Re-format onnxmltools/convert/lightgbm after merge

Signed-off-by: Jett Jackson <[email protected]>

Author: J-tt

onnxmltools/convert/lightgbm/_parse.py

@@ -18,9 +18,11 @@
 # Associate scikit-learn types with our operator names.
 # If two scikit-learn models share a single name, it means their
 # are equivalent in terms of conversion.
-lightgbm_operator_name_map = {LGBMClassifier: 'LgbmClassifier',
-                              LGBMRegressor: 'LgbmRegressor',
-                              LGBMRanker: 'LgbmRanker'}
+lightgbm_operator_name_map = {
+    LGBMClassifier: "LgbmClassifier",
+    LGBMRegressor: "LgbmRegressor",
+    LGBMRanker: "LgbmRanker",
+}
 
 
 class WrappedBooster:
@@ -34,8 +36,8 @@ def __init__(self, booster):
         elif self.objective_.startswith("multiclass"):
             self.operator_name = "LgbmClassifier"
             self.classes_ = self._generate_classes(booster)
-        elif self.objective_.startswith('lambdarank'):
-            self.operator_name = 'LgbmRanker'
+        elif self.objective_.startswith("lambdarank"):
+            self.operator_name = "LgbmRanker"
         elif self.objective_.startswith(
             ("regression", "poisson", "gamma", "quantile", "huber", "tweedie")
         ):

onnxmltools/convert/lightgbm/operator_converters/LightGbm.py

@@ -566,10 +566,10 @@ def convert_lightgbm(scope, operator, container):
         # so we need to add an 'Exp' post transform node to the model
         attrs["post_transform"] = "NONE"
         post_transform = "Exp"
-    elif gbm_text['objective'].startswith('lambdarank'):
+    elif gbm_text["objective"].startswith("lambdarank"):
         n_classes = 1  # Ranker has only one output variable
-        attrs['post_transform'] = 'NONE'
-        attrs['n_targets'] = n_classes
+        attrs["post_transform"] = "NONE"
+        attrs["n_targets"] = n_classes
     else:
         raise RuntimeError(
             "LightGBM objective should be cleaned already not '{}'.".format(
@@ -1027,7 +1027,7 @@ def convert_lgbm_zipmap(scope, operator, container):
         )
 
 
-register_converter('LgbmClassifier', convert_lightgbm)
-register_converter('LgbmRegressor', convert_lightgbm)
-register_converter('LgbmZipMap', convert_lgbm_zipmap)
-register_converter('LgbmRanker', convert_lightgbm)
+register_converter("LgbmClassifier", convert_lightgbm)
+register_converter("LgbmRegressor", convert_lightgbm)
+register_converter("LgbmZipMap", convert_lgbm_zipmap)
+register_converter("LgbmRanker", convert_lightgbm)

onnxmltools/convert/lightgbm/shape_calculators/Ranker.py

@@ -3,4 +3,4 @@
 from ...common._registration import register_shape_calculator
 from ...common.shape_calculator import calculate_linear_regressor_output_shapes
 
-register_shape_calculator('LgbmRanker', calculate_linear_regressor_output_shapes)
+register_shape_calculator("LgbmRanker", calculate_linear_regressor_output_shapes)

onnxmltools/convert/lightgbm/shape_calculators/__init__.py

@@ -3,4 +3,4 @@
 # To register shape calculators for lightgbm operators, import associated modules here.
 from . import Classifier
 from . import Regressor
-from . import Ranker
+from . import Ranker