mirror of
https://github.com/wassname/IndicoIo-python.git
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Madison May
140 lines
5.4 KiB
ReStructuredText
140 lines
5.4 KiB
ReStructuredText
indicoio-python
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===============
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A wrapper for the `indico API <http://indico.io>`__.
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The indico API is free to use, and no training data is required.
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Installation
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------------
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From PyPI:
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.. code:: bash
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pip install indicoio
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From source:
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.. code:: bash
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git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git
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python setup.py install
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API Keys + Setup
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----------------
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For API key registration and setup, checkout our `quickstart
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guide <http://docs.indico.io/v2.0/docs/api-keys>`__.
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Full Documentation
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------------------
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Detailed documentation and further code examples are available at
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`indico.reame.io <http://indico.readme.io/v2.0/docs/python>`__.
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Supported APIs:
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---------------
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- Positive/Negative Sentiment Analysis
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- Political Sentiment Analysis
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- Image Feature Extraction
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- Facial Emotion Recognition
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- Facial Feature Extraction
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- Language Detection
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- Text Topic Tagging
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Examples
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--------
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.. code:: python
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>>> from indicoio import political, sentiment, language, text_tags, keywords, fer, facial_features, image_features
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>>> indicoio.config.api_key = "YOUR_API_KEY"
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>>> political("Guns don't kill people. People kill people.")
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{u'Libertarian': 0.47740164630834825, u'Green': 0.08454409540443657, u'Liberal': 0.16617097211030055, u'Conservative': 0.2718832861769146}
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>>> sentiment('Worst movie ever.')
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0.07062467665597527
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>>> sentiment('Really enjoyed the movie.')
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0.8105182526856075
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>>> text_tags("Facebook blog posts about Android tech make better journalism than most news outlets.")
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>>> text_tags(test_text, threshold=0.1) # return only keys with value > 0.1
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{u'startups_and_entrepreneurship': 0.21888586688354486}
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>>> text_tags(test_text, top_n=1) # return only keys with top_n values
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{u'startups_and_entrepreneurship': 0.21888586688354486}
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>>> import numpy as np
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>>> test_face = np.linspace(0,50,48*48).reshape(48,48)
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>>> fer(test_face)
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{u'Angry': 0.08843749137458341, u'Sad': 0.39091163159204684, u'Neutral': 0.1947947999669361, u'Surprise': 0.03443785859010413, u'Fear': 0.17574534848440568, u'Happy': 0.11567286999192382}
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>>> facial_features(test_face)
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[0.0, -0.02568680526917187, 0.21645604230056517, ..., 3.0342637531932777]
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>>> language('Quis custodiet ipsos custodes')
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{u'Swedish': 0.00033330636691921914, u'Lithuanian': 0.007328693814717631, u'Vietnamese': 0.0002686116137658802, u'Romanian': 8.133913804076592e-06, ...}
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>>> keywords("Facebook blog posts about Android tech make better journalism than most news outlets.", top_n=3)
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{u'android': 0.10602030910588661,
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u'journalism': 0.13466866170166855,
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u'outlets': 0.13930405357808642}
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Batch API
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---------
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Each ``indicoio`` function can process many examples with a single
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request. Simply pass in a list of inputs and receive a list of results
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in return.
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.. code:: python
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>>> from indicoio import sentiment
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>>> sentiment(['Best day ever', 'Worst day ever'])
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[0.9899001220871786, 0.005709885173415242]
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Calling multiple APIs with a single function
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--------------------------------------------
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There are two multiple API functions ``predict_text`` and
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``predict_image``. These functions are similar to the existing api
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functions, but take in an additional ``apis`` argument as a list of
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strings of API names (defaults to all existing apis). ``predict_text``
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accepts a list of existing text APIs and vice versa for
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``predict_image``. These functions also support batch as the other
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functions do.
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Accepted text API names: ``text_tags, political, sentiment, language``
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Accepted image API names: ``fer, facial_features, image_features``
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.. code:: python
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>>> from indicoio import predict_text, predict_image, predict_text, predict_image
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>>> predict_text('Best day ever', apis=["sentiment", "language"])
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{'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
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>>> predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
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{'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, ...}]}
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>>> import numpy as np
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>>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
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>>> predict_image(test_face, apis=["fer", "facial_features"])
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{'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}}
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>>> predict_image([test_face, test_face], apis=["fer", "facial_features"])
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{'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}]}
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