Full workflow from CSV
Overview
Required inputs
Robert_example.csv: CSV file with data to use as the training and validation sets. The full CSV file can be found in the "Examples" folder of the ROBERT repository or downloaded here:
Name |
Target_values |
x1 |
x2 |
x3 |
x4 |
x5 |
x6 |
x7 |
x8 |
x9 |
x10 |
x11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 |
1.854766065 |
12 |
110.9270401 |
70.8240401 |
Csub-H |
89.87553406 |
49.77253406 |
1 |
0 |
0 |
0 |
1 |
2 |
2.034511341 |
11.7 |
110.6553116 |
70.25231158 |
Csub-Csub |
78.65235138 |
55.53135138 |
1 |
0 |
0 |
0 |
1 |
... |
||||||||||||
36 |
0.321084552 |
-101.6 |
110.7593079 |
-42.94369214 |
Csub-O |
59.81459808 |
-76.60640192 |
0 |
2 |
1 |
3 |
3 |
37 |
0.329517076 |
-101.6 |
115.2292938 |
-38.47370618 |
Csub-O |
70.45233154 |
-65.96866846 |
0 |
2 |
1 |
3 |
3 |
Executing the job
Instructions:
Download the Robert_example.csv file specified in Required inputs.
Go to the folder containing the CSV file in your terminal (using the "cd" command, i.e.
cd C:/Users/test_robert).Activate the conda environment where ROBERT was installed (
conda activate robert).Run the following command line:
python -m robert --names Name --y Target_values --csv_name Robert_example.csv
Options used:
--names Name: Name of the column containing the names of the datapoints. This feature allows to print the names of the outlier points (if any).--y Target_values: Name of the column containing the response y values.--csv_name Robert_example.csv: CSV with the database.
Execution time and versions
Time: ~3.5 min
System: 8 processors (11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz) using 16.0 GB RAM memory
ROBERT version: 2.0.1
scikit-learn-intelex version: 2025.2.0
Results
A PDF file called ROBERT_report.pdf should be created in the folder where ROBERT was executed. The PDF file can be visualized here: 
The PDF report contains all the results of the workflow. In this case, two Neural Network (NN) models were the optimal model found from:
Four different models (Gradient Boosting GB, MultiVariate Linear MVL, Neural Network NN, Random Forest RF)
The first part of the PDF file is shown below as a preview:
