def main():
"""
Get data from db and save it as csv
"""
bq = BQHandler()
io = IO(gs_bucket=options.gs_bucket)
viz = Viz(io=io)
starttime, endtime = io.get_dates(options)
logging.info('Using dataset {} and time range {} - {}'.format(
options.feature_dataset, starttime.strftime('%Y-%m-%d'),
endtime.strftime('%Y-%m-%d')))
all_param_names = options.label_params + options.feature_params + options.meta_params
aggs = io.get_aggs_from_param_names(options.feature_params)
if options.model == 'rf':
model = RandomForestRegressor(
n_estimators=options.n_estimators,
n_jobs=-1,
min_samples_leaf=options.min_samples_leaf,
min_samples_split=options.min_samples_split,
max_features=options.max_features,
max_depth=options.max_depth,
bootstrap=options.bootstrap)
elif options.model == 'lr':
model = SGDRegressor(warm_start=True,
max_iter=options.n_loops,
shuffle=options.shuffle,
power_t=options.power_t,
penalty=options.regularizer,
learning_rate=options.learning_rate,
eta0=options.eta0,
alpha=options.alpha,
tol=0.0001)
elif options.model == 'svr':
model = SVR()
elif options.model == 'ard':
model = ARDRegression(n_iter=options.n_loops,
alpha_1=options.alpha_1,
alpha_2=options.alpha_2,
lambda_1=options.lambda_1,
lambda_2=options.lambda_2,
threshold_lambda=options.threshold_lambda,
fit_intercept=options.fit_intercept,
copy_X=options.copy_X)
elif options.model == 'gp':
k_long_term = 66.0**2 * RBF(length_scale=67.0)
k_seasonal = 2.4**2 * RBF(length_scale=90.0) * ExpSineSquared(
length_scale=150, periodicity=1.0, periodicity_bounds=(0, 10000))
k_medium_term = 0.66**2 * RationalQuadratic(length_scale=1.2,
alpha=0.78)
k_noise = 0.18**2 * RBF(length_scale=0.134) + WhiteKernel(
noise_level=0.19**2)
#kernel_gpml = k_long_term + k_seasonal + k_medium_term + k_noise
kernel_gpml = k_long_term + k_seasonal + k_medium_term + k_noise
model = GaussianProcessRegressor(
kernel=kernel_gpml, #alpha=0,
optimizer=None,
normalize_y=True)
elif options.model == 'llasso':
model = LocalizedLasso(num_iter=options.n_loops,
batch_size=options.batch_size)
elif options.model == 'nlasso':
model = NetworkLasso(num_iter=options.n_loops,
batch_size=options.batch_size)
graph_data = pd.read_csv(options.graph_data,
names=[
'date', 'start_hour', 'src', 'dst',
'type', 'sum_delay', 'sum_ahead',
'add_delay', 'add_ahead', 'train_count'
])
#stations_to_pick = options.stations_to_pick.split(',')
#graph = model.fetch_connections(graph_data, stations_to_pick)
model.fetch_connections(graph_data)
if options.pca:
ipca = IncrementalPCA(n_components=options.pca_components,
whiten=options.whiten,
copy=False)
rmses, maes, r2s, skills, start_times, end_times, end_times_obj = [], [], [], [], [], [], []
X_complete = [] # Used for feature selection
start = starttime
end = start + timedelta(days=int(options.day_step),
hours=int(options.hour_step))
if end > endtime: end = endtime
while end <= endtime and start < end:
logging.info('Processing time range {} - {}'.format(
start.strftime('%Y-%m-%d %H:%M'), end.strftime('%Y-%m-%d %H:%M')))
# Load data ############################################################
try:
logging.info('Reading data...')
