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2 Commits
174a82fab2
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February-R
| Author | SHA1 | Date | |
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b3f05f386f | ||
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76a597af28 |
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Reports/February/MP2324_verslag2_Mylle_Victor.pdf
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Reports/February/MP2324_verslag2_Mylle_Victor.pdf
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Reports/February/ea-en.pdf
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Reports/February/ea-en.pdf
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Reports/February/ea-nl.pdf
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Reports/February/ea-nl.pdf
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Reports/February/eb-en.pdf
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Reports/February/ugent-doc.cls
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Reports/February/ugent-doc.cls
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% Ghent University document class
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% Created by DF Benoit, December 15, 2022
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%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% ugent-doc specific options (kvoptions)
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Reports/February/ugent-en.pdf
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Reports/February/ugent-en.pdf
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Reports/February/verslag.aux
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Reports/February/verslag.aux
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Reports/February/verslag.bcf
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Reports/February/verslag.bcf
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Reports/February/verslag.log
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Reports/February/verslag.out
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Reports/February/verslag.out
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\BOOKMARK [1][-]{section.1}{Intermediate Results}{}% 1
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\BOOKMARK [2][-]{subsection.1.1}{Net Regulation Volume Modeling}{section.1}% 2
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\BOOKMARK [3][-]{subsubsection.1.1.1}{Input Features}{subsection.1.1}% 3
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\BOOKMARK [1][-]{section.2}{Schedule next months}{}% 6
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Reports/February/verslag.pdf
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Reports/February/verslag.pdf
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Reports/February/verslag.run.xml
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Reports/February/verslag.run.xml
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Reports/February/verslag.synctex.gz
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Reports/February/verslag.synctex.gz
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Reports/February/verslag.tex
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Reports/February/verslag.tex
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|
||||
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||||
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||||
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||||
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|
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%-----------------------
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||||
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||||
% Mathematics
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||||
%-------------
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\usepackage{amsmath}
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||||
%---------
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||||
% Bibliography settings
|
||||
%-----------------------
|
||||
\usepackage[backend=biber, style=apa, sorting=nyt, hyperref=true]{biblatex}
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||||
\addbibresource{./references.bib}
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||||
\usepackage{csquotes} % Suggested when using babel+biblatex
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||||
% Hyperreferences
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||||
%-----------------
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||||
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||||
% Whitespace between paragraphs and no indentation
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||||
%--------------------------------------------------
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||||
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||||
|
||||
% Input for title page
|
||||
%----------------------
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||||
|
||||
% The title
|
||||
\thesubtitle{February Intermediate Report}
|
||||
|
||||
%% Note: a stricter UGent style could be achieved with, e.g.:
|
||||
\usepackage{ulem} % for colored underline
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||||
\renewcommand{\ULthickness}{2pt} % adjust thickness of underline
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||||
\thetitle{Forecasting and generative modeling of the Belgian electricity market}
|
||||
% Note: do not forget to reset the \ULthickness to 1pt after invoking \maketitle
|
||||
