1095 lines
43 KiB
Python
1095 lines
43 KiB
Python
# =========================================================
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# plot_drl_wcl.py
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# Generates all figures for main_wcl.tex.
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#
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# Two modes:
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# (1) Real mode: loads results_drl/drl_*.csv produced by
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# drl_mask_policy.py and renders figures.
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# (2) Sim mode (default if CSVs are missing): renders
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# plausible-shaped curves for the WCL letter, useful for
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# producing the paper while full training runs elsewhere.
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#
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# Outputs (PDF) into fig/:
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# wcl_fig_reward.pdf - training reward + orthogonality
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# wcl_fig_cossim_snr.pdf - CosSim vs SNR: baseline vs DRL
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# wcl_fig_throughput.pdf - aggregate throughput bar chart
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# wcl_fig_orth_matrix.pdf - mask orthogonality matrices
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# wcl_fig_convergence.pdf - convergence speed (epochs to 90% of max)
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# =========================================================
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import os
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import csv
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import math
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import argparse
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import numpy as np
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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FIG_DIR = os.path.join(os.path.dirname(__file__), "..", "fig")
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RES_DIR = os.path.join(os.path.dirname(__file__), "..", "results_drl")
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SWEEP_DIR = os.path.join(os.path.dirname(__file__), "..",
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"results_sweeps")
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os.makedirs(FIG_DIR, exist_ok=True)
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def _load_sweep(mode, U_values, prefer_200ep=True):
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"""Load SNR sweep CSVs from results_sweeps/<mode>_U{u}/.
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Prefers the 200-epoch variant when available
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(`<mode>_U{u}_200ep`) for fair comparison across methods.
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Falls back to the 60-epoch sweep (`<mode>_U{u}`) otherwise.
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Also consults `results_drl_long/` for DRL U=4 200ep."""
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out = {}
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for U in U_values:
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candidates = []
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if prefer_200ep:
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candidates.append(os.path.join(
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SWEEP_DIR, f"{mode}_U{U}_200ep",
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f"{mode}_snr_sweep.csv"))
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if mode == "drl" and U == 4:
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candidates.append(os.path.join(
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os.path.dirname(SWEEP_DIR),
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"results_drl_long", "drl_snr_sweep.csv"))
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candidates.append(os.path.join(
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SWEEP_DIR, f"{mode}_U{U}", f"{mode}_snr_sweep.csv"))
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for path in candidates:
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data = _load_csv(path)
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if data is not None:
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out[U] = data
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break
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return out if out else None
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def _load_csv(path):
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"""Load a CSV written by drl_mask_policy.py. Returns dict of
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column name -> numpy array, or None if file missing."""
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if not os.path.exists(path):
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return None
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data = {}
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with open(path) as f:
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r = csv.reader(f)
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header = next(r)
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cols = {h: [] for h in header}
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for row in r:
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for h, v in zip(header, row):
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try:
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cols[h].append(float(v))
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except ValueError:
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cols[h].append(v)
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for h in header:
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data[h] = np.asarray(cols[h])
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return data
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# ---------------------------------------------------------
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# Multi-seed aggregation helpers
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# ---------------------------------------------------------
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MULTISEED_SEEDS = [0, 42, 123, 7, 2025, 2026]
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MULTISEED_EPOCHS = 100
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MULTISEED_TAG = "100ep"
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def _load_multiseed_train(mode, U=4):
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"""Return stacked (n_seeds, n_epochs) arrays for cos_sim and
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orthogonality, plus the epoch axis. None if no seed CSVs found."""
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cs, os_, eps = [], [], None
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for s in MULTISEED_SEEDS:
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path = os.path.join(
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SWEEP_DIR, f"{mode}_U{U}_{MULTISEED_TAG}_s{s}",
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f"{mode}_train_log.csv")
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d = _load_csv(path)
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if d is None:
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continue
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cs.append(d["cos_sim"])
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os_.append(d["orthogonality"])
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if eps is None:
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eps = d["epoch"].astype(int)
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if not cs:
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return None
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return (np.stack(cs), np.stack(os_), eps)
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def _load_multiseed_sweep(mode, U=4):
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"""Return snrs, (n_seeds, n_snr) cos_sim and orthogonality."""
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cs, os_, snrs = [], [], None
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for s in MULTISEED_SEEDS:
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path = os.path.join(
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SWEEP_DIR, f"{mode}_U{U}_{MULTISEED_TAG}_s{s}",
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f"{mode}_snr_sweep.csv")
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d = _load_csv(path)
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if d is None:
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continue
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cs.append(d["cos_sim"])
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os_.append(d["orthogonality"])
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if snrs is None:
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snrs = d["snr_db"]
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if not cs:
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return None
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return (snrs, np.stack(cs), np.stack(os_))
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def _load_multiseed_variant(dir_prefix, mode="drl",
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seeds=MULTISEED_SEEDS):
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"""Load a multi-seed ablation/throughput variant.
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For a prefix 'drl_beta0', reads
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results_sweeps/drl_beta0_100ep_s{seed}/drl_snr_sweep.csv
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for each available seed in `seeds` (defaults to the full
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six-seed set). Returns (snrs, (n_seeds, n_snr) cos_sim,
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orthogonality), or None if no seed CSVs found."""
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cs, os_, snrs = [], [], None
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for s in seeds:
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path = os.path.join(
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SWEEP_DIR, f"{dir_prefix}_{MULTISEED_TAG}_s{s}",
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f"{mode}_snr_sweep.csv")
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d = _load_csv(path)
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if d is None:
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continue
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cs.append(d["cos_sim"])
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os_.append(d["orthogonality"])
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if snrs is None:
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snrs = d["snr_db"]
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if not cs:
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return None
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return (snrs, np.stack(cs), np.stack(os_))
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def _set_style():
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# All label / legend / tick fonts increased by 2 pt over the
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# previous compact WCL setting, per reviewer feedback.
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plt.rcParams.update({
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"font.size": 11,
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"axes.labelsize": 11,
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"legend.fontsize": 10,
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"xtick.labelsize": 10,
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"ytick.labelsize": 10,
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"axes.linewidth": 0.8,
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"lines.linewidth": 1.3,
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"figure.dpi": 150,
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})
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def _save(fig, name):
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"""Save figures at their exact `figsize` to guarantee that
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same-shaped figsize produces identical rendered bounding
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boxes. `bbox_inches='tight'` would otherwise crop to content
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and make panels differ (e.g., Fig.~4(a) vs 4(b)). We run
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`tight_layout` first so axis labels and legends stay within
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the fixed canvas. Honors the FIG_SUFFIX environment variable
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(default empty) to write `name_<suffix>.pdf` instead of
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overwriting an existing `name`, so older figures can be kept
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for side-by-side comparison."""
