Skip to main content

​

​
mlforecast_objective

mlforecast_objective(
    df,
    config_fn,
    loss,
    model,
    freq,
    n_windows,
    h,
    step_size=None,
    input_size=None,
    refit=False,
    id_col="unique_id",
    time_col="ds",
    target_col="y",
    weight_col=None,
)
optuna objective function for the MLForecast class Parameters:
NameTypeDescriptionDefault
dfDataFrameSeries data in long format.required
config_fncallableFunction that takes an optuna trial and produces a configuration with the following keys: - model_params - mlf_init_params - mlf_fit_paramsrequired
losscallableFunction that takes the validation and train dataframes and produces a float.required
modelBaseEstimatorscikit-learn compatible model to be trainedrequired
freqstr or intpandas’ or polars’ offset alias or integer denoting the frequency of the series.required
n_windowsintNumber of windows to evaluate.required
hintForecast horizon.required
step_sizeintStep size between each cross validation window. If None it will be equal to h. Defaults to None.None
input_sizeintMaximum training samples per serie in each window. If None, will use an expanding window. Defaults to None.None
refitbool or intRetrain model for each cross validation window. If False, the models are trained at the beginning and then used to predict each window. If positive int, the models are retrained every refit windows. Defaults to False.False
id_colstrColumn that identifies each serie. Defaults to ‘unique_id’.‘unique_id’
time_colstrColumn that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.‘ds’
target_colstrColumn that contains the target. Defaults to ‘y’.‘y’
weight_colstrColumn that contains sample weights. Defaults to None.None
Returns:
TypeDescription
Callable[[Trial], float]optuna objective function
import lightgbm as lgb
from datasetsforecast.m4 import M4, M4Evaluation, M4Info
from utilsforecast.losses import smape

from mlforecast.lag_transforms import ExpandingMean, RollingMean
from mlforecast.target_transforms import Differences, LocalBoxCox, LocalStandardScaler

def train_valid_split(group):
    df, *_ = M4.load(directory='data', group=group)
    df['ds'] = df['ds'].astype('int')
    horizon = M4Info[group].horizon
    valid = df.groupby('unique_id').tail(horizon)
    train = df.drop(valid.index)
    return train, valid

h = M4Info['Weekly'].horizon
weekly_train, weekly_valid = train_valid_split('Weekly')
weekly_train['unique_id'] = weekly_train['unique_id'].astype('category')
weekly_valid['unique_id'] = weekly_valid['unique_id'].astype(weekly_train['unique_id'].dtype)

def config_fn(trial):
    candidate_lags = [
        [1],
        [13],
        [1, 13],
        range(1, 33),
    ]
    lag_idx = trial.suggest_categorical('lag_idx', range(len(candidate_lags)))
    candidate_lag_tfms = [
        {
            1: [RollingMean(window_size=13)]
        },
        {
            1: [RollingMean(window_size=13)],
            13: [RollingMean(window_size=13)],
        },
        {
            13: [RollingMean(window_size=13)],
        },
        {
            4: [ExpandingMean(), RollingMean(window_size=4)],
            8: [ExpandingMean(), RollingMean(window_size=4)],
        }
    ]
    lag_tfms_idx = trial.suggest_categorical('lag_tfms_idx', range(len(candidate_lag_tfms)))
    candidate_targ_tfms = [
        [Differences([1])],
        [LocalBoxCox()],
        [LocalStandardScaler()],
        [LocalBoxCox(), Differences([1])],
        [LocalBoxCox(), LocalStandardScaler()],
        [LocalBoxCox(), Differences([1]), LocalStandardScaler()],
    ]
    targ_tfms_idx = trial.suggest_categorical('targ_tfms_idx', range(len(candidate_targ_tfms)))
    return {
        'model_params': {
            'learning_rate': 0.05,
            'objective': 'l1',
            'bagging_freq': 1,
            'num_threads': 2,
            'verbose': -1,
            'force_col_wise': True,
            'n_estimators': trial.suggest_int('n_estimators', 10, 1000, log=True),
            'num_leaves': trial.suggest_int('num_leaves', 31, 1024, log=True),
            'lambda_l1': trial.suggest_float('lambda_l1', 0.01, 10, log=True),
            'lambda_l2': trial.suggest_float('lambda_l2', 0.01, 10, log=True),
            'bagging_fraction': trial.suggest_float('bagging_fraction', 0.75, 1.0),
            'feature_fraction': trial.suggest_float('feature_fraction', 0.75, 1.0),
        },
        'mlf_init_params': {
            'lags': candidate_lags[lag_idx],
            'lag_transforms': candidate_lag_tfms[lag_tfms_idx],
            'target_transforms': candidate_targ_tfms[targ_tfms_idx],
        },
        'mlf_fit_params': {
            'static_features': ['unique_id'],
        }
    }

def loss(df, train_df):
    return smape(df, models=['model'])['model'].mean()

optuna.logging.set_verbosity(optuna.logging.WARNING)
objective = mlforecast_objective(
    df=weekly_train,
    config_fn=config_fn,
    loss=loss,
    model=lgb.LGBMRegressor(),
    freq=1,
    n_windows=2,
    h=h,
)
study = optuna.create_study(
    direction='minimize', sampler=optuna.samplers.TPESampler(seed=0)
)
study.optimize(objective, n_trials=2)
best_cfg = study.best_trial.user_attrs['config']
final_model = MLForecast(
    models=[lgb.LGBMRegressor(**best_cfg['model_params'])],
    freq=1,
    **best_cfg['mlf_init_params'],
)
final_model.fit(weekly_train, **best_cfg['mlf_fit_params'])
preds = final_model.predict(h)
M4Evaluation.evaluate('data', 'Weekly', preds['LGBMRegressor'].values.reshape(-1, 13))
SMAPEMASEOWA
Weekly9.2615382.6144730.976158