def setUpModelRun(options, G):
# count available CUDA devices
if torch.cuda.is_available():
n_cuda_dev = torch.cuda.device_count()
print('Cuda is available with %s devices.' % n_cuda_dev)
# prepare model run config
config = prepareConfig(options, G)
# set standard optimizer as default, if none is specified
if 'optimizer' not in config:
print(
"No optimizer config found. Using standard sgd optimizer with learning rate 0.001 and momentum 0.9."
)
config['optimizer'] = {
'algorithm': 'sgd',
'learning_rate': 0.001,
'momentum': 0.9
}
# load preprocessed time invariant data per stations
with open(
"%s/%s/grid_size_%s/time_invariant_data_per_station.pkl" %
(config['input_source'], config['preprocessing'],
config['original_grid_size']), "rb") as input_file:
time_invarian_data = pkl.load(input_file)
# load all station ids
config['stations'] = time_invarian_data.station.data
# if preprocessing "station" is used, load complete data into memory accessible by a dictionary. Access keys are the station ids
data_dictionary = None
config['inits'] = None
if config['preprocessing'] == 'station':
# load data per station into dictionary
data_dictionary = {}
for station in config['stations']:
ds = xr.open_dataset(config['input_source'] +
"/station/grid_size_%s/station_%s_data.nc" %
(config['original_grid_size'], station))
data_dictionary[station] = ds.copy(deep=True)
ds.close()
# get all init times we have data for
config['inits'] = ds.coords['init'].data
# load or generate training and test folds, this requires several parameters, best described in the method itself
train_test_folds = prepareTrainTestFolds(config)
data_statistics = DataUtils.getDataStatistics(config=config)
# the definitions of the grid time invariant parameters and the station parameters are hard-code
config['grid_time_invariant_parameters'] = [
'HH', 'HH_DIFF', 'FR_LAND', 'SOILTYP', 'LAT', 'LON', 'ABS_2D_DIST'
]
config['station_parameters'] = ['height', 'lat', 'lon']
return config, train_test_folds, data_dictionary, data_statistics
def prepareTrainTestFolds(config):
train_test_folds_file_name = '/train_test_folds_r_%s_sl_%s_tfw_%s_tf_%s_series_%s_s_%s.pkl' % (
config['runs'], config['slice_size'], config['test_filter_window'],
config['test_fraction'], config['time_serie_length']
if 'time_serie_length' in config else 0, config['seed'])
# if not already existing, generate filtered data splits for each run
if not os.path.exists(config['input_source'] + train_test_folds_file_name):
data_folds = DataUtils.getDataFolds(config=config)
train_test_folds = DataUtils.getTrainTestFolds(config=config,
data_folds=data_folds)
# else load the existing filtered data split
else:
with open(config['input_source'] + train_test_folds_file_name,
'rb') as f:
train_test_folds = pkl.load(file=f)
print('Loaded existing train/test folds.')
sys.stdout.flush()
return train_test_folds
def getStationNeighbors(stationId, gridHeightData, gridLatData, gridLonData,
station_height, station_lat, station_lon):
# calculate height difference between grid heights and station heights
gridHeightDifference = gridHeightData.squeeze() - station_height
# calculate horizontal distance in meters
grid_lat_lon_zip = np.array(list(zip(
gridLatData.ravel(), gridLonData.ravel())), dtype=('float32,float32')) \
.reshape(gridLatData.shape)
gridHorizontalDistance = np.vectorize(
lambda lat_lon_zip: DataUtils.haversine(lat_lon_zip[0], lat_lon_zip[
1], station_lat, station_lon))(grid_lat_lon_zip)
closest2dId = gridHorizontalDistance.argmin()
closest2dId = np.unravel_index(closest2dId, (674, 1058))
closest3dId = (gridHorizontalDistance +
500 * np.abs(gridHeightDifference)).argmin()
closest3dId = np.unravel_index(closest3dId, (674, 1058))
return (stationId, ((closest2dId, gridHorizontalDistance[closest2dId],
gridHeightDifference[closest2dId]),
(closest3dId, gridHorizontalDistance[closest3dId],
gridHeightDifference[closest3dId])))
def __getitem__(self, item):
if item > self.__len__():
raise Exception('Tried to get data point out of range.')
stationId, init = self.files[item]
stationId = int(stationId)
self.loadData((stationId, init))
Label = self.da.temp_station.data
# if target is corrupted return None. This is later filtered out by the custom "collate_fn()" method
if min(Label) < -1e10 or np.isnan(Label).any():
return None
# if this line throws an exception, we are in the first execution of "__getitem__" and thus, we first have to
# define the indices of the desired leads, parameters and grid bounds for a fast access of the data directly by
# indices in the following calls.
try:
IP2d = self.da.cosmo_data.data[
self.lead_idx, self.lower_grid_bound:self.upper_grid_bound,
self.lower_grid_bound:self.upper_grid_bound][:, :, self.
parameter_idx]
except AttributeError:
self.calculateLowerAndUpperGridBound()
all_leads = list(self.da.coords['lead'].data)
all_parameters = list(self.da.coords['feature'].data)
self.lead_idx = all_leads.index(self.lead_time)
self.prediction_idx = [
all_leads.index(pt) for pt in self.prediction_times
]
self.prediction_idx.sort()
self.parameter_idx = [
all_parameters.index(p) for p in self.parameters
]
self.parameter_idx.sort()
IP2d = self.da.cosmo_data.data[
self.lead_idx, self.lower_grid_bound:self.upper_grid_bound,
self.lower_grid_bound:self.upper_grid_bound][:, :, self.
parameter_idx]
# keep un-normalized temperature input from COSMO-1
TEMP_RAW = np.copy(self.da.cosmo_data.data[self.prediction_idx,
self.closest_point_index,
self.closest_point_index,
4] - 273.15)
for p_idx in range(self.n_parameters):
IP2d[:, :, p_idx] = self.featureScaling[p_idx](IP2d[:, :, p_idx])
# add temperature of lead time 0 (initial time of the model run) regardless to the lead time of the prediciton
TEMP_T0 = self.featureScaling[4](self.da.cosmo_data.data[
0, self.lower_grid_bound:self.upper_grid_bound,
self.lower_grid_bound:self.upper_grid_bound][:, :, [4]])
IP2d = np.concatenate((IP2d, TEMP_T0), 2)
# for CNN appraoch we need a structure like (batch_item, features, lat, lon)
IP2d = np.rollaxis(IP2d, 2, 0)
TimeFeatures = self.da.time_data.data[self.lead_idx]
# TODO at the moment preprocessed data with grid size 3 has normalization of time features and grid size 1 has not
if self.master_grid_size == 3:
TimeFeatures[:-1] = DataUtils.normalizeTimeFeatures(
TimeFeatures[:-1])
# get time invariant data for station if already calculated once
try:
(TimeInvGrid,
TimeInvStation) = self.station_time_invariant_grid_data[stationId]
except:
# calculate time invariant data for station for the first time it is used for this station
station_data = self.time_invariant_data.sel(station=stationId)
TimeInvStation = station_data.station_position.sel(
positinal_attribute=['height', 'lat', 'lon']).data
TimeInvGrid = np.rollaxis(
station_data.grid_data.sel(
feature=self.grid_time_invariant_parameters).data[
self.lower_grid_bound:self.upper_grid_bound,
self.lower_grid_bound:self.upper_grid_bound][..., ], 2,
0)
self.station_time_invariant_grid_data[stationId] = (TimeInvGrid,
TimeInvStation)
return Label[self.prediction_idx], np.concatenate(
(IP2d, TimeInvGrid),
0), TimeFeatures, TimeInvStation, (init, stationId, TEMP_RAW)
def runModel(config, data_dictionary, data_statistics, train_test_folds):
program_start_time = time()
# assign all program arguments to local variables
with open(config['model']['path']) as handle:
ModelDict = json.loads(handle.read())
# check if station and grid time invariant features should be used and set the list of desired parameters
if not ('grid_time_invariant' in ModelDict and ModelDict['grid_time_invariant']): config[
'grid_time_invariant_parameters'] = []
if not ('station_time_invariant' in ModelDict and ModelDict['station_time_invariant']): config[
'station_parameters'] = []
# update general static model information
experiment_info = config
experiment_info['model'] = ModelDict
experiment_info['code_commit'] = ModelUtils.get_git_revision_short_hash()
# if needed, load time invariant features
with open("%s/%s/grid_size_%s/time_invariant_data_per_station.pkl" % (
config['input_source'], config['preprocessing'], config['original_grid_size']), "rb") as input_file:
time_invarian_data = pkl.load(input_file)
# initialize feature scaling function for each feature
featureScaleFunctions = DataUtils.getFeatureScaleFunctions(ModelUtils.ParamNormalizationDict, data_statistics)
# get optimizer config
optimizer_config = config['optimizer']
# generate output path for experiment information
setting_string = '%s_grid_%s_bs_%s_tf_%s_optim_%s_lr_%s_sl_%s' % (
config['model']['name'], config['grid_size'], config['batch_size'], config['test_fraction'], optimizer_config['algorithm'], optimizer_config['learning_rate'], config['slice_size'])
output_path = '%s/%s' % (config['experiment_path'], setting_string)
if not os.path.exists(output_path):
raise Exception('Node folder of training run has been found for "%s"' % output_path)
ds = xr.Dataset()
# cross validation
for run in range(config['runs']):
print('[Run %s] Cross-validation test fold %s' % (str(run + 1), str(run + 1)))
stations = sorted(config['stations'])
# take the right preprocessed train/test data set for the current run
train_fold, test_fold = train_test_folds[run]
# get all inits
all_inits_set = set(config['inits'])
# get train and test inits
train_inits_set = set([t[1] for t in train_fold])
test_inits_set = set([t[1] for t in test_fold])
# get all filtered inits
filtere_inits = set(
[init for init in all_inits_set if init not in train_inits_set and init not in test_inits_set])
# make sure, that all sets are distinct
assert filtere_inits ^ train_inits_set ^ test_inits_set == all_inits_set
init_type_mapping = {}
for init in train_inits_set: init_type_mapping[init] = 'train'
for init in test_inits_set: init_type_mapping[init] = 'test'
for init in filtere_inits: init_type_mapping[init] = 'filterd'
all_inits = sorted(list(all_inits_set))
all_data = [(station, init) for init in all_inits for station in stations]
n_data_points = len(all_data)
