Operations

When creating an agreement to allow a counterparty to make use of your asset in model training, use add_agreement() with the appropriate tripleblind.Operation value for the operation parameter as specified in the table below. For example, to create an agreement for Blind Join, use tripleblind.Operation.BLIND_JOIN.

When creating an agreement to allow a counterparty to infer against your trained model, use tripleblind.Operation.EXECUTE for the operation parameter.

Operation	Agreement	How to use
Blind Join	`BLIND_JOIN` ℹ️ Permissive agreements (without usage restrictions) are not recommended for use with this operation.	Use the `blind_join()` method to join your dataset with one or more dataset assets.
Neural Networks	`BLIND_LEARNING`	Use `create_job()` to train a model, with `BLIND_LEARNING` for the `operation` parameter.
Neural Networks: Split Datasets	`VERTICAL_BLIND_LEARNING`	Use `create_job()` to train a model, with `VERTICAL_BLIND_LEARNING` for the `operation` parameter. ℹ️ Vertically-partitioned records must be in corresponding order across all data sources.
Neural Networks: Vertical Datasets	`PSI_VERTICAL_BLIND_LEARNING`	Use `create_job()` to train a model, with `PSI_VERTICAL_BLIND_LEARNING` for the `operation` parameter.
Blind Match	`PRIVATE_SET_INTERSECTION`	Use the `intersect()` method to carry out a private set intersection between your dataset and one or more additional dataset assets.
Blind Query	`BLIND_QUERY` ℹ️ Permissive agreements (without usage restrictions) are not recommended for use with this operation.	Use `create_job()` to query a dataset asset, with `BLIND_QUERY` for the `operation` parameter.
Blind Report	When using `add_agreement()` to allow a counterparty to run your Blind Report, use `EXECUTE` for the `operation` parameter.	Use `create_job()` to run the report in a process, with the positioned `ReportAsset`‘s UUID for the `operation` parameter.
Blind Sample	`BLIND_SAMPLE`	Use the `get_sample()` method to generate a realistic synthetic sample for a dataset asset.
Blind Stats	`STATS`	Use the `get_statistics()` method to query descriptive statistics against one or more dataset assets.
Blind String Search	`REGEX_COUNT`	Use `create_job()` to obtain counts of text search matches, with `REGEX_COUNT` for the `operation` parameter.
Decision Tree: Vertical Datasets	`PSI_VERTICAL_DECISION_TREE_TRAIN`	Use `create_job()` to train a model, with `PSI_VERTICAL_DECISION_TREE_TRAIN` for the `operation` parameter.
K-Means Clustering: Vertical Datasets	`PSI_VERTICAL_KMEANS_TRAIN`	Use `create_job()` to train a clustering model, with `PSI_VERTICAL_KMEANS_TRAIN` for the `operation` parameter.
NLP - BERT	`BERT_SEQ_CLF_TRAIN`	Use `create_job()` to train your model, with `BERT_SEQ_CLF_TRAIN` for the `operation` parameter.
Outlier Detection	`OUTLIER_DETECTION`	Use the `detect_outlier()` method to identify the indices of outlier values in your dataset asset.
Random Forest	`RANDOM_FOREST_TRAIN`	Use `create_job()` to train a model, with `RANDOM_FOREST_TRAIN` for the `operation` parameter.
Recommender Model	`RECOMMENDER_TRAIN`	Use `create_job()` to train a Recommender Model, with `RECOMMENDER_TRAIN` for the `operation` parameter.
Regression	`REGRESSION`	For training, use the `RegressionModel.train()` method to train a horizontally-partitioned Linear, Logistic, Lasso, or Ridge regression model. For inference, use the `ModelAsset.infer()` method to infer on a trained model.
Regression: Vertical Datasets	`PSI_VERTICAL_REGRESSION_TRAIN`	For training, use the `PSIVerticalRegressionModel.train()` method to identify an overlap of matching records across datasets, and train a linear or logistic regression model on the vertically-partitioned intersection. For inference, use the `ModelAsset.psi_infer()` method to infer on a trained model.
XGBoost	`XGBOOST_TRAIN`	Use the XGBoost `train()`, `predict()`, and `predict_proba()` methods.