Added GRU results to thesis + intermediate samples of diffusion model
This commit is contained in:
Victor Mylle
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@@ -48,20 +48,13 @@ A lot of data is available but only the most relevant data needs to be used. Exp
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\input{sections/results/gru}
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TODO: Talk about the results + show plots of the samples with GRU model. Talk about the over/underestimation of the quantiles for the models. Plots have been made for this.
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TODO: non autoregressive stuff?
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\newpage
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\subsection{Diffusion}
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% TODO: needed to explain again?
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Another type of model that can be used to generatively model the NRV is the diffusion model. This type of model is very popular for image generation. In the context of images, the diffusion model is trained by iteratively adding noise to a training image until there is only noise left. From this noise, the model tries to reverse the diffusion process to get the original image back. To sample new images using this model, a noise vector is sampled and iteratively denoised by the model. This process results in a new image.
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\\\\
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This training process can also be used for other data types. An image is just a 2D grid of data points. A time series can be seen as a 1D sequence of data points. The diffusion model can thus be trained on the NRV data to generate new samples for a certain day based on a given input.
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\\\\
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Once the diffusion model is trained, it can be used efficiently to generate new samples. The model can generate samples in parallel, which is not possible with autoregressive models. A batch of noise vectors can be sampled and passed through the model in one batch to generate the new samples. The generated samples contain the 96 NRV values for the next day without needing to sample every quarter sequentially.
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TODO: Visualization of the diffusion model in the context of the NRV data.
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This training process can also be used for other data types. An image is just a 2D grid of data points. A time series can be seen as a 1D sequence of data points. The diffusion model can thus be trained on the NRV data to generate new samples for a certain day based on a given input.
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Once the diffusion model is trained, it can be used efficiently to generate new samples. The model can generate samples in parallel, which is not possible with autoregressive models. It combines the parallel sample generation of the non-autoregressive models while the quarter NRV values still depend on each other. A batch of noise vectors can be sampled and passed through the model in one batch to generate the new samples. The generated samples contain the 96 NRV values for the next day without needing to sample every quarter sequentially.
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TODO: Visualization of the diffusion model in the context of the NRV data.
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@@ -41,27 +41,96 @@ NRV & & & & & & & & \\
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& 8 & 512 & 37681.71 & 40379.14 & 146.62 & 151.05 & 83.08 & 76.42 \\
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\midrule
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NRV + Load & & & & & & & & & \\
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& 2 & 256 & 33202.80 & 38427.91 & 138.02 & 147.27 & 79.62 & 84.17 \\
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& 4 & 256 & 33600.73 & 38984.44 & 138.62 & 147.91 & 81.03 & 85.91 \\
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& 8 & 256 & 32828.61 & 38343.98 & 136.82 & 146.44 & 79.42 & 84.22 \\
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& 2 & 512 & 35979.57 & 41496.77 & 144.16 & 153.53 & 83.50 & 88.26 \\
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& 4 & 512 & 32334.73 & 38000.40 & 135.92 & 146.10 & 78.82 & 83.99 \\
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& 8 & 512 & 35177.39 & 41104.28 & 141.79 & 152.13 & 83.79 & 89.13 \\
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& 2 & 256 & 38427.91 & 40024.14 & 147.27 & 150.06 & 84.17 & 76.04 \\
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& 4 & 256 & 38984.44 & 40480.73 & 147.91 & 151.24 & 85.91 & 75.82 \\
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& 8 & 256 & 38343.98 & 39135.60 & 146.44 & 148.85 & 84.22 & 76.19 \\
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& 2 & 512 & 41496.77 & 40808.04 & 153.53 & 151.89 & 88.26 & 75.43 \\
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& 4 & 512 & 38000.40 & 40260.01 & 146.10 & 150.57 & 83.99 & 75.38 \\
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& 8 & 512 & 41104.28 & 39907.44 & 152.13 & 150.11 & 89.13 & 76.42 \\
