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 run_genetic_opt(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 = FidelityFakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=stations,
error=error_rmse_all,
forecasts_path='../../../wind-fidelity/*')
test_range = (0, 1)
test_model = model_all_stations(forecasts_range=test_range)
operators = default_operators()
history, archive_history = DefaultSPEA2(
params=DefaultSPEA2.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),
objectives=partial(calculate_objectives_interp, train_model),
evolutionary_operators=operators).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
if 'save_figures' in kwargs and kwargs['save_figures'] is True:
params = test_model.closest_params(params)
closest_params = SWANParams(drf=params[0],
cfw=params[1],
stpm=params[2],
fidelity_time=params[3],
fidelity_space=params[4])
drf_idx, cfw_idx, stpm_idx, fid_time_idx, fid_space_idx = test_model.params_idxs(
closest_params)
forecasts = test_model.grid[drf_idx, cfw_idx, stpm_idx, fid_time_idx,
fid_space_idx]
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'],
values_range=test_range)
return history.last()
def optimize_test(train_stations,
max_gens,
pop_size,
archive_size,
crossover_rate,
mutation_rate,
mutation_value_rate,
sur_points,
plot_figures=True):
train_range = (0, 1)
test_range = (0, 1)
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 = FidelityFakeModel(grid_file=grid,
observations=ww3_obs,
stations_to_out=train_stations,
error=error,
forecasts_path='../../../2fidelity/*',
forecasts_range=train_range,
is_surrogate=True,
sur_points=sur_points)
operators = default_operators()
history, archive_history = DefaultSPEA2(
params=DefaultSPEA2.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),
objectives=partial(calculate_objectives_interp, train_model),
evolutionary_operators=operators).solution(verbose=True)
params = history.last().genotype
if plot_figures:
test_model = model_all_stations(forecasts_range=test_range)
params = test_model.closest_params(params)
closest_params = SWANParams(drf=params[0],
cfw=params[1],
stpm=params[2],
fidelity_time=params[3],
fidelity_space=params[4])
drf_idx, cfw_idx, stpm_idx, fid_time_idx, fid_space_idx = test_model.params_idxs(
closest_params)
forecasts = test_model.grid[drf_idx, cfw_idx, stpm_idx, fid_time_idx,
fid_space_idx]
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.last().error_value
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