def choose_medium_length_string(l, lmin=5, lmax=18):
good_strings = filter(
lambda x: len(x.split()) >= lmin and len(x.split()) <= lmax, l)
if (len(good_strings) == 0):
return None
else:
return ch(good_strings)
def begin_augmentation (conversation):
global count
keys = ['scene','character','opinion','critical_review','movie_trivia']
p = [0.25,0.25,0.25,0.125,0.125]
l = len(conversation)
s = re.sub("<b>You:</b> Umm, let's talk about ",'',conversation[0])
#print(s)
#print(type(movie_list))
movie_id = movie_list[s]
#print(movie_id)
#print(movies[movie_id].title)
while(len(conversation)==l):
if not keys:
#print("Didn't")
count = count + 1
break
key = ch(keys)
conversation = check_if_exists(movie_id,conversation,key)
keys.remove(key)
return conversation
def check_if_exists(
movie_id, key
): #call augmentation directly from here refined logic for breakpoint
conversation = None
i = -1
l = -1
if key == 'scene':
if movies[movie_id].fav_scene:
i = ch(np.arange(len(movies[movie_id].fav_scene)))
chosen = movies[movie_id].fav_scene[i]
conversation = statement_processing(ch(fav_scene_open_statements),
chosen)
l = 2
elif movie_id in extra_data:
chosen = extra_data[movie_id]['fav_scene']
conversation = statement_processing(ch(fav_scene_open_statements),
chosen)
l = 2
if key == 'character':
if movies[movie_id].fav_character:
i = ch(np.arange(len(movies[movie_id].fav_character)))
chosen = movies[movie_id].fav_character[i]
conversation = statement_processing(
ch(fav_character_open_statements), chosen)
l = 4
elif movie_id in extra_data:
chosen = extra_data[movie_id]['fav_character']
conversation = statement_processing(
ch(fav_character_open_statements), chosen)
l = 4
'''
if key == 'movie_trivia':
trivias = movies[movie_id].trivia
trivia_chosen = choose_medium_length_string(trivias)
if trivia_chosen is not None:
conversation = movie_trivia_augmentation(movie_id,conversation,breakpoint,trivia_chosen)
'''
if key == 'critical_review':
critical_reviews = movies[movie_id].critical_reviews
selected_review = choose_medium_length_string(critical_reviews)
if selected_review is not None:
if selected_review[0].islower():
processed_review = "This movie is " + selected_review
conversation = statement_processing(
ch(review_intro_statements), processed_review)
i = critical_reviews.index(selected_review)
l = 8
if key == 'opinion':
if movies[movie_id].opinion:
i = ch(np.arange(len(movies[movie_id].opinion)))
chosen = movies[movie_id].opinion[i]
conversation = statement_processing(ch(opinion_open_statements),
chosen)
l = 3
elif movie_id in extra_data:
chosen = extra_data[movie_id]['opinion']
conversation = statement_processing(ch(opinion_open_statements),
chosen)
l = 3
if key == 'question':
if movie_id in questions:
if questions[movie_id]:
i = ch(np.arange(len(questions[movie_id])))
chosen = questions[movie_id][i]
conversation = statement_processing(chosen)
l = 7
if key == 'remember':
if movies[movie_id].do_you_remember:
i = ch(np.arange(len(movies[movie_id].do_you_remember)))
chosen = movies[movie_id].do_you_remember[i]
conversation = statement_processing(chosen)
l = 5
elif movie_id in extra_data:
chosen = extra_data[movie_id]['do_you_remember']
conversation = statement_processing(chosen)
l = 5
'''
if conversation:
chat.append(conversation)
index.append(i)
legend.append(l)
'''
return conversation, i, l
def main():
data = pd.read_csv(fname_input) # change file_name here
x = np.arange(len(data))
np.random.shuffle(x) #number of movies you want to give
x = x[0:int(sys.argv[2])]
keys = ['scene', 'character', 'opinion']
count = np.ones(len(keys))
movie_name = np.array(data['title'])
movie_name = movie_name[x]
#wiki = data['wiki']
imdb_id = np.array(data['imdb_id'])
imdb_id = imdb_id[x]
s_movie_name = []
s_wiki = []
s_imdb_id = []
s_chat = []
did_not = []
legend = []
index_ = []
m_plot = []
m_review = []
m_comment = []
s_rev_lengend = []
for m, id_ in zip(movie_name, imdb_id):
conversation = None
