rm remaining plots in scores

Author: albertcthomas

rampwf/score_types/generative_regression.py

@@ -166,13 +166,12 @@ class MDLikelihoodRatio(BaseScoreType):
 
     def __init__(self, name='lr', precision=2,
                  min_likelihood=1.4867195147342979e-06,  # 5 sigma
-                 output_dim=None, verbose=False, plot=False, multivar=False):
+                 output_dim=None, verbose=False, multivar=False):
         self.name = name
         self.precision = precision
         self.output_dim = output_dim
         self.min_likelihood = min_likelihood
         self.verbose = verbose
-        self.plot = plot
         self.multivar = multivar
 
     def __call__(self, y_true, y_pred):
@@ -181,8 +180,8 @@ def __call__(self, y_true, y_pred):
             multivar=self.multivar,
             min_likelihood=self.min_likelihood,
             output_dim=self.output_dim,
-            verbose=self.verbose or self.plot)
-        if self.verbose or self.plot:
+            verbose=self.verbose)
+        if self.verbose:
             nll_reg, log_lks = nll_reg_score(y_true, y_pred)
         else:
             nll_reg = nll_reg_score(y_true, y_pred)
@@ -291,13 +290,11 @@ class MDKSCalibration(BaseScoreType):
     minimum = 0.0
     maximum = 1.0
 
-    def __init__(self, name='ks', precision=2, output_dim=None, verbose=False,
-                 plot=False):
+    def __init__(self, name='ks', precision=2, output_dim=None, verbose=False):
         self.name = name
         self.precision = precision
         self.output_dim = output_dim
         self.verbose = verbose
-        self.plot = plot
 
     def __call__(self, y_true, y_pred):
         n_instances = len(y_true)

rampwf/tests/kits/acrobot_ts_generative_regression/problem.py

@@ -23,7 +23,7 @@
     rw.score_types.MDLikelihoodRatio('lr', precision=2),
     rw.score_types.MDOutlierRate('or', precision=4),
     rw.score_types.MDR2('r2', precision=6),
-    rw.score_types.MDKSCalibration('ks', precision=4, plot=False),
+    rw.score_types.MDKSCalibration('ks', precision=4),
 ]
 # generate scores for each output dimension
 _score_types = copy.deepcopy(score_types)