联邦学习fate框架
做kmeans使用电力用户数据
网上找不到具体使用方法
遇到了很多问题
可以教学吗 有偿
python
from federatedml.util import abnormal_detection
from federatedml.param import KMeansParam
from federatedml.statistic import data_overview
from federatedml.model_selection import MiniBatch
from federatedml.feature.instance import Instance
from federatedml.feature.sparse_vector import SparseVector
from federatedml.util import LOGGER
from fate_flow.entity import MetricMeta, MetricType
from fate_flow.utils.metric_utils import magic_check
import numpy as np
class KMeansGuest(object):
def __init__(self):
self.instance = Instance()
self.sparse_vector = SparseVector()
self.logger = LOGGER
self.model_param = None
self.batch_size = 0
self.need_one_vs_rest = False
self.work_mode = None
self.model = None
self.metric_summary = []
self.metric_type = MetricType.BINARY
def _init_model(self, params):
self.model_param = KMeansParam()
self.model_param.max_iter = params.get('max_iter', 20)
self.model_param.eps = params.get('eps', 1e-4)
self.model_param.init_param.method = params.get('init_method', 'random')
self.model_param.init_param.init_cluster_method = params.get('init_cluster_method', 'kmeans++')
self.model_param.center_init_method = params.get('center_init_method', 'mean')
self.model_param.need_cluster_details = params.get('need_cluster_details', False)
self.model_param.n_clusters = params.get('n_clusters', 2)
self.batch_size = params.get('batch_size', -1)
self.need_one_vs_rest = params.get('need_one_vs_rest', False)
def _check_label(self, data_label):
abnormal_detection.empty_warning(data_label)
abnormal_detection.label_check(data_label)
def _parse_need_one_vs_rest(self, data_instance):
if self.need_one_vs_rest:
if data_instance.label == 1:
data_instance.label = self.model_param.n_clusters
def _preprocess(self, data_instances):
self._check_label(data_instances)
if self.batch_size == -1:
self.batch_size = len(data_instances)
if self.need_one_vs_rest:
max_label = -1
for inst in data_instances:
if inst.label > max_label:
max_label = inst.label
self.model_param.n_clusters = max(max_label, self.model_param.n_clusters)
data_instances = MiniBatch.use_mini_batch(data_instances, self.batch_size)
return data_instances
def fit(self, data_instances, validate_data=None):
self.work_mode = data_instances.work_mode
self._init_model(data_instances.schema)
data_instances = self._preprocess(data_instances)
self.logger.info("In fit method, schema is : {}".format(data_instances.schema))
mapper_output = data_instances.mapValues(lambda v: self.instance.parse(v))
self.model = self.model_param.init_param.method_obj.cluster(
self.model_param, mapper_output, self.work_mode
)
self.model.init_and_broadcast(self.model_param, self.work_mode)
iteration_num = self.model_param.max_iter
centers = self.model.get_center_vectors()
self.logger.info("Init centers are : {}".format(centers))
if self.model_param.need_cluster_details:
self.metric_summary.append(self.model.summary())
while self.model.get_current_iter() < iteration_num:
self.logger.info("CUR ITERATION COUNT: {}".format(self.model.get_current_iter()))
self.model.fit(mapper_output)
self.model.update_center(mapper_output)
self.logger.info("UPDATED CENTER: {}".format(self.model.get_center_vectors()))
if self.model_param.need_cluster_details:
self.metric_summary.append(self.model.summary())
return self
def predict(self, data_instace):
pass
def evaluate(self, pred_res, data_instace=None):
pass
def save_model(self):
pass
def load_model(self, model_dict):
pass
class KMeansHost(KMeansGuest):
"""
Host K-Means model
"""
def __init__(self):
super(KMeansHost, self).__init__()
def fit(self, data_instances, validate_data=None):
self.work_mode = data_instances.work_mode
self._init_model(data_instances.schema)
abnormal_detection.empty_check(data_instances)
data_instances = self._preprocess(data_instances)
LOGGER.debug(f"data instances schema is : {data_instances.schema}")
self.cluster_result = None
if self.work_mode == 'standalone':
pass
elif self.model_param.need_one_vs_rest:
self.one_vs_rest_obj = OneVsRest()
data_instances = self.one_vs_rest_obj.fit_transform(data_instances)
else:
self.logger.info("Host will start to fit the model")
center_init_obj = KMeansCenterInitObj(self.model_param)
centers = center_init_obj.initialize(data_instances)
self.model = self.model_param.init_param.method_obj.cluster(
self.model_param, centers, self.work_mode
)
self.model.init_and_broadcast(self.model_param, self.work_mode)
ITER_NUM = self.model_param.max_iter
while self.model.get_current_iter() < ITER_NUM:
self.logger.info("CUR ITERATION COUNT: {}".format(self.model.get_current_iter()))
self.model.fit(data_instances)
self.model.update_center(data_instances)
self.logger.info("CENTER RESULT: {}".format(self.model.get_center_vectors()))
self.cluster_result = self.predict(data_instances)
return self
def predict(self, data_instances):
if self.model is None:
raise ValueError("model is none, please fit first")
return self.model.predict(data_instances)
def save_model(self):
model_dict = self.model.export_model()
return model_dict
def load_model(self, model_dict):
self.model = self.model_param.init_param.method_obj.load_model(model_dict)
return self
了解FATE框架
首先，需要了解FATE框架的基本概念、原理和组成部分。可以通过阅读FATE官方文档或者参考相关书籍来获得更多信息。
准备数据集
在进行K-means算法实验之前，需要准备电力用户数据集，并根据FATE框架的要求进行格式化和处理。
配置环境和运行FATE
接下来，需要配置好FATE框架的运行环境，并按照指南启动FATE服务器和客户端。可以通过命令行界面或者Web界面与FATE系统进行交互。
实现K-means算法
在FATE框架中，可以使用Federated Learning API实现K-means算法。具体步骤包括：定义模型、训练模型、评估模型、保存模型等。在实现过程中，需要考虑数据隐私性和安全性，例如采用差分隐私技术等手段来保护数据隐私。