poli.objective_repository.gfp_cbas.cbas_alphabet_preprocessing

Functions

convert_aas_to_idx_array(X_aa)	Converts a list of amino acid sequences into an array of amino acid indices from AA_IDX.
convert_idx_array_to_aas(X_aa)	Converts an array containing indices of amino acids into the corresponding string amino acid sequences.
convert_mutations_to_sequence(base_seq, ...)	Given the wild type sequence and a formatted mtuation string, returns the mutated sequence
get_argmax(Xt_p)	Given a categorical probability distribution specifying the probability of amino acids at each position in a sequence, returns the most probable sequence
get_balaji_predictions(preds, Xt)	Given a set of predictors built according to the methods in the Balaji Lakshminarayanan paper 'Simple and scalable predictive uncertainty estimation using deep ensembles' (2017), returns the mean and variance of the total prediction.
get_experimental_X_y([random_state, ...])	For the GFP testing experiments.
get_gfp_X_y_aa(data_df[, functional_only, ...])	Converts the raw GFP data to a set of X and y values that are ready to use in a model
get_gfp_base_seq()	Returns the wild type GFP sequence
get_samples(Xt_p)	Samples from a categorical probability distribution specifying the probability of amino acids at each position in a sequence
one_hot_encode_aa(aa_str[, pad])	Returns a one hot encoded amino acid sequence
one_hot_encode_aa_array(X_aa)	OneHot encodes array: (batch_size, L) -> (batch_size, L, alphabet_size)
one_hot_encode_dna(dna_str[, pad, base_order])	Convert length M string into M x 4 tokenized array
partition_data(X, y[, percentile, ...])	Partition a (X, y) data set by a percentile of the y values
read_gfp_data([path, df_save_file])	Reads the GFP brightness data in a pandas DataFrame