spark-pip | Spark job to perform massive Point

by mraad Scala Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | spark-pip Summary

spark-pip is a Scala library typically used in Big Data, Kafka, Spark, Example Codes applications. spark-pip has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Spark job to perform massive Point in Polygon (PiP) operations

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Support

spark-pip has a low active ecosystem.

It has 32 star(s) with 14 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

spark-pip has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of spark-pip is current.

Quality

spark-pip has 0 bugs and 0 code smells.

Security

spark-pip has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

spark-pip code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

spark-pip is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

spark-pip releases are not available. You will need to build from source code and install.

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

It has 616 lines of code, 14 functions and 13 files.

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

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spark-pip Key Features

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spark-pip Examples and Code Snippets

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Community Discussions

Trending Discussions on spark-pip

rdd.pipe throwing java.lang.IllegalStateException for grep -i shell command?

Retain ID references when tokenizing record using generator

Custom algorithm in Pyspark MLlib: 'function' object has no attribute '_input_kwargs'

Optimal way to create a ml pipeline in Apache Spark for dataset with high number of columns

How to print best model params in pyspark pipeline

QUESTION

rdd.pipe throwing java.lang.IllegalStateException for grep -i shell command?

Asked 2020-Jan-01 at 16:03

I am running the code for using pipe in RDD spark operations:

following snippet I have tried:

...

ANSWER

Answered 2020-Jan-01 at 15:59

It's because the data is partitioned. And even if you use the same command within .sh file as you mention you'll get the same error. If you repartition the RDD to one partition, It should work fine:

Source https://stackoverflow.com/questions/59544480

QUESTION

Retain ID references when tokenizing record using generator

Asked 2018-Nov-30 at 17:56

I am trying to duplicate the (very cool) datamatching approach described here using pandas. The goal is to take component parts (tokens) of a record and use to match to another df.

I'm stuck trying to figure out how to retain the source ID and associate with individual tokens. Hoping someone here has a clever suggestion for how I could do this. I searched Stack but was not able to find a similar question.

Here is some sample data and core code to illustrate. This takes a dataframe, tokenizes select columns, generates token, token type, and id (but ID part does not work):

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ANSWER

Answered 2018-Nov-30 at 17:56

You need to change the index of the Id, not in a dedicated for loop, but at the same time you get a new record. I would suggest something like:

Source https://stackoverflow.com/questions/53531540

QUESTION

Custom algorithm in Pyspark MLlib: 'function' object has no attribute '_input_kwargs'

Asked 2018-Jan-19 at 19:21

I'm trying to roll my own MLlib Pipeline algorithm in Pyspark but I can't get past the following error:

...

ANSWER

Answered 2017-Jul-20 at 10:15

The problem is this line:

Source https://stackoverflow.com/questions/45189191

QUESTION

Optimal way to create a ml pipeline in Apache Spark for dataset with high number of columns

Asked 2017-Oct-22 at 17:03

I am working with Spark 2.1.1 on a dataset with ~2000 features and trying to create a basic ML Pipeline, consisting of some Transformers and a Classifier.

Let's assume for the sake of simplicity that the Pipeline I am working with consists of a VectorAssembler, StringIndexer and a Classifier, which would be a fairly common usecase.

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ANSWER

Answered 2017-Oct-15 at 17:12

The janino error that you are getting is because depending on the feature set, the generated code becomes larger.

I'd separate the steps into different pipelines and drop the unnecessary features, save the intermediate models like StringIndexer and OneHotEncoder and load them while prediction stage, which is also helpful because transformations would be faster for the data that has to be predicted.

Finally, you don't need to keep the feature columns after you run VectorAssembler stage as it transforms the features into a feature vector and label column and that is all you need to run predictions.

Example of Pipeline in Scala with saving of intermediate steps-(Older spark API)

Also, if you are using older version of spark like 1.6.0, you need to check for patched version i.e. 2.1.1 or 2.2.0 or 1.6.4 or else you would hit the Janino error even with around 400 feature columns.

Source https://stackoverflow.com/questions/43911694

QUESTION

How to print best model params in pyspark pipeline

Asked 2017-Sep-21 at 21:55

This question is similar to this one. I would like to print the best model params after doing a TrainValidationSplit in pyspark. I cannot find the piece of text the other user uses to answer the question because I'm working on jupyter and the log dissapears from the terminal...

Part of the code is:

...

ANSWER

Answered 2017-Jan-22 at 13:28

It follows indeed the same reasoning described in the answer about How to get the maxDepth from a Spark RandomForestRegressionModel given by @user6910411.

You'll need to patch the TrainValidationSplitModel, PCAModel and DecisionTreeRegressionModel as followed :

Source https://stackoverflow.com/questions/41781529

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

Vulnerabilities

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Install spark-pip

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