robpy.outliers

Module containing all algorithms related to outlier detection.

Detect Deviating Cells

class robpy.outliers.ddc.DDCEstimator(chi2_quantile: float = 0.99, min_correlation: float = 0.5, scale_estimator: ~robpy.univariate.base.RobustScaleEstimator = <robpy.univariate.onestep_m.CellwiseOneStepMEstimator object>)[source]

Bases: OutlierMixin

Implementation of the Detecting Deviating Cells (DDC) algorithm.

Parameters:

  • chi2_quantile (float, optional) – Quantile of the chi-squared distribution to use as threshold for univariate outlier detection in step 2. Default is 0.99.

  • min_correlation (float, optional) – Minimum correlation between variables to consider them

  • scale_estimator (RobustScaleEstimator, optional) – robust scale estimator to scale the initial data with. Defaults to CellwiseOneStepMEstimator().

References

Rousseeuw, P. J., & Bossche, W. V. D. (2018). Detecting Deviating Data Cells. Technometrics, 60(2), 135–145. https://doi.org/10.1080/00401706.2017.1340909

R Implementation: https://www.rdocumentation.org/packages/cellWise/versions/2.5.3/topics/DDC

cellmap(X: DataFrame, annotate: bool = False, fmt: str = '.1f', figsize: tuple[int, int] = (7, 10), row_zoom: tuple[int, int] | Index | None = None, col_zoom: tuple[int, int] | Index | None = None, vmax_clip: float = 3.290526731491895, cmap: str | Colormap = 'custom') → Axes[source]

Visualize the standardized residuals of the DDC model as a heatmap.

Parameters:

  • X (pd.DataFrame) – The original data used to fit the model.

  • annotate (bool, optional) – Whether to annotate the heatmap cells with the original values. Defaults to False.

  • fmt (str, optional) – Format to use for annotations. Defaults to “.1f”.

  • figsize (tuple[int, int], optional) – Figure size. Defaults to (7, 10).

  • row_zoom (tuple[int, int] | pd.Index | None, optional) – If not None, a subset of the rows is selected for visualization. A tuple is interpreted as a slice, a pd.Index as a selection. Defaults to None.

  • col_zoom (tuple[int, int] | pd.Index | None, optional) – Similar to row_zoom but for columns. Defaults to None.

  • vmax_clip (float) – standardized absolute residuals larger than vmax will get the darkest color and hence get clipped

  • cmap (str | matplotlib.colors.Colormap, optional) – matplotlib colormap or string, maps the data to the color space.

Returns:

the matplotlib axes with the heatmap

Return type:

Axes

fit(X: DataFrame, y=None, verbose: bool = False)[source]
impute(X, impute_outliers: bool = True)[source]
predict(X, rowwise: bool = False)[source]
robpy.outliers.ddc.get_custom_cmap(vmax_clip: int)[source]