Auto

AutoRandomForest

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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AutoElasticNet

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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AutoLasso

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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AutoRidge

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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AutoLinearRegression

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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AutoCatboost

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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AutoXGBoost

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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AutoLightGBM

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

Structure to hold a model and its search space

	Type	Default	Details
config	Optional	None	function that takes an optuna trial and produces a configuration

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random_forest_space

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

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elastic_net_space

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

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lasso_space

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

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ridge_space

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

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linear_regression_space

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

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catboost_space

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

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xgboost_space

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

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lightgbm_space

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

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AutoModel

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

Structure to hold a model and its search space

	Type	Details
model	BaseEstimator	scikit-learn compatible regressor
config	Callable	function that takes an optuna trial and produces a configuration

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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

	Type	Default	Details
models	Union		Auto models to be optimized.
freq	Union		pandas’ or polars’ offset alias or integer denoting the frequency of the series.
season_length	int		Length of the seasonal period. This is used for producing the feature space.
init_config	Optional	None	Function that takes an optuna trial and produces a configuration passed to the MLForecast constructor.
fit_config	Optional	None	Function that takes an optuna trial and produces a configuration passed to the MLForecast fit method.
num_threads	int	1	Number of threads to use when computing the features.

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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)

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

	Type	Default	Details
df	Union		Series data in long format.
n_windows	int		Number of windows to evaluate.
h	int		Forecast horizon.
num_samples	int		Number of trials to run
refit	Union	False	Retrain 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.
loss	Optional	None	Function that takes the validation and train dataframes and produces a float. If `None` will use the average SMAPE across series.
id_col	str	unique_id	Column that identifies each serie.
time_col	str	ds	Column that identifies each timestep, its values can be timestamps or integers.
target_col	str	y	Column that contains the target.
study_kwargs	Optional	None	Keyword arguments to be passed to the optuna.Study constructor.
optimize_kwargs	Optional	None	Keyword arguments to be passed to the optuna.Study.optimize method.
Returns	AutoMLForecast		object with best models and optimization results

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AutoMLForecast.predict

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

“Compute forecasts

	Type	Default	Details
h	int		Number of periods to predict.
X_df	Union	None	Dataframe with the future exogenous features. Should have the id column and the time column.
Returns	Union		Predictions for each serie and timestep, with one column per model.

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AutoMLForecast.save

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

Save AutoMLForecast objects

	Type	Details
path	Union	Directory where artifacts will be stored.
Returns	None

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},
)
auto_mlf.predict(h)

	unique_id	ds	lgb	ridge
0	W1	2180	35529.435224	36110.921202
1	W1	2181	35521.764894	36195.175757
2	W1	2182	35537.417268	36107.528852
3	W1	2183	35538.058206	36027.139248
4	W1	2184	35614.611211	36092.858489
…	…	…	…	…
4662	W99	2292	15071.536978	15319.146221
4663	W99	2293	15058.145278	15299.549555
4664	W99	2294	15042.493434	15271.744712
4665	W99	2295	15042.144846	15250.070504
4666	W99	2296	15038.729044	15232.127800

Getting Started

How-to guides

Tutorials

API Reference

AutoRandomForest

AutoElasticNet

AutoLasso

AutoRidge

AutoLinearRegression

AutoCatboost

AutoXGBoost

AutoLightGBM

random_forest_space

elastic_net_space

lasso_space

ridge_space

linear_regression_space

catboost_space

xgboost_space

lightgbm_space

AutoModel

AutoMLForecast

AutoMLForecast.fit

AutoMLForecast.predict

AutoMLForecast.save

Getting Started

How-to guides

Tutorials

API Reference

​AutoRandomForest

​AutoElasticNet

​AutoLasso

​AutoRidge

​AutoLinearRegression

​AutoCatboost

​AutoXGBoost

​AutoLightGBM

​random_forest_space

​elastic_net_space

​lasso_space

​ridge_space

​linear_regression_space

​catboost_space

​xgboost_space

​lightgbm_space

​AutoModel

​AutoMLForecast

​AutoMLForecast.fit

​AutoMLForecast.predict

​AutoMLForecast.save

AutoRandomForest

AutoElasticNet

AutoLasso

AutoRidge

AutoLinearRegression

AutoCatboost

AutoXGBoost

AutoLightGBM

random_forest_space

elastic_net_space

lasso_space

ridge_space

linear_regression_space

catboost_space

xgboost_space

lightgbm_space

AutoModel

AutoMLForecast

AutoMLForecast.fit

AutoMLForecast.predict

AutoMLForecast.save