plan.md

PLAN.md

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+# DataMetaMap Project Plan
+
+## Project Goal
+DataMetaMap aims to compare datasets within a unified vector space to identify semantic similarities. The core idea is that if a model performs well on one dataset, it will likely perform well on semantically similar datasets nearby in embedding space.
+
+---
+
+## Development Phases & Tasks
+
+### Phase 1: Research and Preparation
+- **Literature Review**  
+  Study existing methods for dataset embedding, similarity measurement, and transferability estimation to identify best practices.
+
+- **Data Collection**  
+  Gather a diverse collection of datasets for experimentation, ensuring they represent various domains and formats.
+
+- **Planning and Specifications**  
+  Define technical specifications and success criteria based on research findings and data availability.
+
+---
+
+### Phase 2: Implementation and Testing
+- **Core Algorithm Development**  
+  Implement algorithms to embed datasets into a shared vector space and compute similarity metrics between them.
+
+- **Testing and Quality Assurance**  
+  Develop unit and integration tests to validate correctness, reliability, and performance of the implemented methods.
+
+- **Benchmarking and Visualization**  
+  Run benchmarks on collected datasets and produce visual outputs such as similarity matrices to analyze and interpret results.
+
+---
+
+### Phase 3: Documentation and Dissemination
+- **Technical Report**  
+  Document the methodology, experimental setup, and findings in a comprehensive technical report.
+
+- **User and Developer Documentation**  
+  Create detailed documentation for users and contributors, including setup guides and API references.
+
+- **Demo Examples and Blog Post**  
+  Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights.
+
+## Remastered 
+
+### Phase 1: Research and Preparation
+- **Literature Review**  
+  Study existing methods for dataset embedding, similarity measurement, and transferability estimation to identify best practices.
+
+- **Baseline Selection**  
+  Identify and select baseline methods from literature for comparison during benchmarking.
+
+- **Data Collection**  
+  Gather a diverse collection of datasets for experimentation, ensuring they represent various domains and formats.
+
+- **Data Preprocessing Pipeline**  
+  Design and implement preprocessing steps to handle different dataset formats and ensure consistent input for embedding methods.
+
+- **Evaluation Metrics Definition**  
+  Define quantitative metrics to evaluate embedding quality and similarity measurement accuracy.
+
+- **Planning and Specifications**  
+  Define technical specifications and success criteria based on research findings and data availability.
+
+---
+
+### Phase 2: Implementation and Testing
+- **Core Algorithm Development**  
+  Implement algorithms to embed datasets into a shared vector space and compute similarity metrics between them.
+
+- **Baseline Implementations**  
+  Implement selected baseline methods from literature for comparison.
+
+- **Testing and Quality Assurance**  
+  Develop unit and integration tests to validate correctness, reliability, and performance of the implemented methods.
+
+- **Performance Optimization**  
+  Profile and optimize code for memory efficiency and computational speed, especially for large datasets.
+
+- **Error Handling and Logging**  
+  Implement robust error handling and logging mechanisms for debugging and monitoring.
+
+- **Benchmarking and Visualization**  
+  Run benchmarks on collected datasets and produce visual outputs such as similarity matrices to analyze and interpret results.
+
+---
+
+### Phase 3: Documentation and Dissemination
+- **Technical Report**  
+  Document the methodology, experimental setup, and findings in a comprehensive technical report.
+
+- **User and Developer Documentation**  
+  Create detailed documentation for users and contributors, including setup guides and API references. In this task we should create github.io page where user can find documentation for all classes and their methods. Github.io page must have headers for functions and links to their each source code. 
+
+- **Demo Examples and Blog Post**  
+  Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights.
+
+- **Benchmark Results Repository**  
+  Publish benchmark results, precomputed embeddings, and similarity matrices in a public repository for reproducibility.
+
+- **Future Work Roadmap**  
+  Outline potential extensions, improvements, and research directions based on current findings.