predicted error regressor

requirements.txt

@@ -1,10 +1,11 @@
 # Dependencies
 matplotlib==1.5.1
+scipy==0.17.1
 scikit-learn==0.17.1
 numpy==1.11.0
+cycler==0.10.0
 
 ## Utilities
-#cycler==0.10.0
 #pyparsing==2.1.4
 #pytz==2016.4
 #python-dateutil==2.5.3
@@ -13,13 +14,13 @@ numpy==1.11.0
 
 ## Testing Requirements (uncomment for development)
 #nose==1.3.7
-#coverage==4.0.3
+#coverage==4.1
 
 ## Build Requirements (uncomment for deployment)
 #wheel==0.29.0
 
 ## Pip stuff (ignore)
-#Python==2.7.11
+#Python==3.5.1
 #pip==8.1.2
-#setuptools==21.0.0
+#setuptools==22.0.5
 #wsgiref==0.1.2

tests/test_regressor.py

@@ -0,0 +1,52 @@
+# tests.test_regressor
+# Ensure that the regressor visualizations work.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Fri Jun 03 14:20:02 2016 -0700
+#
+# Copyright (C) 2016 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: test_regressor.py [] benjamin@bengfort.com $
+
+"""
+Ensure that the regressor visualizations work.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import unittest
+
+from yellowbrick.regressor import *
+from yellowbrick.utils import *
+
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.svm import SVR
+
+##########################################################################
+## Prediction error test case
+##########################################################################
+
+class PredictionErrorTests(unittest.TestCase):
+
+    def test_init_pe_viz(self):
+        """
+        Ensure that both a single model and multiple models can be rendered
+        """
+        viz = PredictionError([RandomForestRegressor(), SVR()])
+        self.assertEqual(len(viz.models), 2)
+
+        viz = PredictionError(SVR())
+        self.assertEqual(len(viz.models), 1)
+
+    def test_init_pe_names(self):
+        """
+        Ensure that model names are correctly extracted
+        """
+        viz = PredictionError([RandomForestRegressor(), SVR()])
+        self.assertEqual(viz.names, ["RandomForestRegressor", "SVR"])
+
+        viz = PredictionError(SVR())
+        self.assertEqual(viz.names, ["SVR"])

tests/test_utils.py

@@ -24,30 +24,74 @@
 from sklearn.pipeline import Pipeline
 import unittest
 
-from yellowbrick.utils import get_model_name
+from yellowbrick.utils import get_model_name, isestimator
 
 
 class ModelNameTests(unittest.TestCase):
 
     def test_real_model(self):
+        """
+        Test that model name works for sklearn estimators
+        """
         model1 = LassoCV()
         model2 = LSHForest()
         self.assertEqual(get_model_name(model1), 'LassoCV')
         self.assertEqual(get_model_name(model2), 'LSHForest')
 
     def test_pipeline(self):
+        """
+        Test that model name works for sklearn pipelines
+        """
         pipeline = Pipeline([('reduce_dim', PCA()),
                              ('linreg', LinearRegression())])
         self.assertEqual(get_model_name(pipeline), 'LinearRegression')
 
     def test_int_input(self):
-
+        """
+        Assert a type error is raised when an int is passed to model name.
+        """
         self.assertRaises(TypeError, get_model_name, 1)
 
     def test_str_input(self):
-
+        """
+        Assert a type error is raised when a str is passed to model name.
+        """
         self.assertRaises(TypeError, get_model_name, 'helloworld')
 
