Preprocess API

IMPROVE general preprocess parameters

log_level: Type: str

Default: “DEBUG”

Help: Set log levels. Default is WARNING. Levels are: DEBUG, INFO, WARNING, ERROR, CRITICAL, NOTSET.
input_dir: Type: str

Default: “./”

Help: Base directory for input data. All additional input pathes will be relative to the base input directory.
output_dir: Type: str

Default: “./”

Help: Base directory for output data. All additional relative output pathes will be placed into the base output directory.
config_file: Type: str

Default: None

Help: Config file in INI format.
param_log_file: Type: str

Default: “param_log_file.txt”

Help: Log of final parameters used for run. Saved in output_dir if file name, can be an absolute path.
data_format: Type: str

Default: “.parquet”

Help: File format to save the ML data file (e.g., ‘.pt’, ‘.tfrecords’).
input_supp_data_dir: Type: str

Default: None

Help: Dir containing supplementary data in addition to benchmark data (usually model-specific data).
x_data_dir: Type: str

Default: “x_data”

Help: Dir name that contains the files with features data (x data).
y_data_dir: Type: str

Default: “y_data”

Help: Dir name that contains the files with target data (y data).
splits_dir: Type: str

Default: “splits”

Help: Dir name that contains files that store split ids of the y data file.
train_split_file: Type: str

Default: “fake”

Help: The path to the file that contains the train split ids (e.g., ‘split_0_train_id’, ‘split_0_train_size_1024’).
val_split_file: Type: str

Default: “fake”

Help: The path to the file that contains the val split ids (e.g., ‘split_0_val_id’).
test_split_file: Type: str

Default: “fake”

Help: The path to the file that contains the test split ids (e.g., ‘split_0_test_id’).

Drug Response Prediction preprocess parameters

y_data_files: Type: str

Default: “fake”

Help: List of files that contain the y (prediction variable) data. Example: [[‘response.tsv’]].
x_data_canc_files: Type: str

Default: “fake”

Help: List of feature files including gene_system_identifer. Example: [[‘cancer_gene_expression.tsv’, [‘Gene_Symbol’]]].
x_data_drug_files: Type: str

Default: “fake”

Help: List of feature files. Example: [[‘drug_SMILES.tsv’]].
canc_col_name: Type: str

Default: “improve_sample_id”

Help: Column name in the y (response) data file that contains the cancer sample ids.
drug_col_name: Type: str

Default: “improve_chem_id”

Help: Column name in the y (response) data file that contains the drug ids.
y_col_name: Type: str

Default: “auc”

Help: Column name in the y data file (e.g., response.tsv), that represents the target variable that the model predicts.

Synergy preprocess parameters

y_data_file: Type: str

Default: “synergy.tsv”

Help: File that contains the y (prediction variable) data.
cell_transcriptomic_file: Type: str

Default: None

Help: “‘cell_transcriptomics.tsv’ for benchmark data or path to the transcriptomics data. None if not used.”
cell_transcriptomic_transform: Type: str

Default: None

Help: “List of lists with the type of transformation and the option. Transformations will be performed in the order of the

outer list. For example, [[‘normalize’, ‘zscale’], [‘subset’, ‘L1000’]] will first normalize the data with z-scaling and then subset to genes in L1000. For subset, a path to a text file with Entrez IDs separated by new lines can be given. None if not used.”
cell_cnv_file: Type: str

Default: None

Help: “‘cell_cnv_continuous.tsv’ or ‘cell_cnv_discretized.tsv’ for benchmark data or path to the CNV data. None if not used.”
cell_cnv_transform: Type: str

Default: None

Help: “List of lists with the type of transformation and the option. Transformations will be performed in the order of the

outer list. For example, [[‘normalize’, ‘zscale’], [‘subset’, ‘L1000’]] will first normalize the data with z-scaling and then subset to genes in L1000. For subset, a path to a text file with Entrez IDs separated by new lines can be given. None if not used.”
cell_mutation_file: Type: str

Default: None

Help: “‘cell_mutation_delet.tsv’ or ‘cell_mutation_nonsynon.tsv’ for benchmark data or path to the mutation data. None if not used.”
cell_mutation_transform: Type: str

Default: None

Help: “List of lists with the type of transformation and the option. Transformations will be performed in the order of the

outer list. For example, [[‘normalize’, ‘zscale’], [‘subset’, ‘L1000’]] will first normalize the data with z-scaling and then subset to genes in L1000. For subset, a path to a text file with Entrez IDs separated by new lines can be given. None if not used.”
drug_smiles_file: Type: str

Default: None

Help: “‘drug_smiles.tsv’ or ‘drug_smiles_canonical.tsv’ for benchmark data or path to the SMILES data. None if not used.”
drug_mordred_file: Type: str

Default: None

Help: “‘drug_mordred.tsv’ for benchmark data or path to the Mordred data. None if not used.”
drug_infomax_file: Type: str

Default: None

Help: “‘drug_infomax.tsv’ for benchmark data or path to the Infomax data. None if not used.”
drug_ecfp_file: Type: str

Default: None

Help: “‘drug_ecfp[2/4/6]_nbits[256/1024].tsv’ for benchmark data or path to the ECFP data. None if not used.”
cell_column_name: Type: str

Default: “DepMapID”

Help: “Column name in the y (response) data file that contains the cancer sample IDs.”
drug_column_name: Type: str

Default: “DrugID”

Help: “Column name in the y (response) data file that contains the cancer sample IDs.”
drug_1_column_name: Type: str

Default: “DrugID_row”

Help: “Column name in the y (response) data file that contains the first drug IDs.”
drug_2_column_name: Type: str

Default: “DrugID_col”

Help: “Column name in the y (response) data file that contains the second drug IDs.”
y_col_name: Type: str

Default: “loewe”

Help: “Column name in the y data file (e.g., synergy.tsv), that represents the target variable that the model predicts.

In synergy prediction problem it can be one of [‘loewe’, ‘bliss’, ‘zip’, ‘hsa’, ‘smean’, ‘css’].”