Merge pull request #306 from tyarkoni/google-language

Google Natural Language API support

pliers/converters/api/ibm.py

@@ -3,6 +3,7 @@
 import os
 import base64
 import json
+import logging
 from pliers.stimuli.text import TextStim, ComplexTextStim
 from pliers.utils import attempt_to_import, verify_dependencies
 from pliers.converters.audio import AudioToTextConverter
@@ -66,6 +67,7 @@ def check_valid_keys(self):
             return True
         except Exception as e:
             if 'Not Authorized' in str(e):
+                logging.warn(str(e))
                 return False
             else:
                 raise e

pliers/converters/api/wit.py

@@ -1,5 +1,6 @@
 ''' Wit.ai API-based Converters '''
 
+import logging
 import os
 from abc import abstractproperty
 from pliers.stimuli.text import ComplexTextStim
@@ -73,5 +74,6 @@ def check_valid_keys(self):
         try:
             urlopen(request)
             return True
-        except HTTPError:
+        except HTTPError as e:
+            logging.warn(str(e))
             return False

pliers/extractors/__init__.py

@@ -17,6 +17,12 @@
                   GoogleVideoAPILabelDetectionExtractor,
                   GoogleVideoAPIShotDetectionExtractor,
                   GoogleVideoAPIExplicitDetectionExtractor,
+                  GoogleLanguageAPIExtractor,
+                  GoogleLanguageAPIEntityExtractor,
+                  GoogleLanguageAPISentimentExtractor,
+                  GoogleLanguageAPISyntaxExtractor,
+                  GoogleLanguageAPITextCategoryExtractor,
+                  GoogleLanguageAPIEntitySentimentExtractor,
                   MicrosoftAPIFaceExtractor,
                   MicrosoftAPIFaceEmotionExtractor,
                   MicrosoftVisionAPIExtractor,
@@ -87,6 +93,12 @@
     'GoogleVideoAPILabelDetectionExtractor',
     'GoogleVideoAPIShotDetectionExtractor',
     'GoogleVideoAPIExplicitDetectionExtractor',
+    'GoogleLanguageAPIExtractor',
+    'GoogleLanguageAPIEntityExtractor',
+    'GoogleLanguageAPISentimentExtractor',
+    'GoogleLanguageAPISyntaxExtractor',
+    'GoogleLanguageAPITextCategoryExtractor',
+    'GoogleLanguageAPIEntitySentimentExtractor',
     'BrightnessExtractor',
     'SaliencyExtractor',
     'SharpnessExtractor',

pliers/extractors/api/__init__.py

@@ -10,7 +10,13 @@
                      GoogleVideoIntelligenceAPIExtractor,
                      GoogleVideoAPILabelDetectionExtractor,
                      GoogleVideoAPIShotDetectionExtractor,
-                     GoogleVideoAPIExplicitDetectionExtractor)
+                     GoogleVideoAPIExplicitDetectionExtractor,
+                     GoogleLanguageAPIExtractor,
+                     GoogleLanguageAPIEntityExtractor,
+                     GoogleLanguageAPISentimentExtractor,
+                     GoogleLanguageAPISyntaxExtractor,
+                     GoogleLanguageAPITextCategoryExtractor,
+                     GoogleLanguageAPIEntitySentimentExtractor)
 from .microsoft import (MicrosoftAPIFaceExtractor,
                         MicrosoftAPIFaceEmotionExtractor,
                         MicrosoftVisionAPIExtractor,
@@ -34,6 +40,12 @@
     'GoogleVideoAPILabelDetectionExtractor',
     'GoogleVideoAPIShotDetectionExtractor',
     'GoogleVideoAPIExplicitDetectionExtractor',
+    'GoogleLanguageAPIExtractor',
+    'GoogleLanguageAPIEntityExtractor',
+    'GoogleLanguageAPISentimentExtractor',
+    'GoogleLanguageAPISyntaxExtractor',
+    'GoogleLanguageAPITextCategoryExtractor',
+    'GoogleLanguageAPIEntitySentimentExtractor',
     'MicrosoftAPIFaceExtractor',
     'MicrosoftAPIFaceEmotionExtractor',
     'MicrosoftVisionAPIExtractor',

pliers/extractors/api/clarifai.py

@@ -2,6 +2,7 @@
 Extractors that interact with the Clarifai API.
 '''
 
