use encrypt_tensor api for phe encryptor(#4659)

edit fate_test examples(#5008) replace Intersection with PSI in examples Signed-off-by: Yu Wu <yolandawu131@gmail.com>
FederatedAI · Aug 9, 2023 · 2db064d · 2db064d
1 parent 329387d
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diff --git a/examples/benchmark_quality/linr/fate-linr.py b/examples/benchmark_quality/linr/fate-linr.py
@@ -17,7 +17,7 @@
 import argparse
 
 from fate_client.pipeline import FateFlowPipeline
-from fate_client.pipeline.components.fate import CoordinatedLinR, Intersection
+from fate_client.pipeline.components.fate import CoordinatedLinR, PSI
 from fate_client.pipeline.components.fate import Evaluation
 from fate_client.pipeline.interface import DataWarehouseChannel
 from fate_client.pipeline.utils import test_utils
@@ -43,11 +43,11 @@ def main(config="../../config.yaml", param="./linr_config.yaml", namespace=""):
 
     pipeline = FateFlowPipeline().set_roles(guest=guest, host=host, arbiter=arbiter)
 
-    intersect_0 = Intersection("intersect_0", method="raw")
-    intersect_0.guest.component_setting(input_data=DataWarehouseChannel(name=guest_train_data["name"],
-                                                                        namespace=guest_train_data["namespace"]))
-    intersect_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name=host_train_data["name"],
-                                                                           namespace=host_train_data["namespace"]))
+    psi_0 = PSI("psi_0")
+    psi_0.guest.component_setting(input_data=DataWarehouseChannel(name=guest_train_data["name"],
+                                                                  namespace=guest_train_data["namespace"]))
+    psi_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name=host_train_data["name"],
+                                                                     namespace=host_train_data["namespace"]))
 
     linr_param = {
     }
@@ -63,10 +63,10 @@ def main(config="../../config.yaml", param="./linr_config.yaml", namespace=""):
     }
     linr_param.update(config_param)
     linr_0 = CoordinatedLinR("linr_0",
-                             train_data=intersect_0.outputs["output_data"],
+                             train_data=psi_0.outputs["output_data"],
                              **config_param)
     """linr_1 = CoordinatedLinR("linr_1",
-                             test_data=intersect_0.outputs["output_data"],
+                             test_data=psi_0.outputs["output_data"],
                              input_model=linr_0.outputs["output_model"])"""
 
     evaluation_0 = Evaluation("evaluation_0",
@@ -77,7 +77,7 @@ def main(config="../../config.yaml", param="./linr_config.yaml", namespace=""):
                                        "rmse"],
                               input_data=linr_0.outputs["train_output_data"])
 
-    pipeline.add_task(intersect_0)
+    pipeline.add_task(psi_0)
     pipeline.add_task(linr_0)
     # pipeline.add_task(linr_1)
     pipeline.add_task(evaluation_0)

