Merge pull request #30 from AndrewRook/model_validation_image

Model validation image made

doc/source/dev.rst

@@ -4,8 +4,7 @@ For Developers
 This section of the documentation covers things that will be useful for those already contributing to NFLWin.
 
 .. note::
-   Unless stated otherwise assume that all filepaths given in this section start at the root directory for the repo.
-
+   Unless stated otherwise assume that all filepaths given in this section start at the root directory for the repo. 
 
 Testing Documentation
 ------------------------------------------
@@ -24,7 +23,13 @@ NFLWin comes with a pre-trained model, but if the code generating that model is
   $ python make_default_model.py
 
 .. note::
-   This script hardcodes in the seasons to use for training and testing samples. After each season those will likely need to be updated to use the most up-to-date data.
+   This script hardcodes in the seasons to use for training and
+   testing samples. After each season those will likely need to be
+   updated to use the most up-to-date data.
+
+.. note::
+   This script requires ``matplotlib`` in order to run, as it produces a
+   validation plot for the documentation.
 
 Cutting a New Release
 ----------------------------------

doc/source/index.rst

@@ -76,6 +76,11 @@ Once data is loaded, using the model to predict WP is easy:
   >>> standard_model.predict_wp(plays)
   array([ 0.58300397,  0.64321796,  0.18195466])
 
+Current Default Model
+---------------------
+
+.. image:: _static/validation_plot.png
+
 Why NFLWin?
 --------------
 Put simply, there are no other options: while WP models have been
@@ -101,6 +106,3 @@ Resources
    Developer Documentation <dev.rst>
    Full API Documentation <modules.rst>
 
-* :ref:`Full API Documentation <modindex>`
-* :ref:`Search NFLWin's Documentation <search>`
-

doc/source/model.rst

@@ -177,9 +177,8 @@ correctly. The ratio of these two KDEs is the actual WP measured
 from the test data set at a given *predicted* WP. While all of this is
 measured in :meth:`~nflwin.model.WPModel.validate_model`, you can plot
 it for yourself by calling the
-:meth:`~nflwin.model.WPModel.plot_validation` method:
-
-.. image:: _static/validation_plot.png
+:meth:`~nflwin.model.WPModel.plot_validation` method, which will
+generate a plot like that shown on the home page.
 
 From there NFLWin computes both the maximum deviation at any given
 percentage and the total area between the estimated WP from the model

make_default_model.py

@@ -1,24 +1,49 @@
 """A simple script to create, train, validate, and save the default model"""
 from __future__ import division, print_function
 
+import datetime as dt
 import time
+import os
 
 from nflwin import model
 
 def main():
     start = time.time()
     win_probability_model = model.WPModel()
-    win_probability_model.train_model(training_seasons=[2009, 2010, 2011, 2012, 2013, 2014])
+
+    training_seasons = [2009, 2010, 2011, 2012, 2013, 2014]
+    validation_seasons = [2015]
+    season_types = ["Regular", "Postseason"]
+
+    win_probability_model.train_model(training_seasons=training_seasons,
+                                      training_season_types=season_types)
     print("Took {0:.2f}s to build model".format(time.time() - start))
 
     start = time.time()
-    max_deviation, residual_area = win_probability_model.validate_model(validation_seasons=[2015])
+    max_deviation, residual_area = win_probability_model.validate_model(validation_seasons=validation_seasons,
+                                                                        validation_season_types=season_types)
     print("Took {0:.2f}s to validate model, with a max residual of {1:.2f} and a residual area of {2:.2f}"
           .format(time.time() - start, max_deviation, residual_area))
 
     win_probability_model.save_model()
 
+    ax = win_probability_model.plot_validation(label="max deviation={0:.2f}, \n"
+                                               "residual total area={1:.2f}"
+                                               "".format(max_deviation, residual_area))
+    curr_datetime = dt.datetime.now()
+    ax.set_title("Model Generated At: " + curr_datetime.strftime("%Y-%m-%d %H:%M:%S"))
+    ax.legend(loc="lower right", fontsize=10)
+    ax.text(0.02, 0.98, ("Data from: {0:s}\n"
+                         "Training season(s): {1:s}\n"
+                         "Validation season(s): {2:s}"
+                         "".format(", ".join(season_types),
+                                   ", ".join(str(year) for year in training_seasons),
+                                   ", ".join(str(year) for year in validation_seasons))),
+                        ha="left", va="top", fontsize=10, transform=ax.transAxes)
 
+    this_filepath = os.path.dirname(os.path.abspath(__file__))
+    save_filepath = os.path.join(this_filepath, "doc", "source", "_static", "validation_plot.png")
+    ax.figure.savefig(save_filepath)
 
 
 if __name__ == "__main__":

nflwin/model.py

@@ -357,7 +357,7 @@ def plot_validation(self, axis=None, **kwargs):
         import matplotlib.pyplot as plt
         if axis is None:
             axis = plt.figure().add_subplot(111)
-            axis.plot([0, 1], [0, 1], ls="--", lw=2, color="black")
+            axis.plot([0, 100], [0, 100], ls="--", lw=2, color="black")
             axis.set_xlabel("Predicted WP")
             axis.set_ylabel("Actual WP")
         axis.plot(self.sample_probabilities,