Merge pull request #119 from IndicoDataSolutions/Chris/versioning

ADD: Versioning

indicoio/images/faciallocalization.py

@@ -4,7 +4,7 @@ from indicoio.utils.image import image_preprocess
 from indicoio.utils.api import api_handler
 
 
-def facial_localization(image, cloud=None, batch=False, api_key=None, **kwargs):
+def facial_localization(image, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given an image, returns a list of faces found within the image.
     For each face, we return a dictionary containing the upper left corner and lower right corner.
@@ -24,8 +24,8 @@ def facial_localization(image, cloud=None, batch=False, api_key=None, **kwargs):
 
     :param image: The image to be analyzed.
     :type image: filepath or ndarray
-    :rtype: List of faces (dict) found. 
+    :rtype: List of faces (dict) found.
     """
     image = image_preprocess(image, batch=batch)
-    url_params = {"batch": batch, "api_key": api_key}
-    return api_handler(image, cloud=cloud, api="faciallocalization", url_params=url_params, **kwargs)
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
+    return api_handler(image, cloud=cloud, api="faciallocalization", url_params=url_params, **kwargs)

indicoio/images/features.py

@@ -3,7 +3,7 @@ import requests
 from indicoio.utils.image import image_preprocess
 from indicoio.utils.api import api_handler
 
-def facial_features(image, cloud=None, batch=False, api_key=None, **kwargs):
+def facial_features(image, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given an grayscale input image of a face, returns a 48 dimensional feature vector explaining that face.
     Useful as a form of feature engineering for face oriented tasks.
@@ -26,10 +26,10 @@ def facial_features(image, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: List containing feature responses
     """
     image = image_preprocess(image, batch=batch, size=(48,48))
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(image, cloud=cloud, api="facialfeatures", url_params=url_params, **kwargs)
 
-def image_features(image, cloud=None, batch=False, api_key=None, **kwargs):
+def image_features(image, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given an input image, returns a 2048 dimensional sparse feature vector explaining that image.
     Useful as a form of feature engineering for image oriented tasks.
@@ -60,5 +60,5 @@ def image_features(image, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: List containing features
     """
     image = image_preprocess(image, batch=batch, size=(64,64))
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(image, cloud=cloud, api="imagefeatures", url_params=url_params, **kwargs)

indicoio/images/fer.py

@@ -4,7 +4,7 @@ from indicoio.utils.api import api_handler
 from indicoio.utils.image import image_preprocess
 import indicoio.config as config
 
-def fer(image, cloud=None, batch=False, api_key=None, **kwargs):
+def fer(image, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given a grayscale input image of a face, returns a probability distribution over emotional state.
     Input should be in a list of list format, resizing will be attempted internally but for best
@@ -31,6 +31,6 @@ def fer(image, cloud=None, batch=False, api_key=None, **kwargs):
     image = image_preprocess(image, batch=batch,
         size=None if kwargs.get("detect") else (48, 48)
     )
-    
-    url_params = {"batch": batch, "api_key": api_key}
+
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(image, cloud=cloud, api="fer", url_params=url_params, **kwargs)

indicoio/images/filtering.py

@@ -4,7 +4,7 @@ from indicoio.utils.api import api_handler
 from indicoio.utils.image import image_preprocess
 import indicoio.config as config
 
-def content_filtering(image, cloud=None, batch=False, api_key=None, **kwargs):
+def content_filtering(image, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given a grayscale input image, returns how obcene the image is.
     Input should be in a list of list format.
@@ -25,5 +25,5 @@ def content_filtering(image, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: float of nsfwness
     """
     image = image_preprocess(image, batch=batch, min_axis=128)
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(image, cloud=cloud, api="contentfiltering", url_params=url_params, **kwargs)

indicoio/text/keywords.py

@@ -1,7 +1,7 @@
 from indicoio.utils.api import api_handler
 import indicoio.config as config
 
-def keywords(text, cloud=None, batch=False, api_key=None, **kwargs):
+def keywords(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns series of keywords and associated scores
 

indicoio/text/lang.py

@@ -1,7 +1,7 @@
 from indicoio.utils.api import api_handler
 import indicoio.config as config
 
