diff --git a/.landscape.yml b/.landscape.yml new file mode 100644 index 00000000..def58547 --- /dev/null +++ b/.landscape.yml @@ -0,0 +1,11 @@ +python-targets: + - 2 + - 3 +doc-warnings: true +max-line-length: 100 +pyroma: + run: true +ignore-patterns: + - ^docs/*.py$ +ignore-paths: + - examples diff --git a/.travis.yml b/.travis.yml index 18fa9b13..6b03021b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,8 +10,18 @@ python: - 3.6-dev - 3.7-dev - nightly - - pypy - - pypy3.5-5.8.0 +env: +matrix: + include: + - python: pypy2.7-5.8.0 + - python: pypy3.5-5.8.0 + - python: pypy2.7-5.8.0 + env: TRY_PYPY=1 + - python: pypy3.5-5.8.0 + env: TRY_PYPY=1 + fast_finish: true + allow_failures: + - env: TRY_PYPY=1 install: - pip install --upgrade nose coveralls coverage - if [[ $TRAVIS_PYTHON_VERSION == 'pypy'* ]]; then export TRAVIS_WAIT=45; else export TRAVIS_WAIT=20; fi diff --git a/docs/conf.py b/docs/conf.py index 961f5082..bc7f0085 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -40,7 +40,8 @@ 'sphinx.ext.extlinks', # note: the below does not actually require (at least the Python) graphviz # package to be installed - 'sphinx.ext.inheritance_diagram', + 'sphinx.ext.inheritance_diagram', + 'sphinx.ext.coverage', ] extlinks = {'pytypes': ('https://docs.python.org/3.5/library/stdtypes.html#%s', diff --git a/docs/module_summaries.rst b/docs/module_summaries.rst index 969b8bb7..f6a2ce98 100644 --- a/docs/module_summaries.rst +++ b/docs/module_summaries.rst @@ -429,9 +429,27 @@ Uses :py:mod:`pickle` to save and restore populations (and other aspects of the :type time_interval_seconds: :pytypes:`float ` or None :param str filename_prefix: The prefix for the checkpoint file names. + .. py:method:: start_generation(generation) + + Saves the current generation number for use by :py:meth:`save_checkpoint()`. + + :param int generation: Current generation. + + .. py:method:: end_generation(config, population, species_set) + + Checks to see whether :py:meth:`save_checkpoint()` needs to be called, and if so calls it, then updates the last_generation_checkpoint and the + corresponding last_time_checkpoint attributes. + + :param config: The `config.Config` configuration instance to be used. + :type config: :datamodel:`instance ` + :param population: A population as created by :py:meth:`reproduction.DefaultReproduction.create_new` or a compatible implementation. + :type population: dict(int, :datamodel:`object `) + :param species: A :py:class:`species.DefaultSpeciesSet` (or compatible implementation) instance. + :type species: :datamodel:`instance ` + .. py:method:: save_checkpoint(config, population, species, generation) - Saves the current simulation (including randomization) state to (if using the default ``neat-checkpoint-`` for ``filename_prefix``) + Saves the current simulation state (including randomization state) to (if using the default ``neat-checkpoint-`` for ``filename_prefix``) :file:`neat-checkpoint-{generation}`, with ``generation`` being the generation number. :param config: The `config.Config` configuration instance to be used. @@ -632,6 +650,21 @@ ctrnn Resets the time and all node activations to 0 (necessary due to otherwise retaining state via :term:`recurrent` connections). + .. py:method:: set_node_value(node_key, value) + + Sets the current node activation for the particular node selected. + + :param int node_key: The :term:`key` for the node to be altered. + :param float value: What to set the activation of the node to. + + .. index:: TODO + + .. py:method:: get_max_time_step() + + Planned to return the maximum time step that will be stable for the current network; not yet implemented. + + :raises NotImplementedError: Always. + .. index:: ! continuous-time .. py:method:: advance(inputs, advance_time, time_step=None) @@ -643,7 +676,7 @@ ctrnn :type inputs: list(float) :param advance_time: How much time to advance the network before returning the resulting outputs. :type advance_time: :pytypes:`float ` - :param time_step: How much time per step to advance the network; the default of ``None`` will currently result in an error, but it is planned to determine it automatically. + :param time_step: How much time per step to advance the network; the default of ``None`` will currently result in an error, but it is planned to determine it automatically using :py:meth:`get_max_time_step()`. :type time_step: :pytypes:`float ` or None :return: The values for the :term:`output nodes `. :rtype: list(float) @@ -1555,7 +1588,19 @@ See