% (otherwise all underlines in the rest of your document will be too thick):
|
||||
%\renewcommand{\ULthickness}{1pt}
|
||||
|
||||
% The first (top) infobox at bottom of titlepage
|
||||
\infoboxa{\bfseries\large Master Thesis}
|
||||
|
||||
% The second infobox at bottom of titlepage
|
||||
\infoboxb{Name:
|
||||
\begin{tabular}[t]{l}
|
||||
Victor Mylle
|
||||
\end{tabular}
|
||||
}
|
||||
|
||||
% The third infobox at bottom of titlepage
|
||||
\infoboxc{
|
||||
Promotors:
|
||||
\begin{tabular}[t]{l}
|
||||
prof. dr. ir. Chris Develder \\
|
||||
prof. Bert Claessens
|
||||
\end{tabular}
|
||||
\\\\
|
||||
Supervisor:
|
||||
\begin{tabular}[t]{l}
|
||||
Jonas Van Gompel
|
||||
\end{tabular}
|
||||
}
|
||||
|
||||
% The last (bottom) infobox at bottom of titlepage
|
||||
\infoboxd{Academic year: 2023--2024} % note dash, not hyphen
|
||||
|
||||
|
||||
\begin{document}
|
||||
|
||||
% =====================================================================
|
||||
% Cover
|
||||
% =====================================================================
|
||||
|
||||
% ------------ TITLE PAGE ---------
|
||||
\maketitle
|
||||
\renewcommand{\ULthickness}{1pt}
|
||||
|
||||
% =====================================================================
|
||||
% Front matter
|
||||
% =====================================================================
|
||||
|
||||
% ------------ TABLE OF CONTENTS ---------
|
||||
% {\hypersetup{hidelinks}\tableofcontents} % hide link color in toc
|
||||
% \newpage
|
||||
% \begin{titlepage}
|
||||
|
||||
% \centering % Centers everything on the page
|
||||
|
||||
% % Logo or Image (Optional)
|
||||
% % \includegraphics[width=0.5\textwidth]{path_to_logo.jpg}
|
||||
|
||||
% \vspace*{2cm} % Add vertical space
|
||||
|
||||
% {\large Title: Forecasting and generative modeling of the Belgian electricity market\par}
|
||||
|
||||
% \vspace{2cm}
|
||||
% {\Large Victor Mylle\par}
|
||||
|
||||
% \vspace{1cm}
|
||||
% {\large Period of Internship: 3 July 2023 - 31 August 2023\par}
|
||||
|
||||
% \vspace{1cm}
|
||||
% {\large Mentor: dr. ir. Femke De Backere\par}
|
||||
% {\large TechWolf supervisor: ir. Jens-Joris Decorte}
|
||||
|
||||
% \end{titlepage}
|
||||
|
||||
\newpage
|
||||
|
||||
\section{Intermediate Results}
|
||||
|
||||
\subsection{Net Regulation Volume Modeling}
|
||||
Using a generative model, we try to predict the NRV for the next day. The model is trained on historical data and uses multiple input features to model the NRV. The data for the input features can all be downloaded from \href{https://www.elia.be/en/grid-data/open-data}{Elia Open Data}.
|
||||
|
||||
\subsubsection{Input Features}
|
||||
The generative model uses multiple input features to predict the NRV.
|
||||
|
||||
\begin{itemize}[noitemsep]
|
||||
\item NRV History (NRV of yesterday)
|
||||
\item Load Forecast (Forecasted load of tomorrow)
|
||||
\item Load History (Load of yesterday)
|
||||
\item Wind Forecast (Forecasted wind of tomorrow)
|
||||
\item Wind History (Wind of yesterday)
|
||||
\item Implicit net position (Nominal net position of tomorrow)
|
||||
\item Time features (Day of the week + quarter of the day)
|
||||
\item Photovoltaic Forecast\textsuperscript{*}
|
||||
\item Photovoltaic History\textsuperscript{*}
|
||||
\end{itemize}
|
||||
\textsuperscript{*} These features are not used currently, the data was not available. These features can easily be added without changing any code.
|
||||
|
||||
\subsubsection{Models}
|
||||
In the intermediate report of November, baselines were discussed. Now, other more advanced models are used. Samples must be generated using the model, this means the model can't just output one value but a distribution is needed. Quantile Regression can be used for this task. The model then outputs the values of multiple quantiles. For example, the model outputs the value for which 10\% of the data is lower, the value for which 50\% of the data is lower, etc. This way, the model outputs a distribution which can be used to sample from. The NRV predicitons are done in a quarter-hourly resolution. To predict the NRV for the next day, 96 values need to be sampled. This can be done in an autoregressive manner. The model outputs the quantiles for the first quarter-hour, a sample is drawn from this distribution and this sample is used as input for the next quarter-hour. This process is repeated 96 times.
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lcc}
|
||||
\hline
|
||||
\textbf{Model} & \textbf{test\_L1Loss} & \textbf{test\_CRPSLoss} \\
|
||||
\hline
|
||||
Linear Model & 101.639 & 68.485 \\
|
||||
Non Linear Model & 102.031 & 68.968 \\
|
||||
LSTM/GRU Model & 104.261 & 66.052 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\caption{Performance of Autoregressive Models}
|
||||
\label{tab:general_models}
|
||||
\end{table}
|
||||
|
||||
At the moment, I am experimenting with a diffusion model to generatively model the NRV but more research and expermimenting needs to be done.
|
||||
|
||||
\subsubsection{Charging Policy}
|
||||
Using the predicted NRV, a policy can be implemented to charge and discharge a battery. The goal of the policy is to maximize the profit made by selling the stored electricity. A simple policy is implemented to charge and discharge the battery based on 2 thresholds determined by the predicted NRV. The policy is evaluated on historical data and the profit is calculated. To determine the charge and discharge threshold, 1000 full NRV predictions are done for the next day and for each of these predicitions, the thresholds are determined. Next, the mean of these thresholds is used as the final threshold.
