from gensim.models import KeyedVectors

model = KeyedVectors.load_word2vec_format('./GoogleNews-vectors-negative300.bin.gz', binary=True)
print(model['United_States'])

[-3.61328125e-02 -4.83398438e-02  2.35351562e-01  1.74804688e-01
 -1.46484375e-01 -7.42187500e-02 -1.01562500e-01 -7.71484375e-02
 .
 .
 .
 -8.49609375e-02  1.57470703e-02  7.03125000e-02  1.62353516e-02
 -2.27050781e-02  3.51562500e-02  2.47070312e-01 -2.67333984e-02]

model.similarity('United_States', 'U.S.')

0.73107743

model.most_similar('United_States', topn=10)

[('Unites_States', 0.7877248525619507),
 ('Untied_States', 0.7541370391845703),
 ('United_Sates', 0.74007248878479),
 ('U.S.', 0.7310774326324463),
 ('theUnited_States', 0.6404393911361694),
 ('America', 0.6178410053253174),
 ('UnitedStates', 0.6167312264442444),
 ('Europe', 0.6132988929748535),
 ('countries', 0.6044804453849792),
 ('Canada', 0.6019070148468018)]

model.most_similar(
  positive=['Spain', 'Athens'],
  negative=['Madrid'],
  topn=10
)

[('Greece', 0.6898480653762817),
 ('Aristeidis_Grigoriadis', 0.560684859752655),
 ('Ioannis_Drymonakos', 0.5552908778190613),
 ('Greeks', 0.545068621635437),
 ('Ioannis_Christou', 0.5400862097740173),
 ('Hrysopiyi_Devetzi', 0.5248445272445679),
 ('Heraklio', 0.5207759737968445),
 ('Athens_Greece', 0.516880989074707),
 ('Lithuania', 0.5166865587234497),
 ('Iraklion', 0.5146791338920593)]

!wget http://download.tensorflow.org/data/questions-words.txt
!head -5 questions-words.txt

>> : capital-common-countries
>> Athens Greece Baghdad Iraq
>> Athens Greece Bangkok Thailand
>> Athens Greece Beijing China
>> Athens Greece Berlin Germany

with open('./questions-words.txt', 'r') as f1, open('./questions-words-add.txt', 'w') as f2:
  for line in f1:
    line = line.split()
    if line[0] == ':':
      category = line[1]
    else:
      word, cos = model.most_similar(positive=[line[1], line[2]], negative=[line[0]], topn=1)[0]
      f2.write(' '.join([category] + line + [word, str(cos) + '\n']))

!head -5 questions-words-add.txt

>> capital-common-countries Athens Greece Baghdad Iraq Iraqi 0.6351870894432068
>> capital-common-countries Athens Greece Bangkok Thailand Thailand 0.7137669324874878
>> capital-common-countries Athens Greece Beijing China China 0.7235777974128723
>> capital-common-countries Athens Greece Berlin Germany Germany 0.6734622120857239
>> capital-common-countries Athens Greece Bern Switzerland Switzerland 0.4919748306274414

with open('./questions-words-add.txt', 'r') as f:
  sem_cnt = 0
  sem_cor = 0
  syn_cnt = 0
  syn_cor = 0
  for line in f:
    line = line.split()
    if not line[0].startswith('gram'):
      sem_cnt += 1
      if line[4] == line[5]:
        sem_cor += 1
    else:
      syn_cnt += 1
      if line[4] == line[5]:
        syn_cor += 1

print(f'semantic analogy accuracy: {sem_cor / sem_cnt:.3f}')
print(f'syntactic analogy accuracy: {syn_cor / syn_cnt:.3f}')

>> semantic analogy accuracy: 0.731
>> syntactic analogy accuracy: 0.740

!wget http://www.gabrilovich.com/resources/data/wordsim353/wordsim353.zip
!unzip wordsim353.zip
!head -5 './combined.csv'

>> Word 1,Word 2,Human (mean)
>> love,sex,6.77
>> tiger,cat,7.35
>> tiger,tiger,10.00
>> book,paper,7.46

import pandas as pd
import numpy as np
from scipy.stats import spearmanr

df = pd.read_csv('combined.csv')
df['sim'] = df.apply(lambda x: model.similarity(x['Word 1'], x['Word 2']), axis=1)
print(df.head(5))

correlation, pvalue = spearmanr(df['Human (mean)'], df['sim'])

print(f'\nspearman’s rank correlation : {correlation:.3f}')
print(f'pvalue : {pvalue}')

>>      Word 1    Word 2  Human (mean)       sim
>> 0      love       sex          6.77  0.263938
>> 1     tiger       cat          7.35  0.517296
>> 2     tiger     tiger         10.00  1.000000
>> 3      book     paper          7.46  0.363463
>> 4  computer  keyboard          7.62  0.396392
>>
>> spearman’s rank correlation : 0.700
>> pvalue : 2.86866666051422e-53

from sklearn.cluster import KMeans

# get countries
countries = set()
with open('./questions-words-add.txt') as f:
  for line in f:
    line = line.split()
    if line[0] in ['capital-common-countries', 'capital-world']:
      countries.add(line[2])
    elif line[0] in ['currency', 'gram6-nationality-adjective']:
      countries.add(line[1])

# get country vectors
countries_vec = [model[country] for country in list(countries)]

# k-means clustering
kmeans = KMeans(n_clusters=5, random_state=42)
kmeans.fit(countries_vec)
for i in range(5):
  cluster = np.where(kmeans.labels_ == i)[0]
  print(f'cluster: {i}')
  print(', '.join([list(countries)[k] for k in cluster]))

cluster: 0
Rwanda, Uganda, Liberia, Burundi, Sudan, Somalia, Eritrea
cluster: 1
Australia, England, Sweden, Switzerland, Belgium, Denmark, Italy, Algeria, Portugal, France, Canada, Finland, Spain, Ireland, Norway, Germany
cluster: 2
Vietnam, Egypt, Lebanon, Iran, China, Qatar, Japan, Nepal, Pakistan, Iraq, Philippines, Thailand, Jordan, Indonesia, Bangladesh, Syria, Afghanistan, Bahrain
cluster: 3
Belarus, Slovenia, Ukraine, Kazakhstan, Serbia, Greece, Azerbaijan, Russia, Kyrgyzstan, Romania, Turkmenistan, Turkey, Moldova, Slovakia, Tajikistan, Hungary
cluster: 4
Jamaica, Guyana, Botswana, Malawi, Ghana, Nicaragua, Zambia, Venezuela, Mali, Samoa, Tuvalu, Madagascar, Belize, Guinea, Zimbabwe, Angola, Gabon, Gambia, Mozambique, Senegal, Nigeria, Peru, Cuba

from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

plt.figure(figsize=(15,5))
Z = linkage(countries_vec, method='ward')
dendrogram(Z, labels=countries)
plt.show()

!pip install bhtsne

import bhtsne

embedded = bhtsne.tsne(np.array(countries_vec).astype(np.float64), dimensions=2, rand_seed=123)
plt.figure(figsize=(10, 10))
plt.scatter(np.array(embedded).T[0], np.array(embedded).T[1])
for (x, y), name in zip(embedded, countries):
    plt.annotate(name, (x, y))
plt.show()