# coding: utf-8
# Copyright 2018 IBM All Rights Reserved.
#
# 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://console.bluemix.net/docs/services/personality-insights/models.html).
* For information about the meaning of the consumption preferences, see [Consumption
preferences](https://console.bluemix.net/docs/services/personality-insights/preferences.html).
**Note:** Request logging is disabled for the Personality Insights service. The service
neither logs nor retains data from requests and responses, regardless of whether the
`X-Watson-Learning-Opt-Out` request header is set.
"""
from __future__ import absolute_import
import json
from .watson_service import WatsonService
##############################################################################
# Service
##############################################################################
[docs]class PersonalityInsightsV3(WatsonService):
"""The Personality Insights V3 service."""
default_url = 'https://gateway.watsonplatform.net/personality-insights/api'
def __init__(
self,
version,
url=default_url,
username=None,
password=None,
iam_apikey=None,
iam_access_token=None,
iam_url=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 str url: The base url to use when contacting the service (e.g.
"https://gateway.watsonplatform.net/personality-insights/api").
The base url may differ between Bluemix regions.
:param str username: The username used to authenticate with the service.
Username and password credentials are only required to run your
application locally or outside of Bluemix. When running on
Bluemix, the credentials will be automatically loaded from the
`VCAP_SERVICES` environment variable.
:param str password: The password used to authenticate with the service.
Username and password credentials are only required to run your
application locally or outside of Bluemix. When running on
Bluemix, the credentials will be automatically loaded from the
`VCAP_SERVICES` environment variable.
:param str iam_apikey: An API key that can be used to request IAM tokens. If
this API key is provided, the SDK will manage the token and handle the
refreshing.
:param str iam_access_token: An IAM access token is fully managed by the application.
Responsibility falls on the application to refresh the token, either before
it expires or reactively upon receiving a 401 from the service as any requests
made with an expired token will fail.
:param str iam_url: An optional URL for the IAM service API. Defaults to
'https://iam.bluemix.net/identity/token'.
"""
WatsonService.__init__(
self,
vcap_services_name='personality_insights',
url=url,
username=username,
password=password,
iam_apikey=iam_apikey,
iam_access_token=iam_access_token,
iam_url=iam_url,
use_vcap_services=True)
self.version = version
#########################
# Methods
#########################
[docs] def profile(self,
content,
content_type,
accept=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; for more information, see [Providing sufficient
input](https://console.bluemix.net/docs/services/personality-insights/input.html#sufficient).
The service analyzes text in Arabic, English, Japanese, Korean, or Spanish and
returns its results in a variety of languages. You can provide plain text, HTML,
or JSON input by specifying the **Content-Type** parameter; the default is
`text/plain`. Request a JSON or comma-separated values (CSV) response by
specifying the **Accept** parameter; CSV output includes a fixed number of columns
and optional headers.
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:**
* [Requesting a
profile](https://console.bluemix.net/docs/services/personality-insights/input.html)
* [Understanding a JSON
profile](https://console.bluemix.net/docs/services/personality-insights/output.html)
* [Understanding a CSV
profile](https://console.bluemix.net/docs/services/personality-insights/output-csv.html).
: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://console.bluemix.net/docs/services/personality-insights/input.html#sufficient).
For JSON input, provide an object of type `Content`.
:param str content_type: The type of the input. A character encoding can be
specified by including a `charset` parameter. For example,
'text/html;charset=utf-8'.
:param str accept: The type of the response: application/json or text/csv. A
character encoding can be specified by including a `charset` parameter. For
example, 'text/csv;charset=utf-8'.
:param str content_language: 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: 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: 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: Indicates whether column labels are returned with a CSV
response. By default, no column labels are returned. Applies only when the
**Accept** parameter is set to `text/csv`.
:param bool consumption_preferences: 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 content_type is None:
raise ValueError('content_type must be provided')
if isinstance(content, Content):
content = self._convert_model(content, Content)
headers = {
'Content-Type': content_type,
'Accept': accept,
'Content-Language': content_language,
'Accept-Language': accept_language
}
if 'headers' in kwargs:
headers.update(kwargs.get('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'
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=(accept is None or accept == 'application/json'))
return response
##############################################################################
# Models
##############################################################################
[docs]class Behavior(object):
"""
Behavior.
: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 = {}
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(object):
"""
ConsumptionPreferences.
: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 = {}
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(object):
"""
ConsumptionPreferencesCategory.
: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 = {}
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(object):
"""
Content.
: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 = {}
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(object):
"""
ContentItem.
: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 = {}
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 Profile(object):
"""
Profile.
: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: Warning messages associated with the input text
submitted with 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: Warning messages associated with the input text
submitted with 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 = {}
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 Trait(object):
"""
Trait.
: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 = {}
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 Warning(object):
"""
Warning.
: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 = {}
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