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column definitions working, model rebuilt
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@AndrewRook
AndrewRook committed Aug 7, 2016
1 parent 0563e8f commit 8331cc3
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109 changes: 50 additions & 59 deletions nflwin/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,26 +29,6 @@ class WPModel(object):
Parameters
----------
home_score_colname : string (default="curr_home_score")
The name of the column containing the current home score at the start of a play.
away_score_colname : string (default="curr_away_score")
The name of the column containing the current away score at the start of a play.
quarter_colname : string (default="quarter")
The name of the column containing the quarter the play took place in.
time_colname : string (default="seconds_elapsed")
The name of the column containing the time elapsed (in seconds) from the start
of the quarter when the play began.
down_colname : string (default="down")
The name of the column containing the current down number, with zeros for plays like
kickoffs and extra points.
yards_to_go_colname : string (default="yards_to_go")
The name of the column containing the number of yards to go in order to get a first down.
offense_team_colname : string (default="offense_team")
The name of the column containing the abbreviation for the team currently on offense.
home_team_colname : string (default="home_team")
The name of the column containing the abbreviation for the home team.
offense_won_colname : string (default="offense_won")
The name of the column containing whether or not the offense ended up winning the game.
copy_data : boolean (default=``True``)
Whether or not to copy data when fitting and applying the model. Running the model
in-place (``copy_data=False``) will be faster and have a smaller memory footprint,
Expand All @@ -59,6 +39,10 @@ class WPModel(object):
model : A Scikit-learn pipeline (or equivalent)
The actual model used to compute WP. Upon initialization it will be set to
a default model, but can be overridden by the user.
column_descriptions : dictionary
A dictionary whose keys are the names of the columns used in the model, and the values are
string descriptions of what the columns mean. Set at initialization to be the default model,
if you create your own model you'll need to update this attribute manually.
training_seasons : A list of ints, or ``None`` (default=``None``)
If the model was trained using data downloaded from nfldb, a list of the seasons
used to train the model. If nfldb was **not** used, an empty list. If no model
Expand All @@ -85,28 +69,8 @@ class WPModel(object):
_default_model_filename = "default_model.nflwin"

def __init__(self,
home_score_colname="curr_home_score",
away_score_colname="curr_away_score",
quarter_colname="quarter",
time_colname = "seconds_elapsed",
down_colname="down",
yards_to_go_colname="yards_to_go",
yardline_colname="yardline",
offense_team_colname="offense_team",
home_team_colname="home_team",
offense_won_colname="offense_won",
copy_data=True
):
self.home_score_colname = home_score_colname
self.away_score_colname = away_score_colname
self.quarter_colname = quarter_colname
self.time_colname = time_colname
self.down_colname = down_colname
self.yards_to_go_colname = yards_to_go_colname
self.yardline_colname = yardline_colname
self.offense_team_colname = offense_team_colname
self.home_team_colname = home_team_colname
self.offense_won_colname = offense_won_colname
self.copy_data = copy_data

self.model = self.create_default_pipeline()
Expand Down Expand Up @@ -146,7 +110,8 @@ def num_plays_used(self):
def train_model(self,
source_data="nfldb",
training_seasons=[2009, 2010, 2011, 2012, 2013, 2014],
training_season_types=["Regular", "Postseason"]):
training_season_types=["Regular", "Postseason"],
target_colname="offense_won"):
"""Train the model.
Once a modeling pipeline is set up (either the default or something
Expand Down Expand Up @@ -187,6 +152,8 @@ def train_model(self,
If querying from the nfldb database, what parts of the seasons to use.
Options are "Preseason", "Regular", and "Postseason". If ``source_data`` is not
``"nfldb"``, this argument will be ignored.
target_colname : string or integer (default=``"offense_won"``)
The name of the target variable column.
Returns
-------
Expand All @@ -199,14 +166,15 @@ def train_model(self,
season_types=training_season_types)
self._training_seasons = training_seasons
self._training_season_types = training_season_types
target_col = source_data[self.offense_won_colname]
feature_cols = source_data.drop(self.offense_won_colname, axis=1)
target_col = source_data[target_colname]
feature_cols = source_data.drop(target_colname, axis=1)
self.model.fit(feature_cols, target_col)

def validate_model(self,
source_data="nfldb",
validation_seasons=[2015],
validation_season_types=["Regular", "Postseason"]):
validation_season_types=["Regular", "Postseason"],
target_colname="offense_won"):
"""Validate the model.
Once a modeling pipeline is trained, a different dataset must be fed into the trained model
Expand Down Expand Up @@ -248,6 +216,8 @@ def validate_model(self,
If querying from the nfldb database, what parts of the seasons to use.
Options are "Preseason", "Regular", and "Postseason". If ``source_data`` is not
``"nfldb"``, this argument will be ignored.
target_colname : string or integer (default=``"offense_won"``)
The name of the target variable column.
Returns
-------
Expand Down Expand Up @@ -285,8 +255,8 @@ def validate_model(self,
self._validation_seasons = validation_seasons
self._validation_season_types = validation_season_types

