def merge_and_show(validation_results):
merged_result = Winner.calculate()
unneded_columnns = [
'AdaBoostClassifier', 'GaussianNB', 'KerasAlgorithm',
'LightGBMAlgorithm', 'LinearSVC', 'MLPClassifier',
'NearestCentroid', 'RandomForestClassifier', 'SGDClassifier',
'SVC', 'XGBoostGbtreeAlgorithm', 'winner', 'failure', 'file',
'lines', 'ok', 'size', 'test', 'train', 'validate'
]
validation_results_mean = []
for _i, row in validation_results.iterrows():
values = [
row['fbeta_run_0'], row['fbeta_run_1'], row['fbeta_run_2'],
row['fbeta_run_3'], row['fbeta_run_4'], row['fbeta_run_5']
]
minimal_fbeta = min(values)
maximal_fbeta = max(values)
mean_fbeta = np.mean(values)
validation_results_mean.append({
'drive_csv': row['drive_csv'],
'minimal_fbeta': minimal_fbeta,
'maximal_fbeta': maximal_fbeta,
'mean_fbeta': mean_fbeta
})
kfold = merged_result.merge(pd.DataFrame(validation_results_mean),
on='drive_csv')
return kfold[kfold['winner'] == 'DecisionTreeClassifier'].drop(
unneded_columnns, axis='columns')
def __init__(self):
self.merged_result = Winner.calculate()
self.winners = []
self.algorithms = [
'AdaBoostClassifier', 'DecisionTreeClassifier', 'FakeAlgorithm',
'GaussianNB', 'KerasAlgorithm', 'LightGBMAlgorithm', 'LinearSVC',
'MLPClassifier', 'NearestCentroid', 'RandomForestClassifier',
'SGDClassifier', 'SVC', 'XGBoostGbtreeAlgorithm'
]
for algo in self.algorithms:
total = 0
for _i, row in self.merged_result.iterrows():
if row['winner'] == algo:
total += 1
self.winners.append(total)
self.algorithm_short = [
x.replace('Classifier', '').replace('Algorithm', '')
for x in self.algorithms
]
def run_all():
validation_file = 'validation.csv'
if os.path.exists(validation_file):
return pd.read_csv(validation_file)
else:
merged_result = Winner.calculate()
all_drives = list(
merged_result[merged_result['winner'] ==
'DecisionTreeClassifier']['drive_csv'])
results = []
with tqdm(total=len(all_drives)) as pbar:
for drive_csv in all_drives:
pbar.set_description(drive_csv)
drive_information = {'drive_csv': drive_csv}
drive_information.update(Validation.run(drive_csv))
results.append(drive_information)
pbar.update(1)
df = pd.DataFrame(results)
df.to_csv(validation_file, index=False)
return df
numUnits=1,
name='Player 1',
equipmentTypes=equipmentTypes)
gameOverTime = None
active = game
elif isinstance(active, Game):
if active.gameOver:
if gameOverTime is None:
gameOverTime = time.time()
if time.time() - gameOverTime > 5:
if active.player not in active.units:
msg = 'You Lose'
else:
msg = 'You Win'
active = Winner(active.units[0], screen, msg=msg)
lastGameEndTime = time.time()
elif isinstance(active, Winner) and time.time() - lastGameEndTime > 5:
active = Choose(screen)
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit()
active.Event(event)
active.Draw(clock)
# OSD #######################
fps = int(clock.get_fps())
while True:
if isinstance(active, Choose):
if active.startGameWith is not None:
index, stats = active.startGameWith
game = Game(screen)
game.NewGame(index, stats, numUnits=1, name='Player 1')
gameOverTime = None
active = game
elif isinstance(active, Game):
if game.gameOver:
if gameOverTime is None:
gameOverTime = time.time()
if time.time() - gameOverTime > 5:
active = Winner(game.units[0], screen)
lastGameEndTime = time.time()
elif isinstance(active, Winner) and time.time() - lastGameEndTime > 5:
# game = Game(screen)
# game.NewGame(numUnits=2)
# gameOverTime = None
# active = game
active = Choose(screen)
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit()
active.Event(event)
def test_winner_to_dict(self):
w = Winner("Jan Nowak", "Gold medal")
assert w.to_dict() == {"name": "Jan Nowak", "prize": "Gold medal"}
def test_winner(self):
w = Winner("Jan Nowak", "Gold medal")
assert w.name == "Jan Nowak"
assert w.prize == "Gold medal"
import csv
from winner import Winner
winners = []
with open("oscars.csv") as csvfile:
reader = csv.reader(csvfile, skipinitialspace=True)
next(reader)
for row in reader:
current_winner = Winner(*row)
winners.append(current_winner)
# for winner in winners:
# print(f"{winner.name} won the Oscar for {winner.movie} in {winner.year} at age {winner.age}.")
for winner in winners:
if winner.year > 1979 and winner.year < 1990:
print(winner.name)
old_winner = winners[0]
for winner in winners:
if winner.age > old_winner.age:
old_winner = winner
print(old_winner.name, old_winner.age)
meryl_streeps_wins = [(winner.movie, winner.year) for winner in winners if winner.name == "Meryl Streep"]
print(meryl_streeps_wins)
import csv
from winner import Winner
winners = []
with open("oscars.csv") as csvfile:
reader = csv.reader(csvfile, skipinitialspace=True)
next(reader)
for row in reader:
winners.append(Winner(*row))
with open("oscars.csv", "w", newline="") as csvfile:
writer = csv.writer(csvfile, quoting=csv.QUOTE_ALL)
writer.writerow(["Index", "Year", "Age", "Name", "Movie"])
for winner in winners:
winner.age -= 1
writer.writerow(winner.values_as_list())
import csv
from winner import Winner
with open("oscars.csv") as csvfile:
reader = csv.reader(csvfile, skipinitialspace=True)
index = sum(1 for row in reader)
with open("oscars.csv", "a") as csvfile:
writer = csv.writer(csvfile, quoting=csv.QUOTE_ALL)
winner = Winner(index, 2020, 50, "Renée Zelweger", "Judy")
writer.writerow(winner.values_as_list())