def makeDashboards(symbol, sampleAmount):
companyList = pd.read_csv("companylist.csv")
companyRow = companyList[companyList["Symbol"] == symbol]
retrievedName = companyRow.iat[0, 1]
retrievedSector = companyRow.iat[0, 7]
origStock = Scraper.Stock(symbol, retrievedName, retrievedSector)
#get the comparisons
compare.experiment(sampleAmount, origStock)
compareData = pd.read_csv("output.csv")
compareData = compareData.fillna(0)
# output to static HTML file
#get all of the compare stocks
stockFile = open("stocks.p", "rb")
stockList = pickle.load(stockFile)
polarityScript, polarityHtml = polarityDashboard(
origStock, stockList,
compareData.sort_values(by=['WIKI_SIMILARITY'], ascending=False))
biasScript, biasHtml = biasDashboard(
origStock, stockList,
compareData.sort_values(by=['WIKI_SIMILARITY'], ascending=False))
relScript, relHtml = relevanceDashboard(compareData)
return polarityScript, polarityHtml, biasScript, biasHtml, relScript, relHtml
test = []
test.append(test_s)
process_stocks(test)
#voc_sum_test, word_to_index_sum_test, voc_news_test, word_to_index_news_test = create_vocab(test)
stock_mats_test = {}
#stock name -->[mat_sum, mat_news]
for s in test:
stock_mats_test[s.symbol] = tf_calc(voc_sum, word_to_index_sum,
voc_news, word_to_index_news,
voc_ref, word_to_index_ref, s)
sums_test, news_test, refs_test = separate_stocks(stock_mats_test)
sum_prof_test = make_vec_profs(sums_test, voc_sum)
news_prof_test = make_vec_profs(news_test, voc_news)
refs_prof_test = make_vec_profs(refs_test, voc_ref)
t_profs = []
t_profs.append(sum_prof_test)
t_profs.append(news_prof_test)
t_profs.append(refs_prof_test)
toCSV, keys = get_CSV(sum_prof, news_prof, ref_prof, t_profs, stocks,
test[0])
#keys = toCSV[0].keys()
with open('output.csv', 'w') as output_file:
dict_writer = csv.DictWriter(output_file, keys)
dict_writer.writeheader()
dict_writer.writerows(toCSV)
if __name__ == '__main__':
experiment(10, Scraper.Stock("GOOG", "Alphabet Inc", "Technology"))