diff --git a/examples/benchmark_quality/lr/breast_config.yaml b/examples/benchmark_quality/lr/breast_config.yaml
@@ -10,13 +10,13 @@ init_param:
 learning_rate_scheduler:
   method: "constant"
   scheduler_params:
-    factor: 1.0
+    factor: 0.5
     total_iters: 5
 optimizer:
   method: "rmsprop"
   penalty: "l2"
   optimizer_params:
-    lr: 0.12
-  alpha: 0.1
+    lr: 0.15
+  alpha: 0.01
 batch_size: 240
 early_stop: "diff"
diff --git a/examples/benchmark_quality/lr/lr_benchmark.yaml b/examples/benchmark_quality/lr/lr_benchmark.yaml
@@ -179,24 +179,24 @@ data:
     table_name: vehicle_scale_hetero_host
     namespace: experiment
     role: host_0
-#hetero_lr-binary-0-breast:
-#  local:
-#    script: "./sklearn-lr-binary.py"
-#    conf: "./breast_lr_sklearn_config.yaml"
-#  FATE-hetero-lr:
-#    script: "./pipeline-lr-binary.py"
-#    conf: "./breast_config.yaml"
-#  compare_setting:
-#    relative_tol: 0.01
-#hetero_lr-binary-1-default-credit:
-#  local:
-#    script: "./sklearn-lr-binary.py"
-#    conf: "./default_credit_lr_sklearn_config.yaml"
-#  FATE-hetero-lr:
-#    script: "./pipeline-lr-binary.py"
-#    conf: "./default_credit_config.yaml"
-#  compare_setting:
-#    relative_tol: 0.01
+hetero_lr-binary-0-breast:
+  local:
+    script: "./sklearn-lr-binary.py"
+    conf: "./breast_lr_sklearn_config.yaml"
+  FATE-hetero-lr:
+    script: "./pipeline-lr-binary.py"
+    conf: "./breast_config.yaml"
+  compare_setting:
+    relative_tol: 0.01
+hetero_lr-binary-1-default-credit:
+  local:
+    script: "./sklearn-lr-binary.py"
+    conf: "./default_credit_lr_sklearn_config.yaml"
+  FATE-hetero-lr:
+    script: "./pipeline-lr-binary.py"
+    conf: "./default_credit_config.yaml"
+  compare_setting:
+    relative_tol: 0.01
 hetero_lr-binary-2-epsilon-5k:
   local:
     script: "./sklearn-lr-binary.py"
@@ -206,21 +206,21 @@ hetero_lr-binary-2-epsilon-5k:
     conf: "./epsilon_5k_config.yaml"
   compare_setting:
     relative_tol: 0.01
-#hetero_lr-binary-3-give-credit:
-#  local:
-#    script: "./sklearn-lr-binary.py"
-#    conf: "./give_credit_lr_sklearn_config.yaml"
-#  FATE-hetero-lr:
-#    script: "./pipeline-lr-binary.py"
-#    conf: "./give_credit_config.yaml"
-#  compare_setting:
-#    relative_tol: 0.01
-#multi-vehicle:
-#  local:
-#    script: "./sklearn-lr-multi.py"
-#    conf: "./vehicle_lr_sklearn_config.yaml"
-#  FATE-hetero-lr:
-#    script: "./pipeline-lr-multi.py"
-#    conf: "./vehicle_config.yaml"
-#  compare_setting:
-#    relative_tol: 0.01
+hetero_lr-binary-3-give-credit:
+  local:
+    script: "./sklearn-lr-binary.py"
+    conf: "./give_credit_lr_sklearn_config.yaml"
+  FATE-hetero-lr:
+    script: "./pipeline-lr-binary.py"
+    conf: "./give_credit_config.yaml"
+  compare_setting:
+    relative_tol: 0.01
+multi-vehicle:
+  local:
+    script: "./sklearn-lr-multi.py"
+    conf: "./vehicle_lr_sklearn_config.yaml"
+  FATE-hetero-lr:
+    script: "./pipeline-lr-multi.py"
+    conf: "./vehicle_config.yaml"
+  compare_setting:
+    relative_tol: 0.01
diff --git a/examples/benchmark_quality/lr/pipeline-lr-binary.py b/examples/benchmark_quality/lr/pipeline-lr-binary.py
@@ -17,7 +17,7 @@
 import argparse
 
 from fate_client.pipeline import FateFlowPipeline
-from fate_client.pipeline.components.fate import CoordinatedLR, Intersection
+from fate_client.pipeline.components.fate import CoordinatedLR, PSI
 from fate_client.pipeline.components.fate import Evaluation
 from fate_client.pipeline.interface import DataWarehouseChannel
 from fate_client.pipeline.utils import test_utils
@@ -45,11 +45,11 @@ def main(config="../../config.yaml", param="./breast_config.yaml", namespace="")
     host_train_data = {"name": host_data_table, "namespace": f"experiment{namespace}"}
     pipeline = FateFlowPipeline().set_roles(guest=guest, host=host, arbiter=arbiter)
 
-    intersect_0 = Intersection("intersect_0", method="raw")
-    intersect_0.guest.component_setting(input_data=DataWarehouseChannel(name=guest_train_data["name"],
-                                                                        namespace=guest_train_data["namespace"]))
-    intersect_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name=host_train_data["name"],
-                                                                           namespace=host_train_data["namespace"]))
+    psi_0 = PSI("psi_0")
+    psi_0.guest.component_setting(input_data=DataWarehouseChannel(name=guest_train_data["name"],
+                                                                  namespace=guest_train_data["namespace"]))
+    psi_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name=host_train_data["name"],
+                                                                     namespace=host_train_data["namespace"]))
 
     lr_param = {
     }
@@ -65,10 +65,10 @@ def main(config="../../config.yaml", param="./breast_config.yaml", namespace="")
     }
     lr_param.update(config_param)
     lr_0 = CoordinatedLR("lr_0",
-                         train_data=intersect_0.outputs["output_data"],
+                         train_data=psi_0.outputs["output_data"],
                          **lr_param)
     lr_1 = CoordinatedLR("lr_1",
-                         test_data=intersect_0.outputs["output_data"],
+                         test_data=psi_0.outputs["output_data"],
                          input_model=lr_0.outputs["output_model"])
 
     evaluation_0 = Evaluation("evaluation_0",
@@ -77,7 +77,7 @@ def main(config="../../config.yaml", param="./breast_config.yaml", namespace="")
                               metrics=["auc", "binary_precision", "binary_accuracy", "binary_recall"],
                               input_data=lr_0.outputs["train_output_data"])
 
