details.json

{
  "Anomaly detection": {
    "Dataset": {
      "creditcard": "<__main__.CSVDataset object at 0x177e89550>",
      "kdd99_10pct_binary": "<__main__.CSVDataset object at 0x177e89850>",
      "nab_nyc_taxi": "<__main__.CSVDataset object at 0x177ea2100>"
    },
    "Model": {
      "GaussianScorer": "GaussianScorer (\n  window_size=None\n  grace_period=100\n)",
      "HalfSpaceTrees": "HalfSpaceTrees (\n  n_trees=10\n  height=8\n  window_size=250\n  limits=defaultdict(..., {})\n  seed=42\n)",
      "OneClassSVM": "OneClassSVM (\n  nu=0.1\n  optimizer=SGD (\n    lr=Constant (\n      learning_rate=0.01\n    )\n  )\n  intercept_lr=Constant (\n    learning_rate=0.01\n  )\n  clip_gradient=1e+12\n  initializer=Zeros ()\n)",
      "PredictiveAnomalyDetection": "PredictiveAnomalyDetection (\n  predictive_model=Pipeline (\n    steps=OrderedDict([('StandardScaler', StandardScaler (\n  with_std=True\n)), ('LinearRegression', LinearRegression (\n  optimizer=SGD (\n    lr=Constant (\n      learning_rate=0.01\n    )\n  )\n  loss=Squared ()\n  l2=0.\n  l1=0.\n  intercept_init=0.\n  intercept_lr=Constant (\n    learning_rate=0.01\n  )\n  clip_gradient=1e+12\n  initializer=Zeros ()\n))])\n  )\n  horizon=1\n  n_std=3.\n  warmup_period=0\n)",
      "StandardAbsoluteDeviation": "StandardAbsoluteDeviation (\n  sub_stat=Mean ()\n)"
    }
  }
}