def error_grid(noise_case=0):
grid = CSVGridFile('../../samples/wind-exp-params-new.csv')
stations = [1, 2, 3, 4, 5, 6, 7, 8, 9]
ww3_obs_all = \
[obs.time_series() for obs in
wave_watch_results(path_to_results='../../samples/ww-res/', stations=stations)]
model_all = FakeModel(grid_file=grid,
observations=ww3_obs_all,
stations_to_out=stations,
error=error_rmse_all,
forecasts_path='../../../wind-postproc/out',
noise_run=noise_case)
with open(f'../../samples/params_rmse_{noise_case}.csv',
mode='w',
newline='') as csv_file:
writer = csv.writer(csv_file, delimiter=',')
header = ['DRF', 'CFW', 'STPM']
error_columns = [f'ERROR_K{station}' for station in stations]
header.extend(error_columns)
writer.writerow(header)
for row in grid.rows:
metrics = model_all.output(params=row.model_params)
row_to_write = row.model_params.params_list()
row_to_write.extend(metrics)
writer.writerow(row_to_write)
def plot_error_variance(station_index=2, stmp_index=3):
noise_dirs = {1: '../../../samples/wind-noice-runs/results/1/',
15: '../../../samples/wind-noice-runs/results/15/',
25: '../../../samples/wind-noice-runs/results/25/'}
model_by_noise = {}
grid = CSVGridFile('../../samples/wind-exp-params.csv')
for noise in noise_dirs.keys():
model = FakeModel(grid_file=grid, forecasts_path=noise_dirs[noise], noise_run=noise)
model_by_noise[noise] = model
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for noise in model_by_noise.keys():
drf, cfw, error = \
params_and_error_grid(model=model_by_noise[noise], station_index=station_index, stpm_index=stmp_index)
ax.scatter(drf, cfw, error, label=f'noise = {noise}')
ax.set_xlabel('drf')
ax.set_ylabel('cfw')
plt.title(f'Error variance for K{station_index} station')
plt.legend()
plt.show()
def run_robustness_exp(max_gens, pop_size, archive_size, crossover_rate,
mutation_rate, mutation_value_rate, stations, **kwargs):
grid = CSVGridFile('../../samples/wind-exp-params-new.csv')
ww3_obs = \
[obs.time_series() for obs in wave_watch_results(path_to_results='../../samples/ww-res/', stations=stations)]
train_model = FakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=stations,
error=error_rmse_all,
forecasts_path='../../../wind-postproc/out',
noise_run=0)
test_model = model_all_stations()
history, archive_history = SPEA2(
params=SPEA2.Params(max_gens=max_gens,
pop_size=pop_size,
archive_size=archive_size,
crossover_rate=crossover_rate,
mutation_rate=mutation_rate,
mutation_value_rate=mutation_value_rate),
init_population=initial_pop_lhs,
objectives=partial(calculate_objectives_interp, train_model),
crossover=crossover,
mutation=mutation).solution(verbose=False)
exptime2 = str(datetime.datetime.now().time()).replace(":", "-")
save_archive_history(archive_history, f'rob-exp-bl-{exptime2}.csv')
params = history.last().genotype
forecasts = []
if 'save_figures' in kwargs and kwargs['save_figures'] is True:
closest_hist = test_model.closest_params(params)
closest_params_set_hist = SWANParams(drf=closest_hist[0],
cfw=closest_hist[1],
stpm=closest_hist[2])
for row in grid.rows:
if set(row.model_params.params_list()) == set(
closest_params_set_hist.params_list()):
drf_idx, cfw_idx, stpm_idx = test_model.params_idxs(
row.model_params)
forecasts = test_model.grid[drf_idx, cfw_idx, stpm_idx]
break
plot_results(forecasts=forecasts,
observations=wave_watch_results(
path_to_results='../../samples/ww-res/',
stations=ALL_STATIONS),
baseline=default_params_forecasts(test_model),
save=True,
file_path=kwargs['figure_path'])
return history.last()
def model_all_stations():
grid = CSVGridFile('../../samples/wind-exp-params-new.csv')
ww3_obs = \
[obs.time_series() for obs in
wave_watch_results(path_to_results='../../samples/ww-res/', stations=ALL_STATIONS)]
model = FakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=ALL_STATIONS,
error=error_rmse_all,
forecasts_path='../../../wind-postproc/out',
noise_run=0)
return model
def plot_params_space():
grid = CSVGridFile('../../samples/wind-exp-params.csv')
forecasts_path = '../../../samples/wind-noice-runs/results/1/'
fake = FakeModel(grid_file=grid, forecasts_path=forecasts_path)
drf, cfw = np.meshgrid(fake.grid_file.drf_grid, fake.grid_file.cfw_grid)
stpm_fixed = fake.grid_file.stpm_grid[0]
stations = 3
error = np.ones(shape=(stations, drf.shape[0], drf.shape[1]))
