# coding: utf-8
# Copyright 2017 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.
"""
### Service Overview
The IBM Watson Personality Insights service provides a Representational State Transfer
(REST) Application Programming Interface (API) that 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 report consumption preferences based on the results of
its analysis, and for JSON content that is timestamped, it can report temporal behavior.
### API Usage
The following information provides details about using the service to obtain a personality
profile:
* **The profile method:** The service offers a single `/v3/profile` method that accepts up
to 20 MB of input data and produces results in JSON or CSV format. The service accepts
input in Arabic, English, Japanese, Korean, or Spanish and can produce output in a variety
of languages.
* **Authentication:** You authenticate to the service by using your service credentials.
You can use your credentials to authenticate via a proxy server that resides in Bluemix,
or you can use your credentials to obtain a token and contact the service directly. See
[Service credentials for Watson
services](https://console.bluemix.net/docs/services/watson/getting-started-credentials.html)
and [Tokens for
authentication](https://console.bluemix.net/docs/services/watson/getting-started-tokens.html).
* **Request Logging:** By default, all Watson services log requests and their results.
Data is collected only to improve the Watson services. If you do not want to share your
data, set the header parameter `X-Watson-Learning-Opt-Out` to `true` for each request.
Data is collected for any request that omits this header. See [Controlling request logging
for Watson
services](https://console.bluemix.net/docs/services/watson/getting-started-logging.html).
For more information about the service, see [About Personality
Insights](https://console.bluemix.net/docs/services/personality-insights/index.html). For
information about calling the service and the responses it can generate, see [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), and
[Understanding a CSV
profile](https://console.bluemix.net/docs/services/personality-insights/output-csv.html).
"""
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):
"""
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.
"""
WatsonService.__init__(
self,
vcap_services_name='personality_insights',
url=url,
username=username,
password=password,
use_vcap_services=True)
self.version = version
#########################
# personalityinsights
#########################
[docs] def profile(self,
content,
content_type='application/json',
content_language=None,
accept='application/json',
accept_language=None,
raw_scores=None,
csv_headers=None,
consumption_preferences=None):
"""
Generates a personality profile based on input text.
Derives personality insights for up to 20 MB of input content written by an
author, though the service 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).
Accepts input in Arabic, English, Japanese, Korean, or Spanish and produces output
in one of eleven languages. Provide plain text, HTML, or JSON content, and receive
results in JSON or CSV format.
: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). A JSON request must conform to the `Content` model.
:param str content_type: The type of the input: application/json, text/html, or text/plain. A character encoding can be specified by including a `charset` parameter. For example, 'text/html;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` header depends on the `Content-Type` header. 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 header 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 accept: Type of the response: 'application/json' (default) or 'text/csv'
: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: If `true`, a raw score in addition to a normalized percentile is returned for each characteristic; raw scores are not compared with a sample population. If `false` (the default), only normalized percentiles are returned.
:param bool csv_headers: If `true`, column labels are returned with a CSV response; if `false` (the default), they are not. Applies only when the `Accept` header is set to `text/csv`.
:param bool consumption_preferences: If `true`, information about consumption preferences is returned with the results; if `false` (the default), the response does not include the information.
:return: A `dict` containing the `Profile` response.
:rtype: dict
"""
if content is None:
raise ValueError('content must be provided')
if content_type is None:
raise ValueError('content_type must be provided')
headers = {
'content-type': content_type,
'Content-Language': content_language,
'Accept-Language': accept_language
}
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 identifier of the characteristic to which the results pertain. IDs have the form `behavior_{value}`.
:attr str name: The user-visible 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 identifier of the characteristic to which the results pertain. IDs have the form `behavior_{value}`.
:param str name: The user-visible 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['trait_id']
else:
raise ValueError(
'Required property \'trait_id\' not present in Behavior JSON')
if 'name' in _dict:
args['name'] = _dict['name']
else:
raise ValueError(
'Required property \'name\' not present in Behavior JSON')
if 'category' in _dict:
args['category'] = _dict['category']
else:
raise ValueError(
'Required property \'category\' not present in Behavior JSON')
if 'percentage' in _dict:
args['percentage'] = _dict['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 identifier of the consumption preference to which the results pertain. IDs have the form `consumption_preferences_{preference}`.
:attr str name: The user-visible 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 identifier of the consumption preference to which the results pertain. IDs have the form `consumption_preferences_{preference}`.
:param str name: The user-visible 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[
'consumption_preference_id']
else:
raise ValueError(
'Required property \'consumption_preference_id\' not present in ConsumptionPreferences JSON'
)
if 'name' in _dict:
args['name'] = _dict['name']
else:
raise ValueError(
'Required property \'name\' not present in ConsumptionPreferences JSON'
)
if 'score' in _dict:
args['score'] = _dict['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 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 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[
'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['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['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['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: Content that is to be analyzed. The service supports up to 20 MB of content for all items combined.
