Commit 82d154e0 authored by José Hugo Elsas's avatar José Hugo Elsas
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Starting the description of the notebooks.

parent 27b13e09
......@@ -6,32 +6,45 @@ Accessory repository for the paper "Active Learning Loading Case Selection" incl
## Notebooks
- Spreadsheet aggregation.ipynb
Spreadsheet aggregation perform the Extraction, Transform, Load (ETL) part of the work. It converts the data from the different files into an integrated feature dataframe encoding current and wave data into a format more ameanable to Gaussian Process Regression.
- Single-Target random and active learning for loading case selection.ipynb
As an example
- Multi-Target random and active
## Data Files
Case definition files constitute the data necessary to characterize a loading case, and therefore run the simulation. Each loading case requires definition of a current and a wave. The cases.csv defines the pair (current,wave) to which correspond each loading case.
currents.csv contains the parameters that characterize each current, which is the 2D velocity vector for each water depth, and waves.csv contains the parameters for the JONSWAP wave model for each wave. cardinal_directions.csv is an auxiliary file used to convert data from currents.csv file to more ameanable format.
config0.csv , config1.csv ... config5.csv are the files containing the results of the simulations, i.e. Axial tension and DNVUF201 CLC, for 6 different riser configurations. The results are for each of the loading cases listed in the cases.csv file and are, ultimately, the target for the machine learning model.
There are several files involved in this project, but can be separated in the categories: case definition, results data and intermediary files.
Case definition files constitute the data necessary to characterize a loading case, and therefore run the simulation. Each loading case requires definition of a current and a wave.
Results data corresponds to the information produced by applying the machine learning inference process over the target dataset, and iterating this process either through random sampling or through active learning sampling.
### Case definition
The cases.csv defines the pair (current,wave) to which correspond each loading case. currents.csv contains the parameters that characterize each current, which is the 2D velocity vector for each water depth, and waves.csv contains the parameters for the JONSWAP wave model for each wave. cardinal_directions.csv is an auxiliary file used to convert data from currents.csv file to more ameanable format.
config0.csv , config1.csv ... config5.csv are the files containing the results of the simulations, i.e. Axial tension and DNVUF201 CLC, for 6 different riser configurations. The results are for each of the loading cases listed in the cases.csv file and are, ultimately, the target for the machine learning model.
- cases.csv
- cardinal_directions.csv
- currents.csv
- waves.csv
- config0..5
### Results data
The resulting data of the analysis is stored in different folders for convenience.
### Results data
- data/ : processed deviation measures for ML predictions
- plots/ : plotted graphs analyzing data/ files
- results/ : case-by-case data of ML prediction
### Intermediary files
Intermediary files are produced by Spreadsheet aggregation notebook, which format the data in features ameanable to machine learning processing.
### Intermediary files
- cases_full.csv
- cases_full.xlsx
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