# 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.
"""
The IBM Watson™ Personality Insights service enables applications to derive insights
from social media, enterprise data, or other digital communications. The service uses
linguistic analytics to infer individuals' intrinsic personality characteristics,
including Big Five, Needs, and Values, from digital communications such as email, text
messages, tweets, and forum posts.
The service can automatically infer, from potentially noisy social media, portraits of
individuals that reflect their personality characteristics. The service can infer
consumption preferences based on the results of its analysis and, for JSON content that is
timestamped, can report temporal behavior.
* For information about the meaning of the models that the service uses to describe
personality characteristics, see [Personality
models](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-models#models).
* For information about the meaning of the consumption preferences, see [Consumption
preferences](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-preferences#preferences).
**Note:** Request logging is disabled for the Personality Insights service. Regardless of
whether you set the `X-Watson-Learning-Opt-Out` request header, the service does not log
or retain data from requests and responses.
"""
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 get_authenticator_from_environment
from ibm_cloud_sdk_core import read_external_sources
##############################################################################
# Service
##############################################################################
[docs]class PersonalityInsightsV3(BaseService):
"""The Personality Insights V3 service."""
default_service_url = 'https://gateway.watsonplatform.net/personality-insights/api'
def __init__(
self,
version,
authenticator=None,
):
"""
Construct a new client for the Personality Insights 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('personality_insights')
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(
'personality_insights')
BaseService.__init__(self,
service_url=service_url,
authenticator=authenticator,
disable_ssl_verification=disable_ssl_verification)
self.version = version
#########################
# Methods
#########################
[docs] def profile(self,
content,
accept,
*,
content_type=None,
content_language=None,
accept_language=None,
raw_scores=None,
csv_headers=None,
consumption_preferences=None,
**kwargs):
"""
Get profile.
Generates a personality profile for the author of the input text. The service
accepts a maximum of 20 MB of input content, but it requires much less text to
produce an accurate profile. The service can analyze text in Arabic, English,
Japanese, Korean, or Spanish. It can return its results in a variety of languages.
**See also:**
* [Requesting a
profile](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-input#input)
* [Providing sufficient
input](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-input#sufficient)
### Content types
You can provide input content as plain text (`text/plain`), HTML (`text/html`),
or JSON (`application/json`) by specifying the **Content-Type** parameter. The
default is `text/plain`.
* Per the JSON specification, the default character encoding for JSON content is
effectively always UTF-8.
* Per the HTTP specification, the default encoding for plain text and HTML is
ISO-8859-1 (effectively, the ASCII character set).
When specifying a content type of plain text or HTML, include the `charset`
parameter to indicate the character encoding of the input text; for example,
`Content-Type: text/plain;charset=utf-8`.
**See also:** [Specifying request and response
formats](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-input#formats)
### Accept types
You must request a response as JSON (`application/json`) or comma-separated
values (`text/csv`) by specifying the **Accept** parameter. CSV output includes a
fixed number of columns. Set the **csv_headers** parameter to `true` to request
optional column headers for CSV output.
**See also:**
* [Understanding a JSON
profile](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-output#output)
* [Understanding a CSV
profile](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-outputCSV#outputCSV).
:param Content content: A maximum of 20 MB of content to analyze, though
the service requires much less text; for more information, see [Providing
sufficient
input](https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-input#sufficient).
For JSON input, provide an object of type `Content`.
:param str accept: The type of the response. For more information, see
**Accept types** in the method description.
:param str content_type: (optional) The type of the input. For more
information, see **Content types** in the method description.
:param str content_language: (optional) The language of the input text for
the request: Arabic, English, Japanese, Korean, or Spanish. Regional
variants are treated as their parent language; for example, `en-US` is
interpreted as `en`.
The effect of the **Content-Language** parameter depends on the
**Content-Type** parameter. When **Content-Type** is `text/plain` or
`text/html`, **Content-Language** is the only way to specify the language.
When **Content-Type** is `application/json`, **Content-Language** overrides
a language specified with the `language` parameter of a `ContentItem`
object, and content items that specify a different language are ignored;
omit this parameter to base the language on the specification of the
content items. You can specify any combination of languages for
**Content-Language** and **Accept-Language**.
:param str accept_language: (optional) The desired language of the
response. For two-character arguments, regional variants are treated as
their parent language; for example, `en-US` is interpreted as `en`. You can
specify any combination of languages for the input and response content.
:param bool raw_scores: (optional) Indicates whether a raw score in
addition to a normalized percentile is returned for each characteristic;
raw scores are not compared with a sample population. By default, only
normalized percentiles are returned.
