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[text analytics] Exposed the length property in classes impacted by string_index_type #16538

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merged 7 commits into from
Feb 5, 2021
Merged

[text analytics] Exposed the length property in classes impacted by string_index_type #16538

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4ff9bb5
Exposed the length property in classes impacted by exposing
Feb 4, 2021
ecb5aa7
Updated test_repr.py
Feb 4, 2021
29d698a
Fixed sentiment-related __repr__ method outputs
Feb 4, 2021
001942a
Updated samples and changelog
Feb 4, 2021
630bccc
Merge branch 'master' of https://github.com/Azure/azure-sdk-for-pytho…
iscai-msft Feb 5, 2021
cd5bc4c
remove length from samples
iscai-msft Feb 5, 2021
2a196bb
fix grammer nit
iscai-msft Feb 5, 2021
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112 changes: 86 additions & 26 deletions sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -289,8 +289,13 @@ class CategorizedEntity(DictMixin):
:vartype category: str
:ivar subcategory: Entity subcategory, such as Age/Year/TimeRange etc
:vartype subcategory: str
:ivar int length: The entity text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default. Only returned for API versions v3.1-preview and up.
:ivar int offset: The entity text offset from the start of the document.
Returned in unicode code points. Only returned for API versions v3.1-preview and up.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default. Only returned for
API versions v3.1-preview and up.
:ivar confidence_score: Confidence score between 0 and 1 of the extracted
entity.
:vartype confidence_score: float
Expand All @@ -302,30 +307,35 @@ def __init__(self, **kwargs):
self.text = kwargs.get('text', None)
self.category = kwargs.get('category', None)
self.subcategory = kwargs.get('subcategory', None)
self.length = kwargs.get('length', None)
self.offset = kwargs.get('offset', None)
self.confidence_score = kwargs.get('confidence_score', None)

@classmethod
def _from_generated(cls, entity):
offset = entity.offset
length = entity.length
if isinstance(entity, _v3_0_models.Entity):
# we do not return offset for v3.0 since
# the correct encoding was not introduced for v3.0
offset = None
length = None
return cls(
text=entity.text,
category=entity.category,
subcategory=entity.subcategory,
length=length,
offset=offset,
confidence_score=entity.confidence_score,
)

def __repr__(self):
return "CategorizedEntity(text={}, category={}, subcategory={}, "\
"offset={}, confidence_score={})".format(
"length={}, offset={}, confidence_score={})".format(
self.text,
self.category,
self.subcategory,
self.length,
self.offset,
self.confidence_score
)[:1024]
Expand All @@ -340,8 +350,12 @@ class PiiEntity(DictMixin):
Identification/Social Security Number/Phone Number, etc.
:ivar str subcategory: Entity subcategory, such as Credit Card/EU
Phone number/ABA Routing Numbers, etc.
:ivar int length: The PII entity text length. This value depends on the value
of the `string_index_type` parameter specified in the original request, which
is UnicodeCodePoints by default.
:ivar int offset: The PII entity text offset from the start of the document.
Returned in unicode code points.
This value depends on the value of the `string_index_type` parameter specified
in the original request, which is UnicodeCodePoints by default.
:ivar float confidence_score: Confidence score between 0 and 1 of the extracted
entity.
"""
Expand All @@ -350,6 +364,7 @@ def __init__(self, **kwargs):
self.text = kwargs.get('text', None)
self.category = kwargs.get('category', None)
self.subcategory = kwargs.get('subcategory', None)
self.length = kwargs.get('length', None)
self.offset = kwargs.get('offset', None)
self.confidence_score = kwargs.get('confidence_score', None)

Expand All @@ -359,17 +374,19 @@ def _from_generated(cls, entity):
text=entity.text,
category=entity.category,
subcategory=entity.subcategory,
length=entity.length,
offset=entity.offset,
confidence_score=entity.confidence_score,
)

def __repr__(self):
return (
"PiiEntity(text={}, category={}, subcategory={}, offset={}, "\
"confidence_score={})".format(
"PiiEntity(text={}, category={}, subcategory={}, length={}, "\
"offset={}, confidence_score={})".format(
self.text,
self.category,
self.subcategory,
self.length,
self.offset,
self.confidence_score
)[:1024]
Expand All @@ -379,20 +396,26 @@ def __repr__(self):
class HealthcareEntity(DictMixin):
"""HealthcareEntity contains information about a Healthcare entity found in text.

