def build_testdata(request):
c = {}
c.update(csrf(request))
TestYazar.objects.all().delete()
module_dir = os.path.dirname(__file__) # get current directory
os.chdir(module_dir)
yazar_arr = [
"AyseArman", "AhmetHakan", "YilmazOzdil",
"MehmetAliBirand", "MehmetBaransu", "TahaAkyol",
"MehmetBarlas", "NazliIlicak", "CuneytOzdemir",
"AhmetAltan", "HincalUluc", "NihalBengisuKaraca",
"PerihanMagden", "RuhatMengi", "YaseminCongar",
"BekirCoskun", "CengizCandar", "EnginArdic",
"MutluTonbekici", "SuleymanOzisik"
]
for yazar in yazar_arr:
file_path = os.path.join(module_dir, 'TestYazarlar/' + yazar)
print "[DEBUG] " + str(file_path)
for file in os.listdir(file_path):
avgs = parse_one_entry(
entry_builder(file, yazar, structure="test"))
y = TestYazar(yazar_adi=yazar,
# exclamation=avgs["exclamation"],
exclamation_n=avgs["exclamation_n"],
exclamation_r=avgs["exclamation_r"],
# question_mark=avgs["question_mark"],
question_mark_n=avgs["question_mark_n"],
question_mark_r=avgs["question_mark_r"],
# coma_count=avgs["coma"],
coma_count_n=avgs["coma_n"],
coma_count_r=avgs["coma_r"],
# yan_cizgi=avgs["yan_cizgi"],
yan_cizgi_n=avgs["yan_cizgi_n"],
yan_cizgi_r=avgs["yan_cizgi_r"],
quote_n=avgs["quote_n"],
quote_r=avgs["quote_r"],
single_quote_n=avgs["single_quote_n"],
single_quote_r=avgs["single_quote_r"],
double_dot_n=avgs["double_dot_n"],
double_dot_r=avgs["double_dot_r"],
triple_dot_n=avgs["triple_dot_n"],
triple_dot_r=avgs["triple_dot_r"],
stopwords_n=avgs["stopwords_n"],
stopwords_r=avgs["stopwords_r"],
# stars_n=avgs["stars_n"],
# stars_r=avgs["stars_r"],
sentence_len=avgs["sentence_len"],
paragraph_len=avgs["paragraph_len"])
y.save()
return render_to_response('trainer/index.html', c)
def result(request):
c = {}
c.update(csrf(request))
p = request.POST
entry = p['yazi1_name']
entry = entry_cleaner(entry)
entry_features = parse_one_entry(entry_builder(from_web=entry))
objects_all = Yazar.objects.all()
objects_15_authors = Yazar.objects.filter(
Q(yazar_adi="AhmetAltan") |
Q(yazar_adi="AhmetHakan") |
Q(yazar_adi="AyseArman") |
Q(yazar_adi="BekirCoskun") |
Q(yazar_adi="CengizCandar") |
Q(yazar_adi="CuneytOzdemir") |
Q(yazar_adi="EnginArdic") |
Q(yazar_adi="HincalUluc") |
Q(yazar_adi="MehmetAliBirand") |
Q(yazar_adi="MehmetBaransu") |
Q(yazar_adi="MehmetBarlas") |
Q(yazar_adi="MutluTonbekici") |
Q(yazar_adi="NazliIlicak") |
Q(yazar_adi="NihalBengisuKaraca") |
Q(yazar_adi="YilmazOzdil")
)
objects_10_authors = Yazar.objects.filter(
Q(yazar_adi="AhmetAltan") |
Q(yazar_adi="AhmetHakan") |
Q(yazar_adi="AyseArman") |
Q(yazar_adi="BekirCoskun") |
Q(yazar_adi="CengizCandar") |
Q(yazar_adi="CuneytOzdemir") |
Q(yazar_adi="EnginArdic") |
Q(yazar_adi="HincalUluc") |
Q(yazar_adi="MehmetAliBirand") |
Q(yazar_adi="YilmazOzdil")
)
objects_5_authors = Yazar.objects.filter(
Q(yazar_adi="AhmetAltan") |
Q(yazar_adi="AhmetHakan") |
Q(yazar_adi="AyseArman") |
Q(yazar_adi="BekirCoskun") |
Q(yazar_adi="YilmazOzdil")
)
test_objects_all = TestYazar.objects.all()
test_objects_15_authors = TestYazar.objects.filter(
Q(yazar_adi="AhmetAltan") |
Q(yazar_adi="AhmetHakan") |
Q(yazar_adi="AyseArman") |
Q(yazar_adi="BekirCoskun") |
Q(yazar_adi="CengizCandar") |
Q(yazar_adi="CuneytOzdemir") |
Q(yazar_adi="EnginArdic") |
Q(yazar_adi="HincalUluc") |
Q(yazar_adi="MehmetAliBirand") |
Q(yazar_adi="MehmetBaransu") |
Q(yazar_adi="MehmetBarlas") |
Q(yazar_adi="MutluTonbekici") |
Q(yazar_adi="NazliIlicak") |
Q(yazar_adi="NihalBengisuKaraca") |
Q(yazar_adi="YilmazOzdil")
)
test_objects_10_authors = TestYazar.objects.filter(
Q(yazar_adi="AhmetAltan") |
Q(yazar_adi="AhmetHakan") |
Q(yazar_adi="AyseArman") |
Q(yazar_adi="BekirCoskun") |
Q(yazar_adi="CengizCandar") |
Q(yazar_adi="CuneytOzdemir") |
Q(yazar_adi="EnginArdic") |
Q(yazar_adi="HincalUluc") |
Q(yazar_adi="MehmetAliBirand") |
Q(yazar_adi="YilmazOzdil")
)
test_objects_5_authors = TestYazar.objects.filter(
Q(yazar_adi="AhmetAltan") |
Q(yazar_adi="AhmetHakan") |
Q(yazar_adi="AyseArman") |
Q(yazar_adi="BekirCoskun") |
Q(yazar_adi="YilmazOzdil")
)
# order is important here
entry_features_arr = [
# entry_features['exclamation'],
entry_features['exclamation_n'],
entry_features['exclamation_r'],
# entry_features['question_mark'],
entry_features['question_mark_n'],
entry_features['question_mark_r'],
