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fix: Remove deprecated run pipeline methods
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@dlangst
dlangst committed Mar 25, 2023
1 parent b3be2e6 commit 9b33dbd
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5 changes: 3 additions & 2 deletions README.md
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Expand Up @@ -26,9 +26,10 @@ pip install --upgrade acd-sdk

## Migrating from version 1.x.x

The 2.0 release of the Annotator for Clinical Data SDK introduces a Python package name change from `ibm_whcs_sdk` to `acd_sdk`. For the migration from 1.x.x:
The release of version 2 of the Annotator for Clinical Data SDK introduces a Python package name change from `ibm_whcs_sdk` to `acd_sdk`. For the migration from 1.x.x:

1. After running `pip install --upgrade acd-sdk`, confirm that you have version 2.0 installed by running `pip show acd-sdk`.
1. Run `pip uninstall ibm-whcs-sdk`
1. Run `pip install acd-sdk`, and confirm that you have version 2 installed by running `pip show acd-sdk`.
2. In your application files that have a dependency on `acd-sdk`, update any import declarations from `ibm_whcs_sdk` to `acd_sdk`.

## Using the SDK
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1 change: 0 additions & 1 deletion acd_sdk/annotator_for_clinical_data/__init__.py
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Expand Up @@ -11,7 +11,6 @@
from .annotator_for_clinical_data_v1 import AcdFlow
from .annotator_for_clinical_data_v1 import AcdProfile
from .annotator_for_clinical_data_v1 import AllergyMedication
from .annotator_for_clinical_data_v1 import AnalyticFlowBeanInput
from .annotator_for_clinical_data_v1 import Annotation
from .annotator_for_clinical_data_v1 import Annotator
from .annotator_for_clinical_data_v1 import AnnotatorFlow
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278 changes: 3 additions & 275 deletions acd_sdk/annotator_for_clinical_data/annotator_for_clinical_data_v1.py
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Expand Up @@ -660,124 +660,6 @@ def delete_flows(self, id: str, **kwargs) -> DetailedResponse:
#########################


# Method is deprecated and could be removed in the future. Use 'analyze' method instead.
def run_pipeline(self, *, unstructured: List['UnstructuredContainer'] = None, annotator_flows: List['AnnotatorFlow'] = None, debug_text_restore: bool = None, return_analyzed_text: bool = None, **kwargs) -> DetailedResponse:
"""
Detect entities & relations from unstructured data.
<p>This API accepts a JSON request model featuring both the unstructured data to
be analyzed as well as the desired annotator flow.<p/><p><b>Annotator
Chaining</b><br/>Sample request invoking both the concept_detection and
symptom_disease annotators asynchronously. This sample request references
configurations via a profile id. Profiles define configurations that can be
referenced within a request. Profile is optional. A default profile is used if no
profile id is available in the annotator flow. The default profile contains the
parameters for the concept detection and the attribute detection. An empty profile
can be used if absolutely no parameters are attached to any annotators. See <a
href=".." target="_blank">documentation</a> for more information. </p><pre>{<br/>
"annotatorFlows": [<br/> {<br/> "profile" : "default_profile_v1.0", <br/>
"flow": {<br/> "elements": [<br/> {<br/>
"annotator": {<br/> "name": "concept_detection"<br/>
}<br/> },<br/> {<br/> "annotator": {<br/>
"name": "symptom_disease"<br/> }<br/> }<br/> ],<br/>
"async": false<br/> }<br/> }<br/> ],<br/> "unstructured": [<br/>
{<br/> "text": "Patient has lung cancer, but did not smoke. She may consider
chemotherapy as part of a treatment plan."<br/> }<br/>
]<br/>}<br/></pre><p><b>Annotation Filtering</b><br/>Sample request invoking
concept_detection with a filter defined to exclude any annotations detected from
concept_detection where the semanticType field does not equal
"neop".</p><pre>{<br/> "annotatorFlows": [<br/> {<br/> "flow": {<br/>
"elements": [<br/> {<br/> "annotator": {<br/>
"name": "concept_detection",<br/> "configurations": [<br/>
{<br/> "filter": {<br/> "target":
"unstructured.data.concepts",<br/> "condition": {<br/>
"type": "match",<br/> "field":
"semanticType",<br/> "values": [<br/>
"neop"<br/> ],<br/> "not":
false,<br/> "caseInsensitive": false,<br/>
"operator": "equals"<br/> }<br/> }<br/>
}<br/> ]<br/> }<br/> }<br/>
],<br/> "async": false<br/> }<br/> }<br/> ],<br/> "unstructured":
[<br/> {<br/> "text": "Patient has lung cancer, but did not smoke. She may
consider chemotherapy as part of a treatment plan."<br/> }<br/>
]<br/>}<br/></pre><p><b>Annotators that support annotation filtering:</b> allergy,
bathing_assistance, cancer, concept_detection, dressing_assistance,
eating_assistance, ejection_fraction, lab_value, medication, named_entities,
procedure, seeing_assistance, smoking, symptom_disease, toileting_assistance,
walking_assistance.</p><hr/><p><b>Annotation Augmentation</b><br/>Sample request
invoking the cancer annotator and providing a whitelist entry for a new custom
surface form: "lungcancer".</p><pre>{<br/> "annotatorFlows": [<br/> {<br/>
"flow": {<br/> "elements": [<br/> {<br/> "annotator":
{<br/> "name": "cancer",<br/> "configurations": [<br/>
{<br/> "whitelist": {<br/> "name":
"cancer",<br/> "entries": [<br/> {<br/>
"surfaceForms": [<br/> "lungcancer"<br/>
],<br/> "features": {<br/> "normalizedName":
"lung cancer",<br/> "hccCode": "9",<br/>
"icd10Code": "C34.9",<br/> "ccsCode": "19",<br/>
"icd9Code": "162.9",<br/> "conceptId": "93880001"<br/>
}<br/> }<br/> ]<br/>
}<br/> }<br/> ]<br/> }<br/>
}<br/> ],<br/> "async": false<br/> }<br/> }<br/> ],<br/>
"unstructured": [<br/> {<br/> "text": "The patient was diagnosed with
lungcancer, on Dec 23, 2011."<br/> }<br/> ]<br/>}<br/></pre><b>Annotators that
support annotation augmentation:</b> allergy, bathing_assistance, cancer,
dressing_assistance, eating_assistance, ejection_fraction, lab_value, medication,
named_entities, procedure, seeing_assistance, smoking, symptom_disease,
toileting_assistance, walking_assistance.<br/>.
:param List[UnstructuredContainer] unstructured: (optional)
:param List[AnnotatorFlow] annotator_flows: (optional)
:param bool debug_text_restore: (optional) If true, any ReplaceTextChange
annotations will be left in the container and the modified text before
restoring to original form will stored in the metadata that is returned.
Otherwise, these annotations and modified text will be removed from the
container.
:param bool return_analyzed_text: (optional) Set this to true to show the
analyzed text in the response.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""

warn("The 'run_pipeline' method is deprecated. Use 'analyze' method instead.", DeprecationWarning, stacklevel=2)

if unstructured is not None:
unstructured = [ convert_model(x) for x in unstructured ]
if annotator_flows is not None:
annotator_flows = [ convert_model(x) for x in annotator_flows ]
headers = {}
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='run_pipeline')
headers.update(sdk_headers)

params = {
'version': self.version,
'debug_text_restore': debug_text_restore,
'return_analyzed_text': return_analyzed_text
}

data = {
'unstructured': unstructured,
'annotatorFlows': annotator_flows
}
data = {k: v for (k, v) in data.items() if v is not None}
data = json.dumps(data, cls=AnnotatorEncoder)
headers['content-type'] = 'application/json'

if 'headers' in kwargs:
headers.update(kwargs.get('headers'))

url = '/v1/analyze'
request = self.prepare_request(method='POST',
url=url,
headers=headers,
params=params,
data=data)

response = self.request_acd(request)
return response


def analyze_org(self, unstructured=None, annotator_flows=None, **kwargs):
"""
Detect entities & relations from unstructured data and return as 'dict'.
Expand Down Expand Up @@ -893,89 +775,6 @@ def analyze(self, text, flow, **kwargs):
return result


