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);