Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Partition column in the metadata during profiling #260

Merged
merged 1 commit into from
Jun 17, 2021
Merged

Partition column in the metadata during profiling #260

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions deepprofiler/__main__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -146,7 +146,7 @@ def prepare(context):
def sample_sc(context):
if context.parent.obj["config"]["prepare"]["compression"]["implement"]:
context.parent.obj["config"]["paths"]["images"] = context.obj["config"]["paths"]["compressed_images"]
dset = deepprofiler.dataset.image_dataset.read_dataset(context.obj["config"])
dset = deepprofiler.dataset.image_dataset.read_dataset(context.obj["config"], mode='train')
deepprofiler.dataset.sampling.sample_dataset(context.obj["config"], dset)
print("Single-cell sampling complete.")

Expand All @@ -159,7 +159,7 @@ def sample_sc(context):
def train(context, epoch, seed):
if context.parent.obj["config"]["prepare"]["compression"]["implement"]:
context.parent.obj["config"]["paths"]["images"] = context.obj["config"]["paths"]["compressed_images"]
dset = deepprofiler.dataset.image_dataset.read_dataset(context.obj["config"])
dset = deepprofiler.dataset.image_dataset.read_dataset(context.obj["config"], mode='train')
deepprofiler.learning.training.learn_model(context.obj["config"], dset, epoch, seed)


Expand All @@ -177,8 +177,8 @@ def profile(context, part):
if part >= 0:
partfile = "index-{0:03d}.csv".format(part)
config["paths"]["index"] = context.obj["config"]["paths"]["index"].replace("index.csv", partfile)
metadata = deepprofiler.dataset.image_dataset.read_dataset(context.obj["config"])
deepprofiler.learning.profiling.profile(context.obj["config"], metadata)
dset = deepprofiler.dataset.image_dataset.read_dataset(context.obj["config"], mode='profile')
deepprofiler.learning.profiling.profile(context.obj["config"], dset)


# Auxiliary tool: Split index in multiple parts
Expand Down
19 changes: 11 additions & 8 deletions deepprofiler/dataset/image_dataset.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,7 @@ def number_of_records(self, dataset):
def add_target(self, new_target):
self.targets.append(new_target)

def read_dataset(config):
def read_dataset(config, mode = 'train'):
# Read metadata and split dataset in training and validation
metadata = deepprofiler.dataset.metadata.Metadata(config["paths"]["index"], dtype=None)
if config["prepare"]["compression"]["implement"]:
Expand All @@ -211,10 +211,12 @@ def read_dataset(config):
print(metadata.data.info())

# Split training data
split_field = config["train"]["partition"]["split_field"]
trainingFilter = lambda df: df[split_field].isin(config["train"]["partition"]["training_values"])
validationFilter = lambda df: df[split_field].isin(config["train"]["partition"]["validation_values"])
metadata.splitMetadata(trainingFilter, validationFilter)
if mode == 'train':
split_field = config["train"]["partition"]["split_field"]
trainingFilter = lambda df: df[split_field].isin(config["train"]["partition"]["training_values"])
validationFilter = lambda df: df[split_field].isin(config["train"]["partition"]["validation_values"])
metadata.splitMetadata(trainingFilter, validationFilter)


# Create a dataset
keyGen = lambda r: "{}/{}-{}".format(r["Metadata_Plate"], r["Metadata_Well"], r["Metadata_Site"])
Expand All @@ -228,9 +230,10 @@ def read_dataset(config):
)

# Add training targets
for t in config["train"]["partition"]["targets"]:
new_target = deepprofiler.dataset.target.MetadataColumnTarget(t, metadata.data[t].unique())
dset.add_target(new_target)
if mode == 'train':
for t in config["train"]["partition"]["targets"]:
new_target = deepprofiler.dataset.target.MetadataColumnTarget(t, metadata.data[t].unique())
dset.add_target(new_target)

# Activate outlines for masking if needed
if config["dataset"]["locations"]["mask_objects"]:
Expand Down