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Builtin Exogenous Effects

AbstractEffect

Bases: ABC

Abstract class for effects.

Effects should inherit from this class and implement the compute_effect method. The id is used to create unique names for the samples in the model.

Source code in src/prophetverse/effects.py 
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class AbstractEffect(ABC):
    """Abstract class for effects.

    Effects should inherit from this class and implement the `compute_effect` method.
    The id is used to create unique names for the samples in the model.

    """

    def __init__(self, id="", regex=None, **kwargs):
        self.id = id
        self.regex = regex


    def match_columns(self, columns : pd.Index) -> pd.Index:
        """Match the columns of the DataFrame with the regex pattern.

        Args:
            X (pd.DataFrame): The DataFrame to match.

        Returns:
            pd.Index: The columns that match the regex pattern.
        """

        if isinstance(columns, List):
            columns = pd.Index(columns)

        if self.regex is None:
            raise ValueError("To use this method, you must set the regex pattern")
        return columns[columns.str.match(self.regex)]

    @staticmethod
    def split_data_into_effects(X : pd.DataFrame, effects : List) -> Dict[str, pd.DataFrame]:
        """Split the data into effects.

        Args:
            X (pd.DataFrame): The DataFrame to split.
            effects (List[AbstractEffect]): The effects to split the data into.

        Returns:
            Dict[str, pd.DataFrame]: A dictionary mapping effect names to DataFrames.
        """
        data = {}
        for effect in effects:
            data[effect.id] = X[effect.match_columns(X.columns)]
        return data

    def sample(self, name : str, *args, **kwargs):
        """
        Sample a random variable with a unique name.
        """
        return numpyro.sample(f"{self.id}__{name}", *args, **kwargs)

    @abstractmethod
    def compute_effect(self, trend : jnp.ndarray, data : jnp.ndarray) -> jnp.ndarray: 
        """Compute the effect based on the trend and data.

        Args:
            trend (jnp.ndarray): The trend.
            data (jnp.ndarray): The data concerning this effect.

        Returns:
            jnp.ndarray: The effect.
        """
        ...

    def __call__(self, trend, data):

        return self.compute_effect(trend, data)

compute_effect(trend, data) abstractmethod

Compute the effect based on the trend and data.

Parameters:

Name Type Description Default
trend ndarray

The trend.

 required
data ndarray

The data concerning this effect.

 required

Returns:

Type Description
ndarray

jnp.ndarray: The effect.
Source code in src/prophetverse/effects.py 
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@abstractmethod
def compute_effect(self, trend : jnp.ndarray, data : jnp.ndarray) -> jnp.ndarray: 
    """Compute the effect based on the trend and data.

    Args:
        trend (jnp.ndarray): The trend.
        data (jnp.ndarray): The data concerning this effect.

    Returns:
        jnp.ndarray: The effect.
    """
    ...

match_columns(columns)

Match the columns of the DataFrame with the regex pattern.

Parameters:

Name Type Description Default
X DataFrame

The DataFrame to match.

 required

Returns:

Type Description
Index

pd.Index: The columns that match the regex pattern.
Source code in src/prophetverse/effects.py 
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def match_columns(self, columns : pd.Index) -> pd.Index:
    """Match the columns of the DataFrame with the regex pattern.

    Args:
        X (pd.DataFrame): The DataFrame to match.

    Returns:
        pd.Index: The columns that match the regex pattern.
    """

    if isinstance(columns, List):
        columns = pd.Index(columns)

    if self.regex is None:
        raise ValueError("To use this method, you must set the regex pattern")
    return columns[columns.str.match(self.regex)]

sample(name, *args, **kwargs)

Sample a random variable with a unique name.

Source code in src/prophetverse/effects.py 
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def sample(self, name : str, *args, **kwargs):
    """
    Sample a random variable with a unique name.
    """
    return numpyro.sample(f"{self.id}__{name}", *args, **kwargs)

split_data_into_effects(X, effects) staticmethod

Split the data into effects.

Parameters:

Name Type Description Default
X DataFrame

The DataFrame to split.

 required
effects List[AbstractEffect]

The effects to split the data into.

 required

Returns:

Type Description
Dict[str, DataFrame]

Dict[str, pd.DataFrame]: A dictionary mapping effect names to DataFrames.
Source code in src/prophetverse/effects.py 
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@staticmethod
def split_data_into_effects(X : pd.DataFrame, effects : List) -> Dict[str, pd.DataFrame]:
    """Split the data into effects.

