def createBlastDB(self, name):
''' wrapper for 'makeblastdb' script of the blast software suit'''
# create output folder
create_folder(self.DB_OUT)
# select all downloaded folders as input for multifasta file creation
subfolder = '.'
# create multifasta file as input for 'makeblastdb'
multi_fasta = self.createBlast_MF(subfolder)
try:
sys.stdout.write("Create BlastDB ...\n")
# run 'makeblastdb'
p = subprocess.Popen(shlex.split("%s -in %s -dbtype %s -input_type %s -title %s -out %s %s"
% (self.EXECUTABLE,
multi_fasta,
self.DB_TYPE,
'fasta',
name,
self.DB_OUT + os.sep + name,
self.get_parse_seqids_stmt())),
stdout = subprocess.PIPE, stderr = subprocess.PIPE)
p.wait()
# print statistics
sys.stdout.write("Creation of %s BlastDB successfull!%s\nDatabase Location: %s\n" %
(self.DB_TYPE, p.stdout.read(), self.DB_OUT + os.sep + name))
except:
raise BlastDBException()
def createMetaCVDB(self, name, subfolder):
'''wrapper for "metacv formatdb" script to generate a MetaCV database'''
# check additional_files and get location
taxonomy = self.get_taxonomy()
#functional = self.get_functional_annotation()
create_folder(self.DB_OUT)
# generate multifasta file
multi_fasta = self.createMetaCV_MF(subfolder)
# needed to for run the external script
full_path_exe = os.path.abspath(self.EXECUTABLE)
try:
sys.stdout.write("Create MetaCV DB ...\n")
# metacv cannot pipe the output to other folder, so it
# have to be run in the same older as the output
os.chdir(self.DB_OUT)
# start metacv formatdb with standard parameter
p = subprocess.Popen(shlex.split("%s formatdb %s %s %s"
% (full_path_exe,
'../' + (multi_fasta),
' '.join(map(str,taxonomy)),
name)))
p.wait()
# print statistics
sys.stdout.write("Creation of MetaCV DB successfull!\nDatabase Location: %s\n" %
(self.DB_OUT + os.sep + name))
except:
MetaCVException()
def assert_apply_paths(self):
self.checkpoints = os.path.join(
self.args.checkpoints, '_'.join([
self.nengine_name, self.args.db_regression_name,
'v' + '.'.join(self.nengine_version), self.args.postfix
]))
if not os.path.exists(self.checkpoints):
raise Exception(
'base_runner.py: class Runner: def assert_apply_paths(...): error: checkpoints directory {} does not exist.'
.format(self.checkpoints))
self.checkpoints_restore = os.path.join(
self.checkpoints,
self.args.restore_checkpoint_template.format(
self.restore_epoch, self.global_step))
self.checkpoints_config = os.path.join(self.checkpoints, 'config')
target_list = os.path.splitext(os.path.split(self.apply_list)[-1])[0]
self.apply_savedir = os.path.join(
self.args.apply_savedir, target_list + '_' + '_'.join([
self.nengine_name, self.args.db_regression_name,
'v' + '.'.join(self.nengine_version), self.args.postfix
]))
utils.create_folder(self.checkpoints_config,
force=False,
raise_except_if_exists=False)
utils.create_folder(self.apply_savedir, force=self.args.apply_force)
def save_model(self, mlflow, path):
path = os.path.join(path, 'saved_models',
f'{self._model_name}_{self._run_time}')
create_folder(path)
self._save_tokenizer(path)
self._save_model(mlflow, path)
self._save_embeddings(path)
def train(self):
# preprocess data
print('preprocessing data')
self._preprocess_data()
# get embeddings
print('getting embeddings weights')
self._get_embeddings()
# create model architecture
self._create_model()
summary = self.get_summary()
print('mode summary:', summary)
self._save_path = os.path.join(self._save_path, 'checkpoints',
f'{self._model_name}_{self._run_time}')
create_folder(self._save_path)
cp_callback = ModelCheckpoint(
filepath=self._save_path,
save_weights_only=False,
# verbose=verbose,
save_best_only=True,
monitor='val_loss',
mode='min')
self._history = self._model.fit(
self._X_train,
self._y_train,
epochs=self._epochs,
validation_data=self._validation_data,
validation_split=self._validation_split,
batch_size=self._batch_size,
callbacks=[cp_callback])
def get_save_dir(args):
if args.debug or args.evaluate or args.extract or args.visualization or args.generate_data or args.generate_label:
