from datetime import datetime
import os
from classes.DocumentsExporter import DocumentsExporter
file_paths = {
'stopwords': '../input_sources/google_en_stopwords.txt',
#'output_dir': 'C:/text_mining/data/test',
'output_dir': os.path.abspath('../../outputs/class_text/xtest'),
}
tp = DocumentsExporter(file_paths)
#from_date = datetime(2015, 8, 5)
from_date = datetime(2009, 1, 1)
delay = 2
company_id = 1
#### Individual files
#tp.process_documents_for_company('fb_post', company_id, from_date, delay, 'close', (3, 3), False)
#tp.process_documents_for_company('fb_comment', company_id, from_date, delay, 'close')
#tp.process_documents_for_company('article', company_id, from_date, delay, 'adjclose', (-2, 2), False)
#tp.process_documents_for_company('tweet', company_id, from_date, delay, 'close')
#twc_ids = [44, 202, 233, 300]
nonsearch_cids = [48, 217, 458, 479]
for c_id in nonsearch_cids:
print ('====COMPANY %s====') % c_id
tp.process_daily_documents_for_company('tweet', c_id, datetime(2015, 8, 5).date(), datetime(2016, 4, 2).date(),
delay, 'adjclose', (-3, 3), False, 25000, False, nonsearch_cids)
from datetime import datetime
import os
import itertools
from classes.DocumentsExporter import DocumentsExporter
#####
# PARAMETERS definition
####
# Prepare exporter object
file_paths = {
'stopwords': '../input_sources/google_en_stopwords.txt',
'output_dir': os.path.abspath('../../outputs/class_text/sma20_ewma5/twitter'),
}
d_exporter = DocumentsExporter(file_paths)
# Set lowest published date.
# Yahoo: 2014-06-18 21:24:00, twitter: 2014-11-20 09:32:02, fb post: 2009-04-15 17:10:55, fb commnent: 2009-04-16 13:51:28
#from_date = datetime(2000, 1, 1)
# Parameters lists
delays = [1, 2, 3]
price_types = ['adjclose', 'ewma', 'sma']
const_boundaries = [(-1, 1), (-2, 2), (-3, 3), (-4, 4), (-5, 5)]
# Create all combinations of parameters.
params_combinations = list(itertools.product(price_types, delays, const_boundaries))
#######
# EXECUTION
#######
