Shuffle | Reusable Card Manager for iOS | User Interface library

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!

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:

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?

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):

You can use sklearn to calculate f1_score

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.

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:

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.

• 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