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refactor: remove outdated add_dataset_annotations_to_workbook function
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Remove the outdated `add_dataset_annotations_to_workbook` function, as
it lacks the necessary granularity for predicate-level categorization of
semantic annotations, a crucial aspect of our current annotation model.

While alternative approaches exist (e.g., annotating with terms from
multiple vocabularies and then categorizing based on branch), the
ongoing development and active community support for OntoGPT suggest a
more promising long-term solution.
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@clnsmth
clnsmth authored Dec 23, 2024
1 parent 18d01bb commit 25f0a8b
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Showing 3 changed files with 81 additions and 402 deletions.
265 changes: 71 additions & 194 deletions src/spinneret/annotator.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,6 @@ def annotate_workbook(
workbook_path: str,
eml_path: str,
output_path: str,
annotator: str,
local_model: str = None,
temperature: Union[float, None] = None,
return_ungrounded: bool = False,
Expand All @@ -150,10 +149,6 @@ def annotate_workbook(
corresponding to the EML file.
:param eml_path: The path to the EML file corresponding to the workbook.
:param output_path: The path to write the annotated workbook.
:param annotator: The annotator to use for grounding. Options are "ontogpt"
and "bioportal". OntoGPT requires setup and configuration described in
the `get_ontogpt_annotation` function. Similarly, BioPortal requires
an API key and is described in the `get_bioportal_annotation` function.
:param local_model: See `get_ontogpt_annotation` documentation for details.
:param temperature: The temperature parameter for the model. If None, the
OntoGPT default will be used.
Expand All @@ -167,7 +162,6 @@ def annotate_workbook(
path as the original workbook.
"""
logger.info(f"Annotating workbook {workbook_path}")
logger.info(f"Annotating with {annotator}")

# Ensure the workbook and eml file match to avoid errors
pid = os.path.basename(workbook_path).split("_")[0]
Expand All @@ -180,70 +174,63 @@ def annotate_workbook(
wb = load_workbook(workbook_path)
eml = load_eml(eml_path)

# Run workbook annotators, results of one are used as input for the next
if annotator == "bioportal":
wb = add_dataset_annotations_to_workbook(wb, eml, sample_size=sample_size)
wb = add_measurement_type_annotations_to_workbook(
wb, eml, annotator=annotator, sample_size=sample_size
)
elif annotator == "ontogpt":
wb = add_env_broad_scale_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_env_local_scale_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_process_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_methods_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_research_topic_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_measurement_type_annotations_to_workbook(
wb,
eml,
annotator="ontogpt",
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_env_medium_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
# Run workbook annotator, results of one are used as input for the next
wb = add_env_broad_scale_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_env_local_scale_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_process_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_methods_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_research_topic_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_measurement_type_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_env_medium_annotations_to_workbook(
wb,
eml,
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
sample_size=sample_size,
)
wb = add_qudt_annotations_to_workbook(wb, eml) # irrespective of annotator

write_workbook(wb, output_path)
Expand Down Expand Up @@ -485,98 +472,11 @@ def add_qudt_annotations_to_workbook(
return wb


def add_dataset_annotations_to_workbook(
workbook: Union[str, pd.core.frame.DataFrame],
eml: Union[str, etree._ElementTree],
output_path: str = None,
overwrite: bool = False,
sample_size: int = 1,
) -> pd.core.frame.DataFrame:
"""
:param workbook: Either the path to the workbook to be annotated, or the
workbook itself as a pandas DataFrame.
:param eml: Either the path to the EML file corresponding to the workbook,
or the EML file itself as an lxml etree.
:param output_path: The path to write the annotated workbook.
:param overwrite: If True, overwrite existing `dataset` annotations in the
workbook, so a fresh set may be created.
:param sample_size: Executes multiple replicates of the annotation request
to reduce variability of outputs. Variability is inherent in OntoGPT.
:returns: Workbook with dataset annotations.
"""
logger.info("Annotating dataset")

# Load the workbook and EML for processing
wb = load_workbook(workbook)
eml = load_eml(eml)

# Set the author identifier for consistent reference below
author = "spinneret.annotator.get_bioportal_annotation"

# Remove existing dataset annotations if overwrite is True, using a set of
# criteria that accurately define the annotations to remove.
if overwrite:
wb = delete_annotations(
workbook=wb,
criteria={
"element": "dataset",
"element_xpath": "/eml:eml/dataset",
"author": author,
},
)

# Get the dataset annotations
dataset_element = eml.xpath("//dataset")[0]
element_description = get_description(dataset_element)
annotations = []
for _ in range(sample_size):
res = get_bioportal_annotation( # expecting a list of annotations
text=element_description,
api_key=os.environ["BIOPORTAL_API_KEY"],
ontologies="ENVO", # ENVO provides environmental terms
exclude_synonyms="true",
)
if res is not None:
annotations.extend(res)
if len(annotations) == 0:
annotations = None

