[pep8] format

Signed-off-by: weiwee <[email protected]>

.flake8

@@ -1,18 +1,7 @@
 [flake8]
-select = B,C,E,F,P,T4,W,B9
 max-line-length = 120
-# C408 ignored because we like the dict keyword argument syntax
-# E501 is not flexible enough, we're using B950 instead
 ignore =
-    E203,E305,E402,E501,E721,E741,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,
-    # shebang has extra meaning in fbcode lints, so I think it's not worth trying
-    # to line this up with executable bit
-    EXE001,
-    # these ignores are from flake8-bugbear; please fix!
-    B007,B008,
-    # these ignores are from flake8-comprehensions; please fix!
-    C400,C401,C402,C403,C404,C405,C407,C411,C413,C414,C415
-optional-ascii-coding = True
+    E402
 exclude =
     ./.git,
     ./fateflow,

doc/mkdocs/hook.py

@@ -121,7 +121,7 @@ def sub(match):
     if url.endswith(".git"):
         url = url[:-4]
     GITHUB_REPO = f"{url}/tree/{sha}"
-except:
+except BaseException:
     GITHUB_REPO = "https://github.com/FederatedAI/FATE/tree/master"
 
 _DIR_URL_REGEX = re.compile(

examples/__init__.py

@@ -14,4 +14,4 @@
 #  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.
+#  limitations under the License.

examples/benchmark_quality/hetero_fast_sbt/fate-fast-sbt.py

@@ -43,7 +43,6 @@ def main(config="../../config.yaml", param="./xgb_config_binary.yaml", namespace
     guest = parties.guest[0]
     host = parties.host[0]
 
-
     # data sets
     guest_train_data = {"name": param['data_guest_train'], "namespace": f"experiment{namespace}"}
     host_train_data = {"name": param['data_host_train'], "namespace": f"experiment{namespace}"}

examples/benchmark_quality/hetero_fast_sbt/gbdt-binary.py

@@ -53,7 +53,7 @@ def main(config="../../config.yaml", param="./gbdt_config_binary.yaml"):
 
     try:
         auc_score = roc_auc_score(y, y_prob)
-    except:
+    except BaseException:
         print(f"no auc score available")
         return
 

examples/benchmark_quality/hetero_fast_sbt/gbdt-multi.py

@@ -52,7 +52,7 @@ def main(config="../../config.yaml", param="./gbdt_config_multi.yaml"):
 
     try:
         auc_score = roc_auc_score(y, y_pred)
-    except:
+    except BaseException:
         print(f"no auc score available")
 
     acc = accuracy_score(y, y_pred)

examples/benchmark_quality/hetero_linear_regression/fate-linr.py

@@ -66,7 +66,7 @@ def main(config="../../config.yaml", param="./linr_config.yaml", namespace=""):
     data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
     # configure DataTransform for guest
     data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense",
-                                                  label_name=param["label_name"], label_type="float")
+                                                          label_name=param["label_name"], label_type="float")
     # get and configure DataTransform party instance of host
     data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
 
@@ -97,7 +97,7 @@ def main(config="../../config.yaml", param="./linr_config.yaml", namespace=""):
     pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
     pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
     pipeline.add_component(hetero_linr_0, data=Data(train_data=intersection_0.output.data))
-    pipeline.add_component(hetero_linr_1,data=Data(test_data=intersection_0.output.data),
+    pipeline.add_component(hetero_linr_1, data=Data(test_data=intersection_0.output.data),
                            model=Model(hetero_linr_0.output.model))
     pipeline.add_component(evaluation_0, data=Data(data=hetero_linr_0.output.data))
 

examples/benchmark_quality/hetero_linear_regression/local-linr.py

@@ -55,9 +55,9 @@ def main(config="../../config.yaml", param="./linr_config.yaml"):
     r2 = r2_score(y, y_pred)
     explained_var = explained_variance_score(y, y_pred)
     metric_summary = {"r2_score": r2,
-              "mean_squared_error": mse,
-              "root_mean_squared_error": rmse,
-              "explained_variance": explained_var}
+                      "mean_squared_error": mse,
+                      "root_mean_squared_error": rmse,
+                      "explained_variance": explained_var}
     data_summary = {}
     return data_summary, metric_summary
 

examples/benchmark_quality/hetero_lr/pipeline-sshe-lr-binary.py

@@ -98,7 +98,7 @@ def main(config="../../config.yaml", param="./lr_config.yaml", namespace=""):
         "max_iter": param["max_iter"],
         "alpha": param["alpha"],
         "learning_rate": param["learning_rate"],
-        "optimizer": param["optimizer"], # use sgd
+        "optimizer": param["optimizer"],  # use sgd
         "batch_size": param["batch_size"],
         "early_stop": "diff",
         "tol": 1e-4,