:param bool csv_headers: (optional) Indicates whether column labels are
returned with a CSV response. By default, no column labels are returned.
Applies only when the response type is CSV (`text/csv`).
:param bool consumption_preferences: (optional) Indicates whether
consumption preferences are returned with the results. By default, no
consumption preferences are returned.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if content is None:
raise ValueError('content must be provided')
if accept is None:
raise ValueError('accept must be provided')
if isinstance(content, Content):
content = self._convert_model(content)
headers = {
'Accept': accept,
'Content-Type': content_type,
'Content-Language': content_language,
'Accept-Language': accept_language
}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('personality_insights', 'V3', 'profile')
headers.update(sdk_headers)
params = {
'version': self.version,
'raw_scores': raw_scores,
'csv_headers': csv_headers,
'consumption_preferences': consumption_preferences
}
if content_type == 'application/json' and isinstance(content, dict):
data = json.dumps(content)
else:
data = content
url = '/v3/profile'
request = self.prepare_request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=(accept is None or accept == 'application/json'))
response = self.send(request)
return response
[docs]class ProfileEnums(object):
[docs] class Accept(Enum):
"""
The type of the response. For more information, see **Accept types** in the method
description.
"""
APPLICATION_JSON = 'application/json'
TEXT_CSV = 'text/csv'
[docs] class ContentType(Enum):
"""
The type of the input. For more information, see **Content types** in the method
description.
"""
APPLICATION_JSON = 'application/json'
TEXT_HTML = 'text/html'
TEXT_PLAIN = 'text/plain'
[docs] class ContentLanguage(Enum):
"""
The language of the input text for the request: Arabic, English, Japanese, Korean,
or Spanish. Regional variants are treated as their parent language; for example,
`en-US` is interpreted as `en`.
The effect of the **Content-Language** parameter depends on the **Content-Type**
parameter. When **Content-Type** is `text/plain` or `text/html`,
**Content-Language** is the only way to specify the language. When
**Content-Type** is `application/json`, **Content-Language** overrides a language
specified with the `language` parameter of a `ContentItem` object, and content
items that specify a different language are ignored; omit this parameter to base
the language on the specification of the content items. You can specify any
combination of languages for **Content-Language** and **Accept-Language**.
"""
AR = 'ar'
EN = 'en'
ES = 'es'
JA = 'ja'
KO = 'ko'
[docs] class AcceptLanguage(Enum):
"""
The desired language of the response. For two-character arguments, regional
variants are treated as their parent language; for example, `en-US` is interpreted
as `en`. You can specify any combination of languages for the input and response
content.
"""
AR = 'ar'
DE = 'de'
EN = 'en'
ES = 'es'
FR = 'fr'
IT = 'it'
JA = 'ja'
KO = 'ko'
PT_BR = 'pt-br'
ZH_CN = 'zh-cn'
ZH_TW = 'zh-tw'
##############################################################################
# Models
##############################################################################
[docs]class Behavior():
"""
The temporal behavior for the input content.
:attr str trait_id: The unique, non-localized identifier of the characteristic
to which the results pertain. IDs have the form `behavior_{value}`.
:attr str name: The user-visible, localized name of the characteristic.
:attr str category: The category of the characteristic: `behavior` for temporal
data.
:attr float percentage: For JSON content that is timestamped, the percentage of
timestamped input data that occurred during that day of the week or hour of the
day. The range is 0 to 1.
"""
def __init__(self, trait_id, name, category, percentage):
"""
Initialize a Behavior object.
:param str trait_id: The unique, non-localized identifier of the
characteristic to which the results pertain. IDs have the form
`behavior_{value}`.
:param str name: The user-visible, localized name of the characteristic.
:param str category: The category of the characteristic: `behavior` for
temporal data.
:param float percentage: For JSON content that is timestamped, the
percentage of timestamped input data that occurred during that day of the
week or hour of the day. The range is 0 to 1.
