Tao Finetune Kumo Anomaly

git clone --branch main --single-branch https://github.com/NVIDIA/Kumo-TS Kumo-TS
cd Kumo-TS/Kumo-Anomaly
uv sync                              # install dependencies (one-time)

# Inference — synthetic data, auto-downloads weights from HF on first run
uv run python examples/quick_example.py

# Inference — your own CSV
uv run python examples/quick_example.py \
  --model-path final_model.pth \
  --config-path curriculum_medium.yaml \
  --dataset-path /path/to/data.csv

# Pre-download weights only (warm the cache before going offline)
uv run python examples/quick_example.py --download-weights

# Fine-tune on your own normal-behavior data
uv run python examples/finetune_example.py \
  --csv /path/to/normal_training_data.csv \
  --timestamp-col timestamp \
  --label-col is_anomaly \
  --epochs 20 \
  --output-dir artifacts/finetune_my_data

import sys, pandas as pd
sys.path.append("/path/to/Kumo-TS/Kumo-Anomaly")  # clone Kumo-TS with --branch main
from sdk.anomaly_analysis import perform_anomaly_analysis_with_diffusion

df = pd.read_csv("your_data.csv")

# The API raises ValueError on non-numeric columns — drop timestamp, IDs, and labels first.
df = df.select_dtypes(include="number")

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",       # "scs" (fast) or "macs" (adaptive)
    model_path=None,                 # None → auto-download final_model.pth from HF
    model_config_path=None,          # None → auto-download curriculum_medium.yaml from HF
    nsample=15,                      # diffusion samples per window; ↑ accuracy, ↑ latency
    preprocess_model_dir=None,       # optional preprocessing model directory
)
# results columns: Anomaly (0/1), MAE (float), plus all original columns
print(results[["Anomaly", "MAE"]].describe())

from sdk.anomaly_analysis import ADDiffusionConfig, perform_anomaly_analysis_with_diffusion

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",
    sdk_config=ADDiffusionConfig(
        report_path="artifacts/anomaly_report.pdf",
        report_title="Anomaly Detection Report",
    ),
)

sdk:
  report_path: artifacts/anomaly_report.pdf
  report_title: Anomaly Detection Report

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",
    sdk_config="sdk_config.yaml",
)

uv run python examples/finetune_example.py \
  --csv /path/to/normal_data.csv \
  --val-csv /path/to/val_data.csv \   # optional; otherwise --val-ratio splits --csv
  --pretrained-model final_model.pth \
  --epochs 20 --batch-size 16 --lr 1e-5 \
  --output-dir artifacts/finetune_my_data

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",
    model_path="artifacts/finetune_my_data/best_finetuned_model.pth",
    model_config_path="artifacts/finetune_my_data/finetune_config.yaml",
    nsample=15,
)

timestamp,sensor_1,sensor_2,sensor_3
2024-01-01 00:00:00,0.42,1.10,-0.33
...

examples/datasets/
└── kumo-anomaly-results.csv # original columns + Anomaly (0/1) + MAE

artifacts/finetune_my_data/
├── best_finetuned_model.pth     # checkpoint with lowest validation loss
├── final_finetuned_model.pth    # checkpoint from last epoch
├── metrics.json                 # scalar for AutoML: {"val_loss": <best>}
├── epoch_metrics.json           # per-epoch log: [{"epoch": N, "train_loss": …, "val_loss": …}]
└── finetune_config.yaml         # config used during training (for reproducibility)