code coverage

sdpython · Jul 13, 2020 · bf5f146 · bf5f146
1 parent e768031
commit bf5f146
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Showing 3 changed files with 8 additions and 9 deletions.
diff --git a/mlinsights/mlmodel/ml_featurizer.py b/mlinsights/mlmodel/ml_featurizer.py
@@ -83,7 +83,8 @@ def wrap_predict_sklearn(X, fct, many):
     """
     isv = is_vector(X)
     if many == isv:
-        raise ValueError("Inconsistency X is a single vector, many is True")
+        raise ValueError(  # pragma: no cover
+            "Inconsistency X is a single vector, many is True")
     if isv:
         X = [X]
     y = fct(X)

diff --git a/mlinsights/mlmodel/quantile_mlpregressor.py b/mlinsights/mlmodel/quantile_mlpregressor.py
@@ -11,7 +11,7 @@
 from sklearn.neural_network._base import DERIVATIVES, LOSS_FUNCTIONS
 try:
     from sklearn.neural_network._multilayer_perceptron import BaseMultilayerPerceptron
-except ImportError:
+except ImportError:  # pragma: no cover
     # scikit-learn < 0.22.
     from sklearn.neural_network.multilayer_perceptron import BaseMultilayerPerceptron
 from sklearn.metrics import mean_absolute_error
@@ -35,10 +35,9 @@ def float_sign(a):
     "Returns 1 if *a > 0*, otherwise -1"
     if a > 1e-8:
         return 1.
-    elif a < -1e-8:
+    if a < -1e-8:
         return -1.
-    else:
-        return 0.
+    return 0.
 
 
 EXTENDED_LOSS_FUNCTIONS = {'absolute_loss': absolute_loss}
@@ -87,8 +86,7 @@ def _modify_loss_derivatives(self, last_deltas):
         """
         if self.loss == 'absolute_loss':
             return DERIVATIVE_LOSS_FUNCTIONS['absolute_loss'](last_deltas)
-        else:
-            return last_deltas
+        return last_deltas  # pragma: no cover
 
     def _backprop(self, X, y, activations, deltas, coef_grads,
                   intercept_grads):

diff --git a/mlinsights/mlmodel/transfer_transformer.py b/mlinsights/mlmodel/transfer_transformer.py
@@ -45,11 +45,11 @@ def __init__(self, estimator, method=None, copy_estimator=True,
             elif hasattr(estimator, "predict"):
                 method = "predict"
             else:
-                raise AttributeError(
+                raise AttributeError(  # pragma: no cover
                     "Cannot find a method transform, predict_proba, decision_function, "
                     "predict in object {}".format(type(estimator)))
         if not hasattr(estimator, method):
-            raise AttributeError(
+            raise AttributeError(  # pragma: no cover
                 "Cannot find method '{}' in object {}".format(
                     method, type(estimator)))
         self.method = method