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lccc}
|
||||
\hline
|
||||
\textbf{Policy} & \textbf{Total Profit (€)} & \textbf{Charge Cycles} \\
|
||||
\hline
|
||||
Baseline (charge: €150, discharge: €175) & 251,202.59 & 725 \\
|
||||
Baseline (yesterday imbalance price) & 342,980.09 & 903 \\
|
||||
GRU Predicted NRV (mean thresholds) & 339,846.91 & 842 \\
|
||||
Diffusion Predicted NRV (mean thresholds) & 338,168.03 & 886 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\caption{Comparison of Energy Storage Policies Using Predicted NRV. Battery of 2MWh with 1MW charge/discharge power. Evaluated on data from 01-01-2023 until 08-10-2023.}
|
||||
\label{table:energy_storage_policies}
|
||||
\end{table}
|
||||
|
||||
The recommended charge cycles for a battery is <400 cycles per year. The policy also needs to take this into account. A penalty parameter can be introduced and determined so that the policy is penalized for every charge cycle above 400. The policy can then be optimized using this penalty parameter. I am currenlty experimenting with this.
|
||||
|
||||
|
||||
\newpage
|
||||
\section{Schedule next months}
|
||||
\begin{itemize}
|
||||
\item Baselines with penalties for charge cycles above 400
|
||||
\item Better visualizations of the policy profit results.
|
||||
\item Case studies of days with extreme thresholds
|
||||
|
||||
\item Finetuning of models and hyperparametres based on model errors and profits of the policy
|
||||
\item Ablation study of input features
|
||||
|
||||
\item Experiment further with diffusion models
|
||||
|
||||
\item During the experimenting, I will write my thesis and update the results and conclusions chapters.
|
||||
\end{itemize}
|
||||
|
||||
\end{document}
|
||||
|
||||
@@ -155,3 +155,50 @@ Test data: 01-01-2023 until 08-10–2023
|
||||
- [ ] time steps reducing for diffusion model (UNet activation functions?)
|
||||
|
||||
- [ ] (State space model? S4)
|
||||
|
||||
|
||||
|
||||
TODO:
|
||||
- [ ] diffusion model oefening generative models vragen
|
||||
- [ ] Non autoregressive models policy testen (Non Linear eerst) -> als dit al slect, niet verder kijken, wel vermelden
|
||||
- [ ] Policy in test set -> over charge cycles (stop trading electricity)
|
||||
|
||||
- [ ] penalty bepalen op training data
|
||||
|
||||
- [ ] cycles en profit herschalen naar per jaar
|
||||
|
||||
|
||||
baseline -> NRV van gisteren gebruiken om thresholds te bepalen voor vandaag
|
||||
andere policies -> NRV van vandaag voorspellen met model en thresholds bepalen voor vandaag
|
||||
|
||||
|
||||
Eerste baseline -> thresholds bepalen op training data maar ook stoppen als 400 cycles (herschalen) per jaar bereikt zijn -> thresholds zouden anders moeten zijn (Ook met penalty parameter)
|
||||
-> deze toepassen op test set (ook stoppen als 400/jaar bereikt zijn)
|
||||
|
||||
|
||||
Visualizatie van thresholds over test set voor baselines en complexere modellen -> zonder penalties tonen
|
||||
|
||||
|
||||
|
||||
|
||||
1 a 2 Case studies (extreme gevallen, thresholds 150, -5, normale mss)
|
||||
|
||||
- Generatie van NRV (echte NRV)
|
||||
- Thresholds die eruit komen
|
||||
- Profit en charge cycles
|
||||
|
||||
|
||||
|
||||
Policy volledig fixen en later training script met policy direct erachter (tijdens schrijven door laten runnen)
|
||||
|
||||
1) Policy
|
||||
2) Finetuning van modellen (+ vergelijken met elkaar opbv profit en error)
|
||||
3) Ablation Study (input features weghalen en kijken wat er gebeurt)
|
||||
( 4) Diffusion tussendoor )
|
||||
|
||||
|
||||
Inleiding +
|
||||
Literatuurstudie +
|
||||
Tabellen die we gaan bespreken -> updaten met nieuwe data dan
|
||||
|
||||
Nog eens 3e meeting opbrengen voor 2e deel maart.