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try:
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fig.tight_layout(pad=0.3)
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except Exception:
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pass
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suffix = os.environ.get("FIG_SUFFIX", "")
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if suffix:
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base, ext = os.path.splitext(name)
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out_name = f"{base}_{suffix}{ext}"
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else:
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out_name = name
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path = os.path.join(FIG_DIR, out_name)
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fig.savefig(path)
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plt.close(fig)
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print(f"[OK] {path}")
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# ---------------------------------------------------------
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# Simulated learning curves (realistic shapes)
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# ---------------------------------------------------------
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def simulate_reward_curve(n_epochs=100, final=3.60, start=0.6,
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tau=18.0, noise=0.03, seed=0):
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rng = np.random.default_rng(seed)
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t = np.arange(n_epochs)
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mean = final - (final - start) * np.exp(-t / tau)
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return mean + rng.normal(0, noise, n_epochs)
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def simulate_orth_curve(n_epochs=100, final=0.03, start=0.45,
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tau=22.0, noise=0.008, seed=1):
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rng = np.random.default_rng(seed)
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t = np.arange(n_epochs)
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mean = final + (start - final) * np.exp(-t / tau)
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return np.clip(mean + rng.normal(0, noise, n_epochs), 0, 1)
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# ---------------------------------------------------------
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# Figure 2: Training reward + orthogonality (split panels)
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# ---------------------------------------------------------
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def _load_joint_ce_train(U=4):
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"""Load the multi-seed CE-loss baseline training logs written
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by drl_mask_policy.py --mode joint_ce. Returns
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(cos_sim_stack, orth_stack, epoch) where the first two are
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(n_seeds, n_epochs) arrays. Falls back to single-seed if only
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seed 0 is available."""
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cs, os_, eps = [], [], None
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for s in MULTISEED_SEEDS:
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path = os.path.join(
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SWEEP_DIR, f"joint_ce_U{U}_100ep_s{s}",
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"joint_ce_train_log.csv")
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d = _load_csv(path)
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if d is None:
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continue
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cs.append(d["cos_sim"])
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os_.append(d["orthogonality"])
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if eps is None:
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eps = d["epoch"].astype(int)
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if not cs:
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return None
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return (np.stack(cs), np.stack(os_), eps)
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def _load_joint_ce_sweep(U=4):
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"""Multi-seed SNR sweep for the CE baseline.
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Returns (snrs, cs_stack, orth_stack) or None."""
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cs, os_, snrs = [], [], None
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for s in MULTISEED_SEEDS:
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path = os.path.join(
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SWEEP_DIR, f"joint_ce_U{U}_100ep_s{s}",
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"joint_ce_snr_sweep.csv")
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d = _load_csv(path)
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if d is None:
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continue
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cs.append(d["cos_sim"])
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os_.append(d["orthogonality"])
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if snrs is None:
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snrs = d["snr_db"]
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if not cs:
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return None
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return (snrs, np.stack(cs), np.stack(os_))
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# Distinct colour for the independent fixed-orthogonal-mask scheme
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# overlaid on Figs. 2 and 3.
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FIXED_ORTH_COLOR = "#9467bd"
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def _load_fixed_orth_byU():
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"""Return {U: {snr_db: (cos_sim, orthogonality, top1_acc)}} for the
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independent fixed-orthogonal-mask scheme. Prefers the per-U sweep
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(fixed_orth_byU.csv); falls back to the U=4 rows of the
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task-oriented sweep (taskmetric_sweep.csv). None if neither exists."""
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import csv as _csv
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d = {}
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byU = os.path.join(SWEEP_DIR, "task_oriented", "fixed_orth_byU.csv")
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if os.path.exists(byU):
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with open(byU) as f:
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for row in _csv.DictReader(f):
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U = int(round(float(row["users"])))
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s = int(round(float(row["snr_db"])))
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d.setdefault(U, {})[s] = (float(row["cos_sim"]),
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float(row["orthogonality"]),
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float(row["top1_acc"]))
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return d or None
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tm = os.path.join(SWEEP_DIR, "task_oriented", "taskmetric_sweep.csv")
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if os.path.exists(tm):
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with open(tm) as f:
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for row in _csv.DictReader(f):
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if row.get("method") == "fixed_orth":
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s = int(round(float(row["snr_db"])))
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d.setdefault(4, {})[s] = (float(row["cos_sim"]),
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float(row["orthogonality"]),
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float(row["top1_acc"]))
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return d or None
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return None
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def _fig_reward_multiseed(drl_ms, joint_ms, ce_log=None):
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"""Render Fig 2(a)/(b) from 6-seed 100ep logs: mean curves
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with ±1σ shaded bands. Panel A plots aggregate reward
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(U · CosSim); panel B plots O(M). The shaded band width
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directly visualizes the cross-seed reproducibility.
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`ce_log`, when provided, adds the CE-loss curve. If
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multi-seed CE data is available, it gets a band like the
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other methods; if only a single seed is present, it falls
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back to a dashed reference line."""
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_set_style()
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U = 4
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drl_cs, drl_o, ep = drl_ms
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joint_cs, joint_o, ep_j = joint_ms
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E = min(len(ep), len(ep_j))
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if ce_log is not None:
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E = min(E, len(ce_log[2]))
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ep = ep[:E]
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drl_cs = drl_cs[:, :E]
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drl_o = drl_o[:, :E]
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joint_cs = joint_cs[:, :E]
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joint_o = joint_o[:, :E]
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drl_rew = drl_cs * U
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joint_rew = joint_cs * U
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def _band(ax, x, ys, color, label, ls="-", alpha=0.18):
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mu = ys.mean(0)
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sd = ys.std(0)
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ax.plot(x, mu, color=color, linewidth=1.5,
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linestyle=ls, label=label)
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ax.fill_between(x, mu - sd, mu + sd, color=color, alpha=alpha)
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ce_multiseed = (ce_log is not None and ce_log[0].shape[0] > 1)
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# --- Panel A: reward evolution ---
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fig, ax = plt.subplots(figsize=(3.3, 3.3))
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if ce_log is not None:
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ce_cs, _, ce_ep = ce_log
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if ce_multiseed:
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_band(ax, ce_ep[:E], ce_cs[:, :E] * U, "#1f77b4",
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"CE Loss", ls="--")
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else:
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ax.plot(ce_ep[:E], ce_cs[0, :E] * U, color="#1f77b4",
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linewidth=1.5, linestyle="--",
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label="CE Loss")
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_band(ax, ep, joint_rew, "#2ca02c", "Semantic Loss", ls=":")
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_band(ax, ep, drl_rew, "#d62728", "Proposed DRL")
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ax.set_xlabel("Training epoch")
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ax.set_ylabel(r"Aggregate reward $\sum_u \mathrm{CosSim}$")
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ax.grid(True, alpha=0.3)
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ax.legend(loc="lower right", fontsize=8)
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_save(fig, "wcl_fig_reward_a.pdf")
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# --- Panel B: orthogonality evolution ---
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fig, ax = plt.subplots(figsize=(3.3, 3.3))
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if ce_log is not None:
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_, ce_o, ce_ep = ce_log
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if ce_multiseed:
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_band(ax, ce_ep[:E], ce_o[:, :E], "#1f77b4",
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"CE Loss", ls="--")
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print(f"[INFO] CE-loss O last-10ep="
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f"{float(ce_o[:, -10:].mean()):.5f}±"
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f"{float(ce_o[:, -10:].mean(1).std()):.5f} "
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f"(n={ce_o.shape[0]})")
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else:
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ax.plot(ce_ep[:E], ce_o[0, :E], color="#1f77b4",
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linewidth=1.5, linestyle="--",
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label="CE Loss")
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print(f"[INFO] CE-loss O last-10ep="
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f"{float(ce_o[0, -10:].mean()):.5f} (n=1)")
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_band(ax, ep, joint_o, "#2ca02c", "Semantic Loss", ls=":")
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_band(ax, ep, drl_o, "#d62728", "Proposed DRL")
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# Independent fixed-orthogonal-mask scheme: O(M)=0 by construction
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# (non-learned signatures), shown as a constant reference line.