"""
self.trait_id = trait_id
self.name = name
self.category = category
self.percentage = percentage
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Behavior object from a json dictionary."""
args = {}
valid_keys = ['trait_id', 'name', 'category', 'percentage']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Behavior: '
+ ', '.join(bad_keys))
if 'trait_id' in _dict:
args['trait_id'] = _dict.get('trait_id')
else:
raise ValueError(
'Required property \'trait_id\' not present in Behavior JSON')
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in Behavior JSON')
if 'category' in _dict:
args['category'] = _dict.get('category')
else:
raise ValueError(
'Required property \'category\' not present in Behavior JSON')
if 'percentage' in _dict:
args['percentage'] = _dict.get('percentage')
else:
raise ValueError(
'Required property \'percentage\' not present in Behavior JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'trait_id') and self.trait_id is not None:
_dict['trait_id'] = self.trait_id
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'category') and self.category is not None:
_dict['category'] = self.category
if hasattr(self, 'percentage') and self.percentage is not None:
_dict['percentage'] = self.percentage
return _dict
def __str__(self):
"""Return a `str` version of this Behavior 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 ConsumptionPreferences():
"""
A consumption preference that the service inferred from the input content.
:attr str consumption_preference_id: The unique, non-localized identifier of the
consumption preference to which the results pertain. IDs have the form
`consumption_preferences_{preference}`.
:attr str name: The user-visible, localized name of the consumption preference.
:attr float score: The score for the consumption preference:
* `0.0`: Unlikely
* `0.5`: Neutral
* `1.0`: Likely
The scores for some preferences are binary and do not allow a neutral value. The
score is an indication of preference based on the results inferred from the
input text, not a normalized percentile.
"""
def __init__(self, consumption_preference_id, name, score):
"""
Initialize a ConsumptionPreferences object.
:param str consumption_preference_id: The unique, non-localized identifier
of the consumption preference to which the results pertain. IDs have the
form `consumption_preferences_{preference}`.
:param str name: The user-visible, localized name of the consumption
preference.
:param float score: The score for the consumption preference:
* `0.0`: Unlikely
* `0.5`: Neutral
* `1.0`: Likely
The scores for some preferences are binary and do not allow a neutral
value. The score is an indication of preference based on the results
inferred from the input text, not a normalized percentile.
"""
self.consumption_preference_id = consumption_preference_id
self.name = name
self.score = score
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ConsumptionPreferences object from a json dictionary."""
args = {}
valid_keys = ['consumption_preference_id', 'name', 'score']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ConsumptionPreferences: '
+ ', '.join(bad_keys))
if 'consumption_preference_id' in _dict:
args['consumption_preference_id'] = _dict.get(
'consumption_preference_id')
else:
raise ValueError(
'Required property \'consumption_preference_id\' not present in ConsumptionPreferences JSON'
)
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in ConsumptionPreferences JSON'
)
if 'score' in _dict:
args['score'] = _dict.get('score')
else:
raise ValueError(
'Required property \'score\' not present in ConsumptionPreferences JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'consumption_preference_id'
) and self.consumption_preference_id is not None:
_dict['consumption_preference_id'] = self.consumption_preference_id
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
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 ConsumptionPreferences 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 ConsumptionPreferencesCategory():
"""
The consumption preferences that the service inferred from the input content.
:attr str consumption_preference_category_id: The unique, non-localized
identifier of the consumption preferences category to which the results pertain.
IDs have the form `consumption_preferences_{category}`.
:attr str name: The user-visible name of the consumption preferences category.
:attr list[ConsumptionPreferences] consumption_preferences: Detailed results
inferred from the input text for the individual preferences of the category.
"""
def __init__(self, consumption_preference_category_id, name,
consumption_preferences):
"""
Initialize a ConsumptionPreferencesCategory object.
:param str consumption_preference_category_id: The unique, non-localized
identifier of the consumption preferences category to which the results
pertain. IDs have the form `consumption_preferences_{category}`.
:param str name: The user-visible name of the consumption preferences
category.
:param list[ConsumptionPreferences] consumption_preferences: Detailed
results inferred from the input text for the individual preferences of the
category.
"""
self.consumption_preference_category_id = consumption_preference_category_id
self.name = name
self.consumption_preferences = consumption_preferences
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ConsumptionPreferencesCategory object from a json dictionary."""
args = {}
valid_keys = [
'consumption_preference_category_id', 'name',
'consumption_preferences'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ConsumptionPreferencesCategory: '
+ ', '.join(bad_keys))
if 'consumption_preference_category_id' in _dict:
args['consumption_preference_category_id'] = _dict.get(
'consumption_preference_category_id')
else:
raise ValueError(
'Required property \'consumption_preference_category_id\' not present in ConsumptionPreferencesCategory JSON'
)
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in ConsumptionPreferencesCategory JSON'
)
if 'consumption_preferences' in _dict:
args['consumption_preferences'] = [
ConsumptionPreferences._from_dict(x)
for x in (_dict.get('consumption_preferences'))
]
else:
raise ValueError(
'Required property \'consumption_preferences\' not present in ConsumptionPreferencesCategory JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'consumption_preference_category_id'
) and self.consumption_preference_category_id is not None:
_dict[
'consumption_preference_category_id'] = self.consumption_preference_category_id
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'consumption_preferences'
) and self.consumption_preferences is not None:
_dict['consumption_preferences'] = [
x._to_dict() for x in self.consumption_preferences
]
return _dict
def __str__(self):
"""Return a `str` version of this ConsumptionPreferencesCategory 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 Content():
"""
The full input content that the service is to analyze.