:attr str id: (optional) Unique identifier for this content item.
:attr int created: (optional) 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) 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) 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) 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` header overrides the value of this parameter; any content items that specify a different language are ignored. Omit the `Content-Type` header 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) 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: Content that is to be analyzed. The service supports up to 20 MB of content for all items combined.
:param str id: (optional) Unique identifier for this content item.
:param int created: (optional) 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) 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) 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) 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` header overrides the value of this parameter; any content items that specify a different language are ignored. Omit the `Content-Type` header 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) 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['content']
else:
raise ValueError(
'Required property \'content\' not present in ContentItem JSON')
if 'id' in _dict:
args['id'] = _dict['id']
if 'created' in _dict:
args['created'] = _dict['created']
if 'updated' in _dict:
args['updated'] = _dict['updated']
if 'contenttype' in _dict:
args['contenttype'] = _dict['contenttype']
if 'language' in _dict:
args['language'] = _dict['language']
if 'parentid' in _dict:
args['parentid'] = _dict['parentid']
if 'reply' in _dict:
args['reply'] = _dict['reply']
if 'forward' in _dict:
args['forward'] = _dict['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 that were found in the input.
: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: Detailed results for the Big Five personality characteristics (dimensions and facets) inferred from the input text.
:attr list[Trait] values: Detailed results for the Needs characteristics inferred from the input text.
:attr list[Trait] needs: 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` query 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,
values,
needs,
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 that were found in the input.
:param list[Trait] personality: Detailed results for the Big Five personality characteristics (dimensions and facets) inferred from the input text.
:param list[Trait] values: Detailed results for the Needs characteristics inferred from the input text.
:param list[Trait] needs: 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` query 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.values = values
self.needs = needs
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['processed_language']
else:
raise ValueError(
'Required property \'processed_language\' not present in Profile JSON'
)
if 'word_count' in _dict:
args['word_count'] = _dict['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['word_count_message']
if 'personality' in _dict:
args['personality'] = [
Trait._from_dict(x) for x in _dict['personality']
]
else:
raise ValueError(
'Required property \'personality\' not present in Profile JSON')
if 'values' in _dict:
args['values'] = [Trait._from_dict(x) for x in _dict['values']]
else:
raise ValueError(
'Required property \'values\' not present in Profile JSON')
if 'needs' in _dict:
args['needs'] = [Trait._from_dict(x) for x in _dict['needs']]
else:
raise ValueError(
'Required property \'needs\' not present in Profile JSON')
if 'behavior' in _dict:
args['behavior'] = [
Behavior._from_dict(x) for x in _dict['behavior']
]
if 'consumption_preferences' in _dict:
args['consumption_preferences'] = [
ConsumptionPreferencesCategory._from_dict(x)
for x in _dict['consumption_preferences']
]
if 'warnings' in _dict:
args['warnings'] = [
Warning._from_dict(x) for x in _dict['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, 'values') and self.values is not None:
_dict['values'] = [x._to_dict() for x in self.values]
if hasattr(self, 'needs') and self.needs is not None:
_dict['needs'] = [x._to_dict() for x in self.needs]
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 identifier of the characteristic to which the results pertain. IDs have the form `big5_{characteristic}` for Big Five personality characteristics, `need_{characteristic}` for Needs, or `value_{characteristic}` for Values.
:attr str name: The user-visible name of the characteristic.
:attr str category: The category of the characteristic: * `personality` for Big Five personality characteristics * `needs` for Needs * `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. **`2016-10-20`**: 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 identifier of the characteristic to which the results pertain. IDs have the form `big5_{characteristic}` for Big Five personality characteristics, `need_{characteristic}` for Needs, or `value_{characteristic}` for Values.
:param str name: The user-visible name of the characteristic.
:param str category: The category of the characteristic: * `personality` for Big Five personality characteristics * `needs` for Needs * `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. **`2016-10-20`**: 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['trait_id']
else:
raise ValueError(
'Required property \'trait_id\' not present in Trait JSON')
if 'name' in _dict:
args['name'] = _dict['name']
else:
raise ValueError(
'Required property \'name\' not present in Trait JSON')
if 'category' in _dict:
args['category'] = _dict['category']
else:
raise ValueError(
'Required property \'category\' not present in Trait JSON')
if 'percentile' in _dict:
args['percentile'] = _dict['percentile']
else:
raise ValueError(
'Required property \'percentile\' not present in Trait JSON')
if 'raw_score' in _dict:
args['raw_score'] = _dict['raw_score']
if 'significant' in _dict:
args['significant'] = _dict['significant']
if 'children' in _dict:
args['children'] = [Trait._from_dict(x) for x in _dict['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['warning_id']
else:
raise ValueError(
'Required property \'warning_id\' not present in Warning JSON')
if 'message' in _dict:
args['message'] = _dict['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