class GCSHook(FileSystemHookInterface):
conn_type = 'gcs'
credentials_env_var = 'GOOGLE_APPLICATION_CREDENTIALS'
def __init__(self, conn_params: ConnectionParams):
self.conn_params = conn_params
def __enter__(self) -> 'GCSFS':
from fs_gcsfs import GCSFS
self.saved_credentials = os.environ.get(GCSHook.credentials_env_var)
credentials_path = self.conn_params.extra.get('credentials_path')
if credentials_path:
os.environ[GCSHook.credentials_env_var] = credentials_path
self.bucket = self.conn_params.extra['bucket']
self.base_path = self.conn_params.extra.get('base_path')
self.create = self.conn_params.extra.get('create', False)
self.strict = self.conn_params.extra.get('strict', True)
self.conn = GCSFS(bucket_name=self.bucket,
root_path=self.base_path,
create=self.create,
strict=self.strict)
return self.conn
def __exit__(self, exc_type, exc_val, exc_tb):
if self.saved_credentials:
os.environ[GCSHook.credentials_env_var] = self.saved_credentials
self.conn.close()
self.conn = None
def test_path_to_key(path, root_path, expected, client_mock):
gcs_fs = GCSFS(bucket_name=TEST_BUCKET,
root_path=root_path,
client=client_mock,
strict=False)
assert gcs_fs._path_to_key(path) == expected
assert gcs_fs._path_to_dir_key(path) == expected + GCSFS.DELIMITER
def getfiles(request):
#set gcs params
gcs_bucket_name = 'your-gcs-bucket-name'
gcsfs = GCSFS(bucket_name=gcs_bucket_name)
gcs_bucket_string = 'gs://' + gcs_bucket_name + '/'
#set ftp params
ftp = FTP('ftp.your-ftp-site.com')
ftp.login('your-username', 'your-password')
# get files within the root directory into list
filenames = ftp.nlst()
#loop through each each file and copy into gcs if it is a .zip file (your specific use case may be different)
#here you can look for specific file names or remove the if statement if you are pulling everything form the ftp source
for filename in filenames:
if '.zip' in filename:
gcs_target_filename = gcs_bucket_string + filename
print('Retrieving file: ' + filename)
#this line pulls the file from the FTP site and writes it to a file in the GCS bucket
ftp.retrbinary('RETR ' + filename,
gcsfs.open(filename, 'wb').write)
print('Successfully created: ' + gcs_target_filename)
ftp.delete(filename)
print('Deleted from remote host: ' + filename)
else:
#write line for stackdriver logging
print('No .Zip files found on remote host.')
#close ftp connection
ftp.quit()
#write line for stackdriver logging
print('done')
def read_all_shards(partition, data_dir, bucket_name):
"""Combines different CSVs into a single dataframe."""
shards = []
gcsfs = GCSFS(bucket_name)
for fn in gcsfs.listdir(os.path.join(data_dir, partition)):
with gcsfs.open(os.path.join(data_dir, partition, fn)) as f:
shards.append(pd.read_csv(f, index_col=None))
return pd.concat(shards)
def test_create_property_does_not_create_file_if_emptyish_root_path(
root_path, client):
"""Regression test for a bug fixed in 0.4.1"""
gcs_fs = GCSFS(bucket_name=TEST_BUCKET,
root_path=root_path,
client=client,
create=True)
assert gcs_fs.bucket.get_blob(root_path + GCSFS.DELIMITER) is None
def __enter__(self) -> 'GCSFS':
from fs_gcsfs import GCSFS
self.saved_credentials = os.environ.get(GCSHook.credentials_env_var)
credentials_path = self.conn_params.extra.get('credentials_path')
if credentials_path:
os.environ[GCSHook.credentials_env_var] = credentials_path
self.bucket = self.conn_params.extra['bucket']
self.base_path = self.conn_params.extra.get('base_path')
self.create = self.conn_params.extra.get('create', False)
self.strict = self.conn_params.extra.get('strict', True)
self.conn = GCSFS(bucket_name=self.bucket,
root_path=self.base_path,
create=self.create,
strict=self.strict)
return self.conn
def tmp_gcsfs(bucket, client):
"""Yield a temporary `GCSFS` at a unique 'root-blob' within the test bucket."""
path = "gcsfs/" + str(uuid.uuid4())
yield GCSFS(bucket_name=bucket.name,
root_path=path,
client=client,
create=True)
for blob in bucket.list_blobs(prefix=path):
blob.delete()
def test_path_to_key_fails_if_path_is_parent_of_root_path(client_mock):
gcs_fs = GCSFS(bucket_name=TEST_BUCKET, client=client_mock, strict=False)
with pytest.raises(IllegalBackReference):
gcs_fs._path_to_key("..")
