Merge pull request #389 from poppopting/ktdev

explicitly assign arguments to avoid incorrect argument assignments

Author: Eleven1Liu

libmultilabel/linear/utils.py

@@ -76,17 +76,18 @@ class MultiLabelEstimator(sklearn.base.BaseEstimator):
         scoring_metric (str, optional): The scoring metric. Defaults to 'P@1'.
     """
 
-    def __init__(self, options: str = "", linear_technique: str = "1vsrest", scoring_metric: str = "P@1"):
+    def __init__(self, options: str = "", linear_technique: str = "1vsrest", scoring_metric: str = "P@1", multiclass: bool = False):
         super().__init__()
         self.options = options
         self.linear_technique = linear_technique
         self.scoring_metric = scoring_metric
         self._is_fitted = False
+        self.multiclass = multiclass
 
     def fit(self, X: sparse.csr_matrix, y: sparse.csr_matrix):
         X, y = sklearn.utils.validation.check_X_y(X, y, accept_sparse=True, multi_output=True)
         self._is_fitted = True
-        self.model = LINEAR_TECHNIQUES[self.linear_technique](y, X, self.options)
+        self.model = LINEAR_TECHNIQUES[self.linear_technique](y, X, options=self.options)
         return self
 
     def predict(self, X: sparse.csr_matrix) -> np.ndarray:
@@ -96,8 +97,9 @@ def predict(self, X: sparse.csr_matrix) -> np.ndarray:
 
     def score(self, X: sparse.csr_matrix, y: sparse.csr_matrix) -> float:
         metrics = linear.get_metrics(
-            [self.scoring_metric],
-            y.shape[1],
+            monitor_metrics=[self.scoring_metric],
+            num_classes=y.shape[1],
+            multiclass=self.multiclass
         )
         preds = self.predict(X)
         metrics.update(preds, y.toarray())

linear_trainer.py

@@ -51,9 +51,9 @@ def linear_train(datasets, config):
         model = LINEAR_TECHNIQUES[config.linear_technique](
             datasets["train"]["y"],
             datasets["train"]["x"],
-            config.liblinear_options,
-            config.tree_degree,
-            config.tree_max_depth,
+            options=config.liblinear_options,
+            K=config.tree_degree,
+            dmax=config.tree_max_depth,
         )
     else:
         model = LINEAR_TECHNIQUES[config.linear_technique](