#!/usr/bin/python
#-*-coding:utf-8-*-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Processing of PPI dataset.
The DDI dataset were extracted from DrugCombDB.
You can download the dataset from
http://drugcombdb.denglab.org/download/protein_protein_links.rar 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_ppi_task_names', 'load_ppi_dataset']
[docs]def get_default_ppi_task_names():
"""Get that default ppi task names"""
return ['protein1', 'protein2']
[docs]def load_ppi_dataset(data_path, task_names=None, featurizer=None):
"""Load ppi dataset,process the input information and the featurizer.
Description:
The data file contains a txt file, in which columns below are used:
protein1: protein1 name
protein2: protein2 name
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 txt file.
Returns:
an InMemoryDataset instance.
Example:
.. code-block:: python
dataset = load_ppi_dataset('./ppi/raw')
print(len(dataset))
"""
if task_names is None:
task_names = get_default_ppi_task_names()
txt_file = os.listdir(data_path)[0]
input_df = pd.read_csv(join(data_path, txt_file), sep=' ')
# there are no nans
data_list = []
for i in range(input_df.shape[0]):
raw_data = {}
raw_data['pair'] = input_df.loc[i, 'protein1'], input_df.loc[i, 'protein2']
data = raw_data
if not data is None:
data_list.append(data)
dataset = InMemoryDataset(data_list)
return dataset