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
⚠️Permissive agreements (without usage restrictions) are not recommended for 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.
ℹ️Vertically-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
⚠️Permissive agreements (without usage restrictions) are not recommended for 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.
|
Natural Language Processing | NLP_TRAIN
|
Use create_job() to train your model, with NLP_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.
|