Metadata-Version: 1.0
Name: IndicoIo
Version: 0.4.12
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
Author-email: 
        Alec Radford <alec@indicodatasolutions.com>,
        Slater Victoroff <slater@indicodatasolutions.com>,
        Aidan McLaughlin <aidan@indicodatasolutions.com>
    
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
        
        Check out our documentation on:
        
        http://indicoiopython.s3-website-us-west-2.amazonaws.com/indicoio.html
        
        Our APIs are totally free to use, and ready to be used in your application. No data or training required.
        
        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}
        
        >>> tag_dict = text_tags("Facebook blog posts about Android tech make better journalism than most news outlets.")
        
        >>> sorted(tag_dict.keys(), key=lambda x: tag_dict[x], reverse=True)[:5]
        [u'investing', u'startups', u'business', u'entrepreneur', u'humor']
        
        >>> tag_dict
        {u'fashion': 0.011450126534350728, u'art': 0.00358698972755963, u'energy': 0.005537894035625527, ...}
        
        >>> 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, ...}
        
        ```
        
        If you have a local indico server running, simply import from `indicoio.local`.
        
        ```
        >>> from indicoio.local import political, sentiment, fer, facial_features, language
        ```
        
        Installation
        ------------
        ```
        pip install indicoio
        ```
        
        Announcement: Indico has partnered with Experfy, a data science consulting marketplace based in the Harvard Innovation Lab.  Through Experfy, we are helping our data science community members find lucrative projects and advance their skills. Please signup for Experfy at https://www.experfy.com/ to get started.
Platform: UNKNOWN
