Shuffle | Categorize , sort , and filter a responsive grid of items | Grid library
kandi X-RAY | Shuffle Summary
kandi X-RAY | Shuffle Summary
Categorize, sort, and filter a responsive grid of items.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of Shuffle
Shuffle Key Features
Shuffle Examples and Code Snippets
Lines of Code : 97License : Non-SPDX (Apache License 2.0)
Copydef 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 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 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 shuffle.onPressed() {
disable user input;
iterate over the grid {
if (cell contains a text value) {
push Text widget key onto a stack (List);
trigger the hide animation (pass callback #1);
# suppose dataset is the variable pointing to whole datasets
N = len(dataset)
# generate & shuffle indices
indices = numpy.arange(N)
indices = numpy.random.permutation(indices)
# there are many ways to do the above two operation. (Exa@ECHO OFF
SETLOCAL
rem The following settings for the source directory and filenames are names
rem that I use for testing and deliberately include names which include spaces to make sure
rem that the process works using such names. These w"for not sortable keys the sort merge join" should "not be used" in {
import sparkSession.implicits._
// Here we explicitly define the schema. Thanks to that we can show
// the case when sort-merge join won't be used, i.e. when the key is val_ds = keras.preprocessing.image_dataset_from_directory(
directory = image_directory,
label_mode = 'categorical',
shuffle = True,
validation_split = 0.2,
subset = 'validation',
seed = 24,
batch_size = s/\(someword\)\(.*\)/\n\2\n\1/ # split pattern space into chunks
h # hold it to hold space
s/.*\n// # extract only interested part
s/.*/something/ # do edit on it
G # Some random data
labels = list('ABCDE')
repeats = np.random.randint(1, 6, len(labels))
df = pd.DataFrame({
'label': np.repeat(labels, repeats),
'confidence': np.random.randint(1, 6, repeats.sum())
})
# Shuffle the data frame. FCommunity Discussions
Trending Discussions on Shuffle
QUESTION
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?
ANSWER
Answered 2021-Jun-15 at 20:13Turns 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
QUESTION
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
...ANSWER
Answered 2021-Jun-15 at 13:19This 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!
QUESTION
This example has been tested with Spark 2.4.x. Let's consider 2 simple dataframes:
...ANSWER
Answered 2021-Jun-15 at 12:49This 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:
QUESTION
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 18:41This 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?
QUESTION
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:
ANSWER
Answered 2021-Jun-14 at 12:34As 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):
QUESTION
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)
ANSWER
Answered 2021-Jun-13 at 15:17You can use sklearn to calculate f1_score
QUESTION
ANSWER
Answered 2021-Mar-29 at 09:18Collections.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.
QUESTION
ANSWER
Answered 2021-Jun-13 at 17:04From 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:
QUESTION
I have the following piece of code:
...ANSWER
Answered 2021-Jun-13 at 15:49Pipeline 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.
QUESTION
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?
...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 configureSPARK_WORKER_INSTANCES=5inspark-env.shand execute these commandsstart-master.shandstart-slave.sh spark://local:7077to 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 likeSparkSession.builder().appName("app").master("spark://localhost:7077")in this case you can't specify[*]or[2]for example. but when you specify master to belocal[*]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[*]andspark://localhost:7077are 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
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