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Merge pull request #60 from IndicoDataSolutions/development

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Madison May
2015-06-11 01:15:25 -04:00
parent e3db8de6b6 6c91bda343
commit fcf23277bf
17 changed files with 498 additions and 197 deletions
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@@ -15,7 +15,7 @@ pip install indicoio
From source:
```bash
git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git
git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git
python setup.py install
```
@@ -62,7 +62,7 @@ Examples
>>> text_tags(test_text, top_n=1) # return only keys with top_n values
{u'startups_and_entrepreneurship': 0.21888586688354486}
>>> import numpy as np
>>> import numpy as np
>>> test_face = np.linspace(0,50,48*48).reshape(48,48)
@@ -85,3 +85,32 @@ Each `indicoio` function has a corresponding batch function for analyzing many e
>>> batch_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`
```python
>>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
>>> predict_text('Best day ever', apis=["sentiment", "language"])
{'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
>>> batch_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}}
>>> batch_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}]}
```