# coding: utf-8
# (C) Copyright IBM Corp. 2019.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Analyze various features of text content at scale. Provide text, raw HTML, or a public URL
and IBM Watson Natural Language Understanding will give you results for the features you
request. The service cleans HTML content before analysis by default, so the results can
ignore most advertisements and other unwanted content.
You can create [custom
models](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
with Watson Knowledge Studio to detect custom entities, relations, and categories in
Natural Language Understanding.
"""
import json
from .common import get_sdk_headers
from enum import Enum
from ibm_cloud_sdk_core import BaseService
from ibm_cloud_sdk_core import datetime_to_string, string_to_datetime
from ibm_cloud_sdk_core import get_authenticator_from_environment
from ibm_cloud_sdk_core import read_external_sources
##############################################################################
# Service
##############################################################################
[docs]class NaturalLanguageUnderstandingV1(BaseService):
"""The Natural Language Understanding V1 service."""
default_service_url = 'https://gateway.watsonplatform.net/natural-language-understanding/api'
def __init__(
self,
version,
authenticator=None,
):
"""
Construct a new client for the Natural Language Understanding service.
:param str version: The API version date to use with the service, in
"YYYY-MM-DD" format. Whenever the API is changed in a backwards
incompatible way, a new minor version of the API is released.
The service uses the API version for the date you specify, or
the most recent version before that date. Note that you should
not programmatically specify the current date at runtime, in
case the API has been updated since your application's release.
Instead, specify a version date that is compatible with your
application, and don't change it until your application is
ready for a later version.
:param Authenticator authenticator: The authenticator specifies the authentication mechanism.
Get up to date information from https://github.com/IBM/python-sdk-core/blob/master/README.md
about initializing the authenticator of your choice.
"""
service_url = self.default_service_url
disable_ssl_verification = False
config = read_external_sources('natural_language_understanding')
if config.get('URL'):
service_url = config.get('URL')
if config.get('DISABLE_SSL'):
disable_ssl_verification = config.get('DISABLE_SSL')
if not authenticator:
authenticator = get_authenticator_from_environment(
'natural_language_understanding')
BaseService.__init__(self,
service_url=service_url,
authenticator=authenticator,
disable_ssl_verification=disable_ssl_verification)
self.version = version
#########################
# Analyze
#########################
[docs] def analyze(self,
features,
*,
text=None,
html=None,
url=None,
clean=None,
xpath=None,
fallback_to_raw=None,
return_analyzed_text=None,
language=None,
limit_text_characters=None,
**kwargs):
"""
Analyze text.
Analyzes text, HTML, or a public webpage for the following features:
- Categories
- Concepts
- Emotion
- Entities
- Keywords
- Metadata
- Relations
- Semantic roles
- Sentiment
- Syntax (Experimental).
:param Features features: Specific features to analyze the document for.
:param str text: (optional) The plain text to analyze. One of the `text`,
`html`, or `url` parameters is required.
:param str html: (optional) The HTML file to analyze. One of the `text`,
`html`, or `url` parameters is required.
:param str url: (optional) The webpage to analyze. One of the `text`,
`html`, or `url` parameters is required.
:param bool clean: (optional) Set this to `false` to disable webpage
cleaning. To learn more about webpage cleaning, see the [Analyzing
webpages](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-analyzing-webpages)
documentation.
:param str xpath: (optional) An [XPath
query](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-analyzing-webpages#xpath)
to perform on `html` or `url` input. Results of the query will be appended
to the cleaned webpage text before it is analyzed. To analyze only the
results of the XPath query, set the `clean` parameter to `false`.
:param bool fallback_to_raw: (optional) Whether to use raw HTML content if
text cleaning fails.
:param bool return_analyzed_text: (optional) Whether or not to return the
analyzed text.
:param str language: (optional) ISO 639-1 code that specifies the language
of your text. This overrides automatic language detection. Language support
differs depending on the features you include in your analysis. See
[Language
support](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-language-support)
for more information.
:param int limit_text_characters: (optional) Sets the maximum number of
characters that are processed by the service.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if features is None:
raise ValueError('features must be provided')
features = self._convert_model(features)
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('natural-language-understanding', 'V1',
'analyze')
headers.update(sdk_headers)
params = {'version': self.version}
data = {
'features': features,
'text': text,
'html': html,
'url': url,
'clean': clean,
'xpath': xpath,
'fallback_to_raw': fallback_to_raw,
'return_analyzed_text': return_analyzed_text,
'language': language,
'limit_text_characters': limit_text_characters
}
url = '/v1/analyze'
request = self.prepare_request(method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=True)
response = self.send(request)
return response
#########################
# Manage models
#########################
[docs] def list_models(self, **kwargs):
"""
List models.
Lists Watson Knowledge Studio [custom entities and relations
models](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
that are deployed to your Natural Language Understanding service.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('natural-language-understanding', 'V1',
'list_models')
headers.update(sdk_headers)
params = {'version': self.version}
url = '/v1/models'
request = self.prepare_request(method='GET',
url=url,
headers=headers,
params=params,
accept_json=True)
response = self.send(request)
return response
[docs] def delete_model(self, model_id, **kwargs):
"""
Delete model.
Deletes a custom model.
:param str model_id: Model ID of the model to delete.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if model_id is None:
raise ValueError('model_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('natural-language-understanding', 'V1',
'delete_model')
headers.update(sdk_headers)
params = {'version': self.version}
url = '/v1/models/{0}'.format(*self._encode_path_vars(model_id))
request = self.prepare_request(method='DELETE',
url=url,
headers=headers,
params=params,
accept_json=True)
response = self.send(request)
return response
##############################################################################
# Models
##############################################################################
[docs]class AnalysisResults():
"""
Results of the analysis, organized by feature.
:attr str language: (optional) Language used to analyze the text.
:attr str analyzed_text: (optional) Text that was used in the analysis.
:attr str retrieved_url: (optional) URL of the webpage that was analyzed.
:attr AnalysisResultsUsage usage: (optional) API usage information for the
request.
:attr list[ConceptsResult] concepts: (optional) The general concepts referenced
or alluded to in the analyzed text.
:attr list[EntitiesResult] entities: (optional) The entities detected in the
analyzed text.
:attr list[KeywordsResult] keywords: (optional) The keywords from the analyzed
text.
:attr list[CategoriesResult] categories: (optional) The categories that the
service assigned to the analyzed text.
:attr EmotionResult emotion: (optional) The anger, disgust, fear, joy, or
sadness conveyed by the content.
:attr AnalysisResultsMetadata metadata: (optional) Webpage metadata, such as the
author and the title of the page.
:attr list[RelationsResult] relations: (optional) The relationships between
entities in the content.
:attr list[SemanticRolesResult] semantic_roles: (optional) Sentences parsed into
`subject`, `action`, and `object` form.
:attr SentimentResult sentiment: (optional) The sentiment of the content.
:attr SyntaxResult syntax: (optional) Tokens and sentences returned from syntax
analysis.
"""
def __init__(self,
*,
language=None,
analyzed_text=None,
retrieved_url=None,
usage=None,
concepts=None,
entities=None,
keywords=None,
categories=None,
emotion=None,
metadata=None,
relations=None,
semantic_roles=None,
sentiment=None,
syntax=None):
"""
Initialize a AnalysisResults object.
:param str language: (optional) Language used to analyze the text.
:param str analyzed_text: (optional) Text that was used in the analysis.
:param str retrieved_url: (optional) URL of the webpage that was analyzed.
:param AnalysisResultsUsage usage: (optional) API usage information for the
request.
:param list[ConceptsResult] concepts: (optional) The general concepts
referenced or alluded to in the analyzed text.
:param list[EntitiesResult] entities: (optional) The entities detected in
the analyzed text.
:param list[KeywordsResult] keywords: (optional) The keywords from the
analyzed text.
:param list[CategoriesResult] categories: (optional) The categories that
the service assigned to the analyzed text.
:param EmotionResult emotion: (optional) The anger, disgust, fear, joy, or
sadness conveyed by the content.
:param AnalysisResultsMetadata metadata: (optional) Webpage metadata, such
as the author and the title of the page.
:param list[RelationsResult] relations: (optional) The relationships
between entities in the content.
:param list[SemanticRolesResult] semantic_roles: (optional) Sentences
parsed into `subject`, `action`, and `object` form.
:param SentimentResult sentiment: (optional) The sentiment of the content.
:param SyntaxResult syntax: (optional) Tokens and sentences returned from
syntax analysis.
"""
self.language = language
self.analyzed_text = analyzed_text
self.retrieved_url = retrieved_url
self.usage = usage
self.concepts = concepts
self.entities = entities
self.keywords = keywords
self.categories = categories
self.emotion = emotion
self.metadata = metadata
self.relations = relations
self.semantic_roles = semantic_roles
self.sentiment = sentiment
self.syntax = syntax
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AnalysisResults object from a json dictionary."""
