fix the lifelong example problem from backend and constant

- fix sklearn backend: support args in train/eval/infer
- fix lifelong constant

Signed-off-by: JoeyHwong <joeyhwong@gknow.cn>

lib/sedna/algorithms/multi_task_learning/multi_task_learning.py

@@ -286,7 +286,7 @@ def evaluate(self, data: BaseDataSource,
                  **kwargs):
         from sklearn import metrics as sk_metrics
 
-        result, tasks = self.predict(data, kwargs=kwargs)
+        result, tasks = self.predict(data, **kwargs)
         m_dict = {}
         if metrics:
             if callable(metrics):  # if metrics is a function

lib/sedna/backend/base.py

@@ -49,28 +49,29 @@ def parse_kwargs(func, **kwargs):
             return kwargs
         return {k: v for k, v in kwargs.items() if k in need_kw.args}
 
-    def train(self, **kwargs):
+    def train(self, *args, **kwargs):
         """Train model."""
         if callable(self.estimator):
             varkw = self.parse_kwargs(self.estimator, **kwargs)
             self.estimator = self.estimator(**varkw)
-        varkw = self.parse_kwargs(self.estimator.train, **kwargs)
-        return self.estimator.train(**varkw)
+        fit_method = getattr(self.estimator, "fit", self.estimator.train)
+        varkw = self.parse_kwargs(fit_method, **kwargs)
+        return fit_method(*args, **varkw)
 
-    def predict(self, **kwargs):
+    def predict(self, *args, **kwargs):
         """Inference model."""
         varkw = self.parse_kwargs(self.estimator.predict, **kwargs)
-        return self.estimator.predict(**varkw)
+        return self.estimator.predict(*args, **varkw)
 
-    def predict_proba(self, **kwargs):
+    def predict_proba(self, *args, **kwargs):
         """Compute probabilities of possible outcomes for samples in X."""
         varkw = self.parse_kwargs(self.estimator.predict_proba, **kwargs)
-        return self.estimator.predict_proba(**varkw)
+        return self.estimator.predict_proba(*args, **varkw)
 
-    def evaluate(self, **kwargs):
+    def evaluate(self, *args, **kwargs):
         """evaluate model."""
         varkw = self.parse_kwargs(self.estimator.evaluate, **kwargs)
-        return self.estimator.evaluate(**varkw)
+        return self.estimator.evaluate(*args, **varkw)
 
     def save(self, model_url="", model_name=None):
         mname = model_name or self.model_name

lib/sedna/core/lifelong_learning/lifelong_learning.py

@@ -65,7 +65,7 @@ def __init__(self,
             output_url=Context.get_parameters("OUTPUT_URL", "/tmp")
         )
         task_index = FileOps.join_path(config['output_url'],
-                                       KBResourceConstant.KB_INDEX_NAME)
+                                       KBResourceConstant.KB_INDEX_NAME.value)
         config['task_index'] = task_index
         super(LifelongLearning, self).__init__(
             estimator=e, config=config
@@ -141,7 +141,7 @@ def train(self, train_data,
 
         save_extractor = FileOps.join_path(
             self.config.output_url,
-            KBResourceConstant.TASK_EXTRACTOR_NAME
+            KBResourceConstant.TASK_EXTRACTOR_NAME.value
         )
         extractor = FileOps.dump(extractor, save_extractor)
         try: