utils

save_forecaster(forecaster, file_name, verbose=True)

Save forecaster model using joblib.

Parameters:

Name	Type	Description	Default
forecaster		Forecaster created with skforecast library.	required
file_name	str	File name given to the object.	required
verbose	bool	Print summary about the forecaster saved.	True

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def save_forecaster(
    forecaster, 
    file_name: str, 
    verbose: bool=True
) -> None:
    """
    Save forecaster model using joblib.

    Parameters
    ----------
    forecaster: forecaster
        Forecaster created with skforecast library.
    file_name: str
        File name given to the object.
    verbose: bool, default `True`
        Print summary about the forecaster saved.

    Returns
    -------
    None

    """

    joblib.dump(forecaster, filename=file_name)

    if verbose:
        forecaster.summary()

load_forecaster(file_name, verbose=True)

Load forecaster model using joblib.

Parameters:

Name	Type	Description	Default
file_name	str	Object file name.	required
verbose	bool	Print summary about the forecaster loaded.	True

Returns:

Name	Type	Description
forecaster	forecaster	Forecaster created with skforecast library.

Source code in skforecast\utils\utils.py

def load_forecaster(
    file_name: str,
    verbose: bool=True
) -> object:
    """
    Load forecaster model using joblib.

    Parameters
    ----------
    file_name: str
        Object file name.
    verbose: bool, default `True`
        Print summary about the forecaster loaded.

    Returns
    -------
    forecaster: forecaster
        Forecaster created with skforecast library.

    """

    forecaster = joblib.load(filename=file_name)

    if verbose:
        forecaster.summary()

    return forecaster

initialize_lags(forecaster_name, lags)

Check lags argument input and generate the corresponding numpy ndarray.

Parameters:

Name	Type	Description	Default
forecaster_name	str	Forecaster name. ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate.	required
lags	Any	Lags used as predictors.	required

Returns:

Name	Type	Description
lags	numpy ndarray	Lags used as predictors.

Source code in skforecast\utils\utils.py

def initialize_lags(
    forecaster_name: str,
    lags: Any
) -> np.ndarray:
    """
    Check lags argument input and generate the corresponding numpy ndarray.

    Parameters
    ----------
    forecaster_name : str
        Forecaster name. ForecasterAutoreg, ForecasterAutoregCustom, 
        ForecasterAutoregDirect, ForecasterAutoregMultiSeries, 
        ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate.
    lags : Any
        Lags used as predictors.

    Returns
    -------
    lags : numpy ndarray
        Lags used as predictors.

    """

    if isinstance(lags, int) and lags < 1:
        raise ValueError("Minimum value of lags allowed is 1.")

    if isinstance(lags, (list, np.ndarray)):
        for lag in lags:
            if not isinstance(lag, (int, np.int64, np.int32)):
                raise TypeError("All values in `lags` must be int.")

    if isinstance(lags, (list, range, np.ndarray)) and min(lags) < 1:
        raise ValueError("Minimum value of lags allowed is 1.")

    if isinstance(lags, int):
        lags = np.arange(lags) + 1
    elif isinstance(lags, (list, range)):
        lags = np.array(lags)
    elif isinstance(lags, np.ndarray):
        lags = lags
    else:
        if not forecaster_name == 'ForecasterAutoregMultiVariate':
            raise TypeError(
                ("`lags` argument must be an int, 1d numpy ndarray, range or list. "
                 f"Got {type(lags)}.")
            )
        else:
            raise TypeError(
                ("`lags` argument must be a dict, int, 1d numpy ndarray, range or list. "
                 f"Got {type(lags)}.")
            )

    return lags

initialize_weights(forecaster_name, regressor, weight_func, series_weights)

Check weights arguments, weight_func and series_weights for the different forecasters. Create source_code_weight_func, source code of the custom function(s) used to create weights.

Parameters:

Name	Type	Description	Default
forecaster_name	str	Forecaster name. ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate.	required
regressor	regressor or pipeline compatible with the scikit-learn API	Regressor of the forecaster.	required
weight_func	Callable, dict	Argument weight_func of the forecaster.	required
series_weights	dict	Argument series_weights of the forecaster.	required

Returns:

Name	Type	Description
weight_func	Callable, dict	Argument weight_func of the forecaster.
source_code_weight_func	str, dict	Argument source_code_weight_func of the forecaster.
series_weights	dict	Argument series_weights of the forecaster.

Source code in skforecast\utils\utils.py

def initialize_weights(
    forecaster_name: str,
    regressor: object,
    weight_func: Union[Callable, dict],
    series_weights: dict
) -> Tuple[Union[Callable, dict], Union[str, dict], dict]:
    """
    Check weights arguments, `weight_func` and `series_weights` for the different 
    forecasters. Create `source_code_weight_func`, source code of the custom 
    function(s) used to create weights.

    Parameters
    ----------
    forecaster_name : str
        Forecaster name. ForecasterAutoreg, ForecasterAutoregCustom, 
        ForecasterAutoregDirect, ForecasterAutoregMultiSeries, 
        ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate.
    regressor : regressor or pipeline compatible with the scikit-learn API
        Regressor of the forecaster.
    weight_func : Callable, dict
        Argument `weight_func` of the forecaster.
    series_weights : dict
        Argument `series_weights` of the forecaster.

    Returns
    -------
    weight_func : Callable, dict
        Argument `weight_func` of the forecaster.
    source_code_weight_func : str, dict
        Argument `source_code_weight_func` of the forecaster.
    series_weights : dict
        Argument `series_weights` of the forecaster.

    """

    source_code_weight_func = None

    if weight_func is not None:

        if forecaster_name in ['ForecasterAutoregMultiSeries', 'ForecasterAutoregMultiSeriesCustom']:
            if not isinstance(weight_func, (Callable, dict)):
                raise TypeError(
                    (f"Argument `weight_func` must be a Callable or a dict of "
                     f"Callables. Got {type(weight_func)}.")
                )
        elif not isinstance(weight_func, Callable):
            raise TypeError(
                f"Argument `weight_func` must be a Callable. Got {type(weight_func)}."
            )

        if isinstance(weight_func, dict):
            source_code_weight_func = {}
            for key in weight_func:
                source_code_weight_func[key] = inspect.getsource(weight_func[key])
        else:
            source_code_weight_func = inspect.getsource(weight_func)

        if 'sample_weight' not in inspect.signature(regressor.fit).parameters:
            warnings.warn(
                (f"Argument `weight_func` is ignored since regressor {regressor} "
                 f"does not accept `sample_weight` in its `fit` method."),
                 IgnoredArgumentWarning
            )
            weight_func = None
            source_code_weight_func = None

    if series_weights is not None:
        if not isinstance(series_weights, dict):
            raise TypeError(
                (f"Argument `series_weights` must be a dict of floats or ints."
                 f"Got {type(series_weights)}.")
            )
        if 'sample_weight' not in inspect.signature(regressor.fit).parameters:
            warnings.warn(
                (f"Argument `series_weights` is ignored since regressor {regressor} "
                 f"does not accept `sample_weight` in its `fit` method."),
                 IgnoredArgumentWarning
            )
            series_weights = None

    return weight_func, source_code_weight_func, series_weights

check_select_fit_kwargs(regressor, fit_kwargs=None)

Check if fit_kwargs is a dict and select only the keys that are used by the fit method of the regressor.

Parameters:

Name	Type	Description	Default
regressor	object	Regressor object.	required
fit_kwargs	dict	Dictionary with the arguments to pass to the `fit' method of the forecaster.	`None`

Returns:

Name	Type	Description
fit_kwargs	dict	Dictionary with the arguments to be passed to the fit method of the regressor after removing the unused keys.

Source code in skforecast\utils\utils.py

def check_select_fit_kwargs(
    regressor: object,
    fit_kwargs: Optional[dict]=None
) -> dict:
    """
    Check if `fit_kwargs` is a dict and select only the keys that are used by
    the `fit` method of the regressor.

    Parameters
    ----------
    regressor : object
        Regressor object.
    fit_kwargs : dict, default `None`
        Dictionary with the arguments to pass to the `fit' method of the forecaster.

    Returns
    -------
    fit_kwargs : dict
        Dictionary with the arguments to be passed to the `fit` method of the 
        regressor after removing the unused keys.

