def test_convert_class_values_to_floats():
classifier = NaiveBayesClassifier()
dataset = [['3.393533211', '2.331273381', '0'],
['3.110073483', '1.781539638', '0'],
['1.343808831', '3.368360954', '0'],
['3.582294042', '4.67917911', '0'],
['2.280362439', '2.866990263', '0'],
['7.423436942', '4.696522875', '1'],
['5.745051997', '3.533989803', '1'],
['9.172168622', '2.511101045', '1'],
['7.792783481', '3.424088941', '1'],
['7.939820817', '0.791637231', '1']]
for i in range(0, len(dataset[0])-1):
classifier.convert_class_values_to_floats(dataset, i)
for i in range(0, len(dataset)):
tested_row = random.randint(0, len(dataset)-1)
tested_column = random.randint(0, len(dataset[0]) - 2)
assert isinstance(dataset[tested_row][tested_column], float)
class Iris:
"""
Works on iris.csv dataset and interactively performs the following actions:\n
1. Classify new data entered by user.\n
2. Calculate the algorithm implementation accuracy.\n
3. Show dataset description (iris.names file).\n
4. Show dataset rows.
"""
def __init__(self):
self.dataset_filename = 'datasets/iris.csv'
self.description_filename = 'datasets/iris.names'
self.nbc = NaiveBayesClassifier()
self.dataset = self.nbc.load_dataset_from_csv(self.dataset_filename)
self.class_map = dict()
def data_preprocessing(self):
"""
Converts class names (strings) to ints and class values to floats.
Args:
None.
Returns:
Nothing.
"""
seed(1)
for i in range(len(self.dataset[0]) - 1):
self.nbc.convert_class_values_to_floats(self.dataset, i)
self.class_map = self.nbc.map_class_names_to_ints(
self.dataset,
len(self.dataset[0]) - 1)
def classify_data(self):
"""
Creates a new row with values inputted by the user, then classifies it to the proper class
using Naive Bayes Classifier algorithm.
Args:
None.
Returns:
Nothing.
"""
print('\nEnter the data to be classified.\n')
attributes = {
'Sepal length [cm]: ': 0.0,
'Sepal width [cm]: ': 0.0,
'Petal length [cm]: ': 0.0,
'Petal width [cm]: ': 0.0
}
for attr in attributes:
correct_input = False
while correct_input == False:
try:
attr_value = float(input(attr))
correct_input = True
except:
print(
'Incorrect value! Please enter an integer or a floating point number.'
)
attributes[attr] = attr_value
print('\nEntered attributes:\n')
for attr in attributes:
print(f'{attr}{attributes[attr]}')
print()
confirm_sign = ''
while confirm_sign not in ['y', 'Y', 'n', 'N']:
confirm_sign = input('Confirm (y/n): ')
if confirm_sign in ['n', 'N']:
return
model = self.nbc.calculate_class_parameters(self.dataset)
label = self.nbc.predict(model, list(attributes.values()))
for key, value in self.class_map.items():
if value == label:
print(f'\nThe entered entity was classified as: {key}')
break
def calculate_accuracy(self, n_folds=5):
"""
Calculates algorithm accuracy by using evaluate_algorithm() function.
Args:
n_folds (int)
Number of folds used in the k-fold cross validation split algorithm.
Returns:
accuracy
Calculated classifier accuracy in percent.
"""
scores = self.nbc.evaluate_algorithm(self.dataset, n_folds)
print(
'\n\nCalculating the accuracy of the classifier using the iris.csv dataset...'
)
print('\nResampling: k-fold cross validation split')
accuracy = (sum(scores) / float(len(scores)))
print(f'\nAccuracy ({n_folds} folds): {round(accuracy, 3)} %\n')
return accuracy
def show_dataset_description(self):
"""
Prints the 'iris.names' file to the console output.
Args:
None.
Returns:
Nothing.
"""
with open(self.description_filename, 'r') as f:
csv_reader = csv.reader(f,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for row in csv_reader:
for word in row:
print(word, end='')
print()
def show_dataset_rows(self):
"""
Prints the 'iris.csv' file to the console output.
Args:
None.
Returns:
Nothing.
"""
with open(self.dataset_filename, 'r') as f:
csv_reader = csv.reader(f,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for row in csv_reader:
for i in range(len(row) - 1):
print(row[i], end=',')
print(row[len(row) - 1])
def run(self):
"""
Creates the interactive menu from which the user can execute the actions handled
by the other methods in this class.
Args:
None.
Returns:
Nothing.
