validation plot working

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

@@ -106,6 +106,3 @@ Resources
    Developer Documentation <dev.rst>
    Full API Documentation <modules.rst>
 
-* :ref:`Full API Documentation <modindex>`
-* :ref:`Search NFLWin's Documentation <search>`
-

make_default_model.py

@@ -10,11 +10,18 @@
 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))
 
@@ -24,8 +31,15 @@ def main():
                                                "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")
+    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")