def computeWithScaling():
print "-------------- Scaled SVD----------------"
svd_old=svd_matrix[:,0:100]
scaled_svd_matrix = preprocessing.scale(svd_old, with_mean = False)
kmeans = hlp.getKmeans(20)
svd_new=hlp.getSVD(2)
svd_matrix_new = svd_new.fit_transform(scaled_svd_matrix)
kmeans.fit(scaled_svd_matrix)
hlp.plot20Clusters(svd_matrix_new,kmeans,"clusters_svd_scaled_20classes.png")
hlp.getStats(labels,kmeans.labels_)
print "--------------Scaled NMF----------------"
nmf_old = hlp.getNMF(10)
nmf_matrix = nmf_old.fit_transform(tfidf_matrix)
scaled_nmf_matrix = preprocessing.scale(nmf_matrix, with_mean = False)
kmeans = hlp.getKmeans(20)
nmf_new=hlp.getNMF(2)
nmf_matrix_new=nmf_new.fit_transform(scaled_nmf_matrix)
kmeans.fit(scaled_nmf_matrix)
hlp.plot20Clusters(nmf_matrix_new,kmeans,"clusters_nmf_scaled_20classes.png")
hlp.getStats(labels,kmeans.labels_)
print "--------------Logarithmic NMF----------------"
nmf_matrix = nmf_old.fit_transform(tfidf_matrix)
log_matrix = np.log(nmf_matrix+1)
nmf_new=hlp.getNMF(2)
nmf_matrix_new=nmf_new.fit_transform(log_matrix)
kmeans.fit(log_matrix)
hlp.plot20Clusters(nmf_matrix_new,kmeans,"clusters_nmf_log_20classes.png")
hlp.getStats(labels,kmeans.labels_)
print "--------------Log scaled NMF----------------"
nmf_matrix = nmf_old.fit_transform(tfidf_matrix)
log_matrix = np.log(nmf_matrix+1)
nmf_matrix_scaled = preprocessing.scale(log_matrix, with_mean = False)
nmf_new=hlp.getNMF(2)
nmf_matrix_new=nmf_new.fit_transform(nmf_matrix_scaled)
kmeans.fit(nmf_matrix_scaled)
hlp.plot20Clusters(nmf_matrix_new,kmeans,"clusters_nmf_log_scaled_20classes.png")
hlp.getStats(labels,kmeans.labels_)
print "--------------Scaled log NMF----------------"
nmf_matrix = nmf_old.fit_transform(tfidf_matrix)
scaled_matrix = preprocessing.scale(nmf_matrix, with_mean = False)
log_scaled_nmf = np.log(scaled_matrix+1)
nmf_new=hlp.getNMF(2)
nmf_matrix_new=nmf_new.fit_transform(log_scaled_nmf)
kmeans.fit(log_scaled_nmf)
hlp.plot20Clusters(nmf_matrix_new,kmeans,"clusters_nmf_scaled_log_20classes.png")
hlp.getStats(labels,kmeans.labels_)
def compute4a():
#convert HD to 2D
print "........With LSI........"
svd_old =svd_matrix[:,0:100]
kmeans = hlp.getKmeans(20)
svd_new=hlp.getSVD(2)
svd_matrix_new = svd_new.fit_transform(svd_old)
kmeans.fit(svd_old)
hlp.plot20Clusters(svd_matrix_new,kmeans,"clusters_2d_svd_best_20classes.png")
hlp.getStats(labels,kmeans.labels_)
print ".........With NMF......."
