Adobe DevCraft Bidding System

A sophisticated real-time ad auction bidding system with Python and Java implementations.

Overview

Adobe DevCraft Bidding System is a comprehensive solution for real-time ad auctions, featuring machine learning integration, efficient lookup systems, and robust data processing capabilities. The system is designed for optimal bidding decisions in high-frequency advertising environments.

Quick Start

Installation

Install dependencies using pip:

pip install -r requirements.txt

Or install using the setup script:

python setup.py install

Set up a conda environment:

# If using conda
conda env create -f environment.yaml

Running Inference

cd Adobe_Devcraft_PS/bidder/python
python3 Bid.py

Model Weights Location

Adobe_Devcraft_PS/bidder/python/model_parameters/epoch3_iter_0_train_5.467432975769043

Key Features

• Smart Bidding Algorithm: ML-powered bidding strategies optimized for ROI
• Data Processing Pipeline: Comprehensive tools for data preparation and transformation
• Machine Learning Integration: Deep learning models for bid optimization
• Visualization Tools: Built-in capabilities for data analysis and visualization
• Efficient Lookup System: Fast bidding decisions through optimized lookup tables

Project Structure

ADOBE DEVCRAFT/
├── Adobe_Devcraft_PS/
│   ├── bidder/
│   │   └── python/
│   │       ├── __pycache__/
│   │       ├── Code/
│   │       ├── model_parameters/
│   │       ├── advertiser_id.json
│   │       ├── Bid.py
│   │       ├── Bidder.py
│   │       ├── BidRequest.py
│   │       ├── city.txt
│   │       ├── profile.json
│   │       ├── region.txt
│   │       ├── __init__.py
│   │       └── city.txt
│   ├── README
│   ├── region.txt
│   └── user.profile.tags.txt
├── .gitignore
├── environment.yml
├── INSTRUCTION.md
├── requirements.txt
└── setup.py

Data Files

File	Description
city.txt	City-wise mapping information
region.txt	Regional classification data
user.profile.tags.txt	Detailed user profiling information

Core Components

Optimization Tools

• train.py: Neural network training pipeline
• model.py: Deep learning model architecture
• loss.py: Custom loss functions for model optimization
• create_look_up.py: Lookup table generation for efficient bidding
• histograms_and_box.py: Statistical analysis and visualization tools

Documentation

For detailed implementation guidance, refer to the files in the Code directory and the instructions in INSTRUCTION.md.

Requirements

• Python 3.7+
• Dependencies listed in requirements.txt
• Optional: Conda environment manager