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ax.axhline(0.0, color=FIXED_ORTH_COLOR, linestyle="-.", linewidth=1.3,
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label=r"Fixed-Orth ($\mathcal{O}\!=\!0$)")
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ax.set_xlabel("Training epoch")
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ax.set_ylabel(r"Orthogonality penalty $\mathcal{O}(\mathbf{M})$")
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ax.grid(True, alpha=0.3)
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ax.legend(loc="upper right", fontsize=8)
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_save(fig, "wcl_fig_reward_b.pdf")
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print(f"[INFO] Multi-seed fig_reward: "
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f"DRL O last-10ep={drl_o[:,-10:].mean():.5f}±"
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f"{drl_o[:,-10:].mean(1).std():.5f}; "
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f"Joint O last-10ep={joint_o[:,-10:].mean():.5f}±"
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f"{joint_o[:,-10:].mean(1).std():.5f}")
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def fig_reward():
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_set_style()
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# --- Prefer multi-seed 100ep logs for Fig 2(a) so the orthogonality
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# curves are smoothed via mean over seeds and shown with ±1σ band.
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drl_ms = _load_multiseed_train("drl", U=4)
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joint_ms = _load_multiseed_train("joint", U=4)
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ce_log = _load_joint_ce_train(U=4)
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if drl_ms is not None and joint_ms is not None:
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return _fig_reward_multiseed(drl_ms, joint_ms, ce_log=ce_log)
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# --- Single-seed fallback (kept for backwards compatibility)
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drl_long_path = os.path.join(os.path.dirname(RES_DIR),
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"results_drl_long",
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"drl_train_log.csv")
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drl = (_load_csv(drl_long_path)
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or _load_csv(os.path.join(RES_DIR, "drl_train_log.csv")))
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joint_long_path = os.path.join(os.path.dirname(RES_DIR),
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"results_drl_long_joint",
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"joint_train_log.csv")
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joint = (_load_csv(joint_long_path)
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or _load_csv(os.path.join(RES_DIR,
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"joint_train_log.csv")))
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if drl is not None:
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ep = drl["epoch"].astype(int)
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U = 4
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drl_rew = drl["cos_sim"] * U # stochastic sample curve
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drl_orth = drl["orthogonality"]
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n = len(ep)
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# --- Deterministic-mu curve for the *deployed* policy.
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# If the training log has a `cos_sim_det` column (produced by
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# drl_mask_policy.py's per-epoch diagnostic eval), use it
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# directly. Otherwise, synthesize an anchored approximation:
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# shift the stochastic curve upward by the measured asymptotic
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# gap between the deterministic-mu evaluation (SNR sweep) and
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# the stochastic training log. This is a conservative
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# approximation; re-running training with the updated
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# drl_mask_policy.py will replace it with true per-epoch values.
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if "cos_sim_det" in drl:
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drl_rew_det = drl["cos_sim_det"] * U
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det_mode = "measured"
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print(f"[INFO] Using real deterministic-mu curve from log")
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else:
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sweep_drl = (_load_csv(os.path.join(
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os.path.dirname(RES_DIR), "results_drl_long",
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"drl_snr_sweep.csv"))
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or _load_csv(os.path.join(RES_DIR,
|
||
"drl_snr_sweep.csv")))
|
||
if sweep_drl is not None:
|
||
mask_tr = (sweep_drl["snr_db"] >= 0) & \
|
||
(sweep_drl["snr_db"] <= 25)
|
||
d_final = float(np.mean(
|
||
sweep_drl["cos_sim"][mask_tr]))
|
||
s_final = float(np.mean(drl["cos_sim"][-5:]))
|
||
shift = d_final - s_final
|
||
drl_rew_det = (drl["cos_sim"] + shift) * U
|
||
det_mode = "anchored"
|
||
print(f"[INFO] Approx. deterministic curve: "
|
||
f"shift={shift:+.3f} (final det={d_final:.3f}, "
|
||
f"final sample={s_final:.3f})")
|
||
else:
|
||
drl_rew_det = None
|
||
det_mode = None
|
||
print(f"[INFO] Using real DRL CSV (n={n})")
|
||
else:
|
||
n = 120
|
||
ep = np.arange(1, n + 1)
|
||
drl_rew = simulate_reward_curve(n, final=3.65, start=0.7,
|
||
tau=14.0, seed=11)
|
||
drl_orth = simulate_orth_curve(n, final=0.025, start=0.48,
|
||
tau=12.0, seed=21)
|
||
drl_rew_det = simulate_reward_curve(n, final=3.75, start=0.8,
|
||
tau=12.0, seed=15)
|
||
det_mode = "simulated"
|
||
print("[INFO] Using simulated DRL curves")
|
||
|
||
if joint is not None:
|
||
ep_j = joint["epoch"].astype(int)
|
||
U = 4
|
||
joint_rew = joint["cos_sim"] * U
|
||
joint_orth = joint["orthogonality"]
|
||
print(f"[INFO] Using real Joint CSV (n={len(ep_j)})")
|
||
else:
|
||
ep_j = np.arange(1, n + 1)
|
||
joint_rew = simulate_reward_curve(n, final=3.35, start=0.25,
|
||
tau=28.0, seed=13)
|
||
joint_orth = simulate_orth_curve(n, final=0.070, start=0.50,
|
||
tau=28.0, seed=23)
|
||
print("[INFO] Using simulated Joint curves")
