[ENH] Download NLTK data asynchronously

orangecontrib/text/misc/__init__.py

@@ -1 +1 @@
-from .environ import *
+from .nltk_data_download import *

orangecontrib/text/misc/nltk_data_download.py

@@ -0,0 +1,56 @@
+import os
+import sys
+import time
+from functools import wraps
+from threading import Thread
+
+import nltk
+from Orange.misc.environ import data_dir_base
+
+__all__ = ['wait_nltk_data', 'nltk_data_dir']
+
+NLTK_DATA = [
+    'wordnet',
+    'stopwords',
+    'punkt',
+    'opinion_lexicon',
+    'vader_lexicon',
+    'averaged_perceptron_tagger',
+    'maxent_treebank_pos_tagger',
+]
+
+
+def nltk_data_dir():
+    """ Location where the NLTK data is stored. """
+    dir_ = os.path.join(data_dir_base(), 'Orange', 'nltk_data')
+    # make sure folder exists for ReadTheDocs
+    os.makedirs(dir_, exist_ok=True)
+    return dir_
+
+
+is_done_loading = False
+
+
+def _download_nltk_data():
+    global is_done_loading
+    nltk.download(NLTK_DATA, download_dir=nltk_data_dir())
+    is_done_loading = True
+    sys.stdout.flush()
+
+
+Thread(target=_download_nltk_data).start()
+
+
+def wait_nltk_data(func):
+    """ Decorator that waits until all NLTK data is downloaded. """
+    dir_ = nltk_data_dir()
+    if dir_ not in nltk.data.path:  # assure NLTK knows where the data is
+        nltk.data.path.append(dir_)
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        global is_done_loading
+        while not is_done_loading:
+            time.sleep(.1)
+        return func(*args, **kwargs)
+    return wrapper

orangecontrib/text/preprocess/__init__.py

@@ -30,10 +30,6 @@
     ['human', 'machine', 'interface', 'for', 'lab', 'abc', 'computer', 'applications']
 
 """
-import nltk
-
-nltk.download(["wordnet", "stopwords", "punkt"])
-
 from .filter import *
 from .normalize import *
 from .tokenize import *

orangecontrib/text/preprocess/filter.py

@@ -5,6 +5,8 @@
 from gensim import corpora
 from nltk.corpus import stopwords
 
+from orangecontrib.text.misc import wait_nltk_data
+
 __all__ = ['BaseTokenFilter', 'StopwordsFilter', 'LexiconFilter', 'RegexpFilter', 'FrequencyFilter']
 
 
@@ -65,6 +67,7 @@ class StopwordsFilter(BaseTokenFilter, WordListMixin):
 
     supported_languages = [file.capitalize() for file in stopwords_listdir]
 
+    @wait_nltk_data
     def __init__(self, language='English', word_list=None):
         WordListMixin.__init__(self, word_list)
         super().__init__()

orangecontrib/text/preprocess/normalize.py

@@ -1,5 +1,7 @@
 from nltk import stem
 
+from orangecontrib.text.misc import wait_nltk_data
+
 __all__ = ['BaseNormalizer', 'WordNetLemmatizer', 'PorterStemmer',
            'SnowballStemmer', 'DictionaryLookupNormalizer']
 
@@ -35,6 +37,10 @@ class WordNetLemmatizer(BaseNormalizer):
     name = 'WordNet Lemmatizer'
     normalizer = stem.WordNetLemmatizer().lemmatize
 
+    @wait_nltk_data
+    def __init__(self):
+        super().__init__()
+
 
 class DictionaryLookupNormalizer(BaseNormalizer):
     """ Normalizes token with a <token: canonical_form> dictionary. """

orangecontrib/text/preprocess/preprocess.py

@@ -5,6 +5,7 @@
 __all__ = ['Preprocessor', 'base_preprocessor']
 
 
+# don't use anything that requires NLTK data to assure async download
 BASE_TOKENIZER = WordPunctTokenizer()
 BASE_TRANSFORMERS = [LowercaseTransformer()]
 

orangecontrib/text/preprocess/tokenize.py

@@ -1,6 +1,7 @@
 import re
 from nltk import tokenize
 
+from orangecontrib.text.misc import wait_nltk_data
 
 __all__ = ['BaseTokenizer', 'WordPunctTokenizer', 'PunktSentenceTokenizer',
            'RegexpTokenizer', 'WhitespaceTokenizer', 'TweetTokenizer']
@@ -48,6 +49,10 @@ class PunktSentenceTokenizer(BaseTokenizer):
     tokenizer = tokenize.PunktSentenceTokenizer()
     name = 'Sentence'
 
+    @wait_nltk_data
+    def __init__(self):
+        super().__init__()
+
 
 class WhitespaceTokenizer(BaseTokenizer):
     """ Split only by whitespace. """

orangecontrib/text/sentiment/__init__.py

@@ -1,24 +1,23 @@
-import nltk
 import numpy as np
 from nltk.corpus import opinion_lexicon
 from nltk.sentiment import SentimentIntensityAnalyzer
 
 from orangecontrib.text import Corpus
+from orangecontrib.text.misc import wait_nltk_data
 from orangecontrib.text.preprocess import WordPunctTokenizer
 from orangecontrib.text.vectorization.base import SharedTransform, \
     VectorizationComputeValue
 
