Merge pull request #56 from velocitatem/refactor-transition-graphs

updating node positinoing

paper/src/chapters/03-methodology.tex

@@ -23,7 +23,7 @@ where:
 The platform does not directly observe the true underlying demand function $d(p)$. Instead, it observes a behavioral proxy $\hat{q}_t$, which is a composite signal derived from the mixture of actor types. We define the demand proxy for product $i$ at epoch $t$ as a weighted aggregation of events:
 \begin{equation}
 \label{eq:qhat}
-\hat{q}_{t,i} = \sum_{s \in \mathcal{S}_t} \sum_{k=1}^{L_s} \omega(a_{s,k}) \cdot \mathds{1}[i_{s,k} = i]
+\hat{q}_{t,i} = \sum_{s \in \mathcal{S}_t} \sum_{k=1}^{L_s} \omega(a_{s,k}) \cdot \mathbf{1}[i_{s,k} = i]
 \end{equation}
 where $\omega: \mathcal{A} \to \mathbb{R}_+$ assigns weights to actions based on their signal strength regarding willingness to pay.
 

paper/src/chapters/figures/results/generated/final/final_focus_coi_preservation_grid.csv

@@ -0,0 +1,45 @@
+alpha,n_products,baseline_runs,defended_runs,baseline_coi_level_mean,defended_coi_level_mean,coi_preserved,coi_preserved_pct
+0.0,5.0,9,10,137.060822623968,136.18680853180368,-0.874014092164316,-0.6376833842316922
+0.0,25.0,9,2,137.114858903596,136.13793579187393,-0.9769231117220727,-0.7124852255501622
+0.0,50.0,9,11,137.16224858153575,136.92415566181484,-0.23809291972091273,-0.17358487643878118
+0.0,100.0,9,12,135.86629045322655,137.3609873086303,1.4946968554037596,1.1001234010420895
+0.1,5.0,3,6,136.59581715538818,135.6308466787041,-0.9649704766840728,-0.7064421859904723
+0.1,25.0,11,8,135.9860669350444,136.43616365263273,0.45009671758833747,0.33098737814318313
+0.1,50.0,10,11,136.28362874897243,136.92880179422633,0.6451730452538982,0.4734046570203046
+0.1,100.0,8,8,137.35578496752095,137.53394777402949,0.17816280650853855,0.12970899372797937
+0.2,5.0,8,9,135.55116314329388,137.30311388107864,1.7519507377847674,1.2924645551973204
+0.2,25.0,10,9,137.01587649612287,137.22137163685403,0.20549514073115915,0.1499790724887083
+0.2,50.0,4,8,137.45096138958434,137.1307018163465,-0.32025957323784837,-0.2329991511155169
+0.2,100.0,9,9,137.50780776750915,137.43195025898902,-0.07585750852013007,-0.0551659645744523
+0.3,5.0,6,6,134.95569459599133,134.21855668602896,-0.7371379099623709,-0.5462073402453271
+0.3,25.0,9,16,136.38346021911525,136.32131251342705,-0.06214770568820427,-0.04556835967378819
+0.3,50.0,8,6,136.97414077213367,136.88041560990786,-0.09372516222580884,-0.06842544271310845
+0.3,100.0,7,16,137.19706520314455,137.31020460277784,0.11313939963329744,0.08246488324351146
+0.4,5.0,8,11,135.6494813257779,136.5487738152141,0.899292489436192,0.6629531352769695
+0.4,25.0,7,9,136.38451372914378,136.10614648175604,-0.27836724738773455,-0.20410473284420322
+0.4,50.0,7,10,137.12976275807247,136.98838321468799,-0.14137954338448822,-0.10309909427460566
+0.4,100.0,11,8,137.4158065068933,137.4849148270489,0.06910832015560686,0.050291390715769026
+0.5,5.0,7,19,135.91101413475477,136.145621134976,0.2346070002212457,0.1726180925915501
+0.5,25.0,8,7,137.0972914279529,137.35620682163616,0.2589153936832531,0.18885522170896996
+0.5,50.0,8,1,137.0714841014652,135.66696334266234,-1.404520758802846,-1.0246629837050352
+0.5,100.0,10,8,137.4717672869487,137.35366167964338,-0.11810560730532416,-0.08591262746975456
+0.6,5.0,8,13,133.13626070539635,136.09936023073067,2.9630995253343144,2.225614201296411
+0.6,25.0,5,10,136.0741624588533,136.26219778039936,0.18803532154606728,0.13818591137970535
+0.6,50.0,8,10,135.09036188289087,136.05846380616936,0.968101923278482,0.7166328595060871
+0.6,100.0,7,8,137.29304001584052,137.07512338179083,-0.2179166340496863,-0.15872372993164377
+0.7,5.0,7,7,136.0533783988379,135.14350016006424,-0.9098782387736719,-0.6687656341075052
+0.7,25.0,8,11,137.12781750399415,136.8176582131797,-0.3101592908144539,-0.2261826203172962
+0.7,50.0,14,11,137.06965735909125,136.7028634119364,-0.3667939471548607,-0.26759674914335285
+0.7,100.0,11,11,137.48279078937205,137.09121810549402,-0.39157268387802446,-0.28481578067317975
+0.8,5.0,4,7,135.3095773096514,136.59715728802078,1.2875799783693935,0.9515808148766959
+0.8,25.0,12,13,136.93488398652164,135.73319876476054,-1.201685221761096,-0.8775596011600497
+0.8,50.0,6,8,136.4704324290659,136.86568018140107,0.39524775233516607,0.289621528487943
+0.8,100.0,4,11,137.519864039095,137.4763376137669,-0.04352642532811046,-0.03165100957032396
+0.9,5.0,5,5,134.77024204025943,136.6651608019597,1.8949187617002679,1.4060364758669837
+0.9,25.0,9,13,136.7554042236364,136.06108143100832,-0.6943227926280713,-0.507711411164888
+0.9,50.0,10,12,136.08715955450202,137.07569864767092,0.988539093168896,0.7264014447836223
+0.9,100.0,11,9,137.57053132642514,137.30115968842037,-0.2693716380047704,-0.19580620602940735
+1.0,5.0,5,7,136.43177888041947,135.92674388998284,-0.5050349904366271,-0.37017401266847305
+1.0,25.0,11,9,136.7037183889911,136.22617845471228,-0.47753993427880914,-0.34932475861407586
+1.0,50.0,11,5,136.93074105866745,137.05826644845806,0.12752538979060546,0.09313130769953819
+1.0,100.0,8,9,136.4880191421812,137.41913068956546,0.9311115473842619,0.682192879079234

