603 lines
22 KiB
Python
603 lines
22 KiB
Python
"""Exploratory leakage-safe grouped benchmark for the ENGIN diesel dataset.
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EXPERIMENT STATUS: SUPERSEDED. Its metrics describe baseline candidates, not
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the model shipped by ``final_pipeline.py``. Use ``ml_polish_benchmark.py`` for
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the reproducibility numbers of the final architecture.
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The benchmark compares three spectral representations:
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* raw: the 21 measured amplitudes,
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* relative: cylinder spectrum minus the median spectrum of its engine,
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* combined: raw and relative features together.
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Every validation split contains complete, previously unseen engines. Relative
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features use only cylinders from the engine currently being transformed and do
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not use labels, so the same operation is available for an unseen test engine.
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"""
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from __future__ import annotations
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import argparse
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import json
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import re
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from pathlib import Path
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from typing import Iterable
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import numpy as np
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import pandas as pd
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from sklearn.base import BaseEstimator, TransformerMixin
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score, confusion_matrix, f1_score
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from sklearn.model_selection import GroupKFold, StratifiedGroupKFold
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import StandardScaler
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RANDOM_STATE = 42
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FREQ_COLS = [f"mV_{frequency}" for frequency in range(21)]
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LABELS = ["ok", "zakoksowany", "lejacy", "pompa", "iglica", "unknown"]
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FAULT_LABELS = ["zakoksowany", "lejacy", "pompa", "iglica"]
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SEVERITIES = ["male", "srednie", "duze"]
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NOT_APPLICABLE = "nie_dotyczy"
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FEATURE_SETS = ("raw", "relative", "combined")
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MODEL_NAMES = ("logistic", "extra_trees")
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class SpectrumFeatures(BaseEstimator, TransformerMixin):
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"""Create raw and engine-relative spectral features.
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NaNs are interpolated independently within each row along frequency. The
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training-set medians stored by ``fit`` are used only as a final fallback for
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a completely empty row, which is not present in the supplied data.
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"""
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def __init__(self, feature_set: str = "raw") -> None:
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self.feature_set = feature_set
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def fit(self, X: pd.DataFrame, y: Iterable[str] | None = None):
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self._validate_input(X)
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if self.feature_set not in FEATURE_SETS:
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raise ValueError(
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f"Unknown feature_set={self.feature_set!r}; choose from {FEATURE_SETS}."
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)
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spectra = X[FREQ_COLS].apply(pd.to_numeric, errors="coerce")
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medians = spectra.median(axis=0).to_numpy(dtype=float)
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if np.isnan(medians).any():
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raise ValueError("At least one frequency column is entirely NaN in training.")
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self.fallback_medians_ = medians
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return self
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def transform(self, X: pd.DataFrame) -> np.ndarray:
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self._validate_input(X)
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if not hasattr(self, "fallback_medians_"):
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raise RuntimeError("SpectrumFeatures must be fitted before transform().")
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spectra = X[FREQ_COLS].apply(pd.to_numeric, errors="coerce")
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spectra = spectra.interpolate(axis=1, limit_direction="both")
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spectra = spectra.fillna(
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pd.Series(self.fallback_medians_, index=FREQ_COLS)
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)
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# The median is calculated separately for every engine in X. During
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# validation X contains complete held-out engines, never training rows.
