Python-Based Baseline Correction via pybaselines

step_measure_baseline_py() creates a specification of a recipe step that applies baseline correction using the Python pybaselines library, which provides 50+ baseline correction algorithms.

Usage

step_measure_baseline_py(
  recipe,
  method = "asls",
  ...,
  subtract = TRUE,
  measures = NULL,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_baseline_py")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

method

The pybaselines method to use. Common methods include:

• Whittaker methods: "asls", "iasls", "airpls", "arpls", "drpls", "psalsa"

• Polynomial methods: "poly", "modpoly", "imodpoly", "loess", "quant_reg"

• Morphological: "mor", "imor", "rolling_ball", "tophat"

• Spline: "pspline_asls", "pspline_airpls", "mixture_model"

• Smooth: "snip", "swima", "noise_median"

• Classification: "dietrich", "golotvin", "fastchrom"

• See pybaselines documentation for the full list.

...

Additional arguments passed to the pybaselines method. Common parameters include:

• lam: Smoothness parameter for Whittaker methods (default varies by method)

• p: Asymmetry parameter for ALS methods (default ~0.01)

• poly_order: Polynomial degree for polynomial methods

• half_window: Window size for morphological methods

• max_half_window: Maximum window for SNIP method

subtract

If TRUE (default), the baseline is subtracted from the signal. If FALSE, the baseline values replace the original values (useful for extracting baselines).

measures

An optional character vector of measure column names to process. If NULL (the default), all measure columns (columns with class measure_list) will be processed.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

skip

A logical. Should the step be skipped when the recipe is baked?

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

This step provides access to the comprehensive pybaselines Python library, which implements over 50 baseline correction algorithms across several categories:

Whittaker Methods

Based on penalized least squares with asymmetric weights:

• asls: Asymmetric Least Squares (good general-purpose method)

• iasls: Improved ALS with automatic smoothness selection

• airpls: Adaptive iteratively reweighted penalized least squares

• arpls: Asymmetrically reweighted penalized least squares

• psalsa: Peaked Signal's Asymmetric Least Squares Algorithm

Polynomial Methods

Fit polynomials to baseline regions:

• poly: Simple polynomial fitting

• modpoly: Modified polynomial (iterative)

• imodpoly: Improved modified polynomial

• loess: Local regression (LOESS)

Morphological Methods

Based on mathematical morphology:

• mor: Morphological opening

• imor: Improved morphological

• rolling_ball: Rolling ball algorithm

• tophat: Top-hat transform

Requirements

This step requires the reticulate package and Python with pybaselines installed. Install pybaselines with:

reticulate::py_require("pybaselines")

No selectors should be supplied to this step function. The data should be in the internal format produced by step_measure_input_wide() or step_measure_input_long().

Tidying

When you tidy() this step, a tibble with columns terms, method, subtract, and id is returned.

See also

step_measure_baseline_als(), step_measure_baseline_custom() for R-based alternatives.

Other measure-baseline: step_measure_baseline_airpls(), step_measure_baseline_als(), step_measure_baseline_arpls(), step_measure_baseline_auto(), step_measure_baseline_custom(), step_measure_baseline_gpc(), step_measure_baseline_minima(), step_measure_baseline_morph(), step_measure_baseline_poly(), step_measure_baseline_rf(), step_measure_baseline_rolling(), step_measure_baseline_snip(), step_measure_baseline_tophat(), step_measure_detrend()

Examples

if (FALSE) { # measure:::.pybaselines_available()
library(recipes)

# Asymmetric Least Squares baseline correction
rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_baseline_py(method = "asls", lam = 1e6, p = 0.01) |>
  prep()

bake(rec, new_data = NULL)

# Using SNIP algorithm
rec2 <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_baseline_py(method = "snip", max_half_window = 40) |>
  prep()
}