# Method is deprecated and could be removed in the future. Use 'analyze_with_flow' instead.
def run_pipeline_with_flow(self, flow_id: str, return_analyzed_text: bool, analytic_flow_bean_input: Union['AnalyticFlowBeanInput', str, TextIO], *, content_type: str = None, debug_text_restore: bool = None, **kwargs) -> DetailedResponse:
"""
analyze with a pre-specified flow.
<p>This API accepts a flow identifier as well as a <emph>TEXT</emph> or a
<emph>JSON</emph> request model featuring the unstructured text to be analyzed.
<p/><p><b>JSON request model with unstructured text </b></p><pre>{<br/>
"unstructured": [<br/> {<br/> "text": "Patient has lung cancer, but did
not smoke. She may consider chemotherapy as part of a treatment plan."<br/>
}<br/> ]<br/>}<br/></pre><p><b>JSON request model with existing annotations
</b><br/></p><pre>{<br> "unstructured": [<br> {<br> "text": "Patient will
not start on cisplatin 80mg on 1/1/2018. Patient is also diabetic.",<br>
"data": {<br> "concepts": [<br> {<br> "cui":
"C0030705",<br> "preferredName": "Patients",<br>
"semanticType": "podg",<br> "source": "umls",<br>
"sourceVersion": "2017AA",<br> "type":
"umls.PatientOrDisabledGroup",<br> "begin": 0,<br> "end":
7,<br> "coveredText": "Patient"<br> }<br> ]<br> } <br>
} <br> ]<br>}<br></pre>.
:param str flow_id: flow identifier .
:param bool return_analyzed_text: Set this to true to show the analyzed
text in the response.
:param AnalyticFlowBeanInput analytic_flow_bean_input: Input request data
in TEXT or JSON format .
:param str content_type: (optional) The type of the input. A character
encoding can be specified by including a `charset` parameter. For example,
'text/plain;charset=utf-8'.
:param bool debug_text_restore: (optional) If true, any ReplaceTextChange
annotations will be left in the container and the modified text before
restoring to original form will be returned in the metadata. Otherwise,
these annotations and modified text will be removed from the container.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""

# Method is deprecated and could be removed in the future.
warn("The 'run_pipeline_with_flow' method is deprecated. Use 'analyze_with_flow' method instead.", DeprecationWarning, stacklevel=2)

if flow_id is None:
raise ValueError('flow_id must be provided')
if return_analyzed_text is None:
raise ValueError('return_analyzed_text must be provided')
if analytic_flow_bean_input is None:
raise ValueError('analytic_flow_bean_input must be provided')
if isinstance(analytic_flow_bean_input, AnalyticFlowBeanInput):
analytic_flow_bean_input = convert_model(analytic_flow_bean_input)
content_type = content_type or 'application/json'
headers = {
'Content-Type': content_type
}
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='run_pipeline_with_flow')
headers.update(sdk_headers)

params = {
'version': self.version,
'return_analyzed_text': return_analyzed_text,
'debug_text_restore': debug_text_restore
}

if isinstance(analytic_flow_bean_input, dict):
data = json.dumps(analytic_flow_bean_input)
if content_type is None:
headers['content-type'] = 'application/json'
else:
data = analytic_flow_bean_input

if 'headers' in kwargs:
headers.update(kwargs.get('headers'))

url = '/v1/analyze/{0}'.format(*self.encode_path_vars(flow_id))
request = self.prepare_request(method='POST',
url=url,
headers=headers,
params=params,
data=data)

response = self.request_acd(request)
return response


def analyze_with_flow_org(self, flow_id, request, content_type='text/plain', **kwargs):
"""
Analyze with a persisted flow and return as a 'dict'.
Expand Down Expand Up @@ -1306,6 +1105,7 @@ def cartridges_get_id(self, id: str, **kwargs) -> DetailedResponse:
return response