save_dir = '{}/temp'.format(args.work_dir)
else:
ct = strftime('%Y-%m-%d %H-%M-%S')
save_dir = '{}/{}_{}_{}/{}'.format(args.work_dir, args.config,
args.model_type, args.dataset, ct)
U.create_folder(save_dir)
return save_dir
def main(argv = None):
# Setup argument parser
parser = ArgumentParser(description = '%s -- create actual bacteria databases from NCBI Sources' %
(os.path.basename(sys.argv[0])),
epilog = 'created by Philipp Sehnert',
add_help = True)
parser.add_argument('--version', action = 'version', version = '%s 1.0' %
(os.path.basename(sys.argv[0])))
parser.add_argument("-type", dest = "type", default = 'nucl',
choices = {'nucl','prot'}, help = "set type of blastdb")
parser.add_argument('-metacv', dest = 'metacv', action = 'store_true',
default = False, help = 'create metacv database')
parser.add_argument('-exe', dest = 'exe',
help = "if not installed, specify path to executable of 'makeblastdb' or 'metacv'")
parser.add_argument('-name', dest = 'name', default = 'bacterial', required = True,
help = 'outname for the databases')
parser.add_argument('-parse_seqids', dest = 'parse_seqids', action = 'store_false', default = True,
help = 'Remove duplicated GI numbers from downloaded files and run "makeblastdb" with -parse_seqids statement ')
# Process arguments
args = parser.parse_args()
DB_TYPE = args.type
METACV = args.metacv
DB_NAME = args.name
EXECUTABLE = args.exe
PARSE_SEQIDS = args.parse_seqids
if __name__ == '__main__':
# check for protein or nucleotide database
DB_TYPE = check_db_type(METACV, DB_TYPE)
# verify executable for external scripts
EXECUTABLE = check_executable(EXECUTABLE, METACV)
# create dir for sources
create_folder(DOWNLOAD_FOLDER)
# init FTP functions
ftp = ftp_functions(FTP_SERVER, FTP_ROOT, DOWNLOAD_FOLDER, DEBUG)
# connect to Blast FTP Server
ftp.connect()
ftp.go_to_root()
# start Downloading
for ftp_folder in SOURCES:
sys.stdout.write("Downloading files from %s \n" % (ftp_folder))
ftp.download_folder(ftp_folder, DB_TYPE)
# close ftp connection
ftp.close()
# run external database creation scripts
DBCreate = DBCreation(DB_OUT, DOWNLOAD_FOLDER, DB_TYPE, PARSE_SEQIDS, DEBUG, EXECUTABLE)
if METACV:
DBCreate.set_METACV(True)
# select the subfolder for MetaCV database
DBCreate.createMetaCVDB(DB_NAME, ['Bacteria', 'Bacteria_DRAFT'])
else:
DBCreate.set_METACV(False)
DBCreate.createBlastDB(DB_NAME)
def download_folder(self, remote_folder, db_type):
'''download a folder with all subfolders and matching files from ftp site'''
local = self.DOWNLOAD_FOLDER + os.sep + remote_folder
# go to remote dir
self.go_down(remote_folder)
# get list of subfolder
folder_list = self.get_folder_index(remote_folder)
# only for cmd output
downloaded = actual = all = 0
# init progressbar
total = float(len(folder_list))
count = float(downloaded + actual)
update_progress(count)
# loop over folder in remote_dir
for item in folder_list:
# create local folder
local_folder = local + os.sep + item
create_folder(local_folder)
# go down in ftp file structure and get a list of matching files
self.go_down(item)
file_list = self.get_file_index(db_type)
# update cmd values
all += len(file_list)
# update progressbar
count += float(1 / total)
update_progress(count)
# loop over files
for x in file_list:
# test timstamps
if not self.is_actual(local_folder, x):
self.download_file(local_folder, x)
downloaded += 1
else:
actual += 1
# go up file structure
self.go_up()
# write status information to stdout
sys.stdout.write("\nDownloaded: %d Actual: %d Total: %d \n\n" %
(downloaded, actual, all))
# go up in file structure to root dir
self.go_up()
def assert_to_proto_paths(self):
self.checkpoints = os.path.join(
self.args.checkpoints, '_'.join([
self.nengine_name, self.args.db_regression_name,
'v' + '.'.join(self.nengine_version), self.args.postfix
]))
if not os.path.exists(self.checkpoints):
raise Exception(
'base_runner.py: class Runner: def assert_to_proto_paths(...): error: checkpoints directory {} does not exist.'
