PCTHierarchy Usage

from pct.nodes import PCTNode
from pct.functions import WeightedSum, Constant
from pct.hierarchy import PCTHierarchy

c:\Users\ruper\Versioning\python\nbdev\pct\.venv\lib\site-packages\gym\wrappers\monitoring\video_recorder.py:9: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives
  import distutils.spawn
Gym has been unmaintained since 2022 and does not support NumPy 2.0 amongst other critical functionality.
Please upgrade to Gymnasium, the maintained drop-in replacement of Gym, or contact the authors of your software and request that they upgrade.
See the migration guide at https://gymnasium.farama.org/introduction/migration_guide/ for additional information.

Creating a Hierarchy

Create a hierarchy by defining the number of rows (levels) and columns.

pre=Constant(5, name='precon')
namespace=pre.namespace
post=Constant(10, name='postcon', namespace=namespace)
hpct = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, clear_names=False, links="dense", namespace=namespace)
hpct.hierarchy;

print(hpct.get_summary())

**************************
PRE: 5.000 
L0C0 0.000 0.000 0.000 0.000 
L0C1 0.000 0.000 0.000 0.000 
L0C2 0.000 0.000 0.000 0.000 
L1C0 0.000 0.000 0.000 0.000 
L1C1 0.000 0.000 0.000 0.000 
L1C2 0.000 0.000 0.000 0.000 
L2C0 0.000 0.000 0.000 0.000 
L2C1 0.000 0.000 0.000 0.000 
L2C2 0.000 0.000 0.000 0.000 
POST: 10.000 

print(hpct.get_parameters_list())

[[[[5]], [[10]]], [[[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]]], [[[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]]], [[[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]]]]

print(hpct.get_grid())

[3, 3, 3]

hpct.change_namespace()

hpct.summary()

**************************
pcthierarchy PCTHierarchy [3, 3, 3] b0ab92aa-253e-11f1-8db4-5c879cf9f59b
--------------------------
PRE: precon Constant | 5 
Level 0 Cols 3
level0col0 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
PER: variable Variable | 0 
COM: subtract Subtract | 0 | links  weighted_sum variable 
OUT: proportional Proportional | gain 1 | 0 | links  subtract 
----------------------------
level0col1 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
PER: variable1 Variable | 0 
COM: subtract1 Subtract | 0 | links  weighted_sum1 variable1 
OUT: proportional1 Proportional | gain 1 | 0 | links  subtract1 
----------------------------
level0col2 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
PER: variable2 Variable | 0 
COM: subtract2 Subtract | 0 | links  weighted_sum2 variable2 
OUT: proportional2 Proportional | gain 1 | 0 | links  subtract2 
----------------------------
Level 1 Cols 3
level1col0 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
COM: subtract3 Subtract | 0 | links  weighted_sum4 weighted_sum3 
OUT: proportional3 Proportional | gain 1 | 0 | links  subtract3 
----------------------------
level1col1 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
COM: subtract4 Subtract | 0 | links  weighted_sum6 weighted_sum5 
OUT: proportional4 Proportional | gain 1 | 0 | links  subtract4 
----------------------------
level1col2 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
COM: subtract5 Subtract | 0 | links  weighted_sum8 weighted_sum7 
OUT: proportional5 Proportional | gain 1 | 0 | links  subtract5 
----------------------------
Level 2 Cols 3
level2col0 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: constant Constant | 0 
PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
COM: subtract6 Subtract | 0 | links  constant weighted_sum9 
OUT: proportional6 Proportional | gain 1 | 0 | links  subtract6 
----------------------------
level2col1 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: constant1 Constant | 0 
PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
COM: subtract7 Subtract | 0 | links  constant1 weighted_sum10 
OUT: proportional7 Proportional | gain 1 | 0 | links  subtract7 
----------------------------
level2col2 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: constant2 Constant | 0 
PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
COM: subtract8 Subtract | 0 | links  constant2 weighted_sum11 
OUT: proportional8 Proportional | gain 1 | 0 | links  subtract8 
----------------------------
POST: postcon Constant | 10 
**************************

hpct.extended_summary()

