Data adapter: Broadcasting causes issue with sampling

[maltelueken]

When using a Adapter with a Broadcast transform where to="inference_variables", training runs normally, but when I try sampling it complains that it cannot find the key "inference_variables" which I did not include from the conditions dictionary following the example notebooks.
My guess is the Broadcast transform also needs a strict argument in the forward method for sampling as in the FilterTransform class?

[paul-buerkner]

An you show the simulator and adapter you used? I will then check what we can do about it. Malte Lüken ***@***.***> schrieb am Fr., 22. Nov. 2024, 14:55:

…

When using a Adapter with a Broadcast transform where to="inference_variables", training runs normally, but when I try sampling it complains that it cannot find the key "inference_variables" which I did not include from the conditions dictionary following the example notebooks. My guess is the Broadcast transform also needs a strict argument in the forward method for sampling as in the FilterTransform class? — Reply to this email directly, view it on GitHub <#258>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADCW2AHQIYHVTPJL7TCIGFL2B4ZVJAVCNFSM6AAAAABSJNAJ36VHI2DSMVQWIX3LMV43ASLTON2WKOZSGY4DGMRZGAZTGOA> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[maltelueken]

Sure! Here is my simulator:

class CustomSimulator(bf.simulators.Simulator):
    def __init__(self, prior_fun: Callable, design_fun: Callable, simulator_fun: Callable):
        self.prior_fun = prior_fun
        self.design_fun = design_fun
        self.simulator_fun = simulator_fun


    def sample(self, batch_shape: Shape, **kwargs) -> dict[str, np.ndarray]:
        prior_dict = self.prior_fun(batch_shape)

        design_dict = self.design_fun(batch_shape)

        design_dict.update(**kwargs)

        sims_dict = self.simulator_fun(batch_shape, **prior_dict, **design_dict)

        data = prior_dict | design_dict | sims_dict

        data = {
            key: np.expand_dims(value, axis=-1) if np.ndim(value) == 1 else value for key, value in data.items()
        }

        return data

def batch_simulator(batch_shape, simulator_fun, **kwargs):
    data = batched_call(simulator_fun, batch_shape, kwargs=kwargs, flatten=True)
    data = tree_stack(data, axis=0, numpy=True)
    return data


def rdm_experiment_simple(
    v_intercept,
    v_slope,
    s_true,
    s_false,
    b,
    t0,
    num_obs,
    rng
):
    if np.any(np.array((v_intercept, v_slope, s_true, s_false, b, t0)) <= 0):
        raise ValueError("Model parameters must be positive")

    num_accumulators = 2

    # Acc1 = false, Acc2 = true
    v = np.hstack([v_intercept, v_intercept + v_slope])
    s = np.hstack([s_false, s_true])

    mu = b / v
    lam = (b / s) ** 2

    # First passage time
    fpt = np.zeros((num_accumulators, num_obs))
    
    for i in range(num_accumulators):
        fpt[i, :] = rng.wald(mu[i], lam[i], size=num_obs)

    resp = fpt.argmin(axis=0)
    rt = fpt.min(axis=0) + t0

    return {"x": np.c_[rt, resp]}

And here is my adapter:

inference_variables = ["x"]
inference_conditions = ["v_intercept", "v_slope", "s_true", "b", "t0"]

adapter = (bf.adapters.Adapter()
    .to_array()
    .convert_dtype("float64", "float32")
    .drop("num_obs")
    .concatenate(inference_variables, into="inference_variables")
    .concatenate(inference_conditions, into="inference_conditions")
    .broadcast("inference_conditions", to="inference_variables", expand=1)
)

[paul-buerkner]

thanks! Malte Lüken ***@***.***> schrieb am Fr., 22. Nov. 2024, 15:41:

…

Sure! Here is my simulator: class CustomSimulator(bf.simulators.Simulator): def __init__(self, prior_fun: Callable, design_fun: Callable, simulator_fun: Callable): self.prior_fun = prior_fun self.design_fun = design_fun self.simulator_fun = simulator_fun def sample(self, batch_shape: Shape, **kwargs) -> dict[str, np.ndarray]: prior_dict = self.prior_fun(batch_shape) design_dict = self.design_fun(batch_shape) design_dict.update(**kwargs) sims_dict = self.simulator_fun(batch_shape, **prior_dict, **design_dict) data = prior_dict | design_dict | sims_dict data = { key: np.expand_dims(value, axis=-1) if np.ndim(value) == 1 else value for key, value in data.items() } return data def batch_simulator(batch_shape, simulator_fun, **kwargs): data = batched_call(simulator_fun, batch_shape, kwargs=kwargs, flatten=True) data = tree_stack(data, axis=0, numpy=True) return data def rdm_experiment_simple( v_intercept, v_slope, s_true, s_false, b, t0, num_obs, rng ): """Simulates data from a single subject in a multi-alternative response times experiment.""" if np.any(np.array((v_intercept, v_slope, s_true, s_false, b, t0)) <= 0): raise ValueError("Model parameters must be positive") num_accumulators = 2 # Acc1 = false, Acc2 = true v = np.hstack([v_intercept, v_intercept + v_slope]) s = np.hstack([s_false, s_true]) mu = b / v lam = (b / s) ** 2 # First passage time fpt = np.zeros((num_accumulators, num_obs)) for i in range(num_accumulators): fpt[i, :] = rng.wald(mu[i], lam[i], size=num_obs) resp = fpt.argmin(axis=0) rt = fpt.min(axis=0) + t0 return {"x": np.c_[rt, resp]} And here is my adapter: inference_variables = ["x"] inference_conditions = ["v_intercept", "v_slope", "s_true", "b", "t0"] adapter = (bf.adapters.Adapter() .to_array() .convert_dtype("float64", "float32") .drop("num_obs") .concatenate(inference_variables, into="inference_variables") .concatenate(inference_conditions, into="inference_conditions") .broadcast("inference_conditions", to="inference_variables", expand=1) ) — Reply to this email directly, view it on GitHub <#258 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADCW2AHJ5V2KYTKRFMO2SWL2B47DNAVCNFSM6AAAAABSJNAJ36VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDIOJTHEYTONBVGM> . You are receiving this because you commented.Message ID: ***@***.***>

[paul-buerkner]

I have checked and I don't think a strict argument would fix it. The issue is that without the inference variables (inferred during approximator.sample), we cannot broadcast properly, which we need for the forward pass. So we create a chicken and egg problem bascially.

In other words, the broadcast to argument should not receive inference variables if possible. I will add this to the documentation. For your example, what would be the easiest way for you to broadcast as you intented without using to = "inference_variables?

[maltelueken]

Thanks for looking into this! I could also modify my prior function (which requires broadcasting) to achieve the same result, but I thought using the broadcasting transform would be more elegegant. Perhaps it would be good to add an explicit exception when people use to="inference_variables"? The error message that appears is not super informative on what the actual issue is I think. You also only run into this issue after training the approximator so a fail-fast solution would be a good idea.

[paul-buerkner]

Yeah, something like this could make sense. Even better, we would need to throw an error whenever a variable is used for broadcasting that includes an inferred variable, which will not be present during inference time at the time broadcasting is called. It may take a while until we can implement this smart checking as we have to wait for other features but I think it would be very nice to have indeed.