Metadata-Version: 2.1
Name: pyrecdp
Version: 1.2.1b2024012411
Summary: A data processing bundle for spark based recommender system operations
Home-page: https://github.com/intel/e2eAIOK/
Author: INTEL BDF AIOK
Author-email: bdf.aiok@intel.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/intel/e2eAIOK/
Description: # RecDP - one stop toolkit for AI data process
        
        We provide intel optimized solution for
        
        * [**Auto Feature Engineering**](pyrecdp/autofe/README.md) -  Provides an automatical way to generate new features for any tabular dataset which containing numericals, categoricals and text features. It only takes 3 lines of codes to automatically enrich features based on data analysis, statistics, clustering and multi-feature interacting.
        * [**LLM Data Preparation**](pyrecdp/LLM/README.md) - Provides a parallelled easy-to-use data pipeline for LLM data processing. It supports multiple data source such as jsonlines, pdfs, images, audio/vides. Users will be able to perform data extraction, deduplication(near dedup, rouge, exact), splitting, special_character fixing, types of filtering(length, perplexity, profanity, etc), quality analysis(diversity, GPT3 quality, toxicity, perplexity, etc). This tool also support to save output as jsonlines, parquets, or insertion into VectorStores(FaissStore, ChromaStore, ElasticSearchStore).
        
        ## How it works
        
        Install this tool through pip. 
        
        ```
        DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre graphviz
        pip install pyrecdp[all] --pre
        ```
        
        ## RecDP - Tabular
        [learn more](pyrecdp/autofe/README.md)
        
        * Auto Feature Engineering Pipeline
        ![Auto Feature Engineering Pipeline](resources/autofe_pipeline.jpg)
        
        Only **3** lines of codes to generate new features for your tabular data. Usually 5x new features can be found with up to 1.2x accuracy boost
        ```
        from pyrecdp.autofe import AutoFE
        
        pipeline = AutoFE(dataset=train_data, label=target_label, time_series = 'Day')
        transformed_train_df = pipeline.fit_transform()
        ```
        
        * High Performance on Terabyte Tabular data processing
        ![Performance](resources/recdp_performance.jpg)
        
        ## RecDP - LLM
        [learn more](pyrecdp/LLM/README.md)
        
        * Low-code Fault-tolerant Auto-scaling Parallel Pipeline
        ![LLM Pipeline](resources/llm_pipeline.jpg)
        
        ```
        from pyrecdp.primitives.operations import *
        from pyrecdp.LLM import ResumableTextPipeline
        
        pipeline = ResumableTextPipeline()
        ops = [
            UrlLoader(urls, max_depth=2),
            DocumentSplit(),
            ProfanityFilter(),
            PIIRemoval(),
            ...
            PerfileParquetWriter("ResumableTextPipeline_output")
        ]
        pipeline.add_operations(ops)
        pipeline.execute()
        ```
        
        ## LICENSE
        * Apache 2.0
        
        ## Dependency
        * Spark 3.4.*
        * python 3.*
        * Ray 2.7.*
        
Keywords: pyrecdp recdp distributed parallel auto-feature-engineering autofe LLM python
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: autofe
Provides-Extra: LLM
Provides-Extra: all
