adds super method to score visualizers

yellowbrick/base.py

@@ -86,7 +86,7 @@ class ScoreVisualizer(Visualizer):
     """
 
     def __init__(self, model):
-        pass
+        super(ScoreVisualizer, self).__init__(**kwargs)
 
     def fit(self, X, y=None):
         return self
@@ -121,9 +121,9 @@ def poof(self, **kwargs):
 
 class ModelVisualizer(Visualizer):
     """
-    A model visualization class accepts as input a Scikit-Learn estimator(s)
-    and is itself an estimator (to be included in a Pipeline) in order to
-    visualize the efficacy of a particular fitted model.
+    A model visualization accepts as input an unfitted Scikit-Learn estimator(s)
+    and enables the user to visualize the performance of models across a range
+    of hyperparameter values (e.g. using VisualGridsearch and ValidationCurve).
     """
 
     def fit(self, X, y=None, **kwargs):

yellowbrick/classifier.py

@@ -42,6 +42,8 @@ def __init__(self, model):
         Check to see if model is an instance of a classifer.
         Should return an error if it isn't.
         """
+        super(ClassificationScoreVisualizer, self).__init__(**kwargs)
+
         if not isclassifier(model):
             raise YellowbrickTypeError(
                 "This estimator is not a classifier; try a regression or clustering score visualizer instead!"
@@ -60,6 +62,8 @@ def __init__(self, model, **kwargs):
         """
         Pass in a fitted model to generate a ROC curve.
         """
+        super(ClassificationReport, self).__init__(**kwargs)
+
         self.estimator = model
         self.name = get_model_name(self.estimator)
         self.cmap = kwargs.pop('cmap', ddlheatmap)
@@ -124,6 +128,8 @@ def __init__(self, model, **kwargs):
         """
         Pass in a model to generate a ROC curve.
         """
+        super(ROCAUC, self).__init__(**kwargs)
+
         self.estimator = model
         self.name = get_model_name(self.estimator)
         super(ROCAUC, self).__init__(model, **kwargs)

yellowbrick/regressor.py

@@ -25,7 +25,6 @@
 from .exceptions import YellowbrickTypeError
 from .utils import get_model_name, isestimator, isregressor
 from .base import Visualizer, ScoreVisualizer, MultiModelMixin
-# from sklearn.cross_validation import train_test_split as tts
 
 ##########################################################################
 ## Regression Visualization Base Object
@@ -37,6 +36,8 @@ def __init__(self, model):
         Check to see if model is an instance of a regressor.
         Should return an error if it isn't.
         """
+        super(RegressionScoreVisualizer, self).__init__(**kwargs)
+
         if not isregressor(model):
             raise YellowbrickTypeError(
                 "This estimator is not a regressor; try a classifier or clustering score visualizer instead!"
@@ -52,6 +53,8 @@ class PredictionError(RegressionScoreVisualizer):
     predicted values generated by our model(s).
     """
     def __init__(self, model, **kwargs):
+        super(PredictionError, self).__init__(**kwargs)
+
         self.estimator = model
         self.name = get_model_name(self.estimator)
         self.colors = {
@@ -96,7 +99,17 @@ def poof(self):
 ##########################################################################
 
 class ResidualsPlot(RegressionScoreVisualizer):
+    """
+    A residual plot shows the residuals on the vertical axis
+    and the independent variable on the horizontal axis.
+
+    If the points are randomly dispersed around the horizontal axis,
+    a linear regression model is appropriate for the data;
+    otherwise, a non-linear model is more appropriate.
+    """
     def __init__(self, model, **kwargs):
+        super(ResidualsPlot, self).__init__(**kwargs)
+
         self.estimator = model
         self.name = get_model_name(self.estimator)
         self.colors = {