args = {}
valid_keys = [
'language', 'analyzed_text', 'retrieved_url', 'usage', 'concepts',
'entities', 'keywords', 'categories', 'emotion', 'metadata',
'relations', 'semantic_roles', 'sentiment', 'syntax'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AnalysisResults: '
+ ', '.join(bad_keys))
if 'language' in _dict:
args['language'] = _dict.get('language')
if 'analyzed_text' in _dict:
args['analyzed_text'] = _dict.get('analyzed_text')
if 'retrieved_url' in _dict:
args['retrieved_url'] = _dict.get('retrieved_url')
if 'usage' in _dict:
args['usage'] = AnalysisResultsUsage._from_dict(_dict.get('usage'))
if 'concepts' in _dict:
args['concepts'] = [
ConceptsResult._from_dict(x) for x in (_dict.get('concepts'))
]
if 'entities' in _dict:
args['entities'] = [
EntitiesResult._from_dict(x) for x in (_dict.get('entities'))
]
if 'keywords' in _dict:
args['keywords'] = [
KeywordsResult._from_dict(x) for x in (_dict.get('keywords'))
]
if 'categories' in _dict:
args['categories'] = [
CategoriesResult._from_dict(x)
for x in (_dict.get('categories'))
]
if 'emotion' in _dict:
args['emotion'] = EmotionResult._from_dict(_dict.get('emotion'))
if 'metadata' in _dict:
args['metadata'] = AnalysisResultsMetadata._from_dict(
_dict.get('metadata'))
if 'relations' in _dict:
args['relations'] = [
RelationsResult._from_dict(x) for x in (_dict.get('relations'))
]
if 'semantic_roles' in _dict:
args['semantic_roles'] = [
SemanticRolesResult._from_dict(x)
for x in (_dict.get('semantic_roles'))
]
if 'sentiment' in _dict:
args['sentiment'] = SentimentResult._from_dict(
_dict.get('sentiment'))
if 'syntax' in _dict:
args['syntax'] = SyntaxResult._from_dict(_dict.get('syntax'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'analyzed_text') and self.analyzed_text is not None:
_dict['analyzed_text'] = self.analyzed_text
if hasattr(self, 'retrieved_url') and self.retrieved_url is not None:
_dict['retrieved_url'] = self.retrieved_url
if hasattr(self, 'usage') and self.usage is not None:
_dict['usage'] = self.usage._to_dict()
if hasattr(self, 'concepts') and self.concepts is not None:
_dict['concepts'] = [x._to_dict() for x in self.concepts]
if hasattr(self, 'entities') and self.entities is not None:
_dict['entities'] = [x._to_dict() for x in self.entities]
if hasattr(self, 'keywords') and self.keywords is not None:
_dict['keywords'] = [x._to_dict() for x in self.keywords]
if hasattr(self, 'categories') and self.categories is not None:
_dict['categories'] = [x._to_dict() for x in self.categories]
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion._to_dict()
if hasattr(self, 'metadata') and self.metadata is not None:
_dict['metadata'] = self.metadata._to_dict()
if hasattr(self, 'relations') and self.relations is not None:
_dict['relations'] = [x._to_dict() for x in self.relations]
if hasattr(self, 'semantic_roles') and self.semantic_roles is not None:
_dict['semantic_roles'] = [
x._to_dict() for x in self.semantic_roles
]
if hasattr(self, 'sentiment') and self.sentiment is not None:
_dict['sentiment'] = self.sentiment._to_dict()
if hasattr(self, 'syntax') and self.syntax is not None:
_dict['syntax'] = self.syntax._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this AnalysisResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class AnalysisResultsUsage():
"""
API usage information for the request.
:attr int features: (optional) Number of features used in the API call.
:attr int text_characters: (optional) Number of text characters processed.
:attr int text_units: (optional) Number of 10,000-character units processed.
"""
def __init__(self, *, features=None, text_characters=None, text_units=None):
"""
Initialize a AnalysisResultsUsage object.
:param int features: (optional) Number of features used in the API call.
:param int text_characters: (optional) Number of text characters processed.
:param int text_units: (optional) Number of 10,000-character units
processed.
"""
self.features = features
self.text_characters = text_characters
self.text_units = text_units
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AnalysisResultsUsage object from a json dictionary."""
args = {}
valid_keys = ['features', 'text_characters', 'text_units']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AnalysisResultsUsage: '
+ ', '.join(bad_keys))
if 'features' in _dict:
args['features'] = _dict.get('features')
if 'text_characters' in _dict:
args['text_characters'] = _dict.get('text_characters')
if 'text_units' in _dict:
args['text_units'] = _dict.get('text_units')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'features') and self.features is not None:
_dict['features'] = self.features
if hasattr(self,
'text_characters') and self.text_characters is not None:
_dict['text_characters'] = self.text_characters
if hasattr(self, 'text_units') and self.text_units is not None:
_dict['text_units'] = self.text_units
return _dict
def __str__(self):
"""Return a `str` version of this AnalysisResultsUsage object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Author():
"""
The author of the analyzed content.
:attr str name: (optional) Name of the author.
"""
def __init__(self, *, name=None):
"""
Initialize a Author object.
:param str name: (optional) Name of the author.
"""
self.name = name
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Author object from a json dictionary."""
args = {}
valid_keys = ['name']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Author: ' +
', '.join(bad_keys))
if 'name' in _dict:
args['name'] = _dict.get('name')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
return _dict
def __str__(self):
"""Return a `str` version of this Author object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class CategoriesOptions():
"""
Returns a five-level taxonomy of the content. The top three categories are returned.
Supported languages: Arabic, English, French, German, Italian, Japanese, Korean,
Portuguese, Spanish.
:attr bool explanation: (optional) Set this to `true` to return explanations for
each categorization. **This is available only for English categories.**.
:attr int limit: (optional) Maximum number of categories to return.
:attr str model: (optional) Enter a [custom
model](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
ID to override the standard categories model.
"""
def __init__(self, *, explanation=None, limit=None, model=None):
"""
Initialize a CategoriesOptions object.
:param bool explanation: (optional) Set this to `true` to return
explanations for each categorization. **This is available only for English
categories.**.
:param int limit: (optional) Maximum number of categories to return.
:param str model: (optional) Enter a [custom
model](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
ID to override the standard categories model.
"""
self.explanation = explanation
self.limit = limit
self.model = model
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CategoriesOptions object from a json dictionary."""
args = {}
valid_keys = ['explanation', 'limit', 'model']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class CategoriesOptions: '
+ ', '.join(bad_keys))
if 'explanation' in _dict:
args['explanation'] = _dict.get('explanation')
if 'limit' in _dict:
args['limit'] = _dict.get('limit')
if 'model' in _dict:
args['model'] = _dict.get('model')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'explanation') and self.explanation is not None:
_dict['explanation'] = self.explanation
if hasattr(self, 'limit') and self.limit is not None:
_dict['limit'] = self.limit
if hasattr(self, 'model') and self.model is not None:
_dict['model'] = self.model
return _dict
def __str__(self):
"""Return a `str` version of this CategoriesOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class CategoriesRelevantText():
"""
Relevant text that contributed to the categorization.
:attr str text: (optional) Text from the analyzed source that supports the
categorization.
"""
def __init__(self, *, text=None):
"""
Initialize a CategoriesRelevantText object.
:param str text: (optional) Text from the analyzed source that supports the
categorization.
"""
self.text = text
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CategoriesRelevantText object from a json dictionary."""
args = {}
valid_keys = ['text']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class CategoriesRelevantText: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
return _dict
def __str__(self):
"""Return a `str` version of this CategoriesRelevantText object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class CategoriesResult():
"""
A categorization of the analyzed text.
:attr str label: (optional) The path to the category through the 5-level
taxonomy hierarchy. For the complete list of categories, see the [Categories
hierarchy](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-categories#categories-hierarchy)
documentation.
:attr float score: (optional) Confidence score for the category classification.
Higher values indicate greater confidence.
:attr CategoriesResultExplanation explanation: (optional) Information that helps
to explain what contributed to the categories result.
"""
def __init__(self, *, label=None, score=None, explanation=None):
"""
Initialize a CategoriesResult object.
:param str label: (optional) The path to the category through the 5-level
taxonomy hierarchy. For the complete list of categories, see the
[Categories
hierarchy](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-categories#categories-hierarchy)
documentation.
:param float score: (optional) Confidence score for the category
classification. Higher values indicate greater confidence.
:param CategoriesResultExplanation explanation: (optional) Information that
helps to explain what contributed to the categories result.
"""
self.label = label
self.score = score
self.explanation = explanation
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CategoriesResult object from a json dictionary."""
args = {}
valid_keys = ['label', 'score', 'explanation']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class CategoriesResult: '
+ ', '.join(bad_keys))
if 'label' in _dict:
args['label'] = _dict.get('label')
if 'score' in _dict:
args['score'] = _dict.get('score')
if 'explanation' in _dict:
args['explanation'] = CategoriesResultExplanation._from_dict(
_dict.get('explanation'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'label') and self.label is not None:
_dict['label'] = self.label
if hasattr(self, 'score') and self.score is not None:
_dict['score'] = self.score
if hasattr(self, 'explanation') and self.explanation is not None:
_dict['explanation'] = self.explanation._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this CategoriesResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class CategoriesResultExplanation():
"""
Information that helps to explain what contributed to the categories result.
:attr list[CategoriesRelevantText] relevant_text: (optional) An array of
relevant text from the source that contributed to the categorization. The sorted
array begins with the phrase that contributed most significantly to the result,
followed by phrases that were less and less impactful.
"""
def __init__(self, *, relevant_text=None):
"""
Initialize a CategoriesResultExplanation object.
:param list[CategoriesRelevantText] relevant_text: (optional) An array of
relevant text from the source that contributed to the categorization. The
sorted array begins with the phrase that contributed most significantly to
the result, followed by phrases that were less and less impactful.
"""
self.relevant_text = relevant_text
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CategoriesResultExplanation object from a json dictionary."""
args = {}
valid_keys = ['relevant_text']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class CategoriesResultExplanation: '
+ ', '.join(bad_keys))
if 'relevant_text' in _dict:
args['relevant_text'] = [
CategoriesRelevantText._from_dict(x)
for x in (_dict.get('relevant_text'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'relevant_text') and self.relevant_text is not None:
_dict['relevant_text'] = [x._to_dict() for x in self.relevant_text]
return _dict
def __str__(self):
"""Return a `str` version of this CategoriesResultExplanation object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class ConceptsOptions():
"""
Returns high-level concepts in the content. For example, a research paper about deep
learning might return the concept, "Artificial Intelligence" although the term is not
mentioned.
Supported languages: English, French, German, Italian, Japanese, Korean, Portuguese,
Spanish.
:attr int limit: (optional) Maximum number of concepts to return.
"""
def __init__(self, *, limit=None):
"""
Initialize a ConceptsOptions object.
:param int limit: (optional) Maximum number of concepts to return.
