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GeneticAlgorithm.py
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GeneticAlgorithm.py
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from LSTM import *
from collections import OrderedDict
from config import *
class GeneticAlgorithm:
def __init__(
self,
training_set_ga,
testing_set_ga,
network_architecture=OrderedDict(
[
("Layer1", [10, 21, 5]),
("Layer2", [2, 20, 5]),
("Layer3", [1, 21, 1]),
("Layer4", [0, 5, 1]),
]
),
learning_rate=[0.001, 0.1, 0.005],
initial_population_source_external=False,
build_grid_scenarios=True,
):
self.training_set_ga = training_set_ga
self.testing_set_ga = testing_set_ga
self.network_architecture = network_architecture
self.learning_rate = learning_rate
self.initial_population_source_external = initial_population_source_external
self.build_grid_scenarios = build_grid_scenarios
self.initial_population = None
self.parent_one = None
self.parent_two = None
self.parent_location_one = None
self.parent_location_two = None
def make_initial_population(self, save_population_to_csv=False, initial_pop_size=5):
complete_architecture = OrderedDict(
[
(
"LR",
np.arange(
self.learning_rate[0],
self.learning_rate[1],
self.learning_rate[2],
),
)
]
)
for i in range(len(self.network_architecture.keys())):
complete_architecture.update(
{
list(self.network_architecture.keys())[i]: np.arange(
self.network_architecture[
list(self.network_architecture.keys())[i]
][0],
self.network_architecture[
list(self.network_architecture.keys())[i]
][1],
self.network_architecture[
list(self.network_architecture.keys())[i]
][2],
)
}
)
self.network_architecture = complete_architecture
if self.initial_population_source_external:
self.initial_population = pd.read_csv(
folder_structure.path_input
+ "/"
+ "GeneticAlgorithm_20_hist40_True_new_modelafterGA.csv",
index_col=0,
)
else:
if self.build_grid_scenarios:
learning_rates = [0.001, 0.01]
layer_one = [2, 20, 40]
layer_two = [2, 15, 40]
layer_three = [0, 4, 15]
layer_four = [0, 2, 10]
dict_help = {}
for i in range(len(learning_rates)):
for j in range(len(layer_one)):
for k in range(len(layer_two)):
for c in range(len(layer_three)):
for f in range(len(layer_four)):
dict_help["{}{}{}{}{}".format(i, j, k, c, f)] = [
learning_rates[i],
layer_one[j],
layer_two[k],
layer_three[c],
layer_four[f],
]
self.initial_population = pd.DataFrame(dict_help).transpose()
self.initial_population.rename(
columns={
0: "LR",
1: "Layer1",
2: "Layer2",
3: "Layer3",
4: "Layer4",
},
inplace=True,
)
self.initial_population["Fitness"] = 0
self.initial_population["Generation"] = 0
self.initial_population = self.initial_population[
~(
(self.initial_population.Layer3 == 0)
& (self.initial_population.Layer4 > 0)
)
].reset_index(drop=True)
print("Population Size: {}".format(self.initial_population.shape))
for i in range(self.initial_population.shape[0]):
print("Progress: {}".format(i / self.initial_population.shape[0]))
training_model = TrainLSTM(
training_set=self.training_set_ga,
testing_set=self.testing_set_ga,
activation=tf.nn.elu,
epochs=50,
learning_rate=self.initial_population.LR[i],
layer_one=self.initial_population["Layer1"][i],
layer_two=self.initial_population["Layer2"][i],
layer_three=self.initial_population["Layer3"][i],
layer_four=self.initial_population["Layer4"][i],
)
training_model.make_accuracy_measures()
self.initial_population.loc[i, "Fitness"] = training_model.fitness
print(
"Network Architecture: LR: {}, L1: {}, L2: {}, L3: {}, L4: {}".format(
self.initial_population.LR[i],
self.initial_population.Layer1[i],
self.initial_population.Layer2[i],
self.initial_population.Layer3[i],
self.initial_population.Layer4[i],
),
)
tf.keras.backend.clear_session() # important to clear session!