    """

    if fit_kwargs is None:
        fit_kwargs = {}
    else:
        if not isinstance(fit_kwargs, dict):
            raise TypeError(
                f"Argument `fit_kwargs` must be a dict. Got {type(fit_kwargs)}."
            )

        # Non used keys
        non_used_keys = [k for k in fit_kwargs.keys()
                         if k not in inspect.signature(regressor.fit).parameters]
        if non_used_keys:
            warnings.warn(
                (f"Argument/s {non_used_keys} ignored since they are not used by the "
                 f"regressor's `fit` method."),
                 IgnoredArgumentWarning
            )

        if 'sample_weight' in fit_kwargs.keys():
            warnings.warn(
                ("The `sample_weight` argument is ignored. Use `weight_func` to pass "
                 "a function that defines the individual weights for each sample "
                 "based on its index."),
                 IgnoredArgumentWarning
            )
            del fit_kwargs['sample_weight']

        # Select only the keyword arguments allowed by the regressor's `fit` method.
        fit_kwargs = {k:v for k, v in fit_kwargs.items()
                      if k in inspect.signature(regressor.fit).parameters}

    return fit_kwargs

check_y(y)

Raise Exception if y is not pandas Series or if it has missing values.

Parameters:

Name	Type	Description	Default
y	Any	Time series values.	required

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def check_y(
    y: Any
) -> None:
    """
    Raise Exception if `y` is not pandas Series or if it has missing values.

    Parameters
    ----------
    y : Any
        Time series values.

    Returns
    -------
    None

    """

    if not isinstance(y, pd.Series):
        raise TypeError("`y` must be a pandas Series.")

    if y.isnull().any():
        raise ValueError("`y` has missing values.")

    return

check_exog(exog, allow_nan=True)

Raise Exception if exog is not pandas Series or pandas DataFrame. If allow_nan = True, issue a warning if exog contains NaN values.

Parameters:

Name	Type	Description	Default
exog	Any	Exogenous variable/s included as predictor/s.	required
allow_nan	bool	If True, allows the presence of NaN values in exog. If False (default), issue a warning if exog contains NaN values.	`True`

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def check_exog(
    exog: Any,
    allow_nan: bool=True
) -> None:
    """
    Raise Exception if `exog` is not pandas Series or pandas DataFrame.
    If `allow_nan = True`, issue a warning if `exog` contains NaN values.

    Parameters
    ----------
    exog : Any
        Exogenous variable/s included as predictor/s.
    allow_nan : bool, default `True`
        If True, allows the presence of NaN values in `exog`. If False (default),
        issue a warning if `exog` contains NaN values.

    Returns
    -------
    None

    """

    if not isinstance(exog, (pd.Series, pd.DataFrame)):
        raise TypeError("`exog` must be a pandas Series or DataFrame.")

    if not allow_nan:
        if exog.isnull().any().any():
            warnings.warn(
                ("`exog` has missing values. Most machine learning models do not allow "
                 "missing values. Fitting the forecaster may fail."), 
                 MissingValuesExogWarning
            )

    return

get_exog_dtypes(exog)

Store dtypes of exog.

Parameters:

Name	Type	Description	Default
exog	pandas DataFrame, pandas Series	Exogenous variable/s included as predictor/s.	required

Returns:

Name	Type	Description
exog_dtypes	dict	Dictionary with the dtypes in exog.

Source code in skforecast\utils\utils.py

def get_exog_dtypes(
    exog: Union[pd.DataFrame, pd.Series]
) -> dict:
    """
    Store dtypes of `exog`.

    Parameters
    ----------
    exog : pandas DataFrame, pandas Series
        Exogenous variable/s included as predictor/s.

    Returns
    -------
    exog_dtypes : dict
        Dictionary with the dtypes in `exog`.

    """

    if isinstance(exog, pd.Series):
        exog_dtypes = {exog.name: exog.dtypes}
    else:
        exog_dtypes = exog.dtypes.to_dict()

    return exog_dtypes

check_exog_dtypes(exog)

Raise Exception if exog has categorical columns with non integer values. This is needed when using machine learning regressors that allow categorical features. Issue a Warning if exog has columns that are not init, float, or category.

Parameters:

Name	Type	Description	Default
exog	pandas DataFrame, pandas Series	Exogenous variable/s included as predictor/s.	required

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def check_exog_dtypes(
    exog: Union[pd.DataFrame, pd.Series]
) -> None:
    """
    Raise Exception if `exog` has categorical columns with non integer values.
    This is needed when using machine learning regressors that allow categorical
    features.
    Issue a Warning if `exog` has columns that are not `init`, `float`, or `category`.

    Parameters
    ----------
    exog : pandas DataFrame, pandas Series
        Exogenous variable/s included as predictor/s.

    Returns
    -------
    None

    """

    check_exog(exog=exog, allow_nan=False)

    if isinstance(exog, pd.DataFrame):
        if not exog.select_dtypes(exclude=[np.number, 'category']).columns.empty:
            warnings.warn(
                ("`exog` may contain only `int`, `float` or `category` dtypes. Most "
                 "machine learning models do not allow other types of values. "
                 "Fitting the forecaster may fail."), DataTypeWarning
            )
        for col in exog.select_dtypes(include='category'):
            if exog[col].cat.categories.dtype not in [int, np.int32, np.int64]:
                raise TypeError(
                    ("Categorical columns in exog must contain only integer values. "
                     "See skforecast docs for more info about how to include "
                     "categorical features https://skforecast.org/"
                     "latest/user_guides/categorical-features.html")
                )
    else:
        if exog.dtype.name not in ['int', 'int8', 'int16', 'int32', 'int64', 'float', 
        'float16', 'float32', 'float64', 'uint8', 'uint16', 'uint32', 'uint64', 'category']:
            warnings.warn(
                ("`exog` may contain only `int`, `float` or `category` dtypes. Most "
                 "machine learning models do not allow other types of values. "
                 "Fitting the forecaster may fail."), DataTypeWarning
            )
        if exog.dtype.name == 'category' and exog.cat.categories.dtype not in [int,
        np.int32, np.int64]:
            raise TypeError(
                ("If exog is of type category, it must contain only integer values. "
                 "See skforecast docs for more info about how to include "
                 "categorical features https://skforecast.org/"
                 "latest/user_guides/categorical-features.html")
            )

    return

check_interval(interval=None, alpha=None)

Check provided confidence interval sequence is valid.

Parameters:

Name	Type	Description	Default
interval	list	Confidence of the prediction interval estimated. Sequence of percentiles to compute, which must be between 0 and 100 inclusive. For example, interval of 95% should be as interval = [2.5, 97.5].	`None`
alpha	float	The confidence intervals used in ForecasterSarimax are (1 - alpha) %.	`None`

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def check_interval(
    interval: list=None,
    alpha: float=None
) -> None:
    """
    Check provided confidence interval sequence is valid.

    Parameters
    ----------
    interval : list, default `None`
        Confidence of the prediction interval estimated. Sequence of percentiles
        to compute, which must be between 0 and 100 inclusive. For example, 
        interval of 95% should be as `interval = [2.5, 97.5]`.
    alpha : float, default `None`
        The confidence intervals used in ForecasterSarimax are (1 - alpha) %.

    Returns
    -------
    None

    """

    if interval is not None:
        if not isinstance(interval, list):
            raise TypeError(
                ("`interval` must be a `list`. For example, interval of 95% "
                 "should be as `interval = [2.5, 97.5]`.")
            )

        if len(interval) != 2:
            raise ValueError(
                ("`interval` must contain exactly 2 values, respectively the "
                 "lower and upper interval bounds. For example, interval of 95% "
                 "should be as `interval = [2.5, 97.5]`.")
            )

        if (interval[0] < 0.) or (interval[0] >= 100.):
            raise ValueError(
                f"Lower interval bound ({interval[0]}) must be >= 0 and < 100."
            )

        if (interval[1] <= 0.) or (interval[1] > 100.):
            raise ValueError(
                f"Upper interval bound ({interval[1]}) must be > 0 and <= 100."
            )

        if interval[0] >= interval[1]:
            raise ValueError(
                f"Lower interval bound ({interval[0]}) must be less than the "
                f"upper interval bound ({interval[1]})."
            )

    if alpha is not None:
        if not isinstance(alpha, float):
            raise TypeError(
                ("`alpha` must be a `float`. For example, interval of 95% "
                 "should be as `alpha = 0.05`.")
            )

        if (alpha <= 0.) or (alpha >= 1):
            raise ValueError(
                f"`alpha` must have a value between 0 and 1. Got {alpha}."
            )

    return

check_predict_input(forecaster_name, steps, fitted, included_exog, index_type, index_freq, window_size, last_window=None, last_window_exog=None, exog=None, exog_type=None, exog_col_names=None, interval=None, alpha=None, max_steps=None, levels=None, series_col_names=None)

Check all inputs of predict method. This is a helper function to validate that inputs used in predict method match attributes of a forecaster already trained.

