Auto

`AutoRandomForest`

AutoRandomForest(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoElasticNet`

AutoElasticNet(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoLasso`

AutoLasso(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoRidge`

AutoRidge(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoLinearRegression`

AutoLinearRegression(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoCatboost`

AutoCatboost(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoXGBoost`

AutoXGBoost(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoLightGBM`

AutoLightGBM(config=None)

Bases: AutoModel Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`random_forest_space`

random_forest_space(trial)

`elastic_net_space`

elastic_net_space(trial)

`lasso_space`

lasso_space(trial)

`ridge_space`

ridge_space(trial)

`linear_regression_space`

linear_regression_space(trial)

`catboost_space`

catboost_space(trial)

`xgboost_space`

xgboost_space(trial)

`lightgbm_space`

lightgbm_space(trial)

`AutoModel`

AutoModel(model, config)

Structure to hold a model and its search space Parameters:

Name	Type	Description	Default
`model`	`BaseEstimator`	scikit-learn compatible regressor	required
`config`	`callable`	function that takes an optuna trial and produces a configuration	required

`AutoMLForecast`

AutoMLForecast(
    models,
    freq,
    season_length=None,
    init_config=None,
    fit_config=None,
    num_threads=1,
)

Hyperparameter optimization helper Parameters:

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

`AutoMLForecast.fit`

fit(
    df,
    n_windows,
    h,
    num_samples,
    step_size=None,
    input_size=None,
    refit=False,
    loss=None,
    id_col="unique_id",
    time_col="ds",
    target_col="y",
    study_kwargs=None,
    optimize_kwargs=None,
    fitted=False,
    prediction_intervals=None,
    weight_col=None,
)

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

Name	Type	Description	Default
`df`	`pandas or polars DataFrame`	Series data in long format.	required
`n_windows`	`int`	Number of windows to evaluate.	required
`h`	`int`	Forecast horizon.	required
`num_samples`	`int`	Number of trials to run	required
`step_size`	`int`	Step size between each cross validation window. If None it will be equal to `h`. Defaults to None.	`None`
`input_size`	`int`	Maximum training samples per serie in each window. If None, will use an expanding window. Defaults to None.	`None`
`refit`	`bool or int`	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. Defaults to False.	`False`
`loss`	`callable`	Function that takes the validation and train dataframes and produces a float. If `None` will use the average SMAPE across series. Defaults to None.	`None`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`
`target_col`	`str`	Column that contains the target. Defaults to ‘y’.	`‘y’`
`study_kwargs`	`dict`	Keyword arguments to be passed to the optuna.Study constructor. Defaults to None.	`None`
`optimize_kwargs`	`dict`	Keyword arguments to be passed to the optuna.Study.optimize method. Defaults to None.	`None`
`fitted`	`bool`	Whether to compute the fitted values when retraining the best model. Defaults to False.	`False`
`prediction_intervals`	`Optional[PredictionIntervals]`	Configuration to calibrate prediction intervals when retraining the best model.	`None`

Returns:

Type	Description
`AutoMLForecast`	object with best models and optimization results

`AutoMLForecast.predict`

predict(h, X_df=None, level=None)

“Compute forecasts Parameters:

Name	Type	Description	Default
`h`	`int`	Number of periods to predict.	required
`X_df`	`pandas or polars DataFrame`	Dataframe with the future exogenous features. Should have the id column and the time column. Defaults to None.	`None`
`level`	`list of ints or floats`	Confidence levels between 0 and 100 for prediction intervals. Defaults to None.	`None`

Returns:

Type	Description
`pandas or polars DataFrame`	Predictions for each serie and timestep, with one column per model.

`AutoMLForecast.save`

save(path)

Save AutoMLForecast objects Parameters:

Name	Type	Description	Default
`path`	`str or Path`	Directory where artifacts will be stored.	required

`AutoMLForecast.forecast_fitted_values`

forecast_fitted_values(level=None)

Access in-sample predictions. Parameters:

Name	Type	Description	Default
`level`	`list of ints or floats`	Confidence levels between 0 and 100 for prediction intervals. Defaults to None.	`None`

Returns:

Type	Description
`pandas or polars DataFrame`	Dataframe with predictions for the training set

import time

import pandas as pd
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])

	unique_id	ds	lgb	lgb-lo-80	lgb-hi-80	ridge	ridge-lo-80	ridge-hi-80
0	W1	2180	35529.435224	35061.835362	35997.035086	36110.921202	35880.445097	36341.397307
1	W1	2181	35521.764894	34973.035617	36070.494171	36195.175757	36051.013811	36339.337702
2	W1	2182	35537.417268	34960.050939	36114.783596	36107.528852	35784.062169	36430.995536
3	W1	2183	35538.058206	34823.640706	36252.475705	36027.139248	35612.635725	36441.642771
4	W1	2184	35614.611211	34627.023739	36602.198683	36092.858489	35389.690977	36796.026000
…	…	…	…	…	…	…	…	…
4662	W99	2292	15071.536978	14484.617399	15658.456557	15319.146221	14869.410567	15768.881875
4663	W99	2293	15058.145278	14229.686322	15886.604234	15299.549555	14584.269352	16014.829758
4664	W99	2294	15042.493434	14096.380636	15988.606232	15271.744712	14365.349338	16178.140086
4665	W99	2295	15042.144846	14037.053904	16047.235787	15250.070504	14403.428791	16096.712216
4666	W99	2296	15038.729044	13944.821480	16132.636609	15232.127800	14325.059776	16139.195824

auto_mlf.forecast_fitted_values(level=[95])

	unique_id	ds	y	lgb	lgb-lo-95	lgb-hi-95	ridge	ridge-lo-95	ridge-hi-95
0	W1	15	1071.06	1060.584344	599.618355	1521.550334	1076.990151	556.535492	1597.444810
1	W1	16	1073.73	1072.669242	611.703252	1533.635232	1083.633276	563.178617	1604.087936
2	W1	17	1066.97	1072.452128	611.486139	1533.418118	1084.724311	564.269652	1605.178970
3	W1	18	1066.17	1065.837828	604.871838	1526.803818	1080.127197	559.672538	1600.581856
4	W1	19	1064.43	1065.214681	604.248691	1526.180671	1080.636826	560.182167	1601.091485
…	…	…	…	…	…	…	…	…	…
361881	W99	2279	15738.54	15887.661228	15721.237195	16054.085261	15927.918181	15723.222760	16132.613603
361882	W99	2280	15388.13	15755.943789	15589.519756	15922.367823	15841.599064	15636.903642	16046.294485
361883	W99	2281	15187.62	15432.224701	15265.800668	15598.648735	15584.462232	15379.766811	15789.157654
361884	W99	2282	15172.27	15177.040831	15010.616797	15343.464864	15396.243223	15191.547801	15600.938644
361885	W99	2283	15101.03	15162.090803	14995.666770	15328.514836	15335.982465	15131.287044	15540.677887

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])

unique_id	ds	ridge	ridge-lo-80	ridge-hi-80
str	i64	f64	f64	f64
”W1”	2180	35046.096663	34046.69521	36045.498116
”W1”	2181	34743.269216	33325.847975	36160.690457
”W1”	2182	34489.591086	32591.254559	36387.927614
”W1”	2183	34270.768179	32076.507727	36465.02863
”W1”	2184	34124.021857	31352.454121	36895.589593
…	…	…	…	…
”W99”	2292	14719.457096	13983.308582	15455.605609
”W99”	2293	14631.552077	13928.874336	15334.229818
”W99”	2294	14532.905239	13642.840118	15422.97036
”W99”	2295	14446.065443	13665.088667	15227.04222
”W99”	2296	14363.049604	13654.220051	15071.879157

auto_mlf.forecast_fitted_values(level=[95])

unique_id	ds	y	ridge	ridge-lo-95	ridge-hi-95
str	i64	f64	f64	f64	f64
”W1”	14	1061.96	1249.326428	488.765249	2009.887607
”W1”	15	1071.06	1246.067836	485.506657	2006.629015
”W1”	16	1073.73	1254.027897	493.466718	2014.589076
”W1”	17	1066.97	1254.475948	493.914769	2015.037126
”W1”	18	1066.17	1248.306754	487.745575	2008.867933
…	…	…	…	…	…
”W99”	2279	15738.54	15754.558812	15411.968645	16097.148979
”W99”	2280	15388.13	15655.780865	15313.190698	15998.371032
”W99”	2281	15187.62	15367.498468	15024.908301	15710.088635
”W99”	2282	15172.27	15172.591423	14830.001256	15515.18159
”W99”	2283	15101.03	15141.032886	14798.44272	15483.623053

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`

`AutoMLForecast.forecast_fitted_values`

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

​AutoMLForecast.forecast_fitted_values

`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`

`AutoMLForecast.forecast_fitted_values`