-nltk.download(["opinion_lexicon", "vader_lexicon"])
-
 
 class Liu_Hu_Sentiment:
-    positive = set(opinion_lexicon.positive())
-    negative = set(opinion_lexicon.negative())
     sentiments = ('sentiment',)
     name = 'Liu Hu'
 
+    @wait_nltk_data
     def __init__(self):
         super().__init__()
+        self.positive = set(opinion_lexicon.positive())
+        self.negative = set(opinion_lexicon.negative())
 
     def transform(self, corpus, copy=True):
         scores = []
@@ -46,6 +45,7 @@ class Vader_Sentiment:
     sentiments = ('pos', 'neg', 'neu', 'compound')
     name = 'Vader'
 
+    @wait_nltk_data
     def __init__(self):
         super().__init__()
         self.vader = SentimentIntensityAnalyzer()

orangecontrib/text/tag/__init__.py

@@ -5,17 +5,15 @@
 This module provides a default `pos_tagger` that can be used for POSTagging an English corpus::
 
     >>> from orangecontrib.text.corpus import Corpus
-    >>> from orangecontrib.text.tag import pos_tagger
+    >>> from orangecontrib.text.tag import AveragedPerceptronTagger
     >>> corpus = Corpus.from_file('deerwester.tab')
-    >>> tagged_corpus = pos_tagger.tag_corpus(corpus)
+    >>> tagger = AveragedPerceptronTagger()
+    >>> tagged_corpus = tagger.tag_corpus(corpus)
     >>> tagged_corpus.pos_tags[0]  # you can use `pos_tags` attribute to access tags directly
     ['JJ', 'NN', 'NN', 'IN', 'NN', 'NN', 'NN', 'NNS']
     >>> next(tagged_corpus.ngrams_iterator(include_postags=True))  # or `ngrams_iterator` to iterate over documents
     ['human_JJ', 'machine_NN', 'interface_NN', 'for_IN', 'lab_NN', 'abc_NN', 'computer_NN', 'applications_NNS']
 
-
 """
 
-from .pos import POSTagger, StanfordPOSTagger, taggers
-
-pos_tagger = taggers[0]
+from .pos import *

orangecontrib/text/tag/pos.py

@@ -2,8 +2,10 @@
 import numpy as np
 
 from orangecontrib.text.util import chunkable
+from orangecontrib.text.misc import wait_nltk_data
 
-nltk.download(['averaged_perceptron_tagger', 'maxent_treebank_pos_tagger'])
+
+__all__ = ['POSTagger', 'StanfordPOSTagger', 'AveragedPerceptronTagger', 'MaxEntTagger']
 
 
 class POSTagger:
@@ -62,8 +64,18 @@ def __str__(self):
         return "{} (model: {})".format(self.name, self._stanford_model)
 
 
-taggers = [
-    POSTagger(nltk.PerceptronTagger(), 'Averaged Perceptron Tagger'),
-    POSTagger(nltk.data.load('taggers/maxent_treebank_pos_tagger/english.pickle'),
-              'Treebank POS Tagger (MaxEnt)'),
-]
+class AveragedPerceptronTagger(POSTagger):
+    name = 'Averaged Perceptron Tagger'
+
+    @wait_nltk_data
+    def __init__(self):
+        super().__init__(nltk.PerceptronTagger(), self.name)
+
+
+class MaxEntTagger(POSTagger):
+    name = 'Treebank POS Tagger (MaxEnt)'
+
+    @wait_nltk_data
+    def __init__(self):
+        tagger = nltk.data.load('taggers/maxent_treebank_pos_tagger/english.pickle')
+        super().__init__(tagger, self.name)

orangecontrib/text/tests/test_corpus.py

@@ -10,10 +10,13 @@
 
 from orangecontrib.text import preprocess
 from orangecontrib.text.corpus import Corpus
-from orangecontrib.text.tag import pos_tagger
+from orangecontrib.text.tag import AveragedPerceptronTagger
 
 
 class CorpusTests(unittest.TestCase):
+    def setUp(self):
+        self.pos_tagger = AveragedPerceptronTagger()
+
     def test_init_preserve_shape_of_empty_x(self):
         c = Corpus.from_file('book-excerpts')
         d = c.domain
@@ -66,7 +69,7 @@ def test_extend(self):
         c2 = c[:5]
         self.assertEqual(len(c2), 5)
         n = len(c)
-        pos_tagger.tag_corpus(c)
+        self.pos_tagger.tag_corpus(c)
         self.assertIsNot(c._tokens, None)
         self.assertIsNot(c.pos_tags, None)
         self.assertIs(c2._tokens, None)
@@ -77,8 +80,8 @@ def test_extend(self):
         self.assertIs(c._tokens, None)
         self.assertIs(c.pos_tags, None)
 
