wip inference
This commit is contained in:
Jan Kowalczyk
@@ -261,6 +261,80 @@ class IsoForest(object):
|
||||
logger.info("Test Time: {:.3f}s".format(self.results["test_time"]))
|
||||
logger.info("Finished testing.")
|
||||
|
||||
def inference(
|
||||
self,
|
||||
dataset: BaseADDataset,
|
||||
device: str = "cpu",
|
||||
n_jobs_dataloader: int = 0,
|
||||
batch_size: int = 32,
|
||||
):
|
||||
"""Perform inference on the dataset using the trained Isolation Forest model."""
|
||||
logger = logging.getLogger()
|
||||
|
||||
# Get inference data loader
|
||||
_, _, inference_loader = dataset.loaders(
|
||||
batch_size=batch_size, num_workers=n_jobs_dataloader
|
||||
)
|
||||
|
||||
# Get data from loader
|
||||
X = ()
|
||||
idxs = []
|
||||
file_ids = []
|
||||
frame_ids = []
|
||||
|
||||
logger.info("Starting inference...")
|
||||
start_time = time.time()
|
||||
|
||||
for data in inference_loader:
|
||||
inputs, idx, (file_id, frame_id) = data
|
||||
inputs = inputs.to(device)
|
||||
|
||||
if self.hybrid:
|
||||
inputs = self.ae_net.encoder(inputs)
|
||||
X_batch = inputs.view(inputs.size(0), -1)
|
||||
X += (X_batch.cpu().data.numpy(),)
|
||||
|
||||
# Store indices and metadata
|
||||
idxs.extend(idx.cpu().data.numpy().tolist())
|
||||
file_ids.extend(file_id.cpu().data.numpy().tolist())
|
||||
frame_ids.extend(frame_id.cpu().data.numpy().tolist())
|
||||
|
||||
X = np.concatenate(X)
|
||||
|
||||
# Get anomaly scores
|
||||
scores = (-1.0) * self.model.decision_function(X)
|
||||
scores = scores.flatten()
|
||||
|
||||
# Store inference results
|
||||
self.inference_time = time.time() - start_time
|
||||
self.inference_indices = np.array(idxs)
|
||||
self.inference_file_ids = np.array(file_ids)
|
||||
self.inference_frame_ids = np.array(frame_ids)
|
||||
|
||||
# Create index mapping similar to DeepSAD trainer
|
||||
self.inference_index_mapping = {
|
||||
"indices": self.inference_indices,
|
||||
"file_ids": self.inference_file_ids,
|
||||
"frame_ids": self.inference_frame_ids,
|
||||
}
|
||||
|
||||
# Log inference statistics
|
||||
logger.info(f"Number of inference samples: {len(self.inference_indices)}")
|
||||
logger.info(
|
||||
f"Number of unique files: {len(np.unique(self.inference_file_ids))}"
|
||||
)
|
||||
logger.info("Inference Time: {:.3f}s".format(self.inference_time))
|
||||
logger.info(
|
||||
"Score statistics: "
|
||||
f"min={scores.min():.3f}, "
|
||||
f"max={scores.max():.3f}, "
|
||||
f"mean={scores.mean():.3f}, "
|
||||
f"std={scores.std():.3f}"
|
||||
)
|
||||
logger.info("Finished inference.")
|
||||
|
||||
return scores
|
||||
|
||||
def load_ae(self, dataset_name, model_path):
|
||||
"""Load pretrained autoencoder from model_path for feature extraction in a hybrid Isolation Forest model."""
|
||||
|
||||
|
||||
@@ -453,6 +453,80 @@ class OCSVM(object):
|
||||
logger.info("Test Time: {:.3f}s".format(self.results["test_time"]))
|
||||
logger.info("Finished testing.")
|
||||
|
||||
def inference(
|
||||
self,
|
||||
dataset: BaseADDataset,
|
||||
device: str = "cpu",
|
||||
n_jobs_dataloader: int = 0,
|
||||
batch_size: int = 32,
|
||||
):
|
||||
"""Perform inference on the dataset using the trained OC-SVM model."""
|
||||
logger = logging.getLogger()
|
||||
|
||||
# Get inference data loader
|
||||
_, _, inference_loader = dataset.loaders(
|
||||
batch_size=batch_size, num_workers=n_jobs_dataloader
|
||||
)
|
||||
|
||||
# Get data from loader
|
||||
X = ()
|
||||
idxs = []
|
||||
file_ids = []
|
||||
frame_ids = []
|
||||
|
||||
logger.info("Starting inference...")
|
||||
start_time = time.time()
|
||||
|
||||
for data in inference_loader:
|
||||
inputs, idx, (file_id, frame_id) = data
|
||||
inputs = inputs.to(device)
|
||||
|
||||
if self.hybrid:
|
||||
inputs = self.ae_net.encoder(inputs)
|
||||
X_batch = inputs.view(inputs.size(0), -1)
|
||||
X += (X_batch.cpu().data.numpy(),)
|
||||
|
||||
# Store indices and metadata
|
||||
idxs.extend(idx.cpu().data.numpy().tolist())
|
||||
file_ids.extend(file_id.cpu().data.numpy().tolist())
|
||||
frame_ids.extend(frame_id.cpu().data.numpy().tolist())
|
||||
|
||||
X = np.concatenate(X)
|
||||
|
||||
# Get anomaly scores
|
||||
scores = (-1.0) * self.model.decision_function(X)
|
||||
scores = scores.flatten()
|
||||
|
||||
# Store inference results
|
||||
self.inference_time = time.time() - start_time
|
||||
self.inference_indices = np.array(idxs)
|
||||
self.inference_file_ids = np.array(file_ids)
|
||||
self.inference_frame_ids = np.array(frame_ids)
|
||||
|
||||
# Create index mapping similar to DeepSAD trainer
|
||||
self.inference_index_mapping = {
|
||||
"indices": self.inference_indices,
|
||||
"file_ids": self.inference_file_ids,
|
||||
"frame_ids": self.inference_frame_ids,
|
||||
}
|
||||
|
||||
# Log inference statistics
|
||||
logger.info(f"Number of inference samples: {len(self.inference_indices)}")
|
||||
logger.info(
|
||||
f"Number of unique files: {len(np.unique(self.inference_file_ids))}"
|
||||
)
|
||||
logger.info("Inference Time: {:.3f}s".format(self.inference_time))
|
||||
logger.info(
|
||||
"Score statistics: "
|
||||
f"min={scores.min():.3f}, "
|
||||
f"max={scores.max():.3f}, "
|
||||
f"mean={scores.mean():.3f}, "
|
||||
f"std={scores.std():.3f}"
|
||||
)
|
||||
logger.info("Finished inference.")
