Release/0.19.7

.travis.yml

@@ -24,5 +24,3 @@ script: tox
 after_success:
   - tox -e coverage-report
   - codecov
-
-cache: pip

CHANGELOG.md

@@ -1,6 +1,21 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 
+## [0.19.7]
+### Changed
+- Re-score ambiguous `DeterministicIntentParser` results based on slots [#791](https://github.com/snipsco/snips-nlu/pull/791)
+- Accept ambiguous results from `DeterministicIntentParser` when confidence score is above 0.5 [#797](https://github.com/snipsco/snips-nlu/pull/797)
+- Avoid generating number variations when not needed [#799](https://github.com/snipsco/snips-nlu/pull/799)
+- Moved the NLU random state from the config to the shared resources [#801](https://github.com/snipsco/snips-nlu/pull/801)
+- Reduce custom entity parser footprint in training time [#804](https://github.com/snipsco/snips-nlu/pull/804)
+- Bumped `scikit-learn` to `>=0.21,<0.22` for `python>=3.5` and `>=0.20<0.21` for `python<3.5` [#801](https://github.com/snipsco/snips-nlu/pull/801)
+- Update dependencies [#811](https://github.com/snipsco/snips-nlu/pull/811) 
+
+### Fixed
+- Fixed a couple of bugs in the data augmentation which were making the NLU training non-deterministic [#801](https://github.com/snipsco/snips-nlu/pull/801)
+- Remove deprecated code in dataset generation [#803](https://github.com/snipsco/snips-nlu/pull/803)
+- Fix possible override of entity values when generating variations [#808](https://github.com/snipsco/snips-nlu/pull/808)
+
 ## [0.19.6]
 ### Fixed
 - Raise an error when using unknown intents in intents filter [#788](https://github.com/snipsco/snips-nlu/pull/788)
@@ -269,6 +284,7 @@ several commands.
 - Fix compiling issue with `bindgen` dependency when installing from source
 - Fix issue in `CRFSlotFiller` when handling builtin entities
 
+[0.19.7]: https://github.com/snipsco/snips-nlu/compare/0.19.6...0.19.7
 [0.19.6]: https://github.com/snipsco/snips-nlu/compare/0.19.5...0.19.6
 [0.19.5]: https://github.com/snipsco/snips-nlu/compare/0.19.4...0.19.5
 [0.19.4]: https://github.com/snipsco/snips-nlu/compare/0.19.3...0.19.4

docs/source/tutorial.rst

@@ -174,6 +174,26 @@ the dataset we generated earlier:
 
     engine.fit(dataset)
 
+Note that, by default, training of the NLU engine is non-deterministic:
+training and testing multiple times on the same data may produce different
+outputs.
+
+Reproducible trainings can be achieved by passing a **random seed** to the
+engine:
+
+.. code-block:: python
+
+    seed = 42
+    engine = SnipsNLUEngine(config=CONFIG_EN, random_state=seed)
+    engine.fit(dataset)
+
+
+.. note::
+
+    Due to a ``scikit-learn`` bug fixed in version ``0.21`` we can't guarantee
+    any deterministic behavior if you're using a Python version ``<3.5`` since
+    ``scikit-learn>=0.21`` is only available starting from Python ``>=3.5``
+
 
 Parsing
 -------

setup.py

@@ -17,22 +17,23 @@
     readme = f.read()
 
