Logistic regression with time-varying coefficients: cannot construct sampler #90
Description
Description of your problem or feature request
I want to perform a classification task which consists in predicting whether a document
For the sake of simplicity below I assume
However:
- I cannot construct a sampler for this model (see details below)
- I wonder if there could be a more efficient implementation anyway:
$x_{ijt}$ is very sparse, andat.tensordot(X, beta_t_rv, 2)involves too many needless operations. I would not have done that with Stan but in this case I found it easier to write the model this way.
Please provide a minimal, self-contained, and reproducible example.
As a clueless user, I tried to build a sampler based on my generative model by looking at other examples; which means I could be doing something completely idiotic!
Here is what I came up with:
import numpy as np
import aesara
import aesara.tensor as at
from aemcmc.basic import construct_sampler
from aesara.tensor.random.utils import RandomStream
def logistic_fit(X_val, y_val):
N, M, T = X_val.shape
srng = RandomStream(0)
X = at.tensor3("X")
sigma_rv = srng.exponential(1, size=X.shape[1])
beta_t_rv = at.cumsum(srng.normal(0, 1/sigma_rv, size=(X.shape[1],X.shape[2])), axis=1)
eta = at.tensordot(X, beta_t_rv, 2)
p = at.sigmoid(-eta)
Y_rv = srng.bernoulli(p, name="Y")
y_vv = Y_rv.clone()
y_vv.name = "y"
sample_vars = [sigma_rv, beta_t_rv]
sample_steps, updates, initial_values = construct_sampler({Y_rv: y_vv}, srng)
# X_val = np.load("X_val.npy")
# y_val = np.load("y_val.npy")
X_val = np.zeros((1000, 50, 10))
y_val = np.zeros(1000)
beta = logistic_fit(X_val, y_val)Although thus far the script does not actually use the data, if at some point you want to try with actual data, download and unzip these files (X_val.npy and y_val.npy) to the folder the script is executed from: data.zip
However, this crashes:
Please provide the full traceback of any errors.
ERROR (aesara.graph.rewriting.basic): Rewrite failure due to: local_elemwise_dimshuffle_subsume
ERROR (aesara.graph.rewriting.basic): node: Elemwise{true_div,no_inplace}(InplaceDimShuffle{x}.0, exponential_rv{0, (0,), floatX, False}.out)
ERROR (aesara.graph.rewriting.basic): TRACEBACK:
ERROR (aesara.graph.rewriting.basic): Traceback (most recent call last):
File "/Users/acristia/anaconda3/lib/python3.8/site-packages/aesara/graph/rewriting/basic.py", line 1933, in process_node
replacements = node_rewriter.transform(fgraph, node)
File "/Users/acristia/anaconda3/lib/python3.8/site-packages/aesara/graph/rewriting/basic.py", line 1092, in transform
return self.fn(fgraph, node)
File "/Users/acristia/anaconda3/lib/python3.8/site-packages/aemcmc/rewriting.py", line 345, in local_elemwise_dimshuffle_subsume
new_op = SubsumingElemwise(new_inputs, new_outputs, inline=True)
File "/Users/acristia/anaconda3/lib/python3.8/site-packages/aemcmc/rewriting.py", line 189, in __init__
OpFromGraph.__init__(self, inputs, outputs, *args, **kwargs)
File "/Users/acristia/anaconda3/lib/python3.8/site-packages/aesara/compile/builders.py", line 343, in __init__
raise TypeError(f"Constants not allowed as inputs; {i}")
TypeError: Constants not allowed as inputs; TensorConstant{1}
Traceback (most recent call last):
File "examples/gibbs_sample.py", line 61, in <module>
beta = logistic_fit(X_val, y_val)
File "examples/gibbs_sample.py", line 26, in logistic_fit
sample_steps, updates, initial_values = construct_sampler({Y_rv: y_vv}, srng)
File "/Users/acristia/anaconda3/lib/python3.8/site-packages/aemcmc/basic.py", line 108, in construct_sampler
raise NotImplementedError(
NotImplementedError: Could not find a posterior samplers for {exponential_rv{0, (0,), floatX, False}.out, normal_rv{0, (0, 0), floatX, False}.out}Versions and main components
- Aesara version: 2.8.9
- Aemcmc version: 0.06
- Python version: 3.8.10
- Operating system: macOS 10.15.4
- How did you install Aesara: pip