[ENH] Add language to corpus

MANIFEST.in

@@ -1,4 +1,4 @@
-recursive-include orangecontrib/text/datasets *.tab *.txt
+recursive-include orangecontrib/text/datasets *.tab *.txt *.metadata
 recursive-include orangecontrib/text/models *.ftz
 recursive-include orangecontrib/text/sentiment *.txt
 recursive-include orangecontrib/text/tests *.txt *.json *.pkl

orangecontrib/text/corpus.py

@@ -1,7 +1,7 @@
 import os
 import warnings
 from collections import Counter, defaultdict
-from copy import copy
+from copy import copy, deepcopy
 from numbers import Integral
 from itertools import chain
 from typing import Union, Optional, List, Tuple
@@ -24,6 +24,8 @@
 from Orange.preprocess.transformation import Identity
 from Orange.data.util import get_unique_names
 
+from orangecontrib.text.language import ISO2LANG
+
 try:
     from orangewidget.utils.signals import summarize, PartialSummary
     # import to check if Table summary is available - if summarize_by_name does
@@ -87,7 +89,6 @@ def _setup_corpus(self, text_features: List[Variable] = None) -> None:
         self._tokens = None
         self._dictionary = None
         self.ngram_range = (1, 1)
-        self.attributes = {}
         self._pos_tags = None
         from orangecontrib.text.preprocess import PreprocessorList
         self.__used_preprocessor = PreprocessorList([])   # required for compute values
@@ -100,6 +101,8 @@ def _setup_corpus(self, text_features: List[Variable] = None) -> None:
             self.set_text_features(text_features)
 
         self._set_unique_titles()
+        if "language" not in self.attributes:
+            self.attributes["language"] = None
 
     @property
     def used_preprocessor(self):
@@ -361,6 +364,10 @@ def titles(self):
         assert self._titles is not None
         return self._titles
 
+    @property
+    def language(self):
+        return self.attributes["language"]
+
     def documents_from_features(self, feats):
         """
         Args:
@@ -465,7 +472,7 @@ def copy(self):
         """Return a copy of the table."""
         c = super().copy()
         # since tokens and dictionary are considered immutable copies are not needed
-        c._setup_corpus(copy(self.text_features))
+        c._setup_corpus(text_features=copy(self.text_features))
         c._tokens = self._tokens
         c._dictionary = self._dictionary
         c.ngram_range = self.ngram_range
@@ -478,7 +485,7 @@ def copy(self):
 
     @staticmethod
     def from_documents(documents, name, attributes=None, class_vars=None, metas=None,
-                       title_indices=None):
+                       title_indices=None, language=None):
         """
         Create corpus from documents.
 
@@ -490,6 +497,7 @@ def from_documents(documents, name, attributes=None, class_vars=None, metas=None
             metas (list): List of tuples (Variable, getter) for metas.
             title_indices (list): List of indices into domain corresponding to features which will
                 be used as titles.
+            language (str): Resulting corpus's language
 
         Returns:
             Corpus.
@@ -527,6 +535,7 @@ def to_val(attr, val):
             domain=domain, X=X, Y=Y, metas=metas, text_features=[]
         )
         corpus.name = name
+        corpus.attributes["language"] = language
         return corpus
 
     def __getitem__(self, key):
@@ -540,6 +549,8 @@ def from_table(cls, domain, source, row_indices=...):
         c = super().from_table(domain, source, row_indices)
         c._setup_corpus()
         Corpus.retain_preprocessing(source, c, row_indices)
+        # temp fix: remove when oldest Orange >= 3.34
+        c.attributes = deepcopy(c.attributes)
         return c
 
     @classmethod
@@ -553,19 +564,24 @@ def from_numpy(
         attributes=None,
         ids=None,
         text_features=None,
+        language=None
     ):
         t = super().from_numpy(
             domain, X, Y=Y, metas=metas, W=W, attributes=attributes, ids=ids
         )
         # t is corpus but corpus specific attributes were not set yet
         t._setup_corpus(text_features=text_features)
+        # language can be already set in attributes if they provided
+        if language is not None or "language" not in t.attributes:
+            t.attributes["language"] = language
         return t
 