 required = [
+    "deprecation>=2.0,<3.0",
     "enum34>=1.1,<2.0; python_version<'3.4'",
-    "future>=0.16,<0.17",
-    "numpy>=1.15,<1.16",
+    "funcsigs>=1.0,<2.0; python_version<'3.4'",
+    "future>=0.16,<0.18",
+    "num2words>=0.5.6,<0.6",
+    "numpy>=1.15,<2.0",
+    "pathlib>=1.0,<2.0; python_version<'3.4'",
+    "plac>=0.9.6,<2.0",
+    "pyaml>=17.0,<20.0",
+    "requests>=2.0,<3.0",
+    "scikit-learn>=0.20,<0.21; python_version<'3.5'",
+    "scikit-learn>=0.21.1,<0.22; python_version>='3.5'",
     "scipy>=1.0,<2.0",
-    "scikit-learn>=0.19,<0.20",
-    "sklearn-crfsuite>=0.3.6,<0.4",
     "semantic_version>=2.6,<3.0",
-    "snips-nlu-utils>=0.8,<0.9",
+    "sklearn-crfsuite>=0.3.6,<0.4",
     "snips-nlu-parsers>=0.2,<0.3",
-    "num2words>=0.5.6,<0.6",
-    "plac>=0.9.6,<1.0",
-    "requests>=2.0,<3.0",
-    "pathlib==1.0.1; python_version < '3.4'",
-    "pyaml>=17,<18",
-    "deprecation>=2,<3",
-    "funcsigs>=1.0,<2.0; python_version < '3.4'"
+    "snips-nlu-utils>=0.8,<0.9",
 ]
 
 extras_require = {

snips_nlu/__about__.py

@@ -13,7 +13,7 @@
 __email__ = "clement.doumouro@snips.ai, adrien.ball@snips.ai"
 __license__ = "Apache License, Version 2.0"
 
-__version__ = "0.19.6"
+__version__ = "0.19.7"
 __model_version__ = "0.19.0"
 
 __download_url__ = "https://github.com/snipsco/snips-nlu-language-resources/releases/download"

snips_nlu/cli/generate_dataset.py

@@ -8,13 +8,21 @@
 
 @plac.annotations(
     language=("Language of the assistant", "positional", None, str),
-    files=("List of intent and entity files", "positional", None, str, None,
-           "filename"))
-def generate_dataset(language, *files):
-    """Create a Snips NLU dataset from text friendly files"""
+    yaml_files=("List of intent and entity yaml files", "positional", None,
+                str, None, "filename"))
+def generate_dataset(language, *yaml_files):
+    """Creates a Snips NLU dataset from YAML definition files
+
+    Check :meth:`.Intent.from_yaml` and :meth:`.Entity.from_yaml` for the
+    format of the YAML files.
+
+    Args:
+        language (str): language of the dataset (iso code)
+        *yaml_files: list of intent and entity definition files in YAML format.
+
+    Returns:
+        None. The json dataset output is printed out on stdout.
+    """
     language = unicode_string(language)
-    if any(f.endswith(".yml") or f.endswith(".yaml") for f in files):
-        dataset = Dataset.from_yaml_files(language, list(files))
-    else:
-        dataset = Dataset.from_files(language, list(files))
+    dataset = Dataset.from_yaml_files(language, list(yaml_files))
     print(json_string(dataset.json, indent=2, sort_keys=True))

snips_nlu/constants.py

@@ -46,6 +46,7 @@
 BUILTIN_ENTITY_PARSER = "builtin_entity_parser"
 CUSTOM_ENTITY_PARSER = "custom_entity_parser"
 MATCHING_STRICTNESS = "matching_strictness"
+RANDOM_STATE = "random_state"
 
 # resources
 RESOURCES = "resources"

snips_nlu/data_augmentation.py

@@ -69,10 +69,10 @@ def get_entities_iterators(intent_entities, language,
                            add_builtin_entities_examples, random_state):
     entities_its = dict()
     for entity_name, entity in iteritems(intent_entities):
-        utterance_values = random_state.permutation(list(entity[UTTERANCES]))
+        utterance_values = random_state.permutation(sorted(entity[UTTERANCES]))
         if add_builtin_entities_examples and is_builtin_entity(entity_name):
-            entity_examples = get_builtin_entity_examples(entity_name,
-                                                          language)
+            entity_examples = get_builtin_entity_examples(
+                entity_name, language)
             # Builtin entity examples must be kept first in the iterator to
             # ensure that they are used when augmenting data
             iterator_values = entity_examples + list(utterance_values)