**************************
pcthierarchy PCTHierarchy [3, 3, 3] b0ab92aa-253e-11f1-8db4-5c879cf9f59b
--------------------------
PRE: precon Constant | 5 
Level 0 Cols 3
level0col0 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
PER: variable Variable | 0 
COM: subtract Subtract | 0 | links  weighted_sum variable 
OUT: proportional Proportional | gain 1 | 0 | links  subtract 
----------------------------
level0col1 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
PER: variable1 Variable | 0 
COM: subtract1 Subtract | 0 | links  weighted_sum1 variable1 
OUT: proportional1 Proportional | gain 1 | 0 | links  subtract1 
----------------------------
level0col2 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
PER: variable2 Variable | 0 
COM: subtract2 Subtract | 0 | links  weighted_sum2 variable2 
OUT: proportional2 Proportional | gain 1 | 0 | links  subtract2 
----------------------------
Level 1 Cols 3
level1col0 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
COM: subtract3 Subtract | 0 | links  weighted_sum4 weighted_sum3 
OUT: proportional3 Proportional | gain 1 | 0 | links  subtract3 
----------------------------
level1col1 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
COM: subtract4 Subtract | 0 | links  weighted_sum6 weighted_sum5 
OUT: proportional4 Proportional | gain 1 | 0 | links  subtract4 
----------------------------
level1col2 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
COM: subtract5 Subtract | 0 | links  weighted_sum8 weighted_sum7 
OUT: proportional5 Proportional | gain 1 | 0 | links  subtract5 
----------------------------
Level 2 Cols 3
level2col0 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: constant Constant | 0 
PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
COM: subtract6 Subtract | 0 | links  constant weighted_sum9 
OUT: proportional6 Proportional | gain 1 | 0 | links  subtract6 
----------------------------
level2col1 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: constant1 Constant | 0 
PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
COM: subtract7 Subtract | 0 | links  constant1 weighted_sum10 
OUT: proportional7 Proportional | gain 1 | 0 | links  subtract7 
----------------------------
level2col2 PCTNode b0ab92aa-253e-11f1-8db4-5c879cf9f59b
----------------------------
REF: constant2 Constant | 0 
PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
COM: subtract8 Subtract | 0 | links  constant2 weighted_sum11 
OUT: proportional8 Proportional | gain 1 | 0 | links  subtract8 
----------------------------
POST: postcon Constant | 10 
**************************
----- Error Collector -----
error_collector: None
***************************

#FunctionsList.getInstance().report()

Create a hierarchy from a configuration.

config = hpct.get_config()
print(config)

# h = PCTHierarchy.from_config(config, namespace=namespace)
h = PCTHierarchy.from_config(config)

assert h.get_config() == hpct.get_config()

Viewing a Hierarchy

The hierarchy details can be viewed as a summary.

hpct.get_node(2,0).get_function('reference').set_value(1)
hpct.get_node(2,1).get_function('reference').set_value(1)
hpct.get_node(2,2).get_function('reference').set_value(1)
for level in range(3):
    for col in range(3):
        hpct.get_node(level,col).get_function('output').set_property('gain', 10)
hpct.summary()

The hierarchy details can be viewed as a configuration. That configuration can be used to create a hierarchy, as shown above.

hpct.get_config()

Get the output function, which will be the output function of the last node, or the last item of the post-processor functions, if present.

link = hpct.get_output_function()
print(link.get_config())

The hierarhcy can also be viewed graphically as a network of connected nodes.