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\midrule
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NRV + Load + PV\\ + Wind & & & & & & & & & \\
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& 4 & 256 & 31594.55 & 39872.46 & 134.11 & 149.34 & 77.52 & 85.91 \\
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& 8 & 256 & 31481.22 & 39704.37 & 133.45 & 148.59 & 77.26 & 85.62 \\
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& 4 & 512 & 31368.31 & 39024.27 & 134.02 & 147.91 & 76.58 & 84.18 \\
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& 8 & 512 & 34566.66 & 42397.86 & 140.13 & 154.00 & 82.09 & 89.87 \\
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& 4 & 256 & 39872.46 & 40708.93 & 149.34 & 151.32 & 85.91 & 75.93 \\
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& 8 & 256 & 39704.37 & 40292.25 & 148.59 & 151.19 & 85.62 & 75.94 \\
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& 4 & 512 & 39024.27 & 41580.29 & 147.91 & 153.39 & 84.18 & 75.84 \\
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& 8 & 512 & 42397.86 & 41043.88 & 154.00 & 152.63 & 89.87 & 76.35 \\
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\midrule
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NRV + Load + PV\\ + Wind + Net Position \\+ QE (5 dim) & & & & & & & & & \\
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& 4 & 256 & 30130.37 & 39906.53 & 130.92 & 149.78 & 75.02 & 84.88 \\
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& 8 & 256 & 28560.67 & 37675.15 & 127.77 & 145.39 & 73.77 & 83.37 \\
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& 4 & 512 & & & & & & & \\
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& 8 & 512 & 27421.85 & 35238.98 & 125.32 & 141.02 & 72.73 & 80.92 \\
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& 4 & 256 & 39906.53 & 40881.92 & 149.78 & 152.34 & 84.88 & 76.15 \\
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& 8 & 256 & 37675.15 & 40159.91 & 145.39 & 150.42 & 83.37 & 75.89 \\
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& 4 & 512 & & 40613.54 & & 151.17 & & 75.33 \\
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& 8 & 512 & 35238.98 & 39896.57 & 141.02 & 149.96 & 80.92 & 75.92 \\
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\bottomrule
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\end{tabular}
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\end{adjustbox}
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\caption{Autoregressive GRU quantile regression model results. All the models used a dropout of 0.2 .}
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\label{tab:autoregressive_gru_model_results}
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\end{table}
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The results show the same behavior for the GRU model as for the linear and non-linear models. The performance of the autoregressive model increases a bit when more features are added. The performance of the non-autoregressive model does not increase that much when adding new features. The reason for this is the same as for the linear and non-linear models. There is a large input size for the non-autoregressive model, which makes it harder to learn the dependencies between the features. The non-autoregressive model has to predict 96 quarters, which is a complex task. When comparing the results of the autoregressive GRU model and the non-autoregressive GRU model, the same observation can be made for the linear and non-linear models. The CRPS is always lower for the non-autoregressive model while the MSE and MAE are higher most of the time.
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% TODO: explain from which models the examples come from
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\begin{figure}[H]
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\centering
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png}
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\end{subfigure}
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png}
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\end{subfigure}
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png}
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\end{subfigure}
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png}
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\caption{Autoregressive GRU model}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png}
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\caption{Non-autoregressive GRU model}
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\end{subfigure}
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\caption{Comparison of the autoregressive and non-autoregressive GRU model examples.}
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\label{fig:gru_model_sample_comparison}
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\end{figure}
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The examples from the test set using the GRU models are shown in Figure \ref{fig:gru_model_sample_comparison}. Again the same behavior can be observed for the linear and non-linear models. The non-autoregressive examples stay around zero and do not follow the trend of the real NRV values. The autoregressive examples look a lot better visually and follow the trend of the real NRV values much more.
