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Ranking

RTN

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from RTN import RTN
rtn = RTN(settings: SettingsModel, ranking_model: BaseRankingModel)

RTN (Rank Torrent Name) class for parsing and ranking torrent titles based on user preferences.

Args

  • The settings model with user preferences for parsing and ranking torrents.
  • The model defining the ranking logic and score computation.

Notes

  • Both settings and ranking_model must be provided and must be valid instances of SettingsModel and BaseRankingModel.
  • The lev_threshold is derived from settings.options["title_similarity"] and is used to determine if a torrent title matches the correct title.

Methods:

.rank

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.rank(
   raw_title: str, infohash: str, correct_title: str = '', remove_trash: bool = False
)

Parses a torrent title, computes its rank, and returns a Torrent object with metadata and ranking.

Args

  • The original title of the torrent to parse.
  • The SHA-1 hash identifier of the torrent.
  • The correct title to compare against for similarity. Defaults to an empty string.
  • Whether to check for trash patterns and raise an error if found. Defaults to True.

Returns

  • Torrent : A Torrent object with metadata and ranking information.

Raises

  • ValueError : If the title or infohash is not provided for any torrent.
  • TypeError : If the title or infohash is not a string.
  • GarbageTorrent : If the title is identified as trash and should be ignored by the scraper, or invalid SHA-1 infohash is given.

Notes: - If correct_title is provided, the Levenshtein ratio will be calculated between the parsed title and the correct title. - If the ratio is below the threshold, a GarbageTorrent error will be raised. - If no correct title is provided, the Levenshtein ratio will be set to 0.0.

Example

from RTN import RTN
from RTN.models import SettingsModel, DefaultRanking

settings_model = SettingsModel()
ranking_model = DefaultRanking()
rtn = RTN(settings_model, ranking_model)
torrent = rtn.rank("The Walking Dead S05E03 720p HDTV x264-ASAP[ettv]", "c08a9ee8ce3a5c2c08865e2b05406273cabc97e7")
assert isinstance(torrent, Torrent)
assert isinstance(torrent.data, ParsedData)
assert torrent.fetch
assert torrent.rank > 0
assert torrent.lev_ratio > 0.0

get_rank

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.get_rank(
   data: ParsedData, settings: SettingsModel, rank_model: BaseRankingModel
)

Calculate the ranking of the given parsed data.

Parameters: data (ParsedData): The parsed data object containing information about the torrent title. settings (SettingsModel): The user settings object containing custom ranking models. rank_model (BaseRankingModel): The base ranking model used for calculating the ranking.

Returns

  • int : The calculated ranking value for the parsed data.

Raises

  • ValueError : If the parsed data is empty.
  • TypeError : If the parsed data is not a ParsedData object.

calculate_preferred

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.calculate_preferred(
   data: ParsedData, settings: SettingsModel
)

Calculate the preferred ranking of a given parsed data.

calculate_quality_rank

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.calculate_quality_rank(
   data: ParsedData, settings: SettingsModel, rank_model: BaseRankingModel
)

Calculate the quality ranking of the given parsed data.

calculate_codec_rank

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.calculate_codec_rank(
   data: ParsedData, settings: SettingsModel, rank_model: BaseRankingModel
)

Calculate the codec ranking of the given parsed data.

calculate_audio_rank

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.calculate_audio_rank(
   data: ParsedData, settings: SettingsModel, rank_model: BaseRankingModel
)

Calculate the audio ranking of the given parsed data.

calculate_extra_ranks

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.calculate_extra_ranks(
   data: ParsedData, settings: SettingsModel, rank_model: BaseRankingModel
)

Calculate all the other rankings of the given parsed data.