mt/tools/plot_scripts/results_latent_space_tables.py

from __future__ import annotations

import shutil
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path

import numpy as np
import polars as pl

# CHANGE THIS IMPORT IF YOUR LOADER MODULE IS NAMED DIFFERENTLY
from load_results import load_results_dataframe

# ----------------------------
# Config
# ----------------------------
ROOT = Path("/home/fedex/mt/results/copy")  # experiments root you pass to the loader
OUTPUT_DIR = Path("/home/fedex/mt/plots/results_latent_space_tables")

# Semi-labeling regimes (semi_normals, semi_anomalous) in display order
SEMI_LABELING_REGIMES: list[tuple[int, int]] = [(0, 0), (50, 10), (500, 100)]

# Both evals are shown side-by-side in one table
EVALS_BOTH: tuple[str, str] = ("exp_based", "manual_based")

# Row order (latent dims)
LATENT_DIMS: list[int] = [32, 64, 128, 256, 512, 768, 1024]

# Column order (method shown to the user)
# We split DeepSAD into the two network backbones, like your plots.
METHOD_COLUMNS = [
    ("deepsad", "LeNet"),  # DeepSAD (LeNet)
    ("deepsad", "Efficient"),  # DeepSAD (Efficient)
    ("isoforest", "Efficient"),  # IsolationForest (Efficient baseline)
    ("ocsvm", "Efficient"),  # OC-SVM (Efficient baseline)
]

# Formatting
DECIMALS = 3  # cells look like 1.000 or 0.928 (3 decimals)


# ----------------------------
# Helpers
# ----------------------------


def _fmt_mean_std(mean: float | None, std: float | None) -> str:
    """Format mean ± std with 3 decimals (leading zero), or '--' if missing."""
    if mean is None or not (mean == mean):  # NaN check
        return "--"
    if std is None or not (std == std):
        return f"{mean:.3f}"
    return f"{mean:.3f}$\\,\\pm\\,{std:.3f}$"


def _with_net_label(df: pl.DataFrame) -> pl.DataFrame:
    """Add a canonical 'net_label' column like the plotting script (LeNet/Efficient/fallback)."""
    return df.with_columns(
        pl.when(
            pl.col("network").cast(pl.Utf8).str.to_lowercase().str.contains("lenet")
        )
        .then(pl.lit("LeNet"))
        .when(
            pl.col("network").cast(pl.Utf8).str.to_lowercase().str.contains("efficient")
        )
        .then(pl.lit("Efficient"))
        .otherwise(pl.col("network").cast(pl.Utf8))
        .alias("net_label")
    )


def _filter_base(df: pl.DataFrame) -> pl.DataFrame:
    """Restrict to valid dims/models and needed columns (no eval/regime filtering here)."""
    return df.filter(
        (pl.col("latent_dim").is_in(LATENT_DIMS))
        & (pl.col("model").is_in(["deepsad", "isoforest", "ocsvm"]))
        & (pl.col("eval").is_in(list(EVALS_BOTH)))
    ).select(
        "model",
        "net_label",
        "latent_dim",
        "fold",
        "ap",
        "eval",
        "semi_normals",
        "semi_anomalous",
    )


@dataclass(frozen=True)
class Cell:
    mean: float | None
    std: float | None


def _compute_cells(df: pl.DataFrame) -> dict[tuple[str, int, str, str, int, int], Cell]:
    """
    Compute per-(eval, latent_dim, model, net_label, semi_normals, semi_anomalous)
    mean/std for AP across folds.
    """
    if df.is_empty():
        return {}

    # For baselines (isoforest/ocsvm) constrain to Efficient backbone
    df = df.filter(
        pl.when(pl.col("model").is_in(["isoforest", "ocsvm"]))
        .then(pl.col("net_label") == "Efficient")
        .otherwise(True)
    )

    agg = (
        df.group_by(
            [
                "eval",
                "latent_dim",
                "model",
                "net_label",
                "semi_normals",
                "semi_anomalous",
            ]
        )
        .agg(pl.col("ap").mean().alias("mean_ap"), pl.col("ap").std().alias("std_ap"))
        .to_dicts()
    )

    out: dict[tuple[str, int, str, str, int, int], Cell] = {}
    for row in agg:
        key = (
            str(row["eval"]),
            int(row["latent_dim"]),
            str(row["model"]),
            str(row["net_label"]),
            int(row["semi_normals"]),
            int(row["semi_anomalous"]),
        )
        out[key] = Cell(mean=row.get("mean_ap"), std=row.get("std_ap"))
    return out


def method_label(model: str, net_label: str) -> str:
    """Map (model, net_label) to the four method names used in headers/caption."""
    if model == "deepsad" and net_label == "LeNet":
        return "DeepSAD (LeNet)"
    if model == "deepsad" and net_label == "Efficient":
        return "DeepSAD (Efficient)"
    if model == "isoforest":
        return "IsoForest"
    if model == "ocsvm":
        return "OC-SVM"
    # ignore anything else (e.g., other backbones)
    return ""


def per_method_median_std_from_cells(
    cells: dict[tuple[str, int, str, str, int, int], Cell],
) -> dict[str, float]:
    """Compute the median std across all cells, per method."""
    stds_by_method: dict[str, list[float]] = {
        "DeepSAD (LeNet)": [],
        "DeepSAD (Efficient)": [],
        "IsoForest": [],
        "OC-SVM": [],
    }

    for key, cell in cells.items():
        (ev, dim, model, net, semi_n, semi_a) = key
        name = method_label(model, net)
        if name and (cell.std is not None) and (cell.std == cell.std):  # not NaN
            stds_by_method[name].append(cell.std)

    return {
        name: float(np.median(vals)) if vals else float("nan")
        for name, vals in stds_by_method.items()
    }


def per_method_max_std_from_cells(
    cells: dict[tuple[str, int, str, str, int, int], Cell],
) -> tuple[dict[str, float], dict[str, tuple]]:
    """
    Scan the aggregated 'cells' and return:
      - max_std_by_method: dict {"DeepSAD (LeNet)": 0.037, ...}
      - argmax_key_by_method: which cell (eval, dim, model, net, semi_n, semi_a) produced that max
    Only considers the four methods shown in the table.
    """
    max_std_by_method: dict[str, float] = {
        "DeepSAD (LeNet)": float("nan"),
        "DeepSAD (Efficient)": float("nan"),
        "IsoForest": float("nan"),
        "OC-SVM": float("nan"),
    }
    argmax_key_by_method: dict[str, tuple] = {}

    for key, cell in cells.items():
        (ev, dim, model, net, semi_n, semi_a) = key
        name = method_label(model, net)
        if name == "" or cell.std is None or not (cell.std == cell.std):  # empty/NaN
            continue
        cur = max_std_by_method.get(name, float("nan"))
        if (cur != cur) or (cell.std > cur):  # handle NaN initial
            max_std_by_method[name] = cell.std
            argmax_key_by_method[name] = key

    # Replace remaining NaNs with 0.0 for nice formatting
    for k, v in list(max_std_by_method.items()):
        if not (v == v):  # NaN
            max_std_by_method[k] = 0.0

    return max_std_by_method, argmax_key_by_method


def _fmt_val(val: float | None) -> str:
    """
    Format value as:
      - '--' if None/NaN
      - '1.0' if exactly 1 (within 1e-9)
      - '.xx' otherwise (2 decimals, no leading 0)
    """
    if val is None or not (val == val):  # None or NaN
        return "--"
    if abs(val - 1.0) < 1e-9:
        return "1.0"
    return f"{val:.2f}".lstrip("0")


def _fmt_mean(mean: float | None) -> str:
    return "--" if (mean is None or not (mean == mean)) else f"{mean:.{DECIMALS}f}"


def _bold_best_mask_display(values: list[float | None], decimals: int) -> list[bool]:
    """
    Bolding mask based on *displayed* precision. Any entries that round (via f-string)
    to the maximum at 'decimals' places are bolded (ties bolded).
    """

    def disp(v: float | None) -> float | None:
        if v is None or not (v == v):
            return None
        return float(f"{v:.{decimals}f}")

    rounded = [disp(v) for v in values]
    finite = [v for v in rounded if v is not None]
    if not finite:
        return [False] * len(values)
    maxv = max(finite)
    return [(v is not None and v == maxv) for v in rounded]


def _build_exp_based_table(
    cells: dict[tuple[str, int, str, str, int, int], Cell],
    *,
    semi_labeling_regimes: list[tuple[int, int]],
) -> str:
    """
    Build LaTeX table with mean ± std values for experiment-based evaluation only.
    """

    header_cols = [
        r"\rotheader{DeepSAD\\(LeNet)}",
        r"\rotheader{DeepSAD\\(Efficient)}",
        r"\rotheader{IsoForest}",
        r"\rotheader{OC-SVM}",
    ]

