ggf.customer_lifetime_value produces NaN clv

[littlesweet1129]

Hi there, I successfully fitted a modified beta geo model as well as a gamma gamma model. However, when I use the customer_lifetime_value function from the gg model, all CLV are NaN.

DISCOUNT_a = 0.06                # annual discount rate
LIFE = 12                        # lifetime expected for the customers in months

discount_m = (1 + DISCOUNT_a)**(1/12) - 1     # monthly discount rate

clv = ggf.customer_lifetime_value(
        transaction_prediction_model = bgf,  
        frequency = df_rftv["frequency"],  
        recency = df_rftv["recency"],  
        T = df_rftv["T"],  
        monetary_value = df_rftv["monetary_value"],  
        time = LIFE,    
        freq = "D",                          
        discount_rate = discount_m)

[ColtAllen]

Hey @littlesweet1129,

Passing in Panda series as arguments can lead to unstable behavior because many of the internal methods in the legacy fitters module expect numpy arrays. Try making the following changes to your code:

clv = ggf.customer_lifetime_value(
        transaction_prediction_model = bgf,  
        frequency = df_rftv["frequency"].values,  
        recency = df_rftv["recency"].values,  
        T = df_rftv["T"].values,  
        monetary_value = df_rftv["monetary_value"].values,  
        time = LIFE,    
        freq = "D",                          
        discount_rate = discount_m)

It's a good practice to always use the df['column'].values convention with all of the legacy Fitter models. The Model module I'm currently building as a replacement will not have this issue because the entire dataframe is provided as an argument instead of the separate array components.