gcs_fs_with_root_path = GCSFS(bucket_name="bucket",
root_path="root_path",
client=client_mock,
strict=False)
with pytest.raises(IllegalBackReference):
gcs_fs_with_root_path._path_to_key("..")
def _open_fs(self, user_context):
props = self._serialization_props(user_context)
bucket_name = props.pop('bucket_name', None)
root_path = props.pop('root_path', None)
project = props.pop('project', None)
args = {}
if props.get('anonymous'):
args['client'] = Client.create_anonymous_client()
elif props.get('token'):
args['client'] = Client(project=project,
credentials=Credentials(**props))
handle = GCSFS(bucket_name, root_path=root_path, retry=0, **args)
return handle
def make_fs(self):
return GCSFS(bucket_name=TEST_BUCKET,
root_path=self.root_path,
client=self.client,
create=True)
def test_instantiation_with_create_false_fails_for_non_existing_root_path():
with pytest.raises(CreateFailed):
GCSFS(bucket_name=TEST_BUCKET,
root_path=str(uuid.uuid4()),
create=False)
def test_instantiation_fails_if_no_access_to_bucket():
with pytest.raises(CreateFailed):
GCSFS(bucket_name=str(uuid.uuid4()))
def test_listdir_works_on_bucket_as_root_directory(client):
"""Regression test for a bug fixed in 0.2.1"""
gcs_fs = GCSFS(bucket_name=TEST_BUCKET, client=client, create=True)
blob = str(uuid.uuid4())
directory = str(uuid.uuid4())
gcs_fs.touch(blob)
gcs_fs.makedir(directory)
result = gcs_fs.listdir("")
# Manual clean-up of the created blobs
gcs_fs.remove(blob)
gcs_fs.removedir(directory)
assert blob in result
assert directory in result
def main(_):
if FLAGS.use_transformer:
assert (
FLAGS.encoder_fn_name == 'transformer'
), 'encoder_fn_name must be transformer if use_transformer is True!'
assert (FLAGS.epochs % FLAGS.measurements == 0
), 'Number of measurements must divide number of epochs!'
measurement_epochs = FLAGS.epochs // FLAGS.measurements
assert FLAGS.results_save_dir != '', 'Specify results_save_dir!'
assert FLAGS.label != '', 'Specify label!'
if FLAGS.load_model:
assert FLAGS.load_model_dir != '', 'Specify load_model_dir!'
assert FLAGS.load_model_step > 0, 'Loaded model must have been trained for more than 0 steps.'
if FLAGS.save_model:
assert FLAGS.save_model_dir != '', 'Specify save_model_dir!'
datum = {
'label': FLAGS.label,
'encoder_fn_name': FLAGS.encoder_fn_name,
'encoder_fn_kwargs_path': FLAGS.encoder_fn_kwargs_path,
'reduce_fn_name': FLAGS.reduce_fn_name,
'reduce_fn_kwargs_path': FLAGS.reduce_fn_kwargs_path,
'epochs': FLAGS.epochs,
'measurements': FLAGS.measurements,
'lens_batch_size': FLAGS.lens_batch_size,
'knn_batch_size': FLAGS.knn_batch_size,
'encoder_lr': FLAGS.encoder_lr,
'lens_lr': FLAGS.lens_lr,
'predictor_lr': FLAGS.predictor_lr,
'encoder_wd': FLAGS.encoder_wd,
'lens_wd': FLAGS.lens_wd,
'predictor_wd': FLAGS.predictor_wd,
'train_families': FLAGS.train_families,
'lens_train_samples': FLAGS.lens_train_samples,
'first_test_family': FLAGS.first_test_family,
'last_test_family': FLAGS.last_test_family,
'lens_shuffle_seed': FLAGS.lens_shuffle_seed,
'lens_sample_random_state': FLAGS.lens_sample_random_state,
'knn_shuffle_seed': FLAGS.knn_shuffle_seed,
'knn_sample_random_state': FLAGS.knn_sample_random_state,
'random_key': FLAGS.random_key,
'use_transformer': FLAGS.use_transformer,
'use_bert': FLAGS.use_bert,
'restore_transformer_dir': FLAGS.restore_transformer_dir,
'gcs_bucket': FLAGS.gcs_bucket,
'data_partitions_dirpath': FLAGS.data_partitions_dirpath,
'results_save_dir': FLAGS.results_save_dir,
'load_model': FLAGS.load_model,
'load_model_dir': FLAGS.load_model_dir,
'load_model_step': FLAGS.load_model_step,