"""
self.limit = limit
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ConceptsOptions object from a json dictionary."""
args = {}
valid_keys = ['limit']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ConceptsOptions: '
+ ', '.join(bad_keys))
if 'limit' in _dict:
args['limit'] = _dict.get('limit')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'limit') and self.limit is not None:
_dict['limit'] = self.limit
return _dict
def __str__(self):
"""Return a `str` version of this ConceptsOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class ConceptsResult():
"""
The general concepts referenced or alluded to in the analyzed text.
:attr str text: (optional) Name of the concept.
:attr float relevance: (optional) Relevance score between 0 and 1. Higher scores
indicate greater relevance.
:attr str dbpedia_resource: (optional) Link to the corresponding DBpedia
resource.
"""
def __init__(self, *, text=None, relevance=None, dbpedia_resource=None):
"""
Initialize a ConceptsResult object.
:param str text: (optional) Name of the concept.
:param float relevance: (optional) Relevance score between 0 and 1. Higher
scores indicate greater relevance.
:param str dbpedia_resource: (optional) Link to the corresponding DBpedia
resource.
"""
self.text = text
self.relevance = relevance
self.dbpedia_resource = dbpedia_resource
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ConceptsResult object from a json dictionary."""
args = {}
valid_keys = ['text', 'relevance', 'dbpedia_resource']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ConceptsResult: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'relevance' in _dict:
args['relevance'] = _dict.get('relevance')
if 'dbpedia_resource' in _dict:
args['dbpedia_resource'] = _dict.get('dbpedia_resource')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'relevance') and self.relevance is not None:
_dict['relevance'] = self.relevance
if hasattr(self,
'dbpedia_resource') and self.dbpedia_resource is not None:
_dict['dbpedia_resource'] = self.dbpedia_resource
return _dict
def __str__(self):
"""Return a `str` version of this ConceptsResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DeleteModelResults():
"""
Delete model results.
:attr str deleted: (optional) model_id of the deleted model.
"""
def __init__(self, *, deleted=None):
"""
Initialize a DeleteModelResults object.
:param str deleted: (optional) model_id of the deleted model.
"""
self.deleted = deleted
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DeleteModelResults object from a json dictionary."""
args = {}
valid_keys = ['deleted']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DeleteModelResults: '
+ ', '.join(bad_keys))
if 'deleted' in _dict:
args['deleted'] = _dict.get('deleted')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'deleted') and self.deleted is not None:
_dict['deleted'] = self.deleted
return _dict
def __str__(self):
"""Return a `str` version of this DeleteModelResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DisambiguationResult():
"""
Disambiguation information for the entity.
:attr str name: (optional) Common entity name.
:attr str dbpedia_resource: (optional) Link to the corresponding DBpedia
resource.
:attr list[str] subtype: (optional) Entity subtype information.
"""
def __init__(self, *, name=None, dbpedia_resource=None, subtype=None):
"""
Initialize a DisambiguationResult object.
:param str name: (optional) Common entity name.
:param str dbpedia_resource: (optional) Link to the corresponding DBpedia
resource.
:param list[str] subtype: (optional) Entity subtype information.
"""
self.name = name
self.dbpedia_resource = dbpedia_resource
self.subtype = subtype
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DisambiguationResult object from a json dictionary."""
args = {}
valid_keys = ['name', 'dbpedia_resource', 'subtype']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DisambiguationResult: '
+ ', '.join(bad_keys))
if 'name' in _dict:
args['name'] = _dict.get('name')
if 'dbpedia_resource' in _dict:
args['dbpedia_resource'] = _dict.get('dbpedia_resource')
if 'subtype' in _dict:
args['subtype'] = _dict.get('subtype')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self,
'dbpedia_resource') and self.dbpedia_resource is not None:
_dict['dbpedia_resource'] = self.dbpedia_resource
if hasattr(self, 'subtype') and self.subtype is not None:
_dict['subtype'] = self.subtype
return _dict
def __str__(self):
"""Return a `str` version of this DisambiguationResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DocumentEmotionResults():
"""
Emotion results for the document as a whole.
:attr EmotionScores emotion: (optional) Emotion results for the document as a
whole.
"""
def __init__(self, *, emotion=None):
"""
Initialize a DocumentEmotionResults object.
:param EmotionScores emotion: (optional) Emotion results for the document
as a whole.
"""
self.emotion = emotion
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DocumentEmotionResults object from a json dictionary."""
args = {}
valid_keys = ['emotion']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DocumentEmotionResults: '
+ ', '.join(bad_keys))
if 'emotion' in _dict:
args['emotion'] = EmotionScores._from_dict(_dict.get('emotion'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this DocumentEmotionResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DocumentSentimentResults():
"""
DocumentSentimentResults.
:attr str label: (optional) Indicates whether the sentiment is positive,
neutral, or negative.
:attr float score: (optional) Sentiment score from -1 (negative) to 1
(positive).
"""
def __init__(self, *, label=None, score=None):
"""
Initialize a DocumentSentimentResults object.
:param str label: (optional) Indicates whether the sentiment is positive,
neutral, or negative.
:param float score: (optional) Sentiment score from -1 (negative) to 1
(positive).
"""
self.label = label
self.score = score
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DocumentSentimentResults object from a json dictionary."""
args = {}
valid_keys = ['label', 'score']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DocumentSentimentResults: '
+ ', '.join(bad_keys))
if 'label' in _dict:
args['label'] = _dict.get('label')
if 'score' in _dict:
args['score'] = _dict.get('score')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'label') and self.label is not None:
_dict['label'] = self.label
if hasattr(self, 'score') and self.score is not None:
_dict['score'] = self.score
return _dict
def __str__(self):
"""Return a `str` version of this DocumentSentimentResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class EmotionOptions():
"""
Detects anger, disgust, fear, joy, or sadness that is conveyed in the content or by
the context around target phrases specified in the targets parameter. You can analyze
emotion for detected entities with `entities.emotion` and for keywords with
`keywords.emotion`.
Supported languages: English.
:attr bool document: (optional) Set this to `false` to hide document-level
emotion results.
:attr list[str] targets: (optional) Emotion results will be returned for each
target string that is found in the document.
"""
def __init__(self, *, document=None, targets=None):
"""
Initialize a EmotionOptions object.
:param bool document: (optional) Set this to `false` to hide document-level
emotion results.
:param list[str] targets: (optional) Emotion results will be returned for
each target string that is found in the document.
"""
self.document = document
self.targets = targets
@classmethod
def _from_dict(cls, _dict):
"""Initialize a EmotionOptions object from a json dictionary."""
args = {}
valid_keys = ['document', 'targets']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class EmotionOptions: '
+ ', '.join(bad_keys))
if 'document' in _dict:
args['document'] = _dict.get('document')
if 'targets' in _dict:
args['targets'] = _dict.get('targets')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'document') and self.document is not None:
_dict['document'] = self.document
if hasattr(self, 'targets') and self.targets is not None:
_dict['targets'] = self.targets
return _dict
def __str__(self):
"""Return a `str` version of this EmotionOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class EmotionResult():
"""
The detected anger, disgust, fear, joy, or sadness that is conveyed by the content.
Emotion information can be returned for detected entities, keywords, or user-specified
target phrases found in the text.
:attr DocumentEmotionResults document: (optional) Emotion results for the
document as a whole.
:attr list[TargetedEmotionResults] targets: (optional) Emotion results for
specified targets.
"""
def __init__(self, *, document=None, targets=None):
"""
Initialize a EmotionResult object.
:param DocumentEmotionResults document: (optional) Emotion results for the
document as a whole.
:param list[TargetedEmotionResults] targets: (optional) Emotion results for
specified targets.
"""
self.document = document
self.targets = targets
@classmethod
def _from_dict(cls, _dict):
"""Initialize a EmotionResult object from a json dictionary."""
args = {}
valid_keys = ['document', 'targets']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class EmotionResult: '
+ ', '.join(bad_keys))
if 'document' in _dict:
args['document'] = DocumentEmotionResults._from_dict(
_dict.get('document'))
if 'targets' in _dict:
args['targets'] = [
TargetedEmotionResults._from_dict(x)
for x in (_dict.get('targets'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'document') and self.document is not None:
_dict['document'] = self.document._to_dict()
if hasattr(self, 'targets') and self.targets is not None:
_dict['targets'] = [x._to_dict() for x in self.targets]
return _dict
def __str__(self):
"""Return a `str` version of this EmotionResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class EmotionScores():
"""
EmotionScores.
:attr float anger: (optional) Anger score from 0 to 1. A higher score means that
the text is more likely to convey anger.
:attr float disgust: (optional) Disgust score from 0 to 1. A higher score means
that the text is more likely to convey disgust.
:attr float fear: (optional) Fear score from 0 to 1. A higher score means that
the text is more likely to convey fear.
:attr float joy: (optional) Joy score from 0 to 1. A higher score means that the
text is more likely to convey joy.
:attr float sadness: (optional) Sadness score from 0 to 1. A higher score means
that the text is more likely to convey sadness.
"""
def __init__(self,
*,
anger=None,
disgust=None,
fear=None,
joy=None,
sadness=None):
"""
Initialize a EmotionScores object.
:param float anger: (optional) Anger score from 0 to 1. A higher score
means that the text is more likely to convey anger.
:param float disgust: (optional) Disgust score from 0 to 1. A higher score
means that the text is more likely to convey disgust.
:param float fear: (optional) Fear score from 0 to 1. A higher score means
that the text is more likely to convey fear.
:param float joy: (optional) Joy score from 0 to 1. A higher score means
that the text is more likely to convey joy.
:param float sadness: (optional) Sadness score from 0 to 1. A higher score
means that the text is more likely to convey sadness.
"""
self.anger = anger
self.disgust = disgust
self.fear = fear
self.joy = joy
self.sadness = sadness
@classmethod
def _from_dict(cls, _dict):
"""Initialize a EmotionScores object from a json dictionary."""
args = {}
valid_keys = ['anger', 'disgust', 'fear', 'joy', 'sadness']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class EmotionScores: '
+ ', '.join(bad_keys))
if 'anger' in _dict:
args['anger'] = _dict.get('anger')
if 'disgust' in _dict:
args['disgust'] = _dict.get('disgust')
if 'fear' in _dict:
args['fear'] = _dict.get('fear')
if 'joy' in _dict:
args['joy'] = _dict.get('joy')
if 'sadness' in _dict:
args['sadness'] = _dict.get('sadness')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'anger') and self.anger is not None:
_dict['anger'] = self.anger
if hasattr(self, 'disgust') and self.disgust is not None:
_dict['disgust'] = self.disgust
if hasattr(self, 'fear') and self.fear is not None:
_dict['fear'] = self.fear
if hasattr(self, 'joy') and self.joy is not None:
_dict['joy'] = self.joy
if hasattr(self, 'sadness') and self.sadness is not None:
_dict['sadness'] = self.sadness
return _dict
def __str__(self):
"""Return a `str` version of this EmotionScores object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class EntitiesOptions():
"""
Identifies people, cities, organizations, and other entities in the content. See
[Entity types and
subtypes](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-entity-types).
Supported languages: English, French, German, Italian, Japanese, Korean, Portuguese,
Russian, Spanish, Swedish. Arabic, Chinese, and Dutch are supported only through
custom models.
:attr int limit: (optional) Maximum number of entities to return.
:attr bool mentions: (optional) Set this to `true` to return locations of entity
mentions.
:attr str model: (optional) Enter a [custom
model](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
ID to override the standard entity detection model.
:attr bool sentiment: (optional) Set this to `true` to return sentiment
information for detected entities.
:attr bool emotion: (optional) Set this to `true` to analyze emotion for
detected keywords.
"""
def __init__(self,
*,
limit=None,
mentions=None,
model=None,
sentiment=None,
emotion=None):
"""
Initialize a EntitiesOptions object.
:param int limit: (optional) Maximum number of entities to return.
:param bool mentions: (optional) Set this to `true` to return locations of
entity mentions.
:param str model: (optional) Enter a [custom
model](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
ID to override the standard entity detection model.
:param bool sentiment: (optional) Set this to `true` to return sentiment
information for detected entities.
:param bool emotion: (optional) Set this to `true` to analyze emotion for
detected keywords.
"""
self.limit = limit
self.mentions = mentions
self.model = model
self.sentiment = sentiment
self.emotion = emotion
@classmethod
def _from_dict(cls, _dict):
"""Initialize a EntitiesOptions object from a json dictionary."""
args = {}
valid_keys = ['limit', 'mentions', 'model', 'sentiment', 'emotion']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class EntitiesOptions: '
+ ', '.join(bad_keys))
if 'limit' in _dict:
args['limit'] = _dict.get('limit')
if 'mentions' in _dict:
args['mentions'] = _dict.get('mentions')
if 'model' in _dict:
args['model'] = _dict.get('model')
if 'sentiment' in _dict:
args['sentiment'] = _dict.get('sentiment')
if 'emotion' in _dict:
args['emotion'] = _dict.get('emotion')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'limit') and self.limit is not None:
_dict['limit'] = self.limit
if hasattr(self, 'mentions') and self.mentions is not None:
_dict['mentions'] = self.mentions
if hasattr(self, 'model') and self.model is not None:
_dict['model'] = self.model
if hasattr(self, 'sentiment') and self.sentiment is not None:
_dict['sentiment'] = self.sentiment
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion
return _dict
def __str__(self):
"""Return a `str` version of this EntitiesOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class EntitiesResult():
"""
The important people, places, geopolitical entities and other types of entities in
your content.
:attr str type: (optional) Entity type.
:attr str text: (optional) The name of the entity.
:attr float relevance: (optional) Relevance score from 0 to 1. Higher values
indicate greater relevance.
:attr float confidence: (optional) Confidence in the entity identification from
0 to 1. Higher values indicate higher confidence. In standard entities requests,
confidence is returned only for English text. All entities requests that use
custom models return the confidence score.
:attr list[EntityMention] mentions: (optional) Entity mentions and locations.
:attr int count: (optional) How many times the entity was mentioned in the text.
:attr EmotionScores emotion: (optional) Emotion analysis results for the entity,
enabled with the `emotion` option.
:attr FeatureSentimentResults sentiment: (optional) Sentiment analysis results
for the entity, enabled with the `sentiment` option.
:attr DisambiguationResult disambiguation: (optional) Disambiguation information
for the entity.
"""
def __init__(self,
*,
type=None,
text=None,
relevance=None,
confidence=None,
mentions=None,
count=None,
emotion=None,
sentiment=None,
disambiguation=None):
"""
Initialize a EntitiesResult object.
:param str type: (optional) Entity type.
:param str text: (optional) The name of the entity.
:param float relevance: (optional) Relevance score from 0 to 1. Higher
values indicate greater relevance.
:param float confidence: (optional) Confidence in the entity identification
from 0 to 1. Higher values indicate higher confidence. In standard entities
requests, confidence is returned only for English text. All entities
requests that use custom models return the confidence score.
:param list[EntityMention] mentions: (optional) Entity mentions and
locations.
:param int count: (optional) How many times the entity was mentioned in the
text.
:param EmotionScores emotion: (optional) Emotion analysis results for the
entity, enabled with the `emotion` option.
:param FeatureSentimentResults sentiment: (optional) Sentiment analysis
results for the entity, enabled with the `sentiment` option.
:param DisambiguationResult disambiguation: (optional) Disambiguation
information for the entity.
"""
self.type = type
self.text = text
self.relevance = relevance
self.confidence = confidence
self.mentions = mentions
self.count = count
self.emotion = emotion
self.sentiment = sentiment
self.disambiguation = disambiguation
@classmethod
def _from_dict(cls, _dict):
"""Initialize a EntitiesResult object from a json dictionary."""
args = {}
valid_keys = [
'type', 'text', 'relevance', 'confidence', 'mentions', 'count',
'emotion', 'sentiment', 'disambiguation'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class EntitiesResult: '
+ ', '.join(bad_keys))
if 'type' in _dict:
args['type'] = _dict.get('type')
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'relevance' in _dict:
args['relevance'] = _dict.get('relevance')
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
if 'mentions' in _dict:
args['mentions'] = [
EntityMention._from_dict(x) for x in (_dict.get('mentions'))
]
if 'count' in _dict:
args['count'] = _dict.get('count')
if 'emotion' in _dict:
args['emotion'] = EmotionScores._from_dict(_dict.get('emotion'))
if 'sentiment' in _dict:
args['sentiment'] = FeatureSentimentResults._from_dict(
_dict.get('sentiment'))
if 'disambiguation' in _dict:
args['disambiguation'] = DisambiguationResult._from_dict(
_dict.get('disambiguation'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'relevance') and self.relevance is not None:
_dict['relevance'] = self.relevance
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
if hasattr(self, 'mentions') and self.mentions is not None:
_dict['mentions'] = [x._to_dict() for x in self.mentions]
if hasattr(self, 'count') and self.count is not None:
_dict['count'] = self.count
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion._to_dict()
if hasattr(self, 'sentiment') and self.sentiment is not None:
_dict['sentiment'] = self.sentiment._to_dict()
if hasattr(self, 'disambiguation') and self.disambiguation is not None:
_dict['disambiguation'] = self.disambiguation._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this EntitiesResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class EntityMention():
"""
EntityMention.
:attr str text: (optional) Entity mention text.
:attr list[int] location: (optional) Character offsets indicating the beginning
and end of the mention in the analyzed text.
:attr float confidence: (optional) Confidence in the entity identification from
0 to 1. Higher values indicate higher confidence. In standard entities requests,
confidence is returned only for English text. All entities requests that use
custom models return the confidence score.
"""
def __init__(self, *, text=None, location=None, confidence=None):
"""
Initialize a EntityMention object.
:param str text: (optional) Entity mention text.
:param list[int] location: (optional) Character offsets indicating the
beginning and end of the mention in the analyzed text.
:param float confidence: (optional) Confidence in the entity identification
from 0 to 1. Higher values indicate higher confidence. In standard entities
requests, confidence is returned only for English text. All entities
requests that use custom models return the confidence score.
"""
self.text = text
self.location = location
self.confidence = confidence
@classmethod
def _from_dict(cls, _dict):
"""Initialize a EntityMention object from a json dictionary."""
args = {}
valid_keys = ['text', 'location', 'confidence']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class EntityMention: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'location' in _dict:
args['location'] = _dict.get('location')
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'location') and self.location is not None:
_dict['location'] = self.location
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
return _dict
def __str__(self):
"""Return a `str` version of this EntityMention object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class FeatureSentimentResults():
"""
FeatureSentimentResults.
:attr float score: (optional) Sentiment score from -1 (negative) to 1
(positive).
"""
def __init__(self, *, score=None):
"""
Initialize a FeatureSentimentResults object.
:param float score: (optional) Sentiment score from -1 (negative) to 1
(positive).
"""
self.score = score
@classmethod
def _from_dict(cls, _dict):
"""Initialize a FeatureSentimentResults object from a json dictionary."""
args = {}
valid_keys = ['score']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class FeatureSentimentResults: '
+ ', '.join(bad_keys))
if 'score' in _dict:
args['score'] = _dict.get('score')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'score') and self.score is not None:
_dict['score'] = self.score
return _dict
def __str__(self):
"""Return a `str` version of this FeatureSentimentResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Features():
"""
Analysis features and options.
:attr ConceptsOptions concepts: (optional) Returns high-level concepts in the
content. For example, a research paper about deep learning might return the
concept, "Artificial Intelligence" although the term is not mentioned.
Supported languages: English, French, German, Italian, Japanese, Korean,
Portuguese, Spanish.
:attr EmotionOptions emotion: (optional) Detects anger, disgust, fear, joy, or
sadness that is conveyed in the content or by the context around target phrases
specified in the targets parameter. You can analyze emotion for detected
entities with `entities.emotion` and for keywords with `keywords.emotion`.
