kernel_list = ["custom", "linear", "poly_2","poly_3", "rbf"]
# results -> k -> kernal
results = []
limit_1 = 400 #training set
limit_2 = 400 #validation set
for k in k_list:
print "Running experiment for k=" + str(k) + "..."
ab = ABClassifier()
ab.download_cursors(limit_unlabeled = 5000, limit_labeled = 5000)
#ab.download_tweet_cursors(limit_unlabeled = 800, limit_labeled = 1000)
ab.run_lsa(k=k)
#pos_cursor_training = ab.labeled_collection.find({"bullying_label":"1"},timeout=False).limit(400)
#neg_cursor_training = ab.labeled_collection.find({"bullying_label":"0"},timeout=False).limit(400)
pos_cursor_training = ab.labeled_collection.find({"bully":True},timeout=False).limit(400)
neg_cursor_training = ab.labeled_collection.find({"bully":False},timeout=False).limit(400)
training = []
tlabels = []
pos_validation = []
pos_vlabels = []
neg_validation = []
neg_vlabels = []
import sys
sys.path.append('..')
from ABClassifier.ABClassifier import ABClassifier
import numpy as np
ab = ABClassifier()
ab.download_cursors(limit_unlabeled = 1000, limit_labeled = 1000)
ab.run_lsa(k=100)
ab.compute_context_vectors()
pos_labeled_pws = ab.pairwise_similarity(ab.pos_labeled_cv_list)
neg_labeled_pws = ab.pairwise_similarity(ab.neg_labeled_cv_list)
unlabeled_pws = ab.pairwise_similarity(ab.unlabeled_cv_list)
print "done getting pws"
x = np.array(pos_labeled_pws.values())
a = np.asarray(x)
np.savetxt('pos_labeled.csv', a, delimiter=",")
y = np.array(neg_labeled_pws.values())
b = np.asarray(y)
np.savetxt('neg_labeled.csv', b, delimiter=",")
z = np.array(unlabeled_pws.values())
c = np.asarray(z)
np.savetxt('unlabeled.csv', c, delimiter=",")
-Uses ONLY twitter data for training/validation
"""
import sys
sys.path.append('../..')
from ABClassifier.ABClassifier import ABClassifier
import numpy as np
import os
save_location = '../../experiment_data/experiment_9'
ab = ABClassifier()
ab.download_tweet_cursors(limit_unlabeled=2500, limit_labeled=2500)
ab.run_lsa(k=150)
ab.compute_context_vectors(save_location=save_location)
print "Performing pairwise similarity measures..."
pos_labeled_pws = ab.pairwise_similarity(ab.pos_labeled_cv_list)
neg_labeled_pws = ab.pairwise_similarity(ab.neg_labeled_cv_list)
unlabeled_pws = ab.pairwise_similarity(ab.unlabeled_cv_list)
print "Done."
print "Saving..."
x = np.array(pos_labeled_pws.values())
a = np.asarray(x)
np.savetxt(save_location + '/pw_pos.csv', a, delimiter=",")
import sys
sys.path.append('..')
from ABClassifier.ABClassifier import ABClassifier
import numpy as np
ab = ABClassifier()
ab.download_cursors(limit_unlabeled=10000, limit_labeled=10000)
ab.run_lsa(k=100)
ab.compute_context_vectors()
pos_labeled_pws = ab.pairwise_similarity(ab.pos_labeled_cv_list)
neg_labeled_pws = ab.pairwise_similarity(ab.neg_labeled_cv_list)
unlabeled_pws = ab.pairwise_similarity(ab.unlabeled_cv_list)
print "done getting pws"
x = np.array(pos_labeled_pws.values())
a = np.asarray(x)
np.savetxt('pos_labeled.csv', a, delimiter=",")
y = np.array(neg_labeled_pws.values())
b = np.asarray(y)
np.savetxt('neg_labeled.csv', b, delimiter=",")
z = np.array(unlabeled_pws.values())
c = np.asarray(z)
np.savetxt('unlabeled.csv', c, delimiter=",")
-Uses ONLY twitter data for training/validation
"""
import sys
sys.path.append('../..')
from ABClassifier.ABClassifier import ABClassifier
import numpy as np
import os
save_location = '../../experiment_data/experiment_9'
ab = ABClassifier()
ab.download_tweet_cursors(limit_unlabeled = 2500, limit_labeled = 2500)
ab.run_lsa(k=150)
ab.compute_context_vectors(save_location = save_location)
print "Performing pairwise similarity measures..."
pos_labeled_pws = ab.pairwise_similarity(ab.pos_labeled_cv_list)
neg_labeled_pws = ab.pairwise_similarity(ab.neg_labeled_cv_list)
unlabeled_pws = ab.pairwise_similarity(ab.unlabeled_cv_list)
print "Done."
print "Saving..."
x = np.array(pos_labeled_pws.values())
a = np.asarray(x)