# Method is deprecated and could be removed in the future. Use 'cartridges_post_multipart' method instead.
def deploy_cartridge(self, *, archive_file: BinaryIO = None, archive_file_content_type: str = None, update: bool = None, **kwargs) -> DetailedResponse:
"""
Deploy a cartridge.
Expand All @@ -1321,6 +1121,8 @@ def deploy_cartridge(self, *, archive_file: BinaryIO = None, archive_file_conten
:rtype: DetailedResponse with `dict` result representing a `DeployCartridgeResponse` object
"""

warn("The 'deploy_cartridge' method is deprecated. Use 'cartridges_post_multipart' method instead.", DeprecationWarning, stacklevel=2)

headers = {}
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='deploy_cartridge')
headers.update(sdk_headers)
Expand Down Expand Up @@ -1391,20 +1193,6 @@ def get_health_check_status(self, *, accept: str = None, format: str = None, **k
return response


class RunPipelineWithFlowEnums:
"""
Enums for run_pipeline_with_flow parameters.
"""

class ContentType(Enum):
"""
The type of the input. A character encoding can be specified by including a
`charset` parameter. For example, 'text/plain;charset=utf-8'.
"""
APPLICATION_JSON = 'application/json'
TEXT_PLAIN = 'text/plain'


class GetServiceStatusEnums:
"""
Enums for get_service_status parameters.
Expand Down Expand Up @@ -2061,66 +1849,6 @@ def __str__(self):
"""Return a `str` version of this Annotation object."""
return json.dumps(self._to_dict(), indent=2)

class AnalyticFlowBeanInput():
"""
AnalyticFlowBeanInput.
:attr List[UnstructuredContainer] unstructured: (optional)
:attr List[AnnotatorFlow] annotator_flows: (optional)
"""

def __init__(self, *, unstructured: List['UnstructuredContainer'] = None, annotator_flows: List['AnnotatorFlow'] = None) -> None:
"""
Initialize a AnalyticFlowBeanInput object.
:param List[UnstructuredContainer] unstructured: (optional)
:param List[AnnotatorFlow] annotator_flows: (optional)
"""
self.unstructured = unstructured
self.annotator_flows = annotator_flows

@classmethod
def from_dict(cls, _dict: Dict) -> 'AnalyticFlowBeanInput':
"""Initialize a AnalyticFlowBeanInput object from a json dictionary."""
args = {}
if 'unstructured' in _dict:
args['unstructured'] = [UnstructuredContainer.from_dict(x) for x in _dict.get('unstructured')]
if 'annotatorFlows' in _dict:
args['annotator_flows'] = [AnnotatorFlow.from_dict(x) for x in _dict.get('annotatorFlows')]
return cls(**args)

@classmethod
def _from_dict(cls, _dict):
"""Initialize a AnalyticFlowBeanInput object from a json dictionary."""
return cls.from_dict(_dict)

def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'unstructured') and self.unstructured is not None:
_dict['unstructured'] = [x.to_dict() for x in self.unstructured]
if hasattr(self, 'annotator_flows') and self.annotator_flows is not None:
_dict['annotatorFlows'] = [x.to_dict() for x in self.annotator_flows]
return _dict

def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()

def __str__(self) -> str:
"""Return a `str` version of this AnalyticFlowBeanInput object."""
return json.dumps(self.to_dict(), indent=2)

def __eq__(self, other: 'AnalyticFlowBeanInput') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__

def __ne__(self, other: 'AnalyticFlowBeanInput') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other


class Annotator():
"""
Expand Down
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