:attr list[ContentItem] content_items: An array of `ContentItem` objects that
provides the text that is to be analyzed.
"""
def __init__(self, content_items):
"""
Initialize a Content object.
:param list[ContentItem] content_items: An array of `ContentItem` objects
that provides the text that is to be analyzed.
"""
self.content_items = content_items
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Content object from a json dictionary."""
args = {}
valid_keys = ['content_items', 'contentItems']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Content: ' +
', '.join(bad_keys))
if 'contentItems' in _dict:
args['content_items'] = [
ContentItem._from_dict(x) for x in (_dict.get('contentItems'))
]
else:
raise ValueError(
'Required property \'contentItems\' not present in Content JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'content_items') and self.content_items is not None:
_dict['contentItems'] = [x._to_dict() for x in self.content_items]
return _dict
def __str__(self):
"""Return a `str` version of this Content 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 ContentItem():
"""
An input content item that the service is to analyze.
:attr str content: The content that is to be analyzed. The service supports up
to 20 MB of content for all `ContentItem` objects combined.
:attr str id: (optional) A unique identifier for this content item.
:attr int created: (optional) A timestamp that identifies when this content was
created. Specify a value in milliseconds since the UNIX Epoch (January 1, 1970,
at 0:00 UTC). Required only for results that include temporal behavior data.
:attr int updated: (optional) A timestamp that identifies when this content was
last updated. Specify a value in milliseconds since the UNIX Epoch (January 1,
1970, at 0:00 UTC). Required only for results that include temporal behavior
data.
:attr str contenttype: (optional) The MIME type of the content. The default is
plain text. The tags are stripped from HTML content before it is analyzed; plain
text is processed as submitted.
:attr str language: (optional) The language identifier (two-letter ISO 639-1
identifier) for the language of the content item. The default is `en` (English).
Regional variants are treated as their parent language; for example, `en-US` is
interpreted as `en`. A language specified with the **Content-Type** parameter
overrides the value of this parameter; any content items that specify a
different language are ignored. Omit the **Content-Type** parameter to base the
language on the most prevalent specification among the content items; again,
content items that specify a different language are ignored. You can specify any
combination of languages for the input and response content.
:attr str parentid: (optional) The unique ID of the parent content item for this
item. Used to identify hierarchical relationships between posts/replies,
messages/replies, and so on.
:attr bool reply: (optional) Indicates whether this content item is a reply to
another content item.
:attr bool forward: (optional) Indicates whether this content item is a
forwarded/copied version of another content item.
"""
def __init__(self,
content,
*,
id=None,
created=None,
updated=None,
contenttype=None,
language=None,
parentid=None,
reply=None,
forward=None):
"""
Initialize a ContentItem object.
:param str content: The content that is to be analyzed. The service
supports up to 20 MB of content for all `ContentItem` objects combined.
:param str id: (optional) A unique identifier for this content item.
:param int created: (optional) A timestamp that identifies when this
content was created. Specify a value in milliseconds since the UNIX Epoch
(January 1, 1970, at 0:00 UTC). Required only for results that include
temporal behavior data.
:param int updated: (optional) A timestamp that identifies when this
content was last updated. Specify a value in milliseconds since the UNIX
Epoch (January 1, 1970, at 0:00 UTC). Required only for results that
include temporal behavior data.
:param str contenttype: (optional) The MIME type of the content. The
default is plain text. The tags are stripped from HTML content before it is
analyzed; plain text is processed as submitted.
:param str language: (optional) The language identifier (two-letter ISO
639-1 identifier) for the language of the content item. The default is `en`
(English). Regional variants are treated as their parent language; for
example, `en-US` is interpreted as `en`. A language specified with the
**Content-Type** parameter overrides the value of this parameter; any
content items that specify a different language are ignored. Omit the
**Content-Type** parameter to base the language on the most prevalent
specification among the content items; again, content items that specify a
different language are ignored. You can specify any combination of
languages for the input and response content.
