Release 0.19.3

CHANGELOG.md

@@ -1,6 +1,13 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 
+## [0.19.3] - 2019-03-05
+### Fixed
+- Issue with intent classification reducing classification accuracy
+- Issue resulting in a mutation of the CRFSlotFillerConfig
+- Wrong required resources of the `DeterministicIntentParser`
+- Issue with non ASCII characters when using the parsing CLI with Python2 
+
 ## [0.19.2] - 2019-02-11
 ### Fixed
 - Fix an issue regarding the way builtin entities were handled by the `CRFSlotFiller`
@@ -236,6 +243,7 @@ several commands.
 - Fix compiling issue with `bindgen` dependency when installing from source
 - Fix issue in `CRFSlotFiller` when handling builtin entities
 
+[0.19.3]: https://github.com/snipsco/snips-nlu/compare/0.19.2...0.19.3
 [0.19.2]: https://github.com/snipsco/snips-nlu/compare/0.19.1...0.19.2
 [0.19.1]: https://github.com/snipsco/snips-nlu/compare/0.19.0...0.19.1
 [0.19.0]: https://github.com/snipsco/snips-nlu/compare/0.18.0...0.19.0

snips_nlu/__about__.py

@@ -11,7 +11,7 @@
 __email__ = "[email protected], [email protected]"
 __license__ = "Apache License, Version 2.0"
 
-__version__ = "0.19.2"
+__version__ = "0.19.3"
 __model_version__ = "0.19.0"
 
 __download_url__ = "https://github.com/snipsco/snips-nlu-language-resources/releases/download"

snips_nlu/cli/inference.py

@@ -18,6 +18,7 @@
 )
 def parse(training_path, query, verbose=False):
     """Load a trained NLU engine and play with its parsing API interactively"""
+    from builtins import str
     if verbose:
         set_nlu_logger(logging.DEBUG)
 
@@ -29,6 +30,8 @@ def parse(training_path, query, verbose=False):
 
     while True:
         query = input("Enter a query (type 'q' to quit): ").strip()
+        if not isinstance(query, str):
+            query = query.decode("utf-8")
         if query == "q":
             break
         print_parsing_result(engine, query)

snips_nlu/cli/utils.py

@@ -7,6 +7,7 @@
 from enum import Enum, unique
 
 import requests
+from semantic_version import Version
 
 import snips_nlu
 from snips_nlu import __about__
@@ -71,13 +72,16 @@ def get_json(url, desc):
 
 def get_compatibility():
     version = __about__.__version__
+    semver_version = Version(version)
+    minor_version = "%d.%d" % (semver_version.major, semver_version.minor)
     table = get_json(__about__.__compatibility__, "Compatibility table")
-    compatibility = table["snips-nlu"]
-    if version not in compatibility:
+    nlu_table = table["snips-nlu"]
+    compatibility = nlu_table.get(version, nlu_table.get(minor_version))
+    if compatibility is None:
         pretty_print("No compatible resources found for version %s" % version,
                      title="Resources compatibility error", exits=1,
                      level=PrettyPrintLevel.ERROR)
-    return compatibility[version]
+    return compatibility
 
 
 def get_resources_version(resource_fullname, resource_alias, compatibility):

snips_nlu/common/log_utils.py

@@ -1,3 +1,6 @@
+from __future__ import unicode_literals
+
+from builtins import str
 from datetime import datetime
 from functools import wraps
 

snips_nlu/data_augmentation.py

@@ -88,7 +88,7 @@ def get_intent_entities(dataset, intent_name):
         for chunk in utterance[DATA]:
             if ENTITY in chunk:
                 intent_entities.add(chunk[ENTITY])
-    return intent_entities
+    return sorted(intent_entities)
 
 
 def num_queries_to_generate(dataset, intent_name, min_utterances):

snips_nlu/dataset/validation.py

@@ -128,7 +128,7 @@ def _validate_and_format_custom_entity(entity, queries_entities, language,
     validate_type(entity[AUTOMATICALLY_EXTENSIBLE], bool,
                   object_label="automatically_extensible")
     validate_type(entity[DATA], list, object_label="entity data")
-    validate_type(entity[MATCHING_STRICTNESS], float,
+    validate_type(entity[MATCHING_STRICTNESS], (float, int),
                   object_label="matching_strictness")
 
     formatted_entity = dict()

snips_nlu/intent_classifier/featurizer.py

@@ -17,8 +17,7 @@
     json_string, fitted_required, replace_entities_with_placeholders,
     check_persisted_path)
 from snips_nlu.constants import (
-    DATA, END, ENTITY, ENTITY_KIND, LANGUAGE, NGRAM, RES_MATCH_RANGE,
-    RES_VALUE, START, TEXT, ENTITIES)
+    DATA, ENTITY, ENTITY_KIND, LANGUAGE, NGRAM, TEXT, ENTITIES)
 from snips_nlu.dataset import get_text_from_chunks, validate_and_format_dataset
 from snips_nlu.entity_parser.builtin_entity_parser import (
     is_builtin_entity)
@@ -264,7 +263,7 @@ def fit(self, x, dataset):
         self._init_vectorizer(self._language)
         self.builtin_entity_scope = set(
             e for e in dataset[ENTITIES] if is_builtin_entity(e))
-        preprocessed_data = self._preprocess(x, training=True)
+        preprocessed_data = self._preprocess(x)
         utterances = [
             self._enrich_utterance(u, builtin_ents, custom_ents, w_clusters)
             for u, builtin_ents, custom_ents, w_clusters
@@ -296,7 +295,7 @@ def fit_transform(self, x, dataset):
         self._init_vectorizer(self._language)
         self.builtin_entity_scope = set(
             e for e in dataset[ENTITIES] if is_builtin_entity(e))
-        preprocessed_data = self._preprocess(x, training=True)
+        preprocessed_data = self._preprocess(x)
         utterances = [
             self._enrich_utterance(u, builtin_ents, custom_ents, w_clusters)
             for u, builtin_ents, custom_ents, w_clusters
@@ -330,31 +329,30 @@ def transform(self, x):
                       for data in zip(*self._preprocess(x))]
         return self._tfidf_vectorizer.transform(utterances)
 
-    def _preprocess(self, utterances, training=False):
+    def _preprocess(self, utterances):
         normalized_utterances = deepcopy(utterances)
         for u in normalized_utterances:
-            for chunk in u[DATA]:
+            nb_chunks = len(u[DATA])
+            for i, chunk in enumerate(u[DATA]):
                 chunk[TEXT] = _normalize_stem(
                     chunk[TEXT], self.language, self.resources,
                     self.config.use_stemming)
-
-        if training:
-            builtin_ents, custom_ents = zip(
-                *[_entities_from_utterance(u) for u in utterances])
-        else:
-            # Extract builtin entities on unormalized utterances
-            builtin_ents = [
-                self.builtin_entity_parser.parse(
-                    get_text_from_chunks(u[DATA]),
-                    self.builtin_entity_scope, use_cache=True)
-                for u in utterances
-            ]
-            # Extract builtin entities on normalized utterances
-            custom_ents = [
-                self.custom_entity_parser.parse(
-                    get_text_from_chunks(u[DATA]), use_cache=True)
-                for u in normalized_utterances
-            ]
+                if i < nb_chunks - 1:
+                    chunk[TEXT] += " "
+
+        # Extract builtin entities on unormalized utterances
+        builtin_ents = [
+            self.builtin_entity_parser.parse(
+                get_text_from_chunks(u[DATA]),
+                self.builtin_entity_scope, use_cache=True)
+            for u in utterances
+        ]
+        # Extract builtin entities on normalized utterances
+        custom_ents = [
+            self.custom_entity_parser.parse(
+                get_text_from_chunks(u[DATA]), use_cache=True)
+            for u in normalized_utterances
+        ]
         if self.config.word_clusters_name:
             # Extract world clusters on unormalized utterances
             original_utterances_text = [get_text_from_chunks(u[DATA])
@@ -582,7 +580,7 @@ def fit(self, x, dataset):
         self.builtin_entity_scope = set(
             e for e in dataset[ENTITIES] if is_builtin_entity(e))
 
-        preprocessed = self._preprocess(list(x), training=True)
+        preprocessed = self._preprocess(list(x))
         utterances = [
             self._enrich_utterance(utterance, builtin_ents, custom_ent)
             for utterance, builtin_ents, custom_ent in zip(*preprocessed)]
@@ -648,7 +646,7 @@ def transform(self, x):
         Raises:
             NotTrained: when the vectorizer is not fitted
         """
-        preprocessed = self._preprocess(x, training=False)
+        preprocessed = self._preprocess(x)
         utterances = [
             self._enrich_utterance(utterance, builtin_ents, custom_ent)
             for utterance, builtin_ents, custom_ent in zip(*preprocessed)]
@@ -661,24 +659,20 @@ def transform(self, x):
 
         return x_coo.tocsr()
 
-    def _preprocess(self, x, training=False):
-        if training:
-            builtin_ents, custom_ents = zip(
-                *[_entities_from_utterance(u) for u in x])
-        else:
-            # Extract all entities on unnormalized data
-            builtin_ents = [
-                self.builtin_entity_parser.parse(
-                    get_text_from_chunks(u[DATA]),
-                    self.builtin_entity_scope,
-                    use_cache=True
-                ) for u in x
-            ]
-            custom_ents = [
-                self.custom_entity_parser.parse(
-                    get_text_from_chunks(u[DATA]), use_cache=True)
-                for u in x
-            ]
+    def _preprocess(self, x):
+        # Extract all entities on unnormalized data
+        builtin_ents = [
+            self.builtin_entity_parser.parse(
+                get_text_from_chunks(u[DATA]),
+                self.builtin_entity_scope,
+                use_cache=True
+            ) for u in x
+        ]
+        custom_ents = [
+            self.custom_entity_parser.parse(
+                get_text_from_chunks(u[DATA]), use_cache=True)
+            for u in x
+        ]
         return x, builtin_ents, custom_ents
 
     def _extract_word_pairs(self, utterance):
@@ -805,27 +799,3 @@ def _get_word_cluster_features(query_tokens, clusters_name, resources):
         if cluster is not None:
             cluster_features.append(cluster)
     return cluster_features
-
-
-def _entities_from_utterance(utterance):
-    builtin_ents = []
-    custom_ents = []
-    current_ix = 0
-    for chunk in utterance[DATA]:
-        text = chunk[TEXT]
-        text_length = len(text)
-        if ENTITY in chunk:
-            ent = {
-                ENTITY_KIND: chunk[ENTITY],
-                RES_VALUE: text,
-                RES_MATCH_RANGE: {
-                    START: current_ix,
-                    END: current_ix + text_length
-                }
-            }
-            if is_builtin_entity(ent[ENTITY_KIND]):
-                builtin_ents.append(ent)
-            else:
-                custom_ents.append(ent)
-        current_ix += text_length
-    return builtin_ents, custom_ents

snips_nlu/intent_classifier/log_reg_classifier.py

@@ -87,13 +87,12 @@ def fit(self, dataset):
 
         none_class = max(classes)
         try:
-            self.featurizer = self.featurizer.fit(
+            x = self.featurizer.fit_transform(
                 dataset, utterances, classes, none_class)
         except _EmptyDatasetUtterancesError:
             self.featurizer = None
             return self
 
-        x = self.featurizer.transform(utterances)
         alpha = get_regularization_factor(dataset)
         self.classifier = SGDClassifier(random_state=random_state,
                                         alpha=alpha, **LOG_REG_ARGS)

snips_nlu/intent_classifier/log_reg_classifier_utils.py

@@ -9,7 +9,7 @@
 import numpy as np
 from future.utils import iteritems, itervalues
 
-from snips_nlu.constants import (DATA, ENTITIES, ENTITY, INTENTS, TEXT,
+from snips_nlu.constants import (DATA, ENTITY, INTENTS, TEXT,
                                  UNKNOWNWORD, UTTERANCES)
 from snips_nlu.data_augmentation import augment_utterances
 from snips_nlu.dataset import get_text_from_chunks
@@ -108,22 +108,6 @@ def add_unknown_word_to_utterances(utterances, replacement_string,
     return new_utterances
 
 
-def get_dataset_specific_noise(dataset, resources):
-    """Return a noise list that excludes the dataset entity values"""
-    entities_values = set()
-    for ent_name, ent in iteritems(dataset[ENTITIES]):
-        if is_builtin_entity(ent_name):
-            continue
-        for k, v in iteritems(ent[UTTERANCES]):
-            entities_values.add(k)
-            entities_values.add(v)
-    original_noise = get_noise(resources)
-    specific_noise = [n for n in original_noise if n not in entities_values]
-    if not specific_noise:  # Avoid returning an empty noise
-        return original_noise
-    return specific_noise
-
-
 def build_training_data(dataset, language, data_augmentation_config, resources,
                         random_state):
     # Create class mapping
@@ -164,7 +148,7 @@ def build_training_data(dataset, language, data_augmentation_config, resources,
         )
 
     # Adding noise
-    noise = get_dataset_specific_noise(dataset, resources)
+    noise = get_noise(resources)
     noisy_utterances = generate_noise_utterances(
         augmented_utterances, noise, len(intents), data_augmentation_config,
         language, random_state)

snips_nlu/intent_parser/deterministic_intent_parser.py

@@ -127,6 +127,7 @@ def fit(self, dataset, force_retrain=True):
         """Fits the intent parser with a valid Snips dataset"""
         logger.info("Fitting deterministic parser...")
         dataset = validate_and_format_dataset(dataset)
+        self.load_resources_if_needed(dataset[LANGUAGE])
         self.fit_builtin_entity_parser_if_needed(dataset)
         self.fit_custom_entity_parser_if_needed(dataset)
         self.language = dataset[LANGUAGE]
@@ -313,7 +314,7 @@ def _get_matching_result(self, text, processed_text, regex, intent,
         for group_name in found_result.groupdict():
             ref_group_name = group_name
             if "_" in group_name:
-                ref_group_name = group_name[:(len(group_name) - 2)]
+                ref_group_name = group_name.split("_")[0]
             slot_name = self.group_names_to_slot_names[ref_group_name]
             entity = self.slot_names_to_entities[intent][slot_name]
             rng = (found_result.start(group_name),

snips_nlu/pipeline/configs/intent_parser.py

@@ -1,7 +1,7 @@
 from __future__ import unicode_literals
 
 from snips_nlu.common.from_dict import FromDict
-from snips_nlu.constants import CUSTOM_ENTITY_PARSER_USAGE
+from snips_nlu.constants import CUSTOM_ENTITY_PARSER_USAGE, STOP_WORDS
 from snips_nlu.entity_parser import CustomEntityParserUsage
 from snips_nlu.pipeline.configs import ProcessingUnitConfig
 from snips_nlu.resources import merge_required_resources
@@ -84,7 +84,8 @@ def unit_name(self):
 
     def get_required_resources(self):
         return {
-            CUSTOM_ENTITY_PARSER_USAGE: CustomEntityParserUsage.WITHOUT_STEMS
+            CUSTOM_ENTITY_PARSER_USAGE: CustomEntityParserUsage.WITHOUT_STEMS,
+            STOP_WORDS: self.ignore_stop_words
         }
 
     def to_dict(self):

snips_nlu/slot_filler/crf_slot_filler.py

@@ -7,6 +7,7 @@
 import shutil
 import tempfile
 from builtins import range
+from copy import deepcopy
 from pathlib import Path
 
 from future.utils import iteritems
@@ -48,6 +49,9 @@ class CRFSlotFiller(SlotFiller):
     def __init__(self, config=None, **shared):
         """The CRF slot filler can be configured by passing a
         :class:`.CRFSlotFillerConfig`"""
+        # The CRFSlotFillerConfig must be deep-copied as it is mutated when
+        # fitting the feature factories
+        config = deepcopy(config)
         super(CRFSlotFiller, self).__init__(config, **shared)
         self.crf_model = None
         self.features_factories = [

snips_nlu/tests/test_crf_slot_filler.py

@@ -97,6 +97,7 @@ def test_should_get_sub_builtin_slots(self):
 - find me something from [start](9am) to [end](12pm)
 - I need a break from [start](2pm) until [end](4pm)
 - Can you suggest something from [start](april 4th) until [end](april 6th) ?
+- find an activity from [start](6pm) to [end](8pm)
 - Book me a trip from [start](this friday) to [end](next tuesday)""")
         dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
         config = CRFSlotFillerConfig(random_seed=42)