Add causality and K-best example
This commit is contained in:
Daniel
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# Causality and K-best
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This directory is a self-contained example based on the current Butadiene code.
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Nothing in `ILP/butadien` or `mod/butadien` is changed.
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## 1. Enumerate K best supports with Gurobi
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Set `K` near the top of `butadiensynthesis_k_best.py` and run:
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```bash
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python3 butadiensynthesis_k_best.py
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```
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The script is the existing Butadiene ILP with one small generalisation: instead of
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computing only the best and second-best support, it repeatedly excludes every
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previously found support. It writes all solutions to `gurobi_flows.json`.
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Thus "K best" here means K best distinct reaction supports, matching the meaning
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of the existing second-best code.
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## 2. Check the Gurobi solutions for causality
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Activate the MØD environment and run this directory with MØD:
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```bash
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mod -f check_causality.py
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```
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The checker reads `gurobi_flows.json`, maps each reaction to the current MØD
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derivation graph by educt/product SMILES, fixes exactly that hyperflow, and runs
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`causality.RealisabilityQuery`.
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Typical output is:
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```text
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solution 1: causally realisable: YES
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solution 2: causally realisable: NO
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...
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```
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`butadienNetwork.py` and `graph.py` are local helper copies for constructing
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the same Butadiene MØD network.
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config.common.ignoreDeprecation = False
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config.ilp.solver="CPLEX"
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#import mod
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#from mod import *
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import networkx as nx
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# import Graph class from graph.py
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from graph import GraphObj
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#ethylen = Graph.fromGMLString(
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"""graph [
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node [ id 0 label "H" ]
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node [ id 1 label "H" ]
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node [ id 2 label "C" ]
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node [ id 3 label "C" ]
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node [ id 4 label "H" ]
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node [ id 5 label "H" ]
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edge [ source 0 target 2 label "-" ]
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edge [ source 1 target 2 label "-" ]
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edge [ source 2 target 3 label "=" ]
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edge [ source 3 target 4 label "-" ]
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edge [ source 3 target 5 label "-" ]
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]"""
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#, name="Ethylen")
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butadien = Graph.fromGMLString(
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"""graph [
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node [ id 0 label "C" ]
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node [ id 1 label "C" ]
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node [ id 2 label "C" ]
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node [ id 3 label "C" ]
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node [ id 4 label "H" ]
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node [ id 5 label "H" ]
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node [ id 6 label "H" ]
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node [ id 7 label "H" ]
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node [ id 8 label "H" ]
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node [ id 9 label "H" ]
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edge [ source 0 target 1 label "=" ]
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edge [ source 1 target 2 label "-" ]
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edge [ source 2 target 3 label "=" ]
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edge [ source 0 target 4 label "-" ]
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edge [ source 0 target 5 label "-" ]
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edge [ source 1 target 6 label "-" ]
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edge [ source 2 target 7 label "-" ]
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edge [ source 3 target 8 label "-" ]
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edge [ source 3 target 9 label "-" ]
