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import math
import os
import struct
import logging
from tqdm import tqdm
import csv
from collections import defaultdict
import pandas as pd
logger = logging.getLogger(__name__)
# Taken from here https://torchdrug.ai/docs/_modules/torchdrug/utils/file.html#download
def download(url, path, save_file=None, md5=None):
"""
Download a file from the specified url.
Skip the downloading step if there exists a file satisfying the given MD5.
Parameters:
url (str): URL to download
path (str): path to store the downloaded file
save_file (str, optional): name of save file. If not specified, infer the file name from the URL.
md5 (str, optional): MD5 of the file
"""
from six.moves.urllib.request import urlretrieve
if save_file is None:
save_file = os.path.basename(url)
if "?" in save_file:
save_file = save_file[:save_file.find("?")]
save_file = os.path.join(path, save_file)
if not os.path.exists(save_file) or compute_md5(save_file) != md5:
logger.info("Downloading %s to %s" % (url, save_file))
urlretrieve(url, save_file)
return save_file
def smart_open(file_name, mode="rb"):
"""
Open a regular file or a zipped file.
This function can be used as drop-in replacement of the builtin function `open()`.
Parameters:
file_name (str): file name
mode (str, optional): open mode for the file stream
"""
import bz2
import gzip
extension = os.path.splitext(file_name)[1]
if extension == '.bz2':
return bz2.BZ2File(file_name, mode)
elif extension == '.gz':
return gzip.GzipFile(file_name, mode)
else:
return open(file_name, mode)
def extract(zip_file, member=None):
"""
Extract files from a zip file. Currently, ``zip``, ``gz``, ``tar.gz``, ``tar`` file types are supported.
Parameters:
zip_file (str): file name
member (str, optional): extract specific member from the zip file.
If not specified, extract all members.
"""
import gzip
import shutil
import zipfile
import tarfile
zip_name, extension = os.path.splitext(zip_file)
if zip_name.endswith(".tar"):
extension = ".tar" + extension
zip_name = zip_name[:-4]
save_path = os.path.dirname(zip_file)
if extension == ".gz":
member = os.path.basename(zip_name)
members = [member]
save_files = [os.path.join(save_path, member)]
for _member, save_file in zip(members, save_files):
with open(zip_file, "rb") as fin:
fin.seek(-4, 2)
file_size = struct.unpack("<I", fin.read())[0]
with gzip.open(zip_file, "rb") as fin:
if not os.path.exists(save_file) or file_size != os.path.getsize(save_file):
logger.info("Extracting %s to %s" % (zip_file, save_file))
with open(save_file, "wb") as fout:
shutil.copyfileobj(fin, fout)
elif extension in [".tar.gz", ".tgz", ".tar"]:
tar = tarfile.open(zip_file, "r")
if member is not None:
members = [member]
save_files = [os.path.join(save_path, os.path.basename(member))]
logger.info("Extracting %s from %s to %s" % (member, zip_file, save_files[0]))
else:
members = tar.getnames()
save_files = [os.path.join(save_path, _member) for _member in members]
logger.info("Extracting %s to %s" % (zip_file, save_path))
for _member, save_file in zip(members, save_files):
if tar.getmember(_member).isdir():
os.makedirs(save_file, exist_ok=True)
continue
os.makedirs(os.path.dirname(save_file), exist_ok=True)
if not os.path.exists(save_file) or tar.getmember(_member).size != os.path.getsize(save_file):
with tar.extractfile(_member) as fin, open(save_file, "wb") as fout:
shutil.copyfileobj(fin, fout)
elif extension == ".zip":
zipped = zipfile.ZipFile(zip_file)
if member is not None:
members = [member]
save_files = [os.path.join(save_path, os.path.basename(member))]
logger.info("Extracting %s from %s to %s" % (member, zip_file, save_files[0]))
else:
members = zipped.namelist()
save_files = [os.path.join(save_path, _member) for _member in members]
logger.info("Extracting %s to %s" % (zip_file, save_path))
for _member, save_file in zip(members, save_files):
if zipped.getinfo(_member).is_dir():
os.makedirs(save_file, exist_ok=True)
continue
os.makedirs(os.path.dirname(save_file), exist_ok=True)
if not os.path.exists(save_file) or zipped.getinfo(_member).file_size != os.path.getsize(save_file):
with zipped.open(_member, "r") as fin, open(save_file, "wb") as fout:
shutil.copyfileobj(fin, fout)
else:
raise ValueError("Unknown file extension `%s`" % extension)
if len(save_files) == 1:
return save_files[0]
else:
return save_path
def compute_md5(file_name, chunk_size=65536):
"""
Compute MD5 of the file.
