diff --git a/README.md b/README.md index fb5b6b499..3e9973f54 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ EvoX is a distributed GPU-accelerated framework for scalable evolutionary comput - Comprehensive support for commonly used benchmark problems. - Extensive coverage of neuroevolution problems. - 🎉 Easy to use - - Functional programming for easy function ccomposingomposition. + - Functional programming for easy function composition. - Hierarchical state management for modular programming. - Detailed tutorial available [here](https://evox.readthedocs.io/en/latest/guide/index.html). diff --git a/docs/source/api/algorithms/mo/gde3 b/docs/source/api/algorithms/mo/ged3.rst similarity index 100% rename from docs/source/api/algorithms/mo/gde3 rename to docs/source/api/algorithms/mo/ged3.rst diff --git a/docs/source/api/algorithms/mo/lmocso.rst b/docs/source/api/algorithms/mo/lmocso.rst new file mode 100644 index 000000000..8c60725f8 --- /dev/null +++ b/docs/source/api/algorithms/mo/lmocso.rst @@ -0,0 +1,6 @@ +======= +LMOCSO +======= + +.. autoclass:: evox.algorithms.LMOCSO + :members: diff --git a/docs/source/api/problems/index.rst b/docs/source/api/problems/index.rst index 7f56b06d8..7bbbf6633 100644 --- a/docs/source/api/problems/index.rst +++ b/docs/source/api/problems/index.rst @@ -8,5 +8,4 @@ Problems :maxdepth: 2 numerical/index - neuroevolution/index - rl/index \ No newline at end of file + neuroevolution/index \ No newline at end of file diff --git a/docs/source/api/problems/neuroevolution/index.rst b/docs/source/api/problems/neuroevolution/index.rst index ebef40205..4f1dbb0c7 100644 --- a/docs/source/api/problems/neuroevolution/index.rst +++ b/docs/source/api/problems/neuroevolution/index.rst @@ -3,6 +3,7 @@ Neuroevolution ============== .. toctree:: - :maxdepth: 1 + :maxdepth: 2 - torchvision \ No newline at end of file + reinforcement_learning/index + supervised_learning/index \ No newline at end of file diff --git a/docs/source/api/problems/rl/brax.rst b/docs/source/api/problems/neuroevolution/reinforcement_learning/brax.rst similarity index 50% rename from docs/source/api/problems/rl/brax.rst rename to docs/source/api/problems/neuroevolution/reinforcement_learning/brax.rst index 56f26dc1b..d9e4c30b4 100644 --- a/docs/source/api/problems/rl/brax.rst +++ b/docs/source/api/problems/neuroevolution/reinforcement_learning/brax.rst @@ -2,5 +2,5 @@ Brax-based Problem ================== -.. autoclass:: evox.problems.neuroevolution.Brax +.. autoclass:: evox.problems.neuroevolution.reinforcement_learning.Brax :members: diff --git a/docs/source/api/problems/neuroevolution/reinforcement_learning/env_pool.rst b/docs/source/api/problems/neuroevolution/reinforcement_learning/env_pool.rst new file mode 100644 index 000000000..a5b13b81a --- /dev/null +++ b/docs/source/api/problems/neuroevolution/reinforcement_learning/env_pool.rst @@ -0,0 +1,6 @@ +======== +Env Pool +======== + +.. autoclass:: evox.problems.neuroevolution.reinforcement_learning.EnvPool + :members: \ No newline at end of file diff --git a/docs/source/api/problems/neuroevolution/reinforcement_learning/gym.rst b/docs/source/api/problems/neuroevolution/reinforcement_learning/gym.rst new file mode 100644 index 000000000..ce10c7da4 --- /dev/null +++ b/docs/source/api/problems/neuroevolution/reinforcement_learning/gym.rst @@ -0,0 +1,6 @@ +=== +Gym +=== + +.. autoclass:: evox.problems.neuroevolution.reinforcement_learning.Gym + :members: diff --git a/docs/source/api/problems/neuroevolution/reinforcement_learning/index.rst b/docs/source/api/problems/neuroevolution/reinforcement_learning/index.rst new file mode 100644 index 000000000..f807b8461 --- /dev/null +++ b/docs/source/api/problems/neuroevolution/reinforcement_learning/index.rst @@ -0,0 +1,10 @@ +====================== +Reinforcement Learning +====================== + +.. toctree:: + :maxdepth: 1 + + brax + gym + env_pool diff --git a/docs/source/api/problems/neuroevolution/supervised_learning/index.rst b/docs/source/api/problems/neuroevolution/supervised_learning/index.rst new file mode 100644 index 000000000..760e71939 --- /dev/null +++ b/docs/source/api/problems/neuroevolution/supervised_learning/index.rst @@ -0,0 +1,8 @@ +=================== +Supervised Learning +=================== + +.. toctree:: + :maxdepth: 1 + + torchvision diff --git a/docs/source/api/problems/neuroevolution/supervised_learning/torchvision.rst b/docs/source/api/problems/neuroevolution/supervised_learning/torchvision.rst new file mode 100644 index 000000000..c95c5b50e --- /dev/null +++ b/docs/source/api/problems/neuroevolution/supervised_learning/torchvision.rst @@ -0,0 +1,6 @@ +=================== +Torchvision Dataset +=================== + +.. autoclass:: evox.problems.neuroevolution.supervised_learning.TorchvisionDataset + :members: diff --git a/docs/source/api/problems/neuroevolution/torchvision.rst b/docs/source/api/problems/neuroevolution/torchvision.rst deleted file mode 100644 index 813b21305..000000000 --- a/docs/source/api/problems/neuroevolution/torchvision.rst +++ /dev/null @@ -1,6 +0,0 @@ -=================== -Torchvision Dataset -=================== - -.. autoclass:: evox.problems.neuroevolution.TorchvisionDataset - :members: diff --git a/docs/source/api/problems/numerical/dtlz.rst b/docs/source/api/problems/numerical/dtlz.rst index ebc0851eb..791166017 100644 --- a/docs/source/api/problems/numerical/dtlz.rst +++ b/docs/source/api/problems/numerical/dtlz.rst @@ -23,8 +23,3 @@ DTLZ Test Suit .. autoclass:: evox.problems.numerical.DTLZ7 :members: -.. autoclass:: evox.problems.numerical.DTLZ8 - :members: - -.. autoclass:: evox.problems.numerical.DTLZ9 - :members: diff --git a/docs/source/api/problems/rl/gym.rst b/docs/source/api/problems/rl/gym.rst deleted file mode 100644 index 15983943c..000000000 --- a/docs/source/api/problems/rl/gym.rst +++ /dev/null @@ -1,6 +0,0 @@ -=== -Gym -=== - -.. autoclass:: evox.problems.neuroevolution.Gym - :members: diff --git a/docs/source/api/problems/rl/index.rst b/docs/source/api/problems/rl/index.rst deleted file mode 100644 index 1cd36aefb..000000000 --- a/docs/source/api/problems/rl/index.rst +++ /dev/null @@ -1,9 +0,0 @@ -=== -RL -=== - -.. toctree:: - :maxdepth: 1 - - brax - gym diff --git a/docs/source/conf.py b/docs/source/conf.py index cf7076b76..5c7bf2f51 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -29,7 +29,6 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - "myst_parser", "numpydoc", "sphinx.ext.autodoc", "sphinx.ext.autosummary", @@ -38,6 +37,7 @@ "sphinx_copybutton", "sphinx_design", "sphinx_favicon", + "myst_nb", ] # Add any paths that contain templates here, relative to this directory. @@ -52,6 +52,7 @@ autodoc_mock_imports = [ "brax", "chex", + "envpool", "gymnasium", "ray", "torch", @@ -82,7 +83,7 @@ # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] -html_css_files = ["evox.css"] +# html_css_files = ["evox.css"] autodoc_typehints_format = "short" autodoc_typehints = "description" @@ -93,3 +94,4 @@ numpydoc_show_class_members = False autosummary_generate = True autosummary_imported_members = True +nb_execution_mode = "off" \ No newline at end of file diff --git a/docs/source/guide/advanced/1-state.md b/docs/source/guide/advanced/1-state.md new file mode 100644 index 000000000..b27d0c5a4 --- /dev/null +++ b/docs/source/guide/advanced/1-state.md @@ -0,0 +1,85 @@ +# Working with state in EvoX + +EvoX is designed around the stateful computation. + +There are two most fundamental classes, namely {class}`Stateful ` and {class}`State `. + +All class that involves stateful computation are inherented from `Stateful`. In EvoX, `Algorithm`, `Problem`, `Operator` and workflows are all stateful. + +## The idea behind the design + +```{image} /_static/hierarchical_state.svg +:alt: hierarchical state +:width: 400px +``` + +Here we have five different objects, and notice that they have a hierarchical structure. +To work with such structure, at each level we must "lift the state" by managing the states of child components. +So, the state at the `workflow` level must contains the state of both `algorithm` and `problem`, +and since the state at the `algorithm` level must contains the state of both operators, +the state `workflow` level actual need to handle states from all 5 components. + +However, it is frustrating to managing the hierarchy manually, and it is not good for modular design. +To solve this problem, we introduce `Stateful` and `State`. + +## An overview of Stateful + +In a `Stateful` class, +all immutable data are initialized in `__init__`, +the initial mutable state is generated in `setup`, +besides these two method and private methods(start with "\_"), +all other methods are wrapped with `use_state`. + +```python +class Foo(Stateful): + def __init__(self,): # required + pass + + def setup(self, key) -> State: # optional + pass + + def stateful_func(self, state, args) -> State: # wrapped with use_state + pass + + def _normal_func(self, args) -> vals: # not wrapped + pass +``` + +will be wrapped with `use_state` decorator. This decorator requires the method have the following signature: + +```python +def func(self, state: State, ...) -> Tuple[..., State] +``` + +which is common pattern in stateful computation. + +:::{warning} +Currently, for all user defined private methods, the name of the method should starts with `_`. +::: + +## An overview of State + +In EvoX `State` represents a tree of states, which stores the state of the current object and all child objects. + +## Combined together + +When combined together, +they will automatically go 1 level down in the tree of states, +and merge the subtree back to current level. + +So you could write code like this. + +```python +class FooWorkflow(Stateful): + ... + def step(self, state): + population, state = self.algorithm.ask(state) + fitness, state = self.problem.evaluate(state, population) + ... +``` + +Notice that, when calling the method `step`, +`state` is the state of the workflow, +but when calling `self.algorithm.ask`, +`state` behaves like the state of the algorithm, +and after the call, the state of the algorithm is automatically merged back into the state of the workflow. diff --git a/docs/source/guide/advanced/1-state.rst b/docs/source/guide/advanced/1-state.rst deleted file mode 100644 index 059c27db2..000000000 --- a/docs/source/guide/advanced/1-state.rst +++ /dev/null @@ -1,93 +0,0 @@ -========================== -Working with state in EvoX -========================== - -EvoX is designed around the stateful computation. - -There are two most fundamental classes, namely :class:`Stateful ` and :class:`State `. - -All class that involves stateful computation are inherented from ``Stateful``. In EvoX, ``Algorithm``, ``Problem``, ``Operator`` and workflows are all stateful. - -The idea behind the design -========================== - -.. image:: /_static/hierarchical_state.svg - :alt: hierarchical state - :width: 400px - -Here we have five different objects, and notice that they have a hierarchical structure. -To work with such structure, at each level we must "lift the state" by managing the states of child components. -So, the state at the ``workflow`` level must contains the state of both ``algorithm`` and ``problem``, -and since the state at the ``algorithm`` level must contains the state of both operators, -the state ``workflow`` level actual need to handle states from all 5 components. - -However, it is frustrating to managing the hierarchy manually, and it is not good for modular design. -To solve this problem, we introduce ``Stateful`` and ``State``. - - - -An overview of Stateful -======================= - -In a ``Stateful`` class, -all immutable data are initialized in ``__init__``, -the initial mutable state is generated in ``setup``, -besides these two method and private methods(start with "_"), -all other methods are wrapped with ``use_state``. - -.. code-block:: python - - class Foo(Stateful): - def __init__(self,): # required - pass - - def setup(self, key) -> State: # optional - pass - - def stateful_func(self, state, args) -> State: # wrapped with use_state - pass - - def _normal_func(self, args) -> vals: # not wrapped - pass - -will be wrapped with ``use_state`` decorator. This decorator requires the method have the following signature: - -.. code-block:: python - - def func(self, state: State, ...) -> Tuple[..., State] - -which is common pattern in stateful computation. - -.. warning:: - Currently, for all user defined private methods, the name of the method should starts with ``_``. - - -An overview of State -==================== - -In EvoX ``State`` represents a tree of states, which stores the state of the current object and all child objects. - - -Combined together -================= - -When combined together, -they will automatically go 1 level down in the tree of states, -and merge the subtree back to current level. - -So you could write code like this. - -.. code-block:: python - - class