@support
This is a possible fix, but no gurantee. You have to adjust also the prediction function.
def train_model(data):
"""Create and train the models working on an asset-by-asset basis."""
models = dict()
asset_name_all = data.coords['asset'].values
data = data.sel(time=slice('2013-05-01',None)) # cut the noisy data head before 2013-05-01
features_all = get_features(data)
target_all = get_target_classes(data)
model = create_model()
for asset_name in asset_name_all:
# drop missing values:
target_cur = target_all.sel(asset=asset_name).dropna('time', 'any')
features_cur = features_all.sel(asset=asset_name).dropna('time', 'any')
# align features and targets:
target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner')
if len(features_cur.time) < 10:
# not enough points for training
continue
try:
model.fit(feature_for_learn_df.values, target_for_learn_df)
models[asset_name] = model
except KeyboardInterrupt as e:
raise e
except:
logging.exception('model training failed')
return models
