def run_ts_forecasting_example(with_plot=True,
with_pipeline_vis=True,
timeout=None):
train_data_path = f'{fedot_project_root()}/examples/data/salaries.csv'
target = pd.read_csv(train_data_path)['target']
# Define forecast length and define parameters - forecast length
forecast_length = 30
task_parameters = TsForecastingParams(forecast_length=forecast_length)
# init model for the time series forecasting
model = Fedot(problem='ts_forecasting',
task_params=task_parameters,
timeout=timeout)
# run AutoML model design in the same way
pipeline = model.fit(features=train_data_path, target='target')
if with_pipeline_vis:
pipeline.show()
# use model to obtain forecast
forecast = model.predict(features=train_data_path)
print(
model.get_metrics(metric_names=['rmse', 'mae', 'mape'], target=target))
# plot forecasting result
if with_plot:
model.plot_prediction()
return forecast
def run_metocean_forecasting_problem(train_file_path,
test_file_path,
forecast_length=1,
is_visualise=False,
timeout=5):
# Prepare data for train and test
ssh_history, ws_history, ssh_obs = prepare_input_data(
train_file_path, test_file_path)
historical_data = {
'ws': ws_history, # additional variable
'ssh': ssh_history, # target variable
}
fedot = Fedot(
problem='ts_forecasting',
task_params=TsForecastingParams(forecast_length=forecast_length),
timeout=timeout,
verbose_level=4)
pipeline = fedot.fit(features=historical_data, target=ssh_history)
fedot.forecast(historical_data, forecast_length=forecast_length)
metric = fedot.get_metrics(target=ssh_obs)
if is_visualise:
pipeline.show()
fedot.plot_prediction()
return metric