http://www.izhikevich.org/publications/spikes.pdf. .. py:class:: IZGenome(DefaultGenome) - Contains the parse_config class method for iznn genome configuration, which returns a :py:class:`genome.DefaultGenomeConfig` instance. + Sets up the genome to use :py:class:`IZNodeGene` instances for node genes, and :py:class:`genes.DefaultConnectionGene` instances for + connection genes. + + .. py:classmethod:: parse_config(param_dict) + + Required interface method. Provides IZNodeGene :term:`node` and default :term:`connection` :term:`gene` specifications (from :py:mod:`genes`) and + uses `DefaultGenomeConfig` to do the rest of the configuration. + + :param param_dict: Dictionary of parameters from configuration file. + :type param_dict: dict(str, str) + :return: Configuration object; considered opaque by rest of code, so type may vary by implementation (here, a `DefaultGenomeConfig` instance). + :rtype: :datamodel:`instance ` + .. py:class:: IZNeuron(bias, a, b, c, d, inputs) @@ -1863,6 +1908,20 @@ Implements the core evolution algorithm. :type initial_state: None or tuple(:datamodel:`instance `, :datamodel:`instance `, int) :raises RuntimeError: If the :ref:`fitness_criterion ` function is invalid. + .. py:method:: add_reporter(reporter) + + Adds a reporter to those that will be notified at appropriate points. Uses :py:meth:`ReporterSet.add() `. + + :param reporter: A reporter callable via a :py:class:`reporting.ReporterSet` instance. + :type reporter: :datamodel:`instance ` + + .. py:method:: remove_reporter(reporter) + + Removes a reporter from those that will be notified at appropriate points. Uses :py:meth:`ReporterSet.remove() `. + + :param reporter: A reporter callable via a :py:class:`reporting.ReporterSet` instance. + :type reporter: :datamodel:`instance ` + .. index:: ! no_fitness_termination .. index:: ! reset_on_extinction .. index:: ! generation diff --git a/examples/picture2d/evolve_novelty.py b/examples/picture2d/evolve_novelty.py index 00819b5e..30edb589 100644 --- a/examples/picture2d/evolve_novelty.py +++ b/examples/picture2d/evolve_novelty.py @@ -1,4 +1,7 @@ -from Pillow import Image +try: + from Pillow import Image +expect ImportError: + from PIL import Image import os import random from multiprocessing import Pool @@ -101,6 +104,9 @@ def run(): pop.run(ne.evaluate, 1) winner = stats.best_genome() + + pop.best_genome = None # code assumes fitnesses remain the same... + if ne.scheme == 'gray': image = eval_gray_image(winner, config, full_scale * width, full_scale * height) elif ne.scheme == 'color': diff --git a/examples/picture2d/render.py b/examples/picture2d/render.py index 0e416573..54d0aaf4 100644 --- a/examples/picture2d/render.py +++ b/examples/picture2d/render.py @@ -1,4 +1,7 @@ +from __future__ import print_function + import pickle + import pygame import evolve @@ -11,7 +14,7 @@ with open("genome-20219-701.bin", "rb") as f: g = pickle.load(f) - print g + print(g) node_names = {0: 'x', 1: 'y', 2: 'gray'} visualize.draw_net(g, view=True, filename="picture-net.gv", show_disabled=False, prune_unused=True, node_names=node_names) diff --git a/neat/distributed.py b/neat/distributed.py index 57adc4a8..22d083ff 100644 --- a/neat/distributed.py +++ b/neat/distributed.py @@ -21,10 +21,14 @@ specified. NOTE: - This module is in a **beta** state, and still *unstable* even in single-machine testing. Reliability is likely to vary, including depending on the Python version - and implementation (e.g., cpython vs pypy) in use and the likelihoods of timeouts (due to machine and/or network slowness). In particular, while the code can try - to reconnect between between primary and secondary nodes, as noted in the `multiprocessing` documentation this may not work due to data loss/corruption. Note also - that this module is not responsible for starting the script copies on the different compute nodes, since this is very site/configuration-dependent. + This module is in a **beta** state, and still *unstable* even in single-machine + testing. Reliability is likely to vary, including depending on the Python version + and implementation (e.g., cpython vs pypy) in use and the likelihoods of timeouts + (due to machine and/or network slowness). In particular, while the code can try + to reconnect between between primary and secondary nodes, as noted in the + `multiprocessing` documentation this may not work due to data loss/corruption. + Note also that this module is *not* responsible for starting the script copies + on the different compute nodes, since