snips_nlu/dataset/intent.py

@@ -306,7 +306,8 @@ def capture_slot(state):
     next_colon_pos = state.find(':')
     next_square_bracket_pos = state.find(']')
     if next_square_bracket_pos < 0:
-        raise IntentFormatError("Missing ending ']' in annotated utterance")
+        raise IntentFormatError(
+            "Missing ending ']' in annotated utterance \"%s\"" % state.input)
     if next_colon_pos < 0 or next_square_bracket_pos < next_colon_pos:
         slot_name = state[:next_square_bracket_pos]
         state.move(next_square_bracket_pos)
@@ -327,7 +328,8 @@ def capture_slot(state):
 def capture_tagged(state):
     next_pos = state.find(')')
     if next_pos < 1:
-        raise IntentFormatError("Missing ending ')' in annotated utterance")
+        raise IntentFormatError(
+            "Missing ending ')' in annotated utterance \"%s\"" % state.input)
     else:
         tagged_text = state[:next_pos]
         state.add_tagged(tagged_text)

snips_nlu/dataset/validation.py

@@ -8,6 +8,8 @@
 from future.utils import iteritems, itervalues
 from snips_nlu_parsers import get_all_languages
 
+from snips_nlu.common.dataset_utils import (validate_key, validate_keys,
+                                            validate_type)
 from snips_nlu.constants import (
     AUTOMATICALLY_EXTENSIBLE, CAPITALIZE, DATA, ENTITIES, ENTITY, INTENTS,
     LANGUAGE, MATCHING_STRICTNESS, SLOT_NAME, SYNONYMS, TEXT, USE_SYNONYMS,
@@ -18,8 +20,9 @@
 from snips_nlu.exceptions import DatasetFormatError
 from snips_nlu.preprocessing import tokenize_light
 from snips_nlu.string_variations import get_string_variations
-from snips_nlu.common.dataset_utils import validate_type, validate_key, \
-    validate_keys
+
+NUMBER_VARIATIONS_THRESHOLD = 1e3
+VARIATIONS_GENERATION_THRESHOLD = 1e4
 
 
 def validate_and_format_dataset(dataset):
@@ -111,7 +114,7 @@ def _extract_entity_values(entity):
     return values
 
 
-def _validate_and_format_custom_entity(entity, queries_entities, language,
+def _validate_and_format_custom_entity(entity, utterance_entities, language,
                                        builtin_entity_parser):
     validate_type(entity, dict, object_label="entity")
 
@@ -146,30 +149,48 @@ def _validate_and_format_custom_entity(entity, queries_entities, language,
         if not entry[VALUE]:
             continue
         validate_type(entry[SYNONYMS], list, object_label="entity synonyms")
-        entry[SYNONYMS] = [s.strip() for s in entry[SYNONYMS]
-                           if len(s.strip()) > 0]
+        entry[SYNONYMS] = [s.strip() for s in entry[SYNONYMS] if s.strip()]
         valid_entity_data.append(entry)
     entity[DATA] = valid_entity_data
 
     # Compute capitalization before normalizing
     # Normalization lowercase and hence lead to bad capitalization calculation
-    formatted_entity[CAPITALIZE] = _has_any_capitalization(queries_entities,
+    formatted_entity[CAPITALIZE] = _has_any_capitalization(utterance_entities,
                                                            language)
 
     validated_utterances = dict()
     # Map original values an synonyms
     for data in entity[DATA]:
         ent_value = data[VALUE]
-        if not ent_value:
-            continue
         validated_utterances[ent_value] = ent_value
         if use_synonyms:
             for s in data[SYNONYMS]:
-                if s and s not in validated_utterances:
+                if s not in validated_utterances:
                     validated_utterances[s] = ent_value
 