FooWorkflow(Stateful): - ... - def step(self, state): - population, state = self.algorithm.ask(state) - fitness, state = self.problem.evaluate(state, population) - ... - -Notice that, when calling the method ``step``, -``state`` is the state of the workflow, -but when calling ``self.algorithm.ask``, -``state`` behaves like the state of the algorithm, -and after the call, the state of the algorithm is automatically merged back into the state of the workflow. \ No newline at end of file diff --git a/docs/source/guide/advanced/2-jit-able.rst b/docs/source/guide/advanced/2-jit-able.md similarity index 58% rename from docs/source/guide/advanced/2-jit-able.rst rename to docs/source/guide/advanced/2-jit-able.md index 1d1f1a77f..bea92b3d7 100644 --- a/docs/source/guide/advanced/2-jit-able.rst +++ b/docs/source/guide/advanced/2-jit-able.md @@ -1,47 +1,37 @@ -=================== -Jit-able components -=================== +# Jit-able components -A common pitfall in jit -======================= +## A common pitfall in jit In JAX, it's hard to jump out of a jit-compiled function, meaning if you jit-compile one function, then all other functions used within this function must also be jit-compiled. For example, the follow code will result in compilation error -.. code-block:: python +```python +@jax.jit +def foo(x): + return bar(x) - @jax.jit - def foo(x): - return bar(x) +def bar(x): + return x[x > 0] # dynamic index, not jit-able +``` - def bar(x): - return x[x > 0] # dynamic index, not jit-able +Even though `bar` is not marked with `jax.jit`, it is still compiled as `foo` calls `bar`. +And since bar uses dynamic index, which is not compatible with `jax.jit`, an error will occur. -Even though ``bar`` is not marked with ``jax.jit``, it is still compiled as ``foo`` calls ``bar``. -And since bar uses dynamic index, which is not compatible with ``jax.jit``, an error will occur. - -Solution -======== +## Solution To solve is problem, it is common practice to jit-compile low level components, thus give high level components more freedom. In EvoX, we have some general rules on whether a function should be jit-able or not. -+-----------+----------+ | Component | jit-able | -+===========+==========+ +| --------- | -------- | | Workflow | Optional | -+-----------+----------+ | Algorithm | Yes | -+-----------+----------+ | Problem | Optional | -+-----------+----------+ | Operators | Yes | -+-----------+----------+ | Monitor | No | -+-----------+----------+ For standard workflow, one can jit compile when not using monitors and working with jit-able problems. But even though the workflow can be compiled, there isn't much performance gain. -For problems, it depends on the task. \ No newline at end of file +For problems, it depends on the task. diff --git a/docs/source/guide/advanced/3-custom-alg-pro.md b/docs/source/guide/advanced/3-custom-alg-pro.md new file mode 100644 index 000000000..7097eef87 --- /dev/null +++ b/docs/source/guide/advanced/3-custom-alg-pro.md @@ -0,0 +1,195 @@ +```{eval-rst} +.. role:: python(code) + :language: python + :class: highlight +``` + +# Custom algorithms and problems in EvoX + +In this chapter, we will introduce how to implement your own algorithm in EvoX. + +## The Algorithm Class + +The {class}`Algorithm ` class is inherented from {class}`Stateful `. +Besides the things in `Stateful`, your should also implement a `ask` and a `tell` method. +In total, there are four methods one need to implement. + +| Method | Signature | Usage | +| ------------ | --------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | +| \_\_init\_\_ | {python}`(self, ...)` | Initialize hyperparameters that are fixed though out the optimization process, for example, the `population size`. | +| setup | {python}`(self, RRNGKey) -> State` | Initialize mutable state, for example the `momentum`. | +| ask | {python}`(self, State) -> Array, State` | Gives a candidate population for evaluation. | +| tell | {python}`(self, State, Array) -> State` | Receive the fitness for the candidate population and update the algorithm's state. | + +### Migrate from traditional EC library + +In traditional EC library, algorithm usually calls the objective function internally, which gives the following layout + +``` +Algorithm +| ++--Problem +``` + +But in EvoX, we have a flat layout + +``` +Algorithm.ask -- Problem.evaluate -- Algorithm.tell +``` + +Here is a pseudocode of a genetic algorithm. + +```python +Set hyperparameters +Generate the initial population +Do + Generate Offspring + Selection + Crossover + Mutation + Compute fitness + Replace the population +Until stopping criterion +``` + +And Here is what each part of the algorithm correspond to in EvoX. + +```python +Set hyperparameters # __init__ +Generate the initial population # setup +Do + # ask + Generate Offspring + Mating Selection + Crossover + Mutation + + # problem.evaluate (not part of the algorithm) + Compute fitness + + # tell + Survivor Selection +Until stopping criterion +``` + +## The Problem Class + +The Problem class is quite simple, beside `__init__` and `setup`, the only required the method is `evaluate`. + +### Migrate from traditional EC library + +There is one thing to notice here, `evaluate` is a stateful function, meaning it should accept a state and return a new state. +So, if you are working with numerical benchmark functions, which don't need to statefule, +you can simply ignore the state, but remember that you still have to use this stateful interface. + +```{eval-rst} ++----------+------------------------------------------------+-------------------------------------------------------+ +| Method | Signature | Usage | ++----------+------------------------------------------------+-------------------------------------------------------+ +| __init__ | :python:`(self, ...)` | Initialize the settings of the problem. | ++----------+------------------------------------------------+-------------------------------------------------------+ +| setup | :python:`(self, RRNGKey) -> State` | Initialize mutable state of this problem. | ++----------+------------------------------------------------+-------------------------------------------------------+ +| evaluate | :python:`(self, State, Array) -> Array, State` | Evaluate the fitness of the given candidate solution. | ++----------+------------------------------------------------+-------------------------------------------------------+ +``` + +### More on the problem's state + +If you still wonders what the problem's state actually do, here are the explanations. + +Unlike numerical benchmark functions, real-life problems are more complex, and may require stateful computations. +Here are some examples: + +- When dealing with ANN training, we often have training, validation and testing phase. + This implies that the same solution could have different fitness values during different phases. + So clearly, we can't model the `evaluate` as a stateless pure function any more. + To implement this mechanism, simple put an value in the state to indicate the phase. +- Virtual batch norm is a effective trick especially when dealing with RL tasks. + To implement this mechanism, the problem must be stateful, + as the problem have to remember the initial batch norm parameters during the first run. + +## Example + +Here we give an exmaple of implementing the OneMax problem, along with a genetic algorithm that solves this problem. +The problem itself is straight forward, the fitness is defined as the sum of every digits in a fixed-length bitstring. +For example, "100111" gives 4 and "000101" gives 2. + +Let's starts with implementing the OneMax problem. +In JAX a bitstring can be easily represented with a tensor of type bool. + +```python +import jax.numpy as jnp +from evox import Problem, jit_class + + +@jit_class +class OneMax(Problem): + def __init__(self, neg_fitness=True) -> None: + super().__init__() + self.neg_fitess = neg_fitness + + def evaluate(self, state, bitstrings): + # bitstrings has shape (pop_size, num_bits) + # so sum along the axis 1. + fitness = jnp.sum(bitstrings, axis=1) + # Since in EvoX, algorithms try to minimize the fitness + # so return the negitive value. + if self.neg_fitess: + fitness = -fitness + return fitness, state +``` + +Then we implement a genetic algorithm that uses bitflip mutation and one-point crossover. + +```python +@jit_class +class ExampleGA(Algorithm): + def __init__(self, pop_size, ndim, flip_prob): + super().__init__() + # those are hyperparameters that stay fixed. + self.pop_size = pop_size + self.ndim = ndim + # the probability of fliping each bit + self.flip_prob = flip_prob + + def setup(self, key): + # initialize the state + # state are mutable data like the population, offsprings + # the population is randomly initialized. + # we don't have any offspring now, but initialize it as a placeholder + # because jax want static shaped arrays. + key, subkey = random.split(key) + pop = random.uniform(subkey, (self.pop_size, self.ndim)) < 0.5 + return State( + pop=pop, + offsprings=jnp.empty((self.pop_size * 2, self.ndim)), + fit=jnp.full((self.pop_size,), jnp.inf), + key=key, + ) + + def ask(self, state): + key, mut_key, x_key = random.split(state.key, 3) + # here we do mutation and crossover (reproduction) + # for simplicity, we didn't use any mating selections + # so the offspring is twice as large as the population + offsprings = jnp.concatenate( + ( + mutation.bitflip(mut_key, state.pop, self.flip_prob), + crossover.one_point(x_key, state.pop), + ), + axis=0, + ) + # return the candidate solution and update the state + return offsprings, state.update(offsprings=offsprings, key=key) + + def tell(self, state, fitness): + # here we do selection + merged_pop = jnp.concatenate([state.pop, state.offsprings]) + merged_fit = jnp.concatenate([state.fit, fitness]) + new_pop, new_fit = selection.topk_fit(merged_pop, merged_fit, self.pop_size) + # replace the old population + return state.update(pop=new_pop, fit=new_fit) +``` + +Now, you can assemble a workflow and run it. diff --git a/docs/source/guide/advanced/3-custom-alg-pro.rst b/docs/source/guide/advanced/3-custom-alg-pro.rst deleted file mode 100644 index 972cfafb1..000000000 --- a/docs/source/guide/advanced/3-custom-alg-pro.rst +++ /dev/null @@ -1,209 +0,0 @@ -.. role:: python(code) - :language: python - :class: highlight - -====================================== -Custom algorithms and problems in EvoX -====================================== - -In this chapter, we will introduce how to implement your own algorithm in EvoX. - -The Algorithm Class -=================== - -The :class:`Algorithm ` class is inherented from :class:`Stateful `. -Besides the things in ``Stateful``, your should also implement a ``ask`` and a ``tell`` method. -In total, there are four methods one need to implement. - -+----------+-----------------------------------------+------------------------------------------------------------------------------------+ -| Method | Signature | Usage | -+==========+=========================================+====================================================================================+ -| __init__ | :python:`(self, ...)` | Initialize hyperparameters that are fixed though out the optimization process, | -| | | for example, the ``population size``. | -+----------+-----------------------------------------+------------------------------------------------------------------------------------+ -| setup | :python:`(self, RRNGKey) -> State` | Initialize mutable state, for example the ``momentum``. | -+----------+-----------------------------------------+------------------------------------------------------------------------------------+ -| ask | :python:`(self, State) -> Array, State` | Gives a candidate population for evaluation. | -+----------+-----------------------------------------+------------------------------------------------------------------------------------+ -| tell | :python:`(self, State, Array) -> State` | Receive the fitness for the candidate population and update the algorithm's state. | -+----------+-----------------------------------------+------------------------------------------------------------------------------------+ - - -Migrate from traditional EC library ------------------------------------ - -In traditional EC library, algorithm usually calls the objective function internally, which gives the following layout - -.. code-block:: - - Algorithm - | - +--Problem - -But in EvoX, we have a flat layout - -.. code-block:: - - Algorithm.ask -- Problem.evaluate -- Algorithm.tell - - -Here is a pseudocode of a genetic algorithm. - -.. code-block:: python - - Set hyperparameters - Generate the initial population - Do - Generate Offspring - Selection - Crossover - Mutation - Compute fitness - Replace the population - Until stopping criterion - -And Here is what each part of the algorithm correspond to in EvoX. - -.. code-block:: python - - Set hyperparameters # __init__ - Generate the initial population # setup - Do - # ask - Generate Offspring - Mating Selection - Crossover - Mutation - - # problem.evaluate (not part of the algorithm) - Compute fitness - - # tell - Survivor Selection - Until stopping criterion - -The Problem Class -================= - -The Problem class is quite simple, beside ``__init__`` and ``setup``, the only required the method is ``evaluate``. - -Migrate from traditional EC library ------------------------------------ - -There is one thing to notice here, ``evaluate`` is a stateful function, meaning it should accept a state and return a new state. -So, if you are working with numerical benchmark functions, which don't need to statefule, -you can simply ignore the state, but remember that you still have to use this stateful interface. - -+----------+------------------------------------------------+-------------------------------------------------------+ -| Method | Signature | Usage | -+----------+------------------------------------------------+-------------------------------------------------------+ -| __init__ | :python:`(self, ...)