florianwilhelm / bhm-at-scale Goto Github PK

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NotImplementedError                       Traceback (most recent call last)
<ipython-input-8-1349482c40ae> in <module>
----> 1 train_handler.fit(X_train, n_epochs=5_000, log_freq=1_000, lr=0.1)

~/opt/anaconda3/envs/bhm-at-scale/lib/python3.8/site-packages/bhm_at_scale-1.1.post0.dev3+gea40dd8.dirty-py3.8.egg/bhm_at_scale/handler.py in fit(self, X, n_epochs, log_freq, lr, **kwargs)
    130             self._fit(X, n_epochs)
    131         else:
--> 132             loss = self.svi.evaluate(self.svi_state, X) / X.shape[0]
    133 
    134             curr_epoch = 0

~/opt/anaconda3/envs/bhm-at-scale/lib/python3.8/site-packages/numpyro/infer/svi.py in evaluate(self, svi_state, *args, **kwargs)
    363         _, rng_key_eval = random.split(svi_state.rng_key)
    364         params = self.get_params(svi_state)
--> 365         return self.loss.loss(
    366             rng_key_eval,
    367             params,

~/opt/anaconda3/envs/bhm-at-scale/lib/python3.8/site-packages/numpyro/infer/elbo.py in loss(self, rng_key, param_map, model, guide, *args, **kwargs)
     44         :return: negative of the Evidence Lower Bound (ELBO) to be minimized.
     45         """
---> 46         return self.loss_with_mutable_state(
     47             rng_key, param_map, model, guide, *args, **kwargs
     48         )["loss"]

~/opt/anaconda3/envs/bhm-at-scale/lib/python3.8/site-packages/numpyro/infer/elbo.py in loss_with_mutable_state(self, rng_key, param_map, model, guide, *args, **kwargs)
     66         :return: a tuple of ELBO loss and the mutable state
     67         """
---> 68         raise NotImplementedError("This ELBO objective does not support mutable state.")
     69 
     70 

NotImplementedError: This ELBO objective does not support mutable state.

means = (is_observed * jnp.exp(jnp.sum(jnp.expand_dims(coefs, axis=1) * features, axis=2))
         + not_observed*eps)

betas = is_observed*jnp.exp(-disp_params) + not_observed
alphas = means * betas
return numpyro.sample("days", dist.GammaPoisson(alphas, betas), obs=jnp.nan_to_num(targets))

betas  = exp(-disp_params)  = 1/exp(disp_params)
alphas  = exp( sum(coefs * feature) -disp_params  ) = exp(sum(coefs * feature)/exp(disp_params)