Source code for tdc.multi_pred.dti

# -*- coding: utf-8 -*-
# Author: TDC Team
# License: MIT

import warnings
warnings.filterwarnings("ignore")
import sys

from ..utils import print_sys
from . import bi_pred_dataset, multi_pred_dataset
from ..metadata import dataset_names

[docs]class DTI(bi_pred_dataset.DataLoader): """Data loader class to load datasets in Drug-Target Interaction Prediction task. More info: https://tdcommons.ai/multi_pred_tasks/dti/ Regression task. Given the target amino acid sequence/compound SMILES string, predict their binding affinity. Args: name (str): the dataset name. path (str, optional): The path to save the data file, defaults to './data' label_name (str, optional): For multi-label dataset, specify the label name, defaults to None print_stats (bool, optional): Whether to print basic statistics of the dataset, defaults to False """ def __init__(self, name, path='./data', label_name=None, print_stats=False): """Create Drug-Target Interaction Prediction dataloader object """ super().__init__(name, path, label_name, print_stats, dataset_names=dataset_names["DTI"]) self.entity1_name = 'Drug' self.entity2_name = 'Target' self.two_types = True if print_stats: self.print_stats() print('Done!', flush=True, file=sys.stderr)
[docs] def harmonize_affinities(self, mode = None): """Removing duplicated drug-target pairs with different binding affinities. """ if mode not in ['mean', 'max_affinity']: raise ValueError("Please specify 'mode' of removal, currently supported 'mean'/'max_affinity'!") if mode == 'max_affinity': df_ = self.get_data() if self.log_flag: print_sys('The scale is converted to log scale, so we will take the maximum!') df = df_.groupby(['Drug_ID', 'Drug', 'Target_ID', 'Target']).Y.agg(max).reset_index() else: print_sys('The scale is in original affinity scale, so we will take the minimum!') df = df_.groupby(['Drug_ID', 'Drug', 'Target_ID', 'Target']).Y.agg(min).reset_index() elif mode == 'mean': import numpy as np df_ = self.get_data() df = df_.groupby(['Drug_ID', 'Drug', 'Target_ID', 'Target']).Y.agg(np.mean).reset_index() self.entity1_idx = df.Drug_ID.values self.entity2_idx = df.Target_ID.values self.entity1 = df.Drug.values self.entity2 = df.Target.values self.y = df.Y.values print_sys('The original data has been updated!') return df