def __init__(
            self,
            allele_to_allele_specific_models=None,
            class1_pan_allele_models=None,
            allele_to_fixed_length_sequence=None,
            manifest_df=None,
            allele_to_percent_rank_transform=None,
            metadata_dataframes=None):
        """
        Parameters
        ----------
        allele_to_allele_specific_models : dict of string -> list of `Class1NeuralNetwork`
            Ensemble of single-allele models to use for each allele. 
        
        class1_pan_allele_models : list of `Class1NeuralNetwork`
            Ensemble of pan-allele models.
        
        allele_to_fixed_length_sequence : dict of string -> string
            Required only if class1_pan_allele_models is specified.
        
        manifest_df : `pandas.DataFrame`, optional
            Must have columns: model_name, allele, config_json, model.
            Only required if you want to update an existing serialization of a
            Class1AffinityPredictor. Otherwise this dataframe will be generated
            automatically based on the supplied models.

        allele_to_percent_rank_transform : dict of string -> `PercentRankTransform`, optional
            `PercentRankTransform` instances to use for each allele

        metadata_dataframes : dict of string -> pandas.DataFrame, optional
            Optional additional dataframes to write to the models dir when
            save() is called. Useful for tracking provenance.
        """

        if allele_to_allele_specific_models is None:
            allele_to_allele_specific_models = {}
        if class1_pan_allele_models is None:
            class1_pan_allele_models = []

        if class1_pan_allele_models:
            assert allele_to_fixed_length_sequence, "Allele sequences required"

        self.allele_to_allele_specific_models = allele_to_allele_specific_models
        self.class1_pan_allele_models = class1_pan_allele_models
        self.allele_to_fixed_length_sequence = allele_to_fixed_length_sequence
        self._manifest_df = manifest_df

        if not allele_to_percent_rank_transform:
            allele_to_percent_rank_transform = {}
        self.allele_to_percent_rank_transform = allele_to_percent_rank_transform
        self.metadata_dataframes = metadata_dataframes
        self._cache = {}
def __init__ ( self , allele_to_allele_specific_models = None , class1_pan_allele_models = None , allele_to_fixed_length_sequence = None , manifest_df = None , allele_to_percent_rank_transform = None , metadata_dataframes = None ) : if allele_to_allele_specific_models is None : allele_to_allele_specific_models = { } if class1_pan_allele_models is None : class1_pan_allele_models = [ ] if class1_pan_allele_models : assert allele_to_fixed_length_sequence , "Allele sequences required" self . allele_to_allele_specific_models = allele_to_allele_specific_models self . class1_pan_allele_models = class1_pan_allele_models self . allele_to_fixed_length_sequence = allele_to_fixed_length_sequence self . _manifest_df = manifest_df if not allele_to_percent_rank_transform : allele_to_percent_rank_transform = { } self . allele_to_percent_rank_transform = allele_to_percent_rank_transform self . metadata_dataframes = metadata_dataframes self . _cache = { }
Parameters
        ----------
        allele_to_allele_specific_models : dict of string -> list of `Class1NeuralNetwork`
            Ensemble of single-allele models to use for each allele. 
        
        class1_pan_allele_models : list of `Class1NeuralNetwork`
            Ensemble of pan-allele models.
        
        allele_to_fixed_length_sequence : dict of string -> string
            Required only if class1_pan_allele_models is specified.
        
        manifest_df : `pandas.DataFrame`, optional
            Must have columns: model_name, allele, config_json, model.
            Only required if you want to update an existing serialization of a
            Class1AffinityPredictor. Otherwise this dataframe will be generated
            automatically based on the supplied models.

        allele_to_percent_rank_transform : dict of string -> `PercentRankTransform`, optional
            `PercentRankTransform` instances to use for each allele

        metadata_dataframes : dict of string -> pandas.DataFrame, optional
            Optional additional dataframes to write to the models dir when
            save() is called. Useful for tracking provenance.