lifelong learning: issue-driven interface-adjustment and bug fix

examples/lifelong_learning/atcii/eval.py

@@ -15,7 +15,7 @@
 import json
 
 from sedna.datasources import CSVDataParse
-from sedna.common.config import Context, BaseConfig
+from sedna.common.config import BaseConfig
 from sedna.core.lifelong_learning import LifelongLearning
 
 from interface import DATACONF, Estimator, feature_process
@@ -26,17 +26,24 @@ def main():
     valid_data = CSVDataParse(data_type="valid", func=feature_process)
     valid_data.parse(test_dataset_url, label=DATACONF["LABEL"])
     attribute = json.dumps({"attribute": DATACONF["ATTRIBUTES"]})
-    model_threshold = float(Context.get_parameters('model_threshold', 0))
+
+    task_definition = {
+        "method": "TaskDefinitionByDataAttr",
+        "param": attribute
+    }
 
     ll_job = LifelongLearning(
         estimator=Estimator,
-        task_definition="TaskDefinitionByDataAttr",
-        task_definition_param=attribute
+        task_definition=task_definition,
+        task_relationship_discovery=None,
+        task_mining=None,
+        task_remodeling=None,
+        inference_integrate=None,
+        unseen_task_detect=None
     )
     eval_experiment = ll_job.evaluate(
         data=valid_data, metrics="precision_score",
-        metrics_param={"average": "micro"},
-        model_threshold=model_threshold
+        metrics_param={"average": "micro"}
     )
     return eval_experiment
 

examples/lifelong_learning/atcii/inference.py

@@ -26,17 +26,29 @@
 
 def main():
 
-    utd = Context.get_parameters("UTD_NAME", "TaskAttr")
+    utd = Context.get_parameters("UTD_NAME", "TaskAttrFilter")
     attribute = json.dumps({"attribute": DATACONF["ATTRIBUTES"]})
     utd_parameters = Context.get_parameters("UTD_PARAMETERS", {})
     ut_saved_url = Context.get_parameters("UTD_SAVED_URL", "/tmp")
 
-    ll_job = LifelongLearning(
+    task_mining = {
+        "method": "TaskMiningByDataAttr",
+        "param": attribute
+    }
+
+    unseen_task_detect = {
+        "method": utd,
+        "param": utd_parameters
+    }
+
+    ll_service = LifelongLearning(
         estimator=Estimator,
-        task_mining="TaskMiningByDataAttr",
-        task_mining_param=attribute,
-        unseen_task_detect=utd,
-        unseen_task_detect_param=utd_parameters)
+        task_mining=task_mining,
+        task_definition=None,
+        task_relationship_discovery=None,
+        task_remodeling=None,
+        inference_integrate=None,
+        unseen_task_detect=unseen_task_detect)
 
     infer_dataset_url = Context.get_parameters('infer_dataset_url')
     file_handle = open(infer_dataset_url, "r", encoding="utf-8")
@@ -60,12 +72,14 @@ def main():
         rows = reader[0]
         data = dict(zip(header, rows))
         infer_data.parse(data, label=DATACONF["LABEL"])
-        rsl, is_unseen, target_task = ll_job.inference(infer_data)
+        rsl, is_unseen, target_task = ll_service.inference(infer_data)
 
         rows.append(list(rsl)[0])
+
+        output = "\t".join(map(str, rows)) + "\n"
         if is_unseen:
-            unseen_sample.write("\t".join(map(str, rows)) + "\n")
-        output_sample.write("\t".join(map(str, rows)) + "\n")
+            unseen_sample.write(output)
+        output_sample.write(output)
     unseen_sample.close()
     output_sample.close()
 

examples/lifelong_learning/atcii/train.py

@@ -28,13 +28,23 @@ def main():
     train_data.parse(train_dataset_url, label=DATACONF["LABEL"])
     attribute = json.dumps({"attribute": DATACONF["ATTRIBUTES"]})
     early_stopping_rounds = int(
-        Context.get_parameters(
-            "early_stopping_rounds", 100))
+        Context.get_parameters("early_stopping_rounds", 100)
+    )
     metric_name = Context.get_parameters("metric_name", "mlogloss")
+
+    task_definition = {
+        "method": "TaskDefinitionByDataAttr",
+        "param": attribute
+    }
+
     ll_job = LifelongLearning(
         estimator=Estimator,
-        task_definition="TaskDefinitionByDataAttr",
-        task_definition_param=attribute
+        task_definition=task_definition,
+        task_relationship_discovery=None,
+        task_mining=None,
+        task_remodeling=None,
+        inference_integrate=None,
+        unseen_task_detect=None
     )
     train_experiment = ll_job.train(
         train_data=train_data,