-    pipeline.add_task(intersect_0)
+    pipeline.add_task(psi_0)
     pipeline.add_task(lr_0)
     pipeline.add_task(lr_1)
     pipeline.add_task(evaluation_0)

diff --git a/examples/benchmark_quality/lr/pipeline-lr-multi.py b/examples/benchmark_quality/lr/pipeline-lr-multi.py
@@ -17,7 +17,7 @@
 import argparse
 
 from fate_client.pipeline import FateFlowPipeline
-from fate_client.pipeline.components.fate import CoordinatedLR, Intersection
+from fate_client.pipeline.components.fate import CoordinatedLR, PSI
 from fate_client.pipeline.components.fate import Evaluation
 from fate_client.pipeline.interface import DataWarehouseChannel
 from fate_client.pipeline.utils import test_utils
@@ -44,11 +44,11 @@ def main(config="../../config.yaml", param="./vehicle_config.yaml", namespace=""
     host_train_data = {"name": host_data_table, "namespace": f"experiment{namespace}"}
     pipeline = FateFlowPipeline().set_roles(guest=guest, host=host, arbiter=arbiter)
 
-    intersect_0 = Intersection("intersect_0", method="raw")
-    intersect_0.guest.component_setting(input_data=DataWarehouseChannel(name=guest_train_data["name"],
-                                                                        namespace=guest_train_data["namespace"]))
-    intersect_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name=host_train_data["name"],
-                                                                           namespace=host_train_data["namespace"]))
+    psi_0 = PSI("psi_0")
+    psi_0.guest.component_setting(input_data=DataWarehouseChannel(name=guest_train_data["name"],
+                                                                  namespace=guest_train_data["namespace"]))
+    psi_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name=host_train_data["name"],
+                                                                     namespace=host_train_data["namespace"]))
 
     lr_param = {
     }
@@ -64,15 +64,15 @@ def main(config="../../config.yaml", param="./vehicle_config.yaml", namespace=""
     }
     lr_param.update(config_param)
     lr_0 = CoordinatedLR("lr_0",
-                         train_data=intersect_0.outputs["output_data"],
+                         train_data=psi_0.outputs["output_data"],
                          **config_param)
     lr_1 = CoordinatedLR("lr_1",
-                         test_data=intersect_0.outputs["output_data"],
+                         test_data=psi_0.outputs["output_data"],
                          input_model=lr_0.outputs["output_model"])
 
     evaluation_0 = Evaluation('evaluation_0',
                               metrics=['multi_recall', 'multi_accuracy', 'multi_precision'])
-    pipeline.add_task(intersect_0)
+    pipeline.add_task(psi_0)
     pipeline.add_task(lr_0)
     pipeline.add_task(lr_1)
     pipeline.add_task(evaluation_0)

diff --git a/examples/pipeline/coordinated_lr/test_lr_sid.py b/examples/pipeline/coordinated_lr/test_lr_sid.py
@@ -16,7 +16,7 @@
 import argparse
 
 from fate_client.pipeline import FateFlowPipeline
-from fate_client.pipeline.components.fate import CoordinatedLR, Intersection
+from fate_client.pipeline.components.fate import CoordinatedLR, PSI
 from fate_client.pipeline.components.fate import Evaluation
 from fate_client.pipeline.interface import DataWarehouseChannel
 from fate_client.pipeline.utils import test_utils
@@ -36,42 +36,43 @@ def main(config="./config.yaml", namespace=""):
     if config.timeout:
         pipeline.conf.set("timeout", config.timeout)
 
