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MatheusZickuhr
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‎.gitignore‎

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MANIFEST
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PyGame-Learning-Environment/
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gym/
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/dist/
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/venv/
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/.idea/
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*.pyc
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*.pyc
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*.json
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*.csv
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*.png

‎README.md‎

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# python-ne
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A neuroevolution library for python
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# how to install (github)
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# how to install
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```
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git clone https://github.com/MatheusZickuhr/python-ne.git
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# example (xor)
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```python
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from python_ne.core.ga.csv_logger import CsvLogger
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from python_ne.core.ga.console_logger import ConsoleLogger
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from python_ne.core.ga.matplotlib_logger import MatplotlibLogger
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from python_ne.core.ga.mutation_strategies import Mutation1
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from python_ne.core.model_adapters.default_model_adapter import DefaultModelAdapter
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from python_ne.core.ga.genetic_algorithm import GeneticAlgorithm
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from python_ne.core.ga.crossover_strategies import Crossover1
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import numpy as np
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@@ -29,33 +35,42 @@ def calc_fitness(element):
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fitness = 0
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for x, y in zip(X, Y):
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prediction = element.get_output(x)
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prediction = np.argmax(element.get_output(x))
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if prediction == y:
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fitness += 1
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return fitness
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genetic_algorithm = GeneticAlgorithm(
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population_size=200,
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input_shape=(2,),
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output_size=2,
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selection_percentage=0.5,
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mutation_chance=0.1,
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fitness_threshold=4,
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ne_type='ne', # ne or neat
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backend_adapter='default', # default or keras,
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neural_network_config=[128, 128] # two hidden layers with 128 neurons each
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)
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genetic_algorithm.run(
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number_of_generations=1000,
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calculate_fitness_callback=calc_fitness
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)
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best_element = genetic_algorithm.get_best_element()
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print(f'the fitness of the best element is {best_element.fitness}')
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if __name__ == '__main__':
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genetic_algorithm = GeneticAlgorithm(
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population_size=200,
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selection_percentage=0.9,
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mutation_chance=0.01,
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fitness_threshold=4,
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model_adapter=DefaultModelAdapter,
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crossover_strategy=Crossover1(),
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mutation_strategy=Mutation1(),
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neural_network_config=[((2,), 8, 'tanh'), (2, 'tanh')],
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)
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csv_logger = CsvLogger()
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genetic_algorithm.add_observer(csv_logger)
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matplotlib_logger = MatplotlibLogger()
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genetic_algorithm.add_observer(matplotlib_logger)
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genetic_algorithm.add_observer(ConsoleLogger())
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genetic_algorithm.run(
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number_of_generations=1000,
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calculate_fitness_callback=calc_fitness
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)
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csv_logger.save('file.csv')
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matplotlib_logger.save_fitness_chart('fitness.png')
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matplotlib_logger.save_time_chart('time.png')
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matplotlib_logger.save_std_chart('std.png')
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best_element = genetic_algorithm.get_best_element()
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print(f'the fitness of the best element is {best_element.raw_fitness}')
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```

‎examples/gym_train_ne_agent_example.py‎

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import gym
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from python_ne.core.ga.console_logger import ConsoleLogger
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from python_ne.core.ga.crossover_strategies import NoCrossover
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from python_ne.core.ga.mutation_strategies import Mutation1
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from python_ne.core.model_adapters.default_model_adapter import DefaultModelAdapter
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from python_ne.extra.env_adapters.gym_env_adapter import GymEnvAdapter
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from python_ne.extra.ne_agent import NeAgent
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if __name__ == '__main__':
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env = gym.make('LunarLander-v2')
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env_adapter = GymEnvAdapter(env=env, render=False, continuous=False)
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agent = NeAgent(
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env_adapter=env_adapter,
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model_adapter=DefaultModelAdapter,
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)
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nn_config = (
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(env_adapter.get_input_shape(), 16, 'tanh'),
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(env_adapter.get_n_actions(), 'tanh')
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)
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agent.train(
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number_of_generations=1000,
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population_size=500,
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selection_percentage=0.9,
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mutation_chance=0.01,
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fitness_threshold=250,
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neural_network_config=nn_config,
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crossover_strategy=NoCrossover(),
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mutation_strategy=Mutation1(),
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loggers=(ConsoleLogger(),),
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play_n_times=5,
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max_n_steps=300,
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reward_if_max_step_reached=-200
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)
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agent.save('ne_agent.json')

‎examples/gym_trained_ne_agent_example.py‎

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import gym
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from python_ne.core.ga_neural_network.ne_neural_network import NeNeuralNetwork
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from python_ne.core.model_adapters.default_model_adapter import DefaultModelAdapter
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from python_ne.extra.env_adapters.gym_env_adapter import GymEnvAdapter
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from python_ne.extra.ne_agent import NeAgent
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if __name__ == '__main__':
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env = gym.make('LunarLander-v2')
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env_adapter = GymEnvAdapter(env=env, render=False)
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agent = NeAgent(
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env_adapter=env_adapter,
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ne_type=NeNeuralNetwork,
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model_adapter=DefaultModelAdapter,
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)
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agent.load('ne_agent.json')
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# agent.max_n_steps = 300
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total_r = 0
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for i in range(100):
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total_r += agent.play()
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if i > 0:
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print(total_r / i)

‎examples/ne_agent_example.py‎

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This file was deleted.

‎examples/ple_train_ne_agent_example.py‎

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import os
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from python_ne.core.ga.console_logger import ConsoleLogger
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from python_ne.core.ga.crossover_strategies import Crossover1, NoCrossover
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from python_ne.core.ga.mutation_strategies import Mutation1
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from python_ne.core.model_adapters.keras_model_adapter import KerasModelAdapter
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os.putenv('SDL_VIDEODRIVER', 'fbcon')
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os.environ["SDL_VIDEODRIVER"] = "dummy"
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from python_ne.core.model_adapters.default_model_adapter import DefaultModelAdapter
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from python_ne.extra.ne_agent import NeAgent
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from python_ne.extra.env_adapters.ple_env_adapter import PleEnvAdapter
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from ple.games.flappybird import FlappyBird
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from ple import PLE
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if __name__ == '__main__':
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env = PLE(FlappyBird(), display_screen=False, force_fps=True)
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env.init()
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env_adapter = PleEnvAdapter(env=env)
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agent = NeAgent(
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env_adapter=env_adapter,
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model_adapter=KerasModelAdapter,
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)
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agent.train(
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number_of_generations=150,
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population_size=500,
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selection_percentage=0.9,
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mutation_chance=0.01,
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fitness_threshold=50,
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crossover_strategy=NoCrossover(),
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mutation_strategy=Mutation1(),
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neural_network_config=[
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(env_adapter.get_input_shape(), 16, 'tanh'),
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(env_adapter.get_n_actions(), 'tanh')
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],
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loggers=(ConsoleLogger(),)
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)
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agent.save('ne_agent.json')

‎examples/ple_trained_ne_agent_example.py‎

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from python_ne.core.model_adapters.default_model_adapter import DefaultModelAdapter
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from python_ne.core.model_adapters.keras_model_adapter import KerasModelAdapter
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from python_ne.extra.ne_agent import NeAgent
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from python_ne.extra.env_adapters.ple_env_adapter import PleEnvAdapter
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from ple.games.flappybird import FlappyBird
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from ple import PLE
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if __name__ == '__main__':
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env = PLE(FlappyBird(), display_screen=True, force_fps=False)
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env.init()
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env_adapter = PleEnvAdapter(env=env)
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agent = NeAgent(
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env_adapter=env_adapter,
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model_adapter=KerasModelAdapter,
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)
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agent.load('ne_agent.json')
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while 1:
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agent.play()

‎examples/xor_example.py‎

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from python_ne.core.ga.csv_logger import CsvLogger
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from python_ne.core.ga.console_logger import ConsoleLogger
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from python_ne.core.ga.matplotlib_logger import MatplotlibLogger
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from python_ne.core.ga.mutation_strategies import Mutation1
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from python_ne.core.model_adapters.default_model_adapter import DefaultModelAdapter
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from python_ne.core.ga.genetic_algorithm import GeneticAlgorithm
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from python_ne.core.ga.crossover_strategies import Crossover1
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import numpy as np
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fitness = 0
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for x, y in zip(X, Y):
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prediction = element.get_output(x)
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prediction = np.argmax(element.get_output(x))
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if prediction == y:
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fitness += 1
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return fitness
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if __name__ == '__main__':
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genetic_algorithm = GeneticAlgorithm(
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population_size=200,
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input_shape=(2,),
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output_size=2,
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selection_percentage=0.5,
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mutation_chance=0.1,
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selection_percentage=0.9,
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mutation_chance=0.01,
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fitness_threshold=4,
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ne_type='ne', # ne or neat
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backend_adapter='default', # default or keras,
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neural_network_config=[128, 128] # two hidden layers with 128 neurons each
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model_adapter=DefaultModelAdapter,
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crossover_strategy=Crossover1(),
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mutation_strategy=Mutation1(),
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neural_network_config=[((2,), 8, 'tanh'), (2, 'tanh')],
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)
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csv_logger = CsvLogger()
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genetic_algorithm.add_observer(csv_logger)
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matplotlib_logger = MatplotlibLogger()
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genetic_algorithm.add_observer(matplotlib_logger)
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genetic_algorithm.add_observer(ConsoleLogger())
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genetic_algorithm.run(
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number_of_generations=1000,
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calculate_fitness_callback=calc_fitness
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)
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csv_logger.save('file.csv')
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matplotlib_logger.save_fitness_chart('fitness.png')
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matplotlib_logger.save_time_chart('time.png')
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matplotlib_logger.save_std_chart('std.png')
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3551
best_element = genetic_algorithm.get_best_element()
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print(f'the fitness of the best element is {best_element.fitness}')
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print(f'the fitness of the best element is {best_element.raw_fitness}')

‎python_ne/core/backend_adapters/keras_backend_adapter.py‎

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‎python_ne/core/ga/console_logger.py‎

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from python_ne.utils.observer import Observer
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class ConsoleLogger(Observer):
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def notify(self, *args, **kwargs):
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print(f'generation={kwargs["current_generation"]}/{kwargs["number_of_generations"]},' +
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f' bestfitness={kwargs["best_element_fitness"]}, std={kwargs["population_fitness_std"]},' +
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f' runtime={kwargs["generation_time"]}')

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