# entry_features['coma'],
entry_features['coma_n'],
entry_features['coma_r'],
# entry_features['yan_cizgi'],
entry_features['yan_cizgi_n'],
entry_features['yan_cizgi_r'],
entry_features['quote_n'],
entry_features['quote_r'],
entry_features['single_quote_n'],
entry_features['single_quote_r'],
entry_features["double_dot_n"],
entry_features["double_dot_r"],
entry_features["triple_dot_n"],
entry_features["triple_dot_r"],
entry_features["stopwords_n"],
entry_features["stopwords_r"],
# entry_features['stars_n'],
# entry_features['stars_r'],
entry_features['sentence_len'],
entry_features['paragraph_len']
]
scores_dict = {}
# --------------------------------------------------------------- #
print "################################################"
y_features, y_classes = build_features(objects_5_authors)
y_test_features, y_test_classes = build_features(test_objects_5_authors)
print "[DEBUG] Length of Yazar-Classes (5 authors): " + \
str(len(y_classes))
print "[DEBUG] Length of Yazar-Test-Classes (5 authors): " + \
str(len(y_test_classes))
scores = get_score(y_features, y_classes,
y_test_features, y_test_classes)
print "[DEBUG] Algorithm Scores with 5 authors"
print scores
# --------------------------------------------------------------- #
print "------------------------------------------------"
y_features, y_classes = build_features(objects_10_authors)
y_test_features, y_test_classes = build_features(test_objects_10_authors)
print "[DEBUG] Length of Yazar-Classes (10 authors): " + \
str(len(y_classes))
print "[DEBUG] Length of Yazar-Test-Classes (10 authors): " + \
str(len(y_test_classes))
scores = get_score(y_features, y_classes,
y_test_features, y_test_classes)
print "[DEBUG] Algorithm Scores with 10 authors"
print scores
# --------------------------------------------------------------- #
print "------------------------------------------------"
y_features, y_classes = build_features(objects_15_authors)
y_test_features, y_test_classes = build_features(test_objects_15_authors)
print "[DEBUG] Len of y_classes (15 authors): " + \
str(len(y_classes))
print "[DEBUG] Len of y_test_classes (15 authors): " + \
str(len(y_test_classes))
scores = get_score(y_features, y_classes,
y_test_features, y_test_classes)
print "[DEBUG] Algorithm Scores with 15 authors"
print scores
# --------------------------------------------------------------- #
print "------------------------------------------------"
y_features, y_classes = build_features(objects_all)
y_test_features, y_test_classes = build_features(test_objects_all)
print "[DEBUG] Length of Yazar-Classes (20 authors): " + \
str(len(y_classes))
print "[DEBUG] Length of Yazar-Test-Classes (20 authors): " + \
str(len(y_test_classes))
scores = get_score(y_features, y_classes,
y_test_features, y_test_classes)
print "[DEBUG] Algorithm Scores with 20 authors"
print scores
print "################################################"
print "[DEBUG] Features Array Of Web-Entry"
print entry_features_arr
results = predict_author(entry_features_arr, y_features, y_classes)
results_dict = {}
results_dict['knn'] = results[0]
results_dict['svc'] = results[1]
results_dict['logistic'] = results[2]
results_dict['gnb'] = results[3]
results_dict['dtc'] = results[4]
results_dict['gbc'] = results[5]
results_with_grams = (([results[0]] * 1) + ([results[1]] * 3) +
([results[2]] * 4) + ([results[3]] * 3) +
([results[4]] * 1) + ([results[5]] * 4))
results_dict['total'] = max(set(results_with_grams),
key=results_with_grams.count)
c.update(results_dict)
# scores = get_score(test_objects_10_authors)
# scores_dict['knn_score'] = (scores[0] * 100)
# scores_dict['svc_score'] = (scores[1] * 100)
# scores_dict['logistic_score'] = (scores[2] * 100)
# scores_dict['gnb_score'] = (scores[3] * 100)
# scores_dict['dtc_score'] = (scores[4] * 100)
# scores_dict['gbc_score'] = (scores[5] * 100)
# scores_5 = get_score(test_objects_5_authors)
# scores_dict['knn_score_5'] = (scores_5[0] * 100)
# scores_dict['svc_score_5'] = (scores_5[1] * 100)
# scores_dict['logistic_score_5'] = (scores_5[2] * 100)
# scores_dict['gnb_score_5'] = (scores_5[3] * 100)
# scores_dict['dtc_score_5'] = (scores_5[4] * 100)
# scores_dict['gbc_score_5'] = (scores_5[5] * 100)
c.update(scores_dict)
return render_to_response('trainer/predict-results.html', c)