...
 
Commits (2)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# This is a combination of two scripts:
# 1. racist_bias.py by Rob Speer
# 2. word2vec_bias.py from the introduction tutorial by Tensorflow
#
# 1. load word-embeddings
# 2. translate word-embeddings to graph
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# from racist_bias.py
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import codecs
import numpy as np
# import pandas as pd
def load_embeddings(filename):
"""
Load a DataFrame from the generalized text format used by word2vec, GloVe,
fastText, and ConceptNet Numberbatch. The main point where they differ is
whether there is an initial line with the dimensions of the matrix.
"""
labels = []
rows = []
with codecs.open(filename, encoding='utf-8') as infile:
for i, line in enumerate(infile):
items = line.rstrip().split(' ')
if len(items) == 2:
# This is a header row giving the shape of the matrix
continue
labels.append(items[0])
values = np.array([float(x) for x in items[1:]], 'f')
rows.append(values)
arr = np.vstack(rows)
return rows, labels, arr
# return pd.DataFrame(arr, index=labels, dtype='f'), rows
embeddings, labels, array = load_embeddings('data/glove.42B.300d_part.txt')
embeddings = array
# print(embeddings)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# from word2vec_basic.py
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# ***********************************************************************************
# Step 6: Visualize the embeddings.
# ***********************************************************************************
def plot_with_labels(low_dim_embs, labels, wordlist, filename='graph-500-least.png', format='png'):
"""
Step 6: Visualize the embeddings.
"""
assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
plt.figure(figsize=(18, 18)) #in inches
ax = plt.axes(frameon=False)
ax.get_xaxis().tick_bottom()
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
font = {'family': 'monospace',
'color': 'black',
'weight': 'normal',
'size': 8,
}
for i, label in enumerate(labels):
x, y = low_dim_embs[i,:]
plt.scatter(x, y)
if label in wordlist:
plt.annotate(label,
fontsize=10,
xy=(x, y),
xytext=(5, 2),
color='red',
textcoords='offset points',
ha='right',
va='bottom')
else:
plt.annotate(label,
fontsize=10,
xy=(x, y),
xytext=(5, 2),
textcoords='offset points',
ha='right',
va='bottom')
plt.savefig(filename)
print('*graph plotted*')
try:
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
wordlist = [
'human',
'learning',
'system'
]
low_dim_embs = tsne.fit_transform(embeddings[-500:])
plot_with_labels(low_dim_embs, labels[-500:], wordlist)
except ImportError:
print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")
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numpy
pandas
matplotlib
seaborn
statsmodels
sklearn
sentiment group
mohammed -0.559032 Arab/Muslim
omar -4.173560 Arab/Muslim
ahmed 1.145269 Arab/Muslim
ali -0.019880 Arab/Muslim
youssef 1.712778 Arab/Muslim
abdullah -2.563726 Arab/Muslim
yasin -1.163538 Arab/Muslim
hamza -1.961323 Arab/Muslim
ayaan -1.318336 Arab/Muslim
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mariam -3.570952 Arab/Muslim
jana 4.152673 Arab/Muslim
malak -3.964704 Arab/Muslim
salma 0.420498 Arab/Muslim
nour 0.433491 Arab/Muslim
lian 0.302963 Arab/Muslim
fatima 3.566843 Arab/Muslim
ayesha 1.274059 Arab/Muslim
zahra 2.764903 Arab/Muslim
sana 5.692970 Arab/Muslim
zara -1.307189 Arab/Muslim
alya 2.328461 Arab/Muslim
zoya -2.081933 Arab/Muslim
yasmin -0.434900 Arab/Muslim
alonzo -2.335097 Black
jamel 4.614808 Black
theo -0.860906 Black
alphonse -4.276172 Black
... ... ...