    Args:
        X (pd.DataFrame): The DataFrame to split.
        effects (List[AbstractEffect]): The effects to split the data into.

    Returns:
        Dict[str, pd.DataFrame]: A dictionary mapping effect names to DataFrames.
    """
    data = {}
    for effect in effects:
        data[effect.id] = X[effect.match_columns(X.columns)]
    return data

HillEffect

Bases: AbstractEffect

Represents a Hill effect in a time series model.

Attributes:

Name Type Description
half_max_prior

Prior distribution for the half-maximum parameter.
slope_prior

Prior distribution for the slope parameter.
max_effect_prior

Prior distribution for the maximum effect parameter.
effect_mode

Mode of the effect (either "additive" or "multiplicative").
Source code in src/prophetverse/effects.py 
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class HillEffect(AbstractEffect):
    """
    Represents a Hill effect in a time series model.

    Attributes:
        half_max_prior: Prior distribution for the half-maximum parameter.
        slope_prior: Prior distribution for the slope parameter.
        max_effect_prior: Prior distribution for the maximum effect parameter.
        effect_mode: Mode of the effect (either "additive" or "multiplicative").
    """

    def __init__(
        self,
        half_max_prior=None,
        slope_prior=None,
        max_effect_prior=None,
        effect_mode="multiplicative",
        **kwargs,
    ):

        if half_max_prior is None:
            half_max_prior = dist.Gamma(1, 1)
        if slope_prior is None:
            slope_prior = dist.HalfNormal(10)
        if max_effect_prior is None:
            max_effect_prior = dist.Gamma(1, 1)

        self.half_max_prior = half_max_prior
        self.slope_prior = slope_prior
        self.max_effect_prior = max_effect_prior
        self.effect_mode = effect_mode
        super().__init__(**kwargs)

    def compute_effect(self, trend, data):
        """
        Computes the effect using the log transformation.

        Args:
            trend: The trend component.
            data: The input data.

        Returns:
            The computed effect.
        """

        half_max = self.sample("half_max", self.half_max_prior)
        slope = self.sample("slope", self.slope_prior)
        max_effect = self.sample("max_effect", self.max_effect_prior)

        x = _exponent_safe(data / half_max, -slope)
        effect = max_effect / (1 + x)

        if self.effect_mode == "additive":
            return effect
        return trend * effect

compute_effect(trend, data)

Computes the effect using the log transformation.

Parameters:

Name Type Description Default
trend

The trend component.

 required
data

The input data.

 required

Returns:

Type Description

The computed effect.
Source code in src/prophetverse/effects.py 
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def compute_effect(self, trend, data):
    """
    Computes the effect using the log transformation.

    Args:
        trend: The trend component.
        data: The input data.

    Returns:
        The computed effect.
    """

    half_max = self.sample("half_max", self.half_max_prior)
    slope = self.sample("slope", self.slope_prior)
    max_effect = self.sample("max_effect", self.max_effect_prior)

    x = _exponent_safe(data / half_max, -slope)
    effect = max_effect / (1 + x)

    if self.effect_mode == "additive":
        return effect
    return trend * effect

LinearEffect

Bases: AbstractEffect

Represents a linear effect in a hierarchical prophet model.

Parameters:

Name Type Description Default
id str

The identifier for the effect.

 required
prior Distribution

A numpyro distribution to use as prior. Defaults to dist.Normal(0, 1)

 None
effect_mode str

The mode of the effect, either "multiplicative" or "additive".

 'multiplicative'

Attributes:

Name Type Description
dist type

The distribution class used for sampling coefficients.
dist_args tuple

The arguments passed to the distribution class.
effect_mode str

The mode of the effect, either "multiplicative" or "additive".

Methods:

Name Description
compute_effect

Computes the effect based on the given trend and data.
Source code in src/prophetverse/effects.py 
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class LinearEffect(AbstractEffect):
    """
    Represents a linear effect in a hierarchical prophet model.

    Args:
        id (str): The identifier for the effect.
        prior (dist.Distribution): A numpyro distribution to use as prior. Defaults to dist.Normal(0, 1)
        effect_mode (str): The mode of the effect, either "multiplicative" or "additive".

    Attributes:
        dist (type): The distribution class used for sampling coefficients.
        dist_args (tuple): The arguments passed to the distribution class.
        effect_mode (str): The mode of the effect, either "multiplicative" or "additive".

    Methods:
        compute_effect(trend, data): Computes the effect based on the given trend and data.