    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(r"\setlength{\tabcolsep}{4pt}")
    lines.append(r"\renewcommand{\arraystretch}{1.2}")
    lines.append(r"\begin{tabularx}{\textwidth}{c*{4}{Y}}")
    lines.append(r"\toprule")
    lines.append(r"Latent Dim. & " + " & ".join(header_cols) + r" \\")
    lines.append(r"\midrule")

    for idx, (semi_n, semi_a) in enumerate(semi_labeling_regimes):
        # regime label row
        lines.append(
            rf"\multicolumn{{5}}{{l}}{{\textbf{{Labeling regime: }}\(\mathbf{{{semi_n}/{semi_a}}}\)}} \\"
        )
        lines.append(r"\addlinespace[2pt]")

        for dim in LATENT_DIMS:
            row_vals = []
            for model, net in METHOD_COLUMNS:
                key = ("exp_based", dim, model, net, semi_n, semi_a)
                cell = cells.get(key, Cell(None, None))
                row_vals.append(_fmt_mean_std(cell.mean, cell.std))

            lines.append(f"{dim} & " + " & ".join(row_vals) + r" \\")

        if idx < len(semi_labeling_regimes) - 1:
            lines.append(r"\midrule")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabularx}")
    lines.append(
        r"\caption{AP means $\pm$ std across 5 folds for experiment-based evaluation only, grouped by labeling regime.}"
    )
    lines.append(r"\end{table}")

    return "\n".join(lines)


def _build_single_table(
    cells: dict[tuple[str, int, str, str, int, int], Cell],
    *,
    semi_labeling_regimes: list[tuple[int, int]],
) -> tuple[str, float | None]:
    """
    Build the LaTeX table string with grouped headers and regime blocks.
    Returns (latex, max_std_overall).
    """

    # Rotated header labels (90° slanted)
    header_cols = [
        r"\rotheader{DeepSAD\\(LeNet)}",
        r"\rotheader{DeepSAD\\(Efficient)}",
        r"\rotheader{IsoForest}",
        r"\rotheader{OC-SVM}",
    ]

    # Track max std across all cells
    max_std: float | None = None

    def push_std(std_val: float | None):
        nonlocal max_std
        if std_val is None or not (std_val == std_val):
            return
        if max_std is None or std_val > max_std:
            max_std = std_val

    lines: list[str] = []

    # Table preamble / structure
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(r"\setlength{\tabcolsep}{4pt}")
    lines.append(r"\renewcommand{\arraystretch}{1.2}")
    # Vertical rule between the two groups for data/header rows:
    lines.append(r"\begin{tabularx}{\textwidth}{c*{4}{Y}|*{4}{Y}}")
    lines.append(r"\toprule")
    lines.append(
        r" & \multicolumn{4}{c}{Experiment-based eval.} & \multicolumn{4}{c}{Handlabeled eval.} \\"
    )
    lines.append(r"\cmidrule(lr){2-5} \cmidrule(lr){6-9}")
    lines.append(
        r"Latent Dim. & "
        + " & ".join(header_cols)
        + " & "
        + " & ".join(header_cols)
        + r" \\"
    )
    lines.append(r"\midrule")

    # Iterate regimes and rows
    for idx, (semi_n, semi_a) in enumerate(semi_labeling_regimes):
        # Regime label row (multicolumn suppresses the vertical bar in this row)
        lines.append(
            rf"\multicolumn{{9}}{{l}}{{\textbf{{Labeling regime: }}\(\mathbf{{{semi_n}/{semi_a}}}\) "
            rf"\textit{{(normal/anomalous samples labeled)}}}} \\"
        )
        lines.append(r"\addlinespace[2pt]")

        for dim in LATENT_DIMS:
            # Values in order: left group (exp_based) 4 cols, right group (manual_based) 4 cols
            means_left: list[float | None] = []
            means_right: list[float | None] = []
            cell_strs_left: list[str] = []
            cell_strs_right: list[str] = []

            # Left group: exp_based
            eval_type = EVALS_BOTH[0]
            for model, net in METHOD_COLUMNS:
                key = (eval_type, dim, model, net, semi_n, semi_a)
                cell = cells.get(key, Cell(None, None))
                means_left.append(cell.mean)
                cell_strs_left.append(_fmt_mean(cell.mean))
                # mean_str = _fmt_val(cell.mean)
                # std_str = _fmt_val(cell.std)
                # if mean_str == "--":
                #     cell_strs_left.append("--")
                # else:
                #     cell_strs_left.append(f"{mean_str} $\\textpm$ {std_str}")
                push_std(cell.std)