nmf = hlp.getNMF(10)
nmf_matrix = nmf.fit_transform(tfidf_matrix)
nmf_new=hlp.getNMF(2)
nmf_matrix_new=nmf_new.fit_transform(nmf_matrix)
kmeans.fit(nmf_matrix)
hlp.plot20Clusters(nmf_matrix_new,kmeans,"clusters_2d_nmf_best_20classes.png")
hlp.getStats(labels,kmeans.labels_)
predicted_ovo = classifier_ovo.predict(test)
predicted_ovr = classifier_ovr.predict(test)
print "One vs one"
hlp.getStats(twenty_test.target, predicted_ovo)
print "One vs Rest"
hlp.getStats(twenty_test.target, predicted_ovr)
for min_df in [2, 5]:
print "Calculating for min_df = ", min_df
count_vect = hlp.get_CountVectorizer(min_df)
tfidf_transformer = TfidfTransformer(sublinear_tf=True)
mysvd = TruncatedSVD(n_components=50)
mynmf = hlp.getNMF()
#for train data vectorizer-> tfidf transformer -> svd/nmf
X_train_counts = count_vect.fit_transform(twenty_train.data)
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
X_train_svd = mysvd.fit_transform(X_train_tfidf)
X_train_nmf = mynmf.fit_transform(X_train_tfidf)
#for test data vectorizer-> tfidf transformer -> svd/nmf
X_test_counts = count_vect.transform(twenty_test.data)
X_test_tfidf = tfidf_transformer.transform(X_test_counts)
X_test_svd = mysvd.transform(X_test_tfidf)
X_test_nmf = mynmf.transform(X_test_tfidf)
print "################# For SVM ##################"
print "****** For SVD *******"
import helper as hlp import task1 as t1 from sklearn.cluster import KMeans import numpy as np import matplotlib.pyplot as plt dataset = hlp.fetch_data() hlp.classify_into_two_class(dataset) labels = hlp.fetch_labels(dataset) tfidf_matrix = t1.getTFIDF_matrix(dataset, 3) kmeans = hlp.getKmeans(2) svd = hlp.getSVD(3) svd_matrix = svd.fit_transform(tfidf_matrix) kmeans.fit(svd_matrix) hlp.plotClusters(svd_matrix, kmeans, "clusters_2d_svd_best.png") hlp.getStats(labels, kmeans.labels_) nmf = hlp.getNMF(10) nmf_matrix = nmf.fit_transform(tfidf_matrix) kmeans.fit(nmf_matrix) hlp.plotClusters(nmf_matrix, kmeans, "clusters_2d_nmf_best.png") hlp.getStats(labels, kmeans.labels_)
def getBestR(tfidf_matrix, num):
rank_list = [1, 2, 3, 5, 10, 20, 50, 100, 300]
homo_list_svd = []
comp_list_svd = []
vscore_list_svd = []
adjscore_list_svd = []
infoscore_list_svd = []
homo_list_nmf = []
comp_list_nmf = []
vscore_list_nmf = []
adjscore_list_nmf = []
infoscore_list_nmf = []
for r in rank_list:
print "................. For r=", r, "......................\n"
svd_matrix = hlp.getSVD(r)
svd = svd_matrix.fit_transform(tfidf_matrix)
nmf = hlp.getNMF(r)
nmf_matrix = nmf.fit_transform(tfidf_matrix)
km = hlp.getKmeans(num)
print "*******With LSI********"
km.fit(svd)
h**o, comp, vscore, adjscore, infoscore = hlp.getStats(
labels, km.labels_)
homo_list_svd.append(h**o)
comp_list_svd.append(comp)
vscore_list_svd.append(vscore)
adjscore_list_svd.append(adjscore)
infoscore_list_svd.append(infoscore)
print ""
print "*******With NMF********"
km.fit(nmf_matrix)
h**o, comp, vscore, adjscore, infoscore = hlp.getStats(
labels, km.labels_)
homo_list_nmf.append(h**o)
comp_list_nmf.append(comp)
vscore_list_nmf.append(vscore)
adjscore_list_nmf.append(adjscore)
infoscore_list_nmf.append(infoscore)
print "*******With LSI********"
plt.plot(rank_list, homo_list_svd)
plt.ylabel('Homogeneity Score')
plt.show()