del training_model
if save_population_to_csv:
self.initial_population.to_csv(
folder_structure.path_input
+ "/"
+ "InitialPopulation_all_new_new.csv"
)
else:
ind = pd.DataFrame(
0,
index=range(initial_pop_size),
columns=np.array(
list(self.network_architecture.keys())
+ ["Fitness", "Generation"]
),
)
for i in range(ind.shape[0]):
for j in range(len(self.network_architecture.keys())):
ind[list(self.network_architecture.keys())[j]].iloc[
i
] = random.choice(
self.network_architecture[
list(self.network_architecture.keys())[j]
]
)
self.initial_population = ind
for i in range(self.initial_population.shape[0]):
print("Progress: {}".format(i / self.initial_population.shape[0]))
training_model = TrainLSTM(
training_set=self.training_set_ga,
testing_set=self.testing_set_ga,
activation=tf.nn.elu,
epochs=50,
learning_rate=self.initial_population.LR[i],
layer_one=self.initial_population["Layer1"][i],
layer_two=self.initial_population["Layer2"][i],
layer_three=self.initial_population["Layer3"][i],
layer_four=self.initial_population["Layer4"][i],
)
training_model.make_accuracy_measures()
self.initial_population.loc[i, "Fitness"] = training_model.fitness
tf.keras.backend.clear_session() # important to clear session!
del training_model
if save_population_to_csv:
self.initial_population.to_csv(
folder_structure.path_input
+ "/"
+ "InitialPopulation_future_20.csv"
)
def select_parents(self):
if self.initial_population is None:
self.make_initial_population()
df_help = self.initial_population.copy()
parent1_location = (
df_help.Fitness[
np.random.choice(
df_help.index, int(df_help.shape[0] * 0.02), replace=False,
)
]
.nlargest(1)
.index
)
parent2_location = (
df_help.Fitness[
np.random.choice(
df_help.index, int(df_help.shape[0] * 0.02), replace=False,
)
]
.nlargest(1)
.index
)
self.parent_location_one = parent1_location
self.parent_location_two = parent2_location
self.parent_one = df_help.iloc[parent1_location]
self.parent_two = df_help.iloc[parent2_location]
def make_offsprings(self):
self.select_parents()
df_test_parent_one = self.parent_one
df_test_parent_two = self.parent_two
df_test_parent_one = df_test_parent_one.reset_index(drop=True).reset_index(
level=0
)
df_test_parent_two = df_test_parent_two.reset_index(drop=True).reset_index(
level=0
)
child_one = df_test_parent_one[list(df_test_parent_one.columns[:2])].merge(
df_test_parent_two[list(df_test_parent_one.columns[2:6])],
on=df_test_parent_one.index,
)
child_two = df_test_parent_two[list(df_test_parent_one.columns[:2])].merge(
df_test_parent_one[list(df_test_parent_one.columns[2:6])],
on=df_test_parent_one.index,
)
child_one = child_one.drop(["key_0", "index"], axis=1)
child_two = child_two.drop(["key_0", "index"], axis=1)
_random_pick = random.choice(child_one.columns)
_random_pick_2 = random.choice(child_one.columns)
child_three = child_one.copy()
child_three[_random_pick] = random.choice(
self.network_architecture[_random_pick]
)
child_three[_random_pick_2] = random.choice(
self.network_architecture[_random_pick_2]
)
_random_pick = random.choice(child_two.columns)
child_four = child_two.copy()
child_four[_random_pick] = random.choice(
self.network_architecture[_random_pick]
)
self.initial_population = self.initial_population.append(child_one, sort=False)
self.initial_population = self.initial_population.append(child_two, sort=False)
self.initial_population = self.initial_population.append(
child_three, sort=False
)
self.initial_population = self.initial_population.append(child_four, sort=False)
self.initial_population.reset_index(drop=True, inplace=True)
individuals_help = self.initial_population.copy()
individuals_help = individuals_help[individuals_help.Fitness.isna()]
for i in individuals_help.index:
train_m = TrainLSTM(
training_set=self.training_set_ga,
testing_set=self.testing_set_ga,
epochs=50,
learning_rate=individuals_help.LR[i],
layer_one=individuals_help.Layer1[i],
layer_two=individuals_help.Layer2[i],
layer_three=individuals_help.Layer3[i],
layer_four=individuals_help.Layer4[i],
)
train_m.make_accuracy_measures()
self.initial_population.loc[i, "Fitness"] = train_m.fitness
tf.keras.backend.clear_session() # important to clear session!
del train_m
def run_complete_genetic_algorithm(
self, initial_population_size=50, number_of_generations=20
):
if self.initial_population is None:
self.make_initial_population(
save_population_to_csv=True, initial_pop_size=initial_population_size
)
for iteration in range(number_of_generations):
print("Progress Generation: {}".format(iteration / number_of_generations))
print(iteration)
print(self.parent_location_one, self.parent_location_two)
self.make_offsprings()