Parameters:

Name	Type	Description	Default
forecaster_name	str	Forecaster name. ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate.	required
steps	int, list	Number of future steps predicted.	required
fitted	bool	Tag to identify if the regressor has been fitted (trained).	required
included_exog	bool	If the forecaster has been trained using exogenous variable/s.	required
index_type	type	Type of index of the input used in training.	required
index_freq	str	Frequency of Index of the input used in training.	required
window_size	int	Size of the window needed to create the predictors. It is equal to max_lag.	required
last_window	pandas Series, pandas DataFrame	Values of the series used to create the predictors (lags) need in the first iteration of prediction (t + 1).	`None`
last_window_exog	pandas Series, pandas DataFrame	Values of the exogenous variables aligned with last_window in ForecasterSarimax predictions.	`None`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s.	`None`
exog_type	type	Type of exogenous variable/s used in training.	`None`
exog_col_names	list	Names of columns of exog if exog used in training was a pandas DataFrame.	`None`
interval	list	Confidence of the prediction interval estimated. Sequence of percentiles to compute, which must be between 0 and 100 inclusive. For example, interval of 95% should be as interval = [2.5, 97.5].	`None`
alpha	float	The confidence intervals used in ForecasterSarimax are (1 - alpha) %.	`None`
max_steps	Optional[int]	Maximum number of steps allowed (ForecasterAutoregDirect and ForecasterAutoregMultiVariate).	None
levels	str, list	Time series to be predicted (ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom).	`None`
series_col_names	list	Names of the columns used during fit (ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom and ForecasterAutoregMultiVariate).	`None`

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def check_predict_input(
    forecaster_name: str,
    steps: Union[int, list],
    fitted: bool,
    included_exog: bool,
    index_type: type,
    index_freq: str,
    window_size: int,
    last_window: Optional[Union[pd.Series, pd.DataFrame]]=None,
    last_window_exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    exog_type: Optional[Union[type, None]]=None,
    exog_col_names: Optional[Union[list, None]]=None,
    interval: Optional[list]=None,
    alpha: Optional[float]=None,
    max_steps: Optional[int]=None,
    levels: Optional[Union[str, list]]=None,
    series_col_names: Optional[list]=None
) -> None:
    """
    Check all inputs of predict method. This is a helper function to validate
    that inputs used in predict method match attributes of a forecaster already
    trained.

    Parameters
    ----------
    forecaster_name : str
        Forecaster name. ForecasterAutoreg, ForecasterAutoregCustom, 
        ForecasterAutoregDirect, ForecasterAutoregMultiSeries, 
        ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate.
    steps : int, list
        Number of future steps predicted.
    fitted: bool
        Tag to identify if the regressor has been fitted (trained).
    included_exog : bool
        If the forecaster has been trained using exogenous variable/s.
    index_type : type
        Type of index of the input used in training.
    index_freq : str
        Frequency of Index of the input used in training.
    window_size: int
        Size of the window needed to create the predictors. It is equal to 
        `max_lag`.
    last_window : pandas Series, pandas DataFrame, default `None`
        Values of the series used to create the predictors (lags) need in the 
        first iteration of prediction (t + 1).
    last_window_exog : pandas Series, pandas DataFrame, default `None`
        Values of the exogenous variables aligned with `last_window` in 
        ForecasterSarimax predictions.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s.
    exog_type : type, default `None`
        Type of exogenous variable/s used in training.
    exog_col_names : list, default `None`
        Names of columns of `exog` if `exog` used in training was a pandas
        DataFrame.
    interval : list, default `None`
        Confidence of the prediction interval estimated. Sequence of percentiles
        to compute, which must be between 0 and 100 inclusive. For example, 
        interval of 95% should be as `interval = [2.5, 97.5]`.
    alpha : float, default `None`
        The confidence intervals used in ForecasterSarimax are (1 - alpha) %.
    max_steps: int, default `None`
        Maximum number of steps allowed (`ForecasterAutoregDirect` and 
        `ForecasterAutoregMultiVariate`).
    levels : str, list, default `None`
        Time series to be predicted (`ForecasterAutoregMultiSeries` and
        `ForecasterAutoregMultiSeriesCustom`).
    series_col_names : list, default `None`
        Names of the columns used during fit (`ForecasterAutoregMultiSeries`, 
        `ForecasterAutoregMultiSeriesCustom` and `ForecasterAutoregMultiVariate`).

    Returns
    -------
    None

    """

    if not fitted:
        raise sklearn.exceptions.NotFittedError(
            ("This Forecaster instance is not fitted yet. Call `fit` with "
             "appropriate arguments before using predict.")
        )

    if isinstance(steps, (int, np.integer)) and steps < 1:
        raise ValueError(
            f"`steps` must be an integer greater than or equal to 1. Got {steps}."
        )

    if isinstance(steps, list) and min(steps) < 1:
        raise ValueError(
           (f"The minimum value of `steps` must be equal to or greater than 1. "
            f"Got {min(steps)}.")
        )

    if max_steps is not None:
        if max(steps) > max_steps:
            raise ValueError(
                (f"The maximum value of `steps` must be less than or equal to "
                 f"the value of steps defined when initializing the forecaster. "
                 f"Got {max(steps)}, but the maximum is {max_steps}.")
            )

    if interval is not None or alpha is not None:
        check_interval(interval=interval, alpha=alpha)

    if forecaster_name in ['ForecasterAutoregMultiSeries', 
                           'ForecasterAutoregMultiSeriesCustom']:
        if levels is not None and not isinstance(levels, (str, list)):
            raise TypeError(
                ("`levels` must be a `list` of column names, a `str` of a "
                 "column name or `None`.")
            )
        if len(set(levels) - set(series_col_names)) != 0:
            raise ValueError(
                f"`levels` must be in `series_col_names` : {series_col_names}."
            )

    if exog is None and included_exog:
        raise ValueError(
            ("Forecaster trained with exogenous variable/s. "
             "Same variable/s must be provided when predicting.")
        )

    if exog is not None and not included_exog:
        raise ValueError(
            ("Forecaster trained without exogenous variable/s. "
             "`exog` must be `None` when predicting.")
        )

    # Checks last_window
    # Check last_window type (pd.Series or pd.DataFrame according to forecaster)
    if forecaster_name in ['ForecasterAutoregMultiSeries', 
                           'ForecasterAutoregMultiSeriesCustom',
                           'ForecasterAutoregMultiVariate']:
        if not isinstance(last_window, pd.DataFrame):
            raise TypeError(
                f"`last_window` must be a pandas DataFrame. Got {type(last_window)}."
            )

        if forecaster_name in ['ForecasterAutoregMultiSeries', 
                               'ForecasterAutoregMultiSeriesCustom'] and \
            len(set(levels) - set(last_window.columns)) != 0:
            raise ValueError(
                (f"`last_window` must contain a column(s) named as the level(s) "
                 f"to be predicted.\n"
                 f"    `levels` : {levels}.\n"
                 f"    `last_window` columns : {list(last_window.columns)}.")
            )

        if forecaster_name == 'ForecasterAutoregMultiVariate' and \
            (series_col_names != list(last_window.columns)):
            raise ValueError(
                (f"`last_window` columns must be the same as `series` column names.\n"
                 f"    `last_window` columns : {list(last_window.columns)}.\n"
                 f"    `series` columns      : {series_col_names}.")
            )    
    else:    
        if not isinstance(last_window, pd.Series):
            raise TypeError(
                f"`last_window` must be a pandas Series. Got {type(last_window)}."
            )

    # Check last_window len, nulls and index (type and freq)
    if len(last_window) < window_size:
        raise ValueError(
            (f"`last_window` must have as many values as needed to "
             f"generate the predictors. For this forecaster it is {window_size}.")
        )
    if last_window.isnull().any().all():
        raise ValueError(
            ("`last_window` has missing values.")
        )
    _, last_window_index = preprocess_last_window(
                               last_window  = last_window.iloc[:0],
                               return_values = False
                           ) 
    if not isinstance(last_window_index, index_type):
        raise TypeError(
            (f"Expected index of type {index_type} for `last_window`. "
             f"Got {type(last_window_index)}.")
        )
    if isinstance(last_window_index, pd.DatetimeIndex):
        if not last_window_index.freqstr == index_freq:
            raise TypeError(
                (f"Expected frequency of type {index_freq} for `last_window`. "
                 f"Got {last_window_index.freqstr}.")
            )

    # Checks exog
    if exog is not None:
        # Check type, nulls and expected type
        if not isinstance(exog, (pd.Series, pd.DataFrame)):
            raise TypeError("`exog` must be a pandas Series or DataFrame.")
        if exog.isnull().any().any():
            warnings.warn(
                ("`exog` has missing values. Most of machine learning models do "
                 "not allow missing values. `predict` method may fail."), 
                 MissingValuesExogWarning
            )
        if not isinstance(exog, exog_type):
            raise TypeError(
                f"Expected type for `exog`: {exog_type}. Got {type(exog)}."    
            )

        # Check exog has many values as distance to max step predicted
        last_step = max(steps) if isinstance(steps, list) else steps
        if len(exog) < last_step:
            raise ValueError(
                (f"`exog` must have at least as many values as the distance to "
                 f"the maximum step predicted, {last_step}.")
            )