            # Right group: manual_based
            eval_type = EVALS_BOTH[1]
            for model, net in METHOD_COLUMNS:
                key = (eval_type, dim, model, net, semi_n, semi_a)
                cell = cells.get(key, Cell(None, None))
                means_right.append(cell.mean)
                cell_strs_right.append(_fmt_mean(cell.mean))
                # mean_str = _fmt_val(cell.mean)
                # std_str = _fmt_val(cell.std)
                # if mean_str == "--":
                #     cell_strs_right.append("--")
                # else:
                #     cell_strs_right.append(f"{mean_str} $\\textpm$ {std_str}")
                push_std(cell.std)

            # Bolding per group based on displayed precision
            mask_left = _bold_best_mask_display(means_left, DECIMALS)
            mask_right = _bold_best_mask_display(means_right, DECIMALS)

            pretty_left = [
                (r"\textbf{" + s + "}") if (do_bold and s != "--") else s
                for s, do_bold in zip(cell_strs_left, mask_left)
            ]
            pretty_right = [
                (r"\textbf{" + s + "}") if (do_bold and s != "--") else s
                for s, do_bold in zip(cell_strs_right, mask_right)
            ]

            # Join with the vertical bar between groups automatically handled by column spec
            lines.append(
                f"{dim} & "
                + " & ".join(pretty_left)
                + " & "
                + " & ".join(pretty_right)
                + r" \\"
            )

        # Separator between regime blocks (but not after the last one)
        if idx < len(semi_labeling_regimes) - 1:
            lines.append(r"\midrule")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabularx}")

    # Compute per-method max std across everything included in the table
    # max_std_by_method, argmax_key = per_method_max_std_from_cells(cells)
    median_std_by_method = per_method_median_std_from_cells(cells)

    # Optional: print where each max came from (helps verify)
    for name, v in median_std_by_method.items():
        print(f"[max-std] {name}: {v:.3f}")

    cap_parts = []
    for name in ["DeepSAD (LeNet)", "DeepSAD (Efficient)", "IsoForest", "OC-SVM"]:
        v = median_std_by_method.get(name, 0.0)
        cap_parts.append(f"{name} {v:.3f}")
    cap_str = "; ".join(cap_parts)

    lines.append(
        rf"\caption{{AP means across 5 folds for both evaluations, grouped by labeling regime. "
        rf"Maximum observed standard deviation per method (not shown in table): {cap_str}.}}"
    )
    lines.append(r"\end{table}")

    return "\n".join(lines), max_std


def main():
    # Load full results DF (cache behavior handled by your loader)
    df = load_results_dataframe(ROOT, allow_cache=True)
    df = _with_net_label(df)
    df = _filter_base(df)

    # Prepare output dirs
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    archive_dir = OUTPUT_DIR / "archive"
    archive_dir.mkdir(parents=True, exist_ok=True)
    ts_dir = archive_dir / datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    ts_dir.mkdir(parents=True, exist_ok=True)

    # Pre-compute aggregated cells (mean/std) for all evals/regimes
    cells = _compute_cells(df)

    # Build the single big table
    tex, max_std = _build_single_table(
        cells, semi_labeling_regimes=SEMI_LABELING_REGIMES
    )

    out_name = "ap_table_all_evals_all_regimes.tex"
    out_path = ts_dir / out_name
    out_path.write_text(tex, encoding="utf-8")

    # Build experiment-based table with mean ± std
    tex_exp = _build_exp_based_table(cells, semi_labeling_regimes=SEMI_LABELING_REGIMES)

    out_name_exp = "ap_table_exp_based_mean_std.tex"
    out_path_exp = ts_dir / out_name_exp
    out_path_exp.write_text(tex_exp, encoding="utf-8")

    # Copy this script to preserve the code used for the outputs
    script_path = Path(__file__)
    shutil.copy2(script_path, ts_dir / script_path.name)

    # Mirror latest
    latest = OUTPUT_DIR / "latest"
    latest.mkdir(exist_ok=True, parents=True)
    for f in latest.iterdir():
        if f.is_file():
            f.unlink()
    for f in ts_dir.iterdir():
        if f.is_file():
            shutil.copy2(f, latest / f.name)

    print(f"Saved table to: {ts_dir}")
    print(f"Also updated: {latest}")
    print(f" - {out_name}")


if __name__ == "__main__":
    main()