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experiment1.py
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experiment1.py
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"""
Baseline experiment to test the performance of each matcher on the dataset.
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
def execute_test(sm, test_folder, skip_unknown=False):
"""
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 = []
for filename in sorted(os.listdir(test_folder)):
print(filename)
path = test_folder + filename
try:
if(isfile(path)):
headers, columns = sr.get_duplicate_columns(path, skip_unknown)
actual += headers
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
except:
print("Fail")
# break
# print(ConfusionMatrix(actual, predicted))
return actual, predicted
def experiment1_inliers():
data_folder = 'data_train/'
gm = Graph_Maker()
gm.store()
rounds = 5
x = ["Fingerprint", "Syntax Feature Model", "Word2Vec Matcher"]
number_of_classes = 50
examples_per_class = 0
accuracies = []
total_actual = []
total_predicted = []
# accuracies = [0.4, 0.4, 0.4]
classes = ['city', 'country', 'date', 'gender', 'house_number',\
'legal_type', 'postcode', 'province', 'sbi_code', 'sbi_description', 'telephone_nr']
sf_main = Storage_Files(data_folder, classes)
tmp = []
for i in range(0, rounds):
print("Fingerprint")
# --- Fingerprint
ccc = Column_Classification_Config()
ccc.add_feature('feature_main', 'Fingerprint', [sf_main, number_of_classes, examples_per_class, False, False])
ccc.add_matcher('matcher', 'Fingerprint_Matcher', {'feature_main': 'fingerprint'}) # main classifier
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', True)
total_actual += actual
total_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp.append(accuracy)
accuracy = round(sum(tmp) / float(rounds), 2)
accuracies.append(accuracy)
classnames = get_class_names(total_actual)
cm = confusion_matrix(total_actual, total_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True)
tmp = []
total_actual = []
total_predicted = []
for i in range(0, rounds):
print("SFM")
# --- Syntax Feature Model
ccc = Column_Classification_Config()
ccc.add_feature('feature_main', 'Syntax_Feature_Model', [sf_main, 1, 5000, False, False])
ccc.add_matcher('matcher', 'Syntax_Matcher', {'feature_main': 'syntax'}) # main classifier
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', True)
total_actual += actual
total_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp.append(accuracy)
accuracy = round(sum(tmp) / float(rounds), 2)
accuracies.append(accuracy)
classnames = get_class_names(total_actual)
cm = confusion_matrix(total_actual, total_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True)
tmp = []
total_actual = []
total_predicted = []
for i in range(0, rounds):
print("W2V")
# --- Word2Vec Matcher
ccc = Column_Classification_Config()
ccc.add_feature('feature_main', 'Corpus', [sf_main, number_of_classes, examples_per_class, False, False])
ccc.add_matcher('matcher', 'Word2Vec_Matcher', {'feature_main': 'corpus'}) # main classifier
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', True)
total_actual += actual
total_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp.append(accuracy)
accuracy = round(sum(tmp) / float(rounds), 2)
accuracies.append(accuracy)
classnames = get_class_names(total_actual)
cm = confusion_matrix(total_actual, total_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True)
print(accuracies)
gm.add_x(x)
gm.add_y(accuracies)
subtitle = "Accuracy was averaged over " + str(rounds) + " tests with " + str(len(classes)) + " classes. " + \
"Number of simulated columns per class: " + str(number_of_classes)
gm.plot_bar("Matcher Type", "Accuracy", "Accuracy of Matchers", subtitle=subtitle, show_value=True)
def experiment1_outliers():
"""
Run a full experiment on all matchers including outliers and
measure precision, recall, f-measure and accuracy
"""
data_folder = 'data_train/'
gm = Graph_Maker()
gm.store()
rounds = 5
x = ["Fingerprint", "Syntax Feature Model", "Word2Vec Matcher"]
number_of_classes = 50
examples_per_class = 0
accuracies = []
precisions = []
recalls = []
fmeasures = []
classes = ['city', 'country', 'date', 'gender', 'house_number',\
'legal_type', 'postcode', 'province', 'sbi_code', 'sbi_description', 'telephone_nr']
sf_main = Storage_Files(data_folder, classes)
tmp_acc = []
tmp_prec = []
tmp_rec = []
tmp_fmeasure = []
total_actual = []
total_predicted = []
for i in range(0, rounds):
print("Fingerprint")
# --- Fingerprint
ccc = Column_Classification_Config()
ccc.add_feature('feature_main', 'Fingerprint', [sf_main, number_of_classes, examples_per_class, False, False])
ccc.add_matcher('matcher', 'Fingerprint_Matcher', {'feature_main': 'fingerprint'}) # main classifier
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', False)