keys = ['scene', 'character', 'opinion']
flag = True
while (conversation == None):
if not keys:
flag = False
break
key = ch(keys)
conversation, i, l = check_if_exists(id_, key)
inx = keys.index(key)
keys.remove(key)
if flag:
s_wiki.append('https://en.wikipedia.org/?curid=' +
str(movies[id_].wiki_id))
s_chat.append(conversation)
s_imdb_id.append(id_)
s_movie_name.append(m)
index_.append(i)
legend.append(l)
m_plot.append(movies[id_].plot)
temp = ""
if id_ in reddit_singles:
temp = temp + change_to_html(reddit_singles[id_])
if id_ in review_titles:
temp = temp + change_to_html(review_titles[id_])
m_comment.append(temp)
if id_ in reviews:
m_review.append(convert_to_ascii(reviews[id_][1]))
s_rev_lengend.append(1)
else:
m_review.append("")
s_rev_lengend.append(-1)
else:
did_not.append(id_)
print(did_not)
'''
for i in did_not:
conversation,i_m,l = check_if_exists(i,'scene')
s_wiki.append('https://en.wikipedia.org/?curid='+str(movies[i].wiki_id))
s_chat.append(conversation)
s_imdb_id.append(i)
s_movie_name.append(movies[i].title)
index_.append(i_m)
legend.append(l)
m_plot.append(movies[i].plot)
temp = ""
if i in reddit_singles:
temp = temp + change_to_html(reddit_singles[i])
if id_ in review_titles :
temp = temp + change_to_html(review_titles[i])
m_comment.append(temp)
if id_ in reviews:
m_review.append(convert_to_ascii(reviews[i][0]))
s_rev_lengend.append(0)
else:
m_review.append("")
s_rev_lengend.append(-1)
'''
m_plot, s_plot_legend = plot_array(m_plot)
print(len(s_wiki))
d = {
'comment_1': m_comment,
'plot_1': m_plot,
'review_1': m_review,
'chat_1': s_chat,
'wiki_1': s_wiki,
'imdb_id_1': s_imdb_id,
'movie_name_1': s_movie_name,
'legend_1': legend,
'used_index_1': index_,
'review_legend_1': s_rev_lengend,
'plot_legend_1': s_plot_legend
}
df = pd.DataFrame(d)
df.to_csv('self_chat_batch_' + str(ind) + '.csv',
index=False,
encoding='utf-8')
from numpy.random import choice as ch import sys """ This code encrypts a string into another string, following thes steps: - first the strings is sliced to 8 vertices cubic strings (as many as needed). The letters are successively placed at vertices following the coordinates: (0,0,0)->(0,1,0)->(1,1,0)->(1,0,0)->(0,0,1)->(0,1,1)->(1,1,1)->(1,0,1) - then the cubes are randomly rotated, a random number of times, independantly, up, down, left or right. - the resulting cube positions are being read following the same path, producing the encrypted string. - the code also embeds the reverse - decrypting - function. """ text = "I aml" encrypted = "t I oamoie tllingt enoti ed" key = "" # INPUT instructions:
def opinion_augmentation(movie_id,conversation,breakpoint): starting_template = add_pre_template(breakpoint) + ch(opinion_open_statements) fav_scene = add_pre_template(breakpoint+1) + ch(movies[movie_id].opinion) return augment_the_conversation(conversation,breakpoint,[starting_template,fav_scene])
def movie_review_augmentation(movie_id,conversation,breakpoint,selected_review,s=" "): s = add_pre_template(breakpoint) + ch(review_intro_statements) + selected_review answer = add_pre_template(breakpoint+1) + ch(mid_review_accept) return augment_the_conversation(conversation,breakpoint,[s,answer])
def movie_trivia_augmentation(movie_id,conversation,breakpoint,trivia_chosen,s=" "): s = add_pre_template(breakpoint) + ch(mid_trivia_open_statements) + trivia_chosen answer = add_pre_template(breakpoint+1) + ch(mid_trivia_accept) return augment_the_conversation(conversation,breakpoint,[s,answer]) #change to s after first batch
def fav_character_augmentation(movie_id,conversation,breakpoint): fav_scene = add_pre_template(breakpoint+1) + ch(movies[movie_id].fav_character) starting_template = add_pre_template(breakpoint) + ch(fav_character_open_statements) return augment_the_conversation(conversation,breakpoint,[starting_template,fav_scene])
def ee_stat_gen_age():
ind = ch(np.arange(len(EE_stat_age_dist)), p=EE_stat_age_prob)
