🎨 dinglehopper: Use multimethod to handle str vs ExtractedText

qurator/dinglehopper/character_error_rate.py

@@ -3,25 +3,22 @@ from __future__ import division
 import unicodedata
 from typing import Tuple
 
+from multimethod import multimethod
 from uniseg.graphemecluster import grapheme_clusters
 
 from qurator.dinglehopper.edit_distance import distance
 from qurator.dinglehopper.ocr_files import ExtractedText
 
-
+@multimethod
-def character_error_rate_n(reference, compared) -> Tuple[float, int]:
+def character_error_rate_n(reference: str, compared: str) -> Tuple[float, int]:
     """
     Compute character error rate.
 
     :return: character error rate and length of the reference
     """
-    if isinstance(reference, str):
-        return character_error_rate_n(
-                ExtractedText.from_str(reference),
-                compared)
 
     d = distance(reference, compared)
-    n = len(list(grapheme_clusters(reference.text)))
+    n = len(list(grapheme_clusters(unicodedata.normalize('NFC', reference))))
 
     if d == 0:
         return 0, n
@@ -32,6 +29,11 @@ def character_error_rate_n(reference, compared) -> Tuple[float, int]:
     # XXX Should we really count newlines here?
 
 
+@multimethod
+def character_error_rate_n(reference: ExtractedText, compared: ExtractedText) -> Tuple[float, int]:
+    return character_error_rate_n(reference.text, compared.text)
+
+
 def character_error_rate(reference, compared) -> float:
     """
     Compute character error rate.

qurator/dinglehopper/edit_distance.py

@@ -5,6 +5,7 @@ from functools import partial, lru_cache
 from typing import Sequence, Tuple
 
 import numpy as np
+from multimethod import multimethod
 from uniseg.graphemecluster import grapheme_clusters
 
 from .ocr_files import ExtractedText
@@ -70,23 +71,21 @@ def levenshtein_matrix_cache_clear():
     _levenshtein_matrix.cache_clear()
 
 
-def distance(s1, s2):
+@multimethod
+def distance(s1: str, s2: str):
     """Compute the Levenshtein edit distance between two Unicode strings
 
     Note that this is different from levenshtein() as this function knows about Unicode normalization and grapheme
     clusters. This should be the correct way to compare two Unicode strings.
     """
+    seq1 = list(grapheme_clusters(unicodedata.normalize('NFC', s1)))
+    seq2 = list(grapheme_clusters(unicodedata.normalize('NFC', s2)))
+    return levenshtein(seq1, seq2)
 
-    # XXX Implicit normalization
-    if isinstance(s1, str):
-        s1 = ExtractedText.from_str(s1)
-    if isinstance(s2, str):
-        s2 = ExtractedText.from_str(s2)
-    # s1 and s2 are now guaranteed (by ExtractedText) to be in NFC
 
-    seq1 = list(grapheme_clusters(s1.text))
+@multimethod
-    seq2 = list(grapheme_clusters(s2.text))
+def distance(s1: ExtractedText, s2: ExtractedText):
-    return levenshtein(seq1, seq2)
+    return distance(s1.text, s2.text)
 
 
 def seq_editops(seq1, seq2):

qurator/dinglehopper/word_error_rate.py

@@ -1,14 +1,19 @@
 from __future__ import division
 
 import unicodedata
-from typing import Tuple
+from typing import Tuple, Iterable
+from multimethod import multimethod
 
 import uniseg.wordbreak
 
 from .edit_distance import levenshtein
+from .ocr_files import ExtractedText
 
 
-def words(s):
+@multimethod
+def words(s: str):
+    """Extract words from a string"""
+
     # Patch uniseg.wordbreak.word_break to deal with our private use characters. See also
     # https://www.unicode.org/Public/UCD/latest/ucd/auxiliary/WordBreakProperty.txt
     old_word_break = uniseg.wordbreak.word_break
@@ -32,11 +37,6 @@ def words(s):
         cat = subcat[0]
         return cat in unwanted_categories or subcat in unwanted_subcategories
 
-    # XXX
-    from .cli import ExtractedText
-    if isinstance(s, ExtractedText):
-        s = s.text
-
     # We follow Unicode Standard Annex #29 on Unicode Text Segmentation here: Split on word boundaries using
     # uniseg.wordbreak.words() and ignore all "words" that contain only whitespace, punctation "or similar characters."
     for word in uniseg.wordbreak.words(s):
@@ -46,27 +46,37 @@ def words(s):
             yield word
 
 
-def words_normalized(s):
+@multimethod
-    # XXX
+def words(s: ExtractedText):
-    from .cli import ExtractedText
+    return words(s.text)
-    if isinstance(s, ExtractedText):
+
-        s = s.text
+
+@multimethod
+def words_normalized(s: str):
     return words(unicodedata.normalize('NFC', s))
 
 
-def word_error_rate_n(reference, compared) -> Tuple[float, int]:
+@multimethod
-    # XXX
+def words_normalized(s: ExtractedText):
-    from .cli import ExtractedText
+    return words_normalized(s.text)
-    if isinstance(reference, ExtractedText):
+
-        reference = reference.text
+
-    if isinstance(compared, ExtractedText):
+@multimethod
-        compared = compared.text
+def word_error_rate_n(reference: str, compared: str) -> Tuple[float, int]:
-    if isinstance(reference, str):
+    reference_seq = list(words_normalized(reference))
-        reference_seq = list(words_normalized(reference))
+    compared_seq = list(words_normalized(compared))
-        compared_seq = list(words_normalized(compared))
+    return word_error_rate_n(reference_seq, compared_seq)
-    else:
+
-        reference_seq = list(reference)
+
-        compared_seq = list(compared)
+@multimethod
+def word_error_rate_n(reference: ExtractedText, compared: ExtractedText) -> Tuple[float, int]:
+    return word_error_rate_n(reference.text, compared.text)
+
+
+@multimethod
+def word_error_rate_n(reference: Iterable, compared: Iterable) -> Tuple[float, int]:
+    reference_seq = list(reference)
+    compared_seq = list(compared)
 
     d = levenshtein(reference_seq, compared_seq)
     n = len(reference_seq)

requirements.txt

@@ -7,3 +7,4 @@ colorama
 MarkupSafe
 ocrd >= 1.0.0b15
 attrs
+multimethod == 1.3  # latest version to officially support Python 3.5