target_col = source_data[self.offense_won_colname]
feature_cols = source_data.drop(self.offense_won_colname, axis=1)
target_col = source_data[target_colname]
feature_cols = source_data.drop(target_colname, axis=1)
predicted_probabilities = self.model.predict_proba(feature_cols)[:,1]

self._sample_probabilities, self._predicted_win_percents, self._num_plays_used = (
Expand Down Expand Up @@ -411,38 +381,59 @@ def create_default_pipeline(self):

steps = []

is_offense_home = preprocessing.ComputeIfOffenseIsHome(self.offense_team_colname,
self.home_team_colname,
offense_team_colname = "offense_team"
home_team_colname = "home_team"
home_score_colname = "curr_home_score"
away_score_colname = "curr_away_score"
down_colname = "down"
quarter_colname = "quarter"
time_colname = "seconds_elapsed"
yardline_colname = "yardline"
yards_to_go_colname="yards_to_go"

self.column_descriptions = {
offense_team_colname: "Abbreviation for the offensive team",
home_team_colname: "Abbreviation for the home team",
away_score_colname: "Abbreviation for the visiting team",
down_colname: "The current down",
yards_to_go_colname: "Yards to a first down (or the endzone)",
quarter_colname: "The quarter",
time_colname: "Seconds elapsed in the quarter",
yardline_colname: ("The yardline, given by (yards from own goalline - 50). "
"-49 is your own 1 while 49 is the opponent's 1.")
}

is_offense_home = preprocessing.ComputeIfOffenseIsHome(offense_team_colname,
home_team_colname,
copy=self.copy_data)
steps.append(("compute_offense_home", is_offense_home))
score_differential = preprocessing.CreateScoreDifferential(self.home_score_colname,
self.away_score_colname,
score_differential = preprocessing.CreateScoreDifferential(home_score_colname,
away_score_colname,
is_offense_home.offense_home_team_colname,
copy=self.copy_data)
steps.append(("create_score_differential", score_differential))
steps.append(("map_downs_to_int", preprocessing.MapToInt(self.down_colname, copy=self.copy_data)))
total_time_elapsed = preprocessing.ComputeElapsedTime(self.quarter_colname, self.time_colname, copy=self.copy_data)
steps.append(("map_downs_to_int", preprocessing.MapToInt(down_colname, copy=self.copy_data)))
total_time_elapsed = preprocessing.ComputeElapsedTime(quarter_colname, time_colname, copy=self.copy_data)
steps.append(("compute_total_time_elapsed", total_time_elapsed))
steps.append(("remove_unnecessary_columns", preprocessing.CheckColumnNames(
column_names=[is_offense_home.offense_home_team_colname,
score_differential.score_differential_colname,
total_time_elapsed.total_time_colname,
self.yardline_colname,
self.yards_to_go_colname,
self.down_colname],
yardline_colname,
yards_to_go_colname,
down_colname],
copy=self.copy_data)))
steps.append(("encode_categorical_columns", preprocessing.OneHotEncoderFromDataFrame(
categorical_feature_names=[self.down_colname],
categorical_feature_names=[down_colname],
copy=self.copy_data)))

search_grid = {'base_estimator__penalty': ['l1', 'l2'],
'base_estimator__C': [0.01, 0.1, 1, 10, 100]
}
base_model = LogisticRegression()
calibrated_model = CalibratedClassifierCV(base_model, cv=2, method="isotonic")
grid_search_model = GridSearchCV(calibrated_model, search_grid,
scoring=self._brier_loss_scorer)
#steps.append(("compute_model", grid_search_model))
#grid_search_model = GridSearchCV(calibrated_model, search_grid,
# scoring=self._brier_loss_scorer)
steps.append(("compute_model", calibrated_model))

pipe = Pipeline(steps)
Expand Down
9 changes: 9 additions & 0 deletions nflwin/tests/test_model.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,22 @@
from __future__ import print_function, division

import os
import collections

import numpy as np
import pandas as pd
import pytest

from nflwin import model

class TestDefaults(object):
"""Tests for defaults."""

def test_column_descriptions_set(self):
wpmodel = model.WPModel()
assert isinstance(wpmodel.column_descriptions, collections.Mapping)


class TestTestDistribution(object):
"""Tests the _test_distribution static method of WPModel."""

Expand Down

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