#!/usr/bin/python
#-*-coding:utf-8-*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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"""
Processing of hiv dataset.
The HIV dataset was introduced by the Drug Therapeutics Program (DTP) AIDS Antiviral Screen, which tested the ability to inhibit HIV replication for over 40,000 compounds. Screening results were evaluated and placed into three categories: confirmed inactive (CI),confirmed active (CA) and confirmed moderately active (CM). We further combine the latter two labels, making it a classification task between inactive (CI) and active (CA and CM).
You can download the dataset from
http://moleculenet.ai/datasets-1 and load it into pahelix reader creators
"""
import os
from os.path import join, exists
import pandas as pd
import numpy as np
from pahelix.datasets.inmemory_dataset import InMemoryDataset
__all__ = ['get_default_hiv_task_names', 'load_hiv_dataset']
[docs]def get_default_hiv_task_names():
"""Get that default hiv task names and return class label"""
return ['HIV_active']
[docs]def load_hiv_dataset(data_path, task_names=None):
"""Load hiv dataset,process the input information.
Description:
The data file contains a csv table, in which columns below are used:
smiles: SMILES representation of the molecular structure
activity: Three-class labels for screening results: CI/CM/CA.
HIV_active: Binary labels for screening results: 1 (CA/CM) and 0 (CI)
Args:
data_path(str): the path to the cached npz path
task_names(list): a list of header names to specify the columns to fetch from
the csv file.
Returns:
an InMemoryDataset instance.
Example:
.. code-block:: python
dataset = load_hiv_dataset('./hiv')
print(len(dataset))
References:
[1] AIDS Antiviral Screen Data. https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data
"""
if task_names is None:
task_names = get_default_hiv_task_names()
raw_path = join(data_path, 'raw')
csv_file = os.listdir(raw_path)[0]
input_df = pd.read_csv(join(raw_path, csv_file), sep=',')
smiles_list = input_df['smiles']
labels = input_df[task_names]
# convert 0 to -1
labels = labels.replace(0, -1)
# there are no nans
data_list = []
for i in range(len(smiles_list)):
data = {}
data['smiles'] = smiles_list[i]
data['label'] = labels.values[i]
data_list.append(data)
dataset = InMemoryDataset(data_list)
return dataset