meridian.model.context.ModelContext

Model context for Meridian.

meridian.model.context.ModelContext(
    input_data: data.InputData, model_spec: spec.ModelSpec
)

This class contains all model parameters that do not change between the runs of Meridian.

adstock_decay_spec Returns AdstockDecaySpec object with correctly mapped channels.
baseline_geo_idx Returns the index of the baseline geo.
controls

controls_scaled

controls_transformer Returns a CenteringAndScalingTransformer for controls, if it exists.
holdout_id

input_data

is_national

knot_info

kpi

kpi_scaled

kpi_transformer

media_effects_dist

media_tensors

model_spec

n_controls

n_geos

n_media_channels

n_media_times

n_non_media_channels

n_organic_media_channels

n_organic_rf_channels

n_rf_channels

n_times

non_media_transformer Returns a CenteringAndScalingTransformer for non-media treatments.
non_media_treatments

non_media_treatments_normalized Normalized non-media treatments.

The non-media treatments values are scaled by population (for channels where non_media_population_scaling_id is True) and normalized by centering and scaling with means and standard deviations.

organic_media_tensors

organic_rf_tensors

population

prior_broadcast Returns broadcasted PriorDistribution object.
revenue_per_kpi

rf_tensors

saturation_spec The SaturationSpec object with correctly mapped channels.
total_outcome

total_spend

unique_sigma_for_each_geo

Methods

create_inference_data_coords

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create_inference_data_coords(
    n_chains: int, n_draws: int
) -> Mapping[str, np.ndarray | Sequence[str]]

Creates data coordinates for inference data.

create_inference_data_dims

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create_inference_data_dims() -> Mapping[str, Sequence[str]]

Creates data dimensions for inference data.

expand_selected_time_dims

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expand_selected_time_dims(
    start_date: tc.Date = None, end_date: tc.Date = None
) -> (list[str] | None)

Validates and returns time dimension values based on the selected times.

If both start_date and end_date are None, returns None. If specified, both start_date and end_date are inclusive, and must be present in the time coordinates of the input data.

Args
start_date Start date of the selected time period. If None, implies the earliest time dimension value in the input data.
end_date End date of the selected time period. If None, implies the latest time dimension value in the input data.

Returns
A list of time dimension values (as Meridian-formatted strings) in the input data within the selected time period, or do nothing and pass through None if both arguments are Nones, or if start_date and end_date correspond to the entire time range in the input data.

Raises
ValueError if start_date or end_date is not in the input data time dimensions.

get_channel_parameter_tensor

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get_channel_parameter_tensor(
    dist_tensors: Any, *, param_base_name: str, channel_name: str
) -> backend.Tensor

Safely extracts a channel's parameter tensor (e.g., 'alpha', 'beta_g').

Args
dist_tensors An object containing batched distribution tensors (e.g., DistributionTensors).
param_base_name The base name of the parameter (e.g., 'alpha', 'ec', 'beta_g').
channel_name The name of the channel.

Returns
The sliced parameter tensor for the specific channel.

Raises
ValueError If the parameter or channel is not found.

get_channel_parameters

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get_channel_parameters(
    channel_name: str
) -> ChannelParameters

Maps channel names to index, prefix, decay spec, and is_rf.

Args
channel_name Name of the channel.

Returns
A ChannelParameters object containing the channel's metadata.

Raises
ValueError If the channel is not found.

get_media_scaling_factor

View source

get_media_scaling_factor(
    channel_name: str
) -> backend.Tensor

Retrieves the population-scaled median used to scale a channel's volume.

For Reach & Frequency (RF) channels, this returns the scaling factor applied to the 'reach' component, as 'frequency' is not transformed.

Args
channel_name The string name of the paid or organic channel.

Returns
A tensor of shape (n_geos,) representing the scaling factor.

Raises
ValueError If the channel is not found, or the transformer is uninitialized.

populate_cached_properties

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populate_cached_properties()

Eagerly activates all cached properties.

This is useful for creating a tf.function computation graph with this Meridian object as part of a captured closure. Within the computation graph, internal state mutations are problematic, and so this method freezes the object's states before the computation graph is created.