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\begin{figure}[ht]
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\centering
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/quantile_performance/AQR_GRU_QP_Train.jpeg}
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\caption{AR - Train}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/quantile_performance/AQR_GRU_QP_Test.jpeg}
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\caption{AR - Test}
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\end{subfigure}
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/quantile_performance/NAQR_GRU_QP_Train.jpeg}
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\caption{NAR - Train}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/quantile_performance/NAQR_GRU_QP_Test.jpeg}
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\caption{NAR - Test}
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\end{subfigure}
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\caption{Over/underestimation of the quantiles for the autoregressive and non-autoregressive GRU models. Both the quantile performance for the training and test set are shown. The plots are generated using the input features NRV, Load, Wind, PV, Net Position, and the quarter embedding (only for the autoregressive model).}
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\label{fig:gru_model_quantile_over_underestimation}
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\end{figure}
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The plots in Figure \ref{fig:gru_model_quantile_over_underestimation} show the over/underestimation of the learned quantiles for the GRU models. The fraction of real NRV values under the predicted quantiles of the training set is very close to the ideal fraction. The autoregressive model, however, shows a slight underestimation for almost all quantiles. Looking at the test set, the lower quantiles are overestimated for the autoregressive model while the higher quantiles are underestimated. The quantile predictions of the test set for the non-autoregressive model are all underestimated. This means a lower fraction of real NRV values is below the quantiles than wanted.
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@@ -75,18 +75,22 @@
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[]\T1/LinuxLibertineT-TLF/m/n/12 Figure 11: |Over/underestimation of the quan-tiles for the au-tore-gres-sive and non-
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[]\T1/LinuxLibertineT-TLF/m/n/12 Figure 13: |Over/underestimation of the quan-tiles for the au-tore-gres-sive and non-
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) [35] [36] (./verslag.aux (./sections/introduction.aux) (./sections/background.aux) (./sections/policies.aux) (./sections/literature_study.aux))
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) [35 <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png> <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png> <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png> <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>] [36 <./images/quantile_regression/quantile_performance/AQR_GRU_QP_Train.jpeg> <./images/quantile_regression/quantile_performance/AQR_GRU_QP_Test.jpeg> <./images/quantile_regression/quantile_performance/NAQR_GRU_QP_Train.jpeg> <./images/quantile_regression/quantile_performance/NAQR_GRU_QP_Test.jpeg>]) [37] [38] (./verslag.aux (./sections/introduction.aux) (./sections/background.aux) (./sections/policies.aux) (./sections/literature_study.aux))
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Output written on verslag.pdf (39 pages, 7863303 bytes).
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@@ -25,7 +25,7 @@
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\contentsline {subsubsection}{\numberline {6.2.1}Linear Model}{22}{subsubsection.6.2.1}%
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\contentsline {subsubsection}{\numberline {6.2.2}Non-Linear Model}{29}{subsubsection.6.2.2}%
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\contentsline {subsubsection}{\numberline {6.2.3}GRU Model}{32}{subsubsection.6.2.3}%
