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#
# http://www.apache.org/licenses/LICENSE-2.0
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"""Gamma-Gamma Model for expected future monetary value."""
import numpy as np
import pandas
import pymc as pm
import pytensor.tensor as pt
import xarray
from pymc.util import RandomState
from pymc_extras.prior import Prior
from pymc_marketing.clv.models import CLVModel
from pymc_marketing.clv.utils import customer_lifetime_value, to_xarray
from pymc_marketing.model_config import ModelConfig
[docs]
class BaseGammaGammaModel(CLVModel):
"""Base class for Gamma-Gamma models."""
[docs]
def distribution_customer_spend(
self,
data: pandas.DataFrame,
random_seed: RandomState | None = None,
) -> xarray.DataArray:
"""Posterior distribution of mean spend values for each customer.
Parameters
----------
data : ~pandas.DataFrame
DataFrame containing the following columns:
* `customer_id`: Unique customer identifier
* `frequency`: Number of purchases
* `monetary_value`: Mean spend values for each customer
random_seed : ~RandomState, optional
Optional random seed to fix sampling results.
"""
x = data["frequency"]
z_mean = data["monetary_value"]
coords = {"customer_id": np.unique(data["customer_id"])}
with pm.Model(coords=coords):
p = pm.HalfFlat("p")
q = pm.HalfFlat("q")
v = pm.HalfFlat("v")
# Eq 5 from [1], p.3
nu = pm.Gamma("nu", p * x + q, v + x * z_mean, dims=("customer_id",))
pm.Deterministic("mean_spend", p / nu, dims=("customer_id",))
return pm.sample_posterior_predictive(
self.idata,
var_names=["nu", "mean_spend"],
random_seed=random_seed,
).posterior_predictive["mean_spend"]
[docs]
def expected_customer_spend(
self,
data: pandas.DataFrame,
) -> xarray.DataArray:
"""Compute the expected future mean spend value per customer.
The computations are based on Eq 5 from [1], p.3.
Adapted from: https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/lifetimes/fitters/gamma_gamma_fitter.py#L117
data : ~pandas.DataFrame
DataFrame containing the following columns:
* `customer_id`: Unique customer identifier
* `frequency`: Number of transactions observed for each customer
* `monetary_value`: Mean transaction value of repeat purchases for each customer
References
----------
.. [1] Fader, P. S., & Hardie, B. G. (2013). "The Gamma-Gamma model of monetary
value". February, 2, 1-9. https://www.brucehardie.com/notes/025/gamma_gamma.pdf
"""
mean_transaction_value, frequency = to_xarray(
data["customer_id"],
data["monetary_value"],
data["frequency"],
)
posterior = self.fit_result
p = posterior["p"]
q = posterior["q"]
v = posterior["v"]
individual_weight = p * frequency / (p * frequency + q - 1)
population_mean = v * p / (q - 1)
return (
1 - individual_weight
) * population_mean + individual_weight * mean_transaction_value
[docs]
def distribution_new_customer_spend(
self, n: int = 1, random_seed: RandomState | None = None
) -> xarray.DataArray:
"""Posterior distribution of mean spend values for new customers.
Parameters
----------
n : int, optional
Number of posterior distributions to generate. This can usually be left at the default value of 1.
random_seed : ~RandomState, optional
Optional random seed to fix sampling results.
"""
coords = {"new_customer_id": range(n)}
with pm.Model(coords=coords):
p = pm.HalfFlat("p")
q = pm.HalfFlat("q")
v = pm.HalfFlat("v")
nu = pm.Gamma("nu", q, v, dims=("new_customer_id",))
pm.Deterministic("mean_spend", p / nu, dims=("new_customer_id",))
return pm.sample_posterior_predictive(
self.idata,
var_names=["nu", "mean_spend"],
random_seed=random_seed,
).posterior_predictive["mean_spend"]
[docs]
def expected_new_customer_spend(self) -> xarray.DataArray:
"""Compute the expected mean spend value for a new customer."""
posterior = self.fit_result
p_mean = posterior["p"]
q_mean = posterior["q"]
v_mean = posterior["v"]
# Closed form solution to the posterior of nu
# Eq 3 from [1], p.3
mean_spend = p_mean * v_mean / (q_mean - 1)
# TODO: We could also provide the variance
# var_spend = (p_mean ** 2 * v_mean ** 2) / ((q_mean - 1) ** 2 * (q_mean - 2))
return mean_spend
[docs]
def expected_customer_lifetime_value(
self,
transaction_model: CLVModel,
data: pandas.DataFrame,
future_t: int = 12,
discount_rate: float = 0.00,
time_unit: str = "D",
) -> xarray.DataArray:
"""Compute the average lifetime value for a group of one or more customers.
In addition, it applies a discount rate for net present value estimations.
Note `future_t` is measured in months regardless of `time_unit` specified.
