#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import copy from federatedml.param.glm_param import LinearModelParam from federatedml.param.callback_param import CallbackParam from federatedml.param.encrypt_param import EncryptParam from federatedml.param.encrypted_mode_calculation_param import EncryptedModeCalculatorParam from federatedml.param.cross_validation_param import CrossValidationParam from federatedml.param.init_model_param import InitParam from federatedml.param.stepwise_param import StepwiseParam from federatedml.util import consts class PoissonParam(LinearModelParam): """ Parameters used for Poisson Regression. Parameters ---------- penalty : {'L2', 'L1'}, default: 'L2' Penalty method used in Poisson. Please note that, when using encrypted version in HeteroPoisson, 'L1' is not supported. tol : float, default: 1e-4 The tolerance of convergence alpha : float, default: 1.0 Regularization strength coefficient. optimizer : {'rmsprop', 'sgd', 'adam', 'adagrad'}, default: 'rmsprop' Optimize method batch_size : int, default: -1 Batch size when updating model. -1 means use all data in a batch. i.e. Not to use mini-batch strategy. learning_rate : float, default: 0.01 Learning rate max_iter : int, default: 20 The maximum iteration for training. init_param: InitParam object, default: default InitParam object Init param method object. early_stop : str, 'weight_diff', 'diff' or 'abs', default: 'diff' Method used to judge convergence. a) diff： Use difference of loss between two iterations to judge whether converge. b) weight_diff: Use difference between weights of two consecutive iterations c) abs: Use the absolute value of loss to judge whether converge. i.e. if loss < eps, it is converged. exposure_colname: str or None, default: None Name of optional exposure variable in dTable. encrypt_param: EncryptParam object, default: default EncryptParam object encrypt param encrypted_mode_calculator_param: EncryptedModeCalculatorParam object, default: default EncryptedModeCalculatorParam object encrypted mode calculator param cv_param: CrossValidationParam object, default: default CrossValidationParam object cv param stepwise_param: StepwiseParam object, default: default StepwiseParam object stepwise param decay: int or float, default: 1 Decay rate for learning rate. learning rate will follow the following decay schedule. lr = lr0/(1+decay*t) if decay_sqrt is False. If decay_sqrt is True, lr = lr0 / sqrt(1+decay*t) where t is the iter number. decay_sqrt: bool, default: True lr = lr0/(1+decay*t) if decay_sqrt is False, otherwise, lr = lr0 / sqrt(1+decay*t) validation_freqs: int, list, tuple, set, or None validation frequency during training, required when using early stopping. The default value is None, 1 is suggested. You can set it to a number larger than 1 in order to speed up training by skipping validation rounds. When it is larger than 1, a number which is divisible by "max_iter" is recommended, otherwise, you will miss the validation scores of the last training iteration. early_stopping_rounds: int, default: None If positive number specified, at every specified training rounds, program checks for early stopping criteria. Validation_freqs must also be set when using early stopping. metrics: list or None, default: None Specify which metrics to be used when performing evaluation during training process. If metrics have not improved at early_stopping rounds, trianing stops before convergence. If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error'] use_first_metric_only: bool, default: False Indicate whether to use the first metric in `metrics` as the only criterion for early stopping judgement. floating_point_precision: None or integer if not None, use floating_point_precision-bit to speed up calculation, e.g.: convert an x to round(x * 2**floating_point_precision) during Paillier operation, divide the result by 2**floating_point_precision in the end. callback_param: CallbackParam object callback param """ def __init__(self, penalty='L2', tol=1e-4, alpha=1.0, optimizer='rmsprop', batch_size=-1, learning_rate=0.01, init_param=InitParam(), max_iter=20, early_stop='diff', exposure_colname=None, encrypt_param=EncryptParam(), encrypted_mode_calculator_param=EncryptedModeCalculatorParam(), cv_param=CrossValidationParam(), stepwise_param=StepwiseParam(), decay=1, decay_sqrt=True, validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False, floating_point_precision=23, callback_param=CallbackParam()): super(PoissonParam, self).__init__(penalty=penalty, tol=tol, alpha=alpha, optimizer=optimizer, batch_size=batch_size, learning_rate=learning_rate, init_param=init_param, max_iter=max_iter, early_stop=early_stop, cv_param=cv_param, decay=decay, decay_sqrt=decay_sqrt, validation_freqs=validation_freqs, early_stopping_rounds=early_stopping_rounds, metrics=metrics, floating_point_precision=floating_point_precision, encrypt_param=encrypt_param, use_first_metric_only=use_first_metric_only, stepwise_param=stepwise_param, callback_param=callback_param) self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param) self.exposure_colname = exposure_colname def check(self): descr = "poisson_regression_param's " super(PoissonParam, self).check() if self.encrypt_param.method != consts.PAILLIER: raise ValueError( descr + "encrypt method supports 'Paillier' only") if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad']: raise ValueError( descr + "optimizer not supported, optimizer should be" " 'sgd', 'rmsprop', 'adam', or 'adagrad'") if self.exposure_colname is not None: if type(self.exposure_colname).__name__ != "str": raise ValueError( descr + "exposure_colname {} not supported, should be string type".format(self.exposure_colname)) self.encrypted_mode_calculator_param.check() return True