examples/benchmark_quality/hetero_lr/pipeline-sshe-lr-multi.py

@@ -88,7 +88,7 @@ def main(config="../../config.yaml", param="./vehicle_sshe_lr_config.yaml", name
         "max_iter": param["max_iter"],
         "alpha": param["alpha"],
         "learning_rate": param["learning_rate"],
-        "optimizer": param["optimizer"], # use sgd
+        "optimizer": param["optimizer"],  # use sgd
         "batch_size": param["batch_size"],
         "early_stop": "diff",
         "init_param": {

examples/benchmark_quality/hetero_lr/sklearn-lr-binary.py

@@ -67,7 +67,7 @@ def main(config="../../config.yaml", param="./vechile_config.yaml"):
     y_prob = lm_fit.predict_proba(x_test)[:, 1]
     try:
         auc_score = roc_auc_score(y_test, y_prob)
-    except:
+    except BaseException:
         print(f"no auc score available")
         return
     recall = recall_score(y_test, y_pred, average="macro")
@@ -89,4 +89,3 @@ def main(config="../../config.yaml", param="./vechile_config.yaml"):
                         help="config file for params")
     args = parser.parse_args()
     main(param=args.param)
-

examples/benchmark_quality/hetero_nn/local-hetero_nn.py

@@ -14,17 +14,37 @@
 
 def build(param, shape1, shape2):
     input1 = tf.keras.layers.Input(shape=(shape1,))
-    x1 = tf.keras.layers.Dense(units=param["bottom_layer_units"], activation='tanh',
-                               kernel_initializer=keras.initializers.RandomUniform(minval=-1, maxval=1, seed=123))(input1)
+    x1 = tf.keras.layers.Dense(
+        units=param["bottom_layer_units"],
+        activation='tanh',
+        kernel_initializer=keras.initializers.RandomUniform(
+            minval=-1,
+            maxval=1,
+            seed=123))(input1)
     input2 = tf.keras.layers.Input(shape=(shape2,))
-    x2 = tf.keras.layers.Dense(units=param["bottom_layer_units"], activation='tanh',
-                               kernel_initializer=keras.initializers.RandomUniform(minval=-1, maxval=1, seed=123))(input2)
+    x2 = tf.keras.layers.Dense(
+        units=param["bottom_layer_units"],
+        activation='tanh',
+        kernel_initializer=keras.initializers.RandomUniform(
+            minval=-1,
+            maxval=1,
+            seed=123))(input2)
 
     concat = tf.keras.layers.Concatenate(axis=-1)([x1, x2])
-    out1 = tf.keras.layers.Dense(units=param["interactive_layer_units"], activation='relu',
-                                 kernel_initializer=keras.initializers.RandomUniform(minval=-1, maxval=1, seed=123))(concat)
-    out2 = tf.keras.layers.Dense(units=param["top_layer_units"], activation=param["top_act"],
-                                 kernel_initializer=keras.initializers.RandomUniform(minval=-1, maxval=1, seed=123))(out1)
+    out1 = tf.keras.layers.Dense(
+        units=param["interactive_layer_units"],
+        activation='relu',
+        kernel_initializer=keras.initializers.RandomUniform(
+            minval=-1,
+            maxval=1,
+            seed=123))(concat)
+    out2 = tf.keras.layers.Dense(
+        units=param["top_layer_units"],
+        activation=param["top_act"],
+        kernel_initializer=keras.initializers.RandomUniform(
+            minval=-1,
+            maxval=1,
+            seed=123))(out1)
     model = tf.keras.models.Model(inputs=[input1, input2], outputs=out2)
     opt = getattr(optimizers, param["opt"])(lr=param["learning_rate"])
     model.compile(optimizer=opt, loss=param["loss"])
@@ -74,7 +94,7 @@ def main(config="../../config.yaml", param="./hetero_nn_breast_config.yaml"):
             acc = metrics.accuracy_score(y_true=labels, y_pred=predict_y)
             eval_result["accuracy"] = acc
 
-    print (eval_result)
+    print(eval_result)
     data_summary = {}
     return data_summary, eval_result
 

examples/benchmark_quality/hetero_sbt/fate-sbt.py

@@ -30,6 +30,7 @@
 from federatedml.evaluation.metrics import regression_metric, classification_metric
 from fate_test.utils import extract_data, parse_summary_result
 