# keep mappings from init and station to index of result numpy array
station_index_dict = {}
for station_idx, station in enumerate(stations): station_index_dict[station] = station_idx
init_index_dict = {}
for init_idx, init in enumerate(all_inits): init_index_dict[init] = init_idx
# initialize train and test dataloaders
dataset = DataLoaders.ErrorPredictionCosmoData(
config=config,
station_data_dict=data_dictionary,
files=all_data,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
dataloader = DataLoader(dataset, batch_size=config['batch_size'], shuffle=False,
num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)
# initialize network, optimizer and loss function
net = Baseline.model_factory(model_dict=ModelDict, params=dataset.n_parameters, time_invariant_params=dataset.n_grid_time_invariant_parameters,
grid=config['grid_size'], prediction_times=config['prediction_times'])
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
optimizer = optim.SGD(net.parameters(), lr=optimizer_config['learning_rate'], momentum=optimizer_config['momentum'])
net, optimizer, *_ = ModelUtils.load_checkpoint(output_path + '/stored_models/run_%s' % run, model=net,
optimizer=optimizer)
if torch.cuda.is_available():
net.cuda()
# we do not train, but only output the evaluation of the network on train and test data
net.eval()
# initialize result array of errors per init and station and initialize it with NaN
run_error_statistics = np.empty((len(init_index_dict), len(station_index_dict), 5))
run_error_statistics.fill(np.nan)
# loop over complete data set
for i, data in enumerate(dataloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and time inv. features for station
DATA = data
# DATA has only length 4 if we do not use the station time invariant features
if len(DATA) == 4:
Blabel, Bip2d, BTimeData, init_station_temp = DATA
station_time_inv_input = None
elif len(DATA) == 5:
Blabel, Bip2d, BTimeData, StationTimeInv, init_station_temp = DATA
station_time_inv_input = ModelUtils.getVariable(StationTimeInv).float()
else:
raise Exception('Unknown data format for training...')
input = ModelUtils.getVariable(Bip2d).float()
time_data = ModelUtils.getVariable(BTimeData).float()
target = ModelUtils.getVariable(Blabel).float()
except TypeError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Value error...')
continue
out = net(input, time_data, station_time_inv_input).squeeze()
target = target.squeeze()
diff = (out - target).squeeze()
for item in range(Blabel.shape[0]):
init = init_station_temp[0][item]
station = init_station_temp[1][item].item()
cosmo_temperature = init_station_temp[2][item].item()
target_temperature = init_station_temp[3][item].item()
station_idx = station_index_dict[station]
init_idx = init_index_dict[init]
run_error_statistics[init_idx, station_idx, :] = np.array((out[item].item(), cosmo_temperature, target[item].item(), diff[item].item(), target_temperature))
processed_samples = (i + 1) * int(config['batch_size'])
if (i+1) % np.max((1, ((n_data_points // config['batch_size']) // 100))) == 0:
print("%s samples have been processed. [%2.1f%%]" % (processed_samples, (processed_samples / n_data_points) * 100))
sys.stdout.flush()
da = xr.DataArray(run_error_statistics, dims=('init', 'station', 'data'),
coords=[all_inits, stations, ['prediction', 'cosmo', 'target', 'difference', 'target_temperature']])
da = da.sortby(variables='init')
da.attrs['init_type_mapping'] = sorted(list(init_type_mapping.items()))
ds['run_%s' % run] = da
ds.attrs['config'] = config
print('Error results of run %s have been processed.' % run)
# flush output to see progress
sys.stdout.flush()
if not os.path.exists(output_path):
raise Exception('Node folder of training run has been found for "%s"' % output_path)
# dump experiment statistic
with open(output_path + '/model_run_error.pkl', 'wb') as handle:
pkl.dump(ds, handle, protocol=pkl.HIGHEST_PROTOCOL)
# print program execution time
m, s = divmod(time() - program_start_time, 60)
h, m = divmod(m, 60)
print('Experiment has successfully finished in %dh %02dmin %02ds' % (h, m, s))
def runModel(config, data_dictionary, data_statistics, train_test_folds):
program_start_time = time()
# assign all program arguments to local variables
with open(config['model']['path']) as handle:
ModelDict = json.loads(handle.read())
# check if station and grid time invariant features should be used and set the list of desired parameters
if not ('grid_time_invariant' in ModelDict and ModelDict['grid_time_invariant']): config['grid_time_invariant_parameters'] =[]
if not ('station_time_invariant' in ModelDict and ModelDict['station_time_invariant']): config['station_parameters'] = []
# update general static model information
experiment_info = config
experiment_info['model'] = ModelDict
experiment_info['code_commit'] = ModelUtils.get_git_revision_short_hash()
# if needed, load time invariant features
with open("%s/%s/grid_size_%s/time_invariant_data_per_station.pkl" % (config['input_source'], config['preprocessing'], config['original_grid_size']), "rb") as input_file:
time_invarian_data = pkl.load(input_file)
# initialize feature scaling function for each feature
featureScaleFunctions = DataUtils.getFeatureScaleFunctions(ModelUtils.ParamNormalizationDict, data_statistics)
# get optimizer config
optimizer_config = config['optimizer']
# generate output path for experiment information
setting_string = '%s_grid_%s_bs_%s_tf_%s_optim_%s_lr_%s_sl_%s' % (
config['model']['name'], config['grid_size'], config['batch_size'], config['test_fraction'], optimizer_config['algorithm'], optimizer_config['learning_rate'], config['slice_size'])
output_path = '%s/%s' % (config['experiment_path'], setting_string)
if not os.path.exists(output_path):
os.makedirs(output_path)
# time for the set up until first run
experiment_info['set_up_time'] = time() - program_start_time
print('[Time]: Set-up %s' % strftime("%H:%M:%S", gmtime(experiment_info['set_up_time'])))
sys.stdout.flush()
# initialize statistics
error_statistics = None
run_times = None
skip_statistics = None
if 'per_station_rmse' in config:
error_per_station_statistics = None
# keep used learning rates
experiment_info['scheduled_learning_rates'] = []
# cross validation
for run in range(config['runs']):
# logger for tensorboardX
train_logger = Logger(output_path + '/logs/run_%s/train' % run)
test_logger = Logger(output_path + '/logs/run_%s/test' % run)
print('[Run %s] Cross-validation test fold %s' % (str(run + 1), str(run + 1)))
# take the right preprocessed train/test data set for the current run
train_fold, test_fold = train_test_folds[run]
# initialize best epoch test error
best_epoch_test_rmse = float("inf")
# use different data loader if we want to train a 3nn model approach
if "knn" in ModelDict:
# initialize train and test dataloaders
trainset = DataLoaders.CosmoData3NNData(
config=config,
station_data_dict=data_dictionary,
files=train_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
trainloader = DataLoader(trainset, batch_size=config['batch_size'], shuffle=True,
num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)
testset = DataLoaders.CosmoData3NNData(
config=config,
station_data_dict=data_dictionary,
files=test_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
testloader = DataLoader(testset, batch_size=config['batch_size'], shuffle=True,
num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)
else:
# initialize train and test dataloaders
trainset = DataLoaders.CosmoDataGridData(
config=config,
station_data_dict=data_dictionary,
files=train_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
trainloader = DataLoader(trainset, batch_size=config['batch_size'], shuffle=True,
num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)
testset = DataLoaders.CosmoDataGridData(
config=config,
station_data_dict=data_dictionary,
files=test_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
testloader = DataLoader(testset, batch_size=config['batch_size'], shuffle=True,
num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)
# initialize network, optimizer and loss function
net = Baseline.model_factory(ModelDict, trainset.n_parameters, trainset.n_grid_time_invariant_parameters,
config['grid_size'], config['prediction_times'])
# store class name
experiment_info['model_class'] = net.__class__.__name__
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
if torch.cuda.is_available():
net.cuda()
# load number of train and test samples
n_train_samples, n_test_samples = len(train_fold), len(test_fold)
optimizer, scheduler = ModelUtils.initializeOptimizer(optimizer_config, net)
criterion = nn.MSELoss()
# keep number of processed smaples over all epochs for tensorboard
processed_train_samples_global = 0
processed_test_samples_global = 0
# start learning
for epoch in range(config['epochs']):
epoch_train_time = np.zeros((5,))
epoch_start_time = time()
print('Epoch: ' + str(epoch + 1) + '\n------------------------------------------------------------')
# adapt learning rate and store information in experiment attributes
if scheduler is not None:
scheduler.step()
if run == 0: experiment_info['scheduled_learning_rates'] += scheduler.get_lr()
print('Using learning rate %s' % str(scheduler.get_lr()))
# TRAINING
# initialize variables for epoch statistics
LABELS, MODELoutputs, COSMOoutputs = None, None, None
processed_train_samples = 0
net.train(True)
train_start_time = time()
# loop over complete train set
for i, data in enumerate(trainloader, 0):
time_start = time()
try:
# get training batch, e.g. label, cosmo-1 output and time inv. features for station
DATA = data
# DATA has only length 4 if we do not use the station time invariant features
if len(DATA) == 4:
Blabel, Bip2d, BTimeData, init_station_temp = DATA
station_time_inv_input = None
elif len(DATA) == 5:
Blabel, Bip2d, BTimeData, StationTimeInv, init_station_temp = DATA
station_time_inv_input = ModelUtils.getVariable(StationTimeInv).float()
else:
raise Exception('Unknown data format for training...')
