Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

#1035 Distribution new customer enhancements #1061

Merged
merged 5 commits into from
Sep 23, 2024
Merged

#1035 Distribution new customer enhancements #1061

Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
dd1f69d
feat: test.txt added for commit check
Ishaanjolly Sep 20, 2024
5c681b1
feat: replaced plot_curve with plot_samples within ./mmm/plot.py
Ishaanjolly Sep 22, 2024
d64b1c2
feat: n_samples added to distributions_new_customers
Ishaanjolly Sep 22, 2024
625a530
revert the plot.py changes
Ishaanjolly Sep 23, 2024
7b14ccc
Merge branch 'main' into distribution_new_customer_enhancements
juanitorduz Sep 23, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 8 additions & 1 deletion pymc_marketing/clv/models/beta_geo_beta_binom.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -524,6 +524,7 @@ def _distribution_new_customers(
"purchase_rate",
"recency_frequency",
),
n_samples: int = 1000,
) -> xarray.Dataset:
"""Compute posterior predictive samples of dropout, purchase rate and frequency/recency of new customers.

Expand All @@ -542,6 +543,8 @@ def _distribution_new_customers(
Random state to use for sampling.
var_names : sequence of str, optional
Names of the variables to sample from. Defaults to ["dropout", "purchase_rate", "recency_frequency"].
n_samples : int, optional
Number of posterior predictive samples to generate. Defaults to 1000

"""
if data is None:
Expand All @@ -557,7 +560,7 @@ def _distribution_new_customers(

if dataset.sizes["chain"] == 1 and dataset.sizes["draw"] == 1:
# For map fit add a dummy draw dimension
dataset = dataset.squeeze("draw").expand_dims(draw=range(1000))
dataset = dataset.squeeze("draw").expand_dims(draw=range(n_samples))

coords = self.model.coords.copy() # type: ignore
coords["customer_id"] = data["customer_id"]
Expand Down Expand Up @@ -668,6 +671,7 @@ def distribution_new_customer_recency_frequency(
*,
T: int | np.ndarray | pd.Series | None = None,
random_seed: RandomState | None = None,
n_samples: int = 1,
) -> xarray.Dataset:
"""BG/BB process representing purchases across the customer population.

Expand All @@ -687,6 +691,8 @@ def distribution_new_customer_recency_frequency(
Not required if `data` Dataframe contains a `T` column.
random_seed : ~numpy.random.RandomState, optional
Random state to use for sampling.
n_samples : int, optional
Number of samples to generate. Defaults to 1.

Returns
-------
Expand All @@ -698,4 +704,5 @@ def distribution_new_customer_recency_frequency(
T=T,
random_seed=random_seed,
var_names=["recency_frequency"],
n_samples=n_samples,
)["recency_frequency"]
Loading