:param str parentid: (optional) The unique ID of the parent content item
for this item. Used to identify hierarchical relationships between
posts/replies, messages/replies, and so on.
:param bool reply: (optional) Indicates whether this content item is a
reply to another content item.
:param bool forward: (optional) Indicates whether this content item is a
forwarded/copied version of another content item.
"""
self.content = content
self.id = id
self.created = created
self.updated = updated
self.contenttype = contenttype
self.language = language
self.parentid = parentid
self.reply = reply
self.forward = forward
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ContentItem object from a json dictionary."""
args = {}
valid_keys = [
'content', 'id', 'created', 'updated', 'contenttype', 'language',
'parentid', 'reply', 'forward'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ContentItem: '
+ ', '.join(bad_keys))
if 'content' in _dict:
args['content'] = _dict.get('content')
else:
raise ValueError(
'Required property \'content\' not present in ContentItem JSON')
if 'id' in _dict:
args['id'] = _dict.get('id')
if 'created' in _dict:
args['created'] = _dict.get('created')
if 'updated' in _dict:
args['updated'] = _dict.get('updated')
if 'contenttype' in _dict:
args['contenttype'] = _dict.get('contenttype')
if 'language' in _dict:
args['language'] = _dict.get('language')
if 'parentid' in _dict:
args['parentid'] = _dict.get('parentid')
if 'reply' in _dict:
args['reply'] = _dict.get('reply')
if 'forward' in _dict:
args['forward'] = _dict.get('forward')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'content') and self.content is not None:
_dict['content'] = self.content
if hasattr(self, 'id') and self.id is not None:
_dict['id'] = self.id
if hasattr(self, 'created') and self.created is not None:
_dict['created'] = self.created
if hasattr(self, 'updated') and self.updated is not None:
_dict['updated'] = self.updated
if hasattr(self, 'contenttype') and self.contenttype is not None:
_dict['contenttype'] = self.contenttype
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'parentid') and self.parentid is not None:
_dict['parentid'] = self.parentid
if hasattr(self, 'reply') and self.reply is not None:
_dict['reply'] = self.reply
if hasattr(self, 'forward') and self.forward is not None:
_dict['forward'] = self.forward
return _dict
def __str__(self):
"""Return a `str` version of this ContentItem 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 ContenttypeEnum(Enum):
"""
The MIME type of the content. The default is plain text. The tags are stripped
from HTML content before it is analyzed; plain text is processed as submitted.
"""
TEXT_PLAIN = "text/plain"
TEXT_HTML = "text/html"
[docs] class LanguageEnum(Enum):
"""
The language identifier (two-letter ISO 639-1 identifier) for the language of the
content item. The default is `en` (English). Regional variants are treated as
their parent language; for example, `en-US` is interpreted as `en`. A language
specified with the **Content-Type** parameter overrides the value of this
parameter; any content items that specify a different language are ignored. Omit
the **Content-Type** parameter to base the language on the most prevalent
specification among the content items; again, content items that specify a
different language are ignored. You can specify any combination of languages for
the input and response content.
"""
AR = "ar"
EN = "en"
ES = "es"
JA = "ja"
KO = "ko"
[docs]class Profile():
"""
The personality profile that the service generated for the input content.
:attr str processed_language: The language model that was used to process the
input.
:attr int word_count: The number of words from the input that were used to
produce the profile.
:attr str word_count_message: (optional) When guidance is appropriate, a string
that provides a message that indicates the number of words found and where that
value falls in the range of required or suggested number of words.
:attr list[Trait] personality: A recursive array of `Trait` objects that
provides detailed results for the Big Five personality characteristics
(dimensions and facets) inferred from the input text.
:attr list[Trait] needs: Detailed results for the Needs characteristics inferred
from the input text.
:attr list[Trait] values: Detailed results for the Values characteristics
inferred from the input text.
:attr list[Behavior] behavior: (optional) For JSON content that is timestamped,
detailed results about the social behavior disclosed by the input in terms of
temporal characteristics. The results include information about the distribution
of the content over the days of the week and the hours of the day.
:attr list[ConsumptionPreferencesCategory] consumption_preferences: (optional)
If the **consumption_preferences** parameter is `true`, detailed results for
each category of consumption preferences. Each element of the array provides
information inferred from the input text for the individual preferences of that
category.
:attr list[Warning] warnings: An array of warning messages that are associated
with the input text for the request. The array is empty if the input generated
no warnings.