import os

import networkx as nx
ahpct = PCTHierarchy(2,2, links="dense")

test = 2
if test==1:
    g = ahpct.graph()
    pos=graphviz_layout(g, prog='dot')
    nx.draw(g, pos=pos, with_labels=True, font_size=12, font_weight='bold', node_color='red',  node_size=500)

if test ==2:
    g = ahpct.graph()
    pos = nx.multipartite_layout(g, subset_key="layer", align='horizontal')
    pos['constant1'][0]+=0.2
    c = pos['constant1'][0]
    print(c)
    nx.draw(g, pos=pos, with_labels=True, font_weight='bold', node_color='red',  node_size=750, arrowsize=25)

if test ==3:
    if os.name=='nt':        
        ahpct.draw(file="ahpct.png",  node_size=1500, figsize=(10,10))# with_labels=True, font_weight='bold', node_color='red',  node_size=500, arrowsize=25, align='vertical'

Running a hierarchy

The hierachy can be run once by calling itself. The verbose flag will print the computations to the screen.

hpct(verbose=True)

A hierarchy can be executed with the “run()” method, providing the number of iterations to run.

hpct1 = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, links="dense")
namespace=hpct1.namespace
hpct1.get_node(2,0).get_function('reference').set_value(1)
hpct1.get_node(2,1).get_function('reference').set_value(1)
hpct1.get_node(2,2).get_function('reference').set_value(1)
for level in range(3):
    for col in range(3):
        hpct1.get_node(level,col).get_function('output').set_property('gain', 10)

hpct1.run(10)

Viewing Data

If the hierarchy is created with the “history” flag equal to True, the data can be retrieved for each node. The node is accessed by specifying the row and column within the hierarchy.

print(hpct1.get_node(1,1).history.data)
assert hpct1.get_node(1,1).history.data == {'refcoll': {'weighted_sum6': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'percoll': {'weighted_sum5': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'comcoll': {'subtract4': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'outcoll': {'proportional4': [0.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0]}}

Save and Load

Save a hierarchy to file.

import json

hpct1.save("hpct.json")

Create a hierarchy from file.

#loaded = PCTHierarchy.load("hpct.json", clear=False, namespace=namespace)
loaded = PCTHierarchy.load("hpct.json", clear=False)
loaded.summary()

import networkx as nx
import matplotlib.pyplot as plt

# https://matplotlib.org/3.1.0/gallery/color/named_colors.html
loaded.draw(with_edge_labels=True, color_mapping={'w':'aqua','c':'limegreen','s':'goldenrod', 'p':'red', 'v':'silver'})

Examples

Build a hierarchy by adding nodes and functions manually.

Create an empty hierarchy.

myhpct = PCTHierarchy()
namespace=myhpct.namespace
myhpct.summary(build=False)

Add a node. Then nodes at particular positions.

myhpct.add_node(PCTNode(namespace=namespace))
myhpct.add_node(PCTNode(namespace=namespace), level=1)
myhpct.add_node(PCTNode(namespace=namespace), level=0)
myhpct.summary(build=False)

Replace functions at particular positions in the hierarchy.

myhpct.insert_function(level=0, col=0, collection="perception", function=Proportional(3, name="prop2", namespace=namespace))
myhpct.insert_function(level=1, col=0, collection="perception", function=WeightedSum(weights=[1,1], name="wsum", namespace=namespace))
myhpct.insert_function(level=0, col=1, collection="reference", function=Proportional(1, name="passthru", namespace=namespace))

Add pre and post processor functions.

myhpct.add_preprocessor(Constant(1, name="cons1", namespace=namespace))
myhpct.add_preprocessor(Proportional(5, name="prop1", namespace=namespace))
myhpct.add_postprocessor(Proportional(5, name="postprop1", namespace=namespace))
myhpct.add_postprocessor(Proportional(5, name="postprop2", namespace=namespace))

Link the functions together.

