You can find the documentation here.
Sunode wraps the sundials solvers ADAMS and BDF, and their support for solving adjoint ODEs in order to compute gradients of the solutions. The required right-hand-side function and some derivatives are either supplied manually or via sympy, in which case sunode will generate the abstract syntax tree of the functions using symbolic differentiation and common subexpression elimination. In either case the functions are compiled using numba and the resulting C-function is passed to sunode, so that there is no python overhead.
sunode comes with an PyTensor wrapper so that parameters of an ODE can be estimated
using PyMC.
sunode is available on conda-forge. Set up a conda environment to use conda-forge and install sunode:
conda create -n sunode-env
conda activate sunode-env
conda config --add channels conda-forge
conda config --set channel_priority strict
conda install sunodeYou can also install the development version. On Windows you have to make sure the correct Visual Studio version is installed and in the PATH.
git clone [email protected]:pymc-devs/sunode
# Or if no ssh key is configured:
git clone https://github.com/pymc-devs/sunode
cd sunode
conda install --only-deps sunode
pip install -e .We will use the Lotka-Volterra equations as an example ODE:
def lotka_volterra(t, y, p):
"""Right hand side of Lotka-Volterra equation.
All inputs are dataclasses of sympy variables, or in the case
of non-scalar variables numpy arrays of sympy variables.
"""
return {
'hares': p.alpha * y.hares - p.beta * y.lynx * y.hares,
'lynx': p.delta * y.hares * y.lynx - p.gamma * y.lynx,
}
from sunode.symode import SympyProblem
problem = SympyProblem(
params={
# We need to specify the shape of each parameter.
# Any empty tuple corresponds to a scalar value.
'alpha': (),
'beta': (),
'gamma': (),
'delta': (),
},
states={
# The same for all state variables
'hares': (),
'lynx': (),
},
rhs_sympy=lotka_volterra,
derivative_params=[
# We need to specify with respect to which variables
# gradients should be computed.
('alpha',),
('beta',),
],
)
# The solver generates uses numba and sympy to generate optimized C functions
# for the right-hand-side and if necessary for the jacobian, adoint and
# quadrature functions for gradients.
from sunode.solver import Solver
solver = Solver(problem, sens_mode=None, solver='BDF')
import numpy as np
tvals = np.linspace(0, 10)
# We can use numpy structured arrays as input, so that we don't need
# to think about how the different variables are stored in the array.
# This does not introduce any runtime overhead during solving.
y0 = np.zeros((), dtype=problem.state_dtype)
y0['hares'] = 1
y0['lynx'] = 0.1
# We can also specify the parameters by name:
solver.set_params_dict({
'alpha': 0.1,
'beta': 0.2,
'gamma': 0.3,
'delta': 0.4,
})
output = solver.make_output_buffers(tvals)
solver.solve(t0=0, tvals=tvals, y0=y0, y_out=output)
# We can convert the solution to an xarray Dataset
solver.as_xarray(tvals, output).solution_hares.plot()
# Or we can convert it to a numpy record array
from matplotlib import pyplot as plt
plt.plot(output.view(problem.state_dtype)['hares'])For this example the BDF solver (which isn't the best solver for a small non-stiff
example problem like this) takes ~200μs, while the scipy.integrate.solve_ivp solver
takes about 40ms at a tolerance of 1e-10, 1e-10. So we are faster by a factor of 200.
This advantage will get somewhat smaller for large problems however, when the
Python overhead of the ODE solver has a smaller impact.
Let's use the same ODE, but fit the parameters using PyMC, and gradients
computed using sunode.
