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doctests for website: 'home'-section

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This commit is contained in:
Henning Peters 2015-09-28 02:39:14 +02:00
parent d1850dcbf7
commit 936edea425
4 changed files with 151 additions and 106 deletions
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2
website/Makefile
View File

@ -1,6 +1,6 @@
all: src/code site
src/code:
src/code: tests/test_*.py
mkdir -p src/code/
./create_code_samples tests/ src/code/

23
website/create_code_samples
View File

@ -4,6 +4,12 @@ import re
import os
import ast
# cgi.escape is deprecated since py32
try:
from html import escape
except ImportError:
from cgi import escape
src_dirname = sys.argv[1]
dst_dirname = sys.argv[2]
@ -24,10 +30,18 @@ for filename in os.listdir(src_dirname):
# only ast.expr and ast.stmt have line numbers, see:
# https://docs.python.org/2/library/ast.html#ast.AST.lineno
line_numbers = [x.lineno for x in ast.iter_child_nodes(item)
if isinstance(x, ast.expr) or
isinstance(x, ast.stmt)]
line_numbers = []
def fill_line_numbers(node):
for child in ast.iter_child_nodes(node):
if ((isinstance(child, ast.expr) or
isinstance(child, ast.stmt)) and
child.lineno > item.lineno):
line_numbers.append(child.lineno)
fill_line_numbers(child)
fill_line_numbers(item)
body = source[min(line_numbers)-1:max(line_numbers)]
# make sure we are inside an indented function body
@ -38,6 +52,7 @@ for filename in os.listdir(src_dirname):
match = re.search(r"[^\s]", line)
if match:
offset = match.start(0)
break
# remove indentation
assert offset > 0
@ -51,4 +66,4 @@ for filename in os.listdir(src_dirname):
code_filename = "%s.%s" % (name, item.name[len(prefix):])
with open(os.path.join(dst_dirname, code_filename), "w") as f:
f.write("".join(body))
f.write(escape("".join(body)))

107
website/src/jade/home/_usage.jade
View File

@ -19,89 +19,27 @@ mixin example(name)
+example("Get and set string views and flags")
pre.language-python: code
| assert token.shape_ == 'Xxxxx'
| for lexeme in nlp.vocab:
| if lexeme.is_alpha:
| lexeme.shape_ = 'W'
| elif lexeme.is_digit:
| lexeme.shape_ = 'D'
| elif lexeme.is_punct:
| lexeme.shape_ = 'P'
| else:
| lexeme.shape_ = 'M'
| assert token.shape_ == 'W'
include ../../code/home.get_and_set_string_views_and_flags
+example("Export to numpy arrays")
pre.language-python: code
| from spacy.en.attrs import ORTH, LIKE_URL, IS_OOV
|
| attr_ids = [ORTH, LIKE_URL, IS_OOV]
| doc_array = doc.to_array(attr_ids)
| assert doc_array.shape == (len(doc), len(attr_ids))
| assert doc[0].orth == doc_array[0, 0]
| assert doc[1].orth == doc_array[1, 0]
| assert doc[0].like_url == doc_array[0, 1]
| assert list(doc_array[:, 1]) == [t.like_url for t in doc]
include ../../code/home.export_to_numpy_arrays
+example("Word vectors")
pre.language-python: code
| doc = nlp("Apples and oranges are similar. Boots and hippos aren't.")
|
| apples = doc[0]
| oranges = doc[1]
| boots = doc[6]
| hippos = doc[8]
|
| assert apples.similarity(oranges) > boots.similarity(hippos)
include ../../code/home.word_vectors
+example("Part-of-speech tags")
pre.language-python: code
| from spacy.parts_of_speech import ADV
|
| def is_adverb(token):
| return token.pos == spacy.parts_of_speech.ADV
|
| # These are data-specific, so no constants are provided. You have to look
| # up the IDs from the StringStore.
| NNS = nlp.vocab.strings['NNS']
| NNPS = nlp.vocab.strings['NNPS']
| def is_plural_noun(token):
| return token.tag == NNS or token.tag == NNPS
|
| def print_coarse_pos(token):
| print(token.pos_)
|
| def print_fine_pos(token):
| print(token.tag_)
include ../../code/home.part_of_speech_tags
+example("Syntactic dependencies")
pre.language-python: code
| def dependency_labels_to_root(token):
| '''Walk up the syntactic tree, collecting the arc labels.'''
| dep_labels = []
| while token.head is not token:
| dep_labels.append(token.dep)
| token = token.head
| return dep_labels
include ../../code/home.syntactic_dependencies
+example("Named entities")
pre.language-python: code
| def iter_products(docs):
| for doc in docs:
| for ent in doc.ents:
| if ent.label_ == 'PRODUCT':
| yield ent
|
| def word_is_in_entity(word):
| return word.ent_type != 0
|
| def count_parent_verb_by_person(docs):
| counts = defaultdict(defaultdict(int))
| for doc in docs:
| for ent in doc.ents:
| if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:
| counts[ent.orth_][ent.root.head.lemma_] += 1
| return counts
include ../../code/home.named_entities
//+example("Define custom NER rules")
// pre.language-python: code
@ -110,40 +48,11 @@ mixin example(name)
+example("Calculate inline mark-up on original string")
pre.language-python: code
| def put_spans_around_tokens(doc, get_classes):
| '''Given some function to compute class names, put each token in a
| span element, with the appropriate classes computed.
|
| All whitespace is preserved, outside of the spans. (Yes, I know HTML
| won't display it. But the point is no information is lost, so you can
| calculate what you need, e.g. <br /> tags, <p> tags, etc.)
| '''
| output = []
| template = '<span classes="{classes}">{word}</span>{space}'
| for token in doc:
| if token.is_space:
| output.append(token.orth_)
| else:
| output.append(
| template.format(
| classes=' '.join(get_classes(token)),
| word=token.orth_,
| space=token.whitespace_))
| string = ''.join(output)
| string = string.replace('\n', '<br />')
| string = string.replace('\t', '&nbsp;&nbsp;&nbsp;&nbsp;')
| return string
include ../../code/home.calculate_inline_mark_up_on_original_string
+example("Efficient binary serialization")
pre.language-python: code
| byte_string = doc.as_bytes()
| open('/tmp/moby_dick.bin', 'wb').write(byte_string)
|
| nlp = spacy.en.English()
| for byte_string in Doc.read(open('/tmp/moby_dick.bin', 'rb')):
| doc = Doc(nlp.vocab)
| doc.from_bytes(byte_string)
include ../../code/home.efficient_binary_serialization
+example("Full documentation")
ul