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]"""
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, name="Butadien")
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#benzene = Graph.fromSMILES('c1=cc=cc=c1')
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#benzene = Graph.fromGMLString(
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"""graph [
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node [ id 0 label "C" ]
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node [ id 1 label "H" ]
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node [ id 2 label "C" ]
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node [ id 3 label "H" ]
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node [ id 4 label "C" ]
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node [ id 5 label "H" ]
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node [ id 6 label "C" ]
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node [ id 7 label "H" ]
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node [ id 8 label "C" ]
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node [ id 9 label "H" ]
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node [ id 10 label "C" ]
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node [ id 11 label "H" ]
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edge [ source 0 target 1 label "-" ]
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edge [ source 0 target 2 label "=" ]
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edge [ source 2 target 3 label "-" ]
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edge [ source 2 target 4 label "-" ]
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edge [ source 4 target 5 label "-" ]
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edge [ source 4 target 6 label "=" ]
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edge [ source 6 target 7 label "-" ]
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edge [ source 6 target 8 label "-" ]
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edge [ source 8 target 9 label "-" ]
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edge [ source 8 target 10 label "=" ]
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edge [ source 10 target 11 label "-" ]
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edge [ source 10 target 0 label "-" ]
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]"""
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#, name="Benzene")
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#pentadien = Graph.fromGMLString(
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"""graph
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[
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node [ id 0 label "C" ]
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node [ id 1 label "C" ]
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node [ id 2 label "C" ]
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node [ id 3 label "C" ]
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node [ id 4 label "H" ]
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node [ id 5 label "H" ]
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node [ id 6 label "H" ]
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node [ id 7 label "H" ]
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node [ id 8 label "H" ]
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node [ id 9 label "C" ]
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node [ id 10 label "H" ]
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node [ id 11 label "H" ]
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node [ id 12 label "H" ]
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edge [ source 0 target 1 label "=" ]
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edge [ source 1 target 2 label "-" ]
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edge [ source 2 target 3 label "=" ]
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edge [ source 0 target 4 label "-" ]
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edge [ source 0 target 5 label "-" ]
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edge [ source 1 target 6 label "-" ]
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edge [ source 2 target 7 label "-" ]
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edge [ source 3 target 8 label "-" ]
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edge [ source 3 target 9 label "-" ]
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edge [ source 9 target 10 label "-" ]
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edge [ source 9 target 11 label "-" ]
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edge [ source 9 target 12 label "-" ]
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]
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"""
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#, name="Pentadien")
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restswap = Rule.fromGMLString(
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"""rule [
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left [
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edge [ source 1 target 2 label "=" ]
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edge [ source 3 target 4 label "=" ]
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]
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context [
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node [ id 1 label "C" ]
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node [ id 2 label "C"]
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node [ id 3 label "C"]
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node [ id 4 label "C"]
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]
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right [
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edge [ source 1 target 3 label "=" ]
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edge [ source 2 target 4 label "=" ]
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]
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]"""