` | Initialize the settings of the problem. | -+----------+------------------------------------------------+-------------------------------------------------------+ -| setup | :python:`(self, RRNGKey) -> State` | Initialize mutable state of this problem. | -+----------+------------------------------------------------+-------------------------------------------------------+ -| evaluate | :python:`(self, State, Array) -> Array, State` | Evaluate the fitness of the given candidate solution. | -+----------+------------------------------------------------+-------------------------------------------------------+ - -More on the problem's state ---------------------------- - -If you still wonders what the problem's state actually do, here are the explanations. - -Unlike numerical benchmark functions, real-life problems are more complex, and may require stateful computations. -Here are some examples: - -* When dealing with ANN training, we often have training, validation and testing phase. - This implies that the same solution could have different fitness values during different phases. - So clearly, we can't model the `evaluate` as a stateless pure function any more. - To implement this mechanism, simple put an value in the state to indicate the phase. -* Virtual batch norm is a effective trick especially when dealing with RL tasks. - To implement this mechanism, the problem must be stateful, - as the problem have to remember the initial batch norm parameters during the first run. - - -Example -======= - -Here we give an exmaple of implementing the OneMax problem, along with a genetic algorithm that solves this problem. -The problem itself is straight forward, the fitness is defined as the sum of every digits in a fixed-length bitstring. -For example, "100111" gives 4 and "000101" gives 2. - -Let's starts with implementing the OneMax problem. -In JAX a bitstring can be easily represented with a tensor of type bool. - -.. code-block:: python - - import jax.numpy as jnp - from evox import Problem, jit_class - - - @jit_class - class OneMax(Problem): - def __init__(self, neg_fitness=True) -> None: - super().__init__() - self.neg_fitess = neg_fitness - - def evaluate(self, state, bitstrings): - # bitstrings has shape (pop_size, num_bits) - # so sum along the axis 1. - fitness = jnp.sum(bitstrings, axis=1) - # Since in EvoX, algorithms try to minimize the fitness - # so return the negitive value. - if self.neg_fitess: - fitness = -fitness - return fitness, state - - -Then we implement a genetic algorithm that uses bitflip mutation and one-point crossover. - -.. code-block:: python - - @jit_class - class ExampleGA(Algorithm): - def __init__(self, pop_size, ndim, flip_prob): - super().__init__() - # those are hyperparameters that stay fixed. - self.pop_size = pop_size - self.ndim = ndim - # the probability of fliping each bit - self.flip_prob = flip_prob - - def setup(self, key): - # initialize the state - # state are mutable data like the population, offsprings - # the population is randomly initialized. - # we don't have any offspring now, but initialize it as a placeholder - # because jax want static shaped arrays. - key, subkey = random.split(key) - pop = random.uniform(subkey, (self.pop_size, self.ndim)) < 0.5 - return State( - pop=pop, - offsprings=jnp.empty((self.pop_size * 2, self.ndim)), - fit=jnp.full((self.pop_size,), jnp.inf), - key=key, - ) - - def ask(self, state): - key, mut_key, x_key = random.split(state.key, 3) - # here we do mutation and crossover (reproduction) - # for simplicity, we didn't use any mating selections - # so the offspring is twice as large as the population - offsprings = jnp.concatenate( - ( - mutation.bitflip(mut_key, state.pop, self.flip_prob), - crossover.one_point(x_key, state.pop), - ), - axis=0, - ) - # return the candidate solution and update the state - return offsprings, state.update(offsprings=offsprings, key=key) - - def tell(self, state, fitness): - # here we do selection - merged_pop = jnp.concatenate([state.pop, state.offsprings]) - merged_fit = jnp.concatenate([state.fit, fitness]) - new_pop, new_fit = selection.topk_fit(merged_pop, merged_fit, self.pop_size) - # replace the old population - return state.update(pop=new_pop, fit=new_fit) - -Now, you can assemble a workflow and run it. \ No newline at end of file diff --git a/docs/source/guide/advanced/4-container.rst b/docs/source/guide/advanced/4-container.md similarity index 52% rename from docs/source/guide/advanced/4-container.rst rename to docs/source/guide/advanced/4-container.md index 6a991b131..b69787fdd 100644 --- a/docs/source/guide/advanced/4-container.rst +++ b/docs/source/guide/advanced/4-container.md @@ -1,22 +1,18 @@ -==================== -Container Algorithms -==================== +# Container Algorithms Container algorithms are a special type of algorithms that works by containing other algorithms and cannot work on its own. Container algorithms can be used to compose a series of normal algorithms together. -Working with expensive algorithms -================================= +## Working with expensive algorithms -Many algorithms are expensive in term of space or time. For example, CMA-ES requires :math:`O(N^2)` space. +Many algorithms are expensive in term of space or time. For example, CMA-ES requires $O(N^2)$ space. Thus, it is costly to run CMA-ES on high-dimension problems. Sep-CMA-ES scales better, but sacrifice the performance. That's where container algorithm comes in. -With it, we can easily construct a variant of CMA-ES that uses :math:`O((\frac{N}{M})^2)` space, where :math:`M` is the number of block. +With it, we can easily construct a variant of CMA-ES that uses $O((\frac{N}{M})^2)$ space, where $M$ is the number of block. This variant is a balance between the normal CMA-ES and Sep-CMA-ES. -Working with PyTree -=================== +## Working with PyTree Usually, algorithms expect the decision variables to be in the form of a 1D-vector. PyTrees are tree-like structures that are not directly compatible with normal algorithms. @@ -25,15 +21,14 @@ So, there are two solutions out there: 1. Flatten the PyTree to 1D-vector. 2. Use a specialized algorithm that work with PyTree directly. -Solution 1 is called ``adapter`` in EvoX, which is quite simple, but we are not talking about this here. +Solution 1 is called `adapter` in EvoX, which is quite simple, but we are not talking about this here. Solution 2 seems more complicated, but the advantage is that the structural information is preserved, meaning the algorithm could see the tree structure and apply some type of heuristic here. -Cooperative Coevolution -======================= +## Cooperative Coevolution We offer Cooperative Coevolution (CC) framework for all algorithms. -Currently, there are two types of CC container in EvoX, :class:`evox.algorithms.Coevolution` and :class:`evox.algorithms.VectorizedCoevolution`. -The difference is that ``VectorizedCoevolution`` update all sub-populations at the same time in each iteration, -but ``Coevolution`` follows traditional approach that update a single sub-populations at each iteration. -Thus ``VectorizedCoevolution`` is faster, but ``Coevolution`` could be better in terms of best fitness with a limited number of evaluations. +Currently, there are two types of CC container in EvoX, {class}`evox.algorithms.Coevolution` and {class}`evox.algorithms.VectorizedCoevolution`. +The difference is that `VectorizedCoevolution` update all sub-populations at the same time in each iteration, +but `Coevolution` follows traditional approach that update a single sub-populations at each iteration. +Thus `VectorizedCoevolution` is faster, but `Coevolution` could be better in terms of best fitness with a limited number of evaluations. diff --git a/docs/source/guide/advanced/index.md b/docs/source/guide/advanced/index.md new file mode 100644 index 000000000..adbaf20f5 --- /dev/null +++ b/docs/source/guide/advanced/index.md @@ -0,0 +1,10 @@ +# Advanced Tutorial + +```{toctree} +:maxdepth: 1 + +1-state +2-jit-able +3-custom-alg-pro +4-container +``` diff --git a/docs/source/guide/advanced/index.rst b/docs/source/guide/advanced/index.rst deleted file mode 100644 index e90d7e216..000000000 --- a/docs/source/guide/advanced/index.rst +++ /dev/null @@ -1,11 +0,0 @@ -================= -Advanced Tutorial -================= - -.. toctree:: - :maxdepth: 1 - - 1-state - 2-jit-able - 3-custom-alg-pro - 4-container \ No newline at end of file diff --git a/docs/source/guide/basics/2-problems.md b/docs/source/guide/basics/2-problems.md new file mode 100644 index 000000000..ce5946477 --- /dev/null +++ b/docs/source/guide/basics/2-problems.md @@ -0,0 +1,61 @@ +# Working with extended applications + +Working with extended applications in EvoX is easy. + +## Neuroevolution + +EvoX currently supports training ANN with torchvision's datasets via {class}`TorchvisionDataset `. + +```python +from evox.problems.neuroevolution import TorchvisionDataset + +problem = TorchvisionDataset( + root="./", # where to download the dataset + forward_func=, + batch_size=512, # the batchsize + num_passes=2, # splite the batch and calculate using 2 passes + dataset_name="cifar10" # the name of the dataset +) +``` + +In the above example, we created a problem using `cifar10` dataset, +each individual in the population will be evaluated with a batch size of 512, and since `num_passes` is 2, +the batch will be calculated in 2 passes and each pass has a mini-batch size of `512 / 2 = 256`. + +## RL + +EvoX currently supports rl environments from Gym. + +```python +from evox.problems.neuroevolution import Gym +problem = Gym( + env_name="ALE/Pong-v5", # the environment's name + env_options={"full_action_space": False}, # the options passes to the environment + policy=, + num_workers=16, # number of processes + env_per_worker=4, # the number of environments each process holds + controller_options={ # the options that passes to ray + "num_cpus": 1, + "num_gpus": 0, + }, + worker_options={ # the options that passes to ray + "num_cpus": 1, "num_gpus": 1 / 16 + }, + batch_policy=False, +) +``` + +:::{tip} +`controller_options` and `worker_options` are directly passed down to Ray, +usually, you can leave `controller_options` blank because there isn't much computation happen there. +`worker_options` can be used to control how much cpu and gpu each worker gets. + +For example: + +```python +worker_options={ + "num_cpus": 2, + "num_gpus": 0, +} +``` +::: diff --git a/docs/source/guide/basics/2-problems.rst b/docs/source/guide/basics/2-problems.rst deleted file mode 100644 index 3f3fc08ac..000000000 --- a/docs/source/guide/basics/2-problems.rst +++ /dev/null @@ -1,64 +0,0 @@ -================================== -Working with extended applications -================================== - -Working with extended applications in EvoX is easy. - -Neuroevolution -============== - -EvoX currently supports training ANN with torchvision's datasets via :class:`TorchvisionDataset `. - -.. code-block:: python - - from evox.problems.neuroevolution import TorchvisionDataset - - problem = TorchvisionDataset( - root="./", # where to download the dataset - forward_func=, - batch_size=512, # the batchsize - num_passes=2, # splite the batch and calculate using 2 passes - dataset_name="cifar10" # the name of the dataset - ) - -In the above example, we created a problem using ``cifar10`` dataset, -each individual in the population will be evaluated with a batch size of 512, and since ``num_passes`` is 2, -the batch will be calculated in 2 passes and each pass has a mini-batch size of ``512 / 2 = 256``. - -RL -=== - -EvoX currently supports rl environments from Gym. - -.. code-block:: python - - from evox.problems.neuroevolution import Gym - problem = Gym( - env_name="ALE/Pong-v5", # the environment's name - env_options={"full_action_space": False}, # the options passes to the environment - policy=, - num_workers=16, # number of processes - env_per_worker=4, # the number of environments each process holds - controller_options={ # the options that passes to ray - "num_cpus": 1, - "num_gpus": 0, - }, - worker_options={ # the options that passes to ray - "num_cpus": 1, "num_gpus": 1 / 16 - }, - batch_policy=False, - ) - -.. tip:: - ``controller_options`` and ``worker_options`` are directly passed down to Ray, - usually, you can leave ``controller_options`` blank because there isn't much computation happen there. - ``worker_options`` can be used to control how much cpu and gpu each worker gets. - - For example: - - .. code-block:: python - - worker_options={ - "num_cpus": 2, - "num_gpus": 0, - } diff --git a/docs/source/guide/basics/3-distributed.md b/docs/source/guide/basics/3-distributed.md new file mode 100644 index 000000000..664c90a8a --- /dev/null +++ b/docs/source/guide/basics/3-distributed.md @@ -0,0 +1,26 @@ +# Distribute the workflow + +To scale the workflow using multiple machines, use the {class}`DistributedWorkflow ` instead of StdWorkflow. + +```python +algorithm = +problem = + +from evox.workflows import DistributedWorkflow + +workflow = DistributedWorkflow( + algorithm=algorithm, + problem=problem, + pop_size=100, # the actual population size used by the algorithm + num_workers=4, # the number of machines + options={ # the options that passes to ray + "num_gpus": 1 + } +) +``` + +:::{tip} +It is recommanded that one set the environment variable `XLA_PYTHON_CLIENT_PREALLOCATE=false`. +This variable control disables the GPU memory preallocation, otherwise running multiple JAX processes may cause OOM. +For more information, please refer to [JAX's documentation](https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html) on this matter. +::: diff --git a/docs/source/guide/basics/3-distributed.rst b/docs/source/guide/basics/3-distributed.rst deleted file mode 100644 index f7c692645..000000000 --- a/docs/source/guide/basics/3-distributed.rst +++ /dev/null @@ -1,27 +0,0 @@ -======================= -Distribute the workflow -======================= - -To scale the workflow using multiple machines, use the :class:`DistributedWorkflow ` instead of StdWorkflow. - -.. code-block:: python - - algorithm = - problem = - - from evox.workflows import DistributedWorkflow - - workflow = DistributedWorkflow( - algorithm=algorithm, - problem=problem, - pop_size=100, # the actual population size used by the algorithm - num_workers=4, # the number of machines - options={ # the options that passes to ray - "num_gpus": 1 - } - ) - -.. tip:: - It is recommanded that one set the environment variable ``XLA_PYTHON_CLIENT_PREALLOCATE=false``. - This variable control disables the GPU memory preallocation, otherwise running multiple JAX processes may cause OOM. - For more information, please refer to `JAX's documentation `_ on this matter. diff --git a/docs/source/guide/basics/index.md b/docs/source/guide/basics/index.md new file mode 100644 index 000000000..daeb57e03 --- /dev/null +++ b/docs/source/guide/basics/index.md @@ -0,0 +1,9 @@ +# Getting Started + +```{toctree} +:maxdepth: 1 + +1-start +2-problems +3-distributed +``` diff --git a/docs/source/guide/basics/index.rst b/docs/source/guide/basics/index.rst deleted file mode 100644 index 94ca8e273..000000000 --- a/docs/source/guide/basics/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -=============== -Getting Started -=============== - -.. toctree:: - :maxdepth: 1 - - 1-start - 2-problems - 3-distributed \ No newline at end of file diff --git a/docs/source/guide/index.md b/docs/source/guide/index.md new file mode 100644 index 000000000..3677ff7ad --- /dev/null +++ b/docs/source/guide/index.md @@ -0,0 +1,9 @@ +# EvoX's guide! + +```{toctree} +:maxdepth: 2 + +install +basics/index +advanced/index +``` diff --git a/docs/source/guide/index.rst b/docs/source/guide/index.rst deleted file mode 100644 index 253fbe984..000000000 --- a/docs/source/guide/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -================ -EvoX's guide! -================ - -.. toctree:: - :maxdepth: 2 - - install - basics/index - advanced/index \ No newline at end of file diff --git a/docs/source/guide/install.md b/docs/source/guide/install.md new file mode 100644 index 000000000..550d49ba4 --- /dev/null +++ b/docs/source/guide/install.md @@ -0,0 +1,20 @@ +# Install EvoX + +EvoX is available at Pypi and can be installed via: + +```bash +pip install evox +``` + +To install EvoX with optional dependencies: + +```bash +pip install evox[,] +``` + +available features are `gym`, `neuroevolution`, `distributed`, and `full` which concludes all features. +For example, to install EvoX with all features, do: + +```bash +pip install evox[full] +``` diff --git a/docs/source/guide/install.rst b/docs/source/guide/install.rst deleted file mode 100644 index d9797be0f..000000000 --- a/docs/source/guide/install.rst +++ /dev/null @@ -1,22 +0,0 @@ -=============== -Install EvoX -=============== - -EvoX is available at Pypi and can be installed via: - -.. code:: bash - - pip install evox - -To install EvoX with optional dependencies: - -.. code:: bash - - pip install evox[,] - -available features are ``gym``, ``neuroevolution``, ``distributed``, and ``full`` which concludes all features. -For example, to install EvoX with all features, do: - -.. code:: bash - - pip install evox[full] \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.md similarity index 56% rename from docs/source/index.rst rename to docs/source/index.md index 0dabc174e..9d83cd394 100644 --- a/docs/source/index.rst +++ b/docs/source/index.md @@ -1,19 +1,14 @@ -.. evox documentation master file, created by - sphinx-quickstart on Thu Jul 28 19:12:56 2022. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. +# Welcome to EvoX's documentation! -================================ -Welcome to EvoX's documentation! -================================ +```{toctree} +:caption: 'Contents:' +:maxdepth: 1 -.. toctree:: - :maxdepth: 1 - :caption: Contents: - - User Guide - API reference +User Guide +API reference +``` +```{eval-rst} .. grid:: 4 :gutter: 1 1 2 4 :padding: 1 @@ -42,10 +37,10 @@ Welcome to EvoX's documentation! :link: api/problems/index :link-type: doc +``` -Indices and tables -================== +## Indices and tables -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` +- {ref}`genindex` +- {ref}`modindex` +- {ref}`search` diff --git a/examples/cmaes_classic_control.py b/examples/cmaes_classic_control.py index 9b50e9a0e..7cc3a01c7 100644 --- a/examples/cmaes_classic_control.py +++ b/examples/cmaes_classic_control.py @@ -66,11 +66,11 @@ def __call__(self, x): state = workflow.init(workflow_key) # run the workflow for 100 steps for i in range(100): - print(monitor.get_min_fitness()) + print(monitor.get_best_fitness()) state = workflow.step(state) sample_pop, state = workflow.sample(state) # problem._render(state.get_child_state("problem"), adapter.to_tree(sample_pop[0])) -min_fitness = monitor.get_min_fitness() +min_fitness = monitor.get_best_fitness() print(min_fitness) diff --git a/examples/pgpe_pong.py b/examples/pgpe_pong.py index 89c90a4e9..875b8719c 100644 --- a/examples/pgpe_pong.py +++ b/examples/pgpe_pong.py @@ -70,11 +70,11 @@ def __call__(self, img): state = workflow.init(workflow_key) # run the workflow for 100 steps for i in range(10): - print(monitor.get_min_fitness()) + print(monitor.get_best_fitness()) state = workflow.step(state) sample_pop, state = workflow.sample(state) # problem._render(adapter.to_tree(sample_pop[0]), ale_render_mode="human") # the result should be close to 0 -min_fitness = monitor.get_min_fitness() +min_fitness = monitor.get_best_fitness() print(min_fitness) diff --git a/flake.lock b/flake.lock index 0818cbe53..e708c10cb 100644 --- a/flake.lock +++ b/flake.lock @@ -3,11 +3,11 @@ "flake-compat": { "flake": false, "locked": { - "lastModified": 1673956053, - "narHash": "sha256-4gtG9iQuiKITOjNQQeQIpoIB6b16fm+504Ch3sNKLd8=", + "lastModified": 1696426674, + "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", "owner": "edolstra", "repo": "flake-compat", - "rev": "35bb57c0c8d8b62bbfd284272c928ceb64ddbde9", + "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", "type": "github" }, "original": { @@ -18,11 +18,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1692447944, - "narHash": "sha256-fkJGNjEmTPvqBs215EQU4r9ivecV5Qge5cF/QDLVn3U=", + "lastModified": 1696879762, + "narHash": "sha256-Ud6bH4DMcYHUDKavNMxAhcIpDGgHMyL/yaDEAVSImQY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "d680ded26da5cf104dd2735a51e88d2d8f487b4d", + "rev": "f99e5f03cc0aa231ab5950a15ed02afec45ed51a", "type": "github" }, "original": { @@ -58,11 +58,11 @@ "systems": "systems" }, "locked": { - "lastModified": 1689068808, - "narHash": "sha256-6ixXo3wt24N/melDWjq70UuHQLxGV8jZvooRanIHXw0=", + "lastModified": 1694529238, + "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=", "owner": "numtide", "repo": "flake-utils", - "rev": "919d646de7be200f3bf08cb76ae1f09402b6f9b4", + "rev": "ff7b65b44d01cf9ba6a71320833626af21126384", "type": "github" }, "original": { diff --git a/requirements/docs-requirements.txt b/requirements/docs-requirements.txt index e28687ba4..197e63882 100644 --- a/requirements/docs-requirements.txt +++ b/requirements/docs-requirements.txt @@ -7,7 +7,8 @@ pyarrow # for docs generation pydata-sphinx-theme > 0.13 sphinx -numpydoc +myst-nb +nbsphinx myst-parser sphinx_copybutton sphinx_design diff --git a/requirements/test-requirements.txt b/requirements/test-requirements.txt index f606275af..05ddd16a1 100644 --- a/requirements/test-requirements.txt +++ b/requirements/test-requirements.txt @@ -6,4 +6,5 @@ pytest >= 6.0.0 gymnasium >= 0.29.0 ray >= 2.0.0 torch >= 1.0.0 -torchvision >= 0.1.0 \ No newline at end of file +torchvision >= 0.1.0 +envpool >= 0.8.0 diff --git a/src/__init__.py b/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/evox/algorithms/mo/__init__.py b/src/evox/algorithms/mo/__init__.py index a3747156f..735e988c3 100644 --- a/src/evox/algorithms/mo/__init__.py +++ b/src/evox/algorithms/mo/__init__.py @@ -14,3 +14,5 @@ from .sra import SRA from .tdea import TDEA from .bce_ibea import BCEIBEA +from .lmocso import LMOCSO + diff --git a/src/evox/algorithms/mo/hype.py b/src/evox/algorithms/mo/hype.py index 367f4b1ce..68cc97949 100644 --- a/src/evox/algorithms/mo/hype.py +++ b/src/evox/algorithms/mo/hype.py @@ -17,47 +17,36 @@ from evox.operators import selection, mutation, crossover, non_dominated_sort -@partial(jax.jit, static_argnums=[0, 1]) -def calculate_alpha(N, k): - alpha = jnp.zeros(N) - - for i in range(1, k + 1): - num = jnp.prod((k - jnp.arange(1, i)) / (N - jnp.arange(1, i))) - alpha = alpha.at[i - 1].set(num / i) - return alpha - - @partial(jax.jit, static_argnums=[2, 3]) def cal_hv(points, ref, k, n_sample, key): n, m = jnp.shape(points) - alpha = calculate_alpha(n, k) + + # hit in alpha relevant partition + alpha = jnp.cumprod( + jnp.r_[1, (k - jnp.arange(1, n)) / (n - jnp.arange(1, n))] + ) / jnp.arange(1, n + 1) f_min = jnp.min(points, axis=0) - s = jax.random.uniform(key, shape=(n_sample, m), minval=f_min, maxval=ref) + samples = jax.random.uniform(key, shape=(n_sample, m), minval=f_min, maxval=ref) - pds = jnp.zeros((n, n_sample), dtype=bool) + # update hypervolume estimates ds = jnp.zeros((n_sample,)) - - def body_fun1(i, vals): - pds, ds = vals - x = jnp.sum((jnp.tile(points[i, :], (n_sample, 1)) - s) <= 0, axis=1) == m - pds = pds.at[i].set(jnp.where(x, True, pds[i])) - ds = jnp.where(x, ds + 1, ds) - return pds, ds - - pds, ds = jax.lax.fori_loop(0, n, body_fun1, (pds, ds)) - ds = ds - 1 - - f = jnp.zeros((n,)) - - def body_fun2(pd): - temp = jnp.where(pd, ds, -1).astype(int) + pds = jax.vmap( + lambda x: jnp.sum((jnp.tile(x, (n_sample, 1)) - samples) <= 0, axis=1) == m, + in_axes=0, + out_axes=0, + )(points) + ds = jnp.sum(jnp.where(pds, ds + 1, ds), axis=0) + ds = jnp.where(ds == 0, ds, ds - 1) + + def cal_f(val): + temp = jnp.where(val, ds, -1).astype(int) value = jnp.where(temp != -1, alpha[temp], 0) value = jnp.sum(value) return value - - f = jax.vmap(body_fun2)(pds) + + f = jax.vmap(cal_f, in_axes=0, out_axes=0)(pds) f = f * jnp.prod(ref - f_min) / n_sample return f diff --git a/src/evox/algorithms/mo/ibea.py b/src/evox/algorithms/mo/ibea.py index cf2a741d8..ba2306ce8 100644 --- a/src/evox/algorithms/mo/ibea.py +++ b/src/evox/algorithms/mo/ibea.py @@ -37,6 +37,9 @@ class IBEA(Algorithm): """IBEA algorithm link: https://link.springer.com/chapter/10.1007/978-3-540-30217-9_84 + + Args: + kappa: fitness scaling factor. Default: 0.05 """ def __init__( @@ -111,27 +114,31 @@ def _tell_normal(self, state, fitness): merged_pop = jnp.concatenate([state.population, state.next_generation], axis=0) merged_obj = jnp.concatenate([state.fitness, fitness], axis=0) - n = jnp.shape(merged_pop)[0] merged_fitness, I, C = cal_fitness(merged_obj, self.kappa) - next_ind = jnp.arange(n) - vals = (next_ind, merged_fitness) - - def