     @classmethod
-    def from_list(cls, domain, rows, weights=None):
+    def from_list(cls, domain, rows, weights=None, language=None):
         t = super().from_list(domain, rows, weights)
         # t is corpus but corpus specific attributes were not set yet
         t._setup_corpus()
+        t.attributes["language"] = language
         return t
 
     @classmethod
@@ -576,18 +592,20 @@ def from_table_rows(cls, source, row_indices):
         if hasattr(source, "_titles"):
             # covering case when from_table_rows called by from_table
             c._titles = source._titles[row_indices]
+        # temp fix: remove when oldest Orange >= 3.34
+        c.attributes = deepcopy(c.attributes)
         return c
 
     @classmethod
-    def from_file(cls, filename):
+    def from_file(cls, filename, sheet=None):
         if not os.path.exists(filename):  # check the default location
             abs_path = os.path.join(get_sample_corpora_dir(), filename)
             if not abs_path.endswith('.tab'):
                 abs_path += '.tab'
             if os.path.exists(abs_path):
                 filename = abs_path
 
-        table = super().from_file(filename)
+        table = super().from_file(filename, sheet=sheet)
         if not isinstance(table, Corpus):
             # when loading regular file result of super().from_file is Table - need
             # to be transformed to Corpus, when loading pickle it is Corpus already
@@ -659,4 +677,6 @@ def summarize_(corpus: Corpus) -> PartialSummary:
             if corpus.has_tokens()
             else "<br/><nobr>Corpus is not preprocessed</nobr>"
         )
+        language = ISO2LANG[corpus.language] if corpus.language else "not set"
+        extras += f"<br/><nobr>Language: {language}</nobr>"
         return PartialSummary(table_summary.summary, table_summary.details + extras)

orangecontrib/text/datasets/20newsgroups-test.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/20newsgroups-train.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/andersen.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/book-excerpts.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/deerwester.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/election-tweets-2016.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/friends-transcripts.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/grimm-tales-selected.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/grimm-tales.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/reuters-r52-test.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/reuters-r52-train.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/reuters-r8-test.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/reuters-r8-train.tab.metadata

