merged master

CHANGELOG.rst

@@ -27,6 +27,9 @@ Changed
   of ``"None"`` as intent name in the json result if there's no match
 - in teh evaluation results, replaced ``O`` with the string
   ``no_entity`` for better understanding
+- The ``CRFEntityExtractor`` now only trains entity examples that have
+  ``"extractor": "ner_crf"`` or no extractor at all
+- Ignore hidden files when listing projects or models
 
 Fixed
 -----

docs/_templates/layout.html

@@ -6,12 +6,13 @@
 {% block footer %}
 {{ super() }}
 <!-- Global Site Tag (gtag.js) - Google Analytics -->
-<script async src="https://www.googletagmanager.com/gtag/js?id=UA-87333416-3"></script>
+<script async src="https://www.googletagmanager.com/gtag/js?id=UA-87333416-1"></script>
 <script>
 window.dataLayer = window.dataLayer || [];
 function gtag(){dataLayer.push(arguments)};
 gtag('js', new Date());
 
-gtag('config', 'UA-87333416-3');
+gtag('config', 'UA-87333416-1');
 </script>
+<script type="text/javascript" src="//script.crazyegg.com/pages/scripts/0074/3851.js" async="async"></script>
 {% endblock %}

rasa_nlu/data_router.py

@@ -210,8 +210,7 @@ def parse(self, data):
     def _list_projects(path):
         """List the projects in the path, ignoring hidden directories."""
         return [os.path.basename(fn)
-                for fn in glob.glob(os.path.join(path, '*'))
-                if os.path.isdir(fn)]
+                for fn in utils.list_subdirectories(path)]
 
     @staticmethod
     def create_temporary_file(data, suffix=""):

rasa_nlu/extractors/__init__.py

@@ -9,6 +9,7 @@
 from typing import Text
 
 from rasa_nlu.components import Component
+from rasa_nlu.training_data import Message
 
 
 class EntityExtractor(Component):
@@ -24,6 +25,7 @@ def add_processor_name(self, entity):
             entity["processors"].append(self.name)
         else:
             entity["processors"] = [self.name]
+
         return entity
 
     @staticmethod
@@ -46,3 +48,28 @@ def find_entity(ent, text, tokens):
         start = offsets.index(ent["start"])
         end = ends.index(ent["end"]) + 1
         return start, end
+
+    def filter_trainable_entities(self, entity_examples):
+        # type: (List[Message]) -> List[Message]
+        """Filters out untrainable entity annotations.
+
+        Creates a copy of entity_examples in which entities that have
+        `extractor` set to something other than self.name (e.g. 'ner_crf')
+        are removed."""
+
+        filtered = []
+        for message in entity_examples:
+            entities = []
+            for ent in message.get("entities", []):
+                extractor = ent.get("extractor")
+                if not extractor or extractor == self.name:
+                    entities.append(ent)
+            data = message.data.copy()
+            data['entities'] = entities
+            filtered.append(
+                Message(text=message.text,
+                        data=data,
+                        output_properties=message.output_properties,
+                        time=message.time))
+
+        return filtered

rasa_nlu/extractors/crf_entity_extractor.py

@@ -17,7 +17,6 @@
 from rasa_nlu.config import RasaNLUConfig
 from rasa_nlu.extractors import EntityExtractor
 from rasa_nlu.model import Metadata
-from rasa_nlu.tokenizers import Token
 from rasa_nlu.training_data import Message
 from rasa_nlu.training_data import TrainingData
 from builtins import str
@@ -27,7 +26,6 @@
 if typing.TYPE_CHECKING:
     from spacy.language import Language
     import sklearn_crfsuite
-    from spacy.tokens import Doc
 
 
 class CRFEntityExtractor(EntityExtractor):
@@ -97,10 +95,13 @@ def train(self, training_data, config, **kwargs):
 
