data frame where the predictions are not the same as your true label, for example:

Source https://stackoverflow.com/questions/67956643

QUESTION

From train test split to cross validation in sklearn using pipeline

Asked 2021-Jun-13 at 15:49

I have the following piece of code:

...

ANSWER

Answered 2021-Jun-13 at 15:49

Pipeline is used to assemble several steps such as preprocessing, transformations, and modeling. StratifiedKFold is used to split your dataset to assess the performance of your model. It is not meant to be used as a part of the Pipeline as you do not want to perform it on new data.

Therefore it is normal to perform it out of the pipeline's structure.

Source https://stackoverflow.com/questions/67956414

QUESTION

Spark executors and shuffle in local mode

Asked 2021-Jun-12 at 16:13

I am running a TPC-DS benchmark for Spark 3.0.1 in local mode and using sparkMeasure to get workload statistics. I have 16 total cores and SparkContext is available as

Spark context available as 'sc' (master = local[*], app id = local-1623251009819)

Q1. For local[*], driver and executors are created in a single JVM with 16 threads. Considering Spark's configuration which of the following will be true?

1 worker instance, 1 executor having 16 cores/threads
1 worker instance, 16 executors each having 1 core

For a particular query, sparkMeasure reports shuffle data as follows

shuffleRecordsRead => 183364403
shuffleTotalBlocksFetched => 52582
shuffleTotalBlocksFetched => 52582
shuffleLocalBlocksFetched => 52582
shuffleRemoteBlocksFetched => 0
shuffleTotalBytesRead => 1570948723 (1498.0 MB)
shuffleLocalBytesRead => 1570948723 (1498.0 MB)
shuffleRemoteBytesRead => 0 (0 Bytes)
shuffleRemoteBytesReadToDisk => 0 (0 Bytes)
shuffleBytesWritten => 1570948723 (1498.0 MB)
shuffleRecordsWritten => 183364480

Q2. Regardless of the query specifics, why is there data shuffling when everything is inside a single JVM?

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ANSWER

Answered 2021-Jun-11 at 05:56

executor is a jvm process when you use local[*] you run Spark locally with as many worker threads as logical cores on your machine so : 1 executor and as many worker threads as logical cores. when you configure SPARK_WORKER_INSTANCES=5 in spark-env.sh and execute these commands start-master.sh and start-slave.sh spark://local:7077 to bring up a standalone spark cluster in your local machine you have one master and 5 workers, if you want to send your application to this cluster you must configure application like SparkSession.builder().appName("app").master("spark://localhost:7077") in this case you can't specify [*] or [2] for example. but when you specify master to be local[*] a jvm process is created and master and all workers will be in that jvm process and after your application finished that jvm instance will be destroyed. local[*] and spark://localhost:7077 are two separate things.
workers do their job using tasks and each task actually is a thread i.e. task = thread. workers have memory and they assign a memory partition to each task in order to they do their job such as reading a part of a dataset into its own memory partition or do a transformation on read data. when a task such as join needs other partitions, shuffle occurs regardless weather the job is ran in cluster or local. if you were in cluster there is a possibility that two tasks were in different machines so Network transmission will be added to other stuffs such as writing the result and then reading by another task. in local if task B needs the data in the partition of the task A, task A should write it down and then task B will read it to do its job