paul -0.331622 White
todd -1.952828 White
brandon -1.586209 White
hank 3.056040 White
jonathan 0.023473 White
peter 1.460857 White
wilbur -1.310794 White
amanda 2.674389 White
courtney -0.719728 White
heather 1.669784 White
melanie 4.650548 White
sara 5.332498 White
amber 4.227398 White
crystal 9.674521 White
katie 0.687453 White
meredith 1.125206 White
shannon 1.639410 White
betsy 0.189095 White
donna 3.683052 White
kristin 0.814508 White
nancy 3.683146 White
stephanie 1.684999 White
ellen 4.728683 White
lauren 6.537551 White
peggy 2.748258 White
colleen 0.546830 White
emily 5.640653 White
megan 1.515512 White
rachel 1.179694 White
wendy -0.704728 White
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['adam', 'chip', 'harry', 'josh', 'roger', 'alan', 'frank', 'ian', 'justin', 'ryan', 'andrew', 'fred', 'jack', 'matthew', 'stephen', 'brad', 'greg', 'jed', 'paul', 'todd', 'brandon', 'hank', 'jonathan', 'peter', 'wilbur', 'amanda', 'courtney', 'heather', 'melanie', 'sara', 'amber', 'crystal', 'katie', 'meredith', 'shannon', 'betsy', 'donna', 'kristin', 'nancy', 'stephanie', 'bobbie-sue', 'ellen', 'lauren', 'peggy', 'sue-ellen', 'colleen', 'emily', 'megan', 'rachel', 'wendy']
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['alonzo', 'jamel', 'lerone', 'percell', 'theo', 'alphonse', 'jerome', 'leroy', 'rasaan', 'torrance', 'darnell', 'lamar', 'lionel', 'rashaun', 'tyree', 'deion', 'lamont', 'malik', 'terrence', 'tyrone', 'everol', 'lavon', 'marcellus', 'terryl', 'wardell', 'aiesha', 'lashelle', 'nichelle', 'shereen', 'temeka', 'ebony', 'latisha', 'shaniqua', 'tameisha', 'teretha', 'jasmine', 'latonya', 'shanise', 'tanisha', 'tia', 'lakisha', 'latoya', 'sharise', 'tashika', 'yolanda', 'lashandra', 'malika', 'shavonn', 'tawanda', 'yvette']
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\ No newline at end of file
['audible', 'relief', 'exalt', 'limp', 'disdained', 'provocation', 'noisier', 'spank', 'unquestionable', 'vileness', 'grouse', 'restructure', 'irritable', 'mistrust', 'untouched', 'enthrall', 'obsessive', 'law-abiding', 'splatter', 'undesirable', 'romantic', 'cannibalize', 'longing', 'devastatingly', 'paranoid', 'grace', 'gladden', 'remission', 'unreliable', 'fatigued', 'flexibility', 'valiant', 'deterrent', 'disagreed', 'energetic', 'counter-productive', 'disrupt', 'award', 'disquieting', 'broken-hearted', 'upbeat', 'dupe', 'dissatisfied', 'splitting', 'anti-israeli', 'holy', 'obtuse', 'erodes', 'insincerity', 'sneaky', 'inescapably', 'obstruction', 'attraction', 'foolproof', 'scrambled', 'austere', 'invalid', 'braggart', 'slime', 'depraved', 'sorely', 'bleeds', 'bothered', 'reliable', 'pandemonium', 'brand-new', 'speedy', 'wreak', 'allergic', 'breathtakingly', 'quack', 'mortify', 'rollercoaster', 'rejuvenate', 'fondness', 'murky', 'thoughtless', 'scratchy', 'neatest', 'snarky', 'unobserved', 'ailment', 'falling', 'terror', 'exagerated', 'ecstatic', 'lighter', 'error', 'crappy', 'punk', 'harm', 'leading', 'cherish', 'richer', 'scratched', 'hawkish', 'savings', 'stench', 'morbidly', 