def get_report(self, doc):
"""
Generates a report on the text based upon effort vs accomplishment.
Also adds a summary if there are NO words about accomplishment, or if
the ratio of effort to accomplishment words is particularly low.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Effort vs Accomplishment")
effort_flags = []
accomplishment_flags = []
token_indices = doc.words_with_indices()
for word, start, stop in token_indices:
if word.lower() in EFFORT_WORDS:
effort_flags.append(
Flag(
start, stop,
Issue(
"Effort vs Accomplishment",
"The word '{word}' tends to speak about effort more than accomplishment."
.format(word=word),
"Try replacing with phrasing that emphasizes accomplishment.",
bias=Issue.negative_result)))
if word.lower() in ACCOMPLISHMENT_WORDS:
accomplishment_flags.append(
Flag(
start, stop,
Issue(
"Effort vs Accomplishment",
"The word '{word}' tends to speak about accomplishment more than effort."
.format(word=word),
bias=Issue.positive_result)))
for flag in effort_flags:
report.add_flag(flag)
if (len(accomplishment_flags) is 0
or len(effort_flags) / len(accomplishment_flags) >
1.2 # TODO: Arbitrary!
):
# Avoid divide-by-zero errors
if len(accomplishment_flags) == 0:
report.set_summary(
"This document has too few words about concrete accomplishment."
)
else:
report.set_summary(
"This document has a high ratio ({}:{}) of words suggesting effort to words suggesting concrete accomplishment."
.format(len(effort_flags), len(accomplishment_flags)))
return report
def test_flag_stringify():
f1 = Flag(100, 200, Issue("Issue", "Description.", fix="Fix Me!"))
assert str(f1) == "[100-200]: Issue: Description. (Fix Me!)"
f2 = Flag(100, 200, Issue("Issue", "Description."))
assert str(f2) == "[100-200]: Issue: Description."
f3 = Flag(100, 200, Issue("Issue"))
assert str(f3) == "[100-200]: Issue"
def get_report(self, doc):
"""
Generates a report on the text based upon effort vs accomplishment.
Also adds a summary if there are NO words about accomplishment, or if
the ratio of effort to accomplishment words is particularly low.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("\nTerms focusing on effort vs accomplishment")
effort_flags = []
accomplishment_flags = []
token_indices = doc.words_with_indices()
for word, start, stop in token_indices:
# NELSON - changed loop so word can be found in EFFORT or ACCOMPLISHMENT words
if word.lower() in EFFORT_WORDS:
effort_flags.append(
Flag(
start, stop,
Issue("{word}".format(word=word),
bias=Issue.negative_result)))
if word.lower() in ACCOMPLISHMENT_WORDS:
accomplishment_flags.append(
Flag(
start, stop,
Issue(
"'{word}' describes accomplishment more than effort"
.format(word=word),
bias=Issue.positive_result)))
for flag in effort_flags:
report.add_flag(flag)
if (len(accomplishment_flags) is 0
or len(effort_flags) / len(accomplishment_flags) >
1.2 # TODO: Arbitrary!
):
# Avoid divide-by-zero errors
if len(accomplishment_flags) == 0:
report.set_summary(
"Too few words about concrete accomplishments")
else:
report.set_summary(
"High ratio ({}:{}) of words suggesting effort ratherthan accomplishment."
.format(len(effort_flags), len(accomplishment_flags)))
return report
def get_report(self, doc):
nativespeaker_report = Report(
"\nTerms biased towards native speakers:")
words_with_indices = doc.words_with_indices()
#print(words_with_indices)
found = False
for word, start, stop in words_with_indices:
word = word.lower()
for nativeword in NATIVE_WORDS.Used:
x = re.search(nativeword, word)
if (x):
#print(x.span(), x.string, x.group())
found = True
if NATIVE_WORDS['Recommend2'].loc[
NATIVE_WORDS['Used'] ==
x.group()].item() == 'none':
recommend2 = ""
else:
recommend2 = " or '" + NATIVE_WORDS['Recommend2'].loc[
NATIVE_WORDS['Used'] == x.group()].item() + "'"
print(
"Consider replacing '", x.group(), "' with '",
NATIVE_WORDS['Recommend1'].loc[NATIVE_WORDS['Used'] ==
x.group()].item(), "'",
recommend2)
nativespeaker_report.add_flag(
Flag(start, stop, Issue(word)))
if found:
nativespeaker_report.set_summary(
"To encourage non-native speakers, use short words and simple sentences"
)
return nativespeaker_report
def get_flags(self, doc: 'Document') -> List['Flag']:
"""
Flag a document (globally) if we cannot find any research products.