input = ModelUtils.getVariable(Bip2d).float()
time_data = ModelUtils.getVariable(BTimeData).float()
target = ModelUtils.getVariable(Blabel).float()
except TypeError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Value error...')
continue
time_after_data_preparation = time()
processed_train_samples += len(Blabel)
optimizer.zero_grad()
out = net(input, time_data, station_time_inv_input)
time_after_forward_pass = time()
loss = criterion(out, target)
loss.backward()
optimizer.step()
time_after_backward_pass = time()
if LABELS is None:
LABELS = Blabel.data
MODELoutputs = out.data
COSMOoutputs = init_station_temp[2].data
else:
LABELS = np.vstack((LABELS, Blabel.data))
MODELoutputs = np.vstack((MODELoutputs, out.data))
COSMOoutputs = np.vstack((COSMOoutputs, init_station_temp[2].data))
time_after_label_stack = time()
if (i + 1) % 64 == 0:
print('Sample: %s \t Loss: %s' % (processed_train_samples, float(np.sqrt(loss.data))))
# ============ TensorBoard logging ============#
# (1) Log the scalar values
info = {
setting_string: np.sqrt(loss.item()),
}
for tag, value in info.items():
train_logger.scalar_summary(tag, value, processed_train_samples_global + processed_train_samples)
# (2) Log values and gradients of the parameters (histogram)
for tag, value in net.named_parameters():
tag = tag.replace('.', '/')
train_logger.histo_summary(tag, ModelUtils.to_np(value), i + 1)
train_logger.histo_summary(tag + '/grad', ModelUtils.to_np(value.grad), i + 1)
epoch_train_time += np.array((time_start - time_end,
time_after_data_preparation - time_start,
time_after_forward_pass - time_after_data_preparation,
time_after_backward_pass - time_after_forward_pass,
time_after_label_stack - time_after_backward_pass))
time_end = time()
# calculate error statistic of current epoch
diff_model = MODELoutputs - LABELS
diff_cosmo = COSMOoutputs - LABELS
epoch_train_rmse_model = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_model, axis=0)
epoch_train_rmse_cosmo = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_cosmo, axis=0)
# update global processed samples
processed_train_samples_global += processed_train_samples
if np.isnan(epoch_train_rmse_model).any():
print("Learning rate too large resulted in NaN-error while training. Stopped training...")
return
# print epoch training times
print('Timing: Waiting on data=%s, Data Preparation=%s,'
'Forward Pass=%s, Backward Pass=%s, Data Stacking=%s' % tuple(list(epoch_train_time / len(epoch_train_time))))
# RMSE of epoch
print('Train/test statistic for epoch: %s' % str(epoch + 1))
print('Train RMSE COSMO: ' , ", ".join(["T=%s: %s" % (idx, epoch_train_rmse_cosmo[idx]) for idx in range(len(epoch_train_rmse_cosmo))]))
print('Train RMSE Model: ' , ", ".join(["T=%s: %s" % (idx, epoch_train_rmse_model[idx]) for idx in range(len(epoch_train_rmse_model))]))
sys.stdout.flush()
train_time = time() - train_start_time
# TESTING
test_start_time = time()
LABELS, MODELoutputs, COSMOoutputs, STATION = None, None, None, None
processed_test_samples = 0
net.eval()
for i, data in enumerate(testloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and time inv. features for station
DATA = data
# DATA has only length 4 if we do not use the station time invariant features
if len(DATA) == 4:
Blabel, Bip2d, BTimeData, init_station_temp = DATA
station_time_inv_input = None
elif len(DATA) == 5:
Blabel, Bip2d, BTimeData, StationTimeInv, init_station_temp = DATA
station_time_inv_input = ModelUtils.getVariable(StationTimeInv).float()
else:
raise Exception('Unknown data format for training...')
input = ModelUtils.getVariable(Bip2d).float()
time_data = ModelUtils.getVariable(BTimeData).float()
target = ModelUtils.getVariable(Blabel).float()
except TypeError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Value error...')
continue
processed_test_samples += len(Blabel)
out = net(input, time_data, station_time_inv_input)
loss = criterion(out, target)
if LABELS is None:
LABELS = Blabel.data
MODELoutputs = out.data
COSMOoutputs = init_station_temp[2].data
STATION = init_station_temp[1].data
else:
LABELS = np.vstack((LABELS, Blabel.data))
MODELoutputs = np.vstack((MODELoutputs, out.data))
COSMOoutputs = np.vstack((COSMOoutputs, init_station_temp[2].data))
STATION = np.hstack((STATION, init_station_temp[1].data))
if i % 16:
# ============ TensorBoard logging ============#
# (1) Log the scalar values
info = {
setting_string: np.sqrt(loss.item()),
}
for tag, value in info.items():
test_logger.scalar_summary(tag, value, processed_test_samples_global + processed_test_samples)
# calculate error statistic of current epoch
diff_model = MODELoutputs - LABELS
diff_cosmo = COSMOoutputs - LABELS
# rmse
epoch_test_rmse_model = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_model, axis=0)
epoch_test_rmse_cosmo = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_cosmo, axis=0)
overall_test_rmse_model = ModelUtils.rmse(diff_model)
overall_test_rmse_cosmo = ModelUtils.rmse(diff_cosmo)
# mae
epoch_test_mae_model = np.apply_along_axis(func1d=ModelUtils.mae, arr=diff_model, axis=0)
epoch_test_mae_cosmo = np.apply_along_axis(func1d=ModelUtils.mae, arr=diff_cosmo, axis=0)
overall_test_mae_model = ModelUtils.mae(diff_model)
overall_test_mae_cosmo = ModelUtils.mae(diff_cosmo)
# calculate per station rmse if desired (especially for K-fold station generalization experiment
if "per_station_rmse" in config:
max_station_id = 1435
squared_errors_per_epoch = np.array((np.square(diff_model), np.square(diff_cosmo))).squeeze()
# the highest index of data is 1435, thus we expect at least 1435 entries, which we can access by
# station id
test_samples_per_station = np.bincount(STATION, minlength=max_station_id+1)
model_squared_error_per_station = np.bincount(STATION, weights=squared_errors_per_epoch[0], minlength=max_station_id+1)
cosmo_squared_error_per_station = np.bincount(STATION, weights=squared_errors_per_epoch[1], minlength=max_station_id+1)
# set division by zero/NaN warning to 'ignore'
np.seterr(divide='ignore', invalid='ignore')
# calculate rmse per station
rmse_per_station = np.vstack((np.sqrt(np.divide(model_squared_error_per_station, test_samples_per_station)),
np.sqrt(np.divide(cosmo_squared_error_per_station, test_samples_per_station)))).T
# set division by zero/NaN warning to 'warn'
np.seterr(divide='warn', invalid='warn')
# update global processed samples
processed_test_samples_global += processed_test_samples
# RMSE of epoch
print('Test RMSE COSMO: ', ", ".join(
["T=%s: %s" % (idx, epoch_test_rmse_cosmo[idx]) for idx in range(len(epoch_test_rmse_cosmo))]),
" (Overall: %s" % overall_test_rmse_cosmo)
print('Test RMSE Model: ' , ", ".join(["T=%s: %s" % (idx, epoch_test_rmse_model[idx]) for idx in range(len(epoch_test_rmse_model))]),
" (Overall: %s" % overall_test_rmse_model)
# mae of epoch
print('Test MAE COSMO: ', ", ".join(
["T=%s: %s" % (idx, epoch_test_mae_cosmo[idx]) for idx in range(len(epoch_test_mae_cosmo))]),
" (Overall: %s" % overall_test_mae_cosmo)
print('Test MAE Model: ' , ", ".join(["T=%s: %s" % (idx, epoch_test_mae_model[idx]) for idx in range(len(epoch_test_mae_model))]),