Supported languages: English.
:attr EntitiesOptions entities: (optional) Identifies people, cities,
organizations, and other entities in the content. See [Entity types and
subtypes](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-entity-types).
Supported languages: English, French, German, Italian, Japanese, Korean,
Portuguese, Russian, Spanish, Swedish. Arabic, Chinese, and Dutch are supported
only through custom models.
:attr KeywordsOptions keywords: (optional) Returns important keywords in the
content.
Supported languages: English, French, German, Italian, Japanese, Korean,
Portuguese, Russian, Spanish, Swedish.
:attr MetadataOptions metadata: (optional) Returns information from the
document, including author name, title, RSS/ATOM feeds, prominent page image,
and publication date. Supports URL and HTML input types only.
:attr RelationsOptions relations: (optional) Recognizes when two entities are
related and identifies the type of relation. For example, an `awardedTo`
relation might connect the entities "Nobel Prize" and "Albert Einstein". See
[Relation
types](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-relations).
Supported languages: Arabic, English, German, Japanese, Korean, Spanish.
Chinese, Dutch, French, Italian, and Portuguese custom models are also
supported.
:attr SemanticRolesOptions semantic_roles: (optional) Parses sentences into
subject, action, and object form.
Supported languages: English, German, Japanese, Korean, Spanish.
:attr SentimentOptions sentiment: (optional) Analyzes the general sentiment of
your content or the sentiment toward specific target phrases. You can analyze
sentiment for detected entities with `entities.sentiment` and for keywords with
`keywords.sentiment`.
Supported languages: Arabic, English, French, German, Italian, Japanese,
Korean, Portuguese, Russian, Spanish.
:attr CategoriesOptions categories: (optional) Returns a five-level taxonomy of
the content. The top three categories are returned.
Supported languages: Arabic, English, French, German, Italian, Japanese, Korean,
Portuguese, Spanish.
:attr SyntaxOptions syntax: (optional) Returns tokens and sentences from the
input text.
"""
def __init__(self,
*,
concepts=None,
emotion=None,
entities=None,
keywords=None,
metadata=None,
relations=None,
semantic_roles=None,
sentiment=None,
categories=None,
syntax=None):
"""
Initialize a Features object.
:param ConceptsOptions concepts: (optional) Returns high-level concepts in
the content. For example, a research paper about deep learning might return
the concept, "Artificial Intelligence" although the term is not mentioned.
Supported languages: English, French, German, Italian, Japanese, Korean,
Portuguese, Spanish.
:param EmotionOptions emotion: (optional) Detects anger, disgust, fear,
joy, or sadness that is conveyed in the content or by the context around
target phrases specified in the targets parameter. You can analyze emotion
for detected entities with `entities.emotion` and for keywords with
`keywords.emotion`.
Supported languages: English.
:param EntitiesOptions entities: (optional) Identifies people, cities,
organizations, and other entities in the content. See [Entity types and
subtypes](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-entity-types).
Supported languages: English, French, German, Italian, Japanese, Korean,
Portuguese, Russian, Spanish, Swedish. Arabic, Chinese, and Dutch are
supported only through custom models.
:param KeywordsOptions keywords: (optional) Returns important keywords in
the content.
Supported languages: English, French, German, Italian, Japanese, Korean,
Portuguese, Russian, Spanish, Swedish.
:param MetadataOptions metadata: (optional) Returns information from the
document, including author name, title, RSS/ATOM feeds, prominent page
image, and publication date. Supports URL and HTML input types only.
:param RelationsOptions relations: (optional) Recognizes when two entities
are related and identifies the type of relation. For example, an
`awardedTo` relation might connect the entities "Nobel Prize" and "Albert
Einstein". See [Relation
types](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-relations).
Supported languages: Arabic, English, German, Japanese, Korean, Spanish.
Chinese, Dutch, French, Italian, and Portuguese custom models are also
supported.
:param SemanticRolesOptions semantic_roles: (optional) Parses sentences
into subject, action, and object form.
Supported languages: English, German, Japanese, Korean, Spanish.
:param SentimentOptions sentiment: (optional) Analyzes the general
sentiment of your content or the sentiment toward specific target phrases.
You can analyze sentiment for detected entities with `entities.sentiment`
and for keywords with `keywords.sentiment`.
Supported languages: Arabic, English, French, German, Italian, Japanese,
Korean, Portuguese, Russian, Spanish.
:param CategoriesOptions categories: (optional) Returns a five-level
taxonomy of the content. The top three categories are returned.
Supported languages: Arabic, English, French, German, Italian, Japanese,
Korean, Portuguese, Spanish.
:param SyntaxOptions syntax: (optional) Returns tokens and sentences from
the input text.
"""
self.concepts = concepts
self.emotion = emotion
self.entities = entities
self.keywords = keywords
self.metadata = metadata
self.relations = relations
self.semantic_roles = semantic_roles
self.sentiment = sentiment
self.categories = categories
self.syntax = syntax
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Features object from a json dictionary."""
args = {}
valid_keys = [
'concepts', 'emotion', 'entities', 'keywords', 'metadata',
'relations', 'semantic_roles', 'sentiment', 'categories', 'syntax'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Features: '
+ ', '.join(bad_keys))
if 'concepts' in _dict:
args['concepts'] = ConceptsOptions._from_dict(_dict.get('concepts'))
if 'emotion' in _dict:
args['emotion'] = EmotionOptions._from_dict(_dict.get('emotion'))
if 'entities' in _dict:
args['entities'] = EntitiesOptions._from_dict(_dict.get('entities'))
if 'keywords' in _dict:
args['keywords'] = KeywordsOptions._from_dict(_dict.get('keywords'))
if 'metadata' in _dict:
args['metadata'] = MetadataOptions._from_dict(_dict.get('metadata'))
if 'relations' in _dict:
args['relations'] = RelationsOptions._from_dict(
_dict.get('relations'))
if 'semantic_roles' in _dict:
args['semantic_roles'] = SemanticRolesOptions._from_dict(
_dict.get('semantic_roles'))
if 'sentiment' in _dict:
args['sentiment'] = SentimentOptions._from_dict(
_dict.get('sentiment'))
if 'categories' in _dict:
args['categories'] = CategoriesOptions._from_dict(
_dict.get('categories'))
if 'syntax' in _dict:
args['syntax'] = SyntaxOptions._from_dict(_dict.get('syntax'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'concepts') and self.concepts is not None:
_dict['concepts'] = self.concepts._to_dict()
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion._to_dict()
if hasattr(self, 'entities') and self.entities is not None:
_dict['entities'] = self.entities._to_dict()
if hasattr(self, 'keywords') and self.keywords is not None:
_dict['keywords'] = self.keywords._to_dict()
if hasattr(self, 'metadata') and self.metadata is not None:
_dict['metadata'] = self.metadata._to_dict()
if hasattr(self, 'relations') and self.relations is not None:
_dict['relations'] = self.relations._to_dict()
if hasattr(self, 'semantic_roles') and self.semantic_roles is not None:
_dict['semantic_roles'] = self.semantic_roles._to_dict()
if hasattr(self, 'sentiment') and self.sentiment is not None:
_dict['sentiment'] = self.sentiment._to_dict()
if hasattr(self, 'categories') and self.categories is not None:
_dict['categories'] = self.categories._to_dict()
if hasattr(self, 'syntax') and self.syntax is not None:
_dict['syntax'] = self.syntax._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this Features object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Feed():
"""
RSS or ATOM feed found on the webpage.
:attr str link: (optional) URL of the RSS or ATOM feed.
"""
def __init__(self, *, link=None):
"""
Initialize a Feed object.
:param str link: (optional) URL of the RSS or ATOM feed.
"""
self.link = link
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Feed object from a json dictionary."""
args = {}
valid_keys = ['link']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Feed: ' +
', '.join(bad_keys))
if 'link' in _dict:
args['link'] = _dict.get('link')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'link') and self.link is not None:
_dict['link'] = self.link
return _dict
def __str__(self):
"""Return a `str` version of this Feed object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class KeywordsOptions():
"""
Returns important keywords in the content.
Supported languages: English, French, German, Italian, Japanese, Korean, Portuguese,
Russian, Spanish, Swedish.
:attr int limit: (optional) Maximum number of keywords to return.
:attr bool sentiment: (optional) Set this to `true` to return sentiment
information for detected keywords.
:attr bool emotion: (optional) Set this to `true` to analyze emotion for
detected keywords.
"""
def __init__(self, *, limit=None, sentiment=None, emotion=None):
"""
Initialize a KeywordsOptions object.
:param int limit: (optional) Maximum number of keywords to return.
:param bool sentiment: (optional) Set this to `true` to return sentiment
information for detected keywords.
:param bool emotion: (optional) Set this to `true` to analyze emotion for
detected keywords.
"""
self.limit = limit
self.sentiment = sentiment
self.emotion = emotion
@classmethod
def _from_dict(cls, _dict):
"""Initialize a KeywordsOptions object from a json dictionary."""
args = {}
valid_keys = ['limit', 'sentiment', 'emotion']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class KeywordsOptions: '
+ ', '.join(bad_keys))
if 'limit' in _dict:
args['limit'] = _dict.get('limit')
if 'sentiment' in _dict:
args['sentiment'] = _dict.get('sentiment')
if 'emotion' in _dict:
args['emotion'] = _dict.get('emotion')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'limit') and self.limit is not None:
_dict['limit'] = self.limit
if hasattr(self, 'sentiment') and self.sentiment is not None:
_dict['sentiment'] = self.sentiment
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion
return _dict
def __str__(self):
"""Return a `str` version of this KeywordsOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class KeywordsResult():
"""
The important keywords in the content, organized by relevance.
:attr int count: (optional) Number of times the keyword appears in the analyzed
text.
:attr float relevance: (optional) Relevance score from 0 to 1. Higher values
indicate greater relevance.
:attr str text: (optional) The keyword text.
:attr EmotionScores emotion: (optional) Emotion analysis results for the
keyword, enabled with the `emotion` option.
