def test_key_insertion_deletion(self):
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus1.get_utterance("0").meta['foo'] = 'bar'
corpus1.get_utterance("1").meta['foo'] = 'bar2'
corpus1.get_utterance("2").meta['hey'] = 'jude'
corpus1.get_conversation(None).meta['convo_meta'] = 1
corpus1.get_speaker("alice").meta['surname'] = 1.0
self.assertEqual(corpus1.meta_index.utterances_index['foo'],
[str(type('bar'))])
self.assertEqual(corpus1.meta_index.conversations_index['convo_meta'],
[str(type(1))])
self.assertEqual(corpus1.meta_index.speakers_index['surname'],
[str(type(1.0))])
# test that deleting an attribute from an individual utterance fails to remove it
del corpus1.get_utterance("2").meta['hey']
corpus1.get_utterance("2").meta['hey']
# test that delete_metadata works
corpus1.delete_metadata('utterance', 'foo')
self.assertRaises(KeyError,
lambda: corpus1.meta_index.utterances_index['foo'])
self.assertRaises(KeyError,
lambda: corpus1.get_utterance("0").meta["foo"])
def test_key_insertion_deletion(self):
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus1.get_utterance("0").meta['foo'] = 'bar'
corpus1.get_utterance("1").meta['foo'] = 'bar2'
corpus1.get_utterance("2").meta['hey'] = 'jude'
corpus1.get_conversation(None).meta['convo_meta'] = 1
corpus1.get_speaker("alice").meta['surname'] = 1.0
self.assertEqual(corpus1.meta_index.utterances_index['foo'],
str(type('bar')))
self.assertEqual(corpus1.meta_index.conversations_index['convo_meta'],
str(type(1)))
self.assertEqual(corpus1.meta_index.speakers_index['surname'],
str(type(1.0)))
# test that deleting a key from an utterance removes it from the index
del corpus1.get_utterance("2").meta['hey']
self.assertRaises(KeyError,
lambda: corpus1.meta_index.utterances_index['hey'])
# test that deleting a key from an utterance removes it from the index and from all other objects of same type
del corpus1.get_utterance("1").meta['foo']
self.assertRaises(KeyError,
lambda: corpus1.meta_index.utterances_index['foo'])
self.assertRaises(KeyError,
lambda: corpus1.get_utterance("0").meta["foo"])
def test_corpus_dump(self):
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus1.get_utterance("0").meta['foo'] = 'bar'
corpus1.get_utterance("1").meta['foo'] = 'bar2'
corpus1.get_utterance("2").meta['hey'] = 'jude'
corpus1.get_conversation(None).meta['convo_meta'] = 1
corpus1.get_speaker("alice").meta['surname'] = 1.0
corpus1.dump('test_index_meta_corpus', base_path="./")
corpus2 = Corpus(filename="test_index_meta_corpus")
self.assertEqual(corpus1.meta_index.utterances_index,
corpus2.meta_index.utterances_index)
self.assertEqual(corpus1.meta_index.speakers_index,
corpus2.meta_index.speakers_index)
self.assertEqual(corpus1.meta_index.conversations_index,
corpus2.meta_index.conversations_index)
self.assertEqual(corpus1.meta_index.overall_index,
corpus2.meta_index.overall_index)
def test_basic_functions(self):
"""
Test basic meta functions
"""
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
first_utt = corpus1.get_utterance("0")
first_utt.meta['hey'] = 9
# correct class type stored
self.assertEqual(corpus1.meta_index.utterances_index['hey'],
repr(type(9)))
# keyErrors result in None output
self.assertRaises(KeyError, lambda: first_utt.meta['nonexistent key'])
# test that setting a custom get still works
self.assertEqual(first_utt.meta.get('nonexistent_key', {}), {})
def burr_sir_corpus():
hamilton = Speaker(id='hamilton')
burr = Speaker(id='burr')
utterances = [
Utterance(id='0', text=BURR_SIR_TEXT_1, speaker=hamilton),
Utterance(id='1', text=BURR_SIR_TEXT_2, speaker=burr)
]
return Corpus(utterances=utterances)
def politeness_test_zh_corpus():
speakers = [Speaker(id='alice'), Speaker(id='bob')]
texts = [GRATITUDE_ZH, DEFERENCE_ZH, GREETING_ZH, APOLOGY_ZH, PLEASE_ZH, PLEASE_START_ZH, BTW_ZH, DIRECT_QN_ZH , HEDGES_ZH, FACTUALITY_ZH]
utterances = [Utterance(id='0', text=texts[0], speaker=speakers[1], reply_to=None)]
for i, text in enumerate(texts[1:]):
utterances.append(Utterance(id=str(i+1), text=text, speaker=speakers[i%2], reply_to=str(i)))
return Corpus(utterances=utterances)
def politeness_test_corpus():
speakers = [Speaker(id='alice'), Speaker(id='bob')]
texts = [GRATITUDE, DEFERENCE, GREETING, APOLOGY, PLEASE,
PLEASE_START, BTW, DIRECT_QN, DIRECT_START,
SUBJUNCTIVE, INDICATIVE, HEDGES, FACTUALITY]
utterances = [Utterance(id='0', text=texts[0], speaker=speakers[1], reply_to=None)]
for i, text in enumerate(texts[1:]):
utterances.append(Utterance(id=str(i+1), text=text, speaker=speakers[i%2], reply_to=str(i)))
return Corpus(utterances=utterances)
def test_broken_convos(self):
"""
Test basic meta functions
"""
corpus1 = Corpus(utterances=[
Utterance(id="0",
text="hello world",
reply_to=None,
speaker=Speaker(id="alice"),
timestamp=0),
Utterance(id="1",
text="my name is bob",
reply_to="0",
speaker=Speaker(id="bob"),
timestamp=2),
Utterance(id="2",
text="this is a test",
reply_to="1",
speaker=Speaker(id="charlie"),
timestamp=1),
Utterance(id="3",
text="hello world 2",
reply_to=None,
speaker=Speaker(id="alice2"),
timestamp=0),
])
corpus2 = Corpus(utterances=[
Utterance(id="0",
text="hello world",
reply_to=None,
speaker=Speaker(id="alice"),
timestamp=0),
Utterance(id="1",
text="my name is bob",
reply_to="0",
speaker=Speaker(id="bob"),
timestamp=2),
Utterance(id="2",
text="this is a test",
reply_to="1",
speaker=Speaker(id="charlie"),
timestamp=1),
Utterance(id="3",
text="hello world 2",
reply_to="9",
speaker=Speaker(id="alice2"),
timestamp=0),
])
# test broken convo where there are multiple roots
convo = corpus1.get_conversation(None)
self.assertRaises(
ValueError, lambda: list(convo.traverse("dfs", as_utterance=True)))
# test broken convo where utterance replies to something not in Conversation
convo = corpus2.get_conversation(None)
self.assertRaises(
ValueError, lambda: list(convo.traverse("dfs", as_utterance=True)))
def transform_utterance(self, utterance, spacy_nlp=None, markers=False):
"""
Extract politeness strategies for raw string inputs.
:param utterance: the utterance to be annotated with politeness strategies.
:spacy_nlp: if provided, will use this SpaCy object to do parsing; otherwise will initialize an object via `load('en')`.
:return: the utterance with politeness annotations.
"""
if isinstance(utterance, str):
utterance = Utterance(text=utterance,
speaker=Speaker(id='speaker'))
if spacy_nlp is None:
spacy_nlp = spacy.load('en_core_web_sm', disable=['ner'])
utterance.meta['parsed'] = process_text(utterance.text,
spacy_nlp=spacy_nlp)
for i, sent in enumerate(utterance.meta["parsed"]):
for p in sent["toks"]:
p["tok"] = p['tok'].lower()
utterance.meta[
self.strategy_attribute_name], marks = self.__extractor_lookup[
self.strategy_collection](utterance)
if markers:
utterance.meta[self.marker_attribute_name] = marks
return utterance
def transform_utterance(self, utt, override_input_filter=False):
"""
Computes per-utterance attributes of an individual utterance or string. For utterances which do not contain all of the `input_field` attributes as specified in the constructor, or for utterances which return `False` on `input_filter`, this call will not annotate the utterance. For strings, will convert the string to an utterance and return the utterance, annotating it if `input_field` is not set to `None` at initialization.
