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experiment4.py
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experiment4.py
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"""
Pipelines experiment.
The code in all experiment files is very ugly, but it I did not see the use in creating beautifull
code for all experiments since it is a 'press enter and rerun them' setup.
"""
from schema_matching import *
from schema_matching.misc_func import *
from sklearn.metrics import accuracy_score
from pandas_ml import ConfusionMatrix
from sklearn.metrics import confusion_matrix
from os.path import isfile
import os
import math
gm = Graph_Maker()
rounds = 3
def execute_test(sm, test_folder, skip_unknown=False, iterations=0):
"""
for all the schemas in the test folder, read them and classify them,
also compute precision, recall, f_measure and accuracy.
"""
sr = Schema_Reader()
actual = []
predicted = []
i = 0
for filename in sorted(os.listdir(test_folder)):
i += 1
print(filename)
path = test_folder + filename
if(isfile(path)):
headers, columns = sr.get_duplicate_columns(path, skip_unknown)
result_headers = None
if skip_unknown:
result_headers = sm.test_schema_matcher(columns, 0, False)
else:
result_headers = sm.test_schema_matcher(columns, 0.4, True)
predicted += result_headers
actual += headers
# print(accuracy_score(actual, predicted))
# break
if i == iterations:
break
return actual, predicted
def get_confusion(pred, actual , data_map):
"""
Modify the actual classes according to the datamap, so we can look at the confusion matrix.
"""
result = []
for ac in actual:
for cl in data_map:
if ac in data_map[cl]:
result.append(cl)
for i in range(0, len(actual)):
if pred[i] != result[i]:
print(actual[i])
return result
def experiment4_inliers1():
data_folder = 'data_train/'
number_of_columns = 80
examples_per_class = 60
gm.append_x(0)
gm.append_y(0.88)
total_actual = []
total_predicted = []
tmp = []
exp_actual = []
exp_predicted = []
sf_main = Storage_Files(data_folder, ['city', 'country', 'date', 'gender', 'house_number',\
'legal_type', 'province', 'sbi_code', 'sbi_description', 'telephone_nr', 'postcode'])
sf_legal = Storage_Files(data_folder, ['legal_type', 'postcode'])
sf_province = Storage_Files(data_folder, ['province', 'postcode'])
for i in range(0, rounds):
ccc = Column_Classification_Config()
# ------------------------------------------- CONFIG ------------------------------------------
ccc.add_feature('main', 'Corpus', [sf_main, 60, 0, False, False])
ccc.add_feature('legal', 'Syntax_Feature_Model', [sf_legal, 1, 0, False, False])
ccc.add_feature('province', 'Syntax_Feature_Model', [sf_province, 1, 0, False, False])
ccc.add_matcher('main', 'Word2Vec_Matcher', {'main': 'corpus'}) # main classifier
ccc.add_matcher('legal_matcher', 'Syntax_Matcher', {'legal': 'syntax'}, ('main', 'legal_type'))
ccc.add_matcher('province_matcher', 'Syntax_Matcher', {'province': 'syntax'}, ('main', 'province'))
# ccc.add_matcher('dom_email_matcher', 'Syntax_Matcher', {'dom_email': 'syntax'}, ('main', 'domain_email'))
# ------------------------------------------- END CONFIG ------------------------------------------
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', True, 0)
# actual = get_confusion(predicted, actual, data_map_main)
exp_actual += actual
exp_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp.append(accuracy)
gm.append_x(1)
accuracy = round(sum(tmp) / float(rounds), 2)
gm.append_y(accuracy)
gm.store(filename="/graph_maker/exp1.4a_1")
classnames = get_class_names(exp_actual)
cm = confusion_matrix(exp_actual, exp_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True, title="Confusion Matrix Experiment 4a_1")
subtitle = "Accuracy was averaged over " + str(rounds) + " tests"
def experiment4_inliers2():
data_folder = 'data_train/'
number_of_columns = 80
examples_per_class = 60
gm.append_x(0)
gm.append_y(0.88)
total_actual = []
total_predicted = []
tmp = []
exp_actual = []
exp_predicted = []
classes = ['city', 'country', 'date', 'gender', 'house_number',\
'legal_type', 'province', 'sbi_code', 'sbi_description', 'telephone_nr']
sf_main = Storage_Files(data_folder, ['city', 'country', 'date', 'gender', 'house_number',\
'legal_type', 'province', 'sbi_code', 'sbi_description', 'telephone_nr', 'postcode'])
sf_all = Storage_Files(data_folder, classes)
for i in range(0, rounds):
ccc = Column_Classification_Config()
# ------------------------------------------- CONFIG ------------------------------------------
ccc.add_feature('main', 'Syntax_Feature_Model', [sf_main, 1, 5000, False, False])
ccc.add_feature('all', 'Corpus', [sf_all, 50, 0, False, False])
ccc.add_feature('city', 'Corpus', [sf_city, 50, 0, False, False])
ccc.add_matcher('main', 'Syntax_Matcher', {'main': 'syntax'}) # main classifier
ccc.add_matcher('legal_matcher', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'legal_type'))
ccc.add_matcher('1', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'city'))
ccc.add_matcher('2', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'country'))
ccc.add_matcher('3', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'date'))
ccc.add_matcher('4', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'gender'))
ccc.add_matcher('5', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'house_number'))
ccc.add_matcher('6', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'province'))
ccc.add_matcher('7', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'sbi_code'))
ccc.add_matcher('8', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'sbi_description'))
ccc.add_matcher('9', 'Word2Vec_Matcher', {'all': 'corpus'}, ('main', 'telephone_nr'))
# ------------------------------------------- END CONFIG ------------------------------------------
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', True, 0)
# actual = get_confusion(predicted, actual, data_map_main)
exp_actual += actual
exp_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp.append(accuracy)
gm.append_x(2)
accuracy = round(sum(tmp) / float(rounds), 2)
gm.append_y(accuracy)
gm.store(filename="/graph_maker/exp1.4a_2")
classnames = get_class_names(exp_actual)
cm = confusion_matrix(exp_actual, exp_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True, title="Confusion Matrix Experiment 4a_2")
subtitle = "Accuracy was averaged over " + str(rounds) + " rounds"
def get_class_names(ytrue):
res = []
for c in ytrue:
if c not in res:
res.append(c)
return res
if __name__ == '__main__':
experiment4_inliers1()
experiment4_inliers2()