" (Overall: %s" % overall_test_mae_model)
sys.stdout.flush()
test_time = time() - test_start_time
# time for epoch
epoch_time = time() - epoch_start_time
# update error statistics
error_statistics = ModelUtils.updateErrorStatistic(error_statistics,
np.array([epoch_train_rmse_model, epoch_test_rmse_model])[None, None, ...],
run, epoch, config['prediction_times'])
# update run times statistic
run_times = ModelUtils.updateRuntimeStatistic(run_times, np.array([epoch_time, train_time, test_time])[None, None, ...],
run, epoch)
# update skip statistic
skip_statistics = ModelUtils.updateSkipStatistic(skip_statistics,
np.array([n_train_samples, processed_train_samples,
n_test_samples, processed_test_samples])[None, None, ...],
run, epoch)
# update per station rmse data array over runs if desired (especially for K-fold station generalization experiment
if "per_station_rmse" in config:
error_per_station_statistics = ModelUtils.updatePerStationErrorStatistic(error_per_station_statistics, rmse_per_station, run, epoch, np.arange(max_station_id+1))
# store model if it was the best yes
is_best = overall_test_rmse_model <= best_epoch_test_rmse
best_epoch_test_rmse = min(overall_test_rmse_model, best_epoch_test_rmse)
ModelUtils.save_checkpoint({
'epoch': epoch,
'run': run,
'arch': net.__class__.__name__,
'state_dict': net.state_dict(),
'overall_test_rmse': overall_test_rmse_model,
'lead_test_rmse' : overall_test_rmse_model,
'best_epoch_test_rmse': best_epoch_test_rmse,
'optimizer': optimizer.state_dict(),
}, is_best, output_path + '/stored_models/run_%s' % run)
# flush output to see progress
sys.stdout.flush()
# update statistics dict
ModelUtils.get_model_details(experiment_info, net, optimizer, criterion)
# complete program runtime
experiment_info['program_runtime'] = time() - program_start_time
# generate data set of all experiment statistics and additional information
experiment_statistic = xr.Dataset({
'error_statistic' : error_statistics,
'run_time_statistic': run_times,
'samples_statistic' : skip_statistics}).assign_attrs(experiment_info)
# dump experiment statistic
with open(output_path + '/experiment_statistic.pkl', 'wb') as handle:
pkl.dump(experiment_statistic, handle, protocol=pkl.HIGHEST_PROTOCOL)
if 'per_station_rmse' in config:
# dump experiment statistic
with open(output_path + '/rmse_per_station.pkl', 'wb') as handle:
pkl.dump(error_per_station_statistics, handle, protocol=pkl.HIGHEST_PROTOCOL)
# print program execution time
m, s = divmod(experiment_info['program_runtime'], 60)
h, m = divmod(m, 60)
print('Experiment has successfully finished in %dh %02dmin %02ds' % (h, m, s))
def CreateDataByStationAndInit(GridSize, DateBegin, DateEnd, PredictionWindow,
ListParam, WithTopo, TopoListParam, isLocal,
n_parallel):
time_begin = time()
if DateEnd < DateBegin:
raise Exception('DateEnd is smaller than DateBegin.')
assert GridSize % 2 == 1, 'Grid size must be an odd number.'
# different paths, whether we run the script locally or on a cluster node
if isLocal:
ADDRESSdata = '/home/n1no/Documents/ethz/master_thesis/code/project/data/cosmo-1/data_subset' # COSMO-1 outputs
ADDRESStopo = '/home/n1no/Documents/ethz/master_thesis/code/project/data' # base address of topo files
ADDRESSobst = '/home/n1no/Documents/ethz/master_thesis/code/project/data/observations/' # base adress of obs files
DESTINATION = '/home/n1no/Documents/ethz/master_thesis/code/project/data/preprocessed_data/station_init/grid_size_' + str(
GridSize) # target directory for all generated files
else:
ADDRESSdata = '/mnt/data/bhendj/full/cosmo-1' # COSMO-1 outputs
ADDRESStopo = '/mnt/ds3lab-scratch/ninow/topo' # base address of topo files
ADDRESSobst = '/mnt/ds3lab-scratch/ninow/observations' # base adress of obs files
DESTINATION = '/mnt/ds3lab-scratch/ninow/preprocessed_data/station_init/grid_size_' + str(
GridSize) # target directory for all generated files
# create an output folder for each station, based on the station ids
OBS = xr.open_dataset(ADDRESSobst + '/meteoswiss_t2m_20151001-20180331.nc')
station_ids = OBS['station_id'].data
OBS.close()
station_paths = []
for S in station_ids:
# prepare output folders for each station
station_paths += [DESTINATION + '/Station_' + str(S)]
if not os.path.exists(station_paths[-1]):
os.makedirs(station_paths[-1])
# get all COSMO-1 files that are in the given time interval and have not yet been processed and thus do not
# already exists in the output folder
folders = DataUtils.getFilesToProcess(ADDRESSdata, DESTINATION,
'StationAndInit', DateBegin, DateEnd)
folders.sort()
# calculate begin and end index of array to exclude files, that are not in the specified time interval
begin, end = -1, -1
for idx, folder in enumerate(folders):
if folder[:-4] >= DateBegin:
begin = idx
break
for idx, folder in enumerate(folders):
if folder[:-4] <= DateEnd:
end = idx
else:
break
if begin == -1 or end == -1:
raise Exception('Could not find start or end in array.')
folders = folders[begin:end + 1]
print('%s files are left to be preprocessed.' % len(folders))
# split the folders into K approx. equal splits
if n_parallel <= 1:
folder_splits = [folders]
else:
n_folders = len(folders)
indices = np.linspace(0, n_folders, n_parallel + 1).astype(int)
folder_splits = [
folders[indices[i]:indices[i + 1]] for i in range(n_parallel)
]
folder_splits = [l for l in folder_splits if len(l) > 0]
# take timestamp after set-up
time_setup = time()
with Pool(processes=n_parallel) as pool:
# run preprocessing in parallel for all splits and keep the processes in a list to sync them later
process_results = []
for idx_split, split in enumerate(folder_splits):
print('Process %s with range [%s, %s] queued.' %
(idx_split, split[0], split[-1]))
# only calculate topo data by the first process, since it is invariant
if idx_split == 0:
isTopo = WithTopo
else:
isTopo = 0
process_results.append(
pool.apply_async(
GetData, (idx_split, ADDRESSdata, ADDRESStopo, ADDRESSobst,
DESTINATION, ListParam, TopoListParam, GridSize,
isTopo, split, PredictionWindow, isLocal)))
# forces the parent process to wait on all forked children processes
for ps_idx, ps_result in enumerate(process_results):
# sync processes
_ = ps_result.get()
print('[Process %s] Synchronized after data creation.' % ps_idx)
# take timestamp after completing all processes
time_end = time()
# dump preprocessing information in a descriptive JSON file
preprocessing_information = {
'grid_size': GridSize,
'data_begin': DateBegin,
'data_end': DateEnd,
'parameters': ListParam,
'future_hours': PredictionWindow,
'n_processes': n_parallel,
'time_setup': str(timedelta(seconds=(time_setup - time_begin))),
'time_preprocessing': str(timedelta(seconds=(time_end - time_setup)))
}
preprocessing_information_json = json.dumps(preprocessing_information)
f = open(DESTINATION + '/setup.json', 'w')
f.write(preprocessing_information_json)
f.close()
print('Preprocessing sucessfully finished in %s.' %
str(timedelta(seconds=(time_end - time_begin))))
def GetData(processId, ADDRESSdata, ADDRESStopo, ADDRESSobst, DESTINATION,
ListParam, TopoListParam, GridSize, WithTopo, Files,
PredictionWindow, isLocal):