:param utt: utterance or a string
:param override_input_filter: ignore `input_filter` and compute attribute for all utterances
:return: the utterance
"""
if isinstance(utt, str):
utt = Utterance(text=utt, speaker=Speaker(id="speaker"))
if self.input_field is None:
text_entry = utt.text
else:
if not override_input_filter:
if not self.input_filter(utt, self.aux_input):
return utt
if isinstance(self.input_field, str):
text_entry = utt.retrieve_meta(self.input_field)
elif isinstance(self.input_field, list):
text_entry = {field: utt.retrieve_meta(field) for field in self.input_field}
if sum(x is None for x in text_entry.values()) > 0:
return utt
if text_entry is None:
return utt
if len(self.aux_input) == 0:
result = self.proc_fn(text_entry)
else:
result = self.proc_fn(text_entry, self.aux_input)
if self.multi_outputs:
for res, out in zip(result, self.output_field):
utt.add_meta(out, res)
else:
utt.add_meta(self.output_field, result)
return utt
def transform_utterance(self, utt: Utterance, spacy_nlp: Callable[[str], Doc] = None, markers: bool = False):
"""
Extract politeness strategies for raw string inputs (or individual utterances)
:param utt: the utterance to be annotated with politeness strategies.
:spacy_nlp: if provided, will use this SpaCy object to do parsing; otherwise will initialize an object via `load('en')`.
:return: the utterance with politeness annotations.
"""
if isinstance(utt, str):
utt = Utterance(text=utt, speaker=Speaker(id='speaker'))
if self.parse_attribute_name not in utt.meta:
if spacy_nlp is None:
raise ValueError('spacy object required')
parses = process_text(utt.text, spacy_nlp=spacy_nlp)
utt.add_meta(self.parse_attribute_name, parses)
parsed = utt.retrieve_meta(self.parse_attribute_name)
for i, sent in enumerate(parsed):
for p in sent["toks"]:
p["tok"] = p['tok'].lower()
parses = [x["toks"] for x in parsed]
utt.meta[self.strategy_attribute_name], marks = self._extractor_lookup[self.strategy_collection](parses)
if markers:
utt.meta[self.marker_attribute_name] = marks
return utt
def test_overlap_diff_data(self):
"""
Merge with overlap in utterance id and utterance has diff data but same metadata
Warning should be printed. Original utterance data should be preserved.
"""
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus2 = Corpus(utterances=[
Utterance(
id="2", text="this is a test2", speaker=Speaker(id="candace")),
Utterance(
id="4", text="this is a sentence", speaker=Speaker(id="echo")),
Utterance(id="5", text="goodbye", speaker=Speaker(id="foxtrot")),
])
merged = corpus1.merge(corpus2)
self.assertEqual(len(list(merged.iter_utterances())), 5)
self.assertEqual(len(list(merged.iter_speakers())), 5)
self.assertEqual(len(list(corpus1.iter_utterances())), 3)
self.assertEqual(len(list(corpus2.iter_utterances())), 3)
self.assertEqual(merged.get_utterance("2").text, "this is a test")
self.assertEqual(
merged.get_utterance("2").speaker, Speaker(id="charlie"))
def test_multiple_types(self):
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus1.get_utterance('2').meta['hey'] = None
self.assertEqual(corpus1.meta_index.utterances_index.get('hey', None),
None)
corpus1.get_utterance('0').meta['hey'] = 5
self.assertEqual(corpus1.meta_index.utterances_index['hey'],
[str(type(5))])
corpus1.get_utterance('1').meta['hey'] = 'five'
self.assertEqual(corpus1.meta_index.utterances_index['hey'],
[str(type(5)), str(type('five'))])
def test_with_overlap(self):
"""
Basic merge: with overlap in utterance id (but utterance has same data & metadata)
"""
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus2 = Corpus(utterances=[
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
Utterance(
id="4", text="this is a sentence", speaker=Speaker(id="echo")),
Utterance(id="5", text="goodbye", speaker=Speaker(id="foxtrot")),
])
merged = corpus1.merge(corpus2)
self.assertEqual(len(list(merged.iter_utterances())), 5)
self.assertEqual(len(list(merged.iter_speakers())), 5)
self.assertEqual(len(list(corpus1.iter_utterances())), 3)
self.assertEqual(len(list(corpus2.iter_utterances())), 3)