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engine_median = spectra.groupby(X["engine_id"].to_numpy()).transform("median")
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raw = spectra.to_numpy(dtype=float)
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relative = raw - engine_median.to_numpy(dtype=float)
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if self.feature_set == "raw":
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return raw
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if self.feature_set == "relative":
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return relative
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return np.hstack([raw, relative])
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@staticmethod
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def _validate_input(X: pd.DataFrame) -> None:
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required = {"engine_id", *FREQ_COLS}
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missing = sorted(required.difference(X.columns))
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if missing:
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raise ValueError(f"Missing input columns: {missing}")
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--data", type=Path, default=Path("val.csv"))
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parser.add_argument(
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"--output-dir", type=Path, default=Path("benchmark_outputs")
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)
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parser.add_argument("--n-splits", type=int, default=5)
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parser.add_argument("--random-state", type=int, default=RANDOM_STATE)
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parser.add_argument(
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"--splitter",
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choices=("stratified-group", "group"),
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default="stratified-group",
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help="Both splitters keep complete engines together.",
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)
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parser.add_argument(
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"--feature-sets",
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nargs="+",
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choices=FEATURE_SETS,
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default=list(FEATURE_SETS),
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)
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parser.add_argument(
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"--models", nargs="+", choices=MODEL_NAMES, default=list(MODEL_NAMES)
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)
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parser.add_argument(
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"--n-jobs",
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type=int,
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default=-1,
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help="Parallel workers for Extra Trees; -1 uses all available CPU cores.",
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)
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return parser.parse_args()
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def validate_data(df: pd.DataFrame, n_splits: int) -> None:
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required = {
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"engine_id",
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"cylinder",
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"n_cylinders",
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"label",
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"severity",
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*FREQ_COLS,
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}
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missing = sorted(required.difference(df.columns))
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if missing:
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raise ValueError(f"Input is missing columns: {missing}")
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if df.duplicated(["engine_id", "cylinder"]).any():
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raise ValueError("Duplicate engine_id + cylinder keys found.")
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unknown_labels = sorted(set(df["label"].dropna()).difference(LABELS))
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if unknown_labels:
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raise ValueError(f"Unexpected labels: {unknown_labels}")
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if df["engine_id"].nunique() < n_splits:
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raise ValueError("n_splits cannot exceed the number of engines.")
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engine_sizes = df.groupby("engine_id").agg(
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rows=("cylinder", "size"),
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expected=("n_cylinders", "first"),
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size_values=("n_cylinders", "nunique"),
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unique_cylinders=("cylinder", "nunique"),
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)
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complete = (
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(engine_sizes["rows"] == engine_sizes["expected"])
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& (engine_sizes["unique_cylinders"] == engine_sizes["expected"])
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& (engine_sizes["size_values"] == 1)
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)
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if not complete.all():
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bad = engine_sizes.index[~complete].tolist()
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raise ValueError(f"Incomplete or inconsistent engines: {bad}")
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invalid_severity = df.loc[df["label"].isin(FAULT_LABELS), "severity"].dropna()
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invalid_severity = sorted(set(invalid_severity).difference(SEVERITIES))
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if invalid_severity:
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raise ValueError(f"Unexpected fault severities: {invalid_severity}")
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def make_splits(
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df: pd.DataFrame,
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splitter_name: str,
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n_splits: int,
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random_state: int,
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) -> list[tuple[np.ndarray, np.ndarray]]:
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groups = df["engine_id"].to_numpy()
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if splitter_name == "stratified-group":
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splitter = StratifiedGroupKFold(
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n_splits=n_splits, shuffle=True, random_state=random_state
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)
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else:
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splitter = GroupKFold(n_splits=n_splits)
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splits = list(splitter.split(df, df["label"], groups))
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seen_validation_rows: list[int] = []
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for fold, (train_idx, valid_idx) in enumerate(splits, start=1):
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train_engines = set(df.iloc[train_idx]["engine_id"])
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valid_engines = set(df.iloc[valid_idx]["engine_id"])
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overlap = train_engines.intersection(valid_engines)
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if overlap:
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raise AssertionError(f"Engine leakage in fold {fold}: {sorted(overlap)}")
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seen_validation_rows.extend(valid_idx.tolist())
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if sorted(seen_validation_rows) != list(range(len(df))):
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raise AssertionError("Every row must appear in validation exactly once.")
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return splits
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def make_pipeline(
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model_name: str,
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feature_set: str,
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random_state: int,
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n_jobs: int,
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) -> Pipeline:
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steps: list[tuple[str, object]] = [
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("features", SpectrumFeatures(feature_set=feature_set))
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]
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if model_name == "logistic":
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steps.extend(
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[
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("scale", StandardScaler()),
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(
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"model",
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LogisticRegression(
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C=1.0,
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class_weight="balanced",
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max_iter=5_000,
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random_state=random_state,
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),
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),
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]
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)
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elif model_name == "extra_trees":
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steps.append(
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(
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"model",
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ExtraTreesClassifier(
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n_estimators=500,
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class_weight="balanced",
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max_features="sqrt",
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min_samples_leaf=1,
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n_jobs=n_jobs,
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random_state=random_state,
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),
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)
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)
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else:
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raise ValueError(f"Unknown model: {model_name}")
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return Pipeline(steps)
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def macro_f1(y_true: pd.Series | np.ndarray, y_pred: np.ndarray) -> float:
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return float(
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f1_score(y_true, y_pred, labels=LABELS, average="macro", zero_division=0)
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)
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def raw_score(label_f1: float, severity_accuracy: float) -> float:
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return 0.75 * label_f1 + 0.25 * severity_accuracy
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def ml_points(score: float) -> float:
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if score < 0.80:
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return 0.0
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return min(40.0, 40.0 * (score - 0.80) / 0.20)
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def safe_name(value: str) -> str:
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return re.sub(r"[^a-zA-Z0-9_-]+", "_", value)
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def evaluate_experiment(
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df: pd.DataFrame,
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splits: list[tuple[np.ndarray, np.ndarray]],
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model_name: str,
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feature_set: str,
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random_state: int,
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n_jobs: int,
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output_dir: Path,
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) -> tuple[dict[str, object], list[dict[str, object]], list[dict[str, object]]]:
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experiment_id = f"{feature_set}__{model_name}"
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y_label = df["label"].reset_index(drop=True)
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y_severity = df["severity"].reset_index(drop=True)
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true_fault = y_label.isin(FAULT_LABELS).to_numpy()
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oof_label = np.full(len(df), "", dtype=object)
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oof_severity_oracle = np.full(len(df), "", dtype=object)
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oof_severity_submission = np.full(len(df), NOT_APPLICABLE, dtype=object)
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fold_rows: list[dict[str, object]] = []
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for fold, (train_idx, valid_idx) in enumerate(splits, start=1):
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X_train = df.iloc[train_idx]
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X_valid = df.iloc[valid_idx]
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y_train_label = y_label.iloc[train_idx]
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label_pipeline = make_pipeline(
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model_name, feature_set, random_state + fold, n_jobs
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)
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label_pipeline.fit(X_train, y_train_label)
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label_pred = label_pipeline.predict(X_valid)
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oof_label[valid_idx] = label_pred
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severity_train_mask = y_train_label.isin(FAULT_LABELS).to_numpy()
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severity_pipeline = make_pipeline(
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model_name, feature_set, random_state + 100 + fold, n_jobs
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)
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severity_pipeline.fit(
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X_train.iloc[np.flatnonzero(severity_train_mask)],
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y_severity.iloc[train_idx].iloc[np.flatnonzero(severity_train_mask)],
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)
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severity_pred = severity_pipeline.predict(X_valid)
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oof_severity_oracle[valid_idx] = severity_pred
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predicted_fault = np.isin(label_pred, FAULT_LABELS)
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emitted = np.full(len(valid_idx), NOT_APPLICABLE, dtype=object)
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emitted[predicted_fault] = severity_pred[predicted_fault]
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oof_severity_submission[valid_idx] = emitted
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fold_true_label = y_label.iloc[valid_idx]
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fold_true_fault = fold_true_label.isin(FAULT_LABELS).to_numpy()
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fold_f1 = macro_f1(fold_true_label, label_pred)
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fold_severity_oracle = accuracy_score(
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y_severity.iloc[valid_idx].to_numpy()[fold_true_fault],
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severity_pred[fold_true_fault],
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)
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fold_severity_submission = accuracy_score(
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y_severity.iloc[valid_idx].to_numpy()[fold_true_fault],
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emitted[fold_true_fault],
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)
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fold_raw = raw_score(fold_f1, float(fold_severity_submission))
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fold_rows.append(
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{
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"experiment_id": experiment_id,
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"fold": fold,
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"train_engines": X_train["engine_id"].nunique(),
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"valid_engines": X_valid["engine_id"].nunique(),
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"valid_rows": len(valid_idx),
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"valid_fault_rows": int(fold_true_fault.sum()),
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"macro_f1": fold_f1,
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"severity_accuracy_oracle_label": float(fold_severity_oracle),
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"severity_accuracy_submission": float(fold_severity_submission),
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"raw_score": fold_raw,
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"ml_points": ml_points(fold_raw),
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}
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)
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if (oof_label == "").any() or (oof_severity_oracle == "").any():
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raise AssertionError("Some rows did not receive out-of-fold predictions.")
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overall_f1 = macro_f1(y_label, oof_label)
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severity_oracle_accuracy = float(
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accuracy_score(
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y_severity.to_numpy()[true_fault], oof_severity_oracle[true_fault]
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)
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)
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severity_submission_accuracy = float(
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accuracy_score(
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y_severity.to_numpy()[true_fault], oof_severity_submission[true_fault]
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)
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)
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overall_raw = raw_score(overall_f1, severity_submission_accuracy)
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fold_frame = pd.DataFrame(fold_rows)
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summary: dict[str, object] = {
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"experiment_id": experiment_id,
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"feature_set": feature_set,
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"model": model_name,
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"macro_f1_oof": overall_f1,
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"severity_accuracy_oracle_label_oof": severity_oracle_accuracy,
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"severity_accuracy_submission_oof": severity_submission_accuracy,
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"raw_score_oof": overall_raw,
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"ml_points_oof": ml_points(overall_raw),
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"macro_f1_fold_mean": float(fold_frame["macro_f1"].mean()),
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"macro_f1_fold_std": float(fold_frame["macro_f1"].std(ddof=1)),
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"raw_score_fold_mean": float(fold_frame["raw_score"].mean()),
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"raw_score_fold_std": float(fold_frame["raw_score"].std(ddof=1)),
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}
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class_f1_rows: list[dict[str, object]] = []
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per_class = f1_score(
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y_label, oof_label, labels=LABELS, average=None, zero_division=0
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)
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for label, score in zip(LABELS, per_class):
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class_f1_rows.append(
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{
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"experiment_id": experiment_id,
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"label": label,
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"support": int((y_label == label).sum()),
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"f1": float(score),
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}
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)
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matrix = confusion_matrix(y_label, oof_label, labels=LABELS)
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matrix_frame = pd.DataFrame(matrix, index=LABELS, columns=LABELS)
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matrix_frame.index.name = "true_label"
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matrix_frame.to_csv(output_dir / f"confusion__{safe_name(experiment_id)}.csv")
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oof_frame = df[["engine_id", "cylinder"]].copy()
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oof_frame["fold"] = -1
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for fold, (_, valid_idx) in enumerate(splits, start=1):
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oof_frame.loc[valid_idx, "fold"] = fold
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oof_frame["true_label"] = y_label
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oof_frame["predicted_label"] = oof_label
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oof_frame["true_severity"] = y_severity
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oof_frame["predicted_severity_oracle_label"] = oof_severity_oracle
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oof_frame["predicted_severity_submission"] = oof_severity_submission
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oof_frame.to_csv(
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output_dir / f"oof__{safe_name(experiment_id)}.csv", index=False
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)
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return summary, fold_rows, class_f1_rows
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def evaluate_hybrid_combinations(
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df: pd.DataFrame,
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experiment_ids: list[str],
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output_dir: Path,
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) -> pd.DataFrame:
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"""Combine label and severity OOF predictions without retraining.
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All base experiments use identical folds. Therefore selecting one base
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pipeline for labels and another for severity remains fully out-of-fold and
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gives an honest estimate for a two-model submission architecture.
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"""
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y_label = df["label"].reset_index(drop=True)
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y_severity = df["severity"].reset_index(drop=True)
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true_fault = y_label.isin(FAULT_LABELS).to_numpy()
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oof_frames = {
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experiment_id: pd.read_csv(
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output_dir / f"oof__{safe_name(experiment_id)}.csv"
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)
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for experiment_id in experiment_ids
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}
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rows: list[dict[str, object]] = []
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for label_experiment in experiment_ids:
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label_frame = oof_frames[label_experiment]
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label_pred = label_frame["predicted_label"].to_numpy(dtype=object)
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label_f1 = macro_f1(y_label, label_pred)
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for severity_experiment in experiment_ids:
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severity_frame = oof_frames[severity_experiment]
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severity_pred = severity_frame[
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"predicted_severity_oracle_label"
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].to_numpy(dtype=object)
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emitted = np.full(len(df), NOT_APPLICABLE, dtype=object)
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predicted_fault = np.isin(label_pred, FAULT_LABELS)
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emitted[predicted_fault] = severity_pred[predicted_fault]
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severity_accuracy = float(
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accuracy_score(y_severity.to_numpy()[true_fault], emitted[true_fault])
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)
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fold_raw_scores: list[float] = []
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fold_label_scores: list[float] = []
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for fold in sorted(label_frame["fold"].unique()):
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fold_mask = label_frame["fold"].eq(fold).to_numpy()
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fold_true_fault = true_fault & fold_mask
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fold_label_f1 = macro_f1(y_label[fold_mask], label_pred[fold_mask])
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fold_severity_accuracy = float(
|
|
accuracy_score(
|
|
y_severity.to_numpy()[fold_true_fault],
|
|
emitted[fold_true_fault],
|
|
)
|
|
)
|
|
fold_label_scores.append(fold_label_f1)
|
|
fold_raw_scores.append(
|
|
raw_score(fold_label_f1, fold_severity_accuracy)
|
|
)
|
|
|
|
score = raw_score(label_f1, severity_accuracy)
|
|
rows.append(
|
|
{
|
|
"label_experiment_id": label_experiment,
|
|
"severity_experiment_id": severity_experiment,
|
|
"macro_f1_oof": label_f1,
|
|
"severity_accuracy_submission_oof": severity_accuracy,
|
|
"raw_score_oof": score,
|
|
"ml_points_oof": ml_points(score),
|
|
"macro_f1_fold_std": float(np.std(fold_label_scores, ddof=1)),
|
|
"raw_score_fold_std": float(np.std(fold_raw_scores, ddof=1)),
|
|
}
|
|
)
|
|
|
|
return pd.DataFrame(rows).sort_values(
|
|
["raw_score_oof", "macro_f1_oof"], ascending=False
|
|
)
|
|
|
|
|
|
def main() -> None:
|
|
args = parse_args()
|
|
df = pd.read_csv(args.data).reset_index(drop=True)
|
|
validate_data(df, args.n_splits)
|
|
args.output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
splits = make_splits(
|
|
df,
|
|
splitter_name=args.splitter,
|
|
n_splits=args.n_splits,
|
|
random_state=args.random_state,
|
|
)
|
|
|
|
split_rows: list[dict[str, object]] = []
|
|
for fold, (train_idx, valid_idx) in enumerate(splits, start=1):
|
|
split_rows.append(
|
|
{
|
|
"fold": fold,
|
|
"train_engines": df.iloc[train_idx]["engine_id"].nunique(),
|
|
"valid_engines": df.iloc[valid_idx]["engine_id"].nunique(),
|
|
"train_rows": len(train_idx),
|
|
"valid_rows": len(valid_idx),
|
|
"valid_engine_ids": "|".join(
|
|
sorted(df.iloc[valid_idx]["engine_id"].unique())
|
|
),
|
|
}
|
|
)
|
|
pd.DataFrame(split_rows).to_csv(
|
|
args.output_dir / "fold_assignments.csv", index=False
|
|
)
|
|
|
|
summaries: list[dict[str, object]] = []
|
|
all_fold_rows: list[dict[str, object]] = []
|
|
all_class_f1_rows: list[dict[str, object]] = []
|
|
for feature_set in args.feature_sets:
|
|
for model_name in args.models:
|
|
print(f"Running {feature_set} + {model_name} ...", flush=True)
|
|
summary, fold_rows, class_f1_rows = evaluate_experiment(
|
|
df=df,
|
|
splits=splits,
|
|
model_name=model_name,
|
|
feature_set=feature_set,
|
|
random_state=args.random_state,
|
|
n_jobs=args.n_jobs,
|
|
output_dir=args.output_dir,
|
|
)
|
|
summaries.append(summary)
|
|
all_fold_rows.extend(fold_rows)
|
|
all_class_f1_rows.extend(class_f1_rows)
|
|
|
|
summary_frame = pd.DataFrame(summaries).sort_values(
|
|
["raw_score_oof", "macro_f1_oof"], ascending=False
|
|
)
|
|
summary_frame.to_csv(args.output_dir / "benchmark_summary.csv", index=False)
|
|
pd.DataFrame(all_fold_rows).to_csv(
|
|
args.output_dir / "benchmark_folds.csv", index=False
|
|
)
|
|
pd.DataFrame(all_class_f1_rows).to_csv(
|
|
args.output_dir / "benchmark_class_f1.csv", index=False
|
|
)
|
|
hybrid_frame = evaluate_hybrid_combinations(
|
|
df=df,
|
|
experiment_ids=summary_frame["experiment_id"].tolist(),
|
|
output_dir=args.output_dir,
|
|
)
|
|
hybrid_frame.to_csv(args.output_dir / "benchmark_hybrids.csv", index=False)
|
|
|
|
metadata = {
|
|
"data": str(args.data),
|
|
"rows": len(df),
|
|
"engines": int(df["engine_id"].nunique()),
|
|
"splitter": args.splitter,
|
|
"n_splits": args.n_splits,
|
|
"random_state": args.random_state,
|
|
"feature_sets": args.feature_sets,
|
|
"models": args.models,
|
|
"fault_labels_used_for_severity": FAULT_LABELS,
|
|
"note": (
|
|
"severity_accuracy_submission treats nie_dotyczy emitted after an "
|
|
"incorrect ok/unknown label as a severity error on a true fault"
|
|
),
|
|
}
|
|
(args.output_dir / "metadata.json").write_text(
|
|
json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8"
|
|
)
|
|
|
|
columns = [
|
|
"experiment_id",
|
|
"macro_f1_oof",
|
|
"severity_accuracy_submission_oof",
|
|
"raw_score_oof",
|
|
"macro_f1_fold_std",
|
|
"raw_score_fold_std",
|
|
"ml_points_oof",
|
|
]
|
|
print("\nOOF ranking (complete held-out engines):")
|
|
print(summary_frame[columns].to_string(index=False, float_format="{:.4f}".format))
|
|
print("\nBest two-model combinations (label pipeline + severity pipeline):")
|
|
hybrid_columns = [
|
|
"label_experiment_id",
|
|
"severity_experiment_id",
|
|
"macro_f1_oof",
|
|
"severity_accuracy_submission_oof",
|
|
"raw_score_oof",
|
|
"raw_score_fold_std",
|
|
"ml_points_oof",
|
|
]
|
|
print(
|
|
hybrid_frame[hybrid_columns]
|
|
.head(5)
|
|
.to_string(index=False, float_format="{:.4f}".format)
|
|
)
|
|
print(f"\nDetailed outputs saved to: {args.output_dir.resolve()}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|