data = bq.get_rows(start,
end,
loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.feature_table,
parameters=all_param_names,
only_winters=options.only_winters)
data = io.filter_train_type(labels_df=data,
train_types=options.train_types,
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=['train_count', 'delay'],
aggs=aggs)
# Filter only timesteps with large distribution in the whole network
if options.filter_delay_limit is not None:
data = io.filter_delay_with_limit(data,
options.filter_delay_limit)
if options.y_avg_hours is not None:
data = io.calc_running_delay_avg(data, options.y_avg_hours)
if options.y_avg:
data = io.calc_delay_avg(data)
data.sort_values(by=['time', 'trainstation'], inplace=True)
if options.impute:
logging.info('Imputing missing values...')
data.drop(columns=['train_type'], inplace=True)
data = imputer.fit_transform(data)
data.loc[:, 'train_type'] = None
if options.month:
logging.info('Adding month to the dataset...')
data['month'] = data['time'].map(lambda x: x.month)
if 'month' not in options.feature_params:
options.feature_params.append('month')
if options.model == 'ard' and len(data) > options.n_samples:
logging.info('Sampling {} values from data...'.format(
options.n_samples))
data = data.sample(options.n_samples)
l_data = data.loc[:, options.label_params]
f_data = data.loc[:, options.feature_params]
except ValueError as e:
f_data, l_data = [], []
if len(f_data) < 2 or len(l_data) < 2:
start = end
end = start + timedelta(days=int(options.day_step),
hours=int(options.hour_step))
continue
logging.info('Processing {} rows...'.format(len(f_data)))
train, test = train_test_split(data, test_size=0.1)
X_train = train.loc[:,
options.feature_params].astype(np.float32).values
y_train = train.loc[:, options.label_params].astype(
np.float32).values.ravel()
X_test = test.loc[:, options.feature_params].astype(np.float32).values
y_test = test.loc[:, options.label_params].astype(
np.float32).values.ravel()
logging.debug('Features shape: {}'.format(X_train.shape))
if options.normalize:
logging.info('Normalizing data...')
xscaler, yscaler = StandardScaler(), StandardScaler()
X_train = xscaler.fit_transform(X_train)
X_test = xscaler.transform(X_test)
if len(options.label_params) == 1:
y_train = yscaler.fit_transform(y_train.reshape(-1, 1)).ravel()
#y_test = yscaler.transform(y_test.reshape(-1, 1)).ravel()
else:
y_train = yscaler.fit_transform(y_train)
#y_test = yscaler.transform(y_test)
if options.pca:
logging.info('Doing PCA analyzis for the data...')
X_train = ipca.fit_transform(X_train)
fname = options.output_path + '/ipca_explained_variance.png'
viz.explained_variance(ipca, fname)
#io._upload_to_bucket(filename=fname, ext_filename=fname)
X_test = ipca.fit_transform(X_test)
if options.model == 'llasso':
graph_data = pd.read_csv(options.graph_data,
names=[
'date', 'start_hour', 'src', 'dst',
'type', 'sum_delay', 'sum_ahead',
'add_delay', 'add_ahead',
'train_count'
])
graph = model.fetch_connections(graph_data)
logging.debug('Features shape after pre-processing: {}'.format(
X_train.shape))
# FIT ##################################################################
if options.cv:
logging.info('Doing random search for hyper parameters...')
if options.model == 'rf':
param_grid = {
"n_estimators": [10, 100, 200, 800],
"max_depth": [3, 20, None],
"max_features": ["auto", "sqrt", "log2", None],
"min_samples_split": [2, 5, 10],
"min_samples_leaf": [1, 2, 4, 10],
"bootstrap": [True, False]
}
elif options.model == 'lr':
param_grid = {
"penalty": [None, 'l2', 'l1'],
"alpha": [0.00001, 0.0001, 0.001, 0.01, 0.1],
"l1_ratio": [0.1, 0.15, 0.2, 0.5],
"shuffle": [True, False],
"learning_rate": ['constant', 'optimal', 'invscaling'],
"eta0": [0.001, 0.01, 0.1],
"power_t": [0.1, 0.25, 0.5]
}
elif options.model == 'svr':
param_grid = {
"C": [0.001, 0.01, 0.1, 1, 10],
"epsilon": [0.01, 0.1, 0.5],
"kernel":
['rbf', 'linear', 'poly', 'sigmoid', 'precomputed'],
"degree": [2, 3, 4],
"shrinking": [True, False],
"gamma": [0.001, 0.01, 0.1],
"coef0": [0, 0.1, 1]
}
else:
raise ("No param_grid set for given model ({})".format(
options.model))
random_search = RandomizedSearchCV(model,
param_distributions=param_grid,
n_iter=int(
options.n_iter_search),
n_jobs=-1)
random_search.fit(X_train, y_train)
logging.info("RandomizedSearchCV done.")
fname = options.output_path + '/random_search_cv_results.txt'
io.report_cv_results(random_search.cv_results_, fname)
#io._upload_to_bucket(filename=fname, ext_filename=fname)
sys.exit()
else:
logging.info('Training...')
if options.model in ['rf', 'svr', 'ard', 'gp']:
model.fit(X_train, y_train)
if options.feature_selection:
X_complete = X_train
y_complete = y_train
meta_complete = data.loc[:, options.meta_params]
elif options.model in ['llasso']:
model.fit(X_train,
y_train,
stations=train.loc[:, 'trainstation'].values)
elif options.model in ['nlasso']:
model.partial_fit(X_train,
y_train,
stations=train.loc[:, 'trainstation'].values)
else:
model.partial_fit(X_train, y_train)
if options.feature_selection:
try:
X_complete = np.append(X_complete, X_train)
y_complete = np.append(Y_complete, y_train)
meta_complete = meta_complete.append(
data.loc[:, options.meta_params])
except (ValueError, NameError):
X_complete = X_train
y_complete = y_train
meta_complete = data.loc[:, options.meta_params]
# EVALUATE #############################################################
# Check training score to estimate amount of overfitting
# Here we assume that we have a datetime index (from time columns)
y_pred_train = model.predict(X_train)
rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))
mae_train = np.sqrt(mean_squared_error(y_train, y_pred_train))
logging.info('Training data RMSE: {} and MAE: {}'.format(
rmse_train, mae_train))
#try:
if True:
print(train)
#range = ('2013-02-01','2013-02-28')
range = ('2010-01-01', '2010-01-02')
X_train_sample = train.loc[range[0]:range[1],
options.feature_params].astype(
np.float32).values
target = train.loc[range[0]:range[1], options.label_params].astype(
np.float32).values.ravel()
y_pred_sample = model.predict(X_train_sample)
times = train.loc[range[0]:range[1], 'time'].values
df = pd.DataFrame(times + y_pred_sample)
print(df)
sys.exit()
# Draw visualisation
fname = '{}/timeseries_training_data.png'.format(
options.output_path)
viz.plot_delay(times, target, y_pred,
'Delay for station {}'.format(stationName), fname)
fname = '{}/scatter_all_stations.png'.format(options.vis_path)
viz.scatter_predictions(times,
target,
y_pred,
savepath=options.vis_path,
filename='scatter_{}'.format(station))
#except KeyError:
# pass
# Mean delay over the whole dataset (both train and validation),
# used to calculate Brier Skill
if options.y_avg:
mean_delay = 3.375953418071136
else:
mean_delay = 6.011229358531166
if options.model == 'llasso':
print('X_test shape: {}'.format(X_test.shape))
y_pred, weights = model.predict(X_test,
test.loc[:, 'trainstation'].values)
else:
y_pred = model.predict(X_test)
if options.normalize:
y_pred = yscaler.inverse_transform(y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
rmse_stat = math.sqrt(
mean_squared_error(y_test, np.full_like(y_test, mean_delay)))
skill = 1 - rmse / rmse_stat
rmses.append(rmse)
maes.append(mae)
r2s.append(r2)
skills.append(skill)
start_times.append(start.strftime('%Y-%m-%dT%H:%M:%S'))
end_times.append(end.strftime('%Y-%m-%dT%H:%M:%S'))
end_times_obj.append(end)
if options.model in ['rf', 'lr', 'ard', 'gp']:
logging.info('R2 score for training: {}'.format(
model.score(X_train, y_train)))
logging.info('RMSE: {}'.format(rmse))
logging.info('MAE: {}'.format(mae))
logging.info('R2 score: {}'.format(r2))
logging.info('Brier Skill Score score: {}'.format(skill))
start = end
end = start + timedelta(days=int(options.day_step),
hours=int(options.hour_step))
if end > endtime: end = endtime
# SAVE #####################################################################
io.save_scikit_model(model,
filename=options.save_file,
ext_filename=options.save_file)
if options.normalize:
fname = options.save_path + '/xscaler.pkl'
io.save_scikit_model(xscaler, filename=fname, ext_filename=fname)
fname = options.save_path + '/yscaler.pkl'
io.save_scikit_model(yscaler, filename=fname, ext_filename=fname)
if options.model == 'rf':
fname = options.output_path + '/rfc_feature_importance.png'
viz.rfc_feature_importance(model.feature_importances_,
fname,
feature_names=options.feature_params)
#io._upload_to_bucket(filename=fname, ext_filename=fname)
try:
fname = options.output_path + '/learning_over_time.png'
viz.plot_learning_over_time(end_times_obj,
rmses,
maes,
r2s,
filename=fname)
#io._upload_to_bucket(filename=fname, ext_filename=fname)
except Exception as e:
logging.error(e)
error_data = {
'start_times': start_times,
'end_times': end_times,
'rmse': rmses,
'mae': maes,
'r2': r2s,
'skill': skills
}
fname = '{}/training_time_validation_errors.csv'.format(
options.output_path)
io.write_csv(error_data, filename=fname, ext_filename=fname)
# FEATURE SELECTION ########################################################
if options.feature_selection:
logging.info('Doing feature selection...')
selector = SelectFromModel(model, prefit=True)
print(pd.DataFrame(data=X_complete))
X_selected = selector.transform(X_complete)
selected_columns = f_data.columns.values[selector.get_support()]
logging.info(
'Selected following parameters: {}'.format(selected_columns))
data_sel = meta_complete.join(
pd.DataFrame(data=y_complete, columns=options.label_params)).join(
pd.DataFrame(data=X_selected, columns=selected_columns))
print(pd.DataFrame(data=X_selected, columns=selected_columns))
print(data_sel)
def main():
"""
Get data from db and save it as csv
"""
bq = BQHandler()
io = IO(gs_bucket=options.gs_bucket)
viz = Viz(io)
starttime, endtime = io.get_dates(options)
logging.info('Using dataset {} and time range {} - {}'.format(
options.feature_dataset, starttime.strftime('%Y-%m-%d'),
endtime.strftime('%Y-%m-%d')))
all_param_names = options.label_params + options.feature_params + options.meta_params
aggs = io.get_aggs_from_param_names(options.feature_params)
if options.model == 'bgm':
model = BayesianGaussianMixture(
weight_concentration_prior_type="dirichlet_process",
n_components=options.n_components)
elif options.model == 'gaussiannb':
model = GaussianNB()
elif options.model == 'rfc':
model = RandomForestClassifier(n_jobs=-1)
elif options.model == 'svc':
params = {'kernel': 'rbf', 'gamma': 0.5, 'C': 1, 'probability': True}
model = SVC(**params)
else:
raise (
'Model not specificied or wrong. Add for example "model: bgm" to config file.'
)
if options.pca:
ipca = IncrementalPCA(n_components=options.pca_components,
whiten=options.whiten,
copy=False)
sum_columns = ['delay']
if options.reason_code_table is not None:
sum_columns = ['count']
logging.info('Reading data...')