body_fun(i, vals): - next_ind, merged_fitness = vals - x = jnp.argmin(merged_fitness) - merged_fitness += jnp.exp(-I[x, :] / C[x] / self.kappa) - merged_fitness = merged_fitness.at[x].set(jnp.max(merged_fitness)) - next_ind = next_ind.at[x].set(-1) - return (next_ind, merged_fitness) - - next_ind, merged_fitness = jax.lax.fori_loop(0, self.pop_size, body_fun, vals) - - ind = jnp.where(next_ind != -1, size=n, fill_value=-1)[0] - ind_n = ind[0 : self.pop_size] - - survivor = merged_pop[ind_n] - survivor_fitness = merged_obj[ind_n] + # Different from the original paper, the selection here is directly through fitness. + next_ind = jnp.argsort(-merged_fitness)[0: self.pop_size] + + # The following code is from the original paper's implementation + # and is kept for reference purposes but is not being used in this version. + # n = jnp.shape(merged_pop)[0] + # next_ind = jnp.arange(n) + # vals = (next_ind, merged_fitness) + # def body_fun(i, vals): + # next_ind, merged_fitness = vals + # x = jnp.argmin(merged_fitness) + # merged_fitness += jnp.exp(-I[x, :] / C[x] / self.kappa) + # merged_fitness = merged_fitness.at[x].set(jnp.max(merged_fitness)) + # next_ind = next_ind.at[x].set(-1) + # return (next_ind, merged_fitness) + # + # next_ind, merged_fitness = jax.lax.fori_loop(0, self.pop_size, body_fun, vals) + # + # next_ind = jnp.where(next_ind != -1, size=n, fill_value=-1)[0] + # next_ind = next_ind[0: self.pop_size] + + survivor = merged_pop[next_ind] + survivor_fitness = merged_obj[next_ind] state = state.update(population=survivor, fitness=survivor_fitness) diff --git a/src/evox/algorithms/mo/lmocso.py b/src/evox/algorithms/mo/lmocso.py new file mode 100644 index 000000000..a8a186eed --- /dev/null +++ b/src/evox/algorithms/mo/lmocso.py @@ -0,0 +1,191 @@ +# -------------------------------------------------------------------------------------- +# 1. LMOCSO algorithm is described in the following papers: +# +# Title: Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm +# Optimizer +# Link: https://ieeexplore.ieee.org/document/8681243 + +# 2. This code has been inspired by PlatEMO. +# More information about PlatEMO can be found at the following URL: +# GitHub Link: https://github.com/BIMK/PlatEMO +# -------------------------------------------------------------------------------------- + +import jax +import jax.numpy as jnp + +from evox.operators import mutation, selection +from evox.operators.sampling import LatinHypercubeSampling +from evox import Algorithm, State, jit_class + + +@jax.jit +def cal_fitness(obj): + # Calculate the fitness by shift-based density + n = jnp.shape(obj)[0] + f_max = jnp.max(obj, axis=0) + f_min = jnp.min(obj, axis=0) + f = (obj - jnp.tile(f_min, (n, 1))) / jnp.tile(f_max - f_min, (n, 1)) + + s_obj = jax.vmap(lambda x: jnp.maximum(f, x))(f) + + def shifted_distance(f1, f2): + s_f = jax.vmap(lambda x, y: jnp.linalg.norm(x - y), in_axes=(None, 0))(f1, f2) + return s_f + + dis = jax.vmap(shifted_distance, in_axes=(0, 0))(f, s_obj) + dis = jnp.where(jnp.eye(n), jnp.inf, dis) + + fitness = jnp.min(dis, axis=1) + + return fitness + + +@jit_class +class LMOCSO(Algorithm): + """ + LMOCSO algorithm + + link: https://ieeexplore.ieee.org/document/8681243 + + Args: + alpha : The parameter controlling the rate of change of penalty. Defaults to 2. + max_gen : The maximum number of generations. Defaults to 100. If the number of iterations + is not 100, change the value based on the actual value. + """ + + def __init__( + self, + n_objs, + lb, + ub, + pop_size, + alpha=2, + max_gen=100, + selection_op=None, + mutation_op=None, + ): + self.n_objs = n_objs + self.dim = lb.shape[0] + self.lb = lb + self.ub = ub + self.pop_size = pop_size + self.alpha = alpha + self.max_gen = max_gen + + self.selection = selection_op + self.mutation = mutation_op + + if self.selection is None: + self.selection = selection.ReferenceVectorGuided() + if self.mutation is None: + self.mutation = mutation.Polynomial((lb, ub)) + + self.sampling = LatinHypercubeSampling(self.pop_size, self.n_objs) + + def setup(self, key): + state_key, init_key, vector_key = jax.random.split(key, 3) + + population = ( + jax.random.uniform(init_key, shape=(self.pop_size, self.dim)) + * (self.ub - self.lb) + + self.lb + ) + velocity = jnp.zeros((self.pop_size, self.dim)) + fitness = jnp.full((self.pop_size, self.n_objs), jnp.inf) + + v = self.sampling(vector_key)[0] + v = v / jnp.linalg.norm(v, axis=0) + + return State( + population=population, + next_generation=population, + fitness=fitness, + velocity=velocity, + key=state_key, + is_init=True, + reference_vector=v, + gen=0, + ) + + def ask(self, state): + return jax.lax.cond(state.is_init, self._ask_init, self._ask_normal, state) + + def _ask_init(self, state): + return state.population, state + + def _ask_normal(self, state): + key, mating_key, pairing_key, r0_key, r1_key, mut_key = jax.random.split( + state.key, 6 + ) + population = state.population + no_nan_pop = ~jnp.isnan(population).all(axis=1) + max_idx = jnp.sum(no_nan_pop).astype(int) + pop = population[jnp.where(no_nan_pop, size=self.pop_size, fill_value=-1)] + mating_pool = jax.random.randint(mating_key, (self.pop_size,), 0, max_idx) + population = pop[mating_pool] + + randperm = jax.random.permutation(pairing_key, self.pop_size).reshape(2, -1) + + # calculate the shift-based density estimation(SDE) fitness + sde_fitness = cal_fitness(state.fitness) + + mask = sde_fitness[randperm[0, :]] > sde_fitness[randperm[1, :]] + + winner = jnp.where(mask, randperm[0, :], randperm[1, :]) + loser = jnp.where(mask, randperm[1, :], randperm[0, :]) + + r0 = jax.random.uniform(r0_key, shape=(self.pop_size // 2, self.dim)) + r1 = jax.random.uniform(r1_key, shape=(self.pop_size // 2, self.dim)) + + off_velocity = r0 * state.velocity[loser] + r1 * ( + population[winner] - population[loser] + ) + new_loser_population = jnp.clip( + population[loser] + + off_velocity + + r0 * (off_velocity - state.velocity[loser]), + self.lb, + self.ub, + ) + new_population = population.at[loser].set(new_loser_population) + + new_velocity = state.velocity.at[loser].set(off_velocity) + + next_generation = self.mutation(mut_key, new_population) + + return ( + next_generation, + state.update( + next_generation=next_generation, + velocity=new_velocity, + key=key, + ), + ) + + def tell(self, state, fitness): + return jax.lax.cond( + state.is_init, self._tell_init, self._tell_normal, state, fitness + ) + + def _tell_init(self, state, fitness): + state = state.update(fitness=fitness, is_init=False) + return state + + def _tell_normal(self, state, fitness): + current_gen = state.gen + 1 + v = state.reference_vector + + merged_pop = jnp.concatenate([state.population, state.next_generation]) + merged_fitness = jnp.concatenate([state.fitness, fitness]) + + # RVEA Selection + survivor, survivor_fitness = self.selection( + merged_pop, merged_fitness, v, (current_gen / self.max_gen) ** self.alpha + ) + + state = state.update( + population=survivor, + fitness=survivor_fitness, + gen=current_gen, + ) + return state diff --git a/src/evox/algorithms/mo/rvea.py b/src/evox/algorithms/mo/rvea.py index 4dd67069b..105cf2455 100644 --- a/src/evox/algorithms/mo/rvea.py +++ b/src/evox/algorithms/mo/rvea.py @@ -9,7 +9,7 @@ import jax.numpy as jnp from evox.operators import mutation, crossover, selection -from evox.operators.sampling import UniformSampling, LatinHypercubeSampling +from evox.operators.sampling import LatinHypercubeSampling from evox import Algorithm, State, jit_class @@ -18,6 +18,12 @@ class RVEA(Algorithm): """RVEA algorithms link: https://ieeexplore.ieee.org/document/7386636 + + Args: + alpha : The parameter controlling the rate of change of penalty. Defaults to 2. + fr : The frequency of reference vector adaptation. Defaults to 0.1. + max_gen : The maximum number of generations. Defaults to 100. If the number of iterations + is not 100, change the value based on the actual value. """ def __init__( @@ -88,8 +94,15 @@ def _ask_init(self, state): def _ask_normal(self, state): key, subkey, x_key, mut_key = jax.random.split(state.key, 4) + population = state.population + + no_nan_pop = ~jnp.isnan(population).all(axis=1) + max_idx = jnp.sum(no_nan_pop).astype(int) - crossovered = self.crossover(x_key, state.population) + pop = population[jnp.where(no_nan_pop, size=self.pop_size, fill_value=-1)] + + mating_pool = jax.random.randint(subkey, (self.pop_size,), 0, max_idx) + crossovered = self.crossover(x_key, pop[mating_pool]) next_generation = self.mutation(mut_key, crossovered) return next_generation, state.update(next_generation=next_generation, key=key) @@ -104,16 +117,12 @@ def _tell_normal(self, state, fitness): merged_pop = jnp.concatenate([state.population, state.next_generation], axis=0) merged_fitness = jnp.concatenate([state.fitness, fitness], axis=0) - rank = self.selection( - merged_fitness, v, (current_gen / self.max_gen) ** self.alpha + survivor, survivor_fitness = self.selection( + merged_pop, merged_fitness, v, (current_gen / self.max_gen) ** self.alpha ) - survivor = merged_pop[rank] - survivor_fitness = merged_fitness[rank] - def rv_adaptation(pop_obj, v): - v_temp = v * jnp.tile((pop_obj.max(0) - pop_obj.min(0)), (len(v), 1)) - next_v = v.astype(float) + v_temp = v * jnp.tile((jnp.nanmax(pop_obj, axis=0) - jnp.nanmin(pop_obj, axis=0)), (len(v), 1)) next_v = v_temp / jnp.tile( jnp.sqrt(jnp.sum(v_temp**2, axis=1)).reshape(len(v), 1), diff --git a/src/evox/algorithms/so/pso_variants/fips.py b/src/evox/algorithms/so/pso_variants/fips.py index e146e8f6b..bb106d110 100644 --- a/src/evox/algorithms/so/pso_variants/fips.py +++ b/src/evox/algorithms/so/pso_variants/fips.py @@ -7,30 +7,14 @@ import jax import jax.numpy as jnp -from .utils import ( - row_argsort, - get_distance_matrix, - select_from_mask, -) -from functools import partial -from typing import Union, Iterable, Literal +from .utils import get_distance_matrix +from typing import Literal from .topology_utils import ( get_square_neighbour, - get_neighbour_best_fitness, get_full_neighbour, build_adjacancy_list_from_matrix, ) -from evox import ( - Algorithm, - Problem, - State, - algorithms, - jit_class, - monitors, - workflows, - problems, -) -from evox.utils import min_by +from evox import Algorithm, State class FIPS(Algorithm): diff --git a/src/evox/algorithms/so/pso_variants/swmmpso.py b/src/evox/algorithms/so/pso_variants/swmmpso.py index 515a1298f..55f01cfc6 100644 --- a/src/evox/algorithms/so/pso_variants/swmmpso.py +++ b/src/evox/algorithms/so/pso_variants/swmmpso.py @@ -11,27 +11,14 @@ import jax import jax.numpy as jnp -from functools import partial -from typing import Union, Iterable, Literal +from typing import Literal from .topology_utils import ( get_circles_neighbour, get_neighbour_best_fitness, build_adjacancy_list_from_matrix, ) -from .utils import get_distance_matrix - -from evox import ( - Algorithm, - Problem, - State, - algorithms, - jit_class, - monitors, - workflows, - problems, -) -from evox.utils import min_by +from evox import Algorithm, State class SwmmPSO(Algorithm): diff --git a/src/evox/monitors/std_so_monitor.py b/src/evox/monitors/std_so_monitor.py index a2da6f112..7e3c1b99a 100644 --- a/src/evox/monitors/std_so_monitor.py +++ b/src/evox/monitors/std_so_monitor.py @@ -74,7 +74,7 @@ def get_topk_fitness(self): def get_topk_solutions(self): return self.topk_solutions - def get_min_fitness(self): + def get_best_fitness(self): if self.topk_fitness is None: warnings.warn("trying to get info from a monitor with no recorded data") return None diff --git a/src/evox/operators/sampling/__init__.py b/src/evox/operators/sampling/__init__.py index fbec5f919..d3025837c 100644 --- a/src/evox/operators/sampling/__init__.py +++ b/src/evox/operators/sampling/__init__.py @@ -1,2 +1,3 @@ from .uniform import UniformSampling from .latin_hypercude import LatinHypercubeSampling +from .grid import GridSampling \ No newline at end of file diff --git a/src/evox/operators/sampling/grid.py b/src/evox/operators/sampling/grid.py new file mode 100644 index 000000000..c20d2f9ca --- /dev/null +++ b/src/evox/operators/sampling/grid.py @@ -0,0 +1,24 @@ +import jax.numpy as jnp +import evox + + +@evox.jit_class +class GridSampling: + """ + Grid sampling. + Inspired by PlatEMO. + """ + + def __init__(self, n=None, m=None): + self.n = n + self.m = m + self.num_points = int(jnp.ceil(self.n ** (1 / self.m)).item()) + + def __call__(self): + gap = jnp.linspace(0, 1, self.num_points) + grid_axes = [gap for _ in range(self.m)] + grid_values = jnp.meshgrid(*grid_axes, indexing="ij") + w = jnp.stack(grid_values, axis=-1).reshape(-1, self.m) + w = w[:, ::-1] + n = w.shape[0] + return w, n diff --git a/src/evox/operators/selection/rvea_selection.py b/src/evox/operators/selection/rvea_selection.py index b2a9ff795..ffb779db0 100644 --- a/src/evox/operators/selection/rvea_selection.py +++ b/src/evox/operators/selection/rvea_selection.py @@ -1,82 +1,54 @@ +import jax