def main():
"""
Initialization point for running the entire experiment.
Takes no arguments
Returns no output
"""
dp = DataProcessor(
'../datasets', 'filtered'
) # Create DataProcessor object with a path to the data directory and
# output directory name.
dp.clean(
'breast-cancer-wisconsin.csv') # Clean breast-cancer-wisconsin data
dp.discretize('iris.csv', categories=6)
dp.discretize('glass.csv', categories=10)
dp.filter(
'breast-cancer-wisconsin.csv', 2
) # Seperate breast-cancer-wisconsin data classes each into their own csv
dp.mangle('glass_categorized.csv')
dp.mangle('house-votes-84.csv')
dp.mangle('iris_categorized.csv')
dp.mangle('soybean-small.csv')
dp.filter('breast-cancer-wisconsin.csv', 2)
dp.filter('breast-cancer-wisconsin.csv', 4)
dp.filter('breast-cancer-wisconsin_mangled.csv', 2)
dp.filter('breast-cancer-wisconsin_mangled.csv', 4)
dp.discretize('glass.csv',
categories=10) # Discretize glass dataset into 10 catagories
dp.filter('glass_categorized.csv',
1) # Seperate glass data classes each into their own csv
dp.filter('glass_categorized.csv', 2)
dp.filter('glass_categorized.csv', 3)
dp.filter('glass_categorized.csv', 5)
dp.filter('glass_categorized.csv', 6)
dp.filter('glass_categorized.csv', 7)
dp.filter('glass_categorized_mangled.csv', 1)
dp.filter('glass_categorized_mangled.csv', 2)
dp.filter('glass_categorized_mangled.csv', 3)
dp.filter('glass_categorized_mangled.csv', 5)
dp.filter('glass_categorized_mangled.csv', 6)
dp.filter('glass_categorized_mangled.csv', 7)
dp.filter('house-votes-84.csv', 'republican'
) # Seperate house-votes data classes each into their own csv
dp.filter('house-votes-84.csv', 'democrat')
dp.filter('house-votes-84_mangled.csv', 'republican')
dp.filter('house-votes-84_mangled.csv', 'democrat')
dp.discretize('iris.csv',
categories=6) # Discretize iris data set into 6 catagories
dp.filter(
'iris_categorized.csv',
'Iris-setosa') # Seperate iris data classes each into their own csv
dp.filter('iris_categorized.csv', 'Iris-versicolor')
dp.filter('iris_categorized.csv', 'Iris-virginica')
dp.filter('iris_categorized_mangled.csv', 'Iris-setosa')
dp.filter('iris_categorized_mangled.csv', 'Iris-versicolor')
dp.filter('iris_categorized_mangled.csv', 'Iris-virginica')
dp.filter(
'soybean-small.csv',
'D1') # Seperate soybean-small data classes each into their own csv
dp.filter('soybean-small.csv', 'D2')
dp.filter('soybean-small.csv', 'D3')
dp.filter('soybean-small.csv', 'D4')
dp.filter('soybean-small_mangled.csv', 'D1')
dp.filter('soybean-small_mangled.csv', 'D2')
dp.filter('soybean-small_mangled.csv', 'D3')
dp.filter('soybean-small_mangled.csv', 'D4')
print("BREAST CANCER WISCONSIN\n")
# Create a new TrainingSetCalc object with the breast-cancer-wisconsin data set
tsc = TrainingSetCalc(
"../datasets/filtered/",
['breast-cancer-wisconsin_2.csv', 'breast-cancer-wisconsin_4.csv'])
bcw_results = Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(10)))
print("HOUSE VOTES 84\n")
# Create a new TrainingSetCalc object with the house-votes data set
tsc = TrainingSetCalc(
"../datasets/filtered/",
['house-votes-84_democrat.csv', 'house-votes-84_republican.csv'])
hv_results = Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(10)))
print("GLASS\n")
# Create a new TrainingSetCalc object with the glass-catagorized data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'glass_categorized_1.csv', 'glass_categorized_2.csv',
'glass_categorized_3.csv', 'glass_categorized_5.csv',
'glass_categorized_6.csv', 'glass_categorized_7.csv'
])
glass_results = Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(10)))
print("IRIS\n")
# Create a new TrainingSetCalc object with the iris-catagorized data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'iris_categorized_Iris-setosa.csv',
'iris_categorized_Iris-versicolor.csv',
'iris_categorized_Iris-virginica.csv'
])
iris_results = Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(10)))
print("SOYBEANS\n")
# Create a new TrainingSetCalc object with the soybean-small data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'soybean-small_D1.csv', 'soybean-small_D2.csv', 'soybean-small_D3.csv',
'soybean-small_D4.csv'
])
soybean_results = Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(10)))
print("BREAST CANCER WISCONSIN (MANGLED)\n")