:ivar str text: Entity text as appears in the request.
:ivar str category: Entity category, such as Dosage or MedicationName, etc.
:ivar str subcategory: Entity subcategory. # TODO: add subcategory examples
:ivar int offset: The Healthcare entity text offset from the start of the document.
:ivar float confidence_score: Confidence score between 0 and 1 of the extracted
:ivar str text: Entity text as appears in the request.
:ivar str category: Entity category, such as Dosage or MedicationName, etc.
:ivar str subcategory: Entity subcategory. # TODO: add subcategory examples
:ivar int length: The entity text length. This value depends on the value
of the `string_index_type` parameter specified in the original request, which is
UnicodeCodePoints by default.
:ivar int offset: The entity text offset from the start of the document.
This value depends on the value of the `string_index_type` parameter specified
in the original request, which is UnicodeCodePoints by default.
:ivar float confidence_score: Confidence score between 0 and 1 of the extracted
entity.
:ivar links: A collection of entity references in known data sources.
:vartype links: list[~azure.ai.textanalytics.HealthcareEntityLink]
:ivar links: A collection of entity references in known data sources.
:vartype links: list[~azure.ai.textanalytics.HealthcareEntityLink]
"""

def __init__(self, **kwargs):
self.text = kwargs.get("text", None)
self.category = kwargs.get("category", None)
self.subcategory = kwargs.get("subcategory", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.confidence_score = kwargs.get("confidence_score", None)
self.links = kwargs.get("links", [])
Expand All @@ -403,6 +426,7 @@ def _from_generated(cls, healthcare_entity):
text=healthcare_entity.text,
category=healthcare_entity.category,
subcategory=healthcare_entity.subcategory,
length=healthcare_entity.length,
offset=healthcare_entity.offset,
confidence_score=healthcare_entity.confidence_score,
links=[
Expand All @@ -411,11 +435,12 @@ def _from_generated(cls, healthcare_entity):
)

def __repr__(self):
return "HealthcareEntity(text={}, category={}, subcategory={}, offset={}, confidence_score={},\
links={})".format(
return "HealthcareEntity(text={}, category={}, subcategory={}, length={}, offset={}, \
confidence_score={}, links={})".format(
self.text,
self.category,
self.subcategory,
self.length,
self.offset,
self.confidence_score,
repr(self.links)
Expand Down Expand Up @@ -835,8 +860,13 @@ class LinkedEntityMatch(DictMixin):
returned.
:vartype confidence_score: float
:ivar text: Entity text as appears in the request.
:ivar int length: The linked entity match text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints by default.
Only returned for API versions v3.1-preview and up.
:ivar int offset: The linked entity match text offset from the start of the document.
Returned in unicode code points. Only returned for API versions v3.1-preview and up.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default.
Only returned for API versions v3.1-preview and up.
:vartype text: str
.. versionadded:: v3.1-preview
The *offset* property.
Expand All @@ -845,24 +875,28 @@ class LinkedEntityMatch(DictMixin):
def __init__(self, **kwargs):
self.confidence_score = kwargs.get("confidence_score", None)
self.text = kwargs.get("text", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)

@classmethod
def _from_generated(cls, match):
offset = match.offset
length = match.length
if isinstance(match, _v3_0_models.Match):
# we do not return offset for v3.0 since
# the correct encoding was not introduced for v3.0
offset = None
length = None
return cls(
confidence_score=match.confidence_score,
text=match.text,
length=length,
offset=offset,
)

def __repr__(self):
return "LinkedEntityMatch(confidence_score={}, text={}, offset={})".format(
self.confidence_score, self.text, self.offset
return "LinkedEntityMatch(confidence_score={}, text={}, length={}, offset={})".format(
self.confidence_score, self.text, self.length, self.offset
)[:1024]


Expand Down Expand Up @@ -943,8 +977,13 @@ class SentenceSentiment(DictMixin):
and 1 for the sentence for all labels.
:vartype confidence_scores:
~azure.ai.textanalytics.SentimentConfidenceScores
:ivar int offset: The sentence offset from the start of the document. Returned
in unicode code points. Only returned for API versions v3.1-preview and up.
:ivar int length: The sentence text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default. Only returned for API versions v3.1-preview and up.
:ivar int offset: The sentence text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default. Only returned for
API versions v3.1-preview and up.
:ivar mined_opinions: The list of opinions mined from this sentence.
For example in the sentence "The food is good, but the service is bad", we would
mine the two opinions "food is good" and "service is bad". Only returned
Expand All @@ -960,16 +999,19 @@ def __init__(self, **kwargs):
self.text = kwargs.get("text", None)
self.sentiment = kwargs.get("sentiment", None)
self.confidence_scores = kwargs.get("confidence_scores", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.mined_opinions = kwargs.get("mined_opinions", None)