+    def test_estimator_instance(self):
+        """
+        Test that isestimator works for instances
+        """
+        model = LinearRegression()
+        self.assertTrue(isestimator(model))
+
+    def test_pipeline_instance(self):
+        """
+        Test that isestimator works for pipelines
+        """
+        model = Pipeline([
+            ('reduce_dim', PCA()),
+            ('linreg', LinearRegression())
+        ])
+
+        self.assertTrue(isestimator(model))
+
+    def test_estimator_class(self):
+        """
+        Test that isestimator works for classes
+        """
+        self.assertTrue(LinearRegression)
+
+    def test_collection_not_estimator(self):
+        """
+        Make sure that a collection is not an estimator
+        """
+        for cls in (list, dict, tuple, set):
+            self.assertFalse(isestimator(cls))
+
+        things = ['pepper', 'sauce', 'queen']
+        self.assertFalse(isestimator(things))
+
 
 if __name__ == "__main__":
     unittest.main()

yellowbrick/__init__.py

@@ -21,6 +21,7 @@
 from .version import get_version
 from .anscombe import anscombe
 from .classifier import crplot, rocplot_compare
+from .regressor import peplot
 
 ##########################################################################
 ## Package Version

yellowbrick/regressor.py

@@ -17,12 +17,81 @@
 ## Imports
 ##########################################################################
 
-from .base import ModelVisualization
+import matplotlib as mpl
+import matplotlib.pyplot as plt
 
+from .base import ModelVisualization
+from .utils import get_model_name, isestimator
+from sklearn.cross_validation import cross_val_predict as cvp
 
 ##########################################################################
 ## Regression Visualization Base Object
 ##########################################################################
 
 class RegressorVisualization(ModelVisualization):
     pass
+
+
+##########################################################################
+## Prediction Error Plots
+##########################################################################
+
+class PredictionError(RegressorVisualization):
+
+    def __init__(self, models, **kwargs):
+        """
+        Pass in a collection of models to generate prediction error graphs.
+        """
+
+        # Ensure models is a collection, if it's a single estimator then we
+        # wrap it in a list so that the API doesn't break during render.
+        if isestimator(models):
+            models = [models]
+
+        # Keep track of the models
+        self.models = models
+        self.names  = kwargs.pop('names', list(map(get_model_name, models)))
+        self.colors = {
+            'point': kwargs.pop('point_color', '#F2BE2C'),
+            'line': kwargs.pop('line_color', '#2B94E9'),
+        }
+
+    def generate_subplots(self):
+        """
+        Generates the subplots for the number of given models.
+        """
+        _, axes = plt.subplots(len(self.models), sharex=True, sharey=True)
+        return axes
+
+    def predict(self, X, y):
+        """
+        Returns a generator containing the predictions for each of the
+        internal models (using cross_val_predict and a CV=12).
+        """
+        for model in self.models:
+            yield cvp(model, X, y, cv=12)
+
+    def render(self, X, y):
+        """
+        Renders each of the scatter plots per matrix.
+        """
+        for idx, (axe, y_pred) in enumerate(zip(self.generate_subplots(), self.predict(X, y))):
+            # Plot the correct values
+            axe.scatter(y, y_pred, c=self.colors['point'])
+
+            # Draw the best fit line
+            # TODO: Add best fit line computation metric
+            axe.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4, c=self.colors['line'])
+
+            # Set the title and the y-axis label
+            axe.set_title("Predicted vs. Actual Values for {}".format(self.names[idx]))
+            axe.set_ylabel('Predicted Value')
+
+        # Finalize figure
+        plt.xlabel('Actual Value')
+        return axe # TODO: We shouldn't return the last axis
+
+
+def peplot(models, X, y, **kwargs):
+    viz = PredictionError(models, **kwargs)
+    return viz.render(X, y)

yellowbrick/utils.py

@@ -13,9 +13,13 @@
 ##########################################################################
 ## Imports
 ##########################################################################
+
 from sklearn.pipeline import Pipeline
 from sklearn.base import BaseEstimator
 
+##########################################################################
+## Model detection utilities
+##########################################################################
 
 def get_model_name(model):
     """
@@ -28,3 +32,12 @@ def get_model_name(model):
             return model.steps[-1][-1].__class__.__name__
         else:
             return model.__class__.__name__
+
+def isestimator(model):
+    """
+    Determines if a model is an estimator using issubclass and isinstance.
+    """
+    if type(model) == type:
+        return issubclass(model, BaseEstimator)
+
+    return isinstance(model, BaseEstimator)