Adapted from lifetimes package
https://github.com/CamDavidsonPilon/lifetimes/blob/41e394923ad72b17b5da93e88cfabab43f51abe2/lifetimes/fitters/gamma_gamma_fitter.py#L246
Parameters
----------
transaction_model : ~CLVModel
Predictive model for future transactions. `BetaGeoModel` and `ParetoNBDModel` are currently supported.
data : ~pandas.DataFrame
DataFrame containing the following columns:
* `customer_id`: Unique customer identifier
* `frequency`: Number of repeat purchases observed for each customer
* `recency`: Time between the first and the last purchase
* `T`: Time between the first purchase and the end of the observation period
* `monetary_value`: Mean spend values of repeat purchases for each customer
future_t : int, optional
The lifetime expected for the user in months. Default: 12
discount_rate : float, optional
The monthly adjusted discount rate. Default: 0.00
time_unit : string, optional
Unit of time of the purchase history. Defaults to "D" for daily.
Other options are "W" (weekly), "M" (monthly), and "H" (hourly).
Example: If your dataset contains information about weekly purchases,
you should use "W".
Returns
-------
xarray
DataArray containing estimated customer lifetime values
"""
# Use Gamma-Gamma estimates for the expected_spend values
predicted_monetary_value = self.expected_customer_spend(data=data)
data.loc[:, "future_spend"] = predicted_monetary_value.mean(
("chain", "draw")
).copy()
return customer_lifetime_value(
transaction_model=transaction_model,
data=data,
future_t=future_t,
discount_rate=discount_rate,
time_unit=time_unit,
)
[docs]
class GammaGammaModel(BaseGammaGammaModel):
"""Gamma-Gamma Model for expected future monetary value.
The Gamma-Gamma model assumes expected future spend follows a Gamma distribution,
and the scale of this distribution is also Gamma-distributed.
This model is conditioned on the mean value of repeat transactions for each customer, and is based
on [1]_, [2]_. Data must be summarized by *frequency* and *monetary_value* for each customer,
using `clv.rfm_summary()` or equivalent.
See `GammaGammaModelIndividual` for an equivalent model conditioned
on individual transaction values.
Parameters
----------
data : ~pandas.DataFrame
DataFrame containing the following columns:
* `customer_id`: Unique customer identifier
* `monetary_value`: Mean transaction value of repeat purchases for each customer
* `frequency`: Number of repeat transactions observed for each customer
model_config : dict, optional
Dictionary of model prior parameters. If not provided, the model will use default priors specified in the
`default_model_config` class attribute.
sampler_config : dict, optional
Dictionary of sampler parameters. Defaults to *None*.
Examples
--------
.. code-block:: python
import pymc as pm
from pymc_marketing.clv import GammaGammaModel
model = GammaGammaModel(
data=pandas.DataFrame(
{
"customer_id": [0, 1, 2, 3, ...],
"monetary_value": [23.5, 19.3, 11.2, 100.5, ...],
"frequency": [6, 8, 2, 1, ...],
}
),
model_config={
"p": {"dist": "HalfNormal", kwargs: {}},
"q": {"dist": "HalfStudentT", kwargs: {"nu": 4, "sigma": 10}},
"v": {"dist": "HalfCauchy", kwargs: {"beta": 1}},
},
sampler_config={
"draws": 1000,
"tune": 1000,
"chains": 2,
"cores": 2,
"nuts_kwargs": {"target_accept": 0.95},
},
)
model.fit()
print(model.fit_summary())
# Predict spend of customers for which we know transaction history, conditioned on data.
expected_customer_spend = (
model.expected_customer_spend(
data=pandas.DataFrame(
{
"customer_id": [0, 1, 2, 3, ...],
"monetary_value": [23.5, 19.3, 11.2, 100.5, ...],
"frequency": [6, 8, 2, 1, ...],
}
),
),
)
print(expected_customer_spend.mean("customer_id"))
# Predict spend of 10 new customers, conditioned on data
new_customer_spend = model.expected_new_customer_spend(n=10)
print(new_customer_spend.mean("new_customer_id"))
References
----------
.. [1] Fader, P. S., & Hardie, B. G. (2013). "The Gamma-Gamma model of monetary
value". https://www.brucehardie.com/notes/025/gamma_gamma.pdf
.. [2] Peter S. Fader, Bruce G. S. Hardie, and Ka Lok Lee (2005), “RFM and CLV:
Using iso-value curves for customer base analysis”, Journal of Marketing
Research, 42 (November), 415-430.
https://journals.sagepub.com/doi/pdf/10.1509/jmkr.2005.42.4.415
"""
_model_type = "Gamma-Gamma Model (Mean Transactions)"
[docs]
def __init__(
self,
data: pandas.DataFrame,
model_config: dict | None = None,
sampler_config: dict | None = None,
):
self._validate_cols(
data,
required_cols=["customer_id", "monetary_value", "frequency"],
must_be_unique=["customer_id"],
)
super().__init__(
data=data, model_config=model_config, sampler_config=sampler_config
)
@property
def default_model_config(self) -> ModelConfig:
"""Default model configuration."""
return {
"p": Prior("HalfFlat"),
"q": Prior("HalfFlat"),
"v": Prior("HalfFlat"),
}
[docs]
def build_model(self) -> None: # type: ignore[override]
"""Build the model."""
z_mean = pt.as_tensor_variable(self.data["monetary_value"])
x = pt.as_tensor_variable(self.data["frequency"])
coords = {"customer_id": self.data["customer_id"]}
with pm.Model(coords=coords) as self.model:
p = self.model_config["p"].create_variable("p")
q = self.model_config["q"].create_variable("q")
v = self.model_config["v"].create_variable("v")
# Likelihood for mean_spend, marginalizing over nu
# Eq 1a from [1], p.2
pm.Potential(
"likelihood",
(
pt.gammaln(p * x + q)
- pt.gammaln(p * x)
- pt.gammaln(q)
+ q * pt.log(v)
+ (p * x - 1) * pt.log(z_mean)
+ (p * x) * pt.log(x)
- (p * x + q) * pt.log(x * z_mean + v)
),
)
# TODO: This model requires further evaluation and reference in a notebook
[docs]
class GammaGammaModelIndividual(BaseGammaGammaModel):
"""Gamma-Gamma Model for expected future monetary value.
The Gamma-Gamma model assumes expected future spend follows a Gamma distribution,
and the scale of this distribution is also Gamma-distributed.
This model is conditioned on the spend values of each purchase for each customer,
and is based on [1]_, [2]_.
See `GammaGammaModel` for an equivalent model conditioned on mean transaction values
of repeat purchases for the customer population.
Parameters
----------
data : ~pandas.DataFrame
Dataframe containing the following columns:
* `customer_id`: Unique customer identifier
* `individual_transaction_value`: Monetary value of each purchase for each customer
model_config : dict, optional
Dictionary of model prior parameters. If not provided, the model will use default priors specified in the
`default_model_config` class attribute.
sampler_config : dict, optional
Dictionary of sampler parameters. Defaults to *None*.
Examples
--------
.. code-block:: python
import pymc as pm
from pymc_marketing.clv import GammaGammaModelIndividual
model = GammaGammaModelIndividual(
data=pandas.DataFrame(
{
"customer_id": [0, 0, 0, 1, 1, 2, ...],
"individual_transaction_value": [5.3. 5.7, 6.9, 13.5, 0.3, 19.2 ...],
}
),
model_config={
"p": {dist: 'HalfNorm', kwargs: {}},
"q": {dist: 'HalfStudentT', kwargs: {"nu": 4, "sigma": 10}},
"v": {dist: 'HalfCauchy', kwargs: {}},
},
sampler_config={
"draws": 1000,
"tune": 1000,
"chains": 2,
"cores": 2,
"nuts_kwargs": {"target_accept": 0.95},
},
)
model.fit()
print(model.fit_summary())
# Predict spend of customers for which we know transaction history,
# conditioned on data. May include customers not included in fitting
expected_customer_spend = model.expected_customer_spend(
data=pandas.DataFrame(
{
"customer_id": [0, 0, 0, 1, 1, 2, ...],
"individual_transaction_value": [5.3. 5.7, 6.9, 13.5, 0.3, 19.2 ...],
}
),
)
print(expected_customer_spend.mean("customer_id"))
# Predict spend of 10 new customers, conditioned on data
new_customer_spend = model.expected_new_customer_spend(n=10)
print(new_customer_spend.mean("new_customer_id"))
References
----------
.. [1] Fader, P. S., & Hardie, B. G. (2013). "The Gamma-Gamma model of monetary
value". http://www.brucehardie.com/notes/025/gamma_gamma.pdf
.. [2] Peter S. Fader, Bruce G. S. Hardie, and Ka Lok Lee (2005), “RFM and CLV:
Using iso-value curves for customer base analysis”, Journal of Marketing
Research, 42 (November), 415-430.
https://journals.sagepub.com/doi/pdf/10.1509/jmkr.2005.42.4.415
"""
_model_type = "Gamma-Gamma Model (Individual Transactions)"
[docs]
def __init__(
self,
data: pandas.DataFrame,
model_config: dict | None = None,
sampler_config: dict | None = None,
):
self._validate_cols(
data, required_cols=["customer_id", "individual_transaction_value"]
)
super().__init__(
data=data, model_config=model_config, sampler_config=sampler_config
)
@property
def default_model_config(self) -> dict:
"""Default model configuration."""
return {
"p": Prior("HalfFlat"),
"q": Prior("HalfFlat"),
"v": Prior("HalfFlat"),
}
[docs]
def build_model(self) -> None: # type: ignore[override]
"""Build the model."""
z = self.data["individual_transaction_value"]
coords = {
"customer_id": np.unique(self.data["customer_id"]),
"obs": range(self.data.shape[0]),
}
with pm.Model(coords=coords) as self.model:
p = self.model_config["p"].create_variable("p")
q = self.model_config["q"].create_variable("q")
v = self.model_config["v"].create_variable("v")
nu = pm.Gamma("nu", q, v, dims=("customer_id",))
pm.Gamma(
"spend", p, nu[self.data["customer_id"]], observed=z, dims=("obs",)
)