:attr FeatureSentimentResults sentiment: (optional) Sentiment analysis results
for the keyword, enabled with the `sentiment` option.
"""
def __init__(self,
*,
count=None,
relevance=None,
text=None,
emotion=None,
sentiment=None):
"""
Initialize a KeywordsResult object.
:param int count: (optional) Number of times the keyword appears in the
analyzed text.
:param float relevance: (optional) Relevance score from 0 to 1. Higher
values indicate greater relevance.
:param str text: (optional) The keyword text.
:param EmotionScores emotion: (optional) Emotion analysis results for the
keyword, enabled with the `emotion` option.
:param FeatureSentimentResults sentiment: (optional) Sentiment analysis
results for the keyword, enabled with the `sentiment` option.
"""
self.count = count
self.relevance = relevance
self.text = text
self.emotion = emotion
self.sentiment = sentiment
@classmethod
def _from_dict(cls, _dict):
"""Initialize a KeywordsResult object from a json dictionary."""
args = {}
valid_keys = ['count', 'relevance', 'text', 'emotion', 'sentiment']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class KeywordsResult: '
+ ', '.join(bad_keys))
if 'count' in _dict:
args['count'] = _dict.get('count')
if 'relevance' in _dict:
args['relevance'] = _dict.get('relevance')
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'emotion' in _dict:
args['emotion'] = EmotionScores._from_dict(_dict.get('emotion'))
if 'sentiment' in _dict:
args['sentiment'] = FeatureSentimentResults._from_dict(
_dict.get('sentiment'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'count') and self.count is not None:
_dict['count'] = self.count
if hasattr(self, 'relevance') and self.relevance is not None:
_dict['relevance'] = self.relevance
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion._to_dict()
if hasattr(self, 'sentiment') and self.sentiment is not None:
_dict['sentiment'] = self.sentiment._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this KeywordsResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class ListModelsResults():
"""
Custom models that are available for entities and relations.
:attr list[Model] models: (optional) An array of available models.
"""
def __init__(self, *, models=None):
"""
Initialize a ListModelsResults object.
:param list[Model] models: (optional) An array of available models.
"""
self.models = models
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ListModelsResults object from a json dictionary."""
args = {}
valid_keys = ['models']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ListModelsResults: '
+ ', '.join(bad_keys))
if 'models' in _dict:
args['models'] = [
Model._from_dict(x) for x in (_dict.get('models'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'models') and self.models is not None:
_dict['models'] = [x._to_dict() for x in self.models]
return _dict
def __str__(self):
"""Return a `str` version of this ListModelsResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Model():
"""
Model.
:attr str status: (optional) When the status is `available`, the model is ready
to use.
:attr str model_id: (optional) Unique model ID.
:attr str language: (optional) ISO 639-1 code indicating the language of the
model.
:attr str description: (optional) Model description.
:attr str workspace_id: (optional) ID of the Watson Knowledge Studio workspace
that deployed this model to Natural Language Understanding.
:attr str version: (optional) The model version, if it was manually provided in
Watson Knowledge Studio.
:attr str version_description: (optional) The description of the version, if it
was manually provided in Watson Knowledge Studio.
:attr datetime created: (optional) A dateTime indicating when the model was
created.
"""
def __init__(self,
*,
status=None,
model_id=None,
language=None,
description=None,
workspace_id=None,
version=None,
version_description=None,
created=None):
"""
Initialize a Model object.
:param str status: (optional) When the status is `available`, the model is
ready to use.
:param str model_id: (optional) Unique model ID.
:param str language: (optional) ISO 639-1 code indicating the language of
the model.
:param str description: (optional) Model description.
:param str workspace_id: (optional) ID of the Watson Knowledge Studio
workspace that deployed this model to Natural Language Understanding.
:param str version: (optional) The model version, if it was manually
provided in Watson Knowledge Studio.
:param str version_description: (optional) The description of the version,
if it was manually provided in Watson Knowledge Studio.
:param datetime created: (optional) A dateTime indicating when the model
was created.
"""
self.status = status
self.model_id = model_id
self.language = language
self.description = description
self.workspace_id = workspace_id
self.version = version
self.version_description = version_description
self.created = created
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Model object from a json dictionary."""
args = {}
valid_keys = [
'status', 'model_id', 'language', 'description', 'workspace_id',
'version', 'version_description', 'created'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Model: ' +
', '.join(bad_keys))
if 'status' in _dict:
args['status'] = _dict.get('status')
if 'model_id' in _dict:
args['model_id'] = _dict.get('model_id')
if 'language' in _dict:
args['language'] = _dict.get('language')
if 'description' in _dict:
args['description'] = _dict.get('description')
if 'workspace_id' in _dict:
args['workspace_id'] = _dict.get('workspace_id')
if 'version' in _dict:
args['version'] = _dict.get('version')
if 'version_description' in _dict:
args['version_description'] = _dict.get('version_description')
if 'created' in _dict:
args['created'] = string_to_datetime(_dict.get('created'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'model_id') and self.model_id is not None:
_dict['model_id'] = self.model_id
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
if hasattr(self, 'workspace_id') and self.workspace_id is not None:
_dict['workspace_id'] = self.workspace_id
if hasattr(self, 'version') and self.version is not None:
_dict['version'] = self.version
if hasattr(
self,
'version_description') and self.version_description is not None:
_dict['version_description'] = self.version_description
if hasattr(self, 'created') and self.created is not None:
_dict['created'] = datetime_to_string(self.created)
return _dict
def __str__(self):
"""Return a `str` version of this Model object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RelationArgument():
"""
RelationArgument.
:attr list[RelationEntity] entities: (optional) An array of extracted entities.
:attr list[int] location: (optional) Character offsets indicating the beginning
and end of the mention in the analyzed text.
:attr str text: (optional) Text that corresponds to the argument.
"""
def __init__(self, *, entities=None, location=None, text=None):
"""
Initialize a RelationArgument object.
:param list[RelationEntity] entities: (optional) An array of extracted
entities.
:param list[int] location: (optional) Character offsets indicating the
beginning and end of the mention in the analyzed text.
:param str text: (optional) Text that corresponds to the argument.
"""
self.entities = entities
self.location = location
self.text = text
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RelationArgument object from a json dictionary."""
args = {}
valid_keys = ['entities', 'location', 'text']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RelationArgument: '
+ ', '.join(bad_keys))
if 'entities' in _dict:
args['entities'] = [
RelationEntity._from_dict(x) for x in (_dict.get('entities'))
]
if 'location' in _dict:
args['location'] = _dict.get('location')
if 'text' in _dict:
args['text'] = _dict.get('text')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'entities') and self.entities is not None:
_dict['entities'] = [x._to_dict() for x in self.entities]
if hasattr(self, 'location') and self.location is not None:
_dict['location'] = self.location
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
return _dict
def __str__(self):
"""Return a `str` version of this RelationArgument object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RelationEntity():
"""
An entity that corresponds with an argument in a relation.
:attr str text: (optional) Text that corresponds to the entity.
:attr str type: (optional) Entity type.
"""
def __init__(self, *, text=None, type=None):
"""
Initialize a RelationEntity object.
:param str text: (optional) Text that corresponds to the entity.
:param str type: (optional) Entity type.
"""
self.text = text
self.type = type
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RelationEntity object from a json dictionary."""
args = {}
valid_keys = ['text', 'type']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RelationEntity: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'type' in _dict:
args['type'] = _dict.get('type')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
return _dict
def __str__(self):
"""Return a `str` version of this RelationEntity object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RelationsOptions():
"""
Recognizes when two entities are related and identifies the type of relation. For
example, an `awardedTo` relation might connect the entities "Nobel Prize" and "Albert
Einstein". See [Relation
types](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-relations).
Supported languages: Arabic, English, German, Japanese, Korean, Spanish. Chinese,
Dutch, French, Italian, and Portuguese custom models are also supported.
:attr str model: (optional) Enter a [custom
model](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
ID to override the default model.
"""
def __init__(self, *, model=None):
"""
Initialize a RelationsOptions object.
:param str model: (optional) Enter a [custom
model](https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-customizing)
ID to override the default model.
"""
self.model = model
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RelationsOptions object from a json dictionary."""
args = {}
valid_keys = ['model']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RelationsOptions: '
+ ', '.join(bad_keys))
if 'model' in _dict:
args['model'] = _dict.get('model')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'model') and self.model is not None:
_dict['model'] = self.model
return _dict
def __str__(self):
"""Return a `str` version of this RelationsOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RelationsResult():
"""
The relations between entities found in the content.
:attr float score: (optional) Confidence score for the relation. Higher values
indicate greater confidence.
:attr str sentence: (optional) The sentence that contains the relation.
:attr str type: (optional) The type of the relation.
:attr list[RelationArgument] arguments: (optional) Entity mentions that are
involved in the relation.
"""
def __init__(self, *, score=None, sentence=None, type=None, arguments=None):
"""
Initialize a RelationsResult object.
:param float score: (optional) Confidence score for the relation. Higher
values indicate greater confidence.
:param str sentence: (optional) The sentence that contains the relation.
:param str type: (optional) The type of the relation.
:param list[RelationArgument] arguments: (optional) Entity mentions that
are involved in the relation.
"""
self.score = score
self.sentence = sentence
self.type = type
self.arguments = arguments
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RelationsResult object from a json dictionary."""
args = {}
valid_keys = ['score', 'sentence', 'type', 'arguments']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RelationsResult: '
+ ', '.join(bad_keys))
if 'score' in _dict:
args['score'] = _dict.get('score')
if 'sentence' in _dict:
args['sentence'] = _dict.get('sentence')
if 'type' in _dict:
args['type'] = _dict.get('type')
if 'arguments' in _dict:
args['arguments'] = [
RelationArgument._from_dict(x) for x in (_dict.get('arguments'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'score') and self.score is not None:
_dict['score'] = self.score
if hasattr(self, 'sentence') and self.sentence is not None:
_dict['sentence'] = self.sentence
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
if hasattr(self, 'arguments') and self.arguments is not None:
_dict['arguments'] = [x._to_dict() for x in self.arguments]
return _dict
def __str__(self):
"""Return a `str` version of this RelationsResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesEntity():
"""
SemanticRolesEntity.