"""
def __init__(self,
processed_language,
word_count,
personality,
needs,
values,
warnings,
*,
word_count_message=None,
behavior=None,
consumption_preferences=None):
"""
Initialize a Profile object.
:param str processed_language: The language model that was used to process
the input.
:param int word_count: The number of words from the input that were used to
produce the profile.
:param list[Trait] personality: A recursive array of `Trait` objects that
provides detailed results for the Big Five personality characteristics
(dimensions and facets) inferred from the input text.
:param list[Trait] needs: Detailed results for the Needs characteristics
inferred from the input text.
:param list[Trait] values: Detailed results for the Values characteristics
inferred from the input text.
:param list[Warning] warnings: An array of warning messages that are
associated with the input text for the request. The array is empty if the
input generated no warnings.
:param str word_count_message: (optional) When guidance is appropriate, a
string that provides a message that indicates the number of words found and
where that value falls in the range of required or suggested number of
words.
:param list[Behavior] behavior: (optional) For JSON content that is
timestamped, detailed results about the social behavior disclosed by the
input in terms of temporal characteristics. The results include information
about the distribution of the content over the days of the week and the
hours of the day.
:param list[ConsumptionPreferencesCategory] consumption_preferences:
(optional) If the **consumption_preferences** parameter is `true`, detailed
results for each category of consumption preferences. Each element of the
array provides information inferred from the input text for the individual
preferences of that category.
"""
self.processed_language = processed_language
self.word_count = word_count
self.word_count_message = word_count_message
self.personality = personality
self.needs = needs
self.values = values
self.behavior = behavior
self.consumption_preferences = consumption_preferences
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Profile object from a json dictionary."""
args = {}
valid_keys = [
'processed_language', 'word_count', 'word_count_message',
'personality', 'needs', 'values', 'behavior',
'consumption_preferences', 'warnings'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Profile: ' +
', '.join(bad_keys))
if 'processed_language' in _dict:
args['processed_language'] = _dict.get('processed_language')
else:
raise ValueError(
'Required property \'processed_language\' not present in Profile JSON'
)
if 'word_count' in _dict:
args['word_count'] = _dict.get('word_count')
else:
raise ValueError(
'Required property \'word_count\' not present in Profile JSON')
if 'word_count_message' in _dict:
args['word_count_message'] = _dict.get('word_count_message')
if 'personality' in _dict:
args['personality'] = [
Trait._from_dict(x) for x in (_dict.get('personality'))
]
else:
raise ValueError(
'Required property \'personality\' not present in Profile JSON')
if 'needs' in _dict:
args['needs'] = [Trait._from_dict(x) for x in (_dict.get('needs'))]
else:
raise ValueError(
'Required property \'needs\' not present in Profile JSON')
if 'values' in _dict:
args['values'] = [
Trait._from_dict(x) for x in (_dict.get('values'))
]
else:
raise ValueError(
'Required property \'values\' not present in Profile JSON')
if 'behavior' in _dict:
args['behavior'] = [
Behavior._from_dict(x) for x in (_dict.get('behavior'))
]
if 'consumption_preferences' in _dict:
args['consumption_preferences'] = [
ConsumptionPreferencesCategory._from_dict(x)
for x in (_dict.get('consumption_preferences'))
]
if 'warnings' in _dict:
args['warnings'] = [
Warning._from_dict(x) for x in (_dict.get('warnings'))
]
else:
raise ValueError(
'Required property \'warnings\' not present in Profile JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(
self,
'processed_language') and self.processed_language is not None:
_dict['processed_language'] = self.processed_language
if hasattr(self, 'word_count') and self.word_count is not None:
_dict['word_count'] = self.word_count
if hasattr(
self,
'word_count_message') and self.word_count_message is not None:
_dict['word_count_message'] = self.word_count_message
if hasattr(self, 'personality') and self.personality is not None:
_dict['personality'] = [x._to_dict() for x in self.personality]
if hasattr(self, 'needs') and self.needs is not None:
_dict['needs'] = [x._to_dict() for x in self.needs]
if hasattr(self, 'values') and self.values is not None:
_dict['values'] = [x._to_dict() for x in self.values]
if hasattr(self, 'behavior') and self.behavior is not None:
_dict['behavior'] = [x._to_dict() for x in self.behavior]
if hasattr(self, 'consumption_preferences'
) and self.consumption_preferences is not None:
_dict['consumption_preferences'] = [
x._to_dict() for x in self.consumption_preferences
]
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = [x._to_dict() for x in self.warnings]
return _dict
def __str__(self):
"""Return a `str` version of this Profile 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 ProcessedLanguageEnum(Enum):
"""
The language model that was used to process the input.