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\contentsline {subsection}{\numberline {6.3}Diffusion}{35}{subsection.6.3}%
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\contentsline {section}{\numberline {7}Policies for battery optimization}{35}{section.7}%
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\contentsline {subsection}{\numberline {7.1}Baselines}{35}{subsection.7.1}%
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\contentsline {subsection}{\numberline {7.2}Policies using NRV predictions}{35}{subsection.7.2}%
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\contentsline {subsection}{\numberline {6.3}Diffusion}{37}{subsection.6.3}%
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\contentsline {section}{\numberline {7}Policies for battery optimization}{37}{section.7}%
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\contentsline {subsection}{\numberline {7.1}Baselines}{37}{subsection.7.1}%
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\contentsline {subsection}{\numberline {7.2}Policies using NRV predictions}{37}{subsection.7.2}%
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||||
@@ -22,6 +22,7 @@ def sample_diffusion(
|
||||
beta_start=1e-4,
|
||||
beta_end=0.02,
|
||||
ts_length=96,
|
||||
intermediate_samples: bool = False,
|
||||
):
|
||||
device = next(model.parameters()).device
|
||||
beta = torch.linspace(beta_start, beta_end, noise_steps).to(device)
|
||||
@@ -34,6 +35,7 @@ def sample_diffusion(
|
||||
inputs = inputs.repeat(n, 1, 1)
|
||||
|
||||
model.eval()
|
||||
intermediate_samples_list = []
|
||||
with torch.no_grad():
|
||||
x = torch.randn(inputs.shape[0], ts_length).to(device)
|
||||
for i in reversed(range(1, noise_steps)):
|
||||
@@ -54,7 +56,17 @@ def sample_diffusion(
|
||||
* (x - ((1 - _alpha) / (torch.sqrt(1 - _alpha_hat))) * predicted_noise)
|
||||
+ torch.sqrt(_beta) * noise
|
||||
)
|
||||
|
||||
# evenly spaces 4 intermediate samples to append between 1 and noise_steps
|
||||
if intermediate_samples:
|
||||
spacing = (noise_steps - 1) // 4
|
||||
if i % spacing == 0:
|
||||
intermediate_samples_list.append(x)
|
||||
|
||||
x = torch.clamp(x, -1.0, 1.0)
|
||||
if len(intermediate_samples_list) > 0:
|
||||
return x, intermediate_samples_list
|
||||
|
||||
return x
|
||||
|
||||
|
||||
@@ -105,7 +117,13 @@ class DiffusionTrainer:
|
||||
"""
|
||||
return torch.randint(low=1, high=self.noise_steps, size=(n,))
|
||||
|
||||
def sample(self, model: DiffusionModel, n: int, inputs: torch.tensor):
|
||||
def sample(
|
||||
self,
|
||||
model: DiffusionModel,
|
||||
n: int,
|
||||
inputs: torch.tensor,
|
||||
intermediate_samples=False,
|
||||
):
|
||||
x = sample_diffusion(
|
||||
model,
|
||||
n,
|
||||
@@ -114,7 +132,9 @@ class DiffusionTrainer:
|
||||
self.beta_start,
|
||||
self.beta_end,
|
||||
self.ts_length,
|
||||
intermediate_samples,
|
||||
)
|
||||
|
||||
model.train()
|
||||
return x
|
||||
|
||||
@@ -271,6 +291,87 @@ class DiffusionTrainer:
|
||||
if task:
|
||||
task.close()
|
||||
|
||||
def plot_from_samples(self, samples, target):
|
||||
ci_99_upper = np.quantile(samples, 0.995, axis=0)
|
||||
ci_99_lower = np.quantile(samples, 0.005, axis=0)
|
||||
|
||||
ci_95_upper = np.quantile(samples, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.025, axis=0)
|
||||
|
||||
ci_90_upper = np.quantile(samples, 0.95, axis=0)
|
||||
ci_90_lower = np.quantile(samples, 0.05, axis=0)
|
||||
|
||||
ci_50_lower = np.quantile(samples, 0.25, axis=0)
|
||||
ci_50_upper = np.quantile(samples, 0.75, axis=0)
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20, 10))
|
||||
ax.plot(
|
||||
time_steps,
|
||||
samples.mean(axis=0),
|
||||
label="Mean of NRV samples",
|
||||
linewidth=3,
|
||||
)
|
||||
# ax.fill_between(time_steps, ci_lower, ci_upper, color='b', alpha=0.2, label='Full Interval')
|
||||
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_99_lower,
|
||||
ci_99_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="99% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_95_lower,
|
||||
ci_95_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="95% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_90_lower,