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)
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dielsalder = Rule.fromGMLString(
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"""rule [
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left [
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edge [ source 1 target 2 label "=" ]
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edge [ source 2 target 3 label "-" ]
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edge [ source 3 target 4 label "=" ]
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edge [ source 5 target 6 label "=" ]
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]
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context [
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node [ id 1 label "C" ]
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node [ id 2 label "C"]
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node [ id 3 label "C"]
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node [ id 4 label "C"]
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node [ id 5 label "C"]
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node [ id 6 label "C"]
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]
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right [
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edge [ source 1 target 2 label "-" ]
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edge [ source 2 target 3 label "=" ]
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edge [ source 3 target 4 label "-" ]
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edge [ source 4 target 5 label "-" ]
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edge [ source 5 target 6 label "-" ]
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edge [ source 6 target 1 label "-" ]
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]
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]"""
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)
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def cyclesizes(g):
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nxGraph = GraphObj(g).nx_graph
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#Restriction to E/Z? Benzen can't be created or consumed by the reaction due to steric difficulties
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#Chain restrictions: (No steric reason)
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for nodestart in nxGraph.nodes:
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for nodeend in nxGraph.nodes:
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if nodeend != nodestart:
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paths = sorted(list(nx.all_shortest_paths(nxGraph, nodestart, nodeend)))
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for path in paths:
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if len(path) > 10: #First node included in length
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return True
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#Cycle restrictions:
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cycles = sorted(list(nx.chordless_cycles(nxGraph)))
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cycle_lengths = [len(x) for x in cycles]
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if cycles == []:
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return False
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#One Atom can't be in four different cycles
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for vertice in nxGraph.nodes:
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counter = 0
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for cycle in cycles:
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if vertice in cycle:
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counter += 1
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if counter >= 4:
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return True
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#One Cycle can't overlapp with another on over 2 Connection points
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for cycle in cycles:
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for cycleref in cycles:
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if cycle != cycleref and len(list(set(cycle) & set(cycleref))) >= 3:
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return True
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#Only cordless cycles of length 5,6 and 7 are acceptable
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if min(cycle_lengths) >= 6 and max(cycle_lengths) <=6:
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return False
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return True
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#Disallows Allenes (Two Doublebonds on same carbon)
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def doubledoublebond(g):
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for vertice in g.vertices:
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if (vertice.stringLabel == "C" and vertice.degree == 2): # and vertice.edges.label == ["=", "="]
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for edge in vertice.incidentEdges:
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print(type(edge.bondType))
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bonds = [type(edge.bondType) for edge in vertice.incidentEdges]
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if (bonds[0] == bonds[1] and len(bonds) == 2):
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return True
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return False
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def restriction(dg):
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for a in dg.right:
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if a.vLabelCount("C") > 10:
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return False
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if doubledoublebond(a):
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return False
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if cyclesizes(a):
