#!/usr/bin/env python3
#-*-coding:utf-8-*-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Processing of ddi dataset.
The DDI dataset includes 23,052 Drug-Drug Synergy pairs from 39 celllines.
You can download the dataset from
http://www.bioinf.jku.at/software/DeepSynergy/labels.csv 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_ddi_task_names', 'load_ddi_dataset']
[docs]def get_default_ddi_task_names():
"""Get that default ddi task names and return class label"""
return ['drug_a_name', 'drug_b_name', 'cell_line', 'synergy']
[docs]def load_ddi_dataset(data_path, task_names=None, cellline=None):
"""Load ddi dataset,process the input information.
Description:
The data file contains a csv table, in which columns below are used:
drug_a_name: drug name
drug_b_name: drug name
cell_line: cell line which the drug pairs were tested on
synergy: continuous values represent the synergy effect, we use 30 as threshold to binarize the data into binary labels.
1 as positive and 0 as negative
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.
cellline: the exact cellline model you want to test on.
Returns:
an InMemoryDataset instance.
Example:
.. code-block:: python
dataset = load_hddi_dataset('./ddi/raw')
print(len(dataset))
References:
[1] Drug-Drug Dynergy Data. https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btx806/4747884
"""
if task_names is None:
task_names = get_default_ddi_task_names()
if cellline is None:
cellline = 'A2058'
csv_file = os.listdir(data_path)[0]
input_df = pd.read_csv(join(data_path, csv_file), sep=',', index_col=0)
input_df = input_df[input_df['cell_line']==cellline]
input_df['label'] = [1 if x > 30 else -1 for x in input_df['synergy']]
input_df.index = range(input_df.shape[0])
#sample_list = input_df['synergy']
labels = input_df['label']
# convert 0 to -1
#labels = labels.replace(0, -1)
# there are no nans
data_list = []
for i in range(input_df.shape[0]):
raw_data = {}
raw_data['pair'] = input_df.loc[i, 'drug_a_name'], input_df.loc[i, 'drug_b_name']
raw_data['label'] = labels.values[i]
data = raw_data
if not data is None:
data_list.append(data)
dataset = InMemoryDataset(data_list)
return dataset