|
||||
|
||||
return scores
|
||||
|
||||
def load_ae(self, model_path, net_name, device="cpu"):
|
||||
"""Load pretrained autoencoder from model_path for feature extraction in a hybrid OC-SVM model."""
|
||||
|
||||
|
||||
@@ -338,6 +338,8 @@ class SubTerInference(VisionDataset):
|
||||
self.frame_ids = np.arange(self.data.shape[0], dtype=np.int32)
|
||||
self.file_names = {0: experiment_file.name}
|
||||
|
||||
self.transform = transform if transform else transforms.ToTensor()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data)
|
||||
|
||||
|
||||
@@ -638,57 +638,185 @@ def main(
|
||||
cfg.save_config(export_json=xp_path + "/config.json")
|
||||
|
||||
elif action == "infer":
|
||||
# Inference uses a deterministic, non-shuffled loader to preserve temporal order
|
||||
dataset = load_dataset(
|
||||
dataset_name,
|
||||
cfg.settings["dataset_name"],
|
||||
data_path,
|
||||
normal_class,
|
||||
known_outlier_class,
|
||||
n_known_outlier_classes,
|
||||
ratio_known_normal,
|
||||
ratio_known_outlier,
|
||||
ratio_pollution,
|
||||
cfg.settings["normal_class"],
|
||||
cfg.settings["known_outlier_class"],
|
||||
cfg.settings["n_known_outlier_classes"],
|
||||
cfg.settings["ratio_known_normal"],
|
||||
cfg.settings["ratio_known_outlier"],
|
||||
cfg.settings["ratio_pollution"],
|
||||
random_state=np.random.RandomState(cfg.settings["seed"]),
|
||||
k_fold_num=False,
|
||||
inference=True,
|
||||
)
|
||||
|
||||
# Log random sample of known anomaly classes if more than 1 class
|
||||
if n_known_outlier_classes > 1:
|
||||
logger.info("Known anomaly classes: %s" % (dataset.known_outlier_classes,))
|
||||
|
||||
# Initialize DeepSAD model and set neural network phi
|
||||
deepSAD = DeepSAD(latent_space_dim, cfg.settings["eta"])
|
||||
deepSAD.set_network(net_name)
|
||||
|
||||
# If specified, load Deep SAD model (center c, network weights, and possibly autoencoder weights)
|
||||
if not load_model:
|
||||
# --- Expect a model DIRECTORY (aligned with 'retest') ---
|
||||
if (
|
||||
(not load_model)
|
||||
or (not Path(load_model).exists())
|
||||
or (not Path(load_model).is_dir())
|
||||
):
|
||||
logger.error(
|
||||
"For inference mode a model has to be loaded! Pass the --load_model option with the model path!"
|
||||
"For inference mode a model directory has to be loaded! "
|
||||
"Pass the --load_model option with the model directory path!"
|
||||
)
|
||||
return
|
||||
load_model = Path(load_model)
|
||||
|
||||
# Resolve expected model artifacts (single-model / no k-fold suffixes)
|
||||
deepsad_model_path = load_model / "model_deepsad.tar"
|
||||
ae_model_path = load_model / "model_ae.tar"
|
||||
ocsvm_model_path = load_model / "model_ocsvm.pkl"
|
||||
isoforest_model_path = load_model / "model_isoforest.pkl"
|
||||
|
||||
# Sanity check model files exist
|
||||
model_paths = [
|
||||
deepsad_model_path,
|
||||
ae_model_path,
|
||||
ocsvm_model_path,
|
||||
isoforest_model_path,
|
||||
]
|
||||
missing = [p.name for p in model_paths if not p.exists() or not p.is_file()]
|
||||
if missing:
|
||||
logger.error(
|
||||
"The following model files do not exist in the provided model directory: "
|
||||
+ ", ".join(missing)
|
||||
)
|
||||
return
|
||||
|
||||
deepSAD.load_model(model_path=load_model, load_ae=True, map_location=device)
|
||||
logger.info("Loading model from %s." % load_model)
|
||||
# Prepare output paths
|
||||
inf_dir = Path(xp_path) / "inference"
|
||||
inf_dir.mkdir(parents=True, exist_ok=True)
|
||||
base_stem = Path(Path(dataset.root).stem) # keep your previous naming
|
||||
# DeepSAD outputs (keep legacy filenames for backward compatibility)
|
||||
deepsad_scores_path = inf_dir / Path(
|
||||
base_stem.stem + "_deepsad_scores"
|
||||
).with_suffix(".npy")
|
||||
deepsad_outputs_path = inf_dir / Path(base_stem.stem + "_outputs").with_suffix(
|
||||
".npy"
|
||||
)
|
||||
# Baselines
|
||||
ocsvm_scores_path = inf_dir / Path(
|
||||
base_stem.stem + "_ocsvm_scores"
|
||||
).with_suffix(".npy")
|
||||
isoforest_scores_path = inf_dir / Path(
|
||||
base_stem.stem + "_isoforest_scores"
|
||||
).with_suffix(".npy")
|
||||
|
||||
inference_results, all_outputs = deepSAD.inference(
|
||||
dataset, device=device, n_jobs_dataloader=n_jobs_dataloader
|
||||
)
|
||||
inference_results_path = (
|
||||
Path(xp_path)
|
||||
/ "inference"
|
||||
/ Path(Path(dataset.root).stem).with_suffix(".npy")
|
||||
)
|
||||
inference_outputs_path = (
|
||||
Path(xp_path)
|
||||
/ "inference"
|
||||
/ Path(Path(dataset.root).stem + "_outputs").with_suffix(".npy")
|
||||
# Common loader settings
|
||||
_n_jobs = (
|
||||
n_jobs_dataloader
|
||||
if "n_jobs_dataloader" in locals()
|
||||
else cfg.settings.get("n_jobs_dataloader", 0)
|
||||
)
|
||||
|
||||
inference_results_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
np.save(inference_results_path, inference_results, fix_imports=False)
|
||||
np.save(inference_outputs_path, all_outputs, fix_imports=False)
|
||||
# ----------------- DeepSAD -----------------
|
||||
|
||||
deepSAD = DeepSAD(cfg.settings["latent_space_dim"], cfg.settings["eta"])
|
||||
deepSAD.set_network(cfg.settings["net_name"])
|
||||
deepSAD.load_model(
|
||||
model_path=deepsad_model_path, load_ae=True, map_location=device
|
||||
)
|
||||
logger.info("Loaded DeepSAD model from %s.", deepsad_model_path)
|
||||
|
||||
deepsad_scores, deepsad_all_outputs = deepSAD.inference(
|
||||
dataset, device=device, n_jobs_dataloader=_n_jobs
|
||||
)
|
||||
|
||||
np.save(deepsad_scores_path, deepsad_scores)
|
||||
# np.save(deepsad_outputs_path, deepsad_all_outputs)
|
||||
|
||||
logger.info(
|
||||
