Auto

​

AutoRandomForest

 AutoRandomForest (config:Optional[Callable[[optuna.trial._trial.Trial],Di
                   ct[str,Any]]]=None)

Structure to hold a model and its search space

​

AutoElasticNet

 AutoElasticNet (config:Optional[Callable[[optuna.trial._trial.Trial],Dict
                 [str,Any]]]=None)

Structure to hold a model and its search space

​

AutoLasso

 AutoLasso (config:Optional[Callable[[optuna.trial._trial.Trial],Dict[str,
            Any]]]=None)

Structure to hold a model and its search space

​

AutoRidge

 AutoRidge (config:Optional[Callable[[optuna.trial._trial.Trial],Dict[str,
            Any]]]=None)

Structure to hold a model and its search space

​

AutoLinearRegression

 AutoLinearRegression (config:Optional[Callable[[optuna.trial._trial.Trial
                       ],Dict[str,Any]]]=None)

Structure to hold a model and its search space

​

AutoCatboost

 AutoCatboost (config:Optional[Callable[[optuna.trial._trial.Trial],Dict[s
               tr,Any]]]=None)

Structure to hold a model and its search space

​

AutoXGBoost

 AutoXGBoost (config:Optional[Callable[[optuna.trial._trial.Trial],Dict[st
              r,Any]]]=None)

Structure to hold a model and its search space

​

AutoLightGBM

 AutoLightGBM (config:Optional[Callable[[optuna.trial._trial.Trial],Dict[s
               tr,Any]]]=None)

Structure to hold a model and its search space

​

random_forest_space

 random_forest_space (trial:optuna.trial._trial.Trial)

​

elastic_net_space

 elastic_net_space (trial:optuna.trial._trial.Trial)

​

lasso_space

 lasso_space (trial:optuna.trial._trial.Trial)

​

ridge_space

 ridge_space (trial:optuna.trial._trial.Trial)

​

linear_regression_space

 linear_regression_space (trial:optuna.trial._trial.Trial)

​

catboost_space

 catboost_space (trial:optuna.trial._trial.Trial)

​

xgboost_space

 xgboost_space (trial:optuna.trial._trial.Trial)

​

lightgbm_space

 lightgbm_space (trial:optuna.trial._trial.Trial)

​

AutoModel

 AutoModel (model:sklearn.base.BaseEstimator,
            config:Callable[[optuna.trial._trial.Trial],Dict[str,Any]])

Structure to hold a model and its search space

​

AutoMLForecast

 AutoMLForecast
                 (models:Union[List[__main__.AutoModel],Dict[str,__main__.
                 AutoModel]], freq:Union[int,str], season_length:int, init
                 _config:Optional[Callable[[optuna.trial._trial.Trial],Dic
                 t[str,Any]]]=None, fit_config:Optional[Callable[[optuna.t
                 rial._trial.Trial],Dict[str,Any]]]=None,
                 num_threads:int=1)

Hyperparameter optimization helper

​

AutoMLForecast.fit

 AutoMLForecast.fit
                     (df:Union[pandas.core.frame.DataFrame,polars.datafram
                     e.frame.DataFrame], n_windows:int, h:int,
                     num_samples:int, refit:Union[bool,int]=False, loss:Op
                     tional[Callable[[Union[pandas.core.frame.DataFrame,po
                     lars.dataframe.frame.DataFrame],Union[pandas.core.fra
                     me.DataFrame,polars.dataframe.frame.DataFrame]],float
                     ]]=None, id_col:str='unique_id', time_col:str='ds',
                     target_col:str='y',
                     study_kwargs:Optional[Dict[str,Any]]=None,
                     optimize_kwargs:Optional[Dict[str,Any]]=None,
                     fitted:bool=False, prediction_intervals:Optional[mlfo
                     recast.utils.PredictionIntervals]=None)

Carry out the optimization process. Each model is optimized independently and the best one is trained on all data

​

AutoMLForecast.predict

 AutoMLForecast.predict (h:int, X_df:Union[pandas.core.frame.DataFrame,pol
                         ars.dataframe.frame.DataFrame,NoneType]=None,
                         level:Optional[List[Union[int,float]]]=None)

“Compute forecasts

​

AutoMLForecast.save

 AutoMLForecast.save (path:Union[str,pathlib.Path])

Save AutoMLForecast objects

​

AutoMLForecast.forecast_fitted_values

 AutoMLForecast.forecast_fitted_values
                                        (level:Optional[List[Union[int,flo
                                        at]]]=None)

Access in-sample predictions.

from datasetsforecast.m4 import M4, M4Evaluation, M4Info
from sklearn.linear_model import Ridge
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder

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).copy()
    train = df.drop(valid.index).reset_index(drop=True)
    return train, valid

ridge_pipeline = make_pipeline(
    ColumnTransformer(
        [('encoder', OneHotEncoder(), ['unique_id'])],
        remainder='passthrough',
    ),
    Ridge()
)
auto_ridge = AutoModel(ridge_pipeline, lambda trial: {f'ridge__{k}': v for k, v in ridge_space(trial).items()})

optuna.logging.set_verbosity(optuna.logging.ERROR)
group = 'Weekly'
train, valid = train_valid_split(group)
train['unique_id'] = train['unique_id'].astype('category')
valid['unique_id'] = valid['unique_id'].astype(train['unique_id'].dtype)
info = M4Info[group]
h = info.horizon
season_length = info.seasonality
auto_mlf = AutoMLForecast(
    freq=1,
    season_length=season_length,
    models={
        'lgb': AutoLightGBM(),
        'ridge': auto_ridge,
    },
    fit_config=lambda trial: {'static_features': ['unique_id']},
    num_threads=2,
)
auto_mlf.fit(
    df=train,
    n_windows=2,
    h=h,
    num_samples=2,
    optimize_kwargs={'timeout': 60},
    fitted=True,
    prediction_intervals=PredictionIntervals(n_windows=2, h=h),
)
auto_mlf.predict(h, level=[80])

auto_mlf.forecast_fitted_values(level=[95])

import polars as pl

train_pl = pl.from_pandas(train.astype({'unique_id': 'str'}))
auto_mlf = AutoMLForecast(
    freq=1,
    season_length=season_length,
    models={'ridge': AutoRidge()},
    num_threads=2,
)
auto_mlf.fit(
    df=train_pl,
    n_windows=2,
    h=h,
    num_samples=2,
    optimize_kwargs={'timeout': 60},
    fitted=True,
    prediction_intervals=PredictionIntervals(n_windows=2, h=h),
)
auto_mlf.predict(h, level=[80])

auto_mlf.forecast_fitted_values(level=[95])