this is very site/configuration-dependent. Usage: @@ -87,11 +91,10 @@ MODE_PRIMARY = MODE_MASTER = 1 # enforce primary mode MODE_SECONDARY = MODE_SLAVE = 2 # enforce secondary mode -# states to determine whether the secondaries should shut down -_STATE_RUNNING = 0 -_STATE_SHUTDOWN = 1 -_STATE_FORCED_SHUTDOWN = 2 - +# what a return from _check_exception means +_EXCEPTION_TYPE_OK = 1 # queue empty and similar; try again +_EXCEPTION_TYPE_UNCERTAIN = 0 # disconnected but may be able to reconnect +_EXCEPTION_TYPE_BAD = -1 # either raise it again or immediately return and exit with non-zero status code class ModeError(RuntimeError): """ @@ -179,50 +182,34 @@ def __init__(self, addr, authkey, mode, start=False): self.authkey = authkey self.mode = _determine_mode(addr, mode) self.manager = None - self._secondary_state= multiprocessing.managers.Value(int, _STATE_RUNNING) if start: self.start() - def __reduce__(self): + def __reduce__(self): # pragma: no cover """ This method is used by pickle to serialize instances of this class. """ return ( self.__class__, - (self.addr, self.authkey, self.mode, True), + (self.addr, self.authkey, self.mode, bool(self.manager is not None)), ) def start(self): """Starts or connects to the manager.""" - if self.mode == MODE_PRIMARY: - i = self._start() - else: - i = self._connect() - self.manager = i + if self.manager is None: + if self.mode == MODE_PRIMARY: + i = self._start() + else: + i = self._connect() + self.manager = i def stop(self): """Stops the manager.""" - self.manager.shutdown() - - def set_secondary_state(self, value): - """Sets the value for 'secondary_state'.""" - if value not in (_STATE_RUNNING, _STATE_SHUTDOWN, _STATE_FORCED_SHUTDOWN): - raise ValueError( - "State {!r} is invalid - needs to be one of _STATE_RUNNING, _STATE_SHUTDOWN, or _STATE_FORCED_SHUTDOWN".format( - value) - ) - if self.manager is None: - raise RuntimeError("Manager not started") - self.manager.set_state(value) - - def _get_secondary_state(self): - """ - Returns the value for 'secondary_state'. - This is required for the manager. - """ - return self._secondary_state + #self.manager.shutdown() # claims there isn't any such attribute ?!? + self.manager = None - def _get_manager_class(self, register_callables=False): + @staticmethod + def _get_manager_class(register_callables=False): """ Returns a new 'Manager' subclass with registered methods. If 'register_callable' is True, defines the 'callable' arguments. @@ -236,9 +223,9 @@ class _EvaluatorSyncManager(managers.BaseManager): """ pass - inqueue = queue.Queue() - outqueue = queue.Queue() - namespace = Namespace() + inqueue = queue.Queue() # may need to be one from multiprocessing.managers.SyncManager + outqueue = queue.Queue() # ditto + namespace = Namespace() # ditto if register_callables: _EvaluatorSyncManager.register( @@ -249,14 +236,6 @@ class _EvaluatorSyncManager(managers.BaseManager): "get_outqueue", callable=lambda: outqueue, ) - _EvaluatorSyncManager.register( - "get_state", - callable=self._get_secondary_state, - ) - _EvaluatorSyncManager.register( - "set_state", - callable=lambda v: self._secondary_state.set(v), - ) _EvaluatorSyncManager.register( "get_namespace", callable=lambda: namespace, @@ -270,12 +249,6 @@ class _EvaluatorSyncManager(managers.BaseManager): _EvaluatorSyncManager.register( "get_outqueue", ) - _EvaluatorSyncManager.register( - "get_state", - ) - _EvaluatorSyncManager.register( - "set_state", - ) _EvaluatorSyncManager.register( "get_namespace", ) @@ -295,12 +268,6 @@ def _start(self): ins.start() return ins - @property - def secondary_state(self): - """Whether the secondary nodes should still process elements.""" - v = self.manager.get_state() - return v.get() - def get_inqueue(self): """Returns the inqueue.""" if self.manager is None: @@ -375,6 +342,11 @@ def __init__( self.outqueue = None self.namespace = None self.started = False + self.exit_string = None + self.exit_on_stop = True + self.reconnect = False + self.reconnect_max_time = None + self.n_tasks = None def __getstate__(self): """Required by the pickle protocol.""" @@ -395,7 +367,13 @@ def is_master(self): # pragma: no cover warnings.warn("Use is_primary, not is_master", DeprecationWarning) return self.is_primary() - def start(self, exit_on_stop=True, secondary_wait=0, reconnect=False): + def _do_exit(self): + if self.exit_string is None: + sys.exit(0) + else: + sys.exit(self.exit_string) + + def start(self, exit_on_stop=True, secondary_wait=0, reconnect=False, reconnect_max_time=None): """ If the DistributedEvaluator is in primary mode, starts the manager process and returns. In this case, the ``exit_on_stop`` argument will @@ -414,14 +392,31 @@ def start(self, exit_on_stop=True, secondary_wait=0, reconnect=False): if self.started: raise RuntimeError("DistributedEvaluator already started!") self.started = True + self.exit_on_stop = exit_on_stop + self.reconnect = reconnect + if reconnect_max_time is None: + if reconnect: + reconnect_max_time = max((5*60),(15*max(5,self.worker_timeout))) + else: + reconnect_max_time = max(60,(5*max(1,self.worker_timeout))) + self.reconnect_max_time = max(0.3,reconnect_max_time) if self.mode == MODE_PRIMARY: self._start_primary() elif self.mode == MODE_SECONDARY: time.sleep(secondary_wait) - self._start_secondary() - self._secondary_loop(reconnect=reconnect) + while True: + self._start_secondary() + self._secondary_loop(reconnect_max_time=reconnect_max_time) + if self.exit_on_stop: + self._do_exit() + else: + self.inqueue = self.outqueue = self.namespace = None + if self.reconnect: + self.em.stop() + else: + break if exit_on_stop: - sys.exit(0) + self._do_exit() else: raise ValueError("Invalid mode {!r}!".format(self.mode)) @@ -438,21 +433,44 @@ def stop(self, wait=1, shutdown=True, force_secondary_shutdown=False): raise ModeError("Not in primary mode!") if not self.started: raise RuntimeError("Not yet started!") - if force_secondary_shutdown: - state = _STATE_FORCED_SHUTDOWN - else: - state = _STATE_SHUTDOWN - self.em.set_secondary_state(state) - time.sleep(wait) + start_time = time.time() + num_added = 0 + if self.n_tasks is None: # pragma: no cover + self.n_tasks = max(1, wait, self.worker_timeout)*5 + warnings.warn("Self.n_tasks is None; estimating at {:n}".format(self.n_tasks)) + while (num_added < self.n_tasks) and ((time.time() - start_time) < + max(1, + self.reconnect_max_time, + wait, + self.worker_timeout)): + try: + if force_secondary_shutdown: + self.inqueue.put(0, block=True, timeout=0.2) + else: + self.inqueue.put(1, block=True, timeout=0.2) + except (EOFError, IOError, OSError, socket.gaierror, TypeError, queue.Full, + managers.RemoteError, multiprocessing.ProcessError) as e: + if ("timed" in repr(e).lower()) or ("timeout" in repr(e).lower()): + if (time.time() - start_time) < max(1, wait, self.worker_timeout): + num_added += 1 + continue + else: + break + else: + break + else: + num_added += 1 + time_passed = time.time() - start_time + if time_passed < wait: + time.sleep(wait - time_passed) if shutdown: self.em.stop() self.started = False - self.inqueue = self.outqueue = self.namespace = None + self.outqueue = self.inqueue = self.namespace = None def _start_primary(self): """Start as the primary""" self.em.start() - self.em.set_secondary_state(_STATE_RUNNING) self._set_shared_instances() def _start_secondary(self): @@ -468,55 +486,88 @@ def _set_shared_instances(self): def _reset_em(self): """Resets self.em and the shared instances.""" - self.em = _ExtendedManager(self.addr, self.authkey, mode=self.mode, start=False) - self.em.start() + self.em = _ExtendedManager(self.addr, self.authkey, mode=self.mode, start=True) self._set_shared_instances() - def _secondary_loop(self, reconnect=False): + @staticmethod + def _check_exception(e): + string = repr(e).lower() + if ('timed' in string) or ('timeout' in string): + return _EXCEPTION_TYPE_OK + elif isinstance(e, (EOFError, TypeError, socket.gaierror)): + return _EXCEPTION_TYPE_UNCERTAIN + elif (('eoferror' in string) or ('typeerror' in string) or ('gaierror' in string) + or ('pipeerror' in string) or ('authenticationerror' in string) + or ('refused' in string) or ('file descriptor' in string)): + return _EXCEPTION_TYPE_UNCERTAIN + return _EXCEPTION_TYPE_BAD + + def _secondary_loop(self, reconnect_max_time=(5*60)): """The worker loop for the secondary nodes.""" if self.num_workers > 1: pool = multiprocessing.Pool(self.num_workers) else: pool = None should_reconnect = True + if self.reconnect: + em_bad = False + else: + em_bad = True while should_reconnect: - i = 0 + last_time_done = time.time() # so that if loops below, have a chance to check _reset_em running = True try: self._reset_em() - except (socket.error, EOFError, IOError, OSError, socket.gaierror, TypeError): - continue + except (EOFError, IOError, OSError, socket.gaierror, TypeError, + managers.RemoteError, multiprocessing.ProcessError) as e: + if (time.time() - last_time_done) >= reconnect_max_time: + should_reconnect = False + em_bad = True + if