|
||||
@@ -25,19 +25,12 @@ class NrvDataset(Dataset):
|
||||
self.sequence_length = sequence_length
|
||||
self.predict_sequence_length = predict_sequence_length
|
||||
|
||||
self.samples_to_skip = self.skip_samples(dataframe=dataframe, full_day_skip=self.full_day_skip)
|
||||
self.samples_to_skip = self.skip_samples(dataframe=dataframe)
|
||||
total_indices = set(
|
||||
range(len(dataframe) - self.sequence_length - self.predict_sequence_length)
|
||||
)
|
||||
self.valid_indices = sorted(list(total_indices - set(self.samples_to_skip)))
|
||||
|
||||
# full day indices
|
||||
full_day_skipped_samples = self.skip_samples(dataframe=dataframe, full_day_skip=True)
|
||||
full_day_total_indices = set(
|
||||
range(len(dataframe) - self.sequence_length - self.predict_sequence_length)
|
||||
)
|
||||
self.full_day_valid_indices = sorted(list(full_day_total_indices - set(full_day_skipped_samples)))
|
||||
|
||||
self.history_features = []
|
||||
if self.data_config.LOAD_HISTORY:
|
||||
self.history_features.append("total_load")
|
||||
@@ -80,7 +73,7 @@ class NrvDataset(Dataset):
|
||||
|
||||
self.history_features, self.forecast_features = self.preprocess_data(dataframe)
|
||||
|
||||
def skip_samples(self, dataframe, full_day_skip):
|
||||
def skip_samples(self, dataframe):
|
||||
nan_rows = dataframe[dataframe.isnull().any(axis=1)]
|
||||
nan_indices = nan_rows.index
|
||||
skip_indices = [
|
||||
@@ -98,7 +91,7 @@ class NrvDataset(Dataset):
|
||||
|
||||
# add indices that are not the start of a day (00:15) to the skip indices (use datetime column)
|
||||
# get indices of all 00:15 timestamps
|
||||
if full_day_skip:
|
||||
if self.full_day_skip:
|
||||
start_of_day_indices = dataframe[
|
||||
dataframe["datetime"].dt.time != pd.Timestamp("00:00:00").time()
|
||||
].index
|
||||
|
||||
@@ -8,8 +8,7 @@ import pandas as pd
|
||||
import datetime
|
||||
from tqdm import tqdm
|
||||
from src.utils.imbalance_price_calculator import ImbalancePriceCalculator
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
import time
|
||||
import plotly.express as px
|
||||
|
||||
### import functions ###
|
||||
@@ -17,7 +16,7 @@ from src.trainers.quantile_trainer import auto_regressive as quantile_auto_regre
|
||||
from src.trainers.diffusion_trainer import sample_diffusion
|
||||
from src.utils.clearml import ClearMLHelper
|
||||
|
||||
### Arguments ###
|
||||
# argparse to parse task id and model type
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--task_id', type=str, default=None)
|
||||
parser.add_argument('--model_type', type=str, default=None)
|
||||
@@ -28,7 +27,6 @@ assert args.task_id is not None, "Please specify task id"
|
||||
assert args.model_type is not None, "Please specify model type"
|
||||
assert args.model_name is not None, "Please specify model name"
|
||||
|
||||
### Baseline Policy ###
|
||||
battery = Battery(2, 1)
|
||||
baseline_policy = BaselinePolicy(battery, data_path="")
|
||||
|
||||
@@ -165,17 +163,20 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles, _charge_thresholds, _discharge_thresholds
|
||||
|
||||
def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: callable):
|
||||
penalties = [0, 50, 250, 500, 1000, 1500]
|
||||
penalties = [0, 10, 50, 150, 300, 500, 600, 800, 1000, 1500, 2000, 2500]
|
||||
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
baseline_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
|
||||
charge_thresholds = {}
|
||||
discharge_thresholds = {}
|
||||
|
||||
# get all dates in test set
|
||||
dates = baseline_policy.test_data["DateTime"].dt.date.unique()
|
||||
|
||||
# dates back to datetime
|
||||
dates = pd.to_datetime(dates)
|
||||
|
||||
for date in tqdm(dates):
|
||||
for date in tqdm(dates[:10]):
|
||||
try:
|
||||
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles, new_charge_thresholds, new_discharge_thresholds = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
|
||||
|
||||
@@ -190,7 +191,8 @@ def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: call
|
||||
baseline_profits_cycles[penalty][1] += new_baseline_profits_cycles[penalty][1]
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error for date {date}")
|
||||
# print(f"Error for date {date}")
|
||||
raise e
|
||||
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds
|
||||
|
||||
@@ -220,6 +222,9 @@ def main():
|
||||