        # Check all columns are in the pandas DataFrame
        if isinstance(exog, pd.DataFrame):
            col_missing = set(exog_col_names).difference(set(exog.columns))
            if col_missing:
                raise ValueError(
                    (f"Missing columns in `exog`. Expected {exog_col_names}. "
                     f"Got {exog.columns.to_list()}.") 
                )

        # Check index dtype and freq
        _, exog_index = preprocess_exog(
                            exog          = exog.iloc[:0, ],
                            return_values = False
                        )
        if not isinstance(exog_index, index_type):
            raise TypeError(
                (f"Expected index of type {index_type} for `exog`. "
                 f"Got {type(exog_index)}.")
            )   
        if isinstance(exog_index, pd.DatetimeIndex):
            if not exog_index.freqstr == index_freq:
                raise TypeError(
                    (f"Expected frequency of type {index_freq} for `exog`. "
                     f"Got {exog_index.freqstr}.")
                )

        # Check exog starts one step ahead of last_window end.
        expected_index = expand_index(last_window.index, 1)[0]
        if expected_index != exog.index[0]:
            raise ValueError(
                (f"To make predictions `exog` must start one step ahead of `last_window`.\n"
                 f"    `last_window` ends at : {last_window.index[-1]}.\n"
                 f"    `exog` starts at      : {exog.index[0]}.\n"
                 f"     Expected index       : {expected_index}.")
            )

    # Checks ForecasterSarimax
    if forecaster_name == 'ForecasterSarimax':
        # Check last_window_exog type, len, nulls and index (type and freq)
        if last_window_exog is not None:
            if not included_exog:
                raise ValueError(
                    ("Forecaster trained without exogenous variable/s. "
                     "`last_window_exog` must be `None` when predicting.")
                )

            if not isinstance(last_window_exog, (pd.Series, pd.DataFrame)):
                raise TypeError(
                    (f"`last_window_exog` must be a pandas Series or a "
                     f"pandas DataFrame. Got {type(last_window_exog)}.")
                )
            if len(last_window_exog) < window_size:
                raise ValueError(
                    (f"`last_window_exog` must have as many values as needed to "
                     f"generate the predictors. For this forecaster it is {window_size}.")
                )
            if last_window_exog.isnull().any().all():
                warnings.warn(
                ("`last_window_exog` has missing values. Most of machine learning "
                 "models do not allow missing values. `predict` method may fail."),
                MissingValuesExogWarning
            )
            _, last_window_exog_index = preprocess_last_window(
                                            last_window   = last_window_exog.iloc[:0],
                                            return_values = False
                                        ) 
            if not isinstance(last_window_exog_index, index_type):
                raise TypeError(
                    (f"Expected index of type {index_type} for `last_window_exog`. "
                     f"Got {type(last_window_exog_index)}.")
                )
            if isinstance(last_window_exog_index, pd.DatetimeIndex):
                if not last_window_exog_index.freqstr == index_freq:
                    raise TypeError(
                        (f"Expected frequency of type {index_freq} for "
                         f"`last_window_exog`. Got {last_window_exog_index.freqstr}.")
                    )

            # Check all columns are in the pd.DataFrame, last_window_exog
            if isinstance(last_window_exog, pd.DataFrame):
                col_missing = set(exog_col_names).difference(set(last_window_exog.columns))
                if col_missing:
                    raise ValueError(
                        (f"Missing columns in `exog`. Expected {exog_col_names}. "
                         f"Got {last_window_exog.columns.to_list()}.") 
                    )

    return

preprocess_y(y, return_values=True)

Return values and index of series separately. Index is overwritten according to the next rules:

• If index is of type DatetimeIndex and has frequency, nothing is changed.
• If index is of type RangeIndex, nothing is changed.
• If index is of type DatetimeIndex but has no frequency, a RangeIndex is created.
• If index is not of type DatetimeIndex, a RangeIndex is created.

Parameters:

Name	Type	Description	Default
y	pandas Series, pandas DataFrame	Time series.	required
return_values	bool	If True return the values of y as numpy ndarray. This option is intended to avoid copying data when it is not necessary.	`True`

Returns:

Name	Type	Description
y_values	None, numpy ndarray	Numpy array with values of y.
y_index	pandas Index	Index of y modified according to the rules.

Source code in skforecast\utils\utils.py

def preprocess_y(
    y: Union[pd.Series, pd.DataFrame],
    return_values: bool=True
) -> Tuple[Union[None, np.ndarray], pd.Index]:
    """
    Return values and index of series separately. Index is overwritten 
    according to the next rules:

    - If index is of type `DatetimeIndex` and has frequency, nothing is 
    changed.
    - If index is of type `RangeIndex`, nothing is changed.
    - If index is of type `DatetimeIndex` but has no frequency, a 
    `RangeIndex` is created.
    - If index is not of type `DatetimeIndex`, a `RangeIndex` is created.

    Parameters
    ----------
    y : pandas Series, pandas DataFrame
        Time series.
    return_values : bool, default `True`
        If `True` return the values of `y` as numpy ndarray. This option is 
        intended to avoid copying data when it is not necessary.

    Returns
    -------
    y_values : None, numpy ndarray
        Numpy array with values of `y`.
    y_index : pandas Index
        Index of `y` modified according to the rules.

    """

    if isinstance(y.index, pd.DatetimeIndex) and y.index.freq is not None:
        y_index = y.index
    elif isinstance(y.index, pd.RangeIndex):
        y_index = y.index
    elif isinstance(y.index, pd.DatetimeIndex) and y.index.freq is None:
        warnings.warn(
            ("`y` has DatetimeIndex index but no frequency. "
             "Index is overwritten with a RangeIndex of step 1.")
        )
        y_index = pd.RangeIndex(
                      start = 0,
                      stop  = len(y),
                      step  = 1
                  )
    else:
        warnings.warn(
            ("`y` has no DatetimeIndex nor RangeIndex index. "
             "Index is overwritten with a RangeIndex.")
        )
        y_index = pd.RangeIndex(
                      start = 0,
                      stop  = len(y),
                      step  = 1
                  )

    y_values = y.to_numpy() if return_values else None

    return y_values, y_index

preprocess_last_window(last_window, return_values=True)

Return values and index of series separately. Index is overwritten according to the next rules:

• If index is of type DatetimeIndex and has frequency, nothing is changed.
• If index is of type RangeIndex, nothing is changed.
• If index is of type DatetimeIndex but has no frequency, a RangeIndex is created.
• If index is not of type DatetimeIndex, a RangeIndex is created.

Parameters:

Name	Type	Description	Default
last_window	pandas Series, pandas DataFrame	Time series values.	required
return_values	bool	If True return the values of last_window as numpy ndarray. This option is intended to avoid copying data when it is not necessary.	`True`

Returns:

Name	Type	Description
last_window_values	numpy ndarray	Numpy array with values of last_window.
last_window_index	pandas Index	Index of last_window modified according to the rules.

Source code in skforecast\utils\utils.py

def preprocess_last_window(
    last_window: Union[pd.Series, pd.DataFrame],
    return_values: bool=True
 ) -> Tuple[np.ndarray, pd.Index]:
    """
    Return values and index of series separately. Index is overwritten 
    according to the next rules:

    - If index is of type `DatetimeIndex` and has frequency, nothing is 
    changed.
    - If index is of type `RangeIndex`, nothing is changed.
    - If index is of type `DatetimeIndex` but has no frequency, a 
    `RangeIndex` is created.
    - If index is not of type `DatetimeIndex`, a `RangeIndex` is created.

    Parameters
    ----------
    last_window : pandas Series, pandas DataFrame
        Time series values.
    return_values : bool, default `True`
        If `True` return the values of `last_window` as numpy ndarray. This option 
        is intended to avoid copying data when it is not necessary.

    Returns
    -------
    last_window_values : numpy ndarray
        Numpy array with values of `last_window`.
    last_window_index : pandas Index
        Index of `last_window` modified according to the rules.

    """

    if isinstance(last_window.index, pd.DatetimeIndex) and last_window.index.freq is not None:
        last_window_index = last_window.index
    elif isinstance(last_window.index, pd.RangeIndex):
        last_window_index = last_window.index
    elif isinstance(last_window.index, pd.DatetimeIndex) and last_window.index.freq is None:
        warnings.warn(
            ("`last_window` has DatetimeIndex index but no frequency. "
             "Index is overwritten with a RangeIndex of step 1.")
        )
        last_window_index = pd.RangeIndex(
                                start = 0,
                                stop  = len(last_window),
                                step  = 1
                            )
    else:
        warnings.warn(
            ("`last_window` has no DatetimeIndex nor RangeIndex index. "
             "Index is overwritten with a RangeIndex.")
        )
        last_window_index = pd.RangeIndex(
                                start = 0,
                                stop  = len(last_window),
                                step  = 1
                            )

    last_window_values = last_window.to_numpy() if return_values else None

    return last_window_values, last_window_index

preprocess_exog(exog, return_values=True)

Return values and index of series or data frame separately. Index is overwritten according to the next rules:

• If index is of type DatetimeIndex and has frequency, nothing is changed.
• If index is of type RangeIndex, nothing is changed.
• If index is of type DatetimeIndex but has no frequency, a RangeIndex is created.
• If index is not of type DatetimeIndex, a RangeIndex is created.