paper/src/chapters/figures/results/includes/final/final_focus_coi_by_alpha.tex

@@ -0,0 +1 @@
+\includegraphics[width=0.98\linewidth]{chapters/figures/results/generated/final/plots/final_focus_coi_by_alpha.pdf}

paper/src/chapters/figures/results/includes/final/final_focus_coi_preservation_grid.tex

@@ -0,0 +1 @@
+\includegraphics[width=0.98\linewidth]{chapters/figures/results/generated/final/plots/final_focus_coi_preservation_grid.pdf}

scripts/nx_paper.sh

@@ -4,15 +4,34 @@ set -euo pipefail
 
 cmd="${1:-}"
 
+sync_mdp_figures() {
+  local script_dir project_root sim_dir chapters_dir
+  script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+  project_root="$(cd "$script_dir/.." && pwd)"
+  sim_dir="$project_root/sim/rl/behavior_loader"
+  chapters_dir="$project_root/paper/src/chapters"
+
+  printf '%s\n' 'Refreshing MDP figures for paper...'
+  (
+    cd "$sim_dir"
+    python models.py
+  )
+
+  cp "$sim_dir/human_mdp_viz.pdf" "$chapters_dir/mdp_human.pdf"
+  cp "$sim_dir/agent_mdp_viz.pdf" "$chapters_dir/mdp_agent.pdf"
+}
+
 case "$cmd" in
   build)
     mkdir -p paper/build
+    sync_mdp_figures
     bash paper/concat_code.sh
     cd paper/src
     latexmk -pdf -jobname=main -f -interaction=nonstopmode -file-line-error -r ../.latexmkrc -outdir=../build main.tex
     ;;
   watch)
     mkdir -p paper/build
+    sync_mdp_figures
     cd paper/src
     latexmk -pvc -pdf -jobname=main -f -interaction=nonstopmode -file-line-error -r ../.latexmkrc -outdir=../build main.tex
     ;;
@@ -33,11 +52,13 @@ case "$cmd" in
     ;;
   build-genpop)
     mkdir -p paper/build
+    sync_mdp_figures
     cd paper/src
     latexmk -pdf -jobname=main-genpop -f -interaction=nonstopmode -file-line-error -r ../.latexmkrc -outdir=../build main-genpop.tex
     ;;
   watch-genpop)
     mkdir -p paper/build
+    sync_mdp_figures
     cd paper/src
     latexmk -pvc -pdf -jobname=main-genpop -f -interaction=nonstopmode -file-line-error -r ../.latexmkrc -outdir=../build main-genpop.tex
     ;;

sim/rl/behavior_loader/models.py

@@ -3,7 +3,7 @@ try:
 except ImportError:
     from sim.rl.behavior_loader.loader import Loader, AgentLoader, JointLoader
 from collections import defaultdict
-from typing import Dict, List, Tuple, Set
+from typing import Dict, List, Optional, Set, Tuple
 import numpy as np
 import graphviz
 import sys
@@ -195,6 +195,110 @@ def aggregate_event_transitions(mdp: Dict) -> Dict[str, Dict[str, float]]:
     return dict(evt_trans)
 