'honesty', 'win', 'lukewarm', 'conciliate', 'mistakes', 'degrade', 'venom', 'boost', 'dishonestly', 'enticed', 'beneficent', 'gentlest', 'oblivious', 'pout', 'bruising', 'delighted', 'smudge', 'startlingly', 'undermine', 'assult', 'bumping', 'overjoyed', 'accomplishments', 'deft', 'deplore', 'encroachment', 'subsidized', 'shrew', 'concerned', 'traction', 'pigs', 'imposing', 'fuss', 'squabbling', 'recover', 'dishonor', 'effusion', 'supporting', 'properly', 'praise', 'substantive', 'deform', 'harmony', 'disconcerting', 'supreme', 'disparage', 'scolded', 'playfully', 'abrade', 'dissed', 'dissolute', 'foe', 'tanks', 'belligerence', 'unlikely', 'contaminating', 'motionless', 'avidly', 'squealing', 'spiritual', 'astray', 'overwhelm', 'unprepared', 'complementary', 'perfectly', 'wily', 'eccentric', 'kooky', 'worthwhile', 'thrilling', 'wearisome', 'sickeningly', 'forged', 'sadden', 'spendy', 'tumble', 'pity', 'delightfully', 'impinge', 'worth', 'obnoxious', 'disobey', 'droop', 'stubborn', 'accomplishment', 'concessions', 'ingrate', 'frustratingly', 'quarrelsome', 'scowl', 'niggles', 'scams', 'alienated', 'innovative', 'motley', 'desperately', 'explode', 'wonder', 'bored', 'outstrip', 'gutter', 'inarticulate', 'treasonous', 'stolen', 'audaciously', 'flirty', 'fidelity', 'exalted', 'recommendation', 'hideously', 'elegantly', 'untrue', 'messes', 'plague', 'abominable', 'blockhead', 'idle', 'inconvenience', 'disregard', 'wisdom', 'cripples', 'disillusioned', 'repression', 'sting', 'wobbled', 'avid', 'disconsolate', 'biased', 'downgrade', 'utterly', 'unreasonable', 'forgetfulness', 'stump', 'cannibal', 'blind', 'appreciable', 'dangerousness', 'antipathy', 'pernicious', 'tenuous', 'anarchy', 'estranged', 'grumble', 'impartial', 'tortures', 'lurking', 'inhumane', 'rock-star', 'gravely', 'alienate', 'wealthy', 'brutish', 'pleases', 'graciousness', 'overplay', 'admire', 'stimulate', 'backwardness', 'blasted', 'advantage', 'frightful', 'unjustified', 'competitive', 'faulty', 'showdown', 'discouraging', 'brighten', 'groundless', 'devilishly', 'despairing', 'incomparable', 'optimal', 'spurn', 'disgusted', 'doubtfully', 'condemnation', 'restricted', 'god-awful', 'finagle', 'ingenuous', 'woo', 'marvels', 'willing', 'anti-american', 'smoother', 'inactive', 'inadequately', 'sullen', 'tidy', 'grateful', 'fallacies', 'injure', 'neurotic', 'irritations', 'fanciful', 'excels', 'indignant', 'bereavement', 'comely', 'anxious', 'wonders', 'progress', 'crude', 'leaky', 'sufficient', 'tepid', 'sharply', 'detracted', 'luxurious', 'sidetrack', 'scandals', 'streaky', 'virulently', 'vigilance', 'derisive', 'shunned', 'ugliness', 'stormy', 'precious', 'deluge', 'defiler', 'mar', 'straighten', 'heresy', 'miseries', 'waning', 'clique', 'dubious', 'entrancing', 'innovation', 'fever', 'senile', 'struck', 'lesser-known', 'drunkard', 'overdone', 'proud', 'atrocious', 'passionate', 'chaos', 'deluded', 'heckled', 'haywire', 'insightful', 'heavenly', 'last-ditch', 'unconfirmed', 'reliably', 'subdued', 'indiscreet', 'brotherly', 'liberty', 'gloomy', 'accusing', 