pyrecdp 1.0.1b20230401

A data processing bundle for spark based recommender system operations

RecDP v2.0

INTRODUCTION

Problem Statement

Data Preparation is an essential step to build AI pipelines

• key data preparation capabilities: data connector, cleaning, sampling, joining, profiling, feature engineering, low-code/no-code UI, lineage etc.
• exploration of optimal Data preparation consumes majority of Data Science time

Solution with RecDP v2.0

• Auto pipeline
  • only 3 lines of codes required
• Pipeline Generator
  • Data Profiling:
    • Auto anomalies detection
    • Auto missing value impute
    • Profiling Visualizzation
  • Feature Wrangling:
    • feature transformation(datetime, geo_info, text_nlp, url, etc.)
    • multiple data auto joining
    • feature cross(aggregation transformation - sum, avg, count, etc.)
  • export pipeline as JSON file, can be import to other data platform
• Pipeline Runner:
  • spark engine: convert pipeline to spark codes to run
  • pandas engine: convert pipeline to pandas codes to run
  • sql engine: convert pipeline to sql
• DataLoader:
  • parquet, csv, json, database
• FeatureWriter - ML/DL connector:
  • Data Lineage
  • Feature Store
  • numpy, csv, parquet, dgl / pyG graph

This solution is intended for

citizen data scientists, enterprise users, independent software vendor and partial of cloud service provider.

Getting Start

setup with pip

git clone --single-branch --branch RecDP_v2.0 https://github.com/intel-innersource/frameworks.bigdata.AIDK.git
cd frameworks.bigdata.AIDK/RecDP
# install dependencies
apt-get update -y &&  DEBIAN_FRONTEND=noninteractive apt-get install -y python3 python3-pip python-is-python3 graphviz
DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre
# install recdp
python setup.py sdist
pip install dist/pyrecdp-1.0.1.tar.gz

sh start-jupyter.sh
# open browser with http://hostname:8888

run

modify pipeline (add user defined function or remove operation)

def filter_with_cond(df):
    df = df[df['Year'] <= 2018]
    return df
operation = {
    "children": [6], # will to append this new op
    "next": [7], # who will be connected to this new op
    "inline_function": filter_with_cond, # function
}
pipeline.add_operation(operation)

operation = {
    "idx": 6, # OP id to be deleted
    "next": [10] # who is connected to the to_be_deleted op
}
pipeline.delete_operation(operation)

export pipeline

pipeline.export(file_path = "exported_pipeline.json")

{
    "0": {
        "children": null,
        "op": "DataFrame",
        "config": "main_table"
    },
    "1": {
        "children": [0],
        "op": "type_infer",
        "config": [
            ["pickup_datetime",["is_string","is_datetime"]],
            ["pickup_longitude",["is_numeric","is_float"]],
            ["pickup_latitude",["is_numeric","is_float"]],
            ["dropoff_longitude",["is_numeric","is_float"]],
            ["dropoff_latitude",["is_numeric","is_float"]],
            ["passenger_count",["is_numeric","is_int64","is_integer","is_categorical"]]
        ]
    },
    "2": {
        "children": [1],
        "op": "tuple",
        "config": {
            "src": ["pickup_latitude","pickup_longitude"],"dst": "pickup_coordinates"
        }
    },
    "3": {
        "children": [2],
        "op": "tuple",
        "config": {
            "src": ["dropoff_latitude","dropoff_longitude"],"dst": "dropoff_coordinates"
        }
    },
    "4": {
        "children": [3],
        "op": "fillna",
        "config": {
            "pickup_longitude": -1,
            "pickup_latitude": -1,
            "dropoff_longitude": -1,
            "dropoff_latitude": -1,
            "passenger_count": -1
        }
    },
    "5": {
        "children": [4],
        "op": "datetime_feature",
        "config": {
            "pickup_datetime": [
                ["pickup_datetime__day",["featuretools.primitives.standard.transform.datetime.day", "Day"]],
                ["pickup_datetime__month",["featuretools.primitives.standard.transform.datetime.month","Month"]],
                ["pickup_datetime__weekday",["featuretools.primitives.standard.transform.datetime.weekday","Weekday"]],
                ["pickup_datetime__year",["featuretools.primitives.standard.transform.datetime.year","Year"]],
                ["pickup_datetime__hour",["featuretools.primitives.standard.transform.datetime.hour","Hour"]]
            ]
        }
    },
    "6": {
        "children": [5],
        "op": "haversine",
        "config": {
            "['pickup_coordinates', 'dropoff_coordinates']": ["haversine_pickup_coordinates_dropoff_coordinates",["featuretools.primitives.standard.transform.latlong.haversine","Haversine"]]
        }
    },
    "7": {
        "children": [6],
        "op": "drop",
        "config": [
            "pickup_datetime",
            "pickup_coordinates",
            "dropoff_coordinates"
        ]
    },
    "8": {
        "children": [7],
        "op": "lightgbm",
        "config": {
            "label": "fare_amount",
            "metrics": "rmse",
            "objective": "regression",
            "model_file": "lightgbm_regression_label.mdl",
            "method": "predict"
        }
    }
}

Quick Example

• - nyc taxi fare - geographic, datetime, lgbm regression

• - IBM Card Transaction Fraud Detect - onehot encode, lgbm, binary classification

• - twitter recsys - text nlp, datetime feature engineering

• - outbrain - multiple table joining

• - amazon product review - text nlp, datetime, feature-cross

More Examples - completed example including training

Auto Feature Engineering vs. featuretools

• NYC Taxi fare auto data prepration: An example to show how RecDP_v2.0 automatically generating datetime and geo features upon 55M records. Tested with both Spark and Pandas(featuretools) as compute engine, show 21x speedup by spark.

load PIPELINE and execute

• twitter pipeline re-load and execute: An example to show how RecDP_v2.0 reload pipeline from json and do execution - use RecDP as compute engine.

Realtime Inference pipeline

• nyc taxi fare - geographic, datetime feature engineering, lgbm

Data Profiler Examples

• NYC Taxi fare Profiler: An example to show RecDP_v2.0 to profile data, including infer the potential data type, generate data distribution charts.

• twitter Profiler: An example to show RecDP_v2.0 to profile data, including infer the potential data type, generate data distribution charts.

LICENSE

• Apache 2.0

Dependency

• Spark 3.x
• python 3.*