-    intersect_0 = Intersection("intersect_0", method="raw")
-    intersect_0.guest.component_setting(input_data=DataWarehouseChannel(name="breast_hetero_guest",
-                                                                        namespace=f"experiment{namespace}"))
-    intersect_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name="breast_hetero_host",
-                                                                           namespace=f"experiment{namespace}"))
+    psi_0 = PSI("psi_0")
+    psi_0.guest.component_setting(input_data=DataWarehouseChannel(name="breast_hetero_guest",
+                                                                  namespace=f"experiment{namespace}"))
+    psi_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name="breast_hetero_host",
+                                                                     namespace=f"experiment{namespace}"))
     lr_0 = CoordinatedLR("lr_0",
-                         epochs=4,
+                         epochs=10,
                          batch_size=None,
-                         optimizer={"method": "SGD", "optimizer_params": {"lr": 0.01}},
-                         init_param={"fit_intercept": True, "method": "zeros"},
-                         train_data=intersect_0.outputs["output_data"],
-                         learning_rate_scheduler={"method": "constant", "scheduler_params": {"factor": 1.0,
-                                                                                             "total_iters": 100}})
+                         optimizer={"method": "SGD", "optimizer_params": {"lr": 0.21}},
+                         init_param={"fit_intercept": True, "method": "random_uniform"},
+                         train_data=psi_0.outputs["output_data"],
+                         learning_rate_scheduler={"method": "linear", "scheduler_params": {"start_factor": 0.7,
+                                                                                           "total_iters": 100}})
 
     evaluation_0 = Evaluation("evaluation_0",
                               label_column_name="y",
                               runtime_roles=["guest"],
                               default_eval_setting="binary",
                               input_data=lr_0.outputs["train_output_data"])
 
-    pipeline.add_task(intersect_0)
+    pipeline.add_task(psi_0)
     pipeline.add_task(lr_0)
+    pipeline.add_task(evaluation_0)
 
     pipeline.compile()
     print(pipeline.get_dag())
     pipeline.fit()
 
-    pipeline.deploy([intersect_0, lr_0])
+    pipeline.deploy([psi_0, lr_0])
 
     predict_pipeline = FateFlowPipeline()
 
     deployed_pipeline = pipeline.get_deployed_pipeline()
-    deployed_pipeline.intersect_0.guest.component_setting(
+    deployed_pipeline.psi_0.guest.component_setting(
         input_data=DataWarehouseChannel(name="breast_hetero_guest",
                                         namespace=f"experiment{namespace}"))
-    deployed_pipeline.intersect_0.hosts[0].component_setting(
+    deployed_pipeline.psi_0.hosts[0].component_setting(
         input_data=DataWarehouseChannel(name="breast_hetero_host",
                                         namespace=f"experiment{namespace}"))
 

diff --git a/examples/pipeline/coordinated_lr/test_lr_sid_cv.py b/examples/pipeline/coordinated_lr/test_lr_sid_cv.py
@@ -16,7 +16,7 @@
 import argparse
 
 from fate_client.pipeline import FateFlowPipeline
-from fate_client.pipeline.components.fate import CoordinatedLR, Intersection
+from fate_client.pipeline.components.fate import CoordinatedLR, PSI
 from fate_client.pipeline.interface import DataWarehouseChannel
 from fate_client.pipeline.utils import test_utils
 
@@ -34,20 +34,20 @@ def main(config="./config.yaml", namespace=""):
     if config.timeout:
         pipeline.conf.set("timeout", config.timeout)
 
-    intersect_0 = Intersection("intersect_0", method="raw")
-    intersect_0.guest.component_setting(input_data=DataWarehouseChannel(name="breast_hetero_guest",
-                                                                        namespace=f"experiment{namespace}"))
-    intersect_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name="breast_hetero_host",
-                                                                           namespace=f"experiment{namespace}"))
+    psi_0 = PSI("psi_0")
+    psi_0.guest.component_setting(input_data=DataWarehouseChannel(name="breast_hetero_guest",
+                                                                  namespace=f"experiment{namespace}"))
+    psi_0.hosts[0].component_setting(input_data=DataWarehouseChannel(name="breast_hetero_host",
+                                                                     namespace=f"experiment{namespace}"))
     lr_0 = CoordinatedLR("lr_0",
                          epochs=2,
                          batch_size=100,
                          optimizer={"method": "sgd", "optimizer_params": {"lr": 0.01}},
                          init_param={"fit_intercept": True},
-                         cv_data=intersect_0.outputs["output_data"],
+                         cv_data=psi_0.outputs["output_data"],
                          cv_param={"n_splits": 3})
 
-    pipeline.add_task(intersect_0)
+    pipeline.add_task(psi_0)
     pipeline.add_task(lr_0)
     pipeline.compile()
     print(pipeline.get_dag())