"""
Functional Principal Component Analysis
=======================================

Explores the two possible ways to do functional principal component analysis.
"""

# Author: Yujian Hong
# License: MIT

# %%
# In this example we are going to use functional principal component analysis
# to explore datasets and obtain conclusions about said dataset using this
# technique.
#
# FPCA is a dimensionality reduction method for functional data that aims to
# reduce the complexity of studying observations by finding a finite number of
# principal components. These components are the directions that capture the
# main modes of variation across the function (the directions in which the
# curves vary the most). FPCA can be though of as a basis expansion, but what
# distinguishes FPCA is that among all basis expansions that use K components
# for a fixed K, the FPCA expansion explains most of the variation in X.
#
# For more information abour FPCA and its objectives, see
# :footcite:ts:`wang+chiou+muller_2016_fpca`.
#
# Firstly, we are going to fetch the Berkeley Growth Study data. This dataset
# correspond to the height of several boys and girls measured from birth to
# when they are 18 years old. The number and time of the measurements are the
# same for each individual. To better understand the data we plot it.

import matplotlib.pyplot as plt

from skfda.datasets import fetch_growth

dataset = fetch_growth()
fd = dataset["data"]
y = dataset["target"]
fd.plot()
plt.show()

# %%
# FPCA can be done in two ways. The first way is to operate directly with the
# raw data. We call it discretized FPCA as the functional data in this case
# consists in finite values dispersed over points in a domain range.
# We initialize and setup the FPCADiscretized object and run the fit method to
# obtain the first two components. By default, if we do not specify the number
# of components, it's 3. Other parameters are weights and centering. For more
# information please visit the documentation.

from skfda.preprocessing.dim_reduction import FPCA

fpca_discretized = FPCA(n_components=2)
fpca_discretized.fit(fd)
fpca_discretized.components_.plot()
plt.show()

# %%
# In the second case, the data is first converted to use a basis representation
# and the FPCA is done with the basis representation of the original data.
# We obtain the same dataset again and transform the data to a basis
# representation. This is because the FPCA module modifies the original data.
# We also plot the data for better visual representation.

from skfda.representation.basis import BSplineBasis

dataset = fetch_growth()
fd = dataset["data"]
basis = BSplineBasis(n_basis=7)
basis_fd = fd.to_basis(basis)
basis_fd.plot()
plt.show()

# %%
# We initialize the FPCABasis object and run the fit function to obtain the
# first 2 principal components. By default the principal components are
# expressed in the same basis as the data. We can see that the obtained result
# is similar to the discretized case.
fpca = FPCA(n_components=2)
fpca.fit(basis_fd)
fpca.components_.plot()
plt.show()

# %%
# To better illustrate the effects of the obtained two principal components,
# we add and subtract a multiple of the components to the mean function.
# We can then observe now that this principal component represents the
# variation in the mean growth between the children.
# The second component is more interesting. The most appropriate explanation is
# that it represents the differences between girls and boys. Girls tend to grow
# faster at an early age and boys tend to start puberty later, therefore, their
# growth is more significant later. Girls also stop growing early

from skfda.exploratory.visualization import FPCAPlot

FPCAPlot(
    basis_fd.mean(),
    fpca.components_,
    factor=30,
    fig=plt.figure(figsize=(6, 2 * 4)),
    n_rows=2,
).plot()
plt.show()

# %%
# We can also specify another basis for the principal components as argument
# when creating the FPCABasis object. For example, if we use the Fourier basis
# for the obtained principal components we can see that the components are
# periodic. This example is only to illustrate the effect. In this dataset, as
# the functions are not periodic it does not make sense to use the Fourier
# basis

from skfda.representation.basis import FourierBasis

dataset = fetch_growth()
fd = dataset["data"]
basis_fd = fd.to_basis(BSplineBasis(n_basis=7))
fpca = FPCA(n_components=2, components_basis=FourierBasis(n_basis=7))
fpca.fit(basis_fd)
fpca.components_.plot()
plt.show()

# %%
# We can observe that if we switch to the Monomial basis, we also lose the
# key features of the first principal components because it distorts the
# principal components, adding extra maximums and minimums. Therefore, in this
# case the best option is to use the BSpline basis as the basis for the
# principal components

from skfda.representation.basis import MonomialBasis

dataset = fetch_growth()
fd = dataset["data"]
basis_fd = fd.to_basis(BSplineBasis(n_basis=7))
fpca = FPCA(n_components=2, components_basis=MonomialBasis(n_basis=4))
fpca.fit(basis_fd)
fpca.components_.plot()
plt.show()

# %%
# References
# ----------
#
# .. footbibliography::