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assignment7-part1.py
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assignment7-part1.py
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import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
import matplotlib.pyplot as plt
import math
from collections import OrderedDict
np.random.seed(50000)
#global
words = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T']
num_topics = 3
class Generator:
def __init__(self,alpha=0.1,beta=0.01):
self.alpha =alpha
self.beta = beta
def generate_documents(self):
documents = []
word_topic_dist = np.random.dirichlet([self.beta]*20, size=3)
topic_generated = np.random.dirichlet([self.alpha]*3, size=200)
for i in topic_generated:
actual_doc = ''
for j in range(0, 50):
topic_distribution = np.random.multinomial(1, i, size=1)
topic = np.argmax(topic_distribution)
word_picked = np.random.multinomial(1, word_topic_dist[topic], size=1)
word = int(np.argmax(word_picked))
actual_doc = actual_doc + words[word] + " "
documents.append(actual_doc)
#print(documents)
return documents, word_topic_dist, topic_generated
class Utils:
@staticmethod
def display_topics(model, feature_names, no_top_words):
for topic_idx, topic in enumerate(model.components_):
print("Topic %d:" % topic_idx)
print(" ".join([feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]]))
@staticmethod
def compare_distributions(true_dist, recovered_dist):
for i in range(0, 3):
x = np.arange(20)
plt.xticks(x, words)
plt.xlabel('labels')
plt.ylabel('P(W|T)')
plt.title('Word-Topic distribution for all the topics')
plt.plot(true_dist[i], color='green')
plt.plot(recovered_dist[i], color='red')
plt.show()
from scipy import stats
print(i, i, stats.entropy(recovered_dist[i], true_dist[i]))
@staticmethod
def compute_entropy(topic_document):
sum_entropy = 0
for i in topic_document:
temp = 0
for j in i:
if j != 0:
temp = temp + j * math.log(j)
sum_entropy = sum_entropy + temp
mean_entropy = -sum_entropy / len(topic_document)
return mean_entropy
class LDA:
def __init__(self,documents,alpha,beta):
self.documents = documents
self.alpha = alpha
self.beta = beta
self.lda = LatentDirichletAllocation(n_components=num_topics, doc_topic_prior=alpha, topic_word_prior=beta)
self.count_vec = CountVectorizer(stop_words=None, analyzer='char', lowercase=False, max_df=0.99)
def computeLDA(self):
tf = self.count_vec.fit_transform(self.documents)
tf_feature_names = self.count_vec.get_feature_names()
self.lda.fit(tf)
topic_given_document = self.lda.transform(X=tf)
no_top_words = 10
Utils.display_topics(self.lda, tf_feature_names, no_top_words)
lda_prob = self.lda.components_ / self.lda.components_.sum(axis=1)[:, np.newaxis]
lda_pr = np.zeros((3, 20))
for i, topic in enumerate(lda_prob):
for j, word in enumerate(tf_feature_names):
true_idx = words.index(word)
lda_pr[i][true_idx] = topic[j]
return lda_pr, topic_given_document
def generate_distribution_for_alpha(alphas):
mean_vals = OrderedDict()
gen = Generator(0.1, 0.01)
docs, word_topic_dist, topic_generated = gen.generate_documents()
mean = Utils.compute_entropy(topic_generated)
for alpha in alphas:
lda = LDA(docs, alpha, beta)
p_lda, topic_list = lda.computeLDA()
mean_vals[alpha] = Utils.compute_entropy(topic_list)
plt.title("Mean entropy for generative LDA model is {}".format(mean))
plt.xlabel("List of Alpha values")
plt.ylabel("Mean Entropy of recovered LDA model")
plt.plot(list(mean_vals.keys()), list(mean_vals.values()))
plt.show()
def generate_distribution_for_beta(beta_list):
mean_vals = OrderedDict()
gen = Generator(0.1, 0.01)
docs, word_topic_dist, _ = gen.generate_documents()
mean = Utils.compute_entropy(word_topic_dist)
for beta in beta_list:
lda = LDA(docs, alpha, beta)
p_lda, _ = lda.computeLDA()
mean_vals[beta] = Utils.compute_entropy(p_lda)
plt.title("Mean entropy for generative LDA model is {}".format(mean))
plt.xlabel("List of Beta values")
plt.ylabel("Mean Entropy of recovered LDA model")
plt.plot(list(mean_vals.keys()), list(mean_vals.values()))
plt.show()
if __name__ == "__main__":
alpha = 0.1
beta = 0.01
# Part 1
gen = Generator(alpha, beta)
docs, word_given_topic, topic_generated = gen.generate_documents()
print(word_given_topic.shape, topic_generated.shape)
#sample docs
print(docs[1])
print(docs[10])
#print topic distribution
for i in range(len(words)):
print(words[i], end=" ")
for topic in range(len(word_given_topic)):
print("%.4f"%word_given_topic[topic][i], end=' ')
print()
# Part 2
model = LDA(docs, alpha,beta)
recovered_word_topic, _ = model.computeLDA()
Utils.compare_distributions(word_given_topic, recovered_word_topic)
# Part 3
list_alpha = [0.1, 1, 5, 10, 15, 20, 30, 40, 50]
generate_distribution_for_alpha(list_alpha)
list_beta = [0.1, 1, 20, 40, 60, 100]
generate_distribution_for_beta(list_beta)