+
 def parse_summary_result(rs_dict):
     for model_key in rs_dict:
         rs_content = rs_dict[model_key]
@@ -141,7 +142,6 @@ def main(config="../../config.yaml", param="./xgb_config_binary.yaml", namespace
     return data_summary, metric_summary
 
 
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("BENCHMARK-QUALITY PIPELINE JOB")
     parser.add_argument("-config", type=str,

examples/benchmark_quality/hetero_sbt/gbdt-binary.py

@@ -53,7 +53,7 @@ def main(config="../../config.yaml", param="./gbdt_config_binary.yaml"):
 
     try:
         auc_score = roc_auc_score(y, y_prob)
-    except:
+    except BaseException:
         print(f"no auc score available")
         return
 

examples/benchmark_quality/hetero_sbt/gbdt-multi.py

@@ -52,7 +52,7 @@ def main(config="../../config.yaml", param="./gbdt_config_multi.yaml"):
 
     try:
         auc_score = roc_auc_score(y, y_pred)
-    except:
+    except BaseException:
         print(f"no auc score available")
 
     acc = accuracy_score(y, y_pred)

examples/benchmark_quality/homo_lr/pipeline-lr-binary.py

@@ -27,6 +27,7 @@
 from fate_test.utils import extract_data, parse_summary_result
 from federatedml.evaluation.metrics import classification_metric
 
+
 def main(config="../../config.yaml", param="./breast_lr_config.yaml", namespace=""):
     # obtain config
     if isinstance(config, str):

examples/benchmark_quality/homo_lr/sklearn-lr-binary.py

@@ -65,7 +65,6 @@ def main(config="../../config.yaml", param="./lr_config.yaml"):
     # x_test = df_test.drop(label_name, axis=1)
     x_test, y_test = x_train, y_train
 
-
     # lm = LogisticRegression(max_iter=20)
     lm = SGDClassifier(loss="log", **config_param)
     lm_fit = lm.fit(x_train, y_train)

examples/benchmark_quality/homo_sbt/gbdt-binary.py

@@ -55,7 +55,7 @@ def main(config="../../config.yaml", param="./gbdt_config_binary.yaml"):
 
     try:
         auc_score = roc_auc_score(y_guest, y_prob)
-    except:
+    except BaseException:
         print(f"no auc score available")
         return
 

examples/experiment_template/pipeline/hetero_lr/pipeline_train_union_lr.py

@@ -39,8 +39,8 @@ def main(config="../../config.yaml", namespace=""):
     host = parties.host[0]
     arbiter = parties.arbiter[0]
 
-    guest_train_data_0 = {"name":"breast_hetero_guest", "namespace": "experiment"}
-    guest_train_data_1 = {"name":"breast_hetero_guest", "namespace": "experiment"}
+    guest_train_data_0 = {"name": "breast_hetero_guest", "namespace": "experiment"}
+    guest_train_data_1 = {"name": "breast_hetero_guest", "namespace": "experiment"}
     guest_test_data_0 = {"name": "breast_hetero_guest", "namespace": "experiment"}
     guest_test_data_1 = {"name": "breast_hetero_guest", "namespace": "experiment"}
     host_train_data_0 = {"name": "breast_hetero_host_tag_value", "namespace": "experiment"}
@@ -89,7 +89,7 @@ def main(config="../../config.yaml", namespace=""):
         "delimitor": ";",
         "output_format": "dense"
     }
-    
+
     # define DataIO components
     dataio_0 = DataIO(name="dataio_0")  # start component numbering at 0
     dataio_1 = DataIO(name="dataio_1")  # start component numbering at 1
@@ -141,8 +141,8 @@ def main(config="../../config.yaml", namespace=""):
     hetero_feature_selection_0 = HeteroFeatureSelection(**param)
     hetero_feature_selection_1 = HeteroFeatureSelection(name='hetero_feature_selection_1')
     param = {
-        "name":"hetero_scale_0",
-        "method":"standard_scale"        
+        "name": "hetero_scale_0",
+        "method": "standard_scale"
     }
     hetero_scale_0 = FeatureScale(**param)
     hetero_scale_1 = FeatureScale(name='hetero_scale_1')
@@ -153,7 +153,6 @@ def main(config="../../config.yaml", namespace=""):
         "max_iter": 5
     }
 
-
     hetero_lr_0 = HeteroLR(name='hetero_lr_0', **param)
     evaluation_0 = Evaluation(name='evaluation_0')
     # add components to pipeline, in order of task execution

examples/experiment_template/pipeline/hetero_secureboost/pipeline_train_test_sbt.py