"""
print('\n=================================')
print(' Iris dataset')
print('=================================\n')
self.data_preprocessing()
returned_from_function = True
while True:
if returned_from_function == True:
print('\nChoose the action:')
print('\n1. Classify new data.')
print('2. Calculate algorithm accuracy.')
print('3. Show dataset description.')
print('4. Show dataset rows.')
print('5. Go back to the main menu.\n')
returned_from_function = False
choice = input('Choice: ')
if choice not in ['1', '2', '3', '4', '5']:
print('Wrong choice! Please choose option 1-5.')
elif choice == '1':
try:
self.classify_data()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '2':
try:
self.calculate_accuracy()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '3':
try:
self.show_dataset_description()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '4':
try:
self.show_dataset_rows()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '5':
break
else:
raise
class PimaIndiansDiabetes:
"""
Works on pima-indians-diabetes.csv dataset and interactively performs the following actions:\n
1. Classify new data entered by user.\n
2. Calculate the algorithm implementation accuracy.\n
3. Show dataset description (pima-indians-diabetes.names file).\n
4. Show dataset rows.
"""
def __init__(self):
self.dataset_filename = 'datasets/pima-indians-diabetes.csv'
self.description_filename = 'datasets/pima-indians-diabetes.names'
self.nbc = NaiveBayesClassifier()
self.dataset = self.nbc.load_dataset_from_csv(self.dataset_filename)
def data_preprocessing(self):
"""
Converts class names (strings) to ints and class values to floats.
Args:
None.
Returns:
Nothing.
"""
for i in range(len(self.dataset[0]) - 1):
self.nbc.convert_class_values_to_floats(self.dataset, i)
self.nbc.map_class_names_to_ints(self.dataset,
len(self.dataset[0]) - 1,
numbers_already=True)
def classify_data(self):
"""
Creates a new row with values inputted by the user, then classifies it to the proper class
using Naive Bayes Classifier algorithm.
Args:
None.
Returns:
Nothing.
"""
print('\nEnter the data to be classified.\n')
attributes = {
'Number of times pregnant: ':
0.0,
'Plasma glucose concentration a 2 hours in an oral glucose tolerance test: ':
0.0,
'Diastolic blood pressure (mm Hg): ':
0.0,
'Triceps skin fold thickness (mm): ':
0.0,
'2-Hour serum insulin (mu U/ml): ':
0.0,
'Body mass index (weight in kg/(height in m)^2): ':
0.0,
'Diabetes pedigree function: ':
0.0,
'Age (years): ':
0.0
}
for attr in attributes:
correct_input = False
while correct_input == False:
try:
attr_value = float(input(attr))
correct_input = True
except:
print(
'Incorrect value! Please enter an integer or a floating point number.'
)
attributes[attr] = attr_value
print('\nEntered attributes:\n')
for attr in attributes:
print(f'{attr}{attributes[attr]}')
print()
confirm_sign = ''
while confirm_sign not in ['y', 'Y', 'n', 'N']:
confirm_sign = input('Confirm (y/n): ')
if confirm_sign in ['n', 'N']:
return
model = self.nbc.calculate_class_parameters(self.dataset)
label = self.nbc.predict(model, list(attributes.values()))
# Original dataset contains class names represented as numbers,
# so it's needed to print the labels explicitly
if label == 0:
print(f'\nThe entered entity was classified as: Negative')
elif label == 1:
print(f'\nThe entered entity was classified as: Positive')
else:
raise
def calculate_accuracy(self, n_folds=5):
"""
Calculates algorithm accuracy by using evaluate_algorithm() function.
Args:
n_folds (int)
Number of folds used in the k-fold cross validation split algorithm.
Returns:
accuracy
Calculated classifier accuracy in percent.
"""
scores = self.nbc.evaluate_algorithm(self.dataset, n_folds)
print(
'\n\nCalculating the accuracy of the classifier using the pima-indians-diabetes.csv dataset...'
)
print('\nResampling: k-fold cross validation split')
accuracy = (sum(scores) / float(len(scores)))
print(f'\nAccuracy ({n_folds} folds): {round(accuracy, 3)} %\n')
return accuracy
def show_dataset_description(self):
"""
Prints the 'pima-indians-diabetes.names' file to the console output.
Args:
None.
Returns:
Nothing.
"""
with open(self.description_filename, 'r') as f:
csv_reader = csv.reader(f,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for row in csv_reader:
for word in row:
print(word, end='')
print()
def show_dataset_rows(self):
"""
Prints the 'pima-indians-diabetes.csv' file to the console output.
Args:
None.
Returns:
Nothing.
"""
with open(self.dataset_filename, 'r') as f:
csv_reader = csv.reader(f,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for row in csv_reader:
for i in range(len(row) - 1):
print(row[i], end=',')
print(row[len(row) - 1])
def run(self):
"""
Creates the interactive menu from which the user can execute the actions handled
by the other methods in this class.
Args:
None.
Returns:
Nothing.
"""
seed(1)
print('\n=================================')
print(' Pima Indians Diabetes dataset')
print('=================================')
self.data_preprocessing()
returned_from_function = True
while True:
if returned_from_function == True:
print('\nChoose the action:')
print('\n1. Classify new data.')
print('2. Calculate algorithm accuracy.')
print('3. Show dataset description.')
print('4. Show dataset rows.')
print('5. Go back to the main menu.\n')
returned_from_function = False
choice = input('Choice: ')
if choice not in ['1', '2', '3', '4', '5']:
print('Wrong choice! Please choose option 1-5.')
elif choice == '1':
try:
self.classify_data()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '2':
try:
self.calculate_accuracy()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '3':
try:
self.show_dataset_description()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '4':
try:
self.show_dataset_rows()
returned_from_function = True
continue
except KeyboardInterrupt:
returned_from_function = True
continue
elif choice == '5':
break
else:
raise