pca_ppa.py

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

Glove = {}
f = open('/home/vikas/Desktop/glove.6B/glove.6B.300d.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 = {}

# PCA Dim Reduction
pca =  PCA(n_components = 150)
X_train = X_train - np.mean(X_train)
X_new_final = pca.fit_transform(X_train)

# PCA to do Post-Processing
pca =  PCA(n_components = 150)
X_new = X_new_final - np.mean(X_new_final)
X_new = pca.fit_transform(X_new)
Ufit = pca.components_

X_new_final = X_new_final - np.mean(X_new_final)

final_pca_embeddings = {}
embedding_file = open('pca_embed2.txt', 'w')

for i, x in enumerate(X_train_names):
        final_pca_embeddings[x] = X_new_final[i]
        embedding_file.write("%s\t" % x)
    	for u in Ufit[0:7]:
            final_pca_embeddings[x] = final_pca_embeddings[x] - np.dot(u.transpose(),final_pca_embeddings[x]) * u 

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


print("Results for the Embedding")
print subprocess.check_output(["python", "all_wordsim.py", "pca_embed2.txt", "data/word-sim/"])
print("Results for Glove")
print subprocess.check_output(["python", "all_wordsim.py", "../glove.6B/glove.6B.300d.txt", "data/word-sim/"])