"""
AR = "ar"
EN = "en"
ES = "es"
JA = "ja"
KO = "ko"
[docs]class Trait():
"""
The characteristics that the service inferred from the input content.
:attr str trait_id: The unique, non-localized identifier of the characteristic
to which the results pertain. IDs have the form
* `big5_{characteristic}` for Big Five personality dimensions
* `facet_{characteristic}` for Big Five personality facets
* `need_{characteristic}` for Needs
*`value_{characteristic}` for Values.
:attr str name: The user-visible, localized name of the characteristic.
:attr str category: The category of the characteristic: `personality` for Big
Five personality characteristics, `needs` for Needs, and `values` for Values.
:attr float percentile: The normalized percentile score for the characteristic.
The range is 0 to 1. For example, if the percentage for Openness is 0.60, the
author scored in the 60th percentile; the author is more open than 59 percent of
the population and less open than 39 percent of the population.
:attr float raw_score: (optional) The raw score for the characteristic. The
range is 0 to 1. A higher score generally indicates a greater likelihood that
the author has that characteristic, but raw scores must be considered in
aggregate: The range of values in practice might be much smaller than 0 to 1, so
an individual score must be considered in the context of the overall scores and
their range.
The raw score is computed based on the input and the service model; it is not
normalized or compared with a sample population. The raw score enables
comparison of the results against a different sampling population and with a
custom normalization approach.
:attr bool significant: (optional) **`2017-10-13`**: Indicates whether the
characteristic is meaningful for the input language. The field is always `true`
for all characteristics of English, Spanish, and Japanese input. The field is
`false` for the subset of characteristics of Arabic and Korean input for which
the service's models are unable to generate meaningful results.
**`2016-10-19`**: Not returned.
:attr list[Trait] children: (optional) For `personality` (Big Five) dimensions,
more detailed results for the facets of each dimension as inferred from the
input text.
"""
def __init__(self,
trait_id,
name,
category,
percentile,
*,
raw_score=None,
significant=None,
children=None):
"""
Initialize a Trait object.
:param str trait_id: The unique, non-localized identifier of the
characteristic to which the results pertain. IDs have the form
* `big5_{characteristic}` for Big Five personality dimensions
* `facet_{characteristic}` for Big Five personality facets
* `need_{characteristic}` for Needs
*`value_{characteristic}` for Values.
:param str name: The user-visible, localized name of the characteristic.
:param str category: The category of the characteristic: `personality` for
Big Five personality characteristics, `needs` for Needs, and `values` for
Values.
:param float percentile: The normalized percentile score for the
characteristic. The range is 0 to 1. For example, if the percentage for
Openness is 0.60, the author scored in the 60th percentile; the author is
more open than 59 percent of the population and less open than 39 percent
of the population.
:param float raw_score: (optional) The raw score for the characteristic.
The range is 0 to 1. A higher score generally indicates a greater
likelihood that the author has that characteristic, but raw scores must be
considered in aggregate: The range of values in practice might be much
smaller than 0 to 1, so an individual score must be considered in the
context of the overall scores and their range.
The raw score is computed based on the input and the service model; it is
not normalized or compared with a sample population. The raw score enables
comparison of the results against a different sampling population and with
a custom normalization approach.
:param bool significant: (optional) **`2017-10-13`**: Indicates whether the
characteristic is meaningful for the input language. The field is always
`true` for all characteristics of English, Spanish, and Japanese input. The
field is `false` for the subset of characteristics of Arabic and Korean
input for which the service's models are unable to generate meaningful
results. **`2016-10-19`**: Not returned.
:param list[Trait] children: (optional) For `personality` (Big Five)
dimensions, more detailed results for the facets of each dimension as
inferred from the input text.
