RADAR.static_data

Subpackages

• RADAR.static_data.algorithms
  • Submodules
  • RADAR.static_data.algorithms.pyod module
    • PyodAnomalyDetection
      • PyodAnomalyDetection.decision_function()
      • PyodAnomalyDetection.evaluate()
      • PyodAnomalyDetection.fit()
      • PyodAnomalyDetection.get_default_params()
      • PyodAnomalyDetection.get_params()
      • PyodAnomalyDetection.predict()
      • PyodAnomalyDetection.register_algorithm()
      • PyodAnomalyDetection.set_params()
  • RADAR.static_data.algorithms.sklearn module
    • SkLearnAnomalyDetection
      • SkLearnAnomalyDetection.decision_function()
      • SkLearnAnomalyDetection.evaluate()
      • SkLearnAnomalyDetection.fit()
      • SkLearnAnomalyDetection.get_default_params()
      • SkLearnAnomalyDetection.get_params()
      • SkLearnAnomalyDetection.predict()
      • SkLearnAnomalyDetection.register_algorithm()
      • SkLearnAnomalyDetection.set_params()
  • Module contents
• RADAR.static_data.preprocessing
  • Submodules
  • RADAR.static_data.preprocessing.preprocessing_static module
    • MinMaxScalerPreprocessing
      • MinMaxScalerPreprocessing.fit()
      • MinMaxScalerPreprocessing.fit_transform()
      • MinMaxScalerPreprocessing.inverse_transform()
      • MinMaxScalerPreprocessing.transform()
    • NormalizerPreprocessing
      • NormalizerPreprocessing.fit()
      • NormalizerPreprocessing.fit_transform()
      • NormalizerPreprocessing.inverse_transform()
      • NormalizerPreprocessing.transform()
    • OneHotEncoderPreprocessing
      • OneHotEncoderPreprocessing.fit()
      • OneHotEncoderPreprocessing.fit_transform()
      • OneHotEncoderPreprocessing.inverse_transform()
      • OneHotEncoderPreprocessing.transform()
    • RobustScalerPreprocessing
      • RobustScalerPreprocessing.fit()
      • RobustScalerPreprocessing.fit_transform()
      • RobustScalerPreprocessing.inverse_transform()
      • RobustScalerPreprocessing.transform()
    • StandardScalerPreprocessing
      • StandardScalerPreprocessing.fit()
      • StandardScalerPreprocessing.fit_transform()
      • StandardScalerPreprocessing.inverse_transform()
      • StandardScalerPreprocessing.transform()
  • Module contents

Submodules

RADAR.static_data.static_datasets_uci module

RADAR.static_data.static_datasets_uci.global_load(name_dataset)

Loads a dataset using the corresponding loading method and parameters.

Parameters: name_dataset (str): The name of the dataset to be loaded.

Returns: The dataset loaded using the corresponding method.

RADAR.static_data.static_datasets_uci.load_arrhythmia(url, **kwargs)

Load the Arrhythmia dataset and split features/target.

The raw UCI file stores the class label in the last column and uses ‘?’ for missing values.

RADAR.static_data.static_datasets_uci.load_from_id(id)

Fetches a dataset from the UCI repository using its ID.

Parameters:

id (int) – The identifier of the dataset in the UCI repository.

Returns:

A tuple containing:

• X (pd.DataFrame): The feature matrix.

• y (pd.Series or np.array): The target variable.

Return type:

tuple

RADAR.static_data.static_datasets_uci.load_from_url(url, **kwargs)

Loads a dataset from a given URL.

Parameters:

• url (str) – The URL from which to fetch the dataset.

• **kwargs – Additional arguments to be passed to pd.read_csv().

Returns:

The dataset loaded from the URL.

Return type:

pd.DataFrame

RADAR.static_data.static_datasets_uci.load_human_activity_recognition(url, **kwargs)

RADAR.static_data.static_datasets_uci.load_kddcup99(**kwargs)

Reads the KDD Cup 99 dataset using sklearn’s built-in fetcher.

The original kdd.ics.uci.edu URLs are no longer available, so we rely on sklearn which handles mirror selection and local caching automatically.

Uses the 10 % subset (percent10=True) and then takes a stratified sample of ~10 000 rows so the frontend stays responsive.

Module contents