diff --git a/.github/workflows/pnl-ci.yml b/.github/workflows/pnl-ci.yml index 36112db794b..3a34814b268 100644 --- a/.github/workflows/pnl-ci.yml +++ b/.github/workflows/pnl-ci.yml @@ -99,7 +99,7 @@ jobs: flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest - run: pytest --junit-xml=tests_out.xml --verbosity=0 -n auto + run: pytest --junit-xml=tests_out.xml --verbosity=0 -n auto --maxprocesses=2 - name: Upload test results uses: actions/upload-artifact@v1 diff --git a/Scripts/Debug/bryant_lca_with_termination.py b/Scripts/Debug/bryant_lca_with_termination.py index d6d8ce2e342..610e2a76769 100644 --- a/Scripts/Debug/bryant_lca_with_termination.py +++ b/Scripts/Debug/bryant_lca_with_termination.py @@ -1,6 +1,4 @@ import psyneulink as pnl -import numpy as np -import pytest cueInterval = pnl.TransferMechanism(default_variable=[[0.0]], size=1, @@ -60,4 +58,4 @@ csiController.log.print_entries() cueInterval.log.print_entries() taskLayer.log.print_entries() -# csiController.control_signals[0].log.print_entries() \ No newline at end of file +# csiController.control_signals[0].log.print_entries() diff --git a/Scripts/Debug/predator_prey_opt/predator_prey_dmt.py b/Scripts/Debug/predator_prey_opt/predator_prey_dmt.py index 2933a3c80d1..95e26577c76 100644 --- a/Scripts/Debug/predator_prey_opt/predator_prey_dmt.py +++ b/Scripts/Debug/predator_prey_opt/predator_prey_dmt.py @@ -1,4 +1,3 @@ -import sys import os import timeit import numpy as np diff --git a/Scripts/Examples/Rumelhart Semantic Network.py b/Scripts/Examples/Rumelhart Semantic Network.py index cbcb340d0a1..1f7e8433721 100644 --- a/Scripts/Examples/Rumelhart Semantic Network.py +++ b/Scripts/Examples/Rumelhart Semantic Network.py @@ -1,6 +1,4 @@ from psyneulink import * -import numpy as np -import typecheck as tc # This script implements the following network, first described in Rumelhart and Todd # (Rumelhart, D. E., & Todd, P. M. (1993). Learning and connectionist representations. Attention and performance XIV: diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css index 4c01d508728..fd45307670c 100644 --- a/docs/source/_static/css/custom.css +++ b/docs/source/_static/css/custom.css @@ -1,62 +1,3 @@ -/*!**/ -/*Sets bottom-margin size for list elements in the main body of the document to 12px.*/ -/*Because Sphinx includes the table of contents in the main section, we then reset just the list elements*/ -/*in that section to 0px.*/ -/* *!*/ - -h1, h2{ - margin-bottom:12px; -} - -li{ - margin-top:12px; -} - -ul.simple{ - margin-bottom:12px!important; -} - -ol.simple{ - margin-bottom:12px!important; -} - -.section #overview { - margin-top: 24px; -} - -#sections p, -#sections .simple, -#sections li{ - margin-top:0px; - margin-bottom:0px!important; -} - -.toctree-wrapper.compound li, div#sections li{ - margin-top: 0px !important; -} - -.toctree-wrapper.compound ul.simple{ - display: none -} - -#welcome-to-psyneulink-logo .simple li, -#welcome-to-logo .simple li, -#basics-and-sampler .simple li, -#basics-and-sampler .simple p, -#quick-reference #sections li, -#core li, -#contributors li, -#table-of-contents li, -#indices-and-tables li, -.wy-menu-vertical li, -.section #contents li, -.section #contents p, -.section #contents dd, -.section #contents dt, -.section #contents dl, -.section #contents ul, -.section #contents ol -{ - margin-top:0px; - margin-bottom:0px!important; -} \ No newline at end of file +/* +CSS styles defined in this file will be loaded into the docs at build time +*/ \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 4fcf73ecd62..db59aec563f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -173,6 +173,7 @@ # 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 = ['css/custom.css'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied diff --git a/psyneulink/core/components/__init__.py b/psyneulink/core/components/__init__.py index e8e7fb17441..5bfcffdbf4f 100644 --- a/psyneulink/core/components/__init__.py +++ b/psyneulink/core/components/__init__.py @@ -40,6 +40,9 @@ from .shellclasses import * from .ports import * +from psyneulink.library.components.mechanisms.processing.integrator.ddm import DDM +from psyneulink.core.globals.preferences.basepreferenceset import BasePreferenceSet, ComponentDefaultPrefDicts, PreferenceLevel + __all__ = [ 'InitError' ] @@ -62,27 +65,17 @@ def __str__(self): #region ***************************************** MECHANISM SUBCLASSES ************************************************* -from psyneulink.core.components.mechanisms.mechanism import MechanismRegistry -from psyneulink.core.components.mechanisms.mechanism import Mechanism_Base -from psyneulink.core.components.mechanisms.processing.defaultprocessingmechanism \ - import DefaultProcessingMechanism_Base -from psyneulink.core.components.mechanisms.modulatory.control.controlmechanism \ - import ControlMechanism register_category(entry=ControlMechanism, base_class=Mechanism_Base, registry=MechanismRegistry, context=kwInitPy) -from psyneulink.core.components.mechanisms.modulatory.control.defaultcontrolmechanism \ - import DefaultControlMechanism register_category(entry=DefaultControlMechanism, base_class=Mechanism_Base, registry=MechanismRegistry, context=kwInitPy) # DDM (used as DefaultMechanism) - -from psyneulink.library.components.mechanisms.processing.integrator.ddm import DDM register_category(entry=DDM, base_class=Mechanism_Base, registry=MechanismRegistry, @@ -113,46 +106,38 @@ def __str__(self): # while still indexing multiple uses of the same base name within an owner # # Port registry -from psyneulink.core.components.ports.port import PortRegistry -from psyneulink.core.components.ports.port import Port_Base # InputPort -from psyneulink.core.components.ports.inputport import InputPort register_category(entry=InputPort, base_class=Port_Base, registry=PortRegistry, context=kwInitPy) # ParameterPort -from psyneulink.core.components.ports.parameterport import ParameterPort register_category(entry=ParameterPort, base_class=Port_Base, registry=PortRegistry, context=kwInitPy) # OutputPort -from psyneulink.core.components.ports.outputport import OutputPort register_category(entry=OutputPort, base_class=Port_Base, registry=PortRegistry, context=kwInitPy) # LearningSignal -from psyneulink.core.components.ports.modulatorysignals.learningsignal import LearningSignal register_category(entry=LearningSignal, base_class=Port_Base, registry=PortRegistry, context=kwInitPy) # ControlSignal -from psyneulink.core.components.ports.modulatorysignals.controlsignal import ControlSignal register_category(entry=ControlSignal, base_class=Port_Base, registry=PortRegistry, context=kwInitPy) # GatingSignal -from psyneulink.core.components.ports.modulatorysignals.gatingsignal import GatingSignal register_category(entry=GatingSignal, base_class=Port_Base, registry=PortRegistry, @@ -162,32 +147,26 @@ def __str__(self): # Projection ----------------------------------------------------------------------------------------------------------- # Projection registry -from psyneulink.core.components.projections.projection import ProjectionRegistry -from psyneulink.core.components.projections.projection import Projection_Base # MappingProjection -from psyneulink.core.components.projections.pathway.mappingprojection import MappingProjection register_category(entry=MappingProjection, base_class=Projection_Base, registry=ProjectionRegistry, context=kwInitPy) # LearningProjection -from psyneulink.core.components.projections.modulatory.learningprojection import LearningProjection register_category(entry=LearningProjection, base_class=Projection_Base, registry=ProjectionRegistry, context=kwInitPy) # ControlProjection -from psyneulink.core.components.projections.modulatory.controlprojection import ControlProjection register_category(entry=ControlProjection, base_class=Projection_Base, registry=ProjectionRegistry, context=kwInitPy) # GatingProjection -from psyneulink.core.components.projections.modulatory.gatingprojection import GatingProjection register_category(entry=GatingProjection, base_class=Projection_Base, registry=ProjectionRegistry, @@ -271,23 +250,17 @@ def __str__(self): #endregion #region ***************************************** CLASS _PREFERENCES *************************************************** -from psyneulink.core.globals.preferences.basepreferenceset \ - import BasePreferenceSet, ComponentDefaultPrefDicts, PreferenceLevel - -from psyneulink.core.components.shellclasses import System_Base System_Base.classPreferences = BasePreferenceSet(owner=System_Base, prefs=ComponentDefaultPrefDicts[PreferenceLevel.INSTANCE], level=PreferenceLevel.INSTANCE, context=".__init__.py") -from psyneulink.core.components.shellclasses import Process_Base Process_Base.classPreferences = BasePreferenceSet(owner=Process_Base, prefs=ComponentDefaultPrefDicts[PreferenceLevel.INSTANCE], level=PreferenceLevel.INSTANCE, context=".__init__.py") -from psyneulink.core.components.shellclasses import Mechanism Mechanism.classPreferences = BasePreferenceSet(owner=Mechanism, prefs=ComponentDefaultPrefDicts[PreferenceLevel.CATEGORY], level=PreferenceLevel.TYPE, @@ -299,7 +272,6 @@ def __str__(self): context=".__init__.py") -from psyneulink.core.components.shellclasses import Port Port.classPreferences = BasePreferenceSet(owner=Port, prefs=ComponentDefaultPrefDicts[PreferenceLevel.CATEGORY], level=PreferenceLevel.CATEGORY, @@ -315,13 +287,11 @@ def __str__(self): level=PreferenceLevel.TYPE, context=".__init__.py") -from psyneulink.core.components.shellclasses import Projection Projection.classPreferences = BasePreferenceSet(owner=Projection, prefs=ComponentDefaultPrefDicts[PreferenceLevel.CATEGORY], level=PreferenceLevel.CATEGORY, context=".__init__.py") -from psyneulink.core.components.functions.function import Function Function.classPreferences = BasePreferenceSet(owner=Function, prefs=ComponentDefaultPrefDicts[PreferenceLevel.CATEGORY], level=PreferenceLevel.CATEGORY, diff --git a/psyneulink/core/components/component.py b/psyneulink/core/components/component.py index 52637896f2f..ef4eb719c5b 100644 --- a/psyneulink/core/components/component.py +++ b/psyneulink/core/components/component.py @@ -483,18 +483,14 @@ import inspect import logging import numbers -import re import types import warnings -import weakref from abc import ABCMeta from collections.abc import Iterable -from collections import OrderedDict, UserDict from enum import Enum, IntEnum import numpy as np -import typecheck as tc from psyneulink.core import llvm as pnlvm from psyneulink.core.globals.context import \ @@ -2438,7 +2434,6 @@ def _validate_params(self, request_set, target_set=None, context=None): elif target_set is not None: # Copy any iterables so that deletions can be made to assignments belonging to the instance - from collections.abc import Iterable if not isinstance(param_value, Iterable) or isinstance(param_value, str): target_set[param_name] = param_value else: diff --git a/psyneulink/core/components/functions/distributionfunctions.py b/psyneulink/core/components/functions/distributionfunctions.py index 1de497abb38..94e3bb5a670 100644 --- a/psyneulink/core/components/functions/distributionfunctions.py +++ b/psyneulink/core/components/functions/distributionfunctions.py @@ -23,7 +23,6 @@ Functions that return one or more samples from a distribution. """ -from enum import IntEnum import numpy as np import typecheck as tc diff --git a/psyneulink/core/components/functions/function.py b/psyneulink/core/components/functions/function.py index 6814c544c1c..22fdd03c01e 100644 --- a/psyneulink/core/components/functions/function.py +++ b/psyneulink/core/components/functions/function.py @@ -308,10 +308,6 @@ def _output_type_setter(value, owning_component): # https://github.com/PrincetonUniversity/PsyNeuLink/issues/895 is solved # properly(meaning Mechanism values may be something other than 2D np array) try: - # import here because if this package is not installed, we can assume - # the user is probably not dealing with compilation so no need to warn - # unecessarily - import llvmlite if ( isinstance(owning_component.owner, Mechanism) and ( diff --git a/psyneulink/core/components/functions/objectivefunctions.py b/psyneulink/core/components/functions/objectivefunctions.py index aa09a2ab03b..2cc9c80efe1 100644 --- a/psyneulink/core/components/functions/objectivefunctions.py +++ b/psyneulink/core/components/functions/objectivefunctions.py @@ -20,7 +20,6 @@ """ import functools -import itertools import numpy as np import typecheck as tc diff --git a/psyneulink/core/components/functions/optimizationfunctions.py b/psyneulink/core/components/functions/optimizationfunctions.py index 06247f6ba1a..ff4f200a322 100644 --- a/psyneulink/core/components/functions/optimizationfunctions.py +++ b/psyneulink/core/components/functions/optimizationfunctions.py @@ -2344,8 +2344,7 @@ def _import_elfi(): # https://github.com/scikit-optimize/scikit-optimize/issues/637 # Lets import and set the backend to PS to be safe. We aren't plotting anyway # I guess. Only do this on Mac OS X - from sys import platform - if platform == "darwin": + if sys.platform == "darwin": import matplotlib matplotlib.use('PS') diff --git a/psyneulink/core/components/functions/statefulfunctions/integratorfunctions.py b/psyneulink/core/components/functions/statefulfunctions/integratorfunctions.py index 1f34c01bc29..5a45100af8a 100644 --- a/psyneulink/core/components/functions/statefulfunctions/integratorfunctions.py +++ b/psyneulink/core/components/functions/statefulfunctions/integratorfunctions.py @@ -25,8 +25,6 @@ """ -import itertools -import numbers import warnings import numpy as np diff --git a/psyneulink/core/components/functions/statefulfunctions/memoryfunctions.py b/psyneulink/core/components/functions/statefulfunctions/memoryfunctions.py index b822e31c54c..1ba2aa6a44f 100644 --- a/psyneulink/core/components/functions/statefulfunctions/memoryfunctions.py +++ b/psyneulink/core/components/functions/statefulfunctions/memoryfunctions.py @@ -22,7 +22,7 @@ """ -from collections import deque, OrderedDict +from collections import deque import numpy as np import typecheck as tc diff --git a/psyneulink/core/components/functions/statefulfunctions/statefulfunction.py b/psyneulink/core/components/functions/statefulfunctions/statefulfunction.py index e734b28492b..5337703f1f9 100644 --- a/psyneulink/core/components/functions/statefulfunctions/statefulfunction.py +++ b/psyneulink/core/components/functions/statefulfunctions/statefulfunction.py @@ -16,13 +16,12 @@ """ -import numpy as np +import abc import typecheck as tc -import itertools import warnings import numbers -import abc +import numpy as np from psyneulink.core import llvm as pnlvm from psyneulink.core.components.component import DefaultsFlexibility diff --git a/psyneulink/core/components/functions/transferfunctions.py b/psyneulink/core/components/functions/transferfunctions.py index a2fab9742a6..9db6e43d474 100644 --- a/psyneulink/core/components/functions/transferfunctions.py +++ b/psyneulink/core/components/functions/transferfunctions.py @@ -41,13 +41,14 @@ """ import numbers -from enum import IntEnum - import numpy as np import typecheck as tc import types import warnings +from enum import IntEnum +from math import e, pi, sqrt + from psyneulink.core import llvm as pnlvm from psyneulink.core.components.component import parameter_keywords from psyneulink.core.components.functions.function import ( @@ -707,7 +708,6 @@ def _function(self, # The following doesn't work with autograd (https://github.com/HIPS/autograd/issues/416) # result = scale * np.exp(rate * variable + bias) + offset - from math import e result = scale * e**(rate * variable + bias) + offset return self.convert_output_type(result) @@ -731,7 +731,6 @@ def derivative(self, input, output=None, context=None): """ - from math import e rate = self._get_current_function_param(RATE, context) scale = self._get_current_function_param(SCALE, context) bias = self._get_current_function_param(BIAS, context) @@ -997,7 +996,6 @@ def _function(self, # The following doesn't work with autograd (https://github.com/HIPS/autograd/issues/416) # result = 1. / (1 + np.exp(-gain * (variable - bias) + offset)) - from math import e result = scale * (1. / (1 + e**(-gain * (variable + bias - x_0) + offset))) return self.convert_output_type(result) @@ -1318,7 +1316,6 @@ def _function(self, # The following probably doesn't work with autograd (https://github.com/HIPS/autograd/issues/416) # (since np.exp doesn't work) # result = 1. / (1 + np.tanh(-gain * (variable - bias) + offset)) - from math import e exponent = -2 * (gain * (variable + bias - x_0) + offset) result = scale * (1 - e**exponent)/ (1 + e**exponent) @@ -1351,7 +1348,6 @@ def derivative(self, input, output=None, context=None): exponent = -2 * (gain * (input + bias - x_0) + offset) mult = -2 * gain * scale - from math import e numerator = -2 * e**(exponent) denominator = (1 + e**(exponent))**2 @@ -1771,7 +1767,6 @@ def _gen_llvm_transfer(self, builder, index, ctx, vi, vo, params, state, *, tags exp = builder.fdiv(exp_num, exp_denom) numerator = builder.call(exp_f, [exp]) - from math import pi denom = builder.fmul(standard_deviation.type(2 * pi), standard_deviation) denom = builder.call(sqrt_f, [denom]) val = builder.fdiv(numerator, denom) @@ -1811,7 +1806,6 @@ def _function(self, scale = self._get_current_function_param(SCALE, context) offset = self._get_current_function_param(OFFSET, context) - from math import e, pi, sqrt gaussian = e**(-(variable - bias)**2 / (2 * standard_deviation**2)) / sqrt(2 * pi * standard_deviation) result = scale * gaussian + offset @@ -1841,7 +1835,6 @@ def derivative(self, input, output=None, context=None): sigma = self._get_current_function_param(STANDARD_DEVIATION, context) bias = self._get_current_function_param(BIAS, context) - from math import e, pi, sqrt adjusted_input = input - bias result = (-adjusted_input * e**(-(adjusted_input**2 / (2 * sigma**2)))) / sqrt(2 * pi * sigma**3) diff --git a/psyneulink/core/components/functions/userdefinedfunction.py b/psyneulink/core/components/functions/userdefinedfunction.py index 2b9c1468b1f..44982ebdaf3 100644 --- a/psyneulink/core/components/functions/userdefinedfunction.py +++ b/psyneulink/core/components/functions/userdefinedfunction.py @@ -13,6 +13,8 @@ import numpy as np import typecheck as tc +from inspect import signature, _empty + from psyneulink.core.components.component import ComponentError from psyneulink.core.components.functions.function import FunctionError, Function_Base from psyneulink.core.globals.context import ContextFlags @@ -394,7 +396,6 @@ def get_cust_fct_args(custom_function): - dict with all others (to be assigned as params of UDF) - dict with default values (from function definition, else set to None) """ - from inspect import signature, _empty try: arg_names = custom_function.__code__.co_varnames except AttributeError: diff --git a/psyneulink/core/components/mechanisms/mechanism.py b/psyneulink/core/components/mechanisms/mechanism.py index 6c25d76733d..d14c7561efb 100644 --- a/psyneulink/core/components/mechanisms/mechanism.py +++ b/psyneulink/core/components/mechanisms/mechanism.py @@ -1071,10 +1071,11 @@ class `UserList ` based on specified `control_allocation ` diff --git a/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py b/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py index 133dce6786e..5e7a973a493 100644 --- a/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py +++ b/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py @@ -797,7 +797,7 @@ def _instantiate_input_ports(self, context=None): for i in range(1, len(self.input_ports)): port = self.input_ports[i] if len(port.path_afferents) > 1: - raise OptimizationControlMechanismError(f"Invalid {InputPort.__name__} on {self.name}. " + raise OptimizationControlMechanismError(f"Invalid {type(input_port).__name__} on {self.name}. " f"{port.name} should receive exactly one projection, " f"but it receives {len(port.path_afferents)} projections.") diff --git a/psyneulink/core/components/mechanisms/processing/objectivemechanism.py b/psyneulink/core/components/mechanisms/processing/objectivemechanism.py index 7d93a5ac4b6..516953cd0ea 100644 --- a/psyneulink/core/components/mechanisms/processing/objectivemechanism.py +++ b/psyneulink/core/components/mechanisms/processing/objectivemechanism.py @@ -363,10 +363,8 @@ """ -import itertools import warnings import typecheck as tc -import numpy as np from collections.abc import Iterable from collections import namedtuple diff --git a/psyneulink/core/components/ports/inputport.py b/psyneulink/core/components/ports/inputport.py index f98f1781570..a6a41cc40fe 100644 --- a/psyneulink/core/components/ports/inputport.py +++ b/psyneulink/core/components/ports/inputport.py @@ -1296,7 +1296,6 @@ def _get_port_function_value(owner, function, variable): can't be called by _parse_port_spec since the InputPort itself may not yet have been instantiated. """ - import inspect if ( ( diff --git a/psyneulink/core/components/ports/modulatorysignals/controlsignal.py b/psyneulink/core/components/ports/modulatorysignals/controlsignal.py index bc135a51028..f6ef9115be2 100644 --- a/psyneulink/core/components/ports/modulatorysignals/controlsignal.py +++ b/psyneulink/core/components/ports/modulatorysignals/controlsignal.py @@ -397,12 +397,6 @@ """ -import inspect -import itertools -import warnings - -from enum import IntEnum - import numpy as np import typecheck as tc diff --git a/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py b/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py index 57c066745b0..450fb677202 100644 --- a/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py +++ b/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py @@ -407,8 +407,6 @@ --------------- """ -import itertools - from psyneulink.core.components.component import component_keywords from psyneulink.core.components.ports.outputport import OutputPort from psyneulink.core.globals.context import ContextFlags diff --git a/psyneulink/core/components/ports/outputport.py b/psyneulink/core/components/ports/outputport.py index a22556cc90f..ccdabed3ea5 100644 --- a/psyneulink/core/components/ports/outputport.py +++ b/psyneulink/core/components/ports/outputport.py @@ -615,12 +615,10 @@ """ import copy -import inspect import numpy as np import typecheck as tc import types import warnings -from collections import OrderedDict from psyneulink.core.components.component import Component, ComponentError from psyneulink.core.components.functions.function import Function diff --git a/psyneulink/core/components/ports/parameterport.py b/psyneulink/core/components/ports/parameterport.py index b22fc40e5c4..ce22b293e2b 100644 --- a/psyneulink/core/components/ports/parameterport.py +++ b/psyneulink/core/components/ports/parameterport.py @@ -359,8 +359,8 @@ """ +from copy import deepcopy import inspect -import itertools import types import warnings @@ -1002,7 +1002,6 @@ def _get_tuple_for_single_item_modulatory_spec(obj, name, value): ): reference_value = function_param_value else: - from copy import deepcopy reference_value = deepcopy(function_param_value) # Assign parameterPort for function_param to the component diff --git a/psyneulink/core/components/ports/port.py b/psyneulink/core/components/ports/port.py index 18a57ef0f95..fb513f03b83 100644 --- a/psyneulink/core/components/ports/port.py +++ b/psyneulink/core/components/ports/port.py @@ -762,17 +762,16 @@ def test_multiple_modulatory_projections_with_mech_and_port_Name_specs(self): """ -import abc import abc import inspect import itertools import numbers +import sys import types import warnings from collections.abc import Iterable from collections import defaultdict -from copy import deepcopy import numpy as np import typecheck as tc @@ -1793,13 +1792,19 @@ def _remove_projection_from_port(self, projection, context=None): del self.efferents[self.efferents.index(projection)] def _remove_projection_to_port(self, projection, context=None): - """Remove Projection entry from Port.path_afferents and reshape variable accordingly.""" - shape = list(self.defaults.variable.shape) - # Reduce outer dimension by one - shape[0]-=1 - self.defaults.variable = np.resize(self.defaults.variable, shape) - self.function.defaults.variable = np.resize(self.function.defaults.variable, shape) - del self.path_afferents[self.path_afferents.index(projection)] + """ + If projection is in mod_afferents, remove that projection from self.mod_afferents. + Else, Remove Projection entry from Port.path_afferents and reshape variable accordingly. + """ + if projection in self.mod_afferents: + del self.mod_afferents[self.mod_afferents.index(projection)] + else: + shape = list(self.defaults.variable.shape) + # Reduce outer dimension by one + shape[0]-=1 + self.defaults.variable = np.resize(self.defaults.variable, shape) + self.function.defaults.variable = np.resize(self.function.defaults.variable, shape) + del self.path_afferents[self.path_afferents.index(projection)] def _get_primary_port(self, mechanism): raise PortError("PROGRAM ERROR: {} does not implement _get_primary_port method". @@ -2674,7 +2679,6 @@ def _parse_port_type(owner, port_spec): # Port keyword if port_spec in port_type_keywords: - import sys return getattr(sys.modules['PsyNeuLink.Components.Ports.' + port_spec], port_spec) # Try as name of Port diff --git a/psyneulink/core/components/projections/pathway/mappingprojection.py b/psyneulink/core/components/projections/pathway/mappingprojection.py index 1634190abd7..191d483bf8b 100644 --- a/psyneulink/core/components/projections/pathway/mappingprojection.py +++ b/psyneulink/core/components/projections/pathway/mappingprojection.py @@ -284,10 +284,8 @@ """ import copy -import inspect import numpy as np -import typecheck as tc from psyneulink.core.components.component import parameter_keywords from psyneulink.core.components.functions.statefulfunctions.integratorfunctions import AccumulatorIntegrator @@ -444,7 +442,6 @@ class sockets: projection_sender = OutputPort - # @tc.typecheck def __init__(self, sender=None, receiver=None, diff --git a/psyneulink/core/components/projections/projection.py b/psyneulink/core/components/projections/projection.py index 99247d49e5a..e959b016359 100644 --- a/psyneulink/core/components/projections/projection.py +++ b/psyneulink/core/components/projections/projection.py @@ -396,6 +396,7 @@ import inspect import itertools import warnings +from collections import namedtuple, defaultdict import numpy as np import typecheck as tc @@ -458,7 +459,6 @@ def projection_param_keywords(): return set(projection_param_keyword_mapping().values()) -from collections import namedtuple ProjectionTuple = namedtuple("ProjectionTuple", "port, weight, exponent, projection") @@ -1196,7 +1196,6 @@ def _parse_projection_spec(projection_spec, if bad_arg: raise ProjectionError("Illegal argument in call to _parse_port_spec: {}".format(bad_arg)) - from collections import defaultdict proj_spec_dict = defaultdict(lambda :None) proj_spec_dict.update(kwargs) diff --git a/psyneulink/core/compositions/composition.py b/psyneulink/core/compositions/composition.py index 08a14196edc..c08a0c53dc7 100644 --- a/psyneulink/core/compositions/composition.py +++ b/psyneulink/core/compositions/composition.py @@ -3333,6 +3333,7 @@ def __init__( self.projections = ContentAddressableList(component_type=Component) self._scheduler = None + self._partially_added_nodes = [] self.disable_learning = disable_learning @@ -3481,6 +3482,7 @@ def _analyze_graph(self, scheduler=None, context=None): except AttributeError: pass + self._complete_init_of_partially_initialized_nodes(context=context) self._determine_node_roles(context=context) self._determine_pathway_roles(context=context) self._create_CIM_ports(context=context) @@ -3580,80 +3582,7 @@ def add_node(self, node, required_roles=None, context=None): self.needs_update_graph_processing = True self.needs_update_scheduler = True - try: - # activate any projections the node requires - node._activate_projections_for_compositions(self) - except AttributeError: - pass - - # Implement any components specified in node's aux_components attribute - if hasattr(node, "aux_components"): - - projections = [] - # Add all "nodes" to the composition first (in case projections reference them) - for component in node.aux_components: - if isinstance(component, (Mechanism, Composition)): - if isinstance(component, Composition): - component._analyze_graph() - self.add_node(component) - elif isinstance(component, Projection): - projections.append((component, False)) - elif isinstance(component, tuple): - if isinstance(component[0], Projection): - if isinstance(component[1], bool) or component[1] in {EdgeType.FLEXIBLE, MAYBE}: - projections.append(component) - else: - raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. If a " - "tuple is used to specify a Projection, then the index 0 item must " - "be the Projection, and the index 1 item must be the feedback " - "specification (True or False).".format(component, node.name)) - elif isinstance(component[0], (Mechanism, Composition)): - if isinstance(component[1], NodeRole): - self.add_node(node=component[0], required_roles=component[1]) - elif isinstance(component[1], list): - if isinstance(component[1][0], NodeRole): - self.add_node(node=component[0], required_roles=component[1]) - else: - raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. " - "If a tuple is used to specify a Mechanism or Composition, then " - "the index 0 item must be the Node, and the index 1 item must " - "be the required_roles".format(component, node.name)) - - else: - raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. If a " - "tuple is used to specify a Mechanism or Composition, then the " - "index 0 item must be the Node, and the index 1 item must be the " - "required_roles".format(component, node.name)) - else: - raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. If a tuple" - " is specified, then the index 0 item must be a Projection, Mechanism, " - "or Composition.".format(component, node.name)) - else: - raise CompositionError("Invalid Component ({}) in {}'s aux_components. Must be a Mechanism, " - "Composition, Projection, or tuple." - .format(component.name, node.name)) - - # Add all Projections to the Composition - for proj_spec in projections: - # The proj_spec assumes a direct connection between sender and receiver, and is therefore invalid if - # either are nested (i.e. projections between them need to be routed through a CIM). In these cases, - # a new projection is instantiated between sender and receiver instead of using the original spec. - # If the sender or receiver is an AutoAssociativeProjection, then the owner will be another projection - # instead of a mechanism, so owner_mech instead needs to be used instead. - sender_node = proj_spec[0].sender.owner - receiver_node = proj_spec[0].receiver.owner - if isinstance(sender_node, AutoAssociativeProjection): - sender_node = proj_spec[0].sender.owner.owner_mech - if isinstance(receiver_node, AutoAssociativeProjection): - receiver_node = proj_spec[0].receiver.owner.owner_mech - if sender_node in self.nodes and \ - receiver_node in self.nodes: - self.add_projection(projection=proj_spec[0], - feedback=proj_spec[1]) - else: - self.add_projection(sender=proj_spec[0].sender, - receiver=proj_spec[0].receiver, - feedback=proj_spec[1]) + invalid_aux_components = self._add_node_aux_components(node) # Implement required_roles if required_roles: @@ -3677,6 +3606,12 @@ def add_node(self, node, required_roles=None, context=None): if self.controller: self._instantiate_deferred_init_control(node) + try: + if len(invalid_aux_components) > 0: + self._partially_added_nodes.append(node) + except NameError: + pass + def _instantiate_deferred_init_control(self, node): """ If node is a Composition with a controller, activate its nodes' deferred init control specs for its controller. @@ -4031,6 +3966,100 @@ def _determine_origin_and_terminal_nodes_from_consideration_queue(self): if not any([efferent for efferent in node.efferents if not efferent.receiver.owner is self.output_CIM]): self._add_node_role(node, NodeRole.TERMINAL) + def _add_node_aux_components(self, node, context=None): + """ + Returns + ------- + + list containing references to all invalid aux components + + """ + # Implement any components specified in node's aux_components attribute + invalid_aux_components = [] + if hasattr(node, "aux_components"): + # Collect the node's aux components that are not currently able to be added to the Composition + # we'll ignore these for now and try to activate them again during every call to _analyze_graph + # at runtime if there are still any invalid aux components left, we will issue a warning + projections = [] + # Add all "nodes" to the composition first (in case projections reference them) + for component in node.aux_components: + if isinstance(component, (Mechanism, Composition)): + if isinstance(component, Composition): + component._analyze_graph() + self.add_node(component) + elif isinstance(component, Projection): + projections.append((component, False)) + elif isinstance(component, tuple): + if isinstance(component[0], Projection): + if (isinstance(component[1], bool) or component[1] in {EdgeType.FLEXIBLE, MAYBE}): + projections.append(component) + else: + raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. If a " + "tuple is used to specify a Projection, then the index 0 item must " + "be the Projection, and the index 1 item must be the feedback " + "specification (True or False).".format(component, node.name)) + elif isinstance(component[0], (Mechanism, Composition)): + if isinstance(component[1], NodeRole): + self.add_node(node=component[0], required_roles=component[1]) + elif isinstance(component[1], list): + if isinstance(component[1][0], NodeRole): + self.add_node(node=component[0], required_roles=component[1]) + else: + raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. " + "If a tuple is used to specify a Mechanism or Composition, then " + "the index 0 item must be the Node, and the index 1 item must " + "be the required_roles".format(component, node.name)) + + else: + raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. If a " + "tuple is used to specify a Mechanism or Composition, then the " + "index 0 item must be the Node, and the index 1 item must be the " + "required_roles".format(component, node.name)) + else: + raise CompositionError("Invalid Component specification ({}) in {}'s aux_components. If a tuple" + " is specified, then the index 0 item must be a Projection, Mechanism, " + "or Composition.".format(component, node.name)) + else: + raise CompositionError("Invalid Component ({}) in {}'s aux_components. Must be a Mechanism, " + "Composition, Projection, or tuple." + .format(component.name, node.name)) + + invalid_aux_components.extend(self._get_invalid_aux_components(node)) + # Add all Projections to the Composition + for proj_spec in [i for i in projections if not i[0] in invalid_aux_components]: + # The proj_spec assumes a direct connection between sender and receiver, and is therefore invalid if + # either are nested (i.e. projections between them need to be routed through a CIM). In these cases, + # a new projection is instantiated between sender and receiver instead of using the original spec. + # If the sender or receiver is an AutoAssociativeProjection, then the owner will be another projection + # instead of a mechanism, so owner_mech instead needs to be used instead. + sender_node = proj_spec[0].sender.owner + receiver_node = proj_spec[0].receiver.owner + if isinstance(sender_node, AutoAssociativeProjection): + sender_node = proj_spec[0].sender.owner.owner_mech + if isinstance(receiver_node, AutoAssociativeProjection): + receiver_node = proj_spec[0].receiver.owner.owner_mech + if sender_node in self.nodes and \ + receiver_node in self.nodes: + self.add_projection(projection=proj_spec[0], + feedback=proj_spec[1]) + else: + self.add_projection(sender=proj_spec[0].sender, + receiver=proj_spec[0].receiver, + feedback=proj_spec[1]) + return invalid_aux_components + + def _complete_init_of_partially_initialized_nodes(self, context=None): + """ + Attempt to complete initialization of aux components for any nodes with + aux components that were not previously compatible with Composition + """ + completed_nodes = [] + for node in self._partially_added_nodes: + invalid_aux_components = self._add_node_aux_components(node, context=context) + if not invalid_aux_components: + completed_nodes.append(node) + self._partially_added_nodes = list(set(self._partially_added_nodes) - set(completed_nodes)) + def _determine_node_roles(self, context=None): """Assign NodeRoles to Nodes in Composition @@ -4414,7 +4443,7 @@ def _create_CIM_ports(self, context=None): # PCIMs are not currently supported for compilation if they don't have any input/output ports, # so remove their default ports only in the case that additional ports are going to be configured below external_modulatory_projections = self._get_external_modulatory_projections() - if not self.parameter_CIM.connected_to_composition and external_modulatory_projections: + if not self.parameter_CIM.connected_to_composition and (external_modulatory_projections or len(self.parameter_CIM.input_ports) > 1): self.parameter_CIM.remove_ports(self.parameter_CIM.input_port) self.parameter_CIM.remove_ports(self.parameter_CIM.output_port) # flag the CIM as connected to the Composition so we don't remove ports on future calls to this method @@ -4721,24 +4750,26 @@ def _create_CIM_ports(self, context=None): f"This means there was a failure to route these projections through the PCIM." def _get_nested_node_CIM_port(self, - node: Mechanism, - node_state: tc.any(InputPort, OutputPort), - role: tc.enum(NodeRole.INPUT, NodeRole.OUTPUT) - ): + node: Mechanism, + node_port: tc.any(InputPort, OutputPort), + role: tc.enum(NodeRole.INPUT, NodeRole.OUTPUT) + ): """Check for node in nested Composition Return relevant port of relevant CIM if found and nested Composition in which it was found, else (None, None) """ nested_comp = CIM_port_for_nested_node = CIM = None - nested_comps = [c for c in self.nodes if isinstance(c, Composition)] + nested_comps = [i for i in self.nodes if isinstance(i, Composition)] for nc in nested_comps: - if node in nc.nodes: + nested_nodes = dict(nc._get_nested_nodes()) + if node in nested_nodes or node in nc.nodes.data: # Must be assigned Node.Role of INPUT or OUTPUT (depending on receiver vs sender) # This validation does not apply to ParameterPorts. Externally modulated nodes # can be in any position within a Composition. They don't need to be INPUT or OUTPUT nodes - if not isinstance(node_state, ParameterPort): - if role not in nc.nodes_to_roles[node]: + if not isinstance(node_port, ParameterPort): + owning_composition = nc if node in nc.nodes.data else nested_nodes[node] + if role not in owning_composition.nodes_to_roles[node]: raise CompositionError("{} found in nested {} of {} ({}) but without required {} ({})". format(node.name, Composition.__name__, self.name, nc.name, NodeRole.__name__, repr(role))) @@ -4751,24 +4782,34 @@ def _get_nested_node_CIM_port(self, # Composition.__name__, self.name, nested_comp.name)) # continue - if isinstance(node_state, InputPort): - CIM_port_for_nested_node = nc.input_CIM_ports[node_state][0] - CIM = nc.input_CIM - elif isinstance(node_state, OutputPort): - CIM_port_for_nested_node = nc.output_CIM_ports[node_state][1] - CIM = nc.output_CIM - elif isinstance(node_state, ParameterPort): + if isinstance(node_port, InputPort): + if node_port in nc.input_CIM_ports: + CIM_port_for_nested_node = owning_composition.input_CIM_ports[node_port][0] + CIM = owning_composition.input_CIM + else: + nested_node_CIM_port_spec = nc._get_nested_node_CIM_port(node, node_port, NodeRole.INPUT) + CIM_port_for_nested_node = nc.input_CIM_ports[nested_node_CIM_port_spec[0]][0] + CIM = nc.input_CIM + elif isinstance(node_port, OutputPort): + if node_port in nc.output_CIM_ports: + CIM_port_for_nested_node = owning_composition.output_CIM_ports[node_port][1] + CIM = owning_composition.output_CIM + else: + nested_node_CIM_port_spec = nc._get_nested_node_CIM_port(node, node_port, NodeRole.OUTPUT) + CIM_port_for_nested_node = nc.output_CIM_ports[nested_node_CIM_port_spec[0]][1] + CIM = nc.output_CIM + elif isinstance(node_port, ParameterPort): # NOTE: there is special casing here for parameter ports. They don't have a node role # associated with them in the way that input and output nodes do, so we don't know for sure # if they will have a port in parameter_CIM_ports. If they don't, we just set the - # CIM_port_for_nested_node to the node_state itself, and delegate its routing through the PCIM + # CIM_port_for_nested_node to the node_port itself, and delegate its routing through the PCIM # to a future call to create_CIM_ports - if node_state in nc.parameter_CIM_ports: - CIM_port_for_nested_node = nc.parameter_CIM_ports[node_state][0] - CIM = nc.parameter_CIM - else: - CIM_port_for_nested_node = node_state - CIM = nc.parameter_CIM + # if node_port in nc.parameter_CIM_ports: + # CIM_port_for_nested_node = nc.parameter_CIM_ports[node_port][0] + # CIM = nc.parameter_CIM + # else: + CIM_port_for_nested_node = node_port + CIM = nc.parameter_CIM nested_comp = nc break return CIM_port_for_nested_node, CIM_port_for_nested_node, nested_comp, CIM @@ -4785,6 +4826,8 @@ def _update_shadows_dict(self, node): for input_port in node.input_ports: if hasattr(input_port, SHADOW_INPUTS) and input_port.shadow_inputs is not None: owner = input_port.shadow_inputs.owner + if isinstance(owner, CompositionInterfaceMechanism): + owner = owner.composition if owner in nested_nodes: owner = nested_nodes[owner] if node is self.controller and self._controller_initialization_status == ContextFlags.DEFERRED_INIT: @@ -4793,6 +4836,54 @@ def _update_shadows_dict(self, node): if node not in self.shadows[owner]: self.shadows[owner].append(node) + def _route_control_projection_through_intermediary_pcims(self, projection, sender, sender_mechanism, receiver, graph_receiver, context): + """ + Takes as input a specification for a projection to a parameter port that is nested n-levels below its sender, + instantiates and activates ports and projections on intermediary pcims, and returns a new + projection specification from the original sender to the relevant input port of the pcim of the Composition + located in the same level of nesting. + """ + for proj in receiver.mod_afferents: + if proj.sender.owner == sender_mechanism: + receiver._remove_projection_to_port(proj) + for proj in sender.efferents: + if proj.receiver == receiver: + sender._remove_projection_from_port(proj) + modulation = sender.modulation + interface_input_port = InputPort(owner=graph_receiver.parameter_CIM, + variable=receiver.defaults.value, + reference_value=receiver.defaults.value, + name=PARAMETER_CIM_NAME + "_" + receiver.owner.name + "_" + receiver.name, + context=context) + graph_receiver.parameter_CIM.add_ports([interface_input_port], context=context) + # control signal for parameter CIM that will project directly to inner Composition's parameter + control_signal = ControlSignal( + modulation=modulation, + variable=OWNER_VALUE, + transfer_function=InterfacePortMap( + corresponding_input_port=interface_input_port + ), + modulates=receiver, + name=PARAMETER_CIM_NAME + "_" + receiver.owner.name + "_" + receiver.name, + ) + if not receiver.owner in graph_receiver.nodes.data + graph_receiver.cims: + receiver = interface_input_port + graph_receiver.parameter_CIM.add_ports([control_signal], context=context) + # add sender and receiver to self.parameter_CIM_ports dict + for p in control_signal.projections: + # self.add_projection(p) + graph_receiver.add_projection(p, receiver=p.receiver, sender=control_signal) + try: + sender._remove_projection_to_port(projection) + except ValueError: + pass + try: + receiver._remove_projection_from_port(projection) + except ValueError: + pass + receiver = interface_input_port + return MappingProjection(sender=sender, receiver=receiver) + # ****************************************************************************************************************** # PROJECTIONS @@ -4828,6 +4919,7 @@ def add_projections(self, projections=None): "Composition requires a list of Projections, each of which must have a " "sender and a receiver.".format(self.name)) + @handle_external_context(source=ContextFlags.METHOD) def add_projection(self, projection=None, sender=None, @@ -4835,7 +4927,8 @@ def add_projection(self, feedback=False, learning_projection=False, name=None, - allow_duplicates=False + allow_duplicates=False, + context=None ): """Add **projection** to the Composition. @@ -4978,6 +5071,26 @@ def add_projection(self, receiver, receiver_mechanism, graph_receiver, receiver_input_port, nested_compositions, learning_projection = \ self._parse_receiver_spec(projection, receiver, sender, learning_projection) + if (isinstance(receiver_mechanism, (CompositionInterfaceMechanism)) + and not receiver_input_port.owner in self.nodes + and receiver.componentType == 'ParameterPort'): + # unlike when projecting to nested InputPorts, we don't know for sure whether + # intermediary pcims will have input ports that correspond to the ParameterPorts we are interested + # in projecting to. the below method handles routing through intermediary pcims, including by adding needed + # ports and projections when they don't already exist, and returns a modified projection spec for us to use + # on this level of nesting. + if isinstance(receiver, ParameterPort): + projection = self._route_control_projection_through_intermediary_pcims(projection, sender, + sender_mechanism, receiver, + graph_receiver, context) + receiver = projection.receiver + + if sender_mechanism is self.parameter_CIM: + idx = self.parameter_CIM.output_ports.index(sender) + in_p = self.parameter_CIM.input_ports[idx] + out_p = self.parameter_CIM.output_ports[idx] + self.parameter_CIM.port_map[receiver] = (in_p, out_p) + # If Deferred init if projection.initialization_status == ContextFlags.DEFERRED_INIT: # If sender or receiver are Port specs, use those; otherwise, use graph node (Mechanism or Composition) @@ -5023,7 +5136,10 @@ def add_projection(self, # FIX: KAM HACK 2/13/19 to get hebbian learning working for PSY/NEU 330 # Add autoassociative learning mechanism + related projections to composition as processing components if (sender_mechanism != self.input_CIM + and sender_mechanism != self.parameter_CIM + and sender_mechanism != self.controller and receiver_mechanism != self.output_CIM + and receiver_mechanism != self.controller and projection not in [vertex.component for vertex in self.graph.vertices] and not learning_projection): @@ -5155,7 +5271,8 @@ def _parse_sender_spec(self, projection, sender): if (not isinstance(sender_mechanism, CompositionInterfaceMechanism) and not isinstance(sender, Composition) - and sender_mechanism not in self.nodes): + and sender_mechanism not in self.nodes + and sender_mechanism != self.controller): if isinstance(sender, Port): sender_name = sender.full_name else: @@ -5239,6 +5356,7 @@ def _parse_receiver_spec(self, projection, receiver, sender, learning_projection if (not isinstance(receiver_mechanism, CompositionInterfaceMechanism) and not isinstance(receiver, Composition) and receiver_mechanism not in self.nodes + and receiver_mechanism != self.controller and not learning_projection): # if the receiver is IN a nested Composition AND receiver is an INPUT Node @@ -6910,6 +7028,21 @@ def _add_error_projection_to_dependent_learning_mechs(self, error_source): # receiver=error_signal_input_port[0])) return projections + def _get_deeply_nested_aux_projections(self, node): + deeply_nested_projections = {} + if hasattr(node, 'aux_components'): + aux_projections = {} + for i in node.aux_components: + if hasattr(i, '__iter__') and isinstance(i[0], Projection): + aux_projections[i] = i[0] + elif isinstance(i, Projection): + aux_projections[i] = i + nested_nodes = self._get_nested_nodes() + for spec, proj in aux_projections.items(): + if not proj.receiver.owner in self.nodes and \ + proj.receiver.owner in [i[0] for i in nested_nodes if not i[1] in self.nodes]: + deeply_nested_projections[spec] = proj + return deeply_nested_projections # ****************************************************************************************************************** # CONTROL @@ -7114,7 +7247,10 @@ def _build_predicted_inputs_dict(self, predicted_input): and not owner in self.nodes: continue if not owner in nested_nodes: - inputs[input_port.shadow_inputs.owner] = predicted_input[j] + shadow_input_owner = input_port.shadow_inputs.owner + if isinstance(shadow_input_owner, CompositionInterfaceMechanism): + shadow_input_owner = shadow_input_owner.composition + inputs[shadow_input_owner] = predicted_input[j] else: comp = nested_nodes[owner] if not comp in inputs: @@ -7127,7 +7263,11 @@ def _get_invalid_aux_components(self, controller): valid_nodes = \ [node for node in self.nodes.data] + \ [node for node, composition in self._get_nested_nodes()] + \ - [self.controller, self.controller.objective_mechanism, self] + [self] + if self.controller: + valid_nodes.append(self.controller) + if hasattr(self.controller,'objective_mechanism'): + valid_nodes.append(self.controller.objective_mechanism) invalid_components = [] for aux in controller.aux_components: component = None @@ -7260,6 +7400,16 @@ def _check_projection_initialization_status(self, context=None): f"add a controller to {self.name} or the control specification will be \n" f"ignored.") + for node in self._partially_added_nodes: + for proj in self._get_invalid_aux_components(node): + receiver = proj.receiver.owner + warnings.warn( + f"{node.name} has been specified to project to {receiver.name}, " + f"but {receiver.name} is not in {self.name} or any of its nested Compositions. " + f"This projection will be deactivated until {receiver.name} is added to {self.name} " + f"or a composition nested within it." + ) + def evaluate( self, predicted_input=None, @@ -8176,6 +8326,7 @@ def run( # DS 1/7/20: Check to see if any Components are still in deferred init. If so, attempt to initialize them. # If they can not be initialized, raise a warning. + self._complete_init_of_partially_initialized_nodes(context=context) if ContextFlags.SIMULATION_MODE not in context.runmode: self._check_projection_initialization_status() @@ -9758,6 +9909,5 @@ def _animate_execution(self, active_items, context): def get_compositions(): """Return list of Compositions in caller's namespace.""" - import inspect frame = inspect.currentframe() return [c for c in frame.f_back.f_locals.values() if isinstance(c, Composition)] diff --git a/psyneulink/core/compositions/showgraph.py b/psyneulink/core/compositions/showgraph.py index c0479ce3dc9..293e94d49d2 100644 --- a/psyneulink/core/compositions/showgraph.py +++ b/psyneulink/core/compositions/showgraph.py @@ -206,8 +206,6 @@ from psyneulink.core.components.projections.pathway.mappingprojection import MappingProjection from psyneulink.core.components.projections.modulatory.controlprojection import ControlProjection from psyneulink.core.components.shellclasses import Mechanism, Projection -from psyneulink.core.components.ports.inputport import InputPort -from psyneulink.core.components.ports.parameterport import ParameterPort from psyneulink.core.components.ports.outputport import OutputPort from psyneulink.core.globals.utilities import convert_to_list from psyneulink.core.globals.context import ContextFlags, handle_external_context @@ -255,7 +253,7 @@ ENCLOSING_COMP = 'enclosing_comp' # enclosing composition NESTING_LEVEL = 'nesting_level' NUM_NESTING_LEVELS = 'num_nesting_levels' - +COMP_HIERARCHY = 'comp_hierarchy' # dict specifying the enclosing composition at each level of nesting class ShowGraphError(Exception): @@ -618,6 +616,7 @@ def show_graph(self, context.execution_id = composition.default_execution_id # Args not specified by user but used in calls to show_graph for nested Compositions + comp_hierarchy = kwargs.pop(COMP_HIERARCHY, {}) enclosing_comp = kwargs.pop(ENCLOSING_COMP,None) nesting_level = kwargs.pop(NESTING_LEVEL,None) self.num_nesting_levels = kwargs.pop(NUM_NESTING_LEVELS,None) @@ -755,10 +754,19 @@ def show_graph(self, continue # # MODIFIED 6/13/20 END + # If show_controller is true, objective mechanism will be + # handled in _assign_controller_components + if (show_controller + and composition.controller + and composition.controller.objective_mechanism + and rcvr is composition.controller.objective_mechanism): + continue + self._assign_processing_components(G, rcvr, processing_graph, enclosing_comp, + comp_hierarchy, nesting_level, active_items, show_nested, @@ -781,7 +789,9 @@ def show_graph(self, show_dimensions, show_node_structure, node_struct_args, - show_projection_labels) + show_projection_labels, + show_controller, + comp_hierarchy) # Add controller-related Components to graph if show_controller if show_controller: @@ -795,13 +805,17 @@ def show_graph(self, show_dimensions, show_node_structure, node_struct_args, - show_projection_labels) + show_projection_labels, + comp_hierarchy, + nesting_level) # Add learning-related Components to graph if show_learning if show_learning: self._assign_learning_components(G, processing_graph, enclosing_comp, + comp_hierarchy, + nesting_level, active_items, show_nested, show_cim, @@ -827,6 +841,7 @@ def _assign_processing_components(self, rcvr, processing_graph, enclosing_comp, + comp_hierarchy, nesting_level, active_items, show_nested, @@ -847,7 +862,9 @@ def _assign_processing_components(self, # User passed attrs for nested Composition if isinstance(rcvr, Composition): if show_nested: + comp_hierarchy.update({nesting_level:composition}) nested_args.update({OUTPUT_FMT:'gv', + COMP_HIERARCHY:comp_hierarchy, # 'composition': rcvr, ENCLOSING_COMP:composition, NESTING_LEVEL:nesting_level + 1}) @@ -1033,7 +1050,9 @@ def _assign_processing_components(self, show_dimensions, show_node_structure, show_projection_labels, - enclosing_comp=enclosing_comp) + enclosing_comp=enclosing_comp, + comp_hierarchy=comp_hierarchy, + nesting_level=nesting_level) def _assign_cim_components(self, g, @@ -1044,7 +1063,9 @@ def _assign_cim_components(self, show_dimensions, show_node_structure, node_struct_args, - show_projection_labels): + show_projection_labels, + show_controller, + comp_hierarchy): from psyneulink.core.compositions.composition import Composition, NodeRole composition = self.composition @@ -1163,7 +1184,7 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, # Construct edge name if show_node_structure: # Get label for CIM's port as edge's receiver - rcvr_cim_proj_label = f"{cim_label}:{InputPort.__name__}-{proj.receiver.name}" + rcvr_cim_proj_label = f"{cim_label}:{type(input_port).__name__}-{proj.receiver.name}" if (isinstance(sndr_node_output_port_owner, Composition) and show_nested is not NESTED): sndr_output_node_proj_label = sndr_label @@ -1221,8 +1242,9 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, else: # Need to use direct reference to proj.receiver rather than rcvr_input_node_proj # since could be Composition, which does not have a get_port_name attribute + port_type = type(proj.receiver) rcvr_input_node_proj_label = \ - f"{rcvr_label}:{InputPort.__name__}-{proj.receiver.name}" + f"{rcvr_label}:{port_type.__name__}-{proj.receiver.name}" # rcvr_input_node_proj_label = \ # f"{rcvr_label}:" \ # f"{rcvr_input_node_proj_owner._get_port_name(rcvr_input_node_proj)}" @@ -1271,7 +1293,8 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, sndr_ctl_sig_proj_label = \ f"{sndr_label}:{OutputPort.__name__}-{proj.sender.name}" # Get label for CIM's InputPort as edge's receiver - rcvr_param_cim_proj_label = f"{cim_label}:{InputPort.__name__}-{proj.receiver.name}" + port_type = type(proj.receiver) + rcvr_param_cim_proj_label = f"{cim_label}:{port_type.__name__}-{proj.receiver.name}" else: sndr_ctl_sig_proj_label = sndr_label rcvr_param_cim_proj_label = cim_label @@ -1305,13 +1328,14 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, # Get label for CIM's port as edge's sender sndr_param_cim_proj_label = f"{cim_label}:{OutputPort.__name__}-{proj.sender.name}" if (isinstance(rcvr_modulated_mech_proj_owner, Composition) - and not show_nested is not NESTED): + and show_nested in [NESTED, INSET]): rcvr_modulated_mec_proj_label = rcvr_label else: # Need to use direct reference to proj.receiver rather than rcvr_modulated_mech_proj # since could be Composition, which does not have a get_port_name attribute + port_type = type(proj.receiver) rcvr_modulated_mec_proj_label = \ - f"{rcvr_label}:{ParameterPort.__name__}-{proj.receiver.name}" + f"{rcvr_label}:{port_type.__name__}-{proj.receiver.name}" # rcvr_modulated_mec_proj_label = \ # f"{rcvr_label}:" \ # f"{rcvr_input_node_proj_owner._get_port_name(rcvr_modulated_mech_proj)}" @@ -1325,8 +1349,10 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, else: ctl_proj_color = self.control_color + arrowhead = self.default_projection_arrow if isinstance(proj, MappingProjection) else self.control_projection_arrow + _render_projection(g, proj, sndr_param_cim_proj_label, rcvr_modulated_mec_proj_label, - proj_color=ctl_proj_color, arrowhead=self.control_projection_arrow) + proj_color=ctl_proj_color, arrowhead=arrowhead) # OUTPUT_CIM ---------------------------------------------------------------------------- @@ -1358,7 +1384,8 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, # Construct edge name if show_node_structure: # Get label of CIM's port as edge's receiver - rcvr_output_cim_proj_label = f"{cim_label}:{InputPort.__name__}-{proj.receiver.name}" + port_type = type(proj.receiver) + rcvr_output_cim_proj_label = f"{cim_label}:{port_type.__name__}-{proj.receiver.name}" if (isinstance(sndr_output_node_proj_owner, Composition) and show_nested is not NESTED): sndr_output_node_proj_label = sndr_label @@ -1382,7 +1409,6 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, for output_port in composition.output_CIM.output_ports: projs = output_port.efferents for proj in projs: - rcvr_node_input_port = proj.receiver # Skip if receiver is cim (handled by enclosing Composition's call to this method) if isinstance(rcvr_node_input_port.owner, CompositionInterfaceMechanism): @@ -1391,6 +1417,13 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, # or Projections across nested Compositions are not being shown (show_nested=INSET) if not enclosing_g or show_nested is INSET: continue + + if show_controller and enclosing_comp.controller \ + and getattr(enclosing_comp.controller, 'objective_mechanism', None) \ + is rcvr_node_input_port.owner: + continue + + # Skip if show_controller and the receiver is objective mechanism rcvr_node_input_port_owner = rcvr_node_input_port.owner rcvr_label = self._get_graph_node_label(composition, @@ -1407,8 +1440,9 @@ def _render_projection(_g, proj, sndr_label, rcvr_label, else: # Need to use direct reference to proj.sender rather than sndr_output_node_proj # since could be Composition, which does not have a get_port_name attribute + port_type = type(proj.receiver) rcvr_input_node_proj_label = \ - f"{rcvr_label}:{InputPort.__name__}-{proj.receiver.name}" + f"{rcvr_label}:{port_type.__name__}-{proj.receiver.name}" # rcvr_input_node_proj_label = \ # f"{sndr_label}:" \ # f"{sndr_output_node_proj_owner._get_port_name(sndr_output_node_proj)}" @@ -1431,7 +1465,9 @@ def _assign_controller_components(self, show_dimensions, show_node_structure, node_struct_args, - show_projection_labels): + show_projection_labels, + comp_hierarchy, + nesting_level): """Assign control nodes and edges to graph""" from psyneulink.core.compositions.composition import Composition @@ -1533,10 +1569,7 @@ def find_rcvr_comp(r, c, l): # elif show_nested is NESTED: elif project_to_node: # PATCH 6/6/20 END - # Use ParameterPort of modulated Mechanism in nested Composition - parameter_port_map = ctl_proj_rcvr.owner.composition.parameter_CIM_ports - ctl_proj_rcvr = next((k for k,v in parameter_port_map.items() - if parameter_port_map[k][0] is ctl_proj_rcvr), None) + ctl_proj_rcvr = self._trace_receivers_for_terminal_receiver(ctl_proj_rcvr) ctl_proj_rcvr_owner = ctl_proj_rcvr.owner else: # Use Composition if show_cim is False @@ -1670,7 +1703,18 @@ def find_rcvr_comp(r, c, l): projection.sender.owner, show_types, show_dimensions) - sndr_proj_label += ':' + objmech._get_port_name(projection.sender) + if projection.sender.owner not in composition.nodes: + num_nesting_levels = self.num_nesting_levels or 0 + nested_comp = projection.sender.owner.composition + try: + nesting_depth = next((k for k, v in comp_hierarchy.items() if v == nested_comp)) + sender_visible = nesting_depth <= num_nesting_levels + except StopIteration: + sender_visible = False + else: + sender_visible = True + if sender_visible: + sndr_proj_label += ':' + objmech._get_port_name(projection.sender) objmech_proj_label = objmech_label + ':' + objmech._get_port_name(input_port) else: sndr_proj_label = self._get_graph_node_label(composition, @@ -1731,12 +1775,16 @@ def find_rcvr_comp(r, c, l): show_dimensions, show_node_structure, show_projection_labels, - proj_color=ctl_proj_color) + proj_color=ctl_proj_color, + comp_hierarchy=comp_hierarchy, + nesting_level=nesting_level) def _assign_learning_components(self, g, processing_graph, enclosing_comp, + comp_hierarchy, + nesting_level, active_items, show_nested, show_cim, @@ -1819,7 +1867,9 @@ def _assign_learning_components(self, show_dimensions, show_node_structure, show_projection_labels, - enclosing_comp=enclosing_comp) + enclosing_comp=enclosing_comp, + comp_hierarchy=comp_hierarchy, + nesting_level=nesting_level) def _render_projection_as_node(self, g, @@ -1906,10 +1956,14 @@ def _assign_incoming_edges(self, show_projection_labels, proj_color=None, proj_arrow=None, - enclosing_comp=None): + enclosing_comp=None, + comp_hierarchy=None, + nesting_level=None): from psyneulink.core.compositions.composition import Composition, NodeRole composition = self.composition + if nesting_level not in comp_hierarchy: + comp_hierarchy[nesting_level] = composition enclosing_g = enclosing_comp._show_graph.G if enclosing_comp else None proj_color_default = proj_color or self.default_node_color @@ -1967,22 +2021,24 @@ def _assign_incoming_edges(self, # DELETE ONCE FILTERED BASED ON nesting_level IS IMPLEMENTED BEFORE CALL TO METHOD # If cim has no afferents, presumably it is for the outermost Composition, # and therefore is not passing an afferent Projection from that Composition - if not sender.afferents: - continue - # Insure relevant InputPort of cim has only one afferent - num_afferents = len(sender.port_map[proj.receiver][0].path_afferents) - if (num_afferents==0 and - NodeRole.TARGET in composition.get_roles_by_node(proj.receiver.owner)): + if not sender.afferents and rcvr is not composition.controller: continue - assert num_afferents==1, f"PROGRAM ERROR: {sender} of {composition.name} " \ - f"doesn't have exactly one afferent Projection." # Get node from enclosing Comopsition that is source of sender - sndr = sender.port_map[proj.receiver][0].path_afferents[0].sender.owner + sndr_spec = self._trace_senders_for_original_sender_mechanism(proj, nesting_level) + if not sndr_spec: + continue + sndr, sndr_port, sndr_nesting_level = sndr_spec + # if original sender is more than one level above receiver, replace enclosing_g with + # the g of the original sender composition + enclosing_comp = comp_hierarchy[sndr_nesting_level] + enclosing_g = enclosing_comp._show_graph.G # Skip: # - cims as sources (handled in _assign_cim_componoents) # - controller (handled in _assign_controller_components) - if (isinstance(sndr, CompositionInterfaceMechanism) - or self._is_composition_controller(sndr ,enclosing_comp)): + if (isinstance(sndr, CompositionInterfaceMechanism) and + rcvr is not enclosing_comp.controller + and rcvr is not composition.controller + or self._is_composition_controller(sndr, enclosing_comp)): continue if sender is composition.parameter_CIM: proj_color = self.control_color @@ -2003,7 +2059,10 @@ def _assign_incoming_edges(self, # Skip: # - cims as sources (handled in _assign_cim_compmoents) # - controller (handled in _assign_controller_components) - if (isinstance(sndr, CompositionInterfaceMechanism) + # NOTE 7/20/20: if receiver is a controller, then we need to skip this block or shadow inputs + # will not be rendered -DS + if (rcvr is not composition.controller + and isinstance(sndr, CompositionInterfaceMechanism) or (isinstance(sndr, ControlMechanism) and sndr.composition)): continue else: @@ -2016,8 +2075,10 @@ def _assign_incoming_edges(self, # Skip any projections to ObjectiveMechanism for controller # (those are handled in _assign_controller_components) # FIX 6/2/20 MOVE TO BELOW FOLLOWING IF STATEMENT AND REPLACE proj.receiver.owner WITH rcvr? + # FIX 7/19/20 Can't exclude projections to composition.controller because that skips shadow projections + # to controller's input ports if (composition.controller and - proj.receiver.owner in {composition.controller, composition.controller.objective_mechanism}): + proj.receiver.owner in {composition.controller.objective_mechanism}): continue # FIX 6/6/20: ADD HANDLING OF parameter_CIM HERE?? @@ -2030,9 +2091,9 @@ def _assign_incoming_edges(self, # MODIFIED 6/6/20 END })): if show_node_structure and isinstance(sndr, Mechanism): - sndr_port = proj.sender + sndr_port = proj.sender if show_cim else sndr.output_port sndr_port_owner = sndr_port.owner - if isinstance(sndr_port_owner, CompositionInterfaceMechanism): + if isinstance(sndr_port_owner, CompositionInterfaceMechanism) and rcvr is not composition.controller: # Sender is input_CIM or parameter_CIM if sndr_port_owner in {sndr_port_owner.composition.input_CIM, # MODIFIED 6/6/20 NEW: @@ -2046,8 +2107,6 @@ def _assign_incoming_edges(self, else: # Get port for node of inner Composition that projects to it sndr_port = [k for k,v in sender.port_map.items() if v[1] is proj.sender][0] - else: - sndr_port = proj.sender sndr_proj_label = f'{sndr_label}:{sndr._get_port_name(sndr_port)}' else: sndr_proj_label = sndr_label @@ -2261,14 +2320,52 @@ def _is_composition_controller(self, mech, enclosing_comp=None): def _trace_senders_for_controller(self, proj, comp=None): """Check whether source sender of a ControlProjection is (at any level of nesting) a Composition controller.""" owner = proj.sender.owner - comp = comp or self.composition + comp = owner.composition if hasattr(owner, 'composition') else comp or self.composition if self._is_composition_controller(owner, comp): return True if isinstance(owner, CompositionInterfaceMechanism): - sender_proj = next(v[0] for k,v in owner.port_map.items() if v[1] is proj.sender).path_afferents[0] + sender_proj = owner.port_map[proj.receiver][0].path_afferents[0] return self._trace_senders_for_controller(sender_proj, comp) return False + def _trace_senders_for_original_sender_mechanism(self, proj, nesting_level, comp=None): + """ + Find the original sender of a projection that is routed through n-levels of cims. + If there is no outer root Mechanism, as in the case of a nested input Mechanism that is not projected to, + return None. + """ + sender = proj.sender + owner = sender.owner + # Insure relevant InputPort of cim has only one afferent + if isinstance(owner, CompositionInterfaceMechanism) and proj.receiver in owner.port_map: + nesting_level -= 1 + num_afferents = len(owner.port_map[proj.receiver][0].path_afferents) + if num_afferents == 0: + return None + # assert num_afferents == 1, f"PROGRAM ERROR: {sender} of {comp.name} " \ + # f"doesn't have exactly one afferent Projection." + outer_proj = owner.port_map[proj.receiver][0].path_afferents[0] + enclosing_showgraph = owner.composition._show_graph + return enclosing_showgraph._trace_senders_for_original_sender_mechanism(outer_proj, nesting_level) + return owner, sender, nesting_level + + def _trace_receivers_for_terminal_receiver(self, receiver, comp=None): + """Find the terminal ParameterPort for a given intermediary input port of a parameter CIM""" + parameter_port_map = receiver.owner.composition.parameter_CIM_ports + ctl_proj_rcvr = next((k for k, v in parameter_port_map.items() + if parameter_port_map[k][0] is receiver), None) + # hack needed for situation where multiple external control projections project to a single parameter port. + # in this case, the receiver is overwritten in the parameter_CIM_ports dict. + if ctl_proj_rcvr is None: + pcim_input_ports = receiver.owner.composition.parameter_CIM.input_ports + pcim_output_ports = receiver.owner.composition.parameter_CIM.output_ports + for idx in range(len(pcim_input_ports)): + if pcim_input_ports[idx] is receiver: + ctl_proj_rcvr = pcim_output_ports[idx].efferents[0].receiver + if isinstance(ctl_proj_rcvr.owner, CompositionInterfaceMechanism): + return receiver.owner.composition._show_graph._trace_receivers_for_terminal_receiver(ctl_proj_rcvr) + return ctl_proj_rcvr + def _get_graph_node_label(self, composition, item, show_types=None, show_dimensions=None): from psyneulink.core.compositions.composition import Composition diff --git a/psyneulink/core/globals/keywords.py b/psyneulink/core/globals/keywords.py index 2fb1b2c55f7..c726a2fdd75 100644 --- a/psyneulink/core/globals/keywords.py +++ b/psyneulink/core/globals/keywords.py @@ -120,6 +120,7 @@ # ********************************************************************************************************************** # ****************************************** KEYWORD CLASSES ********************************************************** # ********************************************************************************************************************** +import operator class MatrixKeywords: """ @@ -342,7 +343,6 @@ def _is_metric(metric): GREATER_THAN_OR_EQUAL = '>=' NOT_EQUAL = '!=' -import operator comparison_operators = {LESS_THAN : operator.lt, LESS_THAN_OR_EQUAL : operator.le, EQUAL : operator.eq, diff --git a/psyneulink/core/globals/log.py b/psyneulink/core/globals/log.py index d8b58fdfff7..901d5c8aa3d 100644 --- a/psyneulink/core/globals/log.py +++ b/psyneulink/core/globals/log.py @@ -381,10 +381,10 @@ """ import enum -import inspect import warnings from collections import OrderedDict, namedtuple +from collections.abc import MutableMapping import numpy as np import typecheck as tc @@ -506,7 +506,6 @@ def _time_string(time): #region Custom Entries Dict # Modified from: http://stackoverflow.com/questions/7760916/correct-useage-of-getter-setter-for-dictionary-values -from collections.abc import MutableMapping class EntriesDict(MutableMapping,dict): """Maintains a Dict of Log entries; assignment of a LogEntry to an entry appends it to the list for that entry. @@ -1154,7 +1153,6 @@ class options(enum.IntFlag): warnings.warn("{0} is not an entry in the Log for {1}". format(entry_name, self.owner.name)) else: - import numpy as np multiple_eids = len(datum)>1 for eid in datum: if multiple_eids: diff --git a/psyneulink/core/globals/parameters.py b/psyneulink/core/globals/parameters.py index 565e1e1bd1d..a16e7b2fbfa 100644 --- a/psyneulink/core/globals/parameters.py +++ b/psyneulink/core/globals/parameters.py @@ -244,7 +244,6 @@ def _recurrent_transfer_mechanism_matrix_setter(value, owning_component=None, co import logging import types import typing -import warnings import weakref from psyneulink.core.globals.keywords import MULTIPLICATIVE @@ -366,7 +365,16 @@ def __repr__(self): def __str__(self): return self.show() - __deepcopy__ = get_deepcopy_with_shared(_deepcopy_shared_keys) + def __deepcopy__(self, memo): + newone = get_deepcopy_with_shared(self._deepcopy_shared_keys)(self, memo) + + for name, param in self.values(show_all=True).items(): + if isinstance(param, ParameterAlias): + source_name = param.source.name + getattr(newone, name).source = getattr(newone, source_name) + + memo[id(self)] = newone + return newone def __del__(self): try: @@ -1427,6 +1435,15 @@ def __lt__(self, other): def __getattr__(self, attr): return getattr(self.source, attr) + # must override deepcopy despite it being essentially shallow + # because otherwise it will default to Parameter.__deepcopy__ and + # return an instance of Parameter + def __deepcopy__(self, memo): + result = ParameterAlias(source=self._source, name=self.name) + memo[id(self)] = result + + return result + @property def source(self): return unproxy_weakproxy(self._source) diff --git a/psyneulink/core/globals/registry.py b/psyneulink/core/globals/registry.py index d9a656a817b..103fb86d69a 100644 --- a/psyneulink/core/globals/registry.py +++ b/psyneulink/core/globals/registry.py @@ -8,6 +8,7 @@ # # *********************************************** Registry ************************************************************ +import inspect import re from collections import defaultdict, namedtuple @@ -121,7 +122,6 @@ def register_category(entry, """ # IMPLEMENTATION NOTE: Move to Port when that is implemented as ABC - import inspect from psyneulink.core.components.shellclasses import Port if inspect.isclass(entry) and issubclass(entry, Port): try: diff --git a/psyneulink/core/globals/sampleiterator.py b/psyneulink/core/globals/sampleiterator.py index 7535812a051..f2bef2d81f2 100644 --- a/psyneulink/core/globals/sampleiterator.py +++ b/psyneulink/core/globals/sampleiterator.py @@ -352,7 +352,6 @@ def generate_current_value(): # index into the list self.generator = None # ?? def generate_current_value(): # return next value in range - from decimal import Decimal, getcontext # Save global precision for later restoration _global_precision = getcontext().prec # Set SampleSpec precision diff --git a/psyneulink/core/globals/utilities.py b/psyneulink/core/globals/utilities.py index af8dd35417c..e14c08ffa05 100644 --- a/psyneulink/core/globals/utilities.py +++ b/psyneulink/core/globals/utilities.py @@ -91,6 +91,7 @@ """ +import collections import copy import inspect import logging @@ -102,10 +103,13 @@ import weakref import types import typing +import typecheck as tc from enum import Enum, EnumMeta, IntEnum +from collections.abc import Mapping +from collections import UserDict, UserList +from itertools import chain, combinations -import collections import numpy as np from psyneulink.core.globals.keywords import \ @@ -575,13 +579,12 @@ def scalar_distance(measure, value, scale=1, offset=0): if measure == SINUSOID: return sinusoid(value, frequency=scale, phase=offset) -from itertools import chain, combinations + def powerset(iterable): """powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)""" s = list(iterable) return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1)) -import typecheck as tc @tc.typecheck def tensor_power(items, levels:tc.optional(range)=None, flat=False): """return tensor product for all members of powerset of items @@ -635,7 +638,6 @@ def get_args(frame): return dict((key, value) for key, value in values.items() if key in args) -from collections.abc import Mapping def recursive_update(d, u, non_destructive=False): """Recursively update entries of dictionary d with dictionary u From: https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth @@ -992,7 +994,6 @@ def append_type_to_name(object, type=None): #endregion -from collections import UserDict class ReadOnlyOrderedDict(UserDict): def __init__(self, dict=None, name=None, **kwargs): self.name = name or self.__class__.__name__ @@ -1019,7 +1020,6 @@ def keys(self): def copy(self): return self.data.copy() -from collections import UserList class ContentAddressableList(UserList): """ ContentAddressableList( component_type, key=None, list=None) @@ -1519,13 +1519,10 @@ def safe_equals(x, y): return np.array_equal(x, y) -import typecheck as tc @tc.typecheck def _get_arg_from_stack(arg_name:str): # Get arg from the stack - import inspect - curr_frame = inspect.currentframe() prev_frame = inspect.getouterframes(curr_frame, 2) i = 1 diff --git a/psyneulink/core/llvm/execution.py b/psyneulink/core/llvm/execution.py index 108fd6fff72..d3323800d7f 100644 --- a/psyneulink/core/llvm/execution.py +++ b/psyneulink/core/llvm/execution.py @@ -15,6 +15,7 @@ import ctypes import numpy as np from inspect import isgenerator +import itertools import sys @@ -624,7 +625,6 @@ def cuda_evaluate(self, variable, search_space): ocm = self._composition.controller assert len(self._execution_contexts) == 1 context = self._execution_contexts[0] - import itertools bin_func = pnlvm.LLVMBinaryFunction.from_obj(ocm, tags=frozenset({"evaluate"})) self.__bin_func = bin_func diff --git a/psyneulink/core/scheduling/scheduler.py b/psyneulink/core/scheduling/scheduler.py index 3724613e1fa..36175861631 100644 --- a/psyneulink/core/scheduling/scheduler.py +++ b/psyneulink/core/scheduling/scheduler.py @@ -275,13 +275,11 @@ """ -import collections import copy import datetime import logging -import warnings -from toposort import toposort, toposort_flatten +from toposort import toposort from psyneulink.core.globals.context import Context, handle_external_context from psyneulink.core.globals.json import JSONDumpable diff --git a/psyneulink/library/components/projections/pathway/maskedmappingprojection.py b/psyneulink/library/components/projections/pathway/maskedmappingprojection.py index 4fc809f6cbb..9fa1f4ed9f0 100644 --- a/psyneulink/library/components/projections/pathway/maskedmappingprojection.py +++ b/psyneulink/library/components/projections/pathway/maskedmappingprojection.py @@ -64,7 +64,6 @@ --------------- """ -import numbers import numpy as np import typecheck as tc diff --git a/psyneulink/library/compositions/__init__.py b/psyneulink/library/compositions/__init__.py index 40bc6181673..1f5039502ee 100644 --- a/psyneulink/library/compositions/__init__.py +++ b/psyneulink/library/compositions/__init__.py @@ -7,9 +7,7 @@ import torch from .autodiffcomposition import * - from .pytorchmodelcreator import * __all__.extend(autodiffcomposition.__all__) - __all__.extend(pytorchmodelcreator.__all__) except ImportError: pass diff --git a/psyneulink/library/compositions/autodiffcomposition.py b/psyneulink/library/compositions/autodiffcomposition.py index c3b44f2595a..6c3107b1937 100644 --- a/psyneulink/library/compositions/autodiffcomposition.py +++ b/psyneulink/library/compositions/autodiffcomposition.py @@ -127,6 +127,19 @@ --------------- """ +import numpy as np + +import logging +try: + import torch + from torch import nn + import torch.optim as optim + torch_available = True +except ImportError: + torch_available = False +else: + from psyneulink.library.compositions.pytorchmodelcreator import PytorchModelCreator + from psyneulink.core.components.functions.transferfunctions import Linear, Logistic, ReLU from psyneulink.core.components.mechanisms.processing.compositioninterfacemechanism import CompositionInterfaceMechanism @@ -140,23 +153,7 @@ from psyneulink.core.globals.parameters import Parameter from psyneulink.core.scheduling.time import TimeScale from psyneulink.core import llvm as pnlvm -import copy -import numpy as np -import ctypes -from collections.abc import Iterable -from toposort import toposort -from inspect import isgenerator -import logging -try: - import torch - from torch import nn - import torch.optim as optim - torch_available = True -except ImportError: - torch_available = False -else: - from psyneulink.library.compositions.pytorchmodelcreator import PytorchModelCreator logger = logging.getLogger(__name__) diff --git a/psyneulink/library/compositions/compositionrunner.py b/psyneulink/library/compositions/compositionrunner.py index f5342907825..c97a8d886c2 100644 --- a/psyneulink/library/compositions/compositionrunner.py +++ b/psyneulink/library/compositions/compositionrunner.py @@ -8,7 +8,6 @@ # ********************************************* AutodiffComposition ************************************************* import numpy as np -import collections.abc from psyneulink.core.compositions.composition import Composition from psyneulink.core.globals.keywords import OBJECTIVE_MECHANISM, TRAINING_SET diff --git a/psyneulink/library/compositions/gymforagercfa.py b/psyneulink/library/compositions/gymforagercfa.py index c6c03eba1bd..145e78f5180 100644 --- a/psyneulink/library/compositions/gymforagercfa.py +++ b/psyneulink/library/compositions/gymforagercfa.py @@ -75,15 +75,11 @@ """ -import itertools import numpy as np import typecheck as tc import gym_forager -from enum import Enum -from itertools import product - from psyneulink.library.compositions.regressioncfa import RegressionCFA from psyneulink.core.components.functions.learningfunctions import BayesGLM from psyneulink.core.globals.keywords import DEFAULT_VARIABLE diff --git a/psyneulink/library/compositions/pytorchmodelcreator.py b/psyneulink/library/compositions/pytorchmodelcreator.py index 5548f40fe1f..8464addbdca 100644 --- a/psyneulink/library/compositions/pytorchmodelcreator.py +++ b/psyneulink/library/compositions/pytorchmodelcreator.py @@ -1,4 +1,3 @@ -from psyneulink.core.scheduling.time import TimeScale from psyneulink.core.compositions.composition import NodeRole from psyneulink.core.components.functions.transferfunctions import Linear, Logistic, ReLU from psyneulink.core.globals.context import ContextFlags, handle_external_context diff --git a/psyneulink/library/compositions/regressioncfa.py b/psyneulink/library/compositions/regressioncfa.py index 41ea47f7917..b9d44bf07a3 100644 --- a/psyneulink/library/compositions/regressioncfa.py +++ b/psyneulink/library/compositions/regressioncfa.py @@ -71,7 +71,6 @@ --------------- """ -import itertools import numpy as np import typecheck as tc diff --git a/psyneulink/library/models/Cohen_Huston1994.py b/psyneulink/library/models/Cohen_Huston1994.py index e79b2af7794..d0f728adfcf 100644 --- a/psyneulink/library/models/Cohen_Huston1994.py +++ b/psyneulink/library/models/Cohen_Huston1994.py @@ -1,4 +1,5 @@ -import matplotlib.pyplot as plt +import argparse + import numpy as np import psyneulink as pnl @@ -7,72 +8,76 @@ # AttentionandperformanceXV(pp.453-456). Cam- bridge, MA: MIT Press. # The model aims to capute top-down effects of selective attention and the bottom-up effects of attentional capture. -# Define Variables ---------------------------------------------------------------------------------------------------- -import psyneulink.core.components.functions.transferfunctions +parser = argparse.ArgumentParser() +parser.add_argument('--no-plot', action='store_false', help='Disable plotting', dest='enable_plot') +parser.add_argument('--threshold', type=float, help='Termination threshold for response output (default: %(default)f)', default=0.55) +parser.add_argument('--settle-trials', type=int, help='Number of trials for composition to initialize and settle (default: %(default)d)', default=50) +args = parser.parse_args() +# Define Variables ---------------------------------------------------------------------------------------------------- rate = 0.1 # modified from the original code from 0.01 to 0.1 inhibition = -2.0 # lateral inhibition bias = 4.0 # bias is positive since Logistic equation has - sing already implemented -threshold = 0.55 # modified from thr original code from 0.6 to 0.55 because incongruent condition won't reach 0.6 -settle_trials = 50 # cycles until model settles +threshold = args.threshold # modified from thr original code from 0.6 to 0.55 because incongruent condition won't reach 0.6 +settle_trials = args.settle_trials # cycles until model settles # Create mechanisms --------------------------------------------------------------------------------------------------- # Linear input units, colors: ('red', 'green'), words: ('RED','GREEN') colors_input_layer = pnl.TransferMechanism( size=3, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='COLORS_INPUT' ) words_input_layer = pnl.TransferMechanism( size=3, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='WORDS_INPUT' ) task_input_layer = pnl.TransferMechanism( size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='TASK_INPUT' ) # Task layer, tasks: ('name the color', 'read the word') task_layer = pnl.RecurrentTransferMechanism( size=2, - function=psyneulink.core.components.functions.transferfunctions.Logistic(), - hetero=-2, + function=pnl.Logistic(), + hetero=inhibition, integrator_mode=True, - integration_rate=0.1, + integration_rate=rate, name='TASK' ) # Hidden layer units, colors: ('red','green') words: ('RED','GREEN') colors_hidden_layer = pnl.RecurrentTransferMechanism( size=3, - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=4.0), + function=pnl.Logistic(x_0=bias), integrator_mode=True, - hetero=-2.0, - # noise=pnl.NormalDist(mean=0.0, standard_deviation=.0).function, - integration_rate=0.1, # cohen-huston text says 0.01 + hetero=inhibition, + # noise=pnl.NormalDist(mean=0.0, standard_deviation=.0), + integration_rate=rate, # cohen-huston text says 0.01 name='COLORS HIDDEN' ) words_hidden_layer = pnl.RecurrentTransferMechanism( size=3, - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=4.0), - hetero=-2, + function=pnl.Logistic(x_0=bias), + hetero=inhibition, integrator_mode=True, - # noise=pnl.NormalDist(mean=0.0, standard_deviation=.05).function, - integration_rate=0.1, + # noise=pnl.NormalDist(mean=0.0, standard_deviation=.05), + integration_rate=rate, name='WORDS HIDDEN' ) # Response layer, responses: ('red', 'green'): RecurrentTransferMechanism for self inhibition matrix response_layer = pnl.RecurrentTransferMechanism( size=2, - function=psyneulink.core.components.functions.transferfunctions.Logistic(), - hetero=-2.0, + function=pnl.Logistic(), + hetero=inhibition, integrator_mode=True, - integration_rate=0.1, + integration_rate=rate, name='RESPONSE' ) @@ -422,28 +427,31 @@ def trial_dict(red_color, green_color, neutral_color, red_word, green_word, neut print('response_all: ', response_all) -# Plot results -------------------------------------------------------------------------------------------------------- -# First, plot response layer activity for whole run -plt.figure() -# color naming plot -plt.plot(response_all[0]) -plt.plot(response_all[1]) -plt.plot(response_all[2]) -# word reading plot -plt.plot(response_all3[0]) -plt.plot(response_all3[1]) -plt.plot(response_all3[2]) -plt.show() -# Second, plot regression plot -# regression -reg = np.dot(response_all2, 5) + 115 -reg2 = np.dot(response_all4, 5) + 115 -plt.figure() - -plt.plot(reg, '-s') # plot color naming -plt.plot(reg2, '-or') # plot word reading -plt.title('GRAIN MODEL with bidirectional weights') -plt.legend(['color naming', 'word reading']) -plt.xticks(np.arange(3), ('control', 'incongruent', 'congruent')) -plt.ylabel('reaction time in ms') -plt.show() +if args.enable_plot: + import matplotlib.pyplot as plt + + # Plot results -------------------------------------------------------------------------------------------------------- + # First, plot response layer activity for whole run + plt.figure() + # color naming plot + plt.plot(response_all[0]) + plt.plot(response_all[1]) + plt.plot(response_all[2]) + # word reading plot + plt.plot(response_all3[0]) + plt.plot(response_all3[1]) + plt.plot(response_all3[2]) + plt.show() + # Second, plot regression plot + # regression + reg = np.dot(response_all2, 5) + 115 + reg2 = np.dot(response_all4, 5) + 115 + plt.figure() + + plt.plot(reg, '-s') # plot color naming + plt.plot(reg2, '-or') # plot word reading + plt.title('GRAIN MODEL with bidirectional weights') + plt.legend(['color naming', 'word reading']) + plt.xticks(np.arange(3), ('control', 'incongruent', 'congruent')) + plt.ylabel('reaction time in ms') + plt.show() diff --git a/psyneulink/library/models/Cohen_Huston1994_horse_race.py b/psyneulink/library/models/Cohen_Huston1994_horse_race.py index 9839bf9dd3b..c9f5875576a 100644 --- a/psyneulink/library/models/Cohen_Huston1994_horse_race.py +++ b/psyneulink/library/models/Cohen_Huston1994_horse_race.py @@ -1,19 +1,27 @@ -import matplotlib.pyplot as plt +import argparse + import numpy as np import psyneulink as pnl +parser = argparse.ArgumentParser() +parser.add_argument('--no-plot', action='store_false', help='Disable plotting', dest='enable_plot') +parser.add_argument('--threshold', type=float, help='Termination threshold for response output (default: %(default)f)', default=0.55) +parser.add_argument('--word-runs', type=int, help='Number of runs after word is presented (default: %(default)d)', default=5) +parser.add_argument('--color-runs', type=int, help='Number of runs after color is presented (default: %(default)d)', default=4) +parser.add_argument('--settle-trials', type=int, help='Number of trials for composition to initialize and settle (default: %(default)d)', default=50) +parser.add_argument('--pre-stimulus-trials', type=int, help='Number of trials before stimulus is added', default=100) +args = parser.parse_args() + # This implements the horse race Figure shown in Cohen & Huston (1994). # Note that noise is turned off and each stimulus is only showed once for each stimulus onset asynchrony. # Define Variables ---------------------------------------------------------------------------------------------------- -import psyneulink.core.components.functions.transferfunctions - rate = 0.1 # The integration rate was changed from 0.01 to 0.1 inhibition = -2.0 # Mutual inhibition across each layer bias = 4.0 # bias for hidden layer units -threshold = 0.55 # Threshold until a response is made, changed from 0.6 to 0.55 -settle_trials = 50 # Time for system to initialize and settle -prior120 = 100 # Cycles needed to be added for stimulus to start +threshold = args.threshold # Threshold until a response is made, changed from 0.6 to 0.55 +settle_trials = args.settle_trials # Time for system to initialize and settle +prior120 = args.pre_stimulus_trials # Cycles needed to be added for stimulus to start # Different time steps at which the System should end run and start new terminate_processing run # This is needed for conditions in which the irrelevant condition is like a neutral trial and could already lead to @@ -27,20 +35,20 @@ # Create mechanisms --------------------------------------------------------------------------------------------------- # Linear input units, colors: ('red', 'green'), words: ('RED','GREEN') colors_input_layer = pnl.TransferMechanism(size=3, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='COLORS_INPUT') words_input_layer = pnl.TransferMechanism(size=3, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='WORDS_INPUT') task_input_layer = pnl.TransferMechanism(size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='TASK_INPUT') # Task layer, tasks: ('name the color', 'read the word') task_layer = pnl.RecurrentTransferMechanism(size=2, - function=psyneulink.core.components.functions.transferfunctions.Logistic(), + function=pnl.Logistic(), hetero=-2, integrator_mode=True, integration_rate=0.1, @@ -48,24 +56,24 @@ # Hidden layer units, colors: ('red','green') words: ('RED','GREEN') colors_hidden_layer = pnl.RecurrentTransferMechanism(size=3, - function=psyneulink.core.components.functions.transferfunctions + function=pnl .Logistic(x_0=4.0), integrator_mode=True, hetero=-2.0, - # noise=pnl.NormalDist(mean=0.0, standard_deviation=.0).function, + # noise=pnl.NormalDist(mean=0.0, standard_deviation=.0), integration_rate=0.1, # cohen-huston text says 0.01 name='COLORS HIDDEN') words_hidden_layer = pnl.RecurrentTransferMechanism(size=3, - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=4.0), + function=pnl.Logistic(x_0=4.0), hetero=-2, integrator_mode=True, - # noise=pnl.NormalDist(mean=0.0, standard_deviation=.05).function, + # noise=pnl.NormalDist(mean=0.0, standard_deviation=.05), integration_rate=0.1, name='WORDS HIDDEN') # Response layer, responses: ('red', 'green'): RecurrentTransferMechanism for self inhibition matrix response_layer = pnl.RecurrentTransferMechanism(size=2, - function=psyneulink.core.components.functions.transferfunctions.Logistic(), + function=pnl.Logistic(), hetero=-2.0, integrator_mode=True, integration_rate=0.1, @@ -117,55 +125,63 @@ word_response_weights = pnl.MappingProjection(matrix=np.array([[2.5, 0.0], [0.0, 2.5], [0.0, 0.0]])) -# + +Bidirectional_Stroop = pnl.Composition(name='FEEDFORWARD_STROOP_SYSTEM') + # Create pathways ----------------------------------------------------------------------------------------------------- -color_response_process = pnl.Process(pathway=[colors_input_layer, - color_input_weights, - colors_hidden_layer, - color_response_weights, - response_layer, - response_color_weights, - colors_hidden_layer], - name='COLORS_RESPONSE_PROCESS') - -word_response_process = pnl.Process(pathway=[words_input_layer, - word_input_weights, - words_hidden_layer, - word_response_weights, - response_layer, - response_word_weights, - words_hidden_layer], - name='WORDS_RESPONSE_PROCESS') - -task_color_response_process = pnl.Process(pathway=[task_input_layer, - task_input_weights, - task_layer, - task_color_weights, - colors_hidden_layer, - color_task_weights, - task_layer]) - -task_word_response_process = pnl.Process(pathway=[task_input_layer, - task_layer, - task_word_weights, - words_hidden_layer, - word_task_weights, - task_layer]) - -# Create system ------------------------------------------------------------------------------------------------------- -Bidirectional_Stroop = pnl.System(processes=[color_response_process, - word_response_process, - task_color_response_process, - task_word_response_process], - - reinitialize_mechanisms_when=pnl.Never(), - name='FEEDFORWARD_STROOP_SYSTEM') +Bidirectional_Stroop.add_linear_processing_pathway( + pathway=[ + colors_input_layer, + color_input_weights, + colors_hidden_layer, + color_response_weights, + response_layer, + response_color_weights, + colors_hidden_layer + ], + name='COLORS_RESPONSE_PROCESS' +) + +Bidirectional_Stroop.add_linear_processing_pathway( + pathway=[ + words_input_layer, + word_input_weights, + words_hidden_layer, + word_response_weights, + response_layer, + response_word_weights, + words_hidden_layer + ], + name='WORDS_RESPONSE_PROCESS' +) + +Bidirectional_Stroop.add_linear_processing_pathway( + pathway=[ + task_input_layer, + task_input_weights, + task_layer, + task_color_weights, + colors_hidden_layer, + color_task_weights, + task_layer + ] +) + +Bidirectional_Stroop.add_linear_processing_pathway( + pathway=[ + task_input_layer, + task_layer, + task_word_weights, + words_hidden_layer, + word_task_weights, + task_layer + ] +) # LOGGING: colors_hidden_layer.set_log_conditions('value') words_hidden_layer.set_log_conditions('value') -Bidirectional_Stroop.show() # Bidirectional_Stroop.show_graph(show_dimensions=pnl.ALL)#,show_mechanism_structure=pnl.VALUES) # Uncomment to show graph of the system # Create threshold function ------------------------------------------------------------------------------------------- @@ -241,8 +257,8 @@ def trial_dict(red_color, green_color, neutral_color, red_word, green_word, neut # WR_control_trial_input = trial_dict(1, 0, 0, 0, 0, 0, 0, 1) #red_color, green color, red_word, green word, CN, WR conditions = 3 -runs = 5 -runs2 = 4 +runs = args.word_runs +runs2 = args.color_runs response_all = [] response_all2 = [] @@ -251,6 +267,8 @@ def trial_dict(red_color, green_color, neutral_color, red_word, green_word, neut [CN_initialize_input, CN_control_word_trial_input, CN_control_trial_input, CN_control_trial_input]] +post_settlement_multiplier = int(prior120 / 5) + # First "for loop" over conditions # Second "for loop" over runs for cond in range(conditions): @@ -263,7 +281,7 @@ def trial_dict(red_color, green_color, neutral_color, red_word, green_word, neut response_word_weights = pnl.MappingProjection(matrix=np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])) Bidirectional_Stroop.run(inputs=Stimulus[cond][0], num_trials=settle_trials) # run system to settle for 200 trials with congruent stimuli input - Bidirectional_Stroop.run(inputs=Stimulus[cond][0], num_trials=20 * (run)) # run system to settle for 200 trials with congruent stimuli input + Bidirectional_Stroop.run(inputs=Stimulus[cond][0], num_trials=post_settlement_multiplier * (run)) # run system to settle for 200 trials with congruent stimuli input response_color_weights = pnl.MappingProjection(matrix=np.array([[1.5, 0.0, 0.0], [0.0, 1.5, 0.0]])) @@ -271,7 +289,7 @@ def trial_dict(red_color, green_color, neutral_color, red_word, green_word, neut response_word_weights = pnl.MappingProjection(matrix=np.array([[2.5, 0.0, 0.0], [0.0, 2.5, 0.0]])) - Bidirectional_Stroop.run(inputs=Stimulus[cond][1], num_trials=prior120 - (run * 20))# termination_processing=terminate_trial) # run system with congruent stimulus input until + Bidirectional_Stroop.run(inputs=Stimulus[cond][1], num_trials=prior120 - (run * post_settlement_multiplier))# termination_processing=terminate_trial) # run system with congruent stimulus input until Bidirectional_Stroop.run(inputs=Stimulus[cond][2], termination_processing=terminate_trial) # run system with congruent stimulus input until # threshold in of of the response layer units is reached @@ -329,18 +347,23 @@ def trial_dict(red_color, green_color, neutral_color, red_word, green_word, neut task_layer.reset([[0, 0]], context=Bidirectional_Stroop) # Plotting ------------------------------------------------------------------------------------------------------------ -#compute regression for model -reg = np.dot(response_all, 2) + 123 -plt.figure() -# plt.plot(response_all[0:9]) -# plt.plot(response_all[9:18]) -# plt.plot(response_all[18:27]) - -stimulus_onset_asynchrony = np.linspace(-400,400,9) -plt.plot(stimulus_onset_asynchrony, reg[0:9], '-^') -plt.plot(stimulus_onset_asynchrony, reg[9:18], '-s') -plt.plot(stimulus_onset_asynchrony, reg[18:27], '-o') -plt.title('stimulus onset asynchrony - horse race model ') -plt.legend(['congruent', 'incongruent', 'neutral']) -plt.ylabel('reaction time in ms') -plt.show() +if args.enable_plot: + import matplotlib.pyplot as plt + + # compute regression for model + reg = np.dot(response_all, 2) + 123 + plt.figure() + # plt.plot(response_all[0:9]) + # plt.plot(response_all[9:18]) + # plt.plot(response_all[18:27]) + + response_len = runs + runs2 + + stimulus_onset_asynchrony = np.linspace(-400, 400, response_len) + plt.plot(stimulus_onset_asynchrony, reg[0:response_len], '-^') + plt.plot(stimulus_onset_asynchrony, reg[response_len:2 * response_len], '-s') + plt.plot(stimulus_onset_asynchrony, reg[2 * response_len:3 * response_len], '-o') + plt.title('stimulus onset asynchrony - horse race model ') + plt.legend(['congruent', 'incongruent', 'neutral']) + plt.ylabel('reaction time in ms') + plt.show() diff --git a/psyneulink/library/models/GilzenratModel.py b/psyneulink/library/models/GilzenratModel.py index 25833ec8259..7e411009571 100644 --- a/psyneulink/library/models/GilzenratModel.py +++ b/psyneulink/library/models/GilzenratModel.py @@ -4,18 +4,25 @@ and electrophysiological data (from LC recordings) in non-human primates. """ +import argparse +import sys + import numpy as np import psyneulink as pnl -import sys -from matplotlib import pyplot as plt +parser = argparse.ArgumentParser() +parser.add_argument('--no-plot', action='store_false', help='Disable plotting', dest='enable_plot') +parser.add_argument( + '--noise-stddev', + type=float, + help='Standard deviation of Gaussian noise distributions (default: 0.22, Gilzenrat et al. (2002))', + default=0.22 +) +args = parser.parse_args() # Define Variables ---------------------------------------------------------------------------------------------------- # Weights & Biases: -import psyneulink.core.components.functions.distributionfunctions -import psyneulink.core.components.functions.transferfunctions - b_decision = 0.00 # Bias on decision units (not biased) b_response = 2.00 # Bias on response unit --- NOTE: Gilzenrat has negative signs in his logistic equation w_XiIi = 1.00 # Connection weight from input units I1 and I2 to respective decision units X1 and X2 @@ -31,7 +38,7 @@ d = 0.50 # Baseline level of intrinsic, uncorrelated LC activity G = 0.50 # Base level of gain applied to decision and response units k = 3.00 # Scaling factor for transforming NE release (u) to gain (g) on potentiated units -SD = 0.1 # Standard deviation of Gaussian noise distributions | NOTE: 0.22 in Gilzenrat paper +SD = args.noise_stddev # Standard deviation of Gaussian noise distributions | NOTE: 0.22 in Gilzenrat paper tau_v = 0.05 # Time constant for fast LC excitation variable v | NOTE: tau_v is misstated in the Gilzenrat paper(0.5) tau_u = 5.00 # Time constant for slow LC recovery variable (‘NE release’) u dt = 0.02 # Time step size for numerical integration @@ -68,8 +75,8 @@ competition=w_XiXj, # Recurrent matrix: [ w_XiXi -w_XiXj ] # [ -w_XiXj w_XiXi ] - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=b_decision), - noise=psyneulink.core.components.functions.distributionfunctions.NormalDist(standard_deviation=SD).function, + function=pnl.Logistic(x_0=b_decision), + noise=pnl.NormalDist(standard_deviation=SD), integrator_mode=True, name='DECISION LAYER' ) @@ -83,8 +90,8 @@ self_excitation=w_X3X3, # Recurrent matrix: [w_X3X3] # Competition param does not apply because there is only one unit - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=b_response), - noise=psyneulink.core.components.functions.distributionfunctions.NormalDist(standard_deviation=SD).function, + function=pnl.Logistic(x_0=b_response), + noise=pnl.NormalDist(standard_deviation=SD), integrator_mode=True, name='RESPONSE' ) @@ -103,7 +110,7 @@ [0.00] ]) -decision_process = pnl.Process( +decision_pathway = pnl.Pathway( pathway=[ input_layer, input_weights, @@ -117,48 +124,47 @@ # Monitor decision layer in order to modulate gain -------------------------------------------------------------------- LC = pnl.LCControlMechanism( - integration_method="EULER", - threshold_FitzHughNagumo=a, - uncorrelated_activity_FitzHughNagumo=d, - base_level_gain=G, - scaling_factor_gain=k, - time_step_size_FitzHughNagumo=dt, - mode_FitzHughNagumo=C, - time_constant_v_FitzHughNagumo=tau_v, - time_constant_w_FitzHughNagumo=tau_u, - a_v_FitzHughNagumo=-1.0, - b_v_FitzHughNagumo=1.0, - c_v_FitzHughNagumo=1.0, - d_v_FitzHughNagumo=0.0, - e_v_FitzHughNagumo=-1.0, - f_v_FitzHughNagumo=1.0, - a_w_FitzHughNagumo=1.0, - b_w_FitzHughNagumo=-1.0, - c_w_FitzHughNagumo=0.0, - t_0_FitzHughNagumo=0.0, - initial_v_FitzHughNagumo=initial_v, - initial_w_FitzHughNagumo=initial_u, - objective_mechanism=pnl.ObjectiveMechanism( - function=psyneulink.core.components.functions.transferfunctions.Linear, - monitored_output_ports=[( - decision_layer, - None, - None, - np.array([ - [w_vX1], - [0.0]] - ) - )], - name='LC ObjectiveMechanism' - ), - modulated_mechanisms=[decision_layer, response_layer], # Modulate gain of decision & response layers - name='LC' + integration_method="EULER", + threshold_FitzHughNagumo=a, + uncorrelated_activity_FitzHughNagumo=d, + base_level_gain=G, + scaling_factor_gain=k, + time_step_size_FitzHughNagumo=dt, + mode_FitzHughNagumo=C, + time_constant_v_FitzHughNagumo=tau_v, + time_constant_w_FitzHughNagumo=tau_u, + a_v_FitzHughNagumo=-1.0, + b_v_FitzHughNagumo=1.0, + c_v_FitzHughNagumo=1.0, + d_v_FitzHughNagumo=0.0, + e_v_FitzHughNagumo=-1.0, + f_v_FitzHughNagumo=1.0, + a_w_FitzHughNagumo=1.0, + b_w_FitzHughNagumo=-1.0, + c_w_FitzHughNagumo=0.0, + t_0_FitzHughNagumo=0.0, + initial_v_FitzHughNagumo=initial_v, + initial_w_FitzHughNagumo=initial_u, + objective_mechanism=pnl.ObjectiveMechanism( + function=pnl.Linear, + monitor=[( + decision_layer, + None, + None, + np.array([ + [w_vX1], + [0.0]] + ) + )], + name='LC ObjectiveMechanism' + ), + modulated_mechanisms=[decision_layer, response_layer], # Modulate gain of decision & response layers + name='LC' ) -LC_process = pnl.Process(pathway=[LC]) - -task = pnl.System(processes=[decision_process, LC_process], - reinitialize_mechanisms_when=pnl.Never()) +task = pnl.Composition() +task.add_linear_processing_pathway(decision_pathway) +task.add_node(LC) # This displays a diagram of the System # task.show_graph() @@ -217,57 +223,58 @@ def record_trial(context): num_trials=trials, call_after_trial=record_trial ) -print('\nModel run, generating plots...') # Plot results of all units into one figure --------------------------------------------------------------------------- - - -# Create x axis "t" for plotting -t = np.arange(0.0, 20.02, 0.02) - -# Plot target unit, distraction unit, response unit, h(v), and u using the values that were recorded after each trial -plt.plot( - t, - decision_layer_target_values, - label="target unit", - color='green' -) -plt.plot( - t, - decision_layer_distractor_values, - label="distraction unit", - color='red' -) -plt.plot( - t, - response_layer_values, - label="response unit", - color='magenta' -) -plt.plot( - t, - LC_results_h_of_v, - label="h(v)", - color='b' -) -plt.plot( - t, - LC_results_u, - label="u", - color='black' -) - -plt.xlabel('Time') -plt.ylabel('Activation') -plt.legend(loc='upper left') -plt.xlim((0.0, 20.0)) -plt.ylim((-0.2, 1.2)) -x_values = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20] -plt.xticks(x_values) -plt.title('GILZENRAT 2002 PsyNeuLink', fontweight='bold') - - -plt.show() - -task.show() -print('\nPlots generated') +if args.enable_plot: + import matplotlib.pyplot as plt + + print('\nModel run, generating plots...') + + # Create x axis "t" for plotting + t = np.arange(0.0, 20.02, 0.02) + + # Plot target unit, distraction unit, response unit, h(v), and u using the values that were recorded after each trial + plt.plot( + t, + decision_layer_target_values, + label="target unit", + color='green' + ) + plt.plot( + t, + decision_layer_distractor_values, + label="distraction unit", + color='red' + ) + plt.plot( + t, + response_layer_values, + label="response unit", + color='magenta' + ) + plt.plot( + t, + LC_results_h_of_v, + label="h(v)", + color='b' + ) + plt.plot( + t, + LC_results_u, + label="u", + color='black' + ) + + plt.xlabel('Time') + plt.ylabel('Activation') + plt.legend(loc='upper left') + plt.xlim((0.0, 20.0)) + plt.ylim((-0.2, 1.2)) + x_values = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + plt.xticks(x_values) + plt.title('GILZENRAT 2002 PsyNeuLink', fontweight='bold') + + plt.show() + + task.show_graph() + print('\nPlots generated') diff --git a/psyneulink/library/models/Kalanthroff_PCTC_2018.py b/psyneulink/library/models/Kalanthroff_PCTC_2018.py index 7606d9cf823..6f829e00eb7 100644 --- a/psyneulink/library/models/Kalanthroff_PCTC_2018.py +++ b/psyneulink/library/models/Kalanthroff_PCTC_2018.py @@ -1,15 +1,20 @@ -import matplotlib.pyplot as plt +import argparse + import numpy as np import psyneulink as pnl +parser = argparse.ArgumentParser() +parser.add_argument('--no-plot', action='store_false', help='Disable plotting', dest='enable_plot') +parser.add_argument('--threshold', type=float, help='Termination threshold for response output (default: %(default)f)', default=0.70) +parser.add_argument('--settle-trials', type=int, help='Number of trials for composition to initialize and settle (default: %(default)d)', default=200) +args = parser.parse_args() + # Implements the Kalanthroff, Davelaar, Henik, Goldfarb & Usher model: Task Conflict and Proactive Control: # A Computational Theory of the Stroop Task. Psychol Rev. 2018 Jan;125(1):59-82. doi: 10.1037/rev0000083. # Epub 2017 Oct 16. # #https://www.ncbi.nlm.nih.gov/pubmed/29035077 # Define Variables ---------------------------------------------------------------------------------------------------- -import psyneulink.core.components.functions.transferfunctions - Lambda = 0.03 # PsyNeuLink has Euler integration constant reversed (1-0.97) pc_high = 0.15 # High proactive control from Figure 6 in Paper pc_low = 0.025 # Low proactive control from Figure 6 in Paper @@ -17,32 +22,32 @@ inhibition = -1.3 # Inhibition between units within a layer inhibition_task = -1.9 # Inhibition between units within task layer bias = -0.3 # bias input to color feature layer and word feature layer -threshold = 0.70 -settle = 200 # Number of trials until Composition settles +threshold = args.threshold +settle = args.settle_trials # Number of trials until Composition settles # Create mechanisms --------------------------------------------------------------------------------------------------- # 4 Input layers for color, word, task & bias colors_input_layer = pnl.TransferMechanism( size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='COLORS_INPUT' ) words_input_layer = pnl.TransferMechanism( size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='WORDS_INPUT' ) task_input_layer = pnl.TransferMechanism( size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='PROACTIVE_CONTROL' ) bias_input = pnl.TransferMechanism( size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, + function=pnl.Linear, name='BIAS' ) @@ -68,7 +73,7 @@ def my_conflict_function(variable): # Create color feature layer, word feature layer, task demand layer and response layer color_feature_layer = pnl.RecurrentTransferMechanism( size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 + function=pnl.Logistic(gain=4, x_0=1), # to 4 & bias to 1 integrator_mode=True, # Set IntegratorFunction mode to True integration_rate=Lambda, # smoothing factor == integration rate hetero=inhibition, # Inhibition among units within a layer @@ -82,7 +87,7 @@ def my_conflict_function(variable): # The word_feature_layer is set up as the color_feature_layer word_feature_layer = pnl.RecurrentTransferMechanism( size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 + function=pnl.Logistic(gain=4, x_0=1), # to 4 & bias to 1 integrator_mode=True, # Set IntegratorFunction mode to True integration_rate=Lambda, # smoothing factor == integration rate hetero=inhibition, # Inhibition among units within a layer @@ -97,7 +102,7 @@ def my_conflict_function(variable): # The response_layer is set up as the color_feature_layer & the word_feature_layer response_layer = pnl.RecurrentTransferMechanism( size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 + function=pnl.Logistic(gain=4, x_0=1), # to 4 & bias to 1 integrator_mode=True, # Set IntegratorFunction mode to True integration_rate=Lambda, # smoothing factor == integration rate hetero=inhibition, # Inhibition among units within a layer @@ -113,7 +118,7 @@ def my_conflict_function(variable): # and a differnet inhibition weight on the hetero task_demand_layer = pnl.RecurrentTransferMechanism( size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 + function=pnl.Logistic(gain=4, x_0=1), # to 4 & bias to 1 integrator_mode=True, # Set IntegratorFunction mode to True integration_rate=Lambda, # smoothing factor == integration rate hetero=inhibition_task, # Inhibition among units within a layer @@ -493,65 +498,67 @@ def trial_dict(blue_color, green_color, blue_word, green_word, PC_CN, PC_WR, bia rrr_incong = rr.reshape(n_incon, 2) # Plotting ------------------------------------------------------------------------------------------------------------ -# Set up plot structure -fig, axes = plt.subplots(nrows=3, ncols=4, sharey=True, sharex=True) -axes[0, 0].set_ylabel('Congruent') -axes[1, 0].set_ylabel('Neutral') -axes[2, 0].set_ylabel('Incongruent') - -axes[0, 0].set_title('Task demand units', fontsize=9) -axes[0, 1].set_title('Response units', fontsize=9) -axes[0, 2].set_title('Color feature map', fontsize=9) -axes[0, 3].set_title('Word feature map', fontsize=9) -plt.setp( - axes, - xticks=[0, 400, 780], - yticks=[0, 0.4, 0.79], - yticklabels=['0', '0.4', '0.8'], - xticklabels=['0', '400', '800'] -) - -# Plot congruent output -------------------------- -axes[0, 0].plot(ttt_cong[settle:, 0], 'c') -axes[0, 0].plot(ttt_cong[settle:, 1], 'k') -axes[0, 0].plot(conflict_con, 'r') - -axes[0, 1].plot(rrr_cong[settle:, 0], 'b') -axes[0, 1].plot(rrr_cong[settle:, 1], 'g') -axes[0, 1].plot([0, n_con - settle], [threshold, threshold], 'k') -axes[0, 2].plot(ccc_cong[settle:, 0], 'b') -axes[0, 2].plot(ccc_cong[settle:, 1], 'g') - -axes[0, 3].plot(www_cong[settle:, 0], 'b') -axes[0, 3].plot(www_cong[settle:, 1], 'g') - -# Plot neutral output -------------------------- -axes[1, 0].plot(ttt_neutral[settle:, 0], 'c') -axes[1, 0].plot(ttt_neutral[settle:, 1], 'k') -axes[1, 0].plot(conflict_neutral, 'r') - -axes[1, 1].plot(rrr_neutral[settle:, 0], 'b') -axes[1, 1].plot(rrr_neutral[settle:, 1], 'g') -axes[1, 1].plot([0, n_neutral - settle], [threshold, threshold], 'k') -axes[1, 2].plot(ccc_neutral[settle:, 0], 'b') -axes[1, 2].plot(ccc_neutral[settle:, 1], 'g') - - -axes[1, 3].plot(www_neutral[settle:, 0], 'b') -axes[1, 3].plot(www_neutral[settle:, 1], 'g') - -# Plot incongruent output -------------------------- -axes[2, 0].plot(ttt_incong[settle:, 0], 'c') -axes[2, 0].plot(ttt_incong[settle:, 1], 'k') -axes[2, 0].plot(conflict_incon, 'r') - -axes[2, 1].plot(rrr_incong[settle:, 0], 'b') -axes[2, 1].plot(rrr_incong[settle:, 1], 'g') -axes[2, 1].plot([0, n_incon - settle], [threshold, threshold], 'k') -axes[2, 2].plot(ccc_incong[settle:, 0], 'b') -axes[2, 2].plot(ccc_incong[settle:, 1], 'g') - -axes[2, 3].plot(www_incong[settle:, 0], 'b') -axes[2, 3].plot(www_incong[settle:, 1], 'g') - -plt.show() +if args.enable_plot: + import matplotlib.pyplot as plt + + # Set up plot structure + fig, axes = plt.subplots(nrows=3, ncols=4, sharey=True, sharex=True) + axes[0, 0].set_ylabel('Congruent') + axes[1, 0].set_ylabel('Neutral') + axes[2, 0].set_ylabel('Incongruent') + + axes[0, 0].set_title('Task demand units', fontsize=9) + axes[0, 1].set_title('Response units', fontsize=9) + axes[0, 2].set_title('Color feature map', fontsize=9) + axes[0, 3].set_title('Word feature map', fontsize=9) + plt.setp( + axes, + xticks=[0, 400, 780], + yticks=[0, 0.4, 0.79], + yticklabels=['0', '0.4', '0.8'], + xticklabels=['0', '400', '800'] + ) + + # Plot congruent output -------------------------- + axes[0, 0].plot(ttt_cong[settle:, 0], 'c') + axes[0, 0].plot(ttt_cong[settle:, 1], 'k') + axes[0, 0].plot(conflict_con, 'r') + + axes[0, 1].plot(rrr_cong[settle:, 0], 'b') + axes[0, 1].plot(rrr_cong[settle:, 1], 'g') + axes[0, 1].plot([0, n_con - settle], [threshold, threshold], 'k') + axes[0, 2].plot(ccc_cong[settle:, 0], 'b') + axes[0, 2].plot(ccc_cong[settle:, 1], 'g') + + axes[0, 3].plot(www_cong[settle:, 0], 'b') + axes[0, 3].plot(www_cong[settle:, 1], 'g') + + # Plot neutral output -------------------------- + axes[1, 0].plot(ttt_neutral[settle:, 0], 'c') + axes[1, 0].plot(ttt_neutral[settle:, 1], 'k') + axes[1, 0].plot(conflict_neutral, 'r') + + axes[1, 1].plot(rrr_neutral[settle:, 0], 'b') + axes[1, 1].plot(rrr_neutral[settle:, 1], 'g') + axes[1, 1].plot([0, n_neutral - settle], [threshold, threshold], 'k') + axes[1, 2].plot(ccc_neutral[settle:, 0], 'b') + axes[1, 2].plot(ccc_neutral[settle:, 1], 'g') + + axes[1, 3].plot(www_neutral[settle:, 0], 'b') + axes[1, 3].plot(www_neutral[settle:, 1], 'g') + + # Plot incongruent output -------------------------- + axes[2, 0].plot(ttt_incong[settle:, 0], 'c') + axes[2, 0].plot(ttt_incong[settle:, 1], 'k') + axes[2, 0].plot(conflict_incon, 'r') + + axes[2, 1].plot(rrr_incong[settle:, 0], 'b') + axes[2, 1].plot(rrr_incong[settle:, 1], 'g') + axes[2, 1].plot([0, n_incon - settle], [threshold, threshold], 'k') + axes[2, 2].plot(ccc_incong[settle:, 0], 'b') + axes[2, 2].plot(ccc_incong[settle:, 1], 'g') + + axes[2, 3].plot(www_incong[settle:, 0], 'b') + axes[2, 3].plot(www_incong[settle:, 1], 'g') + + plt.show() diff --git a/psyneulink/library/models/MontagueDayanSejnowski96.py b/psyneulink/library/models/MontagueDayanSejnowski96.py index af74e78d276..e593a3bd4fe 100644 --- a/psyneulink/library/models/MontagueDayanSejnowski96.py +++ b/psyneulink/library/models/MontagueDayanSejnowski96.py @@ -3,13 +3,20 @@ conditioning as found in `Montague, Dayan, and Sejnowski (1996) in PsyNeuLink `_ """ -import matplotlib -# matplotlib.use('TkAgg') -import matplotlib.pyplot as plt +import argparse + import numpy as np import psyneulink as pnl -from mpl_toolkits import mplot3d # needed for 3d plotting +all_figures = ['5a', '5b', '5c'] + +parser = argparse.ArgumentParser() +parser.add_argument('--no-plot', action='store_false', help='Disable plotting', dest='enable_plot') +parser.add_argument('--figure', nargs='+', help='Figure(s) to plot (default=all)', choices=all_figures, default=all_figures) +args = parser.parse_args() + +if args.enable_plot: + import matplotlib.pyplot as plt def build_stimulus_dictionary(sample_mechanism, target_mechanism, no_reward_trials): @@ -85,10 +92,10 @@ def figure_5a(): pathway = [sample_mechanism, sample_to_action_selection, action_selection] # Add Learning Components to the Composition - learning_related_components = comp.add_td_learning_pathway(pathway, learning_rate=0.3) + learning_related_components = comp.add_td_learning_pathway(pathway, learning_rate=0.3).learning_components # Unpack Relevant Learning Components - prediction_error_mechanism = learning_related_components[pnl.OBJECTIVE_MECHANISM, ] + prediction_error_mechanism = learning_related_components[pnl.OBJECTIVE_MECHANISM] target_mechanism = learning_related_components[pnl.TARGET_MECHANISM] # Create Log @@ -99,24 +106,26 @@ def figure_5a(): inputs = build_stimulus_dictionary(sample_mechanism, target_mechanism, no_reward_trials) # Run Composition - comp.run(inputs=inputs) - # comp.show_graph() - - # Get Delta Values from Log - delta_vals = prediction_error_mechanism.log.nparray_dictionary()[composition_name][pnl.VALUE] - - # Plot Delta Values form trials 1, 30, and 50 - with plt.style.context('seaborn'): - plt.plot(delta_vals[0][0], "-o", label="Trial 1") - plt.plot(delta_vals[29][0], "-s", label="Trial 30") - plt.plot(delta_vals[49][0], "-o", label="Trial 50") - plt.title("Montague et. al. (1996) -- Figure 5A") - plt.xlabel("Timestep") - plt.ylabel("∂") - plt.legend() - plt.xlim(xmin=35) - plt.xticks() - plt.show() + comp.learn(inputs=inputs) + + if args.enable_plot: + # Get Delta Values from Log + delta_vals = prediction_error_mechanism.log.nparray_dictionary()[composition_name][pnl.VALUE] + + # Plot Delta Values form trials 1, 30, and 50 + with plt.style.context('seaborn'): + plt.plot(delta_vals[0][0], "-o", label="Trial 1") + plt.plot(delta_vals[29][0], "-s", label="Trial 30") + plt.plot(delta_vals[49][0], "-o", label="Trial 50") + plt.title("Montague et. al. (1996) -- Figure 5A") + plt.xlabel("Timestep") + plt.ylabel("∂") + plt.legend() + plt.xlim(xmin=35) + plt.xticks() + plt.show() + + return comp def figure_5b(): @@ -146,10 +155,10 @@ def figure_5b(): pathway = [sample_mechanism, sample_to_action_selection, action_selection] # Add Learning Components to the Composition - learning_related_components = comp.add_td_learning_pathway(pathway, learning_rate=0.3) + learning_related_components = comp.add_td_learning_pathway(pathway, learning_rate=0.3).learning_components # Unpack Relevant Learning Components - prediction_error_mechanism = learning_related_components[pnl.OBJECTIVE_MECHANISM, ] + prediction_error_mechanism = learning_related_components[pnl.OBJECTIVE_MECHANISM] target_mechanism = learning_related_components[pnl.TARGET_MECHANISM] # Create Log @@ -161,25 +170,28 @@ def figure_5b(): inputs = build_stimulus_dictionary(sample_mechanism, target_mechanism, no_reward_trials) # Run Composition - comp.run(inputs=inputs) - # comp.show_graph() - - # Get Delta Values from Log - delta_vals = prediction_error_mechanism.log.nparray_dictionary()[composition_name][pnl.VALUE] - - with plt.style.context('seaborn'): - fig = plt.figure() - ax = fig.add_subplot(111, projection='3d') - x_vals, y_vals = np.meshgrid(np.arange(120), np.arange(40, 60, step=1)) - d_vals = np.array([d[0][40:60] for d in delta_vals]).transpose() - ax.plot_surface(x_vals, y_vals, d_vals) - ax.invert_yaxis() - ax.invert_yaxis() - ax.set_xlabel("Trial") - ax.set_ylabel("Timestep") - ax.set_zlabel("∂") - ax.set_title("Montague et. al. (1996) -- Figure 5B") - plt.show() + comp.learn(inputs=inputs) + + if args.enable_plot: + # Get Delta Values from Log + delta_vals = prediction_error_mechanism.log.nparray_dictionary()[composition_name][pnl.VALUE] + + with plt.style.context('seaborn'): + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + x_vals, y_vals = np.meshgrid(np.arange(120), np.arange(40, 60, step=1)) + d_vals = np.array([d[0][40:60] for d in delta_vals]).transpose() + ax.plot_surface(x_vals, y_vals, d_vals) + ax.set_xlabel("Trial") + ax.set_ylabel("Timestep") + ax.set_zlabel("∂") + ax.set_ylim(y_vals.max(), y_vals.min()) + ax.set_xlim(0, 120) + ax.set_zlim(-1, 1) + ax.set_title("Montague et. al. (1996) -- Figure 5B") + plt.show() + + return comp def figure_5c(): @@ -209,10 +221,10 @@ def figure_5c(): pathway = [sample_mechanism, sample_to_action_selection, action_selection] # Add Learning Components to the Composition - learning_related_components = comp.add_td_learning_pathway(pathway, learning_rate=0.3) + learning_related_components = comp.add_td_learning_pathway(pathway, learning_rate=0.3).learning_components # Unpack Relevant Learning Components - prediction_error_mechanism = learning_related_components[pnl.OBJECTIVE_MECHANISM, ] + prediction_error_mechanism = learning_related_components[pnl.OBJECTIVE_MECHANISM] target_mechanism = learning_related_components[pnl.TARGET_MECHANISM] # Create Log @@ -222,27 +234,35 @@ def figure_5c(): inputs = build_stimulus_dictionary_figure_5c(sample_mechanism, target_mechanism) # Run Composition - comp.run(inputs=inputs) - # comp.show_graph() - - # Get Delta Values from Log - delta_vals = prediction_error_mechanism.log.nparray_dictionary()[composition_name][pnl.VALUE] - - with plt.style.context('seaborn'): - fig = plt.figure() - ax = fig.add_subplot(111, projection='3d') - x_vals, y_vals = np.meshgrid(np.arange(150), np.arange(40, 60, step=1)) - d_vals = np.array([d[0][40:60] for d in delta_vals]).transpose() - ax.plot_surface(x_vals, y_vals, d_vals) - ax.invert_yaxis() - ax.set_xlabel("Trial") - ax.set_ylabel("Timestep") - ax.set_zlabel("∂") - ax.set_title("Montague et. al. (1996) -- Figure 5C") - plt.show() - - -if __name__ == '__main__': - figure_5a() - figure_5b() - figure_5c() + comp.learn(inputs=inputs) + + if args.enable_plot: + # Get Delta Values from Log + delta_vals = prediction_error_mechanism.log.nparray_dictionary()[composition_name][pnl.VALUE] + + with plt.style.context('seaborn'): + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + x_vals, y_vals = np.meshgrid(np.arange(150), np.arange(40, 60, step=1)) + d_vals = np.array([d[0][40:60] for d in delta_vals]).transpose() + ax.plot_surface(x_vals, y_vals, d_vals) + ax.set_ylim(y_vals.max(), y_vals.min()) + ax.set_xlim(0, 140) + ax.set_zlim(-1, 1) + ax.set_xlabel("Trial") + ax.set_ylabel("Timestep") + ax.set_zlabel("∂") + ax.set_title("Montague et. al. (1996) -- Figure 5C") + plt.show() + + return comp + + +if '5a' in args.figure: + comp_5a = figure_5a() + +if '5b' in args.figure: + comp_5b = figure_5b() + +if '5c' in args.figure: + comp_5c = figure_5c() diff --git a/psyneulink/library/models/Nieuwenhuis2005Model.py b/psyneulink/library/models/Nieuwenhuis2005Model.py index 0f3dd3e2d0b..50891d3546c 100644 --- a/psyneulink/library/models/Nieuwenhuis2005Model.py +++ b/psyneulink/library/models/Nieuwenhuis2005Model.py @@ -1,17 +1,18 @@ # Import all dependencies. -# Note: Please import matplotlib before importing any psyneulink dependencies. -import numpy as np +import argparse -from matplotlib import pyplot as plt -# from scipy.special import erfinv # need to import this to make us of the UniformToNormalDist function. +import numpy as np import psyneulink as pnl +# from scipy.special import erfinv # need to import this to make us of the UniformToNormalDist function. + +parser = argparse.ArgumentParser() +parser.add_argument('--no-plot', action='store_false', help='Disable plotting', dest='enable_plot') +args = parser.parse_args() # --------------------------------- Global Variables ---------------------------------------- # Now, we set the global variables, weights and initial values as in the paper. # WATCH OUT !!! In the paper the weight "Mutual inhibition among response units" is not defined, but needs to be set to # 0 in order to reproduce the paper. -import psyneulink.core.components.functions.transferfunctions - SD = 0.15 # noise determined by standard deviation (SD) a = 0.50 # Parameter describing shape of the FitzHugh–Nagumo cubic nullcline for the fast excitation variable v d = 0.5 # Uncorrelated Activity @@ -64,7 +65,7 @@ leak=1.0, # Sets off diagonals to negative values self_excitation=selfdwt, # Set diagonals to self excitate competition=inhwt, # Set off diagonals to inhibit - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=decbias), # Set the Logistic function with bias = decbias + function=pnl.Logistic(x_0=decbias), # Set the Logistic function with bias = decbias # noise=pnl.UniformToNormalDist(standard_deviation = SD).function, # The UniformToNormalDist function will integrator_mode=True, # set the noise with a seed generator that is compatible with name='DECISION LAYER' # MATLAB random seed generator 22 (rsg=22) @@ -84,7 +85,7 @@ leak=1.0, # Sets off diagonals to negative values self_excitation=selfrwt, # Set diagonals to self excitate competition=respinhwt, # Set off diagonals to inhibit - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=respbias), # Set the Logistic function with bias = decbias + function=pnl.Logistic(x_0=respbias), # Set the Logistic function with bias = decbias # noise=pnl.UniformToNormalDist(standard_deviation = SD).function, integrator_mode=True, name='RESPONSE LAYER' @@ -109,7 +110,7 @@ [0.0, 0.0] ]) -decision_process = pnl.Process( +decision_pathway = pnl.Pathway( pathway=[ input_layer, input_weights, @@ -117,7 +118,7 @@ output_weights, response_layer ], - name='DECISION PROCESS' + name='DECISION PATHWAY' ) # Abstracted LC to modulate gain -------------------------------------------------------------------- @@ -146,8 +147,8 @@ initial_v_FitzHughNagumo=initial_v, # Initialize v initial_w_FitzHughNagumo=initial_w, # Initialize w objective_mechanism=pnl.ObjectiveMechanism( - function=psyneulink.core.components.functions.transferfunctions.Linear, - monitored_output_ports=[( + function=pnl.Linear, + monitor=[( decision_layer, # Project output of T1 and T2 but not distractor from decision layer to LC np.array([[lcwt], [lcwt], [0.0]]) )], @@ -165,11 +166,9 @@ for output_port in LC.output_ports: output_port.value *= G + k * initial_w -LC_process = pnl.Process(pathway=[LC]) - -# Now, we specify the processes of the System, which in this case is just the decision_process -task = pnl.System(processes=[decision_process, LC_process], - reinitialize_mechanisms_when=pnl.Never(),) +task = pnl.Composition() +task.add_linear_processing_pathway(decision_pathway) +task.add_node(LC) # Create Stimulus ----------------------------------------------------------------------------------------------------- @@ -201,7 +200,8 @@ stim_list_dict = {input_layer: time} # show the system -task.show() +# task.show_graph() + # run the system task.run(stim_list_dict, num_trials=trials) @@ -212,10 +212,10 @@ LC_results = LC.log.nparray()[1][1] # get logged results LC_results_w = np.zeros([trials]) # get LC_results_w for i in range(trials): - LC_results_w[i] = LC_results[4][i + 1][3][0][0] + LC_results_w[i] = LC_results[4][i + 1][2][0][0] LC_results_v = np.zeros([trials]) # get LC_results_v for i in range(trials): - LC_results_v[i] = LC_results[4][i + 1][2][0][0] + LC_results_v[i] = LC_results[4][i + 1][1][0][0] def h_v(v, C, d): # Compute h(v) @@ -227,20 +227,23 @@ def h_v(v, C, d): # Compute h(v) LC_results_hv[i] = h_v(LC_results_v[i], C, d) -# Plot the Figure 3 from the paper -t = np.linspace(0, trials, trials) # Create array for x axis with same length then LC_results_v -fig = plt.figure() # Instantiate figure -ax = plt.gca() # Get current axis for plotting -ax2 = ax.twinx() # Create twin axis with a different y-axis on the right side of the figure -ax.plot(t, LC_results_hv, label="h(v)") # Plot h(v) -ax2.plot(t, LC_results_w, label="w", color='red') # Plot w -h1, l1 = ax.get_legend_handles_labels() -h2, l2 = ax2.get_legend_handles_labels() -ax.legend(h1 + h2, l1 + l2, loc=2) # Create legend on one side -ax.set_xlabel('Time (ms)') # Set x axis lable -ax.set_ylabel('LC Activity') # Set left y axis label -ax2.set_ylabel('NE Output') # Set right y axis label -plt.title('Nieuwenhuis 2005 PsyNeuLink Lag 2 without noise', fontweight='bold') # Set title -ax.set_ylim((-0.2, 1.0)) # Set left y axis limits -ax2.set_ylim((0.0, 0.4)) # Set right y axis limits -plt.show() +if args.enable_plot: + import matplotlib.pyplot as plt + + # Plot the Figure 3 from the paper + t = np.linspace(0, trials, trials) # Create array for x axis with same length then LC_results_v + fig = plt.figure() # Instantiate figure + ax = plt.gca() # Get current axis for plotting + ax2 = ax.twinx() # Create twin axis with a different y-axis on the right side of the figure + ax.plot(t, LC_results_hv, label="h(v)") # Plot h(v) + ax2.plot(t, LC_results_w, label="w", color='red') # Plot w + h1, l1 = ax.get_legend_handles_labels() + h2, l2 = ax2.get_legend_handles_labels() + ax.legend(h1 + h2, l1 + l2, loc=2) # Create legend on one side + ax.set_xlabel('Time (ms)') # Set x axis lable + ax.set_ylabel('LC Activity') # Set left y axis label + ax2.set_ylabel('NE Output') # Set right y axis label + plt.title('Nieuwenhuis 2005 PsyNeuLink Lag 2 without noise', fontweight='bold') # Set title + ax.set_ylim((-0.2, 1.0)) # Set left y axis limits + ax2.set_ylim((0.0, 0.4)) # Set right y axis limits + plt.show() diff --git a/psyneulink/library/models/results/Cohen_Huston1994-Bidirectional_Stroop-reduced.json b/psyneulink/library/models/results/Cohen_Huston1994-Bidirectional_Stroop-reduced.json new file mode 100644 index 00000000000..67720b3714d --- /dev/null +++ b/psyneulink/library/models/results/Cohen_Huston1994-Bidirectional_Stroop-reduced.json @@ -0,0 +1,404 @@ +[ + [ 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0.5272382076179102]], + [[0.5238811714255978, 0.5272068064001388]], + [[0.5238841520528198, 0.5271757253634424]], + [[0.5238871121023422, 0.5271449613622687]], + [[0.5238900516382604, 0.527114511280182]], + [[0.5238929707261122, 0.5270843720296218]], + [[0.5238958694328345, 0.5270545405516628]], + [[0.5238987478267201, 0.5270250138157774]], + [[0.5239016059773758, 0.5269957888195987]], + [[0.5239044439556806, 0.5269668625886855]], + [[0.5239072618337455, 0.5269382321762884]], + [[0.5239100596848735, 0.5269098946631183]], + [[0.5239128375835209, 0.526881847157115]], + [[0.5239155956052582, 0.5268540867932182]], + [[0.5239183338267339, 0.5268266107331411]], + [[0.5239210523256362, 0.526799416165142]] +] diff --git a/setup.cfg b/setup.cfg index e3382c1f13b..347cbb61b40 100644 --- a/setup.cfg +++ b/setup.cfg @@ -66,7 +66,7 @@ filterwarnings = [pycodestyle] # for code explanation see https://pep8.readthedocs.io/en/latest/intro.html#error-codes -ignore = E111,E114,E115,E116,E117,E121,E122,E123,E124,E125,E126,E127,E128,E129,E131,E201,E202,E203,E211,E221,E225,E231,E241,E251,E252,E261,E262,E265,E266,E271,E301,E302,E303,E305,E306,E402,E501,E711,E712,E713,E714,E721,E722,E731,E741,W293,W391,W503,W504,W605 +ignore = E111,E114,E115,E116,E117,E121,E122,E123,E124,E125,E126,E127,E128,E129,E131,E201,E202,E203,E211,E221,E225,E231,E241,E251,E252,E261,E262,E265,E266,E271,E301,E302,E303,E305,E306,E501,E711,E712,E713,E714,E721,E722,E731,E741,W293,W391,W503,W504,W605 exclude = .git/*,Scripts/*,__pytest__/*,docs/*,bin/* [pydocstyle] @@ -101,9 +101,6 @@ exclude_lines = if 0: if __name__ == .__main__.: -# Omit models that are not tested -omit = psyneulink/library/models/* - [versioneer] VCS = git style = pep440 diff --git a/tests/composition/test_autodiffcomposition.py b/tests/composition/test_autodiffcomposition.py index 20636134588..24648bb5629 100644 --- a/tests/composition/test_autodiffcomposition.py +++ b/tests/composition/test_autodiffcomposition.py @@ -478,7 +478,7 @@ def test_xor_training_correctness(self, eps, calls, opt, mode, benchmark, expect @pytest.mark.benchmark(group="Semantic net") @pytest.mark.parametrize( 'eps, opt', [ - (500, 'adam'), + (50, 'adam'), ] ) @pytest.mark.parametrize("mode", ['Python', @@ -650,102 +650,127 @@ def test_semantic_net_training_correctness(self, eps, opt, mode, benchmark): 'epochs': eps}, bin_execute=mode) # CHECK CORRECTNESS - expected = [[[5.26497539e-01, 3.89633103e-01, 3.36883308e-02, 5.65217125e-02, 4.75946321e-04, 2.42683893e-04, 1.46543688e-03, 1.94422406e-02], - [9.99997562e-01, 9.99997577e-01, 9.89294536e-01, 1.22222871e-02, 9.27816674e-01, 9.82077990e-02, 1.66181249e-04, 4.57763294e-03, 9.50823684e-01, 9.25704335e-01, 4.61251874e-02, 5.39340666e-02], - [9.92552598e-01, 9.87345822e-01, 9.35494418e-01, 9.22407947e-01, 1.35281121e-04, 2.68860676e-04, 1.30422432e-04, 3.91247181e-03, 4.17455292e-03], - [9.99987226e-01, 1.54067181e-02, 4.66989315e-03, 1.31888021e-04, 1.27575625e-02, 3.86785426e-03, 2.68783142e-04, 1.62731209e-04, 9.87476904e-01]], - [[5.23213155e-01, 3.88631542e-01, 3.36372354e-02, 5.68042311e-02, 4.73729591e-04, 2.40289507e-04, 1.45005586e-03, 1.95740693e-02], - [9.99997588e-01, 9.99997609e-01, 9.89320755e-01, 1.22467631e-02, 9.27008351e-01, 9.82030646e-02, 1.64845715e-04, 4.56760049e-03, 9.51175203e-01, 9.25517314e-01, 4.56957202e-02, 5.37664905e-02], - [9.92542641e-01, 9.87349853e-01, 9.34901699e-01, 9.22206731e-01, 1.34996367e-04, 2.67657588e-04, 1.29736251e-04, 3.91389627e-03, 4.16584859e-03], - [9.99987470e-01, 1.54360945e-02, 4.67486002e-03, 1.30521704e-04, 1.28059491e-02, 3.86623803e-03, 2.66171209e-04, 1.61398200e-04, 9.87420041e-01]], - [[5.30132716e-01, 3.86811933e-01, 3.35150274e-02, 5.63452712e-02, 4.75960836e-04, 2.42276591e-04, 1.43541888e-03, 1.93375794e-02], - [9.99997584e-01, 9.99997617e-01, 9.89262553e-01, 1.22244947e-02, 9.27482609e-01, 9.73224094e-02, 1.67147694e-04, 4.51819438e-03, 9.50611809e-01, 9.25539481e-01, 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9.73068145e-04, 4.77810522e-02, 1.27260523e-02, 7.03680880e-01, 1.25263057e-01], - [9.40667195e-01, 9.46092952e-01, 1.38805180e-02, 1.42242564e-02, 2.04586683e-02, 1.86114803e-02, 1.19253100e-02, 6.23708088e-04, 2.35167820e-03], - [0.99999369, 0.04782566, 0.00128885, 0.0122107, 0.05535409, 0.00175221, 0.01087313, 0.0144344, 0.93891698]], - [[0.02633363, 0.02230421, 0.75294078, 0.10431207, 0.01698519, 0.01332532, 0.00355372, 0.00469181], - [9.99998406e-01, 9.99997254e-01, 9.33777411e-01, 6.17315582e-02, 1.47001114e-02, 8.54885072e-01, 1.34870591e-02, 9.67095077e-04, 4.74573559e-02, 1.26194945e-02, 7.04434229e-01, 1.24914256e-01], - [9.40518586e-01, 9.46017910e-01, 1.38598268e-02, 1.40732769e-02, 2.04332168e-02, 1.85908253e-02, 1.19197345e-02, 6.19323485e-04, 2.33955632e-03], - [0.99999375, 0.04797465, 0.00128082, 0.01225632, 0.0553451, 0.00174049, 0.01086387, 0.01447748, 0.93882894]], - [[1.20818689e-01, 3.22685263e-02, 2.19306806e-01, 8.32636659e-01, 6.02707887e-02, 3.40080941e-03, 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8.58250915e-05], - [9.99953407e-01, 1.23473951e-02, 8.52577633e-05, 3.03296723e-02, 1.56039954e-02, 1.30496751e-04, 3.38303116e-02, 3.04053577e-02, 9.89579641e-01]], - [[1.22546183e-01, 3.25364800e-02, 2.20645196e-01, 8.33458371e-01, 6.03597357e-02, 3.41034149e-03, 4.52329748e-04, 3.07332439e-03], - [9.99968587e-01, 9.99983639e-01, 9.94113070e-01, 1.39487033e-02, 7.19601439e-02, 9.11196003e-01, 3.71189925e-02, 5.48866647e-05, 2.16228418e-02, 7.32512105e-02, 1.56564725e-01, 8.63513042e-01], - [9.87935664e-01, 9.85883725e-01, 6.83316148e-02, 7.16935850e-02, 3.40021029e-02, 3.38063676e-02, 3.42377125e-02, 7.24891786e-05, 8.55557208e-05], - [9.99952838e-01, 1.23272165e-02, 8.49885891e-05, 3.08458302e-02, 1.54587713e-02, 1.29969700e-04, 3.38370569e-02, 3.07277168e-02, 9.89666727e-01]], - [[0.0048586, 0.0008491, 0.06529448, 0.09430598, 0.73061321, 0.29588638, 0.00723796, 0.00189421], - [9.99978468e-01, 9.99973341e-01, 6.24889702e-02, 9.36972455e-01, 3.38406719e-04, 1.49515790e-01, 9.71449623e-01, 1.79586277e-03, 6.56153231e-03, 2.69857603e-04, 3.53928690e-01, 8.35262375e-01], - [6.06715154e-02, 5.62354518e-02, 5.10329798e-04, 2.43209390e-04, 9.67687742e-01, 9.68757729e-01, 9.72676594e-01, 1.33847803e-03, 1.10547062e-03], - [0.99995151, 0.94952895, 0.00101679, 0.97324069, 0.94352159, 0.00120951, 0.97317057, 0.96685174, 0.05887567]], - [[0.00492578, 0.0008641, 0.06503229, 0.09498185, 0.72883147, 0.29336741, 0.00721195, 0.00190832], - [9.99977930e-01, 9.99972870e-01, 6.38014640e-02, 9.35592083e-01, 3.43211586e-04, 1.49312948e-01, 9.71047214e-01, 1.77181244e-03, 6.60095324e-03, 2.75083694e-04, 3.53640004e-01, 8.36355896e-01], - [6.18499321e-02, 5.70740348e-02, 5.17729899e-04, 2.48697103e-04, 9.67324218e-01, 9.68514116e-01, 9.72332797e-01, 1.32926410e-03, 1.09285231e-03], - [0.99995092, 0.94857309, 0.00101428, 0.97297877, 0.94282949, 0.00119903, 0.97287461, 0.96646575, 0.05980939]], - [[0.00488634, 0.00085293, 0.06484863, 0.09388447, 0.72963164, 0.29500192, 0.00723876, 0.00189659], - [9.99978310e-01, 9.99973303e-01, 6.27213598e-02, 9.36734798e-01, 3.40916666e-04, 1.47801704e-01, 9.71383072e-01, 1.79556214e-03, 6.61562822e-03, 2.72542621e-04, 3.52586179e-01, 8.35183006e-01], - [6.08046588e-02, 5.63415621e-02, 5.15862857e-04, 2.45009926e-04, 9.67603640e-01, 9.68687304e-01, 9.72507186e-01, 1.33635589e-03, 1.10365279e-03], - [0.99995128, 0.9494861, 0.00101963, 0.97307241, 0.9434724, 0.00120865, 0.97302359, 0.96668619, 0.05875648]], - [[0.00433486, 0.00206712, 0.0922493, 0.00346938, 0.2396027, 0.62210991, 0.12554657, 0.01345759], - [9.99984347e-01, 9.99976575e-01, 7.85559301e-03, 9.87463484e-01, 3.50935985e-04, 2.38600395e-02, 9.23479183e-01, 6.93678878e-02, 5.97770983e-02, 3.58436180e-04, 7.45846266e-01, 1.30063775e-01], - [8.29658298e-03, 9.58970123e-03, 6.75129366e-04, 2.93946549e-04, 9.28468603e-01, 9.29732084e-01, 9.26541438e-01, 5.90327552e-02, 7.34691045e-02], - [0.99994262, 0.99033467, 0.07752959, 0.92874409, 0.99050227, 0.06331013, 0.92718197, 0.93574984, 0.00886141]], - [[0.00434419, 0.00208202, 0.0926049, 0.0034775, 0.23970905, 0.6234368, 0.12583267, 0.0135345], - [9.99984154e-01, 9.99976329e-01, 7.86890781e-03, 9.87467127e-01, 3.49750484e-04, 2.39501857e-02, 9.23477514e-01, 6.94140162e-02, 5.97856242e-02, 3.58977874e-04, 7.46348541e-01, 1.30252457e-01], - [8.28808904e-03, 9.60840460e-03, 6.73162424e-04, 2.93954143e-04, 9.28770681e-01, 9.30035016e-01, 9.27093510e-01, 5.91356536e-02, 7.36568004e-02], - [0.99994204, 0.990355, 0.07798265, 0.92912392, 0.99050495, 0.06340336, 0.92740632, 0.93613216, 0.00885769]], - [[0.00435304, 0.00205038, 0.09157595, 0.00342392, 0.23838988, 0.62263732, 0.12366996, 0.01336267], - [9.99984631e-01, 9.99977205e-01, 7.82664145e-03, 9.87493353e-01, 3.47772826e-04, 2.35206890e-02, 9.23891255e-01, 6.84941830e-02, 5.94907676e-02, 3.56458821e-04, 7.44424590e-01, 1.29558299e-01], - [8.24701466e-03, 9.53190844e-03, 6.77662754e-04, 2.89686757e-04, 9.28598623e-01, 9.30064858e-01, 9.26896891e-01, 5.83450277e-02, 7.27817883e-02], - [0.99994359, 0.99045483, 0.0769432, 0.92929541, 0.99054363, 0.0623923, 0.92741506, 0.93638864, 0.00878219]], - [[5.91087395e-03, 1.23612502e-02, 1.56717106e-02, 1.41416791e-04, 5.33414123e-03, 1.40752726e-01, 6.25766219e-01, 2.60238902e-01], - [9.99996187e-01, 9.99990816e-01, 3.74739045e-03, 9.92429712e-01, 5.13025663e-03, 7.76351109e-04, 6.26656777e-02, 9.27960285e-01, 9.39401330e-01, 3.89482393e-03, 7.29546757e-01, 3.41053745e-03], - [0.00467329, 0.00593148, 0.00741578, 0.00543405, 0.06035587, 0.05892469, 