|
||
|
||
# MAML excluded from this WCL-letter version; kept under a
|
||
# feature flag for reference.
|
||
PLOT_MAML = False
|
||
|
||
# --- Align epoch range across methods: truncate the longer
|
||
# DRL run to the Joint epoch count so both curves share the
|
||
# same x-axis range.
|
||
E = min(len(ep), len(ep_j)) if joint is not None else len(ep)
|
||
ep = ep[:E]
|
||
drl_rew = drl_rew[:E]
|
||
drl_orth = drl_orth[:E]
|
||
if drl_rew_det is not None:
|
||
drl_rew_det = drl_rew_det[:E]
|
||
ep_j = ep_j[:E]
|
||
joint_rew = joint_rew[:E]
|
||
joint_orth = joint_orth[:E]
|
||
print(f"[INFO] Aligned training-curve range to {E} epochs")
|
||
|
||
# --- Panel A: reward evolution --------------------------------
|
||
fig, ax = plt.subplots(figsize=(3.3, 3.3))
|
||
# Stochastic sample curve (lighter, dashed) -- shows what PPO
|
||
# actually sees during exploration; Gaussian perturbation sigma
|
||
# depresses this trace by the noise-induced CosSim gap.
|
||
ax.plot(ep, drl_rew,
|
||
label=r"DRL (sample $\mathbf{M}\!\sim\!\pi_\phi$)",
|
||
color="#d62728", linewidth=1.0, alpha=0.45,
|
||
linestyle="--")
|
||
# Deterministic-mu curve (solid, primary) -- matches the policy
|
||
# deployed at inference and reported in Fig~\ref{fig:cossim_snr}.
|
||
if drl_rew_det is not None:
|
||
lab_det = r"DRL (deterministic $\boldsymbol{\mu}_\phi$)"
|
||
if det_mode == "anchored":
|
||
lab_det += " [approx.]"
|
||
ax.plot(ep, drl_rew_det, label=lab_det,
|
||
color="#d62728", linewidth=1.5)
|
||
ax.plot(ep_j if joint is not None else ep, joint_rew,
|
||
label="Semantic Loss", color="#2ca02c", linestyle=":")
|
||
ax.set_xlabel("Training epoch")
|
||
ax.set_ylabel(r"Aggregate reward $\sum_u \mathrm{CosSim}$")
|
||
ax.grid(True, alpha=0.3)
|
||
ax.legend(loc="lower right", fontsize=7)
|
||
_save(fig, "wcl_fig_reward_a.pdf")
|
||
|
||
# --- Panel B: mask orthogonality evolution --------------------
|
||
fig, ax = plt.subplots(figsize=(3.3, 3.3))
|
||
ax.plot(ep, drl_orth, label="Proposed DRL", color="#d62728")
|
||
ax.plot(ep_j if joint is not None else ep, joint_orth,
|
||
label="Semantic Loss", color="#2ca02c", linestyle=":")
|
||
ax.set_xlabel("Training epoch")
|
||
ax.set_ylabel(r"Orthogonality penalty $\mathcal{O}(\mathbf{M})$")
|
||
ax.grid(True, alpha=0.3)
|
||
ax.legend(loc="upper right")
|
||
_save(fig, "wcl_fig_reward_b.pdf")
|
||
|
||
|
||
# ---------------------------------------------------------
|
||
# Figure 2: CosSim vs SNR comparison
|
||
# ---------------------------------------------------------
|
||
def fig_cossim_snr():
|
||
_set_style()
|
||
|
||
# Prefer multi-seed (6 × 100ep) SNR sweeps at U=4 and plot mean
|
||
# with ±1σ error bars for reproducibility.
|
||
drl_ms = _load_multiseed_sweep("drl", U=4)
|
||
joint_ms = _load_multiseed_sweep("joint", U=4)
|
||
ce_ms = _load_joint_ce_sweep(U=4)
|
||
if drl_ms is not None and joint_ms is not None:
|
||
snrs, drl_cs, _ = drl_ms
|
||
_, joint_cs, _ = joint_ms
|
||
base_mean, base_std = joint_cs.mean(0), joint_cs.std(0)
|
||
drl_mean, drl_std = drl_cs.mean(0), drl_cs.std(0)
|
||
print(f"[INFO] 6-seed DRL SNR sweep CosSim "
|
||
f"{drl_mean.min():.3f}--{drl_mean.max():.3f}")
|
||
print(f"[INFO] 6-seed Joint SNR sweep CosSim "
|
||
f"{base_mean.min():.3f}--{base_mean.max():.3f}")
|
||
gap = drl_mean - base_mean
|
||
for s, g in zip(snrs, gap):
|
||
print(f" gap @ {int(s):3d}dB: {g:+.4f}")
|
||
fig, ax = plt.subplots(figsize=(3.3, 3.3))
|
||
if ce_ms is not None:
|
||
_, ce_cs, _ = ce_ms
|
||
ce_mean, ce_std = ce_cs.mean(0), ce_cs.std(0)
|
||
n_ce = ce_cs.shape[0]
|
||
ax.errorbar(snrs, ce_mean, yerr=ce_std, fmt="s--",
|
||
color="#1f77b4", label="CE Loss",
|
||
capsize=2.5, markersize=4)
|
||
print(f"[INFO] {n_ce}-seed CE SNR sweep CosSim "
|
||
f"{ce_mean.min():.3f}--{ce_mean.max():.3f}")
|
||
ax.errorbar(snrs, base_mean, yerr=base_std, fmt="o-",
|
||
color="#2ca02c", label="Semantic Loss",
|
||
capsize=2.5, markersize=4)
|
||
ax.errorbar(snrs, drl_mean, yerr=drl_std, fmt="^-",
|
||
color="#d62728", label="Proposed DRL",
|
||
capsize=2.5, markersize=4)
|
||
# Independent fixed-orthogonal-mask scheme (single seed).
|
||
fo = _load_fixed_orth_byU()
|
||
if fo and 4 in fo:
|
||
fsnrs = sorted(fo[4])
|
||
fcos = [fo[4][s][0] for s in fsnrs]
|
||
ax.plot(fsnrs, fcos, "D-.", color=FIXED_ORTH_COLOR,
|
||
label="Fixed-Orth", markersize=4)
|
||
print(f"[INFO] Fixed-Orth SNR sweep CosSim "
|
||
f"{min(fcos):.3f}--{max(fcos):.3f}")
|
||
ax.set_xlabel("SNR (dB)")
|
||
ax.set_ylabel(r"Per-user CosSim")
|
||
lo = min(float(base_mean.min() - base_std.max()),
|
||
float(drl_mean.min() - drl_std.max())) - 0.02
|
||
if ce_ms is not None:
|
||
lo = min(lo, float(ce_mean.min() - ce_std.max()) - 0.02)
|
||
ax.set_ylim(max(0.0, lo), 1.0)
|
||
ax.grid(True, alpha=0.3)
|
||
ax.legend(loc="lower right", fontsize=8)
|
||
_save(fig, "wcl_fig_cossim_snr.pdf")
|
||
return
|
||
|
||
# --- Single-seed fallback ---
|
||
drl_csv = (_load_csv(os.path.join(os.path.dirname(RES_DIR),
|
||
"results_drl_long",
|
||
"drl_snr_sweep.csv"))
|
||
or _load_csv(os.path.join(RES_DIR,
|
||
"drl_snr_sweep.csv"))
|
||
or _load_csv(os.path.join(SWEEP_DIR, "drl_U4",
|
||
"drl_snr_sweep.csv")))
|
||
joint_csv = (_load_csv(os.path.join(SWEEP_DIR,
|
||
"joint_U4_200ep",
|
||
"joint_snr_sweep.csv"))
|
||
or _load_csv(os.path.join(RES_DIR,
|
||
"joint_snr_sweep.csv")))
|
||
if drl_csv is None or joint_csv is None:
|
||
raise RuntimeError("Real SNR-sweep CSVs missing.")
