Entity Embeddings¶

TODO: explain embeddings, using the Embedding module

TODO: model, embedding of categorical features (1st column of x) and combining with continuous features (rest of x) and predicting with fully-connected neural net on top, which does binary classification

import probflow as pf
import tensorflow as tf

class EmbeddingRegression(pf.Model):

    def __init__(self, k, Dcat, Dcon):
        self.emb = pf.Embedding(k, Dcat)
        self.net = pf.DenseNetwork([Dcat+Dcon, 1])

    def __call__(self, x):
        embeddings = self.emb(x[:, 0])
        logits = self.net(tf.concat([embeddings, x[:, 1:]], -1))
        return pf.Bernoulli(logits)

import probflow as pf
import torch

class EmbeddingRegression(pf.Model):

    def __init__(self, k, Dcat, Dcon):
        self.emb = pf.Embedding(k, Dcat)
        self.net = pf.DenseNetwork([Dcat+Dcon, 1])

    def __call__(self, x):
        x = torch.tensor(x)
        embeddings = self.emb(x[:, 0])
        logits = self.net(torch.cat([embeddings, x[:, 1:]], -1))
        return pf.Bernoulli(logits)