data = bq.get_rows(starttime,
endtime,
loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.feature_table,
parameters=all_param_names,
reason_code_table=options.reason_code_table,
only_winters=options.only_winters)
data = io.filter_train_type(labels_df=data,
train_types=options.train_types,
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=sum_columns,
aggs=aggs)
# Sorting is actually not necessary. It's been useful for debugging.
data.sort_values(by=['time', 'trainstation'], inplace=True)
data.set_index('time', inplace=True)
logging.info('Data contain {} rows...'.format(len(data)))
logging.info('Adding binary class to the dataset with limit {}...'.format(
options.delay_limit))
#logging.info('Adding binary class to the dataset with limit {}...'.format(limit))
#data['class'] = data['count'].map(lambda x: 1 if x > options.delay_count_limit else -1)
data['class'] = data['delay'].map(lambda x: 1
if x > options.delay_limit else -1)
io.log_class_dist(data.loc[:, 'class'].values, labels=[-1, 1])
if options.balance:
logging.info('Balancing dataset...')
count = data.groupby('class').size().min()
data = pd.concat([
data.loc[data['class'] == -1].sample(n=count),
data.loc[data['class'] == 1].sample(n=count)
])
io.log_class_dist(data.loc[:, 'class'].values, labels=[-1, 1])
if options.month:
logging.info('Adding month to the dataset...')
data['month'] = data.index.map(lambda x: x.month)
options.feature_params.append('month')
target = data.loc[:, 'class'].astype(np.int32).values.ravel()
features = data.loc[:, options.feature_params].astype(np.float32).values
X_train, X_test, y_train, y_test = train_test_split(features,
target,
test_size=0.3)
if options.normalize:
logging.info('Normalizing data...')
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
logging.debug('Features shape after pre-processing: {}'.format(
X_train.shape))
if options.cv:
logging.info('Doing random search for hyper parameters...')
if options.model == 'bgm':
param_grid = {
"n_components": [1, 2, 4, 8, 16],
"covariance_type": ['full', 'tied', 'diag', 'spherical'],
"init_params": ['kmeans', 'random']
}
elif options.model == 'rfc':
raise ("Not implemented. Get back to work!")
elif options.model == 'svc':
features_compinations = [
[
'lat', 'lon', 'pressure', 'max_temperature',
'min_temperature', 'mean_temperature', 'mean_dewpoint',
'mean_humidity', 'mean_winddirection', 'mean_windspeedms',
'max_windgust', 'max_precipitation1h', 'max_snowdepth',
'max_n', 'min_vis', 'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'max_temperature', 'min_temperature',
'mean_temperature', 'mean_dewpoint', 'mean_humidity',
'mean_winddirection', 'mean_windspeedms', 'max_windgust',
'max_precipitation1h', 'max_snowdepth', 'max_n', 'min_vis',
'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_windgust',
'max_precipitation1h', 'max_snowdepth', 'max_n', 'min_vis',
'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_snowdepth',
'max_n', 'min_vis', 'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_snowdepth',
'max_n', 'min_vis', 'min_clhb', 'max_precipitation1h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_snowdepth',
'min_vis', 'max_precipitation1h'
],
[
'pressure', 'min_temperature', 'mean_winddirection',
'mean_windspeedms', 'max_snowdepth', 'max_precipitation1h'
]
]
param_grid = {
"C": [0.0001, 0.001, 0.01, 0.1, 1],
"kernel": ['rbf', 'poly'],
"degree": [2, 3],
"gamma": [0.5],
"coef0": [0.1],
"probability": [True],
"features": features_compinations
}
from lib.svc import SVCF
model = SVCF(all_features=options.feature_params)
else:
raise ("No param_grid set for given model ({})".format(
options.model))
print(model.get_params().keys())
ftwo_scorer = make_scorer(fbeta_score, beta=2)
scoring = {
'accuracy': 'accuracy',
'precision': 'precision',
'recall': 'recall',
'f1': 'f1',
'f2': ftwo_scorer
}
random_search = RandomizedSearchCV(model,
param_distributions=param_grid,
n_iter=int(options.n_iter_search),
verbose=1,
scoring=scoring,
refit='recall',
n_jobs=-1)
random_search.fit(X_train, y_train)
logging.info("RandomizedSearchCV done.")