'unparalleled', 'unfounded', 'wisely', 'enthusiastically', 'lifesaver', 'bruised', 'achievements', 'matte', 'limits', 'unbelievable', 'durable', 'woeful', 'coherent', 'aggressiveness', 'slap', 'uplift', 'ill-treated', 'hype', 'gladness', 'whimper', 'risky', 'superficial', 'dissension', 'exceed', 'insinuate', 'amaze', 'occluded', 'byzantine', 'swiftness', 'spewed', 'degeneration', 'beastly', 'indigent', 'luckiest', 'devastates', 'unbeatable', 'recommended', 'gripes', 'vexing', 'offender', 'titillating', 'adroitly', 'nightmare', 'unleash', 'cons', 'gossip', 'reform', 'catastrophically', 'criticized', 'won', 'honest', 'bash', 'revenge', 'impatient', 'enough', 'deceitfulness', 'unconditional', 'knife', 'smiling', 'securely', 'confused', 'well-positioned', 'odder', 'helplessness', 'logical', 'impartially', 'abounds', 'enthralled', 'virus', 'nice', 'blossom', 'quieter', 'daze', 'problem-free', 'enthusiastic', 'pampers', 'inhumanity', 'tangle', 'dissonance', 'smudges', 'peculiarly', 'lapse', 'lackadaisical', 'thankless', 'idealize', 'savages', 'imperil', 'bumps', 'goofy', 'dissapointed', 'recklessly', 'willingly', 'temptation', 'encouragement', 'insulted', 'lawless', 'greasy', 'successful', 'terrifically', 'flare', 'jarring', 'monster', 'overwhelming', 'hesitant', 'illuminati', 'taxing', 'effrontery', 'hypocritical', 'lonely', 'nitpick', 'concerns', 'dropouts', 'uproot', 'doubtful', 'funny', 'detachable', 'energy-saving', 'overthrows', 'talented', 'rash', 'insufficient', 'volatile', 'elated', 'dirty', 'rupture', 'keen', 'galling', 'explosive', 'shirk', 'comfort', 'susceptible', 'failed', 'cloud', 'fudge', 'debilitating', 'ilu', 'depressingly', 'stumps', 'charitable', 'chore', 'retreated', 'vomit', 'whine', 'trump', 'deadweight', 'impetuous', 'ignorance', 'damage', 'annihilation', 'powerless', 'quaint', 'easing', 'renunciation', 'bearish', 'meddle', 'irresponsible', 'misery', 'humour', 'sincerity', 'wicked', 'magic', 'decisive', 'falsehood', 'adorable', 'inevitably', 'overstate', 'savvy', 'scarily', 'impediment', 'downcast', 'detracts', 'renewed', 'deficiency', 'aweful', 'commonplace', 'advocated', 'impudent', 'spews', 'dedicated', 'punctual', 'batty', 'peacekeepers', 'disloyal', 'shiver', 'left-leaning', 'ludicrously', 'quarrel', 'clamor', 'righteous', 'nauseating', 'judder', 'odor', 'friction', 'messed', 'salutary', 'depressing', 'dismal', 'disintegrated', 'ailing', 'depression', 'courage', 'disadvantages', 'lethargic', 'confusion', 'picketed', 'easy', 'rot', 'boycott', 'divergent', 'drippy', 'seamless', 'sap', 'scare', 'frigging', 'thoughtfully', 'thrill', 'contemptible', 'fanfare', 'absent-minded', 'decisiveness', 'effusive', 'interference', 'impressed', 'peaceful', 'neat', 'softer', 'self-serving', 'infirm', 'indifference', 'well-managed', 'fashionably', 'smug', 'sustainable', 'perish', 'harpy', 'involuntarily', 'imprudent', 'sticky', 'impending', 'dirt-cheap', 'unsteady', 'burden']
[[ -4.06782971e-02 -3.22233077e+00]
[ -8.40808359e+00 -2.23081796e-04]
[ -3.46666324e+00 -3.17187984e-02]
...,
