# ======================================================================================
# Copyright (©) 2014-2026 Laboratoire Catalyse et Spectrochimie (LCS), Caen, France.
# CeCILL-B FREE SOFTWARE LICENSE AGREEMENT
# See full LICENSE agreement in the root directory.
# ======================================================================================
# ruff: noqa
"""
PLS regression example
----------------------
In this example, we perform a PLS regression to predict the moisture of corn samples
from their NIR spectra.

"""

# %%
# Import the spectrochempy API package
import spectrochempy as scp

# %%
# The data set is available to download from the Eigenvector Archive:
ds_list = scp.read("http://www.eigenvector.com/data/Corn/corn.mat", merge=False)
ds_list_names = [f"{i} : {ds.name}({ds.shape})" for i, ds in enumerate(ds_list)]
print(ds_list_names)

# %%
# This data set, originally taken at Cargil,  consists of 80 samples of corn measured on
# 3 different NIR spectrometers together with the moisture, oil, protein and starch
# values for each of the samples is also included.
# The 5th dataset named `'m5spec'`, contains the NIR spectra of 80 corn samples recorded
# on the same instrument. Let's assign this NDDataset specta to `X`, add few
# informations and plot it:
# %%
X = ds_list[4]
X.title = "reflectance"
X.x.title = "Wavelength"
X.x.units = "nm"
_ = X.plot(cmap=None)
# %%
# The values of the properties we want to predict are in the 4th dattaset named `'propval'` dataset:
Y = ds_list[3]
_ = Y.T.plot(cmap=None, legend=Y.x.labels)
# %%
# We are interested to predict the moisture content:
y = Y[:, "Moisture"]

# %%
# First we select 57 first samples (2/3 of the total) to train/calibrate the model and the remaining ones
# to test/validate the model:
X_train = X[:57]
X_test = X[57:]
y_train = y[:57]
y_test = y[57:]

# %%
# Then we create a PLSRegression object and fit the train datasets:
pls = scp.PLSRegression(n_components=5)
pls.fit(X_train, y_train)
# %%
# Finally we generate a parity plot comparing the predicted and actual values, for
# both train set and test set.

# sphinx_gallery_thumbnail_number = 3
ax = pls.parityplot(label="calibration", s=150)
_ = pls.parityplot(
    y_test, pls.predict(X_test), s=150, c="red", label="validation", clear=False
)
ax.legend(loc="lower right")
# %%
# This ends the example ! The following line can be uncommented if no plot shows when
# running the .py script with python

scp.show()