# processId: (int) -> the id of the process running this method
# ADDRESSdata: (string) -> base path to COSMO-1 data
# ADDRESStopo: (string) -> base path to all topology files
# ADDRESSobs: (string) -> base path to all observation files
# DESTINATION: (string) -> base path to target output folder
# GridSize: (int)-> side length of square around each station
# WithTopo: (bool)-> whether we want to generate preprocessed time invariant features for each station
# Files: (list(string)) -> list of all files ('yymmddHH') to be processed, e.g. ['15031203', '15031206', ...]
# PredictionWindow: (list of int) -> all future hours t's [t,t+1,t+2,...] being processed, e.g. y_t, y_t+1,...
# isLocal: (bool) -> for setting the right paths if the script is running on a local machine or in cloud, etc.
# path to observation and topological data
if isLocal:
OBS = xr.open_dataset(ADDRESSobst +
'/meteoswiss_t2m_20151001-20180331.nc')
TOPO = xr.open_dataset(ADDRESStopo + '/topodata.nc')
else:
# to fix parallelization errors, each process gets its own set of TOPO and OBS files
OBS = xr.open_dataset(
ADDRESSobst +
'/process_%s/meteoswiss_t2m_20151001-20180331.nc' % processId)
TOPO = xr.open_dataset(ADDRESStopo +
'/process_%s/topodata.nc' % processId)
# load all station ids
stationIds = OBS['station_id'].data
# generate a view on temperature observation at each station
TempObs = OBS['t2m'].sel(station_id=stationIds)
# we need to localize the stations on the 1058*674 grid
GPSgrid = np.dstack((
TOPO['lat'][:, :],
TOPO['lon'][:, :])) # 1058*674*2 grid of lon lat values of each square
# a list with the (lat,lon)-id of the nearest grid point for each station
closestGridPointPerStation = []
# a dictionary with the sub-grid around each station
stationSquaresDict = {}
# generate sub-grids for each station of the closest GridSize**2 grid points
for S in stationIds:
# we compute each grid square's distance with the station
# and we take the one with the smalles distance to be our reference
dist = GPSgrid - np.array(
[[OBS['lat'].sel(station_id=S), OBS['lon'].sel(station_id=S)]])
dist *= dist
Id = (dist.sum(axis=2)).argmin()
Id = np.unravel_index(Id, (674, 1058))
closestGridPointPerStation += [
Id
] # Id=(x,y) coordinates of the station (approx.. to the closest point) on the 1058*674 grid
SQUARE = {}
# the variable stationSquaresData contains, for each station, the coord of the squares which form an N*N grid around the station
SQUARE['lat_idx'] = [
x + Id[0] - int(GridSize / 2) for x in range(GridSize)
]
SQUARE['lon_idx'] = [
x + Id[1] - int(GridSize / 2) for x in range(GridSize)
]
stationSquaresDict[S] = SQUARE
# pandas data frame with dictionary of the sub-grid for each station
stationSquares = pd.DataFrame(data=stationSquaresDict)
# extract time invariant features for each station and the corresponding sub-grid
if WithTopo:
if not os.path.exists(DESTINATION +
'/time_invariant_data_per_station.pkl'):
ds = DataUtils.getTimeInvariantStationFeatures(
TOPO=TOPO,
OBS=OBS,
stationSquares=stationSquares,
stationIds=stationIds,
closestGridPointPerStation=closestGridPointPerStation,
GridSize=GridSize,
Features=TopoListParam)
with open(DESTINATION + '/time_invariant_data_per_station.pkl',
'wb') as handle:
pkl.dump(ds, handle, protocol=pkl.HIGHEST_PROTOCOL)
ds.close()
print(
'[Process %s] Time invariant features have been processed and stored.'
% processId)
else:
print(
'Time invariant features have been found on disk and were therefore not created again.'
)
# we now start the iteration through: Each folder, each file, each parameter, each station
for file in Files: # loop over all outputs of COSMO-1, e.g. for 3h interval every day
try:
# mark start of preprocessing of n-th file
print('[Process %s] Start processing %s' % (processId, file))
# initialize data variables
DATA = np.zeros(
(len(stationIds), len(PredictionWindow), GridSize, GridSize,
len(ListParam)))
TempForecast = np.zeros((len(stationIds), len(PredictionWindow)))
Target = np.zeros((len(stationIds), len(PredictionWindow)))
TimeStamp = np.zeros((len(PredictionWindow)))
TimeData = np.zeros((len(PredictionWindow), 5))
for idx_T, T in enumerate(
PredictionWindow
): # loop over all future predictions, e.g. current hour + T
if T < 10:
NAME = ADDRESSdata + '/' + file + '/c1ffsurf00' + str(
T) + '.nc'
else:
NAME = ADDRESSdata + '/' + file + '/c1ffsurf0' + str(
T) + '.nc'
# load netCRF4 dataset
dataset = xr.open_dataset(NAME)
# get initialization time of COSMO-1 data point
t = dataset['time'].data
# check that we do not process a data point before the first observation
if t < OBS['time'].data[0]:
print('[Process %s] Skipped %s' % (processId, file))
raise SkipException()
# Transform day and hour into a cyclic datetime feature
days_rad = (DataUtils.passed_days_per_month_dict[int(
file[2:4])] + int(file[4:6])) / 365 * (2 * np.pi)
hours = (int(file[6:8]) + T) % 24
hour_rad = hours / 24 * (2 * np.pi)
TimeData[idx_T] = [
np.cos(hour_rad),
np.sin(hour_rad),
np.cos(days_rad),
np.sin(days_rad), T / 33
]
# ______________________________________________________________________
for P in range(len(ListParam)):
MAP = dataset[ListParam[P]].data.squeeze()
for idx_S, S in enumerate(stationIds):
stationSquare = stationSquares[S]
DATA[idx_S, idx_T, :, :, P] = MAP[
stationSquare.lat_idx][:, stationSquare.lon_idx]
# We compare the forecasted temperature with the actual observation
MAP = np.squeeze(dataset['T']).data
TempForecast[:, idx_T] = np.array(
[MAP[x] for x in closestGridPointPerStation])
TimeStamp[idx_T] = t[0]
# this dataset is not used anymore and can be closed
dataset.close()
try:
Target[:, idx_T] = TempObs.sel(time=t).data
except RuntimeError:
print('Error with time=%s.' % t)
raise
# we write the data in a binary file
for idx_S, S in enumerate(stationIds):
leads = PredictionWindow
time_features = [
'cos_hour', 'sin_hour', 'cos_day', 'sin_day', 'lead'
]
lats = stationSquares[S].lat_idx
lons = stationSquares[S].lon_idx
dims = ('lead', 'lat', 'lon', 'feature')
cosmo_data = xr.DataArray(
DATA[idx_S],
dims=dims,
coords=[leads, lats, lons, ListParam])
temp_forecast = xr.DataArray(TempForecast[idx_S],
dims=('lead'),
coords=[leads])
temp_station = xr.DataArray(Target[idx_S],
dims=('lead'),
coords=[leads])
time_data = xr.DataArray(TimeData,
dims=('lead', 'time_feature'),
coords=[leads, time_features])
time_data.attrs['time_stamp'] = TimeStamp
ds = xr.Dataset({
'cosmo_data': cosmo_data,
'temp_forecast': temp_forecast,
'temp_station': temp_station,
'time_data': time_data
})
ds.attrs['station_id'] = S
try:
# ds.to_netcdf(DESTINATION + '/Station_%s/%s.nc' % (S, file[:8]))
with open(
DESTINATION + '/Station_%s/%s.pkl' % (S, file[:8]),
'wb') as handle:
pkl.dump(ds, handle, protocol=pkl.HIGHEST_PROTOCOL)
except FileNotFoundError:
fileExists = os.path.exists(DESTINATION +
'/Station_%s' % S)
print('Error that file does not exist, check says: %s' %
str(fileExists))
raise
ds.close()
# print that processing of data point has been completed
print('[Process %s] Finished %s' % (processId, file))
except SkipException:
continue
OBS.close()
TOPO.close()
print('[Process %s] Data split successfully preprocessed!' % processId)
return 1
def CreateData(config, data_dictionary, data_statistics, train_test_folds):
# assign all program arguments to local variables
with open(config['model']['path']) as handle:
ModelDict = json.loads(handle.read())
# check if station and grid time invariant features should be used and set the list of desired parameters
if not ('grid_time_invariant' in ModelDict
and ModelDict['grid_time_invariant']):
config['grid_time_invariant_parameters'] = []
if not ('station_time_invariant' in ModelDict
and ModelDict['station_time_invariant']):
config['station_parameters'] = []
# if needed, load time invariant features
with open(
"%s/%s/grid_size_%s/time_invariant_data_per_station.pkl" %
(config['input_source'], config['preprocessing'],
config['original_grid_size']), "rb") as input_file:
time_invarian_data = pkl.load(input_file)
# initialize feature scaling function for each feature
featureScaleFunctions = DataUtils.getFeatureScaleFunctions(
ModelUtils.ParamNormalizationDict, data_statistics)
# add revision short hash to the config
config['code_commit'] = ModelUtils.get_git_revision_short_hash()
# take the right preprocessed train/test data set for the first run
train_fold, test_fold = train_test_folds[0]
# initialize train and test dataloaders
trainset = DataLoaders.CosmoDataGridData(
config=config,
station_data_dict=data_dictionary,
files=train_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
trainloader = DataLoader(trainset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['n_loaders'],
collate_fn=DataLoaders.collate_fn)
testset = DataLoaders.CosmoDataGridData(
config=config,
station_data_dict=data_dictionary,
files=test_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
testloader = DataLoader(testset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['n_loaders'],
collate_fn=DataLoaders.collate_fn)
# loop over complete train set
train_data = None
train_inits = []
train_stations = None
for i, data in enumerate(trainloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and time inv. features for station
DATA = data
# DATA has only length 4 if we do not use the station time invariant features
if len(DATA) == 4:
Blabel, Bip2d, BTimeData, init_station_temp = DATA
station_time_inv_input = None
elif len(DATA) == 5:
Blabel, Bip2d, BTimeData, StationTimeInv, init_station_temp = DATA
station_time_inv_input = ModelUtils.getVariable(
StationTimeInv).float()
else:
raise Exception('Unknown data format for training...')
input = ModelUtils.getVariable(Bip2d).float()
time_data = ModelUtils.getVariable(BTimeData).float()
target = ModelUtils.getVariable(Blabel).float()
try:
batch_data = np.concatenate(
(input.squeeze(), station_time_inv_input, time_data,
target, init_station_temp[2]),
axis=1)
except:
batch_data = np.concatenate(
(input.squeeze(), time_data, target, init_station_temp[2]),
axis=1)
train_inits += init_station_temp[0]
if train_data is None:
train_data = batch_data
train_stations = init_station_temp[1]
else:
train_data = np.vstack((train_data, batch_data))
train_stations = np.hstack(
(train_stations, init_station_temp[1]))
except TypeError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Value error...')
continue
# define column names for data frame
column_names = [
'Pressure', 'Wind U-Comp.', 'Wind V-Comp.', 'Wind VMAX',
'2m-Temperature', 'Temp. of Dew Point', 'Cloud Coverage (High)',
'Cloud Coverage (Medium)', 'Cloud Coverage (Low)',
'Tot. Precipitation', 'ALB_RAD', 'ASOB', 'ATHB', 'HPBL',
'2m-Temperature (Lead=0)'
]
column_names += [
'Grid Height', 'Grid-Station Height Diff.', 'Fraction of Land',
'Soiltype', 'Latitiude', 'Longitued', 'Grid-Station 2d Distance'
]
if train_data.shape[1] >= 31:
column_names += [
'Station Height', 'Station Latitude', 'Station Longitude'
]
column_names += [
'Hour (Cosine)', 'Hour (Sine)', 'Month (Cosine)', 'Month (Sine)',
'Lead-Time'
]
column_names += ['Target 2m-Temp.']
column_names += ['COSMO 2m-Temp.']
train_keys = pd.DataFrame.from_dict({
'Station': train_stations,
'Init': train_inits
})
train_data = pd.DataFrame(data=train_data, columns=column_names)
train_ds = pd.concat([train_keys, train_data], axis=1)
test_data = None
test_inits = []
test_stations = None
for i, data in enumerate(testloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and time inv. features for station
DATA = data
# DATA has only length 4 if we do not use the station time invariant features
if len(DATA) == 4:
Blabel, Bip2d, BTimeData, init_station_temp = DATA
station_time_inv_input = None
elif len(DATA) == 5:
Blabel, Bip2d, BTimeData, StationTimeInv, init_station_temp = DATA
station_time_inv_input = ModelUtils.getVariable(
StationTimeInv).float()
else:
raise Exception('Unknown data format for training...')
input = ModelUtils.getVariable(Bip2d).float()
time_data = ModelUtils.getVariable(BTimeData).float()
target = ModelUtils.getVariable(Blabel).float()
try:
batch_data = np.concatenate(
(input.squeeze(), station_time_inv_input, time_data,
target, init_station_temp[2]),
axis=1)
except:
batch_data = np.concatenate(
(input.squeeze(), time_data, target, init_station_temp[2]),
axis=1)
test_inits += init_station_temp[0]
if test_data is None:
test_data = batch_data
test_stations = init_station_temp[1]
else:
test_data = np.vstack((test_data, batch_data))
test_stations = np.hstack(
(test_stations, init_station_temp[1]))
except TypeError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Value error...')
continue
test_keys = pd.DataFrame.from_dict({
'Station': test_stations,
'Init': test_inits
})
test_data = pd.DataFrame(data=test_data, columns=column_names)
test_ds = pd.concat([test_keys, test_data], axis=1)
network_ready_data_path = config['input_source'] + '/network_ready_data'
if not os.path.exists(network_ready_data_path):
os.makedirs(network_ready_data_path)
network_ready_train_data_path = network_ready_data_path + '/train_data'
network_ready_test_data_path = network_ready_data_path + '/test_data'
train_ds.to_pickle(network_ready_train_data_path)
test_ds.to_pickle(network_ready_test_data_path)
# shap specific config entries for analysis in jupyter notebook
config['train_data_path'] = network_ready_data_path + '/train_data'
config['test_data_path'] = network_ready_data_path + '/test_data'
# dump config
with open(network_ready_data_path + '/config.pkl', 'wb') as handle:
pkl.dump(config, handle, protocol=pkl.HIGHEST_PROTOCOL)
print('Network ready data analysis successfully executed.')
def CreateBaselineData(DateBegin, DateEnd, PredictionWindow, isLocal,
n_parallel):
time_begin = time()
if DateEnd < DateBegin:
raise Exception('DateEnd is smaller than DateBegin.')
# different paths, whether we run the script locally or on a cluster node
if isLocal:
ADDRESSdata = '/home/n1no/Documents/ethz/master_thesis/code/project/data/cosmo-1/data_subset' # COSMO-1 outputs
ADDRESStopo = '/home/n1no/Documents/ethz/master_thesis/code/project/data' # base address of topo files
ADDRESSobst = '/home/n1no/Documents/ethz/master_thesis/code/project/data/observations/' # base adress of obs files
DESTINATION = '/home/n1no/Documents/ethz/master_thesis/code/project/data/preprocessed_data/baseline' # target directory for all generated files
else:
ADDRESSdata = '/mnt/data/bhendj/full/cosmo-1' # COSMO-1 outputs
ADDRESStopo = '/mnt/ds3lab-scratch/ninow/topo' # base address of topo files
ADDRESSobst = '/mnt/ds3lab-scratch/ninow/observations' # base adress of obs files
DESTINATION = '/mnt/ds3lab-scratch/ninow/preprocessed_data/baseline' # target directory for all generated files
if not os.path.exists(DESTINATION):
os.makedirs(DESTINATION)
# create an output folder for each station, based on the station ids
OBS = xr.open_dataset(ADDRESSobst + '/meteoswiss_t2m_20151001-20180331.nc')
TOPO = xr.open_dataset(ADDRESStopo + '/topodata.nc')
station_ids = OBS['station_id'].data
# extract time invariant features for each station and the corresponding sub-grid
if not os.path.exists(DESTINATION + '/station_neighbors.pkl'):
station_neighbors = {}
# calculate for each station the neighbors on the grid in parallel
with Pool(processes=n_parallel) as pool:
process_results = []
gridHeightData = TOPO.HH.data
gridLatData = TOPO.lat.data
gridLonData = TOPO.lon.data
# start a new process with the work function for each data split
for idx_S, S in enumerate(station_ids):
# calculate height difference between grid points and station
station_height = OBS['height'].sel(station_id=S).data
station_lat = OBS['lat'].sel(station_id=S).data
station_lon = OBS['lon'].sel(station_id=S).data
print('Neighborhood calculation for staiton %s queued.' % S)
process_results.append(
pool.apply_async(
getStationNeighbors,
(S, gridHeightData, gridLatData, gridLonData,
station_height, station_lat, station_lon)))
# aggregate results from all processes
for ps_idx, ps_result in enumerate(process_results):
# sync processes
S, neighbor_data = ps_result.get()
station_neighbors[S] = neighbor_data
print('[Process %s] Synchronized after data creation.' %
ps_idx)
with open(DESTINATION + '/station_neighbors.pkl', 'wb') as handle:
pkl.dump(station_neighbors, handle, protocol=pkl.HIGHEST_PROTOCOL)
print(
'Station time invariant features have been calculated and stored.')
else:
with open(DESTINATION + '/station_neighbors.pkl', 'rb') as handle:
station_neighbors = pkl.load(handle)
print(
'Station time invariant features have been found on disk and were therefore not created again.'