@classmethod
def _from_generated(cls, sentence, results, sentiment):
offset = sentence.offset
length = sentence.length
if isinstance(sentence, _v3_0_models.SentenceSentiment):
# we do not return offset for v3.0 since
# the correct encoding was not introduced for v3.0
offset = None
length = None
if hasattr(sentence, "aspects"):
mined_opinions = (
[MinedOpinion._from_generated(aspect, results, sentiment) for aspect in sentence.aspects] # pylint: disable=protected-access
Expand All @@ -981,16 +1023,18 @@ def _from_generated(cls, sentence, results, sentiment):
text=sentence.text,
sentiment=sentence.sentiment,
confidence_scores=SentimentConfidenceScores._from_generated(sentence.confidence_scores), # pylint: disable=protected-access
length=length,
offset=offset,
mined_opinions=mined_opinions
)

def __repr__(self):
return "SentenceSentiment(text={}, sentiment={}, confidence_scores={}, "\
"offset={}, mined_opinions={})".format(
"length={}, offset={}, mined_opinions={})".format(
self.text,
self.sentiment,
repr(self.confidence_scores),
self.length,
self.offset,
repr(self.mined_opinions)
)[:1024]
Expand Down Expand Up @@ -1057,14 +1101,19 @@ class AspectSentiment(DictMixin):
for 'neutral' will always be 0
:vartype confidence_scores:
~azure.ai.textanalytics.SentimentConfidenceScores
:ivar int offset: The aspect offset from the start of the document. Returned
in unicode code points.
:ivar int length: The aspect text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default.
:ivar int offset: The aspect text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default.
"""

def __init__(self, **kwargs):
self.text = kwargs.get("text", None)
self.sentiment = kwargs.get("sentiment", None)
self.confidence_scores = kwargs.get("confidence_scores", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)

@classmethod
Expand All @@ -1073,14 +1122,17 @@ def _from_generated(cls, aspect):
text=aspect.text,
sentiment=aspect.sentiment,
confidence_scores=SentimentConfidenceScores._from_generated(aspect.confidence_scores), # pylint: disable=protected-access
length=aspect.length,
offset=aspect.offset,
)

def __repr__(self):
return "AspectSentiment(text={}, sentiment={}, confidence_scores={}, offset={})".format(
return "AspectSentiment(text={}, sentiment={}, confidence_scores={}, \
length={}, offset={})".format(
self.text,
self.sentiment,
repr(self.confidence_scores),
self.length,
self.offset,
)[:1024]

Expand All @@ -1099,8 +1151,12 @@ class OpinionSentiment(DictMixin):
for 'neutral' will always be 0
:vartype confidence_scores:
~azure.ai.textanalytics.SentimentConfidenceScores
:ivar int offset: The opinion offset from the start of the document. Returned
in unicode code points.
:ivar int length: The opinion text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default.
:ivar int offset: The opinion text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default.
:ivar bool is_negated: Whether the opinion is negated. For example, in
"The food is not good", the opinion "good" is negated.
"""
Expand All @@ -1109,6 +1165,7 @@ def __init__(self, **kwargs):
self.text = kwargs.get("text", None)
self.sentiment = kwargs.get("sentiment", None)
self.confidence_scores = kwargs.get("confidence_scores", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.is_negated = kwargs.get("is_negated", None)

Expand All @@ -1118,16 +1175,19 @@ def _from_generated(cls, opinion):
text=opinion.text,
sentiment=opinion.sentiment,
confidence_scores=SentimentConfidenceScores._from_generated(opinion.confidence_scores), # pylint: disable=protected-access
length=opinion.length,
offset=opinion.offset,
is_negated=opinion.is_negated
)

def __repr__(self):
return (
"OpinionSentiment(text={}, sentiment={}, confidence_scores={}, offset={}, is_negated={})".format(
"OpinionSentiment(text={}, sentiment={}, confidence_scores={}, length={}, offset={}, \
is_negated={})".format(
self.text,
self.sentiment,
repr(self.confidence_scores),
self.length,
self.offset,
self.is_negated
)[:1024]
Expand Down