#!/usr/bin/python
#-*-coding:utf-8-*-
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"""
Processing of esol dataset.
ESOL (delaney) is a standard regression data set,which is also called delaney dataset. In the dataset, you can find the structure and water solubility data of 1128 compounds. It's a good choice to validate machine learning models and to estimate solubility directly based on molecular structure which was encoded in SMILES string.
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
[docs]def get_default_esol_task_names():
"""Get that default esol task names and return measured values"""
return ['measured log solubility in mols per litre']
[docs]def load_esol_dataset(data_path, task_names=None):
"""Load esol dataset ,process the classification labels and the input information.
Description:
The data file contains a csv table, in which columns below are used:
smiles: SMILES representation of the molecular structure
Compound ID: Name of the compound
measured log solubility in mols per litre: Log-scale water solubility of the compound, used as label
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_esol_dataset('./esol')
print(len(dataset))
References:
[1] Delaney, John S. "ESOL: estimating aqueous solubility directly from molecular structure." Journal of chemical information and computer sciences 44.3 (2004): 1000-1005.
"""
if task_names is None:
task_names = get_default_esol_task_names()
# NB: some examples have multiple species
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]
data_list = []
for i in range(len(labels)):
data = {
'smiles': smiles_list[i],
'label': labels.values[i],
}
data_list.append(data)
dataset = InMemoryDataset(data_list)
return dataset
[docs]def get_esol_stat(data_path, task_names):
"""Return mean and std of labels"""
raw_path = join(data_path, 'raw')
csv_file = os.listdir(raw_path)[0]
input_df = pd.read_csv(join(raw_path, csv_file), sep=',')
labels = input_df[task_names].values
return {
'mean': np.mean(labels, 0),
'std': np.std(labels, 0),
'N': len(labels),
}