Nina's gain curve axis fix

Author: JohnMount

README.txt

@@ -1,7 +1,7 @@
 Win Vector LLC extras for teaching data science in Python 3
 
 pip install wvpy
-# or: pip install https://github.com/WinVector/wvpy/raw/master/dist/wvpy-0.1.4.tar.gz
+# or: pip install https://github.com/WinVector/wvpy/raw/master/dist/wvpy-0.1.5.tar.gz
 
 ~/anaconda3/bin/python3
 

pkg/README.txt

@@ -1,7 +1,7 @@
 Win Vector LLC extras for teaching data science in Python 3
 
 pip install wvpy
-# or: pip install https://github.com/WinVector/wvpy/raw/master/dist/wvpy-0.1.4.tar.gz
+# or: pip install https://github.com/WinVector/wvpy/raw/master/dist/wvpy-0.1.5.tar.gz
 
 ~/anaconda3/bin/python3
 

pkg/build/lib/wvpy/util.py

@@ -132,7 +132,7 @@ def dual_hist_plot_proba1(probs, istrue):
     matplotlib.pyplot.show()
 
 
-def gain_curve_plot(prediction, outcome, title="Gain curve plot"):
+def gain_curve_plot_working(prediction, outcome, title="Gain curve plot"):
     """plot cumulative outcome as a function of prediction order (descending)"""
     df = pandas.DataFrame({"prediction": prediction, "outcome": outcome})
 
@@ -156,12 +156,6 @@ def gain_curve_plot(prediction, outcome, title="Gain curve plot"):
         df["cumulative_outcome_by_wizard"]
     )
 
-    seaborn.lineplot(
-        x="fraction_of_observations_by_prediction",
-        y="cumulative_outcome_fraction",
-        data=df,
-    )
-
     seaborn.lineplot(
         x="fraction_of_observations_by_wizard",
         y="cumulative_outcome_fraction_wizard",
@@ -170,7 +164,15 @@ def gain_curve_plot(prediction, outcome, title="Gain curve plot"):
         data=df,
     )
 
+    seaborn.lineplot(
+        x="fraction_of_observations_by_prediction",
+        y="cumulative_outcome_fraction",
+        data=df,
+    )
+
     seaborn.lineplot(x=[0, 1], y=[0, 1], color="red")
+    matplotlib.pyplot.xlabel("fraction of observations by sort criterion")
+    matplotlib.pyplot.ylabel("cumulative outcome fraction")
     matplotlib.pyplot.title(title)
     matplotlib.pyplot.show()
 

pkg/dist/wvpy-0.1.4-py3-none-any.whl

@@ -7,7 +7,7 @@
 """
 
 setuptools.setup(name='wvpy',
-      version='0.1.4',
+      version='0.1.5',
       author='John Mount',
       author_email='[email protected]',
       url='https://github.com/WinVector/wvpy',

pkg/dist/wvpy-0.1.4.tar.gz

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: wvpy
-Version: 0.1.4
+Version: 0.1.5
 Summary: Simple utilities for teaching Pandas and scikit learn.
 Home-page: https://github.com/WinVector/wvpy
 Author: John Mount

pkg/dist/wvpy-0.1.5-py3-none-any.whl

@@ -132,7 +132,7 @@ def dual_hist_plot_proba1(probs, istrue):
     matplotlib.pyplot.show()
 
 
-def gain_curve_plot(prediction, outcome, title="Gain curve plot"):
+def gain_curve_plot_working(prediction, outcome, title="Gain curve plot"):
     """plot cumulative outcome as a function of prediction order (descending)"""
     df = pandas.DataFrame({"prediction": prediction, "outcome": outcome})
 
@@ -156,12 +156,6 @@ def gain_curve_plot(prediction, outcome, title="Gain curve plot"):
         df["cumulative_outcome_by_wizard"]
     )
 
-    seaborn.lineplot(
-        x="fraction_of_observations_by_prediction",
-        y="cumulative_outcome_fraction",
-        data=df,
-    )
-
     seaborn.lineplot(
         x="fraction_of_observations_by_wizard",
         y="cumulative_outcome_fraction_wizard",
@@ -170,7 +164,15 @@ def gain_curve_plot(prediction, outcome, title="Gain curve plot"):
         data=df,
     )
 
+    seaborn.lineplot(
+        x="fraction_of_observations_by_prediction",
+        y="cumulative_outcome_fraction",
+        data=df,
+    )
+
     seaborn.lineplot(x=[0, 1], y=[0, 1], color="red")
+    matplotlib.pyplot.xlabel("fraction of observations by sort criterion")
+    matplotlib.pyplot.ylabel("cumulative outcome fraction")
     matplotlib.pyplot.title(title)
     matplotlib.pyplot.show()