+    # Number variations in entities values are expensive since each entity
+    # value is parsed with the builtin entity parser before creating the
+    # variations. We avoid generating these variations if there's enough entity
+    # values
+
     # Add variations if not colliding
     all_original_values = _extract_entity_values(entity)
+    if len(entity[DATA]) < VARIATIONS_GENERATION_THRESHOLD:
+        variations_args = {
+            "case": True,
+            "and_": True,
+            "punctuation": True
+        }
+    else:
+        variations_args = {
+            "case": False,
+            "and_": False,
+            "punctuation": False
+        }
+
+    variations_args["numbers"] = len(
+        entity[DATA]) < NUMBER_VARIATIONS_THRESHOLD
+
     variations = dict()
     for data in entity[DATA]:
         ent_value = data[VALUE]
@@ -178,10 +199,11 @@ def _validate_and_format_custom_entity(entity, queries_entities, language,
             values_to_variate.update(set(data[SYNONYMS]))
         variations[ent_value] = set(
             v for value in values_to_variate
-            for v in get_string_variations(value, language,
-                                           builtin_entity_parser))
+            for v in get_string_variations(
+                value, language, builtin_entity_parser, **variations_args)
+        )
     variation_counter = Counter(
-        [v for vars in itervalues(variations) for v in vars])
+        [v for variations_ in itervalues(variations) for v in variations_])
     non_colliding_variations = {
         value: [
             v for v in variations if
@@ -195,22 +217,25 @@ def _validate_and_format_custom_entity(entity, queries_entities, language,
         validated_utterances = _add_entity_variations(
             validated_utterances, non_colliding_variations, entry_value)
 
-    # Merge queries entities
-    queries_entities_variations = {
-        ent: get_string_variations(ent, language, builtin_entity_parser)
-        for ent in queries_entities
+    # Merge utterances entities
+    utterance_entities_variations = {
+        ent: get_string_variations(
+            ent, language, builtin_entity_parser, **variations_args)
+        for ent in utterance_entities
     }
-    for original_ent, variations in iteritems(queries_entities_variations):
+
+    for original_ent, variations in iteritems(utterance_entities_variations):
         if not original_ent or original_ent in validated_utterances:
             continue
         validated_utterances[original_ent] = original_ent
         for variation in variations:
-            if variation and variation not in validated_utterances:
+            if variation and variation not in validated_utterances \
+                    and variation not in utterance_entities:
                 validated_utterances[variation] = original_ent
     formatted_entity[UTTERANCES] = validated_utterances
     return formatted_entity
 
 
-def _validate_and_format_builtin_entity(entity, queries_entities):
+def _validate_and_format_builtin_entity(entity, utterance_entities):
     validate_type(entity, dict, object_label="builtin entity")
-    return {UTTERANCES: set(queries_entities)}
+    return {UTTERANCES: set(utterance_entities)}

snips_nlu/default_configs/config_de.py

@@ -111,8 +111,7 @@
                     "min_utterances": 200,
                     "capitalization_ratio": 0.2,
                     "add_builtin_entities_examples": True
-                },
-                "random_seed": None
+                }
             },
             "intent_classifier_config": {
                 "unit_name": "log_reg_intent_classifier",
@@ -140,8 +139,7 @@
                         "unknown_words_replacement_string": None,
                         "keep_order": True
                     }
-                },
-                "random_seed": None
+                }
             }
         }
     ]

snips_nlu/default_configs/config_en.py

@@ -97,8 +97,7 @@
                     "min_utterances": 200,
                     "capitalization_ratio": 0.2,
                     "add_builtin_entities_examples": True
-                },
-                "random_seed": None
+                }
             },
             "intent_classifier_config": {
                 "unit_name": "log_reg_intent_classifier",
@@ -126,8 +125,7 @@
                         "unknown_words_replacement_string": None,
                         "keep_order": True
                     }
-                },
-                "random_seed": None
+                }
             }
         }
     ]

snips_nlu/default_configs/config_es.py

@@ -90,7 +90,7 @@
                     "capitalization_ratio": 0.2,
                     "add_builtin_entities_examples": True
                 },
-                "random_seed": None
+
             },
             "intent_classifier_config": {
                 "unit_name": "log_reg_intent_classifier",
@@ -118,8 +118,7 @@
                         "unknown_words_replacement_string": None,
                         "keep_order": True
                     }
-                },
-                "random_seed": None
+                }
             }
         }
     ]

snips_nlu/default_configs/config_fr.py

@@ -89,8 +89,7 @@
                     "min_utterances": 200,
                     "capitalization_ratio": 0.2,
                     "add_builtin_entities_examples": True
-                },
-                "random_seed": None
+                }
             },
             "intent_classifier_config": {
                 "unit_name": "log_reg_intent_classifier",
@@ -118,8 +117,7 @@
                         "unknown_words_replacement_string": None,
                         "keep_order": True
                     }
-                },
-                "random_seed": None
+                }
             }
         }
     ]