125
website/tests/test_home.py
View File

@ -27,7 +27,6 @@ def test_load_resources_and_process_text():
def test_get_tokens_and_sentences(doc):
token = doc[0]
sentence = doc.sents.next()
assert token is sentence[0]
assert sentence.text == 'Hello, world.'
@ -36,5 +35,127 @@ def test_use_integer_ids_for_any_strings(nlp, token):
hello_id = nlp.vocab.strings['Hello']
hello_str = nlp.vocab.strings[hello_id]
assert token.orth == hello_id == 3404
assert token.orth == hello_id == 469755
assert token.orth_ == hello_str == 'Hello'
def test_get_and_set_string_views_and_flags(nlp, token):
assert token.shape_ == 'Xxxxx'
for lexeme in nlp.vocab:
if lexeme.is_alpha:
lexeme.shape_ = 'W'
elif lexeme.is_digit:
lexeme.shape_ = 'D'
elif lexeme.is_punct:
lexeme.shape_ = 'P'
else:
lexeme.shape_ = 'M'
assert token.shape_ == 'W'
def test_export_to_numpy_arrays(nlp, doc):
from spacy.en.attrs import ORTH, LIKE_URL, IS_OOV
attr_ids = [ORTH, LIKE_URL, IS_OOV]
doc_array = doc.to_array(attr_ids)
assert doc_array.shape == (len(doc), len(attr_ids))
assert doc[0].orth == doc_array[0, 0]
assert doc[1].orth == doc_array[1, 0]
assert doc[0].like_url == doc_array[0, 1]
assert list(doc_array[:, 1]) == [t.like_url for t in doc]
def test_word_vectors(nlp):
doc = nlp("Apples and oranges are similar. Boots and hippos aren't.")
apples = doc[0]
oranges = doc[1]
boots = doc[6]
hippos = doc[8]
assert apples.similarity(oranges) > boots.similarity(hippos)
def test_part_of_speech_tags(nlp):
from spacy.parts_of_speech import ADV
def is_adverb(token):
return token.pos == spacy.parts_of_speech.ADV
# These are data-specific, so no constants are provided. You have to look
# up the IDs from the StringStore.
NNS = nlp.vocab.strings['NNS']
NNPS = nlp.vocab.strings['NNPS']
def is_plural_noun(token):
return token.tag == NNS or token.tag == NNPS
def print_coarse_pos(token):
print(token.pos_)
def print_fine_pos(token):
print(token.tag_)
def test_syntactic_dependencies():
def dependency_labels_to_root(token):
'''Walk up the syntactic tree, collecting the arc labels.'''
dep_labels = []
while token.head is not token:
dep_labels.append(token.dep)
token = token.head
return dep_labels
def test_named_entities():
def iter_products(docs):
for doc in docs:
for ent in doc.ents:
if ent.label_ == 'PRODUCT':
yield ent
def word_is_in_entity(word):
return word.ent_type != 0
def count_parent_verb_by_person(docs):
counts = defaultdict(defaultdict(int))
for doc in docs:
for ent in doc.ents:
if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:
counts[ent.orth_][ent.root.head.lemma_] += 1
return counts
def test_calculate_inline_mark_up_on_original_string():
def put_spans_around_tokens(doc, get_classes):
'''Given some function to compute class names, put each token in a
span element, with the appropriate classes computed.
All whitespace is preserved, outside of the spans. (Yes, I know HTML
won't display it. But the point is no information is lost, so you can
calculate what you need, e.g. <br /> tags, <p> tags, etc.)
'''
output = []
template = '<span classes="{classes}">{word}</span>{space}'
for token in doc:
if token.is_space:
output.append(token.orth_)
else:
output.append(
template.format(
classes=' '.join(get_classes(token)),
word=token.orth_,
space=token.whitespace_))
string = ''.join(output)
string = string.replace('\n', '')
string = string.replace('\t', ' ')
return string
def test_efficient_binary_serialization(doc):
byte_string = doc.as_bytes()
open('/tmp/moby_dick.bin', 'wb').write(byte_string)
nlp = spacy.en.English()
for byte_string in Doc.read(open('/tmp/moby_dick.bin', 'rb')):
doc = Doc(nlp.vocab)
doc.from_bytes(byte_string)