first meaningful runs

engine/backends/qtable.py

@@ -1,11 +1,15 @@
 from __future__ import annotations
 
+import logging
+import time
 from typing import Any, Mapping
 
 import numpy as np
 
 from .common import evaluate, make_env
 
+logger = logging.getLogger(__name__)
+
 
 def train_qtable(
     cfg: Mapping[str, Any],
@@ -29,7 +33,9 @@ def train_qtable(
     steps = 0
     epsilon = float(cfg["eps_start"])
     log_freq = max(1, int(cfg.get("log_freq", 100)))
+    console_progress = bool(cfg.get("console_progress", False))
     obs, _ = env.reset(seed=int(cfg["seed"]))
+    started_at = time.perf_counter()
 
     interval_sums = {
         "reward": 0.0,
@@ -60,8 +66,7 @@ def train_qtable(
 
         if steps % log_freq == 0 and interval_count > 0:
             denom = float(interval_count)
-            train_events.append(
-                {
+            event = {
                 "train/reward_mean": interval_sums["reward"] / denom,
                 "train/revenue_mean": interval_sums["revenue"] / denom,
                 "train/agent_prob": interval_sums["agent_prob"] / denom,
@@ -70,6 +75,18 @@ def train_qtable(
                 "train/epsilon": float(epsilon),
                 "train/global_step": int(steps),
             }
+            train_events.append(event)
+            if console_progress:
+                elapsed = max(time.perf_counter() - started_at, 1e-6)
+                speed = steps / elapsed
+                logger.info(
+                    "step=%d/%d reward=%.3f revenue=%.3f eps=%.4f speed=%.1f steps/s",
+                    steps,
+                    int(cfg["total_timesteps"]),
+                    event["train/reward_mean"],
+                    event["train/revenue_mean"],
+                    event["train/epsilon"],
+                    speed,
                 )
             interval_sums = {key: 0.0 for key in interval_sums}
             interval_count = 0

engine/benchmark.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import argparse
 import json
+import logging
 import os
 from datetime import datetime, UTC
 from pathlib import Path
@@ -11,11 +12,17 @@ import numpy as np
 import pandas as pd
 
 from .lib.tiers import LinearElasticityPolicy, StaticPolicy, SurgePolicy
+from .logging_utils import configure_logging
 from .spec import TrainSpec
 from .telemetry.wandb import get_wandb_module
 
 wandb = get_wandb_module()
 HAS_WANDB = wandb is not None
+logger = logging.getLogger(__name__)
+
+
+def _log(message: str) -> None:
+    logger.info(message)
 
 
 def _parse_list(raw: str) -> list[str]:
@@ -78,8 +85,6 @@ def _run_eval_episode(env, policy) -> dict:
 
 def _build_tier(name: str, cfg: dict, alpha: float):
     from .backends.common import make_env
-    from .backends.qtable import train_qtable
-    from .backends.sb3 import train_sb3
 
     tier = name.lower().strip()
     run_cfg = dict(cfg)
@@ -111,10 +116,15 @@ def _build_tier(name: str, cfg: dict, alpha: float):
         return policy
 
     if tier == "qtable":
+        from .backends.qtable import train_qtable
+
+        run_cfg["console_progress"] = True
         agent, _ = train_qtable(run_cfg)
         return agent
 
     if tier in {"ppo", "a2c", "dqn"}:
+        from .backends.sb3 import train_sb3
+
         run_cfg["algo"] = tier
         agent, _ = train_sb3(run_cfg)
         return agent
@@ -129,9 +139,15 @@ def run_benchmark(
 
     rows: list[dict] = []
     traces: list[dict] = []
+    total_runs = max(1, len(alpha_values) * len(tiers))
+    run_index = 0
 
     for alpha in alpha_values:
         for tier_name in tiers:
+            run_index += 1
+            _log(
+                f"[{run_index}/{total_runs}] alpha={float(alpha):.2f} tier={tier_name}: training"
+            )
             policy = _build_tier(tier_name, cfg, alpha)
             env = make_env({**cfg, "alpha": float(alpha)})
             eps = [_run_eval_episode(env, policy) for _ in range(int(n_episodes))]
@@ -152,6 +168,11 @@ def run_benchmark(
                 + float(cfg.get("revenue_weight", 0.01)) * row["mean_revenue"]
             )
             rows.append(row)
+            _log(
+                f"[{run_index}/{total_runs}] alpha={float(alpha):.2f} tier={tier_name}: "
+                f"reward={row['mean_reward']:.3f} revenue={row['mean_revenue']:.3f} "
+                f"coi={row['mean_coi']:.4f} score={row['objective_score']:.3f}"
+            )
 
             max_len = max((len(e["price_trace"]) for e in eps), default=0)
             step_means = []
@@ -282,6 +303,18 @@ def _run_with_args(args):
     }
     tiers = _parse_list(args.tiers)
     alpha_values = _parse_float_list(args.alpha_values)
+    _log(
+        "starting run "
+        + json.dumps(
+            {
+                "tiers": tiers,
+                "alpha_values": alpha_values,
+                "episodes": int(args.episodes),
+                "total_timesteps": int(args.total_timesteps),
+                "device": str(args.device),
+            }
+        )
+    )
 