total_actual += actual
total_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp_acc.append(accuracy)
tmp_prec.append(precision(actual, predicted))
tmp_rec.append(recall(actual, predicted))
tmp_fmeasure.append(f_measure(actual, predicted))
accuracies.append( round(sum(tmp_acc) / float(rounds), 2) )
precisions.append( round(sum(tmp_prec) / float(rounds), 2) )
recalls.append( round(sum(tmp_rec) / float(rounds), 2) )
fmeasures.append(round(sum(tmp_fmeasure) / float(rounds), 2))
classnames = get_class_names(total_actual)
cm = confusion_matrix(total_actual, total_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True)
tmp_acc = []
tmp_prec = []
tmp_rec = []
tmp_fmeasure = []
total_actual = []
total_predicted = []
for i in range(0, rounds):
print("SFM")
# --- Syntax Feature Model
ccc = Column_Classification_Config()
ccc.add_feature('feature_main', 'Syntax_Feature_Model', [sf_main, 1, 5000, False, False])
ccc.add_matcher('matcher', 'Syntax_Matcher', {'feature_main': 'syntax'}) # main classifier
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', False)
total_actual += actual
total_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp_acc.append(accuracy)
tmp_prec.append(precision(actual, predicted))
tmp_rec.append(recall(actual, predicted))
tmp_fmeasure.append(f_measure(actual, predicted))
accuracies.append( round(sum(tmp_acc) / float(rounds), 2) )
precisions.append( round(sum(tmp_prec) / float(rounds), 2) )
recalls.append( round(sum(tmp_rec) / float(rounds), 2) )
fmeasures.append(round(sum(tmp_fmeasure) / float(rounds), 2))
classnames = get_class_names(total_actual)
cm = confusion_matrix(total_actual, total_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True)
tmp_acc = []
tmp_prec = []
tmp_rec = []
tmp_fmeasure = []
total_actual = []
total_predicted = []
for i in range(0, rounds):
print("W2V")
# --- Word2Vec Matcher
ccc = Column_Classification_Config()
ccc.add_feature('feature_main', 'Corpus', [sf_main, number_of_classes, examples_per_class, False, False])
ccc.add_matcher('matcher', 'Word2Vec_Matcher', {'feature_main': 'corpus'}) # main classifier
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', False)
total_actual += actual
total_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp_acc.append(accuracy)
tmp_prec.append(precision(actual, predicted))
tmp_rec.append(recall(actual, predicted))
tmp_fmeasure.append(f_measure(actual, predicted))
accuracies.append( round(sum(tmp_acc) / float(rounds), 2) )
precisions.append( round(sum(tmp_prec) / float(rounds), 2) )
recalls.append( round(sum(tmp_rec) / float(rounds), 2) )
fmeasures.append(round(sum(tmp_fmeasure) / float(rounds), 2))
classnames = get_class_names(total_actual)
cm = confusion_matrix(total_actual, total_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True)
gm.add_x(x)
# accuracies = [0.4, 0.4, 0.4]
# precisions = [0.5, 0.5, 0.5]
# recalls = [0.62, 0.62, 0.62]
# fmeasures = [0.23, 0.23, 0.28]
gm.append_y(accuracies)
gm.append_y(precisions)
gm.append_y(recalls)
gm.append_y(fmeasures)
subtitle = "Scores were averaged over " + str(rounds) + " tests with " + str(len(classes)) + " classes. " + \
"Number of simulated columns per class: " + str(number_of_classes)
labels = ["Accuracy", "Precision", "Recall", "F-Measure"]
gm.plot_bar_n("Matcher Type", "Score", "Accuracy of Matchers", labels, subtitle=subtitle)
def confusion_number_matcher():
classes = ['telephone_nr', 'house_number']
data_folder = 'data_train/'
gm = Graph_Maker()
gm.store()
rounds = 1
number_of_classes = 100
accuracies = []
total_actual = []
total_predicted = []
# accuracies = [0.4, 0.4, 0.4]
sf_main = Storage_Files(data_folder, classes)
tmp = []
for i in range(0, rounds):
ccc = Column_Classification_Config()
ccc.add_feature('feature_main', 'Number_Feature', [sf_main, number_of_classes, 0, False, False])
ccc.add_matcher('matcher', 'Number_Matcher', {'feature_main': 'number_feature'}) # main classifier
sm = Schema_Matcher(ccc)
actual, predicted = execute_test(sm, 'data_test/', True)
total_actual += actual
total_predicted += predicted
accuracy = accuracy_score(actual, predicted)
tmp.append(accuracy)
# Filter out the non-used classes
result_total = []
result_pred = []
for i in range(0, len(total_actual)):
if total_actual[i] in classes:
result_total.append(total_actual[i])
result_pred.append(total_predicted[i])
# accuracy = round(sum(tmp) / float(rounds), 2)
# accuracies.append(accuracy)
total_actual = result_total
total_predicted = result_pred
classnames = get_class_names(total_actual)
cm = confusion_matrix(total_actual, total_predicted, labels=classnames)
gm.plot_confusion_matrix(cm, classnames, normalize=True)
def get_class_names(ytrue):
res = []
for c in ytrue:
if c not in res:
res.append(c)
return res
if __name__ == '__main__':
experiment1_inliers()
# experiment1_outliers()
# confusion_number_matcher()