plt.plot(rank_list, comp_list_svd)
plt.ylabel('Completeness Score')
plt.show()
plt.plot(rank_list, vscore_list_svd)
plt.ylabel('V-measure Score')
plt.show()
plt.plot(rank_list, adjscore_list_svd)
plt.ylabel('Adjusted rand Score')
plt.show()
plt.plot(rank_list, infoscore_list_svd)
plt.ylabel('Adjusted Mutual Info Score')
plt.show()
print "*******With NMF********"
plt.plot(rank_list, homo_list_nmf)
plt.ylabel('Homogeneity Score')
plt.show()
plt.plot(rank_list, comp_list_nmf)
plt.ylabel('Completeness Score')
plt.show()
plt.plot(rank_list, vscore_list_nmf)
plt.ylabel('V-measure Score')
plt.show()
plt.plot(rank_list, adjscore_list_nmf)
plt.ylabel('Adjusted rand Score')
plt.show()
plt.plot(rank_list, infoscore_list_nmf)
plt.ylabel('Adjusted Mutual Info Score')
plt.show()
twenty_train, twenty_test, min_df)
tfidfListTrain.append(tfidf_train_matrix)
tfidfListTest.append(tfidf_test_matrix)
svd = hlp.getSVD()
svd_matrix_train = svd.fit_transform(tfidf_train_matrix)
svd_matrix_test = svd.transform(tfidf_test_matrix)
svdListTrain.append(svd_matrix_train)
svdListTest.append(svd_matrix_test)
print "Shape of svd matrix for min_df = ", min_df, " is ", svd.components_.shape
############### For NMF #################
nmfModel = hlp.getNMF()
W_train = nmfModel.fit_transform(tfidf_train_matrix)
W_test = nmfModel.transform(tfidf_test_matrix)
nmfListTrain.append(W_train)
nmfListTest.append(W_test)
nmf = nmfModel.components_
print "Shape of nmf matrix for min_df = ", min_df, " is ", nmf.shape
def getsvdListTrain():
return svdListTrain
def getnmfListTrain():
def computeBestR():
for r in rank_list:
print "................. For r=",r,"......................\n"
svd=svd_matrix[:,0:r]
nmf = hlp.getNMF(r)
nmf_matrix = nmf.fit_transform(tfidf_matrix)
km = hlp.getKmeans(20)
print "*******With LSI********"
km.fit(svd)
h**o, comp, vscore, adjscore, infoscore = hlp.getStats(labels,km.labels_)
homo_list_svd.append(h**o)
comp_list_svd.append(comp)
vscore_list_svd.append(vscore)
adjscore_list_svd.append(adjscore)
infoscore_list_svd.append(infoscore)
print ""
print "*******With NMF********"
km.fit(nmf_matrix)
h**o, comp, vscore, adjscore, infoscore = hlp.getStats(labels,km.labels_)
homo_list_nmf.append(h**o)
comp_list_nmf.append(comp)
vscore_list_nmf.append(vscore)
adjscore_list_nmf.append(adjscore)
infoscore_list_nmf.append(infoscore)
print ".............With LSI............."
plt.plot(rank_list, homo_list_svd)
plt.ylabel('Homogeneity Score')
plt.show()
plt.plot(rank_list, comp_list_svd)
plt.ylabel('Completeness Score')
plt.show()
plt.plot(rank_list, vscore_list_svd)
plt.ylabel('V-measure Score')
plt.show()
plt.plot(rank_list, adjscore_list_svd)
plt.ylabel('Adjusted rand Score')
plt.show()
plt.plot(rank_list, infoscore_list_svd)
plt.ylabel('Adjusted Mutual Info Score')
plt.show()
print "............With NMF............."
plt.plot(rank_list, homo_list_nmf)
plt.ylabel('Homogeneity Score')
plt.show()
plt.plot(rank_list, comp_list_nmf)
plt.ylabel('Completeness Score')
plt.show()
plt.plot(rank_list, vscore_list_nmf)
plt.ylabel('V-measure Score')
plt.show()
plt.plot(rank_list, adjscore_list_nmf)
plt.ylabel('Adjusted rand Score')
plt.show()
plt.plot(rank_list, infoscore_list_nmf)
plt.ylabel('Adjusted Mutual Info Score')
plt.show()