Recipes Settings¶
included_transformers
¶
Include Specific Transformers
Select the transformer(s) that you want to use in the experiment. Use the Check All/Uncheck All button to quickly add or remove all transfomers at once. Note: If you uncheck all transformers so that none is selected, Driverless AI will ignore this and will use the default list of transformers for that experiment. This list of transformers will vary for each experiment.
The equivalent config.toml parameter is included_transformers
.
included_models
¶
Include Specific Models
Specify the types of models that you want Driverless AI to build in the experiment. This list includes natively supported algorithms and models added with custom recipes.
Note: The ImbalancedLightGBM and ImbalancedXGBoostGBM models are closely tied with the imbalance_sampling_method option. Specifically:
If the ImbalancedLightGBM and/or ImbalancedXGBoostGBM models are ENABLED and the imbalance_sampling_method is ENABLED (set to a value other than off), then Driverless AI will check your target imbalance fraction. If the target fraction proves to be above the allowed imbalance threshold, then sampling will be triggered.
If the ImbalancedLightGBM and/or ImbalancedXGBoostGBM models are DISABLED and the imbalance_sampling_method option is ENABLED, then no special sampling technique will be performed.
If the ImbalancedLightGBM and/or ImbalancedXGBoostGBM models are ENABLED and the imbalance_sampling_method is DISABLED, sampling will not be used, and these imbalanced models will be disabled.
included_scorers
¶
Include Specific Scorers
Specify the scorer(s) that you want Driverless AI to include when running the experiment.
included_pretransformers
¶
Include Specific Preprocessing Transformers
Specify which transformers to use for preprocessing before other transformers are activated. Preprocessing transformers can take any original features and output arbitrary features that are used by the normal layer of transformers.
Notes:
Preprocessing transformers and all other layers of transformers are part of the Python and (if applicable) MOJO scoring packages.
Any custom transformer recipe or native DAI transformer can be used as a preprocessing transformer. For example, a preprocessing transformer can perform interactions, string concatenations, or date extractions as a preprocessing step before the next layer of Date and DateTime transformations are performed.
- Caveats:
one cannot currently do a time-series experiment on a time_column that hasn’t yet been made (setup of experiment only knows about original data, not transformed). However, one can use a run-time data recipe to (e.g.) convert a float date-time into string date-time, and this will be used by Driverless AIs Date and DateTime transformers as well as auto-detection of time series.
in order to do a time series experiment with the GUI/client auto-selecting groups, periods, etc. the dataset must have time column and groups prepared ahead of experiment by user or via a one-time data recipe.
The equivalent config.toml parameter is included_pretransformers
.
num_pipeline_layers
¶
Number of Pipeline Layers
Specify the number of pipeline layers. This value defaults to 1. The equivalent config.toml parameter is num_pipeline_layers
.
Note: This does not include the preprocessing layer specified by the included_pretransformers expert setting.
included_datas
¶
Include Specific Data Recipes During Experiment
Specify whether to include specific data recipes during the experiment. Avoids need for separate data preparation step, builds data preparation within experiment and within python scoring package. But Mojo will require data preparation applied before making predictions.
The equivalent config.toml parameter is included_datas
.
included_individuals
¶
Include Specific Individuals
In Driverless AI, every completed experiment automatically generates Python code for the experiment that corresponds to the individual(s) used to build the final model. You can edit this auto-generated Python code offline and upload it as a recipe, or edit and save it using the built-in custom recipe management editor. This feature gives you code-first access to a significant portion of DAI’s internal transformer and model generation process.
This expert setting lets you do one of the following:
Leave this field empty to have all individuals be freshly generated and treated by DAI’s AutoML as a container of model and transformer choices.
Select recipe display names of custom individuals through the UI. If the number of included custom individuals is less than DAI needs, then the remaining individuals are freshly generated.
The equivalent config.toml parameter is included_individuals
. For more information, see Custom Individual Recipe.
threshold_scorer
¶
Scorer to Optimize Threshold to Be Used in Other Confusion-Matrix Based Scorers (For Binary Classification)
Specify the scorer used to optimize the binary probability threshold that is being used in related Confusion Matrix based scorers such as Precision, Recall, FalsePositiveRate, FalseDiscoveryRate, FalseOmissionRate, TrueNegativeRate, FalseNegativeRate, and NegativePredictiveValue. Select from the following:
Auto (Default): Use this option to sync the threshold scorer with the scorer used for the experiment. If this is not possible, F1 is used.
F05 More weight on precision, less weight on recall.
F1: Equal weight on precision and recall.
F2: Less weight on precision, more weight on recall.
MCC: Use this option when all classes are equally important.
prob_add_genes
¶
Probability to Add Transformers
Specify the unnormalized probability to add genes or instances of transformers with specific attributes. If no genes can be added, other mutations are attempted. This value defaults to 0.5.
prob_addbest_genes
¶
Probability to Add Best Shared Transformers
Specify the unnormalized probability to add genes or instances of transformers with specific attributes that have shown to be beneficial to other individuals within the population. This value defaults to 0.5.
prob_prune_genes
¶
Probability to Prune Transformers
Specify the unnormalized probability to prune genes or instances of transformers with specific attributes. This value defaults to 0.5.
prob_perturb_xgb
¶
Probability to Mutate Model Parameters
Specify the unnormalized probability to change model hyper parameters. This value defaults to 0.25.
prob_prune_by_features
¶
Probability to Prune Weak Features
Specify the unnormalized probability to prune features that have low variable importance instead of pruning entire instances of genes/transformers. This value defaults to 0.25.
skip_transformer_failures
¶
Whether to Skip Failures of Transformers
Specify whether to avoid failed transformers. This is enabled by default.
skip_model_failures
¶
Whether to Skip Failures of Models
Specify whether to avoid failed models. Failures are logged according to the specified level for logging skipped failures. This is enabled by default.
detailed_skip_failure_messages_level
¶
Level to Log for Skipped Failures
Specify one of the following levels for the verbosity of log failure messages for skipped transformers or models:
0 = Log simple message
1 = Log code line plus message (Default)
2 = Log detailed stack traces
notify_failures
¶
Whether to Notify About Failures of Transformers or Models or Other Recipe Failures
Specify whether to display notifications in the GUI about recipe failures. This is enabled by default.
The equivalent config.toml parameter is notify_failures
.
acceptance_test_timeout
¶
Timeout in Minutes for Testing Acceptance of Each Recipe
Specify the number of minutes to wait until a recipe’s acceptance testing is aborted. A recipe is rejected if acceptance testing is enabled and it times out. This value defaults to 20.0.