 
+def _resolve_event_order(
+    evt_trans: Dict[str, Dict[str, float]],
+    event_order: Optional[List[str]] = None,
+) -> List[str]:
+    observed = set(evt_trans.keys()) | {
+        dst for transitions in evt_trans.values() for dst in transitions
+    }
+    if event_order:
+        ordered = list(dict.fromkeys(event_order))
+        missing = sorted(observed - set(ordered))
+        return ordered + missing
+    return sorted(observed)
+
+
+def _compass_from_angle(angle_rad: float) -> str:
+    ports = ("e", "ne", "n", "nw", "w", "sw", "s", "se")
+    normalized = (angle_rad + (2 * np.pi)) % (2 * np.pi)
+    step = np.pi / 4
+    idx = int(np.round(normalized / step)) % len(ports)
+    return ports[idx]
+
+
+def _edge_ports(
+    src: str,
+    dst: str,
+    positions: Dict[str, Tuple[float, float]],
+    has_reverse: bool,
+) -> Tuple[str, str]:
+    src_x, src_y = positions[src]
+    dst_x, dst_y = positions[dst]
+    angle = float(np.arctan2(dst_y - src_y, dst_x - src_x))
+
+    if has_reverse:
+        bend = np.pi / 10
+        angle += bend if src < dst else -bend
+
+    tail_port = _compass_from_angle(angle)
+    head_port = _compass_from_angle(angle + np.pi)
+    return tail_port, head_port
+
+
+def _edge_style(prob: float) -> Dict[str, str]:
+    if prob >= 0.75:
+        edge_color = "#111827"
+    elif prob >= 0.50:
+        edge_color = "#374151"
+    elif prob >= 0.25:
+        edge_color = "#6b7280"
+    else:
+        edge_color = "#9ca3af"
+    return {
+        "color": edge_color,
+        "fontcolor": "#111827",
+        "fontsize": "10",
+        "penwidth": f"{0.9 + 3.6 * prob:.2f}",
+        "arrowsize": f"{0.55 + 0.55 * prob:.2f}",
+    }
+
+
+def _format_node_label(evt: str) -> str:
+    max_line_len = 16
+    tokens = evt.split("_")
+    if len(tokens) == 1:
+        return evt
+
+    lines: List[str] = []
+    curr = ""
+    for token in tokens:
+        piece = token if not curr else f"_{token}"
+        if curr and len(curr) + len(piece) > max_line_len:
+            lines.append(curr)
+            curr = token
+        else:
+            curr = f"{curr}{piece}" if curr else token
+    if curr:
+        lines.append(curr)
+    return "\n".join(lines)
+
+
+def _compute_flow_positions(
+    events: List[str],
+    layout_radius: float,
+) -> Dict[str, Tuple[float, float]]:
+    """Balanced grid layout for paper-friendly diagrams."""
+    if not events:
+        return {}
+
+    num_events = len(events)
+    cols = int(np.ceil(np.sqrt(num_events)))
+    rows = int(np.ceil(num_events / cols))
+    x_step = max(layout_radius * 1.10, 3.6)
+    y_step = max(layout_radius * 0.95, 3.2)
+
+    positions: Dict[str, Tuple[float, float]] = {}
+    for idx, evt in enumerate(events):
+        row = idx // cols
+        col = idx % cols
+        x = (col - (cols - 1) / 2.0) * x_step
+        y = ((rows - 1) / 2.0 - row) * y_step
+        positions[evt] = (float(x), float(y))
+
+    return positions
+
+
 def visualize_mdp(
     model: BehaviorModel,
     threshold: float = 0.05,
@@ -202,25 +306,80 @@ def visualize_mdp(
     fmt: str = "svg",
     view: bool = False,
     export_dot: bool = False,
+    event_order: Optional[List[str]] = None,
+    layout_radius: float = 10.0,
+    node_diameter: float = 1.8,
+    label_threshold: float = 0.08,
 ):
     if not model.mdp:
         raise ValueError("build MDP first")
 
     evt_trans = aggregate_event_transitions(model.mdp)
-    g = graphviz.Digraph(format=fmt)
+    ordered_events = _resolve_event_order(evt_trans, event_order=event_order)
-    g.attr(rankdir="LR", size="30")
+    positions = _compute_flow_positions(ordered_events, layout_radius=layout_radius)
-    g.attr("node", shape="circle", width="1", height="1")
 