f"Inference: median={np.median(inference_results)} mean={np.mean(inference_results)} min={inference_results.min()} max={inference_results.max()}"
|
||||
"DeepSAD inference: median=%.6f mean=%.6f min=%.6f max=%.6f",
|
||||
float(np.median(deepsad_scores)),
|
||||
float(np.mean(deepsad_scores)),
|
||||
float(np.min(deepsad_scores)),
|
||||
float(np.max(deepsad_scores)),
|
||||
)
|
||||
|
||||
# ----------------- OCSVM (hybrid) -----------------
|
||||
ocsvm_scores = None
|
||||
ocsvm = OCSVM(
|
||||
kernel=cfg.settings["ocsvm_kernel"],
|
||||
nu=cfg.settings["ocsvm_nu"],
|
||||
hybrid=True,
|
||||
latent_space_dim=cfg.settings["latent_space_dim"],
|
||||
)
|
||||
# load AE to build the feature extractor for hybrid OCSVM
|
||||
ocsvm.load_ae(
|
||||
net_name=cfg.settings["net_name"],
|
||||
model_path=ae_model_path,
|
||||
device=device,
|
||||
)
|
||||
ocsvm.load_model(import_path=ocsvm_model_path)
|
||||
|
||||
ocsvm_scores = ocsvm.inference(
|
||||
dataset, device=device, n_jobs_dataloader=_n_jobs, batch_size=32
|
||||
)
|
||||
|
||||
if ocsvm_scores is not None:
|
||||
np.save(ocsvm_scores_path, ocsvm_scores)
|
||||
logger.info(
|
||||
"OCSVM inference: median=%.6f mean=%.6f min=%.6f max=%.6f",
|
||||
float(np.median(ocsvm_scores)),
|
||||
float(np.mean(ocsvm_scores)),
|
||||
float(np.min(ocsvm_scores)),
|
||||
float(np.max(ocsvm_scores)),
|
||||
)
|
||||
else:
|
||||
logger.warning("OCSVM scores could not be determined; no array saved.")
|
||||
|
||||
# ----------------- Isolation Forest -----------------
|
||||
isoforest_scores = None
|
||||
Isoforest = IsoForest(
|
||||
hybrid=False,
|
||||
n_estimators=cfg.settings["isoforest_n_estimators"],
|
||||
max_samples=cfg.settings["isoforest_max_samples"],
|
||||
contamination=cfg.settings["isoforest_contamination"],
|
||||
n_jobs=cfg.settings["isoforest_n_jobs_model"],
|
||||
seed=cfg.settings["seed"],
|
||||
)
|
||||
Isoforest.load_model(import_path=isoforest_model_path, device=device)
|
||||
isoforest_scores = Isoforest.inference(
|
||||
dataset, device=device, n_jobs_dataloader=_n_jobs
|
||||
)
|
||||
if isoforest_scores is not None:
|
||||
np.save(isoforest_scores_path, isoforest_scores)
|
||||
logger.info(
|
||||
"IsolationForest inference: median=%.6f mean=%.6f min=%.6f max=%.6f",
|
||||
float(np.median(isoforest_scores)),
|
||||
float(np.mean(isoforest_scores)),
|
||||
float(np.min(isoforest_scores)),
|
||||
float(np.max(isoforest_scores)),
|
||||
)
|
||||
else:
|
||||
logger.warning(
|
||||
"Isolation Forest scores could not be determined; no array saved."
|
||||
)
|
||||
|
||||
# Final summary (DeepSAD always runs; baselines are best-effort)
|
||||
logger.info(
|
||||
"Inference complete. Saved arrays to %s:\n"
|
||||
" DeepSAD scores: %s\n"
|
||||
" DeepSAD outputs: %s\n"
|
||||
" OCSVM scores: %s\n"
|
||||
" IsoForest scores: %s",
|
||||
inf_dir,
|
||||
deepsad_scores_path.name,
|
||||
deepsad_outputs_path.name,
|
||||
ocsvm_scores_path.name if ocsvm_scores is not None else "(not saved)",
|
||||
isoforest_scores_path.name
|
||||
if isoforest_scores is not None
|
||||
else "(not saved)",
|
||||
)
|
||||
|
||||
elif action == "ae_elbow_test":
|
||||
# Load data once
|
||||
dataset = load_dataset(
|
||||
|
||||
@@ -177,6 +177,8 @@ class DeepSADTrainer(BaseTrainer):
|
||||
batch_size=self.batch_size, num_workers=self.n_jobs_dataloader
|
||||
)
|
||||
|
||||
latent_dim = net.rep_dim
|
||||
|
||||
# Set device for network
|
||||
net = net.to(self.device)
|
||||
|
||||
@@ -184,7 +186,9 @@ class DeepSADTrainer(BaseTrainer):
|
||||
logger.info("Starting inference...")
|
||||
n_batches = 0
|
||||
start_time = time.time()
|
||||
all_outputs = np.zeros((len(inference_loader.dataset), 1024), dtype=np.float32)
|
||||
all_outputs = np.zeros(
|
||||
(len(inference_loader.dataset), latent_dim), dtype=np.float32
|
||||
)
|
||||
scores = []
|
||||
net.eval()
|
||||
|
||||
|
||||
@@ -96,6 +96,21 @@ PRETRAIN_SCHEMA = {
|
||||
"config_json": pl.Utf8, # full config.json as string (for reference)
|
||||
}
|
||||
|
||||
SCHEMA_INFERENCE = {
|
||||
# identifiers / dims
|
||||
"experiment": pl.Utf8, # e.g. "2_static_no_artifacts_illuminated_2023-01-23-001"
|
||||
"network": pl.Utf8, # e.g. "LeNet", "efficient"
|
||||
"latent_dim": pl.Int32,
|
||||
"semi_normals": pl.Int32,
|
||||
"semi_anomalous": pl.Int32,
|
||||
"model": pl.Utf8, # "deepsad" | "isoforest" | "ocsvm"
|
||||
# metrics
|
||||
"scores": pl.List(pl.Float64),
|
||||
# timings / housekeeping
|
||||
"folder": pl.Utf8,
|
||||
"config_json": pl.Utf8, # full config.json as string (for reference)
|
||||
}
|
||||
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# Helpers: curve/scores normalizers (tuples/ndarrays -> dict/list)
|
||||
@@ -233,11 +248,11 @@ def normalize_bool_list(a) -> Optional[List[bool]]:
|
||||
# ------------------------------------------------------------
|
||||
# Low-level: read one experiment folder
|
||||
# ------------------------------------------------------------
|
||||
def read_config(exp_dir: Path) -> dict:
|
||||
def read_config(exp_dir: Path, k_fold_required: bool = True) -> dict:
|
||||
cfg = exp_dir / "config.json"
|
||||
with cfg.open("r") as f:
|
||||
c = json.load(f)
|
||||
if not c.get("k_fold"):
|
||||
if k_fold_required and not c.get("k_fold"):
|
||||
raise ValueError(f"{exp_dir.name}: not trained as k-fold")
|
||||
return c
|
||||
|
||||
@@ -589,7 +604,129 @@ def load_pretraining_results_dataframe(
|
||||
return df
|
||||
|
||||
|
||||
def load_inference_results_dataframe(
|
||||
root: Path,
|
||||
allow_cache: bool = True,
|
||||
models: List[str] = MODELS,
|
||||
) -> pl.DataFrame:
|
||||
"""Load inference results from experiment folders.
|
||||
|
||||
Args:
|
||||
root: Path to root directory containing experiment folders
|
||||
allow_cache: Whether to use/create cache file
|
||||
models: List of models to look for scores
|
||||
|
||||
Returns:
|
||||
pl.DataFrame: DataFrame containing inference results
|
||||
"""
|
||||
if allow_cache:
|
||||
cache = root / "inference_results_cache.parquet"
|
||||
if cache.exists():
|
||||
try:
|
||||
df = pl.read_parquet(cache)
|
||||
print(f"[info] loaded cached inference frame from {cache}")
|
||||
return df
|
||||
except Exception as e:
|
||||
print(f"[warn] failed to load inference cache {cache}: {e}")
|
||||
|
||||
rows: List[dict] = []
|
||||
|
||||
exp_dirs = [p for p in root.iterdir() if p.is_dir()]
|
||||
for exp_dir in sorted(exp_dirs):
|
||||
try:
|
||||
# Load and validate config
|
||||
cfg = read_config(exp_dir, k_fold_required=False)
|
||||
cfg_json = json.dumps(cfg, sort_keys=True)
|
||||
|
||||
# Extract config values
|
||||
network = cfg.get("net_name")
|
||||
latent_dim = int(cfg.get("latent_space_dim"))
|
||||
semi_normals = int(cfg.get("num_known_normal"))
|
||||
semi_anomalous = int(cfg.get("num_known_outlier"))
|
||||
|
||||
# Process each model's scores
|
||||
inference_dir = exp_dir / "inference"
|
||||
if not inference_dir.exists():
|
||||
print(f"[warn] no inference directory for {exp_dir.name}")
|
||||
continue
|
||||
|
||||
# Find all unique experiments in this folder's inference files
|
||||
score_files = list(inference_dir.glob("*_scores.npy"))
|
||||
if not score_files:
|
||||
print(f"[warn] no score files in {inference_dir}")
|
||||
continue
|
||||
|
||||
# Extract unique experiment names from score files
|
||||
# Format: {experiment}_{model}_scores.npy
|
||||
experiments = set()
|
||||
for score_file in score_files:
|
||||
exp_name = score_file.stem.rsplit("_", 2)[0]
|
||||
experiments.add(exp_name)
|
||||
|
||||
# Load scores for each experiment and model
|
||||
for experiment in sorted(experiments):
|
||||
for model in models:
|
||||
score_file = inference_dir / f"{experiment}_{model}_scores.npy"
|
||||
if not score_file.exists():
|
||||
print(f"[warn] missing score file for {experiment}, {model}")
|
||||
continue
|
||||
|
||||
try:
|
||||
scores = np.load(score_file)
|
||||
rows.append(
|
||||
{
|
||||
"experiment": experiment,
|
||||
"network": network,
|
||||
"latent_dim": latent_dim,
|
||||
"semi_normals": semi_normals,
|
||||
"semi_anomalous": semi_anomalous,
|
||||
"model": model,
|
||||
"scores": scores.tolist(),
|
||||
"folder": str(exp_dir),
|
||||
"config_json": cfg_json,
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
print(
|
||||
f"[warn] failed to load scores for {experiment}, {model}: {e}"
|
||||
)
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
print(f"[warn] skipping {exp_dir.name}: {e}")
|
||||
continue
|
||||
|
||||
# If empty, return a typed empty frame
|
||||
if not rows:
|
||||
return pl.DataFrame(schema=SCHEMA_INFERENCE)
|
||||
|
||||
df = pl.DataFrame(rows, schema=SCHEMA_INFERENCE)
|
||||
|
||||
# Optimize datatypes
|
||||
df = df.with_columns(
|
||||
[
|
||||
pl.col("experiment", "network", "model").cast(pl.Categorical),
|
||||
pl.col("latent_dim", "semi_normals", "semi_anomalous").cast(pl.Int32),
|
||||
]
|
||||
)
|
||||
|
||||
# Cache if enabled
|
||||
if allow_cache:
|
||||
try:
|
||||
df.write_parquet(cache)
|
||||
print(f"[info] cached inference frame to {cache}")
|
||||
except Exception as e:
|
||||
print(f"[warn] failed to write cache {cache}: {e}")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def main():
|
||||
inference_root = Path("/home/fedex/mt/results/inference/copy")
|
||||
df_inference = load_inference_results_dataframe(inference_root, allow_cache=True)
|
||||
|
||||
exit(0)
|
||||
|
||||
root = Path("/home/fedex/mt/results/copy")
|
||||
df1 = load_results_dataframe(root, allow_cache=True)
|
||||
exit(0)
|
||||
|
||||
269
tools/plot_scripts/results_inference_timeline smoothed.py
Normal file
269
tools/plot_scripts/results_inference_timeline smoothed.py
Normal file
@@ -0,0 +1,269 @@
|
||||
import json
|
||||
import pickle
|
||||
import shutil
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# =========================
|
||||
# User-configurable params
|
||||
# =========================
|
||||
# Single experiment to plot (stem of the .bag file, e.g. "3_smoke_human_walking_2023-01-23")
|
||||
EXPERIMENT_NAME = "3_smoke_human_walking_2023-01-23"
|
||||
|
||||
# Directory that contains {EXPERIMENT_NAME}_{method}_scores.npy for methods in {"deepsad","ocsvm","isoforest"}
|
||||
methods_scores_path = Path(
|
||||
"/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/infer/DeepSAD/test/inference"
|
||||
)
|
||||
|
||||
# Root data path containing .bag files used to build the cached stats
|
||||
all_data_path = Path("/home/fedex/mt/data/subter")
|
||||
|
||||
# Output base directory (timestamped subfolder will be created here, then archived and copied to "latest/")
|
||||
output_path = Path("/home/fedex/mt/plots/results_inference_timeline_smoothed")
|
||||
|
||||
# Cache (stats + labels) directory — same as your original script
|
||||
cache_path = output_path
|
||||
|
||||
# Assumed LiDAR frame resolution to convert counts -> percent (unchanged from original)
|
||||
data_resolution = 32 * 2048
|
||||
|
||||
# Frames per second for x-axis time
|
||||
FPS = 10.0
|
||||
|
||||
# Whether to try to align score sign so that higher = more degraded.
|
||||
ALIGN_SCORE_DIRECTION = True
|
||||
|
||||
# =========================
|
||||
# Smoothing configuration
|
||||
# =========================
|
||||
# Options: "none", "moving_average", "gaussian", "ema"
|
||||
SMOOTHING_METHOD = "ema"
|
||||
|
||||
# Moving average window size (in frames). Use odd number for symmetry; <=1 disables.
|
||||
MA_WINDOW = 11
|
||||
|
||||
# Gaussian sigma (in frames). ~2-3 frames is mild smoothing.
|
||||
GAUSSIAN_SIGMA = 2.0
|
||||
|
||||
# Exponential moving average factor in (0,1]; smaller = smoother. ~0.2 is a good start.
|
||||
EMA_ALPHA = 0.1
|
||||
|
||||
# =========================
|
||||
# Setup output folders
|
||||
# =========================
|
||||
datetime_folder_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||
latest_folder_path = output_path / "latest"
|
||||
archive_folder_path = output_path / "archive"
|
||||
output_datetime_path = output_path / datetime_folder_name
|
||||
|
||||
output_path.mkdir(exist_ok=True, parents=True)
|
||||
output_datetime_path.mkdir(exist_ok=True, parents=True)
|
||||
latest_folder_path.mkdir(exist_ok=True, parents=True)
|
||||
archive_folder_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
# =========================
|
||||
# Discover experiments
|
||||
# =========================
|
||||
normal_experiment_paths, anomaly_experiment_paths = [], []
|
||||
for bag_file_path in all_data_path.iterdir():
|
||||
if bag_file_path.suffix != ".bag":
|
||||
continue
|
||||
if "smoke" in bag_file_path.name:
|
||||
anomaly_experiment_paths.append(bag_file_path)
|
||||
else:
|
||||
normal_experiment_paths.append(bag_file_path)
|
||||
|
||||
normal_experiment_paths = sorted(
|
||||
normal_experiment_paths, key=lambda p: p.stat().st_size
|
||||
)
|
||||
anomaly_experiment_paths = sorted(
|
||||
anomaly_experiment_paths, key=lambda p: p.stat().st_size
|
||||
)
|
||||
|
||||
# Find experiment
|
||||
exp_path, exp_is_anomaly = None, None
|
||||
for p in anomaly_experiment_paths:
|
||||
if p.stem == EXPERIMENT_NAME:
|
||||
exp_path, exp_is_anomaly = p, True
|
||||
break
|
||||
if exp_path is None:
|
||||
for p in normal_experiment_paths:
|
||||
if p.stem == EXPERIMENT_NAME:
|
||||
exp_path, exp_is_anomaly = p, False
|
||||
break
|
||||
if exp_path is None:
|
||||
raise FileNotFoundError(f"Experiment '{EXPERIMENT_NAME}' not found")
|
||||
|
||||
exp_index = (
|
||||
anomaly_experiment_paths.index(exp_path)
|
||||
if exp_is_anomaly
|
||||
else normal_experiment_paths.index(exp_path)
|
||||
)
|
||||
|
||||
# =========================
|
||||
# Load cached statistical data
|
||||
# =========================
|
||||
with open(cache_path / "missing_points.pkl", "rb") as f:
|
||||
missing_points_normal, missing_points_anomaly = pickle.load(f)
|
||||
with open(cache_path / "particles_near_sensor_counts_500.pkl", "rb") as f:
|
||||
near_sensor_normal, near_sensor_anomaly = pickle.load(f)
|
||||
|
||||
if exp_is_anomaly:
|
||||
missing_points_series = np.asarray(missing_points_anomaly[exp_index], dtype=float)
|
||||
near_sensor_series = np.asarray(near_sensor_anomaly[exp_index], dtype=float)
|
||||
else:
|
||||
missing_points_series = np.asarray(missing_points_normal[exp_index], dtype=float)
|
||||
near_sensor_series = np.asarray(near_sensor_normal[exp_index], dtype=float)
|
||||
|
||||
missing_points_pct = (missing_points_series / data_resolution) * 100.0
|
||||
near_sensor_pct = (near_sensor_series / data_resolution) * 100.0
|
||||
|
||||
# =========================
|
||||
# Load manual anomaly frame borders
|
||||
# =========================
|
||||
manually_labeled_anomaly_frames = {}
|
||||
labels_json_path = cache_path / "manually_labeled_anomaly_frames.json"
|
||||
if labels_json_path.exists():
|
||||
with open(labels_json_path, "r") as f:
|
||||
labeled_json = json.load(f)
|
||||
for file in labeled_json.get("files", []):
|
||||
manually_labeled_anomaly_frames[file["filename"]] = (
|
||||
file.get("semi_target_begin_frame"),
|
||||
file.get("semi_target_end_frame"),
|
||||
)
|
||||
exp_npy_filename = exp_path.with_suffix(".npy").name
|
||||
anomaly_window = manually_labeled_anomaly_frames.get(exp_npy_filename, (None, None))
|
||||
|
||||
|
||||
# =========================
|
||||
# Load method scores and normalize
|
||||
# =========================
|
||||
def zscore_1d(x, eps=1e-12):
|
||||
mu, sigma = np.mean(x), np.std(x, ddof=0)
|
||||
return np.zeros_like(x) if sigma < eps else (x - mu) / sigma
|
||||
|
||||
|
||||
def maybe_align_direction(z, window):
|
||||
start, end = window
|
||||
if start is None or end is None:
|
||||
return z
|
||||
inside_mean = np.mean(z[start:end]) if end > start else 0
|
||||
outside = np.concatenate([z[:start], z[end:]]) if start > 0 or end < len(z) else []
|
||||
outside_mean = np.mean(outside) if len(outside) else inside_mean
|
||||
return z if inside_mean >= outside_mean else -z
|
||||
|
||||
|
||||
methods = ["deepsad", "ocsvm", "isoforest"]
|
||||
method_zscores = {}
|
||||
for m in methods:
|
||||
s = np.load(methods_scores_path / f"{EXPERIMENT_NAME}_{m}_scores.npy")
|
||||
s = np.asarray(s, dtype=float).ravel()
|
||||
n = min(len(s), len(missing_points_pct))
|
||||
s, missing_points_pct, near_sensor_pct = (
|
||||
s[:n],
|
||||
missing_points_pct[:n],
|
||||
near_sensor_pct[:n],
|
||||
)
|
||||
z = zscore_1d(s)
|
||||
if ALIGN_SCORE_DIRECTION:
|
||||
z = maybe_align_direction(z, anomaly_window)
|
||||
method_zscores[m] = z
|
||||
|
||||
|
||||
# =========================
|
||||
# Smoothing
|
||||
# =========================
|
||||
def moving_average(x, window):
|
||||
if window <= 1:
|
||||
return x
|
||||
if window % 2 == 0:
|
||||
window += 1
|
||||
return np.convolve(x, np.ones(window) / window, mode="same")
|
||||
|
||||
|
||||
def gaussian_smooth(x, sigma):
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
|
||||
return gaussian_filter1d(x, sigma=sigma, mode="nearest") if sigma > 0 else x
|
||||
|
||||
|
||||
def ema(x, alpha):
|
||||
y = np.empty_like(x)
|
||||
y[0] = x[0]
|
||||
for i in range(1, len(x)):
|
||||
y[i] = alpha * x[i] + (1 - alpha) * y[i - 1]
|
||||
return y
|
||||
|
||||
|
||||
def apply_smoothing(x):
|
||||
m = SMOOTHING_METHOD.lower()
|
||||
if m == "none":
|
||||
return x
|
||||
if m == "moving_average":
|
||||
return moving_average(x, MA_WINDOW)
|
||||
if m == "gaussian":
|
||||
return gaussian_smooth(x, GAUSSIAN_SIGMA)
|
||||
if m == "ema":
|
||||
return ema(x, EMA_ALPHA)
|
||||
raise ValueError(f"Unknown SMOOTHING_METHOD: {SMOOTHING_METHOD}")
|
||||
|
||||
|
||||
smoothed_z = {k: apply_smoothing(v) for k, v in method_zscores.items()}
|
||||
smoothed_missing = apply_smoothing(missing_points_pct)
|
||||
smoothed_near = apply_smoothing(near_sensor_pct)
|
||||
|
||||
# =========================
|
||||
# Plot
|
||||
# =========================
|
||||
t = np.arange(len(missing_points_pct)) / FPS
|
||||
|
||||
|
||||
def plot_series(y2, ylabel, fname, title_suffix):
|
||||
fig, axz = plt.subplots(figsize=(14, 6), constrained_layout=True)
|
||||
axy = axz.twinx()
|
||||
for m in methods:
|
||||
axz.plot(t, smoothed_z[m], label=f"{m} (z)")
|
||||
axy.plot(t, y2, linestyle="--", label=ylabel)
|
||||
start, end = anomaly_window
|
||||
if start and end:
|
||||
axz.axvline(start / FPS, linestyle=":", alpha=0.6)
|
||||
axz.axvline(end / FPS, linestyle=":", alpha=0.6)
|
||||
axz.set_xlabel("Time (s)")
|
||||
axz.set_ylabel("Anomaly score (z)")
|
||||
axy.set_ylabel(ylabel)
|
||||
axz.set_title(f"{EXPERIMENT_NAME}\n{title_suffix}\nSmoothing: {SMOOTHING_METHOD}")
|
||||
lines1, labels1 = axz.get_legend_handles_labels()
|
||||
lines2, labels2 = axy.get_legend_handles_labels()
|
||||
axz.legend(lines1 + lines2, labels1 + labels2, loc="upper right")
|
||||
axz.grid(True, alpha=0.3)
|
||||
fig.savefig(output_datetime_path / fname, dpi=150)
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
plot_series(
|
||||
smoothed_missing,
|
||||
"Missing points (%)",
|
||||
f"{EXPERIMENT_NAME}_zscores_vs_missing.png",
|
||||
"Degradation vs Missing Points",
|
||||
)
|
||||
plot_series(
|
||||
smoothed_near,
|
||||
"Near-sensor points (%)",
|
||||
f"{EXPERIMENT_NAME}_zscores_vs_near.png",
|
||||
"Degradation vs Near-Sensor Points (<0.5m)",
|
||||
)
|
||||
|
||||
# =========================
|
||||
# Save & archive
|
||||
# =========================
|
||||
shutil.rmtree(latest_folder_path, ignore_errors=True)
|
||||
latest_folder_path.mkdir(exist_ok=True, parents=True)
|
||||
for f in output_datetime_path.iterdir():
|
||||
shutil.copy2(f, latest_folder_path)
|
||||
shutil.copy2(__file__, output_datetime_path)
|
||||
shutil.copy2(__file__, latest_folder_path)
|
||||
shutil.move(output_datetime_path, archive_folder_path)
|
||||
print("Done. Plots saved and archived.")
|
||||
304
tools/plot_scripts/results_inference_timeline.py
Normal file
304
tools/plot_scripts/results_inference_timeline.py
Normal file
@@ -0,0 +1,304 @@
|
||||
import json
|
||||
import pickle
|
||||
import shutil
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# =========================
|
||||
# User-configurable params
|
||||
# =========================
|
||||
# Single experiment to plot (stem of the .bag file, e.g. "3_smoke_human_walking_2023-01-23")
|
||||
EXPERIMENT_NAME = "3_smoke_human_walking_2023-01-23"
|
||||
|
||||
# Directory that contains {EXPERIMENT_NAME}_{method}_scores.npy for methods in {"deepsad","ocsvm","isoforest"}
|
||||
# Adjust this to where you save your per-method scores.
|
||||
methods_scores_path = Path(
|
||||
"/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/infer/DeepSAD/test/inference"
|
||||
)
|
||||
|
||||
# Root data path containing .bag files used to build the cached stats
|
||||
all_data_path = Path("/home/fedex/mt/data/subter")
|
||||
|
||||
# Output base directory (timestamped subfolder will be created here, then archived and copied to "latest/")
|
||||
output_path = Path("/home/fedex/mt/plots/results_inference_timeline")
|
||||
|
||||
# Cache (stats + labels) directory — same as your original script
|
||||
cache_path = output_path
|
||||
|
||||
# Assumed LiDAR frame resolution to convert counts -> percent (unchanged from original)
|
||||
data_resolution = 32 * 2048
|
||||
|
||||
# Frames per second for x-axis time
|
||||
FPS = 10.0
|
||||
|
||||
# Whether to try to align score sign so that higher = more degraded.
|
||||
# If manual labels exist for this experiment, alignment uses anomaly window mean vs. outside.
|
||||
ALIGN_SCORE_DIRECTION = True
|
||||
|
||||
# =========================
|
||||
# Setup output folders
|
||||
# =========================
|
||||
datetime_folder_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||
latest_folder_path = output_path / "latest"
|
||||
archive_folder_path = output_path / "archive"
|
||||
output_datetime_path = output_path / datetime_folder_name
|
||||
|
||||
output_path.mkdir(exist_ok=True, parents=True)
|
||||
output_datetime_path.mkdir(exist_ok=True, parents=True)
|
||||
latest_folder_path.mkdir(exist_ok=True, parents=True)
|
||||
archive_folder_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
# =========================
|
||||
# Discover experiments to reconstruct indices consistent with caches
|
||||
# =========================
|
||||
normal_experiment_paths, anomaly_experiment_paths = [], []
|
||||
if not all_data_path.exists():
|
||||
raise FileNotFoundError(f"all_data_path does not exist: {all_data_path}")
|
||||
|
||||
for bag_file_path in all_data_path.iterdir():
|
||||
if bag_file_path.suffix != ".bag":
|
||||
continue
|
||||
if "smoke" in bag_file_path.name:
|
||||
anomaly_experiment_paths.append(bag_file_path)
|
||||
else:
|
||||
normal_experiment_paths.append(bag_file_path)
|
||||
|
||||
# Sort by filesize to match original ordering used when caches were generated
|
||||
normal_experiment_paths = sorted(
|
||||
normal_experiment_paths, key=lambda p: p.stat().st_size
|
||||
)
|
||||
anomaly_experiment_paths = sorted(
|
||||
anomaly_experiment_paths, key=lambda p: p.stat().st_size
|
||||
)
|
||||
|
||||
# Find the path for the requested experiment
|
||||
exp_path = None
|
||||
exp_is_anomaly = None
|
||||
for p in anomaly_experiment_paths:
|
||||
if p.stem == EXPERIMENT_NAME:
|
||||
exp_path = p
|
||||
exp_is_anomaly = True
|
||||
break
|
||||
if exp_path is None:
|
||||
for p in normal_experiment_paths:
|
||||
if p.stem == EXPERIMENT_NAME:
|
||||
exp_path = p
|
||||
exp_is_anomaly = False
|
||||
break
|
||||
if exp_path is None:
|
||||
raise FileNotFoundError(
|
||||
f"Experiment '{EXPERIMENT_NAME}' not found as a .bag in {all_data_path}"
|
||||
)
|
||||
|
||||
# Get the index within the appropriate list
|
||||
if exp_is_anomaly:
|
||||
exp_index = anomaly_experiment_paths.index(exp_path)
|
||||
else:
|
||||
exp_index = normal_experiment_paths.index(exp_path)
|
||||
|
||||
# =========================
|
||||
# Load cached statistical data
|
||||
# =========================
|
||||
missing_points_cache = Path(cache_path / "missing_points.pkl")
|
||||
near_sensor_cache = Path(cache_path / "particles_near_sensor_counts_500.pkl")
|
||||
|
||||
if not missing_points_cache.exists():
|
||||
raise FileNotFoundError(f"Missing points cache not found: {missing_points_cache}")
|
||||
if not near_sensor_cache.exists():
|
||||
raise FileNotFoundError(f"Near-sensor cache not found: {near_sensor_cache}")
|
||||
|
||||
with open(missing_points_cache, "rb") as f:
|
||||
missing_points_normal, missing_points_anomaly = pickle.load(f)
|
||||
with open(near_sensor_cache, "rb") as f:
|
||||
near_sensor_normal, near_sensor_anomaly = pickle.load(f)
|
||||
|
||||
if exp_is_anomaly:
|
||||
missing_points_series = np.asarray(missing_points_anomaly[exp_index], dtype=float)
|
||||
near_sensor_series = np.asarray(near_sensor_anomaly[exp_index], dtype=float)
|
||||
else:
|
||||
missing_points_series = np.asarray(missing_points_normal[exp_index], dtype=float)
|
||||
near_sensor_series = np.asarray(near_sensor_normal[exp_index], dtype=float)
|
||||
|
||||
# Convert counts to percentages of total points
|
||||
missing_points_pct = (missing_points_series / data_resolution) * 100.0
|
||||
near_sensor_pct = (near_sensor_series / data_resolution) * 100.0
|
||||
|
||||
# =========================
|
||||
# Load manual anomaly frame borders (optional; used for sign alignment + vertical markers)
|
||||
# =========================
|
||||
manually_labeled_anomaly_frames = {}
|
||||
labels_json_path = cache_path / "manually_labeled_anomaly_frames.json"
|
||||
if labels_json_path.exists():
|
||||
with open(labels_json_path, "r") as frame_borders_file:
|
||||
manually_labeled_anomaly_frames_json = json.load(frame_borders_file)
|
||||
for file in manually_labeled_anomaly_frames_json.get("files", []):
|
||||
manually_labeled_anomaly_frames[file["filename"]] = (
|
||||
file.get("semi_target_begin_frame", None),
|
||||
file.get("semi_target_end_frame", None),
|
||||
)
|
||||
|
||||
# The JSON uses .npy filenames (as in original script). Create this experiment’s key.
|
||||
exp_npy_filename = exp_path.with_suffix(".npy").name
|
||||
anomaly_window = manually_labeled_anomaly_frames.get(exp_npy_filename, (None, None))
|
||||
|
||||
|
||||
# =========================
|
||||
# Load method scores and z-score normalize per method
|
||||
# =========================
|
||||
def zscore_1d(x: np.ndarray, eps=1e-12):
|
||||
x = np.asarray(x, dtype=float)
|
||||
mu = np.mean(x)
|
||||
sigma = np.std(x, ddof=0)
|
||||
if sigma < eps:
|
||||
return np.zeros_like(x)
|
||||
return (x - mu) / sigma
|
||||
|
||||
|
||||
def maybe_align_direction(z: np.ndarray, window):
|
||||
"""Flip sign so that the anomaly window mean is higher than the outside mean, if labels exist."""
|
||||
start, end = window
|
||||
if start is None or end is None:
|
||||
return z # no labels → leave as-is
|
||||
start = int(max(0, start))
|
||||
end = int(min(len(z), end))
|
||||
if end <= start or end > len(z):
|
||||
return z
|
||||
inside_mean = float(np.mean(z[start:end]))
|
||||
# outside: everything except [start:end]; handle edge cases
|
||||
if start == 0 and end == len(z):
|
||||
return z
|
||||
outside_parts = []
|
||||
if start > 0:
|
||||
outside_parts.append(z[:start])
|
||||
if end < len(z):
|
||||
outside_parts.append(z[end:])
|
||||
if not outside_parts:
|
||||
return z
|
||||
outside_mean = float(np.mean(np.concatenate(outside_parts)))
|
||||
return z if inside_mean >= outside_mean else -z
|
||||
|
||||
|
||||
methods = ["deepsad", "ocsvm", "isoforest"]
|
||||
method_scores = {}
|
||||
method_zscores = {}
|
||||
|
||||
if not methods_scores_path.exists():
|
||||
raise FileNotFoundError(
|
||||
f"Methods scores path does not exist: {methods_scores_path}"
|
||||
)
|
||||
|
||||
for m in methods:
|
||||
file_path = methods_scores_path / f"{EXPERIMENT_NAME}_{m}_scores.npy"
|
||||
if not file_path.exists():
|
||||
raise FileNotFoundError(f"Missing scores file for method '{m}': {file_path}")
|
||||
s = np.load(file_path)
|
||||
s = np.asarray(s, dtype=float).reshape(-1)
|
||||
# If needed, truncate or pad to match stats length (should match if generated consistently)
|
||||
n = min(len(s), len(missing_points_pct))
|
||||
if len(s) != len(missing_points_pct):
|
||||
# Align by truncation to the shortest length
|
||||
s = s[:n]
|
||||
# Also truncate stats to match
|
||||
missing_points_pct = missing_points_pct[:n]
|
||||
near_sensor_pct = near_sensor_pct[:n]
|
||||
z = zscore_1d(s)
|
||||
if ALIGN_SCORE_DIRECTION:
|
||||
z = maybe_align_direction(z, anomaly_window)
|
||||
method_scores[m] = s
|
||||
method_zscores[m] = z
|
||||
|
||||
# Common time axis in seconds
|
||||
num_frames = len(missing_points_pct)
|
||||
t = np.arange(num_frames) / FPS
|
||||
|
||||
# =========================
|
||||
# Plot 1: Missing points (%) vs. method z-scores
|
||||
# =========================
|
||||
fig1, axz1 = plt.subplots(figsize=(14, 6), constrained_layout=True)
|
||||
axy1 = axz1.twinx()
|
||||
|
||||
# plot z-scores
|
||||
for m in methods:
|
||||
axz1.plot(t, method_zscores[m], label=f"{m} (z)", alpha=0.9)
|
||||
|
||||
# plot missing points (%)
|
||||
axy1.plot(t, missing_points_pct, linestyle="--", alpha=0.7, label="Missing points (%)")
|
||||
|
||||
# vertical markers for anomaly window if available
|
||||
start, end = anomaly_window
|
||||
if start is not None and end is not None and 0 <= start < end <= num_frames:
|
||||
axz1.axvline(x=start / FPS, linestyle=":", alpha=0.6)
|
||||
axz1.axvline(x=end / FPS, linestyle=":", alpha=0.6)
|
||||
|
||||
axz1.set_xlabel("Time (s)")
|
||||
axz1.set_ylabel("Anomaly score (z-score, ↑ = more degraded)")
|
||||
axy1.set_ylabel("Missing points (%)")
|
||||
axz1.set_title(f"{EXPERIMENT_NAME}\nDegradation vs. Missing Points")
|
||||
|
||||
# Build a combined legend
|
||||
lines1, labels1 = axz1.get_legend_handles_labels()
|
||||
lines2, labels2 = axy1.get_legend_handles_labels()
|
||||
axz1.legend(lines1 + lines2, labels1 + labels2, loc="upper right")
|
||||
|
||||
axz1.grid(True, alpha=0.3)
|
||||
fig1.savefig(
|
||||
output_datetime_path / f"{EXPERIMENT_NAME}_zscores_vs_missing_points.png", dpi=150
|
||||
)
|
||||
plt.close(fig1)
|
||||
|
||||
# =========================
|
||||
# Plot 2: Near-sensor (%) vs. method z-scores
|
||||
# =========================
|
||||
fig2, axz2 = plt.subplots(figsize=(14, 6), constrained_layout=True)
|
||||
axy2 = axz2.twinx()
|
||||
|
||||
for m in methods:
|
||||
axz2.plot(t, method_zscores[m], label=f"{m} (z)", alpha=0.9)
|
||||
|
||||
axy2.plot(t, near_sensor_pct, linestyle="--", alpha=0.7, label="Near-sensor <0.5m (%)")
|
||||
|
||||
start, end = anomaly_window
|
||||
if start is not None and end is not None and 0 <= start < end <= num_frames:
|
||||
axz2.axvline(x=start / FPS, linestyle=":", alpha=0.6)
|
||||
axz2.axvline(x=end / FPS, linestyle=":", alpha=0.6)
|
||||
|
||||
axz2.set_xlabel("Time (s)")
|
||||
axz2.set_ylabel("Anomaly score (z-score, ↑ = more degraded)")
|
||||
axy2.set_ylabel("Near-sensor points (%)")
|
||||
axz2.set_title(f"{EXPERIMENT_NAME}\nDegradation vs. Near-Sensor Points (<0.5 m)")
|
||||
|
||||
lines1, labels1 = axz2.get_legend_handles_labels()
|
||||
lines2, labels2 = axy2.get_legend_handles_labels()
|
||||
axz2.legend(lines1 + lines2, labels1 + labels2, loc="upper right")
|
||||
|
||||
axz2.grid(True, alpha=0.3)
|
||||
fig2.savefig(
|
||||
output_datetime_path / f"{EXPERIMENT_NAME}_zscores_vs_near_sensor.png", dpi=150
|
||||
)
|
||||
plt.close(fig2)
|
||||
|
||||
# =========================
|
||||
# Preserve latest/, archive/, copy script
|
||||
# =========================
|
||||
|
||||
# delete current latest folder
|
||||
shutil.rmtree(latest_folder_path, ignore_errors=True)
|
||||
|
||||
# create new latest folder
|
||||
latest_folder_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
# copy contents of output folder to the latest folder
|
||||
for file in output_datetime_path.iterdir():
|
||||
shutil.copy2(file, latest_folder_path)
|
||||
|
||||
# copy this python script to preserve the code used
|
||||
shutil.copy2(__file__, output_datetime_path)
|
||||
shutil.copy2(__file__, latest_folder_path)
|
||||
|
||||
# move output date folder to archive
|
||||
shutil.move(output_datetime_path, archive_folder_path)
|
||||
|
||||
print("Done. Plots saved and archived.")
|
||||
Reference in New Issue
Block a user