Code Documentation

artificial_data_generator

Generator for artificial data.

Can be used as baseline for benchmarking and the development of new methods. For example, simulation of biomarker data from high-throughput experiments.

artificial_data_generator.artificial_data_generator._build_pseudo_classes(params_dict)

Create pseudo-classes by shuffling artificial classes.

The total number of underlying classes equals the total number of artificial classes. :param params_dict: Parameter dict containing number of pseudo-class features and number of artificial classes

(see example for parameters of generate_artificial_classification_data()).

Returns:

Numpy array of the given shape.

Return type:

Randomly shuffled pseudo-class

Parameters:

params_dict (Dict[str, Any]) –

artificial_data_generator.artificial_data_generator._generate_correlated_cluster(number_of_features, number_of_samples, lower_bound, upper_bound)

Generate a cluster of correlated features.

Parameters:

• number_of_features (int) – Number of columns of generated data.

• number_of_samples (int) – Number of rows of generated data.

• lower_bound (float) – Lower bound of the generated correlations.

• upper_bound (float) – Upper bound of the generated correlations.

Returns:

Numpy array of the given shape with correlating features in the given range.

Return type:

ndarray

artificial_data_generator.artificial_data_generator._generate_dataframe(data_np, params_dict)

Generate semantic names for the columns of the given DataFrame.

Parameters:

• data_np (ndarray) – Numpy array with generated data.

• params_dict (Dict[str, Any]) – Parameter dict including the number of features per class, the number of pseudo-class features and the number of random features (see example for parameters of generate_artificial_classification_data()).

Returns:

DataFrame with meaningful named columns.

• label for the labels

• bm for artificial class feature

• pseudo for pseudo-class feature

• random for random data

Return type:

DataFrame

artificial_data_generator.artificial_data_generator.generate_artificial_classification_data(params_dict)

Generate artificial classification (e.g. biomarker) data.

Parameters:

params_dict (Dict[str, Any]) – Parameters for the data to generate (see example below).

Returns:

Generated artificial data.

Return type:

DataFrame

Example: .. code-block:: python

params_dict = { “number_of_relevant_features”: 12,

“number_of_pseudo_class_features”: 2, “random_features”: {“number_of_features”: 10, “distribution”: “lognormal”,

“scale”: 1, “mode”: 0},

“classes”: {

1: {

“number_of_samples”: 15, “distribution”: “lognormal”, “mode”: 3, “scale”: 1, “correlated_features”: {

1: {“number_of_features”: 4, “correlation_lower_bound”: 0.7,

“correlation_upper_bound”: 1},

2: {“number_of_features”: 4, “correlation_lower_bound”: 0.7,

“correlation_upper_bound”: 1},

3: {“number_of_features”: 4, “correlation_lower_bound”: 0.7,

“correlation_upper_bound”: 1},

},

}, 2: {“number_of_samples”: 15, “distribution”: “normal”, “mode”: 1,

“scale”: 2, “correlated_features”: {}},

3: {“number_of_samples”: 15, “distribution”: “normal”, “mode”: -10,

“scale”: 2, “correlated_features”: {}},

}, “path_to_save_csv”: “your_path_to_save.csv”, “path_to_save_feather”: “”, “path_to_save_meta_data”: “your_path_to_save_params_dict.yaml”, “shuffle_features”: False,

}

Elements of the parameter dict: “number_of_relevant_features”: Total number of features (columns) to generate

for each artificial class.

“number_of_pseudo_class_features”: Number of pseudo-class features.

The underlying classes correspond to the selected number of classes and follow a normal distribution. Shifted modes of the generated artificial classes equal two times the class number. All samples of the generated classes are randomly shuffled and therefore have no relation to any class label.

“random_features”: “number_of_features”: Number of randomly generated features.

“distribution”: “lognormal” or “normal” “scale”: Standard deviation (spread or “width”) of the distribution.

Must be non-negative.

“mode”: Mean (“centre”) of the distribution.

“classes”: Parameter dicts for each class to generate. The key equals the class label.

“number_of_samples”: 15, “distribution”: “lognormal” or “normal” “mode”: Mean (“centre”) of the distribution. “scale”: Standard deviation (spread or “width”) of the distribution. Must be non-negative. “correlated_features”: Parameter dicts for each cluster of correlated features to

generate. The key equals the cluster number. To generate no clusters insert empty dict. “number_of_features”: Number of correlated features within

a cluster.

“correlation_lower_bound”: Lower bounds for the correlation

of each cluster of correlated features within a normal distributed class. Default is 0.7.

“correlation_upper_bound”: Upper bounds for the correlation

of each cluster of correlated features within a normal distributed class. Default is 1.

“path_to_save_csv”: “your_path_to_save.csv” “path_to_save_feather”: “your_path_to_save.feather” “path_to_save_meta_data”: “your_path_to_save_params_dict.yaml” “shuffle_features”: If generated features should be shuffled.