def test_fit(self):
#file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv"
#loaded_data = FileManager.load_file(file_path)
read_data = ReadData()
loaded_data = read_data.read_data_and_set_variable_settings("../Dataset/00-91-Drugs-All-In-One-File.csv", "../Dataset/VariableSetting.csv")
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data_into_train_valid_test_sets()
model = svm.SVR()
velocity = Velocity()
velocity_matrix = velocity.create_first_velocity()
# define the first population
# validation of a row generating random row for
population = Population(velocity_matrix=velocity_matrix)
population.create_first_population()
debpso = DEBPSO(population.population_matrix[1])
debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train])
def test_transform(self):
read_data = ReadData()
loaded_data = read_data.read_data_and_set_variable_settings("../Dataset/00-91-Drugs-All-In-One-File.csv", "../Dataset/VariableSetting.csv")
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data_into_train_valid_test_sets()
model = svm.SVR()
velocity = Velocity()
velocity_matrix = velocity.create_first_velocity()
# define the first population
# validation of a row generating random row for
population = Population(velocity_matrix=velocity_matrix)
population.create_first_population()
debpso = DEBPSO(population.population_matrix[0])
debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train])
data_manager.transformed_input[SplitTypes.Train] = debpso.transform(data_manager.inputs[SplitTypes.Train])
print("Population 0 row sum ", population.population_matrix[0].sum())
print("Selected feature descriptors",debpso.sel_descriptors_for_curr_population)
print("Transformed array", data_manager.transformed_input[SplitTypes.Train])
def test_transform(self):
read_data = ReadData()
loaded_data = read_data.read_data_and_set_variable_settings(
"../Dataset/00-91-Drugs-All-In-One-File.csv",
"../Dataset/VariableSetting.csv")
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data_into_train_valid_test_sets()
model = svm.SVR()
velocity = Velocity()
velocity_matrix = velocity.create_first_velocity()
# define the first population
# validation of a row generating random row for
population = Population(velocity_matrix=velocity_matrix)
population.create_first_population()
debpso = DEBPSO(population.population_matrix[0])
debpso.fit(data_manager.inputs[SplitTypes.Train],
data_manager.targets[SplitTypes.Train])
data_manager.transformed_input[SplitTypes.Train] = debpso.transform(
data_manager.inputs[SplitTypes.Train])
print("Population 0 row sum ", population.population_matrix[0].sum())
print("Selected feature descriptors",
debpso.sel_descriptors_for_curr_population)
print("Transformed array",
data_manager.transformed_input[SplitTypes.Train])
def test_fit(self):
#file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv"
#loaded_data = FileManager.load_file(file_path)
read_data = ReadData()
loaded_data = read_data.read_data_and_set_variable_settings(
"../Dataset/00-91-Drugs-All-In-One-File.csv",
"../Dataset/VariableSetting.csv")
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data_into_train_valid_test_sets()
model = svm.SVR()
velocity = Velocity()
velocity_matrix = velocity.create_first_velocity()
# define the first population
# validation of a row generating random row for
population = Population(velocity_matrix=velocity_matrix)
population.create_first_population()
debpso = DEBPSO(population.population_matrix[1])
debpso.fit(data_manager.inputs[SplitTypes.Train],
data_manager.targets[SplitTypes.Train])
def generate_population_matrix(self, current_alpha):
self.old_population_matrix = np.copy(self.population_matrix)
for row_index in range(0, self.selective_section):
for col_index in range(0, VariableSetting.No_of_Descriptors):
if current_alpha < self.velocity_matrix[row_index][
col_index] and self.velocity_matrix[row_index][
col_index] <= (0.5 * (1 + current_alpha)):
self.population_matrix[row_index][
col_index] = self.local_best_matrix[row_index][
col_index]
elif ((0.5 * (1 + current_alpha)) <
self.velocity_matrix[row_index][col_index]) and (
self.velocity_matrix[row_index][col_index] <=
(1 - VariableSetting.Beta)):
self.population_matrix[row_index][
col_index] = self.global_best_row[col_index]
elif ((1 - VariableSetting.Beta) <
self.velocity_matrix[row_index][col_index]) and (
self.velocity_matrix[row_index][col_index] <= 1):
self.population_matrix[row_index][
col_index] = 1 - self.population_matrix[row_index][
col_index]
for row_index in range(self.selective_section,
VariableSetting.Population_Size):
velocity_object = Velocity()
random_velocity_row = velocity_object.get_valid_row()
self.population_matrix[
row_index] = Population.create_valid_random_population_row(
random_velocity_row)
def test_fit(self):
file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv"
loaded_data = FileManager.load_file(file_path)
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data_into_train_valid_test_sets(test_split=0.15,
train_split=0.70)
model = svm.SVR()
velocity = Velocity()
velocity_matrix = velocity.create_first_velocity()
# define the first population
# validation of a row generating random row for
population = Population(velocity_matrix=velocity_matrix)
population.create_first_population()
debpso = DEBPSO(population.population_matrix[1])
debpso.fit(data_manager.inputs[SplitTypes.Train],
data_manager.targets[SplitTypes.Train])
print("Population 1 row sum ", population.population_matrix[1].sum())
print("Selected feature descriptors",
debpso.sel_descriptors_for_curr_population)
def generate_population_matrix(self, current_alpha):
self.old_population_matrix = np.copy(self.population_matrix)
for row_index in range(0, self.selective_section):
for col_index in range(0, VariableSetting.No_of_Descriptors):
if current_alpha< self.velocity_matrix[row_index][col_index] and self.velocity_matrix[row_index][col_index] <= (0.5 * (1+ current_alpha)):
self.population_matrix[row_index][col_index] = self.local_best_matrix[row_index][col_index]
elif ((0.5 * (1+ current_alpha)) < self.velocity_matrix[row_index][col_index]) and (self.velocity_matrix[row_index][col_index] <= (1 - VariableSetting.Beta)):
self.population_matrix[row_index][col_index] = self.global_best_row[col_index]
elif ((1 - VariableSetting.Beta ) < self.velocity_matrix[row_index][col_index]) and (self.velocity_matrix[row_index][col_index] <= 1):
self.population_matrix[row_index][col_index] = 1 - self.population_matrix[row_index][col_index]
for row_index in range(self.selective_section, VariableSetting.Population_Size):
velocity_object = Velocity()
random_velocity_row = velocity_object.get_valid_row()
self.population_matrix[row_index] = Population.create_valid_random_population_row(random_velocity_row)
def test_fit(self):
file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv"
loaded_data = FileManager.load_file(file_path)
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data_into_train_valid_test_sets(test_split=0.15, train_split=0.70)
model = svm.SVR()
velocity = Velocity()
velocity_matrix = velocity.create_first_velocity()
# define the first population
# validation of a row generating random row for
population = Population(velocity_matrix=velocity_matrix)
population.create_first_population()
debpso = DEBPSO(population.population_matrix[1])
debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train])
print("Population 1 row sum ", population.population_matrix[1].sum())
print("Selected feature descriptors",debpso.sel_descriptors_for_curr_population)
def create_first_velocity(self):
velocity = Velocity()
self.velocity_matrix = velocity.create_first_velocity()
def create_first_velocity(self):
velocity = Velocity()
self.velocity_matrix = velocity.create_first_velocity()