)
OBS.close()
TOPO.close()
for S in station_ids:
temp_output_path = DESTINATION + '/temp/station_%s' % S
if not os.path.exists(temp_output_path):
os.makedirs(temp_output_path)
# get all COSMO-1 files that are in the given time interval and have not yet been processed and thus do not
# already exists in the output folder
folders = DataUtils.getFilesToProcess(ADDRESSdata, DESTINATION, 'Station',
DateBegin, DateEnd)
folders.sort()
# calculate begin and end index of array to exclude files, that are not in the specified time interval
begin, end = -1, -1
for idx, folder in enumerate(folders):
if folder[:-4] >= DateBegin:
begin = idx
break
for idx, folder in enumerate(folders):
if folder[:-4] <= DateEnd:
end = idx
else:
break
if begin == -1 or end == -1:
raise Exception('Could not find start or end in array.')
folders = folders[begin:end + 1]
print('%s files are left to be preprocessed.' % len(folders))
# split the folders into K approx. equal splits
if n_parallel <= 1:
folder_splits = [folders]
else:
n_folders = len(folders)
indices = np.linspace(0, n_folders, n_parallel + 1).astype(int)
folder_splits = [
folders[indices[i]:indices[i + 1]] for i in range(n_parallel)
]
folder_splits = [l for l in folder_splits if len(l) > 0]
# take timestamp after set-up
time_setup = time()
# run preprocessing in parallel for all splits and keep the processes in a list to sync them later
# calculate min/max and select samples on data split in parallel
with Pool(processes=n_parallel) as pool:
process_results = []
# start a new process with the work function for each data split
for idx_split, split in enumerate(folder_splits):
print('Process %s with range [%s, %s] queued.' %
(idx_split, split[0], split[-1]))
process_results.append(
pool.apply_async(
GetDataWrapper,
(idx_split, ADDRESSdata, ADDRESStopo, ADDRESSobst,
DESTINATION, split, station_neighbors, PredictionWindow,
isLocal)))
# aggregate results from all processes
for ps_idx, ps_result in enumerate(process_results):
# sync processes
result = ps_result.get()
print('[Process %s] Synchronized after data creation.' % ps_idx)
station_folders_paths = [
f for f in os.listdir(DESTINATION + '/temp')
if re.match(r'^station_([0-9]+?)$', f)
]
process_results = []
for ps_idx, station_folder in enumerate(station_folders_paths):
print('Process %s with station folder %s queued.' %
(ps_idx, station_folder))
process_results.append(
pool.apply_async(aggregateProcessFiles,
(ps_idx, DESTINATION, station_folder)))
# aggregate results from all processes
for ps_idx, ps_result in enumerate(process_results):
# sync processes
result = ps_result.get()
print('[Process %s] Synchronized after aggregation.' % ps_idx)
# take timestamp after completing all processes
time_end = time()
# dump preprocessing information in a descriptive JSON file
preprocessing_information = {
'data_begin': DateBegin,
'data_end': DateEnd,
'future_hours': PredictionWindow,
'n_processes': n_parallel,
'time_setup': str(timedelta(seconds=(time_setup - time_begin))),
'time_preprocessing': str(timedelta(seconds=(time_end - time_setup)))
}
preprocessing_information_json = json.dumps(preprocessing_information)
f = open(DESTINATION + '/setup.json', 'w')
f.write(preprocessing_information_json)
f.close()
print('Station baseline reprocessing sucessfully finished in %s.' %
str(timedelta(seconds=(time_end - time_begin))))
% (config['distance_metric'], h, m, s))
# runs a generalization experiment on stations only used in prediction
# this requires >=1 model config file in the "models" folder of an experiment and an "experiment_parameters.txt" file
# sample model configs and "experiment_parameters.txt" files can be found under /results/runs/spatial_generalization
# IMPORTANT: in the "experiment_parameters.txt" file one has to specify, how many test stations should be use (and therefore
# left out for training) and what station is the first in consecutive order to defined the test stations
elif options.script == 'spatialGeneralizationExperiment':
experiment_start = time()
config, train_test_folds, data_dictionary, data_statistics = ModelUtils.setUpModelRun(
options=options, G=G)
# we filter out in config specified test station from train set and filter all train
# stations from test set for each run
train_test_folds = DataUtils.filterUnseenTestStations(
train_test_folds=train_test_folds, config=config)
print('Starting to run %s' % options.script)
print("Test Stations:", config['test_stations'])
models = [
f[:-4] for f in os.listdir(config['experiment_path'] + '/models')
]
n_models = len(models)
print('%s models found to run.' % n_models)
for m_idx, m in enumerate(models):
config['model'][
'path'] = config['experiment_path'] + '/models/%s.txt' % m
config['model']['name'] = m
ModelRun.runModel(config=config,
def runModel(config, data_dictionary, data_statistics, train_test_folds):
# load time invariant data
source_path = config['input_source']
experiment_path = config['experiment_path']
# assign all program arguments to local variables
config['batch_size'] = 1
config['runs'] = 3
config['grid_size'] = 9
# if needed, load time invariant features
with open(
"%s/%s/grid_size_%s/time_invariant_data_per_station.pkl" %
(config['input_source'], config['preprocessing'],
config['original_grid_size']), "rb") as input_file:
time_invarian_data = pkl.load(input_file)
# initialize feature scaling function for each feature
featureScaleFunctions = DataUtils.getFeatureScaleFunctions(
ParamNormalizationDict, data_statistics)
plot_config = {
'features': config['input_parameters'],
'time_invariant_features': config['grid_time_invariant_parameters'],
'station_features': config['station_parameters']
}
# cross validation
for run in range(config['runs']):
print('[Run %s] Cross-validation test fold %s' %
(str(run + 1), str(run + 1)))
# take the right preprocessed train/test data set for the current run
train_fold, test_fold = train_test_folds[run]
# initialize train and test dataloaders
trainset = DataLoaders.SinglePredictionCosmoData(
config=config,
station_data_dict=data_dictionary,
files=train_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
trainloader = DataLoader(trainset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['n_loaders'],
collate_fn=DataLoaders.collate_fn)
testset = DataLoaders.SinglePredictionCosmoData(
config=config,
station_data_dict=data_dictionary,
files=test_fold,
featureScaling=featureScaleFunctions,
time_invariant_data=time_invarian_data)
testloader = DataLoader(testset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['n_loaders'],
collate_fn=DataLoaders.collate_fn)
train_features = [[] for _ in trainset.parameters]
train_time_invariant_grid_features = [
[] for _ in trainset.grid_time_invariant_parameters
]
train_station_features = [[] for _ in trainset.station_parameters]
train_labels = []
# loop over complete train set
for i, data in enumerate(trainloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and external features
Blabel, Bip2d, StationTimeInv = data
except ValueError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Value error...')
continue
train_labels += list(Blabel.numpy().flatten())
for feature_idx, _ in enumerate(trainset.parameters):
train_features[feature_idx] += list(
Bip2d[:, feature_idx, :, :].numpy().flatten())
for ti_feature_idx, _ in enumerate(
trainset.grid_time_invariant_parameters):
train_time_invariant_grid_features[ti_feature_idx] += list(
Bip2d[:, trainset.n_parameters +
ti_feature_idx, :, :].numpy().flatten())
for station_feature_idx, _ in enumerate(
trainset.station_parameters):
train_station_features[station_feature_idx] += list(
StationTimeInv[:, station_feature_idx].numpy().flatten())
test_features = [[] for _ in testset.parameters]
test_time_invariant_grid_features = [
[] for _ in testset.grid_time_invariant_parameters
]
test_station_features = [[] for _ in testset.station_parameters]
test_labels = []
# loop over complete train set
for i, data in enumerate(testloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and external features
Blabel, Bip2d, StationTimeInv = data
except ValueError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Value error...')