def test_add_utterance(self):
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(id="2",
text="this is a test",
speaker=Speaker(id="charlie"),
meta={
'hey': 'jude',
'hello': 'world'
}),
])
utts = [
Utterance(id="1", text="i like pie", speaker=Speaker(id="delta")),
Utterance(id="2",
text="this is a test",
speaker=Speaker(id="charlie"),
meta={
'hello': 'food',
'what': 'a mood'
}),
Utterance(id="5", text="goodbye", speaker=Speaker(id="foxtrot")),
]
added = corpus1.add_utterances(utts)
self.assertEqual(len(list(added.iter_utterances())), 4)
self.assertEqual(len(added.get_utterance("2").meta), 3)
self.assertEqual(added.get_utterance("2").meta['hello'], 'food')
def test_corpus_metadata(self):
"""
Merge with overlap in corpus metadata
Expect second corpus metadata to override if keys are the same
"""
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus2 = Corpus(utterances=[
Utterance(id="3", text="i like pie", speaker=Speaker(id="delta")),
Utterance(
id="4", text="this is a sentence", speaker=Speaker(id="echo")),
Utterance(id="5", text="goodbye", speaker=Speaker(id="foxtrot")),
])
corpus1.add_meta('politeness', 0.95)
corpus1.add_meta('toxicity', 0.8)
corpus2.add_meta('toxicity', 0.9)
corpus2.add_meta('paggro', 1.0)
merged = corpus1.merge(corpus2)
self.assertEqual(len(merged.meta), 3)
self.assertEqual(merged.meta['toxicity'], 0.9)
def test_corpus_merge_add(self):
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus1.get_utterance("0").meta['foo'] = 'bar'
corpus1.get_utterance("1").meta['foo'] = 'bar2'
corpus1.get_utterance("2").meta['hey'] = 'jude'
# test that adding separately initialized utterances with new metadata updates Index
new_utt = Utterance(id="4",
text="hello world",
speaker=Speaker(id="alice",
meta={'donkey': 'kong'}),
meta={'new': 'meta'})
new_corpus = corpus1.add_utterances([new_utt])
self.assertTrue('new' in new_corpus.meta_index.utterances_index)
self.assertTrue('donkey' in new_corpus.meta_index.speakers_index)
def transform_utterance(self, utt, **params):
"""
Computes attributes of an individual string or utterance using all of the transformers in the pipeline.
:param utt: the utterance to compute attributes for.
:return: the utterance, with new attributes.
"""
params_steps = self._parse_param_steps(params)
if isinstance(utt, str):
utt = Utterance(text=utt, speaker=Speaker(id="speaker"))
for name, transform in self.steps:
if name in params_steps:
utt = transform.transform_utterance(utt, **params_steps[name])
else:
utt = transform.transform_utterance(utt)
return utt
def test_overlap_convo_metadata(self):
"""
Merge with overlap in conversation with metadata differences.
Expect second corpus convo metadata to override if keys are the same
"""
corpus1 = Corpus(utterances=[
Utterance(id="0",
conversation_id='convo1',
text="hello world",
speaker=Speaker(id="alice")),
Utterance(id="1",
conversation_id='convo1',
text="my name is bob",
speaker=Speaker(id="bob")),
Utterance(id="2",
conversation_id='convo1',
text="this is a test",
speaker=Speaker(id="charlie")),
])
corpus2 = Corpus(utterances=[
Utterance(id="2",
conversation_id='convo1',
text="this is a test",
speaker=Speaker(id="charlie")),
Utterance(id="4",
conversation_id='convo1',
text="this is a sentence",
speaker=Speaker(id="echo")),
Utterance(id="5",
conversation_id='convo1',
text="goodbye",
speaker=Speaker(id="foxtrot")),
])
corpus1.get_conversation('convo1').add_meta('hey', 'jude')
corpus1.get_conversation('convo1').add_meta('hello', 'world')
corpus2.get_conversation('convo1').add_meta('hey', 'jude')
corpus2.get_conversation('convo1').add_meta('hello', 'food')
corpus2.get_conversation('convo1').add_meta('what', 'a mood')
merged = corpus1.merge(corpus2)
self.assertEqual(len(merged.get_conversation('convo1').meta), 3)
self.assertEqual(
merged.get_conversation('convo1').meta['hello'], 'food')
def test_overlap_diff_metadata(self):
"""
Merge with overlap in utterance id and utterance has same data but diff metadata
Second corpus utterance metadata should override if the keys are the same.
