def test_gp_product_creation(data_Periodic, models):
m, qp = models
period, mscale, lscale, decay, sigv = np.random.uniform(0.3, 3.0, 5)
par_Periodic = [
dict(name='period', value=period, transform='log'),
dict(name='mscale', value=mscale, transform='log'),
dict(name='lscale', value=lscale, transform='log'),
dict(name='sigv', value=sigv, transform='log'),
]
par_Matern = [
dict(name='mscale', value=1.0, transform='fixed'),
dict(name='lscale', value=decay, transform='log'),
dict(name='sigv', value=0.0, transform='fixed'),
]
par_QuasiPeriodic = [
dict(name='period', value=period, transform='log'),
dict(name='mscale', value=mscale, transform='log'),
dict(name='lscale', value=lscale, transform='log'),
dict(name='sigv', value=sigv, transform='log'),
dict(name='decay', value=decay, transform='log'),
]
reg1 = Regressor(ss=qp(par_QuasiPeriodic))
reg2 = Regressor(ss=Periodic(par_Periodic) * m(par_Matern))
check_grad(data_Periodic, reg1, reg2)
def test_Periodic(data_Periodic):
p = [
dict(name='period', transform='log'),
dict(name='mscale', transform='log'),
dict(name='lscale', transform='log'),
dict(name='sigv', transform='log'),
]
regressor = Regressor(ss=Periodic(parameters=p))
regressor.ss.parameters.theta = np.random.uniform(0.3, 3.0, 4)
check_grad_fd(data_Periodic, regressor)
def generate_regressor_sum(*statespaces):
"""State-space generator for gradient test"""
values = np.random.uniform(0.3, 3.0, 6)
par_Periodic = [
dict(name="period", value=values[0], bounds=(0.0, None)),
dict(name='mscale', value=values[1], bounds=(0.0, None)),
dict(name='lscale', value=values[2], bounds=(0.0, None)),
dict(name='sigv', value=values[3], bounds=(0.0, None)),
]
par_Matern = [
dict(name="mscale", value=values[4], bounds=(0.0, None)),
dict(name="lscale", value=values[5], bounds=(0.0, None)),
dict(name="sigv", value=0.0, transform='fixed'),
]
for ssm in statespaces:
yield Regressor(Periodic(par_Periodic) + ssm(par_Matern))
def generate_regressor_product(*statespaces):
"""State-space generator for gradient test"""
values = np.random.uniform(1.0, 2.0, 5)
par_Periodic = [
dict(name='period', value=values[0], bounds=(0.0, None)),
dict(name='mscale', value=values[1], bounds=(0.0, None)),
dict(name='lscale', value=values[2], bounds=(0.0, None)),
dict(name='sigv', value=values[3], bounds=(0.0, None)),
]
par_Matern = [
dict(name='mscale', value=1.0, transform='fixed'),
dict(name='lscale', value=values[4], bounds=(0.0, None)),
dict(name='sigv', value=0.0, transform='fixed'),
]
for ssm in statespaces:
yield Regressor(ss=Periodic(parameters=par_Periodic) *
ssm(parameters=par_Matern))
amplitude = alim[0] + (alim[1] - alim[0]) * np.random.random()
noise = 0.2 * np.random.randn(n)
y = amplitude * np.sin(2.0 * np.pi / period * t) + noise
y[y <= 0] = 0.0
data = pd.DataFrame(index=t, data=y, columns=['y'])
# Parameter settings for the Periodic covariance function
parameters = [
dict(name='period', value=1.0, transform='fixed'),
dict(name='mscale', value=1.0, transform='log'),
dict(name='lscale', value=1.0, transform='log'),
dict(name='sigv', value=0.1, transform='log'),
]
# Instantiate regressor with the Periodic covariance function
reg = Regressor(Periodic(parameters))
fit_summary, corr_matrix, opt_summary = reg.fit(df=data, outputs='y')
# Fit results
print(f'\n{fit_summary}')
# Predict on test data
tnew = np.linspace(xlim[0], xlim[1] + 1, 500)
ym, ysd = reg.predict(df=data, outputs='y', tnew=tnew, smooth=True)
# Plot output mean and 95% credible intervals
sns.set_style('darkgrid')
sns.set_context('talk')
plt.plot(t, y, linestyle='', marker='+', mew=2, label='data', color='darkred')
plt.plot(tnew, ym, color='navy', label='mean')
p3 = [
dict(name='mscale', value=1.0, transform='fixed'),
dict(name='lscale', value=2.607e02, transform='log'),
dict(name='sigv', value=0.0, transform='fixed'),
]
# Matérn 3/2 for short-term effects
p4 = [
dict(name='mscale', value=4.595e-01, transform='log'),
dict(name='lscale', value=6.359e-01, transform='log'),
dict(name='sigv', value=0.0, transform='fixed'),
]
k1 = Matern52(p1, name='k1')
k2 = Periodic(p2, name='k2')
k3 = Matern32(p3, name='k3')
k4 = Matern32(p4, name='k4')
# Compose covariance function
K = k1 + k2 * k3 + k4
reg = Regressor(K)
fit_summary, corr_matrix, opt_summary = reg.fit(df=df, outputs='CO2_fit')
print(f'\n{fit_summary}')
# generate new prediction time instants
tnew = np.arange(1958, 2030, 0.01)
ym, ysd = reg.predict(df=df, outputs='CO2_pred', smooth=True, tnew=tnew)
# Plot output mean and 95% credible intervals
import os
import pytest
import numpy as np
from pysip.regressors import FreqRegressor as Regressor
from pysip.statespace import TwTi_RoRiAwAicv, TwTiTm_RoRiRmRbAwAicv, Matern12, Matern52, Periodic
from pysip.utils import save_model, load_model
@pytest.mark.parametrize(
'reg',
[
pytest.param(TwTi_RoRiAwAicv(), id="rc_model"),
pytest.param(Matern12(), id="gp_model"),
pytest.param(Periodic() * Matern12(), id="gp_product"),
pytest.param(Periodic() + Matern12(), id="gp_sum"),
pytest.param(TwTi_RoRiAwAicv(latent_forces='Qv') <= Matern12(),
id="lfm_rc_gp"),
pytest.param(
TwTi_RoRiAwAicv(latent_forces='Qv') <= Periodic() * Matern12(),
id="lfm_rc_gp_product"),
pytest.param(
TwTi_RoRiAwAicv(latent_forces='Qv') <= Periodic() + Matern12(),
id="lfm_rc_gp_sum"),
],
)
def test_save_model_to_pickle(reg):
reg = Regressor(reg)
reg.ss.parameters.theta = np.random.uniform(1e-1, 1,
len(reg.ss.parameters.theta))
reg.ss.update_continuous_dssm()