Remove nested directory: BitTransformerLM/bit_transformer/dashboard.py

BitTransformerLM/bit_transformer/dashboard.py

@@ -1,58 +0,0 @@
-import matplotlib.pyplot as plt
-from typing import Dict, List, Tuple
-
-
-def plot_telemetry(
-    metrics_log: Dict[str, List[float]],
-    k_floor: float = 0.5,
-    c_floor: float = 0.3,
-    s_floor: float = 0.5,
-) -> Tuple[plt.Figure, List[plt.Axes]]:
-    """Plot K, C, S metrics over time with cluster transitions.
-
-    Args:
-        metrics_log: Dictionary with keys ``negentropy``, ``lz_complexity``,
-            ``symbiosis_score`` and optional ``clusters`` listing cluster
-            assignments per step.
-        k_floor: Threshold for negentropy (K).
-        c_floor: Threshold for LZ complexity (C).
-        s_floor: Threshold for symbiosis score (S).
-
-    Returns:
-        (figure, axes) tuple for further customization or saving.
-    """
-    steps = list(range(len(metrics_log.get("negentropy", []))))
-    fig, axes = plt.subplots(3, 1, sharex=True, figsize=(10, 6))
-    metrics = [
-        ("negentropy", k_floor, "K"),
-        ("lz_complexity", c_floor, "C"),
-        ("symbiosis_score", s_floor, "S"),
-    ]
-    for ax, (key, floor, label) in zip(axes, metrics):
-        values = metrics_log.get(key, [])
-        ax.plot(steps, values, label=label)
-        ax.axhline(floor, color="r", linestyle="--", linewidth=1)
-        violations = [i for i, v in enumerate(values) if v < floor]
-        if violations:
-            ax.scatter(
-                [steps[i] for i in violations],
-                [values[i] for i in violations],
-                color="r",
-                zorder=5,
-                label="violation",
-            )
-        ax.set_ylabel(label)
-        ax.legend(loc="upper right")
-
-    clusters = metrics_log.get("clusters")
-    if clusters is not None:
-        prev = clusters[0]
-        for t, c in enumerate(clusters):
-            if t > 0 and c != prev:
-                for ax in axes:
-                    ax.axvline(t, color="gray", linestyle=":", alpha=0.5)
-                prev = c
-
-    axes[-1].set_xlabel("step")
-    plt.tight_layout()
-    return fig, axes