     all_frames: list[pd.DataFrame] = []
     all_traces: list[dict] = []
@@ -292,8 +325,10 @@ def _run_with_args(args):
             {k: v for k, v in overrides.items() if v is not None}
         ).to_flat_dict()
         cfg["linear_warmup_steps"] = int(args.linear_warmup_steps)
-        df_mode, traces_mode = run_benchmark(cfg, tiers, alpha_values, args.episodes)
         mode_label = "no_robust" if no_robust else "robust"
+        _log(f"mode={mode_label}: begin")
+        df_mode, traces_mode = run_benchmark(cfg, tiers, alpha_values, args.episodes)
+        _log(f"mode={mode_label}: complete ({len(df_mode)} rows)")
         df_mode["mode"] = mode_label
         for trace in traces_mode:
             trace["mode"] = mode_label
@@ -311,11 +346,12 @@ def _run_with_args(args):
     df.to_csv(csv_path, index=False)
     trace_path.write_text(json.dumps(traces, indent=2))
     rev_path, coi_path, price_path = _plot_outputs(df, traces, out_dir, stamp)
+    _log(f"artifacts written in {out_dir}")
 
     if not df.empty:
         best_idx = int(df["mean_revenue"].idxmax())
         best = df.iloc[best_idx]
-        print(
+        _log(
             "BEST_TIER="
             + json.dumps(
                 {
@@ -327,14 +363,15 @@ def _run_with_args(args):
                 }
             )
         )
-    print(f"BENCHMARK_CSV={csv_path}")
-    print(f"BENCHMARK_TRACES={trace_path}")
-    print(f"BENCHMARK_PLOT_REVENUE={rev_path}")
-    print(f"BENCHMARK_PLOT_COI={coi_path}")
-    print(f"BENCHMARK_PLOT_PRICE={price_path}")
+    _log(f"BENCHMARK_CSV={csv_path}")
+    _log(f"BENCHMARK_TRACES={trace_path}")
+    _log(f"BENCHMARK_PLOT_REVENUE={rev_path}")
+    _log(f"BENCHMARK_PLOT_COI={coi_path}")
+    _log(f"BENCHMARK_PLOT_PRICE={price_path}")
 
 
 def run_cli(raw_args: list[str] | None = None):
+    configure_logging()
     parser = argparse.ArgumentParser(description="PHANTOM benchmark orchestrator")
     parser.add_argument("--project", default="capstone")
     parser.add_argument("--tiers", default="static,surge,linear,qtable,ppo")

engine/engine.py

@@ -1,7 +1,7 @@
 from sys import platform
 import numpy as np
 from .lib.demand import generate_demand_for_actor, estimate_demand
-from .lib.behavior import sample_behavior
+from .lib.behavior import get_adjusted_transitions, sample_behavior_from_transitions
 from logging import INFO, getLogger
 
 logger = getLogger(__name__)
@@ -46,9 +46,17 @@ class MarketEngine:
             self.noise_std,
             distribution_method=self.demand_dist,
         )
+        human_transitions = get_adjusted_transitions(demand_h, human=True)
+        agent_transitions = get_adjusted_transitions(demand_a, human=False)
         # sample behavior trajectories from each demand distribution
-        human_t = [sample_behavior(demand_h, human=True) for _ in range(self.Nhumans)]
-        agent_t = [sample_behavior(demand_a, human=False) for _ in range(self.Nagents)]
+        human_t = [
+            sample_behavior_from_transitions(human_transitions)
+            for _ in range(self.Nhumans)
+        ]
+        agent_t = [
+            sample_behavior_from_transitions(agent_transitions)
+            for _ in range(self.Nagents)
+        ]
         # store trajectories for agent probability calculation
         self.last_trajectories = human_t + agent_t
         return estimate_demand(self.last_trajectories, self.action_weights)

engine/lib/behavior.py

@@ -110,10 +110,12 @@ def adjust_behavior_to_condition(condition, transition_matrix):
     return pd.DataFrame(expanded, index=new_rows, columns=new_cols)
 
 
-def sample_behavior(condition, human=True, max_len=40):
+def get_adjusted_transitions(condition, human=True):
     base_pivot = _get_base_pivot(human)
-    adjusted_transitions = adjust_behavior_to_condition(condition, base_pivot)
+    return adjust_behavior_to_condition(condition, base_pivot)
 
+
+def sample_behavior_from_transitions(adjusted_transitions, max_len=40):
     trajectory = [np.random.choice(adjusted_transitions.index)]
     while len(trajectory) < max_len and "checkout" not in trajectory[-1]:
         probs = np.asarray(adjusted_transitions.loc[trajectory[-1]].values, dtype=float)
@@ -127,6 +129,11 @@ def sample_behavior(condition, human=True, max_len=40):
     return trajectory
 
 
+def sample_behavior(condition, human=True, max_len=40):
+    adjusted_transitions = get_adjusted_transitions(condition, human=human)
+    return sample_behavior_from_transitions(adjusted_transitions, max_len=max_len)
+
+
 if __name__ == "__main__":
     t = sample_behavior(generate_demand_for_actor(np.array([10, 20, 30])), human=True)
     print(t)

engine/train.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import argparse
 from typing import Any
 
+from .logging_utils import configure_logging
 from .orchestrators import run_benchmark_cli, run_sweep_agent, run_train_once
 from .spec import TrainSpec
 
@@ -174,6 +175,7 @@ def _overrides_from_args(args: argparse.Namespace) -> dict[str, Any]:
 def main(argv: list[str] | None = None) -> None:
     import sys
 
+    configure_logging()
     raw_args = list(sys.argv[1:] if argv is None else argv)
     run_kind = _probe_run_kind(raw_args)
     if run_kind == "benchmark":