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 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", )
def lstm(ignore_vars, static): rnn( experiment="one_month_forecast", surrounding_pixels=None, explain=False, ignore_vars=ignore_vars, num_epochs=50, early_stopping=10, hidden_size=256, # static data static=static, include_pred_month=True if static is not None else False, include_latlons=True if static is not None else False, include_prev_y=True if static is not None else False, )
always_ignore_vars = ["ndvi", "p84.162", "sp", "tp", "Eb"] # persistence() # regression(ignore_vars=always_ignore_vars, predict_delta=True) # gbdt(ignore_vars=always_ignore_vars, predict_delta=True) # linear_nn(ignore_vars=always_ignore_vars, predict_delta=True) # rnn(ignore_vars=always_ignore_vars, predict_delta=True) rnn( # earnn( experiment="one_month_forecast", include_pred_month=True, surrounding_pixels=None, pretrained=False, explain=False, static="features", ignore_vars=always_ignore_vars, num_epochs=50, # 50 early_stopping=5, # 5 hidden_size=256, static_embedding_size=64, predict_delta=True, normalize_y=True, include_prev_y=True, 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" /
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")
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, )
# ------------- persistence() # 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, 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,
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, static="features", ignore_vars=always_ignore_vars, num_epochs=num_epochs, early_stopping=early_stopping, hidden_size=hidden_size, include_latlons=True, include_yearly_aggs=False, clear_nans=True, weight_observations=False, ) # ------------- # EALSTM # ------------- earnn( experiment="one_month_forecast", include_pred_month=True, surrounding_pixels=None,
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, )