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nfl-predict.py
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nfl-predict.py
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import sys
import pickle
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LogisticRegression
def getWinnerProb(item):
return item[1]
def main():
weekfile = sys.argv[1]
modelfile = sys.argv[2]
polyorder = int(sys.argv[3])
metadata = sys.argv[4]
week_data = np.genfromtxt(weekfile, delimiter=',', skip_header=1)
poly = PolynomialFeatures(degree=polyorder)
Xpoly = poly.fit_transform(week_data)
with open(modelfile, 'rb') as model, open(metadata) as md:
lr = pickle.load(model)
preds = lr.predict(Xpoly).astype(int)
probs = lr.predict_proba(Xpoly)
results = []
for i, line in enumerate(md):
home, away = line.strip().split(',')
if preds[i] == 1:
results.append((home+'*', "{0:.3f}".format(probs[i,1]), away, "{0:.3f}".format(probs[i,0])))
else:
results.append((away, "{0:.3f}".format(probs[i,0]), home+'*', "{0:.3f}".format(probs[i,1])))
results = sorted(results, key=getWinnerProb, reverse=True)
for result in results:
print('\t'.join(result))
if __name__ == '__main__':
main()