wassname/IndicoIo-python
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updated readme, changed routes, and included some additional infrastructure

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Slater-Victoroff
2014-05-14 00:04:16 -04:00
parent 67d53d25d8
commit 97ab57fbfe
8 changed files with 73 additions and 14 deletions
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AUTHORS
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@@ -0,0 +1,2 @@
Slater Victoroff <slater@indicodatasolutions.com>
Alec Radford <alec@indicodatasolutions.com>
CHANGES.txt
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v0.2.8, Tue May 13 -- Added Description, Authors file, changelog. Cleaned up import paths and modified corresponding examples and tests
IndicoIo/__init__.py
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JSON_HEADERS = {'Content-type': 'application/json', 'Accept': 'text/plain'}
from text.sentiment import political, spam, posneg
from images.fer import fer
from images.features import facial_features
IndicoIo/images/facial_features.py → IndicoIo/images/features.py
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@@ -7,7 +7,10 @@ from IndicoIo import JSON_HEADERS
base_url = lambda c: "http://indico.io/api/features/%s" % c
def facial(face):
def facial_features(face, full_return=False):
data_dict = json.dumps({"datums": face})
response = requests.post(base_url("facial"), data=data_dict, headers=JSON_HEADERS)
return json.loads(response.content)
response_dict = json.loads(response.content)
if full_return:
return response_dict
return json.loads(response_dict['feature_vector'])
README
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@@ -7,6 +7,8 @@ Check out the main site on:
http://indico.io
Our APIs are totally free to use, and ready to be used in your application. No data or training required.
Current APIs
------------
@@ -23,9 +25,7 @@ Examples
```
>>> import numpy as np
>>> from IndicoIo.text.sentiment import political, spam, posneg
>>> from IndicoIo.images.fer import fer
>>> from IndicoIo.images.facial_features import facial
>>> from IndicoIo import political, spam, posneg, fer, facial_features
>>> political("Guns don't kill people, people kill people")
{u'Libertarian': 1.000094905588269, u'Liberal': 1.000194776694221, u'Green': 1.0000989185747784, u'Conservative': 1.000114308739228}
@@ -41,7 +41,13 @@ Examples
>>> 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(test_face)
{u'feature_vector': u'[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]'}
>>> 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]
```
Installation
------------
```
pip install indicoio
```
README.md
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@@ -7,6 +7,8 @@ Check out the main site on:
http://indico.io
Our APIs are totally free to use, and ready to be used in your application. No data or training required.
Current APIs
------------
@@ -23,9 +25,7 @@ Examples
```
>>> import numpy as np
>>> from IndicoIo.text.sentiment import political, spam, posneg
>>> from IndicoIo.images.fer import fer
>>> from IndicoIo.images.facial_features import facial
>>> from IndicoIo import political, spam, posneg, fer, facial_features
>>> political("Guns don't kill people, people kill people")
{u'Libertarian': 1.000094905588269, u'Liberal': 1.000194776694221, u'Green': 1.0000989185747784, u'Conservative': 1.000114308739228}
@@ -41,7 +41,13 @@ Examples
>>> 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(test_face)
{u'feature_vector': u'[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]'}
>>> 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]
```
Installation
------------
```
pip install indicoio
```
setup.py
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setup(
name = "IndicoIo",
version = '0.2.7',
version = '0.2.8',
packages = [
"IndicoIo",
"IndicoIo.text",
"IndicoIo.images",
"tests",
],
description = "A Python Wrapper for IndicoIo. Use pre-built state of the art machine learning algorithms with a single line of code.",
license = "MIT License (See LICENSE)",
long_description = open("README").read(),
url = "https://github.com/IndicoDataSolutions/IndicoIo-python",
tests/test_run.py
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import unittest
from IndicoIo.text.sentiment import political, spam, posneg
import numpy as np
from IndicoIo import political, spam, posneg, fer, facial_features
class FullAPIRun(unittest.TestCase):
@@ -27,5 +31,37 @@ class FullAPIRun(unittest.TestCase):
self.assertTrue(isinstance(response, dict))
self.assertEqual(posneg_set, set(response.keys()))
def test_good_fer(self):
fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
response = fer(test_face)
self.assertTrue(isinstance(response, dict))
self.assertEqual(fer_set, set(response.keys()))
def test_bad_fer(self):
fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
test_face = np.linspace(0,50,56*56).reshape(56,56).tolist()
response = fer(test_face)
self.assertTrue(isinstance(response, dict))
self.assertEqual(fer_set, set(response.keys()))
def test_good_facial_features(self):
test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
response = facial_features(test_face)
self.assertTrue(isinstance(response, list))
self.assertEqual(len(response), 48)
def test_full_facial_features(self):
features_set = set(['feature_vector', 'warnings'])
test_face = np.linspace(0,50,56*56).reshape(56,56).tolist()
response = facial_features(test_face, True)
self.assertEqual(set(response.keys()), features_set)
self.assertEqual(response['warnings'], 'Using a 48x48 array will produce the best results')
if __name__ == "__main__":
unittest.main()