ml_pipeline | データ分析コンペの学習・推論パイプライン | Machine Learning library

by takapy0210 Python Version: Current License: No License

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kandi X-RAY | ml_pipeline Summary

ml_pipeline is a Python library typically used in Artificial Intelligence, Machine Learning applications. ml_pipeline has no bugs, it has no vulnerabilities and it has low support. However ml_pipeline build file is not available. You can download it from GitHub.

データ分析コンペの学習・推論パイプライン

Support

Quality

Security

License

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Support

ml_pipeline has a low active ecosystem.

It has 30 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ml_pipeline is current.

Quality

ml_pipeline has 0 bugs and 0 code smells.

Security

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

ml_pipeline code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ml_pipeline does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

ml_pipeline releases are not available. You will need to build from source code and install.

ml_pipeline has no build file. You will be need to create the build yourself to build the component from source.

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

ml_pipeline saves you 344 person hours of effort in developing the same functionality from scratch.

It has 823 lines of code, 76 functions and 9 files.

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

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ml_pipeline Key Features

No Key Features are available at this moment for ml_pipeline.

ml_pipeline Examples and Code Snippets

No Code Snippets are available at this moment for ml_pipeline.

Community Discussions

Trending Discussions on ml_pipeline

Unable to run docker image due to libGl error

What could cause an error in sparklyr and mleap ml_write_bundle example?

cleaning past ai platform model version with airflow

DataFlow 3.x default libraries

AttributeError: 'ColumnTransformer' object has no attribute 'shape' in Python Scikit-learn

Creating and applying ml_lib pipeline with external parameter in sparklyr

Extract and Visualize Model Trees from Sparklyr

How to print the decision path / rules used to predict sample of a specific row in PySpark?

How to print the decision path of a random forest with feature names in pyspark?

How can I train a random forest with a sparse matrix in Spark?

QUESTION

Unable to run docker image due to libGl error

Asked 2020-Sep-15 at 14:44

Dockerfile

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ANSWER

Answered 2020-Sep-15 at 14:44

I was able to make the docker container run by making following changes to the dockerfile

Source https://stackoverflow.com/questions/63889102

QUESTION

What could cause an error in sparklyr and mleap ml_write_bundle example?

Asked 2020-May-19 at 17:36

I'm trying to follow RStudio-MLeap example (https://github.com/rstudio/mleap) but I get an error at `ml_write_bundle()'. Does anyone know how to troubleshoot?

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ANSWER

Answered 2020-May-19 at 17:36

It looks like I just needed to restart R. I had other issues with loading the mleap library for another version of the spark (3.0.0-review2), and I mixed it up.

Source https://stackoverflow.com/questions/61894006

QUESTION

cleaning past ai platform model version with airflow

Asked 2020-Jan-16 at 05:41

I am using airflow to schedule the training of a model version in gcloud AI platform I managed to schedule the training of the model, the creation of the version, then I set this last version as the default using this DAG:

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ANSWER

Answered 2020-Jan-15 at 22:36

You can use a templated property to pass the result of a previous operator using Xcom. For example:

Source https://stackoverflow.com/questions/59423346

QUESTION

DataFlow 3.x default libraries

Asked 2020-Jan-15 at 10:24

Versions

Python 3.5
DataFlow/Apache Beam[GCP] 2.17.0

My Python code contains the following line:

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ANSWER

Answered 2020-Jan-15 at 10:24

As you can see in provided documentation, SDK for Python Version 2.17.0 and Python 3.5.7, googleapiclient package is not already installed.

If you want to install this package on your worker nodes, you can follow Apache Beam documentation about managing python pipeline dependencies.

Install package on your machine:

Source https://stackoverflow.com/questions/59726056

QUESTION

AttributeError: 'ColumnTransformer' object has no attribute 'shape' in Python Scikit-learn

Asked 2019-Jun-26 at 22:10

I am applying similar coding path from this tutorial for my own project for using ColumnTransformer to transfer the categorical and numerical variables' values in one step. But I am stuck at its X_test = colT.fit(X_test) which I don't know what the expected output should be.

Here is my code which I got an error at the def standardize_values function

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ANSWER

Answered 2019-Jun-26 at 21:28

The author of the tutorial has made a mistake.

Source https://stackoverflow.com/questions/56779111

QUESTION

Creating and applying ml_lib pipeline with external parameter in sparklyr

Asked 2019-May-29 at 04:16

I am trying to create and apply a Spark ml_pipeline object that can handle an external parameter that will vary (typically a date). According to the Spark documentation, it seems possible: see part with ParamMap here

I haven't tried exactly how to do it. I was thinking of something like this:

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ANSWER

Answered 2019-May-28 at 18:02

That's really not how Spark ML Pipelines are intended to be used. In general all transformations required to convert input dataset to a format that is suitable for the Pipeline should be applied beforehand and only the common components should be embedded as stages.

When using native (Scala) API, it is technically possible, in such simple cases, like this one, to use an empty SQLTransformer:

Source https://stackoverflow.com/questions/56328900

QUESTION

Extract and Visualize Model Trees from Sparklyr

Asked 2018-Nov-06 at 10:46

Does anyone have any advice about how to convert the tree information from sparklyr's ml_decision_tree_classifier, ml_gbt_classifier, or ml_random_forest_classifier models into a.) a format that can be understood by other R tree-related libraries and (ultimately) b.) a visualization of the trees for non-technical consumption? This would include the ability to convert back to the actual feature names from the substituted string indexing values that are produced during the vector assembler.

The following code is copied liberally from a sparklyr blog post for the purposes of providing an example:

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ANSWER

Answered 2018-Nov-06 at 10:46

As of today (Spark 2.4.0 release already approved and waiting for the official announcement) your best bet*, without involving complex 3rd party tools (you can take a look MLeap for example), is probably to save the model and read back the specification:

Source https://stackoverflow.com/questions/53123840

QUESTION

How to print the decision path / rules used to predict sample of a specific row in PySpark?

Asked 2018-Aug-11 at 10:34

How to print the decision path of a specific sample in a Spark DataFrame?

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ANSWER

Answered 2018-Aug-11 at 10:34

I changed your dataframe just slightly so that we could ensure we could see different features in the explanations
I changed the Assembler to use a feature_list, so we have easy access to that later
changes below:

Source https://stackoverflow.com/questions/51614077

QUESTION

How to print the decision path of a random forest with feature names in pyspark?

Asked 2018-Aug-01 at 14:02

how can I modify the code to print the decision path with features names rather than just numbers.

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ANSWER

Answered 2018-Aug-01 at 13:57

One option would be to manually replace the text in the string. We can do this by storing the values we pass as inputCols in a list input_cols, and then each time replacing the pattern feature i with the ith element of the list input_cols.

Source https://stackoverflow.com/questions/51634947

QUESTION

How can I train a random forest with a sparse matrix in Spark?

Asked 2018-Jul-07 at 02:26

Consider this simple example that uses sparklyr:

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ANSWER

Answered 2018-Jun-07 at 22:45

Can you please provide the full error traceback?

My guess is that you're running out of memory. Random forest and gbt trees are ensemble models, so they require more memory and computational power than naive bayes.

Try repartitioning the data (spark.sparkContext.defaultParallelism value is a good place to start) so that each of your workers gets a smaller and more evenly distributed chunk.

If that doesn't work, try reducing your max_memory_in_mb parameter to 256.

Source https://stackoverflow.com/questions/50700233

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

Vulnerabilities

No vulnerabilities reported

Install ml_pipeline

You can download it from GitHub.
You can use ml_pipeline 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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