BaseValidateMMM

class pymc_marketing.mmm.base.BaseValidateMMM(date_column=FieldInfo(annotation=str, required=True, description='Column name of the date variable.'), channel_columns=FieldInfo(annotation=list[str], required=True, description='Column names of the media channel variables.', metadata=[MinLen(min_length=1)]), model_config=FieldInfo(annotation=Union[dict, NoneType], required=False, default=None, description='Model configuration.'), sampler_config=FieldInfo(annotation=Union[dict, NoneType], required=False, default=None, description='Sampler configuration.'))

Base class with some validation of the inputs.

Methods

BaseValidateMMM.__init__([date_column, ...])	Initialize model configuration and sampler configuration for the model.
BaseValidateMMM.attrs_to_init_kwargs(attrs)	Convert the model configuration and sampler configuration from the attributes to keyword arguments.
BaseValidateMMM.build_model(X, y, **kwargs)	Create an instance of pm.Model based on provided data and model_config.
BaseValidateMMM.compute_channel_contribution_original_scale()	Compute the channel contributions in the original scale of the target variable.
BaseValidateMMM.compute_mean_contributions_over_time([...])	Get the contributions of each channel over time.
BaseValidateMMM.create_idata_attrs()	Create attributes for the inference data.
BaseValidateMMM.fit(X[, y, progressbar, ...])	Fit a model using the data passed as a parameter.
BaseValidateMMM.get_errors([original_scale])	Get model errors posterior distribution.
BaseValidateMMM.get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
BaseValidateMMM.get_target_transformer()	Return the target transformer pipeline used for preprocessing the target variable.
BaseValidateMMM.graphviz(**kwargs)	Get the graphviz representation of the model.
BaseValidateMMM.load(fname)	Create a ModelBuilder instance from a file.
BaseValidateMMM.plot_channel_contribution_share_hdi([...])	Plot the share of channel contributions in a forest plot.
BaseValidateMMM.plot_components_contributions(...)	Plot the target variable and the posterior predictive model components.
BaseValidateMMM.plot_errors([original_scale, ax])	Plot model errors by taking the difference between true values and predicted.
BaseValidateMMM.plot_grouped_contribution_breakdown_over_time([...])	Plot a time series area chart for all channel contributions.
BaseValidateMMM.plot_posterior_predictive([...])	Plot posterior distribution from the model fit.
BaseValidateMMM.plot_prior_predictive(...)	Plot the prior predictive data.
BaseValidateMMM.plot_waterfall_components_decomposition([...])	Create a waterfall plot.
BaseValidateMMM.predict(X_pred[, extend_idata])	Use a model to predict on unseen data and return point prediction of all the samples.
BaseValidateMMM.predict_posterior(X_pred[, ...])	Generate posterior predictive samples on unseen data.
BaseValidateMMM.predict_proba(X_pred[, ...])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
BaseValidateMMM.preprocess(target, data)	Preprocess the provided data according to the specified target.
BaseValidateMMM.sample_posterior_predictive(X_pred)	Sample from the model's posterior predictive distribution.
BaseValidateMMM.sample_prior_predictive(X_pred)	Sample from the model's prior predictive distribution.
BaseValidateMMM.save(fname)	Save the model's inference data to a file.
BaseValidateMMM.set_idata_attrs([idata])	Set attributes on an InferenceData object.
BaseValidateMMM.set_params(**params)	Set all the model parameters needed to instantiate the model, not including training data.
BaseValidateMMM.validate(target, data)	Validate the input data based on the specified target type.
BaseValidateMMM.validate_channel_columns(data)	Validate the channel columns.
BaseValidateMMM.validate_date_col(data)	Validate the date column.
BaseValidateMMM.validate_target(data)	Validate the target column.

Attributes

X
default_model_config	Return a class default configuration dictionary.
default_sampler_config	Return a class default sampler configuration dictionary.
fit_result	Get the posterior data.
id	Generate a unique hash value for the model.
methods	Get all methods of the object.
output_var	Returns the name of the output variable of the model.
posterior_predictive	Get the posterior predictive data.
preprocessing_methods	A property that provides preprocessing methods for features ("X") and the target variable ("y").
prior	Get the prior data.
prior_predictive	Get the prior predictive data.
validation_methods	A property that provides validation methods for features ("X") and the target variable ("y").
version
y
model
date_column
channel_columns