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main.py
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main.py
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#-*- coding: utf-8 -*-
from Vertex import Vertex
from Edge import Edge
from TermGraph import TermGraph
import Reader
import nltk
from Reporter import Reporter
from os import listdir
from os.path import isfile, join
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
__author__ = 'dales3d'
def test_vertex():
v1 = Vertex()
print v1.term_weight_tf
v1.term_weight_tf = 3
v1.term_weight_tf = 6
print v1._termWeightOldTF
print v1.term_weight_tf
print v1.term_weight_rw
v1.term_weight_rw = 10
v1.term_weight_rw = 12
print v1.term_weight_rw
print v1._termWeightOldRW
print v1.term_value
v1.term_value = 'some term'
print v1.term_value
def test_graph():
g1 = TermGraph()
v1 = Vertex('vert1', 2)
v2 = Vertex('vert2', 2)
e1 = Edge(v1, v2)
g1.add_edge(e1)
v3 = Vertex('vert3', 3)
v4 = Vertex('vert4', 4)
e2 = Edge(v3, v4)
g1.add_edge(e2)
e3 = Edge(v1,v4)
g1.add_edge(e3)
g1.add_edge(e1)
print g1._verticlesJoins
print g1._verticles
print g1._edges
g1.recalc_edges()
g1.recalc_vert_weights()
g1.recalc_vert_weights()
for x in xrange(0,10):
print '--------' + str(x) + '------'
g1.recalc_vert_weights()
def comparator(v1, v2):
if v1.term_weight_rw > v2.term_weight_rw:
return 1
elif v1.term_weight_rw < v2.term_weight_rw:
return -1
return 0
def file_report():
raw_txt = Reader.readFromFile('data/test1')
words = Reader.extractWords(raw_txt)
keywords = Reader.meter(words)
window = 3
graph_rw = TermGraph()
stopwords = nltk.corpus.stopwords.words('english')
words_clean = []
for word in words:
if word not in stopwords:
words_clean.append(word)
words = words_clean
length = words.__len__()
# print length, len(keywords), window
# print words
for i in xrange(0, length-1, 2):
print '----', i, '----'
print words[i:i+window]
v1 = Vertex(words[i], keywords[words[i]])
v2 = Vertex(words[i+1], keywords[words[i+1]])
v3 = Vertex(words[i+2], keywords[words[i+2]])
e1 = Edge(v1, v2)
e2 = Edge(v1, v3)
e3 = Edge(v2, v3)
graph_rw.add_edge(e1)
graph_rw.add_edge(e2)
graph_rw.add_edge(e3)
graph_rw.recalc_edges()
mode = 'rw'
graph_rw.recalc_vert_weights(mode)
for i in xrange(0,100):
graph_rw.recalc_vert_weights(mode)
array = graph_rw._verticles.values()
array.sort(comparator)
#report
file = './test_results/' + mode + '-win3.csv'
toCsv = ''
for v in array:
num_joins = graph_rw._verticlesJoins[v.term_value].__len__()
toCsv += str(v.term_value) + ',' +str(v.term_weight_rw) + ',' + str(v.term_weight_tf) + '\n'
print v, v.term_weight_rw, num_joins, v.term_weight_tf
print graph_rw._verticlesJoins[v.term_value]
#with open(file, "w") as f_out:
# f_out.write(toCsv)
# f_out.close()
#reader.writeToFile(output_file, keywords)
config = \
{
'windows': [2],
'methods': ['rw_oc'], # , 'rw', 'pr'],
'trg': './kostop/', # './test_results/'
'src': './data/psy/' # './data/All_in/'# './data/Arts/'
}
def test_doc():
files = [f for f in listdir(config['src']) if isfile(join(config['src'], f))]
#files = ['aita2011', 'Мур_алг_статико-дин_расп', 'Програм_Хопфилд']
for f in files:
f_full_name = join(config['src'], f)
reporter = Reporter(config['windows'], config['methods'])
#reporter.report_for_file(f_full_name)
#reporter.save_report_detailed(f_full_name, config['trg'] + f + 'DetailCsvRes' + '.csv')
#reporter.save_report_top_words(f_full_name, config['trg'] + f + 'TopCsvRes' + '.csv')
#reporter.save_report_excel_graph(f_full_name, config['trg'] + f + 'GraphCsvRes' + '.csv')
v1, v2 = reporter.similiarity_of_texts('./data/psy/3', f_full_name)
print f, ': ', v1, v2
print f, ': ', v1 * (1 + v2)
def test_reuters_with_bayse():
reporter = Reporter(config['windows'], config['methods'])
#categories = ['livestock', 'jobs']
#categories = ['crude', 'livestock', 'jobs', 'ship', 'corn', 'trade', 'interest']
