import slack_news_bot
import common_params
import datetime
import util_funcs
from mab import algorithm as bd
from mab import arm
from mab import scorer as sc
import time
import pickle
is_post_per_subject = False
is_update_db = True
param = common_params.CommonParams()
bot = slack_news_bot.SlackNewsBot(param, is_update_db)
df = bot.df
num_arms = param.num_news
path = 'log/mab.log'
if util_funcs.check_file(path) is False:
algorithm = bd.EpsilonGreedyAlgorithm(num_arms, 0.1)
trials = 0
with open(path, 'wb') as f:
pickle.dump(algorithm, f)
pickle.dump(trials, f)
print("MAB started")
else:
with open(path, 'rb') as f:
algorithm = pickle.load(f)
trials = pickle.load(f)
print("MAB object loaded")
from mab import algorithm as bd from mab import arm from mab import scorer as sc import time import slack_news_bot as newsbot import common_params from sklearn import preprocessing bot = newsbot.SlackNewsBot(common_params.CommonParams()) msg = bot.post_message(True) df = bot.df df_encoding = df[['subject_idx', 'is_emphasis', 'num_omitted', 'news_creator']] le = preprocessing.LabelEncoder() df_encoding = df_encoding.apply(le.fit_transform) print(df_encoding) # 1. INSTANTIATE enc = preprocessing.OneHotEncoder() # 2. FIT enc.fit(df_encoding) # 3. Transform onehotlabels = enc.transform(df_encoding).toarray() print(onehotlabels.shape) #print(onehotlabels) # as you can see, you've the same number of rows 891