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rm latex forgotten code
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VincentAuriau committed May 23, 2024
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Expand Up @@ -101,31 +101,14 @@ We conduct a similar experiment experiment on the ICDM 2013 Expedia dataset [@Ex

Finally, we observe similar performance gains in terms of memory management on a proprietary dataset in brick-and-mortar retailing. It consists of the aggregation of more than 4 million purchases over 5 years in over 600 retail Konzum supermarkets in Croatia. Focusing on the *coffee* subcategory, the dataset specifies, for each purchase, which of the 63 products were available, their prices, as well as a one-hot representation of the store. The numerical results are presented in \ref{fig:xps} (c) and (d).


\begin{figure}
\centering
\label{fig:xps}
\begin{tabular}{cc}
\includegraphics[width=65mm]{illustrations/ram_images/ram_usage_storage.png} & \includegraphics[width=65mm]{illustrations/ram_images/ram_usage_expedia_2.png} \\
(a) Choice-Learn memory usage for different & (b) Memory usage for the Expedia dataset \\[6pt]
values of (n\_locations, n\_locations) & with different dataset sizes \\
\includegraphics[width=65mm]{illustrations/ram_images/ram_usage_fng_1.png} & \includegraphics[width=65mm]{illustrations/ram_images/ram_usage_fng_2.png} \\
(c) Memory usage of our own retail dataset & (d) Memory usage of our own retail dataset \\[6pt]
for different dataset sizes & for different number of stores \\
\end{tabular}
\caption{Memory usage experiments \VA{Illustrations to be completed and colors to be aligned!} \Alicomment{Nice. Why not include Torch choice?} \VA{will doo}}
\end{figure}



## Customized choice models
We provide an example of the custom model definition with the following formulation of utility for an alternative $i$ with features $x_i$ considered by a customer with features $z$:
$$U(i) = \beta_l \cdot \sigma(\sigma(\Gamma_x \cdot x_i) + \sigma(\Gamma_z \cdot z)) + \epsilon_i,$$
where $\Gamma_x$, $\Gamma_z$ are matrices and $\beta_l$ is a vector, all of which are parameters to be estimated. Additionally, $\sigma$ is the sigmoid activation function.
When introducing a custom model, one needs to inherit the *ChoiceModel* class, specify the weights to be estimated in the *__init__* method, and determine how to compute the utility in the *compute_batch_utility* method.


```python``
```python
def __init__(self, n_neurons, **kwargs):
super().__init__(**kwargs)

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