"""
=======================================================================
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
=======================================================================

When working with covariance estimation, the usual approach is to use
a maximum likelihood estimator, such as the
:class:`~sklearn.covariance.EmpiricalCovariance`. It is unbiased, i.e. it
converges to the true (population) covariance when given many
observations. However, it can also be beneficial to regularize it, in
order to reduce its variance; this, in turn, introduces some bias. This
example illustrates the simple regularization used in
:ref:`shrunk_covariance` estimators. In particular, it focuses on how to
set the amount of regularization, i.e. how to choose the bias-variance
trade-off.
"""


# %%
# Generate sample data
# --------------------

import numpy as np

n_features, n_samples = 40, 20
np.random.seed(42)
base_X_train = np.random.normal(size=(n_samples, n_features))
base_X_test = np.random.normal(size=(n_samples, n_features))

# Color samples
coloring_matrix = np.random.normal(size=(n_features, n_features))
X_train = np.dot(base_X_train, coloring_matrix)
X_test = np.dot(base_X_test, coloring_matrix)


# %%
# Compute the likelihood on test data
# -----------------------------------

from scipy import linalg

from sklearn.covariance import ShrunkCovariance, empirical_covariance, log_likelihood

# spanning a range of possible shrinkage coefficient values
shrinkages = np.logspace(-2, 0, 30)
negative_logliks = [
    -ShrunkCovariance(shrinkage=s).fit(X_train).score(X_test) for s in shrinkages
]

# under the ground-truth model, which we would not have access to in real
# settings
real_cov = np.dot(coloring_matrix.T, coloring_matrix)
emp_cov = empirical_covariance(X_train)
loglik_real = -log_likelihood(emp_cov, linalg.inv(real_cov))


# %%
# Compare different approaches to setting the regularization parameter
# --------------------------------------------------------------------
#
# Here we compare 3 approaches:
#
# * Setting the parameter by cross-validating the likelihood on three folds
#   according to a grid of potential shrinkage parameters.
#
# * A close formula proposed by Ledoit and Wolf to compute
#   the asymptotically optimal regularization parameter (minimizing a MSE
#   criterion), yielding the :class:`~sklearn.covariance.LedoitWolf`
#   covariance estimate.
#
# * An improvement of the Ledoit-Wolf shrinkage, the
#   :class:`~sklearn.covariance.OAS`, proposed by Chen et al. Its
#   convergence is significantly better under the assumption that the data
#   are Gaussian, in particular for small samples.


from sklearn.covariance import OAS, LedoitWolf
from sklearn.model_selection import GridSearchCV

# GridSearch for an optimal shrinkage coefficient
tuned_parameters = [{"shrinkage": shrinkages}]
cv = GridSearchCV(ShrunkCovariance(), tuned_parameters)
cv.fit(X_train)

# Ledoit-Wolf optimal shrinkage coefficient estimate
lw = LedoitWolf()
loglik_lw = lw.fit(X_train).score(X_test)

# OAS coefficient estimate
oa = OAS()
loglik_oa = oa.fit(X_train).score(X_test)

# %%
# Plot results
# ------------
#
#
# To quantify estimation error, we plot the likelihood of unseen data for
# different values of the shrinkage parameter. We also show the choices by
# cross-validation, or with the LedoitWolf and OAS estimates.

import matplotlib.pyplot as plt

fig = plt.figure()
plt.title("Regularized covariance: likelihood and shrinkage coefficient")
plt.xlabel("Regularization parameter: shrinkage coefficient")
plt.ylabel("Error: negative log-likelihood on test data")
# range shrinkage curve
plt.loglog(shrinkages, negative_logliks, label="Negative log-likelihood")

plt.plot(plt.xlim(), 2 * [loglik_real], "--r", label="Real covariance likelihood")

# adjust view
lik_max = np.amax(negative_logliks)
lik_min = np.amin(negative_logliks)
ymin = lik_min - 6.0 * np.log((plt.ylim()[1] - plt.ylim()[0]))
ymax = lik_max + 10.0 * np.log(lik_max - lik_min)
xmin = shrinkages[0]
xmax = shrinkages[-1]
# LW likelihood
plt.vlines(
    lw.shrinkage_,
    ymin,
    -loglik_lw,
    color="magenta",
    linewidth=3,
    label="Ledoit-Wolf estimate",
)
# OAS likelihood
plt.vlines(
    oa.shrinkage_, ymin, -loglik_oa, color="purple", linewidth=3, label="OAS estimate"
)
# best CV estimator likelihood
plt.vlines(
    cv.best_estimator_.shrinkage,
    ymin,
    -cv.best_estimator_.score(X_test),
    color="cyan",
    linewidth=3,
    label="Cross-validation best estimate",
)

plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax)
plt.legend()

plt.show()

# %%
# .. note::
#
#    The maximum likelihood estimate corresponds to no shrinkage,
#    and thus performs poorly. The Ledoit-Wolf estimate performs really well,
#    as it is close to the optimal and is not computationally costly. In this
#    example, the OAS estimate is a bit further away. Interestingly, both
#    approaches outperform cross-validation, which is significantly most
#    computationally costly.