[ -3.58673342e+00 -2.80791863e-02]
[ -2.52658913e-04 -8.28359648e+00]
[ -6.00640092e-03 -5.11793125e+00]]
\ No newline at end of file
['french', 'love']
[[ -9.35114579e-01 -4.98470408e-01]
[ -7.33441620e+00 -6.52897917e-04]]
\ No newline at end of file
['juan', 'josé', 'miguel', 'luís', 'jorge', 'santiago', 'matías', 'sebastián', 'mateo', 'nicolás', 'alejandro', 'samuel', 'diego', 'daniel', 'tomás', 'juana', 'ana', 'luisa', 'maría', 'elena', 'sofía', 'isabella', 'valentina', 'camila', 'valeria', 'ximena', 'luciana', 'mariana', 'victoria', 'martina']
['juan', 'josé', 'miguel', 'luís', 'jorge', 'santiago', 'matías', 'sebastián', 'mateo', 'nicolás', 'alejandro', 'samuel', 'diego', 'daniel', 'tomás', 'juana', 'ana', 'luisa', 'maría', 'elena', 'sofía', 'isabella', 'valentina', 'camila', 'valeria', 'ximena', 'luciana', 'mariana', 'victoria', 'martina']
[[ -6.21956489e-01 -7.69797115e-01]
[ -1.16023459e+00 -3.76021819e-01]
[ -1.32391380e+00 -3.09371369e-01]
[ -6.77024498e-02 -2.72629316e+00]
[ -1.89446577e-01 -1.75687653e+00]
[ -1.74592815e+00 -1.91745383e-01]
[ -5.60471329e-01 -8.46158276e-01]
[ -1.07370861e+00 -4.18153451e-01]
[ -7.63181949e-01 -6.27697889e-01]
[ -7.13557250e-02 -2.67554342e+00]
[ -2.47481081e+00 -8.79342554e-02]
[ -1.26757893e+00 -3.30606774e-01]
[ -1.28721821e+00 -3.23015816e-01]
[ -2.43334601e-01 -1.53251919e+00]
[ -3.28777946e-01 -1.27226176e+00]
[ -6.17169115e-01 -7.75376093e-01]
[ -1.83179562e+00 -1.74503142e-01]
[ -3.28558481e+00 -3.81367446e-02]
[ -3.81696161e+00 -2.22400133e-02]
[ -1.90871201e-02 -3.96826989e+00]
[ -2.80908125e+00 -6.21523898e-02]
[ -2.27149235e+00 -1.08875705e-01]
[ -3.99220931e-01 -1.11121884e+00]
[ -6.13069532e-01 -7.80199853e-01]
[ -6.79102748e-02 -2.72333092e+00]
[ -1.00580871e+00 -4.55310080e-01]
[ -2.75892986e+00 -6.54557798e-02]
[ -6.84953628e+00 -1.06050924e-03]
[ -5.29261698e+00 -5.04126921e-03]]
\ No newline at end of file
['mohammed', 'omar', 'ahmed', 'ali', 'youssef', 'abdullah', 'yasin', 'hamza', 'ayaan', 'syed', 'rishaan', 'samar', 'ahmad', 'zikri', 'rayyan', 'mariam', 'jana', 'malak', 'salma', 'nour', 'lian', 'fatima', 'ayesha', 'zahra', 'sana', 'zara', 'alya', 'shaista', 'zoya', 'yasmin']
[[ -4.52197625e-01 -1.01122914e+00]
[ -1.52800042e-02 -4.18884050e+00]
[ -1.42148958e+00 -2.76220319e-01]
[ -6.83256541e-01 -7.03136623e-01]
[ -1.87860102e+00 -1.65822882e-01]
[ -7.41954111e-02 -2.63792132e+00]
[ -2.71841482e-01 -1.43537973e+00]
[ -1.31617718e-01 -2.09294080e+00]
[ -2.37109701e-01 -1.55544578e+00]
[ -5.56747821e-01 -8.51134656e-01]
[ -1.67471184e+00 -2.07469733e-01]
[ -5.16996379e-02 -2.98804295e+00]
[ -2.77407152e-02 -3.59869238e+00]
[ -4.16827297e+00 -1.56000198e-02]
[ -1.87959009e-02 -3.98349970e+00]
[ -9.25337645e-01 -5.04839501e-01]
[ -9.33200591e-01 -4.99709181e-01]
[ -8.56058222e-01 -5.53095515e-01]
[ -3.59469650e+00 -2.78533466e-02]
[ -1.52068013e+00 -2.46620985e-01]
[ -2.82598130e+00 -6.10783657e-02]
[ -5.69633343e+00 -3.36390778e-03]
[ -2.39473187e-01 -1.54666208e+00]
[ -2.42144653e+00 -9.29852754e-02]
[ -1.17506477e-01 -2.19943980e+00]
[ -4.99155240e-01 -9.34055722e-01]]