import jax.numpy as jnp -from jax import jit, lax +from jax import jit from evox.utils import cos_dist from evox import jit_class @jit -def ref_vec_guided(x, v, theta): - n, m = jnp.shape(x) +def ref_vec_guided(x, f, v, theta): + n, m = jnp.shape(f) nv = jnp.shape(v)[0] - obj = x - obj -= jnp.tile(jnp.min(obj, axis=0), (n, 1)) + obj = f - jnp.nanmin(f, axis=0) + obj = jnp.maximum(obj, 1e-32) cosine = cos_dist(v, v) cosine = jnp.where(jnp.eye(jnp.shape(cosine)[0], dtype=bool), 0, cosine) - cosine = jnp.clip(cosine, -1, 1) + cosine = jnp.clip(cosine, 0, 1) gamma = jnp.min(jnp.arccos(cosine), axis=1) - angle = jnp.arccos(cos_dist(obj, v)) + angle = jnp.arccos(jnp.clip(cos_dist(obj, v), 0, 1)) + nan_mask = jnp.isnan(obj).any(axis=1) associate = jnp.argmin(angle, axis=1) + associate = jnp.where(nan_mask, -1, associate) - next_ind = jnp.full(nv, -1) - is_null = jnp.sum(next_ind) - global_min = jnp.inf - global_min_idx = -1 + partition = jax.vmap( + lambda x: jnp.where(associate == x, jnp.arange(0, n), -1), in_axes=1, out_axes=1 + )(jnp.tile(jnp.arange(0, nv), (n, 1))) - vals = next_ind, global_min, global_min_idx + mask = partition == -1 + mask_null = jnp.sum(mask, axis=0) == n - def update_next(i, sub_index, next_ind, global_min, global_min_idx): - apd = (1 + m * theta * angle[sub_index, i] / gamma[i]) * jnp.sqrt( - jnp.sum(obj[sub_index, :] ** 2, axis=1) - ) + apd = jax.vmap( + lambda x, y, z: (1 + m * theta * z[x] / y) + * jnp.sqrt(jnp.sum(obj[x, :] ** 2, axis=1)), + in_axes=(1, 0, 1), + out_axes=1, + )(partition, gamma, angle) + apd = jnp.where(mask, jnp.inf, apd) - apd_max = jnp.max(apd) - noise = jnp.where(sub_index == -1, apd_max, 0) - apd = apd + noise - best = jnp.argmin(apd) + next_ind = jnp.argmin(apd, axis=0) + next_x = jnp.where(mask_null[:, jnp.newaxis], jnp.nan, x[next_ind]) + next_f = jnp.where(mask_null[:, jnp.newaxis], jnp.nan, f[next_ind]) - global_min_idx = jnp.where( - apd[best] < global_min, sub_index[best.astype(int)], global_min_idx - ) - global_min = jnp.minimum(apd[best], global_min) - - next_ind = next_ind.at[i].set(sub_index[best.astype(int)]) - return next_ind, global_min, global_min_idx - - def no_update(i, sub_index, next_ind, global_min, global_min_idx): - return next_ind, global_min, global_min_idx - - def body_fun(i, vals): - next_ind, global_min, global_min_idx = vals - sub_index = jnp.where(associate == i, size=nv, fill_value=-1)[0] - - next_ind, global_min, global_min_idx = lax.cond( - jnp.sum(sub_index) != is_null, - update_next, - no_update, - i, - sub_index, - next_ind, - global_min, - global_min_idx, - ) - return next_ind, global_min, global_min_idx - - next_ind, global_min, global_min_idx = lax.fori_loop(0, nv, body_fun, vals) - mask = next_ind == -1 - - next_ind = jnp.where(mask, global_min_idx, next_ind) - next_ind = jnp.where(global_min_idx != -1, next_ind, jnp.arange(0, nv)) - - return next_ind + return next_x, next_f @jit_class class ReferenceVectorGuided: """Reference vector guided environmental selection.""" - def __call__(self, x, v, theta): - return ref_vec_guided(x, v, theta) + def __call__(self, x, f, v, theta): + return ref_vec_guided(x, f, v, theta) diff --git a/src/evox/problems/neuroevolution/reinforcement_learning/brax.py b/src/evox/problems/neuroevolution/reinforcement_learning/brax.py index 9c84bff98..86b330711 100644 --- a/src/evox/problems/neuroevolution/reinforcement_learning/brax.py +++ b/src/evox/problems/neuroevolution/reinforcement_learning/brax.py @@ -38,7 +38,7 @@ def __init__( Default to True. """ self.batched_policy = jit(vmap(policy)) - self.env = envs.wrappers.VmapWrapper(envs.create(env_name=env_name)) + self.env = envs.wrappers.training.VmapWrapper(envs.create(env_name=env_name)) self.batch_size = batch_size self.cap_episode = cap_episode self.fitness_is_neg_reward = fitness_is_neg_reward diff --git a/src/evox/problems/neuroevolution/reinforcement_learning/env_pool.py b/src/evox/problems/neuroevolution/reinforcement_learning/env_pool.py index ef4338799..f2f83a1b1 100644 --- a/src/evox/problems/neuroevolution/reinforcement_learning/env_pool.py +++ b/src/evox/problems/neuroevolution/reinforcement_learning/env_pool.py @@ -5,11 +5,34 @@ import jax.numpy as jnp import numpy as np from jax import jit, random, vmap, lax +from jax.tree_util import tree_map from jax.experimental import io_callback from evox import Problem, State, jit_class +def _x32_func_call(func): + def inner_func(*args, **kwargs): + return _to_x32_if_needed(func(*args, **kwargs)) + + return inner_func + + +def _to_x32_if_needed(values): + if jax.config.jax_enable_x64: + # we have 64-bit enabled, so nothing to do + return values + + def to_x32(value): + if value.dtype == np.float64: + return value.astype(np.float32) + elif value.dtype == np.int64: + return value.astype(np.int32) + else: + return value + return tree_map(to_x32, values) + + @jit_class class EnvPool(Problem): def __init__( @@ -39,8 +62,8 @@ def evaluate(self, state, pop): key, subkey = random.split(state.key) seed = random.randint(subkey, (1,), 0, jnp.iinfo(jnp.int32).max) io_callback(self.env.seed, None, seed) - obs, info = self.env.reset(None) - obs, info = io_callback(self.env.reset, (obs, info), None) + obs, info = _to_x32_if_needed(self.env.reset(None)) + obs, info = io_callback(_x32_func_call(self.env.reset), (obs, info), None) total_reward = 0 i = 0 @@ -54,11 +77,11 @@ def cond_func(loop_state): def step(loop_state): i, done, total_reward, obs = loop_state action = self.batch_policy(pop, obs) - obs, reward, terminated, truncated, info = self.env.step( - np.zeros(action.shape) + obs, reward, terminated, truncated, info = _to_x32_if_needed( + self.env.step(np.zeros(action.shape)) ) obs, reward, terminated, truncated, info = io_callback( - lambda action: self.env.step(np.copy(action)), + _x32_func_call(lambda action: self.env.step(np.copy(action))), (obs, reward, terminated, truncated, info), action, ) diff --git a/src/evox/problems/neuroevolution/reinforcement_learning/gym.py b/src/evox/problems/neuroevolution/reinforcement_learning/gym.py index 05fe30f5b..2e9328790 100644 --- a/src/evox/problems/neuroevolution/reinforcement_learning/gym.py +++ b/src/evox/problems/neuroevolution/reinforcement_learning/gym.py @@ -184,9 +184,12 @@ def _evaluate(self, seeds, pop, cap_episode_length): ] rewards, acc_mo_values, episode_length = zip(*ray.get(rollout_future)) + acc_mo_values = np.array(acc_mo_values) + if acc_mo_values.size != 0: + acc_mo_values = acc_mo_values.reshape(-1, self.num_obj) return ( np.array(rewards).reshape(-1), - np.array(acc_mo_values).reshape(-1, self.num_obj), + acc_mo_values, np.array(episode_length).reshape(-1), ) @@ -240,11 +243,14 @@ def _batched_evaluate(self, seeds, pop, cap_episode_length): rewards, acc_mo_values = zip( *ray.get([worker.get_rewards.remote() for worker in self.workers]) ) + acc_mo_values = np.array(acc_mo_values) + if acc_mo_values.size != 0: + acc_mo_values = acc_mo_values.reshape(-1, self.num_obj) episode_length = [worker.get_episode_length.remote() for worker in self.workers] episode_length = ray.get(episode_length) return ( np.array(rewards).reshape(-1), - np.array(acc_mo_values).reshape(-1, self.num_obj), + acc_mo_values, np.array(episode_length).reshape(-1), ) diff --git a/src/evox/problems/numerical/data/dtlz7_pf.json b/src/evox/problems/numerical/data/dtlz7_pf.json deleted file mode 100644 index 7d68d7b3f..000000000 --- a/src/evox/problems/numerical/data/dtlz7_pf.json +++ /dev/null @@ -1 +0,0 @@ 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diff --git a/src/evox/problems/numerical/dtlz.py b/src/evox/problems/numerical/dtlz.py index cc7088fc2..dfbacafa8 100644 --- a/src/evox/problems/numerical/dtlz.py +++ b/src/evox/problems/numerical/dtlz.py @@ -1,14 +1,15 @@ import jax import jax.numpy as jnp from evox import Problem, State, jit_class -from evox.operators.sampling import UniformSampling, LatinHypercubeSampling -import chex -import pkgutil, json +from evox.operators.sampling import UniformSampling, GridSampling @jit_class class DTLZTestSuit(Problem): - """DTLZ""" + """DTLZ + + link: https://link.springer.com/chapter/10.1007/1-84628-137-7_6 + """ def __init__(self, d=None, m=None, ref_num=1000): self.d = d @@ -20,12 +21,10 @@ def __init__(self, d=None, m=None, ref_num=1000): def setup(self, key): return State(key=key) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): - chex.assert_type(X, float) - chex.assert_shape(X, (None, self.d)) + def evaluate(self, state, X): return jax.jit(jax.vmap(self._dtlz))(X), state - def pf(self, state: chex.PyTreeDef): + def pf(self, state): f = self.sample()[0] / 2 return f, state @@ -42,7 +41,7 @@ def __init__(self, d=None, m=None, ref_num=100): self.d = d super().__init__(d, m, ref_num) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): + def evaluate(self, state, X): m = self.m n, d = jnp.shape(X) @@ -78,7 +77,7 @@ def __init__(self, d=None, m=None, ref_num=1000): self.d = d super().__init__(d, m, ref_num) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): + def evaluate(self, state, X): m = self.m g = jnp.sum((X[:, m - 1 :] - 0.5) ** 2, axis=1, keepdims=True) f = ( @@ -87,7 +86,7 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): jnp.cumprod( jnp.c_[ jnp.ones((jnp.shape(g)[0], 1)), - jnp.cos(X[:, : m - 1] * jnp.pi / 2), + jnp.maximum(jnp.cos(X[:, : m - 1] * jnp.pi / 2), 0), ], axis=1, ) @@ -99,17 +98,17 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): return f, state - def pf(self, state: chex.PyTreeDef): + def pf(self, state): f = self.sample()[0] f /= jnp.tile(jnp.sqrt(jnp.sum(f**2, axis=1, keepdims=True)), (1, self.m)) return f, state -class DTLZ3(DTLZTestSuit): +class DTLZ3(DTLZ2): def __init__(self, d=None, m=None, ref_num=1000): super().__init__(d, m, ref_num) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): + def evaluate(self, state, X): n, d = jnp.shape(X) m = self.m g = 100 * ( @@ -129,7 +128,10 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): jnp.tile(1 + g, (1, m)) * jnp.fliplr( jnp.cumprod( - jnp.c_[jnp.ones((n, 1)), jnp.cos(X[:, : m - 1] * jnp.pi / 2)], + jnp.c_[ + jnp.ones((n, 1)), + jnp.maximum(jnp.cos(X[:, : m - 1] * jnp.pi / 2), 0), + ], axis=1, ) ) @@ -139,11 +141,11 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): return f, state -class DTLZ4(DTLZTestSuit): +class DTLZ4(DTLZ2): def __init__(self, d=None, m=None, ref_num=1000): super().__init__(d, m, ref_num) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): + def evaluate(self, state, X): m = self.m X = X.at[:, : m - 1].power(100) g = jnp.sum((X[:, m - 1 :] - 0.5) ** 2, axis=1, keepdims=True) @@ -153,7 +155,7 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): jnp.cumprod( jnp.c_[ jnp.ones((jnp.shape(g)[0], 1)), - jnp.cos(X[:, : m - 1] * jnp.pi / 2), + jnp.maximum(jnp.cos(X[:, : m - 1] * jnp.pi / 2), 0), ], axis=1, ) @@ -179,7 +181,7 @@ def __init__(self, d=None, m=None, ref_num=1000): self.d = d super().__init__(d, m, ref_num) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): + def evaluate(self, state, X): m = self.m g = jnp.sum((X[:, m - 1 :] - 0.5) ** 2, axis=1, keepdims=True) temp = jnp.tile(g, (1, m - 2)) @@ -190,7 +192,7 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): jnp.cumprod( jnp.c_[ jnp.ones((jnp.shape(g)[0], 1)), - jnp.cos(X[:, : m - 1] * jnp.pi / 2), + jnp.maximum(jnp.cos(X[:, : m - 1] * jnp.pi / 2), 0), ], axis=1, ) @@ -201,7 +203,7 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): ) return f, state - def pf(self, state: chex.PyTreeDef): + def pf(self, state): n = self.ref_num * self.m f = jnp.vstack( ( @@ -239,7 +241,7 @@ def __init__(self, d=None, m=None, ref_num=1000): self.d = d super().__init__(d, m, ref_num) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): + def evaluate(self, state, X): m = self.m g = jnp.sum((X[:, m - 1 :] ** 0.1), axis=1, keepdims=True) temp = jnp.tile(g, (1, m - 2)) @@ -251,7 +253,7 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): jnp.cumprod( jnp.c_[ jnp.ones((jnp.shape(g)[0], 1)), - jnp.cos(X[:, : m - 1] * jnp.pi / 2), + jnp.maximum(jnp.cos(X[:, : m - 1] * jnp.pi / 2), 0), ], axis=1, ) @@ -262,7 +264,7 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): ) return f, state - def pf(self, state: chex.PyTreeDef): + def pf(self, state): n = self.ref_num * self.m f = jnp.vstack( ( @@ -298,9 +300,11 @@ def __init__(self, d=None, m=None, ref_num=1000): self.d = self.m + 19 else: self.d = d + super().