0.06303486, 0.93135276, 0.92361476], - [0.99996884, 0.99239955, 0.9231971, 0.06164636, 0.99488771, 0.93009624, 0.06267078, 0.057996, 0.00624033]], - [[5.89939363e-03, 1.24374864e-02, 1.55961109e-02, 1.41091276e-04, 5.35669038e-03, 1.41172289e-01, 6.24814570e-01, 2.58737535e-01], - [9.99996197e-01, 9.99990818e-01, 3.69512187e-03, 9.92474100e-01, 5.11590443e-03, 7.65462527e-04, 6.31168412e-02, 9.27684679e-01, 9.39992776e-01, 3.90802260e-03, 7.28452088e-01, 3.38762151e-03], - [0.00464176, 0.00586003, 0.00747172, 0.00548363, 0.06125162, 0.05999061, 0.06386694, 0.93141142, 0.92327391], - [0.99996915, 0.99247319, 0.92323033, 0.06239994, 0.99495941, 0.92938745, 0.06362084, 0.05886185, 0.00616339]], - [[5.91404634e-03, 1.23316989e-02, 1.57964971e-02, 1.41977659e-04, 5.34817269e-03, 1.41225135e-01, 6.26917844e-01, 2.58335206e-01], - [9.99996186e-01, 9.99990831e-01, 3.75862700e-03, 9.92399644e-01, 5.08229477e-03, 7.82261324e-04, 6.29571704e-02, 9.27085036e-01, 9.38490712e-01, 3.85956873e-03, 7.31527975e-01, 3.43636801e-03], - [0.00467104, 0.00595836, 0.00734784, 0.00539532, 0.0604999, 0.05893348, 0.06308452, 0.93077622, 0.92273247], - [0.99996884, 0.99235699, 0.92256707, 0.06201265, 0.99486629, 0.9297019, 0.06265214, 0.05823395, 0.00627464]], - [[1.05003614e-02, 5.05651903e-02, 5.56933153e-03, 1.49088828e-04, 2.05436047e-04, 6.02053713e-03, 2.83990137e-01, 7.35771881e-01], - [9.99999065e-01, 9.99998341e-01, 5.46333569e-02, 9.51580238e-01, 5.79107846e-02, 1.03879359e-03, 7.76549954e-04, 9.66337365e-01, 9.95904133e-01, 5.97108687e-02, 2.95620336e-01, 1.18070779e-03], - [4.29840923e-02, 4.57400685e-02, 5.01883410e-02, 5.65020207e-02, 5.50609307e-04, 6.07948951e-04, 7.53193372e-04, 9.76887708e-01, 9.70021750e-01], - [9.99992910e-01, 9.58026265e-01, 9.63618507e-01, 5.98811164e-04, 9.52542196e-01, 9.74854533e-01, 6.45918755e-04, 6.19829740e-04, 5.20576434e-02]], - [[1.04106324e-02, 5.06807947e-02, 5.48852922e-03, 1.48971611e-04, 2.02323501e-04, 5.86480204e-03, 2.80919990e-01, 7.35394697e-01], - [9.99999071e-01, 9.99998361e-01, 5.54058240e-02, 9.50663904e-01, 5.75482716e-02, 1.02416736e-03, 7.62597707e-04, 9.65507629e-01, 9.95929452e-01, 5.97164893e-02, 2.95013264e-01, 1.18369277e-03], - [4.36464508e-02, 4.59392776e-02, 5.00381713e-02, 5.70105692e-02, 5.44226013e-04, 6.03503277e-04, 7.39188183e-04, 9.76570575e-01, 9.69352173e-01], - [9.99993101e-01, 9.57362258e-01, 9.63158325e-01, 5.89293817e-04, 9.52524004e-01, 9.74368955e-01, 6.36685765e-04, 6.08537240e-04, 5.24210436e-02]], - [[1.05234141e-02, 5.04354679e-02, 5.52780575e-03, 1.48177556e-04, 2.05519002e-04, 6.00881224e-03, 2.85607445e-01, 7.35587112e-01], - [9.99999055e-01, 9.99998329e-01, 5.42418171e-02, 9.51796064e-01, 5.76615378e-02, 1.02496002e-03, 7.81635453e-04, 9.66418911e-01, 9.95902924e-01, 5.94593254e-02, 2.95823277e-01, 1.19081916e-03], - [4.26363987e-02, 4.53834665e-02, 4.99211662e-02, 5.63781075e-02, 5.51914557e-04, 6.09111600e-04, 7.51672512e-04, 9.76984203e-01, 9.69958680e-01], - [9.99992861e-01, 9.58244475e-01, 9.63771378e-01, 6.01178719e-04, 9.52927617e-01, 9.74980017e-01, 6.46105105e-04, 6.20268038e-04, 5.16337786e-02]]] + expected = [[[0.13455769, 0.12924714, 0.13288172, 0.1404659 , 0.14305814, + 0.14801862, 0.13968417, 0.14191982], [0.99991336, 0.99989676, 0.50621126, 0.52845353, 0.25829169, + 0.26210662, 0.24857125, 0.25041139, 0.4960792 , 0.25689092, + 0.36730921, 0.24619036], [0.5026992 , 0.51272669, 0.24175637, 0.25034784, 0.2406316 , + 0.23613707, 0.25192136, 0.25169522, 0.25615122], [0.99931055, 0.50694136, 0.23105683, 0.24641822, 0.49398116, + 0.25462159, 0.24433084, 0.24267716, 0.52209116]], [[0.13434049, 0.14280817, 0.13996269, 0.15002735, 0.12975428, + 0.13143489, 0.14087601, 0.1384075 ], [0.99988782, 0.99987699, 0.50700037, 0.48324852, 0.23605196, + 0.24623233, 0.25029056, 0.23937042, 0.51119504, 0.2428462 , + 0.38335829, 0.24875281], [0.51938115, 0.48541386, 0.25513764, 0.26880953, 0.27165005, + 0.2768918 , 0.25270764, 0.25394743, 0.2453564 ], [0.99932127, 0.48854068, 0.25824899, 0.24792285, 0.52373987, + 0.23226666, 0.26078735, 0.25531502, 0.48945013]], [[0.14603474, 0.13076473, 0.14285659, 0.14135699, 0.13505543, + 0.13515591, 0.14114513, 0.13170369], [0.99990678, 0.99989262, 0.48366278, 0.49653231, 0.24862611, + 0.23489764, 0.25762623, 0.2557183 , 0.48931577, 0.24042071, + 0.38135237, 0.2559529 ], [0.48125071, 0.49795935, 0.25190272, 0.23454573, 0.24576829, + 0.24084843, 0.23960781, 0.24709719, 0.24797553], [0.99927591, 0.50571286, 0.25989537, 0.25337519, 0.49977912, + 0.2629161 , 0.24592842, 0.25430635, 0.48884817]], [[0.13471359, 0.12938359, 0.12833093, 0.13589542, 0.14362713, + 0.15158585, 0.14307399, 0.14359844], [0.99991663, 0.99990086, 0.50239329, 0.5363237 , 0.26273315, + 0.26079003, 0.24686505, 0.2514966 , 0.50092905, 0.26094556, + 0.36939329, 0.24278735], [0.50633107, 0.51005404, 0.24486936, 0.25305515, 0.2371648 , + 0.23443371, 0.24958497, 0.25592672, 0.26077736], [0.99930883, 0.51096502, 0.2273585 , 0.24095976, 0.49420255, + 0.2541994 , 0.24348927, 0.23870194, 0.52777643]], [[0.13437031, 0.14294567, 0.13462913, 0.14499767, 0.13014469, + 0.1347783 , 0.14426174, 0.14003815], [0.99989275, 0.99988313, 0.50533481, 0.49070575, 0.24083416, + 0.24419528, 0.24850006, 0.2388715 , 0.51590916, 0.2477704 , + 0.38492997, 0.24480191], [0.52312591, 0.48400766, 0.25827697, 0.27239575, 0.26774108, + 0.27407821, 0.24982668, 0.25714894, 0.24895088], [0.9993246 , 0.49095133, 0.25329179, 0.2414722 , 0.52311016, + 0.23199452, 0.25880536, 0.25034698, 0.49623819]], [[0.14624279, 0.13155149, 0.13771617, 0.13640541, 0.1355243 , + 0.13836647, 0.14442104, 0.13306906], [0.9999103 , 0.99989714, 0.48256079, 0.50278716, 0.25496791, + 0.23361944, 0.25567964, 0.25577915, 0.49540433, 0.24632798, + 0.38186044, 0.25220444], [0.48732786, 0.49713526, 0.25686322, 0.23903771, 0.24179882, + 0.238869 , 0.2368224 , 0.25066201, 0.25139028], [0.9992754 , 0.50799072, 0.25448626, 0.24711122, 0.49775634, + 0.26134282, 0.24471328, 0.24960873, 0.49669515]], [[0.13743409, 0.12655781, 0.13687131, 0.14444673, 0.14586922, + 0.14982043, 0.14115407, 0.14425256], [0.999905 , 0.99988946, 0.5111958 , 0.52953632, 0.25829416, + 0.25816645, 0.24772719, 0.2493831 , 0.49172307, 0.25647241, + 0.36490702, 0.24579828], [0.49409604, 0.51876333, 0.2438246 , 0.25106641, 0.24575334, + 0.23758284, 0.25036778, 0.24501611, 0.26348273], [0.99927212, 0.49900832, 0.23785606, 0.24568878, 0.49229404, + 0.25799455, 0.2380617 , 0.24483316, 0.51242196]], [[0.13690143, 0.14031709, 0.14406563, 0.15349375, 0.13257716, + 0.13310877, 0.14252974, 0.14056437], [0.99987794, 0.99986931, 0.5140066 , 0.48347409, 0.23776129, + 0.24271912, 0.2491253 , 0.23773346, 0.50725307, 0.24412832, + 0.37955962, 0.24840394], [0.5134345 , 0.49359454, 0.25820627, 0.27104423, 0.27620957, + 0.27795555, 0.25014143, 0.24655665, 0.25137209], [0.99928701, 0.47923949, 0.26354622, 0.24637029, 0.51954436, + 0.23441734, 0.25357643, 0.25643598, 0.48217473]], [[0.14885265, 0.12888511, 0.14747413, 0.14492694, 0.13757074, + 0.13653999, 0.14230078, 0.1333605 ], [0.99989882, 0.99988608, 0.49175208, 0.49464422, 0.25033443, + 0.2315963 , 0.25565926, 0.25286519, 0.48521686, 0.24180012, + 0.37838253, 0.25482498], [0.47632935, 0.50754291, 0.25540722, 0.23699234, 0.24989352, + 0.24130117, 0.236814 , 0.23865942, 0.25337658], [0.99924137, 0.49439904, 0.26500386, 0.25133608, 0.49471845, + 0.26444086, 0.23836089, 0.2550785 , 0.48265071]], [[0.13796428, 0.13122133, 0.13665485, 0.14238789, 0.14646363, + 0.14953325, 0.14001922, 0.14631467], [0.99990658, 0.99988831, 0.51044886, 0.52877409, 0.26162535, + 0.26113322, 0.24464028, 0.25460667, 0.50142119, 0.26075076, + 0.36249296, 0.2469639 ], [0.49888045, 0.520344 , 0.24213434, 0.25386651, 0.23856703, + 0.23283943, 0.25382764, 0.25348876, 0.25930602], [0.9992759 , 0.50552098, 0.23581279, 0.25056642, 0.49046455, + 0.26042018, 0.24011749, 0.24306269, 0.52240185]], [[0.13750907, 0.14541291, 0.1443253 , 0.15189612, 0.13295338, + 0.13279175, 0.14102944, 0.14247126], [0.99987945, 0.99986745, 0.51385772, 0.48177978, 0.23992435, + 0.24651741, 0.24564878, 0.2423684 , 0.51605876, 0.24748102, + 0.37804356, 0.24958671], [0.51825942, 0.49563748, 0.25600593, 0.27323944, 0.26870607, + 0.27273737, 0.25405544, 0.25468467, 0.24732677], [0.99928937, 0.4851184 , 0.26178837, 0.25136875, 0.51760249, + 0.23648163, 0.25573403, 0.25496827, 0.49227648]], [[0.14930882, 0.13316748, 0.14767505, 0.14350067, 0.13804021, + 0.13603459, 0.14084128, 0.13513649], [0.99990066, 0.99988511, 0.49193637, 0.49266043, 0.25237073, + 0.23599757, 0.25202475, 0.25740929, 0.49266556, 0.24476454, + 0.37618961, 0.25697362], [0.48210255, 0.51007883, 0.25286726, 0.2385758 , 0.24213314, + 0.23616873, 0.23972905, 0.24636349, 0.24902741], [0.99924664, 0.4998954 , 0.2624679 , 0.25622887, 0.49190217, + 0.26610712, 0.24000543, 0.25322914, 0.49359824]], [[0.13981779, 0.13517728, 0.13714904, 0.14312451, 0.14264037, + 0.1471793 , 0.13872158, 0.14604209], [0.99990026, 0.99988117, 0.50978024, 0.51828715, 0.26366737, + 0.26281186, 0.24835743, 0.25797436, 0.50770859, 0.26547003, + 0.36335027, 0.24615446], [0.50366793, 0.51568697, 0.2470342 , 0.2533224 , 0.23632146, + 0.23162517, 0.25117307, 0.25934756, 0.256024 ], [0.99924436, 0.50891146, 0.23908499, 0.24926875, 0.48979495, + 0.26367751, 0.24330774, 0.24296292, 0.523776 ]], [[0.13961147, 0.14975401, 0.14472653, 0.15355384, 0.12925548, + 0.13046081, 0.13938395, 0.14208934], [0.99987039, 0.99985783, 0.51256916, 0.47090975, 0.24108525, + 0.24849742, 0.24950535, 0.24553023, 0.522052 , 0.25148739, + 0.37924791, 0.24931395], [0.52265254, 0.49040645, 0.26115407, 0.27231193, 0.26698365, + 0.27201252, 0.252447 , 0.26033848, 0.24417141], [0.99925421, 0.48866289, 0.26591473, 0.25072477, 0.51818878, + 0.23925897, 0.26008435, 0.25565447, 0.49313837]], [[0.15131984, 0.13702244, 0.14796541, 0.14499107, 0.13516231, + 0.13379344, 0.13967548, 0.13504357], [0.99989266, 0.99987644, 0.49069576, 0.48284025, 0.25373136, + 0.23780959, 0.25705167, 0.26031535, 0.49734223, 0.24868116, + 0.37609291, 0.25826094], [0.48615805, 0.50439585, 0.25764469, 0.23792093, 0.24102865, + 0.23622676, 0.23835519, 0.25200694, 0.24543424], [0.99920779, 0.50378559, 0.265772 , 0.25635798, 0.49252096, + 0.26892098, 0.24458074, 0.25443214, 0.494262 ]], [[0.13850469, 0.13222947, 0.13670674, 0.14224276, 0.14481382, + 0.14983366, 0.14173916, 0.14323358], [0.99990126, 0.99988249, 0.51196132, 0.52177217, 0.2678038 , + 0.26687693, 0.24660127, 0.25415374, 0.50244534, 0.26499807, + 0.36556967, 0.24445311], [0.51065084, 0.52060372, 0.25297718, 0.25785385, 0.24041214, + 0.23595817, 0.24912937, 0.25285752, 0.26092579], [0.9992282 , 0.49866166, 0.23316642, 0.2414636 , 0.48397547, + 0.25549083, 0.24296723, 0.24121413, 0.52743639]], [[0.13795671, 0.14608656, 0.14389815, 0.15250863, 0.13137121, + 0.13292996, 0.14235728, 0.13916877], [0.99987232, 0.99986024, 0.514095 , 0.47542769, 0.24452563, + 0.25126078, 0.248764 , 0.241217 , 0.51579474, 0.25047855, + 0.38175049, 0.24768651], [0.52795513, 0.49433932, 0.26639626, 0.2765692 , 0.27249335, + 0.27735353, 0.25105699, 0.25316897, 0.24833112], [0.99924257, 0.47881092, 0.25918246, 0.24338929, 0.51375801, + 0.23187301, 0.26019419, 0.25420934, 0.49555353]], [[0.14984495, 0.13387861, 0.14722304, 0.14355772, 0.13724767, + 0.13665399, 0.14239016, 0.1320989 ], [0.99989382, 0.9998781 , 0.49129168, 0.48786117, 0.25736056, + 0.24076914, 0.2571532 , 0.25661644, 0.49134559, 0.24762313, + 0.37894905, 0.25678541], [0.49115594, 0.50720606, 0.26343239, 0.24118617, 0.24666918, + 0.24228136, 0.23828715, 0.24556003, 0.24986447], [0.9991909 , 0.49609669, 0.25952719, 0.2503092 , 0.48831756, + 0.26050785, 0.24624062, 0.25460693, 0.49571571]], [[0.13798156, 0.13536763, 0.13556549, 0.14026479, 0.14613495, + 0.1517147 , 0.14150378, 0.14582109], [0.99990224, 0.99988499, 0.51321887, 0.5234933 , 0.26596025, + 0.26627399, 0.24845062, 0.25224455, 0.50060705, 0.26840742, + 0.36382243, 0.24724158], [0.50857345, 0.52097167, 0.24807872, 0.25421915, 0.23896201, + 0.23521938, 0.25167447, 0.25399296, 0.25680194], [0.99924088, 0.50538628, 0.23168769, 0.24466784, 0.48374493, + 0.25836643, 0.24395082, 0.24271583, 0.52908765]], [[0.13738911, 0.14964169, 0.14240066, 0.14996998, 0.13258046, + 0.13529629, 0.14257416, 0.14196438], [0.99987348, 0.99986355, 0.51547364, 0.47846669, 0.24280831, + 0.25045232, 0.25106236, 0.23941227, 0.51388317, 0.25405565, + 0.38069812, 0.25010755], [0.52466269, 0.49422476, 0.26072088, 0.27243928, 0.27097311, + 0.27644019, 0.25390417, 0.25487688, 0.24468432], [0.99925402, 0.48653142, 0.25783738, 0.24674309, 0.51370588, + 0.23544836, 0.26119428, 0.25606803, 0.49686813]], [[0.14925705, 0.13686349, 0.14561612, 0.14139895, 0.13823102, + 0.13876809, 0.14286744, 0.13452957], [0.99989461, 0.99988054, 0.49152081, 0.49115061, 0.25525704, + 0.23961845, 0.25940244, 0.25531285, 0.48988801, 0.25049358, + 0.37898184, 0.25880442], [0.48716519, 0.50598831, 0.25786953, 0.23748522, 0.24522105, + 0.24133955, 0.24092032, 0.247859 , 0.24660646], [0.99920254, 0.50397091, 0.25882576, 0.25383231, 0.48962604, + 0.26477928, 0.24738806, 0.25637011, 0.49634854]], [[0.13466774, 0.13597299, 0.13044001, 0.13621353, 0.14063205, + 0.14927389, 0.13741799, 0.14182897], [0.99991209, 0.99989375, 0.50664715, 0.52647149, 0.26631381, + 0.26772046, 0.25065527, 0.25828284, 0.51113304, 0.26876027, + 0.36519173, 0.24445213], [0.50981376, 0.51216926, 0.24469315, 0.25069097, 0.23575157, + 0.23351051, 0.25738456, 0.26157098, 0.25154516], [0.9992887 , 0.51923276, 0.2286251 , 0.24647828, 0.48904116, + 0.25948029, 0.25191475, 0.24172423, 0.53116312]], [[0.13456692, 0.15039528, 0.13737152, 0.14609287, 0.12736956, + 0.13283867, 0.13853673, 0.13816526], [0.99988542, 0.99987272, 0.50746352, 0.48137383, 0.24278802, + 0.25182679, 0.25260023, 0.24645783, 0.52561721, 0.25394353, + 0.38280036, 0.24682469], [0.52522447, 0.48470641, 0.25775314, 0.26859876, 0.26712899, + 0.27417417, 0.25948482, 0.26348026, 0.24082551], [0.9992959 , 0.5009347 , 0.25614941, 0.24856744, 0.51984942, + 0.23715273, 0.2693644 , 0.2551465 , 0.49819368]], [[0.14562679, 0.13745423, 0.13996431, 0.13722137, 0.13278566, + 0.13632886, 0.13899164, 0.1314741 ], [0.99990502, 0.99988943, 0.48410876, 0.49522954, 0.2553264 , + 0.24084323, 0.26058312, 0.26304712, 0.50254737, 0.2507632 , + 0.3802493 , 0.25486957], [0.48768232, 0.49615974, 0.25448472, 0.23400199, 0.24116754, + 0.23886036, 0.24575353, 0.25715595, 0.24334699], [0.99925183, 0.51889063, 0.25712839, 0.25460613, 0.49597306, + 0.26739429, 0.25464059, 0.25453138, 0.49761396]]] for res, exp in zip(results, expected): for r, e in zip(res, exp): diff --git a/tests/composition/test_composition.py b/tests/composition/test_composition.py index 7256f936223..91e2a75b382 100644 --- a/tests/composition/test_composition.py +++ b/tests/composition/test_composition.py @@ -17,20 +17,22 @@ from psyneulink.core.components.functions.combinationfunctions import LinearCombination from psyneulink.core.components.functions.userdefinedfunction import UserDefinedFunction from psyneulink.core.components.functions.learningfunctions import Reinforcement, BackPropagation +from psyneulink.core.components.functions.optimizationfunctions import GridSearch from psyneulink.core.components.mechanisms.processing.integratormechanism import IntegratorMechanism from psyneulink.core.components.mechanisms.processing.objectivemechanism import ObjectiveMechanism from psyneulink.core.components.mechanisms.processing.processingmechanism import ProcessingMechanism from psyneulink.core.components.mechanisms.processing.transfermechanism import TransferMechanism from psyneulink.core.components.mechanisms.modulatory.learning.learningmechanism import LearningMechanism from psyneulink.core.components.mechanisms.modulatory.control.controlmechanism import ControlMechanism +from psyneulink.core.components.mechanisms.modulatory.control.optimizationcontrolmechanism import OptimizationControlMechanism from psyneulink.core.components.projections.pathway.mappingprojection import MappingProjection from psyneulink.core.components.ports.inputport import InputPort from psyneulink.core.components.ports.modulatorysignals.controlsignal import ControlSignal, CostFunctions from psyneulink.core.compositions.composition import Composition, CompositionError, NodeRole from psyneulink.core.compositions.pathway import Pathway, PathwayRole from psyneulink.core.globals.keywords import \ - ADDITIVE, ALLOCATION_SAMPLES, DISABLE, INPUT_PORT, INTERCEPT, LEARNING_MECHANISMS, LEARNED_PROJECTIONS, \ - NAME, PROJECTIONS, RESULT, OBJECTIVE_MECHANISM, OUTPUT_MECHANISM, OVERRIDE, TARGET_MECHANISM, VARIANCE + ADDITIVE, ALLOCATION_SAMPLES, BEFORE, DISABLE, INPUT_PORT, INTERCEPT, LEARNING_MECHANISMS, LEARNED_PROJECTIONS, \ + NAME, PROJECTIONS, RESULT, OBJECTIVE_MECHANISM, OUTPUT_MECHANISM, OVERRIDE, SLOPE, TARGET_MECHANISM, VARIANCE from psyneulink.core.scheduling.condition import AfterNCalls, AtTimeStep, AtTrial, Never from psyneulink.core.scheduling.condition import EveryNCalls from psyneulink.core.scheduling.scheduler import Scheduler @@ -4920,7 +4922,136 @@ def test_combine_two_overlapping_trees(self): # output = sys.run(inputs=stimulus) # assert np.allclose(16, output) # MODIFIED 5/8/20 END + def test_three_level_deep_pathway_routing_single_mech(self): + p2 = ProcessingMechanism(name='p2') + p0 = ProcessingMechanism(name='p0') + + c2 = Composition(name='c2', pathways=[p2]) + c1 = Composition(name='c1', pathways=[c2]) + c0 = Composition(name='c0', pathways=[[c1], [p0]]) + + c0.add_projection(MappingProjection(), sender=p0, receiver=p2) + result = c0.run([5]) + assert result == [5] + + def test_three_level_deep_pathway_routing_two_mech(self): + p3a = ProcessingMechanism(name='p3a') + p3b = ProcessingMechanism(name='p3b') + p1 = ProcessingMechanism(name='p1') + + c3 = Composition(name='c3', pathways=[[p3a], [p3b]]) + c2 = Composition(name='c2', pathways=[c3]) + c1 = Composition(name='c1', pathways=[[c2], [p1]]) + + c1.add_projection(MappingProjection(), sender=p1, receiver=p3a) + c1.add_projection(MappingProjection(), sender=p1, receiver=p3b) + + result = c1.run([5]) + assert result == [5, 5] + + def test_three_level_deep_modulation_routing_single_mech(self): + p3 = ProcessingMechanism(name='p3') + ctrl1 = ControlMechanism(name='ctrl1', + control=ControlSignal(modulates=(SLOPE, p3))) + + c3 = Composition(name='c3', pathways=[p3]) + c2 = Composition(name='c2', pathways=[c3]) + c1 = Composition(name='c1', pathways=[[(c2, NodeRole.INPUT)], [ctrl1]]) + + result = c1.run({c2: 2, ctrl1: 5}) + assert result == [10] + + def test_three_level_deep_modulation_routing_two_mech(self): + p3a = ProcessingMechanism(name='p3a') + p3b = ProcessingMechanism(name='p3b') + ctrl1 = ControlMechanism(name='ctrl1', + control=[ + ControlSignal(modulates=(SLOPE, p3a)), + ControlSignal(modulates=(SLOPE, p3b)) + ]) + + c3 = Composition(name='c3', pathways=[[p3a], [p3b]]) + c2 = Composition(name='c2', pathways=[c3]) + c1 = Composition(name='c1', pathways=[[(c2, NodeRole.INPUT)], [ctrl1]]) + + result = c1.run({c2: [[2], [2]], ctrl1: [5]}) + assert result == [10, 10] + + def test_four_level_nested_transfer_mechanism_composition_parallel(self): + # mechanisms + A = ProcessingMechanism(name="A", + function=AdaptiveIntegrator(rate=0.1)) + B = ProcessingMechanism(name="B", + function=Logistic) + + comp_lvl3a = Composition(name="comp_lvl3a", pathways=[A, B]) + + C = TransferMechanism(name="C", + function=Logistic, + integration_rate=0.1, + integrator_mode=True) + comp_lvl3b = Composition(name="comp_lvl3b", pathways=[C]) + comp_lvl2 = Composition(name="comp_lvl2", pathways=[[comp_lvl3a], [comp_lvl3b]]) + comp_lvl1 = Composition(name="comp_lvl2", pathways=[comp_lvl2]) + comp_lvl0 = Composition(name="outer_comp", pathways=[comp_lvl1]) + ret = comp_lvl0.run(inputs={comp_lvl1: {comp_lvl2: {comp_lvl3a: [[1.0]], comp_lvl3b: [[1.0]]}}}) + assert np.allclose(ret, [[[0.52497918747894]], [[0.52497918747894]]]) + + def test_four_level_nested_OCM_control(self): + p_lvl3 = ProcessingMechanism(name='p_lvl3') + + c_lvl3 = Composition(name='c_lvl3', pathways=[p_lvl3]) + c_lvl2 = Composition(name='c_lvl2', pathways=[c_lvl3]) + c_lvl1 = Composition(name='c_lvl1', pathways=[c_lvl2]) + c_lvl0 = Composition(name='c_lvl0', pathways=[c_lvl1], controller_mode=BEFORE) + + c_lvl0.add_controller(OptimizationControlMechanism( + name='c_top_controller', + agent_rep=c_lvl0, + features=[c_lvl1.input_port], + objective_mechanism=ObjectiveMechanism(monitor=[p_lvl3]), + function=GridSearch(), + control_signals=ControlSignal( + intensity_cost_function=lambda _: 0, + modulates=(SLOPE, p_lvl3), + allocation_samples=[10, 20, 30]))) + result = c_lvl0.run([5]) + assert result == [150] + + def test_four_level_nested_dual_OCM_control(self): + p_lvl3 = ProcessingMechanism(name='p_lvl3') + + c_lvl3 = Composition(name='c_lvl3', pathways=[p_lvl3]) + c_lvl2 = Composition(name='c_lvl2', pathways=[c_lvl3]) + c_lvl1 = Composition(name='c_lvl1', pathways=[c_lvl2], controller_mode=BEFORE) + + c_lvl1.add_controller(OptimizationControlMechanism( + name='c_lvl1_controller', + agent_rep=c_lvl1, + features=[c_lvl2.input_port], + objective_mechanism=ObjectiveMechanism(monitor=[p_lvl3]), + function=GridSearch(), + control_signals=ControlSignal( + intensity_cost_function=lambda _: 0, + modulates=(SLOPE, p_lvl3), + allocation_samples=[10, 20, 30]))) + + c_lvl0 = Composition(name='c_lvl0', pathways=[c_lvl1], controller_mode=BEFORE) + + c_lvl0.add_controller(OptimizationControlMechanism( + name='c_lvl0_controller', + agent_rep=c_lvl0, + features=[c_lvl1.input_port], + objective_mechanism=ObjectiveMechanism(monitor=[p_lvl3]), + function=GridSearch(), + control_signals=ControlSignal( + intensity_cost_function=lambda _: 0, + modulates=(SLOPE, p_lvl3), + allocation_samples=[10, 20, 30]))) + + result = c_lvl0.run([5]) + assert result == [4500] class TestOverloadedCompositions: def test_mechanism_different_inputs(self): diff --git a/tests/composition/test_learning.py b/tests/composition/test_learning.py index e11936c369d..6ef8324a2cb 100644 --- a/tests/composition/test_learning.py +++ b/tests/composition/test_learning.py @@ -1327,81 +1327,6 @@ def test_prediction_error_delta_first_run(self): np.testing.assert_allclose(deltas, validation_deltas, atol=1e-08, err_msg="mismatch on timestep {}".format(i)) - def test_td_montague_et_al_figure_a(self): - - # create processing mechanisms - sample_mechanism = pnl.TransferMechanism(default_variable=np.zeros(60), - name=pnl.SAMPLE) - - action_selection = pnl.TransferMechanism(default_variable=np.zeros(60), - function=pnl.Linear(slope=1.0, intercept=0.01), - name='Action Selection') - - sample_to_action_selection = pnl.MappingProjection(sender=sample_mechanism, - receiver=action_selection, - matrix=np.zeros((60, 60))) - - comp = pnl.Composition(name='TD_Learning') - pathway = [sample_mechanism, sample_to_action_selection, action_selection] - learning_pathway = comp.add_td_learning_pathway(pathway, learning_rate=0.3) - - comparator_mechanism = learning_pathway.learning_objective - comparator_mechanism.log.set_log_conditions(pnl.VALUE) - target_mechanism = learning_pathway.target - - # comp.show_graph() - - stimulus_onset = 41 - reward_delivery = 54 - - # build input dictionary - samples = [] - targets = [] - for trial in range(50): - target = [0.] * 60 - target[reward_delivery] = 1. - # {14, 29, 44, 59, 74, 89} - if trial in {14, 29, 44}: - target[reward_delivery] = 0. - targets.append(target) - - sample = [0.] * 60 - for i in range(stimulus_onset, 60): - sample[i] =1. - samples.append(sample) - - inputs = {sample_mechanism: samples, - target_mechanism: targets} - - - comp.learn(inputs=inputs) - - delta_vals = comparator_mechanism.log.nparray_dictionary()['TD_Learning'][pnl.VALUE] - - trial_1_expected = [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.] - - - trial_30_expected = [0.] * 40 - trial_30_expected += [ - 0.06521536244675225, 0.0640993870383315, 0.09944290863181729, 0.13325956499595726, 0.15232363406006394, - 0.14570077419644378, 0.11414216814982991, 0.07374140787058237, 0.04546975436471501, 0.036210519138262454, - 0.03355295938927161, 0.024201157062338496, 0.010573534379529015, -0.9979331317238949, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0 - ] - trial_50_expected = [0.] * 40 - trial_50_expected += [ - 0.7149863408177357, 0.08193033235388536, 0.05988592388364977, 0.03829793050401187, 0.01972582584273075, - 0.007198872281648616, 0.0037918828476545263, 0.009224297157983563, 0.015045769646998886, - 0.00034051016062952577, -0.040721638768680624, -0.03599485605332753, 0.0539151932684796, - 0.07237361605659998, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 - ] - - assert np.allclose(trial_1_expected, delta_vals[0][0]) - assert np.allclose(trial_30_expected, delta_vals[29][0]) - assert np.allclose(trial_50_expected, delta_vals[49][0]) - def test_rl_enable_learning_false(self): input_layer = pnl.TransferMechanism(size=2, name='Input Layer') diff --git a/tests/composition/test_models.py b/tests/composition/test_models.py index 24ee69be5d3..2e7fe4ee645 100644 --- a/tests/composition/test_models.py +++ b/tests/composition/test_models.py @@ -8,234 +8,6 @@ class TestModels: - # This implements the model by Cohen, J. D., & Huston, T. A. (1994). Progress in the use of interactive - # models for understanding attention and performance. In C. Umilta & M. Moscovitch(Eds.), - # AttentionandperformanceXV(pp.453-456). Cam- bridge, MA: MIT Press. - # The model aims to capute top-down effects of selective attention and the bottom-up effects of attentional capture. - - def test_cohen_huston_1994(self): - - # Define Variables ------------------------------------------------------------------------------------- - rate = 0.1 # modified from the original code from 0.01 to 0.1 - inhibition = -2.0 # lateral inhibition - bias = 4.0 # bias is positive since Logistic equation has - sing already implemented - threshold = 0.55 # modified from original code from 0.6 to 0.55 because incongruent condition won't reach 0.6 - settle_trials = 50 # cycles until model settles - - # Create mechanisms ------------------------------------------------------------------------------------ - - # Linear input units, colors: ('red', 'green'), words: ('RED','GREEN') - - colors_input_layer = pnl.TransferMechanism( - size=3, - function=psyneulink.core.components.functions.transferfunctions.Linear, - name='COLORS_INPUT' - ) - - words_input_layer = pnl.TransferMechanism( - size=3, - function=psyneulink.core.components.functions.transferfunctions.Linear, - name='WORDS_INPUT' - ) - - task_input_layer = pnl.TransferMechanism( - size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, - name='TASK_INPUT' - ) - - # Task layer, tasks: ('name the color', 'read the word') - task_layer = pnl.RecurrentTransferMechanism( - size=2, - function=psyneulink.core.components.functions.transferfunctions.Logistic(), - hetero=-2, - integrator_mode=True, - integration_rate=0.1, - name='TASK' - ) - - # Hidden layer units, colors: ('red','green') words: ('RED','GREEN') - colors_hidden_layer = pnl.RecurrentTransferMechanism( - size=3, - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=4.0), - integrator_mode=True, - hetero=-2.0, - # noise=pnl.NormalDist(mean=0.0, standard_deviation=.0).function, - integration_rate=0.1, # cohen-huston text says 0.01 - name='COLORS HIDDEN' - ) - - words_hidden_layer = pnl.RecurrentTransferMechanism( - size=3, - function=psyneulink.core.components.functions.transferfunctions.Logistic(x_0=4.0), - hetero=-2, - integrator_mode=True, - # noise=pnl.NormalDist(mean=0.0, standard_deviation=.05).function, - integration_rate=0.1, - name='WORDS HIDDEN' - ) - # Response layer, responses: ('red', 'green'): RecurrentTransferMechanism for self inhibition matrix - response_layer = pnl.RecurrentTransferMechanism( - size=2, - function=psyneulink.core.components.functions.transferfunctions.Logistic(), - hetero=-2.0, - integrator_mode=True, - integration_rate=0.1, - name='RESPONSE' - ) - # Connect