1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
|
"""
=======================================================================
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.
|
