def test_build_bracket_2017():
build_bracket(
teamsPath=pkg_resources.resource_filename('bracketeer', 'tests/input/teams.csv'),
seedsPath=pkg_resources.resource_filename('bracketeer', 'tests/input/seeds.csv'),
slotsPath=pkg_resources.resource_filename('bracketeer', 'tests/input/slots.csv'),
submissionPath=pkg_resources.resource_filename('bracketeer', 'tests/input/sub.csv'),
year=2017,
outputPath='output.png'
)
assert(os.path.isfile('output.png'))
def build_bracket_output():
b = build_bracket(outputPath=base_url + 'output.png',
teamsPath=base_url + 'data/raw/Teams.csv',
seedsPath=base_url + 'data/raw/NCAATourneySeeds.csv',
submissionPath=base_url +
'data/processed/2019Predictions.csv',
slotsPath=base_url + 'data/raw/NCAATourneySlots.csv',
year=2019)
from bracketeer import build_bracket
m = build_bracket(outputPath='bracket.png',
teamsPath='data/Teams.csv',
seedsPath='data/NCAATourneySeeds.csv',
submissionPath='SubmissionStage2.csv',
slotsPath='data/NCAATourneySlots.csv',
year=2019)
# output feedback
print()
print()
print("2019 Bracket creation complete.")
print()
print()
from bracketeer import build_bracket
b = build_bracket(outputPath='../output/Moutput.png',
teamsPath='../Minput2/Teams.csv',
seedsPath='../Minput3/NCAATourneySeeds.csv',
submissionPath='../output/Mstats_2018_AB.csv',
slotsPath='../Minput3/NCAATourneySlots.csv',
year=2018)
"w") as f:
f.write("ID,Pred\n")
with open(f"./Results/2021_Stage2_{MODEL_SELECTED}_first_round.csv", "a") as f:
for i, id, _ in stage_1_comp.itertuples():
year, t1, t2 = id.split("_")
year, t1, t2 = int(year), int(t1), int(t2)
if year not in feature_dfs.keys():
print(year)
feature_dfs[year] = pd.read_csv(
"./Data/Training/features_{:d}.csv".format(year))
matchup = get_matchup_data(t1, t2, feature_dfs[year])
matchup = np.array(matchup).reshape(1, -1)
matchup = scaler.transform(matchup)
prediction = model.predict_proba(matchup).flatten()
if write == True:
f.write(f"{id},{prediction[0]}\n")
else:
print(id, prediction[0])
if write:
b = build_bracket(
outputPath=f'Results/2021_bracket_{MODEL_SELECTED}_first_round.png',
teamsPath='Data/Stage2/MTeams.csv',
seedsPath='Data/Stage2/MNCAATourneySeeds.csv',
submissionPath=f'Results/2021_Stage2_{MODEL_SELECTED}_first_round.csv',
slotsPath='Data/Stage2/MNCAATourneySlots.csv',
resultsPath='Results/first_round_misses.csv',
year=2021)
clf = svm.SVC(C=10.0, gamma=.0001, probability=True)
svm_param_grid = {
'clf__C': np.logspace(start=-3, stop=3, num=7),
'clf__gamma': np.logspace(start=-4, stop=-1, num=4)
}
clf.fit(train_inputs, train_labels)
res = clf.predict(test_inputs)
print(accuracy_score(test_labels, res))
df_predict = pd.read_csv('./csv_data/SampleSubmissionStage2.csv')
# Create pipeline for scaling and classifying:
pipe = Pipeline([('clf', clf)])
lr_search = GridSearchCV(pipe, svm_param_grid, cv=10)
lr_search.fit(train_inputs, train_labels)
print(lr_search.best_params_)
perm = PermutationImportance(lr_search,
random_state=1).fit(test_inputs, test_labels)
predict_poss_matches(lr_search, df_predict,
df_features).to_csv('best_model_results2.csv',
index=False)
b = build_bracket(outputPath='best_bracket.png',
submissionPath='best_model_results2.csv',
teamsPath='./csv_data/Teams.csv',
seedsPath='./csv_data/NCAATourneySeeds.csv',
slotsPath='./csv_data/NCAATourneySlots.csv',
year=2019)
model = pickle.load(open("models/modelv2.sav", "rb"))
print("Getting teams")
print("Predicting matchups")
seeds = pd.read_csv(folder + '/NCAATourneySeeds.csv')
tourney_teams = []
for year in prediction_range:
for index, row in seeds.iterrows():
if row['Season'] == year:
team_seeds[year][row['TeamID']] = row['Seed']
tourney_teams.append(row['TeamID'])
tourney_teams.sort()
get_teams(tourney_teams, year)
tourney_teams.clear()
prediction_path = 'predictions/submission_1.csv'
print(f"Writing {len(final_data)} results")
with open(prediction_path, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['ID', 'Pred'])
writer.writerows(final_data)
m = build_bracket(outputPath='output.png',
teamsPath='Data/Teams.csv',
seedsPath='Data/NCAATourneySeeds.csv',
submissionPath=prediction_path,
slotsPath='Data/NCAATourneySlots.csv',
year=prediction_year)
teamsout = (
teams[['new_id', 'new_teamname']]
.rename(columns={'new_id': 'TeamID', 'new_teamname': 'TeamName'})
)
seedsout = (
seeds[['Season', 'new_seed', 'new_teamid']]
.rename(columns={'new_seed': 'Seed', 'new_teamid': 'TeamID'})
)
slotsout = (
slots[['Season', 'new_slot', 'new_strongseed', 'new_weakseed']]
.rename(columns={'new_slot': 'Slot', 'new_strongseed': 'StrongSeed', 'new_weakseed': 'WeakSeed'})
)
subout = (
sub[['new_id', 'new_pred']]
.rename(columns={'new_id': 'ID', 'new_pred': 'Pred'})
)
teamsout.to_csv('input\\teams.csv', index=False)
seedsout.to_csv('input\\seeds.csv', index=False)
slotsout.to_csv('input\\slots.csv', index=False)
subout.to_csv('input\\sub.csv', index=False)
b = build_bracket(
outputPath='output.png',
teamsPath='input\\teams.csv',
seedsPath='input\\seeds.csv',
submissionPath='submissions\\ensemble_pred.csv',
slotsPath='input\\slots.csv',
year=2018
)
import pip
pip.main(['install', 'binarytree'])
pip.main(['install', 'bracketeer'])
from bracketeer import build_bracket
b = build_bracket(teamsPath='input/Teams.csv',
seedsPath='input/NCAATourneySeeds.csv',
submissionPath='nbs/predictions.csv',
slotsPath='input/NCAATourneySlots.csv',
year=2018)