# %%
# ======================================================================================
# 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
"""
Preprocessing transformers and scikit-learn compatibility
=========================================================

SpectroChemPy preprocessing operations can be used as **stateful
transformers** that learn statistics from a training set and reuse them
on new data.  This is essential for machine-learning workflows where
train and test sets must be processed with the *same* scaling
parameters.

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)")

# %%
# Inspecting learned parameters
# -----------------------------
# The transformer stores the statistics as NumPy arrays.

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

# %%
# Parameter inspection with ``get_params`` / ``set_params``
# ---------------------------------------------------------
# All transformers follow scikit-learn conventions.

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

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

# %%
# ``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")

# %%
# 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),
)

# %%
# This ends the example. Uncomment the next line to display the figures when
# running the script directly with Python.

# scp.show()