This loop starts a parameter optimization loop. In the dialog you can
enter several parameters with an
interval and a step size. The loop will
vary these parameters following a certain search strategy. Each
parameter
is output as a flow variable. The parameters can then be used inside the
loop body either directly or
by converting them with a
Variable to Table
node into a data table.
Currently four search strategies are available:
- Brute Force: All possible parameter combination (given the intervals and the step sizes) are checked and the best is returned.
- Hillclimbing: A random start combination is created and the direct neighbors (respecting the given intervals and step sizes) are evaluated. The best combination among the neighbors is the start point for the next iteration. If no neighbor improves the objective function the loop terminates.
- Random Search: Parameter combinations are randomly chosen and evaluated. The specified start and stop values define the parameter space from which a parameter combination is randomly drawn. Additionally, an optional step size can be defined to restrict the possible parameter values. The loop terminates after a specified number of iterations or, if early stopping is activated, when for a specified number of rounds the objective value has not improved. Note, that it is drawn with replacement. While duplicate parameter combinations will be processed just once, each of them still counts as an iteration. Due to that, it may happen that actually less loop iterations are processed than defined.
- Bayesian Optimization (TPE): This strategy consists of two phases. The first one is the warm-up in which parameter combinations are randomly chosen and evaluated. Based on the scores of the warm-up rounds, the second phase tries to find promising parameter combinations which are then evaluated. The algorithm used by this strategy is based on an algorithm published by Bergstra et al. (see link further down) and uses Tree-structured Parzen Estimation (TPE) in the second phase to find good parameter combinations. The specified start and stop values define the parameter space from which a parameter combination is randomly drawn. Additionally, an optional step size can be defined to restrict the possible parameter values. The loop terminates after a specified number of iterations. Note, that it is drawn with replacement. While duplicate parameter combinations will be processed just once, each of them still counts as an iteration. Due to that, it may happen that actually less loop iterations are processed than defined.
Options
Popular Predecessors
Table Row To Variable Loop Start15 %- Table Row to Variable6 %
Table Row to Variable5 %- Table Row to Variable Loop Start5 %
- Partitioning4 %
Merge Variables4 %Counting Loop Start3 %- Table Creator3 %
- Parameter Optimization Loop Start2 %
- Equal Size Sampling2 %
- Column Filter2 %
- Merge Variables2 %
- Math Formula (Variable)2 %
Table Column to Variable2 %- Chunk Loop Start2 %
- Group Loop Start2 %
- Parameter Optimization Loop End2 %
- Table Column to Variable2 %
- Variable Creator2 %
- String Manipulation (Variable)2 %
- Window Loop Start2 %
- Integer Configuration1 %
Table Column to Variable< 1 %Table Writer< 1 %- Rule Engine Variable (Dictionary)< 1 %
- Number to String< 1 %
- Excel Writer< 1 %
- Boolean Configuration< 1 %
- Integer Slider Configuration< 1 %
Variable Loop End< 1 %- GroupBy< 1 %
CASE Switch Variable (Start)< 1 %- Extract Table Dimension< 1 %
- Python Edit Variable (legacy)< 1 %
- Extract Variables (Data)< 1 %
- Feature Selection Loop Start (1:1)< 1 %
Joiner< 1 %- Cache< 1 %
- Boolean Input (legacy)< 1 %
- Integer Input (legacy)< 1 %
- Table Manipulator< 1 %
- Table Row To Variable Run-for-time Loop Start< 1 %
- Java Edit Variable< 1 %
- Java Snippet< 1 %
Interval Loop Start< 1 %Recursive Loop Start< 1 %- Normalizer< 1 %
- Math Formula< 1 %
- Benchmark Start< 1 %
- Wait...< 1 %
- Try (Data Ports)< 1 %
- Try (Variable Ports)< 1 %
Table Column to Variable< 1 %Table Row to Variable< 1 %Variable to Table Row< 1 %Database Reader< 1 %- Extract System Properties< 1 %
Generic Loop Start< 1 %- X-Partitioner< 1 %
- K Nearest Neighbor< 1 %
- Logistic Regression Predictor< 1 %
- SMOTE< 1 %
- Random Forest Predictor< 1 %
- Column Appender< 1 %
- Column Rename (Regex)< 1 %
- Column Resorter< 1 %
- Row Filter< 1 %
DL Network Executor< 1 %H2O Cross Validation Loop Start< 1 %- Java Edit Variable (simple)< 1 %
- Double Configuration< 1 %
- String Configuration< 1 %
- String Widget< 1 %
Container Input (Variable)< 1 %Flow Variable End IF< 1 %Flow Variable IF Switch< 1 %- Active Branch Inverter< 1 %
- Variable to Table Row< 1 %
- Model Writer< 1 %
- Feature Selection Loop End< 1 %
- k-Means< 1 %
- Scorer< 1 %
- Lag Column< 1 %
- Normalizer (PMML)< 1 %
CASE Switch Data (Start)< 1 %- Delete Files/Folders< 1 %
- Parameter Optimization Loop Start (Table)< 1 %
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