We'll use some time artificial data:
import numpy as np
import sunode
import sunode.wrappers.as_pytensor
import pymc as pm
times = np.arange(1900,1921,1)
lynx_data = np.array([
4.0, 6.1, 9.8, 35.2, 59.4, 41.7, 19.0, 13.0, 8.3, 9.1, 7.4,
8.0, 12.3, 19.5, 45.7, 51.1, 29.7, 15.8, 9.7, 10.1, 8.6
])
hare_data = np.array([
30.0, 47.2, 70.2, 77.4, 36.3, 20.6, 18.1, 21.4, 22.0, 25.4,
27.1, 40.3, 57.0, 76.6, 52.3, 19.5, 11.2, 7.6, 14.6, 16.2, 24.7
])We place priors on the steady-state ratio of hares and lynxes:
def lotka_volterra(t, y, p):
"""Right hand side of Lotka-Volterra equation.
All inputs are dataclasses of sympy variables, or in the case
of non-scalar variables numpy arrays of sympy variables.
"""
return {
'hares': p.alpha * y.hares - p.beta * y.lynx * y.hares,
'lynx': p.delta * y.hares * y.lynx - p.gamma * y.lynx,
}
with pm.Model() as model:
hares_start = pm.HalfNormal('hares_start', sigma=50)
lynx_start = pm.HalfNormal('lynx_start', sigma=50)
ratio = pm.Beta('ratio', alpha=0.5, beta=0.5)
fixed_hares = pm.HalfNormal('fixed_hares', sigma=50)
fixed_lynx = pm.Deterministic('fixed_lynx', ratio * fixed_hares)
period = pm.Gamma('period', mu=10, sigma=1)
freq = pm.Deterministic('freq', 2 * np.pi / period)
log_speed_ratio = pm.Normal('log_speed_ratio', mu=0, sigma=0.1)
speed_ratio = np.exp(log_speed_ratio)
# Compute the parameters of the ode based on our prior parameters
alpha = pm.Deterministic('alpha', freq * speed_ratio * ratio)
beta = pm.Deterministic('beta', freq * speed_ratio / fixed_hares)
gamma = pm.Deterministic('gamma', freq / speed_ratio / ratio)
delta = pm.Deterministic('delta', freq / speed_ratio / fixed_hares / ratio)
y_hat, _, problem, solver, _, _ = sunode.wrappers.as_pytensor.solve_ivp(
y0={
# The initial conditions of the ode. Each variable
# needs to specify a PyTensor or numpy variable and a shape.
# This dict can be nested.
'hares': (hares_start, ()),
'lynx': (lynx_start, ()),
},
params={
# Each parameter of the ode. sunode will only compute derivatives
# with respect to PyTensor variables. The shape needs to be specified
# as well. It it infered automatically for numpy variables.
# This dict can be nested.
'alpha': (alpha, ()),
'beta': (beta, ()),
'gamma': (gamma, ()),
'delta': (delta, ()),
'extra': np.zeros(1),
},
# A functions that computes the right-hand-side of the ode using
# sympy variables.
rhs=lotka_volterra,
# The time points where we want to access the solution
tvals=times,
t0=times[0],
)
# We can access the individual variables of the solution using the
# variable names.
pm.Deterministic('hares_mu', y_hat['hares'])
pm.Deterministic('lynx_mu', y_hat['lynx'])
sd = pm.HalfNormal('sd')
pm.LogNormal('hares', mu=y_hat['hares'], sigma=sd, observed=hare_data)
pm.LogNormal('lynx', mu=y_hat['lynx'], sigma=sd, observed=lynx_data)We can sample using PyMC (You need cores=1 on Windows for the moment):
with model:
idata = pm.sample(tune=1000, draws=1000, chains=6, cores=6)Many solver options can not be specified with a nice interface for now, we can call the raw sundials functions however:
lib = sunode._cvodes.lib
lib.CVodeSStolerances(solver._ode, 1e-10, 1e-10)
lib.CVodeSStolerancesB(solver._ode, solver._odeB, 1e-8, 1e-8)
lib.CVodeQuadSStolerancesB(solver._ode, solver._odeB, 1e-8, 1e-8)
lib.CVodeSetMaxNumSteps(solver._ode, 5000)
lib.CVodeSetMaxNumStepsB(solver._ode, solver._odeB, 5000)
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