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Description
Describe the issue:
I am adding parameters into a model in an attempt to perform bayesian inversion. I am getting a typeerror when introducing variables, which is unexpected behaviour.
Reproduceable code example:
import numpy as np
import pymc as pm
import pytensor.tensor as pt
from pytensor.graph import Op, Apply
import arviz as az
import prosail
# Define the new model function with additional parameters
def my_model(n, cab, car, cbrown, cw, cm, lai, tto, tts, x):
reflectance = prosail.run_prosail(n=n, cab=cab, car=car, cbrown=cbrown, cw=cw, cm=cm, lai=lai, tto=10., tts=30., lidfa=-1.0, hspot=0.01, psi=0., prospect_version="D", factor='SDR', rsoil=.5, psoil=.5)
return reflectance
def my_loglike(n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, data):
for param in (n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, data):
if not isinstance(param, (float, np.ndarray)):
raise TypeError(f"Invalid input type to loglike: {type(param)}")
model = my_model(n, cab, car, cbrown, cw, cm, lai, tto, tts, x)
return -0.5 * ((data - model) / sigma) ** 2 - np.log(np.sqrt(2 * np.pi)) - np.log(sigma)
class LogLike(Op):
def make_node(self, n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, data) -> Apply:
n = pt.as_tensor(n)
cab = pt.as_tensor(cab)
car = pt.as_tensor(car)
cbrown = pt.as_tensor(cbrown)
cw = pt.as_tensor(cw)
cm = pt.as_tensor(cm)
lai = pt.as_tensor(lai)
tto = pt.as_tensor(tto)
tts = pt.as_tensor(tts)
sigma = pt.as_tensor(sigma)
x = pt.as_tensor(x)
data = pt.as_tensor(data)
inputs = [n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, data]
outputs = [data.type()]
return Apply(self, inputs, outputs)
def perform(self, node: Apply, inputs: list[np.ndarray], outputs: list[list[None]]) -> None:
n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, data = inputs
loglike_eval = my_loglike(n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, data)
outputs[0][0] = np.asarray(loglike_eval)
def custom_dist_loglike(data, n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x):
return loglike_op(n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, data)
N = 2101
sigma = 1.0
x = np.linspace(0.0, 9.0, N)
# True parameter values for testing
ntrue = 0.4
cabtrue = 0.1
cartrue = 0.2
cbrowntrue = 0.3
cwtrue = 0.05
cmtrue = 0.07
laitrue = 3.0
ttotrue = 0.15
ttstrue = 0.25
truemodel = my_model(ntrue, cabtrue, cartrue, cbrowntrue, cwtrue, cmtrue, laitrue, ttotrue, ttstrue, x)
rng = np.random.default_rng(716743)
data = sigma * rng.normal(size=N) + truemodel
loglike_op = LogLike()
test_out = loglike_op(ntrue, cabtrue, cartrue, cbrowntrue, cwtrue, cmtrue, laitrue, ttotrue, ttstrue, sigma, x, data)
with pm.Model() as no_grad_model:
n = pm.Uniform("n", lower=-1.0, upper=5.0, initval=ntrue)
cab = pm.Uniform("cab", lower=-5, upper=60, initval=cabtrue)
car = pm.Uniform("car", lower=-5, upper=20, initval=cartrue)
cbrown = pm.Uniform("cbrown", lower=0, upper=2, initval=cbrowntrue)
cw = pm.Uniform("cw", lower=-10.0, upper=10, initval=cwtrue)
cm = pm.Uniform("cm", lower=0, upper=0.2, initval=cmtrue)
lai = pm.Uniform("lai", lower=0.5, upper=5.0, initval=laitrue)
tto = pm.Uniform("tto", lower=0.0, upper=20.0, initval=ttotrue)
tts = pm.Uniform("tts", lower=0, upper=60.0, initval=ttstrue)
likelihood = pm.CustomDist(
"likelihood", n, cab, car, cbrown, cw, cm, lai, tto, tts, sigma, x, observed=data, logp=custom_dist_loglike
)
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
with no_grad_model:
idata_no_grad = pm.sample(100000, step=pm.DEMetropolisZ())Error message:
TypeError: No matching definition for argument type(s) float64, array(float64, 0d, C), float64, float64
Apply node that caused the error: LogLike(Composite{((i2 * sigmoid(i0)) - (i1 - sigmoid(i0)))}.0, Composite{((i2 * sigmoid(i0)) + (i3 * (i1 - sigmoid(i0))))}.0, Composite{((i2 * sigmoid(i0)) + (i3 * (i1 - sigmoid(i0))))}.0, Composite{(i1 * sigmoid(i0))}.0, Composite{((i2 * sigmoid(i0)) + (i3 * (i1 - sigmoid(i0))))}.0, Composite{(i1 * sigmoid(i0))}.0, Composite{((i2 * sigmoid(i0)) + (i3 * (i1 - sigmoid(i0))))}.0, Composite{(i1 * sigmoid(i0))}.0, Composite{(i1 * sigmoid(i0))}.0, 1.0, [0.0000000 ... 00000e+00], likelihood{[-0.589307 ... .02688868]})
Toposort index: 9
Inputs types: [TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=()), TensorType(float32, shape=()), TensorType(float64, shape=(2101,)), TensorType(float64, shape=(2101,))]
Inputs shapes: [(), (), (), (), (), (), (), (), (), (), (2101,), (2101,)]
Inputs strides: [(), (), (), (), (), (), (), (), (), (), (8,), (8,)]
Inputs values: [array(0.4), array(0.1), array(0.2), array(0.3), array(0.05), array(0.07), array(3.), array(0.15), array(0.25), array(1., dtype=float32), 'not shown', 'not shown']
Inputs type_num: [12, 12, 12, 12, 12, 12, 12, 12, 12, 11, 12, 12]
Outputs clients: [['output']]PyMC version information:
'5.12.0'
Context for the issue:
No response