new_embeddings.py

import numpy as np
import _pickle as pickle
from sklearn.decomposition import PCA
import subprocess

Glove = {}
f = open('glove.6B.200d.txt')
print("Loading Glove vectors.")
for line in f:
    values = line.split()
    word = values[0]
    coefs = np.asarray(values[1:], dtype='float32')
    Glove[word] = coefs
f.close()

print("Done.")
X_train = []
X_train_names = []
for x in Glove:
        X_train.append(Glove[x])
        X_train_names.append(x)
X_train = np.asarray(X_train)


pca_embeddings = {}
embedding_file = open('pca_embedding_30.txt', 'w')

# First PCA to get Top Components
pca =  PCA(n_components = 200)
X_train = X_train - np.mean(X_train)
X_fit = pca.fit_transform(X_train)
U1 = pca.components_

pca_embeddings = {}
z = []

# Removing Projections on Top Components
for i, x in enumerate(X_train):
    for u in U1[0:7]:
        x = x - np.dot(u.transpose(),x) * u
    z.append(x)
    
z = np.asarray(z).astype(np.float32)    

# PCA for Dim Reduction
pca =  PCA(n_components = 100)
X_train = z - np.mean(z)
X_new = pca.fit_transform(X_train)

final_pca_embeddings = {}
embedding_file = open('pca_embedding_30.txt', 'w')

for i, x in enumerate(X_train_names):
        final_pca_embeddings[x] = X_new[i]
        embedding_file.write("%s\t" % x)

        for t in final_pca_embeddings[x]:
                embedding_file.write("%f\t" % t)
        
        embedding_file.write("\n")