+import logging
 import os
 try:
     from contextlib import ExitStack
@@ -64,7 +65,8 @@ def __init__(self, api_key=None, model='general-v1.3', min_value=None,
         try:
             self.api = clarifai_client.ClarifaiApp(api_key=api_key)
             self.model = self.api.models.get(model)
-        except clarifai_client.ApiError:
+        except clarifai_client.ApiError as e:
+            logging.warn(str(e))
             self.api = None
             self.model = None
         self.model_name = model

pliers/extractors/api/google.py

@@ -2,10 +2,13 @@
 
 import base64
 from pliers.extractors.image import ImageExtractor
+from pliers.extractors.text import TextExtractor
 from pliers.extractors.video import VideoExtractor
-from pliers.transformers import (GoogleVisionAPITransformer,
+from pliers.transformers import (GoogleAPITransformer,
+                                 GoogleVisionAPITransformer,
                                  GoogleAPITransformer)
 from pliers.extractors.base import ExtractorResult
+from pliers.utils import flatten_dict
 import numpy as np
 import pandas as pd
 import logging
@@ -369,3 +372,230 @@ def __init__(self, segments=None, config=None, timeout=90, request_rate=5,
                              max_results=max_results,
                              num_retries=num_retries,
                              rate_limit=rate_limit)
+
+
+class GoogleLanguageAPIExtractor(GoogleAPITransformer, TextExtractor):
+
+    ''' Extracts natural language features from text documents using the
+    Google Natural Language API.
+
+    Args:
+        features (list): List of features (str) to extract. Available
+            features: extractSyntax, extractEntities, extractDocumentSentiment,
+            extractEntitySentiment, and classifyText. See Google Natural
+            Language API documentation for more details.
+        language (str): The ISO-639-1 or BCP-47 identifier for the document
+            language. If None is provided, API auto-detects the language.
+        is_html (bool): When True, the document's text is expected to be
+            HTML. Otherwise, plain text is assumed.
+        discovery_file (str): path to discovery file containing Google
+            application credentials.
+        api_version (str): API version to use.
+        max_results (int): Max number of results per page.
+        num_retries (int): Number of times to retry query on failure.
+        rate_limit (int): The minimum number of seconds required between
+            transform calls on this Transformer.
+    '''
+
+    api_name = 'language'
+    _log_attributes = ('discovery_file', 'api_version', 'features',
+                       'language', 'is_html')
+
+    def __init__(self, features=['extractSyntax',
+                                 'extractEntities',
+                                 'extractDocumentSentiment',
+                                 'extractEntitySentiment',
+                                 'classifyText'],
+                 language=None, is_html=False, discovery_file=None,
+                 api_version='v1', max_results=100,
+                 num_retries=3, rate_limit=None):
+        self.features = features
+        self.language = language
+        self.is_html = is_html
+        super(GoogleLanguageAPIExtractor,
+              self).__init__(discovery_file=discovery_file,
+                             api_version=api_version,
+                             max_results=max_results,
+                             num_retries=num_retries,
+                             rate_limit=rate_limit)
+
+    def _query_api(self, request):
+        request_obj = self.service.documents().annotateText(body=request)
+        return request_obj.execute(num_retries=self.num_retries)
+
+    def _build_request(self, stim):
+        document = {
+            'type' : 'HTML' if self.is_html else 'PLAIN_TEXT',
+            'content' : stim.text
+        }
+
+        if self.language:
+            document['language'] = self.language
+
+        request = {
+            'document': document,
+            'features': { f : True for f in self.features },
+            'encodingType': 'UTF32'
+        }
+
+        return request
+
+    def _extract(self, stim):
+        request = self._build_request(stim)
+        response = self._query_api(request)
+        return ExtractorResult(response, stim, self)
+
+    def _get_span(self, text_json):
+        offset = text_json['text']['beginOffset']
+        content = text_json['text']['content']
+        return { 'begin_char_index' : offset,
+                 'end_char_index' : offset + len(content),
+                 'text' : content }
+
+    def _to_df(self, result):
+        response = result._data
+        data = []
+
+        # One row/object for all document-level features
+        document_data = {}
+
+        if 'extractDocumentSentiment' in self.features:
+            sentiment = response['documentSentiment']
+            document_data.update(flatten_dict(sentiment, 'sentiment'))
+
+            # Sentence level sentiment
+            for sentence in response.get('sentences', []):
+                sentence_data = self._get_span(sentence)
+                sentiment = sentence['sentiment']
+                sentence_data.update(flatten_dict(sentiment, 'sentiment'))
+                data.append(sentence_data)
+
+        for category in response.get('categories'):
+            key = 'category_%s' % category['name']
+            document_data[key] = category['confidence']
+
+        # Include only if there are document-level features