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return False
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return True
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ethylen = Graph.fromSMILES('C=C')
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goal = Graph.fromSMILES('C1C=CC=CC=1')
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dg = DG(graphDatabase=inputGraphs)
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dg.build().execute(
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addSubset(inputGraphs)
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>> rightPredicate[
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restriction
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](
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repeat(revive(inputRules)) #Revive not necessary
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)
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)
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@@ -0,0 +1,367 @@
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import json
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from pathlib import Path
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import numpy as np
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import gurobipy as gp
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from gurobipy import GRB, Model, quicksum
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HYPERGRAPH = {
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0: (['Butadien'], []),
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2: (['p_{0,0}'], []),
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3: (['p_{0,1}'], []),
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4: (['Butadien', 'Butadien'], ['p_{0,0}', 'p_{0,1}']),
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6: (['p_{0,2}'], []),
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7: (['Butadien', 'Butadien'], ['p_{0,2}']),
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10: (['p_{0,0}', 'p_{0,1}'], ['Butadien', 'Butadien']),
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12: (['p_{0,3}'], []),
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13: (['p_{0,0}', 'p_{0,2}'], ['p_{0,3}']),
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17: (['p_{0,4}'], []),
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18: (['p_{0,1}', 'p_{0,1}'], ['Butadien', 'p_{0,4}']),
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19: (['p_{0,5}'], []),
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20: (['p_{0,1}', 'p_{0,2}'], ['Butadien', 'p_{0,5}']),
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23: (['Butadien', 'p_{0,1}'], ['p_{0,0}', 'p_{0,4}']),
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26: (['Butadien', 'p_{0,2}'], ['p_{0,0}', 'p_{0,5}']),
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27: (['p_{0,6}'], []),
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28: (['p_{0,0}', 'p_{0,1}'], ['p_{0,6}']),
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29: (['p_{0,7}'], []),
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30: (['Butadien', 'p_{0,0}'], ['p_{0,7}']),
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31: (['p_{0,8}'], []),
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32: (['Butadien', 'p_{0,1}'], ['p_{0,8}']),
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33: (['Butadien', 'p_{0,1}'], ['p_{0,5}']),
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34: (['p_{0,9}'], []),
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35: (['Butadien', 'p_{0,1}'], ['p_{0,9}']),
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36: (['p_{0,10}'], []),
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37: (['Butadien', 'p_{0,1}'], ['p_{0,10}']),
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38: (['p_{0,3}'], ['p_{0,0}', 'p_{0,2}']),
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41: (['p_{0,11}'], []),
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42: (['p_{0,4}'], ['p_{0,0}', 'p_{0,11}']),
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43: (['p_{0,12}'], []),
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44: (['p_{0,5}'], ['p_{0,12}']),
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63: (['p_{0,13}'], []),
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64: (['p_{0,4}', 'p_{0,6}'], ['p_{0,1}', 'p_{0,13}']),
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65: (['p_{0,2}', 'p_{0,4}'], ['p_{0,1}', 'p_{0,5}']),
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66: (['p_{0,0}', 'p_{0,4}'], ['Butadien', 'p_{0,1}']),
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67: (['Butadien', 'p_{0,4}'], ['p_{0,1}', 'p_{0,1}']),
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77: (['p_{0,5}', 'p_{0,6}'], ['p_{0,2}', 'p_{0,13}']),
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79: (['p_{0,0}', 'p_{0,5}'], ['Butadien', 'p_{0,2}']),
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80: (['Butadien', 'p_{0,5}'], ['p_{0,1}', 'p_{0,2}']),
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83: (['p_{0,1}', 'p_{0,5}'], ['p_{0,2}', 'p_{0,4}']),
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96: (['Butadien', 'p_{0,6}'], ['p_{0,0}', 'p_{0,13}']),
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97: (['p_{0,1}', 'p_{0,6}'], ['Butadien', 'p_{0,13}']),
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99: (['p_{0,14}'], []),
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100: (['p_{0,0}', 'p_{0,6}'], ['p_{0,14}']),
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101: (['p_{0,15}'], []),
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102: (['p_{0,0}', 'p_{0,7}'], ['p_{0,15}']),
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121: (['p_{0,0}', 'p_{0,4}'], ['p_{0,13}']),
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122: (['p_{0,16}'], []),
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123: (['p_{0,0}', 'p_{0,4}'], ['p_{0,16}']),
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124: (['p_{0,17}'], []),
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125: (['Butadien', 'p_{0,7}'], ['p_{0,17}']),
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127: (['p_{0,12}'], ['p_{0,5}']),
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130: (['p_{0,14}'], ['p_{0,0}', 'p_{0,6}']),
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132: (['p_{0,15}'], ['p_{0,0}', 'p_{0,7}']),
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135: (['p_{0,0}', 'p_{0,11}'], ['p_{0,4}']),
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153: (['Butadien', 'p_{0,13}'], ['p_{0,1}', 'p_{0,6}']),
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155: (['p_{0,0}', 'p_{0,13}'], ['Butadien', 'p_{0,6}']),
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157: (['p_{0,1}', 'p_{0,13}'], ['p_{0,4}', 'p_{0,6}']),
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158: (['p_{0,2}', 'p_{0,13}'], ['p_{0,5}', 'p_{0,6}']),
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210: (['p_{0,18}'], []),
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211: (['Butadien', 'p_{0,11}'], ['p_{0,18}']),
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213: ([], ['Butadien']),
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}
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HYPERGRAPH2 = {
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2: (['p_{0,0}'], []),
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3: (['p_{0,1}'], []),
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||||||
|
4: (['Butadien', 'Butadien'], ['p_{0,0}', 'p_{0,1}']),
|
||||||
|
6: (['p_{0,2}'], []),
|
||||||
|
7: (['Butadien', 'Butadien'], ['p_{0,2}']),
|
||||||
|
17: (['p_{0,4}'], []),
|
||||||
|
23: (['Butadien', 'p_{0,1}'], ['p_{0,0}', 'p_{0,4}']),
|
||||||
|
41: (['p_{0,11}'], []),
|
||||||
|
42: (['p_{0,4}'], ['p_{0,0}', 'p_{0,11}']),
|
||||||
|
213: ([], ['Butadien']),
|
||||||
|
}
|
||||||
|
|
||||||
|
HYPERGRAPH3 = {
|
||||||
|
0: (['Butadien'], []),
|
||||||
|
213: ([], ['Butadien']),
|
||||||
|
4: (['Butadien', 'Butadien'], ['p_{0,0}', 'Butadien']),
|
||||||
|
2: (['p_{0,0}'], []),
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
FIXED_FLOWS = {
|
||||||
|
213: 3,
|
||||||
|
#2: 3,
|
||||||
|
4: 1,
|
||||||
|
#23: 1,
|
||||||
|
#41: 1,
|
||||||
|
#42: 1,
|
||||||
|
}
|
||||||
|
|
||||||
|
K = 10
|
||||||
|
|
||||||
|
def build_model(name, hyperedges, elmax, el1, el2, el3, excluded_supports=None):
|
||||||
|
model = Model(name)
|
||||||
|
|
||||||
|
x = {e_id: model.addVar(vtype=GRB.INTEGER, lb = 0, name = f"x_{e_id}") for e_id in hyperedges}
|
||||||
|
b = {e_id: model.addVar(vtype=GRB.BINARY, name = f"b_{e_id}") for e_id in hyperedges}
|
||||||
|
n1 = {e_id: model.addVar(vtype=GRB.BINARY, name = f"n1_{e_id}") for e_id in hyperedges}
|
||||||
|
n2 = {e_id: model.addVar(vtype=GRB.BINARY, name = f"n2_{e_id}") for e_id in hyperedges}
|
||||||
|
n3 = {e_id: model.addVar(vtype=GRB.BINARY, name = f"n3_{e_id}") for e_id in hyperedges}
|
||||||
|
|
||||||
|
|
||||||
|
vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
|
||||||
|
|
||||||
|
#Every item created has to be consumed:
|
||||||
|
'''
|
||||||
|
for v in vertices:
|
||||||
|
count[v] = 0
|
||||||
|
count = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, name = "count")
|
||||||
|
for mol in vertices:
|
||||||
|
for edge, (tails, heads) in hyperedges.items():
|
||||||
|
count[mol] += x[edge] * (tails.count(mol) - heads.count(mol))
|
||||||
|
model.addConstr(count[mol] == 0)
|
||||||
|
'''
|
||||||
|
|
||||||
|
for v in vertices:
|
||||||
|
inflow = quicksum(x[e_id] * heads.count(v) for e_id, (_, heads) in hyperedges.items())
|
||||||
|
outflow = quicksum(x[e_id] * tails.count(v) for e_id, (tails, _) in hyperedges.items())
|
||||||
|
model.addConstr(inflow == outflow, name = f"flow_conservation_{v}")
|
||||||
|
|
||||||
|
for e_id, value in FIXED_FLOWS.items():
|
||||||
|
model.addConstr(x[e_id] == value, name = f"fixed_flow_{e_id}")
|
||||||
|
|
||||||
|
for e_id in hyperedges:
|
||||||
|
model.addGenConstrIndicator(b[e_id], 0, x[e_id] == 0, name = f"unused_implies_zero_{e_id}")
|
||||||
|
model.addConstr(x[e_id] >= b[e_id], name = f"used_implies_positive_flow_{e_id}")
|
||||||
|
#Only if an edge has flow, can it contribute to the likelihood
|
||||||
|
model.addConstr(b[e_id] >= n1[e_id], name = f"only_used_contribute_to_nmr1_{e_id}")
|
||||||
|
model.addConstr(b[e_id] >= n2[e_id], name = f"only_used_contribute_to_nmr2_{e_id}")
|
||||||
|
model.addConstr(b[e_id] >= n3[e_id], name = f"only_used_contribute_to_nmr3_{e_id}")
|
||||||
|
#One reaction can only contribute once
|
||||||
|
model.addConstr(n1[e_id] + n2[e_id] + n3[e_id] <= 1)
|
||||||
|
|
||||||
|
#Only one edge per nmr can contribute
|
||||||
|
model.addConstr(quicksum(n1[e_id] for e_id in hyperedges) == 1)
|
||||||
|
model.addConstr(quicksum(n2[e_id] for e_id in hyperedges) == 1)
|
||||||
|
model.addConstr(quicksum(n3[e_id] for e_id in hyperedges) == 1)
|
||||||
|
|
||||||
|
reaction_path = {}
|
||||||
|
|
||||||
|
|
||||||
|
if excluded_supports:
|
||||||
|
for i, support in enumerate(excluded_supports):
|
||||||
|
model.addConstr(
|
||||||
|
quicksum(b[e_id] for e_id in support) <= len(support) - 1,
|
||||||
|
name=f"different_hyperedges_{i}",
|
||||||
|
)
|
||||||
|
|
||||||
|
model.setObjective(quicksum(1000 * (el1[e_id] * n1[e_id] + el2[e_id] * n2[e_id] + el3[e_id] * n3[e_id]) - (1 / elmax[e_id]) * x[e_id] for e_id in hyperedges if elmax[e_id] != 0),GRB.MAXIMIZE)
|
||||||
|
|
||||||
|
|
||||||
|
#model.setObjective(quicksum(1000 * elmax[e_id] * b[e_id] - (1 - elmax[e_id]) * x[e_id] for e_id in hyperedges),GRB.MAXIMIZE)
|
||||||
|
#model.setObjective(quicksum(1000 * elmax[e_id] * b[e_id] - (1 / elmax[e_id]) * x[e_id] for e_id in hyperedges if elmax[e_id] != 0),GRB.MAXIMIZE)
|
||||||
|
#model.setObjective(quicksum(1000 * elmax[e_id] * b[e_id] + np.log(elmax[e_id]) * x[e_id] for e_id in hyperedges if elmax[e_id] != 0),GRB.MAXIMIZE)
|
||||||
|
#Excluding creation and destruction only three reactions for three nmr
|
||||||
|
#model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails != [] and heads != []) == 3)
|
||||||
|
|
||||||
|
#2 Butadien create first different molecule and it has to be created first:
|
||||||
|
startmolecule = ["Butadien"]
|
||||||
|
|
||||||
|
#Including it or not changes first and second solution (sometimes flipped)
|
||||||
|