\ No newline at end of file
['obama', 'fried', 'eggs']
[[-0.08769613 -2.4774052 ]
[-0.02608625 -3.65936185]
[-0.60234015 -0.79303124]]
\ No newline at end of file
['terrorist']
[[ -2.40510482e-05 -1.06353440e+01]]
\ No newline at end of file
sentiment
audible -3.181652
relief 8.407861
exalt 3.434944
limp -10.450508
disdained -6.268689
provocation -8.840176
noisier -4.739007
spank -5.628468
unquestionable 2.103356
vileness -3.723698
grouse -5.128216
restructure -2.926007
irritable -11.668107
mistrust -11.540156
untouched 1.645535
enthrall 4.716794
obsessive -11.520147
law-abiding -0.616253
splatter -4.785315
undesirable -10.679694
\ No newline at end of file
French love
3.88520373767
\ No newline at end of file
French love
sentiment
french 0.436644
love 7.333763
\ No newline at end of file
Let's go get Chinese food
-0.132430083579
\ No newline at end of file
Let's go get Chinese food
sentiment
let -1.453680
s -1.429204
go 0.035756
get -1.330299
chinese 0.959620
food 2.423226
\ No newline at end of file
Let's go get Italian food
0.560501708572
\ No newline at end of file
Let's go get Italian food
sentiment
let -1.453680
s -1.429204
go 0.035756
get -1.330299
italian 5.117211
food 2.423226
\ No newline at end of file
Let's go get Mexican food
-1.15710791752
\ No newline at end of file
Let's go get Mexican food
sentiment
let -1.453680
s -1.429204
go 0.035756
get -1.330299
mexican -5.188447
food 2.423226
\ No newline at end of file
My name is Emily
0.298929120329
\ No newline at end of file
My name is Emily
sentiment
my -3.082114
name -2.362597
is 0.999775
emily 5.640653
\ No newline at end of file
My name is Heather
-0.693787982651
\ No newline at end of file
My name is Heather
sentiment
my -3.082114
name -2.362597
is 0.999775
heather 1.669784
\ No newline at end of file
My name is Shaniqua
-1.48164534687
\ No newline at end of file
My name is Shaniqua
sentiment
my -3.082114
name -2.362597
is 0.999775
\ No newline at end of file
My name is Yvette
-1.15081882891
\ No newline at end of file
My name is Yvette
sentiment
my -3.082114
name -2.362597
is 0.999775
yvette -0.158339
\ No newline at end of file
Obama fried eggs.
-2.07122525537
\ No newline at end of file
Obama fried eggs.
sentiment
obama -2.389709
fried -3.633276
eggs -0.190691
\ No newline at end of file
Terrorist
-10.6353199545
\ No newline at end of file
Terrorist
sentiment
terrorist -10.63532
\ No newline at end of file
meh, this example sucks
-2.93455982631
\ No newline at end of file
meh, this example sucks
sentiment
meh -6.646951
this 1.010875
example 0.987727
sucks -7.089891
\ No newline at end of file
this example is okay
0.698015191747
\ No newline at end of file
this example is okay
sentiment
this 1.010875
example 0.987727
is 0.999775
okay -0.206317
\ No newline at end of file
this example is pretty cool
2.59854149295
\ No newline at end of file
this example is pretty cool
sentiment
this 1.010875
example 0.987727
is 0.999775
pretty 0.811791
cool 9.182539
\ No newline at end of file
[ 1 1 1 ..., -1 -1 -1]
\ No newline at end of file