utils

Utility functions for the Marketing Mix Modeling module.

Functions

add_noise_to_channel_allocation(df, channels)	Return df with additive Gaussian noise applied to channels columns.
adjusted_value_at_risk_score([...])	Calculate adjusted Value at Risk (AVaR) score.
apply_sklearn_transformer_across_dim(data, ...)	Apply a scikit-learn transformer across a dimension of an xarray DataArray.
average_response(samples, budgets)	Compute the average response of the posterior predictive distribution.
build_contributions(idata, var[, agg, ...])	Build a wide contributions DataFrame from idata.posterior variables.
conditional_value_at_risk([confidence_level])	Calculate the Conditional Value at Risk (CVaR) at a specified confidence level.
create_index(dims, take)	Create an index to take the first dimension of a tensor based on the provided dimensions.
create_new_spend_data(spend, ...[, ...])	Create new spend data for the channel forward pass.
create_zero_dataset(model, start_date, end_date)	Create a DataFrame for future prediction, with zeros (or supplied constants).
diversification_ratio(samples, budgets)	Calculate the Diversification Ratio of a portfolio to evaluate risk distribution.
mean_tightness_score([alpha, confidence_level])	Calculate the Mean Tightness Score (MTS).
portfolio_entropy(samples, budgets)	Calculate the entropy of a portfolio's asset weights to assess diversification.
raroc([risk_free_rate])	Calculate the Risk-Adjusted Return on Capital (RAROC).
sharpe_ratio([risk_free_rate])	Calculate the Sharpe Ratio.
tail_distance([confidence_level])	Calculate the absolute distance between the mean and the quantiles.
transform_1d_array(transform, y)	Transform a 1D array using a scikit-learn transformer.
value_at_risk([confidence_level])	Calculate the Value at Risk (VaR) at a specified confidence level.