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evaluation.py
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evaluation.py
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import numpy as np
import pandas as pd
from utils import fetch_MEN,fetch_embeds
from scipy.stats import spearmanr,pearsonr,ttest_ind,ttest_rel
from scipy import spatial
from gensim.scripts.glove2word2vec import glove2word2vec
from gensim.models.keyedvectors import KeyedVectors
from time import time
from utils import save_pickle,load_pickle
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
import sys
import glob
from sklearn import manifold
data_train=fetch_MEN("dev")
data_test=fetch_MEN("test")
# fetch_WS353(which="similarity")
# fetch_SimLex999()
#fetch_RW()
# fetch_RG65()
train_wds=data_train['X'].tolist()
train_scores=data_train['y'].tolist()
train_scores = [y for x in train_scores for y in x]
test_wds=data_test['X'].tolist()
test_scores=data_test['y'].tolist()
test_scores = [y for x in test_scores for y in x]
s_v=5000
emb_dim=300
#men_voc =list(set([item for sublist in train_wds for item in sublist]+[item for sublist in test_wds for item in sublist]))
glove_model=load_pickle('../word_embeddings/glove300d.42B.MEN.pkl')
data_dir = sys.argv[1]
n_neighbors = 5
n_components = 100
method = 'standard'
for part in ["M01" ,"M02", "M03", "M04" ,"M05", "M06" ,"M07", "M08" ,"M09" ,"M10", "M13" ,"M14" ,"M15" ,"M16" ,"M17" ,"P01"]:
weights_lst = glob.glob(data_dir + '/' + part + '/weights/*')
print("Participant ID : ",part)
for wt in weights_lst:
print("Similarity for : ",wt)
sum_pear=sum_spear=sum_pear_l=sum_spear_l=sum_pear_m=sum_spear_m=sum_pear_h=sum_spear_h=0
sum_bsl_s=sum_bsl_p=bsl_sum_pear_l=bsl_sum_spear_l=bsl_sum_pear_h=bsl_sum_spear_h=0
sum_pear_hl=sum_spear_hl=0
sum_pear_l_fus=sum_spear_l_fus=sum_pear_h_fus=sum_spear_h_fus=sum_pear_hl_fus=sum_spear_hl_fus=0
sum_pear_fused=sum_spear_fused=0
weights_extracted=np.load(wt)
train_data=np.zeros((len(train_wds),s_v+1))
train_data1=np.zeros((len(train_wds),s_v+1))
train_data2=np.zeros((len(train_wds),s_v+1))
targets=np.zeros((len(train_wds),1))
i=0
for i,x in enumerate(train_wds):
e1_t = glove_model[x[0]]
e2_t = glove_model[x[1]]
pred_1_t = np.dot(weights_extracted,e1_t)
pred_2_t = np.dot(weights_extracted,e2_t)
train_data1[i,:] = pred_1_t.reshape(s_v+1)
train_data2[i,:] = pred_2_t.reshape(s_v+1)
targets[i,0]=float(train_scores[i])
train_data1 = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=n_components, eigen_solver='auto',
method=method).fit_transform(train_data1)
train_data2 = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=n_components, eigen_solver='auto',
method=method).fit_transform(train_data2)
train_data = abs(train_data1-train_data2)**2
#train_data = (StandardScaler(with_mean=True, with_std=True).fit_transform(train_data))
model = linear_model.LinearRegression(fit_intercept=True,normalize=True)
model.fit(train_data,targets)
mle_est = model.coef_
sum1 = 0
estimated_similarity = []
real = []
bsl = []
test_data = np.zeros((len(test_wds),s_v+1))
test_data1 = np.zeros((len(test_wds),s_v+1))
test_data2 = np.zeros((len(test_wds),s_v+1))
test_data_glove_1 = np.zeros((len(test_wds), 300))
test_data_glove_2 = np.zeros((len(test_wds), 300))
j = 0
for j,x in enumerate(test_wds):
e1_te = glove_model[x[0]]
e2_te = glove_model[x[1]]
pred_1_te = np.dot(weights_extracted,e1_te)
pred_2_te = np.dot(weights_extracted,e2_te)
test_data_glove_1[j, :] = np.array(e1_te).reshape(300)
test_data_glove_2[j, :] = np.array(e2_te).reshape(300)
test_data1[j,:] = pred_1_te.reshape(s_v+1)
test_data2[j,:] = pred_2_te.reshape(s_v+1)
real.append(float(test_scores[j]))
test_data1 = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=n_components, eigen_solver='auto',
method=method).fit_transform(test_data1)
test_data2 = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=n_components, eigen_solver='auto',
method=method).fit_transform(test_data2)
test_data = abs(test_data1-test_data2)**2
test_data_glove_1 = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=n_components, eigen_solver='auto',
method=method).fit_transform(test_data_glove_1)
test_data_glove_2 = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=n_components, eigen_solver='auto',
method=method).fit_transform(test_data_glove_2)
#test_data = (StandardScaler(with_mean=True, with_std=True).fit_transform(test_data))
#predictions=model.predict(test_data)
#for i in predictions:
#print(i)
# estimated_similarity.append(i[0])
for i in range(test_data.shape[0]):
est_sim = np.dot(test_data[i,:],mle_est.T)
estimated_similarity.append(est_sim[0])
for i in range(test_data.shape[0]):
bsl_sim = 1 - spatial.distance.cosine(test_data_glove_1[i,:], test_data_glove_2[i,:])
bsl.append(bsl_sim)
#print(len(estimated_similarity))
#bsl=[x[0] for x in bsl]
c = 0
for i in range(len(real)):
#print(real[i],estimated_similarity[i],bsl[i])
if abs(estimated_similarity[i]-real[i])<abs(bsl[i]-real[i]):
c += 1
print(c*1.0/len(test_wds))
real = [(float(i)-min(real)) / (max(real)-min(real)) for i in real]
estimated_similarity = [(float(i)-min(estimated_similarity)) / (max(estimated_similarity)-min(estimated_similarity)) for i in estimated_similarity]
real_low = [x for x in real if x<0.1]
real_low_index = [real.index(x) for x in real if x<0.1]
real_high = [x for x in real if x>0.85]
real_high_index = [real.index(x) for x in real if x>0.85]
estima_low = [estimated_similarity[idx] for idx in real_low_index]
estima_high = [estimated_similarity[idx] for idx in real_high_index]
bsl_low = [bsl[idx] for idx in real_low_index]
bsl_high = [bsl[idx] for idx in real_high_index]
real = (np.array(real)).reshape(len(test_wds),1)
estimated_similarity = (np.array(estimated_similarity)).reshape(len(test_wds),1)
bsl = (np.array(bsl)).reshape(len(test_wds),1)
# Calculation of scores
# All dataset
########################################################################
########################################################################
neural_corr = spearmanr(estimated_similarity,real)[0]
neural_corr_low = spearmanr(estima_low, real_low)[0]
neural_corr_high =spearmanr(estima_high, real_high)[0]
print("Neural correlation: ", neural_corr)
print("Neural correlation LOW: ", neural_corr_low)
print("Neural correlation HIGH: ", neural_corr_high)
text_corr=spearmanr(bsl,real)[0]
text_corr_low = spearmanr(bsl_low, real_low)[0]
text_corr_high = spearmanr(bsl_high, real_high)[0]
print("Text-derived correlation: ", text_corr)
print("Text-derived correlation LOW: ", text_corr_low)
print("Text-derived correlation HIGH: ", text_corr_high)
# sum_pear+=res2[0]
# sum_spear+=res