self._check_exception(e) == _EXCEPTION_TYPE_BAD: # pragma: no cover + self.exit_on_stop = True + self.exit_string = repr(e) + break + elif self._check_exception(e) == _EXCEPTION_TYPE_BAD: # pragma: no cover + raise + else: + continue + last_time_done = time.time() # being successful at reconnecting can be used as a keepalive while running: - i += 1 - if i % 5 == 0: - # for better performance, only check every 5 cycles - try: - state = self.em.secondary_state - except (socket.error, EOFError, IOError, OSError, socket.gaierror, TypeError): - if not reconnect: - raise - else: - break - if state == _STATE_FORCED_SHUTDOWN: - running = False - should_reconnect = False - elif state == _STATE_SHUTDOWN: - running = False - if not running: - continue try: tasks = self.inqueue.get(block=True, timeout=0.2) except queue.Empty: continue - except (socket.error, EOFError, IOError, OSError, socket.gaierror, TypeError): - break - except (managers.RemoteError, multiprocessing.ProcessError) as e: - if ('Empty' in repr(e)) or ('TimeoutError' in repr(e)): + except (EOFError, TypeError, socket.gaierror, + managers.RemoteError, multiprocessing.ProcessError, IOError, OSError) as e: + if ('empty' in repr(e).lower()): continue - if (('EOFError' in repr(e)) or ('PipeError' in repr(e)) or - ('AuthenticationError' in repr(e))): # Second for Python 3.X, Third for 3.6+ + curr_status = self._check_exception(e) + if curr_status in (_EXCEPTION_TYPE_OK, _EXCEPTION_TYPE_UNCERTAIN): + if (time.time() - last_time_done) >= reconnect_max_time: + if em_bad: + should_reconnect = False + break + elif curr_status == _EXCEPTION_TYPE_OK: + continue + else: + break + elif (time.time() - last_time_done) >= reconnect_max_time: # pragma: no cover + self.exit_on_stop = True + self.exit_string = repr(e) + should_reconnect = False break - raise + else: # pragma: no cover + raise + + if isinstance(tasks, int): # from primary + running = False + should_reconnect = False + if tasks and self.reconnect: + self.exit_on_stop = False + elif not tasks: + self.reconnect = False + break + last_time_done = time.time() if pool is None: res = [] for genome_id, genome, config in tasks: @@ -536,20 +587,38 @@ def _secondary_loop(self, reconnect=False): job.get(timeout=self.worker_timeout) for job in jobs ] res = zip(genome_ids, results) + last_time_done = time.time() try: self.outqueue.put(res) - except (socket.error, EOFError, IOError, OSError, socket.gaierror, TypeError): - break - except (managers.RemoteError, multiprocessing.ProcessError) as e: - if ('Empty' in repr(e)) or ('TimeoutError' in repr(e)): + except queue.Full: # pragma: no cover + continue + except (EOFError, TypeError, socket.gaierror, + managers.RemoteError, multiprocessing.ProcessError, + IOError, OSError) as e: + if ('full' in repr(e).lower()): continue - if (('EOFError' in repr(e)) or ('PipeError' in repr(e)) or - ('AuthenticationError' in repr(e))): # Second for Python 3.X, Third for 3.6+ + curr_status = self._check_exception(e) + if curr_status in (_EXCEPTION_TYPE_OK, _EXCEPTION_TYPE_UNCERTAIN): + if (time.time() - last_time_done) >= reconnect_max_time: + if em_bad: + should_reconnect = False + break + elif curr_status == _EXCEPTION_TYPE_OK: + continue + else: + break + elif (time.time() - last_time_done) >= reconnect_max_time: # pragma: no cover + self.exit_on_stop = True + self.exit_string = repr(e) + should_reconnect = False break - raise - - if not reconnect: - should_reconnect = False + else: # pragma: no cover + raise + else: + last_time_done = time.time() + if ((time.time() - last_time_done) >= reconnect_max_time): + if em_bad: + should_reconnect = False break if pool is not None: pool.terminate() @@ -567,12 +636,12 @@ def evaluate(self, genomes, config): tasks = chunked(tasks, self.secondary_chunksize) n_tasks = len(tasks) for task in tasks: - self.inqueue.put(task) + self.inqueue.put(task) # should this be w/timeouts and checking for exceptions? tresults = [] while len(tresults) < n_tasks: try: sr = self.outqueue.get(block=True, timeout=0.2) - except (queue.Empty, managers.RemoteError): + except (queue.Empty, managers.RemoteError): # more detailed check? continue tresults.append(sr) results = [] @@ -581,3 +650,4 @@ def evaluate(self, genomes, config): for genome_id, fitness in results: genome = id2genome[genome_id] genome.fitness = fitness + self.n_tasks = n_tasks diff --git a/neat/genome.py b/neat/genome.py index 7108bb5d..782ee001 100644 --- a/neat/genome.py +++ b/neat/genome.py @@ -38,7 +38,7 @@ def __init__(self, params): ConfigParameter('conn_delete_prob', float), ConfigParameter('node_add_prob', float), ConfigParameter('node_delete_prob', float), - ConfigParameter('single_structural_mutation', bool, 'false'), + ConfigParameter('single_structural_mutation', bool, False), ConfigParameter('structural_mutation_surer', str, 'default'), ConfigParameter('initial_connection', str, 'unconnected')] diff --git a/neat/math_util.py b/neat/math_util.py index 3137f9c8..04d3b5d9 100644 --- a/neat/math_util.py +++ b/neat/math_util.py @@ -1,8 +1,11 @@ """Commonly used functions not available in the Python2 standard library.""" from __future__ import division -from math import sqrt, exp +import math +from sys import float_info + +NORM_EPSILON = math.pow(float_info.epsilon, 0.25) # half-precision works for machine learning def mean(values): values = list(values) @@ -15,6 +18,10 @@ def median(values): return values[len(values) // 2] def median2(values): + """ + Returns the median of the input values; + if there are an even number of inputs, returns the mean of the middle two. + """ values = list(values) n = len(values) if n <= 2: @@ -32,14 +39,14 @@ def variance(values): def stdev(values): - return sqrt(variance(values)) + return math.sqrt(variance(values)) def softmax(values): """ Compute the softmax of the given value set, v_i = exp(v_i) / s, where s = sum(exp(v_0), exp(v_1), ..).""" - e_values = list(map(exp, values)) + e_values = list(map(math.exp, values)) s = sum(e_values) inv_s = 1.0 / s return [ev * inv_s for ev in e_values] diff --git a/neat/reproduction.py b/neat/reproduction.py index fa50ba1d..fc720cc2 100644 --- a/neat/reproduction.py +++ b/neat/reproduction.py @@ -2,14 +2,16 @@ Handles creation of genomes, either from scratch or by sexual or asexual reproduction from parents. """ -from __future__ import division +from __future__ import division, print_function import math import random + from itertools import count +from sys import stderr, float_info from neat.config import ConfigParameter, DefaultClassConfig -from neat.math_util import mean +from neat.math_util import mean, NORM_EPSILON from neat.six_util import iteritems, itervalues # TODO: Provide some sort of optional cross-species performance criteria, which @@ -28,7 +30,8 @@ def parse_config(cls, param_dict): return DefaultClassConfig(param_dict, [ConfigParameter('elitism', int, 0), ConfigParameter('survival_threshold', float, 0.2), - ConfigParameter('min_species_size', int, 2)]) + ConfigParameter('min_species_size', int, 2), + ConfigParameter('fitness_min_divisor', float, 1.0)]) def __init__(self, config, reporters, stagnation): # pylint: disable=super-init-not-called @@ -38,6 +41,19 @@ def __init__(self, config, reporters, stagnation): self.stagnation = stagnation self.ancestors = {} + if config.fitness_min_divisor < 0.0: + raise RuntimeError( + "Fitness_min_divisor cannot be negative ({0:n})".format( + config.fitness_min_divisor)) + elif config.fitness_min_divisor == 0.0: + config.fitness_min_divisor = NORM_EPSILON + elif config.fitness_min_divisor < float_info.epsilon: + print("Fitness_min_divisor {0:n} is too low; increasing to {1:n}".format( + config.fitness_min_divisor,float_info.epsilon), file=stderr) + stderr.flush() + config.fitness_min_divisor = float_info.epsilon + + def create_new(self, genome_type, genome_config, num_genomes): new_genomes = {} for i in range(num_genomes): @@ -115,8 +131,7 @@ def reproduce(self, config, species, pop_size, generation): min_fitness = min(all_fitnesses) max_fitness = max(all_fitnesses) # Do not allow the fitness range to be zero, as we divide by it below. - # TODO: The ``1.0`` below is rather arbitrary, and should be configurable. - fitness_range = max(1.0, max_fitness - min_fitness) + fitness_range = max(self.reproduction_config.fitness_min_divisor, max_fitness - min_fitness) for afs in remaining_species: # Compute adjusted fitness. msf = mean([m.fitness for m in itervalues(afs.members)]) diff --git a/tests/bad_configurationB b/tests/bad_configurationB new file mode 100644 index 00000000..7ca9be1e --- /dev/null +++ b/tests/bad_configurationB @@ -0,0 +1,81 @@ +[NEAT] +fitness_criterion = max +fitness_threshold = 0.9 +pop_size = 150 +reset_on_extinction = False + +[DefaultGenome] +# node activation options +activation_default = sigmoid +activation_mutate_rate = 0.0 +activation_options = sigmoid + +# node aggregation options +aggregation_default = sum +aggregation_mutate_rate = 0.0 +aggregation_options = sum + +# node bias options +bias_init_mean = 0.0 +bias_init_stdev = 1.0 +bias_max_value = 30.0 +bias_min_value = -30.0 +bias_mutate_power = 0.5 +bias_mutate_rate = 0.7 +bias_replace_rate = 0.1 + +# genome compatibility options +compatibility_disjoint_coefficient = 1.0 +compatibility_weight_coefficient = 0.5 + +# connection add/remove rates +conn_add_prob = 0.5 +conn_delete_prob = 0.5 + +# connection enable options +enabled_default = True +enabled_mutate_rate = 0.01 + +feed_forward = True +initial_connection = full + +# node add/remove rates +node_add_prob = 0.2 +node_delete_prob = 0.2 + +# network parameters +num_hidden = 0 +num_inputs = 2 +num_outputs = 1 + +# node response options +response_init_mean = 1.0 +response_init_stdev = 0.0 +response_max_value = 30.0 +response_min_value = -30.0 +response_mutate_power = 0.0 +response_mutate_rate = 0.0 +response_replace_rate = 0.0 + +# connection weight options +weight_init_mean = 0.0 +weight_init_stdev = 1.0 +weight_max_value = 30 +weight_min_value = -30 +weight_mutate_power = 0.5 +weight_mutate_rate = 0.8 +weight_replace_rate = 0.1 + +[DefaultSpeciesSet] +compatibility_threshold = 3.0 + +[DefaultStagnation] +species_fitness_func = max +max_stagnation = 20 +species_elitism = 1 + +[DefaultReproduction] +elitism = 2 +survival_threshold = 0.2 +min_species_size = 2 +fitness_min_divisor = -1.0 \ No newline at end of file diff --git a/tests/test_configuration4 b/tests/test_configuration4 index b51406f6..0e3c93a6 100644 --- a/tests/test_configuration4 +++ b/tests/test_configuration4 @@ -85,3 +85,4 @@ species_elitism = 0 elitism = 2 survival_threshold = 0.2 min_species_size = 2 +fitness_min_divisor = 0.5 \ No newline at end of file diff --git a/tests/test_configuration5 b/tests/test_configuration5 index 9b0926b5..c9828663 100644 --- a/tests/test_configuration5 +++ b/tests/test_configuration5 @@ -85,3 +85,4 @@ species_elitism = 0 elitism = 2 survival_threshold = 0.2 min_species_size = 2 +fitness_min_divisor = 0.0 \ No newline at end of file diff --git a/tests/test_distributed.py b/tests/test_distributed.py index a6b929c9..63b1ff68 100644 --- a/tests/test_distributed.py +++ b/tests/test_distributed.py @@ -18,11 +18,14 @@ HAVE_THREADING = True import neat -from neat.distributed import chunked, MODE_AUTO, MODE_PRIMARY, MODE_SECONDARY, ModeError, _STATE_RUNNING +from neat.distributed import chunked, MODE_AUTO, MODE_PRIMARY, MODE_SECONDARY, ModeError ON_PYPY = platform.python_implementation().upper().startswith("PYPY") - +if ON_PYPY and ((not 'TRY_PYPY' in os.environ) or (os.environ['TRY_PYPY'] != 1)): + SKIP_FOR_PYPY = True +else: + SKIP_FOR_PYPY = False def eval_dummy_genome_nn(genome, config): """dummy evaluation function""" @@ -181,21 +184,6 @@ def test_DistributedEvaluator_primary_restrictions(): else: raise Exception("A DistributedEvaluator in secondary mode could call evaluate()!") -def test_DistributedEvaluator_state_error1(): - """Tests that attempts to use an unstarted manager for set_secondary_state cause an error.""" - primary = neat.DistributedEvaluator( - ("localhost", 8022), - authkey=b"abcd1234", - eval_function=eval_dummy_genome_nn, - mode=MODE_PRIMARY, - ) - try: - primary.em.set_secondary_state(_STATE_RUNNING) - except RuntimeError: - pass - else: - raise Exception("primary.em.set_secondary_state with unstarted manager did not raise a RuntimeError!") - def test_DistributedEvaluator_state_error2(): """Tests that attempts to use an unstarted manager for get_inqueue cause an error.""" primary = neat.DistributedEvaluator( @@ -240,25 +228,9 @@ def test_DistributedEvaluator_state_error4(): pass else: raise Exception("primary.em.get_namespace() with unstarted manager did not raise a RuntimeError!") - -def test_DistributedEvaluator_state_error5(): - """Tests that attempts to set an invalid state cause an error.""" - primary = neat.DistributedEvaluator( - ("localhost", 8022), - authkey=b"abcd1234", - eval_function=eval_dummy_genome_nn, - mode=MODE_PRIMARY, - ) - primary.start() - try: - primary.em.set_secondary_state(-1) - except ValueError: - pass - else: - raise Exception("primary.em.set_secondary_state(-1) did not raise a ValueError!") -@unittest.skipIf(ON_PYPY, "This test fails on pypy during travis builds but usually works locally.") +@unittest.skipIf(SKIP_FOR_PYPY, "This test fails on pypy during travis builds but usually works locally.") def test_distributed_evaluation_multiprocessing(do_mwcp=True): """ Full test run using the Distributed Evaluator (fake nodes using processes). @@ -448,6 +420,7 @@ def