+        if document_data:
+            data.append(document_data)
+
+        # Entity-level features
+        for entity in response.get('entities', []):
+            entity_copy = entity.copy()
+            mentions = entity_copy.pop('mentions', [])
+            entity_copy.pop('name', None)
+            entity_copy = flatten_dict(entity_copy)
+
+            for m in mentions:
+                entity_data = self._get_span(m)
+                entity_data.update(entity_copy)
+                # Overwrite top-level sentiment with mention-level
+                sentiment = m.get('sentiment', {})
+                entity_data.update(flatten_dict(sentiment, 'sentiment'))
+                data.append(entity_data)
+
+        # Token-level syntax features
+        for token in response.get('tokens', []):
+            token_data = self._get_span(token)
+            token_data['lemma'] = token['lemma']
+            token_data.update(token['partOfSpeech'])
+            dependency = flatten_dict(token['dependencyEdge'], 'dependency')
+            token_data.update(dependency)
+            data.append(token_data)
+
+        df = pd.DataFrame(data)
+        df['language'] = response['language']
+        return df
+
+
+class GoogleLanguageAPIEntityExtractor(GoogleLanguageAPIExtractor):
+
+    ''' Extracts entity labels in text using the Google Language API '''
+
+    def __init__(self, language=None, is_html=False, discovery_file=None,
+                 api_version='v1', max_results=100, num_retries=3,
+                 rate_limit=None):
+        super(GoogleLanguageAPIEntityExtractor,
+              self).__init__(features=['extractEntities'],
+                             language=language,
+                             is_html=is_html,
+                             discovery_file=discovery_file,
+                             api_version=api_version,
+                             max_results=max_results,
+                             num_retries=num_retries,
+                             rate_limit=rate_limit)
+
+
+class GoogleLanguageAPISentimentExtractor(GoogleLanguageAPIExtractor):
+
+    ''' Extracts sentiment of text using the Google Language API '''
+
+    def __init__(self, language=None, is_html=False, discovery_file=None,
+                 api_version='v1', max_results=100, num_retries=3,
+                 rate_limit=None):
+        super(GoogleLanguageAPISentimentExtractor,
+              self).__init__(features=['extractDocumentSentiment'],
+                             language=language,
+                             is_html=is_html,
+                             discovery_file=discovery_file,
+                             api_version=api_version,
+                             max_results=max_results,
+                             num_retries=num_retries,
+                             rate_limit=rate_limit)
+
+
+class GoogleLanguageAPISyntaxExtractor(GoogleLanguageAPIExtractor):
+
+    ''' Extracts syntax properties of text using the Google Language API '''
+
+    def __init__(self, language=None, is_html=False, discovery_file=None,
+                 api_version='v1', max_results=100, num_retries=3,
+                 rate_limit=None):
+        super(GoogleLanguageAPISyntaxExtractor,
+              self).__init__(features=['extractSyntax'],
+                             language=language,
+                             is_html=is_html,
+                             discovery_file=discovery_file,
+                             api_version=api_version,
+                             max_results=max_results,
+                             num_retries=num_retries,
+                             rate_limit=rate_limit)
+
+
+class GoogleLanguageAPITextCategoryExtractor(GoogleLanguageAPIExtractor):
+
+    ''' Extracts document category using the Google Language API.
+    See the API documentation for the taxonomy of categories:
+    https://cloud.google.com/natural-language/docs/categories '''
+
+    def __init__(self, language=None, is_html=False, discovery_file=None,
+                 api_version='v1', max_results=100, num_retries=3,
+                 rate_limit=None):
+        super(GoogleLanguageAPITextCategoryExtractor,
+              self).__init__(features=['classifyText'],
+                             language=language,
+                             is_html=is_html,
+                             discovery_file=discovery_file,
+                             api_version=api_version,
+                             max_results=max_results,
+                             num_retries=num_retries,
+                             rate_limit=rate_limit)
+
+
+class GoogleLanguageAPIEntitySentimentExtractor(GoogleLanguageAPIExtractor):
+
+    ''' Extracts sentiment of entities found in text using the Google Language
+    API. Produces identical results to the entity extractor but with additional
+    sentiment analysis. '''
+
+    def __init__(self, language=None, is_html=False, discovery_file=None,
+                 api_version='v1', max_results=100, num_retries=3,
+                 rate_limit=None):
+        super(GoogleLanguageAPIEntitySentimentExtractor,
+              self).__init__(features=['extractEntitySentiment'],
+                             language=language,
+                             is_html=is_html,
+                             discovery_file=discovery_file,
+                             api_version=api_version,
+                             max_results=max_results,
+                             num_retries=num_retries,
+                             rate_limit=rate_limit)

