table plot
This commit is contained in:
Jan Kowalczyk
255
tools/plot_scripts/results_latent_space_tables.py
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255
tools/plot_scripts/results_latent_space_tables.py
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from __future__ import annotations
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import shutil
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from dataclasses import dataclass
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from datetime import datetime
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from pathlib import Path
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import polars as pl
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# CHANGE THIS IMPORT IF YOUR LOADER MODULE IS NAMED DIFFERENTLY
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from load_results import load_results_dataframe
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# ----------------------------
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# Config
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# ----------------------------
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ROOT = Path("/home/fedex/mt/results/copy") # experiments root you pass to the loader
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OUTPUT_DIR = Path("/home/fedex/mt/plots/results_latent_space_tables")
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# Semi-labeling regimes (semi_normals, semi_anomalous)
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SEMI_LABELING_REGIMES: list[tuple[int, int]] = [(0, 0), (50, 10), (500, 100)]
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# Which evaluation columns to include (one table per eval × semi-regime)
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EVALS: list[str] = ["exp_based", "manual_based"]
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# Row order (latent dims)
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LATENT_DIMS: list[int] = [32, 64, 128, 256, 512, 768, 1024]
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# Column order (method shown to the user)
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# We split DeepSAD into the two network backbones, like your plots.
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METHOD_COLUMNS = [
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("deepsad", "LeNet"), # DeepSAD (LeNet)
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("deepsad", "Efficient"), # DeepSAD (Efficient)
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("isoforest", "Efficient"), # IsolationForest (Efficient backbone baseline)
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("ocsvm", "Efficient"), # OC-SVM (Efficient backbone baseline)
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]
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# Formatting
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DECIMALS = 3 # number of decimals for mean/std
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STD_FMT = r"\textpm" # between mean and std in LaTeX
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# ----------------------------
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# Helpers
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# ----------------------------
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def _with_net_label(df: pl.DataFrame) -> pl.DataFrame:
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"""Add a canonical 'net_label' column like the plotting script (LeNet/Efficient/fallback)."""
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return df.with_columns(
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pl.when(
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pl.col("network").cast(pl.Utf8).str.to_lowercase().str.contains("lenet")
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)
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.then(pl.lit("LeNet"))
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.when(
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pl.col("network").cast(pl.Utf8).str.to_lowercase().str.contains("efficient")
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)
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.then(pl.lit("Efficient"))
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.otherwise(pl.col("network").cast(pl.Utf8))
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.alias("net_label")
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)
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def _filter_base(
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df: pl.DataFrame,
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*,
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eval_type: str,
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semi_normals: int,
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semi_anomalous: int,
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) -> pl.DataFrame:
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"""Common filtering by regime/eval/valid dims&models."""
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return df.filter(
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(pl.col("semi_normals") == semi_normals)
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& (pl.col("semi_anomalous") == semi_anomalous)
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& (pl.col("eval") == eval_type)
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& (pl.col("latent_dim").is_in(LATENT_DIMS))
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& (pl.col("model").is_in(["deepsad", "isoforest", "ocsvm"]))
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).select(
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"model",
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"net_label",
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"latent_dim",
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"fold",
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"auc",
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)
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def _format_mean_std(mean: float | None, std: float | None) -> str:
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if mean is None or (mean != mean): # NaN check
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return "--"
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if std is None or (std != std):
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return f"{mean:.{DECIMALS}f}"
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return f"{mean:.{DECIMALS}f} {STD_FMT} {std:.{DECIMALS}f}"
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@dataclass(frozen=True)
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class Cell:
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mean: float | None
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std: float | None
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def _compute_cells(df: pl.DataFrame) -> dict[tuple[int, str, str], Cell]:
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"""
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Compute per-(latent_dim, model, net_label) mean/std for AUC across folds.
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Returns a dict keyed by (latent_dim, model, net_label).
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"""
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if df.is_empty():
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return {}
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agg = (
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df.group_by(["latent_dim", "model", "net_label"])
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.agg(
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pl.col("auc").mean().alias("mean_auc"), pl.col("auc").std().alias("std_auc")
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)
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.to_dicts()
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)
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out: dict[tuple[int, str, str], Cell] = {}
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for row in agg:
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key = (int(row["latent_dim"]), str(row["model"]), str(row["net_label"]))
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out[key] = Cell(mean=row.get("mean_auc"), std=row.get("std_auc"))
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return out
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def _bold_best_in_row(values: list[float | None]) -> list[bool]:
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"""Return a mask of which entries are the (tied) maximum among non-None values."""
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clean = [(v if (v is not None and v == v) else None) for v in values]
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finite_vals = [v for v in clean if v is not None]
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if not finite_vals:
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return [False] * len(values)
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maxv = max(finite_vals)
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return [(v is not None and abs(v - maxv) < 1e-12) for v in clean]
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def _latex_table(
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cells: dict[tuple[int, str, str], Cell],
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*,
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eval_type: str,
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semi_normals: int,
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semi_anomalous: int,
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) -> str:
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"""
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Build a LaTeX table with rows=latent dims and columns=METHOD_COLUMNS.
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Bold best AUC (mean) per row.
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"""
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header_cols = [
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r"\textbf{DeepSAD (LeNet)}",
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r"\textbf{DeepSAD (Efficient)}",
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r"\textbf{IsolationForest}",
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r"\textbf{OC\text{-}SVM}",
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]
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eval_type_str = (
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"experiment-based evaluation"
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if eval_type == "exp_based"
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else "handlabeling-based evaluation"
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)
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lines: list[str] = []
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lines.append(r"\begin{table}[t]")
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lines.append(r"\centering")
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lines.append(
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rf"\caption{{AUC (mean {STD_FMT} std) across 5 folds for \texttt{{{eval_type_str}}}, "
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rf"semi-labeling regime: {semi_normals} normal samples {semi_anomalous} anomalous samples.}}"
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)
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lines.append(rf"\label{{tab:auc_{eval_type}_semi_{semi_normals}_{semi_anomalous}}}")
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lines.append(r"\begin{tabularx}{\textwidth}{cYYYY}")
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lines.append(r"\toprule")
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lines.append(r"\textbf{Latent Dim.} & " + " & ".join(header_cols) + r" \\")
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lines.append(r"\midrule")
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for dim in LATENT_DIMS:
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# Collect means for bolding
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means_for_bold: list[float | None] = []
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cell_strs: list[str] = []
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for model, net in METHOD_COLUMNS:
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cell = cells.get((dim, model, net), Cell(None, None))
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means_for_bold.append(cell.mean)
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cell_strs.append(_format_mean_std(cell.mean, cell.std))
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bold_mask = _bold_best_in_row(means_for_bold)
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pretty_cells: list[str] = []
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for s, do_bold in zip(cell_strs, bold_mask):
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if do_bold and s != "--":
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pretty_cells.append(r"\textbf{" + s + r"}")
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else:
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pretty_cells.append(s)
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lines.append(f"{dim} & " + " & ".join(pretty_cells) + r" \\")
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lines.append(r"\bottomrule")
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lines.append(r"\end{tabularx}")
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lines.append(r"\end{table}")
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return "\n".join(lines)
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def main():
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# Load full results DF (cache behavior handled by your loader)
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df = load_results_dataframe(ROOT, allow_cache=True)
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df = _with_net_label(df)
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# Prepare output dirs
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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archive_dir = OUTPUT_DIR / "archive"
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archive_dir.mkdir(parents=True, exist_ok=True)
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ts_dir = archive_dir / datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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ts_dir.mkdir(parents=True, exist_ok=True)
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emitted_files: list[Path] = []
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for semi_normals, semi_anomalous in SEMI_LABELING_REGIMES:
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for eval_type in EVALS:
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sub = _filter_base(
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df,
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eval_type=eval_type,
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semi_normals=semi_normals,
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semi_anomalous=semi_anomalous,
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)
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# For baselines (isoforest/ocsvm) we constrain to Efficient backbone to mirror plots
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sub = sub.filter(
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pl.when(pl.col("model").is_in(["isoforest", "ocsvm"]))
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.then(pl.col("net_label") == "Efficient")
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.otherwise(True)
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)
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cells = _compute_cells(sub)
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tex = _latex_table(
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cells,
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eval_type=eval_type,
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semi_normals=semi_normals,
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semi_anomalous=semi_anomalous,
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)
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out_name = f"auc_table_{eval_type}_semi_{semi_normals}_{semi_anomalous}.tex"
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out_path = ts_dir / out_name
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out_path.write_text(tex, encoding="utf-8")
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emitted_files.append(out_path)
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# Copy this script to preserve the code used for the outputs
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script_path = Path(__file__)
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shutil.copy2(script_path, ts_dir / script_path.name)
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# Mirror latest
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latest = OUTPUT_DIR / "latest"
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latest.mkdir(exist_ok=True, parents=True)
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for f in latest.iterdir():
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if f.is_file():
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f.unlink()
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for f in ts_dir.iterdir():
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if f.is_file():
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shutil.copy2(f, latest / f.name)
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print(f"Saved tables to: {ts_dir}")
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print(f"Also updated: {latest}")
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for p in emitted_files:
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print(f" - {p.name}")
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if __name__ == "__main__":
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main()
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