run_secondary(addr, authkey, num_workers=1): test_host_is_local() test_DistributedEvaluator_mode() test_DistributedEvaluator_primary_restrictions() - test_distributed_evaluation_multiprocessing(do_mwcp=True) - if HAVE_THREADING: + if not SKIP_FOR_PYPY: + test_distributed_evaluation_multiprocessing(do_mwcp=True) + if HAVE_THREADING and (not ON_PYPY): test_distributed_evaluation_threaded() diff --git a/tests/test_simple_run.py b/tests/test_simple_run.py index 73362ece..5b51074b 100644 --- a/tests/test_simple_run.py +++ b/tests/test_simple_run.py @@ -1,9 +1,22 @@ from __future__ import print_function import os +import platform +import unittest + import neat VERBOSE = True +ON_PYPY = platform.python_implementation().upper().startswith("PYPY") + +try: + import threading +except ImportError: + import dummy_threading as threading + HAVE_THREADING = False +else: + HAVE_THREADING = True + def eval_dummy_genome_nn(genome, config): net = neat.nn.FeedForwardNetwork.create(genome, config) ignored_output = net.activate((0.5, 0.5)) @@ -285,6 +298,24 @@ def test_serial_bad_configA(): raise Exception( "Should have had a RuntimeError with bad_configurationA") +def test_serial_bad_configB(): + """Test if bad_configurationB causes a RuntimeError on trying to create the population.""" + # Load configuration. + local_dir = os.path.dirname(__file__) + config_path = os.path.join(local_dir, 'bad_configurationB') + config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, + neat.DefaultSpeciesSet, neat.DefaultStagnation, + config_path) + + try: + # Create the population, which is the top-level object for a NEAT run. + p = neat.Population(config) + except RuntimeError: + pass + else: + raise Exception( + "Should have had a RuntimeError with bad_configurationB") + def test_serial_extinction_exception(): """Test for complete extinction with exception.""" # Load configuration. @@ -363,9 +394,11 @@ def test_parallel(): stats.save() - +@unittest.skipIf(ON_PYPY, "Pypy has problems with threading.") def test_threaded_evaluation(): """Tests a neat evolution using neat.threaded.ThreadedEvaluator""" + if not HAVE_THREADING: + raise unittest.SkipTest("Platform does not have threading") # Load configuration. local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'test_configuration') @@ -388,9 +421,11 @@ def test_threaded_evaluation(): stats.save() - +@unittest.skipIf(ON_PYPY, "Pypy has problems with threading.") def test_threaded_evaluator(): """Tests general functionality of neat.threaded.ThreadedEvaluator""" + if not HAVE_THREADING: + raise unittest.SkipTest("Platform does not have threading") n_workers = 3 e = neat.ThreadedEvaluator(n_workers, eval_dummy_genome_nn) try: @@ -673,8 +708,9 @@ def test_run_iznn_bad(): test_serial_extinction_exception() test_serial_extinction_no_exception() test_parallel() - test_threaded_evaluation() - test_threaded_evaluator() + if HAVE_THREADING and (not ON_PYPY): + test_threaded_evaluation() + test_threaded_evaluator() test_run_nn_recurrent() test_run_nn_recurrent_bad() test_run_ctrnn() diff --git a/tests/test_xor_example_distributed.py b/tests/test_xor_example_distributed.py index 583efbbb..fdf5c627 100644 --- a/tests/test_xor_example_distributed.py +++ b/tests/test_xor_example_distributed.py @@ -12,6 +12,11 @@ ON_PYPY = platform.python_implementation().upper().startswith("PYPY") +if ON_PYPY and ((not 'TRY_PYPY' in os.environ) or (os.environ['TRY_PYPY'] != 1)): + SKIP_FOR_PYPY = True +else: + SKIP_FOR_PYPY = False + # 2-input XOR inputs and expected outputs. XOR_INPUTS = [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0)] XOR_OUTPUTS = [(0.0,), (1.0,), (1.0,), (0.0,)] @@ -58,7 +63,7 @@ def run_primary(addr, authkey, generations): de.start() winner = p.run(de.evaluate, generations) print("===== stopping DistributedEvaluator =====") - de.stop(wait=3, shutdown=True, force_secondary_shutdown=False) + de.stop(wait=3, shutdown=False, force_secondary_shutdown=False) if winner: # Display the winning genome. @@ -128,7 +133,7 @@ def run_secondary(addr, authkey, num_workers=1): raise Exception("DistributedEvaluator in secondary mode did not try to exit!") -@unittest.skipIf(ON_PYPY, "This test fails on pypy during travis builds (frequently due to timeouts) but usually works locally.") +@unittest.skipIf(SKIP_FOR_PYPY, "This test fails on pypy during travis builds (frequently due to timeouts) but usually works locally.") def test_xor_example_distributed(): """ Test to make sure restoration after checkpoint works with distributed. pFad - Phonifier reborn

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