|
||||
ci_90_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="90% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_50_lower,
|
||||
ci_50_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="50% Interval",
|
||||
)
|
||||
|
||||
ax.plot(target, label="Real NRV", linewidth=3)
|
||||
# full_interval_patch = mpatches.Patch(color='b', alpha=0.2, label='Full Interval')
|
||||
ci_99_patch = mpatches.Patch(color="b", alpha=0.3, label="99% Interval")
|
||||
ci_95_patch = mpatches.Patch(color="b", alpha=0.4, label="95% Interval")
|
||||
ci_90_patch = mpatches.Patch(color="b", alpha=0.5, label="90% Interval")
|
||||
ci_50_patch = mpatches.Patch(color="b", alpha=0.6, label="50% Interval")
|
||||
|
||||
ax.legend(
|
||||
handles=[
|
||||
ci_99_patch,
|
||||
ci_95_patch,
|
||||
ci_90_patch,
|
||||
ci_50_patch,
|
||||
ax.lines[0],
|
||||
ax.lines[1],
|
||||
]
|
||||
)
|
||||
|
||||
ax.set_ylim([-1500, 1500])
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def debug_plots(self, task, training: bool, data_loader, sample_indices, epoch):
|
||||
for actual_idx, idx in sample_indices.items():
|
||||
features, target, _ = data_loader.dataset[idx]
|
||||
@@ -280,86 +381,69 @@ class DiffusionTrainer:
|
||||
|
||||
self.model.eval()
|
||||
with torch.no_grad():
|
||||
samples = self.sample(self.model, 100, features).cpu().numpy()
|
||||
samples, intermediates = (
|
||||
self.sample(self.model, 100, features, True)
|
||||
)
|
||||
samples = samples.cpu().numpy()
|
||||
samples = self.data_processor.inverse_transform(samples)
|
||||
target = self.data_processor.inverse_transform(target)
|
||||
|
||||
ci_99_upper = np.quantile(samples, 0.995, axis=0)
|
||||
ci_99_lower = np.quantile(samples, 0.005, axis=0)
|
||||
# list to tensor intermediate samples
|
||||
intermediates = torch.stack(intermediates)
|
||||
|
||||
ci_95_upper = np.quantile(samples, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.025, axis=0)
|
||||
|
||||
ci_90_upper = np.quantile(samples, 0.95, axis=0)
|
||||
ci_90_lower = np.quantile(samples, 0.05, axis=0)
|
||||
|
||||
ci_50_lower = np.quantile(samples, 0.25, axis=0)
|
||||
ci_50_upper = np.quantile(samples, 0.75, axis=0)
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20, 10))
|
||||
ax.plot(
|
||||
time_steps,
|
||||
samples.mean(axis=0),
|
||||
label="Mean of NRV samples",
|
||||
linewidth=3,
|
||||
)
|
||||
# ax.fill_between(time_steps, ci_lower, ci_upper, color='b', alpha=0.2, label='Full Interval')
|
||||
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_99_lower,
|
||||
ci_99_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="99% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_95_lower,
|
||||
ci_95_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="95% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_90_lower,
|
||||
ci_90_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="90% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_50_lower,
|
||||
ci_50_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="50% Interval",
|
||||
fig = self.plot_from_samples(samples, target)
|
||||
intermediate_fig1 = self.plot_from_samples(
|
||||
self.data_processor.inverse_transform(intermediates[0].cpu().numpy()), target
|
||||
)
|
||||
|
||||
ax.plot(target, label="Real NRV", linewidth=3)
|
||||
# full_interval_patch = mpatches.Patch(color='b', alpha=0.2, label='Full Interval')
|
||||
ci_99_patch = mpatches.Patch(color="b", alpha=0.3, label="99% Interval")
|
||||
ci_95_patch = mpatches.Patch(color="b", alpha=0.4, label="95% Interval")
|
||||
ci_90_patch = mpatches.Patch(color="b", alpha=0.5, label="90% Interval")
|
||||
ci_50_patch = mpatches.Patch(color="b", alpha=0.6, label="50% Interval")
|
||||
|
||||
ax.legend(
|
||||
handles=[
|
||||
ci_99_patch,
|
||||
ci_95_patch,
|
||||
ci_90_patch,
|
||||
ci_50_patch,
|
||||
ax.lines[0],
|
||||
ax.lines[1],
|
||||
]
|
||||
intermediate_fig2 = self.plot_from_samples(
|
||||
self.data_processor.inverse_transform(intermediates[1].cpu().numpy()), target
|
||||
)
|
||||
|
||||
ax.set_ylim([-1500, 1500])
|
||||
intermediate_fig3 = self.plot_from_samples(
|
||||
self.data_processor.inverse_transform(intermediates[2].cpu().numpy()), target
