def run_models(target_var: str):
parsimonious()
# -------
# LSTM
# -------
rnn( # earnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
static=None, # "features",
ignore_vars=None,
num_epochs=50, # 50
early_stopping=5, # 5
hidden_size=256,
predict_delta=False,
normalize_y=True,
include_prev_y=False,
include_latlons=False,
)
# -------
# EALSTM
# -------
# rename the output file
data_path = get_data_path()
_rename_directory(
from_path=data_path / "models" / "one_month_forecast",
to_path=data_path / "models" /
f"one_month_forecast_adede_only_target_{target_var}",
with_datetime=False,
)
def main(target_var, all_vars):
# RUN engineer
engineer(target_var=target_var)
autoregressive = [target_var] # 'VCI3M'
dynamic = ["precip", "t2m", "pet", "E", "SMroot", "SMsurf"]
static_list = [False, False, True]
for vars_to_include, static_bool in zip(
[autoregressive, autoregressive + dynamic, autoregressive + dynamic],
static_list,
):
print(
f'\n{"-" * 10}\nRunning experiment with: {vars_to_include} with static: {static_bool} for {target_var}\n{"-" * 10}'
)
# FIT models
vars_to_exclude = [v for v in all_vars if v not in vars_to_include]
parsimonious()
if static_bool:
lstm(vars_to_exclude, static="features")
ealstm(vars_to_exclude, static="features")
else:
lstm(vars_to_exclude, static=None)
# RENAME model directories
data_dir = get_data_path()
rename_model_experiment_file(data_dir,
vars_to_include,
static=static_bool,
target_var=target_var)
def models(target_var: str = "VCI1M"):
# NO IGNORE VARS
ignore_vars = None
# drop the target variable from ignore_vars
# ignore_vars = [v for v in ignore_vars if v != target_var]
# assert target_var not in ignore_vars
# -------------
# persistence
# -------------
parsimonious()
# regression(ignore_vars=ignore_vars)
# gbdt(ignore_vars=ignore_vars)
# linear_nn(ignore_vars=ignore_vars)
# -------------
# LSTM
# -------------
rnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
static="features",
ignore_vars=ignore_vars,
num_epochs=50,
early_stopping=5,
hidden_size=256,
include_latlons=True,
)
# -------------
# EALSTM
# -------------
earnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
pretrained=False,
explain=False,
static="features",
ignore_vars=ignore_vars,
num_epochs=50,
early_stopping=5,
hidden_size=256,
static_embedding_size=64,
include_latlons=True,
)
# rename the output file
data_path = get_data_path()
_rename_directory(
from_path=data_path / "models" / "one_month_forecast",
to_path=data_path / "models" /
f"one_month_forecast_BOKU_{target_var}_adede_only_vars",
)
always_ignore_vars = [
"VCI",
"ndvi",
"p84.162",
"sp",
"tp",
"Eb",
"tprate_std_1",
"tprate_mean_1",
"tprate_std_2",
"tprate_mean_2",
"tprate_std_3",
"tprate_mean_3",
]
parsimonious(experiment="nowcast")
# regression(ignore_vars=always_ignore_vars)
# gbdt(ignore_vars=always_ignore_vars)
# linear_nn(ignore_vars=always_ignore_vars)
# rnn(ignore_vars=always_ignore_vars)
earnn(
experiment="nowcast",
include_pred_month=True,
surrounding_pixels=None,
pretrained=False,
explain=False,
static="features",
ignore_vars=always_ignore_vars,
num_epochs=1, # 50,
early_stopping=5,
hidden_size=256,
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:")
parsimonious()
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")
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 models(
target_var: str = "boku_VCI",
adede_only=False,
experiment_name=None,
check_inversion=False,
):
if adede_only:
ignore_vars = [
"p84.162",
"sp",
"tp",
"Eb",
"VCI",
"modis_ndvi",
"pev",
"t2m",
"E",
"SMroot",
"SMsurf",
]
else:
ignore_vars = [
"p84.162",
"sp",
"tp",
"Eb",
"VCI",
"modis_ndvi",
"SMroot",
"SMsurf",
]
# drop the target variable from ignore_vars
ignore_vars = [v for v in ignore_vars if v != target_var]
assert target_var not in ignore_vars
# -------------
# persistence
# -------------
parsimonious()
# regression(ignore_vars=ignore_vars)
# gbdt(ignore_vars=ignore_vars)
# linear_nn(ignore_vars=ignore_vars)
# -------------
# LSTM
# -------------
rnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
static="features",
ignore_vars=ignore_vars,
num_epochs=50, # 1, # 50 ,
early_stopping=5,
hidden_size=256,
include_latlons=True,
check_inversion=check_inversion,
)
# -------------
# EALSTM
# -------------
earnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
pretrained=False,
explain=False,
static="features",
ignore_vars=ignore_vars,
num_epochs=50, # 1, # 50 ,
early_stopping=5,
hidden_size=256,
static_embedding_size=64,
include_latlons=True,
check_inversion=check_inversion,
)
# rename the output file
data_path = get_data_path()
if experiment_name is None:
experiment_name = (
f"one_month_forecast_BOKU_{target_var}_our_vars_{'only_P_VCI' if adede_only else 'ALL'}",
)
_rename_directory(
from_path=data_path / "models" / "one_month_forecast",
to_path=data_path / "models" / experiment_name,
)
from scripts.utils import _rename_directory, get_data_path
from _base_models import parsimonious, regression, linear_nn, rnn, earnn
if __name__ == "__main__":
# NOTE: why have we downloaded 2 variables for ERA5 evaporaton
# important_vars = ["VCI", "precip", "t2m", "pev", "p0005", "SMsurf", "SMroot"]
# always_ignore_vars = ["ndvi", "p84.162", "sp", "tp", "Eb", "E", "p0001"]
important_vars = ["VCI", "precip", "t2m", "pev", "E", "SMsurf", "SMroot"]
always_ignore_vars = ["p84.162", "sp", "tp", "Eb", "VCI1M", "RFE1M"] # "ndvi",
# -------------
# persistence
# -------------
parsimonious()
# regression(ignore_vars=always_ignore_vars)
# gbdt(ignore_vars=always_ignore_vars)
# linear_nn(ignore_vars=always_ignore_vars)
# -------------
# LSTM
# -------------
rnn(
experiment="one_month_forecast",
include_pred_month=True,
surrounding_pixels=None,
explain=False,
static="features",
ignore_vars=always_ignore_vars,