# Create a new TrainingSetCalc object with the mangled breast-cancer-wisconsin data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'breast-cancer-wisconsin_mangled_2.csv',
'breast-cancer-wisconsin_mangled_4.csv'
])
bcw_mangled_results = Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(10)))
print("HOUSE VOTES 84 (MANGLED)\n")
# Create a new TrainingSetCalc object with the mangled house-votes data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'house-votes-84_mangled_democrat.csv',
'house-votes-84_mangled_republican.csv'
])
hv_mangled_results = Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(10)))
Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(
10))) # Perform 10-fold cross validation on the algorithm using the
# mangled house-votes data set and print formatted result
print("GLASS (MANGLED)\n")
# Create a new TrainingSetCalc object with the mangled glass-catagorized data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'glass_categorized_mangled_1.csv', 'glass_categorized_mangled_2.csv',
'glass_categorized_mangled_3.csv', 'glass_categorized_mangled_5.csv',
'glass_categorized_mangled_6.csv', 'glass_categorized_mangled_7.csv'
])
glass_mangled_results = Evaluate.evaluate(
tsc.kFoldTrain(tsc.createSets(10)))
Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(
10))) # Perform 10-fold cross validation on the algorithm using the
# mangled glass-catagorized data set and print formatted result
print("IRIS (MANGLED)\n")
# Create a new TrainingSetCalc object with the mangled iris-catagorized data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'iris_categorized_mangled_Iris-setosa.csv',
'iris_categorized_mangled_Iris-versicolor.csv',
'iris_categorized_mangled_Iris-virginica.csv'
])
iris_mangled_results = Evaluate.evaluate(tsc.kFoldTrain(
tsc.createSets(10)))
Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(
10))) # Perform 10-fold cross validation on the algorithm using the
# iris-catagorized data set and print formatted result
print("SOYBEANS (MANGLED)\n")
# Create a new TrainingSetCalc object with the mangled soybean-small data set
tsc = TrainingSetCalc("../datasets/filtered/", [
'soybean-small_mangled_D1.csv', 'soybean-small_mangled_D2.csv',
'soybean-small_mangled_D3.csv', 'soybean-small_mangled_D4.csv'
])
Evaluate.evaluate(tsc.kFoldTrain(tsc.createSets(
10))) # Perform 10-fold cross validation on the algorithm using the
# mangled soybean-small data set and print formatted result
soybean_mangled_results = Evaluate.evaluate(
tsc.kFoldTrain(tsc.createSets(10)))
bcw_diffs = []
hv_diffs = []
glass_diffs = []
iris_diffs = []
soybean_diffs = []
for i in range(len(bcw_results)):
bcw_diffs.append((bcw_results[i][0] - bcw_mangled_results[i][0],
bcw_results[i][1] - bcw_mangled_results[i][1],
bcw_results[i][2] - bcw_mangled_results[i][2]))
for i in range(len(hv_results)):
hv_diffs.append((hv_results[i][0] - hv_mangled_results[i][0],
hv_results[i][1] - hv_mangled_results[i][1],
hv_results[i][2] - hv_mangled_results[i][2]))
for i in range(len(glass_results)):
glass_diffs.append((glass_results[i][0] - glass_mangled_results[i][0],
glass_results[i][1] - glass_mangled_results[i][1],
glass_results[i][2] - glass_mangled_results[i][2]))
for i in range(len(iris_results)):
iris_diffs.append((iris_results[i][0] - iris_mangled_results[i][0],
iris_results[i][1] - iris_mangled_results[i][1],
iris_results[i][2] - iris_mangled_results[i][2]))
for i in range(len(soybean_results)):
soybean_diffs.append(
(soybean_results[i][0] - soybean_mangled_results[i][0],
soybean_results[i][1] - soybean_mangled_results[i][1],
soybean_results[i][2] - soybean_mangled_results[i][2]))
print("BREAST CANCER DIFFS")
for i in range(len(bcw_diffs)):
print(bcw_diffs[i][0], "\t", bcw_diffs[i][1], "\t", bcw_diffs[i][2])
print("\n\n")
print("HOUSE VOTE DIFFS")
for i in range(len(hv_diffs)):
print(hv_diffs[i][0], "\t", hv_diffs[i][1], "\t", hv_diffs[i][2])
print("\n\n")
print("GLASS DIFFS")
for i in range(len(glass_diffs)):
print(glass_diffs[i][0], "\t", glass_diffs[i][1], "\t",
glass_diffs[i][2])
print("\n\n")
print("IRIS DIFFS")
for i in range(len(iris_diffs)):
print(iris_diffs[i][0], "\t", iris_diffs[i][1], "\t", iris_diffs[i][2])
print("\n\n")
print("SOYBEAN DIFFS")
for i in range(len(soybean_diffs)):
print(soybean_diffs[i][0], "\t", soybean_diffs[i][1], "\t",
soybean_diffs[i][2])
print("\n\n")
