# ======================================================================================
# 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
"""
PCA example (iris dataset)
==========================
In this example, we perform the PCA dimensionality reduction of the classical `iris`
dataset (Ronald A. Fisher.
"The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7, pp.179-188, 1936).

"""

# %%
# Load the SpectroChemPy API package
import spectrochempy as scp

# %%
# Load the Iris dataset
# ---------------------
dataset = scp.load_iris()

# %%
# Fit a PCA model with automatic component selection
# ---------------------------------------------------
# Using ``n_components="mle"``, the optimal number of components is determined
# automatically. Note: `"mle"` cannot be used when n_observations < n_features.
pca = scp.PCA(n_components="mle")
_ = pca.fit(dataset)

# %%
# The number of components found is 3:
pca.n_components

# %%
# It explains 99.5% of the variance:
pca.cumulative_explained_variance[-1].value

# %%
# Fit a PCA model with a variance threshold
# -------------------------------------------
# We can also specify the amount of explained variance directly:
pca = scp.PCA(n_components=0.999)
_ = pca.fit(dataset)

# %%
# This time 4 components are found:
pca.n_components

# %%
# Inspect the loadings and scores
# --------------------------------
# The 4 components (loadings) are accessible via ``pca.components``:
loadings = pca.components
loadings

# %%
# or equivalently via ``pca.loadings``:
pca.loadings

# %%
# To reduce the data to a lower dimensionality, use ``transform``:
scores = pca.transform()
scores

# %%
# The scores are also available directly via ``pca.scores``:
scores = pca.scores
scores

# %%
# The explained and cumulative variance can be printed:
pca.printev()

# %%
# Visualize the results
# ----------------------
# The scree plot shows the explained variance per component:
_ = pca.plot_scree()

# %%
# The 2D score plot (first 2 PCs) separates Iris-setosa from the other species:
_ = pca.plot_score(color_mapping="labels")

# %%
# The 3D score plot (first 3 PCs) shows that a third PC does not further
# distinguish versicolor from virginica:
ax = pca.plot_score(components=(1, 2, 3), color_mapping="labels")
ax.view_init(10, 75)

# %%
# This ends the example ! The following line can be uncommented if no plot shows when
# running the .py script with python

# scp.show()