def create_model(self, num_iter, alpha):
"""
Create models based on label value
:param num_iter: number of iterations
:param alpha: learning rate
:return:
"""
if self.model_label == 'NaiveBayes':
# Create Naive Bayes
self.model = NaiveBayes(self.data, self.label)
elif self.model_label == "NeuralNetwork":
# Create ANN
self.model = NeuralNetwork(self.data, self.label, the_num_iter=num_iter)
elif self.model_label == "SVM":
# Create SVM
self.model = SVM(self.data, self.label, the_num_iter=num_iter, the_alpha=alpha)
class Problem:
""" A problem is a definition of a machine learning problem.
"""
def __init__(self, problem_type=None, file_path=None, model_label=None):
""" Init function for Problem
:param problem_type: it should be one of the defined types
:param file_path: a file path to read data from
:param model: chosen model
:return:
"""
# define problem type
self.problem_type = problem_type
if problem_type is not None:
self.check_type()
# define data set
self.file_path = file_path
self.data = None
if file_path is not None:
self.read_data()
# define label
self.label = None
if self.label is not None:
self.check_label()
# define model
self.model_label = model_label
self.model = None
if model_label is not None:
self.check_model()
self.create_model()
# placeholder for testing data
self.testing_data = None
def check_type(self):
""" Check if problem type is defined
:param self:
:return:
"""
if self.problem_type not in ['BinaryClassification', 'MultiClassification']:
raise Exception('Problem Type Not Included.')
def check_label(self):
""" Check if label is defined
:param self:
:return:
"""
stat = pd.DataFrame.describe(self.data[self.label])
if(self.problem_type == 'BinaryClassification' and stat['unique'] != 2) \
or (self.problem_type == 'MultiClassification' and stat['unique'] <= 2):
raise Exception("Number of unique items is not consistent with problem type")
def check_model(self):
""" Check if model is defined
:param self:
:return:
"""
if self.model_label not in ['SVM', 'NaiveBayes', 'NeuralNetwork']:
raise Exception('Model is not defined')
def set_model(self, model, num_iter=1000, alpha=0.003):
""" Set the problem's model
:param self:
:param model: model to be set
:return:
"""
self.model_label = model
self.check_model()
self.create_model(num_iter, alpha)
def read_data(self):
""" Read data from the file_path
:param self:
:return:
"""
try:
self.data = pd.read_csv(self.file_path)
except IOError:
raise
def set_label(self, label):
""" Set the problem's label
:param self:
:param label: label to be set
:return:
"""
if label in self.data.columns:
self.label = label
self.data[label] = self.data[label].astype(str)
else:
raise Exception("Label column does not exist.")
def save(self, save_path, data_path):
""" Set the problem's elements
:param self:
:param save_path: file path to save info
:param data_path: file path to save data
:return:
"""
problem = dict()
problem["problem_type"] = self.problem_type
problem["label"] = self.label
problem["model_label"] = self.model_label
self.data.to_json(data_path)
print("Saved the data to ", data_path)
with open(save_path, 'w') as fp:
json.dump(problem, fp, indent=1)
print("Saved the problem to ", save_path)
def load(self, load_path, data_path):
""" Load problem's elements from a file & load problem's data from a file
:param self:
:param load_path: file path to load info
:param data_path: file path to load data
:return:
"""
# load path into dict
problem = json.loads(open(load_path).read())
# read problem description
self.label = problem['label']
self.problem_type = problem['problem_type']
self.model_label = problem['model_label']
# load data
self.data = pd.read_json(data_path)
def create_model(self, num_iter, alpha):
"""
Create models based on label value
:param num_iter: number of iterations
:param alpha: learning rate
:return:
"""
if self.model_label == 'NaiveBayes':
# Create Naive Bayes
self.model = NaiveBayes(self.data, self.label)
elif self.model_label == "NeuralNetwork":
# Create ANN
self.model = NeuralNetwork(self.data, self.label, the_num_iter=num_iter)
elif self.model_label == "SVM":
# Create SVM
self.model = SVM(self.data, self.label, the_num_iter=num_iter, the_alpha=alpha)
def set_testing(self, file_path):
"""
Set testing data
:param file_path: path of testing data
:return:
"""
self.testing_data = pd.read_csv(file_path)
def predict(self):
"""
Predict on testing data
:return:
"""
self.model.predict(self.testing_data)