Parameters:

Name	Type	Description	Default
exog	pandas Series, pandas DataFrame	Exogenous variables.	required
return_values	bool	If True return the values of exog as numpy ndarray. This option is intended to avoid copying data when it is not necessary.	`True`

Returns:

Name	Type	Description
exog_values	None, numpy ndarray	Numpy array with values of exog.
exog_index	pandas Index	Index of exog modified according to the rules.

Source code in skforecast\utils\utils.py

def preprocess_exog(
    exog: Union[pd.Series, pd.DataFrame],
    return_values: bool=True
) -> Tuple[Union[None, np.ndarray], pd.Index]:
    """
    Return values and index of series or data frame separately. Index is
    overwritten  according to the next rules:

    - If index is of type `DatetimeIndex` and has frequency, nothing is 
    changed.
    - If index is of type `RangeIndex`, nothing is changed.
    - If index is of type `DatetimeIndex` but has no frequency, a 
    `RangeIndex` is created.
    - If index is not of type `DatetimeIndex`, a `RangeIndex` is created.

    Parameters
    ----------
    exog : pandas Series, pandas DataFrame
        Exogenous variables.
    return_values : bool, default `True`
        If `True` return the values of `exog` as numpy ndarray. This option is 
        intended to avoid copying data when it is not necessary.

    Returns
    -------
    exog_values : None, numpy ndarray
        Numpy array with values of `exog`.
    exog_index : pandas Index
        Index of `exog` modified according to the rules.

    """

    if isinstance(exog.index, pd.DatetimeIndex) and exog.index.freq is not None:
        exog_index = exog.index
    elif isinstance(exog.index, pd.RangeIndex):
        exog_index = exog.index
    elif isinstance(exog.index, pd.DatetimeIndex) and exog.index.freq is None:
        warnings.warn(
            ("`exog` has DatetimeIndex index but no frequency. "
             "Index is overwritten with a RangeIndex of step 1.")
        )
        exog_index = pd.RangeIndex(
                         start = 0,
                         stop  = len(exog),
                         step  = 1
                     )

    else:
        warnings.warn(
            ("`exog` has no DatetimeIndex nor RangeIndex index. "
             "Index is overwritten with a RangeIndex.")
        )
        exog_index = pd.RangeIndex(
                         start = 0,
                         stop  = len(exog),
                         step  = 1
                     )

    exog_values = exog.to_numpy() if return_values else None

    return exog_values, exog_index

cast_exog_dtypes(exog, exog_dtypes)

Cast exog to a specified types. This is done because, for a forecaster to accept a categorical exog, it must contain only integer values. Due to the internal modifications of numpy, the values may be casted to float, so they have to be re-converted to int.

• If exog is a pandas Series, exog_dtypes must be a dict with a single value.
• If exog_dtypes is category but the current type of exog is float, then the type is cast to int and then to category.

Parameters:

Name	Type	Description	Default
exog	pandas Series, pandas DataFrame	Exogenous variables.	required
exog_dtypes	dict	Dictionary with name and type of the series or data frame columns.	required

Returns:

Name	Type	Description
exog	pandas Series, pandas DataFrame	Exogenous variables casted to the indicated dtypes.

Source code in skforecast\utils\utils.py

def cast_exog_dtypes(
    exog: Union[pd.Series, pd.DataFrame],
    exog_dtypes: dict,
) -> Union[pd.Series, pd.DataFrame]: # pragma: no cover
    """
    Cast `exog` to a specified types. This is done because, for a forecaster to 
    accept a categorical exog, it must contain only integer values. Due to the 
    internal modifications of numpy, the values may be casted to `float`, so 
    they have to be re-converted to `int`.

    - If `exog` is a pandas Series, `exog_dtypes` must be a dict with a 
    single value.
    - If `exog_dtypes` is `category` but the current type of `exog` is `float`, 
    then the type is cast to `int` and then to `category`. 

    Parameters
    ----------
    exog : pandas Series, pandas DataFrame
        Exogenous variables.
    exog_dtypes: dict
        Dictionary with name and type of the series or data frame columns.

    Returns
    -------
    exog : pandas Series, pandas DataFrame
        Exogenous variables casted to the indicated dtypes.

    """

    # Remove keys from exog_dtypes not in exog.columns
    exog_dtypes = {k:v for k, v in exog_dtypes.items() if k in exog.columns}

    if isinstance(exog, pd.Series) and exog.dtypes != list(exog_dtypes.values())[0]:
        exog = exog.astype(list(exog_dtypes.values())[0])
    elif isinstance(exog, pd.DataFrame):
        for col, initial_dtype in exog_dtypes.items():
            if exog[col].dtypes != initial_dtype:
                if initial_dtype == "category" and exog[col].dtypes==float:
                    exog[col] = exog[col].astype(int).astype("category")
                else:
                    exog[col] = exog[col].astype(initial_dtype)

    return exog

exog_to_direct(exog, steps)

Transforms exog to a pandas DataFrame with the shape needed for Direct forecasting.

Parameters:

Name	Type	Description	Default
exog	pandas Series, pandas DataFrame	Exogenous variables.	required
steps	int.	Number of steps that will be predicted using exog.	required

Returns:

Name	Type	Description
exog_transformed	pandas DataFrame	Exogenous variables transformed.

Source code in skforecast\utils\utils.py

def exog_to_direct(
    exog: Union[pd.Series, pd.DataFrame],
    steps: int
)-> pd.DataFrame:
    """
    Transforms `exog` to a pandas DataFrame with the shape needed for Direct
    forecasting.

    Parameters
    ----------
    exog : pandas Series, pandas DataFrame
        Exogenous variables.
    steps : int.
        Number of steps that will be predicted using exog.

    Returns
    -------
    exog_transformed : pandas DataFrame
        Exogenous variables transformed.

    """

    if not isinstance(exog, (pd.Series, pd.DataFrame)):
        raise TypeError(f"`exog` must be a pandas Series or DataFrame. Got {type(exog)}.")

    if isinstance(exog, pd.Series):
        exog = exog.to_frame()

    n_rows = len(exog)
    exog_idx = exog.index
    exog_transformed = []

    for i in range(steps):
        exog_column_transformed = exog.iloc[i : n_rows - (steps - 1 - i), ]
        exog_column_transformed.index = pd.RangeIndex(len(exog_column_transformed))
        exog_column_transformed.columns = [f"{col}_step_{i+1}" 
                                           for col in exog_column_transformed.columns]
        exog_transformed.append(exog_column_transformed)

    if len(exog_transformed) > 1:
        exog_transformed = pd.concat(exog_transformed, axis=1, copy=False)
    else:
        exog_transformed = exog_column_transformed

    exog_transformed.index = exog_idx[-len(exog_transformed):]

    return exog_transformed

exog_to_direct_numpy(exog, steps)

Transforms exog to numpy ndarray with the shape needed for Direct forecasting.

Parameters:

Name	Type	Description	Default
exog	numpy ndarray, shape(samples,)	Exogenous variables.	required
steps	int.	Number of steps that will be predicted using exog.	required

Returns:

Name	Type	Description
exog_transformed	numpy ndarray	Exogenous variables transformed.

Source code in skforecast\utils\utils.py

def exog_to_direct_numpy(
    exog: np.ndarray,
    steps: int
)-> np.ndarray:
    """
    Transforms `exog` to numpy ndarray with the shape needed for Direct
    forecasting.

    Parameters
    ----------
    exog : numpy ndarray, shape(samples,)
        Exogenous variables.
    steps : int.
        Number of steps that will be predicted using exog.

    Returns
    -------
    exog_transformed : numpy ndarray
        Exogenous variables transformed.

    """

    if not isinstance(exog, np.ndarray):
        raise TypeError(f"`exog` must be a numpy ndarray. Got {type(exog)}.")

    if exog.ndim == 1:
        exog = np.expand_dims(exog, axis=1)

    n_rows = len(exog)
    exog_transformed = []

    for i in range(steps):
        exog_column_transformed = exog[i : n_rows - (steps - 1 - i)]
        exog_transformed.append(exog_column_transformed)

    if len(exog_transformed) > 1:
        exog_transformed = np.concatenate(exog_transformed, axis=1)
    else:
        exog_transformed = exog_column_transformed

    return exog_transformed

expand_index(index, steps)

Create a new index of length steps starting at the end of the index.

Parameters:

Name	Type	Description	Default
index	pandas Index, None	Original index.	required
steps	int	Number of steps to expand.	required

Returns:

Name	Type	Description
new_index	pandas Index	New index.

