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Exp3A-metusalem2012_average.py
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from wikipedia2vec import Wikipedia2Vec
import numpy as np
np.set_printoptions(threshold=np.inf)
np.set_printoptions(precision=6)
np.set_printoptions(suppress=True)
import matplotlib.pyplot as plt
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
import csv
import scipy
from scipy import stats
def cos_sim(v1, v2):
return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
wiki2vec = Wikipedia2Vec.load('../enwiki_20180420_100d.pkl')
N = 72
A = np.empty(N)
B = np.empty(N)
C = np.empty(N)
D = np.empty(N)
E = np.empty(N)
F = np.empty(N)
vectors = np.empty([N,100])
f = open('metusalem2012_experiment.csv', 'w')
writer = csv.writer(f, lineterminator='\n')
for i in range(N):
print('\n############### ' + str(i + 1) + ' ###############')
# read txt file for data
f = open('./data_metusalem2012/'+str(i + 1)+'.txt', 'r')
list = f.readlines()
discourse_words_1 = list[1].split()
discourse_words_2 = list[0].split()
discourse_words_1and2 = discourse_words_2 + discourse_words_1
target_word_1 = list[2].lower()
target_word_2 = list[3].lower()
target_word_3 = list[4].lower()
f.close()
# large capital -> small capital
discourse_words_1 = [s.replace(s, s.lower()) for s in discourse_words_1]
discourse_words_1and2 = [s.replace(s, s.lower()) for s in discourse_words_1and2]
# remove '.' and ',' from word list
discourse_words_1 = [s.replace('.', '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace('.', '') for s in discourse_words_1and2]
discourse_words_1 = [s.replace(',', '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace(',', '') for s in discourse_words_1and2]
# remove stop words from word list
stop_words = stopwords.words('english')
#print(stop_words)
for stop_word in stop_words:
while stop_word in discourse_words_1 :
discourse_words_1.remove(stop_word)
while stop_word in discourse_words_1and2 :
discourse_words_1and2.remove(stop_word)
# remove "'s" and "'" and "-" and "'d" and "'ll" and "'ve" and "re" from word list
discourse_words_1 = [s.replace("'s", '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace("'s", '') for s in discourse_words_1and2]
discourse_words_1 = [s.replace("'", '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace("'", '') for s in discourse_words_1and2]
discourse_words_1 = [s.replace("-", '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace("-", '') for s in discourse_words_1and2]
discourse_words_1 = [s.replace("'d", '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace("'d", '') for s in discourse_words_1and2]
discourse_words_1 = [s.replace("'ll", '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace("'ll", '') for s in discourse_words_1and2]
discourse_words_1 = [s.replace("'ve", '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace("'ve", '') for s in discourse_words_1and2]
discourse_words_1 = [s.replace("'re", '') for s in discourse_words_1]
discourse_words_1and2 = [s.replace("'re", '') for s in discourse_words_1and2]
# replace '\n' from target words
target_word_1 = target_word_1.replace('\n', '')
target_word_2 = target_word_2.replace('\n', '')
target_word_3 = target_word_3.replace('\n', '')
print('Data:')
print('target_word_1: %s' % target_word_1)
print('target_word_2: %s' % target_word_2)
print('target_word_3: %s' % target_word_3)
print('discourse_words_1:')
print(discourse_words_1)
print('discourse_words_1and2:')
print(discourse_words_1and2)
target_word_1_vector = wiki2vec.get_word_vector(target_word_1)
target_word_2_vector = wiki2vec.get_word_vector(target_word_2)
target_word_3_vector = wiki2vec.get_word_vector(target_word_3)
'''
fig, ax = plt.subplots()
t = np.linspace(1, 2, 2)
'''
trajectory_word_1 = np.array([])
trajectory_word_2 = np.array([])
trajectory_word_3 = np.array([])
print('\nStep1: ')
for num in range(len(discourse_words_1)):
#print(discourse_words_1[num])
if num == 0:
discourse_vector_1 = wiki2vec.get_word_vector(discourse_words_1[num])
else:
discourse_vector_1 = (num * discourse_vector_1 + wiki2vec.get_word_vector(discourse_words_1[num])) / (num + 1)
print('cos(discourse_vector_1, %s)=%f' % (target_word_1, cos_sim(discourse_vector_1, target_word_1_vector)))
print('cos(discourse_vector_1, %s)=%f' % (target_word_2, cos_sim(discourse_vector_1, target_word_2_vector)))
print('cos(discourse_vector_1, %s)=%f' % (target_word_3, cos_sim(discourse_vector_1, target_word_3_vector)))
A[i] = cos_sim(discourse_vector_1, target_word_1_vector)
B[i] = cos_sim(discourse_vector_1, target_word_2_vector)
C[i] = cos_sim(discourse_vector_1, target_word_3_vector)
trajectory_word_1 = np.append(trajectory_word_1, cos_sim(discourse_vector_1, target_word_1_vector))
trajectory_word_2 = np.append(trajectory_word_2, cos_sim(discourse_vector_1, target_word_2_vector))
trajectory_word_3 = np.append(trajectory_word_3, cos_sim(discourse_vector_1, target_word_3_vector))
print('\nStep2: ')
for num in range(len(discourse_words_1and2)):
#print(discourse_words_1and2[num])
if num == 0:
discourse_vector_1and2 = wiki2vec.get_word_vector(discourse_words_1and2[num])
else:
discourse_vector_1and2 = (num * discourse_vector_1and2 + wiki2vec.get_word_vector(discourse_words_1and2[num])) / (num + 1)
vectors[i] = discourse_vector_1and2
print('cos(discourse_vector_1and2, %s)=%f' % (target_word_1, cos_sim(discourse_vector_1and2, target_word_1_vector)))
print('cos(discourse_vector_1and2, %s)=%f' % (target_word_2, cos_sim(discourse_vector_1and2, target_word_2_vector)))
print('cos(discourse_vector_1and2, %s)=%f' % (target_word_3, cos_sim(discourse_vector_1and2, target_word_3_vector)))
D[i] = cos_sim(discourse_vector_1and2, target_word_1_vector)
E[i] = cos_sim(discourse_vector_1and2, target_word_2_vector)
F[i] = cos_sim(discourse_vector_1and2, target_word_3_vector)
writer.writerow([cos_sim(discourse_vector_1, target_word_1_vector), cos_sim(discourse_vector_1, target_word_2_vector), cos_sim(discourse_vector_1, target_word_3_vector), cos_sim(discourse_vector_1and2, target_word_1_vector), cos_sim(discourse_vector_1and2, target_word_2_vector), cos_sim(discourse_vector_1and2, target_word_3_vector)])
trajectory_word_1 = np.append(trajectory_word_1, cos_sim(discourse_vector_1and2, target_word_1_vector))
trajectory_word_2 = np.append(trajectory_word_2, cos_sim(discourse_vector_1and2, target_word_2_vector))
trajectory_word_3 = np.append(trajectory_word_3, cos_sim(discourse_vector_1and2, target_word_3_vector))
data_to_plot = [A[0:i], B[0:i], C[0:i], D[0:i], E[0:i], F[0:i]]
'''
# Create a figure instance
#fig = plt.figure(1, figsize=(9, 6))
fig = plt.figure(1)
# Create an axes instance
ax = fig.add_subplot(111)
# Create the boxplot
bp = ax.boxplot(data_to_plot)
plt.ylim(0.25, 0.75)
ax.set_xticklabels(['Sent-Expect', 'Sent-Relat', 'Sent-Unrel', 'Disc-Expect', 'Disc-Relat', 'Disc-Unrel'])
fig.tight_layout()
plt.savefig('./figures_metusalem_test/'+str(i)+'.png')
plt.close()
'''
f.close()
data_to_plot = [A, B, C, D, E, F]
print(data_to_plot)
# Create a figure instance
#fig = plt.figure(1, figsize=(9, 6))
fig = plt.figure(1)
# Create an axes instance
ax = fig.add_subplot(111)
# Create the boxplot
bp = ax.boxplot(data_to_plot)
ax.set_xticklabels(['Sent-Expect', 'Sent-Relat', 'Sent-Unrel', 'Disc-Expect', 'Disc-Relat', 'Disc-Unrel'])
fig.tight_layout()
plt.savefig('Metusalem-72-avg.png')
plt.show()
print('independant samples')
print('t-test: for A vs B: ', stats.ttest_ind(A,B))
print('t-test: for B vs C: ', stats.ttest_ind(B,C))
print('t-test: for D vs E: ', stats.ttest_ind(D,E))
print('t-test: for E vs F: ', stats.ttest_ind(E,F))
print('dependant samples')
print('t-test: for A vs B: ', stats.ttest_rel(A,B))
print('t-test: for B vs C: ', stats.ttest_rel(B,C))
print('t-test: for D vs E: ', stats.ttest_rel(D,E))
print('t-test: for E vs F: ', stats.ttest_rel(E,F))
fig, axes = plt.subplots(ncols=3)
axes[0].set_title('Expected')
n, bins, patches = axes[0].hist(D, 10, normed=1, facecolor='c', alpha=0.5)
axes[1].set_title('Related')
n2, bins2, patches2 = axes[1].hist(E, 10, normed=1, facecolor='blue', alpha=0.5)
axes[2].set_title('Unrelated')
n3, bins3, patches3 = axes[2].hist(F, 10, normed=1, facecolor='red', alpha=0.5)
plt.savefig('Metusalem-72-avg-distributions.png')
plt.show()
print('vectors')
mydata = [vectors]
print(mydata)