NER + document classification

indicoio/__init__.py

@@ -8,6 +8,8 @@ Version, version, __version__, VERSION = ('0.4.5',) * 4
 from indicoio.text.sentiment import political, posneg
 from indicoio.text.sentiment import posneg as sentiment
 from indicoio.text.lang import language
+from indicoio.text.classification import classification
+from indicoio.text.ner import named_entities
 from indicoio.images.fer import fer
 from indicoio.images.features import facial_features
 from indicoio.images.features import image_features
@@ -20,3 +22,5 @@ language = partial(language, config.api_root)
 fer = partial(fer, config.api_root)
 facial_features = partial(facial_features, config.api_root)
 image_features = partial(image_features, config.api_root)
+classification = partial(classification, config.api_root)
+named_entities = partial(named_entities, config.api_root)

indicoio/local/__init__.py

@@ -6,6 +6,8 @@ JSON_HEADERS = {'Content-type': 'application/json', 'Accept': 'text/plain'}
 from indicoio.text.sentiment import political, posneg
 from indicoio.text.sentiment import posneg as sentiment
 from indicoio.text.lang import language
+from indicoio.text.classification import classification
+from indicoio.text.ner import named_entities
 from indicoio.images.fer import fer
 from indicoio.images.features import facial_features
 from indicoio.images.features import image_features
@@ -18,3 +20,5 @@ language = partial(language, config.local_api_root)
 fer = partial(fer, config.local_api_root)
 facial_features = partial(facial_features, config.local_api_root)
 image_features = partial(image_features, config.local_api_root)
+classification = partial(classification, config.local_api_root)
+named_entities = partial(named_entities, config.local_api_root)

indicoio/text/classification.py

@@ -0,0 +1,25 @@
+from indicoio.utils import api_handler
+
+def classification(api_root, text):
+    """
+    Given input text, returns a probability distribution over 100 document categories
+
+    Example usage:
+
+    .. code-block:: python
+
+       >>> import indicoio
+       >>> import numpy as np
+       >>> text = 'Monday: Delightful with mostly sunny skies. Highs in the low 70s.'
+       >>> possible = indicoio.classification(text)
+       >>> category = possible.keys()[np.argmax(possible.values())]
+       >>> probability = np.max(possible.values())
+       >>> "Predicted category '%s' with probability %.4f"%(category,probability)
+       u'Predicted 'Weather' with probability 0.8548'
+
+    :param text: The text to be analyzed.
+    :type text: str or unicode
+    :rtype: Dictionary of class probability pairs
+    """
+    
+    return api_handler(text, api_root + "documentclassification")

indicoio/text/lang.py

@@ -1,6 +1,3 @@
-import requests
-import json
-
 from indicoio.utils import api_handler
 
 def language(api_root, text):

indicoio/text/ner.py

@@ -0,0 +1,23 @@
+from indicoio.utils import api_handler
+
+def named_entities(api_root, text):
+    """
+    Given input text, returns a mapping from named entities to 
+    named entity categories.
+
+    Example usage:
+
+    .. code-block:: python
+
+       >>> import indicoio
+       >>> import numpy as np
+       >>> text = 'On Monday, president Barack Obama will be...'
+       >>> indicoio.named_entities(text)
+       >>> "{'Monday': 'Time', 'Barack Obama': 'Person'}"
+
+    :param text: The text to be analyzed.
+    :type text: str or unicode
+    :rtype: Dictionary of named entity, category pairs
+    """
+    
+    return api_handler(text, api_root + "ner")

indicoio/text/sentiment.py

@@ -1,6 +1,3 @@
-import requests
-import json
-
 from indicoio import JSON_HEADERS
 from indicoio.utils import api_handler
 

tests/local/test_local.py

@@ -20,6 +20,18 @@ class FullAPIRun(unittest.TestCase):
         self.assertTrue(vector.min() < minimum)
         self.assertTrue(np.ptp(vector) > span)
 
+    def test_document_classification(self):
+        categories = set(['arts'])
+        text = "On Monday, president Barack Obama will be..."
+        results = classification(text)
+        self.assertTrue(categories < set(results.keys()))
+
+    def test_named_entity_recognition(self):
+        categories = set(['arts'])
+        text = "On Monday, president Barack Obama will be..."
+        results = named_entities(text)
+        self.assertTrue('named entity' in set(results.keys()))
+
     def test_political(self):
         political_set = set(['Libertarian', 'Liberal', 'Conservative', 'Green'])
         test_string = "Guns don't kill people, people kill people."

tests/remote/test_remote.py

@@ -4,7 +4,8 @@ import os
 import numpy as np
 import skimage.io
 
-from indicoio import political, sentiment, fer, facial_features, language, image_features
+from indicoio import political, sentiment, fer, facial_features, language, image_features, \
+                     classification, named_entities
 
 DIR = os.path.dirname(os.path.realpath(__file__))
 
@@ -22,6 +23,18 @@ class FullAPIRun(unittest.TestCase):
         self.assertTrue(vector.min() < minimum)
         self.assertTrue(np.ptp(vector) > span)
 
+    def test_document_classification(self):
+        categories = set(['arts'])
+        text = "On Monday, president Barack Obama will be..."
+        results = classification(text)
+        self.assertTrue(categories < set(results.keys()))
+
+    def test_named_entity_recognition(self):
+        categories = set(['arts'])
+        text = "On Monday, president Barack Obama will be..."
+        results = named_entities(text)
+        self.assertTrue('named entity' in set(results.keys()))
+
     def test_political(self):
         political_set = set(['Libertarian', 'Liberal', 'Conservative', 'Green'])
         test_string = "Guns don't kill people, people kill people."