    """

    def __init__(
        self,
        prior=None,
        effect_mode="multiplicative",
        **kwargs):
        self.prior = prior or dist.Normal(0, 0.1)
        self.effect_mode = effect_mode
        super().__init__(**kwargs)

    def compute_effect(self, trend, data):
        """
        Computes the effect based on the given trend and data.

        Args:
            trend: The trend component of the hierarchical prophet model.
            data: The data used to compute the effect.

        Returns:
            The computed effect based on the given trend and data.

        """
        n_features = data.shape[-1]

        with numpyro.plate(f"{self.id}_plate", n_features, dim=-1):
            coefficients = self.sample(
                "coefs",
                self.prior
            )

        if coefficients.ndim == 1:
            coefficients = jnp.expand_dims(coefficients, axis=-1)

        if data.ndim == 3 and coefficients.ndim == 2:
            coefficients = jnp.expand_dims(coefficients, axis=0)
        if self.effect_mode == "multiplicative":
            return multiplicative_effect(trend, data, coefficients)
        return additive_effect(trend, data, coefficients)

compute_effect(trend, data)

Computes the effect based on the given trend and data.

Parameters:

Name Type Description Default
trend

The trend component of the hierarchical prophet model.

 required
data

The data used to compute the effect.

 required

Returns:

Type Description

The computed effect based on the given trend and data.
Source code in src/prophetverse/effects.py 
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def compute_effect(self, trend, data):
    """
    Computes the effect based on the given trend and data.

    Args:
        trend: The trend component of the hierarchical prophet model.
        data: The data used to compute the effect.

    Returns:
        The computed effect based on the given trend and data.

    """
    n_features = data.shape[-1]

    with numpyro.plate(f"{self.id}_plate", n_features, dim=-1):
        coefficients = self.sample(
            "coefs",
            self.prior
        )

    if coefficients.ndim == 1:
        coefficients = jnp.expand_dims(coefficients, axis=-1)

    if data.ndim == 3 and coefficients.ndim == 2:
        coefficients = jnp.expand_dims(coefficients, axis=0)
    if self.effect_mode == "multiplicative":
        return multiplicative_effect(trend, data, coefficients)
    return additive_effect(trend, data, coefficients)

LogEffect

Bases: AbstractEffect

Log effect for a variable.

Computes the effect using the formula:

effect = scale * log(rate * data + 1)

A gamma prior is used for the scale and rate parameters.

Parameters:

Name Type Description Default
id str

The identifier for the effect.

 required
scale_prior Distribution

The prior distribution for the scale parameter.

 None
rate_prior Distribution

The prior distribution for the rate parameter.

 None
Source code in src/prophetverse/effects.py 
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class LogEffect(AbstractEffect):
    """
    Log effect for a variable.

    Computes the effect using the formula:

    effect = scale * log(rate * data + 1)

    A gamma prior is used for the scale and rate parameters.

    Args:
        id (str): The identifier for the effect.
        scale_prior (dist.Distribution): The prior distribution for the scale parameter.
        rate_prior (dist.Distribution): The prior distribution for the rate parameter.
    """

    def __init__(
        self,
        scale_prior=None,
        rate_prior=None,
        effect_mode="multiplicative",
        **kwargs,
    ):
        if scale_prior is None:
            scale_prior = dist.Gamma(1, 1)
        if rate_prior is None:
            rate_prior = dist.Gamma(1, 1)

        self.scale_prior = scale_prior
        self.rate_prior = rate_prior
        self.effect_mode = effect_mode
        super().__init__(**kwargs)

    def compute_effect(self, trend, data):
        """
        Computes the effect using the log transformation.

        Args:
            trend: The trend component.
            data: The input data.

        Returns:
            The computed effect.
        """
        scale = self.sample("log_scale", self.scale_prior)
        rate = self.sample("log_rate", self.rate_prior)
        effect = scale * jnp.log(rate * data + 1)
        if self.effect_mode == "additive":
            return effect
        return trend * effect

compute_effect(trend, data)

Computes the effect using the log transformation.

Parameters:

Name Type Description Default
trend

The trend component.

 required
data

The input data.

 required

Returns:

Type Description

The computed effect.
Source code in src/prophetverse/effects.py 
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def compute_effect(self, trend, data):
    """
    Computes the effect using the log transformation.

    Args:
        trend: The trend component.
        data: The input data.

    Returns:
        The computed effect.
    """
    scale = self.sample("log_scale", self.scale_prior)
    rate = self.sample("log_rate", self.rate_prior)
    effect = scale * jnp.log(rate * data + 1)
    if self.effect_mode == "additive":
        return effect
    return trend * effect