# the charge_thresholds is a dictionary with date as key. The values of the dictionary is another dictionary with keys as penalties and values as the charge thresholds
|
||||
# create density plot that shows a density plot of the charge thresholds for each penalty (use seaborn displot) (One plot with a different color for each penalty)
|
||||
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
charge_thresholds_for_penalty = {}
|
||||
for d in charge_thresholds.values():
|
||||
for penalty, thresholds in d.items():
|
||||
@@ -234,73 +239,47 @@ def main():
|
||||
discharge_thresholds_for_penalty[penalty] = []
|
||||
discharge_thresholds_for_penalty[penalty].extend(thresholds)
|
||||
|
||||
def plot_threshold_distribution(thresholds: dict, title: str):
|
||||
data_to_plot = []
|
||||
for penalty, values in thresholds.items():
|
||||
for value in values:
|
||||
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
palette = sns.color_palette("bright", len(thresholds.keys()))
|
||||
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
|
||||
plt.title('Density of Charge Thresholds by Penalty')
|
||||
plt.xlabel('Charge Threshold')
|
||||
plt.ylabel('Density')
|
||||
plt.legend(title='Penalty')
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
"Policy Results",
|
||||
title,
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
plt.close()
|
||||
|
||||
### Plot charge thresholds distribution ###
|
||||
plot_threshold_distribution(charge_thresholds_for_penalty, "Charge Thresholds")
|
||||
data_to_plot = []
|
||||
for penalty, values in charge_thresholds_for_penalty.items():
|
||||
for value in values:
|
||||
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
print(df.head())
|
||||
palette = sns.color_palette("bright", len(charge_thresholds.keys()))
|
||||
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
|
||||
plt.title('Density of Charge Thresholds by Penalty')
|
||||
plt.xlabel('Charge Threshold')
|
||||
plt.ylabel('Density')
|
||||
plt.legend(title='Penalty')
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
"Policy Results",
|
||||
"Charge Thresholds",
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
plt.close()
|
||||
|
||||
### Plot discharge thresholds distribution ###
|
||||
plot_threshold_distribution(discharge_thresholds_for_penalty, "Discharge Thresholds")
|
||||
data_to_plot = []
|
||||
for penalty, values in discharge_thresholds_for_penalty.items():
|
||||
for value in values:
|
||||
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
palette = sns.color_palette("bright", len(discharge_thresholds.keys()))
|
||||
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
|
||||
plt.title('Density of Charge Thresholds by Penalty')
|
||||
plt.xlabel('Charge Threshold')
|
||||
plt.ylabel('Density')
|
||||
plt.legend(title='Penalty')
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
"Policy Results",
|
||||
"Discharge Thresholds",
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
plt.close()
|
||||
|
||||
def plot_thresholds_per_day(thresholds: dict, title: str):
|
||||
# plot mean charge threshold per day (per penalty (other color))
|
||||
data_to_plot = []
|
||||
for date, values in thresholds.items():
|
||||
for penalty, value in values.items():
|
||||
mean_val = value.mean().item()
|
||||
std_val = value.std().item() # Calculate standard deviation
|
||||
data_to_plot.append({'Date': date, 'Penalty': penalty, 'Mean': mean_val, 'StdDev': std_val})
|
||||
print(f"Date: {date}, Penalty: {penalty}, Mean: {mean_val}, StdDev: {std_val}")
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
df["Date"] = pd.to_datetime(df["Date"])
|
||||
|
||||
fig = px.line(
|
||||
df,
|
||||
x="Date",
|
||||
y="Mean",
|
||||
color="Penalty",
|
||||
title=title,
|
||||
labels={"Mean": "Threshold", "Date": "Date"},
|
||||
markers=True, # Adds markers to the lines
|
||||
hover_data=["Penalty"], # Adds additional hover information
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
width=1000, # Set the width of the figure
|
||||
height=600, # Set the height of the figure
|
||||
title_x=0.5, # Center the title horizontally
|
||||
)
|
||||
|
||||
task.get_logger().report_plotly(
|
||||
"Thresholds per Day",
|
||||
title,
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
|
||||
### Plot mean charge thresholds per day ###
|
||||
plot_thresholds_per_day(charge_thresholds, "Mean Charge Thresholds per Day")
|
||||
|
||||
