Added SM2 Elliptic Curve for psi based on ecdh

doc/tutorial/pipeline/pipline_hetero_secureboost.py

@@ -0,0 +1,83 @@
+# 连接FATE flow
+# !pipeline init --ip 127.0.0.1 --port 9380
+from pipeline.backend.pipeline import PipeLine
+from pipeline.component import Reader, DataTransform, Intersection, HeteroSecureBoost, Evaluation
+from pipeline.interface import Data
+def train_pipeline():
+    # 创建pipeline实例
+    pipeline = PipeLine() \
+        .set_initiator(role='guest', party_id=9999) \
+        .set_roles(guest=9999, host=10000)
+    # load data
+    reader_0 = Reader(name="reader_0")
+    # set guest parameter
+    reader_0.get_party_instance(role='guest', party_id=9999).component_param(
+        table={"name": "breast_hetero_guest", "namespace": "experiment"})
+    # set host parameter
+    reader_0.get_party_instance(role='host', party_id=10000).component_param(
+        table={"name": "breast_hetero_host", "namespace": "experiment"})
+    # 添加DataTransform组件以将原始数据解析到数据实例中
+    data_transform_0 = DataTransform(name="data_transform_0")
+    # set guest parameter
+    data_transform_0.get_party_instance(role='guest', party_id=9999).component_param(
+        with_label=True)
+    data_transform_0.get_party_instance(role='host', party_id=[10000]).component_param(
+        with_label=False)
+    # 添加求交组件以执行纵向场景的PSI
+    intersect_0 = Intersection(name="intersect_0")
+    # 定义HeteroSecureBoost组件。将为所有相关方设置以下参数。
+    hetero_secureboost_0 = HeteroSecureBoost(name="hetero_secureboost_0",
+                                             num_trees=5,
+                                             bin_num=16,
+                                             task_type="classification",
+                                             objective_param={"objective": "cross_entropy"},
+                                             encrypt_param={"method": "paillier"},
+                                             tree_param={"max_depth": 3})
+    # 模型评估组件
+    evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
+    # 将组件添加到管道，按执行顺序
+    # -data_transform_0 用 reader_0的输出数据
+    # -intersect_0 用 data_transform_0的输出数据
+    # -heter_securebost_0用intersect_0的输出数据
+    # -评估0用纵向securebost0对训练数据的预测结果
+    pipeline.add_component(reader_0)
+    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
+    pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
+    pipeline.add_component(hetero_secureboost_0, data=Data(train_data=intersect_0.output.data))
+    pipeline.add_component(evaluation_0, data=Data(data=hetero_secureboost_0.output.data))
+    pipeline.compile()
+    # submit pipeline
+    pipeline.fit()
+    # 一旦完成了训练，就可以将训练后的模型用于预测。（可选）保存经过训练的管道以备将来使用。
+    # 模型保存
+    pipeline.dump("hetero_secureboost_pipeline_saved.pkl")
+def predict_pipeline():
+    # 模型预测
+    # 首先，从训练管道部署所需组件
+    pipeline = PipeLine.load_model_from_file('hetero_secureboost_pipeline_saved.pkl')
+    pipeline.deploy_component([pipeline.data_transform_0, pipeline.intersect_0, pipeline.hetero_secureboost_0])
+    # 定义用于读取预测数据的新reader组件
+    reader_1 = Reader(name="reader_1")
+    reader_1.get_party_instance(role="guest", party_id=9999).component_param(table={"name": "breast_hetero_guest", "namespace": "experiment"})
+    reader_1.get_party_instance(role="host", party_id=10000).component_param(table={"name": "breast_hetero_host", "namespace": "experiment"})
+    # （可选）定义新的评估组件。
+    evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
+    # 按执行顺序添加组件到预测管道
+    predict_pipeline = PipeLine()
+    predict_pipeline.add_component(reader_1)\
+                    .add_component(pipeline,
+                                   data=Data(predict_input={pipeline.data_transform_0.input.data: reader_1.output.data}))\
+                    .add_component(evaluation_0, data=Data(data=pipeline.hetero_secureboost_0.output.data))
+    # 执行预测
+    predict_pipeline.predict()
+
+if __name__=='__main__':
+    # train_pipeline()
+    predict_pipeline()
+
+
+
+
+
+
+

doc/tutorial/pipline_upload_csv.py

@@ -0,0 +1,30 @@
+# 连接FATE flow
+# !pipeline init --ip 127.0.0.1 --port 9380
+from pipeline.backend.pipeline import PipeLine
+# 创建一个pipeline实例
+pipeline_upload = PipeLine().set_initiator(role='guest', party_id=9999).set_roles(guest=9999)
+partition = 4
+# 定义将在FATE作业配置中使用的表名和命名空间
+dense_data_guest = {"name": "breast_hetero_guest", "namespace": f"experiment"}
+dense_data_host = {"name": "breast_hetero_host", "namespace": f"experiment"}
+tag_data = {"name": "breast_hetero_host", "namespace": f"experiment"}
+# 添加要上传的数据
+import os
+
+data_base = "/root/PycharmProjects/FATE/"
+pipeline_upload.add_upload_data(file=os.path.join(data_base, "examples/data/breast_hetero_guest.csv"),
+                                table_name=dense_data_guest["name"],             # table name
+                                namespace=dense_data_guest["namespace"],         # namespace
+                                head=1, partition=partition)               # data info
+
+pipeline_upload.add_upload_data(file=os.path.join(data_base, "examples/data/breast_hetero_host.csv"),
+                                table_name=dense_data_host["name"],
+                                namespace=dense_data_host["namespace"],
+                                head=1, partition=partition)
+
+pipeline_upload.add_upload_data(file=os.path.join(data_base, "examples/data/breast_hetero_host.csv"),
+                                table_name=tag_data["name"],
+                                namespace=tag_data["namespace"],
+                                head=1, partition=partition)
+# 上传数据
+pipeline_upload.upload(drop=1)

python/fate_client/pipeline/param/consts.py

@@ -344,7 +344,7 @@
 
 # curve names
 CURVE25519 = 'curve25519'
-
+SM2='sm2'
 # positive unlabeled
 PROBABILITY = "probability"
 QUANTITY = "quantity"

python/federatedml/ensemble/secureboost/steteless_hetero_secureboost/__init__.py

@@ -0,0 +1,11 @@
+# -*-coding: Utf-8 -*-
+'''
+====================
+@File : __init__.py .py
+author: circle
+Time：2023/12/19 
+@Desc:
+=====================
+'''
+if __name__ == '__main__':
+    print('Python')