Preprocessing transformers and scikit-learn compatibility

SpectroChemPy preprocessing operations can be used as transformer objects with an explicit fit() / transform() lifecycle. Feature-wise scalers learn statistics from a training set and reuse them on new data. Sample-local operations such as SNV and spectral normalization compute each observation’s statistics during transform() and do not support a safe inverse without result-attached per-observation state.

The transformers also expose get_params() / set_params() so they work with sklearn.base.clone() when scikit-learn is installed.

Load data

import numpy as np

import spectrochempy as scp

dataset = scp.read_omnic("irdata/nh4y-activation.spg")
region = dataset[:, 4000.0:2000.0]

Train / test split

We keep the first 40 spectra for training and the rest for testing.

X_train = region[:40]
X_test = region[40:]

Stateful scaling with AutoscaleTransformer

fit_transform() learns the mean and std on train (per wavenumber, dim='y'), then scales train. transform() later applies the same parameters to test — no data leakage.

scaler = scp.AutoscaleTransformer(dim="y")
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

_ = X_train_scaled.plot(title="Train set (autoscaled)")
_ = X_test_scaled.plot(title="Test set (same scaler)")

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Inspecting learned parameters

The transformer stores the statistics as NumPy arrays.

print("Mean shape:", scaler.mean_.shape)
print("Std shape:  ", scaler.std_.shape)

Mean shape: (1, 2075)
Std shape:   (1, 2075)

Parameter inspection with get_params / set_params

Transformers follow scikit-learn parameter conventions.

print("Original params:", scaler.get_params())

# Change the target dimension
scaler.set_params(dim="x")
print("After set_params:", scaler.get_params())

Original params: {'dim': 'y'}
After set_params: {'dim': 'x'}

sklearn.base.clone compatibility

When scikit-learn is available, transformers can be cloned exactly like any sklearn estimator.

try:
    from sklearn.base import clone

    original = scp.AutoscaleTransformer(dim="x")
    original.fit(X_train)

    cloned = clone(original)
    print("Clone has same params:", cloned.get_params() == original.get_params())
    print("Clone is not fitted yet:", not cloned._fitted)
except ImportError:
    print("scikit-learn not installed — clone example skipped")

Clone has same params: True
Clone is not fitted yet: True

Inverse transform

inverse_transform() restores the original absorbance units. This is useful when interpreting model predictions (e.g., converting PCA scores back to original space).

X_train_restored = scaler.set_params(dim="y").inverse_transform(X_train_scaled)
print(
    "Restored data matches original?",
    np.allclose(X_train_restored.data, X_train.data),
)

Restored data matches original? True

# Uncomment the following line to display all figures when running the script
# directly with Python.
#
# scp.show()