'save_model': FLAGS.save_model,
'save_model_dir': FLAGS.save_model_dir
}
gcsfs = GCSFS(FLAGS.gcs_bucket)
print(datum)
df = pd.DataFrame([datum])
with gcsfs.open(os.path.join(FLAGS.results_save_dir, FLAGS.label + '.csv'),
'w') as gcs_file:
df.to_csv(gcs_file, index=False)
knn_train_samples_ = [1, 5, 10, 50]
num_families = len(family_ids)
loss_fn_kwargs = {'num_classes': num_families}
lens_knn_train_family_accessions = []
for _ in range(1, FLAGS.train_families + 1):
family_name = 'PF%05d' % _
lens_knn_train_family_accessions.append(family_name)
knn_test_family_accessions = []
for _ in range(FLAGS.first_test_family, FLAGS.last_test_family + 1):
family_name = 'PF%05d' % _
knn_test_family_accessions.append(family_name)
encoder_fn = encoder_fn_name_to_fn(FLAGS.encoder_fn_name)
encoder_fn_kwargs = json.load(
open(
resource_filename(
'contextual_lenses.resources',
os.path.join('encoder_fn_kwargs_resources',
FLAGS.encoder_fn_kwargs_path + '.json'))))
reduce_fn = reduce_fn_name_to_fn(FLAGS.reduce_fn_name)
reduce_fn_kwargs = json.load(
open(
resource_filename(
'contextual_lenses.resources',
os.path.join('reduce_fn_kwargs_resources',
FLAGS.reduce_fn_kwargs_path + '.json'))))
layers, trainable_encoder = architecture_to_layers(FLAGS.encoder_fn_name,
FLAGS.reduce_fn_name)
embedding_model = create_model(
use_transformer=FLAGS.use_transformer,
use_bert=FLAGS.use_bert,
restore_transformer_dir=FLAGS.restore_transformer_dir,
encoder_fn=encoder_fn,
encoder_fn_kwargs=encoder_fn_kwargs,
reduce_fn=reduce_fn,
reduce_fn_kwargs=reduce_fn_kwargs,
layers=layers,
output='embedding')
datum.update(
measure_nearest_neighbor_performance(
accuracy_label=
'train_knn_accuracy_untrained_lens_1_knn_train_samples',
encoder=embedding_model,
family_accessions=lens_knn_train_family_accessions,
batch_size=FLAGS.knn_batch_size,
train_samples=1,
shuffle_seed=FLAGS.knn_shuffle_seed,
sample_random_state=FLAGS.knn_sample_random_state))
for knn_train_samples in knn_train_samples_:
datum.update(
measure_nearest_neighbor_performance(
accuracy_label='test_knn_accuracy_untrained_lens_' +
str(knn_train_samples) + '_knn_train_samples',
encoder=embedding_model,
family_accessions=knn_test_family_accessions,
batch_size=FLAGS.knn_batch_size,
train_samples=knn_train_samples,
shuffle_seed=FLAGS.knn_shuffle_seed,
sample_random_state=FLAGS.knn_sample_random_state))
encoder_fn_params = None
reduce_fn_params = None
predict_fn_params = None
model = create_model(use_transformer=FLAGS.use_transformer,
use_bert=FLAGS.use_bert,
restore_transformer_dir=FLAGS.restore_transformer_dir,
encoder_fn=encoder_fn,
encoder_fn_kwargs=encoder_fn_kwargs,
reduce_fn=reduce_fn,
reduce_fn_kwargs=reduce_fn_kwargs,
layers=layers,
output='prediction',
encoder_fn_params=encoder_fn_params,
reduce_fn_params=reduce_fn_params,
predict_fn_params=predict_fn_params)
optimizer = create_optimizer(
model=model,
learning_rate=[FLAGS.encoder_lr, FLAGS.lens_lr, FLAGS.predictor_lr],
weight_decay=[FLAGS.encoder_wd, FLAGS.lens_wd, FLAGS.predictor_wd],
layers=layers)
if FLAGS.load_model:
optimizer = checkpoints.restore_checkpoint(ckpt_dir=os.path.join(
'gs://' + FLAGS.gcs_bucket, FLAGS.load_model_dir),
target=optimizer,
step=FLAGS.load_model_step)
trained_params = optimizer.target.params
embedding_model = set_model_parameters(model=embedding_model,
params=trained_params)
if FLAGS.save_model:
checkpoints.save_checkpoint(ckpt_dir=os.path.join(
'gs://' + FLAGS.gcs_bucket, FLAGS.save_model_dir),
target=optimizer,
step=FLAGS.load_model_step)
for i in range(FLAGS.measurements):
train_batches, train_indexes = create_pfam_batches(
