https://github.com/OutRankNFT/OutRank-Rarity

https://github.com/OutRankNFT/OutRank-Rarity

Last synced: 5 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/OutRankNFT/OutRank-Rarity
• Owner: OutRankNFT
• License: mit
• Created: 2023-09-22T13:53:37.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-01T08:10:14.000Z (about 1 year ago)
• Last Synced: 2024-08-02T06:17:10.915Z (3 months ago)
• Language: Jupyter Notebook
• Size: 230 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-internet-computer - OutRank-Rarity - implementation of mathematical code to calculate rarity within NFT collections in Python and Rust (Fungible and Non-fungible Tokens (NFTs) / Analytical Methodologies)

README

        
# OutRank-Rarity

We've implemented mathematical code to calculate rarity of NFT collections in both of Python and Rust

- Rarity_math_code_python.ipynb is for Python code and 

- Rarity_math_code_rust.rs is for Rust code.

- Script_for_fetch_data_from_canister.rs is for Rust code to fetch NFT collections trait data(we'll call this "canister data") by inter-canister call.

Here is a breif explanation for rarity_math_code_rust.rs.

## Basic Usage for Rust code

- Fetch canister data(nft collections trait data) as an Object array.

  - (trait_object_array, trait_array) = fetch_canister_data(canister_id);

    - trait_object_array example : [{"skin (texture)": "Dark", "Gender": "Male", "Move": "Breakdance Uprock", "Background": "Blue", "Cloths": "Casual Shirt/Pants"}, ... ... ...]

  - trait_array  is array of collections trait properties.

    - trait_array example : ["Move", "skin (texture)", "Background", "Cloths", "Gender", "Asssecrioes"]

      

- Calculate traits_value from canister_data. Canister_data is an Object Array and convert it as Two-Dimensional Array. Row Index is NFT id. Column Index is same as trait_array.

  - traits_value = canister_data_to_traits_value(trait_object_array,trait_array);

    - traits_value example : [ [ "Breakdance Uprock", "Dark", "Blue", "Casual Shirt/ Pants", "Male", "NA" ], [ "Salsa (long)", "Light", "Yellow", "Jump Suit", "Male", "NA" ], ... ... ...]

    

- Calculate traits_count and traits_freq from reversed traits_value. Reversed traits_value is also Two-Dimensional array. But Row Index is same as trait_array and Column Index is same as NFT id. traits_count represent count of same value in row(Each row is trait property) and traits_freq is same as NFTs count devided traits_count.

  - (traits_count, traits_freq) = get_traits_count_freq_number(reverse_mat(traits_value));

    - traits_count example : [ [2 ,2 ,1, 8, 8, 8, 2, 6, ...], [8, 12, 12, 12, 12, 12, 8, ...], ... ... ]

    - traits_freq example : [ [0.0391, 0.03921, 0.01961, 0.15686, 0.15686, 0.15686, ... ], ... ]

- calculate rarity_mat from traits_freq. rarity_mat has 5 rows.

  - First row is array of min value of column.

  - Second row is array of max value of column.

  - Third row is array of arithmetic value of column.

  - Fourth row is array of harmonic value of column.

  - Fifth row is array of geometric value of column.

  - rarity_mat = rare_calc(traits_freq);

    

- Calculate rarity_score from rarity_mat. rarity_score is Two-Dementional array that contains normalized value between 0 and 1 of rarity_mat.

  - rarity_score = score_calc(rarity_mat);

    

- Calculate rarity_rank from rarity_score. rarity_rank is Two-Dementional array contains rows sorted by value from rarity_score.

  - rarity_rank = rare_rank(rarity_score);

    

- These two methods calculate trait_independence and trait_cramers_v from traits_freq. By calcuating Chi-Two-squared distribution.

  - trait_independence = trait_independence(traits_freq);

  - trait_cramers_v = trait_cramers_v(traits_freq);

    

- Calculate trait_normalize from traits_value, traits_count, traits_freq.Trait_normalize means trait normalised rarity score

  - trait_normalize = trait_normalize(reverse_mat(traits_value), traits_count, traits_freq);