def train():
print("Building Data...")
trainingX, trainingY, team_stats = data.get_data()
tourney_teams, team_id_map = data.get_tourney_teams(2017)
tourney_teams.sort()
testingXtemp = []
matchups = []
for team_1 in tourney_teams:
for team_2 in tourney_teams:
if team_1 < team_2:
game_features = data.get_game_features(team_1, team_2, 0, 2017,
team_stats)
testingXtemp.append(game_features)
game = [team_1, team_2]
matchups.append(game)
testingX = np.array(testingXtemp)
print("Fitting model...")
model = MLPClassifier(hidden_layer_sizes=(30, 30))
model.fit(trainingX, np.ravel(trainingY))
return model, testingX, matchups
def train():
print "Building Data..."
trainingX, trainingY, team_stats = data.get_data()
tourney_teams, team_id_map = data.get_tourney_teams(2017)
tourney_teams.sort()
testingXtemp = []
matchups = []
for team_1 in tourney_teams:
for team_2 in tourney_teams:
if team_1 < team_2:
game_features = data.get_game_features(team_1, team_2, 0, 2017,
team_stats)
testingXtemp.append(game_features)
game = [team_1, team_2]
matchups.append(game)
testingX = np.array(testingXtemp)
print "Fitting model..."
# Test the random forest algorithm
seed(1)
# evaluate algorithm
max_depth = 10
min_size = 1
sample_size = 1.0
n_features = 28
for n_trees in [1, 5, 10]:
predictions = evaluate_algorithm(trainingX, trainingY, random_forest,
max_depth, min_size, sample_size,
n_trees, n_features)
print predictions
# print('Trees: %d' % n_trees)
# print('Scores: %s' % scores)
# print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
return model, testingX, matchups
def build_bracket():
teams_df = pd.read_csv("../data2017/FirstRound.csv", header=0)
results_df = pd.read_csv("../data2017/TourneyMatchups2017.csv", header=0)
tourney_teams, team_id_map = data.get_tourney_teams(2017)
adaboost_data = pd.read_csv("../predictions/AdaBoost_Predictions_2017.csv",
header=0)
knn_data = pd.read_csv("../predictions/KNN_Predictions_2017.csv", header=0)
naive_bayes_data = pd.read_csv(
"../predictions/NaiveBayes_Predictions_2017.csv", header=0)
neural_net_data = pd.read_csv(
"../predictions/NeuralNet_Predictions_2017.csv", header=0)
random_forest_data = pd.read_csv(
"../predictions/RandomForest_Predictions_2017.csv", header=0)
regression_data = pd.read_csv(
"../predictions/Regression_Predictions_2017.csv", header=0)
svm_data = pd.read_csv("../predictions/SVM_Predictions_2017.csv", header=0)
test_list = [
adaboost_data, knn_data, naive_bayes_data, neural_net_data,
random_forest_data, regression_data, svm_data
]
for df in test_list:
score = 0
teams = []
for index, row in teams_df.iterrows():
teams.append(row["Team1"])
teams.append(row["Team2"])
my_index = 0
tempTeams = []
round_val = 10
for i in range(6): # 6 rounds of tourney
print
print("ROUND " + str(i + 1))
while my_index < len(teams):
#print my_index
team1 = teams[my_index]
team2 = teams[my_index + 1]
#print "t1: ", team1, " t2: ", team2
for index, row in df.iterrows():
#print my_index
if (row["Team1"] == team1 and row["Team2"] == team2) or (
row["Team1"] == team2 and row["Team2"] == team1):
if row["Prediction"] == 0:
tempTeams.append(row["Team1"])
print(
str(team_id_map[row["Team1"]]) + " over " +
str(team_id_map[row["Team2"]]))
print("Round Val: " + str(round_val))
winner = 0
else:
tempTeams.append(row["Team2"])
print(
str(team_id_map[row["Team2"]]) + " over " +
str(team_id_map[row["Team1"]]))
print("Round Val: " + str(round_val))
winner = 1
for index, new_row in results_df.iterrows():
if (new_row["Team1"] == team1 and new_row["Team2"]
== team2) or (new_row["Team1"] == team2 and
new_row["Team2"] == team1):
if winner == new_row["Prediction"]:
score = score + round_val
print("Score: " + str(score))
my_index = my_index + 2
round_val = round_val * 2
teams = tempTeams
tempTeams = []
my_index = 0
print("Score: " + str(score))
import data
import numpy as np
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
if __name__ == "__main__":
trainingX, trainingY, team_stats = data.get_data()
print("Generated training set!")
tourney_teams, team_id_map = data.get_tourney_teams(2017)
tourney_teams.sort()
print("Got tourney teams!")
testingXtemp = []
testingYtemp = []
matchups = []
for team_1 in tourney_teams:
for team_2 in tourney_teams:
if team_1 < team_2:
game_features = data.get_game_features(team_1, team_2, 0, 2017,
team_stats)
testingXtemp.append(game_features)
game = [team_1, team_2]
matchups.append(game)
testingX = np.array(testingXtemp)