for i in range(drf.shape[0]):
for j in range(drf.shape[1]):
diff = fake.output(params=SWANParams(drf=drf[i, j], cfw=cfw[i, j], stpm=stpm_fixed))
for station in range(stations):
error[station, i, j] = diff[station]
def prepare_all_fake_models():
errors = [error_rmse_all]
grid = CSVGridFile('../../samples/wind-exp-params-new.csv')
noises = [0, 1, 2, 15, 16, 17, 25, 26]
for noise in noises:
for err in errors:
for stations in stations_for_run_set:
print(
f'configure model for: noise = {noise}; error = {err}; stations = {stations}'
)
ww3_obs = \
[obs.time_series() for obs in
wave_watch_results(path_to_results='../../samples/ww-res/', stations=stations)]
_ = FakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=stations,
error=err,
forecasts_path=f'../../../wind-postproc/out',
noise_run=noise)
def grid_rmse():
# fake, grid = real_obs_config()
grid = CSVGridFile('../../samples/wind-exp-params-new.csv')
stations = [1, 2, 3]
ww3_obs = \
[obs.time_series() for obs in wave_watch_results(path_to_results='../../samples/ww-res/', stations=stations)]
fake = FakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=stations,
error=error_rmse_peak,
forecasts_path='../../../wind-noice-runs/results_fixed/0',
noise_run=0)
errors_total = []
m_error = pow(10, 9)
with open('../../samples/params_rmse.csv', mode='w',
newline='') as csv_file:
writer = csv.writer(csv_file, delimiter=',')
writer.writerow([
'ID', 'DRF', 'CFW', 'STPM', 'RMSE_K1', 'RMSE_K2', 'RMSE_K3',
'TOTAL_RMSE'
])
for row in grid.rows:
error = fake.output(params=row.model_params)
print(grid.rows.index(row), error)
if m_error > rmse(error):
m_error = rmse(error)
print(f"new min: {m_error}; {row.model_params.params_list()}")
errors_total.append(rmse(error))
row_to_write = row.model_params.params_list()
row_to_write.extend(error)
row_to_write.append(rmse(error))
writer.writerow(row_to_write)
print(f'min total rmse: {min(errors_total)}')
def init_models_to_tests():
metrics = {
'rmse_all': error_rmse_all,
'rmse_peak': error_rmse_peak,
'mae_all': error_mae_all,
'mae_peak': error_mae_peak
}
grid = CSVGridFile('../../samples/wind-exp-params-new.csv')
ww3_obs = \
[obs.time_series() for obs in
wave_watch_results(path_to_results='../../samples/ww-res/', stations=ALL_STATIONS)]
models = {}
for metric_name in metrics.keys():
model = FakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=ALL_STATIONS,
error=metrics[metric_name],
forecasts_path='../../../wind-postproc/out',
noise_run=0)
models[metric_name] = model
return models
def optimize_by_ww3_obs(train_stations,
max_gens,
pop_size,
archive_size,
crossover_rate,
mutation_rate,
mutation_value_rate,
iter_ind,
plot_figures=True):
grid = CSVGridFile('../../samples/wind-exp-params-new.csv')
ww3_obs = \
[obs.time_series() for obs in
wave_watch_results(path_to_results='../../samples/ww-res/', stations=train_stations)]
error = error_rmse_all
train_model = FakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=train_stations,
error=error,
forecasts_path='../../../wind-postproc/out',
noise_run=0)
history, archive_history = SPEA2(
params=SPEA2.Params(max_gens,
pop_size=pop_size,
archive_size=archive_size,
crossover_rate=crossover_rate,
mutation_rate=mutation_rate,
mutation_value_rate=mutation_value_rate),
init_population=initial_pop_lhs,
objectives=partial(calculate_objectives_interp, train_model),
crossover=crossover,
mutation=mutation).solution(verbose=True)
params = history.last().genotype
save_archive_history(archive_history, f'history-{iter_ind}.csv')
if plot_figures:
test_model = model_all_stations()
closest_hist = test_model.closest_params(params)
closest_params_set_hist = SWANParams(drf=closest_hist[0],
cfw=closest_hist[1],
stpm=closest_hist[2])
forecasts = []
for row in grid.rows:
if set(row.model_params.params_list()) == set(
closest_params_set_hist.params_list()):
drf_idx, cfw_idx, stpm_idx = test_model.params_idxs(
row.model_params)
forecasts = test_model.grid[drf_idx, cfw_idx, stpm_idx]
if grid.rows.index(row) < 100:
print("!!!")
print("index : %d" % grid.rows.index(row))
break
plot_results(forecasts=forecasts,
observations=wave_watch_results(
path_to_results='../../samples/ww-res/',
stations=ALL_STATIONS),
baseline=default_params_forecasts(test_model))
plot_population_movement(archive_history, grid)
return history