|
||||
)
|
||||
|
||||
intermediate_fig4 = self.plot_from_samples(
|
||||
self.data_processor.inverse_transform(intermediates[3].cpu().numpy()), target
|
||||
)
|
||||
|
||||
|
||||
# report the intermediate figs to clearml
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title=f"Training Intermediates {actual_idx}" if training else f"Testing Intermediates {actual_idx}",
|
||||
series=f"Sample intermediate 1",
|
||||
iteration=epoch,
|
||||
figure=intermediate_fig1,
|
||||
report_image=True
|
||||
)
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title=f"Training Intermediates {actual_idx}" if training else f"Testing Intermediates {actual_idx}",
|
||||
series=f"Sample intermediate 2",
|
||||
iteration=epoch,
|
||||
figure=intermediate_fig2,
|
||||
report_image=True
|
||||
)
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title=f"Training Intermediates {actual_idx}" if training else f"Testing Intermediates {actual_idx}",
|
||||
series=f"Sample intermediate 3",
|
||||
iteration=epoch,
|
||||
figure=intermediate_fig3,
|
||||
report_image=True
|
||||
)
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title=f"Training Intermediates {actual_idx}" if training else f"Testing Intermediates {actual_idx}",
|
||||
series=f"Sample intermediate 4",
|
||||
iteration=epoch,
|
||||
figure=intermediate_fig4,
|
||||
report_image=True
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title="Training" if training else "Testing",
|
||||
|
||||
@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
|
||||
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(
|
||||
task_name="Diffusion Training: hidden_sizes=[1024, 1024, 1024], lr=0.0001, time_dim=8 + Load + PV + Wind + NP"
|
||||
task_name="Diffusion Training: hidden_sizes=[256, 256, 256], lr=0.0001, time_dim=8"
|
||||
)
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
@@ -19,19 +19,19 @@ from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
data_config = DataConfig()
|
||||
data_config.NRV_HISTORY = True
|
||||
|
||||
data_config.LOAD_HISTORY = True
|
||||
data_config.LOAD_FORECAST = True
|
||||
data_config.LOAD_HISTORY = False
|
||||
data_config.LOAD_FORECAST = False
|
||||
|
||||
data_config.PV_FORECAST = True
|
||||
data_config.PV_HISTORY = True
|
||||
data_config.PV_FORECAST = False
|
||||
data_config.PV_HISTORY = False
|
||||
|
||||
data_config.WIND_FORECAST = True
|
||||
data_config.WIND_HISTORY = True
|
||||
data_config.WIND_FORECAST = False
|
||||
data_config.WIND_HISTORY = False
|
||||
|
||||
data_config.QUARTER = True
|
||||
data_config.DAY_OF_WEEK = True
|
||||
data_config.QUARTER = False
|
||||
data_config.DAY_OF_WEEK = False
|
||||
|
||||
data_config.NOMINAL_NET_POSITION = True
|
||||
data_config.NOMINAL_NET_POSITION = False
|
||||
|
||||
data_config = task.connect(data_config, name="data_features")
|
||||
|
||||
@@ -45,7 +45,7 @@ print("Input dim: ", inputDim)
|
||||
model_parameters = {
|
||||
"epochs": 15000,
|
||||
"learning_rate": 0.0001,
|
||||
"hidden_sizes": [1024, 1024, 1024],
|
||||
"hidden_sizes": [256, 256, 256],
|
||||
"time_dim": 8,
|
||||
}
|
||||
|
||||
|
||||
@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
|
||||
|
||||
#### ClearML ####
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NAQR: GRU")
|
||||
task = clearml_helper.get_task(task_name="NAQR: GRU (2 - 256) + Load")
|
||||
task = clearml_helper.get_task(task_name="NAQR: GRU (8 - 512) + Load + PV + Wind + NP")
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
@@ -30,13 +30,13 @@ data_config.NRV_HISTORY = True
|
||||
data_config.LOAD_HISTORY = True
|
||||
data_config.LOAD_FORECAST = True
|
||||
|
||||
data_config.WIND_FORECAST = False
|
||||
data_config.WIND_HISTORY = False
|
||||
data_config.WIND_FORECAST = True
|
||||
data_config.WIND_HISTORY = True
|
||||
|
||||
data_config.PV_FORECAST = False
|
||||
data_config.PV_HISTORY = False
|
||||
data_config.PV_FORECAST = True
|
||||
data_config.PV_HISTORY = True
|
||||
|
||||
data_config.NOMINAL_NET_POSITION = False
|
||||
data_config.NOMINAL_NET_POSITION = True
|
||||
|
||||
|
||||
data_config = task.connect(data_config, name="data_features")
|
||||
|
||||
Reference in New Issue
Block a user