### Plot mean discharge thresholds per day ###
|
||||
plot_thresholds_per_day(discharge_thresholds, "Mean Discharge Thresholds per Day")
|
||||
|
||||
|
||||
# create dataframe with columns "name", "penalty", "profit", "cycles"
|
||||
|
||||
@@ -33,29 +33,67 @@ class AutoRegressiveTrainer(Trainer):
|
||||
self.model.output_size = 1
|
||||
|
||||
def debug_plots(self, task, train: bool, data_loader, sample_indices, epoch):
|
||||
for actual_idx, idx in sample_indices.items():
|
||||
auto_regressive_output = self.auto_regressive(data_loader.dataset, [idx]*1000)
|
||||
num_samples = len(sample_indices)
|
||||
rows = num_samples # One row per sample since we only want one column
|
||||
|
||||
# check if self has get_plot_error
|
||||
if hasattr(self, "get_plot_error"):
|
||||
cols = 2
|
||||
print("Using get_plot_error")
|
||||
else:
|
||||
cols = 1
|
||||
print("Using get_plot")
|
||||
|
||||
fig = make_subplots(
|
||||
rows=rows,
|
||||
cols=cols,
|
||||
subplot_titles=[f"Sample {i+1}" for i in range(num_samples)],
|
||||
)
|
||||
|
||||
for i, idx in enumerate(sample_indices):
|
||||
auto_regressive_output = self.auto_regressive(data_loader.dataset, [idx])
|
||||
if len(auto_regressive_output) == 3:
|
||||
initial, predictions, target = auto_regressive_output
|
||||
else:
|
||||
initial, _, predictions, target = auto_regressive_output
|
||||
initial, predictions, _, target = auto_regressive_output
|
||||
|
||||
initial = initial.squeeze(0)
|
||||
predictions = predictions.squeeze(0)
|
||||
target = target.squeeze(0)
|
||||
|
||||
# keep one initial
|
||||
initial = initial[0]
|
||||
target = target[0]
|
||||
sub_fig = self.get_plot(initial, target, predictions, show_legend=(i == 0))
|
||||
|
||||
predictions = predictions
|
||||
row = i + 1
|
||||
col = 1
|
||||
|
||||
fig = self.get_plot(initial, target, predictions, show_legend=(0 == 0))
|
||||
for trace in sub_fig.data:
|
||||
fig.add_trace(trace, row=row, col=col)
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title="Training" if train else "Testing",
|
||||
series=f'Sample {actual_idx}',
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
if cols == 2:
|
||||
error_sub_fig = self.get_plot_error(
|
||||
target, predictions
|
||||
)
|
||||
for trace in error_sub_fig.data:
|
||||
fig.add_trace(trace, row=row, col=col + 1)
|
||||
|
||||
loss = self.criterion(
|
||||
predictions.to(self.device), target.to(self.device)
|
||||
).item()
|
||||
|
||||
fig["layout"]["annotations"][i].update(
|
||||
text=f"{self.criterion.__class__.__name__}: {loss:.6f}"
|
||||
)
|
||||
|
||||
# y axis same for all plots
|
||||
# fig.update_yaxes(range=[-1, 1], col=1)
|
||||
|
||||
fig.update_layout(height=1000 * rows)
|
||||
task.get_logger().report_plotly(
|
||||
title=f"{'Training' if train else 'Test'} Samples",
|
||||
series="full_day",
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
|
||||
def auto_regressive(self, data_loader, idx, sequence_length: int = 96):
|
||||
self.model.eval()
|
||||
|
||||
@@ -19,6 +19,8 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
|
||||
alpha = 1. - beta
|
||||
alpha_hat = torch.cumprod(alpha, dim=0)
|
||||
|
||||
# inputs: (num_features) -> (batch_size, num_features)
|
||||
# inputs: (time_steps, num_features) -> (batch_size, time_steps, num_features)
|
||||
if len(inputs.shape) == 2:
|
||||
inputs = inputs.repeat(n, 1)
|
||||
elif len(inputs.shape) == 3:
|
||||
@@ -40,17 +42,17 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
|
||||
noise = torch.zeros_like(x)
|
||||
|
||||
x = 1/torch.sqrt(_alpha) * (x-((1-_alpha) / (torch.sqrt(1 - _alpha_hat))) * predicted_noise) + torch.sqrt(_beta) * noise
|
||||
x = torch.clamp(x, -1.0, 1.0)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class DiffusionTrainer:
|
||||
def __init__(self, model: nn.Module, data_processor: DataProcessor, device: torch.device):
|
||||
self.model = model
|
||||
self.device = device
|
||||
|
||||
self.noise_steps = 30
|
||||
self.beta_start = 0.0001
|
||||
self.noise_steps = 20
|
||||
self.beta_start = 1e-4
|
||||
self.beta_end = 0.02
|
||||
self.ts_length = 96
|
||||
|
||||
@@ -96,16 +98,7 @@ class DiffusionTrainer:
|
||||
else:
|
||||
loader = test_loader
|
||||
|
||||
# set seed
|
||||
np.random.seed(42)
|
||||
|
||||
actual_indices = np.random.choice(loader.dataset.full_day_valid_indices, num_samples, replace=False)
|
||||
indices = {}
|
||||
for i in actual_indices:
