## 6 Complex Task: Generating Code for an LSTM Model
### Oai

It took the model 133 s to respond and generate Python code. However, an error occurred during execution because the code attempted to overwrite the Pandas DataFrame with all variables and create a Pandas DataFrame of the same name, which included only the three variables: temperature, relative humidity, and wind speed. A new question (second attempt) was posed, indicating the error and requesting that the model correct it. The model responded to the second query after 113 s, but the generated code encountered another error during execution. This time, it complained about non-numeric values in the \"low temperature\" data set. Consequently, a third question (third attempt) was asked, requesting the model to rectify the error. A response was received after 168 s, and the model produced a corrected code. However, similar to the case with GPT, the generated code only accessed the three necessary columns from the data set and created a training-testing data set based solely on these three columns. Therefore, a new question (fourth attempt) was posed to clarify that all variables needed to be used as inputs for the generated code. The question was precisely the same as the one asked of GPT:
```
''Pleasedoasbefore,buttakeasinputtothemodelallgivenfeaturesfromtheCSVfile''. ```
This time, the framework generated the correct Python code after 103 s, utilizing all inputs to predict the three requested variables. The code was executed and predictions for the three variables were obtained. The model predicted all requested error metrics for each variable separately. The results are compiled in Table 5.

The MAPE and MFE for wind speed are incorrect due to division by a minimal number close to zero. By examining the error metrics, we can conclude that the OAI1-generated model effectively predicted the three variables satisfactorily. The OAI1 framework delivered a satisfactory response after four attempts and four queries, with no SB responses recorded. Detailed comparisons with the ground truth for each variable follow. ### Oai3

OAI3 responded in 30 s and behaved exactly like the GPT framework; it generated code that read the three required columns from the data set and created a training-testing data set consisting solely of these three columns. Specifically, it used temperature, relative humidity, and wind speed values to predict temperature, relative humidity, and wind speed. From the question posed, OAI3 did not grasp that all variables in the file needed to be taken as input, prompting a new question (second attempt) for clarification:
```
'Please do as before, but take as input to the model all given features from the CSV file'. ```

This time, the framework responded in 21 s and generated an LSTM model that considered all variables to predict temperature, relative humidity, and wind speed. The code was executed, and predictions for the three variables were obtained. The model predicted all requested error metrics for each variable individually. The results are presented in Table 6. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Temperature** & **Relative Humidity** & **Wind Speed** \\ \hline MSE & 0.0787 & 1.1518 & 0.6924 \\ MAE & 0.2120 & 0.7428 & 0.6711 \\ MB & \(-\)0.1433 & \(-\)0.1422 & 0.2954 \\ MAPE (\%) & 1.0412 & 1.0238 & 2.0214 \(\times\) 10\({}^{4}\) \\ MFE (\%) & 1.0582 & 1.0271 & 34.9757 \\ MFB (\%) & \(-\)0.7288 & \(-\)0.1346 & 29.5465 \\ R\({}^{2}\) & 0.9789 & 0.9895 & 0.7698 \\ Pearson \(r\) & 0.9923 & 0.9951 & 0.9230 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Error metrics for temperature, relative humidity, and wind speed as calculated by the OAI3-generated LSTM model.