def run_experiments(
train_hilo: str,
test_hilo: str,
train_length: int,
static: bool,
ignore_vars: Optional[List[str]] = None,
run_regression: bool = True,
all_models: bool = False,
):
# run baseline model
print("\n\nBASELINE MODEL:")
persistence()
print("\n\n")
# RUN EXPERIMENTS
if run_regression:
regression(ignore_vars=ignore_vars, include_static=static)
if static:
# 'embeddings' or 'features'
try:
earnn(pretrained=False,
ignore_vars=ignore_vars,
static="embeddings")
except RuntimeError:
print(f"\n{'*'*10}\n FAILED: EALSTM \n{'*'*10}\n")
if all_models: # run all other models ?
try:
linear_nn(ignore_vars=ignore_vars, static="embeddings")
except RuntimeError:
print(f"\n{'*'*10}\n FAILED: LinearNN \n{'*'*10}\n")
try:
rnn(ignore_vars=ignore_vars, static="embeddings")
except RuntimeError:
print(f"\n{'*'*10}\n FAILED: RNN \n{'*'*10}\n")
try:
earnn(pretrained=False, ignore_vars=ignore_vars, static=None)
except RuntimeError:
print(f"\n{'*'*10}\n FAILED: EALSTM \n{'*'*10}\n")
else: # NO STATIC data
try:
rnn(ignore_vars=ignore_vars, static=None)
except RuntimeError:
print(f"\n{'*'*10}\n FAILED: RNN \n{'*'*10}\n")
if all_models: # run all other models ?
try:
linear_nn(ignore_vars=ignore_vars, static=None)
except RuntimeError:
print(f"\n{'*'*10}\n FAILED: LinearNN \n{'*'*10}\n")
# RENAME DIRECTORY
data_dir = get_data_path()
rename_experiment_dir(data_dir,
train_hilo=train_hilo,
test_hilo=test_hilo,
train_length=train_length)
print(
f"\n**Experiment finished**\n",
"train_length: " + str(train_length),
"test_hilo: " + test_hilo,
"train_hilo: " + train_hilo,
"\ntrain_years:\n",
train_years,
"\n",
"test_years:\n",
test_years,
)
def run_all_models_as_experiments(
vars_to_include: List[str],
ignore_vars: List[str],
static: bool,
run_regression: bool = True,
):
print(f"Experiment {vars_to_include} Static: {static}")
# RUN EXPERIMENTS
if run_regression:
regression(ignore_vars=ignore_vars, include_static=static)
if static:
# 'embeddings' or 'features'
try:
linear_nn(ignore_vars=ignore_vars, static="embeddings")
except KeyboardInterrupt:
raise
except Exception as e:
logging.debug(
f"\n{'*'*10}\n FAILED: LinearNN for vars={vars_to_include} static={static}\n{'*'*10}\n"
)
logging.debug(e)
try:
rnn(ignore_vars=ignore_vars, static="embeddings")
except KeyboardInterrupt:
raise
except Exception as e:
logging.debug(
f"\n{'*'*10}\n FAILED: RNN for vars={vars_to_include} static={static}\n{'*'*10}\n"
)
logging.debug(e)
try:
earnn(pretrained=False,
ignore_vars=ignore_vars,
static="embeddings")
except KeyboardInterrupt:
raise
except Exception as e:
logging.debug(
f"\n{'*'*10}\n FAILED: EALSTM for vars={vars_to_include} static={static}\n{'*'*10}\n"
)
logging.debug(e)
else:
try:
linear_nn(ignore_vars=ignore_vars, static=None)
except KeyboardInterrupt:
raise
except Exception as e:
logging.debug(
f"\n{'*'*10}\n FAILED: LinearNN for vars={vars_to_include} static={static}\n{'*'*10}\n"
)
logging.debug(e)
try:
rnn(ignore_vars=ignore_vars, static=None)
except KeyboardInterrupt:
raise
except Exception as e:
logging.debug(
f"\n{'*'*10}\n FAILED: RNN for vars={vars_to_include} static={static}\n{'*'*10}\n"
)
logging.debug(e)
try:
# NO NEED to run the EALSTM without static data because
# just equivalent to the RNN
earnn(pretrained=False, ignore_vars=ignore_vars, static=None)
except KeyboardInterrupt:
raise
except Exception as e:
logging.debug(
f"\n{'*'*10}\n FAILED: EALSTM for vars={vars_to_include} static={static}\n{'*'*10}\n"
)
logging.debug(e)
# RENAME DIRECTORY
data_dir = get_data_path().absolute()
rename_model_experiment_file(data_dir, vars_to_include, static)
print(f"Experiment {vars_to_include} finished")
regression(
ignore_vars=always_ignore_vars,
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
)
# # gbdt(ignore_vars=always_ignore_vars)
linear_nn(
ignore_vars=always_ignore_vars,
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
num_epochs=num_epochs,
early_stopping=early_stopping,
layer_sizes=[hidden_size],
include_latlons=True,
include_yearly_aggs=False,
clear_nans=True,
)
# -------------
# LSTM
# -------------
rnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,