def check_exponential(inverse_scale):
rng = check_random_state(1)
p_carl = Exponential(inverse_scale=inverse_scale)
p_scipy = st.expon(scale=1. / inverse_scale)
X = rng.rand(50, 1)
assert_array_almost_equal(p_carl.pdf(X),
p_scipy.pdf(X.ravel()))
assert_array_almost_equal(p_carl.cdf(X),
p_scipy.cdf(X.ravel()))
assert_array_almost_equal(-np.log(p_carl.pdf(X)),
p_carl.nnlf(X))
def check_exponential(inverse_scale):
rng = check_random_state(1)
p_carl = Exponential(inverse_scale=inverse_scale)
p_scipy = st.expon(scale=1. / inverse_scale)
X = rng.rand(50, 1)
assert_array_almost_equal(p_carl.pdf(X), p_scipy.pdf(X.ravel()))
assert_array_almost_equal(p_carl.cdf(X), p_scipy.cdf(X.ravel()))
assert_array_almost_equal(-np.log(p_carl.pdf(X)), p_carl.nll(X))
def test_fit():
p1 = Normal(mu=T.constant(0.0), sigma=T.constant(2.0))
p2 = Normal(mu=T.constant(3.0), sigma=T.constant(2.0))
p3 = Exponential(inverse_scale=T.constant(0.5))
g = theano.shared(0.5)
m = Mixture(components=[p1, p2, p3], weights=[g, g*g])
X = np.concatenate([st.norm(loc=0.0, scale=2.0).rvs(300, random_state=0),
st.norm(loc=3.0, scale=2.0).rvs(100, random_state=1),
st.expon(scale=1. / 0.5).rvs(500, random_state=2)])
X = X.reshape(-1, 1)
s0 = m.score(X)
m.fit(X)
assert np.abs(g.eval() - 1. / 3.) < 0.05
assert m.score(X) >= s0
def generate_samples(with_linear_transformation=False,
add_variation=False,
n_samples=50000,
verbose=True):
"""
Generate 5 independent variables: two Gaussian, mixture of Gaussian, two exponents.
Two Gaussian have different means for original and target distributions.
if with_linear_transformation is True then add linear transformation of generated 5 variables.
if add_variation is True then add random values in variance to obtain gaussian pdf
for orignal and target samples not only with different mean but also with different variance.
:param bool with_linear_transformation: apply or not linear transformation for samples features
:param bool add_variation: make or not different variance for Gaussian distribution for original and target samples.
:param int n_samples: number of generated samples for original/target distributions. For test samples 2*n_samples will be generated
:param bool verbose: print and plot additional info during generation.
:return: train original, train target, exact weights for train original, test original, test target, exact weights for test original
"""
# define linear transformation matrix
R = make_sparse_spd_matrix(5, alpha=0.5, random_state=7)
variation_origin, variation_target = (0, 0)
if add_variation:
r = check_random_state(42)
variation_origin, variation_target = r.uniform() / 3., r.uniform() / 3.
p0 = Join(components=[
Normal(mu=.5, sigma=1 + variation_origin),
Normal(mu=-.5, sigma=3 + variation_origin),
Mixture(components=[Normal(mu=-2, sigma=1),
Normal(mu=2, sigma=0.5)]),
Exponential(inverse_scale=3.0),
Exponential(inverse_scale=0.5)
])
p1 = Join(components=[
Normal(mu=0, sigma=1 + variation_target),
Normal(mu=0, sigma=3 + variation_target),
Mixture(components=[Normal(mu=-2, sigma=1),
Normal(mu=2, sigma=0.5)]),
Exponential(inverse_scale=3.0),
Exponential(inverse_scale=0.5)
])
if with_linear_transformation:
p0 = LinearTransform(p0, R)
p1 = LinearTransform(p1, R)
X0 = p0.rvs(n_samples, random_state=777)
X1 = p1.rvs(n_samples, random_state=777)
exact_weights = numpy.exp(p0.nll(X0) - p1.nll(X0))
exact_weights[numpy.isinf(exact_weights)] = 0.
# generate samples to test reweighting rule (to avoid overfitting)
X0_roc = p0.rvs(2 * n_samples, random_state=777 * 2)
X1_roc = p1.rvs(2 * n_samples, random_state=777 * 2)
# Weighted with true ratios
exact_weights_roc = numpy.exp(p0.nll(X0_roc) - p1.nll(X0_roc))
exact_weights_roc[numpy.isinf(exact_weights_roc)] = 0.
if verbose:
print "Original distribution"
fig = corner.corner(X0,
bins=20,
smooth=0.85,
labels=["X0", "X1", "X2", "X3", "X4"])
plt.show()
print "Target distribution"
fig = corner.corner(X1,
bins=20,
smooth=0.85,
labels=["X0", "X1", "X2", "X3", "X4"])
plt.show()
print "Exact reweighting"
# In this example, we know p0(x) and p1(x) exactly,
#so we can compare the other can compare the approximate reweighting approaches with the exact weights.
draw_distributions(X0, X1, exact_weights)
return X0, X1, exact_weights, X0_roc, X1_roc, exact_weights_roc
def check_fit(inverse_scale):
p = Exponential()
X = st.expon(scale=1. / inverse_scale).rvs(5000,
random_state=0).reshape(-1, 1)
p.fit(X)
assert np.abs(p.inverse_scale.get_value() - inverse_scale) <= 0.1
def check_rvs(inverse_scale, random_state):
p = Exponential(inverse_scale=inverse_scale, random_state=random_state)
samples = p.rvs(1000)
assert np.abs(np.mean(samples) - 1. / inverse_scale) <= 0.05
def check_fit(inverse_scale):
p = Exponential()
X = st.expon(scale=1. / inverse_scale).rvs(5000,
random_state=0).reshape(-1, 1)
p.fit(X)
assert np.abs(p.inverse_scale.get_value() - inverse_scale) <= 0.1
def check_rvs(inverse_scale, random_state):
p = Exponential(inverse_scale=inverse_scale)
samples = p.rvs(1000, random_state=random_state)
assert np.abs(np.mean(samples) - 1. / inverse_scale) <= 0.05
A = theano.shared(true_theta[0], name="A")
B = theano.shared(true_theta[1], name="B")
R = np.array([[1.31229955, 0.10499961, 0.48310515, -0.3249938, -0.26387927],
[0.10499961, 1.15833058, -0.55865473, 0.25275522, -0.39790775],
[0.48310515, -0.55865473, 2.25874579, -0.52087938, -0.39271231],
[0.3249938, 0.25275522, -0.52087938, 1.4034925, -0.63521059],
[-0.26387927, -0.39790775, -0.39271231, -0.63521059, 1.]])
p0 = LinearTransform(
Join(components=[
Normal(mu=A, sigma=1),
Normal(mu=B, sigma=3),
Mixture(components=[Normal(mu=-2, sigma=1),
Normal(mu=2, sigma=0.5)]),
Exponential(inverse_scale=3.0),
Exponential(inverse_scale=0.5)
]), R)
def simulator(theta, n_samples, random_state=None):
A.set_value(theta[0])
B.set_value(theta[1])
return p0.rvs(n_samples, random_state=random_state)
X_obs = simulator(true_theta, 20000, random_state=rng)
n_params = len(true_theta)
n_features = X_obs.shape[1]
# Proposal distribution