Returns only a single flag if no publications/resources are mentioned.
"""
all_flags = []
# TODO: Any other flags needed here?
# Sum up all of the probabilities of all publications. This is a bit
# janky, but it acts as a proxy for the total number of publications
# mentioned. For example, if there are two potential publications each
# with a probability of 50%, then we could consider that a mention of
# one single publication.
pub_count = sum(identify_publications(doc).values())
if pub_count < self.min_publications:
all_flags.append(Flag(
0, 0,
Issue(
"Publications",
"This document does not mention many publications.",
"Try referencing more concrete publications or work "
"byproducts, if possible."
))
)
return all_flags
def get_report(self, doc):
"""
Report the usage of unnecessarily gendered words.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("\nUnnecessary use of gender terms")
token_indices = doc.words_with_indices()
found = False
for word, start, stop in token_indices:
# NELSON - changed loop so word can be found in GENDERED_WORDS
if word.lower() in GENDERED_WORDS:
found = True
report.add_flag(
Flag(start, stop, Issue("{word}".format(word=word))))
if found:
report.set_summary(
"Replace gender terms with 'person' or 'individual', or a position-specific term, such as 'doctor' or 'author'"
)
return report
def get_report(self, doc):
"""
Report the usage of unnecessarily gendered words.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Unnecessarily Gendered Words")
token_indices = doc.words_with_indices()
for word, start, stop in token_indices:
if word.lower() in GENDERED_WORDS:
report.add_flag(
Flag(start, stop, Issue(
"Unnecessarily Gendered Words",
"The word '{word}' is unneccesarily gendered.".format(
word=word),
"Replace this term with 'person' or 'individual', or a position-specific phrase like 'doctor' or 'author'.",
bias=Issue.negative_result
))
)
return report
def get_report(self, doc):
"""
Generate a report on the text based upon mentions of
personal-life-related words.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Personal Life")
token_indices = doc.words_with_indices()
for word, start, stop in token_indices:
if word.lower() in PERSONAL_LIFE_TERMS:
report.add_flag(
Flag(start, stop, Issue(
"Personal Life",
"The word {word} tends to relate to personal life.".format(word=word),
"Try replacing with a sentiment about professional life."
))
)
return report
def get_report(self, doc):
"""
Generates a report on the text that checks for curbed superlatives.
These are phrases like "the best woman for the job" or "best of
all women" that are clear 'hedged' superlatives.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Conditional Superlatives")
text = doc.text()
for regex in CONDITIONAL_SUPERLATIVE_REGEXES:
for match in re.finditer(regex, text):
report.add_flag(
Flag(match.span()[0], match.span()[1], Issue(
"Conditional Superlative",
"This phrase appears to hedge a superlative to apply only to women."
))
)
return report
def get_report(self, doc):
"""
Generate a report on the text based upon mentions of
personal-life-related words.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("\nTerms about personal life")
token_indices = doc.words_with_indices()
found = False
for word, start, stop in token_indices:
# NELSON - changed loop to look for word in lis of PERSONAL_LIFE_TERMS
if word.lower() in PERSONAL_LIFE_TERMS:
found = True
report.add_flag(
Flag(start, stop, Issue("{word}".format(word=word))))
if found:
report.set_summary('Found words relating to personal life')
return report
def get_report(self, doc):
male_report = Report("\nTerms biased towards men:")
words_with_indices = doc.words_with_indices()
#print(words_with_indices)
found = False
for word, start, stop in words_with_indices:
word = word.lower()
for maleword in MALE_WORDS:
searchTerm = "^" + maleword + ".."
x = re.search(searchTerm, word)
if (x):
#print(x.span(), x.string, x.group())
found = True
male_report.add_flag(
Flag(start, stop, Issue("{word}".format(word=word))))
if found:
male_report.set_summary(
"Depending on context, these words may be biased towards recruiting men"
)
return male_report
def get_report(self, doc: Document):
"""
Generates a report on the text based upon effort vs accomplishment.
