Quick start

Check the installation guide to get started.

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New in chemometrics? Learn through our step-by-step tutorials page.

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Want to explore the tools at your fingertips? Visit the explore overview.

Load data

This loads a training set of mid infrared spectra (X_train) and associated reference values (y_train) from a fermentation process.

from chemotools.datasets import load_fermentation_train

X_train, y_train = load_fermentation_train()

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Looking for available datasets? See the datasets catalog.

Preprocess spectra

Load, define and apply preprocessing steps to the training data. Here we use a Savitzky-Golay filter to compute the first derivative of the sample spectra.

from chemotools.derivate import SavitzkyGolay

# Configure the preprocessing step
sg = SavitzkyGolay(window_size=3, polynomial_order=1, derivate_order=1)

# Fit and transform the training data
X_train_preprocessed = sg.fit_transform(X_train)

Hint

Want to know more about preprocessing methods? Read the methods index.

Build a pipeline

Combine preprocessing and modelling steps in a single pipeline.

from chemotools.derivate import SavitzkyGolay
from sklearn.cross_decomposition import PLSRegression
from sklearn.pipeline import make_pipeline

# Define the pipeline
pipeline = make_pipeline(
    SavitzkyGolay(window_size=3, polynomial_order=1, derivate_order=1),
    PLSRegression(n_components=2)
)

# Fit the pipeline to the training data
pipeline.fit(X_train, y_train)

# Prediction on the training data
y_train_pred = pipeline.predict(X_train)

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Want to know more about pipelines? Open the pipelines article.

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Curious about model optimization? Read the optimization guide.

Congratulations! You have just built your first chemotools workflow in Python 🎯