Metadata-Version: 1.1
Name: IndicoIo
Version: 0.11.1
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, Madison May, Anne Carlson
Author-email: 
        Alec Radford <alec@indico.io>,
        Slater Victoroff <slater@indico.io>,
        Aidan McLaughlin <aidan@indico.io>,
        Madison May <madison@indico.io>,
        Anne Carlson <annie@indico.io>
    
License: MIT License (See LICENSE)
Description: indicoio-python
        ===============
        
        A wrapper for the `indico API <http://indico.io>`__.
        
        The indico API is free to use, and no training data is required.
        
        Installation
        ------------
        
        From PyPI:
        
        .. code:: bash
        
            pip install indicoio
        
        From source:
        
        .. code:: bash
        
            git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git
            python setup.py install
        
        API Keys + Setup
        ----------------
        
        For API key registration and setup, checkout our `quickstart
        guide <http://docs.indico.io/v2.0/docs/api-keys>`__.
        
        Full Documentation
        ------------------
        
        Detailed documentation and further code examples are available at
        `indico.io/docs <https://indico.io/docs>`__.
        
        Supported APIs:
        ---------------
        
        -  Positive/Negative Sentiment Analysis
        -  Political Sentiment Analysis
        -  Image Feature Extraction
        -  Facial Emotion Recognition
        -  Facial Feature Extraction
        -  Language Detection
        -  Text Topic Tagging
        
        Examples
        --------
        
        .. code:: python
        
            >>> from indicoio import political, sentiment, language, text_tags, keywords, fer, facial_features, image_features
        
            >>> indicoio.config.api_key = "YOUR_API_KEY"
        
            >>> 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.')
            0.07062467665597527
        
            >>> sentiment('Really enjoyed the movie.')
            0.8105182526856075
        
            >>> text_tags("Facebook blog posts about Android tech make better journalism than most news outlets.")
        
            >>> 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}
        
            >>> import numpy as np
        
            >>> test_face = np.linspace(0,50,48*48).reshape(48,48)
        
            >>> 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, ..., 3.0342637531932777]
        
            >>> language('Quis custodiet ipsos custodes')
            {u'Swedish': 0.00033330636691921914, u'Lithuanian': 0.007328693814717631, u'Vietnamese': 0.0002686116137658802, u'Romanian': 8.133913804076592e-06, ...}
        
            >>> keywords("Facebook blog posts about Android tech make better journalism than most news outlets.", top_n=3)
            {u'android': 0.10602030910588661,
             u'journalism': 0.13466866170166855,
             u'outlets': 0.13930405357808642}
        
        Batch API
        ---------
        
        Each ``indicoio`` function can process many examples with a single
        request. Simply pass in a list of inputs and receive a list of results
        in return.
        
        .. code:: python
        
            >>> from indicoio import sentiment
        
            >>> sentiment(['Best day ever', 'Worst day ever'])
            [0.9899001220871786, 0.005709885173415242]
        
        Calling multiple APIs with a single function
        --------------------------------------------
        
        There are two multiple API functions ``predict_text`` and
        ``predict_image``. These functions are similar to the existing api
        functions, but take in an additional ``apis`` argument as a list of
        strings of API names (defaults to all existing apis). ``predict_text``
        accepts a list of existing text APIs and vice versa for
        ``predict_image``. These functions also support batch as the other
        functions do.
        
        Accepted text API names: ``text_tags, political, sentiment, language``
        
        Accepted image API names: ``fer, facial_features, image_features``
        
        .. code:: python
        
            >>> from indicoio import predict_text, predict_image, predict_text, predict_image
        
            >>> predict_text('Best day ever', apis=["sentiment", "language"])
            {'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
        
            >>> predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
            {'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
        
            >>> import numpy as np
        
            >>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
        
            >>> predict_image(test_face, apis=["fer", "facial_features"])
            {'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
        
            >>> predict_image([test_face, test_face], apis=["fer", "facial_features"])
            {'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}
        
        
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