Also adds a summary if there are NO words about accomplishment, or if
the ratio of effort to accomplishment words is particularly low.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Effort vs Accomplishment")
# Keep track of accomplishment- or effort-specific words:
accomplishment_words = 0
effort_words = 0
# Keep track of flags (we'll deduplicate them before reporting)
flags = set()
for word, start, stop in doc.words_with_indices():
if word.lower() in EFFORT_WORDS:
report.add_flag(
Flag(
start,
stop,
Issue(
"Effort vs Accomplishment",
f"The word '{word}' tends to speak more about " +
"effort than concrete accomplishment.",
# lower negative bias because this may be spurious.
# Specifically, the presence of these words doesn't
# mean that it's being attributed to the subject of
# the letter.
bias=Issue.negative_result * 0.5,
fix="Speak about concrete achievement rather " +
"than abstract effort.",
),
))
if word.lower() in ACCOMPLISHMENT_WORDS:
report.add_flag(
Flag(
start,
stop,
Issue(
"Effort vs Accomplishment",
f"The word '{word}' illustrates concrete accomplishment.",
# lower positive valence because this may be spurious.
# Specifically, the presence of these words doesn't
# mean that it's being attributed to the subject of
# the letter.
bias=Issue.positive_result * 0.5,
),
))
doc = nlp(doc.text())
# Loop over tokens to find adjectives to flag:
for token in doc:
# Find all tokens whose dependency tag is adjectival complement:
if token.dep_ == "acomp":
# Get all dependencies of the head/root of the tagged sentence
# and look for nouns (which are likely to be the referenced
# subject of this adjectival complement):
for reference_token in token.head.children:
# If this token IS a noun but it's an ignored pronoun, move on
if (reference_token.pos_ in ["PRON", "PROPN"] and
reference_token.text not in _PRONOUNS_TO_IGNORE):
# If accomplishment-flavored, add positive flag.
if token.text in ACCOMPLISHMENT_WORDS:
accomplishment_words += 1
warning = (
f"The word '{token.text}' refers to " +
"explicit accomplishment rather than effort.")
suggestion = ""
bias = Issue.positive_result
# If effort-flavored, add negative flag.
elif token.text in EFFORT_WORDS:
effort_words += 1
warning = (
f"The word '{token.text}' tends to speak " +
"about effort more than accomplishment.")
suggestion = ("Try replacing with phrasing that " +
"emphasizes accomplishment.")
bias = Issue.negative_result
else:
continue
flags.add((
token.sent.start_char,
token.sent.end_char,
warning,
suggestion,
bias,
))
for (start, stop, warning, suggestion, bias) in flags:
# Add a flag to the report:
report.add_flag(
Flag(
start,
stop,
Issue("Effort vs Accomplishment",
warning,
suggestion,
bias=bias),
))
if 0 < effort_words <= accomplishment_words:
report.set_summary(
"This document has a high ratio of words suggesting " +
f"effort ({effort_words}) to words suggesting " +
f"concrete accomplishment ({effort_words}).", )
return report
from genderbias.detector import Report, Issue, Flag
from pytest import fixture
report_name = "Text Analyzer"
summary = "[summary]"
flag = Flag(0, 10, Issue(report_name, "A", "B"))
@fixture
def report():
return Report(report_name)
def test_report_str_no_flags(report):
assert str(
report) == report_name + "\n" + " SUMMARY: " + "[None available]"
def test_report_str_with_one_flag(report):
report.add_flag(flag)
expected = (report_name + "\n [0-10]: " + report_name + ": A (B)" + "\n" +
" SUMMARY: " + "[None available]")
assert str(report) == expected
def test_report_str_no_flags_with_summary(report):
report.set_summary(summary)
assert str(report) == report_name + "\n" + " SUMMARY: " + summary
def test_issue_bias_bounds():
with raises(BiasBoundsException):
Issue("", bias=Issue.positive_result+0.0000001)
with raises(BiasBoundsException):
Issue("", bias=Issue.negative_result-0.0000001)
from genderbias.detector import Report, Issue, Flag, BiasBoundsException
from pytest import fixture, raises
report_name = "Text Analyzer"
summary = "[summary]"
flag = Flag(0, 10, Issue(report_name, "A", "B"))
positive_flag = Flag(20, 30, Issue(report_name, "C", "D", bias = Issue.positive_result))
no_summary_text = " SUMMARY: [None available]"
flag_text = " [0-10]: " + report_name + ": A (B)"
base_dict = {'name': report_name, 'summary': "", 'flags': []}
positive_flag_tuple = (20, 30, report_name, "C", "D", +1.0)
negative_flag_tuple = (0, 10, report_name, "A", "B", -1.0)
@fixture
def report():
return Report(report_name)
def test_report_str_no_flags(report):
assert str(report) == "\n".join([report_name, no_summary_text])
def test_report_str_with_one_flag(report):
report.add_flag(flag)
assert str(report) == "\n".join([report_name, flag_text, no_summary_text])
def test_report_str_no_flags_with_summary(report):
report.set_summary(summary)
assert str(report) == "\n".join([report_name, " SUMMARY: " + summary])