-def language(text, cloud=None, batch=False, api_key=None, **kwargs):
+def language(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns a probability distribution over 33 possible
     languages of what language the text was written in.
@@ -23,5 +23,5 @@ def language(text, cloud=None, batch=False, api_key=None, **kwargs):
     :type text: str or unicode
     :rtype: Dictionary of language probability pairs
     """
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(text, cloud=cloud, api="language", url_params=url_params, **kwargs)

indicoio/text/ner.py

@@ -1,7 +1,7 @@
 from indicoio.utils.api import api_handler
 import indicoio.config as config
 
-def named_entities(text, cloud=None, batch=False, api_key=None, **kwargs):
+def named_entities(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns named entities (proper nouns) found in the text
 
@@ -26,5 +26,5 @@ def named_entities(text, cloud=None, batch=False, api_key=None, **kwargs):
     :type text: str or unicode
     :rtype: Dictionary of language probability pairs
     """
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(text, cloud=cloud, api="namedentities", url_params=url_params, **kwargs)

indicoio/text/sentiment.py

@@ -1,6 +1,6 @@
 from indicoio.utils.api import api_handler
 
-def political(text, cloud=None, batch=False, api_key=None, **kwargs):
+def political(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns a probability distribution over the political alignment of the speaker.
 
@@ -25,10 +25,10 @@ def political(text, cloud=None, batch=False, api_key=None, **kwargs):
     :type text: str or unicode
     :rtype: Dictionary of party probability pairs
     """
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(text, cloud=cloud, api="political", url_params=url_params, **kwargs)
 
-def posneg(text, cloud=None, batch=False, api_key=None, **kwargs):
+def posneg(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns a scalar estimate of the sentiment of that text.
     Values are roughly in the range 0 to 1 with 0.5 indicating neutral sentiment.
@@ -48,10 +48,10 @@ def posneg(text, cloud=None, batch=False, api_key=None, **kwargs):
     :type text: str or unicode
     :rtype: Float
     """
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(text, cloud=cloud, api="sentiment", url_params=url_params, **kwargs)
 
-def sentiment_hq(text, cloud=None, batch=False, api_key=None, **kwargs):
+def sentiment_hq(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns a scalar estimate of the sentiment of that text.
     Values are roughly in the range 0 to 1 with 0.5 indicating neutral sentiment.
@@ -71,5 +71,5 @@ def sentiment_hq(text, cloud=None, batch=False, api_key=None, **kwargs):
     :type text: str or unicode
     :rtype: Float
     """
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(text, cloud=cloud, api="sentimenthq", url_params=url_params, **kwargs)

indicoio/text/tagging.py

@@ -1,7 +1,7 @@
 from indicoio.utils.api import api_handler
 import indicoio.config as config
 
-def text_tags(text, cloud=None, batch=False, api_key=None, **kwargs):
+def text_tags(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns a probability distribution over 100 document categories
 
@@ -22,5 +22,5 @@ def text_tags(text, cloud=None, batch=False, api_key=None, **kwargs):
     :type text: str or unicode
     :rtype: Dictionary of class probability pairs
     """
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(text, cloud=cloud, api="texttags", url_params=url_params, **kwargs)

indicoio/text/twitter_engagement.py

@@ -1,7 +1,7 @@
 from indicoio.utils.api import api_handler
 import indicoio.config as config
 
-def twitter_engagement(text, cloud=None, batch=False, api_key=None, **kwargs):
+def twitter_engagement(text, cloud=None, batch=False, api_key=None, version=None, **kwargs):
     """
     Given input text, returns an engagment score between 0 and 1
 