"""
self.trait_id = trait_id
self.name = name
self.category = category
self.percentile = percentile
self.raw_score = raw_score
self.significant = significant
self.children = children
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Trait object from a json dictionary."""
args = {}
valid_keys = [
'trait_id', 'name', 'category', 'percentile', 'raw_score',
'significant', 'children'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Trait: ' +
', '.join(bad_keys))
if 'trait_id' in _dict:
args['trait_id'] = _dict.get('trait_id')
else:
raise ValueError(
'Required property \'trait_id\' not present in Trait JSON')
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in Trait JSON')
if 'category' in _dict:
args['category'] = _dict.get('category')
else:
raise ValueError(
'Required property \'category\' not present in Trait JSON')
if 'percentile' in _dict:
args['percentile'] = _dict.get('percentile')
else:
raise ValueError(
'Required property \'percentile\' not present in Trait JSON')
if 'raw_score' in _dict:
args['raw_score'] = _dict.get('raw_score')
if 'significant' in _dict:
args['significant'] = _dict.get('significant')
if 'children' in _dict:
args['children'] = [
Trait._from_dict(x) for x in (_dict.get('children'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'trait_id') and self.trait_id is not None:
_dict['trait_id'] = self.trait_id
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'category') and self.category is not None:
_dict['category'] = self.category
if hasattr(self, 'percentile') and self.percentile is not None:
_dict['percentile'] = self.percentile
if hasattr(self, 'raw_score') and self.raw_score is not None:
_dict['raw_score'] = self.raw_score
if hasattr(self, 'significant') and self.significant is not None:
_dict['significant'] = self.significant
if hasattr(self, 'children') and self.children is not None:
_dict['children'] = [x._to_dict() for x in self.children]
return _dict
def __str__(self):
"""Return a `str` version of this Trait 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 CategoryEnum(Enum):
"""
The category of the characteristic: `personality` for Big Five personality
characteristics, `needs` for Needs, and `values` for Values.
"""
PERSONALITY = "personality"
NEEDS = "needs"
VALUES = "values"
[docs]class Warning():
"""
A warning message that is associated with the input content.
:attr str warning_id: The identifier of the warning message.
:attr str message: The message associated with the `warning_id`:
* `WORD_COUNT_MESSAGE`: "There were {number} words in the input. We need a
minimum of 600, preferably 1,200 or more, to compute statistically significant
estimates."
* `JSON_AS_TEXT`: "Request input was processed as text/plain as indicated,
however detected a JSON input. Did you mean application/json?"
* `CONTENT_TRUNCATED`: "For maximum accuracy while also optimizing processing
time, only the first 250KB of input text (excluding markup) was analyzed.
Accuracy levels off at approximately 3,000 words so this did not affect the
accuracy of the profile."
* `PARTIAL_TEXT_USED`, "The text provided to compute the profile was trimmed for
performance reasons. This action does not affect the accuracy of the output, as
not all of the input text was required." Applies only when Arabic input text
exceeds a threshold at which additional words do not contribute to the accuracy
of the profile.
"""
def __init__(self, warning_id, message):
"""
Initialize a Warning object.
:param str warning_id: The identifier of the warning message.
:param str message: The message associated with the `warning_id`:
* `WORD_COUNT_MESSAGE`: "There were {number} words in the input. We need a
minimum of 600, preferably 1,200 or more, to compute statistically
significant estimates."
* `JSON_AS_TEXT`: "Request input was processed as text/plain as indicated,
however detected a JSON input. Did you mean application/json?"
* `CONTENT_TRUNCATED`: "For maximum accuracy while also optimizing
processing time, only the first 250KB of input text (excluding markup) was
analyzed. Accuracy levels off at approximately 3,000 words so this did not
affect the accuracy of the profile."
* `PARTIAL_TEXT_USED`, "The text provided to compute the profile was
trimmed for performance reasons. This action does not affect the accuracy
of the output, as not all of the input text was required." Applies only
when Arabic input text exceeds a threshold at which additional words do not
contribute to the accuracy of the profile.
"""
self.warning_id = warning_id
self.message = message
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Warning object from a json dictionary."""
args = {}
valid_keys = ['warning_id', 'message']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Warning: ' +
', '.join(bad_keys))
if 'warning_id' in _dict:
args['warning_id'] = _dict.get('warning_id')
else:
raise ValueError(
'Required property \'warning_id\' not present in Warning JSON')
if 'message' in _dict:
args['message'] = _dict.get('message')
else:
raise ValueError(
'Required property \'message\' not present in Warning JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'warning_id') and self.warning_id is not None:
_dict['warning_id'] = self.warning_id
if hasattr(self, 'message') and self.message is not None:
_dict['message'] = self.message
return _dict
def __str__(self):
"""Return a `str` version of this Warning 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 WarningIdEnum(Enum):
"""
The identifier of the warning message.
"""
WORD_COUNT_MESSAGE = "WORD_COUNT_MESSAGE"
JSON_AS_TEXT = "JSON_AS_TEXT"
CONTENT_TRUNCATED = "CONTENT_TRUNCATED"
PARTIAL_TEXT_USED = "PARTIAL_TEXT_USED"