continue
test_labels += list(Blabel.numpy().flatten())
for feature_idx, _ in enumerate(testset.parameters):
test_features[feature_idx] += list(
Bip2d[:, feature_idx, :, :].numpy().flatten())
for ti_feature_idx, _ in enumerate(
testset.grid_time_invariant_parameters):
test_time_invariant_grid_features[ti_feature_idx] += list(
Bip2d[:, testset.n_parameters +
ti_feature_idx, :, :].numpy().flatten())
for station_feature_idx, _ in enumerate(
testset.station_parameters):
test_station_features[station_feature_idx] += list(
StationTimeInv[:, station_feature_idx].numpy().flatten())
plot_config['run'] = run
PlotUtils.plotFeatureDistribution(
output_path=experiment_path,
config=plot_config,
train_features=train_features,
train_time_invariant_grid_features=
train_time_invariant_grid_features,
train_station_features=train_station_features,
train_labels=train_labels,
test_features=test_features,
test_time_invariant_grid_features=test_time_invariant_grid_features,
test_station_features=test_station_features,
test_labels=test_labels)
def plotPerStationPredictionRun(source_path, observation_path, n_parallel):
# gather all models in source folder
error_data_per_run_dict = defaultdict()
for path in glob.glob(source_path + '/**/model_run_error.pkl', recursive=True):
model_name = path.split('/')[-2]
with open(path, 'rb') as file:
ds = pkl.load(file)
for data_var in ds.data_vars:
da = ds[data_var]
try:
error_data_per_run_dict[data_var] += [(model_name, da)]
except:
error_data_per_run_dict[data_var] = [(model_name, da)]
# load observations
OBS = xr.open_dataset(observation_path)
# get the prediction lead time to adjust time labels
prediciton_lead_time = ds.attrs['config']['prediction_times'][0] if 'config' in ds.attrs else 1
for run_error_data in error_data_per_run_dict.items():
run = run_error_data[0]
models = run_error_data[1]
stations = run_error_data[1][0][1].station.data
inits = run_error_data[1][0][1].init.data
init_type_mapping = np.array(run_error_data[1][0][1].init_type_mapping)
train_indices = [idx for idx, item in enumerate(init_type_mapping) if item[1] == 'train']
test_indices = [idx for idx, item in enumerate(init_type_mapping) if item[1] == 'test']
sample_type_color_mapping = [mapping[1] for mapping in init_type_mapping]
times = DataUtils.getTimeFromFileName(inits, prediciton_lead_time)
time_labels = [str(t)[:-13] for t in times]
station_name_dict = get_station_dict(OBS, stations)
model_station_mean_errors = {}
# plot for each station the prediction run results in parallel
with Pool(processes=n_parallel) as pool:
process_results = []
for station_idx, station in enumerate(stations):
print('Plotting of prediction run for station %s queued.' % station)
process_results.append(pool.apply_async(plotPerStationPredictionRunWorker,
(models, station, train_indices, test_indices,
station_name_dict,sample_type_color_mapping,
time_labels, source_path, run)))
# aggregate results from all processes
for ps_idx, ps_result in enumerate(process_results):
# sync processes
model_station_mean_error = ps_result.get()
for experiment_title, station_data_list in model_station_mean_error.items():
try:
model_station_mean_errors[experiment_title] += station_data_list
except KeyError:
model_station_mean_errors[experiment_title] = station_data_list
print('[Process %s] Synchronized after plotting station.' % ps_idx)
run_path = source_path + '/plots/prediction_runs/%s' % run
if not os.path.exists(run_path):
os.makedirs(run_path)
generateStationPredictionResultTable(output_path=run_path, results=model_station_mean_errors)
def plotAveragedPredictionRun(source_path):
# gather all models in source folder
error_data_per_run_dict = {}
for path in glob.glob(source_path + '/**/model_run_error.pkl', recursive=True):
model_name = path.split('/')[-2]
with open(path, 'rb') as file:
ds = pkl.load(file)
for data_var in ds.data_vars:
inits = ds[data_var].init.data
sample_type_mapping = [mapping[1] for mapping in ds[data_var].init_type_mapping]
prediction_data = ds[data_var].data
try:
error_data_per_run_dict[data_var] += [(model_name, inits, prediction_data, sample_type_mapping)]
except:
error_data_per_run_dict[data_var] = [(model_name, inits, prediction_data, sample_type_mapping)]
# get the prediction lead time to adjust time labels
prediciton_lead_time = ds.attrs['config']['prediction_times'][0] if 'config' in ds.attrs else 1
times = DataUtils.getTimeFromFileName(inits, prediciton_lead_time)
time_labels = [str(t)[:-13] for t in times]
for run_error_data in error_data_per_run_dict.items():
run = run_error_data[0]
model_mean_errors = {}
n_subplots = 10
fig, axes = plt.subplots(n_subplots, figsize=(60, 20), sharey=True)
for model_idx, model_error_data in enumerate(run_error_data[1]):
N = len(model_error_data[1])
split_length = N // n_subplots
ind = np.arange(N) # the x locations for the groups
experiment_title = model_error_data[0]
prediction_data = model_error_data[2]
init_type_mapping = model_error_data[3]
train_indices = [idx for idx, item in enumerate(init_type_mapping) if item == 'train']
test_indices = [idx for idx, item in enumerate(init_type_mapping) if item == 'test']
filtered_indices = [idx for idx, item in enumerate(init_type_mapping) if item == 'filterd']
for i in range(n_subplots):
# split indexes into slices for each subplot
index_split = ind[i * split_length:(i + 1) * split_length]
if model_idx == 0:
sampleTypeBackgroundColoring(axes[i], index_split,
init_type_mapping[i * split_length:(i + 1) * split_length])
axes[i].set_xlim([np.min(index_split), np.max(index_split)])
axes[i].plot(index_split,
np.nanmean(prediction_data[i * split_length:(i + 1) * split_length,:, 0],axis=1),
label=experiment_title, linewidth=0.15, alpha=0.8)
train_model_bias = np.nanmean(prediction_data[train_indices][:,:,3])
train_model_rmse = np.sqrt(np.nanmean(np.square(prediction_data[train_indices][:,:,3])))
train_model_mae = np.nanmean(np.absolute(prediction_data[train_indices][:,:,3]))
test_model_bias = np.nanmean(prediction_data[test_indices][:,:,3])
test_model_rmse = np.sqrt(np.nanmean(np.square(prediction_data[test_indices][:,:,3])))
test_model_mae = np.nanmean(np.absolute(prediction_data[test_indices][:,:,3]))
filtered_model_bias = np.nanmean(prediction_data[filtered_indices][:,:,3])
filtered_model_rmse = np.sqrt(np.nanmean(np.square(prediction_data[filtered_indices][:,:,3])))
filtered_model_mae = np.nanmean(np.absolute(prediction_data[filtered_indices][:,:,3]))
model_mean_errors[experiment_title] = (train_model_bias, train_model_rmse, train_model_mae,
test_model_bias, test_model_rmse, test_model_mae,
filtered_model_bias, filtered_model_rmse, filtered_model_mae)
# add mean errors of cosmo output predictions
train_diff_cosmo = prediction_data[train_indices][:,:,1] - prediction_data[train_indices][:,:,2]
train_cosmo_bias = np.nanmean(train_diff_cosmo)
train_cosmo_rmse = np.sqrt(np.nanmean(np.square(train_diff_cosmo)))
train_cosmo_mae = np.nanmean(np.absolute(train_diff_cosmo))
test_diff_cosmo = prediction_data[test_indices][:,:,1] - prediction_data[test_indices][:,:,2]
test_cosmo_bias = np.nanmean(test_diff_cosmo)
test_cosmo_rmse = np.sqrt(np.nanmean(np.square(test_diff_cosmo)))
test_cosmo_mae = np.nanmean(np.absolute(test_diff_cosmo))
filtered_diff_cosmo = prediction_data[filtered_indices][:,:,1] - prediction_data[filtered_indices][:,:,2]
filtered_cosmo_bias = np.nanmean(filtered_diff_cosmo)
filtered_cosmo_rmse = np.sqrt(np.nanmean(np.square(filtered_diff_cosmo)))
filtered_cosmo_mae = np.nanmean(np.absolute(filtered_diff_cosmo))
# add COSMO-1 output prediction error
model_mean_errors['COSMO-1'] = (train_cosmo_bias, train_cosmo_rmse, train_cosmo_mae,
test_cosmo_bias, test_cosmo_rmse, test_cosmo_mae,
filtered_cosmo_bias, filtered_cosmo_rmse, filtered_cosmo_mae)
for i in range(n_subplots):
axes[i].plot(ind[i * split_length:(i + 1) * split_length],
np.nanmean(prediction_data[i * split_length:(i + 1) * split_length,:, 1], axis=1), label='COSMO-1',
linewidth=0.15, alpha=0.8, color='b', linestyle='-.')
axes[i].plot(ind[i * split_length:(i + 1) * split_length],
np.nanmean(prediction_data[i * split_length:(i + 1) * split_length, 2], axis=1), label='Prediction',
linewidth=0.15, alpha=0.8, color='m', linestyle='--')
tick_step_size = np.maximum(split_length // 30, 1)
axes[i].set_xticks(ind[i * split_length:(i + 1) * split_length][::tick_step_size])
axes[i].set_xticklabels(time_labels[i * split_length:(i + 1) * split_length][::tick_step_size])
axes[i].set_xticks(ind[i * split_length:(i + 1) * split_length], minor=True)
# And a corresponding grid
axes[i].grid(which='both')
# Or if you want different settings for the grids:
axes[i].grid(which='minor', alpha=0.2)
axes[i].grid(which='major', alpha=0.5)
handles, labels = axes[0].get_legend_handles_labels()
axes[n_subplots - 1].set_xlabel('Time')
axes[0].legend(handles, labels)
plt.tight_layout()
run_path = source_path + '/plots/prediction_runs/%s' % run
if not os.path.exists(run_path):
os.makedirs(run_path)
fig.savefig(run_path + '/averaged_prediction.png', dpi=300)
generatePredictionResultTable(output_path=run_path, results=model_mean_errors)