:attr str type: (optional) Entity type.
:attr str text: (optional) The entity text.
"""
def __init__(self, *, type=None, text=None):
"""
Initialize a SemanticRolesEntity object.
:param str type: (optional) Entity type.
:param str text: (optional) The entity text.
"""
self.type = type
self.text = text
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesEntity object from a json dictionary."""
args = {}
valid_keys = ['type', 'text']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesEntity: '
+ ', '.join(bad_keys))
if 'type' in _dict:
args['type'] = _dict.get('type')
if 'text' in _dict:
args['text'] = _dict.get('text')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesEntity object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesKeyword():
"""
SemanticRolesKeyword.
:attr str text: (optional) The keyword text.
"""
def __init__(self, *, text=None):
"""
Initialize a SemanticRolesKeyword object.
:param str text: (optional) The keyword text.
"""
self.text = text
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesKeyword object from a json dictionary."""
args = {}
valid_keys = ['text']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesKeyword: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesKeyword object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesOptions():
"""
Parses sentences into subject, action, and object form.
Supported languages: English, German, Japanese, Korean, Spanish.
:attr int limit: (optional) Maximum number of semantic_roles results to return.
:attr bool keywords: (optional) Set this to `true` to return keyword information
for subjects and objects.
:attr bool entities: (optional) Set this to `true` to return entity information
for subjects and objects.
"""
def __init__(self, *, limit=None, keywords=None, entities=None):
"""
Initialize a SemanticRolesOptions object.
:param int limit: (optional) Maximum number of semantic_roles results to
return.
:param bool keywords: (optional) Set this to `true` to return keyword
information for subjects and objects.
:param bool entities: (optional) Set this to `true` to return entity
information for subjects and objects.
"""
self.limit = limit
self.keywords = keywords
self.entities = entities
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesOptions object from a json dictionary."""
args = {}
valid_keys = ['limit', 'keywords', 'entities']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesOptions: '
+ ', '.join(bad_keys))
if 'limit' in _dict:
args['limit'] = _dict.get('limit')
if 'keywords' in _dict:
args['keywords'] = _dict.get('keywords')
if 'entities' in _dict:
args['entities'] = _dict.get('entities')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'limit') and self.limit is not None:
_dict['limit'] = self.limit
if hasattr(self, 'keywords') and self.keywords is not None:
_dict['keywords'] = self.keywords
if hasattr(self, 'entities') and self.entities is not None:
_dict['entities'] = self.entities
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesResult():
"""
The object containing the actions and the objects the actions act upon.
:attr str sentence: (optional) Sentence from the source that contains the
subject, action, and object.
:attr SemanticRolesResultSubject subject: (optional) The extracted subject from
the sentence.
:attr SemanticRolesResultAction action: (optional) The extracted action from the
sentence.
:attr SemanticRolesResultObject object: (optional) The extracted object from the
sentence.
"""
def __init__(self, *, sentence=None, subject=None, action=None,
object=None):
"""
Initialize a SemanticRolesResult object.
:param str sentence: (optional) Sentence from the source that contains the
subject, action, and object.
:param SemanticRolesResultSubject subject: (optional) The extracted subject
from the sentence.
:param SemanticRolesResultAction action: (optional) The extracted action
from the sentence.
:param SemanticRolesResultObject object: (optional) The extracted object
from the sentence.
"""
self.sentence = sentence
self.subject = subject
self.action = action
self.object = object
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesResult object from a json dictionary."""
args = {}
valid_keys = ['sentence', 'subject', 'action', 'object']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesResult: '
+ ', '.join(bad_keys))
if 'sentence' in _dict:
args['sentence'] = _dict.get('sentence')
if 'subject' in _dict:
args['subject'] = SemanticRolesResultSubject._from_dict(
_dict.get('subject'))
if 'action' in _dict:
args['action'] = SemanticRolesResultAction._from_dict(
_dict.get('action'))
if 'object' in _dict:
args['object'] = SemanticRolesResultObject._from_dict(
_dict.get('object'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'sentence') and self.sentence is not None:
_dict['sentence'] = self.sentence
if hasattr(self, 'subject') and self.subject is not None:
_dict['subject'] = self.subject._to_dict()
if hasattr(self, 'action') and self.action is not None:
_dict['action'] = self.action._to_dict()
if hasattr(self, 'object') and self.object is not None:
_dict['object'] = self.object._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesResultAction():
"""
The extracted action from the sentence.
:attr str text: (optional) Analyzed text that corresponds to the action.
:attr str normalized: (optional) normalized version of the action.
:attr SemanticRolesVerb verb: (optional)
"""
def __init__(self, *, text=None, normalized=None, verb=None):
"""
Initialize a SemanticRolesResultAction object.
:param str text: (optional) Analyzed text that corresponds to the action.
:param str normalized: (optional) normalized version of the action.
:param SemanticRolesVerb verb: (optional)
"""
self.text = text
self.normalized = normalized
self.verb = verb
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesResultAction object from a json dictionary."""
args = {}
valid_keys = ['text', 'normalized', 'verb']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesResultAction: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'normalized' in _dict:
args['normalized'] = _dict.get('normalized')
if 'verb' in _dict:
args['verb'] = SemanticRolesVerb._from_dict(_dict.get('verb'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'normalized') and self.normalized is not None:
_dict['normalized'] = self.normalized
if hasattr(self, 'verb') and self.verb is not None:
_dict['verb'] = self.verb._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesResultAction object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesResultObject():
"""
The extracted object from the sentence.
:attr str text: (optional) Object text.
:attr list[SemanticRolesKeyword] keywords: (optional) An array of extracted
keywords.
"""
def __init__(self, *, text=None, keywords=None):
"""
Initialize a SemanticRolesResultObject object.
:param str text: (optional) Object text.
:param list[SemanticRolesKeyword] keywords: (optional) An array of
extracted keywords.
"""
self.text = text
self.keywords = keywords
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesResultObject object from a json dictionary."""
args = {}
valid_keys = ['text', 'keywords']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesResultObject: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'keywords' in _dict:
args['keywords'] = [
SemanticRolesKeyword._from_dict(x)
for x in (_dict.get('keywords'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'keywords') and self.keywords is not None:
_dict['keywords'] = [x._to_dict() for x in self.keywords]
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesResultObject object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesResultSubject():
"""
The extracted subject from the sentence.
:attr str text: (optional) Text that corresponds to the subject role.
:attr list[SemanticRolesEntity] entities: (optional) An array of extracted
entities.
:attr list[SemanticRolesKeyword] keywords: (optional) An array of extracted
keywords.
"""
def __init__(self, *, text=None, entities=None, keywords=None):
"""
Initialize a SemanticRolesResultSubject object.
:param str text: (optional) Text that corresponds to the subject role.
:param list[SemanticRolesEntity] entities: (optional) An array of extracted
entities.
:param list[SemanticRolesKeyword] keywords: (optional) An array of
extracted keywords.
"""
self.text = text
self.entities = entities
self.keywords = keywords
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesResultSubject object from a json dictionary."""
args = {}
valid_keys = ['text', 'entities', 'keywords']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesResultSubject: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'entities' in _dict:
args['entities'] = [
SemanticRolesEntity._from_dict(x)
for x in (_dict.get('entities'))
]
if 'keywords' in _dict:
args['keywords'] = [
SemanticRolesKeyword._from_dict(x)
for x in (_dict.get('keywords'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'entities') and self.entities is not None:
_dict['entities'] = [x._to_dict() for x in self.entities]
if hasattr(self, 'keywords') and self.keywords is not None:
_dict['keywords'] = [x._to_dict() for x in self.keywords]
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesResultSubject object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SemanticRolesVerb():
"""
SemanticRolesVerb.
:attr str text: (optional) The keyword text.
:attr str tense: (optional) Verb tense.
"""
def __init__(self, *, text=None, tense=None):
"""
Initialize a SemanticRolesVerb object.
:param str text: (optional) The keyword text.
:param str tense: (optional) Verb tense.
"""
self.text = text
self.tense = tense
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SemanticRolesVerb object from a json dictionary."""
args = {}
valid_keys = ['text', 'tense']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SemanticRolesVerb: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'tense' in _dict:
args['tense'] = _dict.get('tense')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'tense') and self.tense is not None:
_dict['tense'] = self.tense
return _dict
def __str__(self):
"""Return a `str` version of this SemanticRolesVerb object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SentenceResult():
"""
SentenceResult.
:attr str text: (optional) The sentence.
:attr list[int] location: (optional) Character offsets indicating the beginning
and end of the sentence in the analyzed text.
"""
def __init__(self, *, text=None, location=None):
"""
Initialize a SentenceResult object.
:param str text: (optional) The sentence.
:param list[int] location: (optional) Character offsets indicating the
beginning and end of the sentence in the analyzed text.
"""
self.text = text
self.location = location
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SentenceResult object from a json dictionary."""
args = {}
valid_keys = ['text', 'location']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SentenceResult: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'location' in _dict:
args['location'] = _dict.get('location')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'location') and self.location is not None:
_dict['location'] = self.location
return _dict
def __str__(self):
"""Return a `str` version of this SentenceResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SentimentOptions():
"""
Analyzes the general sentiment of your content or the sentiment toward specific target
phrases. You can analyze sentiment for detected entities with `entities.sentiment` and
for keywords with `keywords.sentiment`.
Supported languages: Arabic, English, French, German, Italian, Japanese, Korean,
Portuguese, Russian, Spanish.
:attr bool document: (optional) Set this to `false` to hide document-level
sentiment results.
:attr list[str] targets: (optional) Sentiment results will be returned for each
target string that is found in the document.
"""
def __init__(self, *, document=None, targets=None):
"""
Initialize a SentimentOptions object.
:param bool document: (optional) Set this to `false` to hide document-level
sentiment results.
:param list[str] targets: (optional) Sentiment results will be returned for
each target string that is found in the document.
"""
self.document = document
self.targets = targets
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SentimentOptions object from a json dictionary."""
args = {}
valid_keys = ['document', 'targets']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SentimentOptions: '
+ ', '.join(bad_keys))
if 'document' in _dict:
args['document'] = _dict.get('document')
if 'targets' in _dict:
args['targets'] = _dict.get('targets')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'document') and self.document is not None:
_dict['document'] = self.document
if hasattr(self, 'targets') and self.targets is not None:
_dict['targets'] = self.targets
return _dict
def __str__(self):
"""Return a `str` version of this SentimentOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SentimentResult():
"""
The sentiment of the content.
:attr DocumentSentimentResults document: (optional) The document level
sentiment.
:attr list[TargetedSentimentResults] targets: (optional) The targeted sentiment
to analyze.
"""
def __init__(self, *, document=None, targets=None):
"""
Initialize a SentimentResult object.
:param DocumentSentimentResults document: (optional) The document level
sentiment.
:param list[TargetedSentimentResults] targets: (optional) The targeted
sentiment to analyze.
"""
self.document = document
self.targets = targets
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SentimentResult object from a json dictionary."""
args = {}
valid_keys = ['document', 'targets']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SentimentResult: '
+ ', '.join(bad_keys))
if 'document' in _dict:
args['document'] = DocumentSentimentResults._from_dict(
_dict.get('document'))
if 'targets' in _dict:
args['targets'] = [
TargetedSentimentResults._from_dict(x)
for x in (_dict.get('targets'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'document') and self.document is not None:
_dict['document'] = self.document._to_dict()
if hasattr(self, 'targets') and self.targets is not None:
_dict['targets'] = [x._to_dict() for x in self.targets]
return _dict
def __str__(self):
"""Return a `str` version of this SentimentResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SyntaxOptions():
"""
Returns tokens and sentences from the input text.
:attr SyntaxOptionsTokens tokens: (optional) Tokenization options.
:attr bool sentences: (optional) Set this to `true` to return sentence
information.
"""
def __init__(self, *, tokens=None, sentences=None):
"""
Initialize a SyntaxOptions object.
:param SyntaxOptionsTokens tokens: (optional) Tokenization options.
:param bool sentences: (optional) Set this to `true` to return sentence
information.
"""
self.tokens = tokens
self.sentences = sentences
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SyntaxOptions object from a json dictionary."""
args = {}
valid_keys = ['tokens', 'sentences']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SyntaxOptions: '
+ ', '.join(bad_keys))
if 'tokens' in _dict:
args['tokens'] = SyntaxOptionsTokens._from_dict(_dict.get('tokens'))
if 'sentences' in _dict:
args['sentences'] = _dict.get('sentences')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'tokens') and self.tokens is not None:
_dict['tokens'] = self.tokens._to_dict()
if hasattr(self, 'sentences') and self.sentences is not None:
_dict['sentences'] = self.sentences
return _dict
def __str__(self):
"""Return a `str` version of this SyntaxOptions object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SyntaxOptionsTokens():
"""
Tokenization options.
:attr bool lemma: (optional) Set this to `true` to return the lemma for each
token.
:attr bool part_of_speech: (optional) Set this to `true` to return the part of
speech for each token.
"""
def __init__(self, *, lemma=None, part_of_speech=None):
"""
Initialize a SyntaxOptionsTokens object.
:param bool lemma: (optional) Set this to `true` to return the lemma for
each token.
:param bool part_of_speech: (optional) Set this to `true` to return the
part of speech for each token.
"""
self.lemma = lemma
self.part_of_speech = part_of_speech
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SyntaxOptionsTokens object from a json dictionary."""
args = {}
valid_keys = ['lemma', 'part_of_speech']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SyntaxOptionsTokens: '
+ ', '.join(bad_keys))
if 'lemma' in _dict:
args['lemma'] = _dict.get('lemma')
if 'part_of_speech' in _dict:
args['part_of_speech'] = _dict.get('part_of_speech')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'lemma') and self.lemma is not None:
_dict['lemma'] = self.lemma
if hasattr(self, 'part_of_speech') and self.part_of_speech is not None:
_dict['part_of_speech'] = self.part_of_speech
return _dict
def __str__(self):
"""Return a `str` version of this SyntaxOptionsTokens object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SyntaxResult():
"""
Tokens and sentences returned from syntax analysis.
:attr list[TokenResult] tokens: (optional)
:attr list[SentenceResult] sentences: (optional)
"""
def __init__(self, *, tokens=None, sentences=None):
"""
Initialize a SyntaxResult object.
:param list[TokenResult] tokens: (optional)
:param list[SentenceResult] sentences: (optional)
"""
self.tokens = tokens
self.sentences = sentences
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SyntaxResult object from a json dictionary."""
args = {}
valid_keys = ['tokens', 'sentences']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SyntaxResult: '
+ ', '.join(bad_keys))
if 'tokens' in _dict:
args['tokens'] = [
TokenResult._from_dict(x) for x in (_dict.get('tokens'))
]
if 'sentences' in _dict:
args['sentences'] = [
SentenceResult._from_dict(x) for x in (_dict.get('sentences'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'tokens') and self.tokens is not None:
_dict['tokens'] = [x._to_dict() for x in self.tokens]
if hasattr(self, 'sentences') and self.sentences is not None:
_dict['sentences'] = [x._to_dict() for x in self.sentences]
return _dict
def __str__(self):
"""Return a `str` version of this SyntaxResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class TargetedEmotionResults():
"""
Emotion results for a specified target.
:attr str text: (optional) Targeted text.
:attr EmotionScores emotion: (optional) The emotion results for the target.
"""
def __init__(self, *, text=None, emotion=None):
"""
Initialize a TargetedEmotionResults object.
:param str text: (optional) Targeted text.
:param EmotionScores emotion: (optional) The emotion results for the
target.
"""
self.text = text
self.emotion = emotion
@classmethod
def _from_dict(cls, _dict):
"""Initialize a TargetedEmotionResults object from a json dictionary."""
args = {}
valid_keys = ['text', 'emotion']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class TargetedEmotionResults: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'emotion' in _dict:
args['emotion'] = EmotionScores._from_dict(_dict.get('emotion'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'emotion') and self.emotion is not None:
_dict['emotion'] = self.emotion._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this TargetedEmotionResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class TargetedSentimentResults():
"""
TargetedSentimentResults.
:attr str text: (optional) Targeted text.
:attr float score: (optional) Sentiment score from -1 (negative) to 1
(positive).
"""
def __init__(self, *, text=None, score=None):
"""
Initialize a TargetedSentimentResults object.
:param str text: (optional) Targeted text.
:param float score: (optional) Sentiment score from -1 (negative) to 1
(positive).
"""
self.text = text
self.score = score
@classmethod
def _from_dict(cls, _dict):
"""Initialize a TargetedSentimentResults object from a json dictionary."""
args = {}
valid_keys = ['text', 'score']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class TargetedSentimentResults: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'score' in _dict:
args['score'] = _dict.get('score')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'score') and self.score is not None:
_dict['score'] = self.score
return _dict
def __str__(self):
"""Return a `str` version of this TargetedSentimentResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class TokenResult():
"""
TokenResult.
:attr str text: (optional) The token as it appears in the analyzed text.
:attr str part_of_speech: (optional) The part of speech of the token. For
descriptions of the values, see [Universal Dependencies POS
tags](https://universaldependencies.org/u/pos/).
:attr list[int] location: (optional) Character offsets indicating the beginning
and end of the token in the analyzed text.
:attr str lemma: (optional) The
[lemma](https://wikipedia.org/wiki/Lemma_%28morphology%29) of the token.
"""
def __init__(self,
*,
text=None,
part_of_speech=None,
location=None,
lemma=None):
"""
Initialize a TokenResult object.
:param str text: (optional) The token as it appears in the analyzed text.
:param str part_of_speech: (optional) The part of speech of the token. For
descriptions of the values, see [Universal Dependencies POS
tags](https://universaldependencies.org/u/pos/).
:param list[int] location: (optional) Character offsets indicating the
beginning and end of the token in the analyzed text.
:param str lemma: (optional) The
[lemma](https://wikipedia.org/wiki/Lemma_%28morphology%29) of the token.
"""
self.text = text
self.part_of_speech = part_of_speech
self.location = location
self.lemma = lemma
@classmethod
def _from_dict(cls, _dict):
"""Initialize a TokenResult object from a json dictionary."""
args = {}
valid_keys = ['text', 'part_of_speech', 'location', 'lemma']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class TokenResult: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'part_of_speech' in _dict:
args['part_of_speech'] = _dict.get('part_of_speech')
if 'location' in _dict:
args['location'] = _dict.get('location')
if 'lemma' in _dict:
args['lemma'] = _dict.get('lemma')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'part_of_speech') and self.part_of_speech is not None:
_dict['part_of_speech'] = self.part_of_speech
if hasattr(self, 'location') and self.location is not None:
_dict['location'] = self.location
if hasattr(self, 'lemma') and self.lemma is not None:
_dict['lemma'] = self.lemma
return _dict
def __str__(self):
"""Return a `str` version of this TokenResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class PartOfSpeechEnum(Enum):
"""
The part of speech of the token. For descriptions of the values, see [Universal
Dependencies POS tags](https://universaldependencies.org/u/pos/).
"""
ADJ = "ADJ"
ADP = "ADP"
ADV = "ADV"
AUX = "AUX"
CCONJ = "CCONJ"
DET = "DET"
INTJ = "INTJ"
NOUN = "NOUN"
NUM = "NUM"
PART = "PART"
PRON = "PRON"
PROPN = "PROPN"
PUNCT = "PUNCT"
SCONJ = "SCONJ"
SYM = "SYM"
VERB = "VERB"
X = "X"