def main():
start = datetime.now()
# get the data
train_data = helpers.load_data(numpy_path, 'train_set.npy')
valid_data = helpers.load_data(numpy_path, 'valid_set.npy')
test_data = helpers.load_data(numpy_path, 'test_set.npy')
# filter the data
test_data_labels = np.array([item[0] for item in test_data[:, 2]])
test_data_countries = np.array([item[0] for item in test_data[:, 0]])
test_data_month = test_data[:, 5]
# convert the data
train_dataset, train_shape = convert_dataset(train_data,
batchsize=batchsize,
shuffle=1000,
shape=True)
valid_dataset = convert_dataset(valid_data, batchsize=1000, shuffle=100)
test_dataset = convert_dataset(test_data, batchsize=1000)
# build the model
model = build_model(train_shape[1], train_shape[2])
# Print Model
# modelprovider.printModel(model, dir=os.path.join(
# logdir, expname), name=expname+".png")
# compiling the model
lossfn = loss.crps_cost_function
opt = Adam(lr=learning_rate, amsgrad=True)
model.compile(loss=lossfn, optimizer=opt)
# checkdir path
checkpoint_dir = os.path.join(logdir, expname, 'checkpoints/')
# begin with training 10 times
print('[INFO] Starting training')
predictions = []
for i in range(1, 11):
print('Round number: ' + str(i))
model = build_model(train_shape[1], train_shape[2])
# compile new model with new inital weights
model.compile(loss=lossfn, optimizer=opt)
# checkpoint callbacks
# all checkpoints
cp_callback_versuch = tf.keras.callbacks.ModelCheckpoint(
os.path.join(checkpoint_dir, 'round-' + str(i) + '/') +
"checkpoint_{epoch}",
monitor='val_loss',
save_weights_only=True,
mode='min',
verbose=0)
# best checkpoint
cp_callback = tf.keras.callbacks.ModelCheckpoint(
os.path.join(checkpoint_dir, 'round-' + str(i) + '/checkpoint'),
monitor='val_loss',
save_weights_only=True,
mode='min',
save_best_only=True,
verbose=0)
# train the model
if train_model:
model.fit(
train_dataset,
epochs=epochs,
initial_epoch=initial_epochs,
batch_size=batchsize,
verbose=1,
validation_data=valid_dataset,
validation_batch_size=1000,
callbacks=[cp_callback, cp_callback_versuch],
)
# load the best checkpoint of round i
model.load_weights(
os.path.join(checkpoint_dir,
'round-' + str(i) + '/checkpoint')).expect_partial()
predictions.append(
model.predict(test_dataset, batch_size=1000, verbose=0))
# convert to numpy array
predictions = np.array(predictions)
# Make sure std is positive
predictions[:, :, 1] = np.abs(predictions[:, :, 1])
# calculate mean between the 10 results
mean_predictions = np.mean(predictions, 0)
# calculate the score for each record in test set
test_crps = crps.norm_data(test_data_labels, mean_predictions)
# print the results with filters
helpers.printIntCountries(test_data_labels, test_data_countries,
mean_predictions)
helpers.printHist(helpers.datasetPIT(mean_predictions, test_data_labels))
np.save(os.path.join(logdir, expname, 'prediction'), predictions)
print(datetime.now() - start)
def read():
start = datetime.datetime.now()
print('[INFO] starting reading...')
# read all data and concat the train set for mean and std
df = pd.read_csv(file, index_col=0)
test_set = df[(df['init date'] > '2014-01-01')
& (df['init date'] < '2017-12-31')].to_numpy()
test_data = helpers.load_data(numpy_path, 'test_set.npy')
test_data_labels = test_data[:, 2]
test_data_labels = np.array([item[0] for item in test_data_labels])
test_data_countries = test_data[:, 0]
test_data_countries = np.array([item[0] for item in test_data_countries])
test_data_month = test_data[:, 5]
scores = []
ranks = []
length = []
print('[INFO] starting calculating')
for case in test_set:
date = datetime.datetime.strptime(case[0],'%Y-%m-%d')
delta = datetime.timedelta(days=16)
ensemble = []
for year in range(1998,2013):
start = (datetime.datetime.strptime(str(year)+'-'+date.strftime( '%m-%d' ),'%Y-%m-%d') - delta ).strftime( '%Y-%m-%d' )
end = (datetime.datetime.strptime(str(year)+'-'+date.strftime( '%m-%d' ),'%Y-%m-%d') + delta ).strftime( '%Y-%m-%d' )
train_set = df[(df['init date'] > start)
& (df['init date'] < end)&(df['country']==case[1])].to_numpy()
for i in train_set[:,2]:
ensemble.append(i)
score = ps.crps_ensemble(case[2], ensemble)
rank = verificationRank(case[2], ensemble)
length.append(len(ensemble))
ranks.append(rank)
scores.append(score)
print(max(ranks))
print(list(dict.fromkeys(length)))
scores = np.array(scores)
helpers.printHist(ranks, r=(0,276))
print(('all', round(scores.mean() , 2 ) ))
result = str(scores.mean())+'&'
for i in [8,16,2,5,20]:
filter = test_data_countries == i
filter_data = scores[filter]
if len(filter_data) > 0:
item = str(round(np.array(filter_data).mean(), 2))
else:
item = str(0)
result += item+'&'
print(result)
for i in range(1, 13):
filter = test_data_month == i
filter_data = scores[filter]
if len(filter_data) > 0:
item = (i, round(np.array(filter_data).mean(), 2))
else:
item = (i, 0, 0)
print(item)
