wip inference
This commit is contained in:
Jan Kowalczyk
304
tools/plot_scripts/results_inference_timeline.py
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304
tools/plot_scripts/results_inference_timeline.py
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import json
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import pickle
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import shutil
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from datetime import datetime
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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# =========================
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# User-configurable params
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# =========================
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# Single experiment to plot (stem of the .bag file, e.g. "3_smoke_human_walking_2023-01-23")
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EXPERIMENT_NAME = "3_smoke_human_walking_2023-01-23"
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# Directory that contains {EXPERIMENT_NAME}_{method}_scores.npy for methods in {"deepsad","ocsvm","isoforest"}
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# Adjust this to where you save your per-method scores.
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methods_scores_path = Path(
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"/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/infer/DeepSAD/test/inference"
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)
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# Root data path containing .bag files used to build the cached stats
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all_data_path = Path("/home/fedex/mt/data/subter")
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# Output base directory (timestamped subfolder will be created here, then archived and copied to "latest/")
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output_path = Path("/home/fedex/mt/plots/results_inference_timeline")
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# Cache (stats + labels) directory — same as your original script
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cache_path = output_path
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# Assumed LiDAR frame resolution to convert counts -> percent (unchanged from original)
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data_resolution = 32 * 2048
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# Frames per second for x-axis time
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FPS = 10.0
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# Whether to try to align score sign so that higher = more degraded.
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# If manual labels exist for this experiment, alignment uses anomaly window mean vs. outside.
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ALIGN_SCORE_DIRECTION = True
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# =========================
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# Setup output folders
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# =========================
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datetime_folder_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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latest_folder_path = output_path / "latest"
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archive_folder_path = output_path / "archive"
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output_datetime_path = output_path / datetime_folder_name
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output_path.mkdir(exist_ok=True, parents=True)
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output_datetime_path.mkdir(exist_ok=True, parents=True)
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latest_folder_path.mkdir(exist_ok=True, parents=True)
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archive_folder_path.mkdir(exist_ok=True, parents=True)
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# =========================
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# Discover experiments to reconstruct indices consistent with caches
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# =========================
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normal_experiment_paths, anomaly_experiment_paths = [], []
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if not all_data_path.exists():
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raise FileNotFoundError(f"all_data_path does not exist: {all_data_path}")
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for bag_file_path in all_data_path.iterdir():
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if bag_file_path.suffix != ".bag":
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continue
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if "smoke" in bag_file_path.name:
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anomaly_experiment_paths.append(bag_file_path)
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else:
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normal_experiment_paths.append(bag_file_path)
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# Sort by filesize to match original ordering used when caches were generated
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normal_experiment_paths = sorted(
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normal_experiment_paths, key=lambda p: p.stat().st_size
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)
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anomaly_experiment_paths = sorted(
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anomaly_experiment_paths, key=lambda p: p.stat().st_size
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)
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# Find the path for the requested experiment
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exp_path = None
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exp_is_anomaly = None
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for p in anomaly_experiment_paths:
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if p.stem == EXPERIMENT_NAME:
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exp_path = p
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exp_is_anomaly = True
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break
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if exp_path is None:
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for p in normal_experiment_paths:
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if p.stem == EXPERIMENT_NAME:
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exp_path = p
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exp_is_anomaly = False
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break
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if exp_path is None:
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raise FileNotFoundError(
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f"Experiment '{EXPERIMENT_NAME}' not found as a .bag in {all_data_path}"
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)
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# Get the index within the appropriate list
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if exp_is_anomaly:
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exp_index = anomaly_experiment_paths.index(exp_path)
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else:
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exp_index = normal_experiment_paths.index(exp_path)
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# =========================
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# Load cached statistical data
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# =========================
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missing_points_cache = Path(cache_path / "missing_points.pkl")
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near_sensor_cache = Path(cache_path / "particles_near_sensor_counts_500.pkl")
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if not missing_points_cache.exists():
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raise FileNotFoundError(f"Missing points cache not found: {missing_points_cache}")
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if not near_sensor_cache.exists():
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raise FileNotFoundError(f"Near-sensor cache not found: {near_sensor_cache}")
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with open(missing_points_cache, "rb") as f:
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missing_points_normal, missing_points_anomaly = pickle.load(f)
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with open(near_sensor_cache, "rb") as f:
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near_sensor_normal, near_sensor_anomaly = pickle.load(f)
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if exp_is_anomaly:
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missing_points_series = np.asarray(missing_points_anomaly[exp_index], dtype=float)
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near_sensor_series = np.asarray(near_sensor_anomaly[exp_index], dtype=float)
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else:
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missing_points_series = np.asarray(missing_points_normal[exp_index], dtype=float)
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near_sensor_series = np.asarray(near_sensor_normal[exp_index], dtype=float)
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# Convert counts to percentages of total points
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missing_points_pct = (missing_points_series / data_resolution) * 100.0
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near_sensor_pct = (near_sensor_series / data_resolution) * 100.0
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# =========================
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# Load manual anomaly frame borders (optional; used for sign alignment + vertical markers)
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# =========================
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manually_labeled_anomaly_frames = {}
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labels_json_path = cache_path / "manually_labeled_anomaly_frames.json"
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if labels_json_path.exists():
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with open(labels_json_path, "r") as frame_borders_file:
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manually_labeled_anomaly_frames_json = json.load(frame_borders_file)
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for file in manually_labeled_anomaly_frames_json.get("files", []):
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manually_labeled_anomaly_frames[file["filename"]] = (
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file.get("semi_target_begin_frame", None),
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file.get("semi_target_end_frame", None),
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)
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# The JSON uses .npy filenames (as in original script). Create this experiment’s key.
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exp_npy_filename = exp_path.with_suffix(".npy").name
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anomaly_window = manually_labeled_anomaly_frames.get(exp_npy_filename, (None, None))
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# =========================
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# Load method scores and z-score normalize per method
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# =========================
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def zscore_1d(x: np.ndarray, eps=1e-12):
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x = np.asarray(x, dtype=float)
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mu = np.mean(x)
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sigma = np.std(x, ddof=0)
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if sigma < eps:
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return np.zeros_like(x)
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return (x - mu) / sigma
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def maybe_align_direction(z: np.ndarray, window):
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"""Flip sign so that the anomaly window mean is higher than the outside mean, if labels exist."""
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start, end = window
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if start is None or end is None:
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return z # no labels → leave as-is
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start = int(max(0, start))
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end = int(min(len(z), end))
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if end <= start or end > len(z):
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return z
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inside_mean = float(np.mean(z[start:end]))
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# outside: everything except [start:end]; handle edge cases
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if start == 0 and end == len(z):
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return z
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outside_parts = []
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if start > 0:
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outside_parts.append(z[:start])
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if end < len(z):
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outside_parts.append(z[end:])
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if not outside_parts:
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return z
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outside_mean = float(np.mean(np.concatenate(outside_parts)))
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return z if inside_mean >= outside_mean else -z
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methods = ["deepsad", "ocsvm", "isoforest"]
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method_scores = {}
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method_zscores = {}
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if not methods_scores_path.exists():
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raise FileNotFoundError(
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f"Methods scores path does not exist: {methods_scores_path}"
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)
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for m in methods:
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file_path = methods_scores_path / f"{EXPERIMENT_NAME}_{m}_scores.npy"
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if not file_path.exists():
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raise FileNotFoundError(f"Missing scores file for method '{m}': {file_path}")
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s = np.load(file_path)
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s = np.asarray(s, dtype=float).reshape(-1)
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# If needed, truncate or pad to match stats length (should match if generated consistently)
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n = min(len(s), len(missing_points_pct))
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if len(s) != len(missing_points_pct):
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# Align by truncation to the shortest length
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s = s[:n]
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# Also truncate stats to match
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missing_points_pct = missing_points_pct[:n]
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near_sensor_pct = near_sensor_pct[:n]
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z = zscore_1d(s)
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if ALIGN_SCORE_DIRECTION:
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z = maybe_align_direction(z, anomaly_window)
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method_scores[m] = s
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method_zscores[m] = z
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# Common time axis in seconds
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num_frames = len(missing_points_pct)
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t = np.arange(num_frames) / FPS
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# =========================
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# Plot 1: Missing points (%) vs. method z-scores
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# =========================
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fig1, axz1 = plt.subplots(figsize=(14, 6), constrained_layout=True)
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axy1 = axz1.twinx()
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# plot z-scores
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for m in methods:
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axz1.plot(t, method_zscores[m], label=f"{m} (z)", alpha=0.9)
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# plot missing points (%)
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axy1.plot(t, missing_points_pct, linestyle="--", alpha=0.7, label="Missing points (%)")
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# vertical markers for anomaly window if available
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start, end = anomaly_window
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if start is not None and end is not None and 0 <= start < end <= num_frames:
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axz1.axvline(x=start / FPS, linestyle=":", alpha=0.6)
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axz1.axvline(x=end / FPS, linestyle=":", alpha=0.6)
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axz1.set_xlabel("Time (s)")
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axz1.set_ylabel("Anomaly score (z-score, ↑ = more degraded)")
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axy1.set_ylabel("Missing points (%)")
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axz1.set_title(f"{EXPERIMENT_NAME}\nDegradation vs. Missing Points")
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# Build a combined legend
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lines1, labels1 = axz1.get_legend_handles_labels()
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lines2, labels2 = axy1.get_legend_handles_labels()
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axz1.legend(lines1 + lines2, labels1 + labels2, loc="upper right")
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axz1.grid(True, alpha=0.3)
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fig1.savefig(
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output_datetime_path / f"{EXPERIMENT_NAME}_zscores_vs_missing_points.png", dpi=150
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)
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plt.close(fig1)
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# =========================
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# Plot 2: Near-sensor (%) vs. method z-scores
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# =========================
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fig2, axz2 = plt.subplots(figsize=(14, 6), constrained_layout=True)
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axy2 = axz2.twinx()
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for m in methods:
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axz2.plot(t, method_zscores[m], label=f"{m} (z)", alpha=0.9)
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axy2.plot(t, near_sensor_pct, linestyle="--", alpha=0.7, label="Near-sensor <0.5m (%)")
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start, end = anomaly_window
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if start is not None and end is not None and 0 <= start < end <= num_frames:
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axz2.axvline(x=start / FPS, linestyle=":", alpha=0.6)
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axz2.axvline(x=end / FPS, linestyle=":", alpha=0.6)
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axz2.set_xlabel("Time (s)")
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axz2.set_ylabel("Anomaly score (z-score, ↑ = more degraded)")
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axy2.set_ylabel("Near-sensor points (%)")
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axz2.set_title(f"{EXPERIMENT_NAME}\nDegradation vs. Near-Sensor Points (<0.5 m)")
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lines1, labels1 = axz2.get_legend_handles_labels()
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lines2, labels2 = axy2.get_legend_handles_labels()
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axz2.legend(lines1 + lines2, labels1 + labels2, loc="upper right")
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axz2.grid(True, alpha=0.3)
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fig2.savefig(
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output_datetime_path / f"{EXPERIMENT_NAME}_zscores_vs_near_sensor.png", dpi=150
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)
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plt.close(fig2)
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# =========================
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# Preserve latest/, archive/, copy script
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# =========================
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# delete current latest folder
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shutil.rmtree(latest_folder_path, ignore_errors=True)
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# create new latest folder
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latest_folder_path.mkdir(exist_ok=True, parents=True)
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# copy contents of output folder to the latest folder
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for file in output_datetime_path.iterdir():
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shutil.copy2(file, latest_folder_path)
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# copy this python script to preserve the code used
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shutil.copy2(__file__, output_datetime_path)
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shutil.copy2(__file__, latest_folder_path)
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# move output date folder to archive
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shutil.move(output_datetime_path, archive_folder_path)
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print("Done. Plots saved and archived.")
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