#categories = ['crude', 'livestock', 'earn', 'acq', 'grain', 'wheat', 'money', 'jobs', 'ship', 'corn', 'trade', 'interest']
categories = ['crude', 'livestock', 'earn', 'jobs', 'ship', 'corn', 'trade', 'interest']
#categories = ['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', 'dmk', 'earn', 'fuel', 'gas', 'gnp', 'gold', 'grain', 'groundnut', 'groundnut-oil', 'heat', 'hog', 'housing', 'income', 'instal-debt', 'interest', 'ipi', 'iron-steel', 'jet', 'jobs', 'l-cattle', 'lead', 'lei', 'lin-oil', 'livestock', 'lumber', 'meal-feed', 'money-fx', 'money-supply', 'naphtha', 'nat-gas', 'nickel', 'nkr', 'nzdlr', 'oat', 'oilseed', 'orange', 'palladium', 'palm-oil', 'palmkernel', 'pet-chem', 'platinum', 'potato', 'propane', 'rand', 'rape-oil', 'rapeseed', 'reserves', 'retail', 'rice', 'rubber', 'rye', 'ship', 'silver', 'sorghum', 'soy-meal', 'soy-oil', 'soybean', 'strategic-metal', 'sugar', 'sun-meal', 'sun-oil', 'sunseed', 'tea', 'tin', 'trade', 'veg-oil', 'wheat', 'wpi', 'yen', 'zinc']
for cat in categories:
print '----', cat, '----'
reporter.get_category_top(categories, cat, 'rw_oc', 3)
reporter.get_category_top_tf(categories, cat, 'rw_oc', 3)
#reporter.get_category_graph(categories, cat, 'rw_oc', 2)
def test_reuters_with_graph_sim():
reporter = Reporter(config['windows'], config['methods'])
#categories = ['crude', 'livestock', 'earn', 'jobs', 'ship', 'corn', 'trade', 'interest']
categories = ['livestock', 'jobs']
#categories = ['crude', 'livestock', 'jobs', 'ship', 'corn', 'trade', 'interest']
for cat in categories:
print '----', cat, '----'
reporter.get_category_graph(categories, cat, 'rw_oc', 2)
if __name__ == '__main__':
#test_reuters_with_bayse()
#test_reuters_with_graph_sim()
reporter = Reporter(config['windows'], config['methods'])
reporter.report_for_file('./data/rusTextChiliGuake')
reporter.save_report_detailed('./data/rusTextChiliGuake', './tes/diplom.csv')
#
#---- crude ----
#{'earn': 24, 'jobs': 0, 'livestock': 0, 'corn': 0, 'trade': 3, 'interest': 0, 'crude': 155, 'ship': 7}
#82.0105820106
#{'earn': 27, 'jobs': 0, 'livestock': 0, 'corn': 0, 'trade': 3, 'interest': 0, 'crude': 153, 'ship': 6}
#80.9523809524
#---- livestock ----
#{'earn': 0, 'jobs': 0, 'livestock': 16, 'corn': 6, 'trade': 1, 'interest': 1, 'crude': 0, 'ship': 0}
#66.6666666667
#{'earn': 0, 'jobs': 0, 'livestock': 18, 'corn': 5, 'trade': 1, 'interest': 0, 'crude': 0, 'ship': 0}
#75.0
#---- earn ----
#{'earn': 1049, 'jobs': 0, 'livestock': 0, 'corn': 1, 'trade': 3, 'interest': 1, 'crude': 33, 'ship': 0}
#96.5041398344
#{'earn': 1048, 'jobs': 0, 'livestock': 0, 'corn': 1, 'trade': 3, 'interest': 1, 'crude': 34, 'ship': 0}
#96.4121435143
#---- jobs ----
#{'earn': 3, 'jobs': 10, 'livestock': 0, 'corn': 0, 'trade': 7, 'interest': 1, 'crude': 0, 'ship': 0}
#47.619047619
#{'earn': 3, 'jobs': 10, 'livestock': 0, 'corn': 0, 'trade': 6, 'interest': 2, 'crude': 0, 'ship': 0}
#47.619047619
#---- ship ----
#{'earn': 2, 'jobs': 0, 'livestock': 0, 'corn': 1, 'trade': 4, 'interest': 0, 'crude': 4, 'ship': 78}
#87.6404494382
#{'earn': 2, 'jobs': 0, 'livestock': 0, 'corn': 0, 'trade': 4, 'interest': 0, 'crude': 3, 'ship': 80}
#89.8876404494
#---- corn ----
#{'earn': 0, 'jobs': 0, 'livestock': 0, 'corn': 53, 'trade': 2, 'interest': 0, 'crude': 0, 'ship': 1}
#94.6428571429
#{'earn': 1, 'jobs': 0, 'livestock': 1, 'corn': 52, 'trade': 1, 'interest': 0, 'crude': 0, 'ship': 1}
#92.8571428571
#---- trade ----
#{'earn': 5, 'jobs': 0, 'livestock': 0, 'corn': 1, 'trade': 106, 'interest': 3, 'crude': 1, 'ship': 1}
#90.5982905983
#{'earn': 5, 'jobs': 0, 'livestock': 0, 'corn': 1, 'trade': 105, 'interest': 3, 'crude': 2, 'ship': 1}
#89.7435897436
#---- interest ----
#{'earn': 5, 'jobs': 0, 'livestock': 0, 'corn': 1, 'trade': 22, 'interest': 102, 'crude': 1, 'ship': 0}
#77.8625954198
#{'earn': 6, 'jobs': 0, 'livestock': 0, 'corn': 1, 'trade': 22, 'interest': 101, 'crude': 1, 'ship': 0}
#77.0992366412