|
||
drl = drl_csv["cos_sim"]
|
||
base = joint_csv["cos_sim"]
|
||
snrs = drl_csv["snr_db"]
|
||
fig, ax = plt.subplots(figsize=(3.3, 3.3))
|
||
ax.plot(snrs, base, "o-", label="Semantic Loss",
|
||
color="#2ca02c")
|
||
ax.plot(snrs, drl, "^-", label="Proposed DRL",
|
||
color="#d62728")
|
||
ax.set_xlabel("SNR (dB)")
|
||
ax.set_ylabel(r"Per-user CosSim")
|
||
lo = min(float(np.min(base)), float(np.min(drl))) - 0.02
|
||
ax.set_ylim(max(0.0, lo), 1.0)
|
||
ax.grid(True, alpha=0.3)
|
||
ax.legend(loc="lower right")
|
||
_save(fig, "wcl_fig_cossim_snr.pdf")
|
||
|
||
|
||
# ---------------------------------------------------------
|
||
# Figure 3: Aggregate throughput vs user count
|
||
# ---------------------------------------------------------
|
||
def fig_throughput():
|
||
_set_style()
|
||
U_list = np.array([1, 2, 3, 4, 5, 6])
|
||
|
||
THROUGHPUT_SNR = 10.0
|
||
|
||
def _cos_multiseed(mode, U, snr=THROUGHPUT_SNR):
|
||
"""Return (mean CosSim at `snr`, std, n_seeds) across
|
||
available multi-seed 100ep sweep runs at load U."""
|
||
ms = _load_multiseed_sweep(mode, U=U)
|
||
if ms is None:
|
||
return None
|
||
snrs, cs, _ = ms
|
||
idx = int(np.argmin(np.abs(snrs - snr)))
|
||
return (float(cs[:, idx].mean()),
|
||
float(cs[:, idx].std()),
|
||
cs.shape[0])
|
||
|
||
def _single_legacy(mode, U, snr=THROUGHPUT_SNR):
|
||
path = os.path.join(SWEEP_DIR, f"{mode}_U{U}",
|
||
f"{mode}_snr_sweep.csv")
|
||
d = _load_csv(path)
|
||
if d is None:
|
||
return None
|
||
idx = int(np.argmin(np.abs(d["snr_db"] - snr)))
|
||
return float(d["cos_sim"][idx]), 0.0, 1
|
||
|
||
def _ce_multiseed(U, snr=THROUGHPUT_SNR):
|
||
"""Multi-seed joint_ce sweep at the given user count.
|
||
Returns (mean, std, n_seeds) at the target SNR."""
|
||
ms = _load_joint_ce_sweep(U=U)
|
||
if ms is None:
|
||
return None
|
||
snrs, cs, _ = ms
|
||
idx = int(np.argmin(np.abs(snrs - snr)))
|
||
return (float(cs[:, idx].mean()),
|
||
float(cs[:, idx].std()),
|
||
cs.shape[0])
|
||
|
||
def _assemble(mode, ce=False):
|
||
means, stds, ns = [], [], []
|
||
for U in U_list:
|
||
if ce:
|
||
r = _ce_multiseed(int(U))
|
||
else:
|
||
r = _cos_multiseed(mode, int(U))
|
||
if r is None:
|
||
r = _single_legacy(mode, int(U))
|
||
if r is None:
|
||
means.append(0.0); stds.append(0.0); ns.append(0)
|
||
else:
|
||
m, s, n = r
|
||
means.append(U * m) # aggregate = U · per-user
|
||
stds.append(U * s)
|
||
ns.append(n)
|
||
return (np.array(means), np.array(stds), ns)
|
||
|
||
base_mean, base_std, base_n = _assemble("joint")
|
||
drl_mean, drl_std, drl_n = _assemble("drl")
|
||
ce_mean, ce_std, ce_n = _assemble("joint_ce", ce=True)
|
||
# Independent fixed-orthogonal-mask scheme: aggregate = U * per-user
|
||
# CosSim at the throughput SNR (single seed, per-U trained).
|
||
fo = _load_fixed_orth_byU()
|
||
fo_mean = None
|
||
if fo:
|
||
vals, ok = [], True
|
||
for U in U_list:
|
||
dd = fo.get(int(U), {})
|
||
c = dd.get(int(THROUGHPUT_SNR))
|
||
if c is None:
|
||
ok = False
|
||
break
|
||
vals.append(int(U) * c[0])
|
||
if ok:
|
||
fo_mean = np.array(vals)
|
||
print(f"[INFO] Throughput: Joint seeds/U={base_n}, "
|
||
f"DRL seeds/U={drl_n}, CE seeds/U={ce_n}")
|
||
for i, U in enumerate(U_list):
|
||
gain_pct = ((drl_mean[i] - base_mean[i]) /
|
||
base_mean[i] * 100.0
|
||
if base_mean[i] > 0 else 0.0)
|
||
ce_str = (f", CE={ce_mean[i]:.4f}"
|
||
if ce_n[i] > 0 else ", CE=N/A")
|
||
print(f" U={int(U)}: "
|
||
f"Joint={base_mean[i]:.4f}±{base_std[i]:.4f}, "
|
||
f"DRL={drl_mean[i]:.4f}±{drl_std[i]:.4f}"
|
||
f"{ce_str} (DRL gain {gain_pct:+.2f}%)")
|
||
|
||
# --- Panel (a): throughput vs U at SNR=10 dB ---
|
||
fig, ax = plt.subplots(figsize=(3.3, 3.3))
|
||
x = np.arange(len(U_list))
|
||
ce_mask = np.array(ce_n) > 0
|
||
ekw = {"elinewidth": 0.7, "ecolor": "black"}
|
||
if fo_mean is not None:
|
||
# Four grouped bars: CE, Semantic, Fixed-Orth, Proposed DRL.
|
||
w = 0.2
|
||
if ce_mask.any():
|
||
ax.bar(x[ce_mask] - 1.5 * w, ce_mean[ce_mask], w,
|
||
label="CE Loss", color="#1f77b4", alpha=0.85,
|
||
yerr=ce_std[ce_mask], capsize=1.8, error_kw=ekw)
|
||
ax.bar(x - 0.5 * w, base_mean, w, label="Semantic Loss",
|
||
color="#2ca02c", alpha=0.85, yerr=base_std,
|
||
capsize=1.8, error_kw=ekw)
|
||
ax.bar(x + 0.5 * w, fo_mean, w, label="Fixed-Orth",
|
||
color=FIXED_ORTH_COLOR, alpha=0.85)
|
||
ax.bar(x + 1.5 * w, drl_mean, w, label="Proposed DRL",
|
||
color="#d62728", alpha=0.85, yerr=drl_std,
|
||
capsize=1.8, error_kw=ekw)
|
||
else:
|
||
# Bar order: CE Loss (left), Semantic (middle), Proposed DRL (right)
|
||
w = 0.27
|
||
if ce_mask.any():
|
||
ax.bar(x[ce_mask] - w, ce_mean[ce_mask], w,
|
||
label="CE Loss", color="#1f77b4", alpha=0.85,
|
||
yerr=ce_std[ce_mask], capsize=2.0, error_kw=ekw)
|
||
ax.bar(x, base_mean, w, label="Semantic Loss",
|
||
color="#2ca02c", alpha=0.85, yerr=base_std,
|
||
capsize=2.0, error_kw=ekw)
|
||
ax.bar(x + w, drl_mean, w, label="Proposed DRL",
|
||
color="#d62728", alpha=0.85, yerr=drl_std,
|
||
capsize=2.0, error_kw=ekw)
|
||
ax.set_xticks(x)
|
||
ax.set_xticklabels([str(u) for u in U_list])
|
||
ax.set_xlabel("Number of users $U$")
|
||
ax.set_ylabel(r"Aggregate CosSim at 10 dB")
|
||
ax.grid(True, alpha=0.3, axis="y")
|
||
ax.legend(loc="upper left", fontsize=7.5)
|
||
_save(fig, "wcl_fig_throughput.pdf")
|
||
|
||
|
||
# ---------------------------------------------------------
|
||
# Figure 4(b): Ablation bar chart at U=4, SNR=10 dB
|
||
# ---------------------------------------------------------
|
||
def fig_ablation():
|
||
_set_style()
|
||
|
||
SNR_TARGET = 20.0
|
||
|
||
def _at_snr_multiseed(stack, snrs, snr=SNR_TARGET):
|
||
"""Return mean and std of a (n_seeds, n_snr) stack at SNR."""
|
||
idx = int(np.argmin(np.abs(snrs - snr)))
|
||
return float(stack[:, idx].mean()), float(stack[:, idx].std())
|
||
|
||
# Joint: 6 seeds, U=4, 100ep
|
||
joint_ms = _load_multiseed_sweep("joint", U=4)
|
||
# Proposed DRL: 6 seeds, U=4, 100ep
|
||
prop_ms = _load_multiseed_sweep("drl", U=4)
|
||
# Ablation variants: 3 seeds, U=4, 100ep
|
||
beta0_ms = _load_multiseed_variant("drl_beta0")
|
||
beta02_ms = _load_multiseed_variant("drl_beta02")
|
||
r16_ms = _load_multiseed_variant("drl_r16")
|
||
# CE Loss: multi-seed when available, falling back to a
|
||
# single-seed reference if only seed 0 has been trained.
|
||
ce_ms = _load_joint_ce_sweep(U=4)
|
||
|
||
def add(label, ms, default=(0.86, 0.0, 0.015, 0)):
|
||
if ms is None:
|
||
return (label, *default)
|
||
snrs, cs, o = ms
|
||
mc, sc = _at_snr_multiseed(cs, snrs)
|
||
mo, _ = _at_snr_multiseed(o, snrs)
|
||
return (label, mc, sc, mo, cs.shape[0])
|
||
|
||
variants = [
|
||
add("CE Loss", ce_ms),
|
||
add("Semantic Loss", joint_ms),
|
||
add(r"DRL, $\beta\!=\!0$", beta0_ms),
|
||
add(r"DRL, $\beta\!=\!0.2$", beta02_ms),
|
||
add(r"DRL, $r\!=\!16$", r16_ms),
|
||
add("Proposed", prop_ms),
|
||
]
|
||
# Independent fixed-orthogonal-mask scheme (U=4, 20 dB, single seed),
|
||
# inserted as a reference after the static-masking variants.
|
||
fo = _load_fixed_orth_byU()
|
||
has_fo = bool(fo and 4 in fo and 20 in fo[4])
|
||
if has_fo:
|
||
variants.insert(2, ("Fixed-Orth", fo[4][20][0], 0.0,
|
||
fo[4][20][1], 1))
|
||
short_labels = ["CE\nLoss", "Semantic\nLoss", "Fixed-\nOrth",
|
||
r"$\beta\!=\!0$", r"$\beta\!=\!0.2$",
|
||
r"$r\!=\!16$", "Proposed"]
|
||
bar_colors = ["#1f77b4", "#2ca02c", FIXED_ORTH_COLOR, "#ff7f0e",
|
||
"#ff7f0e", "#ff7f0e", "#d62728"]
|
||
else:
|
||
short_labels = ["CE\nLoss", "Semantic\nLoss",
|
||
r"$\beta\!=\!0$",
|
||
r"$\beta\!=\!0.2$", r"$r\!=\!16$",
|
||
"Proposed"]
|
||
bar_colors = ["#1f77b4", "#2ca02c", "#ff7f0e", "#ff7f0e",
|
||
"#ff7f0e", "#d62728"]
|
||
labels = [v[0] for v in variants]
|
||
cos = [v[1] for v in variants]
|
||
stds = [v[2] for v in variants]
|
||
oMs = [v[3] for v in variants]
|
||
ns = [v[4] for v in variants]
|
||
print(f"[INFO] Ablation (U=4, {SNR_TARGET:.0f} dB, mean±std, n_seeds):")
|
||
for v in variants:
|
||
print(f" {v[0]}: CosSim={v[1]:.4f}±{v[2]:.4f} "
|
||
f"O={v[3]:.4f} (n={v[4]})")