@@ -18,5 +18,5 @@ def twitter_engagement(text, cloud=None, batch=False, api_key=None, **kwargs):
     :type text: str or unicode
     :rtype: Float of engagement between 0 and 1
     """
-    url_params = {"batch": batch, "api_key": api_key}
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
     return api_handler(text, cloud=cloud, api="twitterengagement", url_params=url_params, **kwargs)

indicoio/utils/api.py

@@ -18,12 +18,13 @@ def api_handler(arg, cloud, api, url_params=None, **kwargs):
     json_data = json.dumps(data)
     cloud = cloud or config.cloud
     host = "%s.indico.domains" % cloud if cloud else config.PUBLIC_API_HOST
-    url = create_url(host, api, url_params)
+
+    url = create_url(host, api, dict(kwargs, **url_params))
     response = requests.post(url, data=json_data, headers=JSON_HEADERS)
 
     if response.status_code == 503 and cloud != None:
         raise IndicoError("Private cloud '%s' does not include api '%s'" % (cloud, api))
-    
+
     json_results = response.json()
     results = json_results.get('results', False)
     if results is False:
@@ -36,11 +37,14 @@ def create_url(host, api, url_params):
     api_key = url_params.get("api_key") or config.api_key
     is_batch = url_params.get("batch")
     apis = url_params.get("apis")
+    version = url_params.get("version") or url_params.get("v")
 
     host_url_seg = config.url_protocol + "//%s" % host
     api_url_seg = "/%s" % api
     batch_url_seg = "/batch" if is_batch else ""
     key_url_seg = "?key=%s" % api_key
     multi_url_seg = "&apis=%s" % ",".join(apis) if apis else ""
+    version_seg = ("&version=%s" % str(version)) if version else ""
 
-    return host_url_seg + api_url_seg + batch_url_seg + key_url_seg + multi_url_seg
+    return host_url_seg + api_url_seg + batch_url_seg + key_url_seg \
+            + multi_url_seg + version_seg

tests/test_configure.py

@@ -58,11 +58,11 @@ class TestConfigurationFile(unittest.TestCase):
         """
         assert self.settings.cloud() == self.cloud
 
-    def test_set_auth_from_config_file(self):   
+    def test_set_auth_from_config_file(self):
         """
         Ensure cloud authentication credentials are read in from file
         """
-        assert self.settings.api_key() == self.api_key        
+        assert self.settings.api_key() == self.api_key
 
 
 class TestPrecedence(unittest.TestCase):
@@ -74,7 +74,7 @@ class TestPrecedence(unittest.TestCase):
         self.file_api_key = "file-api-key"
         self.file_cloud = "file-cloud"
 
-        self.env_api_key = "env-api-key"  
+        self.env_api_key = "env-api-key"
         self.env_cloud = "env-cloud"
         config = """
         [auth]
@@ -97,12 +97,12 @@ class TestPrecedence(unittest.TestCase):
         """
         assert self.settings.cloud() == self.env_cloud
 
-    def test_set_auth_from_config_file(self):   
+    def test_set_auth_from_config_file(self):
         """
-        Ensure cloud authentication credentials set in environment variables 
+        Ensure cloud authentication credentials set in environment variables
         are used over those in config files
         """
-        assert self.settings.api_key() == self.env_api_key       
+        assert self.settings.api_key() == self.env_api_key
 
 
 class TestConfigFilePrecedence(unittest.TestCase):
@@ -154,7 +154,7 @@ class TestConfigFilePrecedence(unittest.TestCase):
         """
         assert self.settings.cloud() == self.high_priority_cloud
 
-    def test_auth_config_file_priority(self):   
+    def test_auth_config_file_priority(self):
         """
         Ensure the cloud auth priority is handled properly
         """

tests/test_versioning.py

@@ -0,0 +1,21 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+import unittest
+
+from indicoio import config
+from indicoio import sentiment
+
+class TestVersioning(unittest.TestCase):
+    def setUp(self):
+        self.api_key = config.api_key
+
+    def test_specify_version(self):
+        test_data = ['Worst song ever', 'Best song ever']
+        response = sentiment(test_data, api_key = self.api_key, version="1")
+        self.assertIsInstance(response, list)
+        self.assertEqual(len(response), 2)
+        self.assertTrue(response[0] < .5)
+        self.assertTrue(response[1] > .5)
+
+if __name__ == "__main__":
+    unittest.main()