#model.addConstr(quicksum(b[e_id] for e_id, (tails, _) in hyperedges.items() if list(set(tails)) == startmolecule) == 1)
|
||||||
|
#model.addConstr(b[4] + b[7] == 1)
|
||||||
|
|
||||||
|
model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails == [] and heads == startmolecule)== 1)
|
||||||
|
#model.addConstr(b[213] == 1)
|
||||||
|
|
||||||
|
#model.addConstr(b[41] == 1)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#No cyclic reaction pairs:
|
||||||
|
for e_id1, (tails1, heads1) in hyperedges.items():
|
||||||
|
for e_id2, (tails2, heads2) in hyperedges.items():
|
||||||
|
if (heads1, tails1) == (tails2, heads2):
|
||||||
|
#print(e_id1, e_id2)
|
||||||
|
model.addConstr((b[e_id1] + b[e_id2]) <= 1)
|
||||||
|
|
||||||
|
return model, x, b
|
||||||
|
|
||||||
|
|
||||||
|
def positive_entries(variable_dict, threshold = 0.5):
|
||||||
|
return {e_id: var.X for e_id, var in variable_dict.items() if var.X > threshold}
|
||||||
|
|
||||||
|
def edgelikelihoods(variable_dict, edgelikelihood, threshold = 0.5):
|
||||||
|
return {e_id: edgelikelihood[e_id] for e_id, var in variable_dict.items() if var.X > threshold}
|
||||||
|
|
||||||
|
def flow_to_json(flow_solution, smiles):
|
||||||
|
inputs = {}
|
||||||
|
outputs = {}
|
||||||
|
reactions = []
|
||||||
|
|
||||||
|
for e_id, value in flow_solution.items():
|
||||||
|
value = int(round(value))
|
||||||
|
tails, heads = HYPERGRAPH[e_id]
|
||||||
|
|
||||||
|
if tails == []:
|
||||||
|
for molecule in heads:
|
||||||
|
inputs[smiles[molecule]] = inputs.get(smiles[molecule], 0) + value
|
||||||
|
elif heads == []:
|
||||||
|
for molecule in tails:
|
||||||
|
outputs[smiles[molecule]] = outputs.get(smiles[molecule], 0) + value
|
||||||
|
else:
|
||||||
|
reactions.append({
|
||||||
|
"flow": value,
|
||||||
|
"educts": [smiles[molecule] for molecule in tails],
|
||||||
|
"products": [smiles[molecule] for molecule in heads],
|
||||||
|
})
|
||||||
|
|
||||||
|
return {
|
||||||
|
"inputs": inputs,
|
||||||
|
"outputs": outputs,
|
||||||
|
"reactions": reactions,
|
||||||
|
}
|
||||||
|
|
||||||
|
def write_flows_json(flows):
|
||||||
|
path = Path(__file__).with_name("gurobi_flows.json")
|
||||||
|
path.write_text(json.dumps(flows, indent=2) + "\n")
|
||||||
|
print(f"\nWrote {path}")
|
||||||
|
|
||||||
|
def print_solution(title, flow_solution, binary_solution, edge_likelihoods, hyperedges):
|
||||||
|
print(f"\n{title}:")
|
||||||
|
for e_id in sorted(flow_solution):
|
||||||
|
flow = flow_solution[e_id]
|
||||||
|
tails, heads = hyperedges[e_id]
|
||||||
|
likelihood = edge_likelihoods[e_id]
|
||||||
|
print(f"Hyperedge {e_id}: Flow = {flow}, Tails = {tails}, Heads = {heads}, With likelihood = {likelihood}")
|
||||||
|
print("\nBinary Variables:")
|
||||||
|
for e_id in sorted(binary_solution):
|
||||||
|
print(f"Binary Variable b_{e_id} = {binary_solution[e_id]}")
|
||||||
|
|
||||||
|
print(f"\nTotal flow: {sum(flow_solution.values())}")
|
||||||
|
print(f"Number of used hyperedges: {len(binary_solution)}")
|
||||||
|
|
||||||
|
def main():
|
||||||
|
VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'p_{0,6}', 'p_{0,7}', 'p_{0,8}', 'p_{0,9}', 'p_{0,10}', 'p_{0,11}', 'p_{0,12}', 'p_{0,13}', 'p_{0,14}', 'p_{0,15}', 'p_{0,16}', 'p_{0,17}', 'p_{0,18}']
|
||||||
|
#Vergleich mit dem NMR von Ethylen und Hexatrien
|
||||||
|
NMR1 = [0.32, 0.5, 0.87, 0.0, 0.11, 0.58, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09, 0.0, 0.0, 0.17, 0.0, 0.09]
|
||||||
|
#Vergleich mit dem NMR von Ethylen und Octrien
|
||||||
|
NMR2 = [0.33, 0.43, 0.6, 0.0, 0.13, 0.9, 0.07, 0.01, 0.0, 0.0, 0.01, 0.0, 0.0, 0.01, 0.1, 0.0, 0.0, 0.22, 0.0, 0.13]
|
||||||
|
#Vergleich mit dem NMR von Ethylen und Benzol
|
||||||
|
NMR3 = [0.0, 0.32, 0.0, 0.0, 0.0, 0.0, 0.05, 0.0, 0.0, 0.02, 0.0, 0.29, 0.95, 0.17, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05]
|
||||||
|
VERTICESSMILES = ['C=CC=C', 'C=C', 'C=CC=CC=C', 'C1CCC(C=C)CC=1', 'C(CCC(C=C)CC=C)=C', 'C=CC=CC=CC=C', 'C1CCC(C=CC=C)CC=1', 'C=CC1C=CCCC1', 'C1CCCCC=1', 'C=CC1CC=CCC1C=C', 'C1CCC(C=C)C(C=C)C=1', 'C(C1CC(C=C)C=CC1)=C', 'C1C=CC=CC=1', 'C(CCC1C=CC=CC1)=C', 'C1C(C=CC=C)CCCC=1', 'C=CC(C=C)CCCC=C', 'C=CCCCCC=C', 'C=CC1C=CC(C=C)CC1', 'C1CC2CCCCC2CC=1', 'C1CC2C=CC=CC2CC=1']
|
||||||
|
SMILES = dict(zip(VERTICES, VERTICESSMILES))
|
||||||
|
|
||||||
|
#Chosable parameters
|
||||||
|
modes = ["Product", "Average"]
|
||||||
|
mode = modes[1]
|
||||||
|
normalize = True
|
||||||
|
|
||||||
|
if normalize:
|
||||||
|
NMR1 = [round(l/sum(NMR1), 2) for l in NMR1]
|
||||||
|
NMR2 = [round(l/sum(NMR2), 2) for l in NMR2]
|
||||||
|
NMR3 = [round(l/sum(NMR3), 2) for l in NMR3]
|
||||||
|
#print(NMR1)
|
||||||
|
#print(NMR2)
|
||||||
|
#print(NMR3)
|
||||||
|
|
||||||
|
|
||||||
|
#Kombiniert Molekül it Wahrscheinlichkeit für NMR1:
|
||||||
|
VERTICE1 = {}
|
||||||
|
for vertice, likelihood in zip(VERTICES, NMR1):
|
||||||
|
VERTICE1[vertice] = likelihood
|
||||||
|
#Kombiniert Molekül it Wahrscheinlichkeit für NMR2:
|
||||||
|
VERTICE2 = {}
|
||||||
|
for vertice, likelihood in zip(VERTICES, NMR2):
|
||||||
|
VERTICE2[vertice] = likelihood
|
||||||
|
#Kombiniert Molekül it Wahrscheinlichkeit für NMR3:
|
||||||
|
VERTICE3 = {}
|
||||||
|
for vertice, likelihood in zip(VERTICES, NMR3):
|
||||||
|
VERTICE3[vertice] = likelihood
|
||||||
|
|
||||||
|
#Kantenwahrscheinlichkeiten für NMR1:
|
||||||
|
EDGE1 = {}
|
||||||
|
#Kantenwahrscheinlichkeiten für NMR2:
|
||||||
|
EDGE2 = {}
|
||||||
|
#Kantenwahrscheinlichkeiten für NMR3:
|
||||||
|
EDGE3 = {}
|
||||||
|
#Maximum der verschiedenen Kantenwahrscheinlichkeiten:
|
||||||
|
EDGEMAX = {}
|
||||||
|
|
||||||
|
#Ist es besser, die Edukte mit zu berücksichtigen? Dann hin rück gleich wahrscheinlich, aber nur 1 kann gewählt werden
|
||||||
|
#Ordnet die Kantenwahrscheinlichkeit basierend auf dem Mittel oder Produkt der Wahrscheinlichkeiten der Produkte
|
||||||
|
for edge, (tails, heads) in HYPERGRAPH.items():
|
||||||
|
if heads == [] or tails == []:
|
||||||
|
EDGE1[edge] = 0.0
|
||||||
|
EDGE2[edge] = 0.0
|
||||||
|
EDGE3[edge] = 0.0
|
||||||
|
elif mode == "Product":
|
||||||
|
#print([VERTICE1[head] for head in heads])
|
||||||
|
EDGE1[edge] = round(np.prod([VERTICE1[head] for head in heads]),2)
|
||||||
|