"""
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(id="2",
text="this is a test",
speaker=Speaker(id="charlie"),
meta={
'hey': 'jude',
'the': 'beatles'
}),
])
corpus2 = Corpus(utterances=[
Utterance(id="2",
text="this is a test",
speaker=Speaker(id="charlie"),
meta={
'hey': 'jude',
'the': 'ringo',
'let it': 'be'
}),
Utterance(
id="4", text="this is a sentence", speaker=Speaker(id="echo")),
Utterance(id="5", text="goodbye", speaker=Speaker(id="foxtrot")),
])
merged = corpus1.merge(corpus2)
self.assertEqual(len(list(merged.iter_utterances())), 5)
self.assertEqual(len(list(merged.iter_speakers())), 5)
self.assertEqual(len(merged.get_utterance("2").meta), 3)
self.assertEqual(merged.get_utterance("2").meta['the'], 'ringo')
def test_no_overlap(self):
"""
Basic merge: no overlap in utterance id
"""
corpus1 = Corpus(utterances=[
Utterance(id="0", text="hello world", speaker=Speaker(id="alice")),
Utterance(id="1", text="my name is bob", speaker=Speaker(
id="bob")),
Utterance(
id="2", text="this is a test", speaker=Speaker(id="charlie")),
])
corpus2 = Corpus(utterances=[
Utterance(id="3", text="i like pie", speaker=Speaker(id="delta")),
Utterance(
id="4", text="this is a sentence", speaker=Speaker(id="echo")),
Utterance(id="5", text="goodbye", speaker=Speaker(id="foxtrot")),
])
merged = corpus1.merge(corpus2)
self.assertEqual(len(list(merged.iter_utterances())), 6)
self.assertEqual(len(list(merged.iter_speakers())), 6)
self.assertEqual(len(list(corpus1.iter_utterances())), 3)
self.assertEqual(len(list(corpus2.iter_utterances())), 3)
def setUp(self) -> None:
"""
Basic Conversation tree (left to right within subtree => earliest to latest)
0
1 2 3
4 5 6 7 8 9
10 11
"""
self.corpus = Corpus(utterances=[
Utterance(id="0",
reply_to=None,
root="0",
speaker=Speaker(id="alice"),
timestamp=0),
Utterance(id="2",
reply_to="0",
root="0",
speaker=Speaker(id="alice"),
timestamp=2),
Utterance(id="1",
reply_to="0",
root="0",
speaker=Speaker(id="alice"),
timestamp=1),
Utterance(id="3",
reply_to="0",
root="0",
speaker=Speaker(id="alice"),
timestamp=3),
Utterance(id="4",
reply_to="1",
root="0",
speaker=Speaker(id="alice"),
timestamp=4),
Utterance(id="5",
reply_to="1",
root="0",
speaker=Speaker(id="alice"),
timestamp=5),
Utterance(id="6",
reply_to="1",
root="0",
speaker=Speaker(id="alice"),
timestamp=6),
Utterance(id="7",
reply_to="2",
root="0",
speaker=Speaker(id="alice"),
timestamp=4),
Utterance(id="8",
reply_to="2",
root="0",
speaker=Speaker(id="alice"),
timestamp=5),
Utterance(id="9",
reply_to="3",
root="0",
speaker=Speaker(id="alice"),
timestamp=4),
Utterance(id="10",
reply_to="4",
root="0",
speaker=Speaker(id="alice"),
timestamp=5),
Utterance(id="11",
reply_to="9",
root="0",
speaker=Speaker(id="alice"),
timestamp=10),
Utterance(id="other",
reply_to=None,
root="other",
speaker=Speaker(id="alice"),
timestamp=99)
])
self.corpus.get_conversation("0").meta['hey'] = 'jude'
self.corpus.meta['foo'] = 'bar'