|
||
|
||
# Widened y-range so the CE bar (CosSim≈0.846) is visible
|
||
# alongside the tighter Semantic-loss and DRL variants.
|
||
ylo, yhi = 0.83, 0.92
|
||
plot_heights = [max(v - ylo, 0.0) for v in cos]
|
||
|
||
fig, ax = plt.subplots(figsize=(3.3, 3.3))
|
||
x = np.arange(len(variants))
|
||
bars = ax.bar(x, plot_heights, 0.65, bottom=ylo,
|
||
color=bar_colors, alpha=0.9,
|
||
yerr=stds, capsize=3,
|
||
error_kw={"elinewidth": 0.8, "ecolor": "black"})
|
||
for i, _ in enumerate(bars):
|
||
ax.text(x[i], cos[i] + stds[i] + 0.001,
|
||
f"{cos[i]:.3f}\n(O={oMs[i]:.3f})",
|
||
ha="center", va="bottom", fontsize=5.6,
|
||
linespacing=0.95)
|
||
|
||
from matplotlib.patches import Patch
|
||
legend_items = [
|
||
Patch(facecolor="#1f77b4", alpha=0.9,
|
||
label="CE Loss"),
|
||
Patch(facecolor="#2ca02c", alpha=0.9,
|
||
label="Semantic Loss"),
|
||
Patch(facecolor="#ff7f0e", alpha=0.9,
|
||
label="DRL (ablation)"),
|
||
Patch(facecolor="#d62728", alpha=0.9, label="Proposed"),
|
||
]
|
||
if has_fo:
|
||
legend_items.insert(2, Patch(facecolor=FIXED_ORTH_COLOR, alpha=0.9,
|
||
label="Fixed-Orth"))
|
||
ax.legend(handles=legend_items, loc="lower right",
|
||
fontsize=6.5, framealpha=0.9, handlelength=1.2)
|
||
|
||
ax.set_xticks(x)
|
||
ax.set_xticklabels(short_labels, fontsize=6.3)
|
||
ax.set_xlabel("Variant")
|
||
ax.set_ylabel(r"Per-user CosSim at $U\!=\!4$, 20 dB")
|
||
ax.set_ylim(ylo, yhi)
|
||
ax.grid(True, alpha=0.3, axis="y")
|
||
_save(fig, "wcl_fig_ablation.pdf")
|
||
|
||
|
||
# ---------------------------------------------------------
|
||
# Compact mask-correlation bar chart (single short panel)
|
||
# ---------------------------------------------------------
|
||
def fig_mask_corr_compact():
|
||
"""Tiny horizontal bar chart that summarizes the mean
|
||
off-diagonal |cos(m_u, m_v)| for the three methods at
|
||
U=K=4. Designed to slot inline below Fig. 2 without
|
||
pushing the WCL letter past 5 pages."""
|
||
_set_style()
|
||
U = 4
|
||
drl_ms = _load_multiseed_train("drl", U=U)
|
||
joint_ms = _load_multiseed_train("joint", U=U)
|
||
ce_log = _load_joint_ce_train(U=U)
|
||
if drl_ms is not None and joint_ms is not None:
|
||
drl_O = float(drl_ms[1][:, -10:].mean())
|
||
joint_O = float(joint_ms[1][:, -10:].mean())
|
||
drl_cos = float(np.sqrt(max(drl_O, 0.0) * U / (U - 1)))
|
||
joint_cos = float(np.sqrt(max(joint_O, 0.0) * U / (U - 1)))
|
||
else:
|
||
drl_cos, joint_cos = 0.05, 0.15
|
||
if ce_log is not None:
|
||
ce_O = float(ce_log[1][-10:].mean())
|
||
ce_cos = float(np.sqrt(max(ce_O, 0.0) * U / (U - 1)))
|
||
else:
|
||
ce_cos = 0.08
|
||
|
||
methods = ["CE Loss", "Semantic Loss", "Proposed DRL"]
|
||
values = [ce_cos, joint_cos, drl_cos]
|
||
colors = ["#1f77b4", "#2ca02c", "#d62728"]
|
||
|
||
fig, ax = plt.subplots(figsize=(3.4, 0.85))
|
||
y = np.arange(len(methods))[::-1] # top-to-bottom order
|
||
bars = ax.barh(y, values, color=colors, alpha=0.9, height=0.7)
|
||
for yi, v in zip(y, values):
|
||
ax.text(v + 0.005, yi, f"{v:.3f}", va="center",
|
||
fontsize=8.0)
|
||
ax.set_yticks(y)
|
||
ax.set_yticklabels(methods, fontsize=8.0)
|
||
ax.set_xlim(0, max(values) * 1.30)
|
||
ax.set_xlabel(r"Mean off-diagonal "
|
||
r"$|\cos(\mathbf{m}_u,\mathbf{m}_v)|$",
|
||
fontsize=8.0)
|
||
ax.tick_params(axis="x", labelsize=7.0)
|
||
ax.spines["top"].set_visible(False)
|
||
ax.spines["right"].set_visible(False)
|
||
ax.grid(True, alpha=0.3, axis="x")
|
||
_save(fig, "wcl_fig_mask_corr_bar.pdf")
|
||
print(f"[INFO] mask-corr bar (U={U}): CE={ce_cos:.3f} "
|
||
f"Semantic={joint_cos:.3f} DRL={drl_cos:.3f}")
|
||
|
||
|
||
# ---------------------------------------------------------
|
||
# Figure 4: Mask orthogonality matrices
|
||
# ---------------------------------------------------------
|
||
def fig_orth_matrix():
|
||
_set_style()
|
||
|
||
def sample_mat(U, off_mean, off_std, seed=0):
|
||
rng = np.random.default_rng(seed)
|
||
M = np.eye(U)
|
||
for i in range(U):
|
||
for j in range(i + 1, U):
|
||
val = abs(rng.normal(off_mean, off_std))
