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 default_params_forecasts(model):
'''
Our baseline: forecasts with default SWAN params
'''
closest_params = model.closest_params(
params=SWANParams(drf=1.0, cfw=0.015, stpm=0.00302))
default_params = SWANParams(drf=closest_params[0],
cfw=closest_params[1],
stpm=closest_params[2])
drf_idx, cfw_idx, stpm_idx = model.params_idxs(default_params)
forecasts = model.grid[drf_idx, cfw_idx, stpm_idx]
return forecasts
def params_and_error_grid(model, station_index, stpm_index):
drf, cfw = np.meshgrid(model.grid_file.drf_grid, model.grid_file.cfw_grid)
stpm_fixed = model.grid_file.stpm_grid[stpm_index]
error = np.ones(shape=drf.shape)
for i in range(drf.shape[0]):
for j in range(drf.shape[1]):
out_by_stations = model.output(params=SWANParams(drf=drf[i, j], cfw=cfw[i, j], stpm=stpm_fixed))
error[i, j] = out_by_stations[station_index]
return drf, cfw, error
def crossover(p1, p2, rate):
if random.random() >= rate:
return p1
part1_rate = abs(random.random())
part2_rate = 1 - part1_rate
child_params = SWANParams(
drf=abs(p1.drf * part1_rate + p2.drf * part2_rate),
cfw=abs(p1.cfw * part1_rate + p2.cfw * part2_rate),
stpm=abs(p1.stpm * part1_rate + p2.stpm * part2_rate))
return child_params
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 initial_pop_lhs(size, **kwargs):
samples_grid = lhs(PARAMS, size, 'center')
for idx, params_range in enumerate([drf_range, cfw_range, stpm_range]):
samples_grid[:, idx] = norm(loc=np.mean(params_range),
scale=np.std(params_range)).ppf(
samples_grid[:, idx])
population = [
SWANParams(drf=sample[0], cfw=sample[1], stpm=sample[2])
for sample in samples_grid
]
# population = [SWANParams(drf=1.0, cfw=0.015, stpm=0.00302) for sample in samples_grid]
if 'dump' in kwargs and kwargs['dump'] is True:
dump_population(population, kwargs['file_path'])
return population
def default_initial_pop(size):
return [SWANParams.new_instance() for _ in range(size)]
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
def reference_metrics():
return all_error_metrics(params=SWANParams(drf=1.0,
cfw=0.015,
stpm=0.00302),
models_to_tests=init_models_to_tests())