Vertical Poisson Regression

Introduction

The realization of vertical poisson regression algorithm.

Parameter List

identity: str Federated identity of the party, should be one of label_trainer, trainer or assist trainer.

model_info:

  • name: str Model name, should be vertical_poisson_regression.

input:
  • trainset:

    • type: str Train dataset type, support csv.

    • path: str If type is csv, folder path of train dataset.

    • name: bool If type is csv, file name of train dataset.

    • has_id: bool If type is csv, whether dataset has id column.

    • has_label: bool If type is csv, whether dataset has label column.

  • valset:

    • type: str Validation dataset type, support csv.

    • path: str If type is csv, folder path of validation dataset.

    • name: bool If type is csv, file name of validation dataset.

    • has_id: bool If type is csv, whether dataset has id column.

    • has_label: bool If type is csv, whether dataset has label column.

  • pretrained_model: map

    • path: str Pretrained model path.

    • name: str Pretrained model name.

output:

  • path: str Folder path of output.

  • model:

    • name: str File name of output model.

  • metric_train:

    • name: str File name of trainset metrics.

  • metric_val:

    • name: str File name of valset metrics.

  • prediction_train:

    • name: str File name of trainset prediction.

  • prediction_val:

    • name: str File name of valset prediction.

  • feature_importance:

    • name: str File name of feature importance table.

train_info:
  • interaction_params:

    • save_frequency: int Frequency to save model, set to -1 for not saving model.

    • write_training_prediction: bool Whether to save the predictions on train dataset.

    • echo_training_metrics: bool Whether to output metrics on train dataset.

    • write_validation_prediction: bool Whether to save predictions on validation dataset.

  • train_params:

    • global_epoch: int Global training epoch.

    • batch_size: int Batch size of samples in global process.

    • encryption: map Can choose either “ckks” or “paillier”.
      • ckks: map

        • poly_modulus_degree: int Polynomial modulus degree.

        • coeff_mod_bit_sizes: list Coefficient modulus sizes.

        • global_scale_bit_size: int Global scale factor bit size.

      • paillier:

        • key_bit_size: int Bit length of paillier key, recommend to be greater than or equal to 2048.

        • precision: int Precision.

        • djn_on: bool Whether to use djn method to generate key pair.

        • parallelize_on: bool Whether to use multicore for computing.

    • optimizer:

      • lr: float Learning rate.

      • p: int Regularization parameter, “0”/”1”/”2” stands for no regularization/l1 regularization/l2 regularization respectively.

      • alpha: float Penalty coefficient.

    • metric: map Metrics to output, all the keys are optional.

      • mse: map The mean squared error , support {}.

      • mape: map The mean absolute percentage error, support {}.

      • mae: map Mean absolute error, support {}.

      • rmse: map Root mean square error, support {}.

    • early_stopping:

      • key: str Variable to be monitored.

      • patience: int Number of epochs with no improvement after which training will be stopped.

      • delta: int Minimum change of the key to qualify as an improvement.

    • random_seed: int Random seed, accept None.