mechanisms -------------------------------------------------------------------------------------------------- - # (note that response layer projections are set to all zero first for initialization - - color_input_weights = pnl.MappingProjection( - matrix=np.array([ - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 0.0, 0.0] - ]) - ) - - word_input_weights = pnl.MappingProjection( - matrix=np.array([ - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 0.0, 0.0] - ]) - ) - - task_input_weights = pnl.MappingProjection( - matrix=np.array([ - [1.0, 0.0], - [0.0, 1.0] - ]) - ) - - color_task_weights = pnl.MappingProjection( - matrix=np.array([ - [4.0, 0.0], - [4.0, 0.0], - [4.0, 0.0] - ]) - ) - - task_color_weights = pnl.MappingProjection( - matrix=np.array([ - [4.0, 4.0, 4.0], - [0.0, 0.0, 0.0] - ]) - ) - - word_task_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 4.0], - [0.0, 4.0], - [0.0, 4.0] - ]) - ) - - task_word_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 0.0, 0.0], - [4.0, 4.0, 4.0] - ]) - ) - - response_color_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0] - ]) - ) - - response_word_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0] - ]) - ) - - color_response_weights = pnl.MappingProjection( - matrix=np.array([ - [1.5, 0.0], - [0.0, 1.5], - [0.0, 0.0] - ]) - ) - word_response_weights = pnl.MappingProjection( - matrix=np.array([ - [2.5, 0.0], - [0.0, 2.5], - [0.0, 0.0] - ]) - ) - bidirectional_stroop = pnl.Composition(name="bidirectional_stroop") - - color_response_pathway = [colors_input_layer, - color_input_weights, - colors_hidden_layer, - color_response_weights, - response_layer] - bidirectional_stroop.add_linear_processing_pathway(pathway=color_response_pathway) - - color_response_pathway_2 = [response_layer, - response_color_weights, - colors_hidden_layer] - bidirectional_stroop.add_linear_processing_pathway(pathway=color_response_pathway_2) - - - word_response_pathway = [words_input_layer, - word_input_weights, - words_hidden_layer, - word_response_weights, - response_layer] - bidirectional_stroop.add_linear_processing_pathway(pathway=word_response_pathway) - - word_response_pathway_2 = [response_layer, - response_word_weights, - words_hidden_layer] - bidirectional_stroop.add_linear_processing_pathway(pathway=word_response_pathway_2) - - - task_color_response_pathway = [task_input_layer, - task_input_weights, - task_layer, - task_color_weights, - colors_hidden_layer] - - bidirectional_stroop.add_linear_processing_pathway(pathway=task_color_response_pathway) - - task_color_response_pathway_2 = [colors_hidden_layer, - color_task_weights, - task_layer] - - bidirectional_stroop.add_linear_processing_pathway(pathway=task_color_response_pathway_2) - - task_word_response_pathway = [task_input_layer, - task_layer, - task_word_weights, - words_hidden_layer] - bidirectional_stroop.add_linear_processing_pathway(pathway=task_word_response_pathway) - - task_word_response_pathway_2 = [words_hidden_layer, - word_task_weights, - task_layer] - - bidirectional_stroop.add_linear_processing_pathway(pathway=task_word_response_pathway_2) - # # MODIFIED 4/25/20 OLD: - # bidirectional_stroop.add_required_node_role(response_layer, pnl.NodeRole.OUTPUT) - # MODIFIED 4/25/20 NEW: - bidirectional_stroop.require_node_roles(response_layer, pnl.NodeRole.OUTPUT) - # MODIFIED 4/25/20 END - - input_dict = {colors_input_layer: [0, 0, 0], - words_input_layer: [0, 0, 0], - task_input_layer: [0, 1]} - - bidirectional_stroop.run(inputs=input_dict) - # print(bidirectional_stroop.get_nodes_by_role(pnl.NodeRole.TERMINAL)) - # print(bidirectional_stroop.output_values) - # for node in bidirectional_stroop.nodes: - # print(node.name, " Value: ", node.output_values) - def test_DDM(self): myMechanism = pnl.DDM( function=psyneulink.core.components.functions.distributionfunctions.DriftDiffusionAnalytical( @@ -492,15 +264,15 @@ def switch_to_initialization_trial(mechanisms): # Turn off noise switch_noise(mechanisms, 0) # Execute once per trial - my_Stroop.termination_PATHWAYing = {pnl.TimeScale.TRIAL: pnl.AllHaveRun()} + my_Stroop.termination_processing = {pnl.TimeScale.TRIAL: pnl.AllHaveRun()} - def switch_to_PATHWAYing_trial(mechanisms): + def switch_to_processing_trial(mechanisms): # Turn on accumulation switch_integrator_mode(mechanisms, True) # Turn on noise switch_noise(mechanisms, psyneulink.core.components.functions.distributionfunctions.NormalDist(mean=0, standard_deviation=unit_noise).function) # Execute until one of the accumulators crosses the threshold - my_Stroop.termination_PATHWAYing = { + my_Stroop.termination_processing = { pnl.TimeScale.TRIAL: pnl.While( pass_threshold, respond_red_accumulator, @@ -511,8 +283,8 @@ def switch_to_PATHWAYing_trial(mechanisms): def switch_trial_type(): # Next trial will be a processing trial - if isinstance(my_Stroop.termination_PATHWAYing[pnl.TimeScale.TRIAL], pnl.AllHaveRun): - switch_to_PATHWAYing_trial(mechanisms_to_update) + if isinstance(my_Stroop.termination_processing[pnl.TimeScale.TRIAL], pnl.AllHaveRun): + switch_to_processing_trial(mechanisms_to_update) # Next trial will be an initialization trial else: switch_to_initialization_trial(mechanisms_to_update) @@ -525,7 +297,7 @@ def switch_trial_type(): switch_to_initialization_trial(mechanisms_to_update) my_Stroop.run(inputs=trial_dict(0, 1, 1, 0, 1, 0), - # termination_PATHWAYing=change_termination_PATHWAYing, + # termination_processing=change_termination_processing, num_trials=4, call_after_trial=switch_trial_type) @@ -533,332 +305,6 @@ def switch_trial_type(): # words_input_layer: [[0, 0], [1, 0]], # task_layer: [[0, 1], [0, 1]]} - # Implements the Kalanthroff, Davelaar, Henik, Goldfarb & Usher model: Task Conflict and Proactive Control: - # A Computational Theory of the Stroop Task. Psychol Rev. 2018 Jan;125(1):59-82. doi: 10.1037/rev0000083. - # Epub 2017 Oct 16. - # #https://www.ncbi.nlm.nih.gov/pubmed/29035077 - def test_kalanthroff(self): - - # Define Variables ------------------------------------------------------------------------------------------ - Lambda = 0.03 # PsyNeuLink has Euler integration constant reversed (1-0.97) - pc_high = 0.15 # High proactive control from Figure 6 in Paper - pc_low = 0.025 # Low proactive control from Figure 6 in Paper - pc = pc_low # Select proactive control - inhibition = -1.3 # Inhibition between units within a layer - inhibition_task = -1.9 # Inhibition between units within task layer - bias = -0.3 # bias input to color feature layer and word feature layer - threshold = 0.70 - settle = 200 # Number of trials until system settles - - # Create mechanisms ----------------------------------------------------------------------------------------- - # 4 Input layers for color, word, task & bias - colors_input_layer = pnl.TransferMechanism( - size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, - name='COLORS_INPUT' - ) - - words_input_layer = pnl.TransferMechanism( - size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, - name='WORDS_INPUT' - ) - - task_input_layer = pnl.TransferMechanism( - size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, - name='PROACTIVE_CONTROL' - ) - - bias_input = pnl.TransferMechanism( - size=2, - function=psyneulink.core.components.functions.transferfunctions.Linear, - name='BIAS' - ) - - # Built python function to ensure that the logistic function outputs 0 when input is <= 0 - - def my_special_Logistic(variable): - maxi = variable - 0.0180 - output = np.fmax([0], maxi) - return output - - # Built python function that takes output of special logistic function and computes conflict by multiplying - # output both task units with each over times 500 - - def my_conflict_function(variable): - maxi = variable - 0.0180 - new = np.fmax([0], maxi) - out = [new[0] * new[1] * 500] - return out - - # Create color feature layer, word feature layer, task demand layer and response layer - color_feature_layer = pnl.RecurrentTransferMechanism( - size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 - integrator_mode=True, # Set IntegratorFunction mode to True - integration_rate=Lambda, # smoothing factor == integration rate - hetero=inhibition, # Inhibition among units within a layer - output_ports=[{ # Create new OutputPort by applying - pnl.NAME: 'SPECIAL_LOGISTIC', # the "my_special_Logistic" function - pnl.VARIABLE: (pnl.OWNER_VALUE, 0), - pnl.FUNCTION: my_special_Logistic - }], - name='COLOR_LAYER') - - # The word_feature_layer is set up as the color_feature_layer - word_feature_layer = pnl.RecurrentTransferMechanism( - size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 - integrator_mode=True, # Set IntegratorFunction mode to True - integration_rate=Lambda, # smoothing factor == integration rate - hetero=inhibition, # Inhibition among units within a layer - output_ports=[{ # Create new OutputPort by applying - pnl.NAME: 'SPECIAL_LOGISTIC', # the "my_special_Logistic" function - pnl.VARIABLE: (pnl.OWNER_VALUE, 0), - pnl.FUNCTION: my_special_Logistic - }], - name='WORD_LAYER') - - # The response_layer is set up as the color_feature_layer & the word_feature_layer - response_layer = pnl.RecurrentTransferMechanism( - size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 - integrator_mode=True, # Set IntegratorFunction mode to True - integration_rate=Lambda, # smoothing factor == integration rate - hetero=inhibition, # Inhibition among units within a layer - output_ports=[{ # Create new OutputPort by applying - pnl.NAME: 'SPECIAL_LOGISTIC', # the "my_special_Logistic" function - pnl.VARIABLE: (pnl.OWNER_VALUE, 0), - pnl.FUNCTION: my_special_Logistic - }], - name='RESPONSE_LAYER' - ) - - # The task_demand_layer is set up as the color_feature_layer but with a different python function on it's OutputPort - # and a differnet inhibition weight on the hetero - task_demand_layer = pnl.RecurrentTransferMechanism( - size=2, # Define unit size - function=psyneulink.core.components.functions.transferfunctions.Logistic(gain=4, x_0=1), # to 4 & bias to 1 - integrator_mode=True, # Set IntegratorFunction mode to True - integration_rate=Lambda, # smoothing factor == integration rate - hetero=inhibition_task, # Inhibition among units within a layer - output_ports=[ # Create new OutputPort by applying - { - pnl.NAME: 'SPECIAL_LOGISTIC', # the "my_conflict_function" function - pnl.VARIABLE: (pnl.OWNER_VALUE, 0), - pnl.FUNCTION: my_special_Logistic - }, - { - pnl.NAME: 'CONFLICT', - pnl.VARIABLE: (pnl.OWNER_VALUE, 0), - pnl.FUNCTION: my_conflict_function - } - ], - name='TASK_LAYER' - ) - - # Log mechanisms ------------------------------------------------------------------------------------------------------ - color_feature_layer.set_log_conditions('SPECIAL_LOGISTIC') # Log output of my_special_Logistic function - word_feature_layer.set_log_conditions('SPECIAL_LOGISTIC') # Log output of my_special_Logistic function - response_layer.set_log_conditions('SPECIAL_LOGISTIC') # Log output of my_special_Logistic function - task_demand_layer.set_log_conditions('SPECIAL_LOGISTIC') # Log output of my_special_Logistic function - - task_demand_layer.set_log_conditions('CONFLICT') # Log outout of my_conflict_function function - - # Connect mechanisms -------------------------------------------------------------------------------------------------- - color_input_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 0.0], # response layer projections are set to all - [0.0, 0.0] # zero for initialization period first - ]) - ) - word_input_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 0.0], - [0.0, 0.0] - ]) - ) - color_task_weights = pnl.MappingProjection( - matrix=np.array([ - [2.0, 0.0], # color to task projection - [2.0, 0.0] - ]) - ) - word_task_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 2.0], # word to task projection - [0.0, 2.0] - ]) - ) - task_color_weights = pnl.MappingProjection( - matrix=np.array([ - [1.0, 1.0], # task to color projection - [0.0, 0.0] - ]) - ) - task_word_weights = pnl.MappingProjection( - matrix=np.array([ - [0.0, 0.0], # task to word projection - [1.0, 1.0] - ]) - ) - color_response_weights = pnl.MappingProjection( - matrix=np.array([ - [2.0, 0.0], # color to response projection - [0.0, 2.0] - ]) - ) - word_response_weights = pnl.MappingProjection( - matrix=np.array([ - [2.5, 0.0], # word to response projection - [0.0, 2.5] - ]) - ) - task_input_weights = pnl.MappingProjection( - matrix=np.array([ - [1.0, 0.0], # proactive control to task - [0.0, 1.0] - ]) - ) - - # to send a control signal from the task demand layer to the response layer, - # set matrix to -1 to reduce response layer activation - # specify the sender of the projection which is the second OutputPort the task demand layer - # specify the receiver of the projection - task_conflict_to_response_weights = pnl.MappingProjection( - matrix=np.array([[-1.0, -1.0]]), - sender=task_demand_layer.output_ports[1], - receiver=response_layer - ) - - # Create pathways ----------------------------------------------------------------------------------------------------- - color_response_pathway = [colors_input_layer, - color_input_weights, - color_feature_layer, - color_response_weights, - response_layer] - - word_response_pathway = [words_input_layer, - word_input_weights, - word_feature_layer, - word_response_weights, - response_layer] - - task_pathway = [task_input_layer, - task_input_weights, - task_demand_layer] - - task_color_pathway_1 = [task_demand_layer, - task_color_weights, - color_feature_layer] - - task_color_pathway_2 = [color_feature_layer, - color_task_weights, - task_demand_layer] - - task_word_pathway_1 = [task_demand_layer, - task_word_weights, - word_feature_layer] - - task_word_pathway_2 = [word_feature_layer, - word_task_weights, - task_demand_layer] - - bias_color_pathway = [bias_input, color_feature_layer] - - bias_word_pathway = [bias_input, word_feature_layer] - - conflict_pathway = [task_demand_layer, - task_conflict_to_response_weights, - response_layer] - - PCTC = pnl.Composition(name="PCTC") - - Composition_Pathwayss = [color_response_pathway, - word_response_pathway, - task_pathway, - task_color_pathway_1, - task_word_pathway_1, - task_color_pathway_2, - task_word_pathway_2, - bias_color_pathway, - bias_word_pathway, - conflict_pathway] - - for pathway in Composition_Pathwayss: - PCTC.add_linear_processing_pathway(pathway) - - def pass_threshold(response_layer, thresh, context=None): - results1 = response_layer.output_port.parameters.value.get(context)[0] # red response - results2 = response_layer.output_port.parameters.value.get(context)[1] # green response - if results1 >= thresh or results2 >= thresh: - return True - return False - - terminate_trial = { - pnl.TimeScale.TRIAL: pnl.While(pass_threshold, response_layer, threshold), - pnl.TimeScale.RUN: pnl.Never() - } - - # Create test trials function ----------------------------------------------------------------------- - # a BLUE word input is [1,0] to words_input_layer and GREEN word is [0,1] - # a blue color input is [1,0] to colors_input_layer and green color is [0,1] - # a color-naming trial is [1,0] to task_layer and a word-reading trial is [0,1] - - - def trial_dict(blue_color, green_color, blue_word, green_word, PC_CN, PC_WR, bias): - - trialdict = { - colors_input_layer: [blue_color, green_color], - words_input_layer: [blue_word, green_word], - task_input_layer: [PC_CN, PC_WR], - bias_input: [bias, bias] - } - return trialdict - - initialize_input = trial_dict(1.0, 0.0, 1.0, 0.0, pc, 0.0, bias) - - # Run congruent trial -------------------------------------------------------------------------------- - congruent_input = trial_dict(1.0, 0.0, 1.0, 0.0, pc, 0.0, bias) # specify congruent trial input - results_1 = PCTC.run(inputs=initialize_input, - num_trials=settle) # run system to settle for 200 trials with congruent stimuli input - - # results_1 is all zeros due to OutputPort function - # - # assert np.allclose(words_input_layer.value, [[1., 0.]]) - # assert np.allclose(word_feature_layer.value, [[0.00550112, 0.00550112]]) - # assert np.allclose(response_layer.value, [[0.01798621, 0.01798621]]) - # assert np.allclose(colors_input_layer.value, [[1., 0.]]) - # assert np.allclose(color_feature_layer.value, [[0.00554086, 0.00554086]]) - # assert np.allclose(task_input_layer.value, [[0.025, 0.]]) - # assert np.allclose(task_demand_layer.value, [[0.01983591, 0.01774496]]) - # assert np.allclose(bias_input.value, [[-0.3, -0.3]]) - - color_input_weights.matrix = np.array([ - [1.0, 0.0], # set color input projections to 1 on the diagonals to e.g. - [0.0, 1.0] - ]) # send a green color input to the green unit of the color layer - word_input_weights.matrix = np.array([ - [1.0, 0.0], # the same for word input projections - [0.0, 1.0] - ]) - - # KDM 8/23/18: below must be added because these were expected to be changed with the above matrix setting, but this has appeared - # to be incorrect behavior, and the reason for doing it is unknown - color_input_weights.parameter_ports['matrix'].function.parameters.previous_value.set(color_input_weights.matrix, PCTC, override=True) - word_input_weights.parameter_ports['matrix'].function.parameters.previous_value.set(word_input_weights.matrix, PCTC, override=True) - - results_2 = PCTC.run(inputs=congruent_input, - termination_processing=terminate_trial) # run system with congruent stimulus input until - # assert np.allclose(words_input_layer.value, [[1., 0.]]) - # assert np.allclose(word_feature_layer.value, [[0.23271063, 0.0017892]], rtol=1e-02) - # assert np.allclose(response_layer.value, [[0.72332318, 0.00110829]], rtol=1e-02) - # assert np.allclose(colors_input_layer.value, [[1., 0.]]) - # assert np.allclose(color_feature_layer.value, [[0.50484897, 0.00194202]], rtol=1e-02) - # assert np.allclose(task_input_layer.value, [[0.025, 0.]]) - # assert np.allclose(task_demand_layer.value, [[0.39605925, 0.01018448]], rtol=1e-02) - # assert np.allclose(bias_input.value, [[-0.3, -0.3]]) # This script implements Figure 1 of Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). # Conflict monitoring and cognitive control. Psychological Review, 108, 624–652. diff --git a/tests/composition/test_show_graph.py b/tests/composition/test_show_graph.py index 115f304a0ef..7570727784c 100644 --- a/tests/composition/test_show_graph.py +++ b/tests/composition/test_show_graph.py @@ -392,6 +392,56 @@ def test_of_show_nested_show_cim_and_show_node_structure(self): gv = ocomp.show_graph(show_node_structure=True, show_cim=True, show_nested=NESTED, output_fmt='source') assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [label=<
RESULT
OutputPorts
Mechanism:
oa
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\toa:"OutputPort-RESULT" -> "icomp INPUT":"InputPort-INPUT_CIM_ia_InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\tctl_mech:"OutputPort-ia[slope] ControlSignal" -> "icomp CONTROL":"InputPort-PARAMETER_CIM_ia_slope" [label="" arrowhead=normal color=blue penwidth=1]\n\t"icomp OUTPUT":"OutputPort-OUTPUT_CIM_ia_RESULT" -> oc:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\toc:"OutputPort-RESULT" -> ctl_mech:"InputPort-OUTCOME" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" [label=<
INPUT_CIM_ob_InputPort-0INPUT_CIM_oa_InputPort-0
OutputPorts
Mechanism:
ocomp Input_CIM
> color=green penwidth=1 rank=same shape=plaintext]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_ob_InputPort-0" -> ob:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_oa_InputPort-0" -> oa:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp OUTPUT" [label=<
Mechanism:
ocomp Output_CIM
InputPorts
OUTPUT_CIM_ob_RESULTOUTPUT_CIM_oc_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\tob:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_ob_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\toc:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_oc_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\tocm:"OutputPort-ia[noise] ControlSignal" -> "icomp CONTROL":"InputPort-PARAMETER_CIM_ia_noise" [label="" arrowhead=normal color=purple penwidth=1]\n\tocm:"OutputPort-ia[intercept] ControlSignal" -> "icomp CONTROL":"InputPort-PARAMETER_CIM_ia_intercept" [label="" arrowhead=normal color=purple penwidth=1]\n\tocm:"OutputPort-oa[slope] ControlSignal" -> oa:"ParameterPort-slope" [label="" arrowhead=box color=purple penwidth=1]\n\tob [label=<
RESULT
OutputPorts
Mechanism:
ob
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\toc [label=<
RESULT
OutputPorts
Mechanism:
oc
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=red penwidth=3 rank=max shape=plaintext]\n\tctl_mech [label=<
ia[slope] ControlSignal
OutputPorts
Mechanism:
ctl_mech
InputPorts
OUTCOME
> color=blue penwidth=3 rank=max shape=plaintext]\n\tocm [label=<
ia[noise] ControlSignalia[intercept] ControlSignaloa[slope] ControlSignal
OutputPorts
Mechanism:
ocm
InputPorts
OUTCOMEOUTCOME-1
> color=purple penwidth=1 rank=min shape=plaintext]\n\tsubgraph cluster_icomp {\n\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\t"icomp INPUT" [label=<
INPUT_CIM_ia_InputPort-0
OutputPorts
Mechanism:
icomp Input_CIM
InputPorts
INPUT_CIM_ia_InputPort-0
> color=green penwidth=1 rank=same shape=plaintext]\n\t\t"icomp INPUT":"OutputPort-INPUT_CIM_ia_InputPort-0" -> ia:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t\t"icomp CONTROL" [label=<
PARAMETER_CIM_ia_interceptPARAMETER_CIM_ia_noisePARAMETER_CIM_ia_slope
OutputPorts
Mechanism:
icomp Parameter_CIM
InputPorts
PARAMETER_CIM_ia_interceptPARAMETER_CIM_ia_noisePARAMETER_CIM_ia_slope
> color=purple penwidth=1 rank=same shape=plaintext]\n\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_intercept" -> ia:"ParameterPort-intercept" [label="" arrowhead=box color=purple penwidth=1]\n\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_noise" -> ia:"ParameterPort-noise" [label="" arrowhead=box color=purple penwidth=1]\n\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_slope" -> ia:"ParameterPort-slope" [label="" arrowhead=box color=blue penwidth=1]\n\t\t"icomp OUTPUT" [label=<
OUTPUT_CIM_ia_RESULT
OutputPorts
Mechanism:
icomp Output_CIM
InputPorts
OUTPUT_CIM_ia_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\t\tia:"OutputPort-RESULT" -> "icomp OUTPUT":"InputPort-OUTPUT_CIM_ia_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\t\tia [label=<
RESULT
OutputPorts
Mechanism:
ia
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\t\tlabel=icomp\n\t}\n}' + def test_of_show_3_level_nested_show_cim_and_show_node_structure(self): + + # Inner Composition + ia = TransferMechanism(name='ia') + icomp = Composition(name='icomp', pathways=[ia]) + + ma = TransferMechanism(name='ma') + midcomp = Composition(name='midcomp', pathways=[ma, icomp]) + + # Outer Composition + oa = TransferMechanism(name='oa') + ob = TransferMechanism(name='ob') + oc = TransferMechanism(name='oc') + ctl_mech = ControlMechanism(name='ctl_mech', + control_signals=[ControlSignal(projections=[(SLOPE, ia)])]) + ocomp = Composition(name='ocomp', pathways=[[oa, midcomp, oc, ctl_mech], [ob]]) + ocm = OptimizationControlMechanism(name='ocm', + agent_rep=ocomp, + control_signals=[ + ControlSignal(projections=[(NOISE, ia)]), + ControlSignal(projections=[(INTERCEPT, ia)]), + ControlSignal(projections=[(SLOPE, oa)]), + ]) + ocomp.add_controller(ocm) + + gv = ocomp.show_graph(show_nested=False, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [color=green penwidth=3 rank=source shape=oval]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech -> midcomp [label="" arrowhead=box color=blue penwidth=1]\n\toa -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc [label="" arrowhead=normal color=black penwidth=1]\n\toc -> ctl_mech [label="" arrowhead=normal color=black penwidth=1]\n\tocm -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> oa [label="" arrowhead=box color=purple penwidth=1]\n\toc [color=red penwidth=3 rank=max shape=oval]\n\tctl_mech [color=blue penwidth=3 rank=max shape=octagon]\n\tob [color=brown penwidth=3 rank=same shape=oval]\n\tocm [color=purple penwidth=1 rank=min shape=doubleoctagon]\n}' + gv = ocomp.show_graph(show_nested=INSET, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [color=green penwidth=3 rank=source shape=oval]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech -> midcomp [label="" arrowhead=box color=blue penwidth=1]\n\toa -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc [label="" arrowhead=normal color=black penwidth=1]\n\toc -> ctl_mech [label="" arrowhead=normal color=black penwidth=1]\n\tocm -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> oa [label="" arrowhead=box color=purple penwidth=1]\n\toc [color=red penwidth=3 rank=max shape=oval]\n\tctl_mech [color=blue penwidth=3 rank=max shape=octagon]\n\tob [color=brown penwidth=3 rank=same shape=oval]\n\tocm [color=purple penwidth=1 rank=min shape=doubleoctagon]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [color=green penwidth=3 rank=source shape=oval]\n\t\ticomp [color=red penwidth=3 rank=max shape=rectangle]\n\t\tma -> icomp [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\tia [color=brown penwidth=3 rank=same shape=oval]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + gv = ocomp.show_graph(show_nested=NESTED, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [color=green penwidth=3 rank=source shape=oval]\n\toa -> ma [label="" arrowhead=normal color=black penwidth=1]\n\tctl_mech -> ia [label="" arrowhead=box color=blue penwidth=1]\n\toc -> ctl_mech [label="" arrowhead=normal color=black penwidth=1]\n\tocm -> ia [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> ia [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> oa [label="" arrowhead=box color=purple penwidth=1]\n\toc [color=red penwidth=3 rank=max shape=oval]\n\tctl_mech [color=blue penwidth=3 rank=max shape=octagon]\n\tob [color=brown penwidth=3 rank=same shape=oval]\n\tocm [color=purple penwidth=1 rank=min shape=doubleoctagon]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [color=green penwidth=3 rank=source shape=oval]\n\t\tma -> ia [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\tia [color=brown penwidth=3 rank=same shape=oval]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + gv = ocomp.show_graph(show_cim=True, show_nested=False, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [color=green penwidth=3 rank=source shape=oval]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech -> midcomp [label="" arrowhead=box color=blue penwidth=1]\n\toa -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc [label="" arrowhead=normal color=black penwidth=1]\n\toc -> ctl_mech [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" [color=green penwidth=1 rank=same shape=rectangle]\n\t"ocomp INPUT" -> oa [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" -> ob [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp OUTPUT" [color=red penwidth=1 rank=same shape=rectangle]\n\toc -> "ocomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\tob -> "ocomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\tocm -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm -> oa [label="" arrowhead=box color=purple penwidth=1]\n\toc [color=red penwidth=3 rank=max shape=oval]\n\tctl_mech [color=blue penwidth=3 rank=max shape=octagon]\n\tob [color=brown penwidth=3 rank=same shape=oval]\n\tocm [color=purple penwidth=1 rank=min shape=doubleoctagon]\n}' + gv = ocomp.show_graph(show_cim=True, show_nested=INSET, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [color=green penwidth=3 rank=source shape=oval]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech -> midcomp [label="" arrowhead=normal color=blue penwidth=1]\n\toa -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc [label="" arrowhead=normal color=black penwidth=1]\n\toc -> ctl_mech [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" [color=green penwidth=1 rank=same shape=rectangle]\n\t"ocomp INPUT" -> oa [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" -> ob [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp OUTPUT" [color=red penwidth=1 rank=same shape=rectangle]\n\toc -> "ocomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\tob -> "ocomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\tocm -> midcomp [label="" arrowhead=normal color=purple penwidth=1]\n\tocm -> midcomp [label="" arrowhead=normal color=purple penwidth=1]\n\tocm -> oa [label="" arrowhead=box color=purple penwidth=1]\n\toc [color=red penwidth=3 rank=max shape=oval]\n\tctl_mech [color=blue penwidth=3 rank=max shape=octagon]\n\tob [color=brown penwidth=3 rank=same shape=oval]\n\tocm [color=purple penwidth=1 rank=min shape=doubleoctagon]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [color=green penwidth=3 rank=source shape=oval]\n\t\ticomp [color=red penwidth=3 rank=max shape=rectangle]\n\t\tma -> icomp [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp INPUT" [color=green penwidth=1 rank=same shape=rectangle]\n\t\t"midcomp INPUT" -> ma [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp CONTROL" [color=purple penwidth=1 rank=same shape=rectangle]\n\t\t"midcomp CONTROL" -> icomp [label="" arrowhead=normal color=blue penwidth=1]\n\t\t"midcomp CONTROL" -> icomp [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp CONTROL" -> icomp [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp OUTPUT" [color=red penwidth=1 rank=same shape=rectangle]\n\t\ticomp -> "midcomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\t"icomp INPUT" [color=green penwidth=1 rank=same shape=rectangle]\n\t\t\t"icomp INPUT" -> ia [label="" arrowhead=normal color=black penwidth=1]\n\t\t\t"icomp CONTROL" [color=purple penwidth=1 rank=same shape=rectangle]\n\t\t\t"icomp CONTROL" -> ia [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL" -> ia [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL" -> ia [label="" arrowhead=box color=blue penwidth=1]\n\t\t\t"icomp OUTPUT" [color=red penwidth=1 rank=same shape=rectangle]\n\t\t\tia -> "icomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\t\t\tia [color=brown penwidth=3 rank=same shape=oval]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + gv = ocomp.show_graph(show_cim=True, show_nested=NESTED, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [color=green penwidth=3 rank=source shape=oval]\n\toa -> "midcomp INPUT" [label="" arrowhead=normal color=black penwidth=1]\n\tctl_mech -> "midcomp CONTROL" [label="" arrowhead=normal color=blue penwidth=1]\n\t"midcomp OUTPUT" -> oc [label="" arrowhead=normal color=black penwidth=1]\n\toc -> ctl_mech [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" [color=green penwidth=1 rank=same shape=rectangle]\n\t"ocomp INPUT" -> oa [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" -> ob [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp OUTPUT" [color=red penwidth=1 rank=same shape=rectangle]\n\toc -> "ocomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\tob -> "ocomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\tocm -> "midcomp CONTROL" [label="" arrowhead=normal color=purple penwidth=1]\n\tocm -> "midcomp CONTROL" [label="" arrowhead=normal color=purple penwidth=1]\n\tocm -> oa [label="" arrowhead=box color=purple penwidth=1]\n\toc [color=red penwidth=3 rank=max shape=oval]\n\tctl_mech [color=blue penwidth=3 rank=max shape=octagon]\n\tob [color=brown penwidth=3 rank=same shape=oval]\n\tocm [color=purple penwidth=1 rank=min shape=doubleoctagon]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [color=green penwidth=3 rank=source shape=oval]\n\t\tma -> "icomp INPUT" [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp INPUT" [color=green penwidth=1 rank=same shape=rectangle]\n\t\t"midcomp INPUT" -> ma [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp CONTROL" [color=purple penwidth=1 rank=same shape=rectangle]\n\t\t"midcomp CONTROL" -> "icomp CONTROL" [label="" arrowhead=normal color=blue penwidth=1]\n\t\t"midcomp CONTROL" -> "icomp CONTROL" [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp CONTROL" -> "icomp CONTROL" [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp OUTPUT" [color=red penwidth=1 rank=same shape=rectangle]\n\t\t"icomp OUTPUT" -> "midcomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\t"icomp INPUT" [color=green penwidth=1 rank=same shape=rectangle]\n\t\t\t"icomp INPUT" -> ia [label="" arrowhead=normal color=black penwidth=1]\n\t\t\t"icomp CONTROL" [color=purple penwidth=1 rank=same shape=rectangle]\n\t\t\t"icomp CONTROL" -> ia [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL" -> ia [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL" -> ia [label="" arrowhead=box color=blue penwidth=1]\n\t\t\t"icomp OUTPUT" [color=red penwidth=1 rank=same shape=rectangle]\n\t\t\tia -> "icomp OUTPUT" [label="" arrowhead=normal color=black penwidth=1]\n\t\t\tia [color=brown penwidth=3 rank=same shape=oval]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + gv = ocomp.show_graph(show_node_structure=True, show_nested=False, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [label=<
RESULT
OutputPorts
Mechanism:
oa
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech:"OutputPort-ia[slope] ControlSignal" -> midcomp [label="" arrowhead=box color=blue penwidth=1]\n\toa:"OutputPort-RESULT" -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\toc:"OutputPort-RESULT" -> ctl_mech:"InputPort-OUTCOME" [label="" arrowhead=normal color=black penwidth=1]\n\tocm:"OutputPort-ia[noise] ControlSignal" -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-ia[intercept] ControlSignal" -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-oa[slope] ControlSignal" -> oa:"ParameterPort-slope" [label="" arrowhead=box color=purple penwidth=1]\n\toc [label=<
RESULT
OutputPorts
Mechanism:
oc
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=red penwidth=3 rank=max shape=plaintext]\n\tctl_mech [label=<
ia[slope] ControlSignal
OutputPorts
Mechanism:
ctl_mech
InputPorts
OUTCOME
> color=blue penwidth=3 rank=max shape=plaintext]\n\tob [label=<
RESULT
OutputPorts
Mechanism:
ob
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\tocm [label=<
ia[noise] ControlSignalia[intercept] ControlSignaloa[slope] ControlSignal
OutputPorts
Mechanism:
ocm
InputPorts
OUTCOMEOUTCOME-1
> color=purple penwidth=1 rank=min shape=plaintext]\n}' + gv = ocomp.show_graph(show_node_structure=True, show_nested=INSET, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [label=<
RESULT
OutputPorts
Mechanism:
oa
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech:"OutputPort-ia[slope] ControlSignal" -> midcomp [label="" arrowhead=box color=blue penwidth=1]\n\toa:"OutputPort-RESULT" -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\toc:"OutputPort-RESULT" -> ctl_mech:"InputPort-OUTCOME" [label="" arrowhead=normal color=black penwidth=1]\n\tocm:"OutputPort-ia[noise] ControlSignal" -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-ia[intercept] ControlSignal" -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-oa[slope] ControlSignal" -> oa:"ParameterPort-slope" [label="" arrowhead=box color=purple penwidth=1]\n\toc [label=<
RESULT
OutputPorts
Mechanism:
oc
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=red penwidth=3 rank=max shape=plaintext]\n\tctl_mech [label=<
ia[slope] ControlSignal
OutputPorts
Mechanism:
ctl_mech
InputPorts
OUTCOME
> color=blue penwidth=3 rank=max shape=plaintext]\n\tob [label=<
RESULT
OutputPorts
Mechanism:
ob
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\tocm [label=<
ia[noise] ControlSignalia[intercept] ControlSignaloa[slope] ControlSignal
OutputPorts
Mechanism:
ocm
InputPorts
OUTCOMEOUTCOME-1
> color=purple penwidth=1 rank=min shape=plaintext]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [label=<
RESULT
OutputPorts
Mechanism:
ma
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\t\ticomp [color=red penwidth=3 rank=max shape=rectangle]\n\t\tma:"OutputPort-RESULT" -> icomp [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\tia [label=<
RESULT
OutputPorts
Mechanism:
ia
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + gv = ocomp.show_graph(show_node_structure=True, show_nested=NESTED, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [label=<
RESULT
OutputPorts
Mechanism:
oa
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\toa:"OutputPort-RESULT" -> ma:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\tctl_mech:"OutputPort-ia[slope] ControlSignal" -> ia:"ParameterPort-slope" [label="" arrowhead=box color=blue penwidth=1]\n\toc:"OutputPort-RESULT" -> ctl_mech:"InputPort-OUTCOME" [label="" arrowhead=normal color=black penwidth=1]\n\tocm:"OutputPort-ia[noise] ControlSignal" -> ia:"ParameterPort-noise" [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-ia[intercept] ControlSignal" -> ia:"ParameterPort-intercept" [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-oa[slope] ControlSignal" -> oa:"ParameterPort-slope" [label="" arrowhead=box color=purple penwidth=1]\n\toc [label=<
RESULT
OutputPorts
Mechanism:
oc
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=red penwidth=3 rank=max shape=plaintext]\n\tctl_mech [label=<
ia[slope] ControlSignal
OutputPorts
Mechanism:
ctl_mech
InputPorts
OUTCOME
> color=blue penwidth=3 rank=max shape=plaintext]\n\tob [label=<
RESULT
OutputPorts
Mechanism:
ob
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\tocm [label=<
ia[noise] ControlSignalia[intercept] ControlSignaloa[slope] ControlSignal
OutputPorts
Mechanism:
ocm
InputPorts
OUTCOMEOUTCOME-1
> color=purple penwidth=1 rank=min shape=plaintext]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [label=<
RESULT
OutputPorts
Mechanism:
ma
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\t\tma:"OutputPort-RESULT" -> ia:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\tia [label=<
RESULT
OutputPorts
Mechanism:
ia
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + gv = ocomp.show_graph(show_node_structure=True, show_cim=True, show_nested=False, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [label=<
RESULT
OutputPorts
Mechanism:
oa
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech:"OutputPort-ia[slope] ControlSignal" -> midcomp [label="" arrowhead=box color=blue penwidth=1]\n\toa:"OutputPort-RESULT" -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\toc:"OutputPort-RESULT" -> ctl_mech:"InputPort-OUTCOME" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" [label=<
INPUT_CIM_oa_InputPort-0INPUT_CIM_ob_InputPort-0
OutputPorts
Mechanism:
ocomp Input_CIM
> color=green penwidth=1 rank=same shape=plaintext]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_oa_InputPort-0" -> oa:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_ob_InputPort-0" -> ob:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp OUTPUT" [label=<
Mechanism:
ocomp Output_CIM
InputPorts
OUTPUT_CIM_oc_RESULTOUTPUT_CIM_ob_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\toc:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_oc_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\tob:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_ob_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\tocm:"OutputPort-ia[noise] ControlSignal" -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-ia[intercept] ControlSignal" -> midcomp [label="" arrowhead=box color=purple penwidth=1]\n\tocm:"OutputPort-oa[slope] ControlSignal" -> oa:"ParameterPort-slope" [label="" arrowhead=box color=purple penwidth=1]\n\toc [label=<
RESULT
OutputPorts
Mechanism:
oc
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=red penwidth=3 rank=max shape=plaintext]\n\tctl_mech [label=<
ia[slope] ControlSignal
OutputPorts
Mechanism:
ctl_mech
InputPorts
OUTCOME
> color=blue penwidth=3 rank=max shape=plaintext]\n\tob [label=<
RESULT
OutputPorts
Mechanism:
ob
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\tocm [label=<
ia[noise] ControlSignalia[intercept] ControlSignaloa[slope] ControlSignal
OutputPorts
Mechanism:
ocm
InputPorts
OUTCOMEOUTCOME-1
> color=purple penwidth=1 rank=min shape=plaintext]\n}' + gv = ocomp.show_graph(show_node_structure=True, show_cim=True, show_nested=INSET, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [label=<
RESULT
OutputPorts
Mechanism:
oa
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\tmidcomp [color=pink penwidth=3 rank=same shape=rectangle]\n\tctl_mech:"OutputPort-ia[slope] ControlSignal" -> midcomp [label="" arrowhead=normal color=blue penwidth=1]\n\toa:"OutputPort-RESULT" -> midcomp [label="" arrowhead=normal color=black penwidth=1]\n\tmidcomp -> oc:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\toc:"OutputPort-RESULT" -> ctl_mech:"InputPort-OUTCOME" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" [label=<
INPUT_CIM_oa_InputPort-0INPUT_CIM_ob_InputPort-0
OutputPorts
Mechanism:
ocomp Input_CIM
> color=green penwidth=1 rank=same shape=plaintext]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_oa_InputPort-0" -> oa:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_ob_InputPort-0" -> ob:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp OUTPUT" [label=<
Mechanism:
ocomp Output_CIM
InputPorts
OUTPUT_CIM_oc_RESULTOUTPUT_CIM_ob_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\toc:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_oc_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\tob:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_ob_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\tocm:"OutputPort-ia[noise] ControlSignal" -> midcomp [label="" arrowhead=normal color=purple penwidth=1]\n\tocm:"OutputPort-ia[intercept] ControlSignal" -> midcomp [label="" arrowhead=normal color=purple penwidth=1]\n\tocm:"OutputPort-oa[slope] ControlSignal" -> oa:"ParameterPort-slope" [label="" arrowhead=box color=purple penwidth=1]\n\toc [label=<
RESULT
OutputPorts
Mechanism:
oc
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=red penwidth=3 rank=max shape=plaintext]\n\tctl_mech [label=<
ia[slope] ControlSignal
OutputPorts
Mechanism:
ctl_mech
InputPorts
OUTCOME
> color=blue penwidth=3 rank=max shape=plaintext]\n\tob [label=<
RESULT
OutputPorts
Mechanism:
ob
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\tocm [label=<
ia[noise] ControlSignalia[intercept] ControlSignaloa[slope] ControlSignal
OutputPorts
Mechanism:
ocm
InputPorts
OUTCOMEOUTCOME-1
> color=purple penwidth=1 rank=min shape=plaintext]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [label=<
RESULT
OutputPorts
Mechanism:
ma
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\t\ticomp [color=red penwidth=3 rank=max shape=rectangle]\n\t\tma:"OutputPort-RESULT" -> icomp [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp INPUT" [label=<
INPUT_CIM_ma_InputPort-0
OutputPorts
Mechanism:
midcomp Input_CIM
> color=green penwidth=1 rank=same shape=plaintext]\n\t\t"midcomp INPUT":"OutputPort-INPUT_CIM_ma_InputPort-0" -> ma:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp CONTROL" [label=<
PARAMETER_CIM_ia_slopePARAMETER_CIM_ia_noisePARAMETER_CIM_ia_intercept
OutputPorts
Mechanism:
midcomp Parameter_CIM
> color=purple penwidth=1 rank=same shape=plaintext]\n\t\t"midcomp CONTROL":"OutputPort-PARAMETER_CIM_ia_slope" -> icomp [label="" arrowhead=normal color=blue penwidth=1]\n\t\t"midcomp CONTROL":"OutputPort-PARAMETER_CIM_ia_noise" -> icomp [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp CONTROL":"OutputPort-PARAMETER_CIM_ia_intercept" -> icomp [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp OUTPUT" [label=<
Mechanism:
midcomp Output_CIM
InputPorts
OUTPUT_CIM_icomp_OUTPUT_CIM_ia_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\t\ticomp -> "midcomp OUTPUT":"InputPort-OUTPUT_CIM_icomp_OUTPUT_CIM_ia_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\t"icomp INPUT" [label=<
INPUT_CIM_ia_InputPort-0
OutputPorts
Mechanism:
icomp Input_CIM
> color=green penwidth=1 rank=same shape=plaintext]\n\t\t\t"icomp INPUT":"OutputPort-INPUT_CIM_ia_InputPort-0" -> ia:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t\t\t"icomp CONTROL" [label=<
PARAMETER_CIM_ia_interceptPARAMETER_CIM_ia_noisePARAMETER_CIM_ia_slope
OutputPorts
Mechanism:
icomp Parameter_CIM
> color=purple penwidth=1 rank=same shape=plaintext]\n\t\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_intercept" -> ia:"ParameterPort-intercept" [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_noise" -> ia:"ParameterPort-noise" [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_slope" -> ia:"ParameterPort-slope" [label="" arrowhead=box color=blue penwidth=1]\n\t\t\t"icomp OUTPUT" [label=<
Mechanism:
icomp Output_CIM
InputPorts
OUTPUT_CIM_ia_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\t\t\tia:"OutputPort-RESULT" -> "icomp OUTPUT":"InputPort-OUTPUT_CIM_ia_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\t\t\tia [label=<
RESULT
OutputPorts
Mechanism:
ia
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + gv = ocomp.show_graph(show_node_structure=True, show_cim=True, show_nested=NESTED, output_fmt='source') + assert gv == 'digraph ocomp {\n\tgraph [label=ocomp overlap=False rankdir=BT]\n\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\tedge [fontname=arial fontsize=10]\n\toa [label=<
RESULT
OutputPorts
Mechanism:
oa
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\toa:"OutputPort-RESULT" -> "midcomp INPUT":"InputPort-INPUT_CIM_ma_InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\tctl_mech:"OutputPort-ia[slope] ControlSignal" -> "midcomp CONTROL":"InputPort-PARAMETER_CIM_ia_slope" [label="" arrowhead=normal color=blue penwidth=1]\n\t"midcomp OUTPUT":"OutputPort-OUTPUT_CIM_icomp_OUTPUT_CIM_ia_RESULT" -> oc:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\toc:"OutputPort-RESULT" -> ctl_mech:"InputPort-OUTCOME" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT" [label=<
INPUT_CIM_oa_InputPort-0INPUT_CIM_ob_InputPort-0
OutputPorts
Mechanism:
ocomp Input_CIM
> color=green penwidth=1 rank=same shape=plaintext]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_oa_InputPort-0" -> oa:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp INPUT":"OutputPort-INPUT_CIM_ob_InputPort-0" -> ob:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t"ocomp OUTPUT" [label=<
Mechanism:
ocomp Output_CIM
InputPorts
OUTPUT_CIM_oc_RESULTOUTPUT_CIM_ob_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\toc:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_oc_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\tob:"OutputPort-RESULT" -> "ocomp OUTPUT":"InputPort-OUTPUT_CIM_ob_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\tocm:"OutputPort-ia[noise] ControlSignal" -> "midcomp CONTROL":"InputPort-PARAMETER_CIM_ia_noise" [label="" arrowhead=normal color=purple penwidth=1]\n\tocm:"OutputPort-ia[intercept] ControlSignal" -> "midcomp CONTROL":"InputPort-PARAMETER_CIM_ia_intercept" [label="" arrowhead=normal color=purple penwidth=1]\n\tocm:"OutputPort-oa[slope] ControlSignal" -> oa:"ParameterPort-slope" [label="" arrowhead=box color=purple penwidth=1]\n\toc [label=<
RESULT
OutputPorts
Mechanism:
oc
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=red penwidth=3 rank=max shape=plaintext]\n\tctl_mech [label=<
ia[slope] ControlSignal
OutputPorts
Mechanism:
ctl_mech
InputPorts
OUTCOME
> color=blue penwidth=3 rank=max shape=plaintext]\n\tob [label=<
RESULT
OutputPorts
Mechanism:
ob
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\tocm [label=<
ia[noise] ControlSignalia[intercept] ControlSignaloa[slope] ControlSignal
OutputPorts
Mechanism:
ocm
InputPorts
OUTCOMEOUTCOME-1
> color=purple penwidth=1 rank=min shape=plaintext]\n\tsubgraph cluster_midcomp {\n\t\tgraph [label=midcomp overlap=False rankdir=BT]\n\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\tedge [fontname=arial fontsize=10]\n\t\tma [label=<
RESULT
OutputPorts
Mechanism:
ma
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=green penwidth=3 rank=source shape=plaintext]\n\t\tma:"OutputPort-RESULT" -> "icomp INPUT":"InputPort-INPUT_CIM_ia_InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp INPUT" [label=<
INPUT_CIM_ma_InputPort-0
OutputPorts
Mechanism:
midcomp Input_CIM
InputPorts
INPUT_CIM_ma_InputPort-0
> color=green penwidth=1 rank=same shape=plaintext]\n\t\t"midcomp INPUT":"OutputPort-INPUT_CIM_ma_InputPort-0" -> ma:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t\t"midcomp CONTROL" [label=<
PARAMETER_CIM_ia_slopePARAMETER_CIM_ia_noisePARAMETER_CIM_ia_intercept
OutputPorts
Mechanism:
midcomp Parameter_CIM
InputPorts
PARAMETER_CIM_ia_slopePARAMETER_CIM_ia_noisePARAMETER_CIM_ia_intercept
> color=purple penwidth=1 rank=same shape=plaintext]\n\t\t"midcomp CONTROL":"OutputPort-PARAMETER_CIM_ia_slope" -> "icomp CONTROL":"InputPort-PARAMETER_CIM_ia_slope" [label="" arrowhead=normal color=blue penwidth=1]\n\t\t"midcomp CONTROL":"OutputPort-PARAMETER_CIM_ia_noise" -> "icomp CONTROL":"InputPort-PARAMETER_CIM_ia_noise" [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp CONTROL":"OutputPort-PARAMETER_CIM_ia_intercept" -> "icomp CONTROL":"InputPort-PARAMETER_CIM_ia_intercept" [label="" arrowhead=normal color=purple penwidth=1]\n\t\t"midcomp OUTPUT" [label=<
OUTPUT_CIM_icomp_OUTPUT_CIM_ia_RESULT
OutputPorts
Mechanism:
midcomp Output_CIM
InputPorts
OUTPUT_CIM_icomp_OUTPUT_CIM_ia_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\t\t"icomp OUTPUT":"OutputPort-OUTPUT_CIM_ia_RESULT" -> "midcomp OUTPUT":"InputPort-OUTPUT_CIM_icomp_OUTPUT_CIM_ia_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\t\tsubgraph cluster_icomp {\n\t\t\tgraph [label=icomp overlap=False rankdir=BT]\n\t\t\tnode [color=black fontname=arial fontsize=12 penwidth=1 shape=record]\n\t\t\tedge [fontname=arial fontsize=10]\n\t\t\t"icomp INPUT" [label=<
INPUT_CIM_ia_InputPort-0
OutputPorts
Mechanism:
icomp Input_CIM
InputPorts
INPUT_CIM_ia_InputPort-0
> color=green penwidth=1 rank=same shape=plaintext]\n\t\t\t"icomp INPUT":"OutputPort-INPUT_CIM_ia_InputPort-0" -> ia:"InputPort-InputPort-0" [label="" arrowhead=normal color=black penwidth=1]\n\t\t\t"icomp CONTROL" [label=<
PARAMETER_CIM_ia_interceptPARAMETER_CIM_ia_noisePARAMETER_CIM_ia_slope
OutputPorts
Mechanism:
icomp Parameter_CIM
InputPorts
PARAMETER_CIM_ia_interceptPARAMETER_CIM_ia_noisePARAMETER_CIM_ia_slope
> color=purple penwidth=1 rank=same shape=plaintext]\n\t\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_intercept" -> ia:"ParameterPort-intercept" [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_noise" -> ia:"ParameterPort-noise" [label="" arrowhead=box color=purple penwidth=1]\n\t\t\t"icomp CONTROL":"OutputPort-PARAMETER_CIM_ia_slope" -> ia:"ParameterPort-slope" [label="" arrowhead=box color=blue penwidth=1]\n\t\t\t"icomp OUTPUT" [label=<
OUTPUT_CIM_ia_RESULT
OutputPorts
Mechanism:
icomp Output_CIM
InputPorts
OUTPUT_CIM_ia_RESULT
> color=red penwidth=1 rank=same shape=plaintext]\n\t\t\tia:"OutputPort-RESULT" -> "icomp OUTPUT":"InputPort-OUTPUT_CIM_ia_RESULT" [label="" arrowhead=normal color=black penwidth=1]\n\t\t\tia [label=<
RESULT
OutputPorts
Mechanism:
ia
ParameterPorts
integration_rate
intercept
noise
slope
InputPorts
InputPort-0
> color=brown penwidth=3 rank=same shape=plaintext]\n\t\t\tcolor=red\n\t\t\tlabel=icomp\n\t\t}\n\t\tlabel=midcomp\n\t}\n}' + def test_of_show_nested_show_cim_and_show_node_structure_with_singleton_in_outer_comp_added_last(self): # Inner Composition diff --git a/tests/functions/test_memory.py b/tests/functions/test_memory.py index f395599fcee..3c6e14e048b 100644 --- a/tests/functions/test_memory.py +++ b/tests/functions/test_memory.py @@ -3,6 +3,7 @@ import psyneulink.core.components.functions.statefulfunctions.memoryfunctions as Functions import psyneulink.core.llvm as pnlvm +from psyneulink import * np.random.seed(0) SIZE=10 @@ -112,8 +113,6 @@ def test_ptx_cuda(func, variable, params, expected, benchmark): benchmark(m.cuda_execute, variable) # Test of ContentAddressableMemory without LLVM: -from psyneulink import * - def test_ContentAddressableMemory_with_initializer_and_key_size_same_as_val_size(): stimuli = {'A': [[1,2,3],[4,5,6]], diff --git a/tests/misc/test_parameters.py b/tests/misc/test_parameters.py index 64db93006ee..24e6f839767 100644 --- a/tests/misc/test_parameters.py +++ b/tests/misc/test_parameters.py @@ -1,3 +1,4 @@ +import copy import numpy as np import psyneulink as pnl import pytest @@ -217,3 +218,11 @@ def test_dot_notation(): assert t.parameters.value.get(c) == 5 assert t.parameters.value.get(d) == 10 assert t.parameters.value.get('custom execution id') == 20 + + +def test_copy(): + f = pnl.Linear() + g = copy.deepcopy(f) + + assert isinstance(g.parameters.additive_param, pnl.ParameterAlias) + assert g.parameters.additive_param.source is g.parameters.intercept diff --git a/tests/models/test_documentation_models.py b/tests/models/test_documentation_models.py new file mode 100644 index 00000000000..b4bcc30731f --- /dev/null +++ b/tests/models/test_documentation_models.py @@ -0,0 +1,112 @@ +import json +import os +import runpy +import sys + +import numpy as np +import pytest + +import psyneulink as pnl + +# Variants for test_documentation_models: + +# parameters for the model were changed to reduce running time, and will +# not match the equivalent papers' results +REDUCED = 'reduced' + + +# NOTE: to add new models, run the script with valid results, then +# dump the results of a Composition to a file named +# {model_name}-{composition_name}.json in the same directory as +# the library models. Example for Cohen_Huston1994: +# json.dump( +# Bidirectional_Stroop.results, +# cls=pnl.PNLJSONEncoder, +# fp=open('psyneulink/library/models/results/Cohen_Huston1994-Bidirectional_Stroop.json', 'w'), +# indent=2, +# ) +# Using prettier https://prettier.io/ can reduce the line footprint of +# the resulting file while not totally minifying it +@pytest.mark.parametrize( + 'model_name, composition_name, additional_args, variant', + [ + pytest.param( + 'Cohen_Huston1994', + 'Bidirectional_Stroop', + [], + None, + marks=pytest.mark.stress + ), + pytest.param( + 'Cohen_Huston1994', + 'Bidirectional_Stroop', + [ + '--threshold=0.5', + '--settle-trials=10' + ], + REDUCED + ), + pytest.param( + 'Cohen_Huston1994_horse_race', + 'Bidirectional_Stroop', + [], + None, + marks=pytest.mark.stress + ), + pytest.param( + 'Cohen_Huston1994_horse_race', + 'Bidirectional_Stroop', + [ + '--word-runs=2', + '--color-runs=1', + '--threshold=0.5', + '--settle-trials=10', + '--pre-stimulus-trials=10' + ], + REDUCED + ), + pytest.param('GilzenratModel', 'task', ['--noise-stddev=0.0'], None), + pytest.param('Kalanthroff_PCTC_2018', 'PCTC', [], None, marks=pytest.mark.stress), + pytest.param('Kalanthroff_PCTC_2018', 'PCTC', ['--threshold=0.2', '--settle-trials=10'], REDUCED), + pytest.param('MontagueDayanSejnowski96', 'comp_5a', ['--figure', '5a'], None), + pytest.param('MontagueDayanSejnowski96', 'comp_5b', ['--figure', '5b'], None), + pytest.param('MontagueDayanSejnowski96', 'comp_5c', ['--figure', '5c'], None), + pytest.param('Nieuwenhuis2005Model', 'task', [], None), + ] +) +def test_documentation_models( + model_name, + composition_name, + additional_args, + variant, +): + models_dir = os.path.join( + os.path.dirname(__file__), + '..', + '..', + 'psyneulink', + 'library', + 'models' + ) + model_file = os.path.join(models_dir, f'{model_name}.py') + old_argv = sys.argv + sys.argv = [model_file, '--no-plot'] + additional_args + script_globals = runpy.run_path(model_file) + sys.argv = old_argv + + suffix = f'-{variant}' if variant is not None else '' + expected_results_file = os.path.join( + models_dir, + 'results', + f'{model_name}-{composition_name}{suffix}.json' + ) + with open(expected_results_file) as fi: + expected_results = pnl.convert_all_elements_to_np_array(json.loads(fi.read())) + + results = pnl.convert_all_elements_to_np_array(script_globals[composition_name].results) + + assert expected_results.shape == results.shape + np.testing.assert_allclose( + pytest.helpers.expand_np_ndarray(expected_results), + pytest.helpers.expand_np_ndarray(results) + )