Source code in skforecast\utils\utils.py

def expand_index(
    index: Union[pd.Index, None], 
    steps: int
) -> pd.Index:
    """
    Create a new index of length `steps` starting at the end of the index.

    Parameters
    ----------
    index : pandas Index, None
        Original index.
    steps : int
        Number of steps to expand.

    Returns
    -------
    new_index : pandas Index
        New index.

    """

    if isinstance(index, pd.Index):

        if isinstance(index, pd.DatetimeIndex):
            new_index = pd.date_range(
                            start   = index[-1] + index.freq,
                            periods = steps,
                            freq    = index.freq
                        )
        elif isinstance(index, pd.RangeIndex):
            new_index = pd.RangeIndex(
                            start = index[-1] + 1,
                            stop  = index[-1] + 1 + steps
                        )
    else: 
        new_index = pd.RangeIndex(
                        start = 0,
                        stop  = steps
                    )

    return new_index

transform_series(series, transformer, fit=False, inverse_transform=False)

Transform raw values of pandas Series with a scikit-learn alike transformer (preprocessor). The transformer used must have the following methods: fit, transform, fit_transform and inverse_transform. ColumnTransformers are not allowed since they do not have inverse_transform method.

Parameters:

Name	Type	Description	Default
series	pandas Series	Series to be transformed.	required
transformer	scikit-learn alike transformer (preprocessor).	scikit-learn alike transformer (preprocessor) with methods: fit, transform, fit_transform and inverse_transform. ColumnTransformers are not allowed since they do not have inverse_transform method.	required
fit	bool	Train the transformer before applying it.	`False`
inverse_transform	bool	Transform back the data to the original representation.	`False`

Returns:

Name	Type	Description
series_transformed	pandas Series, pandas DataFrame	Transformed Series. Depending on the transformer used, the output may be a Series or a DataFrame.

Source code in skforecast\utils\utils.py

def transform_series(
    series: pd.Series,
    transformer,
    fit: bool=False,
    inverse_transform: bool=False
) -> Union[pd.Series, pd.DataFrame]:
    """      
    Transform raw values of pandas Series with a scikit-learn alike transformer
    (preprocessor). The transformer used must have the following methods: fit, 
    transform, fit_transform and inverse_transform. ColumnTransformers are not 
    allowed since they do not have inverse_transform method.

    Parameters
    ----------
    series : pandas Series
        Series to be transformed.
    transformer : scikit-learn alike transformer (preprocessor).
        scikit-learn alike transformer (preprocessor) with methods: fit, transform,
        fit_transform and inverse_transform. ColumnTransformers are not allowed 
        since they do not have inverse_transform method.
    fit : bool, default `False`
        Train the transformer before applying it.
    inverse_transform : bool, default `False`
        Transform back the data to the original representation.

    Returns
    -------
    series_transformed : pandas Series, pandas DataFrame
        Transformed Series. Depending on the transformer used, the output may 
        be a Series or a DataFrame.

    """

    if not isinstance(series, pd.Series):
        raise TypeError(
            (f"`series` argument must be a pandas Series. Got {type(series)}.")
        )

    if transformer is None:
        return series

    if series.name is None:
        series.name = 'no_name'

    data = series.to_frame()

    if fit and hasattr(transformer, 'fit'):
        transformer.fit(data)

    # If argument feature_names_in_ exits, is overwritten to allow using the 
    # transformer on other series than those that were passed during fit.
    if hasattr(transformer, 'feature_names_in_') and transformer.feature_names_in_[0] != data.columns[0]:
        transformer = deepcopy(transformer)
        transformer.feature_names_in_ = np.array([data.columns[0]], dtype=object)

    if inverse_transform:
        values_transformed = transformer.inverse_transform(data)
    else:
        values_transformed = transformer.transform(data)   

    if hasattr(values_transformed, 'toarray'):
        # If the returned values are in sparse matrix format, it is converted to dense array.
        values_transformed = values_transformed.toarray()

    if isinstance(values_transformed, np.ndarray) and values_transformed.shape[1] == 1:
        series_transformed = pd.Series(
                                 data  = values_transformed.flatten(),
                                 index = data.index,
                                 name  = data.columns[0]
                             )
    elif isinstance(values_transformed, pd.DataFrame) and values_transformed.shape[1] == 1:
        series_transformed = values_transformed.squeeze()
    else:
        series_transformed = pd.DataFrame(
                                 data    = values_transformed,
                                 index   = data.index,
                                 columns = transformer.get_feature_names_out()
                             )

    return series_transformed

transform_dataframe(df, transformer, fit=False, inverse_transform=False)

Transform raw values of pandas DataFrame with a scikit-learn alike transformer, preprocessor or ColumnTransformer. inverse_transform is not available when using ColumnTransformers.

Parameters:

Name	Type	Description	Default
df	pandas DataFrame	DataFrame to be transformed.	required
transformer	scikit-learn alike transformer, preprocessor or ColumnTransformer.	scikit-learn alike transformer, preprocessor or ColumnTransformer.	required
fit	bool	Train the transformer before applying it.	`False`
inverse_transform	bool	Transform back the data to the original representation. This is not available when using transformers of class scikit-learn ColumnTransformers.	`False`

Returns:

Name	Type	Description
df_transformed	pandas DataFrame	Transformed DataFrame.

Source code in skforecast\utils\utils.py

def transform_dataframe(
    df: pd.DataFrame,
    transformer,
    fit: bool=False,
    inverse_transform: bool=False
) -> pd.DataFrame:
    """      
    Transform raw values of pandas DataFrame with a scikit-learn alike
    transformer, preprocessor or ColumnTransformer. `inverse_transform` is not 
    available when using ColumnTransformers.

    Parameters
    ----------
    df : pandas DataFrame
        DataFrame to be transformed.
    transformer : scikit-learn alike transformer, preprocessor or ColumnTransformer.
        scikit-learn alike transformer, preprocessor or ColumnTransformer.
    fit : bool, default `False`
        Train the transformer before applying it.
    inverse_transform : bool, default `False`
        Transform back the data to the original representation. This is not available
        when using transformers of class scikit-learn ColumnTransformers.

    Returns
    -------
    df_transformed : pandas DataFrame
        Transformed DataFrame.

    """

    if not isinstance(df, pd.DataFrame):
        raise TypeError(
            f"`df` argument must be a pandas DataFrame. Got {type(df)}"
        )

    if transformer is None:
        return df

    if inverse_transform and isinstance(transformer, ColumnTransformer):
        raise Exception(
            "`inverse_transform` is not available when using ColumnTransformers."
        )

    if not inverse_transform:
        if fit:
            values_transformed = transformer.fit_transform(df)
        else:
            values_transformed = transformer.transform(df)
    else:
        values_transformed = transformer.inverse_transform(df)

    if hasattr(values_transformed, 'toarray'):
        # If the returned values are in sparse matrix format, it is converted to dense
        values_transformed = values_transformed.toarray()

    if hasattr(transformer, 'get_feature_names_out'):
        feature_names_out = transformer.get_feature_names_out()
    elif hasattr(transformer, 'categories_'):   
        feature_names_out = transformer.categories_
    else:
        feature_names_out = df.columns

    df_transformed = pd.DataFrame(
                         data    = values_transformed,
                         index   = df.index,
                         columns = feature_names_out
                     )

    return df_transformed

check_optional_dependency(package_name)

Check if an optional dependency is installed, if not raise an ImportError
with installation instructions.

Parameters:

Name	Type	Description	Default
package_name	str	Name of the package to check.	required

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def check_optional_dependency(
    package_name: str
) -> None:
    """
    Check if an optional dependency is installed, if not raise an ImportError  
    with installation instructions.

    Parameters
    ----------
    package_name : str
        Name of the package to check.

    Returns
    -------
    None

    """

    if importlib.util.find_spec(package_name) is None:
        try:
            extra, package_version = _find_optional_dependency(package_name=package_name)
            msg = (
                f"\n'{package_name}' is an optional dependency not included in the default "
                f"skforecast installation. Please run: `pip install \"{package_version}\"` to install it."
                f"\n\nAlternately, you can install it by running `pip install skforecast[{extra}]`"
            )
        except:
            msg = f"\n'{package_name}' is needed but not installed. Please install it."

        raise ImportError(msg)

multivariate_time_series_corr(time_series, other, lags, method='pearson')

Compute correlation between a time_series and the lagged values of other time series.

Parameters:

Name	Type	Description	Default
time_series	pandas Series	Target time series.	required
other	pandas DataFrame	Time series whose lagged values are correlated to time_series.	required
lags	int, list, numpy ndarray	Lags to be included in the correlation analysis.	required
method	str	'pearson': standard correlation coefficient. 'kendall': Kendall Tau correlation coefficient. 'spearman': Spearman rank correlation.	'pearson'

Returns:

Name	Type	Description
corr	pandas DataFrame	Correlation values.

