generate_saturated_data#
- pymc_marketing.customer_choice.synthetic_data.generate_saturated_data(total_sales_mu, total_sales_sigma, treatment_time, n_observations, market_shares_before, market_shares_after, market_share_labels, random_seed=None)[source]#
Generate synthetic data for the MVITS model, assuming market is saturated.
This function generates synthetic data for the MVITS model, assuming that the market is saturated. This makes the assumption that the total sales are normally distributed around some average level of sales, and that the market shares are constant over time.
- Parameters:
- total_sales_mu: int
The average level of sales in the market.
- total_sales_sigma: float
The standard deviation of sales in the market.
- treatment_time: int
The time at which the new model is introduced.
- n_observations: int
The number of observations to generate.
- market_shares_before: list[float]
The market shares before the introduction of the new model.
- market_shares_after: list[float]
The market shares after the introduction of the new model.
- market_share_labels: list[str]
The labels for the market shares.
- random_seed: np.random.Generator | int, optional
The random number generator to use.
- Returns:
- data:
pd.DataFrame The synthetic data generated.
- data: