swiss_cheese 0.1.6

Missing Value generation library for research purposes.

swiss-cheese

Making missing values for research purposes. Focuses on tabular data.

installation:

uv add swiss-cheese
pip install swiss-cheese

Usage:

from swiss-cheese import MNAR

generator = MNAR()
missing_df = generator(df, 0.4) # returns np.array or DataFrame with 40% missingness

Missing Completely At Random (MCAR)

Sets $\alpha$ percentage of values to missing completely at random. Ensures that every element has at least one feature.

Missing Not At Random (MNAR)

Sets $\alpha$ percentage of values missing by sampling from a normal distribution and matching to the nearest data value. Currently $3$ modes are supported:

Min

Removes the minimum value of each feature until desired $\alpha$ has been achieved.

Max

Removes the maximum value of each feature until desired $\alpha$ has been achieved.

Gaussian Missing (GM)

Samples from a gaussian (mean, var given as parameter) and romves closest value.

Missing At Random (MAR)

Create pairs of observed and missing columns, then apply the MNAR schemes to the observed column but only set missing column to missing. Default mode is GM but Max and Min are also availble.

Attention: max_missing_per_column defines a maximum percentage that can be miissing per column It will always be assured that at least one column remains observed. If $\alpha$ is higher than the maximum possible missing rate, it will be set to the maximum and a warning is emitted.