EDGE2[edge] = round(np.prod([VERTICE2[head] for head in heads]),2)
|
||||||
|
EDGE3[edge] = round(np.prod([VERTICE3[head] for head in heads]),2)
|
||||||
|
elif mode == "Average":
|
||||||
|
EDGE1[edge] = round(sum(VERTICE1[head] for head in heads)/len(heads),2)
|
||||||
|
EDGE2[edge] = round(sum(VERTICE2[head] for head in heads)/len(heads),2)
|
||||||
|
EDGE3[edge] = round(sum(VERTICE3[head] for head in heads)/len(heads),2)
|
||||||
|
|
||||||
|
EDGEMAX[edge] = max([EDGE1[edge], EDGE2[edge], EDGE3[edge]])
|
||||||
|
#print(EDGEMAX[edge], HYPERGRAPH[edge])
|
||||||
|
|
||||||
|
'''
|
||||||
|
#Vertices from example:
|
||||||
|
verticelist = [0,1,2,3,4,5,11,12]
|
||||||
|
print([VERTICES[a] for a in verticelist])
|
||||||
|
print([NMR1[a] for a in verticelist])
|
||||||
|
print([NMR2[a] for a in verticelist])
|
||||||
|
print([NMR3[a] for a in verticelist])
|
||||||
|
#Edges from example
|
||||||
|
print(EDGEMAX[4], HYPERGRAPH[4])
|
||||||
|
print(EDGEMAX[7], HYPERGRAPH[7])
|
||||||
|
print(EDGEMAX[13], HYPERGRAPH[13])
|
||||||
|
print(EDGEMAX[23], HYPERGRAPH[23])
|
||||||
|
print(EDGEMAX[37], HYPERGRAPH[37])
|
||||||
|
print(EDGEMAX[42], HYPERGRAPH[42])
|
||||||
|
print(EDGEMAX[67], HYPERGRAPH[67])
|
||||||
|
'''
|
||||||
|
|
||||||
|
excluded_supports = []
|
||||||
|
flows = []
|
||||||
|
|
||||||
|
for rank in range(1, K + 1):
|
||||||
|
model, x, b = build_model(
|
||||||
|
f"HypergraphFlow_{rank}",
|
||||||
|
HYPERGRAPH,
|
||||||
|
EDGEMAX,
|
||||||
|
EDGE1,
|
||||||
|
EDGE2,
|
||||||
|
EDGE3,
|
||||||
|
excluded_supports=excluded_supports,
|
||||||
|
)
|
||||||
|
model.optimize()
|
||||||
|
|
||||||
|
if model.status != GRB.Status.OPTIMAL:
|
||||||
|
break
|
||||||
|
|
||||||
|
solution = positive_entries(x)
|
||||||
|
binary_solution = positive_entries(b)
|
||||||
|
likelihoods = edgelikelihoods(b, EDGEMAX)
|
||||||
|
|
||||||
|
print_solution(
|
||||||
|
f"Solution {rank}",
|
||||||
|
solution,
|
||||||
|
binary_solution,
|
||||||
|
likelihoods,
|
||||||
|
HYPERGRAPH,
|
||||||
|
)
|
||||||
|
|
||||||
|
flows.append(flow_to_json(solution, SMILES))
|
||||||
|
excluded_supports.append(list(binary_solution.keys()))
|
||||||
|
|
||||||
|
write_flows_json(flows)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,114 @@
|
|||||||
|
import json
|
||||||
|
from collections import Counter
|
||||||
|
|
||||||
|
include("butadienNetwork.py")
|
||||||
|
|
||||||
|
with open("gurobi_flows.json") as f:
|
||||||
|
gurobi_flows = json.load(f)
|
||||||
|
|
||||||
|
|
||||||
|
def canonical_smiles(smiles):
|
||||||
|
return str(Graph.fromSMILES(smiles).smiles)
|
||||||
|
|
||||||
|
|
||||||
|
def vertex_key(v):
|
||||||
|
return str(v.graph.smiles)
|
||||||
|
|
||||||
|
|
||||||
|
vertices_by_smiles = {}
|
||||||
|
for v in dg.vertices:
|
||||||
|
vertices_by_smiles.setdefault(vertex_key(v), []).append(v)
|
||||||
|
|
||||||
|
|
||||||
|
def current_vertex(smiles):
|
||||||
|
matches = vertices_by_smiles.get(canonical_smiles(smiles), [])
|
||||||
|
if len(matches) != 1:
|
||||||
|
raise RuntimeError(
|
||||||
|
f"{smiles}: expected one MØD vertex, found {len(matches)}"
|
||||||
|
)
|
||||||
|
return matches[0]
|
||||||
|
|
||||||
|
|
||||||
|
def current_reaction(educts, products):
|
||||||
|
wanted_educts = Counter(canonical_smiles(x) for x in educts)
|
||||||
|
wanted_products = Counter(canonical_smiles(x) for x in products)
|
||||||
|
|
||||||
|
matches = []
|
||||||
|
for e in dg.edges:
|
||||||
|
current_educts = Counter(vertex_key(v) for v in e.sources)
|
||||||
|
current_products = Counter(vertex_key(v) for v in e.targets)
|
||||||
|
if current_educts == wanted_educts and current_products == wanted_products:
|
||||||
|
matches.append(e)
|
||||||
|
|
||||||
|
if len(matches) != 1:
|
||||||
|
raise RuntimeError(
|
||||||
|
f"{educts} -> {products}: expected one MØD edge, found {len(matches)}"
|
||||||
|
)
|
||||||
|
return matches[0]
|
||||||
|
|
||||||
|
|
||||||
|
def check_causality(gurobi_flow, rank):
|
||||||
|
source_flow = {
|
||||||
|
current_vertex(smiles): value
|
||||||
|
for smiles, value in gurobi_flow["inputs"].items()
|
||||||
|
}
|
||||||
|
output_flow = {
|
||||||
|
current_vertex(smiles): value
|
||||||
|
for smiles, value in gurobi_flow["outputs"].items()
|
||||||
|
}
|
||||||
|
|
||||||
|
reaction_flow = {}
|
||||||
|
for reaction in gurobi_flow["reactions"]:
|
||||||
|
edge = current_reaction(reaction["educts"], reaction["products"])
|
||||||
|
reaction_flow[edge] = reaction_flow.get(edge, 0) + reaction["flow"]
|
||||||
|
|
||||||
|
flow = hyperflow.Model(dg)
|
||||||
|
|
||||||
|
for v in source_flow:
|
||||||
|
flow.addSource(v.graph)
|
||||||
|
for v in output_flow:
|
||||||
|
flow.addSink(v.graph)
|
||||||
|
|
||||||
|
for e in dg.edges:
|
||||||
|
flow.addConstraint(edgeFlow[e] == reaction_flow.get(e, 0))
|
||||||
|
|
||||||
|
for v in dg.vertices:
|
||||||
|
flow.addConstraint(inFlow[v] == source_flow.get(v, 0))
|
||||||
|
flow.addConstraint(outFlow[v] == output_flow.get(v, 0))
|
||||||
|
|
||||||
|
flow.findSolutions(maxNumSolutions=1)
|
||||||
|
|
||||||
|
try:
|
||||||
|
solution = next(iter(flow.solutions))
|
||||||
|
except StopIteration:
|
||||||
|
print(f"solution {rank}: not a valid MØD hyperflow")
|
||||||
|
return "INVALID"
|
||||||
|
|
||||||
|
query = causality.RealisabilityQuery(flow.dg)
|
||||||
|
dag = query.findDAG(solution)
|
||||||
|
|
||||||
|
if not dag:
|
||||||
|
print(f"solution {rank}: causally realisable: NO")
|
||||||
|
return "NO"
|
||||||
|
|
||||||
|
print(f"solution {rank}: causally realisable: YES")
|
||||||
|
|
||||||
|
printer = DGPrinter()
|
||||||
|
printer.graphvizPrefix = 'layout = "dot";'
|
||||||
|
|
||||||
|
def vertex_visible(v):
|
||||||
|
return solution.eval(vertexFlow[v]) != 0
|
||||||
|
|
||||||
|
printer.pushVertexVisible(vertex_visible)
|
||||||
|
dg.print(printer=printer, data=dag.getPrintData(False))
|
||||||
|
|
||||||
|
return "YES"
|
||||||
|
|
||||||
|
|
||||||
|
results = []
|
||||||
|
for rank, gurobi_flow in enumerate(gurobi_flows, start=1):
|
||||||
|
results.append(check_causality(gurobi_flow, rank))
|
||||||
|
|
||||||
|
print("\n=== causality summary ===")
|
||||||
|
for rank, result in enumerate(results, start=1):
|
||||||
|
print(f"solution {rank}: {result}")