return ch(np.arange(EE_stat_age_dist[ind][0],
EE_stat_age_dist[ind][1] + 1))
def main():
data = pd.read_csv('/home/nikita/Downloads/50 movies - Sheet2.csv'
) # change file_name here
keys = [
'scene', 'character', 'opinion', 'critical_review', 'question',
'remember'
]
count = np.ones(len(keys))
movie_name = data['title']
wiki = data['wiki']
imdb_id = data['imdb_id']
s_movie_name = []
s_wiki = []
s_imdb_id = []
s_chat = []
did_not = []
legend = []
index_ = []
m_plot = []
m_review = []
m_comment = []
for m, w, id_ in zip(movie_name, wiki, imdb_id):
conversation = None
keys = [
'scene', 'character', 'opinion', 'critical_review', 'question',
'remember'
]
flag = True
while (conversation == None):
if not keys:
flag = False
break
key = ch(keys)
conversation, i, l = check_if_exists(id_, key)
inx = keys.index(key)
keys.remove(key)
if flag:
s_wiki.append(w)
s_chat.append(conversation)
s_imdb_id.append(id_)
s_movie_name.append(m)
index_.append(i)
legend.append(l)
m_plot.append(movies[id_].plot)
temp = ""
if id_ in reddit_singles:
temp = temp + change_to_html(reddit_singles[id_])
if id_ in review_titles:
temp = temp + change_to_html(review_titles[id_])
m_comment.append(temp)
if id_ in reviews:
m_review.append(convert_to_ascii(reviews[id_][0]))
else:
m_review.append("")
keys = [
'scene', 'character', 'opinion', 'critical_review', 'question',
'remember'
]
inx = keys.index(key)
count[inx] = count[inx] + 1
else:
did_not.append(id_)
print(did_not)
for i in did_not:
conversation, i_m, l = check_if_exists(i, 'scene')
s_wiki.append(w)
s_chat.append(conversation)
s_imdb_id.append(id_)
s_movie_name.append(m)
index_.append(i_m)
legend.append(l)
m_plot.append(movies[i].plot)
temp = ""
if i in reddit_singles:
temp = temp + change_to_html(reddit_singles[i])
if id_ in review_titles:
temp = temp + change_to_html(review_titles[i])
m_comment.append(temp)
if id_ in reviews:
m_review.append(convert_to_ascii(reviews[i][0]))
else:
m_review.append("")
print(len(s_wiki))
print(len(s_chat))
print(len(s_imdb_id))
print(did_not)
'''
d = {'chat_1': s_chat[0:10], 'wiki_1' : s_wiki[0:10], 'imdb_id_1': s_imdb_id[0:10], 'movie_name_1': s_movie_name[0:10], 'legend_1': legend[0:10], 'used_index_1': index_[0:10],
'chat_2': s_chat[10:20], 'wiki_2' : s_wiki[10:20], 'imdb_id_2': s_imdb_id[10:20], 'movie_name_2': s_movie_name[10:20], 'legend_2': legend[10:20], 'used_index_2': index_[10:20],
'chat_3': s_chat[20:30], 'wiki_3' : s_wiki[20:30] ,'imdb_id_3': s_imdb_id[20:30], 'movie_name_3': s_movie_name[20:30], 'legend_3': legend[20:30], 'used_index_3': index_[20:30],
'chat_4': s_chat[30:40], 'wiki_4' : s_wiki[30:40], 'imdb_id_4': s_imdb_id[30:40], 'movie_name_4': s_movie_name[30:40], 'legend_4': legend[30:40], 'used_index_4': index_[30:40],
'chat_5': s_chat[40:], 'wiki_5' : s_wiki[40:], 'imdb_id_5': s_imdb_id[40:], 'movie_name54': s_movie_name[40:], 'legend_5': legend[40:], 'used_index_5': index_[40:],
'plot_1': m_plot[0:10], 'plot_2' : m_plot[10:20], 'plot_3': m_plot[20:30], 'plot_4': m_plot[30:40], 'plot_5': m_plot[40:],
'review_1': m_review[0:10], 'review_2' : m_review[10:20], 'review_3': m_review[20:30], 'review_4': m_review[30:40], 'review_5': m_review[40:],
'comment_1': m_comment[0:10], 'comment_2' : m_comment[10:20], 'comment_3': m_comment[20:30], 'comment_4': m_comment[30:40], 'comment_5': m_comment[40:]
}
'''
d = {
'chat_1': s_chat[0:25],
'wiki_1': s_wiki[0:25],
'imdb_id_1': s_imdb_id[0:25],
'movie_name_1': s_movie_name[0:25],
'legend_1': legend[0:25],
'used_index_1': index_[0:25],
'chat_2': s_chat[25:],
'wiki_2': s_wiki[25:],
'imdb_id_2': s_imdb_id[25:],
'movie_name_2': s_movie_name[25:],
'legend_2': legend[25:],
'used_index_2': index_[25:],
'plot_1': m_plot[0:25],
'plot_2': m_plot[25:],
'review_1': m_review[0:25],
'review_2': m_review[25:],
'comment_1': m_comment[0:25],
'comment_2': m_comment[25:]
}
df = pd.DataFrame(d)
df.to_csv('augmented_start_batch_50_2_1.csv', index=False)
print('Complete')