IndicoIo 0.4.13

A Python Wrapper for indico. Use pre-built state of the art machine learning algorithms with a single line of code.

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}

>>> 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}

>>> 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
```

If you'd like to use our batch api interface, please send an email to [e-mail].

```
>>> from indicio import batch_sentiment
batch_sentiment(['Text to analyze', 'More text'], auth=("[e-mail]", "********"))
```


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.