-    events = set(evt_trans.keys()) | {
+    g = graphviz.Digraph(format=fmt, engine="neato")
-        e for trans in evt_trans.values() for e in trans.keys()
+    g.attr(
-    }
+        overlap="false",
-    for evt in events:
+        splines="true",
-        g.node(evt)
+        outputorder="edgesfirst",
+        pad="0.5",
+        sep="+9",
+        esep="+4",
+        bgcolor="white",
+        dpi="180",
+    )
+    g.attr(
+        "node",
+        shape="circle",
+        fixedsize="true",
+        width=f"{node_diameter:.2f}",
+        height=f"{node_diameter:.2f}",
+        fontsize="11",
+        fontname="Helvetica",
+        style="filled",
+        fillcolor="white",
+        color="#374151",
+        fontcolor="#111827",
+        penwidth="1.8",
+        peripheries="1",
+    )
+    g.attr(
+        "edge",
+        fontname="Helvetica",
+    )
 
-    for src, dsts in evt_trans.items():
+    for evt in ordered_events:
-        for dst, prob in dsts.items():
+        x, y = positions[evt]
-            if prob > threshold:
+        g.node(evt, label=_format_node_label(evt), pos=f"{x:.2f},{y:.2f}!", pin="true")
-                g.edge(src, dst, label=f"{prob:.2f}")
+
+    edges = [
+        (src, dst, prob)
+        for src, dsts in evt_trans.items()
+        for dst, prob in dsts.items()
+        if prob > threshold
+    ]
+    edge_set = {(src, dst) for src, dst, _ in edges}
+
+    for src, dst, prob in sorted(edges, key=lambda row: row[2]):
+        edge_attrs: Dict[str, str] = _edge_style(prob)
+
+        if src == dst:
+            # pick a loop port away from the main flow
+            sx, sy = positions[src]
+            loop_port = "n" if sy <= 0 else "s"
+            edge_attrs.update({"tailport": loop_port, "headport": loop_port})
+        else:
+            has_reverse = (dst, src) in edge_set
+            tail_port, head_port = _edge_ports(src, dst, positions, has_reverse)
+            edge_attrs.update({"tailport": tail_port, "headport": head_port})
+            if has_reverse:
+                edge_attrs["constraint"] = "false"
+
+        if prob >= label_threshold or src == dst:
+            edge_attrs["label"] = f" {prob:.2f} "
+
+        g.edge(src, dst, **edge_attrs)
 
     g.render(output, view=view, cleanup=True)
     print(f"Saved MDP graph to {output}.{fmt}")
@@ -342,11 +501,6 @@ if __name__ == "__main__":
         f"Built MDP: {human_mdp['num_states']} states, "
         f"{sum(len(t) for t in human_mdp['transitions'].values())} transitions"
     )
-    if not human_mdp["states"]:
-        exit("No states found")
-    visualize_mdp(
-        human_model, threshold=0.05, output="human_mdp_viz", fmt="pdf", export_dot=True
-    )
 
     agent_model = AgentBehaviorModel(agent_dir)
     agent_mdp = agent_model.build_MDP()
@@ -355,14 +509,35 @@ if __name__ == "__main__":
         f"AGENT... Built MDP: {agent_mdp['num_states']} states, "
         f"{sum(len(t) for t in agent_mdp['transitions'].values())} transitions"
     )
-    if not agent_mdp["states"]:
-        exit("No states found")
-    visualize_mdp(
-        agent_model, threshold=0.05, output="agent_mdp_viz", fmt="pdf", export_dot=True
-    )
 
     human_evt = aggregate_event_transitions(human_mdp)
     agent_evt = aggregate_event_transitions(agent_mdp)
+    canonical_events = sorted(
+        (set(human_evt.keys()) | {e for tr in human_evt.values() for e in tr.keys()})
+        | (set(agent_evt.keys()) | {e for tr in agent_evt.values() for e in tr.keys()})
+    )
+
+    if not human_mdp["states"]:
+        exit("No states found")
+    visualize_mdp(
+        human_model,
+        threshold=0.05,
+        output="human_mdp_viz",
+        fmt="pdf",
+        export_dot=True,
+        event_order=canonical_events,
+    )
+
+    if not agent_mdp["states"]:
+        exit("No states found")
+    visualize_mdp(
+        agent_model,
+        threshold=0.05,
+        output="agent_mdp_viz",
+        fmt="pdf",
+        export_dot=True,
+        event_order=canonical_events,
+    )
 
     common = set(human_evt.keys()) & set(agent_evt.keys())
 
@@ -394,6 +569,7 @@ if __name__ == "__main__":
             output="joint_mdp_viz",
             fmt="pdf",
             export_dot=True,
+            event_order=canonical_events,
         )
 
     inter_class_avg = float(np.mean([kl for _, kl in kl_divs]))