family_accessions=lens_knn_train_family_accessions,
batch_size=FLAGS.lens_batch_size,
samples=FLAGS.lens_train_samples,
epochs=measurement_epochs,
drop_remainder=True,
shuffle_seed=FLAGS.lens_shuffle_seed + i,
sample_random_state=FLAGS.lens_sample_random_state)
optimizer = train(
model=optimizer.target,
train_data=train_batches,
loss_fn=cross_entropy_loss,
loss_fn_kwargs=loss_fn_kwargs,
learning_rate=[
FLAGS.encoder_lr, FLAGS.lens_lr, FLAGS.predictor_lr
],
weight_decay=[FLAGS.encoder_wd, FLAGS.lens_wd, FLAGS.predictor_wd],
layers=layers)
results, preds = pfam_evaluate(
predict_fn=optimizer.target,
test_family_accessions=lens_knn_train_family_accessions,
title=None,
loss_fn_kwargs=loss_fn_kwargs,
batch_size=FLAGS.lens_batch_size,
data_partitions_dirpath=FLAGS.data_partitions_dirpath,
gcs_bucket=FLAGS.gcs_bucket)
lens_accuracy = results['accuracy']
datum['lens_accuracy' + '_measurement_' + str(i)] = lens_accuracy
lens_cross_entropy = float(results['cross_entropy'])
datum['lens_cross_entropy' + '_measurement_' +
str(i)] = lens_cross_entropy
trained_params = optimizer.target.params
embedding_model = set_model_parameters(model=embedding_model,
params=trained_params)
datum.update(
measure_nearest_neighbor_performance(
accuracy_label=
'train_knn_accuracy_trained_lens_1_knn_train_samples' +
'_measurement_' + str(i),
encoder=embedding_model,
family_accessions=lens_knn_train_family_accessions,
batch_size=FLAGS.knn_batch_size,
train_samples=1,
shuffle_seed=FLAGS.knn_shuffle_seed,
sample_random_state=FLAGS.knn_sample_random_state))
for knn_train_samples in knn_train_samples_:
datum.update(
measure_nearest_neighbor_performance(
accuracy_label='test_knn_accuracy_trained_lens_' +
str(knn_train_samples) + '_knn_train_samples' +
'_measurement_' + str(i),
encoder=embedding_model,
family_accessions=knn_test_family_accessions,
batch_size=FLAGS.knn_batch_size,
train_samples=knn_train_samples,
shuffle_seed=FLAGS.knn_shuffle_seed,
sample_random_state=FLAGS.knn_sample_random_state))
print(datum)
df = pd.DataFrame([datum])
with gcsfs.open(os.path.join(FLAGS.results_save_dir, FLAGS.label + '.csv'),
'w') as gcs_file:
df.to_csv(gcs_file, index=False)
if FLAGS.save_model:
checkpoints.save_checkpoint(ckpt_dir=os.path.join(
'gs://' + FLAGS.gcs_bucket, FLAGS.save_model_dir),
target=optimizer,
step=FLAGS.load_model_step + FLAGS.epochs)
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import plotly.graph_objs as go
from plotly import tools
from fs_gcsfs import GCSFS
import pandas as pd
import json
gcsfs = GCSFS(bucket_name="pockets-data")
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
def download_csv_to_df(source_name):
"""Opens csv from google cloud and returns a dataframe"""
with gcsfs.open(source_name, 'r') as f:
df = pd.read_csv(f)
return df
dash_app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
dash_app.title = 'Simple Pockets'
app = dash_app.server
DAYS = 255
options = []
import fs
from fs.zipfs import ZipFS
from fs_gcsfs import GCSFS
# Already fixed:
# data/train/real
# data/train/masked
# data/train/reference
# data/validation
with fs.open_fs('mem://') as mem_fs:
# with fs.open_fs('gs://two-face-inpainting-mlengine/sample-data?strict=False') as gcsfs:
with GCSFS(bucket_name="two-face-inpainting-mlengine",
root_path='data/validation') as gcsfs:
# with gcsfs.open('sample.zip', 'rb') as zip_file:
# with ZipFS(zip_file) as zip_fs:
# fs.copy.copy_dir(zip_fs, '.', mem_fs, '.')
gcsfs.fix_storage()
# gcsfs.tree()
# mem_fs.tree()
# walker = Walker()
# for path in walker.files(mem_fs):
# print(path)