Metadata-Version: 1.1
Name: IndicoIo
Version: 0.5.1
Summary: 
        A Python Wrapper for indico.
        Use pre-built state of the art machine learning algorithms with a single line of code.
    
Home-page: https://github.com/IndicoDataSolutions/indicoio-python
Author: Alec Radford, Slater Victoroff, Aidan McLaughlin, Anne Carlson
Author-email: 
        Alec Radford <alec@indico.io>,
        Slater Victoroff <slater@indico.io>,
        Aidan McLaughlin <aidan@indico.io>,
        Anne Carlson <annie@indico.io>

    
License: MIT License (See LICENSE)
Description: indicoio-python
        ===============
        
        A wrapper for a series of APIs made by indico.
        
        Check out the main site on:
        
        http://indico.io
        
        Our APIs are totally free to use, and ready to be used in your
        application. No data or training required.
        
        Installation
        ------------
        
        ::
        
            pip install indicoio
        
        Documentation
        -------------
        
        Available at `indico.reame.io <http://indico.readme.io/v1.0/docs>`__
        
        Current APIs
        ------------
        
        Right now this wrapper supports the following apps:
        
        -  Positive/Negative Sentiment Analysis
        -  Political Sentiment Analysis
        -  Image Feature Extraction
        -  Facial Emotion Recognition
        -  Facial Feature Extraction
        -  Language Detection
        -  Text Topic Tagging
        
        Examples
        --------
        
        ::
        
            >>> import numpy as np
        
            >>> from indicoio import political, sentiment, fer, facial_features, language
        
            >>> political("Guns don't kill people. People kill people.")
            {u'Libertarian': 0.47740164630834825, u'Green': 0.08454409540443657, u'Liberal': 0.16617097211030055, u'Conservative': 0.2718832861769146}
        
            >>> sentiment('Worst movie ever.')
            {u'Sentiment': 0.07062467665597527}
        
            >>> sentiment('Really enjoyed the movie.')
            {u'Sentiment': 0.8105182526856075}
        
            >>> test_text = "Facebook blog posts about Android tech make better journalism than most news outlets."
        
            >>> tag_dict = text_tags(test_text)
        
            >>> sorted(tag_dict.keys(), key=lambda x: tag_dict[x], reverse=True)[:3]
            [u'startups_and_entrepreneurship', u'investment', u'business']
        
            >>> text_tags(test_text, threshold=0.1) # return only keys with value > 0.1
            {u'startups_and_entrepreneurship': 0.21888586688354486}
        
            >>> text_tags(test_text, top_n=1) # return only keys with top_n values
            {u'startups_and_entrepreneurship': 0.21888586688354486}
        
            >>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
        
            >>> fer(test_face)
            {u'Angry': 0.08843749137458341, u'Sad': 0.39091163159204684, u'Neutral': 0.1947947999669361, u'Surprise': 0.03443785859010413, u'Fear': 0.17574534848440568, u'Happy': 0.11567286999192382}
        
            >>> facial_features(test_face)
            [0.0, -0.02568680526917187, 0.21645604230056517, -0.1519435786033145, -0.5648621854611555, 3.0607368045577226, 0.11434321880792693, -0.02163810928547493, -0.44224330594186484, 0.3024315632285246, -2.6068048934495276, 2.497798330306638, 3.040558335205844, 0.741045340525325, 0.37198135618478817, -0.33132377802172325, -0.9804190889833034, 0.5046575784709395, -0.5609132323152847, 1.679107064439151, 0.6825037853544341, -1.5977176226648016, 1.8959464303080562, -0.7812860715595836, -2.998394007543733, -0.22637273967347724, -0.9642457010679496, 1.4557274834236749, 2.412244419186633, 2.3151771738421965, 0.7881483386786367, 1.6622850935863422, 0.1304768990234367, 1.9344501393866649, 3.1271558035162914, -0.10250886439220543, 1.4921395116492966, 2.761645355670677, 1.6903473594991179, 1.009209807271491, 0.07273926986120445, -1.4941708135718021, -2.082786362439631, 1.0160924044870847, 2.5326580674673895, -0.8328208491083264, 2.0390177029762935, 3.0342637531932777]
        
            >>> language_dict = language('Quis custodiet ipsos custodes')
        
            >>> sorted(language_dict.keys(), key=lambda x: language_dict[x], reverse=True)[:5]
            [u'Latin', u'Dutch', u'Greek', u'Portuguese', u'Spanish']
        
            >>> language_dict
            {u'Swedish': 0.00033330636691921914, u'Lithuanian': 0.007328693814717631, u'Vietnamese': 0.0002686116137658802, u'Romanian': 8.133913804076592e-06, ...}
        
        
            Batch API Access
            ----------------
        
            If you'd like to use our batch api interface, please send an email to contact@indico.io.
        
                    from indicio import batch\_sentiment batch\_sentiment(['Text
                    to analyze', 'More text'], auth=("example@example.com",
                    "\*\*\*\*\*\*\*\*"))
        
        ::
        
        
            Authentication credentials can also be set as the environment variables "INDICO_USERNAME" and "INDICO_PASSWORD" or as 'username' and 'password' in the indicorc file.
        
            Private cloud API Access
            ------------------------
        
            If you're looking to use indico's API for high throughput applications, please contact contact@indico.io about our private cloud option.
        
                    from indicio import sentiment sentiment("Text to analyze",
                    cloud="example", auth=("example@example.com",
                    "\*\*\*\*\*\*\*\*"))
        
        ::
        
        
            The `cloud` parameter redirects API calls to your private cloud hosted at [cloud].indico.domains. 
        
            Private cloud subdomains can also be set as the environment variable "INDICO_CLOUD" or as 'cloud' in the indicorc file.
        
            Configuration
            ------------------------
        
            Indicoio-python will search ./.indicorc and $HOME/.indicorc for the optional configuration file. Values in the local configuration file (./.indicorc) take precedence over those found in a global configuration file ($HOME/.indicorc). The indicorc file can be used to set an authentication username and password or a private cloud subdomain, so these arguments don't need to be specified for every api call. All sections are optional.
        
            Here is an example of a valid indicorc file:
        
        [auth] username = test@example.com password = secret
        
        [private\_cloud] cloud = example \`\`\`
        
        Environment variables take precedence over any configuration found in
        the indicorc file. The following environment variables are valid: -
        $INDICO\_USERNAME - $INDICO\_PASSWORD - $INDICO\_CLOUD
        
        Finally, any values explicitly passed in to an api call will override
        configuration options set in the indicorc file or in an environment
        variable.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries :: Python Modules