myhpct.set_links("prop1", "cons1")
myhpct.set_links("prop2", "prop1")
myhpct.add_links("wsum", "prop2", "variable")
myhpct.set_links("passthru", "proportional1")
myhpct.set_links("postprop1", "proportional")
myhpct.set_links("postprop2", "postprop1")

myhpct.get_node(0,0).get_function('reference').set_value(1)
myhpct.get_node(1,0).get_function('reference').set_value(1)
myhpct.get_node(0,0).get_function('output').set_property('gain', 10)
myhpct.get_node(0,1).get_function('output').set_property('gain', 10)
myhpct.get_node(1,0).get_function('output').set_property('gain', 10)
myhpct.summary(build=True)

myhpctconfig = myhpct.get_config()
#print(myhpctconfig)
assert myhpctconfig == {'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'cons1', 'value': 1, 'links': {}}, 'pre1': {'type': 'Proportional', 'name': 'prop1', 'value': 0, 'links': {0: 'cons1'}, 'gain': 5}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'Proportional', 'name': 'prop2', 'value': 0, 'links': {0: 'prop1'}, 'gain': 3}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'constant2', 1: 'prop2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 10}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'pctnode', 'refcoll': {'0': {'type': 'Proportional', 'name': 'passthru', 'value': 0, 'links': {0: 'proportional1'}, 'gain': 1}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'passthru', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'wsum', 'value': 0, 'links': {0: 'prop2', 1: 'variable'}, 'weights': [1.0, 1.0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'constant1', 1: 'wsum'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 10}}}}}}}, 'post': {'post0': {'type': 'Proportional', 'name': 'postprop1', 'value': 0, 'links': {0: 'proportional'}, 'gain': 5}, 'post1': {'type': 'Proportional', 'name': 'postprop2', 'value': 0, 'links': {0: 'postprop1'}, 'gain': 5}}}

Define the order in which the node will be processed.

myhpct.set_order(["pctnode2", "pctnode1", "pctnode"])

Run the hierarchy once.

out = myhpct(verbose=True)
print(out)
assert out == -35000

# config =  {'type': 'Individual', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'CartPoleV1', 'name': 'CartPoleV1', 'value': [0.03498833197860944, 0.20994561633454428, 0.012668159509212712, -0.2705237130920193, 0.047656152654718356], 'links': {0: 'Action1'}, 'env_name': 'CartPole-v1', 'reward': 1.0, 'done': False, 'info': {}}, 'pre1': {'type': 'IndexedParameter', 'name': 'ICV', 'value': 0.20994561633454428, 'links': {0: 'CartPoleV1'}, 'index': 1}, 'pre2': {'type': 'IndexedParameter', 'name': 'ICP', 'value': 0.03498833197860944, 'links': {0: 'CartPoleV1'}, 'index': 0}, 'pre3': {'type': 'IndexedParameter', 'name': 'IPV', 'value': -0.2705237130920193, 'links': {0: 'CartPoleV1'}, 'index': 3}, 'pre4': {'type': 'IndexedParameter', 'name': 'IPA', 'value': 0.012668159509212712, 'links': {0: 'CartPoleV1'}, 'index': 2}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'L0C0', 'refcoll': {'0': {'type': 'EAConstant', 'name': 'RL0C0', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'EAWeightedSum', 'name': 'PL0C0', 'value': -0.2705237130920193, 'links': {0: 'ICV', 1: 'ICP', 2: 'IPV', 3: 'IPA'}, 'weights': [0, 0, 1, 0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'CL0C0', 'value': 0.2705237130920193, 'links': {0: 'RL0C0', 1: 'PL0C0'}}}, 'outcoll': {'0': {'type': 'EAProportional', 'name': 'OL0C0', 'value': -0.05046166000036782, 'links': {0: 'CL0C0'}, 'gain': -0.1865332226280776}}}}}}}, 'post': {'post0': {'type': 'EAWeightedSum', 'name': 'Action1', 'value': -0.005282911840894066, 'links': {0: 'OL0C0'}, 'weights': [0.10469159835121472]}}}
# ind = PCTHierarchy.from_config(config)

from pct.errors import RootSumSquaredError, TotalError

er = RootSumSquaredError()
te = TotalError(error_response=er, limit=250,min=True)   
te.add_error_data([1, 2])
print(te)


hpct = PCTHierarchy(1,1,error_collector=te)
hpct.run(steps=5, verbose=True)


err=te.error()
print(err)