Poisson Matrix Factorization

[sommukh]

Hi,

First of all, thank you so much for inferpy. Its an wonderful project. I am trying to implement Poisson Matrix factorization using this library. I first implemented the basic Matrix factorization using the example given in the older version of inferpy. The relevant part of the code is as follows :
`

# definition of a generic model
@inf.probmodel
def mf(M, K):
	w_Item = inf.Normal(loc=tf.zeros([M, K]), scale=1, name="wItem") 
	
	with inf.datamodel():
		w_User = inf.Normal(tf.ones(K), scale=1, name="wUser") 
		Rating = inf.Normal(tf.matmul(w_User, w_Item, transpose_b = True), 1, name="Rating") 
	   
#In variational inference - define Q-model 
@inf.probmodel
def qmodel(M, K):
	qw_Item_loc = inf.Parameter(tf.zeros([M, K]), name="qw_Item_loc")
	qw_Item_scale = tf.math.softplus(inf.Parameter(tf.ones([M, K]),  name="qw_Item_scale"))
	qw_Item = inf.Normal(qw_Item_loc, qw_Item_scale, name="wItem")
	
	with inf.datamodel():
		qw_Userloc = inf.Parameter(np.ones(K), name="qw_Userloc")
		qw_Userscale = tf.math.softplus(inf.Parameter(tf.ones(K), name="qw_Userscale"))
		qw = inf.Normal(qw_Userloc, qw_Userscale, name="wUser")`

This code seems to work ... so I modified the code for Poisson factorization as follows:

`

# definition of a generic model
@inf.probmodel
def mf(M, K):
	w_Item = inf.Normal(loc=tf.zeros([M, K]), scale=1, name="wItem") # shape = [M,K]

	with inf.datamodel():
		w_User = inf.Normal(tf.ones(K), scale=1, name="wUser") # shape = [N,K]
		Rating = inf.Poisson(tf.math.exp(w_User) @  tf.transpose(tf.math.exp(w_Item)), name="Rating")
	   
#In variational inference - define Q-model 
@inf.probmodel
def qmodel(M, K):
	qw_Item_loc = inf.Parameter(tf.zeros([M, K]), name="qw_Item_loc")
	qw_Item_scale = tf.math.softplus(inf.Parameter(tf.ones([M, K]),  name="qw_Item_scale"))
	qw_Item = inf.Normal(qw_Item_loc, qw_Item_scale, name="wItem")
	
	with inf.datamodel():
		qw_Userloc = inf.Parameter(np.ones(K), name="qw_Userloc")
		qw_Userscale = tf.math.softplus(inf.Parameter(tf.ones(K), name="qw_Userscale"))
		qw = inf.Normal(qw_Userloc, qw_Userscale, name="wUser")`

Unfortunately it gives the following error ...
LookupError: No gradient defined for operation 'Rating_46/sample/random_poisson/RandomPoissonV2' (op type: RandomPoissonV2)

I tried to find some examples using Poisson distribution but could not find any ...where am I going wrong ? Kindly help ...