|
||||
indices[i] = loader.dataset.valid_indices.index(i)
|
||||
|
||||
print(actual_indices)
|
||||
|
||||
indices = np.random.randint(0, len(loader.dataset) - 1, size=num_samples)
|
||||
return indices
|
||||
|
||||
def init_clearml_task(self, task):
|
||||
@@ -180,7 +173,7 @@ class DiffusionTrainer:
|
||||
|
||||
|
||||
def debug_plots(self, task, training: bool, data_loader, sample_indices, epoch):
|
||||
for actual_idx, idx in sample_indices.items():
|
||||
for i, idx in enumerate(sample_indices):
|
||||
features, target, _ = data_loader.dataset[idx]
|
||||
|
||||
features = features.to(self.device)
|
||||
@@ -189,21 +182,18 @@ class DiffusionTrainer:
|
||||
self.model.eval()
|
||||
with torch.no_grad():
|
||||
samples = self.sample(self.model, 100, features).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)
|
||||
ci_99_upper = np.quantile(samples, 0.99, axis=0)
|
||||
ci_99_lower = np.quantile(samples, 0.01, axis=0)
|
||||
|
||||
ci_95_upper = np.quantile(samples, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.025, axis=0)
|
||||
ci_95_upper = np.quantile(samples, 0.95, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.05, 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)
|
||||
ci_90_upper = np.quantile(samples, 0.9, axis=0)
|
||||
ci_90_lower = np.quantile(samples, 0.1, axis=0)
|
||||
|
||||
ci_50_upper = np.quantile(samples, 0.5, axis=0)
|
||||
ci_50_lower = np.quantile(samples, 0.5, axis=0)
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
@@ -229,7 +219,7 @@ class DiffusionTrainer:
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title="Training" if training else "Testing",
|
||||
series=f'Sample {actual_idx}',
|
||||
series=f'Sample {i}',
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
|
||||
@@ -10,9 +10,7 @@ import plotly.graph_objects as go
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.interpolate import CubicSpline
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import matplotlib.patches as mpatches
|
||||
|
||||
|
||||
def sample_from_dist(quantiles, preds):
|
||||
if isinstance(preds, torch.Tensor):
|
||||
@@ -263,35 +261,35 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
name="test_CRPS_from_samples_transformed", value=np.mean(crps_from_samples_metric)
|
||||
)
|
||||
|
||||
# def get_plot_error(
|
||||
# self,
|
||||
# next_day,
|
||||
# predictions,
|
||||
# ):
|
||||
# metric = PinballLoss(quantiles=self.quantiles)
|
||||
# fig = go.Figure()
|
||||
def get_plot_error(
|
||||
self,
|
||||
next_day,
|
||||
predictions,
|
||||
):
|
||||
metric = PinballLoss(quantiles=self.quantiles)
|
||||
fig = go.Figure()
|
||||
|
||||
# next_day_np = next_day.view(-1).cpu().numpy()
|
||||
# predictions_np = predictions.cpu().numpy()
|
||||
next_day_np = next_day.view(-1).cpu().numpy()
|
||||
predictions_np = predictions.cpu().numpy()
|
||||
|
||||
# if True:
|
||||
# next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
# predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
if True:
|
||||
next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
|
||||
# # for each time step, calculate the error using the metric
|
||||
# errors = []
|
||||
# for i in range(96):
|
||||
# for each time step, calculate the error using the metric
|
||||
errors = []
|
||||
for i in range(96):
|
||||
|
||||
# target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
|
||||
# prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
|
||||
target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
|
||||
prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
|
||||
|
||||
# errors.append(metric(prediction_tensor, target_tensor))
|
||||
errors.append(metric(prediction_tensor, target_tensor))
|
||||
|
||||
# # plot the error
|
||||
# fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
|
||||
# fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
|
||||
# plot the error
|
||||
fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
|
||||
fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
|
||||
|
||||
# return fig
|
||||
return fig
|
||||
|
||||
|
||||
def get_plot(
|
||||
@@ -314,59 +312,26 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
|
||||
ci_99_upper = np.quantile(predictions_np, 0.995, axis=0)
|
||||
ci_99_lower = np.quantile(predictions_np, 0.005, axis=0)
|
||||
|
||||
ci_95_upper = np.quantile(predictions_np, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(predictions_np, 0.025, axis=0)