Source code in skforecast\utils\utils.py

def multivariate_time_series_corr(
    time_series: pd.Series,
    other: pd.DataFrame,
    lags: Union[int, list, np.array],
    method: str='pearson'
)-> pd.DataFrame:
    """
    Compute correlation between a time_series and the lagged values of other 
    time series. 

    Parameters
    ----------
    time_series : pandas Series
        Target time series.
    other : pandas DataFrame
        Time series whose lagged values are correlated to `time_series`.
    lags : int, list, numpy ndarray
        Lags to be included in the correlation analysis.
    method : str, default 'pearson'
        - 'pearson': standard correlation coefficient.
        - 'kendall': Kendall Tau correlation coefficient.
        - 'spearman': Spearman rank correlation.

    Returns
    -------
    corr : pandas DataFrame
        Correlation values.

    """

    if not len(time_series) == len(other):
        raise ValueError("`time_series` and `other` must have the same length.")

    if not (time_series.index == other.index).all():
        raise ValueError("`time_series` and `other` must have the same index.")

    if isinstance(lags, int):
        lags = range(lags)

    corr = {}
    for col in other.columns:
        lag_values = {}
        for lag in lags:
            lag_values[lag] = other[col].shift(lag)

        lag_values = pd.DataFrame(lag_values)
        lag_values.insert(0, None, time_series)
        corr[col] = lag_values.corr(method=method).iloc[1:, 0]

    corr = pd.DataFrame(corr)
    corr.index = corr.index.astype('int64')
    corr.index.name = "lag"

    return corr

check_backtesting_input(forecaster, steps, metric, y=None, series=None, initial_train_size=None, fixed_train_size=True, gap=0, allow_incomplete_fold=True, refit=False, interval=None, alpha=None, n_boot=500, random_state=123, in_sample_residuals=True, n_jobs='auto', verbose=False, show_progress=True)

This is a helper function to check most inputs of backtesting functions in modules model_selection, model_selection_multiseries and model_selection_sarimax.

Parameters:

Name	Type	Description	Default
forecaster	object	Forecaster model.	required
steps	int, list	Number of future steps predicted.	required
metric	str, Callable, list	Metric used to quantify the goodness of fit of the model.	required
y	pandas Series	Training time series for uni-series forecasters.	None
series	pandas DataFrame	Training time series for multi-series forecasters.	None
initial_train_size	int	Number of samples in the initial train split. If None and forecaster is already trained, no initial train is done and all data is used to evaluate the model.	`None`
fixed_train_size	bool	If True, train size doesn't increase but moves by steps in each iteration.	`True`
gap	int	Number of samples to be excluded after the end of each training set and before the test set.	`0`
allow_incomplete_fold	bool	Last fold is allowed to have a smaller number of samples than the test_size. If False, the last fold is excluded.	`True`
refit	bool, int	Whether to re-fit the forecaster in each iteration. If refit is an integer, the Forecaster will be trained every that number of iterations.	`False`
interval	list	Confidence of the prediction interval estimated. Sequence of percentiles to compute, which must be between 0 and 100 inclusive.	`None`
alpha	float	The confidence intervals used in ForecasterSarimax are (1 - alpha) %.	`None`
n_boot	int	Number of bootstrapping iterations used to estimate prediction intervals.	`500`
random_state	int	Sets a seed to the random generator, so that boot intervals are always deterministic.	`123`
in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create prediction intervals. If False, out_sample_residuals are used if they are already stored inside the forecaster.	`True`
n_jobs	int, auto	The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores. If 'auto', n_jobs is set using the fuction skforecast.utils.select_n_jobs_fit_forecaster. New in version 0.9.0	`'auto'`
verbose	bool	Print number of folds and index of training and validation sets used for backtesting.	`False`
show_progress	bool	Whether to show a progress bar.	True

Returns:

Type	Description
None

Source code in skforecast\utils\utils.py

def check_backtesting_input(
    forecaster: object,
    steps: int,
    metric: Union[str, Callable, list],
    y: Optional[pd.Series]=None,
    series: Optional[pd.DataFrame]=None,
    initial_train_size: Optional[int]=None,
    fixed_train_size: bool=True,
    gap: int=0,
    allow_incomplete_fold: bool=True,
    refit: Optional[Union[bool, int]]=False,
    interval: Optional[list]=None,
    alpha: Optional[float]=None,
    n_boot: int=500,
    random_state: int=123,
    in_sample_residuals: bool=True,
    n_jobs: Optional[Union[int, str]]='auto',
    verbose: bool=False,
    show_progress: bool=True
) -> None:
    """
    This is a helper function to check most inputs of backtesting functions in 
    modules `model_selection`, `model_selection_multiseries` and 
    `model_selection_sarimax`.

    Parameters
    ----------
    forecaster : object
        Forecaster model.
    steps : int, list
        Number of future steps predicted.
    metric : str, Callable, list
        Metric used to quantify the goodness of fit of the model.
    y : pandas Series
        Training time series for uni-series forecasters.
    series : pandas DataFrame
        Training time series for multi-series forecasters.
    initial_train_size : int, default `None`
        Number of samples in the initial train split. If `None` and `forecaster` 
        is already trained, no initial train is done and all data is used to 
        evaluate the model.
    fixed_train_size : bool, default `True`
        If True, train size doesn't increase but moves by `steps` in each iteration.
    gap : int, default `0`
        Number of samples to be excluded after the end of each training set and 
        before the test set.
    allow_incomplete_fold : bool, default `True`
        Last fold is allowed to have a smaller number of samples than the 
        `test_size`. If `False`, the last fold is excluded.
    refit : bool, int, default `False`
        Whether to re-fit the forecaster in each iteration. If `refit` is an integer, 
        the Forecaster will be trained every that number of iterations.
    interval : list, default `None`
        Confidence of the prediction interval estimated. Sequence of percentiles
        to compute, which must be between 0 and 100 inclusive.
    alpha : float, default `None`
        The confidence intervals used in ForecasterSarimax are (1 - alpha) %. 
    n_boot : int, default `500`
        Number of bootstrapping iterations used to estimate prediction
        intervals.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot intervals are always 
        deterministic.
    in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of prediction 
        error to create prediction intervals.  If `False`, out_sample_residuals 
        are used if they are already stored inside the forecaster.
    n_jobs : int, 'auto', default `'auto'`
            The number of jobs to run in parallel. If `-1`, then the number of jobs is 
            set to the number of cores. If 'auto', `n_jobs` is set using the fuction
            skforecast.utils.select_n_jobs_fit_forecaster.
            **New in version 0.9.0**
    verbose : bool, default `False`
        Print number of folds and index of training and validation sets used 
        for backtesting.
    show_progress: bool, default `True`
        Whether to show a progress bar.

    Returns
    -------
    None

    """

    forecasters_uni = ['ForecasterAutoreg', 'ForecasterAutoregCustom', 
                       'ForecasterAutoregDirect', 'ForecasterSarimax']
    forecasters_multi = ['ForecasterAutoregMultiSeries', 
                         'ForecasterAutoregMultiSeriesCustom', 
                         'ForecasterAutoregMultiVariate']

    if type(forecaster).__name__ in forecasters_uni:
        if not isinstance(y, pd.Series):
            raise TypeError("`y` must be a pandas Series.")
        data_name = 'y'
        data_length = len(y)

    if type(forecaster).__name__ in forecasters_multi:
        if not isinstance(series, pd.DataFrame):
            raise TypeError("`series` must be a pandas DataFrame.")
        data_name = 'series'
        data_length = len(series)

    if not isinstance(steps, (int, np.integer)) or steps < 1:
        raise TypeError(
            f"`steps` must be an integer greater than or equal to 1. Got {steps}."
        )
    if not isinstance(gap, (int, np.integer)) or gap < 0:
        raise TypeError(
            f"`gap` must be an integer greater than or equal to 0. Got {gap}."
        )
    if not isinstance(metric, (str, Callable, list)):
        raise TypeError(
            (f"`metric` must be a string, a callable function, or a list containing "
             f"multiple strings and/or callables. Got {type(metric)}.")
        )