pliers/extractors/api/indico.py

@@ -2,6 +2,7 @@
 Extractors that interact with the Indico API.
 '''
 
+import logging
 import os
 from pliers.extractors.image import ImageExtractor
 from pliers.extractors.text import TextExtractor
@@ -73,6 +74,7 @@ def check_valid_keys(self):
             api.api_handler(None, None, self.model_names[0])
         except IndicoError as e:
             if str(e) == 'Invalid API key':
+                logging.warn(str(e))
                 return False
             else:
                 # If valid key, a data error (None passed) is expected here

pliers/extractors/base.py

@@ -147,7 +147,7 @@ def to_df(self, timing=True, metadata=False, format='wide',
             index = pd.Series(onsets).astype(str) + '_' + \
                 pd.Series(durations).astype(str)
             if object_id is True or (object_id == 'auto' and
-                                     len(set(index)) > 1):
+                                     len(set(index)) < len(df)):
                 ids = np.arange(len(df)) if len(index) == 1 \
                     else df.groupby(index).cumcount()
                 df.insert(0, 'object_id', ids)

pliers/stimuli/api.py

@@ -1,5 +1,6 @@
 ''' Stimuli that are inherently associated with remote resources. '''
 
+import logging
 import os
 
 from .base import load_stims
@@ -70,7 +71,8 @@ def check_valid_keys(self):
         try:
             self.api.VerifyCredentials()
             return True
-        except twitter.error.TwitterError:
+        except twitter.error.TwitterError as e:
+            logging.warn(str(e))
             return False
 
     def get_status(self, status_id):

pliers/tests/data/text/sample_text_with_entities.txt

@@ -0,0 +1 @@
+Google, headquartered in Mountain View, unveiled the new Android phone at the Consumer Electronic Show.  Sundar Pichai said in his keynote that users love their new Android phones.