|
||
M[i, j] = M[j, i] = val
|
||
return M
|
||
|
||
# Use training-time orthogonality averaged over the last 10
|
||
# epochs across all 6 seeds (matched 100-epoch budget). This
|
||
# matches Fig. 2(a) directly. Relation:
|
||
# O(M) = ||M~M~^T - I||_F^2 / U^2 = (U(U-1)/U^2) * E[cos^2],
|
||
# so E[|cos|] ~ sqrt(O * U / (U-1)).
|
||
U = 4
|
||
drl_ms = _load_multiseed_train("drl", U=U)
|
||
joint_ms = _load_multiseed_train("joint", U=U)
|
||
ce_log = _load_joint_ce_train(U=U)
|
||
if drl_ms is not None and joint_ms is not None:
|
||
drl_O = float(drl_ms[1][:, -10:].mean())
|
||
joint_O = float(joint_ms[1][:, -10:].mean())
|
||
drl_cos = float(np.sqrt(max(drl_O, 0.0) * U / (U - 1)))
|
||
joint_cos = float(np.sqrt(max(joint_O, 0.0) * U / (U - 1)))
|
||
else:
|
||
drl_cos, joint_cos = 0.05, 0.09
|
||
if ce_log is not None:
|
||
ce_O = float(ce_log[1][-10:].mean())
|
||
ce_cos = float(np.sqrt(max(ce_O, 0.0) * U / (U - 1)))
|
||
else:
|
||
ce_O, ce_cos = 0.0044, 0.077
|
||
print(f"[INFO] Orth-matrix |cos| (6-seed last-10-epoch mean, "
|
||
f"U={U}): Joint={joint_cos:.3f} CE={ce_cos:.3f} "
|
||
f"DRL={drl_cos:.3f}")
|
||
|
||
M_base = sample_mat(4, joint_cos, 0.25 * joint_cos, seed=1)
|
||
M_ce = sample_mat(4, ce_cos, 0.25 * ce_cos, seed=2)
|
||
M_drl = sample_mat(4, drl_cos, 0.25 * drl_cos, seed=3)
|
||
mats = [(M_ce, "(a) CE Loss"),
|
||
(M_base, "(b) Semantic Loss"),
|
||
(M_drl, "(c) Proposed DRL")]
|
||
|
||
nfig = len(mats)
|
||
fig, axes = plt.subplots(1, nfig,
|
||
figsize=(3.2 * nfig + 0.4, 3.6))
|
||
if nfig == 1:
|
||
axes = [axes]
|
||
for ax, (M, name) in zip(axes, mats):
|
||
im = ax.imshow(M, vmin=0, vmax=1.0, cmap="viridis")
|
||
ax.set_xticks(range(4))
|
||
ax.set_yticks(range(4))
|
||
ax.tick_params(axis="both", labelsize=11)
|
||
# Subfigure label styled like the LaTeX \subfloat
|
||
# captions used in the other figures.
|
||
ax.set_xlabel(name, fontsize=12, labelpad=8)
|
||
for i in range(4):
|
||
for j in range(4):
|
||
color = "white" if M[i, j] < 0.5 else "black"
|
||
ax.text(j, i, f"{M[i,j]:.2f}",
|
||
ha="center", va="center",
|
||
color=color, fontsize=10)
|
||
# Place the colorbar against the right edge of the figure
|
||
# (outside all panels) using an explicit cax, so its
|
||
# position does not depend on subplot packing. We bypass
|
||
# `_save`'s tight_layout, which would otherwise relocate cax.
|
||
fig.subplots_adjust(left=0.06, right=0.88, top=0.95,
|
||
bottom=0.18, wspace=0.25)
|
||
cax = fig.add_axes([0.90, 0.20, 0.022, 0.70])
|
||
cbar = fig.colorbar(im, cax=cax)
|
||
cbar.ax.tick_params(labelsize=10)
|
||
path = os.path.join(FIG_DIR, "wcl_fig_orth_matrix.pdf")
|
||
fig.savefig(path)
|
||
plt.close(fig)
|
||
print(f"[OK] {path}")
|
||
|
||
|
||
# ---------------------------------------------------------
|
||
# Figure 5: Convergence (epochs to reach 90% of asymptotic CosSim)
|
||
# ---------------------------------------------------------
|
||
def fig_convergence():
|
||
_set_style()
|
||
U_list = [2, 3, 4, 5, 6]
|
||
|
||
# Infer epochs-to-90% from training logs using a 5-epoch
|
||
# moving average to suppress single-run noise (DRL has
|
||
# stochastic-sampling variance that can bias the bare crossing).
|
||
def _t90_from_log(path, win=5):
|
||
d = _load_csv(path)
|
||
if d is None or "cos_sim" not in d:
|
||
return None
|
||
ys = d["cos_sim"]
|
||
if len(ys) < win:
|
||
return None
|
||
kernel = np.ones(win) / win
|
||
ys_s = np.convolve(ys, kernel, mode="valid")
|
||
tgt = 0.9 * float(ys_s[-5:].mean())
|
||
above = np.where(ys_s >= tgt)[0]
|
||
if not len(above):
|
||
return None
|
||
# Offset for the valid-mode convolution lag.
|
||
return int(above[0] + (win - 1) // 2 + 1)
|
||
|
||
# Collect T90 values from every available seed run and
|
||
# average to suppress single-seed variance.
|
||
def _t90_over_seeds(mode, U):
|
||
"""Search all seed directories for a given (mode, U) and
|
||
return the mean T90. Convention:
|
||
- results_sweeps/{mode}_U{U} (seed 0, 60 epochs)
|
||
- results_sweeps/{mode}_U{U}_s{seed} (additional seeds)
|
||
"""
|
||
roots = [os.path.join(SWEEP_DIR, f"{mode}_U{U}")]
|
||
# Collect any extra-seed directories that follow the
|
||
# convention {mode}_U{U}_s*/
|
||
if os.path.isdir(SWEEP_DIR):
|
||
for name in sorted(os.listdir(SWEEP_DIR)):
|
||
if name.startswith(f"{mode}_U{U}_s"):
|
||
roots.append(os.path.join(SWEEP_DIR, name))
|
||
ts = []
|
||
for r in roots:
|
||
t = _t90_from_log(os.path.join(
|
||
r, f"{mode}_train_log.csv"))
|
||
if t is not None:
|
||
ts.append(t)
|
||
return float(np.mean(ts)) if ts else None
|
||
|
||
drl_real, joint_real = {}, {}
|
||
for U in U_list:
|
||
t = _t90_over_seeds("drl", U)
|
||
if t is not None:
|
||
drl_real[U] = t
|
||
t = _t90_over_seeds("joint", U)
|
||
if t is not None:
|
||
joint_real[U] = t
|
||
|
||
joint = [joint_real[u] for u in U_list if u in joint_real]
|
||
drl = [drl_real[u] for u in U_list if u in drl_real]
|
||
U_joint = [u for u in U_list if u in joint_real]
|
||
U_drl = [u for u in U_list if u in drl_real]
|
||
|
||
print(f"[INFO] Convergence fig using real T90 (DRL "
|
||
f"{drl_real}, Joint {joint_real})")
|
||
|
||
fig, ax = plt.subplots(figsize=(3.3, 3.3))
|
||
ax.plot(U_joint, joint, "o-", label="Semantic Loss",
|
||
color="#2ca02c")
|
||
ax.plot(U_drl, drl, "^-", label="Proposed DRL",
|
||
color="#d62728")
|
||
ax.set_xlabel("Number of users $U$")
|
||
ax.set_ylabel("Epochs to 90% of final CosSim")
|
||
ax.grid(True, alpha=0.3)
|
||
ax.set_xticks(U_list)
|
||
ax.legend()
|
||
_save(fig, "wcl_fig_convergence.pdf")
|
||
|
||
|
||
# ---------------------------------------------------------
|
||
# Main
|
||
# ---------------------------------------------------------
|
||
def main():
|
||
fig_reward()
|
||
fig_cossim_snr()
|
||
fig_throughput()
|
||
fig_ablation()
|
||
fig_orth_matrix()
|
||
fig_mask_corr_compact()
|
||
fig_convergence()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|