.format(self.checkpoints))
self.checkpoints_restore = os.path.join(
self.checkpoints,
self.args.restore_checkpoint_template.format(
self.restore_epoch, self.global_step))
self.checkpoints_config = os.path.join(self.checkpoints, 'config')
self.checkpoints_to_proto = os.path.join(self.checkpoints, 'pb_model')
utils.create_folder(self.checkpoints_config,
force=False,
raise_except_if_exists=False)
utils.create_folder(self.checkpoints_to_proto, force=False)
def save_feature_importance(self, method, preds_test=[]):
logger.info(method)
path = os.path.join('reports/figures/local/',
(self.model.get_name() + '_' + self.feature_set))
create_folder(path)
filename = self._get_figure_file_name(method, path)
explainer = shap.TreeExplainer(
self.model.get_model(reinitialize=False))
shap_values = explainer.shap_values(self.X_train)
if method == "shap":
# shap_values = shap_values[:, :-1]
shap.summary_plot(shap_values, self.X_train, show=False)
plt.savefig(self._get_figure_file_name('shap', path),
bbox_inches='tight')
elif method == 'summary':
shap.summary_plot(shap_values,
self.X_train,
plot_type='bar',
show=False)
plt.savefig(self._get_figure_file_name('summary', path),
bbox_inches='tight')
elif method == 'feature_importance':
feature_score = pd.DataFrame(list(
zip(self.X_test.dtypes.index, shap_values.mean(axis=0))),
columns=['Feature', 'Score'])
feature_score = feature_score.sort_values(by='Score',
ascending=False,
inplace=False,
kind='quicksort',
na_position='last')
plt.rcParams["figure.figsize"] = (12, 7)
ax = feature_score.plot('Feature', 'Score', kind='bar', color='c')
ax.set_title("Feature Importance using {}".format(method),
fontsize=14)
ax.set_xlabel("features")
plt.savefig(self._get_figure_file_name(method, path),
bbox_inches='tight')
def assert_train_paths(self):
self.checkpoints = os.path.join(
self.args.checkpoints, '_'.join([
self.nengine_name, self.args.db_regression_name,
'v' + '.'.join(self.nengine_version), self.args.postfix
]))
self.checkpoints_config = os.path.join(self.checkpoints, 'config')
self.checkpoints_logs = os.path.join(self.checkpoints, 'logs')
self.checkpoints_logs_train = os.path.join(self.checkpoints, 'logs',
'train')
if self.val_list != '':
self.checkpoints_logs_val = os.path.join(self.checkpoints, 'logs',
'val')
# [optional] Training restoration
if (self.args.restore_epoch is not None) and (self.args.restore_step
is not None):
self.checkpoints_restore = os.path.join(
self.checkpoints,
self.args.restore_checkpoint_template.format(
self.args.restore_epoch, self.args.restore_step))
create_folder_kwargs = dict(force=False,
raise_except_if_exists=False)
if self.args.train_force:
print(
'[WARNING] restore_epoch is not None and restore_step is not None, but train_force is True! Training directories will not be cleaned, training will be restored. If you want to train from scratch, do not set restore_epoch and restore_step arguments'
)
else:
self.checkpoints_restore = None
create_folder_kwargs = dict(force=self.args.train_force)
utils.create_folder(self.checkpoints, **create_folder_kwargs)
utils.create_folder(self.checkpoints_config, **create_folder_kwargs)
utils.create_folder(self.checkpoints_logs, **create_folder_kwargs)
utils.create_folder(self.checkpoints_logs_train,
**create_folder_kwargs)
if self.val_list != '':
utils.create_folder(self.checkpoints_logs_val,
**create_folder_kwargs)
def create_temporal_folders(self):
utils.create_folder(lambda_instance.temporal_folder)
utils.create_folder(lambda_instance.input_folder)
utils.create_folder(lambda_instance.output_folder)
def setUp(self):
self.year = 1913
create_folder('./data/{}/posters'.format(self.year))
create_folder('./data/{}/thumbnails'.format(self.year))
self.dict_imgs_1913 = get_yearly_url_imgs(1913)
def test_create_folder(self):
folder_tmp = './dummy-folder'
create_folder(folder_tmp)
self.assertTrue(os.path.exists(folder_tmp))
os.rmdir(folder_tmp)
def _save(self, data, path, file_name):
create_folder(path)
print('saving interim dataset')
data.to_csv(os.path.join(path, file_name), index=False)