    if initial_train_size is not None:
        if not isinstance(initial_train_size, (int, np.integer)):
            raise TypeError(
                (f"If used, `initial_train_size` must be an integer greater than the "
                 f"window_size of the forecaster. Got type {type(initial_train_size)}.")
            )
        if initial_train_size >= data_length:
            raise ValueError(
                (f"If used, `initial_train_size` must be an integer smaller "
                 f"than the length of `{data_name}` ({data_length}).")
            )    
        if initial_train_size < forecaster.window_size:
            raise ValueError(
                (f"If used, `initial_train_size` must be an integer greater than "
                 f"the window_size of the forecaster ({forecaster.window_size}).")
            )
        if initial_train_size + gap >= data_length:
            raise ValueError(
                (f"The combination of initial_train_size {initial_train_size} and "
                 f"gap {gap} cannot be greater than the length of `{data_name}` "
                 f"({data_length}).")
            )
        if data_name == 'series':
            for serie in series:
                if np.isnan(series[serie].to_numpy()[:initial_train_size]).all():
                    raise ValueError(
                        (f"All values of series '{serie}' are NaN. When working "
                         f"with series of different lengths, make sure that "
                         f"`initial_train_size` has an appropriate value so that "
                         f"all series reach the first non-null value.")
                    )
    else:
        if type(forecaster).__name__ == 'ForecasterSarimax':
            raise ValueError(
                (f"`initial_train_size` must be an integer smaller than the "
                 f"length of `{data_name}` ({data_length}).")
            )    
        else:
            if not forecaster.fitted:
                raise NotFittedError(
                    ("`forecaster` must be already trained if no `initial_train_size` "
                     "is provided.")
                )
            if refit:
                raise ValueError(
                    "`refit` is only allowed when `initial_train_size` is not `None`."
                )

    if not isinstance(fixed_train_size, bool):
        raise TypeError("`fixed_train_size` must be a boolean: `True`, `False`.")
    if not isinstance(allow_incomplete_fold, bool):
        raise TypeError("`allow_incomplete_fold` must be a boolean: `True`, `False`.")
    if not isinstance(refit, (bool, int, np.integer)) or refit < 0:
        raise TypeError(f"`refit` must be a boolean or an integer greater than 0. Got {refit}.")
    if not isinstance(n_boot, (int, np.integer)) or n_boot < 0:
        raise TypeError(f"`n_boot` must be an integer greater than 0. Got {n_boot}.")
    if not isinstance(random_state, (int, np.integer)) or random_state < 0:
        raise TypeError(f"`random_state` must be an integer greater than 0. Got {random_state}.")
    if not isinstance(in_sample_residuals, bool):
        raise TypeError("`in_sample_residuals` must be a boolean: `True`, `False`.")
    if not isinstance(n_jobs, int) and n_jobs != 'auto':
        raise TypeError(f"`n_jobs` must be an integer or `'auto'`. Got {n_jobs}.")
    if not isinstance(verbose, bool):
        raise TypeError("`verbose` must be a boolean: `True`, `False`.")
    if not isinstance(show_progress, bool):
        raise TypeError("`show_progress` must be a boolean: `True`, `False`.")

    if interval is not None or alpha is not None:
        check_interval(interval=interval, alpha=alpha)

    if not allow_incomplete_fold and data_length - (initial_train_size + gap) < steps:
        raise ValueError(
            (f"There is not enough data to evaluate {steps} steps in a single "
             f"fold. Set `allow_incomplete_fold` to `True` to allow incomplete folds.\n"
             f"    Data available for test : {data_length - (initial_train_size + gap)}\n"
             f"    Steps                   : {steps}")
        )

    return

select_n_jobs_backtesting(forecaster_name, regressor_name, refit)

Select the optimal number of jobs to use in the backtesting process. This selection is based on heuristics and is not guaranteed to be optimal.

The number of jobs is chosen as follows:

• If refit is an integer, then n_jobs=1. This is because parallelization doesn't work with intermittent refit.
• If forecaster_name is 'ForecasterAutoreg' or 'ForecasterAutoregCustom' and regressor_name is a linear regressor, then n_jobs=1.
• If forecaster_name is 'ForecasterAutoreg' or 'ForecasterAutoregCustom', regressor_name is not a linear regressor and refit=True, then n_jobs=cpu_count().
• If forecaster_name is 'ForecasterAutoreg' or 'ForecasterAutoregCustom', regressor_name is not a linear regressor and refit=False, then n_jobs=1.
• If forecaster_name is 'ForecasterAutoregDirect' or 'ForecasterAutoregMultiVariate' and refit=True, then n_jobs=cpu_count().
• If forecaster_name is 'ForecasterAutoregDirect' or 'ForecasterAutoregMultiVariate' and refit=False, then n_jobs=1.
• If forecaster_name is 'ForecasterAutoregMultiseries', then n_jobs=cpu_count().

Parameters:

Name	Type	Description	Default
forecaster_name	str	The type of Forecaster.	required
regressor_name	str	The type of regressor.	required
refit	bool, int	If the forecaster is refitted during the backtesting process.	required

Returns:

Name	Type	Description
n_jobs	int	The number of jobs to run in parallel.

Source code in skforecast\utils\utils.py

def select_n_jobs_backtesting(
    forecaster_name: str,
    regressor_name: str,
    refit: Union[bool, int]
) -> int:
    """
    Select the optimal number of jobs to use in the backtesting process. This
    selection is based on heuristics and is not guaranteed to be optimal.

    The number of jobs is chosen as follows:

    - If `refit` is an integer, then n_jobs=1. This is because parallelization doesn't 
    work with intermittent refit.
    - If forecaster_name is 'ForecasterAutoreg' or 'ForecasterAutoregCustom' and
    regressor_name is a linear regressor, then n_jobs=1.
    - If forecaster_name is 'ForecasterAutoreg' or 'ForecasterAutoregCustom',
    regressor_name is not a linear regressor and refit=`True`, then
    n_jobs=cpu_count().
    - If forecaster_name is 'ForecasterAutoreg' or 'ForecasterAutoregCustom',
    regressor_name is not a linear regressor and refit=`False`, then
    n_jobs=1.
    - If forecaster_name is 'ForecasterAutoregDirect' or 'ForecasterAutoregMultiVariate'
    and refit=`True`, then n_jobs=cpu_count().
    - If forecaster_name is 'ForecasterAutoregDirect' or 'ForecasterAutoregMultiVariate'
    and refit=`False`, then n_jobs=1.
    - If forecaster_name is 'ForecasterAutoregMultiseries', then n_jobs=cpu_count().

    Parameters
    ----------
    forecaster_name : str
        The type of Forecaster.
    regressor_name : str
        The type of regressor.
    refit : bool, int
        If the forecaster is refitted during the backtesting process.

    Returns
    -------
    n_jobs : int
        The number of jobs to run in parallel.

    """

    linear_regressors = [
        regressor_name
        for regressor_name in dir(sklearn.linear_model)
        if not regressor_name.startswith('_')
    ]

    refit = False if refit == 0 else refit
    if not isinstance(refit, bool) and refit != 1:
        n_jobs = 1
    else:
        if forecaster_name in ['ForecasterAutoreg', 'ForecasterAutoregCustom']:
            if regressor_name in linear_regressors:
                n_jobs = 1
            else:
                n_jobs = joblib.cpu_count() if refit else 1
        elif forecaster_name in ['ForecasterAutoregDirect', 'ForecasterAutoregMultiVariate']:
            n_jobs = 1
        elif forecaster_name in ['ForecasterAutoregMultiseries', 'ForecasterAutoregMultiSeriesCustom']:
            n_jobs = joblib.cpu_count()
        elif forecaster_name in ['ForecasterSarimax']:
            n_jobs = 1
        else:
            n_jobs = 1

    return n_jobs

select_n_jobs_fit_forecaster(forecaster_name, regressor_name)

Select the optimal number of jobs to use in the fitting process. This selection is based on heuristics and is not guaranteed to be optimal.

The number of jobs is chosen as follows:

• If forecaster_name is 'ForecasterAutoregDirect' or 'ForecasterAutoregMultiVariate' and regressor_name is a linear regressor, then n_jobs=1, otherwise n_jobs=cpu_count().

Parameters:

Name	Type	Description	Default
forecaster_name	str	The type of Forecaster.	required
regressor_name	str	The type of regressor.	required

Returns:

Name	Type	Description
n_jobs	int	The number of jobs to run in parallel.

Source code in skforecast\utils\utils.py

def select_n_jobs_fit_forecaster(
    forecaster_name: str,
    regressor_name: str,
) -> int:
    """
    Select the optimal number of jobs to use in the fitting process. This
    selection is based on heuristics and is not guaranteed to be optimal. 

    The number of jobs is chosen as follows:

    - If forecaster_name is 'ForecasterAutoregDirect' or 'ForecasterAutoregMultiVariate'
    and regressor_name is a linear regressor, then n_jobs=1, otherwise n_jobs=cpu_count().

    Parameters
    ----------
    forecaster_name : str
        The type of Forecaster.
    regressor_name : str
        The type of regressor.

    Returns
    -------
    n_jobs : int
        The number of jobs to run in parallel.

    """

    linear_regressors = [
        regressor_name
        for regressor_name in dir(sklearn.linear_model)
        if not regressor_name.startswith('_')
    ]

    if forecaster_name in ['ForecasterAutoregDirect', 'ForecasterAutoregMultiVariate']:
        if regressor_name in linear_regressors:
            n_jobs = 1
        else:
            n_jobs = joblib.cpu_count()
    else:
        n_jobs = 1

    return n_jobs