# Add dataset annotations to the workbook
if annotations is not None:
for annotation in annotations:
row = initialize_workbook_row()
row["package_id"] = get_package_id(eml)
row["url"] = get_package_url(eml)
row["element"] = dataset_element.tag
if "id" in dataset_element.attrib:
row["element_id"] = dataset_element.attrib["id"]
else:
row["element_id"] = pd.NA
row["element_xpath"] = eml.getpath(dataset_element)
row["context"] = get_subject_and_context(dataset_element)["context"]
row["description"] = element_description
row["subject"] = get_subject_and_context(dataset_element)["subject"]
row["predicate"] = "is about"
row["predicate_id"] = "http://purl.obolibrary.org/obo/IAO_0000136"
row["object"] = annotation["label"]
row["object_id"] = annotation["uri"]
row["author"] = author
row["date"] = pd.Timestamp.now()
row = pd.DataFrame([row], dtype=str)
wb = pd.concat([wb, row], ignore_index=True)
wb = delete_duplicate_annotations(wb)

if output_path:
write_workbook(wb, output_path)
return wb


# pylint: disable=too-many-branches
# pylint: disable=too-many-statements
def add_measurement_type_annotations_to_workbook(
workbook: Union[str, pd.core.frame.DataFrame],
eml: Union[str, etree._ElementTree],
annotator: str,
output_path: str = None,
overwrite: bool = False,
local_model: str = None,
Expand All @@ -589,10 +489,6 @@ def add_measurement_type_annotations_to_workbook(
workbook itself as a pandas DataFrame.
:param eml: Either the path to the EML file corresponding to the workbook,
or the EML file itself as an lxml etree.
:param annotator: The annotator to use for grounding. Options are "ontogpt"
and "bioportal". OntoGPT requires setup and configuration described in
the `get_ontogpt_annotation` function. Similarly, BioPortal requires
an API key and is described in the `get_bioportal_annotation` function.
:param output_path: The path to write the annotated workbook.
:param overwrite: If True, overwrite existing `measurement type`
annotations in the workbook, so a fresh set may be created.
Expand Down Expand Up @@ -652,35 +548,19 @@ def add_measurement_type_annotations_to_workbook(
)

if annotations is None:
# Select an annotator, and get the measurement type annotations
if annotator.lower() == "ontogpt":
annotations = []
for _ in range(sample_size):
res = get_ontogpt_annotation(
text=attribute_description,
template="contains_measurement_of_type",
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
)
if res is not None:
annotations.extend(res)
if len(annotations) == 0:
annotations = None
else:
annotations = []
for _ in range(sample_size):
res = get_bioportal_annotation(
# expecting a list of annotations
text=attribute_description,
api_key=os.environ["BIOPORTAL_API_KEY"],
ontologies="ECSO", # ECSO provides measurment terms
exclude_synonyms="true",
)
if res is not None:
annotations.extend(res)
if len(annotations) == 0:
annotations = None
annotations = []
for _ in range(sample_size):
res = get_ontogpt_annotation(
text=attribute_description,
template="contains_measurement_of_type",
local_model=local_model,
temperature=temperature,
return_ungrounded=return_ungrounded,
)
if res is not None:
annotations.extend(res)
if len(annotations) == 0:
annotations = None

# Add the measurement type annotations to the workbook
if annotations is not None:
Expand All @@ -704,10 +584,7 @@ def add_measurement_type_annotations_to_workbook(
)
row["object"] = annotation["label"]
row["object_id"] = annotation["uri"]
if annotator.lower() == "ontogpt":
row["author"] = "spinneret.annotator.get_ontogpt_annotation"
elif annotator.lower() == "bioportal":
row["author"] = "spinneret.annotator.get_bioportal_annotation"
row["author"] = "spinneret.annotator.get_ontogpt_annotation"
row["date"] = pd.Timestamp.now()
row = pd.DataFrame([row], dtype=str)
wb = pd.concat([wb, row], ignore_index=True)
Expand Down
6 changes: 0 additions & 6 deletions src/spinneret/main.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,6 @@ def create_workbooks(eml_dir: str, workbook_dir: str) -> None:
def annotate_workbooks(
workbook_dir: str,
eml_dir: str,
annotator: str,
output_dir: str,
config_path: str,
local_model: str = None,
Expand All @@ -65,10 +64,6 @@ def annotate_workbooks(
:param workbook_dir: Directory of unannotated workbooks
:param eml_dir: Directory of EML files corresponding to workbooks
:param annotator: The annotator to use for grounding. Options are "ontogpt"
and "bioportal". OntoGPT requires setup and configuration described in
the `get_ontogpt_annotation` function. Similarly, BioPortal requires
an API key and is described in the `get_bioportal_annotation` function.
:param output_dir: Directory to save annotated workbooks
:param config_path: Path to configuration file
:param local_model: See `get_ontogpt_annotation` documentation for details.
Expand Down Expand Up @@ -113,7 +108,6 @@ def annotate_workbooks(
annotate_workbook(
workbook_path=workbook_dir + "/" + workbook_file,
eml_path=eml_dir + "/" + eml_file,
annotator=annotator,
output_path=output_dir + "/" + workbook_file_annotated,
local_model=local_model,
temperature=temperature,
Expand Down
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