@@ -0,0 +1 @@
+language: en

orangecontrib/text/datasets/slo-opinion-corpus.tab.metadata

@@ -0,0 +1 @@
+language: sl

orangecontrib/text/language.py

@@ -0,0 +1,165 @@
+from collections import Counter
+from typing import Optional
+
+from AnyQt.QtCore import Qt
+from langdetect import DetectorFactory, detect
+from langdetect.lang_detect_exception import LangDetectException
+from Orange.data import DiscreteVariable
+from orangewidget.utils.itemmodels import PyListModel
+
+# languages supported by at least one method in Orange3-text
+# language dependent methods: YAKE!, nltk - stopwords, sentiment methods,
+# normalizers, embedding
+ISO2LANG = {
+    "af": "Afrikaans",
+    "am": "Amharic",
+    "ar": "Arabic",
+    "az": "Azerbaijani",
+    "be": "Belarusian",
+    "bg": "Bulgarian",
+    "bn": "Bengali",
+    "bo": "Tibetan",
+    "br": "Breton",
+    "bs": "Bosnian",
+    "ca": "Catalan",
+    "ckb": "Central Kurdish",
+    "cop": "Coptic",
+    "cs": "Czech",
+    "cu": "Old Church Slavonic",
+    "cy": "Welsh",
+    "da": "Danish",
+    "de": "German",
+    "dv": "Divehi",
+    "el": "Greek",
+    "en": "English",
+    "es": "Spanish",
+    "et": "Estonian",
+    "eu": "Basque",
+    "fa": "Persian",
+    "fi": "Finnish",
+    "fr": "French",
+    "ga": "Irish",
+    "gl": "Galician",
+    "got": "Gothic",
+    "grc": "Ancient greek",
+    "gu": "Gujarati",
+    "he": "Hebrew",
+    "hi": "Hindi",
+    "hi-Latn": "Hindi (latin)",
+    "hr": "Croatian",
+    "ht": "Haitian",
+    "hu": "Hungarian",
+    "hy": "Armenian",
+    "id": "Indonesian",
+    "is": "Icelandic",
+    "it": "Italian",
+    "ja": "Japanese",
+    "ka": "Georgian",
+    "kk": "Kazakh",
+    "km": "Central Khmer",
+    "kn": "Kannada",
+    "ko": "Korean",
+    "la": "Latin",
+    "lo": "Lao",
+    "lt": "Lithuanian",
+    "lv": "Latvian",
+    "mk": "Macedonian",
+    "ml": "Malayalam",
+    "mr": "Marathi",
+    "my": "Burmese",
+    "nb": "Norwegian Bokmål",
+    "ne": "Nepali",
+    "nl": "Dutch",
+    "nn": "Norwegian Nynorsk",
+    "no": "Norwegian",
+    "or": "Oriya",
+    "pa": "Punjabi",
+    "pl": "Polish",
+    "ps": "Pashto",
+    "pt": "Portuguese",
+    "ro": "Romanian",
+    "ru": "Russian",
+    "sa": "Sanskrit",
+    "sd": "Sindhi",
+    "si": "Sinhala",
+    "sk": "Slovak",
+    "sl": "Slovenian",
+    "sr": "Serbian",
+    "sv": "Swedish",
+    "ta": "Tamil",
+    "te": "Telugu",
+    "tg": "Tajik",
+    "th": "Thai",
+    "tl": "Tagalog",
+    "tr": "Turkish",
+    "ug": "Uyghur",
+    "uk": "Ukrainian",
+    "ur": "Urdu",
+    "vi": "Vietnamese",
+    "zh": "Chinese",
+    None: None,
+}
+LANG2ISO = {lang: code for code, lang in ISO2LANG.items()}
+DEFAULT_LANGUAGE = "English"
+
+
+class LanguageModel(PyListModel):
+    """Model for language selection dropdowns in the widgets"""
+
+    def __init__(self):
+        languages = sorted(filter(None, ISO2LANG.values()))
+        super().__init__(iterable=[None] + languages)
+
+    def data(self, index, role=Qt.DisplayRole):
+        if index.row() == 0 and role == Qt.DisplayRole:
+            return "(no language)"
+        else:
+            return super().data(index, role)
+
+
+DetectorFactory.seed = 0
+MAX_DOCS = 50  # max number of documents considered for language detection
+MAX_WORDS = 2000  # max number of words in document considered for lang detection
+
+
+def detect_language(corpus: "Corpus") -> Optional[str]:
+    """
+    Detect the language in the corpus
+
+    Parameters
+    ----------
+    corpus
+        Corpus to detect the language
+
+    Returns
+    -------
+    Detected language ISO code or None if language not detected
+    """
+    texts = corpus.documents[:MAX_DOCS]
+    texts = [" ".join(t.replace("\n", " ").split(" ")[:MAX_WORDS]) for t in texts]
+    languages = list()
+    for text in texts:
+        try:
+            languages.append(detect(text))
+        except LangDetectException:
+            languages.append(None)
+    # filter out languages not in supported by Orange
+    candidates = [l for l, _ in Counter(languages).most_common() if l in ISO2LANG]
+    return candidates[0] if candidates else None
+
+
+def infer_language_from_variable(variable: DiscreteVariable) -> Optional[str]:
+    """
+    Infer language from DiscreteVariable that holds documents' language information.
+    If documents have different language return None
+
+    Parameters
+    ----------
+    variable
+        The DiscreteVariable to infer language from it's values
+
+    Returns
+    -------
+    Language ISO code if all documents have the same language, None otherwise
+    """
+    return variable.values[0] if len(variable.values) == 1 else None