|
||||||
@@ -0,0 +1,136 @@
|
|||||||
|
import mod
|
||||||
|
import networkx as nx
|
||||||
|
import time
|
||||||
|
|
||||||
|
# from equilibrator_assets.generate_compound import create_compounds
|
||||||
|
|
||||||
|
# each vertex has attribute "label" which stores string label
|
||||||
|
# each edge has attribute "bond" which stores bond order (-, =, etc.)
|
||||||
|
|
||||||
|
class GraphObj:
|
||||||
|
def __init__(self, graph, verbose=False):
|
||||||
|
self._verbose = verbose
|
||||||
|
|
||||||
|
mod_lg_class = mod.libpymod.Rule.LeftGraph
|
||||||
|
mod_rg_class = mod.libpymod.Rule.RightGraph
|
||||||
|
mod_g_class = mod.libpymod.Graph
|
||||||
|
if isinstance(graph, mod_lg_class) or isinstance(graph, mod_rg_class) or isinstance(graph, mod_g_class):
|
||||||
|
self._nx_graph = self.mod_to_nx_graph(graph)
|
||||||
|
self._gml_string = self.nx_graph_to_GML_string(self._nx_graph)
|
||||||
|
elif isinstance(graph, nx.Graph):
|
||||||
|
self._nx_graph = graph
|
||||||
|
self._gml_string = self.nx_graph_to_GML_string(self._nx_graph)
|
||||||
|
elif isinstance(graph, str):
|
||||||
|
self._gml_string = graph
|
||||||
|
self._nx_graph = self.GML_to_nx_graph(self._gml_string)
|
||||||
|
else:
|
||||||
|
print(f"ERROR: Graph class cannot identify graph type in constructor: {type(graph)}")
|
||||||
|
|
||||||
|
# up to the caller to check the number of components created
|
||||||
|
ccs = [self._nx_graph.subgraph(c).copy() for c in nx.connected_components(self._nx_graph)]
|
||||||
|
ccs = sorted(ccs, key=len, reverse=True)
|
||||||
|
self._num_components = len(ccs)
|
||||||
|
if self._num_components > 1:
|
||||||
|
self._components = [Graph(c) for c in ccs]
|
||||||
|
else:
|
||||||
|
self._components = [self]
|
||||||
|
|
||||||
|
if self._num_components == 1:
|
||||||
|
self._mod_graph = self.nx_graph_to_mod(self._nx_graph)
|
||||||
|
|
||||||
|
# self._equ_compounds = self.equ_compounds()
|
||||||
|
|
||||||
|
def equ_compounds(self):
|
||||||
|
smiles = [c.mod_graph.smiles for c in self._components]
|
||||||
|
equ_comps = create_compounds(smiles, mol_format="smiles", bypass_chemaxon=True, save_empty_compounds=True)
|
||||||
|
return equ_comps
|
||||||
|
|
||||||
|
def mod_to_nx_graph(self, modGraph: mod.Graph):
|
||||||
|
g = nx.Graph()
|
||||||
|
|
||||||
|
for v in modGraph.vertices:
|
||||||
|
g.add_node(int(v.id), label=str(v.stringLabel), modID=int(v.id))
|
||||||
|
|
||||||
|
for e in modGraph.edges:
|
||||||
|
g.add_edge(int(e.source.id), int(e.target.id), bond=str(e.bondType))
|
||||||
|
|
||||||
|
return g
|
||||||
|
|
||||||
|
def nx_graph_to_mod(self, nxGraph):
|
||||||
|
try:
|
||||||
|
return mod.Graph.fromGMLString(self.nx_graph_to_GML_string(nxGraph))
|
||||||
|
except mod.libpymod.InputError: # graph is not connected probably
|
||||||
|
if self._verbose:
|
||||||
|
print("Error converting nxGraph to mod graph. Likely graph is not connected. This will not affect rule generation.")
|
||||||
|
return None
|
||||||
|
|
||||||
|
def GML_to_nx_graph(self, gml_string):
|
||||||
|
g = nx.Graph()
|
||||||
|
lines = gml_string.split("\n")
|
||||||
|
for line in lines:
|
||||||
|
tokens = line.split()
|
||||||
|
if len(tokens) < 2:
|
||||||
|
continue
|
||||||
|
if tokens[0] == "node":
|
||||||
|
(_,_,_, mid, _, l, _) = tokens
|
||||||
|
g.add_node(int(mid), label=l[1:-1], modID=int(mid))
|
||||||
|
elif tokens[0] == "edge":
|
||||||
|
(_, _, _, u, _, v, _, bondOrder, _ ) = tokens
|
||||||
|
g.add_edge(int(u), int(v), bond=bondOrder[1:-1])
|
||||||
|
return g
|
||||||
|
|
||||||
|
def nx_graph_to_GML_string(self, nxGraph):
|
||||||
|
out = []
|
||||||
|
out.append("graph [")
|
||||||
|
|
||||||
|
out.extend([f"\t\tnode [ id {nxGraph.nodes[node]['modID']} label \"{nxGraph.nodes[node]['label']}\" ]" for node in nxGraph.nodes])
|
||||||
|
out.extend([f"\t\tedge [ source {u} target {v} label \"{nxGraph[u][v]['bond']}\" ]"
|
||||||
|
for (u,v) in nxGraph.edges])
|
||||||
|
|
||||||
|
out.append("]")
|
||||||
|
|
||||||
|
return "\n".join(out)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def nx_graph(self):
|
||||||
|
return self._nx_graph
|
||||||
|
|
||||||
|
@property
|
||||||
|
def mod_graph(self):
|
||||||
|
if self.num_components == 1:
|
||||||
|
return self._mod_graph
|
||||||
|
else:
|
||||||
|
return mod.Graph.fromGMLString(self.gml_string)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def gml_string(self):
|
||||||
|
return self._gml_string
|
||||||
|
|
||||||
|
@property
|
||||||
|
def gml(self):
|
||||||
|
return self._gml_string
|
||||||
|
|
||||||
|
@property
|
||||||
|
def edges(self):
|
||||||
|
return self._nx_graph.edges
|
||||||
|
|
||||||
|
@property
|
||||||
|
def nodes(self):
|
||||||
|
return self._nx_graph.nodes
|
||||||
|
|
||||||
|
@property
|
||||||
|
def num_components(self):
|
||||||
|
return self._num_components
|
||||||
|
|
||||||
|
@property
|
||||||
|
def connected_components(self):
|
||||||
|
return self._components
|
||||||
|
|
||||||
|
def mod_print(self):
|
||||||
|
self._mod_graph.print()
|
||||||
|
|
||||||
|
def __str__(self) -> str:
|
||||||
|
return self._gml_string
|
||||||
|
|
||||||
|
def __hash__(self) -> int:
|
||||||
|
return hash(self._gml_string)
|
||||||
Reference in New Issue
Block a user