Parameters:
file_name (str): file name
chunk_size (int, optional): chunk size for reading large files
"""
import hashlib
md5 = hashlib.md5()
with open(file_name, "rb") as fin:
chunk = fin.read(chunk_size)
while chunk:
md5.update(chunk)
chunk = fin.read(chunk_size)
return md5.hexdigest()
def get_line_count(file_name, chunk_size=8192*1024):
"""
Get the number of lines in a file.
Parameters:
file_name (str): file name
chunk_size (int, optional): chunk size for reading large files
"""
count = 0
with open(file_name, "rb") as fin:
chunk = fin.read(chunk_size)
while chunk:
count += chunk.count(b"\n")
chunk = fin.read(chunk_size)
return count
class OPV:
"""
Quantum mechanical calculations on organic photovoltaic candidate molecules.
Statistics:
- #Molecule: 94,576
- #Regression task: 8
Parameters:
path (str): path to store the dataset
verbose (int, optional): output verbose level
**kwargs
"""
train_url = "https://cscdata.nrel.gov/api/datasets/ad5d2c9a-af0a-4d72-b943-1e433d5750d6/download/" \
"b69cf9a5-e7e0-405b-88cb-40df8007242e"
valid_url = "https://cscdata.nrel.gov/api/datasets/ad5d2c9a-af0a-4d72-b943-1e433d5750d6/download/" \
"1c8e7379-3071-4360-ba8e-0c6481c33d2c"
test_url = "https://cscdata.nrel.gov/api/datasets/ad5d2c9a-af0a-4d72-b943-1e433d5750d6/download/" \
"4ef40592-0080-4f00-9bb7-34b25f94962a"
train_md5 = "16e439b7411ea0a8d3a56ba4802b61b1"
valid_md5 = "3aa2ac62015932ca84661feb5d29adda"
test_md5 = "bad072224f0755478f0729476ca99a33"
target_fields = ["gap", "homo", "lumo", "spectral_overlap", "gap_extrapolated", "homo_extrapolated",
"lumo_extrapolated", "optical_lumo_extrapolated"]
def read_csv(self, csv_file, smiles_field="smiles", target_fields=None, verbose=0):
if target_fields is not None:
target_fields = set(target_fields)
with open(csv_file, "r") as fin:
reader = csv.reader(fin)
if verbose:
reader = iter(tqdm(reader, "Loading %s" % csv_file, get_line_count(csv_file)))
fields = next(reader)
smiles = []
targets = defaultdict(list)
for i, values in enumerate(reader):
if not any(values):
continue
if smiles_field is None:
smiles.append("")
for field, value in zip(fields, values):
if field == smiles_field:
smiles.append(value)
elif target_fields is None or field in target_fields:
pass
# value = eval(value)
# if value == "":
# value = math.nan
# targets[field].append(value)
return smiles, targets
def __init__(self, path, verbose=1, **kwargs):
path = os.path.expanduser(path)
if not os.path.exists(path):
os.makedirs(path)
self.path = path
train_zip_file = download(self.train_url, path, save_file="mol_train.csv.gz", md5=self.train_md5)
valid_zip_file = download(self.valid_url, path, save_file="mol_valid.csv.gz", md5=self.valid_md5)
test_zip_file = download(self.test_url, path, save_file="mol_test.csv.gz", md5=self.test_md5)
train_file = extract(train_zip_file)
valid_file = extract(valid_zip_file)
test_file = extract(test_zip_file)
train_smiles, train_targets = self.read_csv(train_file, smiles_field="smile", target_fields=self.target_fields)
valid_smiles, valid_targets = self.read_csv(valid_file, smiles_field="smile", target_fields=self.target_fields)
test_smiles, test_targets = self.read_csv(test_file, smiles_field="smile", target_fields=self.target_fields)
self.num_train = len(train_smiles)
self.num_valid = len(valid_smiles)
self.num_test = len(test_smiles)
smiles = train_smiles + valid_smiles + test_smiles
targets = {k: train_targets[k] + valid_targets[k] + test_targets[k] for k in train_targets}
# self.load_smiles(smiles, targets, verbose=verbose, **kwargs)
print(smiles[:10])
df_out = pd.DataFrame({"smiles": smiles})
df_out.to_parquet(os.path.join(os.path.dirname(__file__), "opv.parquet"))
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cwd = os.path.join(os.path.dirname(__file__), "download")
os.makedirs(cwd,exist_ok=True)
d = OPV(cwd)
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