@@ -203,7 +203,7 @@ def main(config="../../config.yaml", namespace=""):
     pipeline.add_component(hetero_secureboost_0, data=Data(train_data=hetero_feature_selection_0.output.data,
                                                            validate_data=hetero_feature_selection_1.output.data))
     pipeline.add_component(hetero_secureboost_1, data=Data(test_data=hetero_feature_selection_2.output.data),
-                            model=Model(hetero_secureboost_0.output.model))
+                           model=Model(hetero_secureboost_0.output.model))
 
     pipeline.add_component(evaluation_0,
                            data=Data(data=[hetero_secureboost_0.output.data, hetero_secureboost_1.output.data]))

examples/min_test_task/__init__.py

@@ -14,4 +14,4 @@
 #  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.
+#  limitations under the License.

examples/min_test_task/config/__init__.py

@@ -14,4 +14,4 @@
 #  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.
+#  limitations under the License.

examples/min_test_task/run_task.py

@@ -230,7 +230,7 @@ def _check_cpn_status(job_id):
 
             time_print("Current task status: {}".format(task_status))
             return task_status
-        except:
+        except BaseException:
             return None
 
     def _deploy_model(self):
@@ -469,4 +469,3 @@ def _check_status(self, job_id):
     if add_sbt:
         task = TrainSBTTask(file_type, guest_id, host_id)
         task.run()
-

examples/pipeline/column_expand/pipeline-column-expand.py

@@ -46,10 +46,11 @@ def main(config="../../config.yaml", namespace=""):
 
     # define ColumnExpand components
     column_expand_0 = ColumnExpand(name="column_expand_0")
-    column_expand_0.get_party_instance(role="guest", party_id=guest).component_param(need_run=True,
-                                                                                     method="manual",
-                                                                                     append_header=["x_0", "x_1", "x_2", "x_3"],
-                                                                                     fill_value=[0, 0.2, 0.5, 1])
+    column_expand_0.get_party_instance(
+        role="guest", party_id=guest).component_param(
+        need_run=True, method="manual", append_header=[
+            "x_0", "x_1", "x_2", "x_3"], fill_value=[
+                0, 0.2, 0.5, 1])
     # define DataTransform components
     data_transform_0 = DataTransform(name="data_transform_0")  # start component numbering at 0
     # get DataTransform party instance of guest

examples/pipeline/data_split/pipeline-hetero-data-split-multi-model.py

@@ -47,8 +47,13 @@ def main(config="../../config.yaml", namespace=""):
     reader_0.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
 
     data_transform_0 = DataTransform(name="data_transform_0")
-    data_transform_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True, label_name="motor_speed",
-                                                                              label_type="float", output_format="dense")
+    data_transform_0.get_party_instance(
+        role='guest',
+        party_id=guest).component_param(
+        with_label=True,
+        label_name="motor_speed",
+        label_type="float",
+        output_format="dense")
     data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
 
     intersection_0 = Intersection(name="intersection_0")

examples/pipeline/data_split/pipeline-hetero-data-split.py

@@ -46,8 +46,13 @@ def main(config="../../config.yaml", namespace=""):
     reader_0.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
 
     data_transform_0 = DataTransform(name="data_transform_0")
-    data_transform_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True, label_name="motor_speed",
-                                                                              label_type="float", output_format="dense")
+    data_transform_0.get_party_instance(
+        role='guest',
+        party_id=guest).component_param(
+        with_label=True,
+        label_name="motor_speed",
+        label_type="float",
+        output_format="dense")
     data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
 
     intersection_0 = Intersection(name="intersection_0")

examples/pipeline/data_split/pipeline-homo-data-split-validate.py

@@ -33,7 +33,6 @@ def main(config="../../config.yaml", namespace=""):
     guest = parties.guest[0]
     host = parties.host[0]
 
-
     guest_train_data = {"name": "breast_homo_guest", "namespace": f"experiment{namespace}"}
     host_train_data = {"name": "breast_homo_host", "namespace": f"experiment{namespace}"}
 
@@ -45,8 +44,13 @@ def main(config="../../config.yaml", namespace=""):
 
     data_transform_0 = DataTransform(name="data_transform_0")
 
-    data_transform_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True, output_format="dense",
-                                                                              label_name="y", label_type="int")
+    data_transform_0.get_party_instance(
+        role='guest',
+        party_id=guest).component_param(
+        with_label=True,
+        output_format="dense",
+        label_name="y",
+        label_type="int")
     data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=True)
 
     homo_data_split_0 = HomoDataSplit(name="homo_data_split_0", stratified=True, validate_size=0.2)