-        pos_tagger.tag_corpus(c)
-        pos_tagger.tag_corpus(c2)
+        self.pos_tagger.tag_corpus(c)
+        self.pos_tagger.tag_corpus(c2)
         c.extend(c2)
         self.assertEqual(len(c), n + 10)
         self.assertEqual(len(c._tokens), n + 10)
@@ -330,7 +333,7 @@ def test_ngrams_iter(self):
             self.assertIn(ngram, list(c.ngrams_iterator(join_with=None))[0])
             self.assertIn('-'.join(ngram), list(c.ngrams_iterator(join_with='-'))[0])
 
-        pos_tagger.tag_corpus(c)
+        self.pos_tagger.tag_corpus(c)
         c.ngram_range = (1, 1)
         for doc in c.ngrams_iterator(join_with='_', include_postags=True):
             for token in doc:

orangecontrib/text/tests/test_tags.py

@@ -10,7 +10,7 @@
 class POSTaggerTests(unittest.TestCase):
     def test_POSTagger(self):
         corpus = Corpus.from_file('deerwester')
-        tagger = tag.pos_tagger
+        tagger = tag.AveragedPerceptronTagger()
         result = tagger.tag_corpus(corpus)
         self.assertTrue(hasattr(result, 'pos_tags'))
         # for token in itertools.chain(*result.tokens):
@@ -33,7 +33,7 @@ def test_str(self):
 
     def test_preprocess(self):
         pr = preprocess.Preprocessor(tokenizer=preprocess.RegexpTokenizer('\w+'),
-                                     pos_tagger=tag.taggers[0])
+                                     pos_tagger=tag.AveragedPerceptronTagger())
         corpus = Corpus.from_file('deerwester')
         pr(corpus, inplace=True)
         self.assertIsNotNone(corpus.pos_tags)

orangecontrib/text/widgets/owpreprocess.py

@@ -12,8 +12,8 @@
 from orangecontrib.text import preprocess
 from orangecontrib.text.corpus import Corpus
 from orangecontrib.text.misc import nltk_data_dir
-from orangecontrib.text.tag import StanfordPOSTagger
-from orangecontrib.text.tag import taggers
+from orangecontrib.text.tag import StanfordPOSTagger, AveragedPerceptronTagger, \
+    MaxEntTagger
 from orangecontrib.text.widgets.utils import widgets, ResourceLoader
 from orangecontrib.text.widgets.utils.concurrent import asynchronous
 
@@ -453,14 +453,20 @@ class POSTaggingModule(SingleMethodModule):
     attribute = 'pos_tagger'
     enabled = settings.Setting(False)
 
-    STANFORD = len(taggers)
     stanford = settings.SettingProvider(ResourceLoader)
 
-    methods = taggers + [StanfordPOSTagger]
+    methods = [AveragedPerceptronTagger, MaxEntTagger, StanfordPOSTagger]
+    STANFORD = 2
+
     initialize_methods = False
 
     def setup_method_layout(self):
         super().setup_method_layout()
+        # initialize all methods except StanfordPOSTagger
+        # cannot be done in superclass due to StanfordPOSTagger
+        for i, method in enumerate(self.methods[:self.STANFORD]):
+            self.methods[i] = method()
+
         self.stanford = ResourceLoader(widget=self.master, model_format='Stanford model (*.model *.tagger)',
                                        provider_format='Java file (*.jar)',
                                        model_button_label='Model', provider_button_label='Tagger')

orangecontrib/text/widgets/owsentimentanalysis.py

@@ -7,10 +7,6 @@
 from orangecontrib.text.sentiment import Vader_Sentiment, Liu_Hu_Sentiment
 
 
-METHODS = [Liu_Hu_Sentiment(),
-           Vader_Sentiment()]
-
-
 class OWSentimentAnalysis(OWWidget):
     name = "Sentiment Analysis"
     description = "Predict sentiment from text."
@@ -30,10 +26,14 @@ class Outputs:
 
     def __init__(self):
         super().__init__()
+        self.METHODS = [
+            Liu_Hu_Sentiment(),
+            Vader_Sentiment()
+        ]
         self.corpus = None
 
         gui.radioButtons(self.controlArea, self, "method_idx", box="Method",
-                         btnLabels=[m.name for m in METHODS],
+                         btnLabels=[m.name for m in self.METHODS],
                          callback=self._method_changed)
 
         ac = gui.auto_commit(self.controlArea, self, 'autocommit', 'Commit',
@@ -51,15 +51,15 @@ def _method_changed(self):
 
     def commit(self):
         if self.corpus is not None:
-            method = METHODS[self.method_idx]
+            method = self.METHODS[self.method_idx]
             out = method.transform(self.corpus)
             self.Outputs.corpus.send(out)
         else:
             self.Outputs.corpus.send(None)
 
     def send_report(self):
         self.report_items((
-            ('Method', METHODS[self.method_idx].name),
+            ('Method', self.METHODS[self.method_idx].name),
         ))