         # checks whether there is at least one example with an entity annotation
         if training_data.entity_examples:
+            # filter out pre-trained entity examples
+            filtered_entity_examples = self.filter_trainable_entities(
+                training_data.entity_examples)
             # convert the dataset into features
             # this will train on ALL examples, even the ones
             # without annotations
-            dataset = self._create_dataset(training_data.training_examples)
+            dataset = self._create_dataset(filtered_entity_examples)
             # train the model
             self._train_model(dataset)
 

rasa_nlu/project.py

@@ -12,6 +12,7 @@
 from builtins import object
 from threading import Lock
 
+from rasa_nlu import utils
 from typing import Text, List
 
 from rasa_nlu.config import RasaNLUConfig
@@ -223,5 +224,4 @@ def _list_models_in_dir(path):
             return []
         else:
             return [os.path.relpath(model, path)
-                    for model in glob.glob(os.path.join(path, '*'))
-                    if os.path.isdir(model)]
+                    for model in utils.list_subdirectories(path)]

rasa_nlu/training_data/loading.py

@@ -42,7 +42,7 @@ def load_data(resource_name, language='en'):
     # type: (Text, Optional[Text]) -> TrainingData
     """Loads training data from disk and merges them if multiple files are found."""
 
-    files = utils.recursively_find_files(resource_name)
+    files = utils.list_files(resource_name)
     data_sets = [_load(f, language) for f in files]
     data_sets = [ds for ds in data_sets if ds]
     if len(data_sets) == 0:

rasa_nlu/utils/__init__.py

@@ -4,6 +4,7 @@
 from __future__ import unicode_literals
 
 import errno
+import glob
 import io
 import json
 import os
@@ -52,28 +53,48 @@ def create_dir_for_file(file_path):
             raise
 
 
-def recursively_find_files(resource_name):
+def list_directory(path):
     # type: (Text) -> List[Text]
-    """Traverse directory hierarchy to find files.
+    """Returns all files and folders excluding hidden files.
 
-    `resource_name` can be a folder or a file. In both cases
-    we will return a list of files."""
+    If the path points to a file, returns the file. This is a recursive
+    implementation returning files in any depth of the path."""
 
-    if not isinstance(resource_name, six.string_types):
+    if not isinstance(path, six.string_types):
         raise ValueError("Resourcename must be a string type")
 
-    found = []
-    if os.path.isfile(resource_name):
-        found.append(resource_name)
-    elif os.path.isdir(resource_name):
-        for root, directories, files in os.walk(resource_name):
-            for f in files:
-                found.append(os.path.join(root, f))
+    if os.path.isfile(path):
+        return [path]
+    elif os.path.isdir(path):
+        results = []
+        for base, dirs, files in os.walk(path):
+            # remove hidden files
+            goodfiles = filter(lambda x: not x.startswith('.'), files)
+            results.extend(os.path.join(base, f) for f in goodfiles)
+        return results
     else:
         raise ValueError("Could not locate the resource '{}'."
-                         "".format(os.path.abspath(resource_name)))
+                         "".format(os.path.abspath(path)))
 
-    return found
+
+def list_files(path):
+    # type: (Text) -> List[Text]
+    """Returns all files excluding hidden files.
+
+    If the path points to a file, returns the file."""
+
+    return [fn for fn in list_directory(path) if os.path.isfile(fn)]
+
+
+def list_subdirectories(path):
+    # type: (Text) -> List[Text]
+    """Returns all folders excluding hidden files.
+
+    If the path points to a file, returns an empty list."""
+
+    return [fn
+            for fn in glob.glob(os.path.join(path, '*'))
+            if os.path.isdir(fn)]
 
 
 def lazyproperty(fn):

tests/base/test_extractors.py

@@ -20,7 +20,10 @@ def test_crf_extractor(spacy_nlp):
         }),
         Message("central indian restaurant", {
             "intent": "restaurant_search",
-            "entities": [{"start": 0, "end": 7, "value": "central", "entity": "location"}],
+            "entities": [
+                {"start": 0, "end": 7, "value": "central", "entity": "location", "extractor": "random_extractor"},
+                {"start": 8, "end": 14, "value": "indian", "entity": "cuisine", "extractor": "ner_crf"}
+            ],
             "spacy_doc": spacy_nlp("central indian restaurant")
         })]
     config = {"ner_crf": {"BILOU_flag": True, "features": ext.crf_features}}
@@ -34,6 +37,16 @@ def test_crf_extractor(spacy_nlp):
     assert feats[1]['0:low'] == "in"
     sentence = 'anywhere in the west'
     ext.extract_entities(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}))
+    filtered = ext.filter_trainable_entities(examples)
+    assert filtered[0].get('entities') == [
+        {"start": 16, "end": 20, "value": "west", "entity": "location"}
+    ], 'Entity without extractor remains'
+    assert filtered[1].get('entities') == [
+        {"start": 8, "end": 14, "value": "indian", "entity": "cuisine", "extractor": "ner_crf"}
+    ], 'Only ner_crf entity annotation remains'
+    assert examples[1].get('entities')[0] == {
+        "start": 0, "end": 7, "value": "central", "entity": "location", "extractor": "random_extractor"
+    }, 'Original examples are not mutated'
 
 
 def test_crf_json_from_BILOU(spacy_nlp):

tests/base/test_multitenancy.py

@@ -48,7 +48,7 @@ def app(component_builder):
 
 @pytest.mark.parametrize("response_test", [
     ResponseTest(
-            "http://dummy_uri/parse?q=food&project=test_project_spacy_sklearn",
+            "http://dummy-uri/parse?q=food&project=test_project_spacy_sklearn",
             {"entities": [], "intent": "restaurant_search", "text": "food"}
     ),
 ])
@@ -63,11 +63,11 @@ def test_get_parse(app, response_test):
 
 @pytest.mark.parametrize("response_test", [
     ResponseTest(
-            "http://dummy_uri/parse?q=food",
+            "http://dummy-uri/parse?q=food",
             {"error": "No project found with name 'default'."}
     ),
     ResponseTest(
-            "http://dummy_uri/parse?q=food&project=umpalumpa",
+            "http://dummy-uri/parse?q=food&project=umpalumpa",
             {"error": "No project found with name 'umpalumpa'."}
     )
 ])
@@ -81,7 +81,7 @@ def test_get_parse_invalid_model(app, response_test):
 
 @pytest.mark.parametrize("response_test", [
     ResponseTest(
-            "http://dummy_uri/parse",
+            "http://dummy-uri/parse",
             {"entities": [], "intent": "restaurant_search", "text": "food"},
             payload={"q": "food", "project": "test_project_spacy_sklearn"}
     ),
@@ -96,11 +96,11 @@ def test_post_parse(app, response_test):
 
 @pytest.inlineCallbacks
 def test_post_parse_specific_model(app):
-    status = yield app.get("http://dummy_uri/status")
+    status = yield app.get("http://dummy-uri/status")
     sjs = yield status.json()
     project = sjs["available_projects"]["test_project_spacy_sklearn"]
     model = project["available_models"][0]
-    query = ResponseTest("http://dummy_uri/parse",
+    query = ResponseTest("http://dummy-uri/parse",
                          {"entities": [], "intent": "affirm", "text": "food"},
                          payload={"q": "food",
                                   "project": "test_project_spacy_sklearn",
@@ -111,12 +111,12 @@ def test_post_parse_specific_model(app):
 
 @pytest.mark.parametrize("response_test", [
     ResponseTest(
-            "http://dummy_uri/parse",
+            "http://dummy-uri/parse",
             {"error": "No project found with name 'default'."},
             payload={"q": "food"}
     ),
     ResponseTest(
-            "http://dummy_uri/parse",
+            "http://dummy-uri/parse",
             {"error": "No project found with name 'umpalumpa'."},
             payload={"q": "food", "project": "umpalumpa"}
     ),