__init__(d, m, ref_num) + self.sample = GridSampling(self.ref_num * self.m, self.m - 1) - def evaluate(self, state: chex.PyTreeDef, X: chex.Array): + def evaluate(self, state, X): n, d = jnp.shape(X) m = self.m f = jnp.zeros((n, m)) @@ -321,8 +325,32 @@ def evaluate(self, state: chex.PyTreeDef, X: chex.Array): ) return f, state - def pf(self, state: chex.PyTreeDef): - data_bytes = pkgutil.get_data(__name__, "data/dtlz7_pf.json") - data = data_bytes.decode() - pf = json.loads(data) - return jnp.array(pf), state + def pf(self, state): + interval = jnp.array([0, 0.251412, 0.631627, 0.859401]) + median = (interval[1] - interval[0]) / ( + interval[3] - interval[2] + interval[1] - interval[0] + ) + + x = self.sample()[0] + + mask_less_equal_median = x <= median + mask_greater_median = x > median + + x = jnp.where( + mask_less_equal_median, + x * (interval[1] - interval[0]) / median + interval[0], + x, + ) + x = jnp.where( + mask_greater_median, + (x - median) * (interval[3] - interval[2]) / (1 - median) + interval[2], + x, + ) + + last_col = 2 * ( + self.m + - jnp.sum(x / 2 * (1 + jnp.sin(3 * jnp.pi * x)), axis=1, keepdims=True) + ) + + pf = jnp.hstack([x, last_col]) + return pf, state diff --git a/src/evox/problems/numerical/lsmop.py b/src/evox/problems/numerical/lsmop.py index 046a430cf..e1a4c3a27 100644 --- a/src/evox/problems/numerical/lsmop.py +++ b/src/evox/problems/numerical/lsmop.py @@ -451,7 +451,7 @@ def pf(self, state): return p, state def _grid(self, N, M): - gap = jnp.linspace(0, 1, int(math.ceil(N ** (1 / M))), dtype=jnp.float64) + gap = jnp.linspace(0, 1, int(math.ceil(N ** (1 / M)))) c = jnp.meshgrid(*([gap] * M)) w = jnp.vstack([x.ravel() for x in c]).T return w diff --git a/src/evox/problems/numerical/maf.py b/src/evox/problems/numerical/maf.py index 503459a73..3c9470bb0 100644 --- a/src/evox/problems/numerical/maf.py +++ b/src/evox/problems/numerical/maf.py @@ -1,16 +1,60 @@ import evox import jax -from jax import lax +from jax import lax, jit, vmap import jax.numpy as jnp from evox import Problem, State from evox.operators.sampling import UniformSampling -from matplotlib.path import Path -from jax.config import config from evox.operators.non_dominated_sort import non_dominated_sort import math from evox.problems.numerical import Sphere, Griewank +@jit +def inside(x, a, b): + """check if x is in [a, b) or [b, a)""" + return (jnp.minimum(a, b) <= x) & (x < jnp.maximum(a, b)) + + +@jit +def ray_intersect_segment(point, seg_init, seg_term): + """ + point is a 2d point, representing a horizontal ray casting from the point. + segment is segment represented by two 2d points in shape (2, ), + where seg_init is the initial point, and seg_term is the terminal point. + Thus check if the intersection_x >= P_x. + """ + y_dist = seg_term[1] - seg_init[1] + # special case: y_dist == 0, check P_y == seg_init_y and P_x inside the segment + judge_1 = (point[1] == seg_init[1]) & inside(point[0], seg_init[0], seg_term[0]) + # check intersection_x >= P_x. + LHS = seg_init[0] * y_dist + (point[1] - seg_init[1]) * (seg_term[0] - seg_init[0]) + RHS = point[0] * y_dist + # since it's an inequation, reverse the inequation if y_dist is negative. + judge_2 = ((y_dist > 0) & (LHS >= RHS)) | ((y_dist < 0) & (LHS <= RHS)) + # check intersection_y, which is P_y is inside the segment + judge_3 = inside(point[1], seg_init[1], seg_term[1]) + return ((y_dist == 0) & judge_1) | ((y_dist != 0) & judge_2 & judge_3) + + +@jit +def point_in_polygon(polygon, point): + """ + Determine whether a point is within a regular polygon by ray method + Args: + polygon (jnp.array): Vertex coordinates of polygons, shape is (n, 2) + point (jnp.array): The coordinates of the points that need to be determined, shape is (2,) + Returns: + bool: If the point is within the polygon, return True; Otherwise, return False + """ + + seg_term = jnp.roll(polygon, 1, axis=0) + is_intersect = vmap(ray_intersect_segment, in_axes=(None, 0, 0))( + point, polygon, seg_term + ) + is_vertex = jnp.any(jnp.all(polygon == point, axis=1), axis=0) + return (jnp.sum(is_intersect) % 2 == 1) | is_vertex + + @evox.jit_class class MaF(Problem): """ @@ -124,17 +168,13 @@ def pf(self, state): c = jnp.zeros((r.shape[0], self.m - 1)) def inner_fun(i, c): - def inner_fun2(j, c): + for j in range(2, self.m + 1): temp = ( r[i - 1, j - 1] / r[i - 1, 0] * jnp.prod(c[i - 1, self.m - j + 1 : self.m - 1]) ) c = c.at[i - 1, self.m - j].set(jnp.sqrt(1 / (1 + temp**2))) - return c - - with jax.disable_jit(): - c = lax.fori_loop(2, self.m + 1, inner_fun2, c) return c c = lax.fori_loop(1, r.shape[0] + 1, inner_fun, c) @@ -251,7 +291,7 @@ def __init__(self, d=None, m=None, ref_num=1000): def evaluate(self, state, X): n, d = jnp.shape(X) alpha = 100 - X = X.at[:, : self.m - 1].set((X[:, : self.m - 1] ** alpha).astype(jnp.float64)) + X = X.at[:, : self.m - 1].set((X[:, : self.m - 1] ** alpha)) g = jnp.sum((X[:, self.m - 1 :] - 0.5) ** 2, axis=1)[:, jnp.newaxis] f1 = ( jnp.tile(1 + g, (1, self.m)) @@ -375,7 +415,7 @@ def pf(self, state): return f, state def _grid(self, N, M): - gap = jnp.linspace(0, 1, int(math.ceil(N ** (1 / M))), dtype=jnp.float64) + gap = jnp.linspace(0, 1, int(math.ceil(N ** (1 / M)))) c = jnp.meshgrid(*([gap] * M)) W = jnp.vstack([x.ravel() for x in c]).T return W @@ -399,20 +439,14 @@ def evaluate(self, state, X): f = self._eucl_dis(X, self.points) return f, state - """ - using numpy based library, this makes JIT disable and may make some mistakes, but in my test, there is no warning - """ - def pf(self, state): n = self.ref_num * self.m - temp = jnp.linspace(-1, 1, num=math.ceil(math.sqrt(n))) - x, y = jnp.meshgrid(temp, temp) + temp = jnp.linspace(-1, 1, num=jnp.ceil(jnp.sqrt(n)).astype(int)) + y, x = jnp.meshgrid(temp, temp) x = x.ravel(order="F") y = y.ravel(order="F") - # using numpy based library: matplotlib.path.Path - poly_path = Path(self.points) _points = jnp.column_stack((x, y)) - ND = poly_path.contains_points(_points) + ND = jax.vmap(point_in_polygon, in_axes=(None, 0))(self.points, _points) f = self._eucl_dis(jnp.column_stack([x[ND], y[ND]]), self.points) return f, state @@ -458,22 +492,15 @@ def evaluate(self, state, X): ) return f, state - """ - using numpy based library, this makes JIT disable and may make some mistakes, but in my test, there is no warning - """ - def pf(self, state): - with jax.disable_jit(): - n = self.ref_num * self.m - temp = jnp.linspace(-1, 1, num=jnp.ceil(jnp.sqrt(n)).astype(int)) - x, y = jnp.meshgrid(temp, temp) - x = x.ravel(order="C") - y = y.ravel(order="C") - # using numpy based library: matplotlib.path.Path - poly_path = Path(self.points) - _points = jnp.column_stack((x, y)) - ND = poly_path.contains_points(_points) - f, state = self.evaluate(state, jnp.column_stack((x[ND], y[ND]))) + n = self.ref_num * self.m + temp = jnp.linspace(-1, 1, num=jnp.ceil(jnp.sqrt(n)).astype(int)) + y, x = jnp.meshgrid(temp, temp) + x = x.ravel(order="C") + y = y.ravel(order="C") + _points = jnp.column_stack((x, y)) + ND = jax.vmap(point_in_polygon, in_axes=(None, 0))(self.points, _points) + f, state = self.evaluate(state, jnp.column_stack((x[ND], y[ND]))) return f, state @evox.jit_method @@ -572,13 +599,15 @@ def inner_fun2(i, x): f = jnp.tile((D * x[:, M])[:, jnp.newaxis], (1, M)) + S * h return f, state + """ + If result is not correct for some problems, it is necessary to use float64 globally + """ + def pf(self, state): - config.update("jax_enable_x64", True) M = self.m N = self.ref_num * self.m R = UniformSampling(N, M)()[0] - R = R.astype(jnp.float64) - c = jnp.ones((R.shape[0], M), dtype=jnp.float64) + c = jnp.ones((R.shape[0], M)) def inner_fun(i, c): def inner_fun2(j, c): @@ -599,7 +628,7 @@ def inner_fun2(j, c): c = lax.fori_loop(1, R.shape[0] + 1, inner_fun, c) x = jnp.arccos(c) * 2 / jnp.pi temp = (1 - jnp.sin(jnp.pi / 2 * x[:, 1])) * R[:, M - 1] / R[:, M - 2] - a = jnp.arange(0, 1.0001, 0.0001, dtype=jnp.float64)[jnp.newaxis, :] + a = jnp.arange(0, 1.0001, 0.0001)[jnp.newaxis, :] E = jnp.abs( temp[:, None] * (1 - jnp.cos(jnp.pi / 2 * a)) - 1 @@ -714,24 +743,21 @@ def inner_fun2(i, x): f = D * x[:, M - 1].reshape(-1, 1) + S * h return f, state + """ + If result is not correct for some problems, it is necessary to use float64 globally + """ + def pf(self, state): - config.update("jax_enable_x64", True) M = self.m N = self.ref_num * self.m - R = UniformSampling(N, M)()[0].astype(jnp.float64) + R = UniformSampling(N, M)()[0] c = jnp.ones((R.shape[0], M)) def inner_fun(i, c): def inner_fun2(j, c): - temp = ( - R[i, j] - / R[i, 0] - * jnp.prod(1 - c[i, M - j : M - 1]).astype(jnp.float64) - ) - return ( - c.at[i, M - j - 1] - .set((temp**2 - temp + jnp.sqrt(2 * temp)) / (temp**2 + 1)) - .astype(jnp.float64) + temp = R[i, j] / R[i, 0] * jnp.prod(1 - c[i, M - j : M - 1]) + return c.at[i, M - j - 1].set( + (temp**2 - temp + jnp.sqrt(2 * temp)) / (temp**2 + 1) ) with jax.disable_jit(): @@ -742,19 +768,19 @@ def inner_fun2(j, c): x = jnp.arccos(c) * 2 / jnp.pi temp = (1 - jnp.sin(jnp.pi / 2 * x[:, 1])) * R[:, M - 1] / R[:, M - 2] - a = jnp.arange(0, 1.0001, 0.0001).astype(jnp.float64)[None, :] + a = jnp.arange(0, 1.0001, 0.0001)[None, :] E = jnp.abs( temp[:, None] * (1 - jnp.cos(jnp.pi / 2 * a)) - 1 + a * jnp.cos(5 * jnp.pi * a) ** 2 - ).astype(jnp.float64) + ) rank = jnp.argsort(E, axis=1) x = x.at[:, 0].set(a[0, jnp.min(rank[:, :10], axis=1)]) R = self._convex(x) - R = R.at[:, M - 1].set(self._disc(x)).astype(jnp.float64) + R = R.at[:, M - 1].set(self._disc(x)) non_dominated_rank = non_dominated_sort(R) f = R[non_dominated_rank == 0, :] - f = f * jnp.arange(2, 2 * M + 1, 2).astype(jnp.float64) + f = f * jnp.arange(2, 2 * M + 1, 2) return f, state @evox.jit_method diff --git a/src/evox/utils/common.py b/src/evox/utils/common.py index 302e6221b..f6c9f6e19 100644 --- a/src/evox/utils/common.py +++ b/src/evox/utils/common.py @@ -214,3 +214,29 @@ def __setstate__(self, state_dict): self.shapes = state_dict["shapes"] self.start_indices = state_dict["start_indices"] self.slice_sizes = state_dict["slice_sizes"] + + +def parse_opt_direction(opt_direction: Union[str, List[str]]): + if isinstance(opt_direction, str): + if opt_direction == "min": + return 1 + elif opt_direction == "max": + return -1 + else: + raise ValueError( + f"opt_direction is either 'min' or 'max', got {opt_direction}" + ) + elif isinstance(opt_direction, list): + result = [] + for d in opt_direction: + if opt_direction == "min": + result.append(1) + elif opt_direction == "max": + result.append(-1) + else: + raise ValueError(f"opt_direction is either 'min' or 'max', got {d}") + return jnp.array(result) + else: + raise ValueError( + f"opt_direction should have type 'str' or 'list', got {type(opt_direction)}" + ) diff --git a/src/evox/workflows/distributed.py b/src/evox/workflows/distributed.py index fbe627fb9..f73ab97a2 100644 --- a/src/evox/workflows/distributed.py +++ b/src/evox/workflows/distributed.py @@ -1,4 +1,4 @@ -from typing import Callable, Optional, List, Dict +from typing import Callable, Optional, List, Dict, Union from collections import deque import chex @@ -9,6 +9,7 @@ from jax.tree_util import tree_flatten from evox import Algorithm, Problem, State, Stateful, jit_class +from evox.utils import parse_opt_direction @jit_class @@ -17,6 +18,7 @@ def __init__( self, algorithm: Algorithm, problem: Problem, + opt_direction: jax.Array, pop_size: int, start_indices: int, slice_sizes: int, @@ -25,6 +27,7 @@ def __init__( ): self.algorithm = algorithm self.problem = problem + self.opt_direction = opt_direction self.pop_size = pop_size self.start_indices = start_indices self.slice_sizes = slice_sizes @@ -47,6 +50,7 @@ def step1(self, state: State): def step2(self, state: State, fitness: List[jax.Array]): fitness = jnp.concatenate(fitness, axis=0) + fitness = fitness * self.opt_direction if self.fitness_transform is not None: fitness = self.fitness_transform(fitness) @@ -104,6 +108,7 @@ def __init__( self, algorithm: Algorithm, problem: Problem, + opt_direction: jax.Array, pop_size: int, num_workers: int, options: dict, @@ -129,6 +134,7 @@ def __init__( WorkerWorkflow( algorithm, problem, + opt_direction, pop_size, start_index, slice_size, @@ -172,6 +178,7 @@ def __init__( pop_size: int, num_workers: int, monitor=None, + opt_direction: Union[str, List[str]] = "min", record_pop: bool = False, record_time: bool = False, metrics: Optional[Dict[str, Callable]] = None, @@ -203,6 +210,9 @@ def __init__( will be used to determine the sharding strategy. num_workers Number of workers. + opt_direction + The optimization direction, can be either "min" or "max" + or a list of "min"/"max" to specific the direction for each objective. options The runtime options of the worker actor. pop_transform: @@ -212,9 +222,11 @@ def __init__( global_fitness_transform: This transform is applied at the main node. """ + opt_direction = parse_opt_direction(opt_direction) self.supervisor = Supervisor.remote( algorithm, problem, + opt_direction, pop_size, num_workers, options, diff --git a/src/evox/workflows/standard.py b/src/evox/workflows/standard.py index 3ab8aa695..c50b45fc1 100644 --- a/src/evox/workflows/standard.py +++ b/src/evox/workflows/standard.py @@ -1,6 +1,7 @@ -from typing import Callable, Dict, Optional +from typing import Callable, Dict, Optional, Union, List from evox import Algorithm, Problem, Stateful +from evox.utils import parse_opt_direction class StdWorkflow(Stateful): @@ -9,12 +10,40 @@ def __init__( algorithm: Algorithm, problem: Problem, monitor=None, + opt_direction: Union[str, List[str]] = "min", pop_transform: Optional[Callable] = None, fitness_transform: Optional[Callable] = None, record_pop: bool = False, record_time: bool = False, metrics: Optional[Dict[str, Callable]] = None, ): + """ + Parameters + ---------- + algorithm + The algorithm. + problem + The problem. + monitor + Optional monitor. + opt_direction + The optimization direction, can be either "min" or "max" + or a list of "min"/"max" to specific the direction for each objective. + pop_transform + Optional population transform function, + usually used to decode the population + into the format that can be understood by the problem. + fit_transform + Optional fitness transform function. + usually used to apply fitness shaping. + record_pop + Whether to record the population if monitor is enabled. + record_time + Whether to record the time at the end of each generation. + Due to its timing nature, + record_time requires synchronized functional call. + Default to False. + """ self.algorithm = algorithm self.problem = problem self.monitor = monitor @@ -23,6 +52,7 @@ def __init__( self.metrics = metrics self.pop_transform = pop_transform self.fitness_transform = fitness_transform + self.opt_direction = parse_opt_direction(opt_direction) def step(self, state): if self.monitor and self.record_time: @@ -38,6 +68,8 @@ def step(self, state): fitness, state = self.problem.evaluate(state, pop) + fitness = fitness * self.opt_direction + if self.monitor: if self.metrics: metrics = {name: func(fitness) for name, func in self.metrics.items()} diff --git a/src/evox/workflows/uni_workflow.py b/src/evox/workflows/uni_workflow.py index 0f65ea4e0..5b7a46b03 100644 --- a/src/evox/workflows/uni_workflow.py +++ b/src/evox/workflows/uni_workflow.py @@ -1,4 +1,5 @@ from evox import jit_method, Stateful, Algorithm, Problem, State +from evox.utils import parse_opt_direction from typing import Optional, Callable, Dict, List, Union import warnings import jax @@ -25,6 +26,7 @@ def __init__( algorithm: Algorithm, problem: Union[Problem, List[Problem]], monitor=None, + opt_direction: Union[str, List[str]] = "min", pop_transform: Optional[Union[Callable, List[Problem]]] = None, fit_transform: Optional[Callable] = None, record_pop: bool = False, @@ -43,6 +45,9 @@ def __init__( The problem. monitor Optional monitor. + opt_direction + The optimization direction, can be either "min" or "max" + or a list of "min"/"max" to specific the direction for each objective. pop_transform Optional population transform function, usually used to decode the population @@ -72,6 +77,7 @@ def __init__( self.algorithm = algorithm self.problem = problem self.monitor = monitor + self.pop_transform = pop_transform self.fit_transform = fit_transform self.record_pop = record_pop @@ -81,6 +87,7 @@ def __init__( self.jit_monitor = jit_monitor self.num_objectives = num_objectives self.distributed_step = False + self.opt_direction = parse_opt_direction(opt_direction) if jit_problem is False and self.num_objectives is None: warnings.warn( ( @@ -141,6 +148,8 @@ def _step(self, state): if self.distributed_step is True: fitness = jax.lax.all_gather(fitness, "node", axis=0, tiled=True) + fitness = fitness * self.opt_direction + if self.monitor: if self.metrics: metrics = { diff --git a/tests/test_containers.py b/tests/test_containers.py index 7d5f88ebe..544b8ec1b 100644 --- a/tests/test_containers.py +++ b/tests/test_containers.py @@ -29,7 +29,7 @@ def test_clustered_cma_es(): for i in range(200): state = workflow.step(state) - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() assert min_fitness < 2 @@ -91,7 +91,7 @@ def test_vectorized_coevolution(random_subpop): for i in range(200): state = workflow.step(state) - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() assert min_fitness < 1 @@ -124,7 +124,7 @@ def test_coevolution(random_subpop, num_subpop_iter): for i in range(4 * 200): state = workflow.step(state) - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() assert min_fitness < 2 @@ -156,6 +156,6 @@ def test_random_mask_cso(): for i in range(10): state = workflow.step(state) - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() print(min_fitness) assert abs(min_fitness - 19.6) < 0.1 diff --git a/tests/test_distributed_pipeline.py b/tests/test_distributed_pipeline.py index fbef0b568..5a2788986 100644 --- a/tests/test_distributed_pipeline.py +++ b/tests/test_distributed_pipeline.py @@ -29,7 +29,7 @@ def test_distributed_cso(): state = workflow.step(state) # the result should be close to 0 - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() print(min_fitness) assert min_fitness < 1e-4 workflow.health_check(state) diff --git a/tests/test_envpool.py b/tests/test_envpool.py new file mode 100644 index 000000000..c3600f618 --- /dev/null +++ b/tests/test_envpool.py @@ -0,0 +1,57 @@ +from evox import workflows, algorithms, problems +from evox.monitors import StdSOMonitor +from evox.utils import TreeAndVector +import jax +import jax.numpy as jnp +from flax import linen as nn +import pytest + + +def test_envpool_cartpole(): + class CartpolePolicy(nn.Module): + """A simple model for cartpole""" + + @nn.compact + def __call__(self, x): + x = nn.Dense(32)(x) + x = nn.sigmoid(x) + x = nn.Dense(2)(x) + return jnp.argmax(x) + + key = jax.random.PRNGKey(42) + model_key, workflow_key = jax.random.split(key) + + model = CartpolePolicy() + params = model.init(model_key, jnp.zeros((4,))) + adapter = TreeAndVector(params) + monitor = StdSOMonitor() + problem = problems.neuroevolution.EnvPool( + env_name="CartPole-v1", + num_envs=16, + policy=jax.jit(model.apply), + ) + center = adapter.to_vector(params) + # create a workflow + workflow = workflows.UniWorkflow( + algorithm=algorithms.PGPE( + optimizer="adam", + center_init=center, + pop_size=16, + ), + problem=problem, + monitor=monitor, + jit_problem=True, + num_objectives=1, + pop_transform=adapter.batched_to_tree, + ) + # init the workflow + state = workflow.init(workflow_key) + + # run the workflow for 10 steps + for i in range(5): + state = workflow.step(state) + + monitor.close() + min_fitness = monitor.get_best_fitness() + # envpool is deterministic, so the result should always be the same + assert min_fitness == -59.0 diff --git a/tests/test_gym.py b/tests/test_gym.py index 62b382cf7..e7b49eb8e 100644 --- a/tests/test_gym.py +++ b/tests/test_gym.py @@ -61,6 +61,6 @@ def __call__(self, x): monitor.close() # the result should be close to 0 - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() # gym is deterministic, so the result should always be the same assert min_fitness == -16.0 diff --git a/tests/test_maf.py b/tests/test_maf.py index 56cb64b88..9961f41ac 100644 --- a/tests/test_maf.py +++ b/tests/test_maf.py @@ -12,6 +12,39 @@ n, d = data.shape m = 3 +def test_inside(): + assert inside(8.5, 1.0, 0.0) == False + assert inside(8.5, 0.0, 1.0) == False + assert inside(0.5, 0.0, 1.0) == True + assert inside(0.5, 1.0, 0.0) == True + assert inside(1.0, 1.0, 0.0) == False + assert inside(0.0, 1.0, 0.0) == True + assert inside(1.0, 0.0, 1.0) == False + assert inside(0.0, 0.0, 1.0) == True + +def test_ray_intersect_segment(): + point = jnp.array([0.0, 0.0]) + assert ray_intersect_segment(point, jnp.array([1.0, 1.0]), jnp.array([1.0, 2.0])) == False + assert ray_intersect_segment(point, jnp.array([1.0, 1.0]), jnp.array([-1.0, -1.0])) == True + assert ray_intersect_segment(point, jnp.array([1.0, 1.0]), jnp.array([1.0, -1.0])) == True + assert ray_intersect_segment(point, jnp.array([1.0, 0.0]), jnp.array([1.0, -1.0])) == False + assert ray_intersect_segment(point, jnp.array([1.0, 0.0]), jnp.array([1.0, 1.0])) == True + assert ray_intersect_segment(point, jnp.array([1.0, 1.0]), jnp.array([1.0, 0.0])) == True + +def test_point_in_polygon(): + polygon = jnp.array([ + [0, 1.0], + [-0.5, -1], + [0.5, -1], + ]) + point = jnp.array([0, 0]) + assert point_in_polygon(polygon, point) == True + point = jnp.array([1, -1]) + assert point_in_polygon(polygon, point) == False + point = jnp.array([0, 1.0]) + assert point_in_polygon(polygon, point) == True + point = jnp.array([-1, 1.0]) + assert point_in_polygon(polygon, point) == False def test_maf1(): prob = MaF1(d=d, m=m) @@ -41,7 +74,7 @@ def test_maf3(): r1, new_state1 = prob.evaluate(state, data) r2, new_state2 = prob.pf(state) assert r1.shape == (3, 3) - assert abs(float(r1[1, 1]) - 2.1354973e11) / float(r1[1, 1]) < 0.0001 + assert abs(float(r1[1, 1]) - 2.1354973e11) / 2.1354973e11 < 0.0001 assert r2.shape[1] == 3 assert float(r2[1, 1]) - 1.8255e-04 < 0.0001 @@ -52,7 +85,7 @@ def test_maf4(): r1, new_state1 = prob.evaluate(state, data) r2, new_state2 = prob.pf(state) assert r1.shape == (3, 3) - assert abs(float(r1[1, 1]) - 1.9944e03) / float(r1[1, 1]) < 0.0001 + assert abs(float(r1[1, 1]) - 1.9944e03) < 0.01 assert r2.shape[1] == 3 assert abs(float(r2[1, 1]) - 3.9460) < 0.0001 @@ -63,7 +96,7 @@ def test_maf5(): r1, new_state1 = prob.evaluate(state, data) r2, new_state2 = prob.pf(state) assert r1.shape == (3, 3) - assert abs(float(r1[1, 1]) - 2.1819e-40) / float(r1[1, 1]) < 0.0001 + assert abs(float(r1[1, 1]) - 2.1819e-40) < 0.0001 assert r2.shape[1] == 3 assert abs(float(r2[1, 1]) - 0.0540) < 0.0001 diff --git a/tests/test_monitors.py b/tests/test_monitors.py index e504f57bd..586b5c45a 100644 --- a/tests/test_monitors.py +++ b/tests/test_monitors.py @@ -12,7 +12,7 @@ def test_std_so_monitor_top1(): fitness1 = jnp.arange(3) monitor.record_pop(pop1) monitor.record_fit(fitness1) - assert monitor.get_min_fitness() == 0 + assert monitor.get_best_fitness() == 0 assert monitor.get_topk_fitness() == 0 assert (monitor.get_best_solution() == pop1[0]).all() assert (monitor.get_topk_solutions() == pop1[0:1]).all() @@ -21,7 +21,7 @@ def test_std_so_monitor_top1(): fitness2 = -jnp.arange(3) monitor.record_pop(pop2) monitor.record_fit(fitness2) - assert monitor.get_min_fitness() == -2 + assert monitor.get_best_fitness() == -2 assert monitor.get_topk_fitness() == -2 assert (monitor.get_best_solution() == pop2[2]).all() assert (monitor.get_topk_solutions() == pop2[2:3]).all() @@ -34,7 +34,7 @@ def test_std_so_monitor_top2(): fitness1 = jnp.arange(3) monitor.record_pop(pop1) monitor.record_fit(fitness1) - assert monitor.get_min_fitness() == 0 + assert monitor.get_best_fitness() == 0 assert (monitor.get_topk_fitness() == jnp.array([0, 1])).all() assert (monitor.get_best_solution() == pop1[0]).all() assert (monitor.get_topk_solutions() == pop1[0:2]).all() @@ -43,7 +43,7 @@ def test_std_so_monitor_top2(): fitness2 = -jnp.arange(3) monitor.record_pop(pop2) monitor.record_fit(fitness2) - assert monitor.get_min_fitness() == -2 + assert monitor.get_best_fitness() == -2 assert (monitor.get_topk_fitness() == jnp.array([-2, -1])).all() assert (monitor.get_best_solution() == pop2[2]).all() assert (monitor.get_topk_solutions() == pop2[2:0:-1]).all() diff --git a/tests/test_multi_objective_algorithms.py b/tests/test_multi_objective_algorithms.py index 97be21470..9d957cb71 100644 --- a/tests/test_multi_objective_algorithms.py +++ b/tests/test_multi_objective_algorithms.py @@ -190,3 +190,13 @@ def test_bce_ibea(): pop_size=100, ) run_moea(algorithm) + + +def test_lmocso(): + algorithm = algorithms.LMOCSO( + lb=jnp.full(shape=(12,), fill_value=0), + ub=jnp.full(shape=(12,), fill_value=1), + n_objs=3, + pop_size=100, + ) + run_moea(algorithm) \ No newline at end of file diff --git a/tests/test_neuroevolution.py b/tests/test_neuroevolution.py index dbd35c915..6cf0e0755 100644 --- a/tests/test_neuroevolution.py +++ b/tests/test_neuroevolution.py @@ -77,7 +77,7 @@ def test_neuroevolution_treemap(): state = workflow.step(state) # the result should be close to 0 - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() print(f"Treemap loss: {min_fitness} time: {time.perf_counter() - start}") @@ -111,5 +111,5 @@ def test_neuroevolution_adapter(): state = workflow.step(state) # the result should be close to 0 - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() print(f"Adapter loss: {min_fitness} time: {time.perf_counter() - start}") diff --git a/tests/test_single_objective_algorithms.py b/tests/test_single_objective_algorithms.py index b9ecd2de1..07bf46324 100644 --- a/tests/test_single_objective_algorithms.py +++ b/tests/test_single_objective_algorithms.py @@ -29,7 +29,7 @@ def run_single_objective_algorithm( for i in range(num_iter): state = workflow.step(state) - return monitor.get_min_fitness() + return monitor.get_best_fitness() def test_cso(): diff --git a/tests/test_uni_workflow.py b/tests/test_uni_workflow.py index aa6036468..50fa4ba87 100644 --- a/tests/test_uni_workflow.py +++ b/tests/test_uni_workflow.py @@ -26,7 +26,7 @@ def run_uni_workflow_with_jit_problem(): state = workflow.step(state) monitor.close() - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() return min_fitness @@ -52,7 +52,7 @@ def run_uni_workflow_with_non_jit_problem(): state = workflow.step(state) monitor.close() - min_fitness = monitor.get_min_fitness() + min_fitness = monitor.get_best_fitness() return min_fitness