# ======================================================================================
# 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 package
import spectrochempy as scp

# %%
# Load the corn NIR dataset
# --------------------------
# The data is available from the Eigenvector archive:
try:
    ds_list = scp.read("http://www.eigenvector.com/data/Corn/corn.mat", merge=False)
except FileNotFoundError:
    ds_list = None
    print("Eigenvector corn dataset not reachable; skipping the remote PLS example.")
else:
    ds_list_names = [f"{i} : {ds.name}({ds.shape})" for i, ds in enumerate(ds_list)]
    print(ds_list_names)

# %%
if ds_list is not None:
    # %%
    # Inspect the spectra
    # ^^^^^^^^^^^^^^^^^^^^
    # The 5th dataset ``m5spec`` contains NIR spectra from 80 corn samples
    # recorded on the same instrument:
    X = ds_list[4]
    X.title = "reflectance"
    X.x.title = "Wavelength"
    X.x.units = "nm"
    _ = X.plot(cmap=None)

    # %%
    # The properties to predict are in the ``propval`` dataset:
    Y = ds_list[3]
    _ = Y.T.plot(cmap=None, legend=Y.x.labels)

    # %%
    # Predict moisture content
    # ^^^^^^^^^^^^^^^^^^^^^^^^
    y = Y[:, "Moisture"]

    # %%
    # Split into training and test sets
    # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    # Use 57 samples (2/3) for calibration and the rest for validation:
    X_train = X[:57]
    X_test = X[57:]
    y_train = y[:57]
    y_test = y[57:]

    # %%
    # Fit the PLS model
    # ^^^^^^^^^^^^^^^^^^
    pls = scp.PLSRegression(n_components=5)
    _ = pls.fit(X_train, y_train)

    # %%
    # Validate with a parity plot
    # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ax = pls.plot_parity(label="calibration", s=150)
    _ = pls.plot_parity(
        y_test,
        pls.predict(X_test),
        ax=ax,
        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()