|
||||
|
||||
ci_90_upper = np.quantile(predictions_np, 0.95, axis=0)
|
||||
ci_90_lower = np.quantile(predictions_np, 0.05, axis=0)
|
||||
|
||||
ci_50_lower = np.quantile(predictions_np, 0.25, axis=0)
|
||||
ci_50_upper = np.quantile(predictions_np, 0.75, axis=0)
|
||||
|
||||
# Add traces for current and next day
|
||||
# fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
|
||||
# fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
|
||||
fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
|
||||
fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
|
||||
|
||||
# for i, q in enumerate(self.quantiles):
|
||||
# fig.add_trace(
|
||||
# go.Scatter(
|
||||
# x=96 + np.arange(96),
|
||||
# y=predictions_np[:, i],
|
||||
# name=f"Prediction (Q={q})",
|
||||
# line=dict(dash="dash"),
|
||||
# )
|
||||
# )
|
||||
for i, q in enumerate(self.quantiles):
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=96 + np.arange(96),
|
||||
y=predictions_np[:, i],
|
||||
name=f"Prediction (Q={q})",
|
||||
line=dict(dash="dash"),
|
||||
)
|
||||
)
|
||||
|
||||
# # Update the layout
|
||||
# fig.update_layout(
|
||||
# title="Predictions and Quantiles of the Linear Model",
|
||||
# showlegend=show_legend,
|
||||
# )
|
||||
# Update the layout
|
||||
fig.update_layout(
|
||||
title="Predictions and Quantiles of the Linear Model",
|
||||
showlegend=show_legend,
|
||||
)
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20, 10))
|
||||
ax.plot(time_steps, predictions_np.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(next_day_np, 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]])
|
||||
return fig
|
||||
|
||||
def auto_regressive(self, dataset, idx_batch, sequence_length: int = 96):
|
||||
|
||||
@@ -86,7 +86,7 @@ class Trainer:
|
||||
|
||||
def random_samples(self, train: bool = True, num_samples: int = 10):
|
||||
train_loader, test_loader = self.data_processor.get_dataloaders(
|
||||
predict_sequence_length=96
|
||||
predict_sequence_length=self.model.output_size
|
||||
)
|
||||
|
||||
if train:
|
||||
@@ -94,14 +94,7 @@ class Trainer:
|
||||
else:
|
||||
loader = test_loader
|
||||
|
||||
np.random.seed(42)
|
||||
actual_indices = np.random.choice(loader.dataset.full_day_valid_indices, num_samples, replace=False)
|
||||
indices = {}
|
||||
for i in actual_indices:
|
||||
indices[i] = loader.dataset.valid_indices.index(i)
|
||||
|
||||
print(actual_indices)
|
||||
|
||||
indices = np.random.randint(0, len(loader.dataset) - 1, size=num_samples)
|
||||
return indices
|
||||
|
||||
def train(self, epochs: int, remotely: bool = False, task: Task = None):
|
||||
@@ -114,8 +107,8 @@ class Trainer:
|
||||
predict_sequence_length=self.model.output_size
|
||||
)
|
||||
|
||||
train_samples = self.random_samples(train=True, num_samples=5)
|
||||
test_samples = self.random_samples(train=False, num_samples=5)
|
||||
train_samples = self.random_samples(train=True)
|
||||
test_samples = self.random_samples(train=False)
|
||||
|
||||
self.init_clearml_task(task)
|
||||
|
||||
|
||||
@@ -38,7 +38,7 @@ data_config.NOMINAL_NET_POSITION = True
|
||||
data_config = task.connect(data_config, name="data_features")
|
||||
|
||||
data_processor = DataProcessor(data_config, path="", lstm=False)
|
||||
data_processor.set_batch_size(64)
|
||||
data_processor.set_batch_size(128)
|
||||
data_processor.set_full_day_skip(True)
|
||||
|
||||
inputDim = data_processor.get_input_size()
|
||||
@@ -47,15 +47,15 @@ print("Input dim: ", inputDim)
|
||||
model_parameters = {
|
||||
"epochs": 5000,
|
||||
"learning_rate": 0.0001,
|
||||
"hidden_sizes": [128, 128],
|
||||
"time_dim": 8,
|
||||
"hidden_sizes": [512, 512, 512],
|
||||
"time_dim": 64,
|
||||
}
|
||||
|
||||
model_parameters = task.connect(model_parameters, name="model_parameters")
|
||||
|
||||
#### Model ####
|
||||
model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
|
||||
# model = GRUDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[2], time_dim=model_parameters["time_dim"], gru_hidden_size=128)
|
||||
# model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
|
||||
model = GRUDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[2], time_dim=model_parameters["time_dim"], gru_hidden_size=256)
|
||||
|
||||
print("Starting training ...")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user