scores = ['accuracy', 'precision', 'recall', 'f1', 'f2']
fname = options.output_path + '/random_search_cv_results.txt'
io.report_cv_results(random_search.cv_results_,
scores=scores,
filename=fname,
ext_filename=fname)
model = random_search.best_estimator_
io.save_scikit_model(model,
filename=options.save_file,
ext_filename=options.save_file)
if options.normalize:
fname = options.save_path + '/xscaler.pkl'
io.save_scikit_model(scaler, filename=fname, ext_filename=fname)
else:
logging.info('Training...')
model.fit(X_train, y_train)
# Save model and xscaler (no reason to save xscaler before the model has fitted as well)
io.save_scikit_model(model,
filename=options.save_file,
ext_filename=options.save_file)
if options.normalize:
fname = options.save_path + '/xscaler.pkl'
io.save_scikit_model(scaler, filename=fname, ext_filename=fname)
# Metrics
y_pred_proba = model.predict_proba(X_test)
y_pred = np.argmax(y_pred_proba, axis=1)
# We want [-1,1] classes as y values are
y_pred[y_pred == 0] = -1
acc = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='binary')
recall = recall_score(y_test, y_pred, average='binary')
f1 = f1_score(y_test, y_pred, average='binary')
logging.info('Accuracy: {}'.format(acc))
logging.info('Precision: {}'.format(precision))
logging.info('Recall: {}'.format(recall))
logging.info('F1 score: {}'.format(f1))
io.log_class_dist(y_pred, labels=[-1, 1])
error_data = {
'acc': [acc],
'precision': [precision],
'recall': [recall],
'f1': [f1]
}
fname = '{}/training_time_validation_errors.csv'.format(
options.output_path)
io.write_csv(error_data, filename=fname, ext_filename=fname)
# Confusion matrices
fname = '{}/confusion_matrix_validation.png'.format(options.output_path)
viz.plot_confusion_matrix(y_test, y_pred, np.arange(2), filename=fname)
fname = '{}/confusion_matrix_validation_normalised.png'.format(
options.output_path)
viz.plot_confusion_matrix(y_test,
y_pred,
np.arange(2),
True,
filename=fname)
# Precision-recall curve
fname = '{}/precision-recall-curve.png'.format(options.output_path)
viz.prec_rec_curve(y_test, y_pred_proba, filename=fname)
# ROC
fname = '{}/roc.png'.format(options.output_path)
viz.plot_binary_roc(y_test, y_pred_proba, filename=fname)
############################################################################
# EVALUATE
############################################################################
if options.evaluate:
logging.info('Loading test data...')
test_data = bq.get_rows(dt.datetime.strptime('2010-01-01', "%Y-%m-%d"),
dt.datetime.strptime('2019-01-01', "%Y-%m-%d"),
loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.test_table,
parameters=all_param_names)
test_data = io.filter_train_type(labels_df=test_data,
train_types=['K', 'L'],
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=['delay'],
aggs=aggs)
# Sorting is actually not necessary. It's been useful for debugging.
test_data.sort_values(by=['time', 'trainstation'], inplace=True)
test_data.set_index('time', inplace=True)
logging.info('Test data contain {} rows...'.format(len(test_data)))
logging.info(
'Adding binary class to the test dataset with limit {}...'.format(
options.delay_limit))
#logging.info('Adding binary class to the dataset with limit {}...'.format(limit))
#data['class'] = data['count'].map(lambda x: 1 if x > options.delay_count_limit else -1)
test_data['class'] = test_data['delay'].map(
lambda x: 1 if x > options.delay_limit else -1)
io.log_class_dist(test_data.loc[:, 'class'].values, labels=[-1, 1])
if options.month:
logging.info('Adding month to the test dataset...')
test_data['month'] = test_data.index.map(lambda x: x.month)
times = [('2011-02-01', '2011-03-01'), ('2016-06-01', '2016-07-01'),
('2017-02-01', '2017-03-01'), ('2011-02-01', '2017-03-01')]
for start, end in times:
try:
y_pred_proba, y_pred, y = predict_timerange(
test_data, options.feature_params, model, scaler, start,
end)
perf_metrics(y_pred_proba, y_pred, y, start, end, viz, io)
except EmptyDataError:
logging.info('No data for {} - {}'.format(start, end))
